Search

Background: Pancreatic ductal adenocarcinoma (PDAC) is dominated by an immunosuppressive microenvironment, which makes immune checkpoint blockade (ICB) often non-responsive. Understanding the mechanisms by which PDAC forms an immunosuppressive microenvironment is important for the development of new effective immunotherapy strategies.Methods: This study comprehensively evaluated the cell-cell communications between malignant cells and immune cells by integrative analyses of single-cell RNA sequencing data and bulk RNA sequencing data of PDAC. A Malignant-Immune cell crosstalk (MIT) score was constructed to predict survival and therapy response in PDAC patients. Immunological characteristics, enriched pathways, and mutations were evaluated in high- and low MIT groups.Results: We found that PDAC had high level of immune cell infiltrations, mainly were tumor-promoting immune cells. Frequent communication between malignant cells and tumor-promoting immune cells were observed. 15 ligand-receptor pairs between malignant cells and tumor-promoting immune cells were identified. We selected genes highly expressed on malignant cells to construct a Malignant-Immune Crosstalk (MIT) score. MIT score was positively correlated with tumor-promoting immune infiltrations. PDAC patients with high MIT score usually had a worse response to immune checkpoint blockade (ICB) immunotherapy.Conclusion: The ligand-receptor pairs identified in this study may provide potential targets for the development of new immunotherapy strategy. MIT score was established to measure tumor-promoting immunocyte infiltration. It can serve as a prognostic indicator for long-term survival of PDAC, and a predictor to ICB immunotherapy response.

In single cell analyses, cell types are conventionally identified based on expressions of known marker genes, whose identifications are time-consuming and irreproducible. To solve this issue, many supervised approaches have been developed to identify cell types based on the rapid accumulation of public datasets. However, these approaches are sensitive to batch effects or biological variations since the data distributions are different in cross-platforms or species predictions. In this study, we developed scAdapt, a virtual adversarial domain adaptation network, to transfer cell labels between datasets with batch effects. scAdapt used both the labeled source and unlabeled target data to train an enhanced classifier and aligned the labeled source centroids and pseudo-labeled target centroids to generate a joint embedding. The scAdapt was demonstrated to outperform existing methods for classification in simulated, cross-platforms, cross-species, spatial transcriptomic and COVID-19 immune datasets. Further quantitative evaluations and visualizations for the aligned embeddings confirm the superiority in cell mixing and the ability to preserve discriminative cluster structure present in the original datasets. [ABSTRACT FROM AUTHOR]

Although some co-risk factors and hemodynamic alterations are involved in hypertension progression, their direct biomechanical effects are unclear. Here, we constructed a high-hydrostatic-pressure cell-culture system to imitate constant hypertension and identified novel molecular classifications of human aortic smooth muscle cells (HASMCs) by single-cell transcriptome analysis. Under 100-mmHg (analogous to healthy human blood pressure) or 200-mmHg (analogous to hypertension) hydrostatic pressure for 48 h, HASMCs showed six distinct vascular SMC (VSMC) clusters according to differential gene expression and gene ontology enrichment analysis. Especially, two novel HASMC subsets were identified, named the inflammatory subset, with CXCL2, CXCL3 and CCL2 as markers, and the endothelial-function inhibitory subset, with AKR1C2, AKR1C3, SERPINF1 as markers. The inflammatory subset promoted CXCL2&3 and CCL2 chemokine expression and secretion, triggering monocyte migration; the endothelial-function inhibitory subset secreted SERPINF1 and accelerated prostaglandin F2α generation to inhibit angiogenesis. The expression of the two VSMC subsets was greatly increased in arterial media from patients with hypertension and experimental animal models of hypertension. Collectively, we identified high hydrostatic pressure directly driving VSMCs into two new subsets, promoting or exacerbating endothelial dysfunction, thereby contributing to the pathogenesis of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Alignment of scRNA-Seq data are the first and one of the most critical steps of the scRNA-Seq analysis workflow, and thus the choice of proper aligners is of paramount importance. Recently, STAR an alignment method and Kallisto a pseudoalignment method have both gained a vast amount of popularity in the single cell sequencing field. However, an unbiased third-party comparison of these two methods in scRNA-Seq is lacking. Here we conduct a systematic comparison of them on a variety of Drop-seq, Fluidigm and 10x genomics data, from the aspects of gene abundance, alignment accuracy, as well as computational speed and memory use. We observe that STAR globally produces more genes and higher gene-expression values, compared to Kallisto, as well as Bowtie2, another popular alignment method for bulk RNA-Seq. STAR also yields higher correlations of the Gini index for the genes with RNA-FISH validation results. Using 10x genomics PBMC 3K scRNA-Seq and mouse cortex single nuclei RNA-Seq data, STAR shows similar or better cell-type annotation results, by detecting a larger subset of known gene markers. However, the gain of accuracy and gene abundance of STAR alignment comes with the price of significantly slower computation time (4 folds) and more memory (7.7 folds), compared to Kallisto.

Cell is the fundamental structural and functional unit of complex multicellular organisms. Conventional methods which involve average analysis of cells in bulk populations can undermine physiologically significant cell populations, whereas analysis of cells at a single cell level may reveal unique biomarkers and other mechanisms that govern the genotype and phenotype in various physiological processes in presumed homogenous cell populations. Cellular abnormalities such as irregularities in cellular mechanisms have been linked to human aging and other major diseases including neurodegenerative, vascular, autoimmune, and cancer. Aging is a functional decline associated with various diseases in an organism, majorly arising from cellular abnormalities. Single cell analysis (SCA) which involves isolation and study of single cell proteomics, genomics, transcriptomics and metabolomics which enables research of cellular abnormalities with a molecular resolution, is gaining recognition in the research of human aging and disease. The advances in SCA are producing breakthrough information about cellular heterogeneity, disease progression, cellular microenvironment and its interactions, early diagnostics, improving precision medicine through high throughput drug screening and discovery of novel biomarkers; combinedly, these advances exhibit the potential of SCA to study of human aging and disease. Primarily, we review the role of SCA in understanding cellular mechanisms involved in aging and other major diseases including neurological, vascular, autoimmunity and cancer. Secondly, we also include review of SCA role in studying cell adhesion mechanisms which are involved in tissue development and maintenance and disease progression. Finally, SCA potential to empower precision medicine and its overall challenges along with future directions are discussed.

Abstract Cell clustering is one of the most common routines in single cell RNA-seq data analyses, for which a number of specialized methods are available. The evaluation of these methods ignores an important biological characteristic that the structure for a population of cells is hierarchical, which could result in misleading evaluation results. In this work, we develop two new metrics that take into account the hierarchical structure of cell types. We illustrate the application of the new metrics in constructed examples as well as several real single cell datasets and show that they provide more biologically plausible results.

Abstract Background With the rapid development of single-cell RNA sequencing technology, it is possible to dissect cell-type composition at high resolution. A number of methods have been developed with the purpose to identify rare cell types. However, existing methods are still not scalable to large datasets, limiting their utility. To overcome this limitation, we present a new software package, called GiniClust3, which is an extension of GiniClust2 and significantly faster and memory-efficient than previous versions. Results Using GiniClust3, it only takes about 7 h to identify both common and rare cell clusters from a dataset that contains more than one million cells. Cell type mapping and perturbation analyses show that GiniClust3 could robustly identify cell clusters. Conclusions Taken together, these results suggest that GiniClust3 is a powerful tool to identify both common and rare cell population and can handle large dataset. GiniCluster3 is implemented in the open-source python package and available at https://github.com/rdong08/GiniClust3.

The brain is a highly complex organ consisting of numerous types of cells with ample diversity at the epigenetic level to achieve distinct gene expression profiles. During neuronal cell specification, transcription factors (TFs) form regulatory modules with chromatin remodeling proteins to initiate the cascade of epigenetic changes. Currently, little is known about brain epigenetic regulatory modules and how they regulate gene expression in a cell-type specific manner. To infer TFs involved in neuronal specification, we applied a recursive motif search approach on the differentially methylated regions identified from single-cell methylomes. The epigenetic transcription regulatory modules (ETRM), including EGR1 and MEF2C, were predicted and the co-expression of TFs in ETRMs were examined with RNA-seq data from single or sorted brain cells using a conditional probability matrix. Lastly, computational predications were validated with EGR1 ChIP-seq data. In addition, methylome and RNA-seq data generated from Egr1 knockout mice supported the essential role of EGR1 in brain epigenome programming, in particular for excitatory neurons. In summary, we demonstrated that brain single cell methylome and RNA-seq data can be integrated to gain a better understanding of how ETRMs control cell specification. The analytical pipeline implemented in this study is freely accessible in the Github repository (https://github.com/Gavin-Yinld/brain_TF).

Abstract Background Various statistical models have been developed to model the single cell RNA-seq expression profiles, capture its multimodality, and conduct differential gene expression test. However, for expression data generated by different experimental design and platforms, there is currently lack of capability to determine the most proper statistical model. Results We developed an R package, namely Multi-Modal Model Selection (M3S), for gene-wise selection of the most proper multi-modality statistical model and downstream analysis, useful in a single-cell or large scale bulk tissue transcriptomic data. M3S is featured with (1) gene-wise selection of the most parsimonious model among 11 most commonly utilized ones, that can best fit the expression distribution of the gene, (2) parameter estimation of a selected model, and (3) differential gene expression test based on the selected model. Conclusion A comprehensive evaluation suggested that M3S can accurately capture the multimodality on simulated and real single cell data. An open source package and is available through GitHub at https://github.com/zy26/M3S.

Abstract Background CCCTC-Binding Factor (CTCF), also known as 11-zinc finger protein, participates in many cellular processes, including insulator activity, transcriptional regulation and organization of chromatin architecture. Based on single cell flow cytometry and single cell RNA-FISH analyses, our previous study showed that deletion of CTCF binding site led to a significantly increase of cellular variation of its target gene. However, the effect of CTCF on genome-wide landscape of cell-to-cell variation remains unclear. Results We knocked down CTCF in EL4 cells using shRNA, and conducted single cell RNA-seq on both wild type (WT) cells and CTCF-Knockdown (CTCF-KD) cells using Fluidigm C1 system. Principal component analysis of single cell RNA-seq data showed that WT and CTCF-KD cells concentrated in two different clusters on PC1, indicating that gene expression profiles of WT and CTCF-KD cells were systematically different. Interestingly, GO terms including regulation of transcription, DNA binding, zinc finger and transcription factor binding were significantly enriched in CTCF-KD-specific highly variable genes, implying tissue-specific genes such as transcription factors were highly sensitive to CTCF level. The dysregulation of transcription factors potentially explains why knockdown of CTCF leads to systematic change of gene expression. In contrast, housekeeping genes such as rRNA processing, DNA repair and tRNA processing were significantly enriched in WT-specific highly variable genes, potentially due to a higher cellular variation of cell activity in WT cells compared to CTCF-KD cells. We further found that cellular variation-increased genes were significantly enriched in down-regulated genes, indicating CTCF knockdown simultaneously reduced the expression levels and increased the expression noise of its regulated genes. Conclusions To our knowledge, this is the first attempt to explore genome-wide landscape of cellular variation after CTCF knockdown. Our study not only advances our understanding of CTCF function in maintaining gene expression and reducing expression noise, but also provides a framework for examining gene function.

Mesenchymal stem/stromal cells (MSCs) are promising cell sources for regenerative medicine and the treatment of autoimmune disorders. Comparing MSCs from different tissues at the single-cell level is fundamental for optimizing clinical applications. Here we analyzed single-cell RNA-seq data of MSCs from four tissues, namely umbilical cord, bone marrow, synovial tissue, and adipose tissue. We identified three major cell subpopulations, namely osteo-MSCs, chondro-MSCs, and adipo/myo-MSCs, across all MSC samples. MSCs from the umbilical cord exhibited the highest immunosuppression, potentially indicating it is the best immune modulator for autoimmune diseases. MSC subpopulations, with different subtypes and tissue sources, showed pronounced differences in differentiation potentials. After we compared the cell subpopulations and cell status pre-and-post chondrogenesis induction, osteogenesis induction, and adipogenesis induction, respectively, we found MSC subpopulations expanded and differentiated when their subtypes consist with induction directions, while the other subpopulations shrank. We identified the genes and transcription factors underlying each induction at the single-cell level and subpopulation level, providing better targets for improving induction efficiency.

Objective: The current study addresses the cellular complexity and plasticity of subcutaneous (inguinal) white adipose tissue (iWAT) in mice during the critical periods of perinatal growth and establishment. Methods: We performed a large-scale single cell transcriptomic (scRNA-seq) and epigenomic (snATAC-seq) characterization of cellular subtypes (adipose stromal cells (ASC) and adipocyte nuclei) during inguinal WAT (subcutaneous; iWAT) development in mice, capturing the early postnatal period (postnatal days (PND) 06 and 18) through adulthood (PND56). Results: Perinatal and adult iWAT contain 3 major ASC subtypes that can be independently identified by RNA expression profiles and DNA transposase accessibility. Furthermore, the transcriptomes and enhancer landscapes of both ASC and adipocytes dynamically change during postnatal development. Perinatal ASC (PND06) are highly enriched for several imprinted genes (IGs; e.g., Mest, H19, Igf2) and extracellular matrix proteins whose expression then declines prior to weaning (PND18). By comparison, adult ASC (PND56) are more enriched for transcripts associated with immunoregulation, oxidative stress, and integrin signaling. Two clusters of mature adipocytes, identified through single nuclei RNA sequencing (snRNA-seq), were distinctive for proinflammatory/immune or metabolic gene expression patterns that became more transcriptionally diverse in adult animals. Single nuclei assay for transposase-accessible chromatin (snATAC-seq) revealed that differences in gene expression were associated with developmental changes in chromatin accessibility and predicted transcription factor motifs (e.g., Plagl1, Ar) in both stromal cells and adipocytes. Conclusions: Our data provide new insights into transcriptional and epigenomic signaling networks important during iWAT establishment at a single cell resolution, with important implications for the field of metabolic programming.

Objective: Homo- or heterodimerization of G protein–coupled receptors (GPCRs) generally alters the normal functioning of these receptors and mediates their responses to a variety of physiological stimuli in vivo. It is well known that melanocortin-3 receptor (MC3R) and melanocortin-4 receptor (MC4R) are key regulators of appetite and energy homeostasis in the central nervous system (CNS). However, the GPCR partners of MC3R and MC4R are not well understood. Our objective is to analyze single cell RNA-seq datasets of the hypothalamus to explore and identify novel GPCR partners of MC3R and MC4R and examine the pharmacological effect on the downstream signal transduction and membrane translocation of melanocortin receptors. Methods: We conducted an integrative analysis of multiple single cell RNA-seq datasets to reveal the expression pattern and correlation of GPCR families in the mouse hypothalamus. The emerging GPCRs with important metabolic functions were selected for cloning and co-immunoprecipitation validation. The positive GPCR partners were then tested for the pharmacological activation, competitive binding assay and surface translocation ELISA experiments. Results: Based on the expression pattern of GPCRs and their function enrichment results, we narrowed down the range of potential GPCR interaction with MC3R and MC4R for further confirmation. Co-immunoprecipitation assay verified 23 and 32 novel GPCR partners that interacted with MC3R and MC4R in vitro. The presence of these GPCR partners exhibited different effects in the physiological regulation and signal transduction of MC3R and MC4R. Conclusions: This work represented the first large-scale screen for the functional GPCR complex of central melanocortin receptors and defined a composite metabolic regulatory GPCR network of the hypothalamic nucleuses.

Background: The development of multiple myeloma (MM) is considered to involve a multistep transformation process, but the role of cytogenetic abnormalities and molecular alterations in determining the cell fate of multiple myeloma (MM) remains unclear. Here, we have analyzed single cell RNA-seq data and bulk gene profiles to reveal a novel signature associated with MM development. Methods: The scRNA-seq data from GSE118900 was used to profile the transcriptomes of cells from MM patients at different stages. Pseudotemporal ordering of the single cells was performed using Monocle package to feature distinct transcriptomic states of the developing MM cells. The bulk microarray profiles from GSE24080 and GSE9782 were applied to identify a signature associated with MM development. Results: The 597 cells were divided into 7 clusters according to different risk levels. They were initiated mainly from monoclonal gammopathy of undetermined significance (MGUS), newly diagnosed MM (NDMM), or relapsed and/or refractory myeloma (RRMM) with cytogenetically favorable t(11;14), moved towards the cells from smoldering MM (SMM) or NDMM without t(11;14) or t(4;14), and then finally to cells from SMM or RRMM with t(4;14). Based on the markers identified in the late stage, the bulk data was used to develop a 20-gene signature stratifying patients into high and low-risk groups (GSE24080: HR = 3.759, 95% CI 2.746–5.145; GSE9782: HR = 2.612, 95% CI 1.894–3.603), which was better than the previously published gene signatures (EMC92, UAMS70, and UAMS17) and International Staging System. This signature also succeeded in predicting the clinical outcome of patients treated with bortezomib (HR = 2.884, 95% CI 1.994–4.172, P = 1.89e−8). The 20 genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients with MM. Conclusion: Our comprehensive analyses offered new insights in MM development, and established a 20-gene signature as an independent biomarker for MM. [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA sequencing (scRNA-seq) has led to remarkable progress in our understanding of tissue heterogeneity in health and disease. Recently, the need for scRNA-seq sample fixation has emerged in many scenarios, such as when samples need long-term transportation, or when experiments need to be temporally synchronized. Methanol fixation is a simple and gentle method that has been routinely applied in scRNA-sEq. Yet, concerns remain that fixation may result in biases which may change the RNA-seq outcome. Results: We adapted an existing methanol fixation protocol and performed scRNA-seq on both live and methanol fixed cells. Analyses of the results show methanol fixation can faithfully preserve biological related signals, while the discrepancy caused by fixation is subtle and relevant to library construction methods. By grouping transcripts based on their lengths and GC content, we find that transcripts with different features are affected by fixation to different degrees in full-length sequencing data, while the effect is alleviated in Drop-seq result. Conclusions: Our deep analysis reveals the effects of methanol fixation on sample RNA integrity and elucidates the potential consequences of using fixation in various scRNA-seq experiment designs. [ABSTRACT FROM AUTHOR]

The prevalence of dropout events is a serious problem for single-cell Hi-C (scHiC) data due to insufficient sequencing depth and data coverage, which brings difficulties in downstream studies such as clustering and structural analysis. Complicating things further is the fact that dropouts are confounded with structural zeros due to underlying properties, leading to observed zeros being a mixture of both types of events. Although a great deal of progress has been made in imputing dropout events for single cell RNA-sequencing (RNA-seq) data, little has been done in identifying structural zeros and imputing dropouts for scHiC data. In this paper, we adapted several methods from the single-cell RNA-seq literature for inference on observed zeros in scHiC data and evaluated their effectiveness. Through an extensive simulation study and real data analysis, we have shown that a couple of the adapted single-cell RNA-seq algorithms can be powerful for correctly identifying structural zeros and accurately imputing dropout values. Downstream analysis using the imputed values showed considerable improvement for clustering cells of the same types together over clustering results before imputation. [ABSTRACT FROM AUTHOR]

Background: Genetic programs underlying preimplantation development and early lineage segregation are highly conserved across mammals. It has been suggested that nonhuman primates would be better model organisms for human embryogenesis, but a limited number of studies have investigated the monkey preimplantation development. In this study, we collect single cells from cynomolgus monkey preimplantation embryos for transcriptome profiling and compare with single‐cell RNA‐seq data derived from human and mouse embryos. Results: By weighted gene‐coexpression network analysis, we found that cynomolgus gene networks have greater conservation with human embryos including a greater number of conserved hub genes than that of mouse embryos. Consistently, we found that early ICM/TE lineage‐segregating genes in monkeys exhibit greater similarity with human when compared to mouse, so are the genes in signaling pathways such as LRP1 and TCF7 involving in WNT pathway. Last, we tested the role of one conserved pre‐EGA hub gene, SIN3A, using a morpholino knockdown of maternal RNA transcripts in monkey embryos followed by single‐cell RNA‐seq. We found that SIN3A knockdown disrupts the gene‐silencing program during the embryonic genome activation transition and results in developmental delay of cynomolgus embryos. Conclusion: Taken together, our study provided new insight into evolutionarily conserved and divergent transcriptome dynamics during mammalian preimplantation development. Key Findings: A dynamic transcriptome for pre‐implantation embryos in cynomolgus monkey.SIN3A is a critical regulatory gene for pre‐implantation development in among three mammalian species.SIN3A is important for gene silencing during the EGA.New knowledge of evolutionarily conserved and divergent transcriptome dynamics during mammalian pre‐implantation development. [ABSTRACT FROM AUTHOR]

Analyzing single cell RNA-seq data is important for deciphering the spatial relationships, expression patterns, and developmental processes of cells. Combining in situ hybridization-based gene expression atlas images, some works have successfully recovered spatial locations of cells in zebrafish and Drosophila embryos. In this article, we describe a highly ranked method in the DREAM Single Cell Transcriptomics Challenge for predicting cell positions in the Drosophila embryo. The method performs unsupervised feature extraction to select a small number of driver genes and then uses them to predict gene expression and spatial position of each individual cell. First, hierarchical clustering is used to select a subset of driver genes. Second, the similarity matrix of single cells in the bins of the reference atlas is computed. Based on the similarity matrix, the spatial positions of cells are then determined by hierarchical clustering. This method is evaluated on the cell positions and gene expressions in the DREAM Single Cell Transcriptomics Challenge. The comparison with the “silver standard” suggests that our method is effective in reconstructing the cell spatial positions and gene expression patterns in tissues.

During aging and neuromuscular diseases, there is a progressive loss of skeletal muscle volume and function impacting mobility and quality of life. Muscle loss is often associated with denervation and a loss of resident muscle stem cells (satellite cells or MuSCs); however, the relationship between MuSCs and innervation has not been established. Herein, we administered severe neuromuscular trauma to a transgenic murine model that permits MuSC lineage tracing. We show that a subset of MuSCs specifically engraft in a position proximal to the neuromuscular junction (NMJ), the synapse between myofibers and motor neurons, in healthy young adult muscles. In aging and in a mouse model of neuromuscular degeneration (Cu/Zn superoxide dismutase knockout – Sod1-/-), this localized engraftment behavior was reduced. Genetic rescue of motor neurons in Sod1-/- mice reestablished integrity of the NMJ in a manner akin to young muscle and partially restored MuSC ability to engraft into positions proximal to the NMJ. Using single cell RNA-sequencing of MuSCs isolated from aged muscle, we demonstrate that a subset of MuSCs are molecularly distinguishable from MuSCs responding to myofiber injury and share similarity to synaptic myonuclei. Collectively, these data reveal unique features of MuSCs that respond to synaptic perturbations caused by aging and other stressors.

Axotomy in the CNS activates retrograde signals that can trigger regeneration or cell death. Whether these outcomes use different injury signals is not known. Local protein synthesis in axon tips plays an important role in axon retraction and regeneration. Microarray and RNA-seq studies on cultured mammalian embryonic or early postnatal peripheral neurons showed that axon growth cones contain hundreds to thousands of mRNAs. In the lamprey, identified reticulospinal neurons vary in the probability that their axons will regenerate after axotomy. The bad regenerators undergo early severe axon retraction and very delayed apoptosis. We micro-aspirated axoplasms from 10 growing, 9 static and 5 retracting axon tips of spinal cord transected lampreys and performed single-cell RNA-seq, analyzing the results bioinformatically. Genes were identified that were upregulated selectively in growing (n = 38), static (20) or retracting tips (18). Among them, map3k2, csnk1e and gtf2h were expressed in growing tips, mapk8(1) was expressed in static tips and prkcq was expressed in retracting tips. Venn diagrams revealed more than 40 components of MAPK signaling pathways, including jnk and p38 isoforms, which were differentially distributed in growing, static and/or retracting tips. Real-time q-PCR and immunohistochemistry verified the colocalization of map3k2 and csnk1e in growing axon tips. Thus, differentially regulated MAPK and circadian rhythm signaling pathways may be involved in activating either programs for axon regeneration or axon retraction and apoptosis.

The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.

Pancreatic ductal adenocarcinoma (PDAC) is a highly devastating disease with poor prognosis and rising incidence worldwide. Late detection and particularly aggressive characteristics are the major challenges that lead to therapeutic failure of this disease. A well described gene program and core regulators are yet to be discovered to drive the metastasis of the PDAC cells. As the development of single cell omics technologies including single cell RNA-sequencing (scRNA-seq), detailed characterization of the cellular composition of solid tumors and their microenvironments are well elaborated. In the current study, we accessed a recently published scRNA-seq dataset on primary and metastatic PDAC tissues and subset the tumor cells. By comparative analysis, we profiled the differentially expressed gene programs of primary and metastatic PDAC and found several long intergenic non-coding RNAs (LincRNAs) in top genes. The PDAC cancer cells showed some heterogeneity and were divided into four major subclusters based on gene profiles, one of which was mostly contributed by metastatic PDAC. Interestingly, this subcluster was remarkably marked by one of the above LincRNAs, MEG3, and exhibited significantly increased Epithelial–Mesenchymal-Transition (EMT) signatures. Ingenuity Pathway Analysis (IPA) on the signature genes of this subcluster gave multiple cancer metastasis associated and EMT signaling pathways, suggesting a critical role of this cluster in leading tumor cell metastasis. Taken together, this study displayed a PDAC cancer subcluster and its marker gene, biologically targeting of which might significantly attenuate the metastasis of tumor and might be a potential strategy for the therapeutic treatment of cancer.

Objective: Early treatment-naïve rheumatoid arthritis (RA) has defective regulatory T (Treg) cells and increased inflammation response. In this study, we aim to illustrate the regulation of Treg cells in pathogenesis of early rheumatoid arthritis by arsenic trioxide (As2O3).Methods: We studied the effects of As2O3 on gene expression in early treatment-naïve RA Treg cells with single cell RNA-seq (scRNA-seq). Treg cells were sorted from peripheral blood mononuclear cells (PBMCs) and purified by fluorescence-activated cell sorting (FACS) and cultured with or without As2O3 (at 0.1 µM) for 24 h. Total RNA was isolated and sequenced, and functional analysis was performed against the Gene Ontology (GO) database. Results for selected genes were confirmed with RT-qPCR.Results: As2O3 exerts no significant effect on CD4+ T-cell apoptosis under physical condition, and selectively modulate CD4+ T cells toward Treg cells not Th17 cells under special polarizing stimulators. As2O3 increased the expression of 200 and reduced that of 272 genes with fold change (FC) 2.0 or greater. Several genes associated with inflammation, Treg-cell activation and differentiation as well as glucose and amino acids metabolism were among the most strongly affected genes. GO function analysis identified top ten ranked significant biological process (BPs), molecular functions (MFs), and cell components (CCs) in treatment and nontreatment Treg cells. In GO analysis, genes involved in the immunoregulation, cell apoptosis and cycle, inflammation, and cellular metabolism were enriched among the significantly affected genes. The KEGG pathway enrichment analysis identified the forkhead box O (FoxO) signal pathway, apoptosis, cytokine–cytokine receptor interaction, cell cycle, nuclear factor-kappa B (NF-κB) signaling pathway, tumor necrosis factor α (TNF-α), p53 signaling pathway, and phosphatidylinositol 3′-kinase (PI3K)-Akt signaling pathway were involved in the pathogenesis of early treatment-naïve RA.Conclusion: This is the first study investigating the genome-wide effects of As2O3 on the gene expression of treatment-naïve Treg cells. In addition to clear anti-inflammatory and immunoregulation effects, As2O3 affect amino acids and glucose metabolism in Treg cells, an observation that might be particularly important in the metabolic phenotype of treatment-naïve RA.

Objective: Early treatment-naïve rheumatoid arthritis (RA) has defective regulatory T (Treg) cells and increased inflammation response. In this study, we aim to illustrate the regulation of Treg cells in pathogenesis of early rheumatoid arthritis by arsenic trioxide (As2O3). Methods: We studied the effects of As2O3 on gene expression in early treatment-naïve RA Treg cells with single cell RNA-seq (scRNA-seq). Treg cells were sorted from peripheral blood mononuclear cells (PBMCs) and purified by fluorescence-activated cell sorting (FACS) and cultured with or without As2O3 (at 0.1 µM) for 24 h. Total RNA was isolated and sequenced, and functional analysis was performed against the Gene Ontology (GO) database. Results for selected genes were confirmed with RT-qPCR. Results: As2O3 exerts no significant effect on CD4+ T-cell apoptosis under physical condition, and selectively modulate CD4+ T cells toward Treg cells not Th17 cells under special polarizing stimulators. As2O3 increased the expression of 200 and reduced that of 272 genes with fold change (FC) 2.0 or greater. Several genes associated with inflammation, Treg-cell activation and differentiation as well as glucose and amino acids metabolism were among the most strongly affected genes. GO function analysis identified top ten ranked significant biological process (BPs), molecular functions (MFs), and cell components (CCs) in treatment and nontreatment Treg cells. In GO analysis, genes involved in the immunoregulation, cell apoptosis and cycle, inflammation, and cellular metabolism were enriched among the significantly affected genes. The KEGG pathway enrichment analysis identified the forkhead box O (FoxO) signal pathway, apoptosis, cytokine–cytokine receptor interaction, cell cycle, nuclear factor-kappa B (NF-κB) signaling pathway, tumor necrosis factor α (TNF-α), p53 signaling pathway, and phosphatidylinositol 3′-kinase (PI3K)-Akt signaling pathway were involved in the pathogenesis of early treatment-naïve RA. Conclusion: This is the first study investigating the genome-wide effects of As2O3 on the gene expression of treatment-naïve Treg cells. In addition to clear anti-inflammatory and immunoregulation effects, As2O3 affect amino acids and glucose metabolism in Treg cells, an observation that might be particularly important in the metabolic phenotype of treatment-naïve RA. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is a highly devastating disease with poor prognosis and rising incidence worldwide. Late detection and particularly aggressive characteristics are the major challenges that lead to therapeutic failure of this disease. A well described gene program and core regulators are yet to be discovered to drive the metastasis of the PDAC cells. As the development of single cell omics technologies including single cell RNA-sequencing (scRNA-seq), detailed characterization of the cellular composition of solid tumors and their microenvironments are well elaborated. In the current study, we accessed a recently published scRNA-seq dataset on primary and metastatic PDAC tissues and subset the tumor cells. By comparative analysis, we profiled the differentially expressed gene programs of primary and metastatic PDAC and found several long intergenic non-coding RNAs (LincRNAs) in top genes. The PDAC cancer cells showed some heterogeneity and were divided into four major subclusters based on gene profiles, one of which was mostly contributed by metastatic PDAC. Interestingly, this subcluster was remarkably marked by one of the above LincRNAs, MEG3 , and exhibited significantly increased Epithelial–Mesenchymal-Transition (EMT) signatures. Ingenuity Pathway Analysis (IPA) on the signature genes of this subcluster gave multiple cancer metastasis associated and EMT signaling pathways, suggesting a critical role of this cluster in leading tumor cell metastasis. Taken together, this study displayed a PDAC cancer subcluster and its marker gene, biologically targeting of which might significantly attenuate the metastasis of tumor and might be a potential strategy for the therapeutic treatment of cancer. [ABSTRACT FROM AUTHOR]

Cellular analysis of developmental processes and toxicities has traditionally entailed bulk methods (e.g., transcriptomics) that lack single cell resolution or tissue localization methods (e.g., immunostaining) that allow only a few genes to be monitored in each experiment. Recent technological advances have enabled interrogation of genomic function at the single‐cell level, providing new opportunities to unravel developmental pathways and processes with unprecedented resolution. Here, we review emerging technologies of single‐cell RNA‐sequencing (scRNA‐seq) to globally characterize the gene expression sets of different cell types and how different cell types emerge from earlier cell states in development. Cell atlases of experimental embryology and human embryogenesis at single‐cell resolution will provide an encyclopedia of genes that define key stages from gastrulation to organogenesis. This technology, combined with computational models to discover key organizational principles, was recognized by Science magazine as the "Breakthrough of the year" for 2018 due to transformative potential on the way we study how human cells mature over a lifetime, how tissues regenerate, and how cells change in diseases (e.g., patient‐derived organoids to screen disease‐specific targets and design precision therapy). Profiling transcriptomes at the single‐cell level can fulfill the need for greater detail in the molecular progression of all cell lineages, from pluripotency to adulthood and how cell–cell signaling pathways control progression at every step. Translational opportunities emerge for elucidating pathogenesis of genetic birth defects with cellular precision and improvements for predictive toxicology of chemical teratogenesis. [ABSTRACT FROM AUTHOR]

Background: Whole lung tissue transcriptomic profiling studies in chronic obstructive pulmonary disease (COPD) have led to the identification of several genes associated with the severity of airflow limitation and/or the presence of emphysema, however, the cell types driving these gene expression signatures remain unidentified.Methods: To determine cell specific transcriptomic changes in severe COPD, we conducted single-cell RNA sequencing (scRNA seq) on n = 29,961 cells from the peripheral lung parenchymal tissue of nonsmoking subjects without underlying lung disease (n = 3) and patients with severe COPD (n = 3). The cell type composition and cell specific gene expression signature was assessed. Gene set enrichment analysis (GSEA) was used to identify the specific cell types contributing to the previously reported transcriptomic signatures.Results: T-distributed stochastic neighbor embedding and clustering of scRNA seq data revealed a total of 17 distinct populations. Among them, the populations with more differentially expressed genes in cases vs. controls (log fold change >|0.4| and FDR = 0.05) were: monocytes (n = 1499); macrophages (n = 868) and ciliated epithelial cells (n = 590), respectively. Using GSEA, we found that only ciliated and cytotoxic T cells manifested a trend towards enrichment of the previously reported 127 regional emphysema gene signatures (normalized enrichment score [NES] = 1.28 and = 1.33, FDR = 0.085 and = 0.092 respectively). Among the significantly altered genes present in ciliated epithelial cells of the COPD lungs, QKI and IGFBP5 protein levels were also found to be altered in the COPD lungs.Conclusions: scRNA seq is useful for identifying transcriptional changes and possibly individual protein levels that may contribute to the development of emphysema in a cell-type specific manner. [ABSTRACT FROM AUTHOR]

Purpose of Review: The ability to analyze the molecular events occurring within individual cells as opposed to populations of cells is revolutionizing our understanding of musculoskeletal tissue development and disease. Single cell studies have the great potential of identifying cellular subpopulations that work in a synchronized fashion to regenerate and repair damaged tissues during normal homeostasis. In addition, such studies can elucidate how these processes break down in disease as well as identify cellular subpopulations that drive the disease. This review highlights three emerging technologies: single cell RNA sequencing (scRNA-seq), Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), and Cytometry by Time-Of-Flight (CyTOF) mass cytometry. Recent Findings: Technological and bioinformatic tools to analyze the transcriptome, epigenome, and proteome at the individual cell level have advanced rapidly making data collection relatively easy; however, understanding how to access and interpret the data remains a challenge for many scientists. It is, therefore, of paramount significance to educate the musculoskeletal community on how single cell technologies can be used to answer research questions and advance translation. Summary: This article summarizes talks given during a workshop on "Single Cell Omics" at the 2020 annual meeting of the Orthopedic Research Society. Studies that applied scRNA-seq, ATAC-seq, and CyTOF mass cytometry to cartilage development and osteoarthritis are reviewed. This body of work shows how these cutting-edge tools can advance our understanding of the cellular heterogeneity and trajectories of lineage specification during development and disease. [ABSTRACT FROM AUTHOR]

Cells in the tumor microenvironment (TME) influence each other through secretion and sensing of soluble mediators, such as cytokines and chemokines. While signaling of interferon γ (IFNγ) and tumor necrosis factor α (TNFα) is integral to anti-tumor immune responses, our understanding of the spatiotemporal behavior of these cytokines is limited. Here, we describe a single cell transcriptome-based approach to infer which signal(s) an individual cell has received. We demonstrate that, contrary to expectations, CD8+ T cell-derived IFNγ is the dominant modifier of the TME relative to TNFα. Furthermore, we demonstrate that cell pools that show abundant IFNγ sensing are characterized by decreased expression of transforming growth factor β (TGFβ)-induced genes, consistent with IFNγ-mediated TME remodeling. Collectively, these data provide evidence that CD8+ T cell-secreted cytokines should be categorized into local and global tissue modifiers, and describe a broadly applicable approach to dissect cytokine and chemokine modulation of the TME. [Display omitted] • Single cell transcriptomes inform on prior cytokine sensing • CD8+ T cell-derived interferon γ (IFNγ) is a dominant modifier of the tumor microenvironment (TME) as compared to tumor necrosis factor α (TNFα) • TGFβ sensing appears lowered in IFNγ sensing tumor cells • Cytokines may be distinguished into local and global tissue modifiers Hoekstra et al. describe a single-cell RNA-seq-based approach for inference of prior in vivo cytokine sensing. They demonstrate that CD8+ T cell-derived TNFα is only sensed locally in the TME, whereas IFNγ forms a global tumor tissue modifier, and such IFNγ sensing appears associated with suppressed TGFβ sensing. [ABSTRACT FROM AUTHOR]

Abstract Background Classic dendritic cells (cDCs) play a central role in the immune system by processing and presenting antigens to activate T cells, and consist of two major subsets: CD141+ cDC (cDC1) and CD1c+ cDC (cDC2). A population of migratory precursor cells, the pre-cDCs, is the immediate precursors to both cDC subsets. Previous studies showed that there were two pre-committed pre-cDC subpopulations. However, the key molecular drivers of pre-commitment in human pre-cDCs were not investigated. Results To identify the key molecular drivers for pre-commitment in human pre-cDCs, we performed single cell RNA sequencing (RNA-Seq) of two cDC subsets and pre-cDCs, and bulk RNA-Seq of pre-cDCs and cDCs from human peripheral blood. We found that pre-DC subpopulations cannot be separated by either variable genes within pre-cDCs or differentially expressed genes between cDC1 and cDC2. In contrast, they were separated by 16 transcription factors that are themselves differentially expressed or have regulated targets enriched in the differentially expressed genes between bulk cDC1 and cDC2, with one subpopulation close to cDC1 and the other close to cDC2. More importantly, these two pre-cDC sub-populations are correlated with ratio of IRF8 to IRF4 expression level more than their individual expression level. We also verified these findings using three recently published datasets. Conclusions In this study, we demonstrate that single cell transcriptome profiling can reveal pre-cDCs differentiation map, and our results suggest the concept that combinatorial dose of transcription factors determines cell differentiation fate.

Abstract Background The advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification. Results We have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment. Conclusions Based on our study, we found that when marker genes are expressed at fold change of 4 or more, either Seurat or SIMLR algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fold change of 2, choice of the single cell algorithm is dependent on the number of single cells isolated and rarity of cell types to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in the design of single cell experiments.

Until the discovery of human embryonic stem cells and human induced pluripotent stem cells, biotechnology companies were severely limited in the number of human tissues that they could model in large-scale in vitro studies. Until this point, companies have been limited to immortalized cancer lines or a small number of primary cell types that could be extracted and expanded. Nowadays, protocols continue to be developed in the stem cell field, enabling researchers to model an ever-growing library of cell types in controlled, large-scale screens. One differentiation method in particular- cerebral organoids- shows substantial potential in the field of neuroscience and developmental neurobiology. Cerebral organoid technology is still in an early phase of development, and there are several challenges that are currently being addressed by academic and industrial researchers alike. Here we briefly describe some of the early adopters of cerebral organoids, several of the challenges that they are likely facing, and various technologies that are currently being implemented to overcome them. [ABSTRACT FROM AUTHOR]

• Emergence of an NKT cell cluster upon bacterial infection. • Reduction of B cell population. • Significant increase in low-density neutrophils. • Heterogeneous alteration of immune cells among patients. • Subtle details revealed by single-cell RNA-Seq. Bacterial infection remains one of the greatest threats to human health. However, how human hosts respond to bacterial infection has not been thoroughly understood. Better understanding of this response will improve human health. Here, we conducted an investigation on host response to bacterial infection using unperturbed clinical samples and single-cell RNA-Seq (scRNA-Seq) technology. To evaluate immune alteration upon bacterial infection in scRNA-Seq data of peripheral blood mononuclear cells (PBMCs), we developed a barcode analytical framework named PBMCode. Using this PBMCode framework, we revealed profound immune alteration in peripheral blood under bacterial infection, including the emergence of natural killer T (NKT) cell cluster, reduction of B cell population, and considerable changes in T cells and monocytes. In addition, we also observed a large quantity of low-density neutrophils. Our investigation on single cells provided unprecedented details in the alteration of both cell population and cell state under bacterial infection. These findings may be relevant to clinical decisions. The complexity of host response to bacterial infection revealed by scRNA-Seq deserves further attention in future studies. [ABSTRACT FROM AUTHOR]

The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.

Single cell RNA sequencing (scRNA-seq) data analysis is important for building a global transcription landscape of all cell types in tissues, tracing cell lineages, and reconstructing cell spatial organizations. In this article, we propose an unsupervised learning method to predict spatial positions and gene expression of individual cells in Drosophila embryos using a small number of driver genes. Specifically, we develop a two-stage clustering approach, and compute a probability matrix of the spatial positions of single cells. This method is applied to dataset in the DREAM Single Cell Transcriptomics Challenge. The comparison with the “gold standard” suggests that our method is effective in reconstructing the cell positions and gene expression patterns in spatial tissues.

Gene duplication is a key evolutionary phenomenon, prevalent in all organisms but particularly so in plants, where whole genome duplication (WGD; polyploidy) is a major force in genome evolution. Much effort has been expended in attempting to understand the evolution of duplicate genes, addressing such questions as why some paralog pairs rapidly return to single copy status whereas, in other pairs, both paralogs are retained and may diverge in expression pattern or function. The effect of a gene – its site of expression and thus the initial locus of its function – occurs at the level of a cell comprising a single cell type at a given state of the cell’s development. Using Arabidopsis thaliana single cell transcriptomic data we categorized patterns of expression for 11,470 duplicate gene pairs across 36 cell clusters comprising nine cell types and their developmental states. Among these 11,470 pairs, 10,187 (88.8%) had at least one copy expressed in at least one of the 36 cell clusters. Pairs produced by WGD more often had both paralogs expressed in root cells than did pairs produced by small scale duplications. Three quarters of gene pairs expressed in the 36 cell clusters (7,608/10,187) showed extreme expression bias in at least one cluster, including 352 cases of reciprocal bias, a pattern consistent with expression subfunctionalization. More than twice as many pairs showed reciprocal expression bias between cell states than between cell types or between roots and leaves. A group of 33 gene pairs with reciprocal expression bias showed evidence of concerted divergence of gene networks in stele vs. epidermis. Pairs with both paralogs expressed without bias were less likely to have paralogs with divergent mutant phenotypes; such bias-free pairs showed evidence of preservation by maintenance of dosage balance. Overall, we found considerable evidence of shifts in gene expression following duplication, including in >80% of pairs encoding 7,653 genes expressed ubiquitously in all root cell types and states for which we inferred the polarity of change.

Background Single-cell RNA-sequencing (scRNA-seq) technology is a powerful tool to study organism from a single cell perspective and explore the heterogeneity between cells. Clustering is a fundamental step in scRNA-seq data analysis and it is the key to understand cell function and constitutes the basis of other advanced analysis. Nonnegative Matrix Factorization (NMF) has been widely used in clustering analysis of transcriptome data and achieved good performance. However, the existing NMF model is unsupervised and ignores known gene functions in the process of clustering. Knowledges of cell markers genes (genes that only express in specific cells) in human and model organisms have been accumulated a lot, such as the Molecular Signatures Database (MSigDB), which can be used as prior information in the clustering analysis of scRNA-seq data. Because the same kind of cells is likely to have similar biological functions and specific gene expression patterns, the marker genes of cells can be utilized as prior knowledge in the clustering analysis. Methods We propose a robust and semi-supervised NMF (rssNMF) model, which introduces a new variable to absorb noises of data and incorporates marker genes as prior information into a graph regularization term. We use rssNMF to solve the clustering problem of scRNA-seq data. Results Twelve scRNA-seq datasets with true labels are used to test the model performance and the results illustrate that our model outperforms original NMF and other common methods such as KMeans and Hierarchical Clustering. Biological significance analysis shows that rssNMF can identify key subclasses and latent biological processes. To our knowledge, this study is the first method that incorporates prior knowledge into the clustering analysis of scRNA-seq data.

BackgroundImmunotherapies targeting CTLA-4 and PD-1 have elicited promising responses in a variety of cancers. However, the relatively low response rates warrant the identification of additional immunosuppressive pathways. V domain immunoglobulin suppressor of T cell activation (VISTA) plays a critical role in antitumor immunity and is a valuable target in cancer immunotherapy.MethodsHere, we used single-cell RNA-seq to analyze the gene expression levels of 14897 cells from a breast cancer sample and its paired 7,320 normal cells. Then, we validated the protein expression of immune checkpoint molecules (VISTA, PD-1, PD-L1, TIGIT, TIM3, and LAG3) in 324 human breast cancer samples by immunohistochemistry and quantitative immunofluorescence (QIF) approaches.ResultsSingle cell RNA-seq results show a higher level of immune checkpoint VISTA expression in breast cancer tissue compared to adjacent normal tissue. We also found that VISTA expressed highest in breast cancer tissue than other immune-checkpoints. Immunohistochemical results showed that VISTA was detected with a membranous/cytoplasmic staining pattern in intratumoral immune cells and breast cancer cells. Additionally, VISTA was positively correlated with pathological grade, lymph node status and the levels of PD-1 according to the chi-square test or Fisher’s test. Furthermore, VISTA expression was higher in CD68+ tumor-associated macrophages (TAMs) than in CD4+ T cells, CD8+ cytotoxic T cells or CD20+ B cells.ConclusionsThese findings therefore support the immunoregulatory role of VISTA in breast cancer and indicate that targeting VISTA may benefit breast cancer immunotherapy.

Severe COVID-19 patients show various immunological abnormalities including T-cell reduction and cytokine release syndrome, which can be fatal and is a major concern of the pandemic. However, it is poorly understood how T-cell dysregulation can contribute to the pathogenesis of severe COVID-19. Here we show single cell-level mechanisms for T-cell dysregulation in severe COVID-19, demonstrating new pathogenetic mechanisms of T-cell activation and differentiation underlying severe COVID-19. By in silico sorting CD4+ T-cells from a single cell RNA-seq dataset, we found that CD4+ T-cells were highly activated and showed unique differentiation pathways in the lung of severe COVID-19 patients. Notably, those T-cells in severe COVID-19 patients highly expressed immunoregulatory receptors and CD25, whilst repressing the expression of FOXP3. Furthermore, we show that CD25+ hyperactivated T-cells differentiate into multiple helper T-cell lineages, showing multifaceted effector T-cells with Th1 and Th2 characteristics. Lastly, we show that CD25-expressing hyperactivated T-cells produce the protease Furin, which facilitates the viral entry of SARS-CoV-2. Collectively, CD4+ T-cells from severe COVID-19 patients are hyperactivated and FOXP3-mediated negative feedback mechanisms are impaired in the lung, which may promote immunopathology. Therefore, our study proposes a new model of T-cell hyperactivation and paralysis that drives immunopathology in severe COVID-19.

Colorectal cancer (CRC), also known as colon cancer, is the third most common cancer and the fourth most cause of cancer-related death in the world. CRC can be classified into two major subtypes, including microsatellite instability (MSI) and microsatellite stability (MSS), which showed different characteristics in immunotherapy. Low sensitivity of diagnostic biomarkers and metastasis are still the principal cause of mortality, especially in MSI. Here, applying computational programs, we identified recurring expression programs based on single cell RNA sequencing (scRNA-Seq) data of CRC cell lines. Notably, three MSI specific recurring modules were identified by non-negative matrix factorization (NMF). High NMF score genes enriched in the function of metabolism and inflammatory response. Focusing on top specific active transcription factor (TF), RUNX3 (Runt-related transcription factor 3), our results suggest that T cell infiltration was increased in RUNX3 high MSI CRC samples. Unbiased Gene Set Enrichment Analysis (GSEA) showed that RUNX3 was strongly associated with immune and metastasis related functions, such as Interferon Gamma (IFN-γ) and EPITHELIAL MESENCHYMAL TRANSITION (EMT). In addition, RUNX3 shows specific highly activated at epigenetic level in MSI compared with other gastrointestinal carcinomas. Positive correlation between RUNX3 and most immune checkpoints further confirmed RUNX3 might have crucial roles in MSI cancer progression and immunotherapy. Taken together, these results indicate significant tumor heterogeneity of two CRC subtypes at single-cell level and epigenetic modification level. These results also linked transcriptional dysregulation with immune infiltration at single-cell level in MSI, which may advance the application of scRNA-Seq technology in immunotherapy and contribute to developing novel biomarkers of this malignancy., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

Background: Malignant tumours, particularly non-small cell lung cancer (NSCLC), pose a significant threat to human health due to their prevalence and lethality. Treatment methods for NSCLC vary greatly among individuals, making it crucial to identify predictive markers. Moreover, during tumour initiation and progression, tumour cells can release signaling molecules to induce polarization of macrophages towards a more tumour friendly M2 phenotype, which can promote tumour growth, metastasis, and drug resistance., Methods: We employed a comprehensive approach, combining bulk RNA-seq and single-cell sequencing analysis., Results: In our study, we used bulk RNA-seq and single-cell sequencing methods to analyze differential cells in NSCLC and adjacent tissues, searching for relevant marker genes that can predict prognosis and drug efficacy. We scrutinized biological phenomena such as macrophage-related gene methylation, copy number variation, and alternative splicing. Additionally, we utilized a co-culture technique of immune and tumour cells to explore the role of these genes in macrophage polarization. Our findings revealed distinct differences in macrophages between cancerous and adjacent tissues. We identified ANP32A, CCL20, ERAP2, MYD88, TMEM126B, TUBB6, and ZNF655 as macrophage-related genes that correlate with NSCLC patient prognosis and immunotherapy efficacy. Notably, ERAP2, TUBB6, CCL20, and TMEM126B can induce macrophage M0 to M2 polarization, promoting tumour proliferation., Conclusion: These findings significantly contribute to our understanding of the NSCLC tumour immune microenvironment. They pave the way for further research into the potential of these genes as targets for regulating tumour occurrence and development., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Gene duplication is a key evolutionary phenomenon, prevalent in all organisms but particularly so in plants, where whole genome duplication (WGD; polyploidy) is a major force in genome evolution. Much effort has been expended in attempting to understand the evolution of duplicate genes, addressing such questions as why some paralog pairs rapidly return to single copy status whereas, in other pairs, both paralogs are retained and may diverge in expression pattern or function. The effect of a gene – its site of expression and thus the initial locus of its function – occurs at the level of a cell comprising a single cell type at a given state of the cell's development. Using Arabidopsis thaliana single cell transcriptomic data we categorized patterns of expression for 11,470 duplicate gene pairs across 36 cell clusters comprising nine cell types and their developmental states. Among these 11,470 pairs, 10,187 (88.8%) had at least one copy expressed in at least one of the 36 cell clusters. Pairs produced by WGD more often had both paralogs expressed in root cells than did pairs produced by small scale duplications. Three quarters of gene pairs expressed in the 36 cell clusters (7,608/10,187) showed extreme expression bias in at least one cluster, including 352 cases of reciprocal bias, a pattern consistent with expression subfunctionalization. More than twice as many pairs showed reciprocal expression bias between cell states than between cell types or between roots and leaves. A group of 33 gene pairs with reciprocal expression bias showed evidence of concerted divergence of gene networks in stele vs. epidermis. Pairs with both paralogs expressed without bias were less likely to have paralogs with divergent mutant phenotypes; such bias-free pairs showed evidence of preservation by maintenance of dosage balance. Overall, we found considerable evidence of shifts in gene expression following duplication, including in >80% of pairs encoding 7,653 genes expressed ubiquitously in all root cell types and states for which we inferred the polarity of change. [ABSTRACT FROM AUTHOR]

Oocytes of better quality and developmental competence are highly demanded, which is affected by many intrinsic and external factors, including environmental pollutants. We have previously demonstrated that 7, 12-dimethylbenz [ a ]anthracene (DMBA) reduces the developmental competence of porcine oocytes, by desynchronizing nuclear and ooplasmic maturation. However, the underlying molecular mechanism remains obscure. Here we performed single cell RNA-seq to study the transcriptome changes in DMBA-treated porcine MII oocytes, and identified 19 protein-coding genes and 156 novel long non-coding RNAs (lncRNAs) with abundance to be significantly different (P < 0.05), which enriched in signaling pathways such as glycosphingolipid biosynthesis, nicotine addiction, basal transcription factors and nucleotide excision repair. RT-qPCR on oocyte pools confirmed ornithine aminotransferase (Oat) and serine/arginine-rich splicing factor 4 (Srsf4) to be significantly up- and down-regulated, respectively (P < 0.05). Treating porcine COCs with MAPK and PLC pathway inhibitors suppressed DMBA's effects on increasing PB1 extrusion rate. In addition, DMBA co-incubation with 250 μM vitamin C derivative (l -ascorbic acid 2-phosphate sesquimagnesium salt hydrate, AA2P) and 100 μM co-enzyme Q10 (CoQ10) could significantly reduce the DMBA-induced high ROS level, and partially alleviate the DMBA-induced high PB1 rate, whereas the cleavage and blastocyst rates of parthenotes derived from treated mature oocytes remained to be low. Collectively, our findings indicate that single cell RNA-seq can help reveal the dynamics of molecular signaling pathways for porcine oocytes treated by DMBA, and supplement of anti-oxidative reagents could not sufficiently rescue DMBA-induced defects of porcine oocytes. • Single cell RNA-seq identified DMBA-induced 19 DE mRNAs and 156 DE novel lncRNAs in pig oocytes. • MAPK and PLC pathway inhibitors suppressed DMBA's effects on increasing PB1 rate of pig oocytes. • 250 μM AA2P and 100 μM CoQ10 could reduce ROS level and partially alleviate PB1 rate as induced by DMBA in pig oocytes. • 250 μM AA2P and 100 μM CoQ10 could not recover the subsequent development ability of DMBA-treated pig oocytes. [ABSTRACT FROM AUTHOR]

Recently, immune cell‐mediated tissue repair and regeneration has been an emerging paradigm of regenerative medicine. Immune cells form an essential part of the wound as induction of inflammation is a necessary step to elicit tissue healing. Rapid progress in transcriptomic analyses by high‐throughput next‐generation sequencing has been developed to study gene regulatory network and establish molecular signatures of immune cells that could potentially predict their functional roles in tissue repair and regeneration. However, the identification of cellular heterogeneity especially on the rare cell subsets has been limited in transcriptomic analyses of bulk cell populations. Therefore, genome‐wide, single‐cell RNA sequencing (scRNA‐Seq) has offered an unprecedented approach to unravel cellular diversity and to study novel immune cell populations involved in tissue repair and regeneration through unsupervised sampling of individual cells without the need to rely on prior knowledge about cell‐specific markers. The analysis of gene expression patterns at a single‐cell resolution also holds promises to uncover the mechanisms and therefore the development of therapeutic strategy promoting immunoregenerative medicine. In this review, we will discuss how scRNA‐Seq facilitates the characterization of immune cells, including macrophages, innate lymphoid cells and T and B lymphocytes, discovery of immune cell heterogeneity, identification of novel subsets, and tracking of developmental trajectories of distinct immune cells during tissue homeostasis, repair, and regeneration. [ABSTRACT FROM AUTHOR]