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We describe a protocol to perform fast and non-arbitrary quality control of single-cell RNA sequencing (scRNA-seq) raw data using scKB and COPILOT. scKB is a wrapper script of kallisto and bustools for accelerated alignment and transcript count matrix generation, which runs significantly faster than the popular tool Cell Ranger. COPILOT then offers non-arbitrary background noise removal by comparing distributions of low-quality and high-quality cells. Together, this protocol streamlines the processing workflow and provides an easy entry for new scRNA-seq users. For complete details on the use and execution of this protocol, please refer to Shahan et al. (2022).

Genetic variants associated with human diseases are often located outside the protein coding regions of the genome. Identification and functional characterization of the regulatory elements in the non-coding genome is therefore of crucial importance for understanding the consequences of genetic variation and the mechanisms of disease. The past decade has seen rapid progress in high-throughput analysis and mapping of chromatin accessibility, looping, structure, and occupancy by transcription factors, as well as epigenetic modifications, all of which contribute to the proper execution of regulatory functions in the non-coding genome. Here, we review the current technologies for the definition and functional validation of non-coding regulatory regions in the genome.

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single-cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.

Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a statistically powerful and computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.

Translation modulates the timing and amplification of gene expression after transcription. Development of the brain’s neocortex requires precisely timed and spatially targeted gene expression, but the relationship between mRNA vs. protein synthesis throughout the genome is unknown. We perform a comprehensive analysis of the reactants, synthesis, and products of mRNA translation spanning mouse neocortex neurogenesis. Ribosome number in the cortical plate decreases sharply at mid-neurogenesis during a transition in neuronal subtype specification, shifting the fundamental kinetics of protein synthesis, with mRNA and protein levels frequently divergent. Satb2, which drives an essential neuronal subtype-specific program, is a highly dynamically translated mRNA with surprisingly broad transcription across diverse neuronal lineages. Satb2 protein achieves its neuronal subtype expression through timed regulation by the RNA-binding protein Pumilio2. Thus, the refinement of transcriptional programs by protein synthesis is a widespread feature of neuronal specification. Developmental neocortex translatome data are provided in an open-source resource: https://shiny.mdc-berlin.de/cortexomics/.

Ribosome profiling (Ribo-seq) has catalyzed a paradigm shift in our understanding of the translational ‘vocabulary’ of the human genome, discovering thousands of translated open reading frames (ORFs) within long non-coding RNAs and presumed untranslated regions of protein-coding genes. However, reference gene annotation projects have been circumspect in their incorporation of these ORFs due to uncertainties about their experimental reproducibility and physiological roles. Yet, it is indisputable that certain Ribo-seq ORFs make stable proteins, others mediate gene regulation, and many have medical implications. Ultimately, the absence of standardized ORF annotation has created a circular problem: while Ribo-seq ORFs remain unannotated by reference biological databases, this lack of characterisation will thwart research efforts examining their roles. Here, we outline the initial stages of a community-led effort supported by GENCODE / Ensembl, HGNC and UniProt to produce a consolidated catalog of human Ribo-seq ORFs.

In pluripotent cells, a delicate activation-repression balance maintains pro-differentiation genes ready for rapid activation. The identity of transcription factors (TFs) that specifically repress pro-differentiation genes remains obscure. By targeting ~1,700 TFs with CRISPR loss-of-function screen, we found that ZBTB11 and ZFP131 are required for embryonic stem cell (ESC) pluripotency. ZBTB11 and ZFP131 maintain promoter-proximally paused Polymerase II at pro-differentiation genes in ESCs. ZBTB11 or ZFP131 loss leads to NELF pausing factor release, an increase in H3K4me3, and transcriptional upregulation of genes associated with all three germ layers. Together, our results suggest that ZBTB11 and ZFP131 maintain pluripotency by preventing premature expression of pro-differentiation genes and present a generalizable framework to maintain cellular potency.

Early stages of embryogenesis depend heavily on subcellular localization and transport of maternally deposited mRNA. However, systematic analysis of these processes is currently hindered by a lack of spatio-temporal information in single-cell RNA sequencing. Here, we combined spatially-resolved transcriptomics and single-cell RNA labeling to study the spatio-temporal dynamics of the transcriptome during the first few hours of zebrafish development. We measured spatial localization of mRNA molecules with sub-single-cell resolution at the one-cell stage, which allowed us to identify a class of mRNAs that are specifically localized at an extraembryonic position, the vegetal pole. Furthermore, we established a method for high-throughput single-cell RNA labeling in early zebrafish embryos, which enabled us to follow the fate of individual maternal transcripts until gastrulation. This approach revealed that many localized transcripts are specifically transported to the primordial germ cells. Finally, we acquired spatial transcriptomes of two xenopus species, and we compared evolutionary conservation of localized genes as well as enriched sequence motifs. In summary, we established sub-single-cell spatial transcriptomics and single-cell RNA labeling to reveal principles of mRNA localization in early vertebrate development.

Chloroplast RNA processing requires a large number of nuclear-encoded RNA binding proteins (RBPs) that are imported post-translationally into the organelle. Most of these RBPs are highly specific for one or few target RNAs. By contrast, members of the chloroplast ribonucleoprotein family (cpRNPs) have a wider RNA target range. We here present a quantitative analysis of RNA targets of the cpRNP CP31A using digestion-optimized RNA co-immunoprecipitation with deep sequencing (DO-RIP-seq). This identifies the mRNAs coding for subunits of the chloroplast NAD(P)H dehydrogenase (NDH) complex as main targets for CP31A. We demonstrate using whole-genome gene expression analysis and targeted RNA gel blot hybridization that the ndh mRNAs are all down-regulated in cp31a mutants. This diminishes the activity of the NDH complex. Our findings demonstrate how a chloroplast RNA binding protein can combine functionally related RNAs into one post-transcriptional operon.

Cell fate acquisition is a fundamental developmental process in all multicellular organisms. Yet, much is unknown regarding how a cell traverses the pathway from stem cell to terminal differentiation. Advances in single cell genomics1 hold promise for unraveling developmental mechanisms2–3 in tissues4, organs5–6, and organisms7–8. However, lineage tracing can be challenging for some tissues9 and integration of high-quality datasets is often necessary to detect rare cell populations and developmental states10,11. Here, we harmonized single cell mRNA sequencing data from over 110,000 cells to construct a comprehensive atlas for a stereotypically developing organ with indeterminate growth, the Arabidopsis root. To test the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single cell resolution, shortroot and scarecrow. Although both transcription factors are required for early specification of cell identity12, our results suggest the existence of an alternative pathway acting in mature cells to specify endodermal identity, for which SHORTROOT is required. Uncovering the architecture of this pathway will provide insight into specification and stabilization of the endodermis, a tissue analogous to the mammalian epithelium. Thus, the atlas is a pivotal advance for unraveling the transcriptional programs that specify and maintain cell identity to regulate organ development in space and time.

DNA accessibility of cis regulatory elements (CREs) dictates transcriptional activity and drives cell differentiation during development. While many of the genes that regulate embryonic development have been described, the underlying CRE dynamics controlling their expression remain largely unknown. To address this, we applied single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq) to whole 24 hours post fertilization (hpf) stage zebrafish embryos and developed a new computational tool, ScregSeg, that selects informative genome segments and classifies complex accessibility dynamics. We integrated the ScregSeg output with bulk measurements for histone post-translational modifications and 3D genome organization, expanding knowledge of regulatory principles between chromatin modalities. Sci-ATAC-seq profiling of npas4l/cloche mutant embryos revealed novel cellular roles for this hemato-vascular transcriptional master regulator and suggests an intricate mechanism regulating its expression. Our work constitutes a valuable resource for future studies in developmental, molecular, and computational biology.

Deep sequencing methods have matured to comprehensively detect the full set of transcribed loci, but there is a gap to determine the function of the resulting highly complex transcriptomes. At the center of the gene expression cascade, translation is fundamental in defining the fate of much of the transcribed genome. We have developed a new approach (SaTAnn, Splice-aware Translatome Annotation) to annotate and quantify translation at the single open reading frame (ORF) level, that uses information from ribosome profiling to determine the translational state of each isoform in a comprehensive annotation. For most genes, one ORF represents the dominant translation product, but our approach also detects translation from ORFs belonging to multiple transcripts per gene, including targets of RNA surveillance mechanisms such as nonsense-mediated decay. Diversity in the translation output across human cell lines reveals the extent of gene-specific differences in protein production, which are supported by steady-state protein abundance estimates. Computational analysis of Ribo-seq data with SaTAnn (available at https://github.com/lcalviell/SaTAnn) provides a window into the functions of the heterogeneous transcriptome

Control of gene activity through transcriptional regulatory elements is a major driving force in human evolution. A new study measures nascent transcription directly and shows that sequence, activity and three-dimensional organization of transcriptional regulatory elements all contribute to the evolution of gene activity within primate CD4(+) T cells.

Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target genes to putative enhancers via a semi-supervised learning algorithm that predicts gene expression patterns based on enriched sequence features. Predicted expression patterns were 73-98% accurate, predicted assignments showed strong Hi-C interaction enrichment, enhancer-associated histone modifications were evident, and known functional motifs were recovered. Our model provides a general framework to link globally identified enhancers to targets and contributes to deciphering the regulatory genome.

The cellular response to genotoxic stress is mediated by a well-characterized network of DNA surveillance pathways. The contribution of posttranscriptional gene regulatory networks to the DNA damage response (DDR) has not been extensively studied. Here, we systematically identified RNA-binding proteins differentially interacting with polyadenylated transcripts upon exposure of human breast carcinoma cells to ionizing radiation (IR). Interestingly, more than 260 proteins including many nucleolar proteins showed increased binding to poly(A) RNA in IR-exposed cells. The functional analysis of DDX54, a candidate genotoxic stress responsive RNA helicase, revealed that this protein is an immediate-to-early DDR regulator required for the splicing efficacy of its target IR-induced pre-mRNAs. Upon IR exposure, DDX54 acts by increased interaction with a well-defined class of pre-mRNAs which harbor introns with weak acceptor splice sites, as well as by protein-protein contacts within components of U2 snRNP and spliceosomal B complex, resulting in lower intron retention and higher processing rates of its target transcripts. Since DDX54 promotes survival after exposure to IR its expression and/or mutation rate may impact DDR-related pathologies. Our work indicates the relevance of many uncharacterized RBPs potentially involved in the DDR.

Early embryogenesis is characterized by the maternal to zygotic transition (MZT), in which maternally deposited messenger RNAs are degraded while zygotic transcription begins. Before the MZT, post-transcriptional gene regulation by RNA-binding proteins (RBPs) is the dominant force in embryo patterning. We used two mRNA interactome capture methods to identify RBPs bound to polyadenylated transcripts within the first two hours of D. melanogaster embryogenesis. We identified a high-confidence set of 476 putative RBPs and confirmed RNA-binding activities for most of 24 tested candidates. Most proteins in the interactome are known RBPs or harbor canonical RBP features, but 99 exhibited previously uncharacterized RNA-binding activity. mRNA-bound RBPs and TFs exhibit distinct expression dynamics, in which the newly identified RBPs dominate the first two hours of embryonic development. Integrating our resource with in situ hybridization data from existing databases showed that mRNAs encoding RBPs are enriched in posterior regions of the early embryo, suggesting their general importance in posterior patterning and germ cell maturation.

DNaseI footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF-DNA interactions. DNase-seq footprints were absent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF-DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.

Most transcription factors (TFs) belong to protein families that share a common DNA binding domain and have very similar DNA binding preferences. However, many paralogous TFs (i.e. members of the same TF family) perform different regulatory functions and interact with different genomic regions in the cell. A potential mechanism for achieving this differential in vivo specificity is through interactions with protein co-factors. Computational tools for studying the genomic binding profiles of paralogous TFs and identifying their putative co-factors are currently lacking. Here, we present an interactive web implementation of COUGER, a classification-based framework for identifying protein co-factors that might provide specificity to paralogous TFs. COUGER takes as input two sets of genomic regions bound by paralogous TFs, and it identifies a small set of putative co-factors that best distinguish the two sets of sequences. To achieve this task, COUGER uses a classification approach, with features that reflect the DNA-binding specificities of the putative co-factors. The identified co-factors are presented in a user-friendly output page, together with information that allows the user to understand and to explore the contributions of individual co-factor features. COUGER can be run as a stand-alone tool or through a web interface: http://couger.oit.duke.edu.

BACKGROUND: ZFP36, also known as tristetraprolin or TTP, and ELAVL1, also known as HuR, are two disease-relevant RNA-binding proteins (RBPs) that both interact with AU-rich sequences but have antagonistic roles. While ELAVL1 binding has been profiled in several studies, the precise in vivo binding specificity of ZFP36 has not been investigated on a global scale. We determined ZFP36 binding preferences using cross-linking and immunoprecipitation in human embryonic kidney cells, and examined the combinatorial regulation of AU-rich elements by ZFP36 and ELAVL1. RESULTS: Targets bound and negatively regulated by ZFP36 include transcripts encoding proteins necessary for immune function and cancer, and transcripts encoding other RBPs. Using partial correlation analysis, we were able to quantify the association between ZFP36 binding sites and differential target RNA abundance upon ZFP36 overexpression independent of effects from confounding features. Genes with increased mRNA half-lives in ZFP36 knockout versus wild-type mouse cells were significantly enriched for our human ZFP36 targets. We identified thousands of overlapping ZFP36 and ELAVL1 binding sites, in 1,313 genes, and found that ZFP36 degrades transcripts through specific AU-rich sequences, representing a subset of the U-rich sequences ELAVL1 interacts with to stabilize transcripts. CONCLUSIONS: ZFP36-RNA target specificities in vivo are quantitatively similar to previously reported in vitro binding affinities. ZFP36 and ELAVL1 bind an overlapping spectrum of RNA sequences, yet with differential relative preferences that dictate combinatorial regulatory potential. Our findings and methodology delineate an approach to unravel in vivo combinatorial regulation by RNA-binding proteins.

BACKGROUND: Epigenetic mechanisms such as chromatin accessibility impact transcription factor binding to DNA and transcriptional specificity. The androgen receptor (AR), a master regulator of the male phenotype and prostate cancer pathogenesis, acts primarily through ligand-activated transcription of target genes. Although several determinants of AR transcriptional specificity have been elucidated, our understanding of the interplay between chromatin accessibility and AR function remains incomplete. RESULTS: We used deep sequencing to assess chromatin structure via DNase I hypersensitivity and mRNA abundance, and paired these datasets with three independent AR ChIP-seq datasets. Our analysis revealed qualitative and quantitative differences in chromatin accessibility that corresponded to both AR binding and an enrichment of motifs for potential collaborating factors, one of which was identified as SP1. These quantitative differences were significantly associated with AR-regulated mRNA transcription across the genome. Base-pair resolution of the DNase I cleavage profile revealed three distinct footprinting patterns associated with the AR-DNA interaction, suggesting multiple modes of AR interaction with the genome. CONCLUSIONS: In contrast with other DNA-binding factors, AR binding to the genome does not only target regions that are accessible to DNase I cleavage prior to hormone induction. AR binding is invariably associated with an increase in chromatin accessibility and, consequently, changes in gene expression. Furthermore, we present the first in vivo evidence that a significant fraction of AR binds only to half of the full AR DNA motif. These findings indicate a dynamic quantitative relationship between chromatin structure and AR-DNA binding that impacts AR transcriptional specificity.

Background: RNA editing is a co-transcriptional modification that increases the molecular diversity, alters secondary structure and protein coding sequences by changing the sequence of transcripts. The most common RNA editing modification is the single base substitution (A→I) that is catalyzed by the members of the Adenosine deaminases that act on RNA (ADAR) family. Typically, editing sites are identified as RNA-DNA-differences (RDDs) in a comparison of genome and transcriptome data from next-generation sequencing experiments. However, a method for robust detection of site-specific editing events from replicate RNA-seq data has not been published so far. Even more surprising, condition-specific editing events, which would show up as differences in RNA-RNA comparisons (RRDs) and depend on particular cellular states, are rarely discussed in the literature. Results: We present JACUSA, a versatile one-stop solution to detect single nucleotide variant positions from comparing RNA-DNA and/or RNA-RNA sequencing samples. The performance of JACUSA has been carefully evaluated and compared to other variant callers in an in silico benchmark. JACUSA outperforms other algorithms in terms of the F measure, which combines precision and recall, in all benchmark scenarios. This performance margin is highest for the RNA-RNA comparison scenario. We further validated JACUSA’s performance by testing its ability to detect A→I events using sequencing data from a human cell culture experiment and publicly available RNA-seq data from Drosophila melanogaster heads. To this end, we performed whole genome and RNA sequencing of HEK-293 cells on samples with lowered activity of candidate RNA editing enzymes. JACUSA has a higher recall and comparable precision for detecting true editing sites in RDD comparisons of HEK-293 data. Intriguingly, JACUSA captures most A→I events from RRD comparisons of RNA sequencing data derived from Drosophila and HEK-293 data sets. Conclusion: Our software JACUSA detects single nucleotide variants by comparing data from next-generation sequencing experiments (RNA-DNA or RNA-RNA). In practice, JACUSA shows higher recall and comparable precision in detecting A→I sites from RNA-DNA comparisons, while showing higher precision and recall in RNA-RNA comparisons.