Singlecell transcriptome sequencing now routinely samples thousands of cells, potentially providing enough data to reconstruct causal gene regulatory networks from observational data. Here, we present Scribe, a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for singlecell experiments to power network reconstruction. Scribe employs Restricted Directed Information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of singlecell measurements and show that there is a dramatic drop in performance for "pseudotime" ordered singlecell data compared to true time series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses therefore highlight an important shortcoming in experimental and computational methods for analyzing gene regulation at singlecell resolution and point the way towards overcoming it.

Over a million DNA regulatory elements have been cataloged in the human genome, but linking these elements to the genes that they regulate remains challenging. We introduce Cicero, a statistical method that connects regulatory elements to target genes using single cell chromatin accessibility data. We apply Cicero to investigate how thousands of dynamically accessible elements orchestrate gene regulation in differentiating myoblasts. Groups of co-accessible regulatory elements linked by Cicero meet criteria of "chromatin hubs", in that they are physically proximal, interact with a common set of transcription factors, and undergo coordinated changes in histone marks that are predictive of gene expression. Pseudotemporal analysis revealed a subset of elements bound by MYOD in myoblasts that exhibit early opening, potentially serving as the initial sites of recruitment of chromatin remodeling and histonemodifying enzymes. The methodological framework described here constitutes a powerful new approach for elucidating the architecture, grammar and mechanisms of cis-regulation on a genome-wide basis.

Expression quantitative trait locus (eQTL) and genomewide association studies (GWAS) are powerful paradigms for mapping the determinants of gene expression and organismal phenotypes, respectively. However, eQTL mapping and GWAS are limited in scope (to naturally occurring, common genetic variants) and resolution (by linkage disequilibrium). Here, we present crisprQTL mapping, a framework in which large numbers of CRISPR/Cas9 perturbations are introduced to each cell on an isogenic background, followed by singlecell RNAseq (scRNAseq). crisprQTL mapping is analogous to conventional human eQTL studies, but with individual humans replaced by individual cells; genetic variants replaced by unique combinations of 'unlinked' guide RNA (gRNA)programmed perturbations per cell; and tissuelevel RNAseq of many individuals replaced by scRNAseq of many cells. By randomly introducing gRNAs, a single population of cells can be leveraged to test for association between each perturbation and the expression of any potential target gene, analogous to how eQTL studies leverage populations of humans to test millions of genetic variants for associations with expression in a genomewide manner. However, crisprQTL mapping is neither limited to naturally occurring, common genetic variants nor by linkage disequilibrium. As a proofofconcept, we applied crisprQTL mapping to evaluate 1,119 candidate enhancers with no strong a priori hypothesis as to their target gene(s). Perturbations were made by a nucleasedead Cas9 (dCas9) tethered to KRAB, and introduced at a mean 'allele frequency' of 1.1% into a population of 47,650 profiled human K562 cells (median of 15 gRNAs identified per cell). We tested for differential expression of all genes within 1 megabase (Mb) of each candidate enhancer, effectively evaluating 17,584 potential enhancertarget gene relationships within a single experiment. At an empirical false discovery rate (FDR) of 10%, we identify 128 cis crisprQTLs (11%) whose targeting resulted in downregulation of 105 nearby genes. crisprQTLs were strongly enriched for proximity to their target genes (median 34.3 kilobases (Kb)) and the strength of H3K27ac, p300, and lineagespecific transcription factor (TF) ChIPseq peaks. Our results establish the power of the eQTL mapping paradigm as applied to programmed variation in populations of cells, rather than natural variation in populations of individuals. We anticipate that crisprQTL mapping will facilitate the comprehensive elucidation of the cis regulatory architecture of the human genome. 2. CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/314344 doi: bioRxiv preprint first posted online May. 4, 2018; Main TextConsequent to an era of biochemical surveys of the human genome (such as the ENCODE Project, ( 1 ) ) and 'common variant' human genetics ( i.e. GWAS, eQTL, ( 2 , 3 ...