TY - JOUR
T1 - Cell Type Purification by Single-Cell Transcriptome-Trained Sorting
AU - Baron, Chloé S.
AU - Barve, Aditya
AU - Muraro, Mauro J.
AU - van der Linden, Reinier
AU - Dharmadhikari, Gitanjali
AU - Lyubimova, Anna
AU - de Koning, Eelco J.P.
AU - van Oudenaarden, Alexander
N1 - Funding Information:
This work was supported by a European Research Council advanced grant (ERC-AdG 742225-IntScOmics) and a Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) TOP award (NWO-CW 714.016.001). Financial support was also provided by the Dutch Diabetes Research Foundation and the DON Foundation. This work is part of the Oncode Institute, which is partly financed by the Dutch Cancer Society. We especially thank Dr. Jake Yeung, Josi Peterson-Maduro, Dr. Lennart Kester, and all other members of the A.v.O. laboratory for discussions and input. In addition, we thank the Hubrecht Sorting Facility and the Utrecht Sequencing Facility, subsidized by the University Medical Center Utrecht; the Hubrecht Institute, and Utrecht University. A.v.O. and A.B. conceived and designed the project. A.B. developed the GateID algorithm. A.B. C.S.B. M.J.M. and A.v.O. further refined the algorithm. A.B. performed the gate design and normalization for BD FACSJazz WKM and pancreas experiments. C.S.B. performed the gate design and normalization for BD FACSInflux WKM experiments. R.v.d.L. operated both FACS machines used in this study and assisted with gate normalization. C.S.B. performed zebrafish WKM scRNA-seq experiments. A.B. and C.S.B. analyzed the zebrafish WKM scRNA-seq data. M.J.M. and G.D. performed human pancreas scRNA-seq experiments. M.J.M. analyzed the human pancreas scRNA-seq data. G.D. and E.J.P.d.K. provided human pancreatic tissue. All authors discussed and interpreted the results. C.S.B. A.B. M.J.M. and A.v.O. wrote the manuscript. The authors declare no competing interests.
Funding Information:
This work was supported by a European Research Council advanced grant ( ERC-AdG 742225-IntScOmics ) and a Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) TOP award ( NWO-CW 714.016.001 ). Financial support was also provided by the Dutch Diabetes Research Foundation and the DON Foundation. This work is part of the Oncode Institute, which is partly financed by the Dutch Cancer Society . We especially thank Dr. Jake Yeung, Josi Peterson-Maduro, Dr. Lennart Kester, and all other members of the A.v.O. laboratory for discussions and input. In addition, we thank the Hubrecht Sorting Facility and the Utrecht Sequencing Facility, subsidized by the University Medical Center Utrecht; the Hubrecht Institute, and Utrecht University.
Publisher Copyright:
© 2019 The Author(s)
PY - 2019/10/3
Y1 - 2019/10/3
N2 - Much of current molecular and cell biology research relies on the ability to purify cell types by fluorescence-activated cell sorting (FACS). FACS typically relies on the ability to label cell types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for cell types without a priori knowledge of cell-type markers. Here, we propose GateID, a computational method that combines single-cell transcriptomics with FACS index sorting to purify cell types of choice using only native cellular properties such as cell size, granularity, and mitochondrial content. We validate GateID by purifying various cell types from zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.
AB - Much of current molecular and cell biology research relies on the ability to purify cell types by fluorescence-activated cell sorting (FACS). FACS typically relies on the ability to label cell types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for cell types without a priori knowledge of cell-type markers. Here, we propose GateID, a computational method that combines single-cell transcriptomics with FACS index sorting to purify cell types of choice using only native cellular properties such as cell size, granularity, and mitochondrial content. We validate GateID by purifying various cell types from zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.
KW - bisulphite sequencing
KW - cell type purification
KW - FACS gate prediction and normalization
KW - flow cytometry
KW - human pancreas
KW - machine learning
KW - optimization algorithm
KW - single-cell transcriptomics
KW - zebrafish hematopoiesis
UR - http://www.scopus.com/inward/record.url?scp=85071284516&partnerID=8YFLogxK
U2 - 10.1016/j.cell.2019.08.006
DO - 10.1016/j.cell.2019.08.006
M3 - Article
C2 - 31585086
AN - SCOPUS:85071284516
SN - 0092-8674
VL - 179
SP - 527-542.e19
JO - Cell
JF - Cell
IS - 2
ER -