Single-cell Datasets Research Articles

Bulk and single-cell transcriptome datasets of the mouse fetal and adult rete ovarii and surrounding tissues

The rete ovarii (RO) is an epithelial structure that arises during development in close proximity to the ovary and persists throughout adulthood. However, the functional significance of the RO remains elusive, and it is absent from recent discussions of female reproductive anatomy. The RO comprises three regions: the intraovarian rete within the ovary, the extraovarian rete in the periovarian tissue, and the connecting rete linking the two. We hypothesize that the RO plays a pivotal role in ovarian homeostasis and responses to physiological changes. To begin to uncover the nature and function of RO cells, we conducted transcriptomic profiling of the RO. This study presents three datasets, and reports our analysis and quality control approaches for bulk, single-cell, and nucleus-level transcriptomics of the fetal and adult RO tissues using the Pax8-rtTA; Tre-H2B-GFP mouse line, where all RO regions express nuclear GFP. The integration and rigorous validation of these datasets will advance our understanding of the RO’s roles in ovarian development, female maturation, and adult female fertility.

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

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

The integration of single-cell and bulk RNA-seq atlas reveals ERS-mediated acinar cell damage in acute pancreatitis

BackgroundAcute pancreatitis (AP) is a clinically common acute abdominal disease, whose pathogenesis remains unclear. The severe patients usually have multiple complications and lack specific drugs, leading to a high mortality and poor outcome. Acinar cells are recognized as the initial site of AP. However, there are no precise single-cell transcriptomic profiles to decipher the landscape of acinar cells during AP, which are the missing pieces of jigsaw we aimed to complete in this study.MethodsA single-cell sequencing dataset was used to identify the cell types in pancreas of AP mice and to depict the transcriptomic maps in acinar cells. The pathways’ activities were evaluated by gene sets enrichment analysis (GSEA) and single-cell gene sets variation analysis (GSVA). Pseudotime analysis was performed to describe the development trajectories of acinar cells. We also constructed the protein–protein interaction (PPI) network and identified the hub genes. Another independent single-cell sequencing dataset of pancreas samples from AP mice and a bulk RNA sequencing dataset of peripheral blood samples from AP patients were also analyzed.ResultsIn this study, we identified genetic markers of each cell type in the pancreas of AP mice based on single-cell sequencing datasets and analyzed the transcription changes in acinar cells. We found that acinar cells featured acinar-ductal metaplasia (ADM), as well as increased endocytosis and vesicle transport activity during AP. Notably, the endoplasmic reticulum stress (ERS) and ER-associated degradation (ERAD) pathways activated by accumulation of unfolded/misfolded proteins in acinar cells could be pivotal for the development of AP.ConclusionWe deciphered the distinct roadmap of acinar cells in the early stage of AP at single-cell level. ERS and ERAD pathways are crucially important for acinar homeostasis and the pathogenesis of AP.

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

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

PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization.

Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of uncertainty. Here we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single-cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships. Using variational inference allows hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using real single-cell datasets and synthetic data. We show that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates.

Scholarly publications and data set evidence for the Human Reference Atlas

Abstract Experts from 17 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the human body at single cell resolution. To bridge across scales—from the meter size human body to the micrometer size single-cell level—organ experts are constructing anatomical structures, cell types plus biomarkers (ASCT+B) tables, and associated spatial reference objects. The 3rd HRA (v1.2) release features 26 organ-specific ASCT+B tables that cite 456 scholarly papers and are linked to 61 spatial reference objects and Organ Mapping Antibody Panels (OMAPs); it is authored by more than 120 experts. This paper presents the first analyses and visualizations showcasing what data and scholarly evidence exist for which organs and how experts relate to the organs covered in the HRA. To identify potential HRA authors and reviewers, we queried the Web of Science database for authors who work on the 33 organs targeted for the next HRA release (v1.3). To provide scientific evidence for the HRA, we identified 620 high-quality, single-cell experimental data sets for 58 organs published in 561 unique papers. The results presented are critical for understanding and communicating the quality of the HRA, planning for future tissue data collection, and inviting leading experts to contribute to the evolving atlas.

Abstract LB338: Targeted deletion of CD244 on monocytes promotes differentiation into anti-tumorigenic macrophages and potentiates PD-L1 blockade in melanoma

Abstract Background: In the myeloid compartment of the tumor microenvironment, CD244 signaling has been implicated in immunosuppressive phenotype of monocytes. However, the precise molecular mechanism and contribution of CD244 to tumor immunity in monocytes/macrophages remains elusive due to the co-existing lymphoid cells expressing CD244. Methods: To directly assess the role of CD244 in tumor-associated macrophages, monocyte-lineage-specific CD244-deficient mice were generated using cre-lox recombination and challenged with B16F10 melanoma. The phenotype and function of tumor-infiltrating macrophages along with antigen-specific CD8 T cells were analyzed by flow cytometry and single cell RNA sequencing data analysis, and the molecular mechanism underlying anti-tumorigenic macrophage differentiation, antigen presentation, phagocytosis was investigated ex vivo. Finally, the clinical feasibility of CD244-negative monocytes as a therapeutic modality in melanoma was confirmed by adoptive transfer experiments. Results: CD244fl/flLysMcre mice demonstrated a significant reduction in tumor volume (61% relative to that of the CD244fl/fl control group) 14 days after tumor implantation. Within tumor mass, CD244fl/flLysMcre mice also showed higher percentages of Ly6Clow macrophages, along with elevated gp100+IFN-γ+ CD8 T cells. Flow cytometry and RNA sequencing data demonstrated that ER stress resulted in increased CD244 expression on monocytes. This, in turn, impeded the generation of anti-tumorigenic Ly6Clow macrophages, phagocytosis and MHC-I antigen presentation by suppressing autophagy pathways. Combining anti-PD-L1 antibody with CD244-/- bone marrow-derived macrophages markedly improved tumor rejection compared to the anti-PD-L1 antibody alone or in combination with wild-type macrophages. Consistent with the murine data, transcriptome analysis of human melanoma tissue single-cell RNA-sequencing dataset revealed close association between CD244 and the inhibition of macrophage maturation and function. Furthermore, the presence of CD244-negative monocytes/macrophages significantly increased patient survival in primary and metastatic tumors. Conclusion: Our study highlights the novel role of CD244 on monocytes/macrophages in restraining anti-tumorigenic macrophage generation and tumor antigen-specific T cell response in melanoma. Importantly, our findings suggest that CD244-deficient macrophages could potentially be used as a therapeutic agent in combination with immune checkpoint inhibitors. Furthermore, CD244 expression in monocyte-lineage cells serve as a prognostic marker in cancer patients. Citation Format: Jeongsoo Kim, Tae-Jin Kim, Sehyun Chae, Hyojeong Ha, Yejin Park, Seon Ah Lim, Hyemin Lee, Younji Sim, Seyoung Oh, Geon Mo Koo, Minkyung Jo, Daehee Hwang, Kyung-Mi Lee. Targeted deletion of CD244 on monocytes promotes differentiation into anti-tumorigenic macrophages and potentiates PD-L1 blockade in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB338.

Abstract SY39-03: Illuminating the tumor immune GPCRome: Novel immunotherapy targets

Abstract Immune checkpoint blockade (ICB) targeting PD-1 and CTLA-4 has revolutionized cancer treatment. However, many cancers do not respond to ICB, prompting the search for additional strategies to achieve durable cancer remission. In this regard, G-protein-coupled receptors (GPCRs) represent the largest family of cell surface receptors involved in signal transduction and are the target of >30% of all approved drugs. GPCRs are the most pharmacologically favorable targets due to their druggability and direct relevance to many key physiological processes and a myriad of human diseases. However, GPCRs have been largely underexploited in oncology, and specifically understudied in cancer immunotherapy. GPCRs are primarily coupled to 4 distinct families of G proteins α subunit (Gαi, Gαs, Gαq, and Gα12), which dictate the signaling events controlled by each GPCR. Recently, we cross-integrated large single-cell RNA-sequencing datasets from CD8+ T cells covering 19 distinct cancer types and identified an enrichment of Gαs-coupled GPCRs on exhausted CD8+ T cells. These include GPCRs activated by inflammatory mediators, protons, neurotransmitters, and metabolites that accumulate in the tumor microenvironment, thereby promoting T cell dysfunction. We also developed transgenic mice expressing a chemogenetic CD8-restricted Gαs-GPCR and showed that a Gαs-PKA signaling axis promotes CD8+ T cell exhaustion and immunotherapy failure. These studies revealed that tumor cells may avoid immune elimination by stimulating Gαs-GPCRs on infiltrating CD8 T cells, thereby exposing novel druggable immune checkpoints that can be exploited therapeutically. We have recently extended these studies and mapped the expression patterns of all human GPCRs in nearly a million-tumor infiltrating immune cells. The emerging tumor-immune GPCR atlas may provide a valuable resource to explore the role of GPCRs in cancer immunology. Ultimately, our study may reveal new and exciting GPCR therapeutic targets to enhance the response to ICB, and help identify potential co-medications that may limit the success of ICB therapies. Citation Format: J. Silvio Gutkind. Illuminating the tumor immune GPCRome: Novel immunotherapy targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY39-03.

Abstract LB242: Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome

Abstract Introduction: The tumor microenvironment (TME) is a complex and dynamic ecosystem that plays critical roles in tumor development and clinical outcome. While the multifarious cellular interactions within the TME have been extensively studied with a significant emphasis on immunotherapy, understanding its role in chemotherapy outcome remains less explored. To this end, we present a novel generic computational framework named DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning). DECODEM facilitates the exploration of the association of cell-type-specific gene expression profiles with the effectiveness of neoadjuvant chemotherapy (NAC) in treating breast cancer (BC). This approach holds promise for elucidating the nuanced ways in which the TME can modulate therapeutic efficacy, potentially paving the way for more personalized and effective cancer treatments. Methods: DECODEM employs cellular deconvolution of bulk transcriptomics to extract cell-type-specific gene expression profiles within the TME, and subsequently harnesses machine learning to construct cell-type-specific predictors of clinical outcomes. Utilizing DECODEM, we analyzed three publicly available cohorts of HER2-negative BC patients who received NAC. We assessed the predictive capabilities of the individual cell types as well as their collective influence, benchmarking against a state-of-the-art predictor that employs both clinical and transcriptomic features. We corroborated our findings in a single-cell (SC) transcriptomics cohort of patients with triple negative breast cancer (TNBC) undergoing chemotherapy. Further, we expanded DECODEM to DECODEMi, which accounts for cell-cell interactions (CCIs) among the predominant cell types, offering potential insights into the mechanistic processes governing clinical responses. Results: Our analysis indicated that the heterogeneous composition of the TME is crucial for chemotherapy response prediction in HER2-negative BC, beyond the influence of malignant cells alone. The key insights from our study are as follows:1. Gene expression of immune cells (myeloid, plasmablasts, B-cells) and stromal cells (endothelial, normal epithelial, cancer-associated fibroblasts) displayed comparable-to-superior predictive powers over both bulk expression and the state-of-the-art predictor.2. The predictive accuracy is further augmented by multi-cell-type ensembles, with a triad ensemble comprising endothelial, myeloid, and plasmablast cells achieving the highest predictive accuracy across multiple cohorts.3. DECODEMi can effectively capture the regulatory impact of CCIs (validated in a SC dataset sourced from the SC-TNBC cohort) and has identified novel CCIs likely to influence chemotherapy response in HER2-negative BC. Conclusion: We provide an unprecedented computational methodology to quantitatively assess the cell-type-specific influences of the TME on clinical outcome. Our analysis offers insights into the complex interactions among TME cells and their correlation with chemotherapy efficacy in HER2-negative BC, thereby enriching the knowledge base of BC treatment effectiveness. Citation Format: Saugato Rahman Dhruba, Sahil Sahni, Binbin Wang, Di Wu, Yael Schmidt, Eldad Shulman, Sanju Sinha, Stephen-John Sammut, Carlos Caldas, Kun Wang, Eytan Ruppin. Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB242.

Single-cell transcriptome sequencing analysis reveals intra-tumor heterogeneity in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignant tumor of the digestive system that poses a significant threat to human life and health. It is crucial to thoroughly investigate the mechanisms of esophageal carcinogenesis and identify potential key molecular events in its carcinogenesis. Single-cell transcriptome sequencing is an emerging technology that has gained prominence in recent years for studying molecular mechanisms, which may help to further explore the underlying mechanisms of the ESCC tumor microenvironment in depth. The single-cell dataset was obtained from GSE160269 in the Gene Expression Omnibus database, including 60 tumor samples and four paracancer samples. The single-cell data underwent dimensional reduction clustering analysis to identify clusters and annotate expression profiles. Subcluster analysis was conducted for each cellular taxon. Copy number variation analysis of tumor cell subpopulations was performed to primarily identify malignant cells within them. A proposed chronological analysis was performed to obtain the process of cell differentiation. In addition, cell communication, transcription factor analysis, and tumor pathway analysis were also performed. Relevant risk models and key genes were established by univariate COX regression and LASSO analysis. The key genes obtained from the screen were subjected to appropriate silencing and cellular assays, including CCK-8, 5-ethynyl-2'-deoxyuridine, colony formation, and western blot. Single-cell analysis revealed that normal samples contained a large number of fibroblasts, T cells, and B cells, with fewer other cell types, whereas tumor samples exhibited a relatively balanced distribution of cell types. Subclassification analysis of immune cells, fibroblasts, endothelial cells, and epithelial cells revealed their specific spatial characteristics. The prognostic risk model, we constructed successfully, achieved accurate prognostic stratification for ESCC patients. The screened key gene, UPF3A, was found to be significantly associated with the development of ESCC by cellular assays. This process might be linked to the phosphorylation of ERK and P38. Single-cell transcriptome analysis successfully revealed the distribution of cell types and major expressed factors in ESCC patients, which could facilitate future in-depth studies on the therapeutic mechanisms of ESCC.

GPR65 sensing tumor-derived lactate induces HMGB1 release from TAM via the cAMP/PKA/CREB pathway to promote glioma progression

BackgroundLactate has emerged as a critical regulator within the tumor microenvironment, including glioma. However, the precise mechanisms underlying how lactate influences the communication between tumor cells and tumor-associated macrophages (TAMs), the most abundant immune cells in glioma, remain poorly understood. This study aims to elucidate the impact of tumor-derived lactate on TAMs and investigate the regulatory pathways governing TAM-mediated tumor-promotion in glioma.MethodsBioinformatic analysis was conducted using datasets from TCGA and CGGA. Single-cell RNA-seq datasets were analyzed by using UCSC Cell Browser and Single Cell Portal. Cell proliferation and mobility were evaluated through CCK8, colony formation, wound healing, and transwell assays. Western blot and immunofluorescence staining were applied to assess protein expression and cell distribution. RT-PCR and ELISA were employed to identify the potential secretory factors. Mechanistic pathways were explored by western blotting, ELISA, shRNA knockdown, and specific inhibitors and activators. The effects of pathway blockades were further assessed using subcutaneous and intracranial xenograft tumor models in vivo.ResultsElevated expressions of LDHA and MCT1 were observed in glioma and exhibited a positive correlation with M2-type TAM infiltration. Lactate derived from glioma cells induced TAMs towards M2-subtype polarization, subsequently promoting glioma cells proliferation, migration, invasion, and mesenchymal transition. GPR65, highly expressed on TAMs, sensed lactate-stimulation in the TME, fueling glioma cells malignant progression through the secretion of HMGB1. GPR65 on TAMs triggered HMGB1 release in response to lactate stimulation via the cAMP/PKA/CREB signaling pathway. Disrupting this feedback loop by GPR65-knockdown or HMGB1 inhibition mitigated glioma progression in vivo.ConclusionThese findings unveil the intricate interplay between TAMs and tumor cells mediated by lactate and HMGB1, driving tumor progression in glioma. GPR65, selectively highly expressed on TAMs in glioma, sensed lactate stimulation and fostered HMGB1 secretion via the cAMP/PKA/CREB signaling pathway. Blocking this feedback loop presents a promising therapeutic strategy for GBM.

SingmiR: a single-cell miRNA alignment and analysis tool.

Single-cell RNA sequencing(RNA-seq) has revolutionized our understanding of cell biology, developmental and pathophysiological molecular processes, paving the way toward novel diagnostic and therapeutic approaches. However, most of the gene regulatory processes on the single-cell level are still unknown, including post-transcriptional control conferred by microRNAs (miRNAs). Like the established single-cell gene expression analysis, advanced computational expertise is required to comprehensively process newly emerging single-cell miRNA-seq datasets. A web server providing a workflow tailored for single-cell miRNA-seq data with a self-explanatory interface is currently not available. Here, we present SingmiR, enabling the rapid (pre-)processing and quantification of human miRNAs from noncoding single-cell samples. It performs read trimming for different library preparation protocols, generates automated quality control reports and provides feature-normalized count files. Numerous standard and advanced analyses such as dimension reduction, clustered feature heatmaps, sample correlation heatmaps and differential expression statistics are implemented. We aim to speed up the prototyping pipeline for biologists developing single-cell miRNA-seq protocols on small to medium-sized datasets. SingmiR is freely available to all users without the need for a login athttps://www.ccb.uni-saarland.de/singmir.

Intestinal cell diversity and treatment responses in a parasitic nematode at single cell resolution

BackgroundParasitic nematodes, significant pathogens for humans, animals, and plants, depend on diverse organ systems for intra-host survival. Understanding the cellular diversity and molecular variations underlying these functions holds promise for developing novel therapeutics, with specific emphasis on the neuromuscular system’s functional diversity. The nematode intestine, crucial for anthelmintic therapies, exhibits diverse cellular phenotypes, and unraveling this diversity at the single-cell level is essential for advancing knowledge in anthelmintic research across various organ systems.ResultsHere, using novel single-cell transcriptomics datasets, we delineate cellular diversity within the intestine of adult female Ascaris suum, a parasitic nematode species that infects animals and people. Gene transcripts expressed in individual nuclei of untreated intestinal cells resolved three phenotypic clusters, while lower stringency resolved additional subclusters and more potential diversity. Clusters 1 and 3 phenotypes displayed variable congruence with scRNA phenotypes of C. elegans intestinal cells, whereas the A. suum cluster 2 phenotype was markedly unique. Distinct functional pathway enrichment characterized each A. suum intestinal cell cluster. Cluster 2 was distinctly enriched for Clade III-associated genes, suggesting it evolved within clade III nematodes. Clusters also demonstrated differential transcriptional responsiveness to nematode intestinal toxic treatments, with Cluster 2 displaying the least responses to short-term intra-pseudocoelomic nematode intestinal toxin treatments.ConclusionsThis investigation presents advances in knowledge related to biological differences among major cell populations of adult A. suum intestinal cells. For the first time, diverse nematode intestinal cell populations were characterized, and associated biological markers of these cells were identified to support tracking of constituent cells under experimental conditions. These advances will promote better understanding of this and other parasitic nematodes of global importance, and will help to guide future anthelmintic treatments.

Single cell RNA-sequencing reveals the cellular senescence of placental mesenchymal stem/stromal cell in preeclampsia

IntroductionPreeclampsia (PE) is a severe obstetric complication closely associated with placental dysfunction. Placental mesenchymal stem/stromal cells (PMSCs) modulate placental development while PE PMSCs are excessively senescent to disturb placental function. Nevertheless, the senescence mechanism of PE PMSCs remains unclear. MethodsPE-related single-cell RNA sequencing datasets (GSE173193), data of chip detection (GSE99007) and bulk transcriptome RNA sequencing datasets (GSE75010) were extracted from the GEO database. Firstly, the functional enrichment analyses of the differentially expressed genes (DEGs) in PMSCs were conducted. Then, the clusters of PE PMSCs were distinguished according to the expressions of senescence-related genes (SRGs) by consensus clustering analysis. Cell cycle analysis, senescence β-galactosidase, Transwell, and tube formation were conducted. Next, the expressions of the senescence-associated secretory phenotype (SASPs) were displayed. The characteristic genes of PE were screened by the least absolute shrinkage and selection operator analysis. CTSZ was suppressed in PMSCs and the cellular senescence levels were evaluated. ResultsIn this study, The DEGs in PMSCs were closely associated with cellular senescence. The score of SRGs was significantly higher and most of the SASPs were abnormally expressed in the senescent group. Seven characteristic genes of PE were identified, thereinto, CTSZ reduction may accelerate the senescence in PMSCs in vitro. DiscussionCombined with bioinformatic analysis and lab experiments, our study emphatically revealed the abnormal cellular senescence in PE PMSCs, in which CTSZ, one of the PE characteristic genes, regulated the cellular senescence levels in PMSCs. These findings might help to deepen the understanding of the senescence mechanism of PMSCs in PE.

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

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

Aortic Cellular Heterogeneity in Health and Disease: Novel Insights Into Aortic Diseases From Single-Cell RNA Transcriptomic Data Sets.

Aortic diseases such as atherosclerosis, aortic aneurysms, and aortic stiffening are significant complications that can have significant impact on end-stage cardiovascular disease. With limited pharmacological therapeutic strategies that target the structural changes in the aorta, surgical intervention remains the only option for some patients with these diseases. Although there have been significant contributions to our understanding of the cellular architecture of the diseased aorta, particularly in the context of atherosclerosis, furthering our insight into the cellular drivers of disease is required. The major cell types of the aorta are well defined; however, the advent of single-cell RNA sequencing provides unrivaled insights into the cellular heterogeneity of each aortic cell type and the inferred biological processes associated with each cell in health and disease. This review discusses previous concepts that have now been enhanced with recent advances made by single-cell RNA sequencing with a focus on aortic cellular heterogeneity.

Characterization of a G2M checkpoint-related gene model and subtypes associated with immunotherapy response for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) presents challenges in early diagnosis and effective treatment. In this study, we aimed to establish a prognostic model based on G2M checkpoint-related genes and identify associated clusters in ccRCC through clinical bioinformatic analysis and experimental validation. Utilizing a single-cell RNA dataset (GSE159115) and bulk-sequencing data from The Cancer Genome Atlas (TCGA) database, we analyzed the G2M checkpoint pathway in ccRCC. Differential expression analysis identified 45 genes associated with the G2M checkpoint, leading to the construction of a predictive model with four key genes (E2F2, GTSE1, RAD54L, and UBE2C). The model demonstrated reliable predictive ability for 1-, 3-, and 5-year overall survival, with AUC values of 0.794, 0.790, and 0.794, respectively. Patients in the high-risk group exhibited a worse prognosis, accompanied by significant differences in immune cell infiltration, immune function, TIDE and IPS scores, and drug sensitivities. Two clusters of ccRCC were identified using the "ConsensusClusterPlus" package, cluster 1 exhibited a worse survival rate and was resistant to chemotherapeutic drugs of Axitinib, Erlotinib, Pazopanib, Sunitinib, and Temsirolimus, but not Sorafenib. Targeted experiments on RAD54L, a gene involved in DNA repair processes, revealed its crucial role in inhibiting proliferation, invasion, and migration in 786-O cells. In conclusion, our study offers valuable insights into the molecular mechanisms underlying ccRCC, identifying potential prognostic genes and molecular subtypes associated with the G2M checkpoint. These findings hold promise for guiding personalized treatment strategies in the management of ccRCC.

Causal association of monocytes with chronic kidney disease and the mediation role of frailty: A study integrating large-scale two-sample Mendelian randomization and single-cell analysis

BackgroundRecent research reported that frailty was prevalent among adults with chronic kidney disease (CKD) in clinical trials, and monocytes illustrated a similar difference in these two diseases compared to the normal. However, the scientific evidence for a causal relationship between these two diseases was lacking, with further exploration into whether monocytes co-regulate them. MethodsWe aimed to integrate large-scale Mendelian randomization (MR) and single-cell transcriptome analysis to determine whether there was a causal relationship between frailty and CKD (Bidirectional two-sample Mendelian determined the causal direction), whether monocytes impacted them, and whether the two diseases shared genetic variation sites. Based on 441 Genome-wide association study datasets, this study utilized five MR methods, multiple sensitivity analysis, and corresponding single-cell transcriptome datasets as proof. ResultsThe association between frailty and CKD was significantly causal, and frailty increased the risk of CKD in patients (OR (95 %CI): 3.5597 (1.8369–6.8982), p = 0.000168909). The exposure monocyte can increase the risk of frailty and CKD in patients, especially with high expression of HLA genes in these cells. The existing two-sample MR results cannot reject the hypothesis that monocytes increase the risk of CKD by inducing frailty. rs9275271’ 1mb genetic location above and below had been proven to be an effective genetic space for both frailty and CKD. ConclusionWe conducted the largest MR to date on frailty, monocyte, and CKD, and found a significant causal association between frailty and CKD, with the single-cell analysis confirmed. The exposure monocytes increased the risk of frailty and CKD, particularly with high expression of HLA genes in these cells. We identified a potential common genetic variant space, rs9275271, associated with frailty and CKD, providing insights into the genetic basis of these conditions.

Stromal rigidity stress accelerates pancreatic intraepithelial neoplasia progression and chromosomal instability via nuclear PTK2 localization

Since the mechanotransduction by stromal stiffness stimulates the rupture and repair of the nuclear envelope in pancreatic progenitor cells, accumulated genomic aberrations are under selection in the tumor microenvironment. Analysis of cell growth, micronuclei, and γH2AX foci links to mechanotransduction pressure in vivo during serial orthotopic passages of mouse KrasLSL-G12D/+;Trp53flox/flox;Pdx1-Cre (KPC) cancer cells in the tumor and in migrating through the size-restricted 3μm micropores. To search for pancreatic cancer cell-of-origin, analysis of single-cell datasets revealed that the ECM shapes an alternate route of acinar-ductal transdifferentiation of acinar cells into a central hub of elegantly restrained TOP2A-overexpressing cancer cells that spread out as unique cancer clusters with copy number amplifications in MYC-PTK2 locus and PIK3CA. High-PTK2 expression is associated with 171 differentially methylated CpG loci, 319 differentially expressed genes, and poor overall survival in TCGA-PAAD patients. Abolished RGD-integrin signaling by disintegrin KG blocked the PTK2 phosphorylation, increased cancer apoptosis, decreased VAV1 expression, and prolonged overall survival in the KPC mice. Decreases of αSMA deposition in the CD248 knockout KPC mice remodel the tissue stroma and downregulated TOP2A expression in the epithelium. In summary, stromal stiffness induces the onset of cells-of-origin of cancer by ectopic TOP2A expression, and the genomic amplification of MYC-PTK2 locus via alternative transdifferentiation of pancreatic progenitor cells is the vulnerability useful for disintegrin KG treatment against cells-of-origin cancer.

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach.

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single-cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple-negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long non-coding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and -resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers an optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.