02286nas a2200421   4500000000100000008004100001260001200042653001300054653000900067653002000076653002300096653001200119653001400131653001400145653003100159653002200190100001400212700001600226700001400242700000900256700001200265700001400277700000900291700001200300700001100312700001100323700002300334700001700357700001300374700001200387700001300399245015000412856005800562300001500620490000700635520120800642022001401850       2020                            d  c10/202010aSeq-Well10aacne10aalopecia areata10agranuloma annulare10aleprosy10apsoriasis10ascRNA-seq10asingle-cell RNA sequencing10askin inflammation1 aHughes TK1 aWadsworth M1 aGierahn T1 aDo T1 aWeiss D1 aAndrade P1 aMa F1 aSilva B1 aShao S1 aTsoi L1 aOrdovas-Montanes J1 aGudjonsson J1 aModlin R1 aLove CJ1 aShalek A00aSecond-Strand Synthesis-Based Massively Parallel scRNA-Seq Reveals Cellular States and Molecular Features of Human Inflammatory Skin Pathologies.  uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7562821/  a878-894.e70 v533 a<p>High-throughput single-cell RNA-sequencing (scRNA-seq) methodologies enable characterization of complex biological samples by increasing the number of cells that can be profiled contemporaneously. Nevertheless, these approaches recover less information per cell than low-throughput strategies. To accurately report the expression of key phenotypic features of cells, scRNA-seq platforms are needed that are both high fidelity and high throughput. To address this need, we created Seq-Well S ("Second-Strand Synthesis"), a massively parallel scRNA-seq protocol that uses a randomly primed second-strand synthesis to recover complementary DNA (cDNA) molecules that were successfully reverse transcribed but to which a second oligonucleotide handle, necessary for subsequent whole transcriptome amplification, was not appended due to inefficient template switching. Seq-Well S increased the efficiency of transcript capture and gene detection compared with that of previous iterations by up to 10- and 5-fold, respectively. We used Seq-Well S to chart the transcriptional landscape of five human inflammatory skin diseases, thus providing a resource for the further study of human skin inflammation.</p>  a1097-4180