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A novel scoring method based on RNA-Seq immunograms describing individual cancer-immunity interactions.
Because of the complexity of cancer-immune system interactions, combinations of biomarkers will be required for predicting individual patient responses to treatment and for monitoring combination strategies to overcome treatment resistance. To this end, the "immunogram" has been proposed as a comprehensive framework to capture all relevant immunological variables. Here, we developed a method to convert transcriptomic data into immunogram scores (IGS). This immunogram includes 10 molecular profiles, consisting of innate immunity, priming and activation, T cell response, interferon γ (IFNG) response, inhibitory molecules, regulatory T cells, myeloid-derived suppressor cells (MDSCs), recognition of tumor cells, proliferation, and glycolysis. Using genes related to these 10 parameters, we applied single-sample gene set enrichment analysis (ssGSEA) to 9417 bulk RNA-Seq data from 9362 cancer patients with 29 different solid cancers in The Cancer Genome Atlas (TCGA). Enrichment scores were z-score normalized (Z) for each cancer type or the entire TCGA cohort. The IGS was defined by the formula IGS = 3 + 1.5 × Z so that patients would be well distributed over a range of scores from 1 to 5. The immunograms constructed in this way for all individual patients in the entire TCGA cohort can be accessed at "The RNA-Seq based Cancer Immunogram Web" (https://yamashige33.shinyapps.io/immunogram/).
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Atlas-guided discovery of transcription factors for T cell programming.
CD8+ T cells differentiate into diverse states that shape immune outcomes in cancer and chronic infection1-4. To define systematically the transcription factors (TFs) driving these states, we built a comprehensive atlas integrating transcriptional and epigenetic data across nine CD8+ T cell states and inferred TF activity profiles. Our analysis catalogued TF activity fingerprints, uncovering regulatory mechanisms governing selective cell state differentiation. Leveraging this platform, we focused on two transcriptionally similar but functionally opposing states that are critical in tumour and viral contexts: terminally exhausted T (TEXterm) cells, which are dysfunctional5-8, and tissue-resident memory T (TRM) cells, which are protective9-13. Global TF community analysis revealed distinct biological pathways and TF-driven networks underlying protective versus dysfunctional states. Through in vivo CRISPR screening integrated with single-cell RNA sequencing (in vivo Perturb-seq) we delineated several TFs that selectively govern TEXterm cell differentiation. We also identified HIC1 and GFI1 as shared regulators of TEXterm and TRM cell differentiation and KLF6 as a unique regulator of TRM cells. We discovered new TEXterm-selective TFs, including ZSCAN20 and JDP2, with no previous known function in T cells. Targeted deletion of these TFs enhanced tumour control and synergized with immune checkpoint blockade but did not interfere with TRM cell formation. Consistently, their depletion in human T cells reduces the expression of inhibitory receptors and improves effector function. By decoupling exhaustion TEX-selective from protective TRM cell programmes, our platform enables more precise engineering of T cell states, accelerating the rational design of more effective cellular immunotherapies.
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The Single-Cell Atlas Reveals Subset-Specific Transcriptional Programs of NK Cells in Skin Inflammation.
BACKGROUND: Natural killer (NK) cells are crucial in inflammatory skin diseases, but their diverse functions and lack of a standardized classification system across diseases have limited deeper insights into their roles. METHODOLOGY: We merged single-cell transcriptomic data from 40 skin samples to create a comprehensive atlas of NK cells across various skin diseases, identifying nine distinct NK cell subsets with unique functions. RESULTS: Our analysis revealed a conserved Aryl Hydrocarbon Receptor (AHR)+ NK cell subset that is broadly present across multiple skin diseases. Notably, the Granzyme B (GZMB)+ NK cell subset may be associated with the pathogenesis of psoriasis (PSO) and appears to undergo a differentiation trajectory toward IL13+ NK cells in the pseudotime analysis. This finding suggests a potential role for these cells in mediating paradoxical cutaneous inflammation. Our analysis identified NK cells expressing GZMB in a variety of skin diseases. Notably, the NK cell subpopulation expressing GZMB appears to be associated with the pathogenesis of PSO and ultimately exhibits the expression of IL13 in pseudotime analysis, suggesting that it may play a role in regulating contradictory skin inflammation. CONCLUSION: This study offers a comprehensive overview of skin NK cells, identifies pathogenic subsets that may drive skin disease progression, and provides novel insights for future targeted therapies.
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Construction and differential analysis of testicular atlas between 10-week-old and 23-week-old ducks using single-cell RNA sequencing.
While spermatogenesis has been extensively characterized in mammals, its molecular underpinnings in avian species remain largely unexplored. To address this knowledge gap, we performed single-cell transcriptomic profiling of duck testes across developmental stages (10-week immature vs. 23-week mature). Our analysis generated a comprehensive cellular atlas comprising 54,702 cells, resolving eight germ cell clusters (three spermatogonia [SPG], three spermatocytes [SPC], two spermatozoa [SPT]) and nine somatic populations, including peritubular myoid cells, immune subsets (T cells, macrophages, granulocytes), endothelial cells, Leydig cells, and three Sertoli cell subtypes, each defined by unique marker gene signatures. Furthermore, novel marker genes were identified, including EXFABP for granulocyte, ARHGAP15 for T cell regulation, FDX1 specific to Leydig cells (LC), and TSSK3/TSSK2 linked to elongated spermatid formation (SPT). Notably, we identified some novel molecular markers distinguishing these populations. Pseudotemporal trajectory reconstruction of germline development revealed stage-specific enrichment of ribosome, endoplasmic reticulum protein processing, and autophagy pathways. Core regulators MRPL13, MRPL2, MRPL22, MRPS14, MRPS7 (ribosome), HSPA5 (ER stress response), and PIK3C3 (autophagy) emerged as molecular hubs showing progressive downregulation during differentiation. Comparative transcriptomic analysis of germ cells and Sertoli cells between immature (IMT) and mature (MT) testes revealed significant enrichment of the spliceosome pathway in both germ and Sertoli cells. Critical spliceosome components SNRPG, SF3B3, and SNRPF exhibited coordinated downregulation during testicular maturation, suggesting their role as negative regulators of spermatogenic progression. This study establishes the first high-resolution cellular blueprint of avian spermatogenesis, delineating regulatory networks of duck testis cell development. Our findings provide valuable datasets and mechanistic insights into the evolutionary specialization of reproductive strategies in poultry.
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Single-cell atlas of domestic pig cerebral cortex and hypothalamus.
The brain of the domestic pig (Sus scrofa domesticus) has drawn considerable attention due to its high similarities to that of humans. However, the cellular compositions of the pig brain (PB) remain elusive. Here we investigated the single-nucleus transcriptomic profiles of five regions of the PB (frontal lobe, parietal lobe, temporal lobe, occipital lobe, and hypothalamus) and identified 21 cell subpopulations. The cross-species comparison of mouse and pig hypothalamus revealed the shared and specific gene expression patterns at the single-cell resolution. Furthermore, we identified cell types and molecular pathways closely associated with neurological disorders, bridging the gap between gene mutations and pathogenesis. We reported, to our knowledge, the first single-cell atlas of domestic pig cerebral cortex and hypothalamus combined with a comprehensive analysis across species, providing extensive resources for future research regarding neural science, evolutionary developmental biology, and regenerative medicine.
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Early neuronal reprogramming and cell cycle reentry shape Alzheimer's disease progression.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by hallmark pathologies, synaptic dysfunction, neuronal loss, gliosis and cognitive decline-dementia. Recent large-scale cell atlases of human AD brains exposed vulnerability of specific neuronal subtypes and highlighted early, coordinated glial responses, suggesting glial involvement in disease progression. However, the timing and nature of neuronal changes, differences between neuronal subtypes, and their coordination with glia remain unclear. Here, we used non-negative matrix factorization to identify co-expression gene programs in single-nucleus RNA profiles from the prefrontal cortex of 437 samples from donors whose clinical symptoms ranged between no cognitive impairment and AD dementia. This approach identified early coordinated transcriptional changes across all neuronal subtypes, preceding clinical symptoms of cognitive decline, and validated in independent snRNA-seq, proteomics, and ELISA datasets. We found neurons in AD undergo rapid modulation of synaptic genes, accompanied by convergence of neurons into two distinct programs: An oxidative stress and apoptosis program abundant in vulnerable neuronal subtypes, and a DNA damage and cell-cycle reentry program associated with resilient subtypes. Moreover, neuronal reprogramming was closely tied to glial responses, and diverged between AD to non-AD brain aging, suggesting neuro-glial coordinated reprogramming shapes the AD cascade and influences disease outcomes.
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Genetic insights into drug targets for alzheimer's disease: integrative multi-omics analysis.
BACKGROUND: The development of effective disease-modifying therapies for Alzheimer's disease (AD) remains a critical unmet need. While Mendelian randomization (MR) has been leveraged to identify genetic variants to accelerate AD target discovery, previous studies have been limited by narrow phenotypic coverage, insufficient multiomics validation, and inadequate mechanistic exploration. This study aims to overcome these limitations via comprehensive MR to identify robust therapeutic targets. METHODS: We performed an integrative multiomics MR analysis leveraging over 50 genome-wide association study (GWAS) datasets spanning AD, cerebrospinal fluid (CSF) biomarkers (Aβ42, p-tau), neuroimaging endophenotypes, cognitive traits, and risk factors. Blood/CSF/brain protein quantitative trait loci (pQTLs) from large-scale proteomics studies were analyzed to identify druggable targets. A rigorous validation cascade was subsequently performed: Bayesian colocalization was performed to assess whether the same variants are associated with the protein and other traits; summary-data-based MR was performed to distinguish pleiotropy from linkage; mediation analysis was performed to quantify biomarker-driven causal pathways; integrated analysis of multiomics (single-cell RNA-seq and proteome) data was performed to resolve cellular specificity, and (PPI) interaction networks were generated; phenome-wide MR (Phe-MR) was performed across 679 traits to evaluate on-target side effects; and structure-based druggability screening was conducted. RESULTS: Proteome-wide MR analysis revealed 15 potential drug targets for AD; six of these targets (PILRA, GRN, ACE, TIMD3, TREM2) were validated as Tier 1 (highest-confidence targets with external validation and causal consistency). Mediation analysis revealed that IDUA reduced the risk of AD through Aβ42 and p-tau in the CSF, whereas Siglec-7/9 increased the risk of AD through p-tau in the CSF. Additional targets revealed associations with AD biomarkers, neuroimaging, and cognitive function. Single-cell analysis highlighted the enrichment of key microglial and astrocyte targets. PPI network analysis revealed interaction pathways between seven drug targets and four AD therapeutics, and druggability assessment revealed seven potential therapeutics. CONCLUSIONS: This study established a comprehensive AD target atlas, revealing mechanism-anchored targets that were validated across multiomics analyses and a clinically actionable framework integrating efficacy, biology, and safety profiling. Overall, these results advance AD drug discovery by revealing prioritized targets with causal biological support and providing a validated development roadmap.
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Screening of PDSS1 as a Potential Biomarker for Hepatocellular Carcinoma Based on a Copper-Related Prognostic Signature through Bulk and Single-cell RNA-sequencing Analysis.
Background: Currently, there is few literature comprehensively analyzing landscape of cuproptosis-related genes (CRGs) in liver hepatocellular carcinoma (LIHC) with multiple omics approaches. Aims: Using comprehensive analysis, we aim to find out how CRGs works on LIHC. Method: With data from The Cancer Genome Atlas (TCGA) database, we constructed a prognostic prediction model for CGRs using LASSO regression analysis and performed immune infiltration analysis using the same dataset. To validate findings, we utilized RNA expression data from the International Cancer Genome Consortium (ICGC). Furthermore, we analyzed the enrichment and features of CRGs in epithelial cells using single-cell RNA sequencing (scRNA-seq) data. To validate the reliability of findings, we performed several experiments including RT-PCR, cloning formation assay, scratch assay, and Transwell assay. Result: We have constructed a high-precision risk scoring model composed of CRGs for predicting prognosis in TCGA-LIHC. Reliability of the risk prognosis model was confirmed through Kaplan-Meier curve analysis, time-dependent ROC analysis, and multivariate regression analysis. Furthermore, we found knocking down PDSS1 increased sensitivity of LIHC cells to copper ions, and both proliferation and migration abilities were significantly reduced. Finally, we comprehensively characterized the features of CRGs in LIHC through scRNA-seq. Conclusion: In this study, we introduce PDSS1 as a novel CRG in HCC. Utilizing scRNA-seq, we provide a comprehensive landscape of cuproptosis across various cell subtypes within the HCC tumor microenvironment. Furthermore, we detailed the characteristics of high PDSS1-expressing tumor cells, including their distinctive transcription factors, metabolic profiles, and interactions with different subtypes within the tumor microenvironment. This work not only elucidated the role of PDSS1 in HCC but also enhanced our understanding of cuproptosis dynamics during tumor progression.
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Integrative single-cell transcriptomic investigation unveils long non-coding RNAs associated with localized cellular inflammation in psoriasis.
Psoriasis is a complex, chronic autoimmune disorder predominantly affecting the skin. Accumulating evidence underscores the critical role of localized cellular inflammation in the development and persistence of psoriatic skin lesions, involving cell types such as keratinocytes, mesenchymal cells, and Schwann cells. However, the underlying mechanisms remain largely unexplored. Long non-coding RNAs (lncRNAs), known to regulate gene expression across various cellular processes, have been particularly implicated in immune regulation. We utilized our neural-network learning pipeline to integrate 106,675 cells from healthy human skin and 79,887 cells from psoriatic human skin. This formed the most extensive cell transcriptomic atlas of human psoriatic skin to date. The robustness of our reclassified cell-types, representing full-layer zonation in human skin, was affirmed through neural-network learning-based cross-validation. We then developed a publicly available website to present this integrated dataset. We carried out analysis for differentially expressed lncRNAs, co-regulated gene patterns, and GO-bioprocess enrichment, enabling us to pinpoint lncRNAs that modulate localized cellular inflammation in psoriasis at the single-cell level. Subsequent experimental validation with skin cell lines and primary cells from psoriatic skin confirmed these lncRNAs' functional role in localized cellular inflammation. Our study provides a comprehensive cell transcriptomic atlas of full-layer human skin in both healthy and psoriatic conditions, unveiling a new regulatory mechanism that governs localized cellular inflammation in psoriasis and highlights the therapeutic potential of lncRNAs in this disease's management.
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Using the Allen gene expression atlas of the adult mouse brain to gain further insight into the physiological significance of TAG-1/Contactin-2.
The anatomic gene expression atlas (AGEA) of the adult mouse brain of the Allen Institute for Brain Science is a transcriptome-based atlas of the adult C57Bl/6 J mouse brain, based on the extensive in situ hybridization dataset of the Institute. This spatial mapping of the gene expression levels of mice under baseline conditions could assist in the formation of new, reasonable transcriptome-derived hypotheses on brain structure and underlying biochemistry, which could also have functional implications. The aim of this work is to use the data of the AGEA (in combination with Tabula Muris, a compendium of single cell transcriptome data collected from mice, enabling direct and controlled comparison of gene expression among cell types) to provide further insights into the physiology of TAG-1/Contactin-2 and its interactions, by presenting the expression of the corresponding gene across the adult mouse brain under baseline conditions and to investigate any spatial genomic correlations between TAG-1/Contactin-2 and its interacting proteins and markers of mature and immature oligodendrocytes, based on the pre-existing experimental or bibliographical evidence. The across-brain correlation analysis on the gene expression intensities showed a positive spatial correlation of TAG-1/Contactin-2 with the gene expression of Plp1, Myrf, Mbp, Mog, Cldn11, Bace1, Kcna1, Kcna2, App and Nfasc and a negative spatial correlation with the gene expression of Cspg4, Pdgfra, L1cam, Ncam1, Ncam2 and Ptprz1. Spatially correlated genes are mainly expressed by mature oligodendrocytes (like Cntn2), while spatially anticorrelated genes are mainly expressed by oligodendrocyte precursor cells. According to the data presented in this work, we propose that even though Contactin-2 expression during development correlates with high plasticity events, such as neuritogenesis, in adulthood it correlates with pathways characterized by low plasticity.