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      De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data

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          Summary

          Adult mitotic tissues like the intestine, skin, and blood undergo constant turnover throughout the life of an organism. Knowing the identity of the stem cell is crucial to understanding tissue homeostasis and its aberrations upon disease. Here we present a computational method for the derivation of a lineage tree from single-cell transcriptome data. By exploiting the tree topology and the transcriptome composition, we establish StemID, an algorithm for identifying stem cells among all detectable cell types within a population. We demonstrate that StemID recovers two known adult stem cell populations, Lgr5+ cells in the small intestine and hematopoietic stem cells in the bone marrow. We apply StemID to predict candidate multipotent cell populations in the human pancreas, a tissue with largely uncharacterized turnover dynamics. We hope that StemID will accelerate the search for novel stem cells by providing concrete markers for biological follow-up and validation.

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          Highlights

          • StemID infers the lineage tree and identifies stem cells from single-cell mRNA-seq data

          • Direct links of stem cells to distinct sub-types reflect transcriptome plasticity

          • The permissive stem cell transcriptome is characterized by high entropy

          • StemID infers candidate multipotent cell populations in the human pancreas

          Abstract

          Grün et al. developed an algorithm, StemID, for the derivation of cell lineage trees and identification of stem cells from single-cell mRNA sequencing data. StemID successfully recovered known adult stem cell populations from the small intestine and bone marrow and was then used to predict a novel multipotent cell population in the human pancreas.

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          Most cited references24

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          Nature, nurture, or chance: stochastic gene expression and its consequences.

          Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.
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            Accounting for technical noise in single-cell RNA-seq experiments.

            Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
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              • Article: not found

              Adult intestinal stem cells: critical drivers of epithelial homeostasis and regeneration.

              Small populations of adult stem cells are responsible for the remarkable ability of the epithelial lining of the intestine to be efficiently renewed and repaired throughout life. The recent discovery of specific markers for these stem cells, together with the development of new technologies to track endogenous stem cell activity in vivo and to exploit their ability to generate new epithelia ex vivo, has greatly improved our understanding of stem cell-driven homeostasis, regeneration and cancer in the intestine. These exciting new insights into the biology of intestinal stem cells have the potential to accelerate the development of stem cell-based therapies and ameliorate cancer treatments.
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                Author and article information

                Contributors
                Journal
                Cell Stem Cell
                Cell Stem Cell
                Cell Stem Cell
                Cell Press
                1934-5909
                1875-9777
                04 August 2016
                04 August 2016
                : 19
                : 2
                : 266-277
                Affiliations
                [1 ]Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, 3584 CT Utrecht, the Netherlands
                [2 ]Cancer Genomics Netherlands, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands
                [3 ]Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany
                [4 ]Department of Medicine, Section of Nephrology and Section of Endocrinology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands
                [5 ]Princess Maxima Center for Pediatric Oncology, 3508 AB Utrecht, the Netherlands
                Author notes
                []Corresponding author gruen@ 123456ie-freibug.mpg.de
                [∗∗ ]Corresponding author a.vanoudenaarden@ 123456hubrecht.eu
                Article
                S1934-5909(16)30094-7
                10.1016/j.stem.2016.05.010
                4985539
                27345837
                87a26999-8daa-41b8-b4d2-111ea9c7f0d1
                © 2016 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 24 February 2016
                : 4 April 2016
                : 12 May 2016
                Categories
                Resource

                Molecular medicine
                Molecular medicine

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