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Colorectal Liver Metastasis Pathomics Model: Integrating Single-Cell and Spatial Transcriptome Analysis With Pathomics for Predicting Liver Metastasis in Colorectal Cancer.
The liver is the primary target organ for hematologic metastasis of colorectal cancer (CRC), and CRC liver metastasis (CRLM) often precludes radical resection, making it the leading cause of death in patients with CRC. To improve the identification and prediction of liver metastasis risk, we identified a cell type of liver metastasis--triggering malignant cells (LMTMCs) through integrating single-cell RNA sequencing and spatial transcriptome analysis. Multiomics cell communication analysis indicated that the interaction between fibroblasts and LMTMCs through the COL1A1-CD44/SDC4 and LAMA4-CD44 signaling axes could promote CRLM. By applying the one-class logistic regression algorithm, we developed a CRLM scoring system in the bulk RNA-sequencing data according to the abundance of LMTMCs in each individual. Using the grouping labels derived from the CRLM scoring system in the bulk data and the corresponding whole-slide images without any manual annotations at the region or pixel level, processed via slide-level weakly supervised learning, a deep-learning model based on the ResNet18 architecture, called Colorectal Liver Metastasis Pathomics Model, was developed to predict the risk of liver metastasis in patients with CRC. The Colorectal Liver Metastasis Pathomics Model achieved an area under the curve of 0.84 at the internal test set of The Cancer Genome Atlas-CRC histology images. In the external independent validation sets, namely the Affiliated Hospital of Southwest Medical University and the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University cohorts, the areas under the curve were 0.89 and 0.72, respectively, indicating effective classification performances. This study provided new insights and tools for the early identification of CRLM and demonstrated the potential of combining multiomics with deep learning-based pathomics in cancer research.
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Spatially resolved single cell atlas deciphers SAA1 inflammatory epithelial cells.
Disruption of epithelial integrity is a pivotal event in inflammation, disease pathogenesis, and tissue homeostasis. To investigate these processes in chronic inflammatory disease, we performed spatial transcriptomics integrated with single-cell RNA sequencing (scRNA-seq) on human gingival tissue, coupled with metagenomic analysis of matched subgingival plaque. This approach allowed in situ characterization of epithelial heterogeneity and microbiome-epithelium-connective tissue crosstalk. We identified a distinct inflammatory epithelial subpopulation (SAA1+Epi), situated within the junctional epithelium, that becomes activated through the TLR2-PITX2 axis by Porphyromonas gingivalis lipopolysaccharide. These SAA1+Epi cells secrete TGFβ, which induces an inflammatory program in the connective tissue by driving the differentiation of inflammation-associated fibroblasts (C3+FB) via the PI3K/Akt pathway. Concurrently, SAA1+Epi cells express chemotactic factors such as CXCL6 to recruit NK cells, thereby sustaining the inflammatory niche. The transcription factor PITX2 emerged as a critical regulator of SAA1+Epi differentiation; targeting PITX2 suppressed C3+FB induction and natural killer (NK) cells recruitment, ultimately attenuating periodontitis progression. Our findings position SAA1+Epi as a frontline responder to dysbiotic bacteria at the inflammatory interface and underscore its essential role in regulating epithelial-connective tissue homeostasis during inflammation.
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Atlas of amnion development during the first trimester of human pregnancy.
The amnion is a critical extra-embryonic structure that supports foetal development, yet its ontogeny remains poorly defined. Here, using single-cell transcriptomics, we identified major cell types and subtypes in the human amnion across the first trimester of pregnancy, broadly categorized into epithelial, mesenchymal and macrophage lineages. We uncovered epithelial-mesenchymal and epithelial-immune transitions, highlighting dynamic remodelling during early pregnancy. Our results further revealed key intercellular communication pathways, including BMP4 signalling from mesenchymal to epithelial cells and TGF-β signalling from macrophages to mesenchymal cells, suggesting coordinated interactions that drive amnion morphogenesis. In addition, integrative comparisons across humans, non-human primates and in vitro stem cell-based models reveal that stem cell-based models recapitulate various stages of amnion development, emphasizing the need for careful selection of model systems to accurately recapitulate in vivo amnion formation. Collectively, our findings provide a detailed view of amnion cellular composition and interactions, advancing our understanding of its developmental role and regenerative potential.
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Fibulin-5 (FBLN5) as a context-dependent pan-cancer regulator: integrated multi-omics analysis reveals dual roles in tumor progression and therapeutic resistance.
BACKGROUND: Fibulin-5 (FBLN5) is a key member of the extracellular matrix (ECM), which plays an important role in the occurrence and development of tumors. This study aimed to determine the role of FBLN5 in the diagnosis, prognosis, and immune regulation of tumors using a pan-cancer analysis. METHODS: By integrating and analyzing data from multiple public databases, including The Cancer Genome Atlas (TCGA), Firehose, University of California, Santa Cruz (UCSC) Xena, cBioPortal, Gene Expression Omnibus (GEO), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Tumor Immune Estimation Resource 2.0 (TIMER2.0), Tumor Immune Single-cell Hub (TISCH), CellMiner, Genomics of Drug Sensitivity in Cancer (GDSC), and Cancer Therapeutics Response Portal (CTRP), we comprehensively investigated the diagnostic and prognostic potential of FBLN5 across multiple types. We further performed a gene set enrichment analysis (GSEA) to elucidate the biological functions associated with FBLN5 expression. Additionally, a survival analysis was conducted to assess its prognostic value, and genetic alteration, methylation, and pathway analyses were also performed. Finally, we examined the relationship between FBLN5 and immunity response, and identified potential drugs that may affect the efficacy of tumor treatment. RESULTS: The analysis of TCGA data revealed that FBLN5 RNA was differentially expressed across multiple tumor types, and in 92% of tumor types, the expression level of FBLN5 protein is lower. Genetic and epigenetic alterations-including mutations, somatic copy number alterations, and DNA methylation-were associated with its dysregulated expression. FBLN5 expression was also correlated with several key clinical features. Further, functional analyses linked FBLN5 expression to various metabolism-, metastasis-, and immune-related pathways. Elevated FBLN5 expression had a controversial effect on prognosis across multiple tumors, and influenced sensitivity to numerous therapeutic agents. Additionally, FBLN5 expression showed significant correlations with immunoregulatory molecules and biomarkers indicative of lymphocyte subpopulation infiltration. CONCLUSIONS: This study conducted a systematic analysis of FBLN5 and its regulatory network, demonstrating that FBLN5 expression is closely associated with DNA methylation and drug resistance across multiple tumor types. Further, FBLN5 was found to remodel the tumor microenvironment and cause resistance to immunotherapy.
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Artificial intelligence-based multi-modal multi-tasks analysis reveals tumor molecular heterogeneity, predicts preoperative lymph node metastasis and prognosis in papillary thyroid carcinoma: a retrospective study.
BACKGROUND: Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. Molecular heterogeneity, driven by genetic alterations and tumor microenvironment components, contributes to the complexity of PTC. Understanding these complexities is essential for precise risk stratification and therapeutic decisions. METHODS: This study involved a comprehensive analysis of 521 patients with PTC from our hospital and 499 patients from The Cancer Genome Atlas (TCGA). The real-world cohort 1 comprised 256 patients with stage I-III PTC. Tissues from 252 patients were analyzed by DNA-based next-generation sequencing, and tissues from four patients were analyzed by single-cell RNA sequencing (scRNA-seq). Additionally, 586 PTC pathological sections were collected from TCGA, and 275 PTC pathological sections were collected from the real-world cohort 2. A deep-learning multi-modal model was developed using matched histopathology images, genomic, transcriptomic, and immune cell data to predict LNM and disease-free survival (DFS). RESULTS: This study included a total of 1011 PTC patients, comprising 256 patients from cohort 1, 275 patients from cohort 2, and 499 patients from TCGA. In cohort 1, the authors categorized PTC into four molecular subtypes based on BRAF, RAS, RET, and other mutations. BRAF mutations were significantly associated with LNM and impacted DFS. ScRNA-seq identified distinct T-cell subtypes and reduced B-cell diversity in BRAF-mutated PTC with LNM. The study also explored cancer-associated fibroblasts and macrophages, highlighting their associations with LNM. The deep-learning model was trained using 405 pathology slides and RNA sequences from 328 PTC patients and validated with 181 slides and RNA sequences from 140 PTC patients in the TCGA cohort. It achieved high accuracy, with an area under the curve (AUC) of 0.86 in the training cohort, 0.84 in the validation cohort, and 0.83 in the real-world cohort 2. High-risk patients in the training cohort had significantly lower DFS rates ( P <0.001). Model AUCs were 0.91 at 1 year, 0.93 at 3 years, and 0.87 at 5 years. In the validation cohort, high-risk patients also had lower DFS ( P <0.001); the AUCs were 0.89, 0.87, and 0.80 at 1, 3, and 5 years. The authors utilized the GradCAM algorithm to generate heatmaps from pathology-based deep-learning models, which visually highlighted high-risk tumor areas in PTC patients. This enhanced clinicians' understanding of the model's predictions and improved diagnostic accuracy, especially in cases with lymph node metastasis. CONCLUSION: The artificial intelligence (AI)-based analysis uncovered vital insights into PTC molecular heterogeneity, emphasizing BRAF mutations' impact. The integrated deep-learning model shows promise in predicting metastasis, offering valuable contributions to improved diagnostic and therapeutic strategies.
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A Novel Pyroptosis-Related Gene Signature for Early-Stage Lung Squamous Cell Carcinoma.
BACKGROUND: Diagnosis of early stage lung squamous cell carcinoma (LUSC) has improved; however, a comprehensive analysis of prognostic signatures is needed. PURPOSE: To identify, establish, and validate a signature model based on pyroptosis-related genes for prognostic predictions of early stage LUSC. PATIENTS AND METHODS: Two independent cohorts were included. RNA-seq transcriptome data from patients with early stage LUSC were obtained from The Cancer Genome Atlas (TCGA) database. Thirty-three pyroptosis-related genes were analyzed between early stage LUSC and normal lung tissues. Cox regression analysis, random survival forest, and least absolute shrinkage and selection operator algorithms established a three-gene signature. Kaplan-Meier survival and receiver-operating characteristic curves assessed the prognostic efficacy of the model. Single-sample gene set enrichment analysis (ssGSEA) assessed the relationship between pyroptosis and immune cells. Patients with early stage LUSC from the GSE74777 dataset were used for validation. Pyroptosis-related genes were verified by RT-qPCR and Western blotting. RESULTS: Twenty-three differentially expressed pyroptosis-related genes were identified in the LUSC and adjacent normal tissues. Three differentially expressed pyroptosis-related genes were identified as hub genes in early stage LUSC. Patients with early stage LUSC in the TCGA cohort were classified into low- and high-risk subgroups according to the risk score. Overall survival (OS) was significantly short in the high-risk subgroup versus the low-risk subgroup. A similar result was found for the GSE74777 dataset. ssGSEA of immune cells and immune-related pathways between the low- and high-risk subgroups may explain the different OS for patients with early-stage LUSC. IL-6 expression was upregulated, which was inconsistent with the bioinformatic analysis. NOD1 and CASP4 were downregulated in LUSC (all P < 0.05) versus normal lung tissues. CONCLUSION: Differentially expressed pyroptosis-related genes may be involved in early stage LUSC. Pyroptosis-related genes are important in tumor immunity and may be potential prognostic predictors for early stage LUSC.
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Transcriptomic landscape of mammalian ventral pallidum at single-cell resolution.
The ventral pallidum (VP) is critical for motivated behaviors. While contemporary work has begun to elucidate the functional diversity of VP neurons, the molecular heterogeneity underlying this functional diversity remains incompletely understood. We used single-nucleus RNA sequencing and in situ hybridization to define the transcriptional taxonomy of VP cell types in mice, macaques, and baboons. We found transcriptional conservation between all three species, within the broader neurochemical cell types. Unique dopaminoceptive and cholinergic subclusters were identified and conserved across both primate species but had no homolog in mice. This harmonized consensus VP cellular atlas will pave the way for understanding the structure and function of the VP and identified key neuropeptides, neurotransmitters, and neurotransmitter receptors that could be targeted within specific VP cell types for functional investigations.
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Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex.
Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.
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Decrypting spatiotemporal code of human endometrial receptivity.
The mechanism underlying the establishment of human endometrial receptivity remains elusive, constituting a significant obstacle to advancing our knowledge of female infertility. Via integrating high-resolution spatiotemporal and single-cell transcriptomic profiling and in situ sequencing, we construct a spatiotemporal atlas of human endometrial receptivity at single-cell resolution. Our study depicts detailed spatial molecular topography governing the opening and closing of human endometrial receptivity. Notably, the results indicate that stromal-specific NR2F1, CEBPD and epithelial-specific SGK1, KLF5 and ELF3 are closely associated with the opening of implantation window. Furthermore, cholesterol metabolism is found to promote ciliogenesis. Remarkably, we identify stroma- and epithelium-specific factors and unravel a previously unappreciated role of FGFs-FGFR2 signaling pathway mediating stroma-epithelium crosstalk in epithelial differentiation during the onset of receptivity. This spatiotemporally resolved receptivity code provides insights into the female fertility, with potential implications for the intervention of female infertility.
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Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma.
Paediatric rhabdomyosarcoma (RMS) is a soft tissue malignancy of mesenchymal origin that is thought to arise as a consequence of derailed myogenic differentiation. Despite intensive treatment regimens, the prognosis for high-risk patients remains dismal. The cellular differentiation states underlying RMS and how these relate to patient outcomes remain largely elusive. Here, we use single-cell mRNA sequencing to generate a transcriptomic atlas of RMS. Analysis of the RMS tumour niche reveals evidence of an immunosuppressive microenvironment. We also identify a putative interaction between NECTIN3 and TIGIT, specific to the more aggressive fusion-positive (FP) RMS subtype, as a potential cause of tumour-induced T-cell dysfunction. In malignant RMS cells, we define transcriptional programs reflective of normal myogenic differentiation and show that these cellular differentiation states are predictive of patient outcomes in both FP RMS and the less aggressive fusion-negative subtype. Our study reveals the potential of therapies targeting the immune microenvironment of RMS and suggests that assessing tumour differentiation states may enable a more refined risk stratification.