A Single-Cell and Single-Nucleus RNA-Seq Toolbox for Fresh and Frozen Human Tumors

May 11, 2020·
M. slyper
,
C. b. m. porter
,
O. ashenberg
,
J. waldman
,
E. drokhlyansky
,
I. wakiro
,
C. smillie
,
G. smith rosario
,
J. wu
,
D. dionne
,
S. vigneau
,
J. jane valbuena
,
T. l. tickle
,
S. napolitano
,
M. su
,
A. g. patel
,
A. karlstrom
,
S. gritsch
,
M. nomura
,
A. waghray
,
S. h. gohil
,
A. m. tsankov
,
L. jerby arnon
,
O. cohen
,
J. klughammer
,
Y. rosen
,
J. gould
,
L. nguyen
,
M. hofree
,
P. j. tramontozzi
Dr. Bo Li
Dr. Bo Li
,
C. j. wu
,
B. izar
,
R. haq
,
F. s. hodi
,
C. h. yoon
,
A. n. hata
,
S. j. baker
,
M. l. suva
,
R. bueno
,
E. h. stover
,
M. r. clay
,
M. a. dyer
,
N. b. collins
,
U. a. matulonis
,
N. wagle
,
B. e. johnson
,
A. rotem
,
O. rozenblatt rosen odagger
,
A. regevdagger
· 0 min read
Abstract
Single-cell genomics is essential to chart tumor ecosystems. Although single-cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumors, single-nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each requires customization to different tissue and tumor types, posing a barrier to adoption. Here, we have developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We analyzed 216,490 cells and nuclei from 40 samples across 23 specimens spanning eight tumor types of varying tissue and sample characteristics. We evaluated protocols by cell and nucleus quality, recovery rate and cellular composition. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types, but at different proportions. Our work provides guidance for studies in a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.
Type
Publication
Nature Medicine
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Dr. Bo Li
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Principal Scientist II
Dr. Bo Li is a Principal Scientist at Genentech, Inc. His research focuses on large-scale single-cell genomics data analysis. Before joining in Genentech, he was an Assistant Professor of Medicine at Harvard Medical School and the director of Bioinformatics and Computational Biology at Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital. He received his Ph.D. in computer science from UW-Madison and completed two postdoctoral trainings with Dr. Lior Pachter at UC Berkeley and Dr. Aviv Regev at Broad Institute. He is best known for developing RSEM, an impactful RNA-seq transcript quantification software. RSEM is cited 22,602 times (Google Scholar) and adopted by several big consortia such as TCGA, ENCODE, GTEx and TOPMed.
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