 |
 |
GEO help: Mouse over screen elements for information. |
|
| 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 |
| E-mail(s) |
Ken.Dower@pfizer.com
|
| 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)
|
|
| Relations |
| BioProject |
PRJNA648161 |
| SRA |
SRP273339 |
| 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 |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
| Processed data are available on Series record |
|
|
|
|
 |
Less...
More...