TY - JOUR
T1 - De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data
AU - Grün, Dominic
AU - Muraro, Mauro J
AU - Boisset, Jean-Charles
AU - Wiebrands, Kay
AU - Lyubimova, Anna
AU - Dharmadhikari, Gitanjali
AU - van den Born, Maaike
AU - van Es, Johan
AU - Jansen, Erik
AU - Clevers, Hans
AU - de Koning, Eelco J P
AU - van Oudenaarden, Alexander
N1 - Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
PY - 2016/6/21
Y1 - 2016/6/21
N2 - 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.
AB - 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.
U2 - 10.1016/j.stem.2016.05.010
DO - 10.1016/j.stem.2016.05.010
M3 - Article
C2 - 27345837
SN - 1934-5909
JO - Cell Stem Cell
JF - Cell Stem Cell
ER -