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The Human Microglia Atlas (HuMicA) unravels changes in disease-associated microglia subsets across neurodegenerative conditions.
Dysregulated microglia activation, leading to neuroinflammation, is crucial in neurodegenerative disease development and progression. We constructed an atlas of human brain immune cells by integrating nineteen single-nucleus RNA-seq and single-cell RNA-seq datasets from multiple neurodegenerative conditions, comprising 241 samples from patients with Alzheimer's disease, autism spectrum disorder, epilepsy, multiple sclerosis, Lewy body diseases, COVID-19, and healthy controls. The integrated Human Microglia Atlas (HuMicA) included 90,716 nuclei/cells and revealed nine populations distributed across all conditions. We identified four subtypes of disease-associated microglia and disease-inflammatory macrophages, recently described in mice, and shown here to be prevalent in human tissue. The high versatility of microglia is evident through changes in subset distribution across various pathologies, suggesting their contribution in shaping pathological phenotypes. A GPNMB-high subpopulation was expanded in AD and MS. In situ hybridization corroborated this increase in AD, opening the question on the relevance of this population in other pathologies.
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Integrated Analysis of Single-Cell and Bulk RNA Sequencing Reveals HSD3B7 as a Prognostic Biomarker and Potential Therapeutic Target in ccRCC.
Clear cell renal cell carcinoma (ccRCC) is the most common kidney malignancy, with a poor prognosis for advanced-stage patients. Identifying key biomarkers involved in tumor progression is crucial for improving treatment outcomes. In this study, we employed an integrated approach combining single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) to identify biomarkers associated with ccRCC progression and prognosis. Single-cell transcriptomic data were obtained from publicly available datasets, and genes related to tumor progression were screened using Monocle2. Bulk RNA-seq data for ccRCC were retrieved from The Cancer Genome Atlas (TCGA) and integrated with scRNA-seq data to explore tumor heterogeneity. We identified 3 beta-hydroxy steroid dehydrogenase type 7 (HSD3B7) as a candidate biomarker for ccRCC, associated with poor overall survival, disease-specific survival, and progression-free interval. Elevated HSD3B7 expression correlated with aggressive clinical features such as advanced TNM stages, histologic grades, and metastasis. Functional studies demonstrated that HSD3B7 promotes cell proliferation, migration, and invasion in vitro, while its silencing significantly inhibits tumor growth in vivo. Our findings reveal that HSD3B7 is a novel biomarker for ccRCC, providing insights into its role in tumor progression and potential as a target for therapy. This study highlights the value of integrating scRNA-seq and bulk RNA-seq data to uncover key regulators of tumor biology and lays the foundation for developing personalized therapeutic strategies for ccRCC patients.
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Single-cell RNA sequencing reveals a putative lncRNA-associated ceRNA network in high-grade serous ovarian cancer.
BACKGROUND: High-grade serous ovarian cancer (HGSOC) is the most aggressive ovarian cancer subtype, characterized by high recurrence, chemoresistance, and poor prognosis. Although competing endogenous RNA (ceRNA) networks involving long non-coding RNAs (lncRNAs) have been proposed as key regulators of tumor progression, their cell type-specific roles in HGSOC remain unclear. OBJECTIVES: We aimed to identify lncRNA-associated ceRNA networks active within the HGSOC tumor microenvironment. METHODS: We performed an integrative analysis combining single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq datasets. Cancer cells were isolated, and coexpression analyses were conducted using high-dimensional weighted gene coexpression network analysis. The resulting ceRNA modules were validated and functionally annotated using The Cancer Genome Atlas and pathway enrichment analysis. RESULTS: Our analysis identified a cancer cell-specific ceRNA network involving MIR100HG, mir-224-5p, and EYA4. Interaction prediction and expression correlation analyses indicated that MIR100HG may function as a molecular sponge for mir-224-5p, thereby alleviating its suppression of EYA4. The presence of a 7mer-m8 seed match between mir-224-5p and EYA4 supported this interaction. Pathway analysis suggested a link between the identified ceRNA network and the Wnt signaling pathway, a key driver of tumor initiation and metastasis. CONCLUSION: The MIR100HG-mir-224-5p-EYA4 ceRNA network may promote tumor progression by modulating Wnt signaling. These findings offer insights into a potential posttranscriptional regulatory mechanism in tumor development and therapeutic targeting in HGSOC.
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CytoVerse: Single-Cell AI Foundation Models in the Browser.
Mapping single-cell datasets to large atlases is often hindered by server constraints and privacy concerns. We present CytoVerse, a framework that runs scRNA-seq Foundation Models (scFM) entirely in the browser. Three key contributions enable this: (1) deploying models via ONNX without server side compute; (2) using compressed indexing (IVFPQ) to search a more then 20 million cell reference from the client; and (3) a lightweight protocol for sharing embeddings across consortia without exposing raw data. CytoVerse thereby provides a scalable, privacy preserving framework for distributed single-cell analysis.
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Single-cell transcriptome profiling reveals vascular endothelial cell heterogeneity in human skin.
Vascular endothelial cells (ECs) are increasingly recognized as active players in intercellular crosstalk more than passive linings of a conduit for nutrition delivery. Yet, their functional roles and heterogeneity in skin remain uncharacterized. We have used single-cell RNA sequencing (scRNA-seq) as a profiling strategy to investigate the tissue-specific features and intra-tissue heterogeneity in dermal ECs at single-cell level. Methods: Skin tissues collected from 10 donors were subjected to scRNA-seq. Human dermal EC atlas of over 23,000 single-cell transcriptomes was obtained and further analyzed. Arteriovenous markers discovered in scRNA-seq were validated in human skin samples via immunofluorescence. To illustrate tissue-specific characteristics of dermal ECs, ECs from other human tissues were extracted from previously reported data and compared with our transcriptomic data. Results: In comparison with ECs from other human tissues, dermal ECs possess unique characteristics in metabolism, cytokine signaling, chemotaxis, and cell adhesions. Within dermal ECs, 5 major subtypes were identified, which varied in molecular signatures and biological activities. Metabolic transcriptome analysis revealed a preference for oxidative phosphorylation in arteriole ECs when compared to capillary and venule ECs. Capillary ECs abundantly expressed HLA-II molecules, suggesting its immune-surveillance role. Post-capillary venule ECs, with high levels of adhesion molecules, were equipped with the capacity in immune cell arrest, adhesion, and infiltration. Conclusion: Our study provides a comprehensive characterization of EC features and heterogeneity in human dermis and sets the stage for future research in identifying disease-specific alterations of dermal ECs in various dermatoses.
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CellCommuNet: an atlas of cell-cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states.
Cell-cell communication, as a basic feature of multicellular organisms, is crucial for maintaining the biological functions and microenvironmental homeostasis of cells, organs, and whole organisms. Alterations in cell-cell communication contribute to many diseases, including cancers. Single-cell RNA sequencing (scRNA-seq) provides a powerful method for studying cell-cell communication by enabling the analysis of ligand-receptor interactions. Here, we introduce CellCommuNet (http://www.inbirg.com/cellcommunet/), a comprehensive data resource for exploring cell-cell communication networks in scRNA-seq data from human and mouse tissues in normal and disease states. CellCommuNet currently includes 376 single datasets from multiple sources, and 118 comparison datasets between disease and normal samples originating from the same study. CellCommuNet provides information on the strength of communication between cells and related signalling pathways and facilitates the exploration of differences in cell-cell communication between healthy and disease states. Users can also search for specific signalling pathways, ligand-receptor pairs, and cell types of interest. CellCommuNet provides interactive graphics illustrating cell-cell communication in different states, enabling differential analysis of communication strength between disease and control samples. This comprehensive database aims to be a valuable resource for biologists studying cell-cell communication networks.
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A single-cell transcriptomic atlas of the developing chicken limb.
BACKGROUND: Through precise implementation of distinct cell type specification programs, differentially regulated in both space and time, complex patterns emerge during organogenesis. Thanks to its easy experimental accessibility, the developing chicken limb has long served as a paradigm to study vertebrate pattern formation. Through decades' worth of research, we now have a firm grasp on the molecular mechanisms driving limb formation at the tissue-level. However, to elucidate the dynamic interplay between transcriptional cell type specification programs and pattern formation at its relevant cellular scale, we lack appropriately resolved molecular data at the genome-wide level. Here, making use of droplet-based single-cell RNA-sequencing, we catalogue the developmental emergence of distinct tissue types and their transcriptome dynamics in the distal chicken limb, the so-called autopod, at cellular resolution. RESULTS: Using single-cell RNA-sequencing technology, we sequenced a total of 17,628 cells coming from three key developmental stages of chicken autopod patterning. Overall, we identified 23 cell populations with distinct transcriptional profiles. Amongst them were small, albeit essential populations like the apical ectodermal ridge, demonstrating the ability to detect even rare cell types. Moreover, we uncovered the existence of molecularly distinct sub-populations within previously defined compartments of the developing limb, some of which have important signaling functions during autopod pattern formation. Finally, we inferred gene co-expression modules that coincide with distinct tissue types across developmental time, and used them to track patterning-relevant cell populations of the forming digits. CONCLUSIONS: We provide a comprehensive functional genomics resource to study the molecular effectors of chicken limb patterning at cellular resolution. Our single-cell transcriptomic atlas captures all major cell populations of the developing autopod, and highlights the transcriptional complexity in many of its components. Finally, integrating our data-set with other single-cell transcriptomics resources will enable researchers to assess molecular similarities in orthologous cell types across the major tetrapod clades, and provide an extensive candidate gene list to functionally test cell-type-specific drivers of limb morphological diversification.
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Immune Cell Landscape of Patients With Diabetic Macular Edema by Single-Cell RNA Analysis.
Purpose: We performed single-cell RNA sequencing (scRNA-seq), an unbiased and high-throughput single cell technology, to determine phenotype and function of peripheral immune cells in patients with diabetic macular edema (DME). Methods: Peripheral blood mononuclear cells (PBMCs) were isolated from DME patients and healthy controls (HC). The single-cell samples were loaded on the Chromium platform (10x Genomics) for sequencing. R package Seurat v3 was used for data normalizing, clustering, dimensionality reduction, differential expression analysis, and visualization. Results: We constructed a single-cell RNA atlas comprising 57,650 PBMCs (24,919 HC, 32,731 DME). We divided all immune cells into five major immune cell lineages, including monocytes (MC), T cells (TC), NK cells (NK), B cells (BC), and dendritic cells (DC). Our differential expression gene (DEG) analysis showed that MC was enriched of genes participating in the cytokine pathway and inflammation activation. We further subdivided MC into five subsets: resting CD14++ MC, proinflammatory CD14++ MC, intermediate MC, resting CD16++ MC and pro-inflammatory CD16++ MC. Remarkably, we revealed that the proinflammatory CD14++ monocytes predominated in promoting inflammation, mainly by increasingly production of inflammatory cytokines (TNF, IL1B, and NFKBIA) and chemokines (CCL3, CCL3L1, CCL4L2, CXCL2, and CXCL8). Gene Ontology (GO) and pathway analysis of the DEGs demonstrated that the proinflammatory CD14++ monocytes, especially in DME patients, upregulated inflammatory pathways including tumor necrosis factor-mediated signaling pathway, I-kappaB kinase/NF-kappaB signaling, and toll-like receptor signaling pathway. Conclusion: In this study, we construct the first immune landscape of DME patients with T2D and confirmed innate immune dysregulation in peripheral blood based on an unbiased scRNA-seq approach. And these results demonstrate potential target cell population for anti-inflammation treatments.
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Tumor γδ T-cell abundance is associated with favorable cancer treatment outcomes.
PURPOSE: Clinical response to immune checkpoint blockade (ICB) remains variable. We performed a reference-wide screen to identify immune-cell populations in the tumor microenvironment (TME) associated with benefit across treatment modalities and tumor types. EXPERIMENTAL DESIGN: We analyzed pretreatment bulk tumor RNA-seq to identify RNA-abundance patterns associated with favorable outcomes in (1) published ICB cohorts with response labels and (2) pretreatment TCGA cohorts with survival outcomes. To identify associated cell types, we compared favorable expression patterns to previously determined patterns for 157 cell types defined by the Human Primary Cell Atlas (HPCA), spanning immune, stromal, vascular, and epithelial lineages. Cell types were associated with favorable outcomes using Cox models (TCGA survival) and mixed-effects models of responder status that accounted for cohort, therapy class, tumor type (ICB cohorts). ICB analyses were restricted to pretreatment samples. Random-effects meta-analysis was used for the PRECOG cross-platform replication. After γδ T-cells emerged as the top-ranked population associated with ICB response, we adjusted γδ associations for CD8 + T-cell abundance, and evaluated γδ signals with TRUST4-based T-cell receptor gamma (TRG) and delta (TRD) gene reconstruction and single-cell RNA-seq. To test whether ICB-favorable cellular landscapes were shared with non-immunotherapy treatment response, we used TCGA RECIST-evaluable treatment records to derive immune-cell profiles associated with treatment response for chemotherapy, radiation, targeted therapy, and hormone therapy. RESULTS: The γδ T-cell program was the top-ranked cell-type program associated with ICB response and was also among the programs most strongly associated with favorable TCGA survival. Cell types associated with ICB response showed strong descriptive concordance with the chemotherapy-response profile derived from recorded TCGA outcomes (Pearson r of ranks = 0.92) and moderate concordance with the analogous radiation-associated profile (r = 0.58). Analyses of targeted and hormone therapies were limited by small sample sizes and showed weaker concordance. In pretreatment tumor biopsies, inferred γδ T-cell abundance was associated with ICB response and with survival, and these associations were not explained simply by CD8 + T-cell abundance. In TCGA, TRUST4-derived γδ T-cell receptor (TCR) abundance estimates correlated with signature-based γδ scores and stratified survival. In independent single-cell RNA-seq datasets, higher γδ TCR RNA abundance was also associated with a higher rate of ICB response. CONCLUSION: A reference-wide cell-type screen identified γδ T-cells as the leading population associated with ICB response, and pretreatment γδ T-cell abundance was also associated with favorable outcomes in immunotherapy and non-immunotherapy settings. The evidence is associative and varies in strength by tumor context but supports prospective evaluation of γδ T-cell abundance as a component of pretreatment immune profiling alongside CD8, B-cell/TLS, stromal, and epithelial-state features. TRANSLATIONAL RELEVANCE: Across cancers, clinical response to immune checkpoint blockade (ICB) is variable, with inconsistent performance from established biomarkers such as PD-L1 and tumor mutation burden. We asked which immune-cell transcriptional programs were associated with favorable outcomes across immunotherapy and non-immunotherapy treatment settings. Using pretreatment bulk tumor RNA-seq from published ICB cohorts and TCGA, we evaluated the inferred abundance of 157 subtypes in the tumor microenvironment (TME) with a common gene-level and sample-level framework. Pretreatment TMEs enriched in lymphoid cells or γδ T-cells were associated with ICB response and with favorable survival. The immune-cell landscape associated with favorable ICB outcomes was strongly concordant with chemotherapy response and moderately concordant with radiation response. The association between γδ abundance and favorable outcomes remained after accounting for CD8 + T-cell abundance. These observational findings support γδ T-cell abundance as a candidate for pretreatment immune profiling that warrants prospective evaluation.
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Construction and verification of a hypoxia-related nine-gene prognostic model in uveal melanoma based on integrated single-cell and bulk RNA sequencing analyses.
Uveal melanoma (UM) is the most common primary intraocular tumor with high metastasis and poor prognosis among adults. Hypoxia participates in the metastasis process in various types of cancers. It is reported that the increased expression of hypoxia inducible factor 1 alpha subunit (HIF1A), a hypoxia-related molecule, is associated with worse prognoses of UM patients. Based on the integrated analysis of single-cell sequencing (scRNA-seq) dataset from Gene Expression Omnibus (GEO) and bulk RNA-seq dataset from the Cancer Genome Atlas (TCGA), we found hypoxia was the key feature in UM progression and identified 47 common hypoxia-related differentially expressed genes (DEGs) for the following research. Univariate cox analysis and LASSO-Cox regression analysis were performed to establish a nine-gene prognostic model. According to this model, UM patients could be divided into high- and low-risk groups, with a significant difference in overall survival and progression free survival between the two groups (P < 0.001). The accuracy of the predictive model was also verified on two other independent datasets. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these hypoxia-related DEGs were enriched in immune and cancer related pathways. The proportion of immune infiltration and the expression of immune biomarkers were different between high- and low-risk UM patients, providing potential targets for UM immunotherapy. Hence, our hypoxia-related nine-gene model could efficiently predict the prognosis and guide personalized therapies for UM patients.