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Cell Stem Cell. 2016 Aug 4;19(2):266-277. doi: 10.1016/j.stem.2016.05.010. Epub 2016 Jun 23.

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

Author information

1
Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, 3584 CT Utrecht, the Netherlands; Cancer Genomics Netherlands, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands; Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany. Electronic address: gruen@ie-freibug.mpg.de.
2
Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, 3584 CT Utrecht, the Netherlands; Cancer Genomics Netherlands, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands.
3
Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, 3584 CT Utrecht, the Netherlands; Cancer Genomics Netherlands, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands; Department of Medicine, Section of Nephrology and Section of Endocrinology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands.
4
Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, 3584 CT Utrecht, the Netherlands; Cancer Genomics Netherlands, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands; Princess Maxima Center for Pediatric Oncology, 3508 AB Utrecht, the Netherlands.
5
Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, 3584 CT Utrecht, the Netherlands; Cancer Genomics Netherlands, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands. Electronic address: a.vanoudenaarden@hubrecht.eu.

Abstract

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.

PMID:
27345837
PMCID:
PMC4985539
DOI:
10.1016/j.stem.2016.05.010
[Indexed for MEDLINE]
Free PMC Article

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