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Computational prediction of neural progenitor cell fates


Understanding how stem and progenitor cells choose between alternative cell fates is a major challenge in developmental biology. Efforts to tackle this problem have been hampered by the scarcity of markers that can be used to predict cell division outcomes. Here we present a computational method, based on algorithmic information theory, to analyze dynamic features of living cells over time. Using this method, we asked whether rat retinal progenitor cells (RPCs) display characteristic phenotypes before undergoing mitosis that could foretell their fate. We predicted whether RPCs will undergo a self-renewing or terminal division with 99% accuracy, or whether they will produce two photoreceptors or another combination of offspring with 87% accuracy. Our implementation can segment, track and generate predictions for 40 cells simultaneously on a standard computer at 5 min per frame. This method could be used to isolate cell populations with specific developmental potential, enabling previously impossible investigations.

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Figure 1: Retinal cell type identification.
Figure 2: Self-renewing divisions.
Figure 3: Terminal division.
Figure 4: Automated cell segmentation and tracking.


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The computational aspects of this work were supported by the Center for Subsurface Sensing and Imaging Systems (US National Science Foundation; EEC-9986821), by the Rensselaer Polytechnic Institute and by the University of Wisconsin–Milwaukee. We thank A. Todorski and the staff at Rensselaer's Computational Center for Nanotechnology Innovations supercomputing center; S. Temple for helpful comments and feedback, and on collaborations leading up to the present work; M. Kmita and J. Chan for insightful comments on the manuscript; A. Daigneault for expert technical assistance with the animal colony; and C. Jolicoeur for help with the oligodendrocyte precursor cell culture. This work was supported by grants from the Canadian Institutes of Health Research and the Foundation Fighting Blindness-Canada (to M.C.). M.C. is supported by the Canadian Institutes of Health Research New Investigator program and the W.K. Stell scholarship from the Foundation Fighting Blindness, Canada.

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Authors and Affiliations



A.R.C. and B.R. developed the computational methods; F.L.A.F.G. and M.C. proposed the initial hypothesis and developed the cell culture, immunostaining and imaging methods. All authors contributed to writing the manuscript.

Corresponding authors

Correspondence to Badrinath Roysam or Michel Cayouette.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1 and 2 and Supplementary Tables 1–3 (PDF 917 kb)

Supplementary Movie 1

Retinal progenitor cell undergoing a self-renewing division (Fig. 2). A RPC divides to give rise to a photoreceptor and another RPC at the first and second division (self-renewing) and finally undergoes a terminal division to give rise to two photoreceptors. (MOV 899 kb)

Supplementary Movie 2

Retinal progenitor cell undergoing a terminal division (Fig. 3). A RPC divides to give rise to a photoreceptor and an amacrine cell. (MOV 2637 kb)

Supplementary Movie 3

Example of a retinal progenitor cell segmentation and tracking results. (MOV 986 kb)

Supplementary Movie 4

Oligodendrocyte precursor cell undergoing a terminal division (Supplementary Figure 1). An OPC divides to give rise to two oligodendrocytes. (MOV 2834 kb)

Supplementary Movie 5

Oligodendrocyte precursor cell undergoing a self-renewing division (Supplementary Figure 2). An OPC divides to give rise to another OPC and an oligodendrocyte (self-renewing). (MOV 4085 kb)

Supplementary Software

Source code for the AITP software. (ZIP 28619 kb)

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Cohen, A., Gomes, F., Roysam, B. et al. Computational prediction of neural progenitor cell fates. Nat Methods 7, 213–218 (2010).

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