PubAnnotation

Id	Subject	Object	Predicate	Lexical cue
T12	0-12	Sentence	denotes	INTRODUCTION
T13	13-166	Sentence	denotes	The world population is undergoing a rapid expansion of older adults, and thus, exploring how to stay healthy with age has become an urgent global focus.
T14	167-468	Sentence	denotes	Aging leads to numerous physiological changes, including the deterioration of the immune system, rendering the elderly more susceptible to infections, such as the COVID-19 pandemic, and poor responses to vaccines (Ciabattini et al., 2018; Alpert et al., 2019; Onder et al., 2020; Verity et al., 2020).
T15	469-601	Sentence	denotes	Changes observed during aging are often reflected as alterations in the composition and functional declines of diverse immune cells.
T16	602-942	Sentence	denotes	For T cells (TCs), the high frequency of naive cells in young humans progressively decreases along with the accumulation of highly differentiated memory cells (Hakim and Gress, 2007), whereas nonclassical monocytes (MCs) with high levels of plasma tumor necrosis factor (TNF)-α and interleukin (IL)-8 accumulate with age (Ong et al., 2018).
T17	943-1208	Sentence	denotes	In addition, senescence of the immune system in the elderly has been termed “inflammaging”, which refers to increased levels of tissue and circulating proinflammatory cytokines in the absence of an immunological threat (Panda et al., 2009; Franceschi et al., 2018).
T18	1209-1370	Sentence	denotes	Overall, aging is associated with changes in the structure of diverse immune compartments, where accumulating dysfunctional subsets contribute to immune failure.
T19	1371-1581	Sentence	denotes	Seminal studies have provided insights into the compositions and functional alterations occurring during aging, primarily based on previously described markers detected in pooled heterogeneous cell populations.
T20	1582-1813	Sentence	denotes	The recent development of unbiased high-throughput single-cell technologies with high accuracy and specificity has begun to change immunological studies, as researchers worldwide are ushering in the new field of systems immunology.
T21	1814-2069	Sentence	denotes	By using single-cell sequencing, recent studies have reported that cell-to-cell transcriptional variability increases with age in CD4+ TCs (Bahar et al., 2006; Martinez-Jimenez et al., 2017) and in leukocytes from old mouse lungs (Angelidis et al., 2019).
T22	2070-2177	Sentence	denotes	Aging also increases the variations in chromatin modifications of human immune cells (Cheung et al., 2018).
T23	2178-2422	Sentence	denotes	Very recently, many immunological phenotypes, such as intratissue accumulation of proinflammatory cells, have been reported in aging rodent and primate models (Messaoudi et al., 2006; Watson et al., 2017; Hammond et al., 2019; Ma et al., 2020).
T24	2423-2590	Sentence	denotes	However, a comprehensive aging cell atlas of human peripheral blood that systematically connects all the blood lineages and cell subtypes has not yet been constructed.
T25	2591-2872	Sentence	denotes	Here, we applied single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to comprehensively characterize the properties of peripheral blood mononuclear cells (PBMCs) in young and old adults.
T26	2873-3143	Sentence	denotes	We also enrolled young and aged COVID-19 patients in the incipient stage and recovery stage to explore how age influenced the capacity for recovery and prognosis of COVID-19 infection and to better understand the influence of immune dysregulation in aging and infection.
T27	3144-3425	Sentence	denotes	Our data revealed that aging promotes the polarization of TCs from naive and memory to effector, exhausted and regulatory subtypes and increases the numbers of late natural killer cells (NKs), age-associated B cells (BCs), inflammatory MCs, and dysfunctional dendritic cells (DCs).
T28	3426-3658	Sentence	denotes	With single-cell paired T/B cell receptor sequencing (scTCR/BCR-seq), we uncovered decreased diversity and increased clonality of effector, cytotoxic and exhausted CD8+ TC subsets in TCs and age-associated B subsets in BCs with age.
T29	3659-3819	Sentence	denotes	Notably, aging increased the expression of inflammation-related genes, senescence-related genes, and coronavirus susceptibility genes in specific cell subtypes.
T30	3820-4010	Sentence	denotes	Most impressively, COVID-19 caused similar immune cell landscape changes to that of aging and further increased aging-induced immune cell polarization and upregulation of inflammatory genes.
T31	4011-4286	Sentence	denotes	Increased SARS-CoV-2 susceptibility gene expression and inflammatory MCs and decreased TCs aggravate inflammatory storms and lymphopenia (Mehta et al., 2020; Merad and Martin, 2020; Zhou et al., 2020) and likely underlie the high susceptibility and mortality of old patients.
T32	4287-4568	Sentence	denotes	Overall, this work expands our knowledge of aging via single-cell transcriptomic, proteomic and chromatin accessibility immune cell profiling and highlights critical nodes between the dysregulated immune system and infections that may serve to modulate the process of inflammaging.
T33	4570-4577	Sentence	denotes	RESULTS
T34	4579-4660	Sentence	denotes	Cohort characteristics and single-cell analysis of PBMCs in young and aged adults
T35	4661-4956	Sentence	denotes	To generate a comprehensive immune cell atlas reflecting cellular and systemic adaptations resulting from age and/or COVID-19 infection, we integrated scRNA-seq, CyTOF, scATAC-seq and scTCR/BCR-seq of single-cell PBMC suspensions collected from 3 separate cohorts (Fig. 1A, 1B, and Table S1A–G).
T36	4957-5662	Sentence	denotes	In cohort-1, comprising young healthy adults (YA) (20–45 years old) and aged healthy adults (AA) (≥60 years old), we combined CyTOF (n = 10) and scATAC-seq (n = 10) with scRNA-seq (n = 16) and scTCR/BCR-seq (n = 16); in cohort-2, comprising young healthy (YH) individuals (30–45 years old), aged healthy (AH) individuals (≥60 years old), young COVID-19 onset patients (YCO) (30–50 years old) and aged COVID-19 onset patients (ACO) (≥70 years old), we performed CyTOF analysis (n = 8); and in cohort-3, comprising YH individuals, AH individuals, young recovered COVID-19 patients (YCR) (30–50 years old) and aged recovered COVID-19 patients (ACR) (≥70 years old), we performed scRNA-seq (n = 22) (Fig. 1B).
T37	5663-5932	Sentence	denotes	By combining scRNA-seq, CyTOF, scATAC-seq and scTCR/BCR-seq analysis, we created a comparative framework detailing the impact of aging on cell type distribution and immune cell functions at the transcriptional, proteomic, and chromatin accessibility levels in cohort-1.
T38	5933-6128	Sentence	denotes	In cohort-2, we measured single-cell protein expression using a 26-marker CyTOF panel to discover early cellular changes in incipient COVID-19 patients and how those changes were affected by age.
T39	6129-6267	Sentence	denotes	Finally, in cohort-3, we compared cellular differences between young and aged recovered COVID-19 patients by scRNA-seq analysis (Fig. 1B).
T40	6268-7190	Sentence	denotes	Figure 1 Schematic illustration of the collection and data processing of PBMC from young and aged group. (A) Flowchart overview of PBMC collection in young and aged adults followed by scRNA-seq, mass cytometry, scATAC-seq and scTCR/BCR-seq experiments. (B) Schematic illustration of experimental cohorts; cohort-1: young and aged adults, cohort-2: young and aged healthy individuals, young and aged adults with COVID-19 onset, cohort-3: young and aged healthy individuals, young and aged adults recovered from COVID-19, matched with analysis as indicated: single-cell proteomic data from CyTOF studies, gene expression data from scRNA-seq studies, chromosomal accessibility data from scATAC-seq, and TCR and BCR repertoire data from scTCR/BCR-seq. (C) t-SNE projections of PBMCs derived from scRNA-seq data in cohort-1. (D) Heatmaps showing scaled expression of discriminative gene sets for each cell type and cell subset.
T41	7191-7267	Sentence	denotes	Color scheme is based on z-score distribution from −3 (purple) to 3 (yellow)
T42	7268-7547	Sentence	denotes	We analyzed PBMC single-cell suspensions by CyTOF for the protein expression of several lineage-, activation- and trafficking-associated markers and converted them to barcoded scRNA-seq libraries using 10x Genomics for downstream scRNA-seq, scATAC-seq and scTCR/BCR-seq analysis.
T43	7548-7647	Sentence	denotes	CellRanger software and the Seurat package were used for initial processing of the sequencing data.
T44	7648-7820	Sentence	denotes	Quality metrics included numbers of unique molecular identifiers (UMIs), genes detected per cell, and reads aligned that were comparable across different research subjects.
T45	7821-8082	Sentence	denotes	We identified red blood cells (RBCs), megakaryocytes (MEGAs) and five major immune cell lineages (TCs, NKs, BCs, MCs and DCs) based on the expression of canonical lineage markers and other genes specifically upregulated in each cluster (Figs. 1C, 1D and S1A–C).
T46	8083-8362	Sentence	denotes	In accordance with the scRNA-seq results, we identified five immune cell lineages (TCs, NKs, BCs, MCs and DCs) in CyTOF using t-distributed stochastic neighbor embedding (t-SNE), an unbiased dimensionality reduction algorithm (See Table S2 for a list of antibodies) (Fig. S2A–D).
T47	8363-8515	Sentence	denotes	Cell-type-specific marker genes were determined by differential gene expression values between clusters positioned and visualized in a t-SNE plot (Figs.
T48	8516-8527	Sentence	denotes	S1 and S2).
T49	8528-8642	Sentence	denotes	The definition of cell types in clusters in the t-SNE maps was comparable between old and young individuals (Figs.
T50	8643-8748	Sentence	denotes	S1B and 2B) both by scRNA-seq and CyTOF, indicating that the cell type identity was not altered with age.
T51	8749-9001	Sentence	denotes	Figure 2 Changes in cell proportions during aging. (A) Bar chart of the relative percentage of immune cell types derived from scRNA-seq data in PBMCs. (B) Bar chart of the relative percentage of immune cell subsets derived from scRNA-seq data in PBMCs.
T52	9002-10292	Sentence	denotes	The focused cell-subsets have been marked red. (C) Pie charts showing relative cluster abundances derived from mass cytometry data in the YA and AA groups. (D) Percentage of CD4 Naive cells in PBMCs from YA (n = 8) and AA (n = 8) groups. (E) Percentage of CD8 Naive cells in PBMCs from YA (n = 8) and AA (n = 8) groups. (F) Percentage of CD4 Naive cells in CD45+ cells from YA (n = 5) and AA (n = 5) groups. (G) Percentage of CD8 Naive cells in CD45+ cells from YA (n = 5) and AA (n = 5) groups. (H) Bar chart of the relative percentage of CD4+ T cell subsets derived from scRNA-seq data in PBMCs. (I) Bar chart of the relative percentage of CD8+ T cell subsets derived from scRNA-seq data in PBMCs. (J) Bar chart of the relative percentage of CD4+ T cell subsets derived from mass cytometry data in CD45+ cells. (K) Bar chart of the relative percentage of CD8+ T cell subsets derived from mass cytometry data in CD45+ cells. (L) Percentage of CD14 monocytes in PBMCs from YA (n = 8) and AA (n = 8) groups. (M) Percentage of CD14 monocytes in CD45+ cells from YA (n = 5) and AA (n = 5) groups. (N) Bar chart of the relative percentage of DC subsets derived from scRNA-seq data in PBMCs. (O) Bar chart of the relative percentage of DC subsets derived from mass cytometry data in CD45+ cells.
T53	10293-10368	Sentence	denotes	P values are based on two-tailed Mann-Whitney-Wilcoxon tests between groups
T54	10370-10436	Sentence	denotes	Dissection of immune cell subtypes in the cellular aging ecosystem
T55	10437-10548	Sentence	denotes	To classify each cell subpopulation in an unbiased manner, we separately reclustered the cells of each lineage.
T56	10549-10993	Sentence	denotes	By analyzing the most significantly upregulated genes in each cluster in scRNA-seq analysis, we identified five distinct subsets of CD3+ TCs (Fig. S3A), five distinct subsets of CD4+ TCs (Fig. S3B), four distinct subsets of CD8+ TCs (Fig. S3C), three distinct subsets of NKs (Fig. S3D), four distinct subsets of BCs (Fig. S3E), three distinct subsets of MCs (Fig. S3F) and four distinct subsets of DCs (Fig. S3G, see Table S3A for the details).
T57	10994-11073	Sentence	denotes	Aging affects the development and function of TCs and NKs (Pinti et al., 2016).
T58	11074-11268	Sentence	denotes	We identified known T cell subsets, including CD4+, CD8+, CD4+CD8+, CD4−CD8− and proliferative T cells (mitotic T cells, T-mito), based on the expression of canonical lineage markers (Fig. S3H).
T59	11269-11570	Sentence	denotes	The CD4+ T cells were subdivided into five classes: CCR7high CD69low naive CD4+ T cells (CD4 Naive); CCR7med CD69high CCR6− central memory CD4+ T cells (CD4 Tcm); CCR6+ effector memory CD4+ T cells (CD4 Tem); FOXP3+ regulatory T cells (CD4 Treg) and PDCD1+ exhausted CD4+ T cells (CD4 Tex) (Fig. S3I).
T60	11571-11803	Sentence	denotes	The CD8+ T cells were subdivided into four classes: CCR7+ naive CD8+ T cells (CD8 Naive); GZMK+ effector memory CD8+ T cells (CD8 Tem); GZMB+ GNLY+ cytotoxic CD8+ TCs (CD8 CTL) and PDCD1+ exhausted CD8+ T cells (CD8 Tex) (Fig. S3J).
T61	11804-12104	Sentence	denotes	Analysis of NK cell-status identified circulating NKs with three separate immune states (Fig. S 3D): the CD16 (FCGR3A)low CD56 (NCAM1)bright NK population (NK1), the CD16high CD56dim CD57 (B3GAT1)− low-cytotoxic NK compartment (NK2) and the CD16high CD56dim CD57+ late NK population (NK3) (Fig. S3K).
T62	12105-12445	Sentence	denotes	In addition, we identified four major peripheral B cell subsets: IL4R+ IGHD+ naive B cells (Naive BCs); CD27+ IGHG1+ memory B cells (Memory BCs); plasma cells or so-called antibody-secreting cells (ASCs), expressing high level of immunoglobulin genes MZB1; and a subset of ITGAX+ B cells defined as age-associated B cells (ABCs) (Fig. S3L).
T63	12446-13050	Sentence	denotes	In human peripheral blood myeloid cells (including MCs and DCs), known to promote antigen presentation and inflammatory activities, we identified seven transcriptionally distinct subsets: CD14high CD16− classical monocytes (CD14 MCs), CD14+/− CD16high nonclassical monocytes (CD16 MCs), CD14+ CD16+/− intermediate monocytes (Intermed MCs) (Fig. S3M), CLEC9A+ conventional DC1 (cDC1), CD1c+ cDC2 conventional DC2 (cDC2), CD123 (IL3RA)+ CLEC4C+ plasmacytoid DCs (pDCs) (Fig. S3N), and dendritic cell precursors (pre-DCs) expressing AXL and CD123 (Grabiec and Hussell, 2016; Ruffin et al., 2019) (Fig. S3N).
T64	13051-13152	Sentence	denotes	Therefore, we targeted the immune cell changes based on more precise classification of each subgroup.
T65	13153-13273	Sentence	denotes	To further verify the aging-associated change in the cell ratio, we performed single-cell analysis at the protein level.
T66	13274-13791	Sentence	denotes	Similar to the cell clusters and subsets in scRNA-seq results, in CyTOF analysis, we identified 21 sub-clusters with nine subsets of TCs (CD4 Naive, CD4 Tcm, CD4 Tem, CD4 Treg, CD8 Naive, CD8 Tem, CD8 CTL, CD4+ CD8+, CD4− CD8−), three subsets of NKs (CD56bright NK1, CD16+CD57− NK2 and CD16+CD57+ NK3), four subsets of BCs (Naive BC, Memory BC, ASCs, and ABCs), three subsets of MCs (CD14high MCs, CD16high MCs and intermediate MCs), and two subsets of DCs (pDCs and cDCs) (Fig. S4A–K, see Table S3B for the details).
T67	13793-13865	Sentence	denotes	Aging shifts the cellular composition toward extreme effector phenotypes
T68	13866-14035	Sentence	denotes	To delineate how cell-type composition changed with aging, we separately compared the proportions of each cell type across major cell types between the YA and AA groups.
T69	14036-14157	Sentence	denotes	We observed changes at the single-cell transcriptional level, which were further confirmed at the protein level by CyTOF.
T70	14158-14350	Sentence	denotes	Globally, we found that TCs and BCs, especially the former, decreased by approximately 10% in all PBMCs with scRNA-seq analysis (Fig. 2A, 2B, and S5A) and by 15% with CyTOF (Figs. 2C and S5B).
T71	14351-14483	Sentence	denotes	In contrast, MCs increased by approximately 7% in scRNA-seq analysis (Figs. 2A, 2B, and S5A) and by 10% in CyTOF (Figs. 2C and S5B).
T72	14484-14579	Sentence	denotes	The composition of cell subsets across all cell lineages differed between the YA and AA groups.
T73	14580-14728	Sentence	denotes	Among TCs, CD4+ TCs were increased, CD8+ TCs were decreased, and CD4+CD8+ and proliferating T cells were increased in the AA group (Fig. 2B and 2C).
T74	14729-14890	Sentence	denotes	Moreover, naive TCs, especially CD4 Naive and CD8 Naive, showed a common distribution in the YA group but were reduced in the aged group (P = 0.0175, Fig. 2D–G).
T75	14891-14991	Sentence	denotes	Conversely, effector, memory and exhausted cell subsets were dominant in the aged group (Fig. 2H–K).
T76	14992-15143	Sentence	denotes	The AA group also had a diminished proportion of the CD56bright NK1 population and an expansion of the NK2 and late NK3 populations (Fig. S5C and S5D).
T77	15144-15300	Sentence	denotes	Analysis of BC clusters revealed that Naive BCs were decreased while ABCs were mildly increased in the AA group compared to the YA group (Fig. S5E and S5F).
T78	15301-15504	Sentence	denotes	Our data also showed that elderly research subjects had increased MC subsets, particularly classical CD14 MCs and, to some extent, nonclassical CD16 MCs and intermediate MCs (Figs. 2L, 2M, S5G, and S5H).
T79	15505-15585	Sentence	denotes	Overall MC growth mainly resulted from CD14 MC enrichment (P = 0.0012, Fig. 2M).
T80	15586-15868	Sentence	denotes	However, given that CD14 MCs made up 70%–80% of the MCs population, the increase we observed in CD16 MCs was more remarkable as a change in the overall population proportion between the AA and YA groups, which was not observed for intermediate MCs between these groups (Fig. S5G–J).
T81	15869-16042	Sentence	denotes	A similar analysis of the DC subset composition showed that the percentage of cDC2 cells increased, whereas cDC1, pDC, and pre-DC decreased with age (Figs. 2N, 2O, and S5I).
T82	16043-16176	Sentence	denotes	In summary, these results demonstrate that aging induces an immune dysfunction shift into effector and inflammatory cell populations.
T83	16178-16263	Sentence	denotes	Identification of aging-related cell-type-specific transcriptional expression changes
T84	16264-16474	Sentence	denotes	To identify cell-subtype-specific gene signatures associated with aging, we performed an integrated comparative analysis of differentially expressed genes (DEGs) from blood immune cells in the YA and AA groups.
T85	16475-16599	Sentence	denotes	We found that blood immune cells showed heterogeneous transcriptional changes affected by aging based on the number of DEGs.
T86	16600-16708	Sentence	denotes	Strikingly, BC was the cell type most affected by aging, followed by TC and MC (Figs. 3A, S6A; Table S4A–E).
T87	16709-16974	Sentence	denotes	Specifically, we found a set of 60 genes whose expression was increased in all kinds of immune cells, indicative of general oxidative stress (e.g., DDIT4, CASP4, TSPO) and an inflammatory state (e.g., DUSP2, S100A10, COX5A, PSMB6) across cell populations (Fig. 3A).
T88	16975-17143	Sentence	denotes	Conversely, genes with decreased expression shared across all cell populations included DDX17, RBM39, and SCAF11, which are involved in RNA splicing (Fig. S6A and S6B).
T89	17144-17374	Sentence	denotes	Consistent with our understanding of the main immune cell lineages, we found that the myeloid and lymphocyte cell lineages were characterized by unique gene expression spectra, whereas TCs showed the highest heterogeneity in DEGs.
T90	17375-17531	Sentence	denotes	To explore the biological implications of our data in the context of aging, we used Gene Ontology (GO) and pathway analysis for each immune cell population.
T91	17532-17693	Sentence	denotes	Common aging-upregulated biological pathways included TNF signaling, IL-1 signaling, the apoptotic signaling pathway, and the adaptive immune response (Fig. 3B).
T92	17694-17755	Sentence	denotes	We found that these pathways were especially enhanced in TCs.
T93	17756-17892	Sentence	denotes	In addition, aging-upregulated biological pathways in MCs were enriched for interferon-gamma (IFN-γ) signaling and cell aging (Fig. 3B).
T94	17893-18112	Sentence	denotes	To assess the impact of aging on circulating immune cells, we selected the top 20 genes of the 60 total genes that were upregulated in all immune cells (Fig. 3A) and calculated aging scores across all immune cell types.
T95	18113-18255	Sentence	denotes	We found that MCs and DCs had the highest scores, suggesting that senescent cells are most likely present in these cell populations (Fig. 3C).
T96	18256-18533	Sentence	denotes	Moreover, when calculating the scores of individual samples, we found that individuals in the AA group had consistently higher scores than individuals in the YA group (Fig. 3D), suggesting that aging-score assessments are suitable for studying aging-related immune dysfunction.
T97	18534-18727	Sentence	denotes	Figure 3 Changes in transcriptional profiles during aging. (A) UpSet Plot showing the integrated comparative analysis of upregulated DEGs in major immune cell lineages between YA and AA groups.
T98	18728-18935	Sentence	denotes	Upregulated DEGs: upregulated in AA, downregulated in YA group. (B) Representative GO terms and pathways enriched in upregulated DEGs based on functional enrichment analysis in major immune cell populations.
T99	18936-19261	Sentence	denotes	P value was derived by a hypergeometric test. (C) Distribution and comparison of the aging score in immune cell populations. (D) Distribution and comparison of the aging score in all cells of each sample. (E) UpSet plot showing the integrated comparative analysis of upregulated DEGs in CD4+ T cells between YA and AA groups.
T100	19262-19319	Sentence	denotes	Upregulated DEGs: upregulated in AA, downregulated in YA.
T101	19320-19484	Sentence	denotes	The count showing the number of DEGs. (F) Representative GO terms and pathways enriched in upregulated DEGs based on functional enrichment analysis in CD4+ T cells.
T102	19485-19646	Sentence	denotes	P value was derived by a hypergeometric test. (G) Venn diagram showing integrated comparative analysis of upregulated DEGs in monocytes between YA and AA groups.
T103	19647-19704	Sentence	denotes	Upregulated DEGs: upregulated in AA, downregulated in YA.
T104	19705-19866	Sentence	denotes	The count showing the number of DEGs. (H) Representative GO terms and pathways enriched in upregulated DEGs based on functional enrichment analysis in monocytes.
T105	19867-20216	Sentence	denotes	P value was derived by a hypergeometric test. (I) Violin plots showing the distribution of normalized expression levels of selected aging-associated genes in all DC cluster between YA and AA groups. (J) t-SNE plots segregated on the basis of DC subsets. (K) Representative GO terms and pathways enriched in biased DEGs of cDC2-A and cDC2-B clusters.
T106	20217-20373	Sentence	denotes	P value was derived by a hypergeometric test. (L) CLEC12A expression in cDC2 is shown as flow cytometry histogram. (M) Percentage of CLEC12A+ cells in cDC2.
T107	20374-20472	Sentence	denotes	P value are based on two-tailed Mann-Whitney-Wilcoxon tests between YA and AA groups (n = 3/group)
T108	20473-20596	Sentence	denotes	By analyzing age-associated DEGs in CD4+ TCs, we found enrichment in inflammatory and effector genes (Tables S5A and 6A–E).
T109	20597-20763	Sentence	denotes	To determine cell-subtype-specific gene signatures within different CD4+ TC subpopulations, we generated UpSet plots of upregulated DEGs in different CD4+ TC subsets.
T110	20764-20915	Sentence	denotes	We found a range of subtype-specific patterns, including the IL2 receptor (IL2RA) in Naive cells, CCR10 in Tem, and GZMB and TRBV11-2 in Tex (Fig. 3E).
T111	20916-21054	Sentence	denotes	GO and pathway analysis of the DEGs demonstrated that effector and memory subsets were most affected by aging based on the number of DEGs.
T112	21055-21201	Sentence	denotes	For example, in CD4 Tem, TNF, interleukin signaling and apoptotic pathways were enhanced, whereas mRNA processing was impaired (Figs. 3F and S6C).
T113	21202-21356	Sentence	denotes	Analysis of CD8+ TC status indicated that the AA group had increased expression of chemokines and granzyme family members (Fig. S6D; Tables S5B and 7A–D).
T114	21357-21651	Sentence	denotes	Moreover, aging was associated with a decreased proportion of CD8 Naive with increased apoptotic signaling pathway and lymphocyte activation and an expanded CD8 Tem compartment with increased cytokine production as well as reduced chromatin remodeling and antiviral function (Fig. S6E and S6F).
T115	21652-21840	Sentence	denotes	In addition, T-mito in aged group was associated with the upregulated inflammatory signaling molecules HLA-DRB5, PDCD5 and PSMA2 (Fig. S6G; Table S5C) and inflammatory pathways (Fig. S6H).
T116	21841-22008	Sentence	denotes	Analysis of NKs status revealed that the AA group had a smaller fraction of the CD56bright NK1 population and expanded late low-cytotoxic NK subsets than the YA group.
T117	22009-22171	Sentence	denotes	Notably, NKs in the AA group had increased expression of DDIT4, ISG20, and CASP4 and decreased expression of DDX17, PCBP1 and TRIM56 (Figs. 3A, S6A; Table S8A–C).
T118	22172-22347	Sentence	denotes	These genes were mostly enriched in apoptotic signaling pathways and cellular responses to lipopolysaccharide, along with decreased virus defense responses (Fig. S6I and S6J).
T119	22348-22521	Sentence	denotes	As for BCs, we found increased expression of JUNB, IGHA1, SSR4 and CXCR4, indicative of increased memory BC signature and activity during aging (Figs. 3A, S7A; Table S9A–D).
T120	22522-22694	Sentence	denotes	Moreover, the comparative functional analysis of aging-associated DEGs revealed that Naive BC in the AA group had increased cytokine-mediated signaling pathways (Fig. S7B).
T121	22695-22855	Sentence	denotes	Additionally, analysis of downregulated DEGs and pathways in the AA group demonstrated that BCs were associated with reduced viral defense responses (Fig. S7C).
T122	22856-22985	Sentence	denotes	These results indicate that NKs and BCs lose their capacity for antiviral activity with upregulated inflammatory states in aging.
T123	22986-23130	Sentence	denotes	We next studied aging-associated DEGs in MCs and found enrichment in inflammatory genes, such as IL1B, TNF and CXCL8, in the AA group (Fig. 3A).
T124	23131-23290	Sentence	denotes	All MC subsets had increased expression of the chemokines, TNF, IL1B and CDKN1A and decreased expression of SIGLEC14 and CLEC12A (Figs. 3G, S6A; Table S10A–C).
T125	23291-23661	Sentence	denotes	Analysis of aging-related DEGs demonstrated that the CD14 MC subset was most affected by aging, as reflected by the increased NOD-like receptor signaling pathway, NF-κB signaling pathway, Toll-like receptor signaling pathway, inflammasome pathway, and MAPK pathway (Fig. 3H) and the obvious decrease in RNA splicing, autophagy, and vesicle-mediated transport (Fig. S7D).
T126	23662-23848	Sentence	denotes	To complete our DEG and GO survey of immune lineage cells and their subtypes, we next analyzed aging-associated DEGs in DCs in the YA and AA groups (Figs. 3I, S7E and S7I; Table S11A–D).
T127	23849-24034	Sentence	denotes	Among the upregulated DEGs, IFN-stimulated genes (IFITM2, ISG20), TNF and IL1B indicated an overactive inflammatory response in DC clusters in the AA group (Figs. 3A, 3I, S7E, and S7I).
T128	24035-24174	Sentence	denotes	We observed that overrepresented pathways in DCs from the AA group included apoptotic, MAPK, IL-1, and IFN-γ signaling pathways (Fig. S7F).
T129	24175-24519	Sentence	denotes	Notably, CLEC12A and TXNIP, which are critical for the antigen-presentation function of DCs; and MALAT1 and AHR, which are critical to inducing tolerogenic DCs (Son et al., 2008; Hutten et al., 2016; Wu et al., 2018), were decreased in AA DCs (Figs. 3I, S7G, and S7I), reflecting the decreased antigen-presenting ability of aged DCs (Fig. S7H).
T130	24520-24631	Sentence	denotes	These results indicate that DCs acquire an inflammatory state with age but lose the antigen-presenting ability.
T131	24632-24763	Sentence	denotes	Within DC clusters, we found distinct aging manifestations in the cDC2 subsets by comparing DC clusters in the t-SNE map (Fig. 3J).
T132	24764-24932	Sentence	denotes	Cells from the YA group grouped together in clusters 0 and 1 (named cDC2-A), whereas cells in AA group grouped distinctively in clusters 3, 4, 10 and 11 (named cDC2-B).
T133	24933-25237	Sentence	denotes	The expression signature of cDC2-A cells included antigen presentation-related genes such as AHR, CLEC4E, and CLEC12A, whereas the expression signature of cDC2-B cells included inflammatory and aging-associated genes such as IFN-stimulated genes, IL1B, CDKN2D, DDIT4, CXCL8, and DUSP2 (Fig. S7J and S7K).
T134	25238-25574	Sentence	denotes	Moreover, the comparative functional analysis of DEGs between the two clusters indicated that cDC2-A had intact immune regulation and antigen presentation function, while aging-related cDC2-B with high HLA-DQA2 expression exhibited increased inflammatory signaling pathways, such as the response to hypoxia and IL-1 signaling (Fig. 3K).
T135	25575-25665	Sentence	denotes	We further confirmed that CLEC12A+ cDC2s were decreased in aging by FACS (Fig. 3L and 3M).
T136	25666-25831	Sentence	denotes	Taken together, these findings indicate that aging curtails DC antigen presentation ability and upregulates inflammatory and aging-associated gene expression in DCs.
T137	25833-25917	Sentence	denotes	Identification of aging-related cell-type-specific chromosomal accessibility changes
T138	25918-26045	Sentence	denotes	After quality control, a total of 74,102 cells (33,004 YA, 41,098 AA) were used to generate a PBMC chromatin-accessibility map.
T139	26046-26177	Sentence	denotes	MEGAs, TCs, NKs, BCs and myeloid cells were identified based on the promoter sum of genes specifically upregulated in each cluster.
T140	26178-26580	Sentence	denotes	After separately reclustering each lineage population, we identified 3 distinct subsets in CD4+ TCs, 3 distinct subsets in CD8+ TCs, 3 distinct subsets in NKs, 3 distinct subsets in BCs, 3 distinct subsets in DCs and 2 distinct subsets in MCs according to gene peaks and transcription factor (TF) activity using chromVAR (Satpathy et al., 2019) (Fig. 4A, 4B, and S8A–D, see Table S 3C for the details).
T141	26581-26711	Sentence	denotes	Consistent with the scRNA-seq and CyTOF data, we observed a decrease in naive TCs and an increase in MCs in the elderly (Fig. 4C).
T142	26712-26874	Sentence	denotes	Figure 4 Changes in chromosomal accessibility during aging. (A) Heatmaps showing scaled expression of discriminative gene sets for each cell type and cell subset.
T143	26875-27252	Sentence	denotes	Color scheme is based on z-score distribution from −1.5 (purple) to 1.5 (yellow). (B) t-SNE projections of PBMCs derived from scATAC-seq data. (C) t-SNE plots segregated by YA and AA groups. (D) UpSet plot showing the integrated comparative analysis of upregulated differentially expressed transcription factors (DETs) in major immune cell populations between YA and AA groups.
T144	27253-27310	Sentence	denotes	Upregulated DETs: upregulated in AA, downregulated in YA.
T145	27311-27489	Sentence	denotes	The count showing the number of DETs. (E) UpSet plot showing the integrative comparative analysis of downregulated DETs in major immune cell populations between YA and AA groups.
T146	27490-27549	Sentence	denotes	Downregulated DETs: upregulated in YA, downregulated in AA.
T147	27550-27706	Sentence	denotes	The count showing the number of DETs. (F) Venn diagram showing integrated comparative analysis of upregulated DETs in CD4+ T cells between YA and AA groups.
T148	27707-27764	Sentence	denotes	Upregulated DETs: upregulated in AA, downregulated in YA.
T149	27765-27921	Sentence	denotes	The count showing the number of DETs. (G) Venn diagram showing integrated comparative analysis of upregulated DETs in CD8+ T cells between YA and AA groups.
T150	27922-27979	Sentence	denotes	Upregulated DETs: upregulated in AA, downregulated in YA.
T151	27980-28132	Sentence	denotes	The count showing the number of DETs. (H) Venn diagram showing integrated comparative analysis of upregulated DETs in NK cells between YA and AA groups.
T152	28133-28190	Sentence	denotes	Upregulated DETs: upregulated in AA, downregulated in YA.
T153	28191-28371	Sentence	denotes	The count showing the number of DETs. (I) Venn diagram showing integrated comparative analysis of upregulated DETs in B cells (top) and monocytes (bottom) between YA and AA groups.
T154	28372-28429	Sentence	denotes	Upregulated DETs: upregulated in AA, downregulated in YA.
T155	28430-28945	Sentence	denotes	The count showing the number of DETs. (J) Mean scATAC-seq coverage at FOSL2 loci in CD8+ T cells. (K) Mean scATAC-seq coverage at NFATC2 loci in CD8+ T cells. (L) Mean scATAC-seq coverage at CDKN2B loci in B cells. (M) Mean scATAC-seq coverage at SIRT7 loci in NK1 cells. (N) Mean scATAC-seq coverage at GLI2 loci in CD4 Naive cells. (O) Mean scATAC-seq coverage at IFNG loci in CD8 Naive cells. (P) Mean scATAC-seq coverage at DUSP5 loci in CD8 Memory cells. (Q) Mean scATAC-seq coverage at PDCD1 loci in NK3 cells
T156	28946-29081	Sentence	denotes	Next, we focused on the differentially expressed transcription factors (DETs) in immune cells in the AA group compared to the YA group.
T157	29082-29211	Sentence	denotes	At the TF level, MCs were the most affected by aging based on the numbers of upregulated and downregulated DETs (Fig. 4D and 4E).
T158	29212-29437	Sentence	denotes	To identify aging-associated TF events, we performed an integrated comparative analysis of these DETs and found that AP-1 family TFs, including FOSL2 and JUNB, were increased in all immune cells during aging (Fig. 4D and 4E).
T159	29438-29602	Sentence	denotes	Upregulation of AP-1 family TFs, including FOS, FOSB, FOSL1, FOSL2, JUN, JUNB, and JUND, was also observed in almost all cell subsets during aging (Fig. 4F and 4I).
T160	29603-29732	Sentence	denotes	The AP-1 family regulates a wide range of cellular processes, including cell proliferation, death, survival, and differentiation.
T161	29733-30008	Sentence	denotes	The effects of the activated AP-1 TFs, associated with the active inflammatory state, are primarily mediated through combinatorial regulation with the NFAT family, both of which are key regulators of TC activation and are enriched in TCs (Fig. 4D) (Shaulian and Karin, 2002).
T162	30009-30226	Sentence	denotes	In addition, we visualized the chromosomal accessibility of FOSL2 loci and NFATC2 loci and found that the chromosomal accessibility of the FOSL2 and NFATC2 gene regions was also increased in aged TCs (Fig. 4J and 4K).
T163	30227-30319	Sentence	denotes	CDKN2B, an aging hallmark gene, also showed an increase in accessibility with age (Fig. 4L).
T164	30320-30548	Sentence	denotes	In parallel, we found 25 common decreased TFs, including nuclear respiratory factor 1 (NRF1) and ELK4, which are involved in antioxidant stress and negatively regulate cell differentiation and proliferation (Figs. 4E and S8E–I).
T165	30549-30755	Sentence	denotes	Consistently, we found that chromatin accessibility also decreased at the loci of SIRT7 (Fig. 4M), which coordinates with NRF1 to maintain cellular energy metabolism and proliferation (Mohrin et al., 2015).
T166	30756-30946	Sentence	denotes	In TCs, a series of subset-specific TF changes were observed, such as GLI2 in naive cells, which has been associated with decreased TC function and impaired immune defenses (Fig. 4F and 4G).
T167	30947-31031	Sentence	denotes	Consistently, increased chromatin accessibility was detected in GLI2 loci (Fig. 4N).
T168	31032-31222	Sentence	denotes	Analysis of differentially accessible regions (DARs) demonstrated that the IFNG, DUSP5, and GZMB loci were highly accessible, which indicated activated CD8+ TC states (Figs. 4O, 4P and S8J).
T169	31223-31458	Sentence	denotes	In our analysis of NK status, we identified the key TF changes in NK subsets during aging (Fig. 4H), and found that the chromatin accessibility of the inhibitory receptor gene increased, while that of the activating receptor decreased.
T170	31459-31526	Sentence	denotes	These changes may weaken the ability to clear virus-infected cells.
T171	31527-31722	Sentence	denotes	For example, the PDCD1 exhibited higher chromatin accessibility in the gene region of the elderly group, which might be part of the reason why older individuals were prone to infection (Fig. 4Q).
T172	31723-31866	Sentence	denotes	In our analysis of BCs, we identified aging-related TF changes, such as TBX21, IRF4, which are consistent with our scRNA-seq results (Fig. 4I).
T173	31867-32062	Sentence	denotes	Aging-associated TFs and DARs in MCs demonstrated enrichment in inflammatory-related TFs and gene loci in the AA group, such as NF-κB family (REL, RELA), IL1B, TNF and CXCL8 (Figs. 4I and S8K–M).
T174	32063-32215	Sentence	denotes	In summary, aging-related chromosomal accessibility changes are associated with an increase in the inflammatory pathway and an impaired immune response.
T175	32217-32299	Sentence	denotes	Aging-associated heterogeneous changes in clonality and diversity of TCRs and BCRs
T176	32300-32586	Sentence	denotes	Although the antigen repertoire sensed by immunoglobulin receptors on both TCs (TCRs) and BCs (BCRs) is known to continuously evolve with age (Yuseff et al., 2013), the phenomenon of aging-associated TCR and BCR repertoire constriction has not yet been studied at the single-cell level.
T177	32587-32682	Sentence	denotes	Here, we employed scTCR/BCR-seq to assess immune cell clonal expansion in the YA and AA groups.
T178	32683-32902	Sentence	denotes	We found that relative to the YA group, the AA group was associated with a substantial decrease in unique clonotypes both in TCRs and BCRs (Fig. 5A and 5B), suggesting that both TCR and BCR clonality increased with age.
T179	32903-33102	Sentence	denotes	Moreover, quantification of the most highly expanded (maximum) clone for each research subject showed that the ratios of the maximum clones were higher in the AA group than in the YA group (Fig. 5C).
T180	33103-33303	Sentence	denotes	Although an aging-related clonal lymphocyte population may reflect an existing adaptive immunity of the elderly, the overall diversity was decreased in the AA group compared to the YA group (Fig. 5D).
T181	33304-33500	Sentence	denotes	Analysis of TCR and BCR distributions across different TC and BC subtypes revealed that loss of repertoire diversity was pronounced in CD8+, T-mito and memory BCs of the AA group (Fig. 5E and 5F).
T182	33501-33804	Sentence	denotes	To understand how clonally expanded TCs could be affected by aging, we performed DEG analysis of clonal cells between YA and AA groups and revealed increased expression of effector and memory TC signatures, including GZMB, GZMK, CXCR4, CCL3 and various TCR genes in the aged group (Fig. 5G; Table S12A).
T183	33805-34060	Sentence	denotes	In addition, clonal BCs showed aging-associated changes, including increased expression of S100A family genes and decreased levels of naive signature genes such as IGHM and TCL1A in the aged BCs compared to their young counterparts (Fig. 5H; Table S 12B).
T184	34061-34639	Sentence	denotes	Figure 5 Abnormal TCR and BCR repertoire during aging. (A) Pie plots showing TCR clone differences across YA and AA groups. (B) Pie plots showing BCR clone differences across YA and AA groups. (C) Percentage of maximum clones between YA (n = 8) and AA groups (n = 8). (D) Diversity of TCR and BCR between YA (n = 8) and AA groups (n = 8). (E) Diversity of TCR in T cell subsets between YA (n = 8) and AA groups (n = 8). (F) Diversity of BCR in B cell subsets between YA (n = 8) and AA groups (n = 8). (G) Volcano plot showing DEGs of clonal T cells between the YA and AA groups.
T185	34640-34844	Sentence	denotes	P values were calculated using a paired, two-sided Wilcoxon test and FDR was corrected using the Benjamini-Hochberg procedure. (H) Volcano plot showing DEGs of clonal B cells between the YA and AA groups.
T186	34845-35066	Sentence	denotes	P values were calculated using a paired, two-sided Wilcoxon test and FDR was corrected using the Benjamini-Hochberg procedure. (I) Chord diagram showing pairing of V and J segments within the TRB subset from the AA group.
T187	35067-35267	Sentence	denotes	Chord widths represent the proportion of sequences with a given V (colored) and J (gray) segment pairing. (J) Chord diagram showing pairing of V and J segments within the IGH subset from the AA group.
T188	35268-35372	Sentence	denotes	Chord widths represent the proportion of sequences with a given V (colored) and J (gray) segment pairing
T189	35373-35781	Sentence	denotes	To further explore the aging-associated changes on V(D)J rearrangements in TC and BC, we next examined the frequency of genes (variable region) in the YA and AA groups and found that the frequency of several TRAVs, TRBVs, IGHVs, IGKVs and IGLVs changed with age (Fig. S9A–E), indicating that TCs and BCs had experienced unique clonal V(D)J rearrangements under the adaptive immune environment of the elderly.
T190	35782-36033	Sentence	denotes	When analyzing isotype use in BCR repertoires in the YA and AA groups (Fig. S9F), we found that IGHA and IGHG were overrepresented in the AA group compared to the YA group, suggesting that aging might induce more frequent isotype switching (Fig. S9G).
T191	36034-36233	Sentence	denotes	In addition, the chord diagrams of the V-J arrangement for each group showed that aging resulted in multiple cloning sites, suggesting increased antigen exposure with age (Figs. 5I, 5J, S9H and S9I).
T192	36234-36386	Sentence	denotes	The enriched arrangements associated with aging were mainly TRBV6-5, TRBV20-1, and TRBV28 in the TRB subset and IGHV3-33 and IGHV5-51 in the IGH subset.
T193	36387-36684	Sentence	denotes	Taken together, these data show that increased clonality and decreased diversity in aging immune cells, combined with a skewed use of variable region genes, underlie aging-associated abnormalities of TCR and BCR repertoires, elucidating the abnormal immune states and disease spectra during aging.
T194	36686-36790	Sentence	denotes	Age-related imbalance in cellular composition is associated with poor outcomes in patients with COVID-19
T195	36791-37140	Sentence	denotes	To depict how the immune landscape changes with aging and SARS-CoV-2 infection, we enrolled young (YCO, n = 2) and aged (ACO, n = 2) patients with incipient COVID-19 (to assess the acute inflammatory state) in cohort-2 and young (YCR, n = 2) and aged (ACR, n = 2) patients who had recovered from COVID-19 (to assess the recovered state) in cohort-3.
T196	37141-37287	Sentence	denotes	In addition, we performed CyTOF analysis of PBMCs from YH, AH, YCO and ACO individuals in cohort-2 (n = 2 for each group) (Figs. 6A, S10 and S11).
T197	37288-37357	Sentence	denotes	Similar to our CyTOF analysis in cohort-1, we identified 21 clusters:
T198	37358-37470	Sentence	denotes	9 subsets of TCs, 3 subsets of NKs, 4 subsets of BCs, 3 subsets of MCs, and 2 subsets of DCs (Figs. 6B and S11).
T199	37471-37623	Sentence	denotes	We first compared the peripheral immune cell composition between COVID-19 patients (at the onset stage, CO) and their age-matched healthy controls (HC).
T200	37624-37793	Sentence	denotes	Between the CO and HC groups, we found a similar trend of variation to aging, reflected in a decreased percentage of TCs and increased MC and NK populations (Fig. 6C–E).
T201	37794-38023	Sentence	denotes	This trend was also observed at the cell subtype levels, as evidenced by decreased pDC, naive and memory TCs and BCs and increased populations of effector TCs, CD16 MCs, intermediate MCs, ASCs and ABCs (Figs. 6F, 6H, and S12A–L).
T202	38024-38184	Sentence	denotes	Importantly, the aging-associated increase in MCs and decrease in TCs were amplified by COVID-19 in aged patients compared with healthy aged controls (Fig. 6I).
T203	38185-38441	Sentence	denotes	This trend was also observed at the cell subtype level, as reflected by decreased naive TCs and BCs and increased populations of effector TCs, CD16 MCs, ASCs and ABCs in each immune cell composition and total circulating immune cells (Figs. 6J–N and S12M).
T204	38442-38729	Sentence	denotes	Figure 6 Poor outcomes upon COVID-19 infection is associated with imbalanced cellular aging. (A) t-SNE projections of PBMCs derived from mass cytometry data in cohort-2. (B) Heatmap showing mean population expression levels of all markers. (C) t-SNE plots segregated by HC and CO groups.
T205	38730-40280	Sentence	denotes	HC includes YH (n = 2) and AH (n = 2); CO includes YCO (n = 2) and ACO (n = 2). (D) Percentage of immune cell populations in PBMC between HC (n = 4) and CO (n = 4) groups. (E) Bar chart of the relative percentage of major immune cell populations derived from mass cytometry data between HC and CO groups. (F) Percentage of CD4 Naive cells in CD45+ cells between HC (n = 4) and CO (n = 4) groups. (G) Percentage of NK2 cells in CD45+ cells between HC (n = 4) and CO (n = 4) groups. (H) Percentage of CD16 monocytes in CD45+ cells between HC (n = 4) and CO (n = 4) groups. (I) Bar chart of the relative percentage of major immune cells derived from mass cytometry data from YH, AH and ACO groups. (J) Bar chart of the relative percentage of T cell subsets derived from mass cytometry data from YH, AH and ACO groups. (K) Bar chart of the relative percentage of NK cell subsets derived from mass cytometry data from YH, AH and ACO groups. (L) Bar chart of the relative percentage of B cell subsets derived from mass cytometry data from YH, AH and ACO groups. (M) Bar chart of the relative percentage of DC subsets derived from mass cytometry data from YH, AH and ACO groups. (N) Bar chart of the relative percentage of monocyte subsets derived from mass cytometry data from YH, AH and ACO groups. (O) Bar chart of the relative percentage of major immune cell populations derived from mass cytometry data between YCO and ACO groups. (P) CT photography showing the different evolution of Lung Ground-Glass Opacity in young and aged patients with COVID-19.
T206	40281-40305	Sentence	denotes	CT, computed tomography.
T207	40306-40381	Sentence	denotes	P values are based on two-tailed Mann-Whitney-Wilcoxon tests between groups
T208	40382-40557	Sentence	denotes	Notably, we found a higher ratio of MCs, especially inflammatory MCs, and a lower percentage of TCs in aged COVID-19 patients than young COVID-19 patients (Figs. 6O and S12N).
T209	40558-40680	Sentence	denotes	Notably, comparative subgroup analysis demonstrated that naive BCs and pDCs were decreased in aged patients (Fig. S12O–S).
T210	40681-40803	Sentence	denotes	The patients in cohort-2 were diagnosed with severe COVID-19 and presented with similar clinical symptoms and CT findings.
T211	40804-40912	Sentence	denotes	Despite these similarities, the recovery and outcomes in the young and aged patients differed substantially.
T212	40913-41187	Sentence	denotes	As was evident in high-resolution CT scans, ground-glass opacity in the lungs of young patients gradually dissipated after a period of treatment, but this parameter remained associated with extensive fluid buildup (exudation) and pleural effusion in aged patients (Fig. 6P).
T213	41188-41368	Sentence	denotes	Infiltrating MCs can enter the lung and other organs and release abundant levels of inflammatory cytokines and chemokines, exacerbating the infection and leading to fatal outcomes.
T214	41369-41556	Sentence	denotes	Aged COVID-19 patients had more MCs and fewer TCs than young patients, thus lowering the threshold of developing hyperinflammatory states that may trigger cytokine storms and lymphopenia.
T215	41558-41697	Sentence	denotes	Aging increases the expression of susceptibility genes for COVID-19, and COVID-19 enhances upregulation of aging-induced inflammatory genes
T216	41698-41979	Sentence	denotes	To determine how an increased MCs population and decreased TCs population at the onset of SARS-CoV-2 infection contribute to faster disease progression in the elderly at the cellular and molecular levels, we used scRNA-seq to investigate the association between aging and COVID-19.
T217	41980-42211	Sentence	denotes	Specifically, we analyzed DEGs to explore whether differentially expressed SARS-CoV-2-related genes in aged patients could explain the impact that aging had on the susceptibility and recovery of COVID-19 patients in cohort-3 (Figs.
T218	42212-42229	Sentence	denotes	S13A–C and S14A).
T219	42230-42504	Sentence	denotes	ACE2 is not expressed by any blood immune cells, and recent studies have reported that CD147 (encoded by BSG), CD26 and ANPEP might be alternative cellular entry receptors for SARS-CoV-2, especially CD147, in TCs (Han et al., 2020; Qi et al., 2020; Ulrich and Pillat, 2020).
T220	42505-42611	Sentence	denotes	Anti-CD147 antibody has been tested to treat COVID-19 patients with promising effects (Bian et al., 2020).
T221	42612-42742	Sentence	denotes	We found that BSG expression in the AH group was increased in TCs, BCs and DCs, while ANPEP was only upregulated in MCs (Fig. 7A).
T222	42743-42852	Sentence	denotes	Moreover, we found that aging increased the frequency of immune cells that expressed BSG and ANPEP (Fig. 7B).
T223	42853-43033	Sentence	denotes	This result was validated using flow cytometry analysis, which showed increased CD147 expression in CD3+ TCs in the aged people compared with the young group (P = 0.0010, Fig. 7C).
T224	43034-43166	Sentence	denotes	We further observed higher expression of the CD147-related genes NFATC1, ITGB1, and PPIB in CD4 Naive of the AH group (Fig. S14B–D).
T225	43167-43332	Sentence	denotes	In addition, CD26 (encoded by DPP4), another potential SARS-CoV-2 receptor (Radzikowska et al., 2020), was only upregulated in CD4 Naive of the AH group (Fig. S14E).
T226	43333-43519	Sentence	denotes	Altered expression of these molecules in circulating immune cells, especially in CD4 Naive, with age might contribute to increased susceptibility and severity of COVID-19 in the elderly.
T227	43520-43742	Sentence	denotes	Figure 7 Aging and SARS-CoV-2 infection are characterized by similar hyper-inflammatory states. (A) Dot plot showing increased BSG and ANPEP expression in major immune cell populations in the AH group compared to YH group.
T228	43743-43975	Sentence	denotes	P values are based on two-tailed Mann-Whitney-Wilcoxon tests between groups. (B) Expression levels of BSG and ANPEP in specific cell types in YH and AH groups. (C) Recapitulative graph of the MFI of CD147 expression in CD3+ T cells.
T229	43976-44324	Sentence	denotes	MFI, mean fluorescence intensity. (D) Bar charts of the relative percentage of major immune cell populations derived from scRNA-seq data in YH, AH and ACR group (left), YCR and ACR group (right). (E) Venn diagram showing the integrated comparative analysis of upregulated DEGs in T cells between YH and AH group, YCR and YH group, ACR and AH group.
T230	44325-44518	Sentence	denotes	The count shows the number of DEGs. (F) Venn diagram showing the integrated comparative analysis of upregulated DEGs in monocytes between AH and YH groups, YCR and YH groups, ACR and AH groups.
T231	44519-44623	Sentence	denotes	The count shows the number of DEGs. (G) Volcano plot showing DEGs in T cells between YCR and ACR groups.
T232	44624-44821	Sentence	denotes	P values were calculated using a paired, two-sided Wilcoxon test and FDR was corrected using the Benjamini-Hochberg procedure. (H) Volcano plot showing DEGs in monocytes between YCR and ACR groups.
T233	44822-45348	Sentence	denotes	P values were calculated using a paired, two-sided Wilcoxon test and FDR was corrected using the Benjamini-Hochberg procedure. (I) Dot plot showing expression levels of the top 20 aging-induced and disease-associated genes in T cells per group in cohort-3. (J) Dot plot showing expression levels of the top 20 aging-induced and disease-associated genes in monocytes per group in cohort-3. (K) CT photography showing the different manifestation of evolution of Lung Ground-Glass Opacity in young and aged patients with COVID-19
T234	45349-45474	Sentence	denotes	Upregulation of SARS-CoV-2-related genes in aged individuals indicates that aging increases susceptibility to this infection.
T235	45475-45644	Sentence	denotes	Our data demonstrated that the inflammatory response was sustained in the blood environment of COVID-19 patients recovering from SARS-CoV-2 infection (Wen et al., 2020).
T236	45645-45813	Sentence	denotes	In the recovery stage, the aging-associated increase in MCs and decrease in TCs were amplified by COVID-19 in aged covered patients compared with healthy aged controls.
T237	45814-45913	Sentence	denotes	Importantly, the ACR group still had more MCs and fewer TCs than the YCR group (Figs. 7D and S14F).
T238	45914-46151	Sentence	denotes	To compare the effects of age on disease recovery, we next analyzed the upregulated DEGs between the YCR and YH groups and between the ACR and AH groups, along with combined analysis of the upregulated aging-related DEGs (Table S 13-14).
T239	46152-46305	Sentence	denotes	We identified aging-induced and disease-associated genes in TCs, including CD69, JUNB, CDKN2A, and IFN-related genes, including IRF1 and ISG15 (Fig. 7E).
T240	46306-46501	Sentence	denotes	In MCs, we identified several aging-induced genes involved in disease development, such as TNF, IL1B, JUNB, DUSP2, OSM, the CDKN family, IFN-related genes, and chemokine family members (Fig. 7F).
T241	46502-46677	Sentence	denotes	Analysis of the DEGs between TCs of the YCR and ACR groups demonstrated that several granzyme genes and inflammatory genes were increased in aged recovered patients (Fig. 7G).
T242	46678-46889	Sentence	denotes	Moreover, we found that MCs in the ACR group had increased expression of inflammatory genes such as FOS, DUSP1, IL1B, and JUN and chemokines including CXCL8 and CCL3 compared to those of the YCR group (Fig. 7H).
T243	46890-47056	Sentence	denotes	We finally compared the expression of the top 20 specific aging-induced and disease-associated genes among the 4 groups in MCs and TCs, respectively (Fig. 7I and 7J).
T244	47057-47219	Sentence	denotes	The results showed that COVID-19 amplifies aging-induced upregulation of inflammatory genes and senescence hallmark genes (CDKN family) (López-Otín et al., 2013).
T245	47220-47505	Sentence	denotes	As expected, although the initial clinical manifestations and diagnosis were similar, lung ground-glass opacity in young patients had been dissipated and absorbed completely, but in aged patients, it was not absorbed completely at one week after a negative nucleic acid test (Fig. 7K).
T246	47506-47602	Sentence	denotes	These findings indicate that aged people have a slower recovery from COVID-19 than young people.
T247	47603-47769	Sentence	denotes	We next predicted cell-to-cell interactions that might contribute to the distinct functional status of circulating TCs and BCs of the YCR and ACR patients (Fig. S15).
T248	47770-47904	Sentence	denotes	In ACR patients, we discovered that TCs expressed high levels of IFNG, the ligands for IFNGR1, which was expressed on MCs (Fig. S15A).
T249	47905-48004	Sentence	denotes	Other TC-MC interactions involved the inflammatory response, cell-cell signaling and cell adhesion.
T250	48005-48136	Sentence	denotes	Notably, TCs might activate MCs through the expression of CCL5 ligands that bind to CCR1 and contribute to inflammatory activation.
T251	48137-48339	Sentence	denotes	Interestingly, TCs in the ACR patients expressed high levels of IL-4, which was predicted to bind IL-4R and IL-2R in TC-MC interactions and was reported to enhance viral infection (Rogers et al., 2019).
T252	48340-48448	Sentence	denotes	In the ACR group, BCs expressed increased levels of genes encoding ligands of IL1R and TNFRSF1B (Fig. S15B).
T253	48449-48586	Sentence	denotes	The expression of these molecules in MCs suggests that BCs may contribute to the activation of IL1B and TNF signaling in circulating MCs.
T254	48587-48782	Sentence	denotes	Compared with the ACR group, the YCR group was characterized by the presence of signals that negatively regulate the inflammatory response molecules IL10-IL10RA in TC or BC interactions with MCs.
T255	48783-48904	Sentence	denotes	Downregulation of negative regulatory signals may also contribute to the slow dissipation of inflammation in the elderly.
T256	48905-49048	Sentence	denotes	Overall, enhanced inflammatory signals and impaired regulatory signals between TCs and MCs, or, BCs and MCs, slow recovery in elderly patients.
T257	49050-49060	Sentence	denotes	DISCUSSION
T258	49061-49287	Sentence	denotes	Here, we present a comprehensive and integrated single-cell landscape of human circulating immune cell aging and single-cell analysis of immune cells in young and aged COVID-19 patients at the transcriptomic and protein level.
T259	49288-49348	Sentence	denotes	The primary discoveries in the current study are as follows:
T260	49349-50179	Sentence	denotes	1) aging reprograms the human immune cell landscape toward polarized and inflammatory states; 2) aging increases the expression of SARS-CoV-2 susceptibility genes, especially in TCs; 3) an increase in immune cell polarization and circulatory inflammation during aging can be amplified by virus infection in COVID-19; 4) age-associated dendritic cells have increased IFN-stimulated gene expression and a decreased antigen-presenting ability; 5) single-cell TCR and BCR analysis shows that aging is associated with decreased diversity and increased clonality of effector, cytotoxic and exhausted CD8+ TC subsets and ABC subset; 6) single-cell chromosomal accessibility profiles of immune cells shows that the AP-1 family TFs are the most affected by ageing across all cell types and subtypes and are further upregulated in COVID-19.
T261	50180-50322	Sentence	denotes	Numerous studies have reported important observations about the composition and functional alterations of immune cells in animal aging models.
T262	50323-50407	Sentence	denotes	However, animal models fail to recapitulate the human immune environment adequately.
T263	50408-50562	Sentence	denotes	What we know about human immune cells is primarily based on flow cytometric analysis, relying on previously described markers for pooled cell populations.
T264	50563-50675	Sentence	denotes	These analytical methods are too biased to reveal information on selected and not all cells or cell populations.
T265	50676-50912	Sentence	denotes	Single-cell technologies open new avenues in many research fields but are particularly important for analyzing human cells in aging and diseases in an unbiased and global fashion (He et al., 2020; Wang et al., 2020; Zhang et al., 2020).
T266	50913-51290	Sentence	denotes	Using scRNA-seq, recent studies have reported transcriptomic and functional changes in immune cells during aging in mouse cells and tissues such as the central nervous system (Mrdjen et al., 2018), macrophages in brain (Martinez-Jimenez et al., 2017; Van Hove et al., 2019), TCs in spleen (Dulken et al., 2019), and hematopoietic stem cells in bone marrow (Leins et al., 2018).
T267	51291-51382	Sentence	denotes	Recently, our group revealed how aging affects the immune system in rats (Ma et al., 2020).
T268	51383-51519	Sentence	denotes	For humans, mass cytometry analysis showed that aging increased epigenetic variations in circulatory immune cells (Cheung et al., 2018).
T269	51520-51601	Sentence	denotes	However, a comprehensive atlas of immune cell aging has not yet been constructed.
T270	51602-51741	Sentence	denotes	Here, we depicted such an atlas from PBMCs harvested from healthy young and old research subjects and young and old patients with COVID-19.
T271	51742-51857	Sentence	denotes	First, scRNA-seq and CyTOF reveal that aging causes cell compositional changes at the cell type and subtype levels.
T272	51858-51997	Sentence	denotes	Second, our study provides the first high-quality analysis of TCR and BCR repertoires in young and aged adults at a single-cell resolution.
T273	51998-52160	Sentence	denotes	Third, our study provides the first chromosomal accessibility profiles of major immune cells in young and aged healthy research subjects at the single-cell level.
T274	52161-52317	Sentence	denotes	Combined with several novel single-cell methodologies, this study represents a state-of-the-art unbiased and systematic analysis of human immune cell aging.
T275	52318-52584	Sentence	denotes	Mechanistically, we observed age-associated alterations in immune cell type and subtype composition, gene expression, transcriptional regulation, chromosomal accessibility, TCR and BCR repertoires, and cell-cell communication across multiple cell types and subtypes.
T276	52585-52935	Sentence	denotes	Our data suggest that increased numbers of MCs may contribute to cytokine storms in coronavirus infection, as indicated by increased numbers of MCs during aging and further increases in COVID-19, whereas TCs that are critical for virus clearance (Hickman et al., 2015; Herzig et al., 2019) were decreased during aging and further reduced in COVID-19.
T277	52936-53121	Sentence	denotes	Through the analysis of cell subtype composition, we found that naive subsets were profoundly decreased with age, likely weakening the responsive capacity of TCs during viral infection.
T278	53122-53238	Sentence	denotes	In addition, the polarization from naive to effector cells was further enhanced by SARS-CoV-2 infection in COVID-19.
T279	53239-53361	Sentence	denotes	Inflammatory genes such as IL1B, TNF, and CXCL8 were also increased during aging and were further upregulated in COVID-19.
T280	53362-53539	Sentence	denotes	Notably, aging promoted the expression of coronavirus receptor-related genes, such as BSG (encoding CD147), DPP4 (encoding CD26), ITGB1, NFATC1, PPIB and ANPEP, in immune cells.
T281	53540-53740	Sentence	denotes	Collectively, these findings reveal that aging reprograms the landscape of human immune cells toward polarized and inflammatory states and thus increases the susceptibility of COVID-19 in the elderly.
T282	53741-53835	Sentence	denotes	In turn, COVID-19 causes more “aging” of polarization and inflammatory states in immune cells.
T283	53836-53922	Sentence	denotes	This reinforcing feedback loop may underlie the immune system collapse in aged people.
T284	53923-54037	Sentence	denotes	Due to technical limitations, high-dimensional molecular profiles in aging for rare cells such as DCs are lacking.
T285	54038-54103	Sentence	denotes	Here, we overcame this challenge with a novel single-cell method.
T286	54104-54239	Sentence	denotes	Aging increased the percentage of cDC2 cells and decreased the percentage of pDCs that engage antiviral activities by priming CD8+ TCs.
T287	54240-54386	Sentence	denotes	By comparison, aging decreased the expression of CLEC12A, TXNIP, AHR and MALAT1 and increased the expression of HLA-DQA2 and IFN-stimulated genes.
T288	54387-54668	Sentence	denotes	CLEC12A (Hutten et al., 2016) and TXNIP (Son et al., 2008) are critical for the antigen-presentation function of DCs, whereas MALAT1 and AHR are critical for tolerogenic DC differentiation (Takenaka and Quintana, 2017; Wu et al., 2018), and their dysregulation hampers DC function.
T289	54669-54776	Sentence	denotes	Interestingly, HLA-DQA2 and IFN-stimulated genes were distinctly expressed in the cDC2 subset during aging.
T290	54777-55032	Sentence	denotes	Moreover, our functional analysis of DEGs indicates that the aging of DCs was associated with a decrease in the antigen-presenting ability and an increase in activation of inflammatory signaling pathways, such as the response to hypoxia and IFN signaling.
T291	55033-55105	Sentence	denotes	These findings highlight how aging affects DCs composition and function.
T292	55106-55193	Sentence	denotes	In this study, we provide a comprehensive atlas of human circulating immune cell aging.
T293	55194-55426	Sentence	denotes	Furthermore, we reveal novel aging-related genes and adaptive immune dysregulation, thus defining the potential contributions of aging-related immune cell disorganization to the high severity rate of aged COVID-19 patients (Fig. 8).
T294	55427-55626	Sentence	denotes	We believe that these findings will serve as a foundation from which to explore unknown facets of aging etiology and a reference for the broad scientific community interested in immunology and aging.
T295	55627-55745	Sentence	denotes	Figure 8 Aging reprograms human immune cell landscape, and increases the susceptibility and vulnerability of COVID-19.
T296	55746-55880	Sentence	denotes	Schematic illustrating the key innate and adaptive immune functional alterations identified in PBMCs influenced by aging and COVID-19.
T297	55881-55983	Sentence	denotes	Young healthy individuals maintain homeostasis in immune system which could timely eliminate pathogen.
T298	55984-56086	Sentence	denotes	Aging leads to the increase of monocytes (MCs) and the decrease of T cells (TCs) in the immune system.
T299	56087-56338	Sentence	denotes	Aging promotes the polarization of TCs from naive and memory to effector, exhausted and regulatory subtypes and increases the numbers of late natural killer (NK) cells, age-associated B cells, inflammatory MCs, and dysfunctional dendritic cells (DCs).
T300	56339-56482	Sentence	denotes	Moreover, aging induces increased expression of genes related to SARS-CoV-2 susceptibility, suggesting increased susceptibility in the elderly.
T301	56483-56588	Sentence	denotes	Importantly, aging induces DCs to lose the antigen-presenting ability, and turn to an inflammatory state.
T302	56589-56776	Sentence	denotes	Together, a dysregulated immune system and increased expression of genes associated with SARS-CoV-2 susceptibility may at least partially account for COVID-19 vulnerability in the elderly
T303	56778-56799	Sentence	denotes	MATERIALS AND METHODS
T304	56801-56815	Sentence	denotes	Human subjects
T305	56816-56958	Sentence	denotes	The study was approved by the Ethics Committee of Zhongshan Ophthalmic Center, China and the Ethics Committee of Wuhan Hankou Hospital, China.
T306	56959-57146	Sentence	denotes	A written informed consent was routinely obtained from all individuals participating in the study and all relevant ethical regulations regarding human research participants were followed.
T307	57147-57315	Sentence	denotes	Healthy non-frail individuals were recruited in the Zhongshan Ophthalmic Center, and divided by age into two groups in cohort-1: young adults (YA) and aged adults (AA).
T308	57316-57418	Sentence	denotes	The YA group ranged from ages 20 to 45 years old and the AA group ranged from ages 60 to 80 years old.
T309	57419-57607	Sentence	denotes	COVID-19 patients diagnosed by real-time fluorescent quantitative reverse transcription polymerase chain reaction (RT-qPCR) and CT images were enrolled in the Wuhan Hankou Hospital, China.
T310	57608-57884	Sentence	denotes	Based on their clinical history, patients were divided into incipient and recovered groups in cohort-2 and cohort-3 respectively, and the incipient hospitalized patients were further divided by age into young COVID-19 patient onset (YCO) and aged COVID-19 patient onset (ACO).
T311	57885-58071	Sentence	denotes	Enrolled patients that tested negative with nucleic acid transfer in 7–14 days were further divided into young COVID-19 patient recovered (YCR) and aged COVID-19 patient recovered (ACR).
T312	58072-58212	Sentence	denotes	Individuals with comorbid conditions including cancer, immunocompromising disorders, hypertension, diabetes and steroid usage were excluded.
T313	58213-58435	Sentence	denotes	No significant gender differences were detected between YA group and AA group in cohort-1 (Table S1C–E), between YH, AH, YCO and ACO group in cohort-2 (Table S1F), between YH, AH, YCR and ACR group in cohort-3 (Table S1G).
T314	58437-58460	Sentence	denotes	Antibodies and reagents
T315	58461-58614	Sentence	denotes	Antibodies against the following markers in flow cytometric analysis were purchased from Biolegend, BD biosciences and Abcam: CD3 (clone SK7) BV785 (Cat.
T316	58615-58651	Sentence	denotes	344842), CD19 (clone HB19) APC (Cat.
T317	58652-58691	Sentence	denotes	302212), CD88 (clone S5/1) PE/Cy7 (Cat.
T318	58692-58730	Sentence	denotes	344307), CD89 (clone A59) PE/Cy7 (Cat.
T319	58731-58770	Sentence	denotes	354107), HLA-DR (clone L243) FITC (Cat.
T320	58771-58809	Sentence	denotes	307604), CD11c (clone 3.9) BV421 (Cat.
T321	58810-58858	Sentence	denotes	301627), FcεRIa (clone AER-37) PercP/Cy5.5 (Cat.
T322	58859-58897	Sentence	denotes	334622), CD1c (clone L161) BV650 (Cat.
T323	58898-58944	Sentence	denotes	331541), CD371 (CLEC12A) (clone 50C1) PE (Cat.
T324	58945-59011	Sentence	denotes	353603) were purchased from Biolegend, CD147 (clone HIM6) PE (Cat.
T325	59012-59054	Sentence	denotes	562552) was purchased from BD biosciences.
T326	59055-59085	Sentence	denotes	Fetal bovine serum (FBS) (Cat.
T327	59086-59127	Sentence	denotes	10270-106), penicillin/streptomycin (Cat.
T328	59128-59170	Sentence	denotes	15140-122), and Trypsin-EDTA (0.25%) (Cat.
T329	59171-59208	Sentence	denotes	25200-072) were purchased from GIBCO.
T330	59209-59226	Sentence	denotes	RT-qPCR kit (Cat.
T331	59227-59264	Sentence	denotes	25200-072) was purchased from TaKaRa.
T332	59266-59302	Sentence	denotes	Detection of SARS-Cov-2 with RT-qPCR
T333	59303-59458	Sentence	denotes	Samples used for RT-qPCR were blood, upper respiratory tract sputum and throat swab obtained from patients at specified time-points during hospitalization.
T334	59459-59564	Sentence	denotes	The patient samples were collected, processed and analyzed following the guideline stipulated by the WHO.
T335	59565-59710	Sentence	denotes	To extract viral RNA, the specimens were treated with the QIAamp RNA Viral Kit (Qiagen, Heiden, Germany) following the manufacturer’s guidelines.
T336	59711-59926	Sentence	denotes	The presence of SARS-CoV-2 infection was confirmed with a China CDC recommended RT-qPCR kit (TaKaRa, Dalian, China). qPCR was performed as previously described (Zhang et al., 2019; Bi et al., 2020; Li et al., 2020).
T337	59928-59991	Sentence	denotes	Isolation of PBMCs for mass cytometry, scRNA-seq and scATAC-seq
T338	59992-60223	Sentence	denotes	For pipeline analysis, venous blood samples were derived from each healthy donor or patient using Ficoll-Hypaque density solution, heparinized and then processed by standard density gradient centrifugation methods to isolate PBMCs.
T339	60224-60321	Sentence	denotes	The viability and quantity of PBMCs in single-cell suspensions were determined using Trypan Blue.
T340	60322-60371	Sentence	denotes	For each sample, the cell viability exceeded 90%.
T341	60372-60551	Sentence	denotes	For each sample with more than 1 × 107 viable cells, a fraction of PBMCs was extracted for scRNA-seq analysis, a fraction of PBMCs was allocated for scATAC-seq and mass cytometry.
T342	60553-60577	Sentence	denotes	Flow cytometric analysis
T343	60578-60788	Sentence	denotes	PBMCs suspended in phosphate buffered saline (PBS) were cultured with Live/Dead yellow dye (Invitrogen) at 4 °C for 30 min and then washed once with 1 mL of PBS containing 1% FBS (GIBCO, Grand Island, NY, USA).
T344	60789-60857	Sentence	denotes	Subsequently, cells were treated with antibodies for 30 min at 4 °C.
T345	60858-61264	Sentence	denotes	These antibodies included: CD3-BV785 (clone SK7, Biolegend), CD19-APC (clone HB19, Biolegend), CD88-PE/Cy7 (clone S5/1, Biolegend), CD89-PE/Cy7 (clone A59, Biolegend), HLA-DR-FITC (clone L243, Biolegend), CD11c-BV421 (clone 3.9, Biolegend), FcεRIa- PercP/Cy5.5 (clone AER-37, Biolegend), CD1c-BV650 (clone L161, Biolegend), CD147-PE (clone HIM6, BD biosciences), CD371 (CLEC12A)-PE (clone 50C1, Biolegend).
T346	61265-61440	Sentence	denotes	Analysis of PBMCs with flow cytometry was conducted with BD Fortessa (BD Biosciences) and the results were evaluated with FlowJo (version 10.0.7, Tree Star, Ashland, OR, USA).
T347	61442-61497	Sentence	denotes	Mass cytometry live cell barcoding and surface staining
T348	61498-61637	Sentence	denotes	We made use of a live cell barcoding approach to minimize inter-sample staining variability, sample handling time and antibody consumption.
T349	61638-61796	Sentence	denotes	After incubating with anti-human CD45 loaded with different isotopes (89Y, 162Dy, 165Ho, 169Tm, 175Lu), all the samples were then pooled for surface staining.
T350	61797-61965	Sentence	denotes	The Maxpar Direct Immune Profiling Assay (Fluidigm) was used for cell surface staining and the monoclonal anti-human antibodies in the assay kit are listed as Table S2.
T351	61966-62388	Sentence	denotes	Barcoded and combined samples were washed and stained with viability dyes cisplatin-195pt (0.5 μmolL) (Fluidigm, 201064) and vortexed to mix thoroughly for 2 min at room temperature for cell viability, terminated with Maxpar Cell Staining buffer at room temperature (400 rcf.), washed, fixed with 1.6% paraformaldehyde (PFA; Electron Microscopy Sciences) in PBS for 10 min at room temperature on a rotary shaker (500 rpm).
T352	62389-62479	Sentence	denotes	The fixed cells were resuspended in pre-cooling Maxpar Cell Staining to slow fix reaction.
T353	62480-62636	Sentence	denotes	Fixed samples were washed twice with PBS/bovine serum albumin and once with double-distilled water before resuspended in 400 μL of surface-antibody mixture.
T354	62637-62719	Sentence	denotes	Surface staining was performed for 30 min at 37 °C on a rotating shaker (500 rpm).
T355	62720-62907	Sentence	denotes	The samples then stored in freshly diluted 2% formaldehyde (Electron Microscopy Sciences) in PBS containing 0.125 nmol/L iridium 191/193 intercalator (Fluidigm, 201192) at 4 °C overnight.
T356	62909-62968	Sentence	denotes	scRNA-seq data alignment, processing and sample aggregation
T357	62969-63204	Sentence	denotes	The Chromium Single Cell 5′ Library (the 10x Genomics chromium platform Illumina NovaSeq6000), Gel Bead and Multiplex Kit, and Chip Kit (10x Genomics) were used to convert single-cell suspension samples to barcoded scRNA-seq libraries.
T358	63205-63353	Sentence	denotes	Single-cell RNA libraries were prepared using the Chromium Single Cell 5′ v2 Reagent (10x Genomics, 120237) kit as per the manufacturer’s protocols.
T359	63354-63421	Sentence	denotes	The quality of the libraries was checked using the FastQC software.
T360	63422-63552	Sentence	denotes	Initial processing of the sequenced data was performed using CellRanger software (https://support.10xgenomics.com, version 3.1.0).
T361	63553-63736	Sentence	denotes	The command Cell Ranger count in CellRanger Software Suite (10x Genomics) was used to demultiplex and barcode the sequences derived from the 10x Genomics single-cell RNA-seq platform.
T362	63737-64041	Sentence	denotes	The data was filtered, normalized, dimensionality was reduced, clustered, and differential gene expression analysis were performed after calculation of the single-cell expression matrix by CellRanger using Python (version 3.7.7) Scanpy (https://scanpy.readthedocs.io/en/stable/index.html, version 1.4.6).
T363	64042-64181	Sentence	denotes	Data collection and the subsequent analyses were performed in an unsupervised manner, but not blinded to the conditions of the experiments.
T364	64182-64383	Sentence	denotes	For quality control, the filtered cell population was mainly those cells that express HBB, HBA1, and several light and heavy chain transcripts, which identified as the RBC-contaminated cell population.
T365	64384-64529	Sentence	denotes	Likewise, several clusters expressing genes has no significance (P ≥ 0.1, calculate by 10x genomics Loupe Cell Browser with it default algorithm.
T366	64530-64625	Sentence	denotes	P values are adjusted using the Benjamini-Hochberg correction for multiple tests) were removed.
T367	64626-64767	Sentence	denotes	A total of 16 libraries were sequenced, and 166,609 cells (YA 77,652 cells, AA 88,957 cells) were analyzed after quality control in cohort-1.
T368	64768-64926	Sentence	denotes	For cohort-3, 22 libraries and 205,434 cells (YH 79,039 cells, AH 88,750 cells, YCR 19,533 cells, ACR 18,112 cells) were remained for the subsequent analysis.
T369	64927-65226	Sentence	denotes	The genes used in principal component analysis (PCA) analysis have eliminated mitochondria (MT), and ribosomes (RPL and RPS) genes with 50 principal components, and then aligned together, followed by t-distributed stochastic neighbor embedding (t-SNE) are both used after the results of the aligned.
T370	65227-65409	Sentence	denotes	And using the run_harmony function (in pyharmony package, version 1.0.7) and combat function (in Scanpy) methods to deal with batch effect issues if batch effect existing in dataset.
T371	65410-65479	Sentence	denotes	Genes not detected in any cell were removed from subsequent analysis.
T372	65481-65551	Sentence	denotes	Dimensionality reduction and clustering analysis of scRNA-seq datasets
T373	65552-65798	Sentence	denotes	To analyze the scRNA-seq data, we log normalized data (1 + counts per 10,000) with the ‘‘sc.pp.normalize_total’’ function before clustering, reduction and performing 2-dimensional t-SNE algorithm clustering with the first 50 principal components.
T374	65799-65946	Sentence	denotes	This was done following PCA on top 5,000 most variable genes by using “sc.pp.highly_variable_genes” function in Scanpy with the default parameters.
T375	65947-66153	Sentence	denotes	Dimensionality method and identification of significant clusters and was performed using Leiden clustering algorithm which uses a shared nearest neighbour modularity optimization-based clustering algorithm.
T376	66154-66274	Sentence	denotes	Marker genes for each significant cluster were found using the function sc.tl.rank_genes_groups with default parameters.
T377	66276-66308	Sentence	denotes	Differential expression analysis
T378	66309-66558	Sentence	denotes	Differential expression analysis for each cell type between different groups (YA and AA in cohort-1 and YH, AH, YCR and ACR in cohort-3) was performed using the t-test as implemented in the ‘‘sc.tl.rank_genes_groups’’ function of the Scanpy package.
T379	66559-66696	Sentence	denotes	For each cluster, differentially-expressed genes (DEGs) were performed using the t-test and generated relative to all of the other cells.
T380	66697-66856	Sentence	denotes	Before executing the differential expression analysis, we filtered out the cell types that were missing or had fewer than three cells in the comparison groups.
T381	66857-67126	Sentence	denotes	An aging-associated and disease-related DEG dataset was established (adjusted P value < 0.05, \|Log2FC\| > 0.25) after identification of DEGs between AA and YA groups in cohort-1, AH and YH groups in cohort-3, ACR and AH groups in cohort-3, YCR and YH groups in cohort-3.
T382	67127-67243	Sentence	denotes	The ‘‘upregulated DEGs during aging’’ were defined as the DEGs that increased in AA group and decreased in YA group.
T383	67244-67358	Sentence	denotes	The ‘‘downregulated DEGs in aging’’ were defined as the DEGs that decreased in AA group and increased in YA group.
T384	67360-67386	Sentence	denotes	Gene functional annotation
T385	67387-67607	Sentence	denotes	The Metascape webtool (www.metascape.org) (Zhou et al., 2019) that allow visualization of functional patterns of gene clusters and statistical analysis was used to conduct DEGs gene ontology, pathway enrichment analyses.
T386	67608-67771	Sentence	denotes	Among the top 30 enriched GO terms or pathways across various types of cells and tissues, 10 GO terms or pathways which were associated with aging were visualized.
T387	67772-67947	Sentence	denotes	Gene expression profile cluster plots and heatmaps were established using the pheatmap R package (https://cran.r-project.org/web/packages/pheatmap/index.html, version 1.0.12).
T388	67949-67969	Sentence	denotes	Aging score analysis
T389	67970-68113	Sentence	denotes	To assess the impact of aging in circulating immune cells, we selected the top 20 genes out of 60 common upregulated genes in all immune cells.
T390	68114-68236	Sentence	denotes	Aging scores were estimated for all cells as the average of the scaled (Z-normalized) expression of the genes in the list.
T391	68237-68365	Sentence	denotes	The score of aging for all immune cell types can be used to predict the effect of aging on single cells and cell subtype levels.
T392	68366-68633	Sentence	denotes	Calculation of the scores was done as follows: the score of the aging gene set in the given cell-subset (named as X) was computed as the sum of all UMI for all the aging genes expressed in X cell, divided by the sum of all UMI expressed by X cell (Pont et al., 2019).
T393	68635-68679	Sentence	denotes	Sequencing and analysis of TCR and BCR V(D)J
T394	68680-68860	Sentence	denotes	PCR amplification was done to enrich the full-length TCR/BCR V(D)J segments for the amplified cDNA from 5′ libraries with a Chromium Single-Cell V(D)J Enrichment kit (10 Genomics).
T395	68861-69038	Sentence	denotes	The TCR/BCR sequences of each T/B cell were clustered using the CellRanger vdj pipeline (version 3.1.0, allowing identification of CDR3 sequence and the rearranged TCR/BCR gene.
T396	69039-69150	Sentence	denotes	Analysis was performed using Loupe V(D)J Browser version 2.0.1 (https://support.10xgenomics.com, 10x Genomics).
T397	69151-69259	Sentence	denotes	In summary, barcode information a containing clonotype frequency and TCR/BCR diversity metric were obtained.
T398	69260-69378	Sentence	denotes	We projected T /B cells with dominant TCR/BCR clonotypes on a t-SNE plot using barcode information (Wen et al., 2020).
T399	69380-69418	Sentence	denotes	Determination of cell-cell interaction
T400	69419-69541	Sentence	denotes	We employed the expression of immune-related ligands and receptors to assess the cell-cell interactions (Ma et al., 2020).
T401	69542-69812	Sentence	denotes	The possible ligand-receptor interactions between one set of receptor-expressing cells and then next ligand-expressing cells were determined as the average of the product of receptor and ligand expression (respectively from set one and two) across all single-cell pairs:
T402	69813-70561	Sentence	denotes	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$I = \mathop \sum \limits_{i}^{n} I_{i} \times \mathop \sum \limits_{j}^{m} r_{j} \left(\frac{1}{m*n}\right)$$\end{document}I=∑inIi×∑jmrj1m∗nwhere I refers to the interaction score between receptor expressing cells in set one and ligand-expressing cells in set two, Ii stands for the ligand expression of cell i in cell set one, rj represents the receptor expression of cell j in cell set two, n stands for the number of cells in set one and m denotes the number of cells in set two.
T403	70562-70700	Sentence	denotes	In the gene list, there were 168 pairs of well-annotated ligands and receptors, among which were co-stimulators, chemokines and cytokines.
T404	70701-70923	Sentence	denotes	The possible interactions between two cell types were orchestrated by receptor-ligand pairs by the product of the average expression levels of the ligand in one cell type and the respective receptor in the other cell type.
T405	70925-70970	Sentence	denotes	Mass cytometry processing and quality control
T406	70971-71194	Sentence	denotes	CyTOF data were acquired with a CyTOF2 system using a SuperSampler fluidics system (Victorian Airships) at an event rate of < 400 events per second and normalized with Helios normalizer software (Fluidigm version 6.7.1016).
T407	71195-71328	Sentence	denotes	Acquisitions from different days (three independent acquisitions were performed) were normalized using five-element beads (Fluidigm).
T408	71329-71431	Sentence	denotes	Barcoded samples were deconvoluted and cross-sample doublets were filtered using cytobank application.
T409	71432-71717	Sentence	denotes	CyTOF data was pre-processed with Cytobank (https://mtsinai.cytobank.org; Cytobank, 7.0) to sequentially remove calibration beads, dead cells, debris and barcodes for CD45+ PBMCs based on event length, DNA (191Ir and 193Ir) and live cell (195Pt) channels and then export the FCS files.
T410	71718-71829	Sentence	denotes	We analyzed 200,000 PBMCs in cohort-1 and 160,000 PBMC in cohort-2, with an average of 20,000 cells per sample.
T411	71831-71872	Sentence	denotes	Mass cytometry visualizing and clustering
T412	71873-71984	Sentence	denotes	We created mass cytometry datasets for analysis by concatenating cells from all individuals for each cell type.
T413	71985-72196	Sentence	denotes	In this way, we created downsampled datasets of 95,316 TCs, 35,254 NKs, 22,042 BCs, 39,144 MCs and 8,244 DCs in cohort-1 and 57,910 TCs, 34,857 NKs, 13,812 BCs, 45,431 MCs and 7,990 DCs in cohort-2 for analysis.
T414	72197-72227	Sentence	denotes	We used FlowCore (65 flowCore:
T415	72228-72322	Sentence	denotes	Basic structures for flow cytometry data.) to read and process FCS files for further analysis.
T416	72323-72445	Sentence	denotes	For sample with more than 20,000 cells, we randomly selected 20,000 cells to ensure that samples were equally represented.
T417	72446-72644	Sentence	denotes	At last, we run the t-SNE dimensionality reduction algorithm on a combined data sample using the Seurat package based on harmony embedding (https://github.com/immunogenomics/harmony, version 1.0.0).
T418	72646-72685	Sentence	denotes	Batch correction of mass cytometry data
T419	72686-72827	Sentence	denotes	PBMC mass cytometry data from 10 subjects of cohort-1 or 8 subjects of cohort-2 was combined and batch normalized using harmony respectively.
T420	72828-72920	Sentence	denotes	First, mass cytometry data from each cohort all subjects was combined into a single dataset.
T421	72921-72993	Sentence	denotes	Second, harmony batch correction was performed using one of the samples.
T422	72994-73109	Sentence	denotes	Third, mass cytometry data were lognormalized in the Seurat’s NormalizeData function across the aggregated dataset.
T423	73111-73189	Sentence	denotes	Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq)
T424	73190-73329	Sentence	denotes	scATAC-seq targeting 4,000 cells per sample was performed using Chromium Single Cell ATAC Library and Gel Bead kit (10x Genomics, 1000110).
T425	73330-73398	Sentence	denotes	Each sample library was uniquely barcoded and quantified by RT-qPCR.
T426	73399-73632	Sentence	denotes	Libraries were then pooled and loaded on an Illumina Novaseq 6000 sequencer (3.5 pmol/L loading concentration, 50 + 8 + 16 + 49 bp read configuration) and sequenced to either 90% saturation or 30,000 unique reads per cell on average.
T427	73633-73864	Sentence	denotes	All protocols to generate scATAC-seq data on the 10x Chromium platform, including sample preparation, library preparation and instrument and sequencing settings, are available here: https://support.10xgenomics.com/single-cell-atac.
T428	73866-73892	Sentence	denotes	scATAC-seq data processing
T429	73894-73915	Sentence	denotes	scATAC-seq processing
T430	73916-74051	Sentence	denotes	scATAC-seq reads were aligned to the GRCh38 (hg38) reference genome and quantified using CellRanger-ATAC count (10x Genomics, v.1.0.0).
T431	74053-74079	Sentence	denotes	scATAC-seq quality control
T432	74080-74261	Sentence	denotes	To ensure that each cell was both adequately sequenced and had a high signal-to-background ratio, we filtered cells with less than 1,000 unique fragments and enrichment at TSSs < 8.
T433	74262-74410	Sentence	denotes	To calculate TSS enrichment > 2, genome-wide Tn5-corrected insertions were aggregated ± 2,000 bp relative (TSS-strand-corrected) to each unique TSS.
T434	74411-74584	Sentence	denotes	This profile was normalized to the mean accessibility ± 1,900–2,000 bp from the TSS, smoothed every 51 bp and the maximum smoothed value was reported as TSS enrichment in R.
T435	74585-74715	Sentence	denotes	To construct a counts matrix for each cell by each feature (peaks), we read each fragment.tsv.gz fill into a GenomicRanges object.
T436	74716-74885	Sentence	denotes	For each Tn5 insertion, which can be thought of as the “start” and “end” of the ATAC fragments, we used findOverlaps to find all overlaps with the feature by insertions.
T437	74886-75012	Sentence	denotes	Then we added a column with the unique id (integer) cell barcode to the overlaps object and fed this into a sparseMatrix in R.
T438	75013-75194	Sentence	denotes	To calculate the fraction of reads/insertions in peaks, we used the colSums of the sparseMatrix and divided it by the number of insertions for each cell id barcode using table in R.
T439	75196-75239	Sentence	denotes	scATAC-seq visualization in genomic regions
T440	75240-75325	Sentence	denotes	To visualize scATAC-seq data, we read the fragments into a GenomicRanges object in R.
T441	75326-75454	Sentence	denotes	We then computed sliding windows across each region we wanted to visualize for every 100 bp “slidingWindows (region, 100, 100)”.
T442	75455-75562	Sentence	denotes	We computed a counts matrix for Tn5-corrected insertions as described above and then binarized this matrix.
T443	75563-75708	Sentence	denotes	We then returned all non-zero indices (binarization) from the matrix (cell × 100-bp intervals) and plotted them in ggplot2 in R with “geom_tile”.
T444	75709-75805	Sentence	denotes	For visualizing aggregate scATAC-seq data, the binarized matrix above was summed and normalized.
T445	75806-75915	Sentence	denotes	Scale factors were computed by taking the binarized sum in the global peak set and normalizing to 10,000,000.
T446	75916-76238	Sentence	denotes	Tracks were then plotted in Loupe Cell Browser, an interactive visualization software that shows scATAC-seq peak profiles for scATAC-seq cell clusters, similar to the analysis done in this manuscript and described at https://support.10xgenomics.com/single-cellatac/software/visualization/latest/what-is-loupe-cell-browser.
T447	76240-76248	Sentence	denotes	chromVAR
T448	76249-76297	Sentence	denotes	We measured global TF activity using chromVAR15.
T449	76298-76479	Sentence	denotes	We used the cell-by-peaks and the Catalog of Inferred Sequence Binding Preferences (CIS-BP) motif (from chromVAR motifs “human_pwms_v1”) matches within these peaks from motifmatchr.
T450	76480-76582	Sentence	denotes	We then computed the GC-bias-corrected deviation scores using the chromVAR “deviationScores” function.
T451	76584-76604	Sentence	denotes	Statistical analysis
T452	76605-76697	Sentence	denotes	The GraphPad Prism Software (version 8.0.2) was employed for data analysis and presentation.
T453	76698-76739	Sentence	denotes	All results are presented as means ± SEM.
T454	76740-76866	Sentence	denotes	Groups were compared with two-tailed Mann-Whitney-Wilcoxon tests and FDR was corrected using the Benjamini-Hochberg procedure.
T455	76867-76952	Sentence	denotes	P value was derived by a hypergeometric test in representative GO terms and pathways.
T456	76954-76987	Sentence	denotes	Electronic supplementary material
T457	76989-77087	Sentence	denotes	Below is the link to the electronic supplementary material.Supplementary material 1 (PDF 10889 kb)
T458	77088-77126	Sentence	denotes	Supplementary material 2 (XLS 3266 kb)

T12

0-12

Sentence

denotes

INTRODUCTION

T13

13-166

Sentence

denotes

The world population is undergoing a rapid expansion of older adults, and thus, exploring how to stay healthy with age has become an urgent global focus.

T14

167-468

Sentence

denotes

Aging leads to numerous physiological changes, including the deterioration of the immune system, rendering the elderly more susceptible to infections, such as the COVID-19 pandemic, and poor responses to vaccines (Ciabattini et al., 2018; Alpert et al., 2019; Onder et al., 2020; Verity et al., 2020).

T15

469-601

Sentence

denotes

Changes observed during aging are often reflected as alterations in the composition and functional declines of diverse immune cells.

T16

602-942

Sentence

denotes

For T cells (TCs), the high frequency of naive cells in young humans progressively decreases along with the accumulation of highly differentiated memory cells (Hakim and Gress, 2007), whereas nonclassical monocytes (MCs) with high levels of plasma tumor necrosis factor (TNF)-α and interleukin (IL)-8 accumulate with age (Ong et al., 2018).

T17

943-1208

Sentence

denotes

In addition, senescence of the immune system in the elderly has been termed “inflammaging”, which refers to increased levels of tissue and circulating proinflammatory cytokines in the absence of an immunological threat (Panda et al., 2009; Franceschi et al., 2018).

T18

1209-1370

Sentence

denotes

Overall, aging is associated with changes in the structure of diverse immune compartments, where accumulating dysfunctional subsets contribute to immune failure.

T19

1371-1581

Sentence

denotes

Seminal studies have provided insights into the compositions and functional alterations occurring during aging, primarily based on previously described markers detected in pooled heterogeneous cell populations.

T20

1582-1813

Sentence

denotes

The recent development of unbiased high-throughput single-cell technologies with high accuracy and specificity has begun to change immunological studies, as researchers worldwide are ushering in the new field of systems immunology.

T21

1814-2069

Sentence

denotes

By using single-cell sequencing, recent studies have reported that cell-to-cell transcriptional variability increases with age in CD4+ TCs (Bahar et al., 2006; Martinez-Jimenez et al., 2017) and in leukocytes from old mouse lungs (Angelidis et al., 2019).

T22

2070-2177

Sentence

denotes

Aging also increases the variations in chromatin modifications of human immune cells (Cheung et al., 2018).

T23

2178-2422

Sentence

denotes

Very recently, many immunological phenotypes, such as intratissue accumulation of proinflammatory cells, have been reported in aging rodent and primate models (Messaoudi et al., 2006; Watson et al., 2017; Hammond et al., 2019; Ma et al., 2020).

T24

2423-2590

Sentence

denotes

However, a comprehensive aging cell atlas of human peripheral blood that systematically connects all the blood lineages and cell subtypes has not yet been constructed.

T25

2591-2872

Sentence

denotes

Here, we applied single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to comprehensively characterize the properties of peripheral blood mononuclear cells (PBMCs) in young and old adults.

T26

2873-3143

Sentence

denotes

We also enrolled young and aged COVID-19 patients in the incipient stage and recovery stage to explore how age influenced the capacity for recovery and prognosis of COVID-19 infection and to better understand the influence of immune dysregulation in aging and infection.

T27

3144-3425

Sentence

denotes

Our data revealed that aging promotes the polarization of TCs from naive and memory to effector, exhausted and regulatory subtypes and increases the numbers of late natural killer cells (NKs), age-associated B cells (BCs), inflammatory MCs, and dysfunctional dendritic cells (DCs).

T28

3426-3658

Sentence

denotes

With single-cell paired T/B cell receptor sequencing (scTCR/BCR-seq), we uncovered decreased diversity and increased clonality of effector, cytotoxic and exhausted CD8+ TC subsets in TCs and age-associated B subsets in BCs with age.

T29

3659-3819

Sentence

denotes

Notably, aging increased the expression of inflammation-related genes, senescence-related genes, and coronavirus susceptibility genes in specific cell subtypes.

T30

3820-4010

Sentence

denotes

Most impressively, COVID-19 caused similar immune cell landscape changes to that of aging and further increased aging-induced immune cell polarization and upregulation of inflammatory genes.

T31

4011-4286

Sentence

denotes

Increased SARS-CoV-2 susceptibility gene expression and inflammatory MCs and decreased TCs aggravate inflammatory storms and lymphopenia (Mehta et al., 2020; Merad and Martin, 2020; Zhou et al., 2020) and likely underlie the high susceptibility and mortality of old patients.

T32

4287-4568

Sentence

denotes

Overall, this work expands our knowledge of aging via single-cell transcriptomic, proteomic and chromatin accessibility immune cell profiling and highlights critical nodes between the dysregulated immune system and infections that may serve to modulate the process of inflammaging.

T33

4570-4577

Sentence

denotes

RESULTS

T34

4579-4660

Sentence

denotes

Cohort characteristics and single-cell analysis of PBMCs in young and aged adults

T35

4661-4956

Sentence

denotes

To generate a comprehensive immune cell atlas reflecting cellular and systemic adaptations resulting from age and/or COVID-19 infection, we integrated scRNA-seq, CyTOF, scATAC-seq and scTCR/BCR-seq of single-cell PBMC suspensions collected from 3 separate cohorts (Fig. 1A, 1B, and Table S1A–G).

T36

4957-5662

Sentence

denotes

In cohort-1, comprising young healthy adults (YA) (20–45 years old) and aged healthy adults (AA) (≥60 years old), we combined CyTOF (n = 10) and scATAC-seq (n = 10) with scRNA-seq (n = 16) and scTCR/BCR-seq (n = 16); in cohort-2, comprising young healthy (YH) individuals (30–45 years old), aged healthy (AH) individuals (≥60 years old), young COVID-19 onset patients (YCO) (30–50 years old) and aged COVID-19 onset patients (ACO) (≥70 years old), we performed CyTOF analysis (n = 8); and in cohort-3, comprising YH individuals, AH individuals, young recovered COVID-19 patients (YCR) (30–50 years old) and aged recovered COVID-19 patients (ACR) (≥70 years old), we performed scRNA-seq (n = 22) (Fig. 1B).

T37

5663-5932

Sentence

denotes

By combining scRNA-seq, CyTOF, scATAC-seq and scTCR/BCR-seq analysis, we created a comparative framework detailing the impact of aging on cell type distribution and immune cell functions at the transcriptional, proteomic, and chromatin accessibility levels in cohort-1.

T38

5933-6128

Sentence

denotes

In cohort-2, we measured single-cell protein expression using a 26-marker CyTOF panel to discover early cellular changes in incipient COVID-19 patients and how those changes were affected by age.

T39

6129-6267

Sentence

denotes

Finally, in cohort-3, we compared cellular differences between young and aged recovered COVID-19 patients by scRNA-seq analysis (Fig. 1B).

T40

6268-7190

Sentence

denotes

Figure 1 Schematic illustration of the collection and data processing of PBMC from young and aged group. (A) Flowchart overview of PBMC collection in young and aged adults followed by scRNA-seq, mass cytometry, scATAC-seq and scTCR/BCR-seq experiments. (B) Schematic illustration of experimental cohorts; cohort-1: young and aged adults, cohort-2: young and aged healthy individuals, young and aged adults with COVID-19 onset, cohort-3: young and aged healthy individuals, young and aged adults recovered from COVID-19, matched with analysis as indicated: single-cell proteomic data from CyTOF studies, gene expression data from scRNA-seq studies, chromosomal accessibility data from scATAC-seq, and TCR and BCR repertoire data from scTCR/BCR-seq. (C) t-SNE projections of PBMCs derived from scRNA-seq data in cohort-1. (D) Heatmaps showing scaled expression of discriminative gene sets for each cell type and cell subset.

T41

7191-7267

Sentence

denotes

Color scheme is based on z-score distribution from −3 (purple) to 3 (yellow)

T42

7268-7547

Sentence

denotes

We analyzed PBMC single-cell suspensions by CyTOF for the protein expression of several lineage-, activation- and trafficking-associated markers and converted them to barcoded scRNA-seq libraries using 10x Genomics for downstream scRNA-seq, scATAC-seq and scTCR/BCR-seq analysis.

T43

7548-7647

Sentence

denotes

CellRanger software and the Seurat package were used for initial processing of the sequencing data.

T44

7648-7820

Sentence

denotes

Quality metrics included numbers of unique molecular identifiers (UMIs), genes detected per cell, and reads aligned that were comparable across different research subjects.

T45

7821-8082

Sentence

denotes

We identified red blood cells (RBCs), megakaryocytes (MEGAs) and five major immune cell lineages (TCs, NKs, BCs, MCs and DCs) based on the expression of canonical lineage markers and other genes specifically upregulated in each cluster (Figs. 1C, 1D and S1A–C).

T46

8083-8362

Sentence

denotes

In accordance with the scRNA-seq results, we identified five immune cell lineages (TCs, NKs, BCs, MCs and DCs) in CyTOF using t-distributed stochastic neighbor embedding (t-SNE), an unbiased dimensionality reduction algorithm (See Table S2 for a list of antibodies) (Fig. S2A–D).

T47

8363-8515

Sentence

denotes

Cell-type-specific marker genes were determined by differential gene expression values between clusters positioned and visualized in a t-SNE plot (Figs.

T48

8516-8527

Sentence

denotes

S1 and S2).

T49

8528-8642

Sentence

denotes

The definition of cell types in clusters in the t-SNE maps was comparable between old and young individuals (Figs.

T50

8643-8748

Sentence

denotes

S1B and 2B) both by scRNA-seq and CyTOF, indicating that the cell type identity was not altered with age.

T51

8749-9001

Sentence

denotes

Figure 2 Changes in cell proportions during aging. (A) Bar chart of the relative percentage of immune cell types derived from scRNA-seq data in PBMCs. (B) Bar chart of the relative percentage of immune cell subsets derived from scRNA-seq data in PBMCs.

T52

9002-10292

Sentence

denotes

The focused cell-subsets have been marked red. (C) Pie charts showing relative cluster abundances derived from mass cytometry data in the YA and AA groups. (D) Percentage of CD4 Naive cells in PBMCs from YA (n = 8) and AA (n = 8) groups. (E) Percentage of CD8 Naive cells in PBMCs from YA (n = 8) and AA (n = 8) groups. (F) Percentage of CD4 Naive cells in CD45+ cells from YA (n = 5) and AA (n = 5) groups. (G) Percentage of CD8 Naive cells in CD45+ cells from YA (n = 5) and AA (n = 5) groups. (H) Bar chart of the relative percentage of CD4+ T cell subsets derived from scRNA-seq data in PBMCs. (I) Bar chart of the relative percentage of CD8+ T cell subsets derived from scRNA-seq data in PBMCs. (J) Bar chart of the relative percentage of CD4+ T cell subsets derived from mass cytometry data in CD45+ cells. (K) Bar chart of the relative percentage of CD8+ T cell subsets derived from mass cytometry data in CD45+ cells. (L) Percentage of CD14 monocytes in PBMCs from YA (n = 8) and AA (n = 8) groups. (M) Percentage of CD14 monocytes in CD45+ cells from YA (n = 5) and AA (n = 5) groups. (N) Bar chart of the relative percentage of DC subsets derived from scRNA-seq data in PBMCs. (O) Bar chart of the relative percentage of DC subsets derived from mass cytometry data in CD45+ cells.

T53

10293-10368

Sentence

denotes

P values are based on two-tailed Mann-Whitney-Wilcoxon tests between groups

T54

10370-10436

Sentence

denotes

Dissection of immune cell subtypes in the cellular aging ecosystem

T55

10437-10548

Sentence

denotes

To classify each cell subpopulation in an unbiased manner, we separately reclustered the cells of each lineage.

T56

10549-10993

Sentence

denotes

By analyzing the most significantly upregulated genes in each cluster in scRNA-seq analysis, we identified five distinct subsets of CD3+ TCs (Fig. S3A), five distinct subsets of CD4+ TCs (Fig. S3B), four distinct subsets of CD8+ TCs (Fig. S3C), three distinct subsets of NKs (Fig. S3D), four distinct subsets of BCs (Fig. S3E), three distinct subsets of MCs (Fig. S3F) and four distinct subsets of DCs (Fig. S3G, see Table S3A for the details).

T57

10994-11073

Sentence

denotes

Aging affects the development and function of TCs and NKs (Pinti et al., 2016).

T58

11074-11268

Sentence

denotes

We identified known T cell subsets, including CD4+, CD8+, CD4+CD8+, CD4−CD8− and proliferative T cells (mitotic T cells, T-mito), based on the expression of canonical lineage markers (Fig. S3H).

T59

11269-11570

Sentence

denotes

The CD4+ T cells were subdivided into five classes: CCR7high CD69low naive CD4+ T cells (CD4 Naive); CCR7med CD69high CCR6− central memory CD4+ T cells (CD4 Tcm); CCR6+ effector memory CD4+ T cells (CD4 Tem); FOXP3+ regulatory T cells (CD4 Treg) and PDCD1+ exhausted CD4+ T cells (CD4 Tex) (Fig. S3I).

T60

11571-11803

Sentence

denotes

The CD8+ T cells were subdivided into four classes: CCR7+ naive CD8+ T cells (CD8 Naive); GZMK+ effector memory CD8+ T cells (CD8 Tem); GZMB+ GNLY+ cytotoxic CD8+ TCs (CD8 CTL) and PDCD1+ exhausted CD8+ T cells (CD8 Tex) (Fig. S3J).

T61

11804-12104

Sentence

denotes

Analysis of NK cell-status identified circulating NKs with three separate immune states (Fig. S 3D): the CD16 (FCGR3A)low CD56 (NCAM1)bright NK population (NK1), the CD16high CD56dim CD57 (B3GAT1)− low-cytotoxic NK compartment (NK2) and the CD16high CD56dim CD57+ late NK population (NK3) (Fig. S3K).

T62

12105-12445

Sentence

denotes

In addition, we identified four major peripheral B cell subsets: IL4R+ IGHD+ naive B cells (Naive BCs); CD27+ IGHG1+ memory B cells (Memory BCs); plasma cells or so-called antibody-secreting cells (ASCs), expressing high level of immunoglobulin genes MZB1; and a subset of ITGAX+ B cells defined as age-associated B cells (ABCs) (Fig. S3L).

T63

12446-13050

Sentence

denotes

In human peripheral blood myeloid cells (including MCs and DCs), known to promote antigen presentation and inflammatory activities, we identified seven transcriptionally distinct subsets: CD14high CD16− classical monocytes (CD14 MCs), CD14+/− CD16high nonclassical monocytes (CD16 MCs), CD14+ CD16+/− intermediate monocytes (Intermed MCs) (Fig. S3M), CLEC9A+ conventional DC1 (cDC1), CD1c+ cDC2 conventional DC2 (cDC2), CD123 (IL3RA)+ CLEC4C+ plasmacytoid DCs (pDCs) (Fig. S3N), and dendritic cell precursors (pre-DCs) expressing AXL and CD123 (Grabiec and Hussell, 2016; Ruffin et al., 2019) (Fig. S3N).

T64

13051-13152

Sentence

denotes

Therefore, we targeted the immune cell changes based on more precise classification of each subgroup.

T65

13153-13273

Sentence

denotes

To further verify the aging-associated change in the cell ratio, we performed single-cell analysis at the protein level.

T66

13274-13791

Sentence

denotes

Similar to the cell clusters and subsets in scRNA-seq results, in CyTOF analysis, we identified 21 sub-clusters with nine subsets of TCs (CD4 Naive, CD4 Tcm, CD4 Tem, CD4 Treg, CD8 Naive, CD8 Tem, CD8 CTL, CD4+ CD8+, CD4− CD8−), three subsets of NKs (CD56bright NK1, CD16+CD57− NK2 and CD16+CD57+ NK3), four subsets of BCs (Naive BC, Memory BC, ASCs, and ABCs), three subsets of MCs (CD14high MCs, CD16high MCs and intermediate MCs), and two subsets of DCs (pDCs and cDCs) (Fig. S4A–K, see Table S3B for the details).

T67

13793-13865

Sentence

denotes

Aging shifts the cellular composition toward extreme effector phenotypes

T68

13866-14035

Sentence

denotes

To delineate how cell-type composition changed with aging, we separately compared the proportions of each cell type across major cell types between the YA and AA groups.

T69

14036-14157

Sentence

denotes

We observed changes at the single-cell transcriptional level, which were further confirmed at the protein level by CyTOF.

T70

14158-14350

Sentence

denotes

Globally, we found that TCs and BCs, especially the former, decreased by approximately 10% in all PBMCs with scRNA-seq analysis (Fig. 2A, 2B, and S5A) and by 15% with CyTOF (Figs. 2C and S5B).

T71

14351-14483

Sentence

denotes

In contrast, MCs increased by approximately 7% in scRNA-seq analysis (Figs. 2A, 2B, and S5A) and by 10% in CyTOF (Figs. 2C and S5B).

T72

14484-14579

Sentence

denotes

The composition of cell subsets across all cell lineages differed between the YA and AA groups.

T73

14580-14728

Sentence

denotes

Among TCs, CD4+ TCs were increased, CD8+ TCs were decreased, and CD4+CD8+ and proliferating T cells were increased in the AA group (Fig. 2B and 2C).

T74

14729-14890

Sentence

denotes

Moreover, naive TCs, especially CD4 Naive and CD8 Naive, showed a common distribution in the YA group but were reduced in the aged group (P = 0.0175, Fig. 2D–G).

T75

14891-14991

Sentence

denotes

Conversely, effector, memory and exhausted cell subsets were dominant in the aged group (Fig. 2H–K).

T76

14992-15143

Sentence

denotes

The AA group also had a diminished proportion of the CD56bright NK1 population and an expansion of the NK2 and late NK3 populations (Fig. S5C and S5D).

T77

15144-15300

Sentence

denotes

Analysis of BC clusters revealed that Naive BCs were decreased while ABCs were mildly increased in the AA group compared to the YA group (Fig. S5E and S5F).

T78

15301-15504

Sentence

denotes

Our data also showed that elderly research subjects had increased MC subsets, particularly classical CD14 MCs and, to some extent, nonclassical CD16 MCs and intermediate MCs (Figs. 2L, 2M, S5G, and S5H).

T79

15505-15585

Sentence

denotes

Overall MC growth mainly resulted from CD14 MC enrichment (P = 0.0012, Fig. 2M).

T80

15586-15868

Sentence

denotes

However, given that CD14 MCs made up 70%–80% of the MCs population, the increase we observed in CD16 MCs was more remarkable as a change in the overall population proportion between the AA and YA groups, which was not observed for intermediate MCs between these groups (Fig. S5G–J).

T81

15869-16042

Sentence

denotes

A similar analysis of the DC subset composition showed that the percentage of cDC2 cells increased, whereas cDC1, pDC, and pre-DC decreased with age (Figs. 2N, 2O, and S5I).

T82

16043-16176

Sentence

denotes

In summary, these results demonstrate that aging induces an immune dysfunction shift into effector and inflammatory cell populations.

T83

16178-16263

Sentence

denotes

Identification of aging-related cell-type-specific transcriptional expression changes

T84

16264-16474

Sentence

denotes

To identify cell-subtype-specific gene signatures associated with aging, we performed an integrated comparative analysis of differentially expressed genes (DEGs) from blood immune cells in the YA and AA groups.

T85

16475-16599

Sentence

denotes

We found that blood immune cells showed heterogeneous transcriptional changes affected by aging based on the number of DEGs.

T86

16600-16708

Sentence

denotes

Strikingly, BC was the cell type most affected by aging, followed by TC and MC (Figs. 3A, S6A; Table S4A–E).

T87

16709-16974

Sentence

denotes

Specifically, we found a set of 60 genes whose expression was increased in all kinds of immune cells, indicative of general oxidative stress (e.g., DDIT4, CASP4, TSPO) and an inflammatory state (e.g., DUSP2, S100A10, COX5A, PSMB6) across cell populations (Fig. 3A).

T88

16975-17143

Sentence

denotes

Conversely, genes with decreased expression shared across all cell populations included DDX17, RBM39, and SCAF11, which are involved in RNA splicing (Fig. S6A and S6B).

T89

17144-17374

Sentence

denotes

Consistent with our understanding of the main immune cell lineages, we found that the myeloid and lymphocyte cell lineages were characterized by unique gene expression spectra, whereas TCs showed the highest heterogeneity in DEGs.

T90

17375-17531

Sentence

denotes

To explore the biological implications of our data in the context of aging, we used Gene Ontology (GO) and pathway analysis for each immune cell population.

T91

17532-17693

Sentence

denotes

Common aging-upregulated biological pathways included TNF signaling, IL-1 signaling, the apoptotic signaling pathway, and the adaptive immune response (Fig. 3B).

T92

17694-17755

Sentence

denotes

We found that these pathways were especially enhanced in TCs.

T93

17756-17892

Sentence

denotes

In addition, aging-upregulated biological pathways in MCs were enriched for interferon-gamma (IFN-γ) signaling and cell aging (Fig. 3B).

T94

17893-18112

Sentence

denotes

To assess the impact of aging on circulating immune cells, we selected the top 20 genes of the 60 total genes that were upregulated in all immune cells (Fig. 3A) and calculated aging scores across all immune cell types.

T95

18113-18255

Sentence

denotes

We found that MCs and DCs had the highest scores, suggesting that senescent cells are most likely present in these cell populations (Fig. 3C).

T96

18256-18533

Sentence

denotes

Moreover, when calculating the scores of individual samples, we found that individuals in the AA group had consistently higher scores than individuals in the YA group (Fig. 3D), suggesting that aging-score assessments are suitable for studying aging-related immune dysfunction.

T97

18534-18727

Sentence

denotes

Figure 3 Changes in transcriptional profiles during aging. (A) UpSet Plot showing the integrated comparative analysis of upregulated DEGs in major immune cell lineages between YA and AA groups.

T98

18728-18935

Sentence

denotes

Upregulated DEGs: upregulated in AA, downregulated in YA group. (B) Representative GO terms and pathways enriched in upregulated DEGs based on functional enrichment analysis in major immune cell populations.

T99

18936-19261

Sentence

denotes

P value was derived by a hypergeometric test. (C) Distribution and comparison of the aging score in immune cell populations. (D) Distribution and comparison of the aging score in all cells of each sample. (E) UpSet plot showing the integrated comparative analysis of upregulated DEGs in CD4+ T cells between YA and AA groups.

T100

19262-19319

Sentence

denotes

Upregulated DEGs: upregulated in AA, downregulated in YA.

T101

19320-19484

Sentence

denotes

The count showing the number of DEGs. (F) Representative GO terms and pathways enriched in upregulated DEGs based on functional enrichment analysis in CD4+ T cells.

T102

19485-19646

Sentence

denotes

P value was derived by a hypergeometric test. (G) Venn diagram showing integrated comparative analysis of upregulated DEGs in monocytes between YA and AA groups.

T103

19647-19704

Sentence

denotes

Upregulated DEGs: upregulated in AA, downregulated in YA.

T104

19705-19866

Sentence

denotes

The count showing the number of DEGs. (H) Representative GO terms and pathways enriched in upregulated DEGs based on functional enrichment analysis in monocytes.

T105

19867-20216

Sentence

denotes

P value was derived by a hypergeometric test. (I) Violin plots showing the distribution of normalized expression levels of selected aging-associated genes in all DC cluster between YA and AA groups. (J) t-SNE plots segregated on the basis of DC subsets. (K) Representative GO terms and pathways enriched in biased DEGs of cDC2-A and cDC2-B clusters.

T106

20217-20373

Sentence

denotes

P value was derived by a hypergeometric test. (L) CLEC12A expression in cDC2 is shown as flow cytometry histogram. (M) Percentage of CLEC12A+ cells in cDC2.

T107

20374-20472

Sentence

denotes

P value are based on two-tailed Mann-Whitney-Wilcoxon tests between YA and AA groups (n = 3/group)

T108

20473-20596

Sentence

denotes

By analyzing age-associated DEGs in CD4+ TCs, we found enrichment in inflammatory and effector genes (Tables S5A and 6A–E).

T109

20597-20763

Sentence

denotes

To determine cell-subtype-specific gene signatures within different CD4+ TC subpopulations, we generated UpSet plots of upregulated DEGs in different CD4+ TC subsets.

T110

20764-20915

Sentence

denotes

We found a range of subtype-specific patterns, including the IL2 receptor (IL2RA) in Naive cells, CCR10 in Tem, and GZMB and TRBV11-2 in Tex (Fig. 3E).

T111

20916-21054

Sentence

denotes

GO and pathway analysis of the DEGs demonstrated that effector and memory subsets were most affected by aging based on the number of DEGs.

T112

21055-21201

Sentence

denotes

For example, in CD4 Tem, TNF, interleukin signaling and apoptotic pathways were enhanced, whereas mRNA processing was impaired (Figs. 3F and S6C).

T113

21202-21356

Sentence

denotes

Analysis of CD8+ TC status indicated that the AA group had increased expression of chemokines and granzyme family members (Fig. S6D; Tables S5B and 7A–D).

T114

21357-21651

Sentence

denotes

Moreover, aging was associated with a decreased proportion of CD8 Naive with increased apoptotic signaling pathway and lymphocyte activation and an expanded CD8 Tem compartment with increased cytokine production as well as reduced chromatin remodeling and antiviral function (Fig. S6E and S6F).

T115

21652-21840

Sentence

denotes

In addition, T-mito in aged group was associated with the upregulated inflammatory signaling molecules HLA-DRB5, PDCD5 and PSMA2 (Fig. S6G; Table S5C) and inflammatory pathways (Fig. S6H).

T116

21841-22008

Sentence

denotes

Analysis of NKs status revealed that the AA group had a smaller fraction of the CD56bright NK1 population and expanded late low-cytotoxic NK subsets than the YA group.

T117

22009-22171

Sentence

denotes

Notably, NKs in the AA group had increased expression of DDIT4, ISG20, and CASP4 and decreased expression of DDX17, PCBP1 and TRIM56 (Figs. 3A, S6A; Table S8A–C).

T118

22172-22347

Sentence

denotes

These genes were mostly enriched in apoptotic signaling pathways and cellular responses to lipopolysaccharide, along with decreased virus defense responses (Fig. S6I and S6J).

T119

22348-22521

Sentence

denotes

As for BCs, we found increased expression of JUNB, IGHA1, SSR4 and CXCR4, indicative of increased memory BC signature and activity during aging (Figs. 3A, S7A; Table S9A–D).

T120

22522-22694

Sentence

denotes

Moreover, the comparative functional analysis of aging-associated DEGs revealed that Naive BC in the AA group had increased cytokine-mediated signaling pathways (Fig. S7B).

T121

22695-22855

Sentence

denotes

Additionally, analysis of downregulated DEGs and pathways in the AA group demonstrated that BCs were associated with reduced viral defense responses (Fig. S7C).

T122

22856-22985

Sentence

denotes

These results indicate that NKs and BCs lose their capacity for antiviral activity with upregulated inflammatory states in aging.

T123

22986-23130

Sentence

denotes

We next studied aging-associated DEGs in MCs and found enrichment in inflammatory genes, such as IL1B, TNF and CXCL8, in the AA group (Fig. 3A).

T124

23131-23290

Sentence

denotes

All MC subsets had increased expression of the chemokines, TNF, IL1B and CDKN1A and decreased expression of SIGLEC14 and CLEC12A (Figs. 3G, S6A; Table S10A–C).

T125

23291-23661

Sentence

denotes

Analysis of aging-related DEGs demonstrated that the CD14 MC subset was most affected by aging, as reflected by the increased NOD-like receptor signaling pathway, NF-κB signaling pathway, Toll-like receptor signaling pathway, inflammasome pathway, and MAPK pathway (Fig. 3H) and the obvious decrease in RNA splicing, autophagy, and vesicle-mediated transport (Fig. S7D).

T126

23662-23848

Sentence

denotes

To complete our DEG and GO survey of immune lineage cells and their subtypes, we next analyzed aging-associated DEGs in DCs in the YA and AA groups (Figs. 3I, S7E and S7I; Table S11A–D).

T127

23849-24034

Sentence

denotes

Among the upregulated DEGs, IFN-stimulated genes (IFITM2, ISG20), TNF and IL1B indicated an overactive inflammatory response in DC clusters in the AA group (Figs. 3A, 3I, S7E, and S7I).

T128

24035-24174

Sentence

denotes

We observed that overrepresented pathways in DCs from the AA group included apoptotic, MAPK, IL-1, and IFN-γ signaling pathways (Fig. S7F).

T129

24175-24519

Sentence

denotes

Notably, CLEC12A and TXNIP, which are critical for the antigen-presentation function of DCs; and MALAT1 and AHR, which are critical to inducing tolerogenic DCs (Son et al., 2008; Hutten et al., 2016; Wu et al., 2018), were decreased in AA DCs (Figs. 3I, S7G, and S7I), reflecting the decreased antigen-presenting ability of aged DCs (Fig. S7H).

T130

24520-24631

Sentence

denotes

These results indicate that DCs acquire an inflammatory state with age but lose the antigen-presenting ability.

T131

24632-24763

Sentence

denotes

Within DC clusters, we found distinct aging manifestations in the cDC2 subsets by comparing DC clusters in the t-SNE map (Fig. 3J).

T132

24764-24932

Sentence

denotes

Cells from the YA group grouped together in clusters 0 and 1 (named cDC2-A), whereas cells in AA group grouped distinctively in clusters 3, 4, 10 and 11 (named cDC2-B).

T133

24933-25237

Sentence

denotes

The expression signature of cDC2-A cells included antigen presentation-related genes such as AHR, CLEC4E, and CLEC12A, whereas the expression signature of cDC2-B cells included inflammatory and aging-associated genes such as IFN-stimulated genes, IL1B, CDKN2D, DDIT4, CXCL8, and DUSP2 (Fig. S7J and S7K).

T134

25238-25574

Sentence

denotes

Moreover, the comparative functional analysis of DEGs between the two clusters indicated that cDC2-A had intact immune regulation and antigen presentation function, while aging-related cDC2-B with high HLA-DQA2 expression exhibited increased inflammatory signaling pathways, such as the response to hypoxia and IL-1 signaling (Fig. 3K).

T135

25575-25665

Sentence

denotes

We further confirmed that CLEC12A+ cDC2s were decreased in aging by FACS (Fig. 3L and 3M).

T136

25666-25831

Sentence

denotes

Taken together, these findings indicate that aging curtails DC antigen presentation ability and upregulates inflammatory and aging-associated gene expression in DCs.

T137

25833-25917

Sentence

denotes

Identification of aging-related cell-type-specific chromosomal accessibility changes

T138

25918-26045

Sentence

denotes

After quality control, a total of 74,102 cells (33,004 YA, 41,098 AA) were used to generate a PBMC chromatin-accessibility map.

T139

26046-26177

Sentence

denotes

MEGAs, TCs, NKs, BCs and myeloid cells were identified based on the promoter sum of genes specifically upregulated in each cluster.

T140

26178-26580

Sentence

denotes

After separately reclustering each lineage population, we identified 3 distinct subsets in CD4+ TCs, 3 distinct subsets in CD8+ TCs, 3 distinct subsets in NKs, 3 distinct subsets in BCs, 3 distinct subsets in DCs and 2 distinct subsets in MCs according to gene peaks and transcription factor (TF) activity using chromVAR (Satpathy et al., 2019) (Fig. 4A, 4B, and S8A–D, see Table S 3C for the details).

T141

26581-26711

Sentence

denotes

Consistent with the scRNA-seq and CyTOF data, we observed a decrease in naive TCs and an increase in MCs in the elderly (Fig. 4C).

T142

26712-26874

Sentence

denotes

Figure 4 Changes in chromosomal accessibility during aging. (A) Heatmaps showing scaled expression of discriminative gene sets for each cell type and cell subset.

T143

26875-27252

Sentence

denotes

Color scheme is based on z-score distribution from −1.5 (purple) to 1.5 (yellow). (B) t-SNE projections of PBMCs derived from scATAC-seq data. (C) t-SNE plots segregated by YA and AA groups. (D) UpSet plot showing the integrated comparative analysis of upregulated differentially expressed transcription factors (DETs) in major immune cell populations between YA and AA groups.

T144

27253-27310

Sentence

denotes

Upregulated DETs: upregulated in AA, downregulated in YA.

T145

27311-27489

Sentence

denotes

The count showing the number of DETs. (E) UpSet plot showing the integrative comparative analysis of downregulated DETs in major immune cell populations between YA and AA groups.

T146

27490-27549

Sentence

denotes

Downregulated DETs: upregulated in YA, downregulated in AA.

T147

27550-27706

Sentence

denotes

The count showing the number of DETs. (F) Venn diagram showing integrated comparative analysis of upregulated DETs in CD4+ T cells between YA and AA groups.

T148

27707-27764

Sentence

denotes

Upregulated DETs: upregulated in AA, downregulated in YA.

T149

27765-27921

Sentence

denotes

The count showing the number of DETs. (G) Venn diagram showing integrated comparative analysis of upregulated DETs in CD8+ T cells between YA and AA groups.

T150

27922-27979

Sentence

denotes

Upregulated DETs: upregulated in AA, downregulated in YA.

T151

27980-28132

Sentence

denotes

The count showing the number of DETs. (H) Venn diagram showing integrated comparative analysis of upregulated DETs in NK cells between YA and AA groups.

T152

28133-28190

Sentence

denotes

Upregulated DETs: upregulated in AA, downregulated in YA.

T153

28191-28371

Sentence

denotes

The count showing the number of DETs. (I) Venn diagram showing integrated comparative analysis of upregulated DETs in B cells (top) and monocytes (bottom) between YA and AA groups.

T154

28372-28429

Sentence

denotes

Upregulated DETs: upregulated in AA, downregulated in YA.

T155

28430-28945

Sentence

denotes

The count showing the number of DETs. (J) Mean scATAC-seq coverage at FOSL2 loci in CD8+ T cells. (K) Mean scATAC-seq coverage at NFATC2 loci in CD8+ T cells. (L) Mean scATAC-seq coverage at CDKN2B loci in B cells. (M) Mean scATAC-seq coverage at SIRT7 loci in NK1 cells. (N) Mean scATAC-seq coverage at GLI2 loci in CD4 Naive cells. (O) Mean scATAC-seq coverage at IFNG loci in CD8 Naive cells. (P) Mean scATAC-seq coverage at DUSP5 loci in CD8 Memory cells. (Q) Mean scATAC-seq coverage at PDCD1 loci in NK3 cells

T156

28946-29081

Sentence

denotes

Next, we focused on the differentially expressed transcription factors (DETs) in immune cells in the AA group compared to the YA group.

T157

29082-29211

Sentence

denotes

At the TF level, MCs were the most affected by aging based on the numbers of upregulated and downregulated DETs (Fig. 4D and 4E).

T158

29212-29437

Sentence

denotes

To identify aging-associated TF events, we performed an integrated comparative analysis of these DETs and found that AP-1 family TFs, including FOSL2 and JUNB, were increased in all immune cells during aging (Fig. 4D and 4E).

T159

29438-29602

Sentence

denotes

Upregulation of AP-1 family TFs, including FOS, FOSB, FOSL1, FOSL2, JUN, JUNB, and JUND, was also observed in almost all cell subsets during aging (Fig. 4F and 4I).

T160

29603-29732

Sentence

denotes

The AP-1 family regulates a wide range of cellular processes, including cell proliferation, death, survival, and differentiation.

T161

29733-30008

Sentence

denotes

The effects of the activated AP-1 TFs, associated with the active inflammatory state, are primarily mediated through combinatorial regulation with the NFAT family, both of which are key regulators of TC activation and are enriched in TCs (Fig. 4D) (Shaulian and Karin, 2002).

T162

30009-30226

Sentence

denotes

In addition, we visualized the chromosomal accessibility of FOSL2 loci and NFATC2 loci and found that the chromosomal accessibility of the FOSL2 and NFATC2 gene regions was also increased in aged TCs (Fig. 4J and 4K).

T163

30227-30319

Sentence

denotes

CDKN2B, an aging hallmark gene, also showed an increase in accessibility with age (Fig. 4L).

T164

30320-30548

Sentence

denotes

In parallel, we found 25 common decreased TFs, including nuclear respiratory factor 1 (NRF1) and ELK4, which are involved in antioxidant stress and negatively regulate cell differentiation and proliferation (Figs. 4E and S8E–I).

T165

30549-30755

Sentence

denotes

Consistently, we found that chromatin accessibility also decreased at the loci of SIRT7 (Fig. 4M), which coordinates with NRF1 to maintain cellular energy metabolism and proliferation (Mohrin et al., 2015).

T166

30756-30946

Sentence

denotes

In TCs, a series of subset-specific TF changes were observed, such as GLI2 in naive cells, which has been associated with decreased TC function and impaired immune defenses (Fig. 4F and 4G).

T167

30947-31031

Sentence

denotes

Consistently, increased chromatin accessibility was detected in GLI2 loci (Fig. 4N).

T168

31032-31222

Sentence

denotes

Analysis of differentially accessible regions (DARs) demonstrated that the IFNG, DUSP5, and GZMB loci were highly accessible, which indicated activated CD8+ TC states (Figs. 4O, 4P and S8J).

T169

31223-31458

Sentence

denotes

In our analysis of NK status, we identified the key TF changes in NK subsets during aging (Fig. 4H), and found that the chromatin accessibility of the inhibitory receptor gene increased, while that of the activating receptor decreased.

T170

31459-31526

Sentence

denotes

These changes may weaken the ability to clear virus-infected cells.

T171

31527-31722

Sentence

denotes

For example, the PDCD1 exhibited higher chromatin accessibility in the gene region of the elderly group, which might be part of the reason why older individuals were prone to infection (Fig. 4Q).

T172

31723-31866

Sentence

denotes

In our analysis of BCs, we identified aging-related TF changes, such as TBX21, IRF4, which are consistent with our scRNA-seq results (Fig. 4I).

T173

31867-32062

Sentence

denotes

Aging-associated TFs and DARs in MCs demonstrated enrichment in inflammatory-related TFs and gene loci in the AA group, such as NF-κB family (REL, RELA), IL1B, TNF and CXCL8 (Figs. 4I and S8K–M).

T174

32063-32215

Sentence

denotes

In summary, aging-related chromosomal accessibility changes are associated with an increase in the inflammatory pathway and an impaired immune response.

T175

32217-32299

Sentence

denotes

Aging-associated heterogeneous changes in clonality and diversity of TCRs and BCRs

T176

32300-32586

Sentence

denotes

Although the antigen repertoire sensed by immunoglobulin receptors on both TCs (TCRs) and BCs (BCRs) is known to continuously evolve with age (Yuseff et al., 2013), the phenomenon of aging-associated TCR and BCR repertoire constriction has not yet been studied at the single-cell level.

T177

32587-32682

Sentence

denotes

Here, we employed scTCR/BCR-seq to assess immune cell clonal expansion in the YA and AA groups.

T178

32683-32902

Sentence

denotes

We found that relative to the YA group, the AA group was associated with a substantial decrease in unique clonotypes both in TCRs and BCRs (Fig. 5A and 5B), suggesting that both TCR and BCR clonality increased with age.

T179

32903-33102

Sentence

denotes

Moreover, quantification of the most highly expanded (maximum) clone for each research subject showed that the ratios of the maximum clones were higher in the AA group than in the YA group (Fig. 5C).

T180

33103-33303

Sentence

denotes

Although an aging-related clonal lymphocyte population may reflect an existing adaptive immunity of the elderly, the overall diversity was decreased in the AA group compared to the YA group (Fig. 5D).

T181

33304-33500

Sentence

denotes

Analysis of TCR and BCR distributions across different TC and BC subtypes revealed that loss of repertoire diversity was pronounced in CD8+, T-mito and memory BCs of the AA group (Fig. 5E and 5F).

T182

33501-33804

Sentence

denotes

To understand how clonally expanded TCs could be affected by aging, we performed DEG analysis of clonal cells between YA and AA groups and revealed increased expression of effector and memory TC signatures, including GZMB, GZMK, CXCR4, CCL3 and various TCR genes in the aged group (Fig. 5G; Table S12A).

T183

33805-34060

Sentence

denotes

In addition, clonal BCs showed aging-associated changes, including increased expression of S100A family genes and decreased levels of naive signature genes such as IGHM and TCL1A in the aged BCs compared to their young counterparts (Fig. 5H; Table S 12B).

T184

34061-34639

Sentence

denotes

Figure 5 Abnormal TCR and BCR repertoire during aging. (A) Pie plots showing TCR clone differences across YA and AA groups. (B) Pie plots showing BCR clone differences across YA and AA groups. (C) Percentage of maximum clones between YA (n = 8) and AA groups (n = 8). (D) Diversity of TCR and BCR between YA (n = 8) and AA groups (n = 8). (E) Diversity of TCR in T cell subsets between YA (n = 8) and AA groups (n = 8). (F) Diversity of BCR in B cell subsets between YA (n = 8) and AA groups (n = 8). (G) Volcano plot showing DEGs of clonal T cells between the YA and AA groups.

T185

34640-34844

Sentence

denotes

P values were calculated using a paired, two-sided Wilcoxon test and FDR was corrected using the Benjamini-Hochberg procedure. (H) Volcano plot showing DEGs of clonal B cells between the YA and AA groups.

T186

34845-35066

Sentence

denotes

P values were calculated using a paired, two-sided Wilcoxon test and FDR was corrected using the Benjamini-Hochberg procedure. (I) Chord diagram showing pairing of V and J segments within the TRB subset from the AA group.

T187

35067-35267

Sentence

denotes

Chord widths represent the proportion of sequences with a given V (colored) and J (gray) segment pairing. (J) Chord diagram showing pairing of V and J segments within the IGH subset from the AA group.

T188

35268-35372

Sentence

denotes

Chord widths represent the proportion of sequences with a given V (colored) and J (gray) segment pairing

T189

35373-35781

Sentence

denotes

To further explore the aging-associated changes on V(D)J rearrangements in TC and BC, we next examined the frequency of genes (variable region) in the YA and AA groups and found that the frequency of several TRAVs, TRBVs, IGHVs, IGKVs and IGLVs changed with age (Fig. S9A–E), indicating that TCs and BCs had experienced unique clonal V(D)J rearrangements under the adaptive immune environment of the elderly.

T190

35782-36033

Sentence

denotes

When analyzing isotype use in BCR repertoires in the YA and AA groups (Fig. S9F), we found that IGHA and IGHG were overrepresented in the AA group compared to the YA group, suggesting that aging might induce more frequent isotype switching (Fig. S9G).

T191

36034-36233

Sentence

denotes

In addition, the chord diagrams of the V-J arrangement for each group showed that aging resulted in multiple cloning sites, suggesting increased antigen exposure with age (Figs. 5I, 5J, S9H and S9I).

T192

36234-36386

Sentence

denotes

The enriched arrangements associated with aging were mainly TRBV6-5, TRBV20-1, and TRBV28 in the TRB subset and IGHV3-33 and IGHV5-51 in the IGH subset.

T193

36387-36684

Sentence

denotes

Taken together, these data show that increased clonality and decreased diversity in aging immune cells, combined with a skewed use of variable region genes, underlie aging-associated abnormalities of TCR and BCR repertoires, elucidating the abnormal immune states and disease spectra during aging.

T194

36686-36790

Sentence

denotes

Age-related imbalance in cellular composition is associated with poor outcomes in patients with COVID-19

T195

36791-37140

Sentence

denotes

To depict how the immune landscape changes with aging and SARS-CoV-2 infection, we enrolled young (YCO, n = 2) and aged (ACO, n = 2) patients with incipient COVID-19 (to assess the acute inflammatory state) in cohort-2 and young (YCR, n = 2) and aged (ACR, n = 2) patients who had recovered from COVID-19 (to assess the recovered state) in cohort-3.

T196

37141-37287

Sentence

denotes

In addition, we performed CyTOF analysis of PBMCs from YH, AH, YCO and ACO individuals in cohort-2 (n = 2 for each group) (Figs. 6A, S10 and S11).

T197

37288-37357

Sentence

denotes

Similar to our CyTOF analysis in cohort-1, we identified 21 clusters:

T198

37358-37470

Sentence

denotes

9 subsets of TCs, 3 subsets of NKs, 4 subsets of BCs, 3 subsets of MCs, and 2 subsets of DCs (Figs. 6B and S11).

T199

37471-37623

Sentence

denotes

We first compared the peripheral immune cell composition between COVID-19 patients (at the onset stage, CO) and their age-matched healthy controls (HC).

T200

37624-37793

Sentence

denotes

Between the CO and HC groups, we found a similar trend of variation to aging, reflected in a decreased percentage of TCs and increased MC and NK populations (Fig. 6C–E).

T201

37794-38023

Sentence

denotes

This trend was also observed at the cell subtype levels, as evidenced by decreased pDC, naive and memory TCs and BCs and increased populations of effector TCs, CD16 MCs, intermediate MCs, ASCs and ABCs (Figs. 6F, 6H, and S12A–L).

T202

38024-38184

Sentence

denotes

Importantly, the aging-associated increase in MCs and decrease in TCs were amplified by COVID-19 in aged patients compared with healthy aged controls (Fig. 6I).

T203

38185-38441

Sentence

denotes

This trend was also observed at the cell subtype level, as reflected by decreased naive TCs and BCs and increased populations of effector TCs, CD16 MCs, ASCs and ABCs in each immune cell composition and total circulating immune cells (Figs. 6J–N and S12M).

T204

38442-38729

Sentence

denotes

Figure 6 Poor outcomes upon COVID-19 infection is associated with imbalanced cellular aging. (A) t-SNE projections of PBMCs derived from mass cytometry data in cohort-2. (B) Heatmap showing mean population expression levels of all markers. (C) t-SNE plots segregated by HC and CO groups.

T205

38730-40280

Sentence

denotes

HC includes YH (n = 2) and AH (n = 2); CO includes YCO (n = 2) and ACO (n = 2). (D) Percentage of immune cell populations in PBMC between HC (n = 4) and CO (n = 4) groups. (E) Bar chart of the relative percentage of major immune cell populations derived from mass cytometry data between HC and CO groups. (F) Percentage of CD4 Naive cells in CD45+ cells between HC (n = 4) and CO (n = 4) groups. (G) Percentage of NK2 cells in CD45+ cells between HC (n = 4) and CO (n = 4) groups. (H) Percentage of CD16 monocytes in CD45+ cells between HC (n = 4) and CO (n = 4) groups. (I) Bar chart of the relative percentage of major immune cells derived from mass cytometry data from YH, AH and ACO groups. (J) Bar chart of the relative percentage of T cell subsets derived from mass cytometry data from YH, AH and ACO groups. (K) Bar chart of the relative percentage of NK cell subsets derived from mass cytometry data from YH, AH and ACO groups. (L) Bar chart of the relative percentage of B cell subsets derived from mass cytometry data from YH, AH and ACO groups. (M) Bar chart of the relative percentage of DC subsets derived from mass cytometry data from YH, AH and ACO groups. (N) Bar chart of the relative percentage of monocyte subsets derived from mass cytometry data from YH, AH and ACO groups. (O) Bar chart of the relative percentage of major immune cell populations derived from mass cytometry data between YCO and ACO groups. (P) CT photography showing the different evolution of Lung Ground-Glass Opacity in young and aged patients with COVID-19.

T206

40281-40305

Sentence

denotes

CT, computed tomography.

T207

40306-40381

Sentence

denotes

P values are based on two-tailed Mann-Whitney-Wilcoxon tests between groups

T208

40382-40557

Sentence

denotes

Notably, we found a higher ratio of MCs, especially inflammatory MCs, and a lower percentage of TCs in aged COVID-19 patients than young COVID-19 patients (Figs. 6O and S12N).

T209

40558-40680

Sentence

denotes

Notably, comparative subgroup analysis demonstrated that naive BCs and pDCs were decreased in aged patients (Fig. S12O–S).

T210

40681-40803

Sentence

denotes

The patients in cohort-2 were diagnosed with severe COVID-19 and presented with similar clinical symptoms and CT findings.

T211

40804-40912

Sentence

denotes

Despite these similarities, the recovery and outcomes in the young and aged patients differed substantially.

T212

40913-41187

Sentence

denotes

As was evident in high-resolution CT scans, ground-glass opacity in the lungs of young patients gradually dissipated after a period of treatment, but this parameter remained associated with extensive fluid buildup (exudation) and pleural effusion in aged patients (Fig. 6P).

T213

41188-41368

Sentence

denotes

Infiltrating MCs can enter the lung and other organs and release abundant levels of inflammatory cytokines and chemokines, exacerbating the infection and leading to fatal outcomes.

T214

41369-41556

Sentence

denotes

Aged COVID-19 patients had more MCs and fewer TCs than young patients, thus lowering the threshold of developing hyperinflammatory states that may trigger cytokine storms and lymphopenia.

T215

41558-41697

Sentence

denotes

Aging increases the expression of susceptibility genes for COVID-19, and COVID-19 enhances upregulation of aging-induced inflammatory genes

T216

41698-41979

Sentence

denotes

To determine how an increased MCs population and decreased TCs population at the onset of SARS-CoV-2 infection contribute to faster disease progression in the elderly at the cellular and molecular levels, we used scRNA-seq to investigate the association between aging and COVID-19.

T217

41980-42211

Sentence

denotes

Specifically, we analyzed DEGs to explore whether differentially expressed SARS-CoV-2-related genes in aged patients could explain the impact that aging had on the susceptibility and recovery of COVID-19 patients in cohort-3 (Figs.

T218

42212-42229

Sentence

denotes

S13A–C and S14A).

T219

42230-42504

Sentence

denotes

ACE2 is not expressed by any blood immune cells, and recent studies have reported that CD147 (encoded by BSG), CD26 and ANPEP might be alternative cellular entry receptors for SARS-CoV-2, especially CD147, in TCs (Han et al., 2020; Qi et al., 2020; Ulrich and Pillat, 2020).

T220

42505-42611

Sentence

denotes

Anti-CD147 antibody has been tested to treat COVID-19 patients with promising effects (Bian et al., 2020).

T221

42612-42742

Sentence

denotes

We found that BSG expression in the AH group was increased in TCs, BCs and DCs, while ANPEP was only upregulated in MCs (Fig. 7A).

T222

42743-42852

Sentence

denotes

Moreover, we found that aging increased the frequency of immune cells that expressed BSG and ANPEP (Fig. 7B).

T223

42853-43033

Sentence

denotes

This result was validated using flow cytometry analysis, which showed increased CD147 expression in CD3+ TCs in the aged people compared with the young group (P = 0.0010, Fig. 7C).

T224

43034-43166

Sentence

denotes

We further observed higher expression of the CD147-related genes NFATC1, ITGB1, and PPIB in CD4 Naive of the AH group (Fig. S14B–D).

T225

43167-43332

Sentence

denotes

In addition, CD26 (encoded by DPP4), another potential SARS-CoV-2 receptor (Radzikowska et al., 2020), was only upregulated in CD4 Naive of the AH group (Fig. S14E).

T226

43333-43519

Sentence

denotes

Altered expression of these molecules in circulating immune cells, especially in CD4 Naive, with age might contribute to increased susceptibility and severity of COVID-19 in the elderly.

T227

43520-43742

Sentence

denotes

Figure 7 Aging and SARS-CoV-2 infection are characterized by similar hyper-inflammatory states. (A) Dot plot showing increased BSG and ANPEP expression in major immune cell populations in the AH group compared to YH group.

T228

43743-43975

Sentence

denotes

P values are based on two-tailed Mann-Whitney-Wilcoxon tests between groups. (B) Expression levels of BSG and ANPEP in specific cell types in YH and AH groups. (C) Recapitulative graph of the MFI of CD147 expression in CD3+ T cells.

T229

43976-44324

Sentence

denotes

MFI, mean fluorescence intensity. (D) Bar charts of the relative percentage of major immune cell populations derived from scRNA-seq data in YH, AH and ACR group (left), YCR and ACR group (right). (E) Venn diagram showing the integrated comparative analysis of upregulated DEGs in T cells between YH and AH group, YCR and YH group, ACR and AH group.

T230

44325-44518

Sentence

denotes

The count shows the number of DEGs. (F) Venn diagram showing the integrated comparative analysis of upregulated DEGs in monocytes between AH and YH groups, YCR and YH groups, ACR and AH groups.

T231

44519-44623

Sentence

denotes

The count shows the number of DEGs. (G) Volcano plot showing DEGs in T cells between YCR and ACR groups.

T232

44624-44821

Sentence

denotes

P values were calculated using a paired, two-sided Wilcoxon test and FDR was corrected using the Benjamini-Hochberg procedure. (H) Volcano plot showing DEGs in monocytes between YCR and ACR groups.

T233

44822-45348

Sentence

denotes

P values were calculated using a paired, two-sided Wilcoxon test and FDR was corrected using the Benjamini-Hochberg procedure. (I) Dot plot showing expression levels of the top 20 aging-induced and disease-associated genes in T cells per group in cohort-3. (J) Dot plot showing expression levels of the top 20 aging-induced and disease-associated genes in monocytes per group in cohort-3. (K) CT photography showing the different manifestation of evolution of Lung Ground-Glass Opacity in young and aged patients with COVID-19

T234

45349-45474

Sentence

denotes

Upregulation of SARS-CoV-2-related genes in aged individuals indicates that aging increases susceptibility to this infection.

T235

45475-45644

Sentence

denotes

Our data demonstrated that the inflammatory response was sustained in the blood environment of COVID-19 patients recovering from SARS-CoV-2 infection (Wen et al., 2020).

T236

45645-45813

Sentence

denotes

In the recovery stage, the aging-associated increase in MCs and decrease in TCs were amplified by COVID-19 in aged covered patients compared with healthy aged controls.

T237

45814-45913

Sentence

denotes

Importantly, the ACR group still had more MCs and fewer TCs than the YCR group (Figs. 7D and S14F).

T238

45914-46151

Sentence

denotes

To compare the effects of age on disease recovery, we next analyzed the upregulated DEGs between the YCR and YH groups and between the ACR and AH groups, along with combined analysis of the upregulated aging-related DEGs (Table S 13-14).

T239

46152-46305

Sentence

denotes

We identified aging-induced and disease-associated genes in TCs, including CD69, JUNB, CDKN2A, and IFN-related genes, including IRF1 and ISG15 (Fig. 7E).

T240

46306-46501

Sentence

denotes

In MCs, we identified several aging-induced genes involved in disease development, such as TNF, IL1B, JUNB, DUSP2, OSM, the CDKN family, IFN-related genes, and chemokine family members (Fig. 7F).

T241

46502-46677

Sentence

denotes

Analysis of the DEGs between TCs of the YCR and ACR groups demonstrated that several granzyme genes and inflammatory genes were increased in aged recovered patients (Fig. 7G).

T242

46678-46889

Sentence

denotes

Moreover, we found that MCs in the ACR group had increased expression of inflammatory genes such as FOS, DUSP1, IL1B, and JUN and chemokines including CXCL8 and CCL3 compared to those of the YCR group (Fig. 7H).

T243

46890-47056

Sentence

denotes

We finally compared the expression of the top 20 specific aging-induced and disease-associated genes among the 4 groups in MCs and TCs, respectively (Fig. 7I and 7J).

T244

47057-47219

Sentence

denotes

The results showed that COVID-19 amplifies aging-induced upregulation of inflammatory genes and senescence hallmark genes (CDKN family) (López-Otín et al., 2013).

T245

47220-47505

Sentence

denotes

As expected, although the initial clinical manifestations and diagnosis were similar, lung ground-glass opacity in young patients had been dissipated and absorbed completely, but in aged patients, it was not absorbed completely at one week after a negative nucleic acid test (Fig. 7K).

T246

47506-47602

Sentence

denotes

These findings indicate that aged people have a slower recovery from COVID-19 than young people.

T247

47603-47769

Sentence

denotes

We next predicted cell-to-cell interactions that might contribute to the distinct functional status of circulating TCs and BCs of the YCR and ACR patients (Fig. S15).

T248

47770-47904

Sentence

denotes

In ACR patients, we discovered that TCs expressed high levels of IFNG, the ligands for IFNGR1, which was expressed on MCs (Fig. S15A).

T249

47905-48004

Sentence

denotes

Other TC-MC interactions involved the inflammatory response, cell-cell signaling and cell adhesion.

T250

48005-48136

Sentence

denotes

Notably, TCs might activate MCs through the expression of CCL5 ligands that bind to CCR1 and contribute to inflammatory activation.

T251

48137-48339

Sentence

denotes

Interestingly, TCs in the ACR patients expressed high levels of IL-4, which was predicted to bind IL-4R and IL-2R in TC-MC interactions and was reported to enhance viral infection (Rogers et al., 2019).

T252

48340-48448

Sentence

denotes

In the ACR group, BCs expressed increased levels of genes encoding ligands of IL1R and TNFRSF1B (Fig. S15B).

T253

48449-48586

Sentence

denotes

The expression of these molecules in MCs suggests that BCs may contribute to the activation of IL1B and TNF signaling in circulating MCs.

T254

48587-48782

Sentence

denotes

Compared with the ACR group, the YCR group was characterized by the presence of signals that negatively regulate the inflammatory response molecules IL10-IL10RA in TC or BC interactions with MCs.

T255

48783-48904

Sentence

denotes

Downregulation of negative regulatory signals may also contribute to the slow dissipation of inflammation in the elderly.

T256

48905-49048

Sentence

denotes

Overall, enhanced inflammatory signals and impaired regulatory signals between TCs and MCs, or, BCs and MCs, slow recovery in elderly patients.

T257

49050-49060

Sentence

denotes

DISCUSSION

T258

49061-49287

Sentence

denotes

Here, we present a comprehensive and integrated single-cell landscape of human circulating immune cell aging and single-cell analysis of immune cells in young and aged COVID-19 patients at the transcriptomic and protein level.

T259

49288-49348

Sentence

denotes

The primary discoveries in the current study are as follows:

T260

49349-50179

Sentence

denotes

1) aging reprograms the human immune cell landscape toward polarized and inflammatory states; 2) aging increases the expression of SARS-CoV-2 susceptibility genes, especially in TCs; 3) an increase in immune cell polarization and circulatory inflammation during aging can be amplified by virus infection in COVID-19; 4) age-associated dendritic cells have increased IFN-stimulated gene expression and a decreased antigen-presenting ability; 5) single-cell TCR and BCR analysis shows that aging is associated with decreased diversity and increased clonality of effector, cytotoxic and exhausted CD8+ TC subsets and ABC subset; 6) single-cell chromosomal accessibility profiles of immune cells shows that the AP-1 family TFs are the most affected by ageing across all cell types and subtypes and are further upregulated in COVID-19.

T261

50180-50322

Sentence

denotes

Numerous studies have reported important observations about the composition and functional alterations of immune cells in animal aging models.

T262

50323-50407

Sentence

denotes

However, animal models fail to recapitulate the human immune environment adequately.

T263

50408-50562

Sentence

denotes

What we know about human immune cells is primarily based on flow cytometric analysis, relying on previously described markers for pooled cell populations.

T264

50563-50675

Sentence

denotes

These analytical methods are too biased to reveal information on selected and not all cells or cell populations.

T265

50676-50912

Sentence

denotes

Single-cell technologies open new avenues in many research fields but are particularly important for analyzing human cells in aging and diseases in an unbiased and global fashion (He et al., 2020; Wang et al., 2020; Zhang et al., 2020).

T266

50913-51290

Sentence

denotes

Using scRNA-seq, recent studies have reported transcriptomic and functional changes in immune cells during aging in mouse cells and tissues such as the central nervous system (Mrdjen et al., 2018), macrophages in brain (Martinez-Jimenez et al., 2017; Van Hove et al., 2019), TCs in spleen (Dulken et al., 2019), and hematopoietic stem cells in bone marrow (Leins et al., 2018).

T267

51291-51382

Sentence

denotes

Recently, our group revealed how aging affects the immune system in rats (Ma et al., 2020).

T268

51383-51519

Sentence

denotes

For humans, mass cytometry analysis showed that aging increased epigenetic variations in circulatory immune cells (Cheung et al., 2018).

T269

51520-51601

Sentence

denotes

However, a comprehensive atlas of immune cell aging has not yet been constructed.

T270

51602-51741

Sentence

denotes

Here, we depicted such an atlas from PBMCs harvested from healthy young and old research subjects and young and old patients with COVID-19.

T271

51742-51857

Sentence

denotes

First, scRNA-seq and CyTOF reveal that aging causes cell compositional changes at the cell type and subtype levels.

T272

51858-51997

Sentence

denotes

Second, our study provides the first high-quality analysis of TCR and BCR repertoires in young and aged adults at a single-cell resolution.

T273

51998-52160

Sentence

denotes

Third, our study provides the first chromosomal accessibility profiles of major immune cells in young and aged healthy research subjects at the single-cell level.

T274

52161-52317

Sentence

denotes

Combined with several novel single-cell methodologies, this study represents a state-of-the-art unbiased and systematic analysis of human immune cell aging.

T275

52318-52584

Sentence

denotes

Mechanistically, we observed age-associated alterations in immune cell type and subtype composition, gene expression, transcriptional regulation, chromosomal accessibility, TCR and BCR repertoires, and cell-cell communication across multiple cell types and subtypes.

T276

52585-52935

Sentence

denotes

Our data suggest that increased numbers of MCs may contribute to cytokine storms in coronavirus infection, as indicated by increased numbers of MCs during aging and further increases in COVID-19, whereas TCs that are critical for virus clearance (Hickman et al., 2015; Herzig et al., 2019) were decreased during aging and further reduced in COVID-19.

T277

52936-53121

Sentence

denotes

Through the analysis of cell subtype composition, we found that naive subsets were profoundly decreased with age, likely weakening the responsive capacity of TCs during viral infection.

T278

53122-53238

Sentence

denotes

In addition, the polarization from naive to effector cells was further enhanced by SARS-CoV-2 infection in COVID-19.

T279

53239-53361

Sentence

denotes

Inflammatory genes such as IL1B, TNF, and CXCL8 were also increased during aging and were further upregulated in COVID-19.

T280

53362-53539

Sentence

denotes

Notably, aging promoted the expression of coronavirus receptor-related genes, such as BSG (encoding CD147), DPP4 (encoding CD26), ITGB1, NFATC1, PPIB and ANPEP, in immune cells.

T281

53540-53740

Sentence

denotes

Collectively, these findings reveal that aging reprograms the landscape of human immune cells toward polarized and inflammatory states and thus increases the susceptibility of COVID-19 in the elderly.

T282

53741-53835

Sentence

denotes

In turn, COVID-19 causes more “aging” of polarization and inflammatory states in immune cells.

T283

53836-53922

Sentence

denotes

This reinforcing feedback loop may underlie the immune system collapse in aged people.

T284

53923-54037

Sentence

denotes

Due to technical limitations, high-dimensional molecular profiles in aging for rare cells such as DCs are lacking.

T285

54038-54103

Sentence

denotes

Here, we overcame this challenge with a novel single-cell method.

T286

54104-54239

Sentence

denotes

Aging increased the percentage of cDC2 cells and decreased the percentage of pDCs that engage antiviral activities by priming CD8+ TCs.

T287

54240-54386

Sentence

denotes

By comparison, aging decreased the expression of CLEC12A, TXNIP, AHR and MALAT1 and increased the expression of HLA-DQA2 and IFN-stimulated genes.

T288

54387-54668

Sentence

denotes

CLEC12A (Hutten et al., 2016) and TXNIP (Son et al., 2008) are critical for the antigen-presentation function of DCs, whereas MALAT1 and AHR are critical for tolerogenic DC differentiation (Takenaka and Quintana, 2017; Wu et al., 2018), and their dysregulation hampers DC function.

T289

54669-54776

Sentence

denotes

Interestingly, HLA-DQA2 and IFN-stimulated genes were distinctly expressed in the cDC2 subset during aging.

T290

54777-55032

Sentence

denotes

Moreover, our functional analysis of DEGs indicates that the aging of DCs was associated with a decrease in the antigen-presenting ability and an increase in activation of inflammatory signaling pathways, such as the response to hypoxia and IFN signaling.

T291

55033-55105

Sentence

denotes

These findings highlight how aging affects DCs composition and function.

T292

55106-55193

Sentence

denotes

In this study, we provide a comprehensive atlas of human circulating immune cell aging.

T293

55194-55426

Sentence

denotes

Furthermore, we reveal novel aging-related genes and adaptive immune dysregulation, thus defining the potential contributions of aging-related immune cell disorganization to the high severity rate of aged COVID-19 patients (Fig. 8).

T294

55427-55626

Sentence

denotes

We believe that these findings will serve as a foundation from which to explore unknown facets of aging etiology and a reference for the broad scientific community interested in immunology and aging.

T295

55627-55745

Sentence

denotes

Figure 8 Aging reprograms human immune cell landscape, and increases the susceptibility and vulnerability of COVID-19.

T296

55746-55880

Sentence

denotes

Schematic illustrating the key innate and adaptive immune functional alterations identified in PBMCs influenced by aging and COVID-19.

T297

55881-55983

Sentence

denotes

Young healthy individuals maintain homeostasis in immune system which could timely eliminate pathogen.

T298

55984-56086

Sentence

denotes

Aging leads to the increase of monocytes (MCs) and the decrease of T cells (TCs) in the immune system.

T299

56087-56338

Sentence

denotes

Aging promotes the polarization of TCs from naive and memory to effector, exhausted and regulatory subtypes and increases the numbers of late natural killer (NK) cells, age-associated B cells, inflammatory MCs, and dysfunctional dendritic cells (DCs).

T300

56339-56482

Sentence

denotes

Moreover, aging induces increased expression of genes related to SARS-CoV-2 susceptibility, suggesting increased susceptibility in the elderly.

T301

56483-56588

Sentence

denotes

Importantly, aging induces DCs to lose the antigen-presenting ability, and turn to an inflammatory state.

T302

56589-56776

Sentence

denotes

Together, a dysregulated immune system and increased expression of genes associated with SARS-CoV-2 susceptibility may at least partially account for COVID-19 vulnerability in the elderly

T303

56778-56799

Sentence

denotes

MATERIALS AND METHODS

T304

56801-56815

Sentence

denotes

Human subjects

T305

56816-56958

Sentence

denotes

The study was approved by the Ethics Committee of Zhongshan Ophthalmic Center, China and the Ethics Committee of Wuhan Hankou Hospital, China.

T306

56959-57146

Sentence

denotes

A written informed consent was routinely obtained from all individuals participating in the study and all relevant ethical regulations regarding human research participants were followed.

T307

57147-57315

Sentence

denotes

Healthy non-frail individuals were recruited in the Zhongshan Ophthalmic Center, and divided by age into two groups in cohort-1: young adults (YA) and aged adults (AA).

T308

57316-57418

Sentence

denotes

The YA group ranged from ages 20 to 45 years old and the AA group ranged from ages 60 to 80 years old.

T309

57419-57607

Sentence

denotes

COVID-19 patients diagnosed by real-time fluorescent quantitative reverse transcription polymerase chain reaction (RT-qPCR) and CT images were enrolled in the Wuhan Hankou Hospital, China.

T310

57608-57884

Sentence

denotes

Based on their clinical history, patients were divided into incipient and recovered groups in cohort-2 and cohort-3 respectively, and the incipient hospitalized patients were further divided by age into young COVID-19 patient onset (YCO) and aged COVID-19 patient onset (ACO).

T311

57885-58071

Sentence

denotes

Enrolled patients that tested negative with nucleic acid transfer in 7–14 days were further divided into young COVID-19 patient recovered (YCR) and aged COVID-19 patient recovered (ACR).

T312

58072-58212

Sentence

denotes

Individuals with comorbid conditions including cancer, immunocompromising disorders, hypertension, diabetes and steroid usage were excluded.

T313

58213-58435

Sentence

denotes

No significant gender differences were detected between YA group and AA group in cohort-1 (Table S1C–E), between YH, AH, YCO and ACO group in cohort-2 (Table S1F), between YH, AH, YCR and ACR group in cohort-3 (Table S1G).

T314

58437-58460

Sentence

denotes

Antibodies and reagents

T315

58461-58614

Sentence

denotes

Antibodies against the following markers in flow cytometric analysis were purchased from Biolegend, BD biosciences and Abcam: CD3 (clone SK7) BV785 (Cat.

T316

58615-58651

Sentence

denotes

344842), CD19 (clone HB19) APC (Cat.

T317

58652-58691

Sentence

denotes

302212), CD88 (clone S5/1) PE/Cy7 (Cat.

T318

58692-58730

Sentence

denotes

344307), CD89 (clone A59) PE/Cy7 (Cat.

T319

58731-58770

Sentence

denotes

354107), HLA-DR (clone L243) FITC (Cat.

T320

58771-58809

Sentence

denotes

307604), CD11c (clone 3.9) BV421 (Cat.

T321

58810-58858

Sentence

denotes

301627), FcεRIa (clone AER-37) PercP/Cy5.5 (Cat.

T322

58859-58897

Sentence

denotes

334622), CD1c (clone L161) BV650 (Cat.

T323

58898-58944

Sentence

denotes

331541), CD371 (CLEC12A) (clone 50C1) PE (Cat.

T324

58945-59011

Sentence

denotes

353603) were purchased from Biolegend, CD147 (clone HIM6) PE (Cat.

T325

59012-59054

Sentence

denotes

562552) was purchased from BD biosciences.

T326

59055-59085

Sentence

denotes

Fetal bovine serum (FBS) (Cat.

T327

59086-59127

Sentence

denotes

10270-106), penicillin/streptomycin (Cat.

T328

59128-59170

Sentence

denotes

15140-122), and Trypsin-EDTA (0.25%) (Cat.

T329

59171-59208

Sentence

denotes

25200-072) were purchased from GIBCO.

T330

59209-59226

Sentence

denotes

RT-qPCR kit (Cat.

T331

59227-59264

Sentence

denotes

25200-072) was purchased from TaKaRa.

T332

59266-59302

Sentence

denotes

Detection of SARS-Cov-2 with RT-qPCR

T333

59303-59458

Sentence

denotes

Samples used for RT-qPCR were blood, upper respiratory tract sputum and throat swab obtained from patients at specified time-points during hospitalization.

T334

59459-59564

Sentence

denotes

The patient samples were collected, processed and analyzed following the guideline stipulated by the WHO.

T335

59565-59710

Sentence

denotes

To extract viral RNA, the specimens were treated with the QIAamp RNA Viral Kit (Qiagen, Heiden, Germany) following the manufacturer’s guidelines.

T336

59711-59926

Sentence

denotes

The presence of SARS-CoV-2 infection was confirmed with a China CDC recommended RT-qPCR kit (TaKaRa, Dalian, China). qPCR was performed as previously described (Zhang et al., 2019; Bi et al., 2020; Li et al., 2020).

T337

59928-59991

Sentence

denotes

Isolation of PBMCs for mass cytometry, scRNA-seq and scATAC-seq

T338

59992-60223

Sentence

denotes

For pipeline analysis, venous blood samples were derived from each healthy donor or patient using Ficoll-Hypaque density solution, heparinized and then processed by standard density gradient centrifugation methods to isolate PBMCs.

T339

60224-60321

Sentence

denotes

The viability and quantity of PBMCs in single-cell suspensions were determined using Trypan Blue.

T340

60322-60371

Sentence

denotes

For each sample, the cell viability exceeded 90%.

T341

60372-60551

Sentence

denotes

For each sample with more than 1 × 107 viable cells, a fraction of PBMCs was extracted for scRNA-seq analysis, a fraction of PBMCs was allocated for scATAC-seq and mass cytometry.

T342

60553-60577

Sentence

denotes

Flow cytometric analysis

T343

60578-60788

Sentence

denotes

PBMCs suspended in phosphate buffered saline (PBS) were cultured with Live/Dead yellow dye (Invitrogen) at 4 °C for 30 min and then washed once with 1 mL of PBS containing 1% FBS (GIBCO, Grand Island, NY, USA).

T344

60789-60857

Sentence

denotes

Subsequently, cells were treated with antibodies for 30 min at 4 °C.

T345

60858-61264

Sentence

denotes

These antibodies included: CD3-BV785 (clone SK7, Biolegend), CD19-APC (clone HB19, Biolegend), CD88-PE/Cy7 (clone S5/1, Biolegend), CD89-PE/Cy7 (clone A59, Biolegend), HLA-DR-FITC (clone L243, Biolegend), CD11c-BV421 (clone 3.9, Biolegend), FcεRIa- PercP/Cy5.5 (clone AER-37, Biolegend), CD1c-BV650 (clone L161, Biolegend), CD147-PE (clone HIM6, BD biosciences), CD371 (CLEC12A)-PE (clone 50C1, Biolegend).

T346

61265-61440

Sentence

denotes

Analysis of PBMCs with flow cytometry was conducted with BD Fortessa (BD Biosciences) and the results were evaluated with FlowJo (version 10.0.7, Tree Star, Ashland, OR, USA).

T347

61442-61497

Sentence

denotes

Mass cytometry live cell barcoding and surface staining

T348

61498-61637

Sentence

denotes

We made use of a live cell barcoding approach to minimize inter-sample staining variability, sample handling time and antibody consumption.

T349

61638-61796

Sentence

denotes

After incubating with anti-human CD45 loaded with different isotopes (89Y, 162Dy, 165Ho, 169Tm, 175Lu), all the samples were then pooled for surface staining.

T350

61797-61965

Sentence

denotes

The Maxpar Direct Immune Profiling Assay (Fluidigm) was used for cell surface staining and the monoclonal anti-human antibodies in the assay kit are listed as Table S2.

T351

61966-62388

Sentence

denotes

Barcoded and combined samples were washed and stained with viability dyes cisplatin-195pt (0.5 μmolL) (Fluidigm, 201064) and vortexed to mix thoroughly for 2 min at room temperature for cell viability, terminated with Maxpar Cell Staining buffer at room temperature (400 rcf.), washed, fixed with 1.6% paraformaldehyde (PFA; Electron Microscopy Sciences) in PBS for 10 min at room temperature on a rotary shaker (500 rpm).

T352

62389-62479

Sentence

denotes

The fixed cells were resuspended in pre-cooling Maxpar Cell Staining to slow fix reaction.

T353

62480-62636

Sentence

denotes

Fixed samples were washed twice with PBS/bovine serum albumin and once with double-distilled water before resuspended in 400 μL of surface-antibody mixture.

T354

62637-62719

Sentence

denotes

Surface staining was performed for 30 min at 37 °C on a rotating shaker (500 rpm).

T355

62720-62907

Sentence

denotes

The samples then stored in freshly diluted 2% formaldehyde (Electron Microscopy Sciences) in PBS containing 0.125 nmol/L iridium 191/193 intercalator (Fluidigm, 201192) at 4 °C overnight.

T356

62909-62968

Sentence

denotes

scRNA-seq data alignment, processing and sample aggregation

T357

62969-63204

Sentence

denotes

The Chromium Single Cell 5′ Library (the 10x Genomics chromium platform Illumina NovaSeq6000), Gel Bead and Multiplex Kit, and Chip Kit (10x Genomics) were used to convert single-cell suspension samples to barcoded scRNA-seq libraries.

T358

63205-63353

Sentence

denotes

Single-cell RNA libraries were prepared using the Chromium Single Cell 5′ v2 Reagent (10x Genomics, 120237) kit as per the manufacturer’s protocols.

T359

63354-63421

Sentence

denotes

The quality of the libraries was checked using the FastQC software.

T360

63422-63552

Sentence

denotes

Initial processing of the sequenced data was performed using CellRanger software (https://support.10xgenomics.com, version 3.1.0).

T361

63553-63736

Sentence

denotes

The command Cell Ranger count in CellRanger Software Suite (10x Genomics) was used to demultiplex and barcode the sequences derived from the 10x Genomics single-cell RNA-seq platform.

T362

63737-64041

Sentence

denotes

The data was filtered, normalized, dimensionality was reduced, clustered, and differential gene expression analysis were performed after calculation of the single-cell expression matrix by CellRanger using Python (version 3.7.7) Scanpy (https://scanpy.readthedocs.io/en/stable/index.html, version 1.4.6).

T363

64042-64181

Sentence

denotes

Data collection and the subsequent analyses were performed in an unsupervised manner, but not blinded to the conditions of the experiments.

T364

64182-64383

Sentence

denotes

For quality control, the filtered cell population was mainly those cells that express HBB, HBA1, and several light and heavy chain transcripts, which identified as the RBC-contaminated cell population.

T365

64384-64529

Sentence

denotes

Likewise, several clusters expressing genes has no significance (P ≥ 0.1, calculate by 10x genomics Loupe Cell Browser with it default algorithm.

T366

64530-64625

Sentence

denotes

P values are adjusted using the Benjamini-Hochberg correction for multiple tests) were removed.

T367

64626-64767

Sentence

denotes

A total of 16 libraries were sequenced, and 166,609 cells (YA 77,652 cells, AA 88,957 cells) were analyzed after quality control in cohort-1.

T368

64768-64926

Sentence

denotes

For cohort-3, 22 libraries and 205,434 cells (YH 79,039 cells, AH 88,750 cells, YCR 19,533 cells, ACR 18,112 cells) were remained for the subsequent analysis.

T369

64927-65226

Sentence

denotes

The genes used in principal component analysis (PCA) analysis have eliminated mitochondria (MT), and ribosomes (RPL and RPS) genes with 50 principal components, and then aligned together, followed by t-distributed stochastic neighbor embedding (t-SNE) are both used after the results of the aligned.

T370

65227-65409

Sentence

denotes

And using the run_harmony function (in pyharmony package, version 1.0.7) and combat function (in Scanpy) methods to deal with batch effect issues if batch effect existing in dataset.

T371

65410-65479

Sentence

denotes

Genes not detected in any cell were removed from subsequent analysis.

T372

65481-65551

Sentence

denotes

Dimensionality reduction and clustering analysis of scRNA-seq datasets

T373

65552-65798

Sentence

denotes

To analyze the scRNA-seq data, we log normalized data (1 + counts per 10,000) with the ‘‘sc.pp.normalize_total’’ function before clustering, reduction and performing 2-dimensional t-SNE algorithm clustering with the first 50 principal components.

T374

65799-65946

Sentence

denotes

This was done following PCA on top 5,000 most variable genes by using “sc.pp.highly_variable_genes” function in Scanpy with the default parameters.

T375

65947-66153

Sentence

denotes

Dimensionality method and identification of significant clusters and was performed using Leiden clustering algorithm which uses a shared nearest neighbour modularity optimization-based clustering algorithm.

T376

66154-66274

Sentence

denotes

Marker genes for each significant cluster were found using the function sc.tl.rank_genes_groups with default parameters.

T377

66276-66308

Sentence

denotes

Differential expression analysis

T378

66309-66558

Sentence

denotes

Differential expression analysis for each cell type between different groups (YA and AA in cohort-1 and YH, AH, YCR and ACR in cohort-3) was performed using the t-test as implemented in the ‘‘sc.tl.rank_genes_groups’’ function of the Scanpy package.

T379

66559-66696

Sentence

denotes

For each cluster, differentially-expressed genes (DEGs) were performed using the t-test and generated relative to all of the other cells.

T380

66697-66856

Sentence

denotes

Before executing the differential expression analysis, we filtered out the cell types that were missing or had fewer than three cells in the comparison groups.

T381

66857-67126

Sentence

denotes

An aging-associated and disease-related DEG dataset was established (adjusted P value < 0.05, |Log2FC| > 0.25) after identification of DEGs between AA and YA groups in cohort-1, AH and YH groups in cohort-3, ACR and AH groups in cohort-3, YCR and YH groups in cohort-3.

T382

67127-67243

Sentence

denotes

The ‘‘upregulated DEGs during aging’’ were defined as the DEGs that increased in AA group and decreased in YA group.

T383

67244-67358

Sentence

denotes

The ‘‘downregulated DEGs in aging’’ were defined as the DEGs that decreased in AA group and increased in YA group.

T384

67360-67386

Sentence

denotes

Gene functional annotation

T385

67387-67607

Sentence

denotes

The Metascape webtool (www.metascape.org) (Zhou et al., 2019) that allow visualization of functional patterns of gene clusters and statistical analysis was used to conduct DEGs gene ontology, pathway enrichment analyses.

T386

67608-67771

Sentence

denotes

Among the top 30 enriched GO terms or pathways across various types of cells and tissues, 10 GO terms or pathways which were associated with aging were visualized.

T387

67772-67947

Sentence

denotes

Gene expression profile cluster plots and heatmaps were established using the pheatmap R package (https://cran.r-project.org/web/packages/pheatmap/index.html, version 1.0.12).

T388

67949-67969

Sentence

denotes

Aging score analysis

T389

67970-68113

Sentence

denotes

To assess the impact of aging in circulating immune cells, we selected the top 20 genes out of 60 common upregulated genes in all immune cells.

T390

68114-68236

Sentence

denotes

Aging scores were estimated for all cells as the average of the scaled (Z-normalized) expression of the genes in the list.

T391

68237-68365

Sentence

denotes

The score of aging for all immune cell types can be used to predict the effect of aging on single cells and cell subtype levels.

T392

68366-68633

Sentence

denotes

Calculation of the scores was done as follows: the score of the aging gene set in the given cell-subset (named as X) was computed as the sum of all UMI for all the aging genes expressed in X cell, divided by the sum of all UMI expressed by X cell (Pont et al., 2019).

T393

68635-68679

Sentence

denotes

Sequencing and analysis of TCR and BCR V(D)J

T394

68680-68860

Sentence

denotes

PCR amplification was done to enrich the full-length TCR/BCR V(D)J segments for the amplified cDNA from 5′ libraries with a Chromium Single-Cell V(D)J Enrichment kit (10 Genomics).

T395

68861-69038

Sentence

denotes

The TCR/BCR sequences of each T/B cell were clustered using the CellRanger vdj pipeline (version 3.1.0, allowing identification of CDR3 sequence and the rearranged TCR/BCR gene.

T396

69039-69150

Sentence

denotes

Analysis was performed using Loupe V(D)J Browser version 2.0.1 (https://support.10xgenomics.com, 10x Genomics).

T397

69151-69259

Sentence

denotes

In summary, barcode information a containing clonotype frequency and TCR/BCR diversity metric were obtained.

T398

69260-69378

Sentence

denotes

We projected T /B cells with dominant TCR/BCR clonotypes on a t-SNE plot using barcode information (Wen et al., 2020).

T399

69380-69418

Sentence

denotes

Determination of cell-cell interaction

T400

69419-69541

Sentence

denotes

We employed the expression of immune-related ligands and receptors to assess the cell-cell interactions (Ma et al., 2020).

T401

69542-69812

Sentence

denotes

The possible ligand-receptor interactions between one set of receptor-expressing cells and then next ligand-expressing cells were determined as the average of the product of receptor and ligand expression (respectively from set one and two) across all single-cell pairs:

T402

69813-70561

Sentence

denotes

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$I = \mathop \sum \limits_{i}^{n} I_{i} \times \mathop \sum \limits_{j}^{m} r_{j} \left(\frac{1}{m*n}\right)$$\end{document}I=∑inIi×∑jmrj1m∗nwhere I refers to the interaction score between receptor expressing cells in set one and ligand-expressing cells in set two, Ii stands for the ligand expression of cell i in cell set one, rj represents the receptor expression of cell j in cell set two, n stands for the number of cells in set one and m denotes the number of cells in set two.

T403

70562-70700

Sentence

denotes

In the gene list, there were 168 pairs of well-annotated ligands and receptors, among which were co-stimulators, chemokines and cytokines.

T404

70701-70923

Sentence

denotes

The possible interactions between two cell types were orchestrated by receptor-ligand pairs by the product of the average expression levels of the ligand in one cell type and the respective receptor in the other cell type.

T405

70925-70970

Sentence

denotes

Mass cytometry processing and quality control

T406

70971-71194

Sentence

denotes

CyTOF data were acquired with a CyTOF2 system using a SuperSampler fluidics system (Victorian Airships) at an event rate of < 400 events per second and normalized with Helios normalizer software (Fluidigm version 6.7.1016).

T407

71195-71328

Sentence

denotes

Acquisitions from different days (three independent acquisitions were performed) were normalized using five-element beads (Fluidigm).

T408

71329-71431

Sentence

denotes

Barcoded samples were deconvoluted and cross-sample doublets were filtered using cytobank application.

T409

71432-71717

Sentence

denotes

CyTOF data was pre-processed with Cytobank (https://mtsinai.cytobank.org; Cytobank, 7.0) to sequentially remove calibration beads, dead cells, debris and barcodes for CD45+ PBMCs based on event length, DNA (191Ir and 193Ir) and live cell (195Pt) channels and then export the FCS files.

T410

71718-71829

Sentence

denotes

We analyzed 200,000 PBMCs in cohort-1 and 160,000 PBMC in cohort-2, with an average of 20,000 cells per sample.

T411

71831-71872

Sentence

denotes

Mass cytometry visualizing and clustering

T412

71873-71984

Sentence

denotes

We created mass cytometry datasets for analysis by concatenating cells from all individuals for each cell type.

T413

71985-72196

Sentence

denotes

In this way, we created downsampled datasets of 95,316 TCs, 35,254 NKs, 22,042 BCs, 39,144 MCs and 8,244 DCs in cohort-1 and 57,910 TCs, 34,857 NKs, 13,812 BCs, 45,431 MCs and 7,990 DCs in cohort-2 for analysis.

T414

72197-72227

Sentence

denotes

We used FlowCore (65 flowCore:

T415

72228-72322

Sentence

denotes

Basic structures for flow cytometry data.) to read and process FCS files for further analysis.

T416

72323-72445

Sentence

denotes

For sample with more than 20,000 cells, we randomly selected 20,000 cells to ensure that samples were equally represented.

T417

72446-72644

Sentence

denotes

At last, we run the t-SNE dimensionality reduction algorithm on a combined data sample using the Seurat package based on harmony embedding (https://github.com/immunogenomics/harmony, version 1.0.0).

T418

72646-72685

Sentence

denotes

Batch correction of mass cytometry data

T419

72686-72827

Sentence

denotes

PBMC mass cytometry data from 10 subjects of cohort-1 or 8 subjects of cohort-2 was combined and batch normalized using harmony respectively.

T420

72828-72920

Sentence

denotes

First, mass cytometry data from each cohort all subjects was combined into a single dataset.

T421

72921-72993

Sentence

denotes

Second, harmony batch correction was performed using one of the samples.

T422

72994-73109

Sentence

denotes

Third, mass cytometry data were lognormalized in the Seurat’s NormalizeData function across the aggregated dataset.

T423

73111-73189

Sentence

denotes

Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq)

T424

73190-73329

Sentence

denotes

scATAC-seq targeting 4,000 cells per sample was performed using Chromium Single Cell ATAC Library and Gel Bead kit (10x Genomics, 1000110).

T425

73330-73398

Sentence

denotes

Each sample library was uniquely barcoded and quantified by RT-qPCR.

T426

73399-73632

Sentence

denotes

Libraries were then pooled and loaded on an Illumina Novaseq 6000 sequencer (3.5 pmol/L loading concentration, 50 + 8 + 16 + 49 bp read configuration) and sequenced to either 90% saturation or 30,000 unique reads per cell on average.

T427

73633-73864

Sentence

denotes

All protocols to generate scATAC-seq data on the 10x Chromium platform, including sample preparation, library preparation and instrument and sequencing settings, are available here: https://support.10xgenomics.com/single-cell-atac.

T428

73866-73892

Sentence

denotes

scATAC-seq data processing

T429

73894-73915

Sentence

denotes

scATAC-seq processing

T430

73916-74051

Sentence

denotes

scATAC-seq reads were aligned to the GRCh38 (hg38) reference genome and quantified using CellRanger-ATAC count (10x Genomics, v.1.0.0).

T431

74053-74079

Sentence

denotes

scATAC-seq quality control

T432

74080-74261

Sentence

denotes

To ensure that each cell was both adequately sequenced and had a high signal-to-background ratio, we filtered cells with less than 1,000 unique fragments and enrichment at TSSs < 8.

T433

74262-74410

Sentence

denotes

To calculate TSS enrichment > 2, genome-wide Tn5-corrected insertions were aggregated ± 2,000 bp relative (TSS-strand-corrected) to each unique TSS.

T434

74411-74584

Sentence

denotes

This profile was normalized to the mean accessibility ± 1,900–2,000 bp from the TSS, smoothed every 51 bp and the maximum smoothed value was reported as TSS enrichment in R.

T435

74585-74715

Sentence

denotes

To construct a counts matrix for each cell by each feature (peaks), we read each fragment.tsv.gz fill into a GenomicRanges object.

T436

74716-74885

Sentence

denotes

For each Tn5 insertion, which can be thought of as the “start” and “end” of the ATAC fragments, we used findOverlaps to find all overlaps with the feature by insertions.

T437

74886-75012

Sentence

denotes

Then we added a column with the unique id (integer) cell barcode to the overlaps object and fed this into a sparseMatrix in R.

T438

75013-75194

Sentence

denotes

To calculate the fraction of reads/insertions in peaks, we used the colSums of the sparseMatrix and divided it by the number of insertions for each cell id barcode using table in R.

T439

75196-75239

Sentence

denotes

scATAC-seq visualization in genomic regions

T440

75240-75325

Sentence

denotes

To visualize scATAC-seq data, we read the fragments into a GenomicRanges object in R.

T441

75326-75454

Sentence

denotes

We then computed sliding windows across each region we wanted to visualize for every 100 bp “slidingWindows (region, 100, 100)”.

T442

75455-75562

Sentence

denotes

We computed a counts matrix for Tn5-corrected insertions as described above and then binarized this matrix.

T443

75563-75708

Sentence

denotes

We then returned all non-zero indices (binarization) from the matrix (cell × 100-bp intervals) and plotted them in ggplot2 in R with “geom_tile”.

T444

75709-75805

Sentence

denotes

For visualizing aggregate scATAC-seq data, the binarized matrix above was summed and normalized.

T445

75806-75915

Sentence

denotes

Scale factors were computed by taking the binarized sum in the global peak set and normalizing to 10,000,000.

T446

75916-76238

Sentence

denotes

Tracks were then plotted in Loupe Cell Browser, an interactive visualization software that shows scATAC-seq peak profiles for scATAC-seq cell clusters, similar to the analysis done in this manuscript and described at https://support.10xgenomics.com/single-cellatac/software/visualization/latest/what-is-loupe-cell-browser.

T447

76240-76248

Sentence

denotes

chromVAR

T448

76249-76297

Sentence

denotes

We measured global TF activity using chromVAR15.

T449

76298-76479

Sentence

denotes

We used the cell-by-peaks and the Catalog of Inferred Sequence Binding Preferences (CIS-BP) motif (from chromVAR motifs “human_pwms_v1”) matches within these peaks from motifmatchr.

T450

76480-76582

Sentence

denotes

We then computed the GC-bias-corrected deviation scores using the chromVAR “deviationScores” function.

T451

76584-76604

Sentence

denotes

Statistical analysis

T452

76605-76697

Sentence

denotes

The GraphPad Prism Software (version 8.0.2) was employed for data analysis and presentation.

T453

76698-76739

Sentence

denotes

All results are presented as means ± SEM.

T454

76740-76866

Sentence

denotes

Groups were compared with two-tailed Mann-Whitney-Wilcoxon tests and FDR was corrected using the Benjamini-Hochberg procedure.

T455

76867-76952

Sentence

denotes

P value was derived by a hypergeometric test in representative GO terms and pathways.

T456

76954-76987

Sentence

denotes

Electronic supplementary material

T457

76989-77087

Sentence

denotes

Below is the link to the electronic supplementary material.Supplementary material 1 (PDF 10889 kb)

T458

77088-77126

Sentence

denotes

Supplementary material 2 (XLS 3266 kb)

PMC:7417788 / 1465-78591 JSON TXT 14 Projects

Annnotations TAB TSV DIC JSON TextAE Lectin_function

PMC:7417788 / 1465-78591 JSONTXT 14 Projects

Annnotations TAB TSV DIC JSON TextAE Lectin_function

PMC:7417788 / 1465-78591 JSON TXT 14 Projects