Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods.

BACKGROUND: Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. RESULTS: In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. CONCLUSIONS: Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.

Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes.

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1Β and CXCL8, while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.

Integrated analysis of single-cell RNA-seq, bulk RNA-seq, Mendelian randomization, and eQTL reveals T cell-related nomogram model and subtype classification in rheumatoid arthritis.

Objective: Rheumatoid arthritis (RA) is a systemic disease that attacks the joints and causes a heavy economic burden on humans worldwide. T cells regulate RA progression and are considered crucial targets for therapy. Therefore, we aimed to integrate multiple datasets to explore the mechanisms of RA. Moreover, we established a T cell-related diagnostic model to provide a new method for RA immunotherapy. Methods: scRNA-seq and bulk-seq datasets for RA were obtained from the Gene Expression Omnibus (GEO) database. Various methods were used to analyze and characterize the T cell heterogeneity of RA. Using Mendelian randomization (MR) and expression quantitative trait loci (eQTL), we screened for potential pathogenic T cell marker genes in RA. Subsequently, we selected an optimal machine learning approach by comparing the nine types of machine learning in predicting RA to identify T cell-related diagnostic features to construct a nomogram model. Patients with RA were divided into different T cell-related clusters using the consensus clustering method. Finally, we performed immune cell infiltration and clinical correlation analyses of T cell-related diagnostic features. Results: By analyzing the scRNA-seq dataset, we obtained 10,211 cells that were annotated into 7 different subtypes based on specific marker genes. By integrating the eQTL from blood and RA GWAS, combined with XGB machine learning, we identified a total of 8 T cell-related diagnostic features (MIER1, PPP1CB, ICOS, GADD45A, CD3D, SLFN5, PIP4K2A, and IL6ST). Consensus clustering analysis showed that RA could be classified into two different T-cell patterns (Cluster 1 and Cluster 2), with Cluster 2 having a higher T-cell score than Cluster 1. The two clusters involved different pathways and had different immune cell infiltration states. There was no difference in age or sex between the two different T cell patterns. In addition, ICOS and IL6ST were negatively correlated with age in RA patients. Conclusion: Our findings elucidate the heterogeneity of T cells in RA and the communication role of these cells in an RA immune microenvironment. The construction of T cell-related diagnostic models provides a resource for guiding RA immunotherapeutic strategies.

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.

Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks.

Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.

ezSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists.

ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell's streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application ( https://immunesinglecell.org/ezsc/ ) or a software package with a shinyApp interface ( https://github.com/JinmiaoChenLab/ezSingleCell2 ) for offline analysis.

Characterizing efficient feature selection for single-cell expression analysis.

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS.

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing.

Single-cell sequencing is frequently affected by "omission" due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly "omitted" cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of "omitted" cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.

A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials.

Translating RNA-seq into clinical diagnostics requires ensuring the reliability and cross-laboratory consistency of detecting clinically relevant subtle differential expressions, such as those between different disease subtypes or stages. As part of the Quartet project, we present an RNA-seq benchmarking study across 45 laboratories using the Quartet and MAQC reference samples spiked with ERCC controls. Based on multiple types of 'ground truth', we systematically assess the real-world RNA-seq performance and investigate the influencing factors involved in 26 experimental processes and 140 bioinformatics pipelines. Here we show greater inter-laboratory variations in detecting subtle differential expressions among the Quartet samples. Experimental factors including mRNA enrichment and strandedness, and each bioinformatics step, emerge as primary sources of variations in gene expression. We underscore the profound influence of experimental execution, and provide best practice recommendations for experimental designs, strategies for filtering low-expression genes, and the optimal gene annotation and analysis pipelines. In summary, this study lays the foundation for developing and quality control of RNA-seq for clinical diagnostic purposes.

[Exploration of cancer biological characteristics in people with different five movements and six climates constitution based on pan-cancer Bulk RNA-Seq].

According to the theory of five movements and six climates, the innate constitution plays a crucial role in determining the underlyingpa thological mechanisms of diseases later in life. Previous studies have demonstrated a close association between the constitution, as defined by the theory of five movements and six climates, and the development of various types of tumors. Furt hermore,the tumorsubtype determined by the constitution has prognostic implications. This highlights the potential of utilizing the fivemovements and six climates theory to guide the implementation of precision medicine strategies in thefield of oncology. However, no resear ch has yet been conducted to investigate the use of this theory in guiding the development of tumor molecular classification and precisi onmedicine strategies. The objective of this research is to uncover the biological characteristics of each constitution within a pancanc ercohort and identify potential anti-tumor drugs that are applicable to patients with different constitutional types. By doing so, we aimto c ontribute to the establishment of a precision medicine strategy for tumors derived from the original concepts of traditional Chi nesemedicine(TCM). In this study, we obtainedpan-cancer Bulk RNA-Seq data from UCSC Xena, GWAS cohort data from the UKBiobank, and cis-eQTLs data from eQ TLGen and GTEx V8. We employed machine learning methods to screen for hub genes associated with each constitution. Subsequently, we utilized informatics tools to explore the biological characteristics of each constitut iondefined by the theory of five movements and six bioclimates. Further, potential anti-tumor drugs suitable for patients with differen tconstitutional types were identified through mendelian randomization, molecular docking, and drug-like prediction techniques. Withinthe pan-cancer cohort, significant differences were observed among different constitutions in terms of progression-free interval, biological f unctions, immune cell abundance, tumor drug sensitivity, and immunotherapy response. These findings suggest that the five movements and six climates theory can guide tumor molecular classification and the development of precision medicine strategies. Moreover,the biological characteristics inherent to each constitution partially shed light on the scientific implications of Chinese medicinetheories, offering a fresh perspective towards clinical cancer treatment. Through molecular docking and drug-like prediction, several po tential anti-tumor drugs such as 17-beta-estradiol, serotonin, trans-resveratrol, and linoleic acid were identified. Overall, the util izationof multi-omics approaches pro vides a powerful tool to unravel the scientific foundations of TCM theories. The elucidation of themu lti-omics features associated witheach constitution in tumors serves as the basis for applying the five movements and six climates theoryto tumor molecular classification and the development of precision medicine strategies.

Identification of candidate genes controlling cold tolerance at the early seedling stage from Dongxiang wild rice by QTL mapping, BSA-Seq and RNA-Seq.

BACKGROUND: The cold tolerance of rice is closely related to its production and geographic distribution. The identification of cold tolerance-related genes is of important significance for developing cold-tolerant rice. Dongxiang wild rice (Oryza rufipogon Griff.) (DXWR) is well-adapted to the cold climate of northernmost-latitude habitats ever found in the world, and is one of the most valuable rice germplasms for cold tolerance improvement. RESULTS: Transcriptome analysis revealed genes differentially expressed between Xieqingzao B (XB; a cold sensitive variety) and 19H19 (derived from an interspecific cross between DXWR and XB) in the room temperature (RT), low temperature (LT), and recovery treatments. The results demonstrated that chloroplast genes might be involved in the regulation of cold tolerance in rice. A high-resolution SNP genetic map was constructed using 120 BC5F2 lines derived from a cross between 19H19 and XB based on the genotyping-by-sequencing (GBS) technique. Two quantitative trait loci (QTLs) for cold tolerance at the early seedling stage (CTS), qCTS12 and qCTS8, were detected. Moreover, a total of 112 candidate genes associated with cold tolerance were identified based on bulked segregant analysis sequencing (BSA-seq). These candidate genes were divided into eight functional categories, and the expression trend of candidate genes related to 'oxidation-reduction process' and 'response to stress' differed between XB and 19H19 in the RT, LT and recovery treatments. Among these candidate genes, the expression level of LOC_Os12g18729 in 19H19 (related to 'response to stress') decreased in the LT treatment but restored and enhanced during the recovery treatment whereas the expression level of LOC_Os12g18729 in XB declined during recovery treatment. Additionally, XB contained a 42-bp deletion in the third exon of LOC_Os12g18729, and the genotype of BC5F2 individuals with a survival percentage (SP) lower than 15% was consistent with that of XB. Weighted gene coexpression network analysis (WGCNA) and modular regulatory network learning with per gene information (MERLIN) algorithm revealed a gene interaction/coexpression network regulating cold tolerance in rice. In the network, differentially expressed genes (DEGs) related to 'oxidation-reduction process', 'response to stress' and 'protein phosphorylation' interacted with LOC_Os12g18729. Moreover, the knockout mutant of LOC_Os12g18729 decreased cold tolerance in early rice seedling stage signifcantly compared with that of wild type. CONCLUSIONS: In general, study of the genetic basis of cold tolerance of rice is important for the development of cold-tolerant rice varieties. In the present study, QTL mapping, BSA-seq and RNA-seq were integrated to identify two CTS QTLs qCTS8 and qCTS12. Furthermore, qRT-PCR, genotype sequencing and knockout analysis indicated that LOC_Os12g18729 could be the candidate gene of qCTS12. These results are expected to further exploration of the genetic mechanism of CTS in rice and improve cold tolerance of cultivated rice by introducing the cold tolerant genes from DXWR through marker-assisted selection.

DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.

Single Cell RNAseq Analysis of Cytokine-Treated Human Islets: Association of Cellular Stress with Impaired Cytokine Responsiveness.

Pancreatic ß-cells are essential for survival, being the only cell type capable of insulin secretion. While they are believed to be vulnerable to damage by inflammatory cytokines such as interleukin-1 beta (IL-1ß) and interferon-gamma, we have recently identified physiological roles for cytokine signaling in rodent ß-cells that include the stimulation of antiviral and antimicrobial gene expression and the inhibition of viral replication. In this study, we examine cytokine-stimulated changes in gene expression in human islets using single-cell RNA sequencing. Surprisingly, the global responses of human islets to cytokine exposure were remarkably blunted compared to our previous observations in the mouse. The small population of human islet cells that were cytokine responsive exhibited increased expression of IL-1ß-stimulated antiviral guanylate-binding proteins, just like in the mouse. Most human islet cells were not responsive to cytokines, and this lack of responsiveness was associated with high expression of genes encoding ribosomal proteins. We further correlated the expression levels of RPL5 with stress response genes, and when expressed at high levels, RPL5 is predictive of failure to respond to cytokines in all endocrine cells. We postulate that donor causes of death and isolation methodologies may contribute to stress of the islet preparation. Our findings indicate that activation of stress responses in human islets limits cytokine-stimulated gene expression, and we urge caution in the evaluation of studies that have examined cytokine-stimulated gene expression in human islets without evaluation of stress-related gene expression.

A dual RNA-seq analyses revealed dynamic arms race during the invasion of walnut by Colletotrichum gloeosporioides.

BACKGROUND: Walnut anthracnose caused by Colletotrichum gloeosporioides seriously endangers the yield and quality of walnut, and has now become a catastrophic disease in the walnut industry. Therefore, understanding both pathogen invasion mechanisms and host response processes is crucial to defense against C. gloeosporioides infection. RESULTS: Here, we investigated the mechanisms of interaction between walnut fruits (anthracnose-resistant F26 fruit bracts and anthracnose-susceptible F423 fruit bracts) and C. gloeosporioides at three infection time points (24hpi, 48hpi, and 72hpi) using a high-resolution time series dual transcriptomic analysis, characterizing the arms race between walnut and C. gloeosporioides. A total of 20,780 and 6670 differentially expressed genes (DEGs) were identified in walnut and C. gloeosporioides against 24hpi, respectively. Generous DEGs in walnut exhibited opposite expression patterns between F26 and F423, which indicated that different resistant materials exhibited different transcriptional responses to C. gloeosporioides during the infection process. KEGG functional enrichment analysis indicated that F26 displayed a broader response to C. gloeosporioides than F423. Meanwhile, the functional analysis of the C. gloeosporioides transcriptome was conducted and found that PHI, SignalP, CAZy, TCDB genes, the Fungal Zn (2)-Cys (6) binuclear cluster domain (PF00172.19) and the Cytochrome P450 (PF00067.23) were largely prominent in F26 fruit. These results suggested that C. gloeosporioides secreted some type of effector proteins in walnut fruit and appeared a different behavior based on the developmental stage of the walnut. CONCLUSIONS: Our present results shed light on the arms race process by which C. gloeosporioides attacked host and walnut against pathogen infection, laying the foundation for the green prevention of walnut anthracnose.

Using a pan-cancer atlas to investigate tumour associated macrophages as regulators of immunotherapy response.

The paradigm for macrophage characterization has evolved from the simple M1/M2 dichotomy to a more complex model that encompasses the broad spectrum of macrophage phenotypic diversity, due to differences in ontogeny and/or local stimuli. We currently lack an in-depth pan-cancer single cell RNA-seq (scRNAseq) atlas of tumour-associated macrophages (TAMs) that fully captures this complexity. In addition, an increased understanding of macrophage diversity could help to explain the variable responses of cancer patients to immunotherapy. Our atlas includes well established macrophage subsets as well as a number of additional ones. We associate macrophage composition with tumour phenotype and show macrophage subsets can vary between primary and metastatic tumours growing in sites like the liver. We also examine macrophage-T cell functional cross talk and identify two subsets of TAMs associated with T cell activation. Analysis of TAM signatures in a large cohort of immune checkpoint inhibitor-treated patients (CPI1000 + ) identify multiple TAM subsets associated with response, including the presence of a subset of TAMs that upregulate collagen-related genes. Finally, we demonstrate the utility of our data as a resource and reference atlas for mapping of novel macrophage datasets using projection. Overall, these advances represent an important step in both macrophage classification and overcoming resistance to immunotherapies in cancer.

Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq.

Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.

Long-read RNA-Seq for the discovery of long noncoding and antisense RNAs in plant organelles.

Plant organelle transcription has been studied for decades. As techniques advanced, so did the fields of mitochondrial and plastid transcriptomics. The current view is that organelle genomes are pervasively transcribed, irrespective of their size, content, structure, and taxonomic origin. However, little is known about the nature of organelle noncoding transcriptomes, including pervasively transcribed noncoding RNAs (ncRNAs). Next-generation sequencing data have uncovered small ncRNAs in the organelles of plants and other organisms, but long ncRNAs remain poorly understood. Here, we argue that publicly available third-generation long-read RNA sequencing data from plants can provide a fine-tuned picture of long ncRNAs within organelles. Indeed, given their bloated architectures, plant mitochondrial genomes are well suited for studying pervasive transcription of ncRNAs. Ultimately, we hope to showcase this new avenue of plant research while also underlining the limitations of the proposed approach.

Single-cell total-RNA profiling unveils regulatory hubs of transcription factors.

Recent development of RNA velocity uses master equations to establish the kinetics of the life cycle of RNAs from unspliced RNA to spliced RNA (i.e., mature RNA) to degradation. To feed this kinetic analysis, simultaneous measurement of unspliced RNA and spliced RNA in single cells is greatly desired. However, the majority of single-cell RNA-seq chemistry primarily captures mature RNA species to measure gene expressions. Here, we develop a one-step total-RNA chemistry-based single-cell RNA-seq method: snapTotal-seq. We benchmark this method with multiple single-cell RNA-seq assays in their performance in kinetic analysis of cell cycle by RNA velocity. Next, with LASSO regression between transcription factors, we identify the critical regulatory hubs mediating the cell cycle dynamics. We also apply snapTotal-seq to profile the oncogene-induced senescence and identify the key regulatory hubs governing the entry of senescence. Furthermore, from the comparative analysis of unspliced RNA and spliced RNA, we identify a significant portion of genes whose expression changes occur in spliced RNA but not to the same degree in unspliced RNA, indicating these gene expression changes are mainly controlled by post-transcriptional regulation. Overall, we demonstrate that snapTotal-seq can provide enriched information about gene regulation, especially during the transition between cell states.

Differentially expressed heterogeneous overdispersion genes testing for count data.

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size, where overdispersion refers to the empirical phenomenon that the variance of read counts is larger than the mean of read counts. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes when the number of replicates is limited as long as the number of conditions is large. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq2 and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.