title: Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape.
abstract: We present single-cell clustering using bifurcation analysis (SCUBA), a novel computational method for extracting lineage relationships from single-cell gene expression data and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multilineage specifications. By applying SCUBA to analyze two complementary, publicly available datasets we successfully reconstructed the cellular hierarchy during early development of mouse embryos, modeled the dynamic changes in gene expression patterns, and predicted the effects of perturbing key transcriptional regulators on inducing lineage biases. The results were robust with respect to experimental platform differences between RT-PCR and RNA sequencing. We selectively tested our predictions in Nanog mutants and found good agreement between SCUBA predictions and the experimental data. We further extended the utility of SCUBA by developing a method to reconstruct missing temporal-order information from a typical single-cell dataset. Analysis of a hematopoietic dataset suggests that our method is effective for reconstructing gene expression dynamics during human B-cell development. In summary, SCUBA provides a useful single-cell data analysis tool that is well-suited for the investigation of developmental processes.
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115;
authors.forename: Eugenio
authors.initials: E
authors.lastname: Marco
authors.affiliation: Department of Systems Biology, Harvard Medical School, Boston, MA 02115;
authors.forename: Robert L
authors.initials: RL
authors.lastname: Karp
authors.affiliation: Division of Pediatric Hematology/Oncology, Boston Children's Hospital and Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Harvard Medical School, Boston, MA 02115;
authors.forename: Guoji
authors.initials: G
authors.lastname: Guo
authors.affiliation: Department of Biological Sciences, National University of Singapore and Genome Institute of Singapore, Singapore 138672; and.
authors.forename: Paul
authors.identifier: http://orcid.org/0000-0002-0191-3958
authors.initials: P
authors.lastname: Robson
authors.affiliation: Department of Genetics, La Trobe University, Melbourne, VIC 3086, Australia.
authors.forename: Adam H
authors.initials: AH
authors.lastname: Hart
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115;
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Trippa
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115; gcyuan@jimmy.harvard.edu.
authors.forename: Guo-Cheng
authors.initials: GC
authors.lastname: Yuan
mesh_terms: D000818:Animals; D001402:B-Lymphocytes / Q000166:cytology / Q000378:metabolism; D002454:Cell Differentiation / Q000502:physiology*; D004622:Embryo, Mammalian / Q000166:cytology / Q000378:metabolism; D044127:Epigenesis, Genetic / Q000502:physiology*; D018507:Gene Expression Regulation, Developmental / Q000502:physiology*; D006410:Hematopoiesis / Q000502:physiology*; D018398:Homeodomain Proteins / Q000235:genetics / Q000378:metabolism; D006801:Humans; D051379:Mice; D008954:Models, Biological; D000071317:Nanog Homeobox Protein; D013269:Stochastic Processes
chemical_list: D018398:Homeodomain Proteins; C486780:NANOG protein, human; D000071317:Nanog Homeobox Protein; C475540:Nanog protein, mouse
keywords: bifurcation; differentiation; gene expression; single cell