PMC:7252096 / 118463-120734 JSONTXT

{"target":"http://pubannotation.org/docs/sourcedb/PMC/sourceid/7252096","sourcedb":"PMC","sourceid":"7252096","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7252096","text":"We began by quantifying how likely we are to capture and transcribe at least one ACE2 mRNA molecule, as a function of the number ACE2 mRNA molecules per cell and a protocol’s efficiency (Figure S6A). Drop-Seq has a capture/transcription efficiency of ∼10% (as estimated using ERCC spike ins; see (Macosko et al., 2015), and the experimental platforms used in this study are either equivalent (e.g., Seq-Well v1, (Gierahn et al., 2017) or superior (e.g., 10-fold better unique molecule detection, 5-fold better gene detection using Seq-Well S3;(Hughes et al., 2019)). Most relevant to this context, inferior turbinate scrapings were processed using both Seq-Well v1 and Seq-Well S3 (Figure S3B). Importantly, Seq-Well S3 provided \u003e two-fold increase in the detection frequency of rare ACE2 transcripts (i.e., ACE2+: 4.7% for v1 versus 9.8% for S3), making it reasonable to expect that such improvements in single-cell experimental technologies have yielded corresponding improvements in capture and transcription efficiency. Based on Drop-Seq’s 10% efficiency, even if ACE2 is expressed at the low level of 5 mRNA molecules per cell (a reasonable order-of-magnitude estimate, given that non-human primate ileum cells had a maximum of 10 ACE2 unique molecules per cell observed via sequencing and an average of 1.93 molecules per cell in expressing cells, see Figures 3B and 3C), our experimental platforms have a minimum likelihood of 41% to capture and reverse transcribe at least one ACE2 mRNA molecule in any given individual cell. This likelihood rapidly increases if we estimate higher efficiencies for improved scRNA-seq technologies (e.g., 67% likelihood within any individual cell at 20% capture/transcription efficiency, 76% likelihood at 25% efficiency, Figure S6A). Thus, while transcript drop-out may reduce the fraction of positive cells, with the capture and transcription efficiencies of improved single-cell technologies, the impact is likely to be minor (reads are likely underestimated by up to a factor of ∼2.5x), given a sufficient depth of sequencing (see below). We note that this impacts both clusters deemed to contain and not contain ACE2+ cells, and suggests our percentages are likely lower bounds for true expression (within a factor of 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