Existing single-cell RNA sequencing (scRNA-seq) methods rely on reverse transcription (RT) and second-strand synthesis (SSS) to convert single-stranded RNA into double-stranded DNA prior to amplification, with the limited RT/SSS efficiency compromising RNA detectability. Here, we develop a new scRNA-seq method, Linearly Amplified Single-stranded-RNA-derived Transcriptome sequencing (LAST-seq), which directly amplifies the original single-stranded RNA molecules without prior RT/SSS. LAST-seq offers a high single-molecule capture efficiency and a low level of technical noise for single-cell transcriptome analyses. Using LAST-seq, we characterize transcriptional bursting kinetics in human cells, revealing a role of topologically associating domains in transcription regulation.

BACKGROUND: Cumulative sequencing efforts have yielded enough genomes to construct pangenomes for dozens of bacterial species and elucidate intraspecies gene conservation. Given the diversity of organisms for which this is achievable, similar analyses for ancestral species are feasible through the integration of pangenomics and phylogenetics, promising deeper insights into the nature of ancient life. RESULTS: We construct pangenomes for 183 bacterial species from 54,085 genomes and identify their core genomes using a novel statistical model to estimate genome-specific error rates and underlying gene frequencies. The core genomes are then integrated into a phylogenetic tree to reconstruct the core genome of the last bacterial common ancestor (LBCA), yielding three main results: First, the gene content of modern and ancestral core genomes are diverse at the level of individual genes but are similarly distributed by functional category and share several poorly characterized genes. Second, the LBCA core genome is distinct from any individual modern core genome but has many fundamental biological systems intact, especially those involving translation machinery and biosynthetic pathways to all major nucleotides and amino acids. Third, despite this metabolic versatility, the LBCA core genome likely requires additional non-core genes for viability, based on comparisons with the minimal organism, JCVI-Syn3A. CONCLUSIONS: These results suggest that many cellular systems commonly conserved in modern bacteria were not just present in ancient bacteria but were nearly immutable with respect to short-term intraspecies variation. Extending this analysis to other domains of life will likely provide similar insights into more distant ancestral species.

BACKGROUND: Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation mutagenesis experiments to construct accurate computational predictors of proteome-wide missense variant pathogenicity. RESULTS: We train cross-protein transfer (CPT) models using deep mutational scanning (DMS) data from only five proteins and achieve state-of-the-art performance on clinical variant interpretation for unseen proteins across the human proteome. We also improve predictive accuracy on DMS data from held-out proteins. High sensitivity is crucial for clinical applications and our model CPT-1 particularly excels in this regime. For instance, at 95% sensitivity of detecting human disease variants annotated in ClinVar, CPT-1 improves specificity to 68%, from 27% for ESM-1v and 55% for EVE. Furthermore, for genes not used to train REVEL, a supervised method widely used by clinicians, we show that CPT-1 compares favorably with REVEL. Our framework combines predictive features derived from general protein sequence models, vertebrate sequence alignments, and AlphaFold structures, and it is adaptable to the future inclusion of other sources of information. We find that vertebrate alignments, albeit rather shallow with only 100 genomes, provide a strong signal for variant pathogenicity prediction that is complementary to recent deep learning-based models trained on massive amounts of protein sequence data. We release predictions for all possible missense variants in 90% of human genes. CONCLUSIONS: Our results demonstrate the utility of mutational scanning data for learning properties of variants that transfer to unseen proteins.

BACKGROUND: Although spatial organization of compartments and topologically associating domains at large scale is relatively well studied, the spatial organization of regulatory elements at fine scale is poorly understood in plants. RESULTS: Here we perform high-resolution chromatin interaction analysis using paired-end tag sequencing approach. We map chromatin interactions tethered with RNA polymerase II and associated with heterochromatic, transcriptionally active, and Polycomb-repressive histone modifications in Arabidopsis. Analysis of the regulatory repertoire shows that distal active cis-regulatory elements are linked to their target genes through long-range chromatin interactions with increased expression of the target genes, while poised cis-regulatory elements are linked to their target genes through long-range chromatin interactions with depressed expression of the target genes. Furthermore, we demonstrate that transcription factor MYC2 is critical for chromatin spatial organization, and propose that MYC2 occupancy and MYC2-mediated chromatin interactions coordinately facilitate transcription within the framework of 3D chromatin architecture. Analysis of functionally related gene-defined chromatin connectivity networks reveals that genes implicated in flowering-time control are functionally compartmentalized into separate subdomains via their spatial activity in the leaf or shoot apical meristem, linking active mark- or Polycomb-repressive mark-associated chromatin conformation to coordinated gene expression. CONCLUSION: The results reveal that the regulation of gene transcription in Arabidopsis is not only by linear juxtaposition, but also by long-range chromatin interactions. Our study uncovers the fine scale genome organization of Arabidopsis and the potential roles of such organization in orchestrating transcription and development.

We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.

BACKGROUND: Cancer patients can achieve dramatic responses to chemotherapy yet retain resistant tumor cells, which ultimately results in relapse. Although xenograft model studies have identified several cellular and molecular features that are associated with chemoresistance in acute myeloid leukemia (AML), to what extent AML patients exhibit these properties remains largely unknown. RESULTS: We apply single-cell RNA sequencing to paired pre- and post-chemotherapy whole bone marrow samples obtained from 13 pediatric AML patients who had achieved disease remission, and distinguish AML clusters from normal cells based on their unique transcriptomic profiles. Approximately 50% of leukemic stem and progenitor populations actively express leukemia stem cell (LSC) and oxidative phosphorylation (OXPHOS) signatures, respectively. These clusters have a higher chance of tolerating therapy and exhibit an enhanced metabolic program in response to treatment. Interestingly, the transmembrane receptor CD69 is highly expressed in chemoresistant hematopoietic stem cell (HSC)-like populations (named the CD69+ HSC-like subpopulation). Furthermore, overexpression of CD69 results in suppression of the mTOR signaling pathway and promotion of cell quiescence and adhesion in vitro. Finally, the presence of CD69+ HSC-like cells is associated with unfavorable genetic mutations, the persistence of residual tumor cells in chemotherapy, and poor outcomes in independent pediatric and adult public AML cohorts. CONCLUSIONS: Our analysis reveals leukemia stem cell and OXPHOS as two major chemoresistant features in human AML patients. CD69 may serve as a potential biomarker in defining a subpopulation of chemoresistant leukemia stem cells. These findings have important implications for targeting residual chemo-surviving AML cells.

BACKGROUND: The Fe (II)- and α-ketoglutarate-dependent AlkB family dioxygenases are implicated in nucleotide demethylation. AlkB homolog1 (ALKBH1) is shown to demethylate DNA adenine methylation (6mA) preferentially from single-stranded or unpaired DNA, while its demethylase activity and function in the chromatin context are unclear. RESULTS: Here, we find that loss-of-function of the rice ALKBH1 gene leads to increased 6mA in the R-loop regions of the genome but has a limited effect on the overall 6mA level. However, in the context of mixed tissues, rather than on individual loci, the ALKBH1 mutation or overexpression mainly affects the expression of genes with a specific combination of chromatin modifications in the body region marked with H3K4me3 and H3K27me3 but depleted of DNA CG methylation. In the similar context of mixed tissues, further analysis reveals that the ALKBH1 protein preferentially binds to genes marked by the chromatin signature and has a function to maintain a high H3K4me3/H3K27me3 ratio by impairing the binding of Polycomb repressive complex 2 (PRC2) to the targets, which is required for both the basal and stress-induced expression of the genes. CONCLUSION: Our findings unravel a function of ALKBH1 to control the balance between the antagonistic histone methylations for gene activity and provide insight into the regulatory mechanism of PRC2-mediated H3K27me3 deposition within the gene body region.

BACKGROUND: Asian rice is one of the world's most widely cultivated crops. Large-scale resequencing analyses have been undertaken to explore the domestication and de-domestication genomic history of Asian rice, but the evolution of rice is still under debate. RESULTS: Here, we construct a syntelog-based rice pan-genome by integrating and merging 74 high-accuracy genomes based on long-read sequencing, encompassing all ecotypes and taxa of Oryza sativa and Oryza rufipogon. Analyses of syntelog groups illustrate subspecies divergence in gene presence-and-absence and haplotype composition and identify massive genomic regions putatively introgressed from ancient Geng/japonica to ancient Xian/indica or its wild ancestor, including almost all well-known domestication genes and a 4.5-Mbp centromere-spanning block, supporting a single domestication event in main rice subspecies. Genomic comparisons between weedy and cultivated rice highlight the contribution from wild introgression to the emergence of de-domestication syndromes in weedy rice. CONCLUSIONS: This work highlights the significance of inter-taxa introgression in shaping diversification and divergence in rice evolution and provides an exploratory attempt by utilizing the advantages of pan-genomes in evolutionary studies.

Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportions of cell types. Expression deconvolution methods can help differentiate these scenarios. BEDwARS is a Bayesian deconvolution method designed to address differences between reference signatures of cell types and corresponding true signatures underlying bulk transcriptomic profiles. BEDwARS is more robust to noisy reference signatures and outperforms leading in-class methods for estimating cell type proportions and signatures. Application of BEDwARS to dihydropyridine dehydrogenase deficiency identified the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.

Synthetic long read sequencing techniques such as UST's TELL-Seq and Loop Genomics' LoopSeq combine 3[Formula: see text] barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3[Formula: see text] unique molecular identifier confounds the assignment of linkage between short reads. We introduce Ariadne, a novel assembly graph-based synthetic long read deconvolution algorithm, that can be used to extract single-species read-clouds from synthetic long read datasets to improve the taxonomic classification and de novo assembly of complex populations, such as metagenomes.

BACKGROUND: In humans, muscle-invasive bladder cancer (MIBC) is highly aggressive and associated with a poor prognosis. With a high mutation load and large number of altered genes, strategies to delineate key driver events are necessary. Dogs and cats develop urothelial carcinoma (UC) with histological and clinical similarities to human MIBC. Cattle that graze on bracken fern also develop UC, associated with exposure to the carcinogen ptaquiloside. These species may represent relevant animal models of spontaneous and carcinogen-induced UC that can provide insight into human MIBC. RESULTS: Whole-exome sequencing of domestic canine (n = 87) and feline (n = 23) UC, and comparative analysis with human MIBC reveals a lower mutation rate in animal cases and the absence of APOBEC mutational signatures. A convergence of driver genes (ARID1A, KDM6A, TP53, FAT1, and NRAS) is discovered, along with common focally amplified and deleted genes involved in regulation of the cell cycle and chromatin remodelling. We identify mismatch repair deficiency in a subset of canine and feline UCs with biallelic inactivation of MSH2. Bovine UC (n = 8) is distinctly different; we identify novel mutational signatures which are recapitulated in vitro in human urinary bladder UC cells treated with bracken fern extracts or purified ptaquiloside. CONCLUSION: Canine and feline urinary bladder UC represent relevant models of MIBC in humans, and cross-species analysis can identify evolutionarily conserved driver genes. We characterize mutational signatures in bovine UC associated with bracken fern and ptaquiloside exposure, a human-linked cancer exposure. Our work demonstrates the relevance of cross-species comparative analysis in understanding both human and animal UC.

BACKGROUND: Large-scale genotype-phenotype association studies of crop germplasm are important for identifying alleles associated with favorable traits. The limited number of single-nucleotide polymorphisms (SNPs) in most wheat genome-wide association studies (GWASs) restricts their power to detect marker-trait associations. Additionally, only a few genes regulating grain number per spikelet have been reported due to sensitivity of this trait to variable environments. RESULTS: We perform a large-scale GWAS using approximately 40 million filtered SNPs for 27 spike morphology traits. We detect 132,086 significant marker-trait associations and the associated SNP markers are located within 590 associated peaks. We detect additional and stronger peaks by dividing spike morphology into sub-traits relative to GWAS results of spike morphology traits. We propose that the genetic dissection of spike morphology is a powerful strategy to detect signals for grain yield traits in wheat. The GWAS results reveal that TaSPL17 positively controls grain size and number by regulating spikelet and floret meristem development, which in turn leads to enhanced grain yield per plant. The haplotypes at TaSPL17 indicate geographical differentiation, domestication effects, and breeding selection. CONCLUSION: Our study provides valuable resources for genetic improvement of spike morphology and a fast-forward genetic solution for candidate gene detection and cloning in wheat.

Dimensionality reduction summarizes the complex transcriptomic landscape of single-cell datasets for downstream analyses. Current approaches favor large cellular populations defined by many genes, at the expense of smaller and more subtly defined populations. Here, we present surprisal component analysis (SCA), a technique that newly leverages the information-theoretic notion of surprisal for dimensionality reduction to promote more meaningful signal extraction. For example, SCA uncovers clinically important cytotoxic T-cell subpopulations that are indistinguishable using existing pipelines. We also demonstrate that SCA substantially improves downstream imputation. SCA's efficient information-theoretic paradigm has broad applications to the study of complex biological tissues in health and disease.

BACKGROUND: As one of the most common malignancies, esophageal cancer has two subtypes, squamous cell carcinoma and adenocarcinoma, arising from distinct cells-of-origin. Distinguishing cell-type-specific molecular features from cancer-specific characteristics is challenging. RESULTS: We analyze whole-genome bisulfite sequencing data on 45 esophageal tumor and nonmalignant samples from both subtypes. We develop a novel sequence-aware method to identify large partially methylated domains (PMDs), revealing profound heterogeneity at both methylation level and genomic distribution of PMDs across tumor samples. We identify subtype-specific PMDs that are associated with repressive transcription, chromatin B compartments and high somatic mutation rate. While genomic locations of these PMDs are pre-established in normal cells, the degree of loss is significantly higher in tumors. We find that cell-type-specific deposition of H3K36me2 may underlie genomic distribution of PMDs. At a smaller genomic scale, both cell-type- and cancer-specific differentially methylated regions (DMRs) are identified for each subtype. Using binding motif analysis within these DMRs, we show that a cell-type-specific transcription factor HNF4A maintains the binding sites that it generates in normal cells, while establishing new binding sites cooperatively with novel partners such as FOSL1 in esophageal adenocarcinoma. Finally, leveraging pan-tissue single-cell and pan-cancer epigenomic datasets, we demonstrate that a substantial fraction of cell-type-specific PMDs and DMRs identified here in esophageal cancer are actually markers that co-occur in other cancers originating from related cell types. CONCLUSIONS: These findings advance our understanding of DNA methylation dynamics at various genomic scales in normal and malignant states, providing novel mechanistic insights into cell-type- and cancer-specific epigenetic regulations.

BACKGROUND: Hundreds of functional genomic screens have been performed across a diverse set of cancer contexts, as part of efforts such as the Cancer Dependency Map, to identify gene dependencies-genes whose loss of function reduces cell viability or fitness. Recently, large-scale screening efforts have shifted from RNAi to CRISPR-Cas9, due to superior efficacy and specificity. However, many effective oncology drugs only partially inhibit their protein targets, leading us to question whether partial suppression of genes using RNAi could reveal cancer vulnerabilities that are missed by complete knockout using CRISPR-Cas9. Here, we compare CRISPR-Cas9 and RNAi dependency profiles of genes across approximately 400 matched cancer cell lines. RESULTS: We find that CRISPR screens accurately identify more gene dependencies per cell line, but the majority of each cell line's dependencies are part of a set of 1867 genes that are shared dependencies across the entire collection (pan-lethals). While RNAi knockdown of about 30% of these genes is also pan-lethal, approximately 50% have selective dependency patterns across cell lines, suggesting they could still be cancer vulnerabilities. The accuracy of the unique RNAi selectivity is supported by associations to multi-omics profiles, drug sensitivity, and other expected co-dependencies. CONCLUSIONS: Incorporating RNAi data for genes that are pan-lethal knockouts facilitates the discovery of a wider range of gene targets than could be detected using the CRISPR dataset alone. This can aid in the interpretation of contrasting results obtained from CRISPR and RNAi screens and reinforce the importance of partial gene suppression methods in building a cancer dependency map.

Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.

The binding of transcription factors at proximal promoters and distal enhancers is central to gene regulation. Identifying regulatory motifs and quantifying their impact on expression remains challenging. Using a convolutional neural network trained on single-cell data, we infer putative regulatory motifs and cell type-specific importance. Our model, scover, explains 29% of the variance in gene expression in multiple mouse tissues. Applying scover to distal enhancers identified using scATAC-seq from the developing human brain, we identify cell type-specific motif activities in distal enhancers. Scover can identify regulatory motifs and their importance from single-cell data where all parameters and outputs are easily interpretable.

MHC-I-associated peptides deriving from non-coding genomic regions and mutations can generate tumor-specific antigens, including neoantigens. Quantifying tumor-specific antigens' RNA expression in malignant and benign tissues is critical for discriminating actionable targets. We present BamQuery, a tool attributing an exhaustive RNA expression to MHC-I-associated peptides of any origin from bulk and single-cell RNA-sequencing data. We show that many cryptic and mutated tumor-specific antigens can derive from multiple discrete genomic regions, abundantly expressed in normal tissues. BamQuery can also be used to predict MHC-I-associated peptides immunogenicity and identify actionable tumor-specific antigens de novo.

BACKGROUND: The international Dog10K project aims to sequence and analyze several thousand canine genomes. Incorporating 20 × data from 1987 individuals, including 1611 dogs (321 breeds), 309 village dogs, 63 wolves, and four coyotes, we identify genomic variation across the canid family, setting the stage for detailed studies of domestication, behavior, morphology, disease susceptibility, and genome architecture and function. RESULTS: We report the analysis of > 48 M single-nucleotide, indel, and structural variants spanning the autosomes, X chromosome, and mitochondria. We discover more than 75% of variation for 239 sampled breeds. Allele sharing analysis indicates that 94.9% of breeds form monophyletic clusters and 25 major clades. German Shepherd Dogs and related breeds show the highest allele sharing with independent breeds from multiple clades. On average, each breed dog differs from the UU_Cfam_GSD_1.0 reference at 26,960 deletions and 14,034 insertions greater than 50 bp, with wolves having 14% more variants. Discovered variants include retrogene insertions from 926 parent genes. To aid functional prioritization, single-nucleotide variants were annotated with SnpEff and Zoonomia phyloP constraint scores. Constrained positions were negatively correlated with allele frequency. Finally, the utility of the Dog10K data as an imputation reference panel is assessed, generating high-confidence calls across varied genotyping platform densities including for breeds not included in the Dog10K collection. CONCLUSIONS: We have developed a dense dataset of 1987 sequenced canids that reveals patterns of allele sharing, identifies likely functional variants, informs breed structure, and enables accurate imputation. Dog10K data are publicly available.

Existing single nucleotide polymorphism (SNP) genotyping algorithms do not scale for species with thousands of sequenced strains, nor do they account for conspecific redundancy. Here we present a bioinformatics tool, Maast, which empowers population genetic meta-analysis of microbes at an unrivaled scale. Maast implements a novel algorithm to heuristically identify a minimal set of diverse conspecific genomes, then constructs a reliable SNP panel for each species, and enables rapid and accurate genotyping using a hybrid of whole-genome alignment and k-mer exact matching. We demonstrate Maast's utility by genotyping thousands of Helicobacter pylori strains and tracking SARS-CoV-2 diversification.

BACKGROUND: Cleavage Under Targets and Release Using Nuclease (CUT&RUN) is an increasingly popular technique to map genome-wide binding profiles of histone modifications, transcription factors, and co-factors. The ENCODE project and others have compiled blacklists for ChIP-seq which have been widely adopted: these lists contain regions of high and unstructured signal, regardless of cell type or protein target, indicating that these are false positives. While CUT&RUN obtains similar results to ChIP-seq, its biochemistry and subsequent data analyses are different. We found that this results in a CUT&RUN-specific set of undesired high-signal regions. RESULTS: We compile suspect lists based on CUT&RUN data for the human and mouse genomes, identifying regions consistently called as peaks in negative controls. Using published CUT&RUN data from our and other labs, we show that the CUT&RUN suspect regions can persist even when peak calling is performed with SEACR or MACS2 against a negative control and after ENCODE blacklist removal. Moreover, we experimentally validate the CUT&RUN suspect lists by performing reiterative negative control experiments in which no specific protein is targeted, showing that they capture more than 80% of the peaks identified. CONCLUSIONS: We propose that removing these problematic regions can substantially improve peak calling in CUT&RUN experiments, resulting in more reliable datasets.

BACKGROUND: RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial. RESULTS: We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples. CONCLUSIONS: We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.