Single-cell RNA-sequencing of virus-specific cellular immune responses in chronic hepatitis B patients.

Chronic hepatitis B (CHB) is a major global health challenge. CHB can be controlled by antivirals but a therapeutic cure is lacking. CHB is characterized by limited HBV-specific T cell reactivity and functionality and expression of inhibitory receptors. The mechanisms driving these T cell phenotypes are only partially understood. Here, we created a single-cell RNA-sequencing dataset of HBV immune responses in patients to contribute to a better understanding of the dysregulated immunity. Blood samples of a well-defined cohort of 21 CHB and 10 healthy controls, including a subset of 5 matched liver biopsies, were collected. scRNA-seq data of total immune cells (55,825) plus sorted HBV-specific (1,963), non-naive (32,773) and PD1+ T cells (96,631) was generated using the 10X Genomics platform (186,123 cells) or the full-length Smart-seq2 protocol (1,069 cells). The shared transcript count matrices of single-cells serve as a valuable resource describing transcriptional changes underlying dysfunctional HBV-related T cell responses in blood and liver tissue and offers the opportunity to identify targets or biomarkers for HBV-related immune exhaustion.

Elucidating the changes in the heterogeneity and function of radiation-induced cardiac macrophages using single-cell RNA sequencing.

Purpose: A mouse model of irradiation (IR)-induced heart injury was established to investigate the early changes in cardiac function after radiation and the role of cardiac macrophages in this process. Methods: Cardiac function was evaluated by heart-to-tibia ratio, lung-to-heart ratio and echocardiography. Immunofluorescence staining and flow cytometry analysis were used to evaluate the changes of macrophages in the heart. Immune cells from heart tissues were sorted by magnetic beads for single-cell RNA sequencing, and the subsets of macrophages were identified and analyzed. Trajectory analysis was used to explore the differentiation relationship of each macrophage subset. The differentially expressed genes (DEGs) were compared, and the related enriched pathways were identified. Single-cell regulatory network inference and clustering (SCENIC) analysis was performed to identify the potential transcription factors (TFs) which participated in this process. Results: Cardiac function temporarily decreased on Day 7 and returned to normal level on Day 35, accompanied by macrophages decreased and increased respectively. Then, we identified 7 clusters of macrophages by single-cell RNA sequencing and found two kinds of stage specific macrophages: senescence-associated macrophage (Cdkn1ahighC5ar1high) on Day 7 and interferon-associated macrophage (Ccr2highIsg15high) on Day 35. Moreover, we observed cardiac macrophages polarized over these two-time points based on M1/M2 and CCR2/major histocompatibility complex II (MHCII) expression. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses suggested that macrophages on Day 7 were characterized by an inflammatory senescent phenotype with enhanced chemotaxis and inflammatory factors, while macrophages on Day 35 showed enhanced phagocytosis with reduced inflammation, which was associated with interferon-related pathways. SCENIC analysis showed AP-1 family members were associated with IR-induced macrophages changes. Conclusion: We are the first study to characterize the diversity, features, and evolution of macrophages during the early stages in an IR-induced cardiac injury animal model.

Demuxafy: improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods.

Recent innovations in single-cell RNA-sequencing (scRNA-seq) provide the technology to investigate biological questions at cellular resolution. Pooling cells from multiple individuals has become a common strategy, and droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences. An implicit challenge with scRNA-seq is the occurrence of doublets-droplets containing two or more cells. We develop Demuxafy, a framework to enhance donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. Demuxafy significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq.

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10x Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10x data where samples were not multiplexed. Despite relatively lower cell capture affecting library preparation, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, a comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.

Cellular senescence and metabolic reprogramming model based on bulk/single-cell RNA sequencing reveals PTGER4 as a therapeutic target for ccRCC.

Clear cell renal cell carcinoma (ccRCC) is the prevailing histological subtype of renal cell carcinoma and has unique metabolic reprogramming during its occurrence and development. Cell senescence is one of the newly identified tumor characteristics. However, there is a dearth of methodical and all-encompassing investigations regarding the correlation between the broad-ranging alterations in metabolic processes associated with aging and ccRCC. We utilized a range of analytical methodologies, such as protein‒protein interaction network analysis and least absolute shrinkage and selection operator (LASSO) regression analysis, to form and validate a risk score model known as the senescence-metabolism-related risk model (SeMRM). Our study demonstrated that SeMRM could more precisely predict the OS of ccRCC patients than the clinical prognostic markers in use. By utilizing two distinct datasets of ccRCC, ICGC-KIRC (the International Cancer Genome Consortium) and GSE29609, as well as a single-cell dataset (GSE156632) and real patient clinical information, and further confirmed the relationship between the senescence-metabolism-related risk score (SeMRS) and ccRCC patient progression. It is worth noting that patients who were classified into different subgroups based on the SeMRS exhibited notable variations in metabolic activity, immune microenvironment, immune cell type transformation, mutant landscape, and drug responsiveness. We also demonstrated that PTGER4, a key gene in SeMRM, regulated ccRCC cell proliferation, lipid levels and the cell cycle in vivo and in vitro. Together, the utilization of SeMRM has the potential to function as a dependable clinical characteristic to increase the accuracy of prognostic assessment for patients diagnosed with ccRCC, thereby facilitating the selection of suitable treatment strategies.

Spatial analysis toolkits for RNA in situ sequencing.

Spatial transcriptomics (ST) is featured by high-throughput gene expression profiling within their native cell and tissue context, offering a means to investigate gene regulatory networks in tissue microenvironment. In situ sequencing (ISS) is an imaging-based ST technology that simultaneously detects hundreds to thousands of genes at subcellular resolution. As a highly reproducible and robust technique, ISS has been widely adapted and undergone a series of technical iterations. As the interest in ISS-based spatial transcriptomic analysis grows, scalable and integrated data analysis workflows are needed to facilitate the applications of ISS in different research fields. This review presents the state-of-the-art bioinformatic toolkits for ISS data analysis, which covers the upstream and downstream analysis workflows, including image analysis, cell segmentation, clustering, functional enrichment, detection of spatially variable genes and cell clusters, spatial cell-cell interactions, and trajectory inference. To assist the community in choosing the right tools for their research, the application of each tool and its compatibility with ISS data are reviewed in detailed. Finally, future perspectives and challenges concerning how to integrate heterogeneous tools into a user-friendly analysis pipeline are discussed. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico.

CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny.

BACKGROUND: The rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) simultaneously profiles the RNA transcriptome and the surface protein expression. The surface protein markers in CITE-Seq can be used to identify cell populations similar to the iterative filtration process in flow cytometry, also called "gating", and is an essential step for downstream analyses and data interpretation. While several packages allow users to interactively gate cells, they often do not process multi-omic sequencing datasets and may require writing redundant code to specify gate boundaries. To streamline the gating process, we developed CITEViz which allows users to interactively gate cells in Seurat-processed CITE-Seq data. CITEViz can also visualize basic quality control (QC) metrics allowing for a rapid and holistic evaluation of CITE-Seq data. RESULTS: We applied CITEViz to a peripheral blood mononuclear cell CITE-Seq dataset and gated for several major blood cell populations (CD14 monocytes, CD4 T cells, CD8 T cells, NK cells, B cells, and platelets) using canonical surface protein markers. The visualization features of CITEViz were used to investigate cellular heterogeneity in CD14 and CD16-expressing monocytes and to detect differential numbers of detected antibodies per patient donor. These results highlight the utility of CITEViz to enable the robust classification of single cell populations. CONCLUSIONS: CITEViz is an R-Shiny app that standardizes the gating workflow in CITE-Seq data for efficient classification of cell populations. Its secondary function is to generate basic feature plots and QC figures specific to multi-omic data. The user interface and internal workflow of CITEViz uniquely work together to produce an organized workflow and sensible data structures for easy data retrieval. This package leverages the strengths of biologists and computational scientists to assess and analyze multi-omic single-cell datasets. In conclusion, CITEViz streamlines the flow cytometry gating workflow in CITE-Seq data to help facilitate novel hypothesis generation.

Comparative single-cell RNA sequencing analysis of immune response to inactivated vaccine and natural SARS-CoV-2 infection.

Uncovering the immune response to an inactivated SARS-CoV-2 vaccine (In-Vac) and natural infection is crucial for comprehending COVID-19 immunology. Here we conducted an integrated analysis of single-cell RNA sequencing (scRNA-seq) data from serial peripheral blood mononuclear cell (PBMC) samples derived from 12 individuals receiving In-Vac compared with those from COVID-19 patients. Our study reveals that In-Vac induces subtle immunological changes in PBMC, including cell proportions and transcriptomes, compared with profound changes for natural infection. In-Vac modestly upregulates IFN-α but downregulates NF-κB pathways, while natural infection triggers hyperactive IFN-α and NF-κB pathways. Both In-Vac and natural infection alter T/B cell receptor repertoires, but COVID-19 has more significant change in preferential VJ gene, indicating a vigorous immune response. Our study reveals distinct patterns of cellular communications, including a selective activation of IL-15RA/IL-15 receptor pathway after In-Vac boost, suggesting its potential role in enhancing In-Vac-induced immunity. Collectively, our study illuminates multifaceted immune responses to In-Vac and natural infection, providing insights for optimizing SARS-CoV-2 vaccine efficacy.

Single-cell and spatial RNA sequencing reveal the spatiotemporal trajectories of fruit senescence.

The senescence of fruit is a complex physiological process, with various cell types within the pericarp, making it highly challenging to elucidate their individual roles in fruit senescence. In this study, a single-cell expression atlas of the pericarp of pitaya (Hylocereus undatus) is constructed, revealing exocarp and mesocarp cells undergoing the most significant changes during the fruit senescence process. Pseudotime analysis establishes cellular differentiation and gene expression trajectories during senescence. Early-stage oxidative stress imbalance is followed by the activation of resistance in exocarp cells, subsequently senescence-associated proteins accumulate in the mesocarp cells at late-stage senescence. The central role of the early response factor HuCMB1 is unveiled in the senescence regulatory network. This study provides a spatiotemporal perspective for a deeper understanding of the dynamic senescence process in plants.

Identification of microRNAs and their target genes associated with chasmogamous and cleistogamous flower development in Viola prionantha.

MAIN CONCLUSION: Differentially expressed microRNAs were found associated with the development of chasmogamous and cleistogamous flowers in Viola prionantha, revealing potential roles of microRNAs in the developmental evolution of dimorphic flowers. In Viola prionantha, chasmogamous (CH) flowers are induced by short daylight, while cleistogamous (CL) flowers are triggered by long daylight. How environmental factors and microRNAs (miRNAs) affect dimorphic flower formation remains unknown. In this study, small RNA sequencing was performed on CH and CL floral buds at different developmental stages in V. prionantha, differentially expressed miRNAs (DEmiRNAs) were identified, and their target genes were predicted. In CL flowers, Viola prionantha miR393 (vpr-miR393a/b) and vpr-miRN3366 were highly expressed, while in CH flowers, vpr-miRN2005, vpr-miR172e-2, vpr-miR166m-3, vpr-miR396f-2, and vpr-miR482d-2 were highly expressed. In the auxin-activated signaling pathway, vpr-miR393a/b and vpr-miRN2005 could target Vpr-TIR1/AFB and Vpr-ARF2, respectively, and other DEmiRNAs could target genes involved in the regulation of transcription, e.g., Vpr-AP2-7. Moreover, Vpr-UFO and Vpr-YAB5, the main regulators in petal and stamen development, were co-expressed with Vpr-TIR1/AFB and Vpr-ARF2 and showed lower expression in CL flowers than in CH flowers. Some V. prionantha genes relating to the stress/defense responses were co-expressed with Vpr-TIR1/AFB, Vpr-ARF2, and Vpr-AP2-7 and highly expressed in CL flowers. Therefore, in V. prionantha, CH-CL flower development may be regulated by the identified DEmiRNAs and their target genes, thus providing the first insight into the formation of dimorphic flowers in Viola.

BEERS2: RNA-Seq simulation through high fidelity in silico modeling.

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully length messenger RNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in polymerase chain reaction (PCR) amplification, barcode read errors and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

Integrated analysis of single-cell and bulk RNA sequencing data reveals prognostic characteristics of lysosome-dependent cell death-related genes in osteosarcoma.

BACKGROUND: Tumor cells exhibit a heightened susceptibility to lysosomal-dependent cell death (LCD) compared to normal cells. However, the role of LCD-related genes (LCD-RGs) in Osteosarcoma (OS) remains unelucidated. This study aimed to elucidate the role of LCD-RGs and their mechanisms in OS using several existing OS related datasets, including TCGA-OS, GSE16088, GSE14359, GSE21257 and GSE162454. RESULTS: Analysis identified a total of 8,629 DEGs1, 2,777 DEGs2 and 21 intersection genes. Importantly, two biomarkers (ATP6V0D1 and HDAC6) linked to OS prognosis were identified to establish the prognostic model. Significant differences in risk scores for OS survival were observed between high and low-risk cohorts. Additionally, scores of dendritic cells (DC), immature DCs and γδT cells differed significantly between the two risk cohorts. Cell annotations from GSE162454 encompassed eight types (myeloid cells, osteoblastic OS cells and plasma cells). ATP6V0D1 was found to be significantly over-expressed in myeloid cells and osteoclasts, while HDAC6 was under-expressed across all cell types. Moreover, single-cell trajectory mapping revealed that myeloid cells and osteoclasts differentiated first, underscoring their pivotal role in patients with OS. Furthermore, ATP6V0D1 expression progressively decreased with time. CONCLUSIONS: A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS.

Topological and geometric analysis of cell states in single-cell transcriptomic data.

Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate the local and first-order structures of scRNA-seq data using graph representations, while scRNA-seq data frequently display complex high-dimensional structures. Here, we introduce scGeom, a tool that exploits the multiscale and multidimensional structures in scRNA-seq data by analyzing the geometry and topology through curvature and persistent homology of both cell and gene networks. We demonstrate the utility of these structural features to reflect biological properties and functions in several applications, where we show that curvatures and topological signatures of cell and gene networks can help indicate transition cells and the differentiation potential of cells. We also illustrate that structural characteristics can improve the classification of cell types.

Detecting m6A at single-molecular resolution via direct RNA sequencing and realistic training data.

Direct RNA sequencing offers the possibility to simultaneously identify canonical bases and epi-transcriptomic modifications in each single RNA molecule. Thus far, the development of computational methods has been hampered by the lack of biologically realistic training data that carries modification labels at molecular resolution. Here, we report on the synthesis of such samples and the development of a bespoke algorithm, mAFiA (m6A Finding Algorithm), that accurately detects single m6A nucleotides in both synthetic RNAs and natural mRNA on single read level. Our approach uncovers distinct modification patterns in single molecules that would appear identical at the ensemble level. Compared to existing methods, mAFiA also demonstrates improved accuracy in measuring site-level m6A stoichiometry in biological samples.

Library size confounds biology in spatial transcriptomics data.

Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools including normalization methods. Here, we demonstrate that library size is associated with tissue structure and that normalizing these effects out using commonly applied scRNA-seq normalization methods will negatively affect spatial domain identification. Spatial data should not be specifically corrected for library size prior to analysis, and algorithms designed for scRNA-seq data should be adopted with caution.

Enhanced bovine genome annotation through integration of transcriptomics and epi-transcriptomics datasets facilitates genomic biology.

BACKGROUND: The accurate identification of the functional elements in the bovine genome is a fundamental requirement for high-quality analysis of data informing both genome biology and genomic selection. Functional annotation of the bovine genome was performed to identify a more complete catalog of transcript isoforms across bovine tissues. RESULTS: A total of 160,820 unique transcripts (50% protein coding) representing 34,882 unique genes (60% protein coding) were identified across tissues. Among them, 118,563 transcripts (73% of the total) were structurally validated by independent datasets (PacBio isoform sequencing data, Oxford Nanopore Technologies sequencing data, de novo assembled transcripts from RNA sequencing data) and comparison with Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive data from different technologies such as whole transcriptome termini site sequencing, RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin using sequencing. A large proportion of identified transcripts (69%) were unannotated, of which 86% were produced by annotated genes and 14% by unannotated genes. A median of two 5' untranslated regions were expressed per gene. Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. Furthermore, we identified 3,744 genes that functioned as noncoding genes in fetal tissues but as protein-coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 annotated gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available quantitative trait loci data to study tissue-tissue interconnection involved in different traits and construct the first bovine trait similarity network. CONCLUSIONS: These validated results show significant improvement over current bovine genome annotations.

stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics.

Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST. stDiff employs a conditional diffusion model, capturing gene expression abundance relationships in scRNA-seq data through two Markov processes: one introducing noise to transcriptomics data and the other denoising to recover them. The missing portion of ST is predicted by incorporating the original ST data into the denoising process. In our comprehensive performance evaluation across 16 datasets, utilizing multiple clustering and similarity metrics, stDiff stands out for its exceptional ability to preserve topological structures among cells, positioning itself as a robust solution for cell population identification. Moreover, stDiff's enhancement outcomes closely mirror the actual ST data within the batch space. Across diverse spatial expression patterns, our model accurately reconstructs them, delineating distinct spatial boundaries. This highlights stDiff's capability to unify the observed and predicted segments of ST data for subsequent analysis. We anticipate that stDiff, with its innovative approach, will contribute to advancing ST imputation methodologies.

Single-cell RNA sequencing reveals Immune Education promotes T cell survival in mice subjected to the cecal ligation and puncture sepsis model.

Background: Individual T cell responses vary significantly based on the microenvironment present at the time of immune response and on prior induced T cell memory. While the cecal ligation and puncture (CLP) model is the most commonly used murine sepsis model, the contribution of diverse T cell responses has not been explored. We defined T cell subset responses to CLP using single-cell RNA sequencing and examined the effects of prior induced T cell memory (Immune Education) on these responses. We hypothesized that Immune Education prior to CLP would alter T cell responses at the single cell level at a single, early post-CLP time point. Methods: Splenic T cells were isolated from C57BL/6 mice. Four cohorts were studied: Control, Immune-Educated, CLP, and Immune-Educated CLP. At age 8 weeks, Immune-Educated and Immune-Educated CLP mice received anti-CD3ϵ antibody; Control and CLP mice were administered an isotype control. CLP (two punctures with a 22-gauge needle) was performed at 12-13 weeks of life. Mice were sacrificed at baseline or 24-hours post-CLP. Unsupervised clustering of the transcriptome library identified six distinct T cell subsets: quiescent naïve CD4+, primed naïve CD4+, memory CD4+, naïve CD8+, activated CD8+, and CD8+ cytotoxic T cell subsets. T cell subset specific gene set enrichment analysis and Hurdle analysis for differentially expressed genes (DEGs) were performed. Results: T cell responses to CLP were not uniform - subsets of activated and suppressed T cells were identified. Immune Education augmented specific T cell subsets and led to genomic signatures favoring T cell survival in unoperated and CLP mice. Additionally, the combination of Immune Education and CLP effected the expression of genes related to T cell activity in ways that differed from CLP alone. Validating our finding that IL7R pathway markers were upregulated in Immune-Educated CLP mice, we found that Immune Education increased T cell surface IL7R expression in post-CLP mice. Conclusion: Immune Education enhanced the expression of genes associated with T cell survival in unoperated and CLP mice. Induction of memory T cell compartments via Immune Education combined with CLP may increase the model's concordance to human sepsis.