Linking regulatory DNA elements to their target genes, which may be located hundreds of kilobases away, remains challenging. Here, we introduce Cicero, an algorithm that identifies co-accessible pairs of DNA elements using single-cell chromatin accessibility data and so connects regulatory elements to their putative target genes. We apply Cicero to investigate how dynamically accessible elements orchestrate gene regulation in differentiating myoblasts. Groups of Cicero-linked regulatory elements meet criteria of "chromatin hubs"-they are enriched for physical proximity, interact with a common set of transcription factors, and undergo coordinated changes in histone marks that are predictive of changes in gene expression. Pseudotemporal analysis revealed that most DNA elements remain in chromatin hubs throughout differentiation. A subset of elements bound by MYOD1 in myoblasts exhibit early opening in a PBX1- and MEIS1-dependent manner. Our strategy can be applied to dissect the architecture, sequence determinants, and mechanisms of cis-regulation on a genome-wide scale.

Although we can increasingly measure transcription, chromatin, methylation, and other aspects of molecular biology at single-cell resolution, most assays survey only one aspect of cellular biology. Here we describe sci-CAR, a combinatorial indexing-based coassay that jointly profiles chromatin accessibility and mRNA (CAR) in each of thousands of single cells. As a proof of concept, we apply sci-CAR to 4825 cells, including a time series of dexamethasone treatment, as well as to 11,296 cells from the adult mouse kidney. With the resulting data, we compare the pseudotemporal dynamics of chromatin accessibility and gene expression, reconstruct the chromatin accessibility profiles of cell types defined by RNA profiles, and link cis-regulatory sites to their target genes on the basis of the covariance of chromatin accessibility and transcription across large numbers of single cells.

In Brief A highly multiplexed CRISPRi screen uncovers gene-enhancer relationships at scale.

High-throughput chemical screens typically use coarse assays such as cell survival, limiting what can be learned about mechanisms of action, off-target effects, and heterogeneous responses. Here, we introduce “sci-Plex,” which uses “nuclear hashing” to quantify global transcriptional responses to thousands of independent perturbations at single-cell resolution. As a proof of concept, we applied sci-Plex to screen three cancer cell lines exposed to 188 compounds. In total, we profiled ~650,000 single-cell transcriptomes across ~5000 independent samples in one experiment. Our results reveal substantial intercellular heterogeneity in response to specific compounds, commonalities in response to families of compounds, and insight into differential properties within families. In particular, our results with histone deacetylase inhibitors support the view that chromatin acts as an important reservoir of acetate in cancer cells.

Single cell RNA sequencing can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach to Arabidopsis (Arabidopsis thaliana) root cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat-shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution. RESULTS Single-Cell RNA-Seq of Whole A. thaliana Roots Reveals Distinct Populations of Cortex, Endodermis, Hair, Nonhair, and Stele CellsWe used whole Arabidopsis roots from 7d-old seedlings to generate protoplasts for transcriptome analysis using the 103