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.

/pdf/comprehensive-single-cell-transcriptional-profiling-of-a-4eahlwn5oc.pdf

Comprehensive single-cell transcriptional profiling of a multicellular organism

Caenorhabditis elegans is an animal with few cells but a wide diversity of cell types. In this study, we characterize the molecular basis for their specification by profiling the transcriptomes of 86,024 single embryonic cells. We identify 502 terminal and preterminal cell types, mapping most single-cell transcriptomes to their exact position in C. elegans' invariant lineage. Using these annotations, we find that (i) the correlation between a cell's lineage and its transcriptome increases from middle to late gastrulation, then falls substantially as cells in the nervous system and pharynx adopt their terminal fates; (ii) multilineage priming contributes to the differentiation of sister cells at dozens of lineage branches; and (iii) most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state.

A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters.

/pdf/comparative-analysis-of-the-transcriptome-across-distant-hoblv052zp.pdf

Comparative analysis of the transcriptome across distant species

https://projects.iq.harvard.edu/files/mango_lab/files/deplancke_cell_2006.pdf

A gene-centered C. elegans protein-DNA interaction network

Various methodologies have been developed to characterize diverse features of chromatin, but understanding how epigenomic states contribute to cellular heterogeneity requires adoption of these techniques at the single-cell level. This article discusses the technological developments driving single-cell epigenomics, including the practical and bioinformatic challenges and emerging biological insights.

Single-cell epigenomics: techniques and emerging applications

Nature 512, 400–405 (2014); doi:10.1038/nature13497 In this Article, when processing C. elegans ChIP-seq libraries, the gene label ZK337.2 (KLU-1, a C2H2 Zn-finger protein) was mis-transcribed to ZK377.2 (SAX-3), a neuronal fate regulator. To clarify, ZK337.2 (KLU-1) is not an established neuronal fate regulator, but joins FKH-10 and C34F6.

/pdf/corrigendum-regulatory-analysis-of-the-c-elegans-genome-with-2l0fr1s7o2.pdf

Corrigendum: Regulatory analysis of the C. elegans genome with spatiotemporal resolution.

Discovering the structure and dynamics of transcriptional regulatory events in the genome with cellular and temporal resolution is crucial to understanding the regulatory underpinnings of development and disease. We determined the genomic distribution of binding sites for 92 transcription factors and regulatory proteins across multiple stages of Caenorhabditis elegans development by performing 241 ChIP-seq (chromatin immunoprecipitation followed by sequencing) experiments. Integration of regulatory binding and cellular-resolution expression data produced a spatiotemporally resolved metazoan transcription factor binding map. Using this map, we explore developmental regulatory circuits that encode combinatorial logic at the levels of co-binding and co-expression of transcription factors, characterizing the genomic coverage and clustering of regulatory binding, the binding preferences of, and biological processes regulated by, transcription factors, the global transcription factor co-associations and genomic subdomains that suggest shared patterns of regulation, and identifying key transcription factors and transcription factor co-associations for fate specification of individual lineages and cell types.

/pdf/regulatory-analysis-of-the-c-elegans-genome-with-36cptlucrj.pdf

Regulatory analysis of the C. elegans genome with spatiotemporal resolution

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Corrigendum: Regulatory analysis of the C. elegans genome with spatiotemporal resolution.

Regulatory analysis of the C. elegans genome with spatiotemporal resolution