The brain emerges from the primitive ectoderm as a sheet of neuroepithelial cells which folds into the neural tube during neurulation 1 . The developing nervous system is unique for the length of the developmental window, the extent of the interplay between different anatomical regions and lineages, and the diversity of cell types generated. Therefore, the ability of single-cell RNA-seq to disentangle the molecular heterogeneity of a complex cell pool has been particularly useful to study nervous system development [2][3][4][5][6][7][8][9][10] . Recent studies have shed light on the developing telencephalon 5,11 , the hippocampus 9,12,13 , the developing ventral midbrain 14-16, the developing spinal cord and cerebellum 17,18 , and the hypothalamic arcuate nucleus and diencephalon 19,20 . Single-cell RNA-seq has elucidated the differences between embryonic, postnatal and adult neural progenitors 9,21,22 , and compared normal glial progenitors with their malignant counterparts 23,24 .To map mouse brain development in detail, we collected embryonic brain tissue from 43 pregnant CD-1 mice, sampling each day from E7 to E18 (Extended Data Figure 1a-b, Table S1). We prepared 105 samples by droplet-based single-cell RNA sequencing. After removing low-quality cells and doublets (Methods), 96 samples remained with a mean of 5 766 transcripts (unique molecular identifiers, UMIs) and 1 934 genes detected per cell (Extended Data Figure 1c-f). The total cellular RNA content dropped as a function of

Our understanding of how genotype controls phenotype is limited by the scale at which we can precisely alter the genome and assess phenotypic consequences of each perturbation. Here we describe a CRISPR/Cas9-based method for multiplexed accurate genome editing with short, trackable, integrated cellular barcodes (MAGESTIC) in S. cerevisiae. MAGESTIC uses array-synthesized guide-donor oligos for plasmid-based high-throughput editing and features genomic barcode integration to prevent plasmid barcode loss and to enable robust phenotyping. We demonstrate that editing efficiency can be increased >5-fold by recruiting donor DNA to the site of breaks using the LexA-Fkh1p fusion protein. We performed saturation editing of the essential gene SEC14 and identified amino acids critical for chemical inhibition of lipid signaling. We also constructed thousands of natural genetic variants, characterized guide mismatch tolerance at the genome-scale, and ascertained that cryptic Pol III termination elements substantially reduce guide efficacy. MAGESTIC will be broadly useful to uncover the genetic basis of phenotypes in yeast.

Human cells produce thousands of lipids that change during cell differentiation and can vary across individual cells of the same type. However, we are only starting to characterize the function of these cell-to-cell differences in lipid composition. Here, we measured the lipidomes and transcriptomes of individual human dermal fibroblasts by coupling high-resolution mass spectrometry imaging with single-cell transcriptomics. We found that the cell-to-cell variations of specific lipid metabolic pathways contribute to the establishment of cell states involved in the organization of skin architecture. Sphingolipid composition is shown to define fibroblast subpopulations, with sphingolipid metabolic rewiring driving cell-state transitions. Therefore, cell-to-cell lipid heterogeneity affects the determination of cell states, adding a new regulatory component to the self-organization of multicellular systems.

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The Polymerase Associated Factor 1 complex (Paf1C) is a multifunctional regulator of eukaryotic gene expression important for the coordination of transcription with chromatin modification and post-transcriptional processes. In this study, we investigated the extent to which the functions of Paf1C combine to regulate the Saccharomyces cerevisiae transcriptome. While previous studies focused on the roles of Paf1C in controlling mRNA levels, here, we took advantage of a genetic background that enriches for unstable transcripts, and demonstrate that deletion of PAF1 affects all classes of Pol II transcripts including multiple classes of noncoding RNAs (ncRNAs). By conducting a de novo differential expression analysis independent of gene annotations, we found that Paf1 positively and negatively regulates antisense transcription at multiple loci. Comparisons with nascent transcript data revealed that many, but not all, changes in RNA levels detected by our analysis are due to changes in transcription instead of post-transcriptional events. To investigate the mechanisms by which Paf1 regulates protein-coding genes, we focused on genes involved in iron and phosphate homeostasis, which were differentially affected by PAF1 deletion. Our results indicate that Paf1 stimulates phosphate gene expression through a mechanism that is independent of any individual Paf1C-dependent histone modification. In contrast, the inhibition of iron gene expression by Paf1 correlates with a defect in H3 K36 trimethylation. Finally, we showed that one iron regulon gene, FET4, is coordinately controlled by Paf1 and transcription of upstream noncoding DNA. Together, these data identify roles for Paf1C in controlling both coding and noncoding regions of the yeast genome.