Single-cell RNA-seq Data Research Articles

Prediction of distinct populations of innate lymphoid cells by transcriptional profiles.

Innate lymphoid cells (ILCs) are a unique type of lymphocyte that differ from adaptive lymphocytes in that they lack antigen receptors, which primarily reside in tissues and are closely associated with fibers. Despite their plasticity and heterogeneity, identifying ILCs in peripheral blood can be difficult due to their small numbers. Accurately and rapidly identifying ILCs is critical for studying homeostasis and inflammation. To address this challenge, we collect single-cell RNA-seq data from 647 patients, including 26,087 transcripts. Background screening, Lasso analysis, and principal component analysis (PCA) are used to select features. Finally, we employ a deep neural network to classify lymphocytes. Our method achieved the highest accuracy compared to other approaches. Furthermore, we identified four genes that play a vital role in lymphocyte development. Adding these gene transcripts into model, we were able to increase the model's AUC. In summary, our study demonstrates the effectiveness of using single-cell transcriptomic analysis combined with machine learning techniques to accurately identify congenital lymphoid cells and advance our understanding of their development and function in the body.

ScTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.

Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-the-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same-cell type between prostatic cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-seq data.

Novel CAF-identifiers via transcriptomic and protein level analysis in HNSC patients

Cancer-associated fibroblasts (CAFs), a prominent component of the tumor microenvironment, play an important role in tumor development, invasion, and drug resistance. The expression of distinct “CAF-markers” which separates CAFs from normal fibroblasts and epithelial cells, have traditionally been used to identify them. These commonly used CAF-markers have been reported to differ greatly across different CAF subpopulations, even within a cancer type. Using an unbiased -omic approach from public data and in-house RNAseq data from patient derived novel CAF cells, TIMP-1, SPARC, COL1A2, COL3A1 and COL1A1 were identified as potential CAF-markers by differential gene expression analysis using publicly available single cell sequencing data and in-house RNAseq data to distinguish CAF populations from tumor epithelia and normal oral fibroblasts. Experimental validation using qPCR and immunofluorescence revealed CAF-specific higher expression of TIMP-1 and COL1A2 as compared to other markers in 5 novel CAF cells, derived from patients of diverse gender, habits and different locations of head and neck squamous cell carcinoma (HNSC). Upon immunohistochemical (IHC) analysis of FFPE blocks however, COL1A2 showed better differential staining between tumor epithelia and tumor stroma. Similar data science driven approach utilizing single cell sequencing and RNAseq data from stabilized CAFs can be employed to identify CAF-markers in various cancers.

Integrating Single-Cell RNA-Seq and Bulk RNA-Seq Data to Explore the Key Role of Fatty Acid Metabolism in Breast Cancer.

Cancer immune escape is associated with the metabolic reprogramming of the various infiltrating cells in the tumor microenvironment (TME), and combining metabolic targets with immunotherapy shows great promise for improving clinical outcomes. Among all metabolic processes, lipid metabolism, especially fatty acid metabolism (FAM), plays a major role in cancer cell survival, migration, and proliferation. However, the mechanisms and functions of FAM in the tumor immune microenvironment remain poorly understood. We screened 309 fatty acid metabolism-related genes (FMGs) for differential expression, identifying 121 differentially expressed genes. Univariate Cox regression models in The Cancer Genome Atlas (TCGA) database were then utilized to identify the 15 FMGs associated with overall survival. We systematically evaluated the correlation between FMGs' modification patterns and the TME, prognosis, and immunotherapy. The FMGsScore was constructed to quantify the FMG modification patterns using principal component analysis. Three clusters based on FMGs were demonstrated in breast cancer, with three patterns of distinct immune cell infiltration and biological behavior. An FMGsScore signature was constructed to reveal that patients with a low FMGsScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, better survival advantage, and were more sensitive to immunotherapy than those with a high FMGsScore. Finally, the expression and function of the signature key gene NDUFAB1 were examined by in vitro experiments. This study significantly demonstrates the substantial impact of FMGs on the immune microenvironment of breast cancer, and that FMGsScores can be used to guide the prediction of immunotherapy efficacy in breast cancer patients. In vitro experiments, knockdown of the NDUFAB1 gene resulted in reduced proliferation and migration of MCF-7 and MDA-MB-231 cell lines.

A cancer-associated fibroblast subtypes-based signature enables the evaluation of immunotherapy response and prognosis in bladder cancer

Bladder cancer (BLCA) is one of the most prevalent and heterogeneous urinary malignant tumors. Previous researches have reported a significant association between cancer-associated fibroblasts (CAFs) and poor prognosis of tumor patients. However, uncertainty surrounds the role of CAFs in the BLCA tumor microenvironment, necessitating further investigation into the CAFs-related gene signatures in BLCA. In this study, we identified three CAF subtypes in BLCA according to single-cell RNA-seq data and constructed CAFs-related risk score (CRRS) by screening 102,714 signatures. The survival analysis, ROC curves, and nomogram suggested that CRRS was a valuable predictor in 2,042 patients from 9 available public datasets and Xiangya real-world cohort. We further revealed the significant correlation between CRRS and clinicopathological characteristics, genome alterations, and epithelial-mesenchymal transition (EMT). A high CRRS indicated a non-inflamed phenotype and a lower remission rate of immunotherapy in BLCA. In conclusion, the CRRS had the potential to predict the prognosis and immunotherapy response of BLCA patients.

The core role of macrophages in hepatocellular carcinoma: the definition of molecular subtypes and the prognostic risk system.

Background: In patients with hepatocellular carcinoma (HCC), the tumor microenvironment (TME) is resistant to immunotherapy because of its specificity. It is meaningful to explore the role of macrophage, which is one of the most abundant immune cells in the TME, in cellular communication and its effect on the prognosis and immunotherapy of HCC. Methods: Dimensionality reduction and clustering of the single-cell RNA-seq data from the GSE149614 dataset were carried out to identify the cellular composition of HCC. CellChat was used to analyze the communication between different cells. The specifically highly expressed genes of macrophages were extracted for univariate Cox regression analysis to obtain prognostic genes for HCC cluster analysis, and the risk system of macrophage-specifically highly expressed genes was developed by random forest analysis and multivariate Cox regression analysis. Prognosis, TME infiltration, potential responses to immunotherapy, and antineoplastic drugs were compared among molecular subtypes and between risk groups. Results: We found that HCC included nine identifiable cell types, of which macrophages had the highest communication intensity with each of the other eight cell types. Of the 179 specifically highly expressed genes of macrophage, 56 were significantly correlated with the prognosis of HCC, which classified HCC into three subtypes, which were reproducible and produced different survival outcomes, TME infiltration, and immunotherapy responses among the subtypes. In the integration of four macrophage-specifically highly expressed genes for the development of a risk system, the risk score was significantly involved in higher immune cell infiltration, poor prognosis, immunotherapy response rate, and sensitivity of six drugs. Conclusion: In this study, through single-cell RNA-seq data, we identified nine cell types, among which macrophage had the highest communication intensity with the rest of the cell types. Based on specifically highly expressed genes of macrophage, we successfully divided HCC patients into three clusters with distinct prognosis, TME, and therapeutic response. Additionally, a risk system was constructed, which provided a potential reference index for the prognostic target and preclinical individualized treatment of HCC.

C-JUN is a barrier in hESC to cardiomyocyte transition.

Loss of c-JUN leads to early mouse embryonic death, possibly because of a failure to develop a normal cardiac system. How c-JUN regulates human cardiomyocyte cell fate remains unknown. Here, we used the in vitro differentiation of human pluripotent stem cells into cardiomyocytes to study the role of c-JUN. Surprisingly, the knockout of c-JUN improved cardiomyocyte generation, as determined by the number of TNNT2+ cells. ATAC-seq data showed that the c-JUN defect led to increased chromatin accessibility on critical regulatory elements related to cardiomyocyte development. ChIP-seq data showed that the knockout c-JUN increased RBBP5 and SETD1B expression, leading to improved H3K4me3 deposition on key genes that regulate cardiogenesis. The c-JUN KO phenotype could be copied using the histone demethylase inhibitor CPI-455, which also up-regulated H3K4me3 levels and increased cardiomyocyte generation. Single-cell RNA-seq data defined three cell branches, and knockout c-JUN activated more regulons that are related to cardiogenesis. In summary, our data demonstrated that c-JUN could regulate cardiomyocyte cell fate by modulating H3K4me3 modification and chromatin accessibility and shed light on how c-JUN regulates heart development in humans.

GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes

Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.

Meta-Analysis of Single-Cell RNA-Seq Data Reveals the Mechanism of Formation and Heterogeneity of Tertiary Lymphoid Organ in Vascular Disease.

Tertiary lymphoid organs (TLOs) are ectopic lymphoid organs developed in nonlymphoid tissues with chronic inflammation, but little is known about their existence in different types of vascular diseases and the mechanism that mediated their development. To take advantage of single-cell RNA sequencing techniques, we integrated 28 single-cell RNA sequencing data sets containing 5 vascular disease models (atherosclerosis, abdominal aortic aneurysm, intimal hyperplasia, isograft, and allograft) to explore TLOs existence and environment supporting its growth systematically. We also searched Medline, Embase, PubMed, and Web of Science from inception to January 2022 for published histological images of vascular remodeling for histological evidence to support TLO genesis. Accumulation and infiltration of innate and adaptive immune cells have been observed in various remodeling vessels. Interestingly, the proportion of such immune cells incrementally increases from atherosclerosis to intimal hyperplasia, abdominal aortic aneurysm, isograft, and allograft. Importantly, we uncovered that TLO structure cells, such as follicular helper T cells and germinal center B cells, present in all remodeled vessels. Among myeloid cells and lymphocytes, inflammatory macrophages, and T helper 17 cells are the major lymphoid tissue inducer cells which were found to be positively associated with the numbers of TLO structural cells in remodeled vessels. Vascular stromal cells also actively participate in vascular TLO genesis by communicating with myeloid cells and lymphocytes via CCLs (C-C motif chemokine ligands), CXCL (C-X-C motif ligand), lymphotoxin, BMP (bone morphogenetic protein) chemotactic, FGF-2 (fibroblast growth factor-2), and IGF (insulin growth factor) proliferation mechanisms, particularly for lymphoid tissue inducer cell aggregation. Additionally, the interaction between stromal cells and immune cells modulates extracellular matrix remodeling. Among TLO structure cells, follicular helper T, and germinal center B cells have strong interactions via TCR (T-cell receptor), CD40 (cluster of differentiation 40), and CXCL signaling, to promote the development and maturation of the germinal center in TLO. Consistently, by reviewing the histological images from the literature, TLO genesis was found in those vascular remodeling models. Our analysis showed the existence of TLOs across 5 models of vascular diseases. The mechanisms that support TLOs formation in different models are heterogeneous. This study could be a valuable resource for understanding and discovering new therapeutic targets for various forms of vascular disease.

LIDER: cell embedding based deep neural network classifier for supervised cell type identification.

Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation. Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier. LIDER was developed upon a stacked denoising autoencoder to learn encoder-decoder structures for identifying cell embedding. LIDER accurately identifies cell types by using stacked denoising autoencoder. Benchmarking against state-of-the-art methods across eight types of single-cell data, LIDER achieves comparable or even superior enhancement performance. Moreover, LIDER suggests comparable robust to batch effects. Our results show a potential in deep supervised learning for automatic cell type identification of single-cell RNA-seq data. The LIDER codes are available at https://github.com/ShiMGLab/LIDER.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq to unravel the molecular mechanisms underlying the immune microenvironment in the development of intestinal-type gastric cancer

Intestinal-type gastric cancer (IGC) is the most frequent type of gastric cancer in high-incidence populations. The early stages of IGC growth successively include nonatrophic gastritis (NAG), chronic atrophic gastritis (CAG) and intestinal metaplasia (IM). However, the mechanisms of IGC development through these stages remain unclear. For this study, single-cell RNA-seq data related to IGC were downloaded from the GEO database, and immune cells of the tumor microenvironment (TME) were annotated using R software. Changes in the proportion of immune cells and altered cell-to-cell interactions were explored at different disease stages using R software, with a focus on plasma cells. Additionally, IGC samples from the TCGA database were used for immune cell infiltration analysis, and a Cox proportional risk regression model was constructed to identify possible prognostic genes. The results indicated that for precancerous lesions, interactions between immune cells were mainly dominated by chemokines to stimulate the infiltration and activation of immune cells. In tumors, intercellular movement of upregulated molecules and amplified signals were associated with the tumor necrosis factor family and immunosuppression to escape immune surveillance and promote tumor growth. Regarding prognostic analysis, IGLC3, IGLV1–44, IGKV1–16, IGHV3–21, IGLV1–51, and IGLV3–19 were found to be novel biomarkers for IGC. Our analysis of the IGC single-cell atlas together with bulk transcriptome data contributes to understanding TME heterogeneity at the molecular level during IGC development and provides insights for elucidating the mechanism of IGC and discovering novel targets for precise therapy.

Dissecting and improving gene regulatory network inference using single-cell transcriptome data.

Single-cell transcriptome data has been widely used to reconstruct gene regulatory networks (GRNs) controlling critical biological processes such as development and differentiation. Although a growing list of algorithms has been developed to infer GRNs using such data, achieving an inference accuracy consistently higher than random guessing has remained challenging. To address this, it is essential to delineate how the accuracy of regulatory inference is limited. Here, we systematically characterized factors limiting the accuracy of inferred GRNs and demonstrated that using pre-mRNA information can help improve regulatory inference compared to the typically used information (i.e., mature mRNA). Using kinetic modeling and simulated single-cell data sets, we showed that target genes' mature mRNA levels often fail to accurately report upstream regulatory activities because of gene-level and network-level factors, which can be improved by using pre-mRNA levels. We tested this finding on public single-cell RNA-seq data sets using intronic reads as proxies of pre-mRNA levels and can indeed achieve a higher inference accuracy compared to using exonic reads (corresponding to mature mRNAs). Using experimental data sets, we further validated findings from the simulated data sets and identified factors such as transcription factor activity dynamics influencing the accuracy of pre-mRNA-based inference. This work delineates the fundamental limitations of gene regulatory inference and helps improve GRN inference using single-cell RNA-seq data.

Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux’s capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.

Single-cell RNA sequencing reveals blastomere heterogeneity of 2-cell embryos in pigs.

In mammals, single blastomeres from as early as 2-cell embryos demonstrate heterogeneous developmental capacity and fate decision into different cell lineages. However, mechanisms underlying blastomere heterogeneity of 2-cell embryos remain largely unresolved. Here, we analysed the molecular heterogeneity of full-length mRNAs and their 3'UTR regions, based on the single-cell RNA-seq data of pig 2-cell embryos generated from in vivo fertilization (in vivo), in vitro fertilization (in vitro) and parthenogenetic activation (PA), respectively. First, unsupervised clustering helped discover two different groups of blastomeres for 2-cell pig embryos. Between these two groups of blastomeres in pig 2-cell embryos, 35, 301 and 428 full-length mRNAs respectively in in vivo, in vitro and PA embryo types were identified to be differentially expressed (padj ≤ .05 and |log2 [fold change]| ≥1) (DE mRNAs), while 92, 89 and 42 mRNAs were shown to be with significantly different 3'UTR lengths (3'UTR DE) (padj ≤ .05). Gene enrichment for both DE mRNAs and 3'UTR DE mRNAs found multiple signalling pathways, including cell cycle, RNA processing. Few numbers of common DE mRNAs and 3'UTR DE mRNAs existed between in vitro and in vivo blastomeres derived from 2-cell embryos, indicating the larger differences between in vitro and in vivo fertilized embryos. Integrative genomics viewer analysis further identified that 3'UTRs of HSDL2 and SGTA (in vivo), FAM204A and phosphoserine phosphatase (in vitro), PRPF40A and RPIA (PA) had >100 nt average length changes. Moreover, numbers and locations of regulatory elements (polyadenylation site, cytoplasmic polyadenylation element and microRNA binding sites) within 3'UTRs of these DE mRNAs were predicted. These results indicate that molecular heterogeneity existed among blastomeres from different types of pig 2-cell embryos, providing useful information and novel insights into future functional investigation on its relationship with the subsequent embryo development and differentiation.

SARS-CoV-2 infection perturbs enhancer mediated transcriptional regulation of key pathways.

Despite extensive studies on the effects of SARS-CoV-2 infection, there is still a lack of understanding of the downstream epigenetic and regulatory alterations in infected cells. In this study, we investigated changes in enhancer acetylation in epithelial lung cells infected with SARS-CoV-2 and their influence on transcriptional regulation and pathway activity. To achieve this, we integrated and reanalyzed data of enhancer acetylation, ex-vivo infection and single cell RNA-seq data from human patients. Our findings revealed coordinated changes in enhancers and transcriptional networks. We found that infected cells lose the WT1 transcription factor and demonstrate disruption of WT1-bound enhancers and of their associated target genes. Downstream targets of WT1 are involved in the regulation of the Wnt signaling and the mitogen-activated protein kinase cascade, which indeed exhibit increased activation levels. These findings may provide a potential explanation for the development of pulmonary fibrosis, a lethal complication of COVID-19. Moreover, we revealed over-acetylated enhancers associated with upregulated genes involved in cell adhesion, which could contribute to cell-cell infection of SARS-CoV-2. Furthermore, we demonstrated that enhancers may play a role in the activation of pro-inflammatory cytokines and contribute to excessive inflammation in the lungs, a typical complication of COVID-19. Overall, our analysis provided novel insights into the cell-autonomous dysregulation of enhancer regulation caused by SARS-CoV-2 infection, a step on the path to a deeper molecular understanding of the disease.

Disease-associated astrocytes and microglia markers are upregulated in mice fed high fat diet

High-fat diet (HFD) is associated with Alzheimer’s disease (AD) and type 2 diabetes risk, which share features such as insulin resistance and amylin deposition. We examined gene expression associated with astrocytes and microglia since dysfunction of these cell types is implicated in AD pathogenesis. We hypothesize gene expression changes in disease-associated astrocytes (DAA), disease-associated microglia and human Alzheimer’s microglia exist in diabetic and obese individuals before AD development. By analyzing bulk RNA-sequencing (RNA-seq) data generated from brains of mice fed HFD and humans with AD, 11 overlapping AD-associated differentially expressed genes were identified, including Kcnj2, C4b and Ddr1, which are upregulated in response to both HFD and AD. Analysis of single cell RNA-seq (scRNA-seq) data indicated C4b is astrocyte specific. Spatial transcriptomics (ST) revealed C4b colocalizes with Gfad, a known astrocyte marker, and the colocalization of C4b expressing cells with Gad2 expressing cells, i.e., GABAergic neurons, in mouse brain. There also exists a positive correlation between C4b and Gad2 expression in ST indicating a potential interaction between DAA and GABAergic neurons. These findings provide novel links between the pathogenesis of obesity, diabetes and AD and identify C4b as a potential early marker for AD in obese or diabetic individuals.

Interleukin-34-NF-κB signaling aggravates myocardial ischemic/reperfusion injury by facilitating macrophage recruitment and polarization

Macrophage infiltration and polarization are integral to the progression of heart failure and cardiac fibrosis after ischemia/reperfusion (IR). Interleukin 34 (IL-34) is an inflammatory regulator related to a series of autoimmune diseases. Whether IL-34 mediates inflammatory responses and contributes to cardiac remodeling and heart failure post-IR remains unclear. IL-34 knock-out mice were used to determine the role of IL-34 on cardiac remodeling after IR surgery. Then, immunofluorescence, flow cytometry assays, and RNA-seq analysis were performed to explore the underlying mechanisms of IL-34-induced macrophage recruitment and polarization, and further heart failure after IR. By re-analyzing single-cell RNA-seq and single-nucleus RNA-seq data of murine and human ischemic hearts, we showed that IL-34 expression was upregulated after IR. IL-34 knockout mitigated cardiac remodeling, cardiac dysfunction, and fibrosis after IR and vice versa. RNA-seq analysis revealed that IL-34 deletion correlated negatively with immune responses and chemotaxis after IR injury. Consistently, immunofluorescence and flow cytometry assays demonstrated that IL-34 deletion attenuated macrophage recruitment and CCR2+ macrophage polarization. Mechanistically, IL-34 deficiency repressed both the canonical and noncanonical NF-κB signaling pathway, leading to marked reduction of P-IKKβ and P-IκBα kinase levels; downregulation of NF-κB p65, RelB, and p52 expression, which drove the decline in chemokine CCL2 expression. Finally, IL-34 and CCL2 levels were increased in the serum of acute coronary syndrome patients, with a positive correlation between circulating IL-34 and CCL2 levels in clinical patients. In conclusion, IL-34 sustains NF-κB pathway activation to elicit increased CCL2 expression, which contributes to macrophage recruitment and polarization, and subsequently exacerbates cardiac remodeling and heart failure post-IR. Strategies targeting IL-34-centered immunomodulation may provide new therapeutic approaches to prevent and reverse cardiac remodeling and heart failure in clinical MI patients after percutaneous coronary intervention. This study was supported by the National Nature Science Foundation of China (81670352 and 81970327 to R T, 82000368 to Q F).

Transcriptome-wide association study-derived genes as potential visceral adipose tissue-specific targets for type 2 diabetes.

This study aimed to assess the causal relationship between visceral obesity and type 2 diabetes and subsequently to screen visceral adipose tissue (VAT)-specific targets for type 2 diabetes. We examined the causal relationship between VAT and type 2 diabetes using bidirectional Mendelian randomisation (MR) followed by multivariable MR. We conducted a transcriptome-wide association study (TWAS) leveraging prediction models and a large-scale type 2 diabetes genome-wide association study (74,124 cases and 824,006 controls) to identify candidate genes in VAT and used summary-data-based MR (SMR) and co-localisation analysis to map causal genes. We performed enrichment and single-cell RNA-seq analyses to determine the cell-specific localisation of the TWAS-identified genes. We also conducted knockdown experiments in 3T3-L1 pre-adipocytes. MR analyses showed a causal relationship between genetically increased VAT mass and type 2 diabetes (inverse-variance weighted OR 2.48 [95% CI 2.21, 2.79]). Ten VAT-specific candidate genes were associated with type 2 diabetes after Bonferroni correction, including five causal genes supported by SMR and co-localisation: PABPC4 (1p34.3); CCNE2 (8q22.1); HAUS6 (9p22.1); CWF19L1 (10q24.31); and CCDC92 (12q24.31). Combined with enrichment analyses, clarifying cell-type specificity with single-cell RNA-seq data indicated that most TWAS-identified candidate genes appear more likely to be associated with adipocytes in VAT. Knockdown experiments suggested that Pabpc4 likely contributes to regulating differentiation and energy metabolism in 3T3-L1 adipocytes. Our findings provide new insights into the genetic basis and biological processes of the association between VAT accumulation and type 2 diabetes and warrant investigation through further functional studies to validate these VAT-specific candidate genes.

Integration of single-cell sequencing and bulk expression data reveals chemokine signaling pathway in proliferating cells is associated with the survival outcome of osteosarcoma

BackgroundOsteosarcoma, as the most common primary bone malignancy, is urgent to be well-studied on the biomarkers and therapeutic targets to improve the five-year survival rate. Transcriptomic analysis using single-cell RNA or bulk RNA sequencing has been developed to detect biomarkers in various cancer types.Methods and resultsWe applied Scissor to combine single-cell RNA-seq data and bulk transcriptome data of osteosarcoma, providing cell-level information and sample phenotypes to identify the survival-associated cell subpopulations. By investigating the differences between the survival-associated cell subpopulations, we identified CCL21, CCL22, CCL24, CXCL11, CXCL12, CXCL13, GNAI2, and RAC2 in the proliferating cells that are significantly associated with osteosarcoma patient outcome. Then we assigned the risk score for each sample based on the cell proportion-normalized gene expression and validated it in the public dataset.ConclusionsThis study provides the clinical insight that chemokine signaling pathway genes (CCL21, CCL22, CCL24, CXCL11, CXCL12, CXCL13, GNAI2, and RAC2) in proliferating cells might be the potential biomarkers for treatment of osteosarcoma.

Location of CD39+ T cell subpopulations within tumors predict differential outcomes in non-small cell lung cancer

PurposeAn improved mechanistic understanding of immunosuppressive pathways in non-small cell lung cancer (NSCLC) is important to develop novel diagnostic and therapeutic approaches. Here, we investigate the prognostic significance of the...