A generic, cost-effective, and scalable cell lineage analysis platform

Tamir Biezuner; Adam Spiro; Ofir Raz; Shiran Amir; Lilach Milo; Rivka Adar; Ilani, Noa Chapal Ilani; Veronika Berman; Yael Fried; Elena Ainbinder; Galit Cohen; Haim M. Barr; Ruth Halaban; Ehud Shapiro

doi:10.1101/gr.202903.115

A generic, cost-effective, and scalable cell lineage analysis platform

Tamir Biezuner, Adam Spiro, Ofir Raz, Shiran Amir, Lilach Milo, Rivka Adar, Ilani, Noa Chapal Ilani, Veronika Berman, Yael Fried, Elena Ainbinder, Galit Cohen, Haim M. Barr, Ruth Halaban, Ehud Shapiro

Research output: Contribution to journal › Article › peer-review

32 Citations (Scopus)

Abstract

Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing-based methods for cell lineage analysis depend on low-resolution bulk analysis or rely on extensive single-cell sequencing, which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective, and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data, and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way toward large-scale human cell lineage discovery.

Original language	English
Pages (from-to)	1588-1599
Number of pages	12
Journal	Genome Research
Volume	26
Issue number	11
DOIs	https://doi.org/10.1101/gr.202903.115
Publication status	Published - 24 Aug 2016

Funding

European Union FP7-ERC-AdG (European Research Council) [233047]; EU-H2020-ERC-AdG (European Research Council) [670535]; Deutsche Forschungsgemeinschaft (DFG) [611042]; Israeli Science Foundation (ISF) [P14587]; Israeli Science Foundation-BROAD (ISF) [P15439]; National Institutes of Health [VUMC 38347]; Kenneth and Sally Leafman Appelbaum Discovery Fund; Harry Weinrebe Professorial Chair of Computer Science and Biology. We thank our collaborators Dr. V.A. Adalsteinsson from the MIT laboratory of Professor J.C. Love, Dr. L. Shlush from the Toronto University laboratory of Professor J.E. Dick, Professor C. Wu at the Broad Institute, and Professor C. Klein at Regensburg University for providing us with SC genomic DNA samples, thus supporting the individual’s cell lineage tree experiment (Fig. 4). We thank Dr. D. Pilzer for performing Access Array runs, and T. Wiesel and A. Hendler for the prompt and excellent preparation and design of figures. This research was supported by the following foundations: the European Union FP7-ERC-AdG (European Research Council, 233047); The EU-H2020-ERC-AdG (European Research Council, 670535); the Deutsche Forschungsgemeinschaft (DFG, 611042); the Israeli Science Foundation (ISF, P14587); the Israeli Science Foundation-BROAD (ISF, P15439); the National Institutes of Health (VUMC 38347); and the Kenneth and Sally Leafman Appelbaum Discovery Fund. Ehud Shapiro is the incumbent of The Harry Weinrebe Professorial Chair of Computer Science and Biology.

All Science Journal Classification (ASJC) codes

Genetics
Genetics(clinical)

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Cite this

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abstract = "Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing-based methods for cell lineage analysis depend on low-resolution bulk analysis or rely on extensive single-cell sequencing, which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective, and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data, and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way toward large-scale human cell lineage discovery.",

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AU - Adar, Rivka

AU - Chapal Ilani, Ilani, Noa

AU - Berman, Veronika

AU - Fried, Yael

AU - Ainbinder, Elena

AU - Cohen, Galit

AU - Barr, Haim M.

AU - Halaban, Ruth

AU - Shapiro, Ehud

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AB - Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing-based methods for cell lineage analysis depend on low-resolution bulk analysis or rely on extensive single-cell sequencing, which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective, and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data, and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way toward large-scale human cell lineage discovery.

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A generic, cost-effective, and scalable cell lineage analysis platform

Abstract

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All Science Journal Classification (ASJC) codes

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