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

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


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Animals
  • Bone Marrow Cells / cytology
  • Bone Marrow Cells / metabolism
  • Cell Lineage
  • Entropy
  • Hematopoietic Stem Cells / cytology
  • Hematopoietic Stem Cells / metabolism
  • Humans
  • Intestines / cytology
  • Mice, Inbred C57BL
  • Multipotent Stem Cells / cytology
  • Multipotent Stem Cells / metabolism
  • Pancreatic Ducts / cytology
  • Pluripotent Stem Cells / cytology
  • Pluripotent Stem Cells / metabolism
  • Reproducibility of Results
  • Single-Cell Analysis / methods*
  • Stem Cells / cytology*
  • Transcriptome / genetics*