Lineage motifs as developmental modules for control of cell type proportions

Creators: Tran, Martin; Askary, Amjad; Elowitz, Michael B.¹

1. California Institute of Technology

Abstract

In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells or extrinsic interactions that produce specific patterns of descendant cell types on lineage trees. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer these dynamics and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states or extrinsic interactions. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in zebrafish and rat retina and early mouse embryonic development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.

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Acknowledgement

We thank Duncan Chadly, Ben Emert, Dongyang Li, Jacob Parres-Gold, Yodai Takei, and other members of the Elowitz lab for scientific input and feedback. We thank Mykel Barrett, Marianne Bronner, Rusty Lansford, Carlos Lois, Magda Zernicka-Goetz, and Meng Zhu for helpful discussion and feedback. This research was supported by Paul G. Allen Frontiers Group and Prime Awarding Agency under award no. UWSC10142 and the National Institute of Health grant R01MH116508. M.T. was supported with funding from the Tianqiao and Chrissy Chen Institute for Neuroscience at Caltech and by the National Eye Institute of the National Institutes of Health under award number F31EY033220. A.A. was supported with funding from National Eye Institute of NIH under award number R00EY031782. M.B.E. is a Howard Hughes Medical Institute investigator. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This article is subject to HHMI’s open access to publications policy. HHMI lab heads have previously granted a nonexclusive CC BY 4.0 license to the public and a sublicensable license to HHMI in their research articles. Pursuant to those licenses, the author-accepted manuscript of this article can be made freely available under a CC BY 4.0 license immediately upon publication.

Contributions

Conceptualization, M.T., A.A., and M.B.E.; methodology, M.T., A.A., and M.B.E.; software, M.T. and A.A.; formal analysis, M.T. and A.A.; writing – original draft, M.T.; writing – review & editing, M.T., A.A., and M.B.E.; visualization, M.T.; supervision, A.A. and M.B.E.; funding acquisition, M.T., A.A., and M.B.E.

Conflict of Interest

The authors declare no competing interests.

Code Availability

GitHub repository: https://github.com/labowitz/linmo

linmo package documentation: https://labowitz.github.io/linmo/

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