Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps.

Colorectal cancers (CRCs) arise from precursor polyps whose cellular origins, molecular heterogeneity, and immunogenic potential may reveal diagnostic and therapeutic insights when analyzed at high resolution. We present a single-cell transcriptomic and imaging atlas of the two most common human colorectal polyps, conventional adenomas and serrated polyps, and their resulting CRC counterparts. Integrative analysis of 128 datasets from 62 participants reveals adenomas arise from WNT-driven expansion of stem cells, while serrated polyps derive from differentiated cells through gastric metaplasia. Metaplasia-associated damage is coupled to a cytotoxic immune microenvironment preceding hypermutation, driven partly by antigen-presentation differences associated with tumor cell-differentiation status. Microsatellite unstable CRCs contain distinct non-metaplastic regions where tumor cells acquire stem cell properties and cytotoxic immune cells are depleted. Our multi-omic atlas provides insights into malignant progression of colorectal polyps and their microenvironment, serving as a framework for precision surveillance and prevention of CRC.

In vivo base editing rescues Hutchinson-Gilford progeria syndrome in mice.

Hutchinson-Gilford progeria syndrome (HGPS or progeria) is typically caused by a dominant-negative C•G-to-T•A mutation (c.1824 C>T; p.G608G) in LMNA, the gene that encodes nuclear lamin A. This mutation causes RNA mis-splicing that produces progerin, a toxic protein that induces rapid ageing and shortens the lifespan of children with progeria to approximately 14 years1-4. Adenine base editors (ABEs) convert targeted A•T base pairs to G•C base pairs with minimal by-products and without requiring double-strand DNA breaks or donor DNA templates5,6. Here we describe the use of an ABE to directly correct the pathogenic HGPS mutation in cultured fibroblasts derived from children with progeria and in a mouse model of HGPS. Lentiviral delivery of the ABE to fibroblasts from children with HGPS resulted in 87-91% correction of the pathogenic allele, mitigation of RNA mis-splicing, reduced levels of progerin and correction of nuclear abnormalities. Unbiased off-target DNA and RNA editing analysis did not detect off-target editing in treated patient-derived fibroblasts. In transgenic mice that are homozygous for the human LMNA c.1824 C>T allele, a single retro-orbital injection of adeno-associated virus 9 (AAV9) encoding the ABE resulted in substantial, durable correction of the pathogenic mutation (around 20-60% across various organs six months after injection), restoration of normal RNA splicing and reduction of progerin protein levels. In vivo base editing rescued the vascular pathology of the mice, preserving vascular smooth muscle cell counts and preventing adventitial fibrosis. A single injection of ABE-expressing AAV9 at postnatal day 14 improved vitality and greatly extended the median lifespan of the mice from 215 to 510 days. These findings demonstrate the potential of in vivo base editing as a possible treatment for HGPS and other genetic diseases by directly correcting their root cause.

Proteomic Biomarkers of Quantitative Interstitial Abnormalities in COPDGene and CARDIA Lung Study.

Rationale: Quantitative interstitial abnormalities (QIAs) are early measures of lung injury automatically detected on chest computed tomography scans. QIAs are associated with impaired respiratory health and share features with advanced lung diseases, but their biological underpinnings are not well understood. Objectives: To identify novel protein biomarkers of QIAs using high-throughput plasma proteomic panels within two multicenter cohorts. Methods: We measured the plasma proteomics of 4,383 participants in an older, ever-smoker cohort (COPDGene [Genetic Epidemiology of Chronic Obstructive Pulmonary Disease]) and 2,925 participants in a younger population cohort (CARDIA [Coronary Artery Disease Risk in Young Adults]) using the SomaLogic SomaScan assays. We measured QIAs using a local density histogram method. We assessed the associations between proteomic biomarker concentrations and QIAs using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, and study center (Benjamini-Hochberg false discovery rate-corrected P ⩽ 0.05). Measurements and Main Results: In total, 852 proteins were significantly associated with QIAs in COPDGene and 185 in CARDIA. Of the 144 proteins that overlapped between COPDGene and CARDIA, all but one shared directionalities and magnitudes. These proteins were enriched for 49 Gene Ontology pathways, including biological processes in inflammatory response, cell adhesion, immune response, ERK1/2 regulation, and signaling; cellular components in extracellular regions; and molecular functions including calcium ion and heparin binding. Conclusions: We identified the proteomic biomarkers of QIAs in an older, smoking population with a higher prevalence of pulmonary disease and in a younger, healthier community cohort. These proteomics features may be markers of early precursors of advanced lung diseases.

Transcriptional Dysregulation Underlies Both Monogenic Arrhythmia Syndrome and Common Modifiers of Cardiac Repolarization.

BACKGROUND: Brugada syndrome (BrS) is an inherited arrhythmia syndrome caused by loss-of-function variants in the cardiac sodium channel gene SCN5A (sodium voltage-gated channel alpha subunit 5) in ≈20% of subjects. We identified a family with 4 individuals diagnosed with BrS harboring the rare G145R missense variant in the cardiac transcription factor TBX5 (T-box transcription factor 5) and no SCN5A variant. METHODS: We generated induced pluripotent stem cells (iPSCs) from 2 members of a family carrying TBX5-G145R and diagnosed with Brugada syndrome. After differentiation to iPSC-derived cardiomyocytes (iPSC-CMs), electrophysiologic characteristics were assessed by voltage- and current-clamp experiments (n=9 to 21 cells per group) and transcriptional differences by RNA sequencing (n=3 samples per group), and compared with iPSC-CMs in which G145R was corrected by CRISPR/Cas9 approaches. The role of platelet-derived growth factor (PDGF)/phosphoinositide 3-kinase (PI3K) pathway was elucidated by small molecule perturbation. The rate-corrected QT (QTc) interval association with serum PDGF was tested in the Framingham Heart Study cohort (n=1893 individuals). RESULTS: TBX5-G145R reduced transcriptional activity and caused multiple electrophysiologic abnormalities, including decreased peak and enhanced "late" cardiac sodium current (INa), which were entirely corrected by editing G145R to wild-type. Transcriptional profiling and functional assays in genome-unedited and -edited iPSC-CMs showed direct SCN5A down-regulation caused decreased peak INa, and that reduced PDGF receptor (PDGFRA [platelet-derived growth factor receptor α]) expression and blunted signal transduction to PI3K was implicated in enhanced late INa. Tbx5 regulation of the PDGF axis increased arrhythmia risk due to disruption of PDGF signaling and was conserved in murine model systems. PDGF receptor blockade markedly prolonged normal iPSC-CM action potentials and plasma levels of PDGF in the Framingham Heart Study were inversely correlated with the QTc interval (P<0.001). CONCLUSIONS: These results not only establish decreased SCN5A transcription by the TBX5 variant as a cause of BrS, but also reveal a new general transcriptional mechanism of arrhythmogenesis of enhanced late sodium current caused by reduced PDGF receptor-mediated PI3K signaling.

DC ENaC-Dependent Inflammasome Activation Contributes to Salt-Sensitive Hypertension.

BACKGROUND: Salt sensitivity of blood pressure is an independent predictor of cardiovascular morbidity and mortality. The exact mechanism by which salt intake increases blood pressure and cardiovascular risk is unknown. We previously found that sodium entry into antigen-presenting cells (APCs) via the amiloride-sensitive epithelial sodium channel EnaC (epithelial sodium channel) leads to the formation of IsoLGs (isolevuglandins) and release of proinflammatory cytokines to activate T cells and modulate salt-sensitive hypertension. In the current study, we hypothesized that ENaC-dependent entry of sodium into APCs activates the NLRP3 (NOD [nucleotide-binding and oligomerization domain]-like receptor family pyrin domain containing 3) inflammasome via IsoLG formation leading to salt-sensitive hypertension. METHODS: We performed RNA sequencing on human monocytes treated with elevated sodium in vitro and Cellular Indexing of Transcriptomes and Epitopes by Sequencing analysis of peripheral blood mononuclear cells from participants rigorously phenotyped for salt sensitivity of blood pressure using an established inpatient protocol. To determine mechanisms, we analyzed inflammasome activation in mouse models of deoxycorticosterone acetate salt-induced hypertension as well as salt-sensitive mice with ENaC inhibition or expression, IsoLG scavenging, and adoptive transfer of wild-type dendritic cells into NLRP3 deficient mice. RESULTS: We found that high levels of salt exposure upregulates the NLRP3 inflammasome, pyroptotic and apoptotic caspases, and IL (interleukin)-1ß transcription in human monocytes. Cellular Indexing of Transcriptomes and Epitopes by Sequencing revealed that components of the NLRP3 inflammasome and activation marker IL-1ß dynamically vary with changes in salt loading/depletion. Mechanistically, we found that sodium-induced activation of the NLRP3 inflammasome is ENaC and IsoLG dependent. NLRP3 deficient mice develop a blunted hypertensive response to elevated sodium, and this is restored by the adoptive transfer of NLRP3 replete APCs. CONCLUSIONS: These findings reveal a mechanistic link between ENaC, inflammation, and salt-sensitive hypertension involving NLRP3 inflammasome activation in APCs. APC activation via the NLRP3 inflammasome can serve as a potential diagnostic biomarker for salt sensitivity of blood pressure.

The Genetic Landscape of Familial Pulmonary Fibrosis.

Rationale and Objectives: Up to 20% of idiopathic interstitial lung disease is familial, referred to as familial pulmonary fibrosis (FPF). An integrated analysis of FPF genetic risk was performed by comprehensively evaluating for genetic rare variants (RVs) in a large cohort of FPF kindreds. Methods: Whole-exome sequencing and/or candidate gene sequencing from affected individuals in 569 FPF kindreds was performed, followed by cosegregation analysis in large kindreds, gene burden analysis, gene-based risk scoring, cell-type enrichment analysis, and coexpression network construction. Measurements and Main Results: It was found that 14.9-23.4% of genetic risk in kindreds could be explained by RVs in genes previously linked to FPF, predominantly telomere-related genes. New candidate genes were identified in a small number of families-including SYDE1, SERPINB8, GPR87, and NETO1-and tools were developed for evaluation and prioritization of RV-containing genes across kindreds. Several pathways were enriched for RV-containing genes in FPF, including focal adhesion and mitochondrial complex I assembly. By combining single-cell transcriptomics with prioritized candidate genes, expression of RV-containing genes was discovered to be enriched in smooth muscle cells, type II alveolar epithelial cells, and endothelial cells. Conclusions: In the most comprehensive FPF genetic study to date, the prevalence of RVs in known FPF-related genes was defined, and new candidate genes and pathways relevant to FPF were identified. However, new RV-containing genes shared across multiple kindreds were not identified, thereby suggesting that heterogeneous genetic variants involving a variety of genes and pathways mediate genetic risk in most FPF kindreds.

scMRMA: single cell multiresolution marker-based annotation.

Single-cell RNA sequencing has become a powerful tool for identifying and characterizing cellular heterogeneity. One essential step to understanding cellular heterogeneity is determining cell identities. The widely used strategy predicts identities by projecting cells or cell clusters unidirectionally against a reference to find the best match. Here, we develop a bidirectional method, scMRMA, where a hierarchical reference guides iterative clustering and deep annotation with enhanced resolutions. Taking full advantage of the reference, scMRMA greatly improves the annotation accuracy. scMRMA achieved better performance than existing methods in four benchmark datasets and successfully revealed the expansion of CD8 T cell populations in squamous cell carcinoma after anti-PD-1 treatment.

Elucidation of physico-chemical principles of high-density lipoprotein-small RNA binding interactions.

Extracellular small RNAs (sRNAs) are abundant in many biofluids, but little is known about their mechanisms of transport and stability in RNase-rich environments. We previously reported that high-density lipoproteins (HDLs) in mice were enriched with multiple classes of sRNAs derived from the endogenous transcriptome, but also from exogenous organisms. Here, we show that human HDL transports tRNA-derived sRNAs (tDRs) from host and nonhost species, the profiles of which were found to be altered in human atherosclerosis. We hypothesized that HDL binds to tDRs through apolipoprotein A-I (apoA-I) and that these interactions are conferred by RNA-specific features. We tested this using microscale thermophoresis and electrophoretic mobility shift assays and found that HDL binds to tDRs and other single-stranded sRNAs with strong affinity but did not bind to double-stranded RNA or DNA. Furthermore, we show that natural and synthetic RNA modifications influenced tDR binding to HDL. We demonstrate that reconstituted HDL bound to tDRs only in the presence of apoA-I, and purified apoA-I alone were able to bind sRNA. Conversely, phosphatidylcholine vesicles did not bind tDRs. In summary, we conclude that HDL binds to single-stranded sRNAs likely through nonionic interactions with apoA-I. These results highlight binding properties that likely enable extracellular RNA communication and provide a foundation for future studies to manipulate HDL-sRNA interactions for therapeutic approaches to prevent or treat disease.

Combined Dusp4 and p53 loss with Dbf4 amplification drives tumorigenesis via cell cycle restriction and replication stress escape in breast cancer.

AIM: Deregulated signaling pathways are a hallmark feature of oncogenesis and driver of tumor progression. Dual specificity protein phosphatase 4 (DUSP4) is a critical negative regulator of the mitogen-activated protein kinase (MAPK) pathway and is often deleted or epigenetically silenced in tumors. DUSP4 alterations lead to hyperactivation of MAPK signaling in many cancers, including breast cancer, which often harbor mutations in cell cycle checkpoint genes, particularly in TP53. METHODS: Using a genetically engineered mouse model, we generated mammary-specific Dusp4-deleted primary epithelial cells to investigate the necessary conditions in which DUSP4 loss may drive breast cancer oncogenesis. RESULTS: We found that Dusp4 loss alone is insufficient in mediating tumorigenesis, but alternatively converges with loss in Trp53 and MYC amplification to induce tumorigenesis primarily through chromosome 5 amplification, which specifically upregulates Dbf4, a cell cycle gene that promotes cellular replication by mediating cell cycle checkpoint escape. CONCLUSIONS: This study identifies a novel mechanism for breast tumorigenesis implicating Dusp4 loss and p53 mutations in cellular acquisition of Dbf4 upregulation as a driver of cellular replication and cell cycle checkpoint escape.

Cancer-specific expression quantitative loci are affected by expression dysregulation.

Expression quantitative trait loci (eQTLs) have been touted as the missing piece that can bridge the gap between genetic variants and phenotypes. Over the past decade, we have witnessed a sharp rise of effort in the identification and application of eQTLs. The successful application of eQTLs relies heavily on their reproducibility. The current eQTL databases such as Genotype-Tissue Expression (GTEx) were populated primarily with eQTLs deriving from germline single nucleotide polymorphisms and normal tissue gene expression. The novel scenarios that employ eQTL models for prediction purposes often involve disease phenotypes characterized by altered gene expressions. To evaluate eQTL reproducibility across diverse data sources and the effect of disease-specific gene expression alteration on eQTL identification, we conducted an eQTL study using 5178 samples from The Cancer Genome Atlas (TCGA). We found that the reproducibility of eQTLs between normal and tumor tissues was low in terms of the number of shared eQTLs. However, among the shared eQTLs, the effect directions were generally concordant. This suggests that the source of the gene expression (normal or tumor tissue) has a strong effect on the detectable eQTLs and the effect direction of the eQTLs. Additional analyses demonstrated good directional concordance of eQTLs between GTEx and TCGA. Furthermore, we found that multi-tissue eQTLs may exert opposite effects across multiple tissue types. In summary, our results suggest that eQTL prediction models need to carefully address tissue and disease dependency of eQTLs. Tissue-disease-specific eQTL databases can afford more accurate prediction models for future studies.

EditPredict: Prediction of RNA editable sites with convolutional neural network.

RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to predict human RNA editing events in both Alu regions and non-Alu regions. With a validation dataset resulting from CRISPR/Cas9 knockout of the ADAR1 enzyme, the validation accuracies reached 99.5% and 93.6% for Alu and non-Alu regions, respectively. We ported our CNN models in a web service named EditPredict. EditPredict not only works on reference genome sequences but can also take into consideration single nucleotide variants in personal genomes. In addition to the human genome, EditPredict tackles other model organisms including bumblebee, fruitfly, mouse, and squid genomes. EditPredict can be used stand-alone to predict novel RNA editing and it can be used to assist in filtering for candidate RNA editing detected from RNA-Seq data.

Inflammatory heterogeneity in aspirin-exacerbated respiratory disease.

BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) is a mechanistically distinct subtype of chronic rhinosinusitis with nasal polyps (CRSwNP). Although frequently associated with type 2 inflammation, literature characterizing the milieu of inflammatory cytokines and lipid mediators in AERD has been conflicting. OBJECTIVE: We sought to identify differences in the upper airway inflammatory signature between CRSwNP and AERD and determine whether endotypic subtypes of AERD may exist. METHODS: Levels of 7 cytokines representative of type 1, type 2, and type 3 inflammation, and 21 lipid mediators were measured in nasal mucus from 109 patients with CRSwNP, 30 patients with AERD, and 64 non-CRS controls. Differences in inflammatory mediators were identified between groups, and patterns of inflammation among patients with AERD were determined by hierarchical cluster analysis. RESULTS: AERD could be distinguished from CRSwNP by profound elevations in IL-5, IL-6, IL-13, and IFN-γ; however, significant heterogeneity existed between patients. Hierarchical cluster analysis identified 3 inflammatory subendotypes of AERD characterized by (1) low inflammatory burden, (2) high type 2 cytokines, and (3) comparatively low type 2 cytokines and high levels of type 1 and type 3 cytokines. Several lipid mediators were associated with asthma and sinonasal disease severity; however, lipid mediators showed less variability than cytokines. CONCLUSIONS: AERD is associated with elevations in type 2 cytokines (IL-5 and IL-13) and the type 1 cytokine, IFN-γ. Among patients with AERD, the inflammatory signature is heterogeneous, supporting subendotypes of the disease. Variability in AERD immune signatures should be further clarified because this may predict clinical response to biologic medications that target type 2 inflammation.

AnnoGen: annotating genome-wide pragmatic features.

MOTIVATION: Genome annotation is an important step for all in-depth bioinformatics analysis. It is imperative to augment quantity and diversity of genome-wide annotation data for the latest reference genome to promote its adoption by ongoing and future impactful studies. RESULTS: We developed a python toolkit AnnoGen, which at the first time, allows the annotation of three pragmatic genomic features for the GRCh38 genome in enormous base-wise quantities. The three features are chemical binding Energy, sequence information Entropy and Homology Score. The Homology Score is an exceptional feature that captures the genome-wide homology through single-base-offset tiling windows of 100 continual nucleotide bases. AnnoGen is capable of annotating the proprietary pragmatic features for variable user-interested genomic regions and optionally comparing two parallel sets of genomic regions. AnnoGen is characterized with simple utility modes and succinct HTML report of informative statistical tables and plots. AVAILABILITY AND IMPLEMENTATION: https://github.com/shengqh/annogen.

Quantitative assessment of cell population diversity in single-cell landscapes.

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.

Non-canonical RNA-DNA differences and other human genomic features are enriched within very short tandem repeats.

Very short tandem repeats bear substantial genetic, evolutional, and pathological significance in genome analyses. Here, we compiled a census of tandem mono-nucleotide/di-nucleotide/tri-nucleotide repeats (MNRs/DNRs/TNRs) in GRCh38, which we term "polytracts" in general. Of the human genome, 144.4 million nucleotides (4.7%) are occupied by polytracts, and 0.47 million single nucleotides are identified as polytract hinges, i.e., break-points of tandem polytracts. Preliminary exploration of the census suggested polytract hinge sites and boundaries of AAC polytracts may bear a higher mapping error rate than other polytract regions. Further, we revealed landscapes of polytract enrichment with respect to nearly a hundred genomic features. We found MNRs, DNRs, and TNRs displayed noticeable difference in terms of locational enrichment for miscellaneous genomic features, especially RNA editing events. Non-canonical and C-to-U RNA-editing events are enriched inside and/or adjacent to MNRs, while all categories of RNA-editing events are under-represented in DNRs. A-to-I RNA-editing events are generally under-represented in polytracts. The selective enrichment of non-canonical RNA-editing events within MNR adjacency provides a negative evidence against their authenticity. To enable similar locational enrichment analyses in relation to polytracts, we developed a software Polytrap which can handle 11 reference genomes. Additionally, we compiled polytracts of four model organisms into a Track Hub which can be integrated into USCS Genome Browser as an official track for convenient visualization of polytracts.

Hyperoxia Injury in the Developing Lung Is Mediated by Mesenchymal Expression of Wnt5A.

Rationale: Bronchopulmonary dysplasia (BPD) is a leading complication of preterm birth that affects infants born in the saccular stage of lung development at <32 weeks of gestation. Although the mechanisms driving BPD remain uncertain, exposure to hyperoxia is thought to contribute to disease pathogenesis.Objectives: To determine the effects of hyperoxia on epithelial-mesenchymal interactions and to define the mediators of activated Wnt/ß-catenin signaling after hyperoxia injury.Methods: Three hyperoxia models were used: A three-dimensional organotypic coculture using primary human lung cells, precision-cut lung slices (PCLS), and a murine in vivo hyperoxia model. Comparisons of normoxia- and hyperoxia-exposed samples were made by real-time quantitative PCR, RNA in situ hybridization, quantitative confocal microscopy, and lung morphometry.Measurements and Main Results: Examination of an array of Wnt ligands in the three-dimensional organotypic coculture revealed increased mesenchymal expression of WNT5A. Inhibition of Wnt5A abrogated the BPD transcriptomic phenotype induced by hyperoxia. In the PCLS model, Wnt5A inhibition improved alveolarization following hyperoxia exposure, and treatment with recombinant Wnt5a reproduced features of the BPD phenotype in PCLS cultured in normoxic conditions. Chemical inhibition of NF-κB with BAY11-7082 reduced Wnt5a expression in the PCLS hyperoxia model and in vivo mouse hyperoxia model, with improved alveolarization in the PCLS model.Conclusions: Increased mesenchymal Wnt5A during saccular-stage hyperoxia injury contributes to the impaired alveolarization and septal thickening observed in BPD. Precise targeting of Wnt5A may represent a potential therapeutic strategy for the treatment of BPD.

beRBP: binding estimation for human RNA-binding proteins.

Identifying binding targets of RNA-binding proteins (RBPs) can greatly facilitate our understanding of their functional mechanisms. Most computational methods employ machine learning to train classifiers on either RBP-specific targets or pooled RBP-RNA interactions. The former strategy is more powerful, but it only applies to a few RBPs with a large number of known targets; conversely, the latter strategy sacrifices prediction accuracy for a wider application, since specific interaction features are inevitably obscured through pooling heterogeneous datasets. Here, we present beRBP, a dual approach to predict human RBP-RNA interaction given PWM of a RBP and one RNA sequence. Based on Random Forests, beRBP not only builds a specific model for each RBP with a decent number of known targets, but also develops a general model for RBPs with limited or null known targets. The specific and general models both compared well with existing methods on three benchmark datasets. Notably, the general model achieved a better performance than existing methods on most novel RBPs. Overall, as a composite solution overarching the RBP-specific and RBP-General strategies, beRBP is a promising tool for human RBP binding estimation with good prediction accuracy and a broad application scope.

BET bromodomain proteins regulate enhancer function during adipogenesis.

Developmental transitions are guided by master regulatory transcription factors. During adipogenesis, a transcriptional cascade culminates in the expression of PPARγ and C/EBPα, which orchestrate activation of the adipocyte gene expression program. However, the coactivators controlling PPARγ and C/EBPα expression are less well characterized. Here, we show the bromodomain-containing protein, BRD4, regulates transcription of PPARγ and C/EBPα. Analysis of BRD4 chromatin occupancy reveals that induction of adipogenesis in 3T3L1 fibroblasts provokes dynamic redistribution of BRD4 to de novo super-enhancers proximal to genes controlling adipocyte differentiation. Inhibition of the bromodomain and extraterminal domain (BET) family of bromodomain-containing proteins impedes BRD4 occupancy at these de novo enhancers and disrupts transcription of Pparg and Cebpa, thereby blocking adipogenesis. Furthermore, silencing of these BRD4-occupied distal regulatory elements at the Pparg locus by CRISPRi demonstrates a critical role for these enhancers in the control of Pparg gene expression and adipogenesis in 3T3L1s. Together, these data establish BET bromodomain proteins as time- and context-dependent coactivators of the adipocyte cell state transition.

scRNABatchQC: multi-samples quality control for single cell RNA-seq data.

SUMMARY: Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers. AVAILABILITY AND IMPLEMENTATION: scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.