When looking at populations of cells, features such as cell heterogeneity and localization are masked. At the onset of gastrulation, the fly embryo consists of about 6000 cells with distinct gene expression profiles. Karaiskos et al. developed an algorithm to generate an interactive three-dimensional (3D) "virtual embryo," with the expression of more than 8000 genes per cell measured for most cells (see the Perspective by Stadler and Eisen). The virtual embryo offers insights into developmental mechanisms--from local expression of regulators such as transcription factors and long noncoding RNAs to spatial modulation of signaling pathways. Science, this issue p. 194; see also p. 172
We generated single-cell transcriptomes from 38,731 cells during early zebrafish embryogenesis at high temporal resolution, spanning 12 stages from the onset of zygotic transcription through early somitogenesis. We took two complementary approaches to identify the transcriptional trajectories in the data. First, we developed a simulated diffusion-based computational approach, URD, which identified the trajectories describing the specification of 25 cell types in the form of a branching tree. Second, we identified modules of coexpressed genes and connected them across developmental time. Combining the reconstructed developmental trajectories with differential gene expression analysis uncovered gene expression cascades leading to each cell type, including previously unidentified markers and candidate regulators.
To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided 50-fold "shotgun" cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type–specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.
We began by (i) detecting and quantifying a focused 160-gene set (including cell type markers and activity-regulated genes) simultaneously in mouse primary visual cortex; (ii) clustering resulting per-cell gene expression patterns into a dozen distinct inhibitory, excitatory, and non-neuronal cell types; and (iii) mapping the spatial distribution of all of these cell types across layers of cortex. For validation, per-cell-type gene expression was found to correlate well both with in situ hybridization results and with single-cell RNA sequencing, and widespread up-regulation of activity-regulated genes was observed in response to visual stimulation. We next applied STARmap to a higher cognitive area (the medial prefrontal cortex) and discovered a more complex distribution of cell types. Last, we extended STARmap to much larger numbers of genes and spatial scales; we measured 1020 genes simultaneously in sections--obtaining results concordant with the 160-gene set--and measured 28 genes across millimeter-scale volumes encompassing 30,000 cells, revealing 3D patterning principles that jointly characterize a broad and diverse spectrum of cell types.
The recent development of single-cell genomic techniques allows us to profile gene expression at the single-cell level easily, although many of these methods have limited throughput. Rosenberg et al. describe a strategy called split-pool ligation-based transcriptome sequencing, or SPLiT-seq, which uses combinatorial barcoding to profile single-cell transcriptomes without requiring the physical isolation of each cell. The authors used their method to profile 100,000 single-cell transcriptomes from mouse brains and spinal cords at 2 and 11 days after birth. Comparisons with in situ hybridization data on RNA expression from Allen Institute atlases linked these transcriptomes with spatial mapping, from which developmental lineages could be identified.