Details

Autor(en) / Beteiligte

• Grün, Dominic
• Muraro, Mauro J.
• Boisset, Jean-Charles
• Wiebrands, Kay
• Lyubimova, Anna
• Dharmadhikari, Gitanjali
• van den Born, Maaike
• van Es, Johan
• Jansen, Erik
• Clevers, Hans
• de Koning, Eelco J.P.
• van Oudenaarden, Alexander

Titel

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

Ist Teil von

• Cell stem cell, 2016-08, Vol.19 (2), p.266-277

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2016

Quelle

Elsevier ScienceDirect Journals

Beschreibungen/Notizen

• 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. [Display omitted] •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 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.