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Second-Strand Synthesis-Based Massively Parallel scRNA-Seq Reveals Cellular States and Molecular Features of Human Inflammatory Skin Pathologies

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 acc...

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Detalles Bibliográficos
Autores principales: Hughes, Travis K., Wadsworth, Marc H., Gierahn, Todd M., Do, Tran, Weiss, David, Andrade, Priscila R., Ma, Feiyang, de Andrade Silva, Bruno J., Shao, Shuai, Tsoi, Lam C., Ordovas-Montanes, Jose, Gudjonsson, Johann E., Modlin, Robert L., Love, J. Christopher, Shalek, Alex K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cell Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7562821/
https://www.ncbi.nlm.nih.gov/pubmed/33053333
http://dx.doi.org/10.1016/j.immuni.2020.09.015
Descripción
Sumario: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(3) (“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(3) 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(3) to chart the transcriptional landscape of five human inflammatory skin diseases, thus providing a resource for the further study of human skin inflammation.