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Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be informative of both the individual cell clusters and various cell types present in the sample. Here, we developed a computational platform, ScType, which enables a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information. Using six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides unbiased and accurate cell type annotations by guaranteeing the specificity of positive and negative marker genes across cell clusters and cell types. We also demonstrate how ScType distinguishes between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for anticancer applications. The widely applicable method is deployed both as an interactive web-tool ( https://sctype.app ), and as an open-source R-package. Cell types are typically identified in single cell transcriptomic data by manual annotation of cell clusters using established marker genes. Here the authors present a fully-automated computational platform that can quickly and accurately distinguish between cell types.

Tibetan wheat is grown under environmental constraints at high-altitude conditions, but its underlying adaptation mechanism remains unknown. Here, we present a draft genome sequence of a Tibetan semi-wild wheat ( Triticum aestivum ssp. tibetanum Shao) accession Zang1817 and re-sequence 245 wheat accessions, including world-wide wheat landraces, cultivars as well as Tibetan landraces. We demonstrate that high-altitude environments can trigger extensive reshaping of wheat genomes, and also uncover that Tibetan wheat accessions accumulate high-altitude adapted haplotypes of related genes in response to harsh environmental constraints. Moreover, we find that Tibetan semi-wild wheat is a feral form of Tibetan landrace, and identify two associated loci, including a 0.8-Mb deletion region containing Brt1/2 homologs and a genomic region with TaQ-5A gene, responsible for rachis brittleness during the de-domestication episode. Our study provides confident evidence to support the hypothesis that Tibetan semi-wild wheat is de-domesticated from local landraces, in response to high-altitude extremes. Mechanism of high altitude adaptation of wheat remains unknown. Here, the authors assemble the draft genome of a Tibetan semi-wild wheat accession and resequence 245 wheat accessions to reveal that Tibetan semi-wild wheat has been de-domesticated from local landraces to adapt to high altitude.

Esophageal squamous-cell carcinoma (ESCC), one of the most prevalent and lethal malignant disease, has a complex but unknown tumor ecosystem. Here, we investigate the composition of ESCC tumors based on 208,659 single-cell transcriptomes derived from 60 individuals. We identify 8 common expression programs from malignant epithelial cells and discover 42 cell types, including 26 immune cell and 16 nonimmune stromal cell subtypes in the tumor microenvironment (TME), and analyse the interactions between cancer cells and other cells and the interactions among different cell types in the TME. Moreover, we link the cancer cell transcriptomes to the somatic mutations and identify several markers significantly associated with patients’ survival, which may be relevant to precision care of ESCC patients. These results reveal the immunosuppressive status in the ESCC TME and further our understanding of ESCC. Esophageal squamous-cell carcinomas (ESCC) have poor prognosis, and detailed molecular profiles are necessary to identify prognostic markers. Here the authors analyse 60 ESCC patient samples using scRNA-seq, TCR-seq and genomics; they find mucosal immunity markers associated with survival and immunosuppressive microenvironments.

Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA ( https://github.com/theislab/scCODA ), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance, while reliably controlling for false discoveries, and identified experimentally verified cell type changes that were missed in original analyses. Imbalance and loss of cell types is a hallmark in many diseases. Still, quantifying compositional changes in scRNAseq data remains challenging. Here the authors present scCODA, a Bayesian model to assess cell type compositions in scRNA-seq data.

Macroautophagy decreases with age, and this change is considered a hallmark of the aging process. It remains unknown whether mitophagy, the essential selective autophagic degradation of mitochondria, also decreases with age. In our analysis of mitophagy in multiple organs in the mito-QC reporter mouse, mitophagy is either increased or unchanged in old versus young mice. Transcriptomic analysis shows marked upregulation of the type I interferon response in the retina of old mice, which correlates with increased levels of cytosolic mtDNA and activation of the cGAS/STING pathway. Crucially, these same alterations are replicated in primary human fibroblasts from elderly donors. In old mice, pharmacological induction of mitophagy with urolithin A attenuates cGAS/STING activation and ameliorates deterioration of neurological function. These findings point to mitophagy induction as a strategy to decrease age-associated inflammation and increase healthspan. Dysregulated autophagy and mitochondrial function are two well-described hallmarks of aging. Here, the authors describe an unexpected age-associated upregulation of mitophagy in response to neuroinflammation triggered by leaked mtDNA.

The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing, N 6 -methyladenosine (m 6 A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m 6 A-modified and unmodified synthetic sequences, can predict m 6 A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m 6 A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these ‘errors’ are typically not observed in yeast ime4 -knockout strains, which lack m 6 A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context. We currently lack generic methods to map RNA modifications across the entire transcriptome. Here, the authors demonstrate that m 6 A RNA modifications can be detected with high accuracy using nanopore direct RNA sequencing.

The hypoxic tumor microenvironment has been implicated in immune escape, but the underlying mechanism remains elusive. Using an in vitro culture system modeling human T cell dysfunction and exhaustion in triple-negative breast cancer (TNBC), we find that hypoxia suppresses immune effector gene expression, including in T and NK cells, resulting in immune effector cell dysfunction and resistance to immunotherapy. We demonstrate that hypoxia-induced factor 1α (HIF1α) interaction with HDAC1 and concurrent PRC2 dependency causes chromatin remolding resulting in epigenetic suppression of effector genes and subsequent immune dysfunction. Targeting HIF1α and the associated epigenetic machinery can reverse the immune effector dysfunction and overcome resistance to PD-1 blockade, as demonstrated both in vitro and in vivo using syngeneic and humanized mice models. These findings identify a HIF1α-mediated epigenetic mechanism in immune dysfunction and provide a potential strategy to overcome immune resistance in TNBC. Hypoxia can promote tumor escape from immune surveillance and immunotherapy. Here, the authors show that hypoxia induces T and NK cell dysfunction through HIF1α-mediated epigenetic suppression of effector gene expression, conferring resistance to anti-PD1 blockade in triple negative breast cancer models.

Remove unwanted variation (RUV) is a new statistical method for RNA-seq data normalization that uses control genes or samples to improve differential expression analysis. Normalization of RNA-sequencing (RNA-seq) data has proven essential to ensure accurate inference of expression levels. Here, we show that usual normalization approaches mostly account for sequencing depth and fail to correct for library preparation and other more complex unwanted technical effects. We evaluate the performance of the External RNA Control Consortium (ERCC) spike-in controls and investigate the possibility of using them directly for normalization. We show that the spike-ins are not reliable enough to be used in standard global-scaling or regression-based normalization procedures. We propose a normalization strategy, called remove unwanted variation (RUV), that adjusts for nuisance technical effects by performing factor analysis on suitable sets of control genes (e.g., ERCC spike-ins) or samples (e.g., replicate libraries). Our approach leads to more accurate estimates of expression fold-changes and tests of differential expression compared to state-of-the-art normalization methods. In particular, RUV promises to be valuable for large collaborative projects involving multiple laboratories, technicians, and/or sequencing platforms.

To the Editor: Over the past two decades, the scale and complexity of genomics technologies and data have advanced from sequencing genomes of a few organisms to generating metagenomes, genome variation, gene expression, metabolites, and phenotype data for thousands of organisms and their communities. A major challenge in this data-rich age of biology is integrating heterogeneous and distributed data into predictive models of biological function, ranging from a single gene to entire organisms and their ecologies. Here we present the DOE Systems Biology Knowledgebase (KBase, http://kbase.us), an open-source software and data platform that enables data sharing, integration, and analysis of microbes, plants, and their communities. Once a Narrative has been shared or made public, other users can copy the Narrative and rerun it on their own data, or modify it to suit their scientific needs. [...]public Narratives serve as resources for the user community by capturing valuable data sets, associated computational analyses, and scientific context describing the rationale behind a scientific study in a form that is immediately reproducible and reusable.

Adeno-associated virus (AAV) forms the basis for several commercial gene therapy products and for countless gene transfer vectors derived from natural or synthetic viral isolates that are under intense preclinical evaluation. Here, we report a versatile pipeline that enables the direct side-by-side comparison of pre-selected AAV capsids in high-throughput and in the same animal, by combining DNA/RNA barcoding with multiplexed next-generation sequencing. For validation, we create three independent libraries comprising 183 different AAV variants including widely used benchmarks and screened them in all major tissues in adult mice. Thereby, we discover a peptide-displaying AAV9 mutant called AAVMYO that exhibits superior efficiency and specificity in the musculature including skeletal muscle, heart and diaphragm following peripheral delivery, and that holds great potential for muscle gene therapy. Our comprehensive methodology is compatible with any capsids, targets and species, and will thus facilitate and accelerate the stratification of optimal AAV vectors for human gene therapy. Adeno-associated virus is the basis of many gene therapies and gene transfer vectors. Here the authors report a pipeline to enable side-by-side comparison of pre-selected capsids in a high throughput manner.