Cell Stem Cell. 2016 Jun 21. pii: S1934-5909(16)30094-7. doi: 10.1016/j.stem.2016.05.010. [Epub ahead of print]
De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data.
Grün D, Muraro MJ, Boisset JC, Wiebrands K, Lyubimova A, Dharmadhikari G, van den Born M, van Es J, Jansen E, Clevers H, de Koning EJ, van Oudenaarden A.
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.
A cikknek különösen a bioinformatikai része, az adatok feldolgozásának módja az újszerű és elgodolkodtató.
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