Single-cell and spatial transcriptome databases as well as the codes for analysing these data are stored. We refer the following papers and repositories: * Stewart et al., Nat. Cancer 1, 2020 * Reyes et al., Nat. Med. 26, 2020 * Moncada et al., Nat. Biotechnol. 38, 2020 * 10x Genomics repository

IntroductionThe role of mast cells (MCs) in clear cell renal carcinoma (ccRCC) is unclear, and comprehensive single-cell studies of ccRCC MCs have not yet been performed.MethodsTo investigate the heterogeneity and effects of MCs in ccRCC, we studied single-cell transcriptomes from four ccRCC patients, integrating both single-cell sequencing and bulk tissue sequencing data from online sequencing databases, followed by validation via spatial transcriptomics and multiplex immunohistochemistry (mIHC).ResultsWe identified four MC signature genes (TPSB2, TPSAB1, CPA3, and HPGDS). MC density was significantly greater in ccRCC tissues than in normal tissues, but MC activation characteristics were not significantly different between ccRCC and normal tissues. Activated and resting MCs were defined as having high and low expression of MC receptors and mediators, respectively, whereas proliferating MCs had high expression of proliferation-related genes. The overall percentage of activated MCs in ccRCC tissues did not change significantly but shifted toward a more activated subpopulation (VEGFA+ MCs), with a concomitant decrease in proliferative MCs (TNF+ MCs) and resting MCs. An analysis of the ratio of TNF+/VEGFA+ MCs in tumors revealed that MCs exerted antitumor effects on ccRCC. However, VEGFA+MC was produced in large quantities in ccRCC tissues and promoted tumor angiogenesis compared with adjacent normal tissues, which aroused our concern. In addition, MC signature genes were associated with a better prognosis in the KIRC patient cohort in the TCGA database, which is consistent with our findings. Furthermore, the highest level of IL1B expression was observed in macrophages in ccRCC samples, and spatial transcriptome analysis revealed the colocalization of VEGFA+ MCs with IL1B+ macrophages at the tumor–normal interface.DiscussionIn conclusion, this study revealed increased MC density in ccRCC. Although the proportion of activated MCs was not significantly altered in ccRCC tissues compared with normal tissues, this finding highlights a shift in the MC phenotype from CTSGhighMCs to more activated VEGFA+MCs, providing a potential therapeutic target for inhibiting ccRCC progression.

BackgroundKidney renal clear cell carcinoma (KIRC) is the most frequently diagnosed subtype of renal cell carcinoma (RCC); however, the pathogenesis and diagnostic approaches for KIRC remain elusive. Using single-cell transcriptomic information of KIRC, we constructed a diagnostic model depicting the landscape of programmed cell death (PCD)-associated genes, namely cell death-related genes (CDRGs).MethodsIn this study, six CDRG categories, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA sequencing (RNA-seq) data of blood-derived exosomes from the exoRBase database, RNA-seq data of tissues from The Cancer Genome Atlas (TCGA) combined with control samples from the GTEx databases, and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database were downloaded. Next, we intersected the differentially expressed genes (DEGs) of the KIRC cohort from exoRBase and the TCGA databases with CDRGs and DEGs obtained from single-cell datasets, further screening out the candidate biomarker genes using clinical indicators and machine learning methods and thus constructing a diagnostic model for KIRC. Finally, we investigated the underlying mechanisms of key genes and their roles in the tumor microenvironment using scRNA-seq, single-cell assays for transposase-accessible chromatin sequencing (scATAC-seq), and the spatial transcriptomics sequencing (stRNA-seq) data of KIRC provided by the GEO database.ResultWe obtained 1,428 samples and 216,155 single cells. After the rational screening, we constructed a 13-gene diagnostic model for KIRC, which had high diagnostic efficacy in the exoRBase KIRC cohort (training set: AUC = 1; testing set: AUC = 0.965) and TCGA KIRC cohort (training set: AUC = 1; testing set: AUC = 0.982), with an additional validation cohort from GEO databases presenting an AUC value of 0.914. The results of a subsequent analysis revealed a specific tumor epithelial cell of TRIB3high subset. Moreover, the results of a mechanical analysis showed the relatively elevated chromatin accessibility of TRIB3 in tumor epithelial cells in the scATAC data, while stRNA-seq verified that TRIB3 was predominantly expressed in cancer tissues.ConclusionsThe 13-gene diagnostic model yielded high accuracy in KIRC screening, and TRIB3high tumor epithelial cells could be a promising therapeutic target for KIRC.

IntroductionThe role of mast cells (MCs) in clear cell renal carcinoma (ccRCC) is unclear, and comprehensive single-cell studies of ccRCC MCs have not yet been performed.MethodsTo investigate the heterogeneity and effects of MCs in ccRCC, we studied single-cell transcriptomes from four ccRCC patients, integrating both single-cell sequencing and bulk tissue sequencing data from online sequencing databases, followed by validation via spatial transcriptomics and multiplex immunohistochemistry (mIHC).ResultsWe identified four MC signature genes (TPSB2, TPSAB1, CPA3, and HPGDS). MC density was significantly greater in ccRCC tissues than in normal tissues, but MC activation characteristics were not significantly different between ccRCC and normal tissues. Activated and resting MCs were defined as having high and low expression of MC receptors and mediators, respectively, whereas proliferating MCs had high expression of proliferation-related genes. The overall percentage of activated MCs in ccRCC tissues did not change significantly but shifted toward a more activated subpopulation (VEGFA+ MCs), with a concomitant decrease in proliferative MCs (TNF+ MCs) and resting MCs. An analysis of the ratio of TNF+/VEGFA+ MCs in tumors revealed that MCs exerted antitumor effects on ccRCC. However, VEGFA+MC was produced in large quantities in ccRCC tissues and promoted tumor angiogenesis compared with adjacent normal tissues, which aroused our concern. In addition, MC signature genes were associated with a better prognosis in the KIRC patient cohort in the TCGA database, which is consistent with our findings. Furthermore, the highest level of IL1B expression was observed in macrophages in ccRCC samples, and spatial transcriptome analysis revealed the colocalization of VEGFA+ MCs with IL1B+ macrophages at the tumor–normal interface.DiscussionIn conclusion, this study revealed increased MC density in ccRCC. Although the proportion of activated MCs was not significantly altered in ccRCC tissues compared with normal tissues, this finding highlights a shift in the MC phenotype from CTSGhighMCs to more activated VEGFA+MCs, providing a potential therapeutic target for inhibiting ccRCC progression.

IntroductionDiffuse high-grade gliomas are the most common malignant adult neuroepithelial tumors in humans and a leading cause of cancer-related death worldwide. The advancement of high throughput transcriptome sequencing technology enables rapid and comprehensive acquisition of transcriptome data from target cells or tissues. This technology aids researchers in understanding and identifying critical therapeutic targets for the prognosis and treatment of diffuse high-grade glioma.MethodsSpatial transcriptomics was conducted on two cases of isocitrate dehydrogenase (IDH) wild-type diffuse high-grade glioma (Glio-IDH-wt) and two cases of IDH-mutant diffuse high-grade glioma (Glio-IDH-mut). Gene set enrichment analysis and clustering analysis were employed to pinpoint differentially expressed genes (DEGs) involved in the progression of diffuse high-grade gliomas. The spatial distribution of DEGs in the spatially defined regions of human glioma tissues was overlaid in the t-distributed stochastic neighbor embedding (t-SNE) plots.ResultsWe identified a total of 10,693 DEGs, with 5,677 upregulated and 5,016 downregulated, in spatially defined regions of diffuse high-grade gliomas. Specifically, SPP1, IGFBP2, CALD1, and TMSB4X exhibited high expression in carcinoma regions of both Glio-IDH-wt and Glio-IDH-mut, and 3 upregulated DEGs (SMOC1, APOE, and HIPK2) and 4 upregulated DEGs (PPP1CB, UBA52, S100A6, and CTSB) were only identified in tumor regions of Glio-IDH-wt and Glio-IDH-mut, respectively. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses revealed that upregulated DEGs were closely related to PI3K/Akt signaling pathway, virus infection, and cytokine-cytokine receptor interaction. Importantly, the expression of these DEGs was validated using GEPIA databases. Furthermore, the study identified spatial expression patterns of key regulatory genes, including those involved in protein post-translational modification and RNA binding protein-encoding genes, with spatially defined regions of diffuse high-grade glioma.DiscussionSpatial transcriptome analysis is one of the breakthroughs in the field of medical biotechnology as this can map the analytes such as RNA information in their physical location in tissue sections. Our findings illuminate previously unexplored spatial expression profiles of key biomarkers in diffuse high-grade glioma, offering novel insight for the development of therapeutic strategies in glioma.

This resource provides comprehensive spatial profiling of the Brain, including overview of protein expression in the mammalian brain based on integration of data from human, pig and mouse. Transcriptomics data combined with affinity-based protein in situ localization down to single cell detail is available in this brain-centric sub atlas of the Human Protein Atlas. The data presented are for human genes and their one-to-one orthologues in pig and mouse. Gene summary pages provide the hierarchical expression landscape form 13 main regions of the brain to individual nuclei and subfields for every protein coding gene. For selected proteins, high content images are available to explore the cellular and subcellular protein distribution. In addition, the Brain resource contains lists of genes with elevated expression in one or a group of regions to help the user identify unique protein expression profiles linked to physiology and function. More information about the specific content and the generation and analysis of the data in this resource can be found on the Methods Summary. Learn about:

Expression levels for all human proteins in regions and subregions of the human brain Expression levels for all proteins with human orthologs in regions and subregions of the pig and mouse brain Brain enriched genes with higher expression in any of the regions of the brain compared to peripheral organs Regional enriched genes with higher expression in a single or few regions of the brain Cell-type and cell-compartment distribution of selected proteins in the human and mouse brain Differences in gene expression between mammalian species

Additional information: In addition to the data provided in the brain resource there is also data on human retina and single cell data containing information on protein expression in human neuronal and non-neuronal cell-types in the central nervous system.

An AnnData object for the Stereo-seq axolotl subset (30DPI, 60DPI and Adult) data from Wei et al.The Stereo-seq axolotl data [Wei et al., 2022] are available in the Spatial Transcript Omics DataBase (STOmics DB) under https://db.cngb.org/stomics/ artista/.

Background and aimsThe mitotic catastrophe (MC) pathway plays an important role in hepatocellular carcinoma (HCC) progression and tumor microenvironment (TME) regulation. However, the mechanisms linking MC heterogeneity to immune evasion and treatment response remain unclear.MethodsBased on 94 previously published highly correlated genes for MC, HCC patients’ data from the Cancer Genome Atlas (TCGA) and changes in immune signatures and prognostic stratification were studied. Time and spatial-specific differences for MCGs were assessed by single-cell RNA sequencing and spatial transcriptome (ST) analysis. Multiple external databases (GEO, ICGC) were employed to construct an MC-related riskscore model.ResultsIdentification of two MC-related subtypes in HCC patients from TCGA, with clear differences in immune signatures and prognostic risk stratification. Spatial mapping further associates low MC tumor regions with significant immune escape-related signaling. Nomogram combining MC riskscore and traditional indicators was validated great effect for early prediction of HCC patient outcomes.ConclusionMC heterogeneity enables immune escape and therapy resistance in HCC. The MC gene signature serves as a reliable prognostic indicator for liver cancer. By revealing clear immune and spatial heterogeneity of HCC, our integrated approach provides contextual therapeutic strategies for optimal clinical decision-making.

In the quest for “missing proteins” (MPs), the proteins encoded by the human genome still lacking evidence of existence at the protein level, novel approaches are needed to detect this challenging group of proteins. The current count stands at 1,343 MPs, and it is likely that many of these proteins are expressed at low levels, in rare cell or tissue types, or the cells in which they are expressed may only represent a small minority of the tissue. Here, we used an integrated omics approach to identify and explore MPs in human ovaries. By taking advantage of publicly available transcriptomics and antibody-based proteomics data in the Human Protein Atlas (HPA), we selected 18 candidates for further immunohistochemical analysis using an exclusive collection of ovarian tissues from women and patients of reproductive age. The results were compared with data from single-cell mRNA sequencing, and seven proteins (CTXN1, MRO, RERGL, TTLL3, TRIM61, TRIM73, and ZNF793) could be validated at the single-cell type level with both methods. We present for the first time the cell type-specific spatial localization of 18 MPs in human ovarian follicles, thereby showcasing the utility of the HPA database as an important resource for identification of MPs suitable for exploration in specialized tissue samples. The results constitute a starting point for further quantitative and qualitative analysis of the human ovaries, and the novel data for the seven proteins that were validated with both methods should be considered as evidence of existence of these proteins in human ovary.

Supplementary Table S1: Detailed sources and clinical information for each 22 single-cell RNA-seq sample.

IntroductionHepatocellular carcinoma (HCC), representing more than 80% of primary liver cancer cases, lacks satisfactory etiology and diagnostic methods. This study aimed to elucidate the role of programmed cell death-associated genes (CDRGs) in HCC by constructing a diagnostic model using single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data.MethodsSix categories of CDRGs, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA-seq data from blood-derived exosomes were sourced from the exoRBase database, RNA-seq data from cancer tissues from the TCGA database, and scRNA-seq data from the GEO database. Subsequently, we intersected the differentially expressed genes (DEGs) of the HCC cohort from exoRBase and TCGA databases with CDRGs, as well as DEGs obtained from single-cell datasets. Candidate biomarker genes were then screened using clinical indicators and a machine learning approach, resulting in the construction of a seven-gene diagnostic model for HCC. Additionally, scRNA-seq and spatial transcriptome sequencing (stRNA-seq) data of HCC from the Mendeley data portal were used to investigate the underlying mechanisms of these seven key genes and their association with immune checkpoint blockade (ICB) therapy. Finally, we validated the expression of key molecules in tissues and blood-derived exosomes through quantitative Polymerase Chain Reaction (qPCR) and immunohistochemistry experiments.ResultsCollectively, we obtained a total of 50 samples and 104,288 single cells. Following the meticulous screening, we established a seven-gene diagnostic model for HCC, demonstrating high diagnostic efficacy in both the exoRBase HCC cohort (training set: AUC = 1; testing set: AUC = 0.847) and TCGA HCC cohort (training set: AUC = 1; testing set: AUC = 0.976). Subsequent analysis revealed that HCC cluster 3 exhibited a higher stemness index and could serve as the starting point for the differentiation trajectory of HCC cells, also displaying more abundant interactions with other cell types in the microenvironment. Notably, key genes TRIB3 and NQO1 displayed elevated expression levels in HCC cells. Experimental validation further confirmed their elevated expression in both tumor tissues and blood-derived exosomes of cancer patients. Additionally, stRNA analysis not only substantiated these findings but also suggested that patients with high TRIB3 and NQO1 expression might respond more favorably to ICB therapy.ConclusionsThe seven-gene diagnostic model demonstrated remarkable accuracy in HCC screening, with TRIB3 emerging as a promising diagnostic tool and therapeutic target for HCC.

BackgroundWilms tumor (WT) is the most common childhood renal malignancy, with recurrence linked to poor prognosis. Identifying the molecular features of tumor phenotypes that drive recurrence and discovering novel targets are crucial for improving treatment strategies and enhancing patient outcomes.MethodsSingle-nuclei RNA sequencing (snRNA-seq), spatial transcriptomics (ST), bulk RNA-seq, and mutation/copy number data were curated from public databases. The Seurat package was used to process snRNA-seq and ST data. Scissor analysis was applied to identify tumor subpopulations associated with poor relapse-free survival (RFS). Univariate Cox and LASSO analyses were utilized to reduce features. A prognostic ensemble machine learning model was developed. Immunohistochemistry was used to validate the expression of key features in tumor tissues. The CellChat and Commot package was utilized to infer cellular interactions. The PERCEPTION computational pipeline was used to predict the response of tumor cells to chemotherapy and targeted therapies.ResultsBy integrating snRNA-seq and bulk RNA-seq data, we identified a subtype of Scissor+ tumor cells associated with poor RFS, predominantly derived from cap mesenchyme-like blastemal and fibroblast-like tumor subgroups. These cells displayed nephron progenitor signatures and cancer stem cell markers. A prognostic ensemble machine learning model was constructed based on the Scissor+ tumor signature to accurately predict patient RFS. TGFA was identified as the most significant feature in this model and validated by immunohistochemistry. Cellular communication analysis revealed strong associations between Scissor+ tumor cells and cancer-associated fibroblasts (CAFs) through IGF, SLIT, FGF, and PDGF pathways. ST data revealed that Scissor+ tumor cells were primarily located in immune-desert niche surrounded by CAFs. Despite reduced responsiveness to conventional chemotherapy, Scissor+ tumor cells were sensitive to EGFR inhibitors, providing insights into clinical intervention strategies for WT patients at high risk of recurrence.ConclusionThis study identified a relapse-associated tumor subtype resembling nephron progenitor cells, residing in immune-desert niches through interactions with CAFs. The proposed prognostic model could accurately identify patients at high risk of relapse, offering a promising method for clinical risk stratification. Targeting these cells with EGFR inhibitors, in combination with conventional chemotherapy, may provide a potential therapeutic strategy for WT patients.

BackgroundBone is the most common metastatic site of Breast invasive carcinoma (BRCA). In this study, the bone metastasis-specific regulation network of BRCA was constructed based on prognostic stemness-related signatures (PSRSs), their upstream transcription factors (TFs) and downstream pathways.MethodsClinical information and RNA-seq data of 1,080 primary BRCA samples (1,048 samples without bone metastasis and 32 samples with bone metastasis) were downloaded from The Cancer Genome Atlas (TCGA). The edgeR method was performed to identify differential expressed genes (DEGs). Next, mRNA stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR). To analyze DEGs by classification, similar genes were integrated into the same module by weighted gene co-expression network analysis (WGCNA). Then, univariate and multivariate Cox proportional hazard regression were applied to find the PSRSs. Furthermore, PSRSs, 318 TFs obtained from Cistrome database and 50 hallmark pathways quantified by GSVA were integrated into co-expression analysis. Significant co-expression patterns were used to construct the bone metastasis-specific regulation network. Finally, spatial single-cell RNA-seq and chromatin immunoprecipitation sequence (ChIP-seq) data and multi-omics databases were applied to validate the key scientific hypothesis in the regulation network. Additionally, Connectivity Map (CMap) was utilized to select the potential inhibitors of bone metastasis-specific regulation network in BRCA.ResultsBased on edgeR and WGCNA method, 43 PSRSs were identified. In the bone metastasis-specific regulation network, MAF positively regulated CD248 (R = 0.435, P < 0.001), and hallmark apical junction was the potential pathway of CD248 (R = 0.353, P < 0.001). This regulatory pattern was supported by spatial single-cell RNA sequence, ChIP-seq data and multi-omics online databases. Additionally, alexidine was identified as the possible inhibitor for bone metastasis of BRCA by CMap analysis.ConclusionPSRSs played important roles in bone metastasis of BRCA, and the prognostic model based on PSRSs showed good performance. Especially, we proposed that CD248 was the most significant PSRS, which was positively regulated by MAF, influenced bone metastasis via apical junction pathway. And this axis might be inhibited by alexidine, which providing a potential treatment strategy for bone metastasis of BRCA.

Adenocarcinoma not otherwise specified (AC) and mucinous adenocarcinoma (MC) have different biological behaviors and clinical features. We utilized our previous proteomic data and public transcriptome, single-cell transcriptome, and spatial transcriptome databases to profile the molecular atlas of the tumor microenvironments of MC, AC, and normal colon tissues. By exploring the general and specific molecular features of AC and MC, we found that AC was immune-active but exposed to a hypoxic microenvironment. MC cells could protect against DNA damage, and the microenvironment was unfavorable to leukocyte transendothelial migration. We identified several potential molecular and cellular targets of AC and MC for future research. We also highlighted that the major difference between AC and MC was not the variety of cell types and functions but possibly cell interactions. Stromal and epithelial cell interactions play important roles in both MC and AC, but different regulatory pathways were involved.