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A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma.
BACKGROUND: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking. METHODS: We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions. RESULTS: We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. CONCLUSIONS: This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .
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Multi-omics reveals a monocyte-macrophage-fibroblast axis in post-COVID-19 fibroinflammatory lung remodelling.
Post-COVID-19 residual lung abnormalities (RLA) are associated with persistent respiratory symptoms and radiological changes, yet the underlying mechanisms remain unclear. We performed integrated multi-omic profiling of paired bronchoalveolar lavage and blood samples from patients with post-COVID-19 RLA and healthy controls, combining single-cell RNA sequencing, CITE-seq, single-cell T cell receptor sequencing, bronchoalveolar lavage fluid proteomics and functional fibroblast assays. In post-COVID-19 RLA lungs, we identified an increased abundance of profibrotic SPP1hi monocyte-derived alveolar macrophages, arising from an expanded circulating HLA-DRlowCD163+PDE4Dhi classical monocyte progenitor population, supporting a blood-lung myeloid axis. Cell-cell communication modelling positioned macrophages as central hubs of immune-stromal crosstalk, promoting monocyte recruitment with profibrotic priming, and fibroblast activation. Proteomic analysis of bronchoalveolar lavage fluid from post-COVID-19 RLA and idiopathic pulmonary fibrosis, compared with healthy controls, revealed shared and distinct signatures. These alveolar proteins in post-COVID-19 RLA were predominantly attributed to myeloid cells and predicted to engage fibroblast receptors. Bronchoalveolar lavage fluid induced fibroblast proliferation, differentiation and collagen deposition in vitro, with proliferation attenuated by the antifibrotic drug nintedanib. We also identified compartment-specific lymphoid dysregulation, including depletion of mucosal-associated invariant T (MAIT) cells in both the lung and blood, decreased natural killer (NK) cells with oligoclonal T cell expansion in the lung, and expansion of regulatory and cytotoxic T cells in the blood. These findings support a persistent monocyte-macrophage-fibroblast axis linking immune dysregulation to fibroproliferative remodelling after COVID-19 and highlights candidate therapeutic targets for post-viral lung fibrosis. We provide a publicly available atlas at x (TBA).
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Multi-omics dissection of CPNE1 reveals its prognostic value and immune-regulatory function in liver cancer.
BACKGROUND: Hepatocellular carcinoma (HCC) is known for its high malignancy and poor prognosis. Despite some progress in treatment approaches in recent years, the prognosis of HCC remains unfavorable. CPNE1 (Copine 1), a calcium-dependent phospholipid-binding protein, has been shown to be involved in the development of various cancers. However, the precise role of CPNE1 in HCC and its underlying molecular mechanisms remain unclear. This study aims to elucidate the role of CPNE1 in HCC and investigate its relationship with the immune microenvironment. METHODS: In this study, we conducted a pan-cancer analysis of CPNE1 expression and prognosis using data from the Cancer Genome Atlas (TCGA). Using a series of bioinformatics analyses, including expression, correlation, and survival analyses, we predicted the upstream miRNAs and lncRNAs of CPNE1 and explored its regulatory network through the competitive endogenous RNA (ceRNA) mechanism. Subsequently, functional enrichment analysis was conducted to explore the related functions and signaling pathways of the target genes. Finally, using single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data from the GEO database, we analyzed the spatial distribution of CPNE1 in liver cancer tissues and its expression profiles in immune cells. RESULTS: Differential expression analysis revealed that CPNE1 expression is higher in hepatocellular carcinoma tissues than in adjacent non-cancerous tissues, and HCC patients with higher CPNE1 expression tend to have a worse prognosis. The SNHG6-hsa-miR-101-3p axis was identified as a promising CPNE1 upstream ncRNA pathway in HCC. Functional enrichment analysis revealed that CPNE1 is significantly enriched in several tumor-associated pathways. Single-cell sequencing and spatial transcriptomics analyses revealed that CPNE1 expression is elevated in malignant hepatocytes and immune cells, particularly in NK cells. Further cell communication analysis suggested that CPNE1 may influence the immune microenvironment of liver cancer through the MIF and PPIA-BSG signaling pathways. CONCLUSION: CPNE1 is linked to poor prognosis in hepatocellular carcinoma (HCC) and proposes the SNHG6/hsa-miR-101-3p/CPNE1 regulatory axis. The study investigates the role of CPNE1 in modulating the immune microenvironment of liver cancer and suggests that it may serve as a biomarker for poor prognosis in HCC patients.
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Graphical-model framework for automated annotation of cell identities in dense cellular images.
Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers' experiences. Here, we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in Caenorhabditis elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems.
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Spatial mapping, isolation, and culture of primary human microglia and astrocytes: A systematic review and framework for glial cell-based models.
Microglia and astrocytes are essential glial cells in the CNS that play key roles in brain development, homeostasis, and disease. While immortalized cell lines are convenient, they lack the physiological relevance of primary cells, highlighting the need for reliable human-based isolation protocols. This systematic review synthesizes 21 studies describing methods for isolating and culturing primary human microglia and astrocytes from adult surgical resections, postmortem tissues, and fetal brains. Using the Human Brain Cell Atlas v1.0 single-nuclei RNA-Seq dataset and voxel-wise mapping on the MNI152 template, we provide a spatial transcriptomic context for region-specific glial isolation. We critically assess tissue dissociation techniques, enrichment strategies, and culture conditions, including substrate coatings and growth factors. Additionally, we summarize phenotypic and functional assays used for characterization including surface marker expression, phagocytosis, and cytokine profiling. Despite variability across protocols, we propose an optimized, spatially informed workflow to reproducibly isolate viable, pure glial populations from human brain tissues. This approach supports New Approach Methodologies and the 3Rs-Replacement, Reduction, and Refinement by promoting ethical, human-relevant in vitro models. Our framework advances glial cell-based systems, improving the translational relevance of neurodegenerative and neuroinflammatory disease research and aligns with multiple United Nations Sustainable Development Goals through responsible scientific practices.
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KNSTRN Is a Prognostic Biomarker That Is Correlated with Immune Infiltration in Breast Cancer and Promotes Cell Cycle and Proliferation.
Kinetochore-localized astrin/SPAG5-binding protein (KNSTRN) promotes the progression of bladder cancer and lung adenocarcinoma. However, its expression and biological function in breast cancer remain largely unknown. Therefore, this study aimed to analyze KNSTRN expression, prognoses, correlation with immune infiltration, expression-associated genes, and regulated signaling pathways to characterize its role in regulating the cell cycle using both bioinformatics and in vitro functional experiments. Analyses of The Cancer Genome Atlas, Gene Expression Omnibus, TIMER, and The Human Protein Atlas databases revealed a significant upregulation of KNSTRN transcript and protein levels in breast cancer. Kaplan-Meier survival analyses demonstrated a significant association between high expression of KNSTRN and poor overall survival, relapse-free survival, post-progression survival, and distant metastases-free survival in patients with breast cancer. Furthermore, multivariate Cox regression analyses confirmed that KNSTRN is an independent prognostic factor for breast cancer. Immune infiltration analysis indicated a positive correlation between KNSTRN expression and T regulatory cell infiltration while showing a negative correlation with Tgd and natural killer cell infiltration. Gene set enrichment analysis along with single-cell transcriptome data analysis suggested that KNSTRN promoted cell cycle progression by regulating the expression of key cell cycle proteins. The overexpression and silencing of KNSTRN in vitro, respectively, promoted and inhibited the proliferation of breast cancer cells. The overexpression of KNSTRN enhanced the expression of key cell cycle regulators, including CDK4, CDK6, and cyclin D3, thereby accelerating the G1/S phase transition and leading to aberrant proliferation of breast cancer cells. In conclusion, our study demonstrates that KNSTRN functions as an oncogene in breast cancer by regulating immune response, promoting G1/S transition, and facilitating breast cancer cell proliferation. Moreover, KNSTRN has potential as a molecular biomarker for diagnostic and prognostic prediction in breast cancer.
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CAFs activated by YAP1 upregulate cancer matrix stiffness to mediate hepatocellular carcinoma progression.
BACKGROUND: The stiffness of the matrix is closely related to the progression of hepatocellular carcinoma (HCC). Although direct targeting of stromal rigidity in HCC remains a clinical challenge, cancer-associated fibroblasts (CAFs) are considered key contributors to this process. Given the heterogeneity of CAFs, this study explored the relationship between specific CAF subsets and liver cancer matrix stiffness, aiming to identify novel therapeutic targets for HCC patients. METHODS: Single-cell sequencing datasets were leveraged to identify cell types within liver cancer and characterize the transcriptomic profiles of CAFs. Prognostic analysis, utilizing the Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) liver cancer datasets, assessed the correlation between matrix stiffness-related genes and HCC patient outcomes. Pseudo-time analysis was applied to trace the developmental trajectories of CAFs. By calculating intercellular communication probabilities and analyzing transcription factor activity, the functions and interactions of different CAF subsets were elucidated. Gene Ontology (GO) analysis was used to explore the functional roles of CAFs in distinct Yes-associated protein 1 (YAP1) groups. Finally, cellular experiments and animal experiments were further conducted to validate the hypotheses of this study. RESULTS: This study identified CAF subpopulations based on single-cell sequencing data and analyzed transcriptional changes within these subpopulations. Key findings include the identification of collagen type I alpha 1 (COL1A1), collagen type III alpha 1 (COL3A1), and lysyloxidase (LOX) as pivotal node genes during CAF development. Moreover, the expression of matrix stiffness-related genes was inversely correlated with the prognosis of HCC patients. Notably, the YAP1-positive CAF subpopulation emerged as the primary contributor to matrix stiffness in liver cancer. This subpopulation upregulates the expression of matrix stiffness-related genes and promotes tumor progression by activating signaling pathways such as autophagy and GTPase activity regulation. Cellular experiments and animal studies further validated this conclusion. CONCLUSION: This single-cell analysis uncovered the functional roles of CAFs in liver cancer. The YAP1-positive CAF subpopulation, in particular, was shown to contribute to matrix stiffness by upregulating the expression of relevant genes and promoting tumor progression through the activation of specific signaling pathways.
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The role of KIAA1467 in breast cancer: insights from pan-cancer and single-cell sequencing analysis.
BACKGROUND: Improving the response rate of single-agent immune checkpoint blockade (ICB) urgently requires the discovery of new therapeutic targets for combinatorial regimens. Analyses of tumor microenvironment (TME)-associated biomarkers have verified that KIAA1467 drives the formation of an immune-excluded, non-inflamed TME in breast cancer (BRCA). This study systematically explores the expression pattern, prognostic value, immune regulatory function, biological effects, and drug resistance relevance of FAM234B (also known as KIAA1467) in BRCA. METHODS: We performed pan-cancer survival analysis using The Cancer Genome Atlas (TCGA) datasets. Multi-omics bioinformatics analyses were conducted to evaluate KIAA1467 expression across malignancies. Single-cell RNA sequencing (scRNA-seq) data from GSE176078 was utilized to localize KIAA1467 expression at the cellular level. Immunohistochemistry and western blot assays validated KIAA1467 expression in BRCA clinical specimens. Correlation analyses were implemented to assess relationships between KIAA1467 expression, clinicopathological features, immune modulators, tumor-infiltrating immune cells, and p53 mutation status. Functional enrichment analysis uncovered relevant signaling pathways. Bioinformatic half maximal inhibitory concentration (IC50) prediction and in vitro cellular experiments were applied to evaluate associations between KIAA1467 and chemotherapeutic drug sensitivity. RESULTS: TCGA pan-cancer survival analysis demonstrated that elevated KIAA1467 expression significantly predicted shortened overall survival in BRCA and multiple other tumor types. KIAA1467 displayed distinct expression patterns across cancers, with prominent upregulation in BRCA. scRNA-seq confirmed enriched KIAA1467 expression within BRCA cells, and its upregulation in BRCA tissues was further verified by immunohistochemistry and western blot. High KIAA1467 expression was positively correlated with advanced tumor grade and lymphatic metastasis. KIAA1467 showed negative correlations with most immune modulators and core immune checkpoint molecules, as well as tumor-infiltrating immune cells in the TME, implying its potential function in tumor immune evasion. Low KIAA1467 expression was tightly linked to p53 mutations. Enrichment analysis indicated participation of KIAA1467 in epithelial-mesenchymal transition, apoptosis and cell cycle arrest. Furthermore, high KIAA1467 expression corresponded to higher estimated IC50 values of cisplatin, gefitinib, paclitaxel and gemcitabine, consistent with reduced chemosensitivity observed in vitro. CONCLUSIONS: This study reveals the multifaceted oncogenic role of KIAA1467 in BRCA. KIAA1467 participates in remodeling an immunosuppressive TME, correlates with malignant progression and chemoresistance, and may serve as a promising candidate target to optimize ICB-based combination therapy for BRCA. These findings offer new perspectives for the clinical treatment and comprehensive management of BRCA.
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Establishment of a new prognostic risk model of MAPK pathway-related molecules in kidney renal clear cell carcinoma based on genomes and transcriptomes analysis.
PURPOSE: The mitogen-activated protein kinase (MAPK) signaling pathway is often studied in oncology as the most easily mentioned signaling pathway. This study aims to establish a new prognostic risk model of MAPK pathway related molecules in kidney renal clear cell carcinoma (KIRC) based on genome and transcriptome analysis. METHODS: In our study, RNA-seq data were acquired from the KIRC dataset of The Cancer Genome Atlas (TCGA) database. MAPK signaling pathway-related genes were obtained from the gene enrichment analysis (GSEA) database. We used "glmnet" and the "survival" extension package for LASSO (Least absolute shrinkage and selection operator) regression curve analysis and constructed a prognosis-related risk model. The survival curve and the COX regression analysis were used the "survival" expansion packages. The ROC curve was plotted using the "survival ROC" extension package. We then used the "rms" expansion package to construct a nomogram plot. We performed a pan-cancer analysis of CNV (copy number variation), SNV (single nucleotide variant), drug sensitivity, immune infiltration, and overall survival (OS) of 14 MAPK signaling pathway-related genes using several analysis websites, such as GEPIA website and TIMER database. Besides, the immunohistochemistry and pathway enrichment analysis used The Human Protein Atlas (THPA) database and the GSEA method. Finally, the mRNA expression of risk model genes in clinical renal cancer tissues versus adjacent normal tissues was further verified by real-time quantitative reverse transcription (qRT-PCR). RESULTS: We performed Lasso regression analysis using 14 genes and created a new KIRC prognosis-related risk model. High-risk scores suggested that KIRC patients with lower-risk scores had a significantly worse prognosis. Based on the multivariate Cox analysis, we found that the risk score of this model could serve as an independent risk factor for KIRC patients. In addition, we used the THPA database to verify the differential expression of proteins between normal kidney tissues and KIRC tumor tissues. Finally, the results of qRT-PCR experiments suggested large differences in the mRNA expression of risk model genes. CONCLUSIONS: This study constructs a KIRC prognosis prediction model involving 14 MAPK signaling pathway-related genes, which is essential for exploring potential biomarkers for KIRC diagnosis.
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Brain-based gene expression and corresponding behavioural relevance of risk genes for broad antisocial behaviour.
Antisocial behaviour (ASB) involves persistent irresponsible, delinquent activities violating rights and safety of others. A meta-analysis of genome-wide association studies revealed significant genetic associations with ASB, yet their brain expression patterns and behavioural relevance remain unclear. Our investigation of fifteen genes associated with ASB examined their biological role and distribution across tissues, integrating post-mortem brain sample data from the Allen-Human-Brain Atlas and the Genotype-Tissue Expression project. We found that these genes were differentially expressed in the brain, particularly in regions like the cerebellum, putamen, and caudate, and were notably downregulated in the pancreas. Single cell type expression analysis revealed that ASB-associated genes had strong correlations with ductal and endothelial cells in the pancreas, indicating a possible metabolic influence on ASB. Certain genes like NTN1, SMAD5, NCAM2, and CDC42EP3 displayed specificity for cognitive terms including chronic pain, heart rate, and aphasia. These expression patterns aligned with neurocognitive domains related to thinking, and learning, distress, motor skills, as determined by fMRI analysis. This study connects specific brain gene expression with potential genetic and metabolic factors in ASB, offering novel insights into its biological basis and possible interdisciplinary approaches to understanding and addressing aggressive behaviours.