Single-cell RNA-seq Data Research Articles

Retrotrans-genomics identifies aberrant THE1B endogenous retrovirus fusion transcripts in the pathogenesis of sarcoidosis

Transposon-like human element 1B (THE1B) originates from ancient retroviral sequences integrated into the primate genome approximately 50 million years ago, now accounting for at least 27,233 copies in the human genome, suggesting their extensive influence on human genomic architecture. Here we report identification of 19 THE1B fusion transcripts through short- and long-read RNA-seq analysis, 15 of which are previously unmapped, showing elevated expression in 16 individuals with sarcoid myopathy (SM), as compared to 400 controls with various other muscle diseases. Analysis of publicly available RNA-seq data indicated a correlation between the reduced expression of eight THE1B fusion transcripts and clinical improvement in individuals with cutaneous sarcoidosis receiving tofacitinib treatment. Single-cell or single-nucleus RNA-seq analyses of sarcoidosis not only confirmed these transcripts but also revealed a novel read-through transcript, SIRPB1-SIRPD, and TREM2.1, predominantly in granuloma-associated macrophages. The expression profiles of THE1B fusion transcripts in tuberculosis (TB) significantly differed from SM in single-cell RNA-seq data, suggesting that the differences between TB’s caseous granulomas and sarcoidosis’s non-caseous granulomas might be linked to disparate expression patterns of THE1B fusion transcripts. Our retrotrans-genomics approach has not only identified the genomic landscape of sarcoidosis but also provided new insights into its etiology.

ScRecover: Discriminating True and False Zeros in Single-Cell RNA-Seq Data for Imputation.

High-throughput single-cell RNA-seq (scRNA-seq) data contains an excess of zero values, which can be contributed by unexpressed genes and detection signal dropouts. Existing imputation methods fail to distinguish between these two types of zeros. In this study, we introduce a statistical framework that effectively differentiates true zeros (lack of expression) from false zeros (dropouts). By focusing only on imputing the dropout zeros, we developed a new imputation tool, scRecover. Our approach utilizes a zero-inflated negative binomial framework to model the gene expression of each gene in each cell, enabling the estimation of zero-dropout probability. Additionally, we employ a modified version of the Good and Toulmin model to identify true zeros for each gene. To achieve imputation, scRecover is combined with other imputation methods such as scImpute, SAVER and MAGIC. Down-sampling experiments show that it recovers dropout zeros with higher accuracy and avoids over-imputing true zero values. Experiments conducted on real world data highlight the ability of scRecover to enhance downstream analysis and visualization.

Myeloma cell-derived CXCL7 facilitates proliferation of tumor cells and occurrence of osteolytic lesions through JAK/STAT3 pathway

Osteolytic lesions and pathological fractures are hallmark manifestations of multiple myeloma (MM), profoundly influencing the quality of life and self-care ability of MM patients. By analyzing transcriptome data and single-cell RNA-seq data from our center and the GEO database, a subset of MM cells with high expression levels of chemokine CXCL7 was identified. This subset of MM cells possesses a high capacity for proliferation and activation of osteoclast signaling pathway. CXCL7 might be a crucial regulator of osteolytic damage in MM. Subsequently, the association between the expression level of CXCL7 and pathological fractures in patients was investigated, and the impact of CXCL7 on MM proliferation was confirmed both in vivo and in vitro. A mouse xenograft tumor model was established through intravenous injection of myeloma cell lines based on the homing ability of plasma cells. Moreover, the mechanism by which CXCL7 promotes the activation of osteoclast signaling pathways in MM was explored. Our findings reveal that elevated CXCL7 levels significantly enhance MM cell proliferation, increasing the risk of pathological fractures in MM patients. Additionally, our mouse xenograft tumor model demonstrated that CXCL7 can induce femoral fractures and reduce bone mineral density. Concurrently, it was discovered that CXCL7 could activate the JAK/STAT3 pathway via CXCR2 and upregulate the expression levels of MMP13 and C-myc, facilitating MM cell proliferation and activation of the osteoclast signaling pathway. Our study offers novel insights into the pathogenic mechanism of osteolytic lesions and implies that targeting CXCL7 may present a new therapeutic avenue for MM.

TransCAE: Enhancing Cell Type Annotation in Single-cell RNA-seq Data with Transfer Learning and Convolutional Autoencoder.

Single-cell RNA sequencing (scRNA-seq) analysis offers tremendous potential for addressing various biological questions, with one key application being the annotation of query datasets with unknown cell types using well-annotated external reference datasets. However, the performance of existing supervised or semi-supervised methods largely depends on the quality of source data. Furthermore, these methods often struggle with the batch effects arising from different platforms when handling multiple reference or query datasets, making precise annotation challenging. We developed transCAE, a robust transfer learning-based algorithm for single-cell annotation that integrates unsupervised dimensionality reduction with supervised cell type classification. This approach fully leverages information from both reference and query datasets to achieve precise cell classification within the query data. Extensive evaluations show that transCAE significantly enhances classification accuracy and efficiently mitigates batch effects. Compared to other state-of-the-art methods, transCAE demonstrates superior performance in experiments involving multiple reference or query datasets. These strengths position transCAE as an optimal annotation method for scRNA-seq datasets.

Pre-immunotherapy alters stereotactic ablative radiotherapy-induced systemic T cell responses in early-stage NSCLC

BackgroundStereotactic ablative radiotherapy (SABR) is thought to activate T cell responses in patients with cancer, leading to its combination with immunotherapy and chemotherapy for treatment of non-small-cell lung cancer (NSCLC). Here, we aimed to provide a high-resolution transcriptomic profiling of the systemic T cell response following SABR, with or without preceding immunotherapy/chemotherapy.MethodsWe conducted single-cell RNA and T cell receptor (TCR) sequencing of T cells from peripheral blood of seven patients with early-stage NSCLC taken pre- and post-SABR without or with prior immunotherapy and chemotherapy (icSABR). Other flow cytometry, single-cell RNA-seq data and bulk RNA-seq data were used to validate the results.ResultsWe uncovered distinct T cell response patterns induced by these treatments: while terminal effector CD8+ T cells showed increased cytotoxic and inhibitory scores, and upregulated immune-activated pathways post-SABR, the reverse responses occurred post-icSABR. Furthermore, the proportion of large T cell clones increased and single clone decreased post-SABR, while the opposite was seen post-icSABR. Of note, both SABR and icSABR largely changed TCR clonotypes, which were mainly large clones post-SABR but single clone post-icSABR, and predominantly from terminal effector CD8+ T cells and T helper cells, respectively.ConclusionsThese findings reveal a complex interplay between SABR and immunotherapy, with potentially valuable implications for treatment strategies involving SABR and immunotherapy to induce systemic T cell responses for tumor eradication in patients with NSCLC.

MAP3K4 signaling regulates HDAC6 and TRAF4 coexpression and stabilization in trophoblast stem cells.

Mitogen-activated protein kinase kinase kinase 4 (MAP3K4) promotes fetal and placental growth and development, with MAP3K4 kinase inactivation resulting in placental insufficiency and fetal growth restriction. MAP3K4 promotes key signaling pathways including JNK, p38, and PI3K/Akt, leading to activation of CREB-binding protein. MAP3K4 kinase inactivation results in loss of these pathways and gain of histone deacetylase 6 (HDAC6) expression and activity. Tumor necrosis factor receptor-associated factor 4 (TRAF4) binds MAP3K4 and promotes MAP3K4 activation of downstream pathways in the embryo; however, the role of TRAF4 and its association with MAP3K4 in the placenta is unknown. Our analyses of murine placenta single-cell RNA-Seq data showed that Traf4 is coexpressed with Map3k4 in trophoblast stem (TS) cells and labyrinth progenitors, whereas Hdac6 expression is higher in differentiated trophoblasts. We demonstrate that, like HDAC6, TRAF4 expression is increased in MAP3K4 kinase-inactive TS (TSKI) cells and upon inhibition of MAP3K4-dependent pathways in WT TS cells. Moreover, Hdac6 shRNA knockdown in TSKI cells reduces TRAF4 protein expression. We found that HDAC6 forms a protein complex with TRAF4 in TS cells and promotes TRAF4 expression in the absence of HDAC6 deacetylase activity. Finally, we examine the relationships among MAP3K4, TRAF4, and HDAC6 in the developing placenta, finding a previously unknown switch in the coexpression of Traf4 with Map3k4 versus Traf4 with Hdac6 during differentiation of the placental labyrinth. Together, our findings identify previously unknown mechanisms of MAP3K4 and HDAC6 coregulation of TRAF4 in TS cells and highlight these MAP3K4, TRAF4, and HDAC6 associations during placental development.

Multiomic analysis uncovers a continuous spectrum of differentiation and Wnt-MDK-driven immune evasion in hepatoblastoma.

Hepatoblastoma is the most common pediatric cancer of the liver and the majority of cases display activating mutations in the Wnt/β-catenin pathway. Understanding the complex milieu of the tumor microenvironment has resulted in promising new therapies for adult cancers, but similar approaches in pediatric cancers are still lacking. We aimed to provide a comprehensive analysis of the tumor microenvironment of hepatoblastoma unveiling its spatial architecture and key signaling mechanisms. Single-cell/-nucleus RNA-seq (n=15), spatial transcriptomics (n=22), and multiplex immunofluorescence stainings (n=7) of treated, untreated, and metastasized pediatric hepatoblastomas were performed. An RNA-seq validation cohort (n=110) including hepatoblastoma, non-tumor and fetal liver samples and single-cell RNA-seq data of healthy immune cells were used for further analysis. Western blotting and RNA-seq of hepatoblastoma and macrophage cell lines were conducted for experimental validation. Of four identified transcriptional tumor programs, "Developmental" and "Metabolic" reflected different hepatic differentiation stages, while "Cycling" was enriched in undifferentiated cells and relapsed samples, and "Intermediate" displayed high activity in samples from patients with poor outcomes. We discovered an increased ratio of anti- to pro-inflammatory immune cells and evidence of immune exclusion from tumor areas. Wnt-responsive upregulation of the immunomodulator midkine in hepatoblastoma cells was associated with a change in macrophage phenotype, which could be partially reversed through midkine inhibition. Hepatoblastoma cells exist along a continuous spectrum of hepatic differentiation and inhabit an altered immune environment. Wnt signaling augments midkine expression, which appears to be involved in shaping the immune environment by modifying macrophages to enable immune evasion, thereby providing a potential therapeutic target. Despite hepatoblastoma being the most common pediatric liver cancer, there has been a critical knowledge gap in understanding how the tumor microenvironment and immune landscape contribute to disease progression. Our novel findings, revealing a continuous spectrum of tumor differentiation states and Wnt-MDK-driven immune evasion, are significant for pediatric oncology clinicians and researchers, improving our functional understanding of the immune environment of hepatoblastoma. The identification of midkine as a tumor-specific immunomodulator suggests a potential for developing new targeted therapies, though further mechanistic and practical validation would be needed to realize clinical translation of these findings.

BRD4 inhibition rewires cardiac macrophages toward a protective phenotype marked by low MHC class II expression.

Bromodomain and extraterminal domain (BET) proteins, including BRD4, bind acetylated chromatin and coactivate gene transcription. A BET inhibitor, JQ1, prevents and reverses pathological cardiac remodeling in preclinical models of heart failure. However, the underlying cellular mechanisms by which JQ1 improves cardiac structure and function remain poorly defined. Here, we demonstrate that BRD4 knockdown reduced expression of genes encoding CC chemokines in cardiac fibroblasts, suggesting a role for this epigenetic reader in controlling fibroblast-immune cell cross talk. Consistent with this, JQ1 dramatically suppressed recruitment of monocytes to the heart in response to stress. Normal mouse hearts were found to have approximately equivalent numbers of major histocompatibility complex (MHC-II)high and MHC-IIlow resident macrophages, whereas MHC-IIlow macrophages predominated following JQ1 treatment. Single-cell RNA-seq data confirmed that JQ1 treatment or BRD4 knockout in CX3CR1+ cells reduced MHC-II gene expression in cardiac macrophages, and studies with cultured macrophages further illustrated a cell autonomous role for BET proteins in controlling the MHC-II axis. Bulk RNA-seq analysis demonstrated that JQ1 blocked pro-inflammatory macrophage gene expression through a mechanism that likely involves repression of NF-κB signaling. JQ1 treatment reduced cardiac infarct size in mice subjected to ischemia/reperfusion. Our findings illustrate that BET inhibition affords a powerful pharmacological approach to manipulate monocyte-derived and resident macrophages in the heart. Such an approach has the potential to enhance the reparative phenotype of macrophages to promote wound healing and limit infarct expansion following myocardial ischemia.NEW & NOTEWORTHY BRD4 inhibition blocks stress-induced recruitment of pro-inflammatory monocytes to the heart. BRD4 inhibition reprograms resident cardiac macrophages toward a reparative phenotype marked by reduced NF-κB signaling and diminished MHC-II expression. BRD4 inhibition reduces infarct size in an acute model of ischemia/reperfusion injury in mice.

ScHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss.

The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant. However, existing methods utilizing neural networks typically focus on minimizing reconstruction loss and maintaining similarity in embeddings of directly related cells, but fail to consider dissimilarity, thus lacking separability and limiting the performance of clustering. We propose a novel clustering algorithm, called scHNTL (scRNA-seq data clustering augmented by high-order neighbors and triplet loss). It first constructs an auxiliary similarity graph and uses a Graph Attentional Autoencoder to learn initial embeddings of cells. Then it identifies similar and dissimilar cells by exploring high-order structures of the similarity graph and exploits a triplet loss of contrastive learning, to improve the embeddings in preserving structural information by separating dissimilar pairs. Finally, this improvement for embedding and the target of clustering are fused in a self-optimizing clustering framework to obtain the clusters. Experimental evaluations on 16 real-world datasets demonstrate the superiority of scHNTL in clustering over the state-of-the-arts single-cell clustering algorithms. Python implementation of scHNTL is available at Figshare (https://doi.org/10.6084/m9.figshare.27001090) and Github (https://github.com/SWJTU-ML/scHNTL-code). Supplementary data are available at Bioinformatics online.

Improving Spatial Transcriptomics with Membrane-Based Boundary Definition and Enhanced Single-Cell Resolution.

Accurately defining cell boundaries for spatial transcriptomics is technically challenging. The current major approaches are nuclear staining or mathematical inference, which either exclude the cytoplasm or determine a hypothetical boundary. Here, a new method is introduced for defining cell boundaries: labeling cell membranes using genetically coded fluorescent proteins, which allows precise indexing of sequencing spots and transcripts within cells on sections. Use of this membrane-based method greatly increases the number of genes captured in cells compared to the number captured using nucleus-based methods; the numbers of genes are increased by 67% and 119% in mouse and axolotl livers, respectively. The obtained expression profiles are more consistent with single-cell RNA-seq data, demonstrating more rational clustering and apparent cell type-specific markers. Furthermore, improved single-cell resolution is achieved to better identify rare cell types and elaborate spatial domains in the axolotl brain and intestine. In addition to regular cells, accurate recognition of multinucleated cells and cells lacking nuclei in the mouse liver is achieved, demonstrating its ability to analyze complex tissues and organs, which is not achievable using previous methods. This study provides a powerful tool for improving spatial transcriptomics that has broad potential for its applications in the biological and medical sciences.

Single Cell Transcriptome Signatures of Sarcoidosis in Lung Immune Cell Populations.

To identify cell specific molecular changes associated with sarcoidosis risk and progression, we aimed to characterize the cellular composition, gene expression patterns, and cell-cell interactions in BAL cells from patients with sarcoidosis (both progressive and non-progressive) and healthy controls. Single cell RNA-seq data were collected on 12 sarcoidosis and 4 control participants. We combined scRNA-seq data from these participants with our previously collected data on 4 sarcoidosis and 10 control participants for a final sample size of 16 sarcoidosis cases (8 progressive and 8 non-progressive) and 14 controls. Following initial preprocessing in CellRanger, data were quality controlled, combined, and clustered in Seurat. We tested differences in cell proportions by disease group using F-tests on cell proportions and differences in gene expression using pseudobulk analysis. Cell to cell communication and pathway analysis were performed using CellChat. We identified five macrophage populations: resident, high metallothionein (MT) resident, recruited, profibrotic recruited, and proliferating macrophages. Each subpopulation displayed unique gene expression profiles, with notable differential expression of genes and pathways linked to sarcoidosis in resident macrophages, recruited macrophages, and proliferating macrophages. We also observed changes in gene expression associated with disease progression in resident and recruited macrophages. In non-macrophages cells, we observed a significant reduction in the number of B cells in sarcoidosis patients compared to controls. Among T cell populations, we identified specific transcriptional alterations at gene and pathway level. Additionally, we observed distinct differences in cell-to-cell interactions of macrophages and T cells between sarcoidosis patients and healthy controls. These findings underscore the complexity of immune cell involvement in sarcoidosis and highlight potential cellular and molecular targets for further investigation.

DOP113 Residual TYK2 signalling is associated with non-response to vedolizumab and systemic TNF inhibitors among patients with ulcerative colitis: post hoc analyses of mucosal transcriptome data

Abstract Background Understanding the cellular and molecular mechanisms underlying response to treatment in patients with inflammatory bowel disease (IBD) is crucial to guide the development of new drugs. We have used transcriptomic approaches to assess biomarkers of response to treatment among patients with active ulcerative colitis (UC) receiving the integrin inhibitor vedolizumab (VDZ) or systemic tumor necrosis factor (TNF) inhibitor therapy. Methods In VARSITY, 769 patients with moderately to severely active ulcerative colitis were randomized to receive VDZ (n = 383) or adalimumab (ADA; n = 386) for 52 weeks; superiority was demonstrated for VDZ relative to ADA for the primary endpoint of clinical remission at Week 52 (31.3% vs 22.5%; p = 0.006).1 An unbiased cell and gene/pathway module analysis was conducted using pre-treatment (baseline), Week 14, and Week 52 intestinal biopsy samples from responders and non-responders to treatment, with response defined according to the primary endpoint. Longitudinal analysis was conducted focusing on tyrosine kinase 2 (TYK2)-dependent signalling pathways, including type I interferon (IFN) and interleukin (IL)-23. In addition, cell–cell signalling network analysis was conducted using single-cell RNA-seq data from patients with UC.2 Results Using single-cell transcriptomic data to assess downstream targets of ligand molecules,2 distinct but overlapping signalling networks were observed for α4β7 integrin, IL-23 and TNF. Cell and gene/pathway module analysis of transcriptomic data from VARSITY identified biomarkers at baseline, Week 14, and Week 52 associated with non-response to VDZ at Week 52, including CD4+, CD8+, B-cell and fibroblast cell subsets, and elevated transcription of gene modules associated with JAK/STAT and IFN signalling. Similar regulation differences were seen for response to ADA at Weeks 14 and 52, but, contrary to VDZ, no predictors of ADA response were identified at baseline. Targeted longitudinal analysis demonstrated enrichment of IL-23 and type I IFN pathways among VDZ and ADA non-responders throughout the study, and incomplete resolution of IL-23 and Type I IFN pathway modulation among treatment responders. Conclusion Elevations in gene modules associated with TYK2-dependent signalling were identified as biomarkers of non-response to both VDZ and ADA, suggestive of mechanistic complementarity between α4β7 integrin, TNF and TYK2 inhibition. These results have been further validated in additional transcriptomic studies of VDZ and infliximab.3,4. Findings support further investigation of the role of TYK2 inhibitors in IBD, irrespective of prior response to VDZ or ADA.

OP03 Mapping the observable IBDverse: Using massive-scale single-cell RNA sequencing of gut and blood samples to nominate genes, cell types and pathways driving IBD susceptibility

Abstract Background Genome-wide association studies (GWAS) have identified 370 loci associated with inflammatory bowel disease (IBD) susceptibility1. The majority of these are driven by non-coding variants, thought to dysregulate the expression of a nearby gene. The dysregulated disease effector gene, and the operative cell-type(s) is often unknown. Previous efforts to nominate effector genes have relied on identifying expression quantitative trait loci (eQTL) and expression-linked genes (eGenes). However, gene expression is often derived from healthy individuals, a single, non-primary site, and measured using bulk RNA sequencing. This likely misses cell-, site-, or disease-specific genetic effects, and has nominated disease effector genes for only a minority of IBD loci. Methods To better characterise the universe of IBD effector genes, we created IBDverse - the largest collection of single-cell RNAseq data from the terminal ileum (TI, nCD=119, nHealthy=243), rectum (nHealthy=275) and blood (nCD=95) - a total of >2.4 million high quality transcriptomes from 732 samples across 396 individuals. After grouping cells into different cell types, we mapped genetic variants associated with gene expression changes. Disease effector genes were nominated by identifying expression-linked genetic effects likely driven by the same causal variant as an IBD GWAS signal, as determined by colocalisation analysis2. Results Across all tissues, we identify 102 transcriptionally distinct cell types (Fig 1), and genetic effects associated with the expression of >17,000 genes. For 507 genes, the genetic association for their expression was strongly associated with genetic effects for IBD susceptibility, suggesting them to be drivers of disease risk. Remarkably, this doubles the previous best efforts to nominate disease effector genes3, doing so at >52% of IBD susceptibility loci. As well as their nomination, this study also helps contextualise disease effector expression variation. Disease effector genes were detectable in only a single tissue or cell annotation in >40% of cases, highlighting the need for high resolution data obtained across multiple tissues. We also find the first evidence for upregulation of RASGRP1, a gene previously correlated with exacerbated colitis in mice4, to drive Crohn’s disease susceptibility. As this is identified in a subset of tissue-resident memory T cells, and only from the TI, this also suggests this cell type and tissue as governing this effect. Conclusion This study serves as a step change in the understanding of cellular heterogeneity of these tissues, provides a compendium of genetic regulatory effects across constituent cell types, and quantifies the relevance of these to IBD susceptibility. References Liu et al., (2023). Nat. Genetics. ‘Genetic architecture of the inflammatory bowel diseases across East Asian and European ancestries’ Giambartolemi et al., (2014) PLOS Genetics. ‘Bayesian test for colocalisation between pairs of genetic association studies using summary statistics’ Panousis et al., (2024) Nat. Comms (in press). Wang et al., (2022) Nat. Comms. ‘RasGRP1 promotes the acute inflammatory response and restricts inflammation-associated cancer cell growth’

Anti-correlation of KLRG1 and PD-1 expression in human tumor CD8 T cells.

Recently, combination checkpoint therapy of cancer has been recognized as producing additive as opposed to synergistic benefit due in part to positively correlated effects. The potential for uncorrelated or negatively correlated therapies to produce true synergistic benefits has been noted. Whereas the inhibitory receptors PD-1, CTLA-4, TIM-3, LAG-3, and TIGIT have been collectively characterized as exhaustion receptors, another inhibitory receptor KLRG1 was historically characterized as a senescent receptor and received relatively little attention as a potential checkpoint inhibitor target. The anti-tumor effects of KLRG1 blockade has relatively recently been demonstrated in preclinical in vivo studies. Here, expression of the inhibitory receptors PD-1, CTLA-4, TIM-3, LAG-3, TIGIT, and KLRG1 was studied in publicly available gene expression datasets. Bulk RNA microarray and RNAseq, and single cell RNAseq data from healthy blood and tumor tissue samples were analyzed for Pearson correlation. CD8 T cell differentiation of memory T cells from the TEM to TEMRA states is characterized by PD-1/KLRG1 anti-correlation, with decreased PD-1 expression but increased KLRG1 expression. Single cell RNAseq analysis of tumor infiltrating CD8 T cells shows positive correlation of CTLA-4, TIM-3, LAG-3, TIGIT, GITR, 4-1BB, and OX40 with PD-1 but negative correlation of KLRG1 with PD-1. The anti-correlation of PD-1 and KLRG1 expression in human tumor infiltrating CD8 T cells suggests the potential for combination therapy supra-additive benefits of anti-PD-1 and anti-KLRG1 therapies.

Integration of Chromogenic RNAscope In Situ Hybridization for Target Validation in Drug Discovery.

Characterizing the expression of novel targets in normal and diseased tissues is a fundamental component of a target validation data package. Often these targets are presented to the pathology team for assessment with bulk or single-cell RNAseq data and limited to no spatial tissue expression data. In situ hybridization to detect mRNA (RNAscope) is a valuable tool to (1) identify cells that may express the target protein and to corroborate protein expression during immunohistochemical (IHC) assay development or (2) to use as surrogate for single-cell expression IHC when antibodies are not available. Chromogenic RNAscope in situ hybridization (CISH) can be performed on frozen or formalin-fixed, paraffin-embedded (FFPE) tissues. This CISH workflow starts with RNA qualification of the tissue (to assess RNA integrity) by measuring the expression of housekeeping genes. RNA-qualified tissues then undergo CISH for the target in question, and positive CISH signals are quantified in VisioPharm by a combination of color deconvolution, size gating, and dot density thresholding. This RNA workflow can complement IHC or standalone in target validation for spatial characterization of novel targets.

Cellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.

Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune microenvironment remain unclear. Three machine learning methods, namely k nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), are utilized to identify eight key HCC cell senescence markers (HCC-CSMs). Consensus clustering revealed molecular subtypes. The single-cell analysis explored the tumor microenvironment, immune checkpoints, and immunotherapy responses. In vitro, RNA interference mediated BIRC5 knockdown, and co-culture experiments assessed its impact. Cellular senescence-related genes predicted HCC survival information better than differential expression genes (DEGs). Eight key HCC-CSMs were identified, which revealed two distinct clusters with different clinical characteristics and mutation patterns. By single-cell RNA-seq data, we investigated the immunological microenvironment and observed that increasing immune cells allow hepatocytes to regain population dominance. This phenomenon may be associated with the HCC-CSMs identified in our study. By combining bulk RNA sequencing and single-cell RNA sequencing data, we identified the key gene BIRC5 and the natural killer (NK) cells that express BIRC5 at the highest levels. BIRC5 knockdown increased NK cell proliferation but reduced function, potentially aiding tumor survival. These findings provide insights into senescence-driven HCC progression and potential therapeutic targets.

Integrative genetics and multiomics analysis reveal mechanisms and therapeutic targets in vitiligo highlighting JAK STAT pathway regulation of CTSS

Vitiligo is a complex autoimmune disease characterized by the loss of melanocytes, leading to skin depigmentation. Despite advances in understanding its genetic and molecular basis, the precise mechanisms driving vitiligo remain elusive. Integrating multiple layers of omics data can provide a comprehensive view of disease pathogenesis and identify potential therapeutic targets. The study aims to delineate the genetic and molecular mechanisms of vitiligo pathogenesis using an integrative multiomics strategy. We focus on exploring the regulatory influence of the JAK/STAT pathway on Cathepsin S, a potential therapeutic target in vitiligo. Our GWAS-meta analysis pinpointed five druggable genes: ERBB3, RHOH, CDK10, MC1R, and NDUFAF3, and underwent drug target exploration and molecular docking. SMR analysis linked CTSS, CTSH, STX8, KIR2DL3, and GRHPR to vitiligo through pQTL and eQTL associations. Microarray and single-cell RNA-seq data showed differential expression of CTSS and STAT1/3 in vitiligo patients’ blood and skin lesions. Our study offers novel perspectives on vitiligo’s genetic and molecular basis, highlighting the JAK/STAT pathway’s role in regulating CTSS for antigen processing in melanocytes. Further research is needed to confirm these results and assess the therapeutic potential of CTSS and related genes.

Less is more: relative rank is more informative than absolute abundance for compositional NGS data.

High-throughput gene expression data have been extensively generated and utilized in biological mechanism investigations, biomarker detection, disease diagnosis and prognosis. These applications encompass not only bulk transcriptome, but also single cell RNA-seq data. However, extracting reliable biological information from transcriptome data remains challenging due to the constrains of Compositional Data Analysis. Current data preprocessing methods, including dataset normalization and batch effect correction, are insufficient to address these issues and improve data quality for downstream analysis. Alternatively, qualification methods focusing on the relative order of gene expression (ROGER) are more informative than the quantification methods that rely on gene expression abundance. The Pairwise Analysis of Gene expression method is an enhancement of ROGER, designed for data integration in either sample space or feature space. In this review, we summarize the methods applied to transcriptome data analysis and discuss their potentials in predicting clinical outcomes.

Integrating single-cell RNA-Seq and bulk RNA-Seq data to explore the key role of fatty acid metabolism in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a predominant cause of cancer-related mortality globally, noted for its propensity towards late-stage diagnosis and scarcity of effective treatment modalities. The process of metabolic reprogramming, with a specific emphasis on lipid metabolism, is instrumental in the progression of HCC. Nevertheless, the precise mechanisms through which lipid metabolism impacts HCC and its viability as a therapeutic target have yet to be fully elucidated. In the current investigation, single-cell RNA sequencing in conjunction with weighted gene co-expression network analysis (WGCNA) was utilized to delineate lipid metabolism-related genes correlated with the prognostic outcomes of hepatocellular carcinoma (HCC). Data procurement encompassed transcriptomic and clinical datasets from HCC patients, sourced from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories. Subsequent to this, consensus clustering analysis was implemented to stratify patients into distinct subgroups, contingent upon the expression patterns of lipid metabolism genes. Further analytical procedures involved functional enrichment analysis, evaluation of immune infiltration, and examination of the mutation landscape.PTGES3 was identified as a pivotal gene associated with lipid metabolism. Subsequent to its identification, cellular communication analysis was employed to assess the immunological attributes of PTGES3 within the tumor microenvironment. The functional role of PTGES3 was further corroborated through molecular docking simulations and in vitro experimental assays. We identified 27 genes associated with lipid metabolism, 18 of which exhibited significant correlation with overall survival in HCC patients. PTGES3 emerged as a central gene, demonstrating a robust association with immune cell infiltration and unfavorable prognosis. Cellular communication analysis revealed that PTGES3 exhibits the highest communication intensity with T cells, modulating the tumor microenvironment by potentiating the FN1/CD44 + MDK/NCL signaling pathway. Elevated expression of PTGES3 was linked to immunosuppressive cascades, diminished responsiveness to immunotherapy, and inferior overall survival outcomes. Molecular docking analysis indicated that etoposide, methotrexate, and doxorubicin could effectively bind to PTGES3. In vitro experiments confirmed that PTGES3 knockdown significantly impaired the proliferation, invasion, and migration of HCC cells. This study highlights the pivotal role of lipid metabolism in HCC progression and identifies PTGES3 as a potential prognostic biomarker and therapeutic target. These findings offer new insights into the development of targeted therapies for HCC, particularly in patients with high PTGES3 expression.

P-clustval: a novel $$p$$-adic approach for enhanced clustering of high-dimensional single-cell RNASeq data

p-clustval: a novel $$p$$-adic approach for enhanced clustering of high-dimensional single-cell RNASeq data