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LitCovid-PD-FMA-UBERON

LitCovid-PD-UBERON

LitCovid-PubTator

LitCovid-PD-MONDO

LitCovid-PD-CLO

LitCovid-PD-CHEBI

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T134","span":{"begin":2476,"end":2481},"obj":"http://purl.obolibrary.org/obo/GO_0019835"},{"id":"T135","span":{"begin":5368,"end":5374},"obj":"http://purl.obolibrary.org/obo/GO_0005152"},{"id":"T136","span":{"begin":13789,"end":13805},"obj":"http://purl.obolibrary.org/obo/GO_0006955"}],"text":"Materials and methods\n\nPatients, subjects, and clinical data collection\nPatients admitted to the Hospital of the University of Pennsylvania with a positive SARS-CoV2 PCR test were screened and approached for informed consent within 3 days of hospitalization. Healthy donors (HD) were adults with no prior diagnosis of or recent symptoms consistent with COVID-19. Normal reference ranges for HDs were the UPenn clinical laboratory values shaded in green. Recovered COVID-19 subjects (RD) were adults with a prior positive COVID-19 PCR test by self-report who met the definition of recovery by the Centers for Disease Control. HD and RD were recruited initially by word of mouth and subsequently through a centralized University of Pennsylvania resource website for COVID-19-related studies. Peripheral blood was collected from all subjects. For inpatients, clinical data were abstracted from the electronic medical record into standardized case report forms. ARDS was categorized in accordance with the Berlin definition reflecting each subject’s worst oxygenation level and with physician adjudication of chest radiographs. APACHE III scoring was based on data collected in the first 24 hours of ICU admission or the first 24 hours of hospital admission for subjects admitted to general inpatient units. Clinical laboratory data were abstracted from the date closest to research blood collection. HD and RD completed a survey about symptoms. After enrollment, the clinical team determined three patients to be COVID-negative and/or PCR false positive. Two of these patients were classified as Immunotype 3. In keeping with inclusion criteria, these subjects were maintained in the analysis. The statistical significance reported in Fig. 6K did not change when analysis was repeated without the three patients. All participants or their surrogates provided informed consent in accordance with protocols approved by the regional ethical research boards and the Declaration of Helsinki.\n\nSample processing\nPeripheral blood was collected into sodium heparin tubes (BD, Cat#367874). Tubes were spun (15 min, 3000 rpm, RT), plasma removed, and banked. Remaining whole blood was diluted 1:1 with 1% RPMI (table S7) and layered into a SEPMATE tube (STEMCELL Technologies, Cat#85450) pre-loaded with lymphoprep (Alere Technologies, Cat#1114547). SEPMATE tubes were spun (10 min, 1200xg, RT) and the PBMC layer collected, washed with 1% RPMI (10 min, 1600 rpm, RT) and treated with ACK lysis buffer (5 min, ThermoFisher, Cat#A1049201). Samples were filtered with a 70 μm filter, counted, and aliquoted for staining.\n\nAntibody panels and staining\nApproximately 1-5×106 freshly isolated PBMCs were used per patient per stain. See table S7 for buffer information and table S8 for antibody panel information. PBMCs were stained with live/dead mix (100 μl, 10 min, RT), washed with FACS buffer, and spun down (1500 rpm, 5 min, RT). PBMCs were incubated with 100 μl of Fc block (RT, 10 min) before a second wash (FACS buffer, 1500 rpm, 5 min, RT). Pellet was resuspended in 25 μl of chemokine receptor staining mix, and incubated at 37°C for 20 min. Following incubation, 25 μl of surface receptor staining mix was directly added and the PBMCs were incubated at RT for a further 45 min. PBMCs were washed (FACS buffer, 1500 rpm, 5 min, RT) and stained with 50 μl of secondary antibody mix for 20 min at RT, then washed again (FACS buffer, 1500 rpm, 5 min, RT). Samples were fixed and permeabilized by incubating in 100 μl of Fix/Perm buffer (RT, 30 min) and washed in Perm Buffer (1800 rpm, 5 min, RT). PBMCs were stained with 50μl of intracellular mix overnight at 4°C. The following morning, samples were washed (Perm Buffer, 1800 rpm, 5 min, RT) and further fixed in 50 μl of 4% PFA. Prior to acquisition, samples were diluted to 1% PFA and 10,000 counting beads added per sample (BD, Cat#335925). Live/dead mix was prepared in PBS. For the surface receptor and chemokine staining mix, antibodies were diluted in FACS buffer with 50% BD Brilliant Buffer (BD, Cat#566349). Intracellular mix was diluted in Perm Buffer.\n\nFlow cytometry\nSamples were acquired on a 5 laser BD FACS Symphony A5. Standardized SPHERO rainbow beads (Spherotech, Cat#RFP-30-5A) were used to track and adjust PMTs over time. UltraComp eBeads (ThermoFisher, Cat#01-2222-42) were used for compensation. Up to 2 × 106 live PBMC were acquired per each sample.\n\nLuminex\nPBMCs from patients were thawed and rested overnight at 37°C in complete RPMI (cRPMI, table S7). 96-well flat bottom plates were coated with 1 μg/mL of anti-CD3 (UCHT1, #BE0231, BioXell) in PBS at 4°C overnight. The next day, cells were collected and plated at 1 × 105/well in 100 μl in duplicate. 2 μg/mL of anti-human CD28/CD49d was added to the wells containing plate-bound anti-CD3 (Clone L293, 347690, BD). PBMCs were stimulated or left unstimulated for 16 hours, spun down (1200 rpm, 10 min) and 85 μL/well of supernatant was collected. Plasma from matched subjects was thawed on ice, spun (3000 rpm, 1 min) to remove debris, and 85 μl collected in duplicate. Luminex assay was run according to manufacturer’s instructions, using a custom human cytokine 31-plex panel (EMD Millipore Corporation, SPRCUS707). The panel included: EGF, FGF-2, Eotaxin, sIL-2Ra, G-CSF, GM-CSF, IFN-α2, IFN-γ, IL-10, IL-12P40, IL-12P70, IL-13, IL-15, IL-17A, IL-1Ra, HGF, IL-1β, CXCL9/MIG, IL-2, IL-4, IL-5, IL-6, IL-7, CXCL8/IL-8, CXCL10/IP-10, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, RANTES, TNF-α, and VEGF. Assay plates were measured using a Luminex FlexMAP 3D instrument (Thermofisher, Cat#APX1342).\nData acquisition and analysis were done using xPONENT software www.luminexcorp.com/xponent/). Data quality was examined based on the following criteria: The standard curve for each analyte has a 5P R2 value \u003e 0.95 with or without minor fitting using xPONENT software. To pass assay technical quality control, the results for two controls in the kit needed to be within the 95% of CI (confidence interval) provided by the vendor for \u003e25 of the tested analytes. No further tests were done on samples with results out of range low (\u003cOOR). Samples with results that were out of range high (\u003eOOR) or greater than the standard curve maximum value (SC max) were not tested at higher dilutions without further request.\n\nIntracellular stain after CD3/CD28 stimulation\n96-well flat bottom plates were coated with 1μg/mL of anti-CD3 (UCHT1, #BE0231, BioXell) in PBS at 4°C overnight. The next day, cells were collected and plated at 1 × 105/well in 100 μl with 1/1000 of GolgiPlug (BD #555029). 2μg/mL of anti-human CD28/CD49d was added to the wells containing plate-bound anti-CD3 (Clone L293, 347690, BD). GolgiPlug-treated PBMCs were stimulated or left unstimulated for 16 hours, spun down (1200 rpm, 10 min) and were stained for intracellular IFNɣ.\n\nLongitudinal analysis D0-D7 and patient grouping\nTo identify subjects where the frequency of specific immune cell populations increased, decreased or stayed stable over time (day 0 to day 7), where data was available we used a previously published dataset to establish a standard range of fold change over time in a healthy cohort (44). A fold change greater than the mean fold change ± 2 standard deviations was considered an increase, less than this range was considered a decrease, and within this range was considered stable. Where this data was not available, a fold change from day 0 to day 7 of between 0.5 and 1.5 was considered stable. A fold change \u003c0.5 was considered decreased, and \u003e1.5 was considered increased. In order to eliminate redundant tests and maximize statistical power, the pairwise statistical tests shown in Fig. 5G were performed using fold-change as a continuous metric, irrespective of the discrete up/stable/down classification described above. Similarly, in fig. S9G, pairwise association tests between changes in UMAP Component coordinates and clinical data were performed using each difference value as a continuous metric, irrespective of the up/stable/down classification.\n\nCorrelation plots and heatmap visualization\nPairwise correlations between variables were calculated and visualized as a correlogram using R function corrplot. Spearman's Rank Correlation coefficient (ρ) was indicated by square size and heat scale; significance indicated by: *p \u003c 0.05, **p \u003c 0.01, and ***p \u003c 0.001; black box indicates FDR \u003c 0.05. Heatmaps were created to visualize variable values using R function pheatmap or complexheatmap.\n\nStatistics\nDue to the heterogeneity of clinical and flow cytometric data, non-parametric tests of association were preferentially used throughout this study unless otherwise specified. Correlation coefficients between ordered features (including discrete ordinal, continuous scale, or a mixture of the two) were quantified by the Spearman rank correlation coefficient and significance was assessed by the corresponding non-parametric methods (null hypothesis: ρ = 0). Tests of association between mixed continuous versus non-ordered categorical variables were performed by unpaired Wilcoxon test (for n = 2 categories) or by Kruskal-Wallis test (for n \u003e 2 categories). Association between categorical variables was assessed by Fisher-exact test. For association testing illustrated in heatmaps, categorical variables with more than 2 categories (e.g., ABO blood type) were transformed into binary dummy variables for each category versus the rest. All tests were performed two-sided, using a nominal significance threshold of P \u003c 0.05 unless otherwise specified. When appropriate to adjust for multiple hypothesis testing, false discovery rate (FDR) correction was performed using the Benjamini-Hochberg procedure at the FDR \u003c 0.05 significance threshold. Joint statistical modeling to adjust for confounding of demographic factors (age, sex, and race) when testing for association of UMAP Components 1 and 2 with the NIH Ordinal Severity Scale was performed using ordinal logistic regression provided by the polr function of the R package MASS. Statistical analysis of flow cytometry data was performed using R package rstatix. Other details, if any, for each experiment are provided within the relevant figure legends.\n\nHigh dimensional data analysis of flow cytometry data\nviSNE and FlowSOM analysis were performed on Cytobank (https://cytobank.org). B cells, non-naïve CD4 T cells, and non-naïve CD8 T cells were analyzed separately. viSNE analysis was performed using equal sampling of 1000 cells from each FCS file, with 5000 iterations, a perplexity of 30, and a theta of 0.5. For B cells, the following markers were used to generate the viSNE maps: CD45RA, IgD, CXCR5, CD138, Eomes, TCF-1, CD38, CD95, CCR7, CD21, KI67, CD27, CX3CR1, CD39, T-bet, HLA-DR, CD16, CD19 and CD20. For non-naïve CD4 and CD8 T cells, the following markers were used: CD45RA, PD1, CXCR5, TCF-1, CD38, CD95, Eomes, CCR7, KI67, CD16, CD27, CX3CR1, CD39, CD20, T-bet, and HLA-DR. Resulting viSNE maps were fed into the FlowSOM clustering algorithm (59). For each cell subset, a new self-organizing map (SOM) was generated using hierarchical consensus clustering on the tSNE axes. For each SOM, 225 clusters and 10 or 15 metaclusters were identified for B cells and T cells respectively.\nTo group individuals based on B cell landscape, pairwise Earth Mover’s Distance (EMD) value was calculated on the B cell tSNE axes for all COVID-19 day 0 patients, healthy donors, and recovered donors using the emdist package in R as previously described (60). Resulting scores were hierarchically clustered using the hclust package in R.\n\nBatch correction\nDuring the sample acquisition period, the flow panel was changed to remove one antibody. Batch correction was performed for samples acquired before and after this change to remove potential bias from downstream analysis. Because the primary flow features were expressed as a fraction of the parent population (falling in the 0-to-1 interval) a variance stabilizing transform (logit) was first applied to each data value, prior to re-centering the second panel to have the same mean as the first. After mean-centering, data were transformed back to the original fraction of parent scale by inverse transform. This procedure was applied separately to all 553 flow features annotated in the main text and supplemental data. Notably, this procedure avoids any batch-corrected feature values artificially falling outside of the original 0 to 1 range. Following batch correction, neither UMAP Component 1 nor Component 2 had a statistically significant difference between panels by unpaired Wilcoxon test.\n\nVisualizing variation of flow cytometric features across the UMAP embedding space\nA feature-weighted kernel density was computed across all COVID-19 patients, and was displayed as a contour plot (Fig. 6G and fig. S8, A to D). Whereas traditional kernel density methods apply the same base kernel function to every point to visualize point density, here the base kernel function centered at each individual COVID-19 patient sample was instead weighted (multiplied) by the Z-transform (mean-centered and standard deviation-scaled) of the log-transformed input feature prior to computing the overall kernel density. This weighting procedure facilitated visualization of the overall feature gradients (going from relatively low-to-high expression) across UMAP coordinates independent of the different range of each input feature. A radially symmetric two-dimensional Gaussian was used as the base kernel function with a variance parameter equal to one-half, which was tuned to be sufficiently broad in order to smooth out local discontinuities and best visualize feature gradients.\n\nDefinition of immunotype 3\nTo define COVID-19 patients with low or absent immune responses, classified as immunotype 3, the intersection of the bottom 50% of 5 different flow parameters was used: PB as % of B cells, KI67+ as % of non-naïve CD4 T cells, KI67+ as % of non-naïve CD8 T cells, HLA-DR+CD38+ as % of non-naïve CD4 T cells, HLA-DR+CD38+ as % of non-naïve CD8 T cells—graphically displayed in fig. S10."}

LitCovid-sentences

2_test

{"project":"2_test","denotations":[{"id":"32669297-25573116-135105235","span":{"begin":11040,"end":11042},"obj":"25573116"},{"id":"32669297-30413431-135105236","span":{"begin":11534,"end":11536},"obj":"30413431"}],"text":"Materials and methods\n\nPatients, subjects, and clinical data collection\nPatients admitted to the Hospital of the University of Pennsylvania with a positive SARS-CoV2 PCR test were screened and approached for informed consent within 3 days of hospitalization. Healthy donors (HD) were adults with no prior diagnosis of or recent symptoms consistent with COVID-19. Normal reference ranges for HDs were the UPenn clinical laboratory values shaded in green. Recovered COVID-19 subjects (RD) were adults with a prior positive COVID-19 PCR test by self-report who met the definition of recovery by the Centers for Disease Control. HD and RD were recruited initially by word of mouth and subsequently through a centralized University of Pennsylvania resource website for COVID-19-related studies. Peripheral blood was collected from all subjects. For inpatients, clinical data were abstracted from the electronic medical record into standardized case report forms. ARDS was categorized in accordance with the Berlin definition reflecting each subject’s worst oxygenation level and with physician adjudication of chest radiographs. APACHE III scoring was based on data collected in the first 24 hours of ICU admission or the first 24 hours of hospital admission for subjects admitted to general inpatient units. Clinical laboratory data were abstracted from the date closest to research blood collection. HD and RD completed a survey about symptoms. After enrollment, the clinical team determined three patients to be COVID-negative and/or PCR false positive. Two of these patients were classified as Immunotype 3. In keeping with inclusion criteria, these subjects were maintained in the analysis. The statistical significance reported in Fig. 6K did not change when analysis was repeated without the three patients. All participants or their surrogates provided informed consent in accordance with protocols approved by the regional ethical research boards and the Declaration of Helsinki.\n\nSample processing\nPeripheral blood was collected into sodium heparin tubes (BD, Cat#367874). Tubes were spun (15 min, 3000 rpm, RT), plasma removed, and banked. Remaining whole blood was diluted 1:1 with 1% RPMI (table S7) and layered into a SEPMATE tube (STEMCELL Technologies, Cat#85450) pre-loaded with lymphoprep (Alere Technologies, Cat#1114547). SEPMATE tubes were spun (10 min, 1200xg, RT) and the PBMC layer collected, washed with 1% RPMI (10 min, 1600 rpm, RT) and treated with ACK lysis buffer (5 min, ThermoFisher, Cat#A1049201). Samples were filtered with a 70 μm filter, counted, and aliquoted for staining.\n\nAntibody panels and staining\nApproximately 1-5×106 freshly isolated PBMCs were used per patient per stain. See table S7 for buffer information and table S8 for antibody panel information. PBMCs were stained with live/dead mix (100 μl, 10 min, RT), washed with FACS buffer, and spun down (1500 rpm, 5 min, RT). PBMCs were incubated with 100 μl of Fc block (RT, 10 min) before a second wash (FACS buffer, 1500 rpm, 5 min, RT). Pellet was resuspended in 25 μl of chemokine receptor staining mix, and incubated at 37°C for 20 min. Following incubation, 25 μl of surface receptor staining mix was directly added and the PBMCs were incubated at RT for a further 45 min. PBMCs were washed (FACS buffer, 1500 rpm, 5 min, RT) and stained with 50 μl of secondary antibody mix for 20 min at RT, then washed again (FACS buffer, 1500 rpm, 5 min, RT). Samples were fixed and permeabilized by incubating in 100 μl of Fix/Perm buffer (RT, 30 min) and washed in Perm Buffer (1800 rpm, 5 min, RT). PBMCs were stained with 50μl of intracellular mix overnight at 4°C. The following morning, samples were washed (Perm Buffer, 1800 rpm, 5 min, RT) and further fixed in 50 μl of 4% PFA. Prior to acquisition, samples were diluted to 1% PFA and 10,000 counting beads added per sample (BD, Cat#335925). Live/dead mix was prepared in PBS. For the surface receptor and chemokine staining mix, antibodies were diluted in FACS buffer with 50% BD Brilliant Buffer (BD, Cat#566349). Intracellular mix was diluted in Perm Buffer.\n\nFlow cytometry\nSamples were acquired on a 5 laser BD FACS Symphony A5. Standardized SPHERO rainbow beads (Spherotech, Cat#RFP-30-5A) were used to track and adjust PMTs over time. UltraComp eBeads (ThermoFisher, Cat#01-2222-42) were used for compensation. Up to 2 × 106 live PBMC were acquired per each sample.\n\nLuminex\nPBMCs from patients were thawed and rested overnight at 37°C in complete RPMI (cRPMI, table S7). 96-well flat bottom plates were coated with 1 μg/mL of anti-CD3 (UCHT1, #BE0231, BioXell) in PBS at 4°C overnight. The next day, cells were collected and plated at 1 × 105/well in 100 μl in duplicate. 2 μg/mL of anti-human CD28/CD49d was added to the wells containing plate-bound anti-CD3 (Clone L293, 347690, BD). PBMCs were stimulated or left unstimulated for 16 hours, spun down (1200 rpm, 10 min) and 85 μL/well of supernatant was collected. Plasma from matched subjects was thawed on ice, spun (3000 rpm, 1 min) to remove debris, and 85 μl collected in duplicate. Luminex assay was run according to manufacturer’s instructions, using a custom human cytokine 31-plex panel (EMD Millipore Corporation, SPRCUS707). The panel included: EGF, FGF-2, Eotaxin, sIL-2Ra, G-CSF, GM-CSF, IFN-α2, IFN-γ, IL-10, IL-12P40, IL-12P70, IL-13, IL-15, IL-17A, IL-1Ra, HGF, IL-1β, CXCL9/MIG, IL-2, IL-4, IL-5, IL-6, IL-7, CXCL8/IL-8, CXCL10/IP-10, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, RANTES, TNF-α, and VEGF. Assay plates were measured using a Luminex FlexMAP 3D instrument (Thermofisher, Cat#APX1342).\nData acquisition and analysis were done using xPONENT software www.luminexcorp.com/xponent/). Data quality was examined based on the following criteria: The standard curve for each analyte has a 5P R2 value \u003e 0.95 with or without minor fitting using xPONENT software. To pass assay technical quality control, the results for two controls in the kit needed to be within the 95% of CI (confidence interval) provided by the vendor for \u003e25 of the tested analytes. No further tests were done on samples with results out of range low (\u003cOOR). Samples with results that were out of range high (\u003eOOR) or greater than the standard curve maximum value (SC max) were not tested at higher dilutions without further request.\n\nIntracellular stain after CD3/CD28 stimulation\n96-well flat bottom plates were coated with 1μg/mL of anti-CD3 (UCHT1, #BE0231, BioXell) in PBS at 4°C overnight. The next day, cells were collected and plated at 1 × 105/well in 100 μl with 1/1000 of GolgiPlug (BD #555029). 2μg/mL of anti-human CD28/CD49d was added to the wells containing plate-bound anti-CD3 (Clone L293, 347690, BD). GolgiPlug-treated PBMCs were stimulated or left unstimulated for 16 hours, spun down (1200 rpm, 10 min) and were stained for intracellular IFNɣ.\n\nLongitudinal analysis D0-D7 and patient grouping\nTo identify subjects where the frequency of specific immune cell populations increased, decreased or stayed stable over time (day 0 to day 7), where data was available we used a previously published dataset to establish a standard range of fold change over time in a healthy cohort (44). A fold change greater than the mean fold change ± 2 standard deviations was considered an increase, less than this range was considered a decrease, and within this range was considered stable. Where this data was not available, a fold change from day 0 to day 7 of between 0.5 and 1.5 was considered stable. A fold change \u003c0.5 was considered decreased, and \u003e1.5 was considered increased. In order to eliminate redundant tests and maximize statistical power, the pairwise statistical tests shown in Fig. 5G were performed using fold-change as a continuous metric, irrespective of the discrete up/stable/down classification described above. Similarly, in fig. S9G, pairwise association tests between changes in UMAP Component coordinates and clinical data were performed using each difference value as a continuous metric, irrespective of the up/stable/down classification.\n\nCorrelation plots and heatmap visualization\nPairwise correlations between variables were calculated and visualized as a correlogram using R function corrplot. Spearman's Rank Correlation coefficient (ρ) was indicated by square size and heat scale; significance indicated by: *p \u003c 0.05, **p \u003c 0.01, and ***p \u003c 0.001; black box indicates FDR \u003c 0.05. Heatmaps were created to visualize variable values using R function pheatmap or complexheatmap.\n\nStatistics\nDue to the heterogeneity of clinical and flow cytometric data, non-parametric tests of association were preferentially used throughout this study unless otherwise specified. Correlation coefficients between ordered features (including discrete ordinal, continuous scale, or a mixture of the two) were quantified by the Spearman rank correlation coefficient and significance was assessed by the corresponding non-parametric methods (null hypothesis: ρ = 0). Tests of association between mixed continuous versus non-ordered categorical variables were performed by unpaired Wilcoxon test (for n = 2 categories) or by Kruskal-Wallis test (for n \u003e 2 categories). Association between categorical variables was assessed by Fisher-exact test. For association testing illustrated in heatmaps, categorical variables with more than 2 categories (e.g., ABO blood type) were transformed into binary dummy variables for each category versus the rest. All tests were performed two-sided, using a nominal significance threshold of P \u003c 0.05 unless otherwise specified. When appropriate to adjust for multiple hypothesis testing, false discovery rate (FDR) correction was performed using the Benjamini-Hochberg procedure at the FDR \u003c 0.05 significance threshold. Joint statistical modeling to adjust for confounding of demographic factors (age, sex, and race) when testing for association of UMAP Components 1 and 2 with the NIH Ordinal Severity Scale was performed using ordinal logistic regression provided by the polr function of the R package MASS. Statistical analysis of flow cytometry data was performed using R package rstatix. Other details, if any, for each experiment are provided within the relevant figure legends.\n\nHigh dimensional data analysis of flow cytometry data\nviSNE and FlowSOM analysis were performed on Cytobank (https://cytobank.org). B cells, non-naïve CD4 T cells, and non-naïve CD8 T cells were analyzed separately. viSNE analysis was performed using equal sampling of 1000 cells from each FCS file, with 5000 iterations, a perplexity of 30, and a theta of 0.5. For B cells, the following markers were used to generate the viSNE maps: CD45RA, IgD, CXCR5, CD138, Eomes, TCF-1, CD38, CD95, CCR7, CD21, KI67, CD27, CX3CR1, CD39, T-bet, HLA-DR, CD16, CD19 and CD20. For non-naïve CD4 and CD8 T cells, the following markers were used: CD45RA, PD1, CXCR5, TCF-1, CD38, CD95, Eomes, CCR7, KI67, CD16, CD27, CX3CR1, CD39, CD20, T-bet, and HLA-DR. Resulting viSNE maps were fed into the FlowSOM clustering algorithm (59). For each cell subset, a new self-organizing map (SOM) was generated using hierarchical consensus clustering on the tSNE axes. For each SOM, 225 clusters and 10 or 15 metaclusters were identified for B cells and T cells respectively.\nTo group individuals based on B cell landscape, pairwise Earth Mover’s Distance (EMD) value was calculated on the B cell tSNE axes for all COVID-19 day 0 patients, healthy donors, and recovered donors using the emdist package in R as previously described (60). Resulting scores were hierarchically clustered using the hclust package in R.\n\nBatch correction\nDuring the sample acquisition period, the flow panel was changed to remove one antibody. Batch correction was performed for samples acquired before and after this change to remove potential bias from downstream analysis. Because the primary flow features were expressed as a fraction of the parent population (falling in the 0-to-1 interval) a variance stabilizing transform (logit) was first applied to each data value, prior to re-centering the second panel to have the same mean as the first. After mean-centering, data were transformed back to the original fraction of parent scale by inverse transform. This procedure was applied separately to all 553 flow features annotated in the main text and supplemental data. Notably, this procedure avoids any batch-corrected feature values artificially falling outside of the original 0 to 1 range. Following batch correction, neither UMAP Component 1 nor Component 2 had a statistically significant difference between panels by unpaired Wilcoxon test.\n\nVisualizing variation of flow cytometric features across the UMAP embedding space\nA feature-weighted kernel density was computed across all COVID-19 patients, and was displayed as a contour plot (Fig. 6G and fig. S8, A to D). Whereas traditional kernel density methods apply the same base kernel function to every point to visualize point density, here the base kernel function centered at each individual COVID-19 patient sample was instead weighted (multiplied) by the Z-transform (mean-centered and standard deviation-scaled) of the log-transformed input feature prior to computing the overall kernel density. This weighting procedure facilitated visualization of the overall feature gradients (going from relatively low-to-high expression) across UMAP coordinates independent of the different range of each input feature. A radially symmetric two-dimensional Gaussian was used as the base kernel function with a variance parameter equal to one-half, which was tuned to be sufficiently broad in order to smooth out local discontinuities and best visualize feature gradients.\n\nDefinition of immunotype 3\nTo define COVID-19 patients with low or absent immune responses, classified as immunotype 3, the intersection of the bottom 50% of 5 different flow parameters was used: PB as % of B cells, KI67+ as % of non-naïve CD4 T cells, KI67+ as % of non-naïve CD8 T cells, HLA-DR+CD38+ as % of non-naïve CD4 T cells, HLA-DR+CD38+ as % of non-naïve CD8 T cells—graphically displayed in fig. S10."}

PMC:7402624 / 61473-75599 JSONTXT

Annnotations TAB JSON ListView MergeView

PMC:7402624 / 61473-75599 JSON TXT