Cell Type Purification by Single-Cell Transcriptome-Trained Sorting
Baron, Chloé S.; Barve, Aditya; Muraro, Mauro J.; van der Linden, Reinier; Dharmadhikari, Gitanjali; Lyubimova, Anna; de Koning, Eelco J.P.; van Oudenaarden, Alexander
(2019) Cell, volume 179, issue 2, pp. 527 - 542.e19
(Article)
Abstract
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
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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.
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Keywords: bisulphite sequencing, cell type purification, FACS gate prediction and normalization, flow cytometry, human pancreas, machine learning, optimization algorithm, single-cell transcriptomics, zebrafish hematopoiesis, General Biochemistry,Genetics and Molecular Biology
ISSN: 0092-8674
Publisher: Cell Press
Note: 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)
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