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      Single cell RNA-seq identifies the origins of heterogeneity in efficient cell transdifferentiation and reprogramming

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          Abstract

          Forced transcription factor expression can transdifferentiate somatic cells into other specialised cell types or reprogram them into induced pluripotent stem cells (iPSCs) with variable efficiency. To better understand the heterogeneity of these processes, we used single-cell RNA sequencing to follow the transdifferentation of murine pre-B cells into macrophages as well as their reprogramming into iPSCs. Even in these highly efficient systems, there was substantial variation in the speed and path of fate conversion. We predicted and validated that these differences are inversely coupled and arise in the starting cell population, with Myc high large pre-BII cells transdifferentiating slowly but reprogramming efficiently and Myc low small pre-BII cells transdifferentiating rapidly but failing to reprogram. Strikingly, differences in Myc activity predict the efficiency of reprogramming across a wide range of somatic cell types. These results illustrate how single cell expression and computational analyses can identify the origins of heterogeneity in cell fate conversion processes.

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          Most cited references35

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          Direct conversion of fibroblasts to functional neurons by defined factors

          Cellular differentiation and lineage commitment are considered robust and irreversible processes during development. Recent work has shown that mouse and human fibroblasts can be reprogrammed to a pluripotent state with a combination of four transcription factors. This raised the question of whether transcription factors could directly induce other defined somatic cell fates, and not only an undifferentiated state. We hypothesized that combinatorial expression of neural lineage-specific transcription factors could directly convert fibroblasts into neurons. Starting from a pool of nineteen candidate genes, we identified a combination of only three factors, Ascl1, Brn2, and Myt1l, that suffice to rapidly and efficiently convert mouse embryonic and postnatal fibroblasts into functional neurons in vitro. These induced neuronal (iN) cells express multiple neuron-specific proteins, generate action potentials, and form functional synapses. Generation of iN cells from non-neural lineages could have important implications for studies of neural development, neurological disease modeling, and regenerative medicine.
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            Forcing cells to change lineages.

            The ability to produce stem cells by induced pluripotency (iPS reprogramming) has rekindled an interest in earlier studies showing that transcription factors can directly convert specialized cells from one lineage to another. Lineage reprogramming has become a powerful tool to study cell fate choice during differentiation, akin to inducing mutations for the discovery of gene functions. The lessons learnt provide a rubric for how cells may be manipulated for therapeutic purposes.
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              Diffusion maps for high-dimensional single-cell analysis of differentiation data.

              Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                12 March 2019
                2019
                : 8
                : e41627
                Affiliations
                [1 ]Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST) BarcelonaSpain
                [2 ]deptDepartment of Stem Cell and Regenerative Biology Harvard University CambridgeUnited States
                [3 ]deptCNAG-CRG Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST) BarcelonaSpain
                [4 ]Institució Catalana de Recerca i Estudis Avançats (ICREA) BarcelonaSpain
                [5 ]Universitat Pompeu Fabra (UPF) BarcelonaSpain
                University of Edinburgh United Kingdom
                Max Planck Institute for Developmental Biology Germany
                University of Edinburgh United Kingdom
                University of Edinburgh United Kingdom
                Author notes
                [‡]

                Laboratoire de Biologie et Modélisation de la Cellule (LBMC), Universite Lyon, Ecole Normale Superieure de Lyon, CNRS UMR 5239, INSERM U 1210, Universite Claude Bernard Lyon 1, Lyon, France.

                [§]

                Institute of Molecular Biology, Mainz, Germany.

                [#]

                Department of Pediatrics, Dana Farber Cancer Institute, Harvard Medical School, Boston, United States.

                [†]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-8702-0877
                http://orcid.org/0000-0003-2532-3087
                https://orcid.org/0000-0002-1976-7506
                http://orcid.org/0000-0001-7219-632X
                https://orcid.org/0000-0002-8817-1124
                https://orcid.org/0000-0003-2774-4117
                Article
                41627
                10.7554/eLife.41627
                6435319
                30860479
                50095051-7a3a-41bf-8c31-9bc42320ffaf
                © 2019, Francesconi et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 01 September 2018
                : 11 March 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: Synergy Grant (4D-Genome)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003030, Agency for Management of University and Research Grants;
                Award ID: SGR-1136
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: Consolidator grant (616434)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003329, Ministry of Economy and Competitiveness;
                Award ID: BFU2011-26206
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001961, AXA Research Fund;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100007492, Fondation Bettencourt Schueller;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003030, Agency for Management of University and Research Grants;
                Award ID: SGR-831
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Computational and Systems Biology
                Stem Cells and Regenerative Medicine
                Custom metadata
                Variation in efficiency, speed and path during transdifferentiation and reprogramming originates from two pre-B cell subpopulations with reciprocal propensity towards each fate conversion.

                Life sciences
                single cell,reprogramming,transdifferentiation,mouse
                Life sciences
                single cell, reprogramming, transdifferentiation, mouse

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