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Cellular heterogeneity within and across tumors has been a major obstacle in understanding and treating cancer, and the complex heterogeneity is masked if bulk tumor tissues are used for analysis. The advent of rapidly developing single-cell sequencing technologies, which include methods related to single-cell genome, epigenome, transcriptome, and multi-omics sequencing, have been applied to cancer research and led to exciting new findings in the fields of cancer evolution, metastasis, resistance to therapy, and tumor microenvironment. In this review, we discuss recent advances and limitations of these new technologies and their potential applications in cancer studies.

Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch. Computational deconvolution with single-cell RNA sequencing data as a reference is pivotal for interpreting spatial transcriptomics data. Here, authors present Redeconve, which improves the resolution by more than 100-fold with higher accuracy and speed.

Single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic studies by providing unprecedented cellular and molecular throughputs, but spatial information of individual cells is lost during tissue dissociation. While imaging-based technologies such as in situ sequencing show great promise, technical difficulties currently limit their wide usage. Here we hypothesize that cellular spatial organization is inherently encoded by cell identity and can be reconstructed, at least in part, by ligand-receptor interactions, and we present CSOmap, a computational tool to infer cellular interaction de novo from scRNA-seq. We show that CSOmap can successfully recapitulate the spatial organization of multiple organs of human and mouse including tumor microenvironments for multiple cancers in pseudo-space, and reveal molecular determinants of cellular interactions. Further, CSOmap readily simulates perturbation of genes or cell types to gain novel biological insights, especially into how immune cells interact in the tumor microenvironment. CSOmap can be a widely applicable tool to interrogate cellular organizations based on scRNA-seq data for various tissues in diverse systems.

Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrate that our ROGUE metric is broadly applicable, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast, B cell and brain data, we identify additional subtypes and demonstrate the application of ROGUE-guided analyses to detect precise signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas. Single cell RNA-seq is a powerful method to assign cell identity, but the purity of cell clusters arising from this data is not clear. Here the authors present an entropy-based statistic called ROGUE to quantify the purity of cell clusters, and identify subtypes within clusters.

Background Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed. Results Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies. Conclusions We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

Grain production is becoming increasingly vulnerable to climate change globally. Highland barley (HB) is the most important cereal crop in the Qinghai-Tibet Plateau (QTP), so assessing HB productivity and its response to climate change could help to understand the capacity of grain production and food security. This study simulated the potential yield of HB annually at 72 meteorological stations for 1978–2017 using the WOFOST model, and then analyzed the spatiotemporal changes of HB potential yield and climatic factors in the growing season. Further, the influence of climate change on HB potential yield was explored in different temperature zones (TZ). Results indicate that the annual average of HB potential yield ranged from 3.5 to 8.1 t/ha in the QTP, and it was averaged at 6.5 t/ha in TZ-3, higher than other zones. From 1978 to 2017, HB potential yield for the whole QTP decreased slightly by 2.1 kg/ha per year, and its change rates were 23.9, 10.1, − 15.9, − 23.8 and − 16.7 kg/ha/year from TZ-1 to TZ-5 ( p  < 0.05), respectively. In all zones, average (Tave), maximum (Tmax) and minimum temperature (Tmin) showed a significantly warming trend ( p  < 0.01), and Tmin increased by 0.53, 0.45, 0.44, 0.40 and 0.69 °C per decade, higher than that of Tave and Tmax. However, temperature diurnal range (TDR) and radiation (RA) showed a downward trend, and their decrease rates were far higher in TZ-5 and TZ-3. In TZ-1, ΔTDR was the critical factor to the change in HB potential yield, which would increase by 420.30 kg/ha for 1 °C increase of ΔTDR ( p  < 0.01). From TZ-2 to TZ-5, ΔRA was the critical factor, but the influence amplitude in terms of the elastic coefficient, decreased from 4.08 to 0.99 ( p  < 0.01). In addition, other factors such as ΔTmax in TZ-3 and ΔTmin in TZ-4 and TZ-5 also had an important influence on the potential yield. To improve the HB productivity in the QTP, suitable varieties should be developed and introduced to adapt the climate warming in different temperature zones. In addition, efforts are needed to adjust the strategies of fertilizers and irrigation applications.

RNA splicing shapes the gene regulatory programs that underlie various physiological and disease processes. Here, we present the SCASL (single-cell clustering based on alternative splicing landscapes) method for interrogating the heterogeneity of RNA splicing with single-cell RNA-seq data. SCASL resolves the issue of biased and sparse data coverage on single-cell RNA splicing and provides a new scheme for classifications of cell identities. With previously published datasets as examples, SCASL identifies new cell clusters indicating potentially precancerous and early-tumor stages in triple-negative breast cancer, illustrates cell lineages of embryonic liver development, and provides fine clusters of highly heterogeneous tumor-associated CD4 and CD8 T cells with functional and physiological relevance. Most of these findings are not readily available via conventional cell clustering based on single-cell gene expression data. Our study shows the potential of SCASL in revealing the intrinsic RNA splicing heterogeneity and generating biological insights into the dynamic and functional cell landscapes in complex tissues. RNA splicing serves as a critical layer of gene expression regulation. Here, authors introduce SCASL for investigating the heterogeneity of RNA splicing landscapes at single-cell resolution, offering a novel scheme for classifying cell identities with physiological relevance.

ObjectiveA comprehensive immune landscape for HBV infection is pivotal to achieve HBV cure.DesignWe performed single-cell RNA sequencing of 2 43 000 cells from 46 paired liver and blood samples of 23 individuals, including six immune tolerant, 5 immune active (IA), 3 acute recovery (AR), 3 chronic resolved and 6 HBV-free healthy controls (HCs). Flow cytometry and histological assays were applied in a second HBV cohort for validation.ResultsBoth IA and AR were characterised by high levels of intrahepatic exhausted CD8+ T (Tex) cells. In IA, Tex cells were mainly derived from liver-resident GZMK+ effector memory T cells and self-expansion. By contrast, peripheral CX3CR1+ effector T cells and GZMK+ effector memory T cells were the main source of Tex cells in AR. In IA but not AR, significant cell–cell interactions were observed between Tex cells and regulatory CD4+ T cells, as well as between Tex and FCGR3A+ macrophages. Such interactions were potentially mediated through human leukocyte antigen class I molecules together with their receptors CANX and LILRBs, respectively, contributing to the dysfunction of antiviral immune responses. By contrast, CX3CR1+GNLY+ central memory CD8+ T cells were concurrently expanded in both liver and blood of AR, providing a potential surrogate marker for viral resolution. In clinic, intrahepatic Tex cells were positively correlated with serum alanine aminotransferase levels and histological grading scores.ConclusionOur study dissects the coordinated immune responses for different HBV infection phases and provides a rich resource for fully understanding immunopathogenesis and developing effective therapeutic strategies.

The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-to-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch effect removal framework, called iMAP, based on both deep autoencoders and generative adversarial networks. Compared with current methods, iMAP shows superior, robust, and scalable performance in terms of both reliably detecting the batch-specific cells and effectively mixing distributions of the batch-shared cell types. Applying iMAP to tumor microenvironment datasets from two platforms, Smart-seq2 and 10x Genomics, we find that iMAP can leverage the powers of both platforms to discover novel cell-cell interactions.

Background Tumor-reactive CD8+ tumor-infiltrating lymphocytes (TILs) represent a subtype of T cells that can recognize and destroy tumor specifically. Understanding the regulatory mechanism of tumor-reactive CD8+ T cells has important therapeutic implications. Yet the DNA methylation status of this T cell subtype has not been elucidated. Results In this study, we segregate tumor-reactive and bystander CD8+ TILs, as well as naïve and effector memory CD8+ T cell subtypes as controls from colorectal cancer patients, to compare their transcriptome and methylome characteristics. Transcriptome profiling confirms previous conclusions that tumor-reactive TILs have an exhausted tissue-resident memory signature. Whole-genome methylation profiling identifies a distinct methylome pattern of tumor-reactive CD8+ T cells, with tumor-reactive markers CD39 and CD103 being specifically demethylated. In addition, dynamic changes are observed during the transition of naïve T cells into tumor-reactive CD8+ T cells. Transcription factor binding motif enrichment analysis identifies several immune-related transcription factors, including three exhaustion-related genes ( NR4A1 , BATF , and EGR2 ) and VDR , which potentially play an important regulatory role in tumor-reactive CD8+ T cells. Conclusion Our study supports the involvement of DNA methylation in shaping tumor-reactive and bystander CD8+ TILs, and provides a valuable resource for the development of novel DNA methylation markers and future therapeutics.