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Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements

Hormoz, Sahand and Singer, Zakary S. and Linton, James M. and Antebi, Yaron E. and Shraiman , Boris I. and Elowitz, Michael B. (2016) Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements. Cell Systems, 3 (5). pp. 419-433. ISSN 2405-4712. PMCID PMC5142829. http://resolver.caltech.edu/CaltechAUTHORS:20161128-114511280

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Abstract

As they proliferate, living cells undergo transitions between specific molecularly and developmentally distinct states. Despite the functional centrality of these transitions in multicellular organisms, it has remained challenging to determine which transitions occur and at what rates without perturbations and cell engineering. Here, we introduce kin correlation analysis (KCA) and show that quantitative cell-state transition dynamics can be inferred, without direct observation, from the clustering of cell states on pedigrees (lineage trees). Combining KCA with pedigrees obtained from time-lapse imaging and endpoint single-molecule RNA-fluorescence in situ hybridization (RNA-FISH) measurements of gene expression, we determined the cell-state transition network of mouse embryonic stem (ES) cells. This analysis revealed that mouse ES cells exhibit stochastic and reversible transitions along a linear chain of states ranging from 2C-like to epiblast-like. Our approach is broadly applicable and may be applied to systems with irreversible transitions and non-stationary dynamics, such as in cancer and development.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1016/j.cels.2016.10.015DOIArticle
http://www.sciencedirect.com/science/article/pii/S2405471216303349PublisherArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5142829/PubMed CentralArticle
ORCID:
AuthorORCID
Elowitz, Michael B.0000-0002-1221-0967
Additional Information:© 2016 Elsevier. Received 24 November 2015; revised 1 June 2016; accepted 18 October 2016; published 23 November 2016. We thank Jordi Garcia-Ojalvo and David Sprinzak for helpful comments on the manuscript. We thank Fred Tan for construction of the Zscan4 reporter, and members of the M.B.E. Lab for fruitful discussions. This work was supported by the NIH grants R01HD075605A, R01GM086793A, and P50GM068763, Human Frontiers Science Program, grant RGP0020/2012, and in part by the National Science Foundation under grant NSF PHY-1125915. S.H. also acknowledges support from NIH grant K99GM118910. This work is funded by the Gordon and Betty Moore Foundation through grant GBMF2809 to the Caltech Programmable Molecular Technology Initiative. Author Contributions: S.H., Z.S.S., and M.B.E. designed experiments. S.H. and Z.S.S. performed experiments and analyzed data. J.M.L. and Z.S.S. constructed cell lines with help from S.H. Y.E.A. developed the movie tracking system. B.I.S. and M.B.E. supervised research. S.H., Z.S.S., and M.B.E. wrote the manuscript with substantial input from all authors. Data and Software Availability: All the analysis software, including those used for movie tracking, FISH dot detection/counting, and KCA analysis is available upon request (see CONTACT FOR REAGENT AND RESOURCE SHARING). The data visualization package is also hosted on the Elowitz lab website: http://www.elowitz.caltech.edu/. The accession number for the data reported in this paper is NCBI’s GEO: GSE86417. Additional Resources: The complete end-point FISH data and the associated lineage relationships is available on the Elowitz lab website (http://www.elowitz.caltech.edu/) for interactive viewing using a novel visualization tool, CellLines, developed by the Elowitz Lab in collaboration with the Caltech Data Visualization Program.
Funders:
Funding AgencyGrant Number
NIHR01HD075605A
NIHR01GM086793A
NIHP50GM068763
Human Frontier Science ProgramRGP0020/2012
NSFPHY-1125915
NIHK99GM118910
Gordon and Betty Moore FoundationGBMF2809
Howard Hughes Medical Institute (HHMI)UNSPECIFIED
Subject Keywords:Lineage; Cell State Transition; Inference; Dynamics; Single Cell; Heterogeneity; Stochasticity; Stem Cells; Time-lapse Microscopy; Single-molecule FISH
PubMed Central ID:PMC5142829
Record Number:CaltechAUTHORS:20161128-114511280
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20161128-114511280
Official Citation:Hormoz, S., Singer, Zakary S., Linton, James M., Antebi, Yaron E., Shraiman, Boris I., and Elowitz, Michael B. (2016). Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements. Cell Systems 3, 419-433.e418.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72313
Collection:CaltechAUTHORS
Deposited By: Melissa Ray
Deposited On:28 Nov 2016 21:02
Last Modified:10 Jul 2017 22:21

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