Revealing the vectors of cellular identity with single-cell genomics.
Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.
Department of Electrical Engineering and Computer Science and the Center for Computational Biology, University of California, Berkeley, California, USA.
Allon
A
Wagner
Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.|Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Aviv
A
Regev
Department of Electrical Engineering and Computer Science and the Center for Computational Biology, University of California, Berkeley, California, USA.|Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Boston, Massachusetts, USA.
Nir
N
Yosef
D000818:Animals; D002468:Cell Physiological Phenomena / Q000502:physiology*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology; D059467:Transcriptome / Q000502:physiology*
D020543:Proteome