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Series GSE150672 Query DataSets for GSE150672
Status Public on Sep 30, 2020
Title Highly Efficient, Massively-Parallel Single-Cell RNA-Seq Reveals Cellular States and Molecular Features of Human Skin Pathology
Organisms Homo sapiens; Mus musculus
Experiment type Expression profiling by high throughput sequencing
Summary The development of high-throughput single-cell RNA-sequencing (scRNA-Seq) methodologies has empowered the characterization of complex biological samples by dramatically increasing the number of constituent cells that can be examined concurrently. Nevertheless, these approaches typically recover substantially less information per-cell as compared to lower-throughput microtiter plate-based strategies. To uncover critical phenotypic differences among cells and effectively link scRNA-Seq observations to legacy datasets, reliable detection of phenotype-defining transcripts – such as transcription factors, affinity receptors, and signaling molecules – by these methods is essential. Here, we describe a substantially improved massively-parallel scRNA-Seq protocol we term Seq-Well S^3 (“Second-Strand Synthesis”) that increases the efficiency of transcript capture and gene detection by up to 10- and 5-fold, respectively, relative to previous iterations, surpassing best-in-class commercial analogs. We first characterized the performance of Seq-Well S^3 in cell lines and PBMCs, and then examined five different inflammatory skin diseases, illustrative of distinct types of inflammation, to explore the breadth of potential immune and parenchymal cell states. Our work presents an essential methodological advance as well as a valuable resource for studying the cellular and molecular features that inform human skin inflammation.
Overall design Profile five different inflammatory skin diseases using scRNA-Seq
Contributor(s) Wadsworth II MH, Hughes TK, Gierahn TM, Modlin RL, Love JC, Shalek AK
Citation(s) 33053333
Submission date May 15, 2020
Last update date Dec 30, 2020
Contact name Ken Dower
Organization name Pfizer
Department Inflammation & Immunology
Street address 1 Portland Street
City Cambridge
State/province MA
ZIP/Postal code 02139
Country USA
Platforms (2)
GPL18573 Illumina NextSeq 500 (Homo sapiens)
GPL19415 Illumina NextSeq 500 (Homo sapiens; Mus musculus)
Samples (438)
GSM4693356 S3Comp1217
GSM4693357 A1V2_PBMCs
GSM4693358 171129LovA_D17-9763
BioProject PRJNA648161
SRA SRP273339

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE150672_RAW.tar 69.0 Mb (http)(custom) TAR (of CSV, TXT)
GSE150672_Skin_Expression_counts.csv.gz 75.1 Mb (ftp)(http) CSV
GSE150672_Skin_Normalized_Expression_counts.csv.gz 458.2 Mb (ftp)(http) CSV
GSE150672_rsem.genes.count.matrix.txt.gz 4.3 Mb (ftp)(http) TXT
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Raw data are available in SRA
Processed data provided as supplementary file
Processed data are available on Series record

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