Cell-specific metabolic models reveal targets that mitigate pathogenicity in Th17 cells: towards an actionable human cell atlas
Allon Wagner1,2,#,*, Chao Wang3,#, David DeTomaso2, Aviv Regev3,4,5, Vijay K. Kuchroo3,6,#,*, and Nir Yosef1,2,#,*
1The department of electrical engineering and computer science, University of California, Berkeley, CA 94720; 2The center for computational biology, University of California, Berkeley , CA 94720; 3Broad Institute of MIT and Harvard, Cambridge, MA 02142; 4Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02140; 5Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139; 6Evergrande center for immunologic diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA 02115;
This project addresses a longstanding goal of developmental biology: to map the detailed molecular events beginning with the expansion of pluripotent blastomeres to the differentiation of all mature cell types in the body. To date, many key molecular components and cell types have been catalogued through the use of genetic screens, perturbations, and fate mapping. However, a precise understanding of how cells choose their final identities requires deeper examinations of transitional states in development.
Here, we use high-throughput single-cell transcriptomics to deliver a quantitative map of early vertebrate development, using the zebrafish as a model. Single-cell suspensions were generated from dissociated zebrafish embryos from 7 timepoints spanning the first 24 hours post-fertilization. Over 35,000 individual cells were then encapsulated using a microfluidic droplet-based barcoding platform (“inDrops”, Klein et al 2015) and analyzed by single-cell RNAseq. Established dimensionality reduction and clustering analyses revealed a comprehensive atlas of cell states, which increased in complexity over developmental time. In total, we annotated over 194 cell states (representing both stable cell types as well as dynamic processes) using data from the ZFIN gene expression database. Furthermore, we developed a quantitative strategy for inferring cell state progression over time and constructed a cell state “tree” for development. This tree recapitulates a branching pattern: cells initially expressing early pluripotency markers give rise to distinct germ layers, and progressively discrete tissue-specific compartments. In addition to providing a rich resource for gene discovery, our data also reveal several novel cell types, and elucidate a previously unknown lineage branching event.