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Fragment-sequencing unveils local tissue microenvironments at single-cell resolution.
Cells collectively determine biological functions by communicating with each other-both through direct physical contact and secreted factors. Consequently, the local microenvironment of a cell influences its behavior, gene expression, and cellular crosstalk. Disruption of this microenvironment causes reciprocal changes in those features, which can lead to the development and progression of diseases. Hence, assessing the cellular transcriptome while simultaneously capturing the spatial relationships of cells within a tissue provides highly valuable insights into how cells communicate in health and disease. Yet, methods to probe the transcriptome often fail to preserve native spatial relationships, lack single-cell resolution, or are highly limited in throughput, i.e. lack the capacity to assess multiple environments simultaneously. Here, we introduce fragment-sequencing (fragment-seq), a method that enables the characterization of single-cell transcriptomes within multiple spatially distinct tissue microenvironments. We apply fragment-seq to a murine model of the metastatic liver to study liver zonation and the metastatic niche. This analysis reveals zonated genes and ligand-receptor interactions enriched in specific hepatic microenvironments. Finally, we apply fragment-seq to other tissues and species, demonstrating the adaptability of our method.
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Machine learning integration of single-cell and bulk transcriptomics identifies fibroblast-driven prognostic markers in colorectal cancer.
Single-cell RNA sequencing (scRNA-seq) has significantly advanced our understanding of cellular heterogeneity and the complex interplay within the tumor microenvironment (TME) of colorectal cancer (CRC). However, translating these molecular insights into clinically actionable prognostic biomarkers and therapeutic strategies remains a considerable challenge. In this study, we conducted a comprehensive scRNA-seq analysis of 306 CRC samples comprising 448,255 cells to characterize the TME in depth. By constructing intercellular communication networks based on connection counts and communication probabilities, we identified fibroblasts as central regulatory hubs within the TME. Using Wilcoxon rank-sum tests and univariate survival analyses, we initially identified 23 prognostic fibroblast markers. These were refined to a seven-gene fibroblast-related prognostic signature via an integrated machine learning approach. The signature exhibited robust predictive performance in the The Cancer Genome Atlas - Colon Adenocarcinoma (TCGA-COAD) training cohort (n=351; C-index=0.65) and was successfully validated in the GSE17536 dataset (n=177; C-index=0.63). Functional enrichment analyses revealed that this signature is involved in immune regulation and multiple tumor-associated cellular pathways. Notably, high-risk patients displayed increased macrophage and NK cell infiltration, impaired immune function, and elevated immune rejection scores, while low-risk patients demonstrated heightened sensitivity to camptothecin and irinotecan. Together, our findings underscore the prognostic value of fibroblast-derived signatures in CRC and support their potential utility in risk stratification and the development of personalized therapeutic strategies, contributing to the advancement of precision oncology.
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TNFRSF12A expression in stomach adenocarcinoma and its preliminary role in predicting immunotherapy response.
BACKGROUND: TNFRSF12A is abnormally expressed in various malignancies, especially in stomach adenocarcinoma (STAD), which is related to tumor invasiveness and prognosis of patients. This study examined the expression pattern of TNFRSF12A in STAD and predicted immunotherapy response. METHODS: Data were derived from The Cancer Gene Atlas (TCGA), Gene Expression Omnibus (GEO), and Gene Expression Profiling Interactive Analysis (GEPIA) to analyze the expression pattern of TNFRSF12A in pan-cancer and STAD, as well as its correlation with clinical features. Biological pathways involved in TNFRSF12A were analyzed by "clusterProfiler" package. Immune cell infiltration was evaluated by "GSVA" and "CIBERSORT" packages. Immunotherapy response was assessed by TIDE score and tumor mutation burden (TMB) level. Expression level of TNFRSF12A in the single cell of STAD was analyzed by scRNA-seq. Finally, in vitro test detected the mRNA expression of TNFRSF12A in STAD cells, Wound healing and Transwell assays were performed to measure the capabilities of STAD cell to migrate and invade. RESULTS: TNFRSF12A was highly expressed in STAD. However, TNFRSF12A expression did not shown significant difference in relation to clinical features. TNFRSF12A exhibited notably positive correlation with many carcinogenic signaling pathways and immune cells infiltration such as T cells and macrophages. High TNFRSF12A expression group showed a higher TIDE score, Exclusion score, and TMB level than the low TNFRSF12A expression group, which indicated that STAD patients with high TNFRSF12A expression responded more poorly to immunotherapy. TNFRSF12A showed a positive relation with most of immune checkpoint genes. By scRNA-seq analysis, TNFRSF12A was chiefly expressed in Fibroblasts and Mast cells of STAD. Further, in vitro assays verified the high expression of TNFRSF12A in STAD cells, and the migration and invasion capabilities of STAD cells were notably suppressed by TNFRSF12A silencing (p<0.05). CONCLUSION: The present study not only reveals the potential of TNFRSF12A as a therapeutic target for STAD, but also explores its great potential in STAD immunotherapy. This finding opens up a new way of thinking for the personalized treatment of STAD.
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Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients.
BACKGROUND: Breast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis. METHODS: We used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level. RESULTS: Our single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets. CONCLUSIONS: This study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.
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Atlas of temporal molecular pathological alterations after traumatic brain injury based on RNA-Seq.
Traumatic brain injury (TBI) involves diverse molecular pathological alterations and biological processes in a temporally dynamic manner. However, current knowledge on the various processes during the acute phase of TBI is still rather limited. RNA-seq analysis was performed on brain tissues from C57/BL6 mice at 10 time points(0 h, 1 h, 2 h, 3 h, 4 h, 6 h, 12 h, 1d, 3d, and 7d) following TBI modeling. Subsequently, a bioinformatics approach, Weighted Gene Co-expression Network Analysis (WGCNA), was employed to identify characteristic modules, which were then validated using the Mfuzz method. Pathway enrichment analysis was conducted on WGCNA module genes, and hub genes were screened using the STRING database. After exploring the various potential pathways and expression patterns (neuroinflammation, cognition, gliosis and myelin regeneration etc.), we focus on pyroptosis, a inflammatory cell death influencing immune response, for in-depth analysis. RT-qPCR, Western blot(WB) and Immunofluorescence(IF) were used to validate the hub genes and key pyroptosis-related genes(Casp1, Casp11, GSDMD). Additionally, single-cell RNA sequencing data at 7 day post injury(dpi) was also used to validate the expression of the identified hub genes. Our approach to intensive transcriptomic analysis comprehensively reveals the temporal molecular pathological alterations during TBI progression. Pyroptosis may be a key mechanism in the neuroinflammatory process. Intervention strategies targeting specific molecular pathways may offer novel approach for the treatment of TBI.
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[Single-cell sequencing reveals the temporal expression characteristics of key molecules related to tooth agenesis and dental hard tissues in mouse molars].
Objective: To utilize single-cell RNA sequencing (scRNA-seq) to untangle the temporal expression profiles of molecules associated with congenital tooth agenesis and dental hard tissue formation during mouse molar development, and to construct a comprehensive cell atlas spanning the entire developmental period from E13.5 to P7.5, thereby providing new insights into the molecular mechanisms underlying abnormal tooth development. Methods: scRNA-seq data of murine mandibular molar tooth germs at five developmental stages (E13.5, E14.5, E16.5, P3.5, P7.5) were obtained from the GEO database (accession: GSE189381). The Seurat pipeline was employed for quality control, data normalization, dimensionality reduction, and Harmony-based batch effect correction. Cellular subpopulations were identified through uniform manifold approximation and projection dimensionality reduction, while developmental trajectories were reconstructed using Monocle for pseudotime analysis. Results: scRNA-seq analysis profiling identified 27 distinct cellular clusters, which were annotated into twelve major cell types including epithelial cells, mesenchymal cells, and endothelial cells. Msx1 exhibited a bimodal expression pattern. Pax9 reached its peak at E14.5 and then gradually decreased. Eda had a low expression level with a diffuse distribution. In contrast, Amelx and Enam were barely expressed during the embryonic stage and were activated at P3.5. Dspp was ectopically highly expressed in epithelial cells from P3.5 to P7.5, while Dmp1 was specifically upregulated in mesenchymal cells at P7.5. Conclusions: The temporal expression patterns of key regulatory genes for tooth agenesis (Msx1, Pax9, Eda), ameloblast differentiation (Amelx, Enam), and odontoblast development (Dspp, Dmp1) during mouse molar development. These findings provide a theoretical foundation and potential therapeutic targets for deciphering the molecular mechanisms underlying tooth agenesis and other developmental dental anomalies, paving the way for targeted clinical interventions.
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A Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation.
We performed RNA sequencing on 40,000 cells to create a high-resolution single-cell gene expression atlas of developing human cortex, providing the first single-cell characterization of previously uncharacterized cell types, including human subplate neurons, comparisons with bulk tissue, and systematic analyses of technical factors. These data permit deconvolution of regulatory networks connecting regulatory elements and transcriptional drivers to single-cell gene expression programs, significantly extending our understanding of human neurogenesis, cortical evolution, and the cellular basis of neuropsychiatric disease. We tie cell-cycle progression with early cell fate decisions during neurogenesis, demonstrating that differentiation occurs on a transcriptomic continuum; rather than only expressing a few transcription factors that drive cell fates, differentiating cells express broad, mixed cell-type transcriptomes before telophase. By mapping neuropsychiatric disease genes to cell types, we implicate dysregulation of specific cell types in ASD, ID, and epilepsy. We developed CoDEx, an online portal to facilitate data access and browsing.
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A rice single cell transcriptomic atlas defines the developmental trajectories of rice floret and inflorescence meristems.
Rice inflorescence development determines yield and relies on the activity of axillary meristems (AMs); however, high-resolution analysis of its early development is lacking. Here, we have used high-throughput single-cell RNA sequencing to profile 37 571 rice inflorescence cells and constructed a genome-scale gene expression resource covering the inflorescence-to-floret transition during early reproductive development. The differentiation trajectories of florets and AMs were reconstructed, and discrete cell types and groups of regulators in the highly heterogeneous young inflorescence were identified and then validated by in situ hybridization and with fluorescent marker lines. Our data demonstrate that a WOX transcription factor, DWARF TILLER1, regulates flower meristem activity, and provide evidence for the role of auxin in rice inflorescence branching by exploring the expression and biological role of the auxin importer OsAUX1. Our comprehensive transcriptomic atlas of early rice inflorescence development, supported by genetic evidence, provides single-cell-level insights into AM differentiation and floret development.
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A machine learning-based epigenetic signature reveals YTHDC1 stabilizes POU5F1 to oppose tumor progression.
BACKGROUND: The prognosis and progression mechanisms of bladder cancer (BLCA) are highly heterogeneous, driven by complex genetic and epigenetic alterations. This study aimed to construct a robust prognostic signature using epigenetic modification-related genes and to investigate the underlying molecular mechanisms driving its predictive power. METHODS: We developed a prognostic signature by applying a machine learning-based approach to screen epigenetic genes in the TCGA (The Cancer Genome Atlas)-BLCA cohort. Its performance was rigorously evaluated against 101 other machine learning algorithms and 110 previously published signatures across four independent validation datasets (IMvigor210, E-MTAB-4321, GSE31684, GSE48075). Associations with clinical, genetic, and transcriptomic features were analyzed. Immune infiltration, cell-cell interactions, and drug responses were assessed using both bulk and single-cell RNA-seq data. The functional role of a key signature gene, YTHDC1 (YTH Domain-Containing 1), was investigated through in vitro assays. RESULTS: A six-gene epigenetic signature was constructed. It significantly stratified patients into high- and low-risk groups with distinct overall survival (median survival 20.5 vs. 86.8 months, HR = 2.12, p = 7.7e-7). Our signature demonstrated superior predictive accuracy (C-index and 1-year AUROC) compared to other models. High-risk scores correlated with adverse clinical features (e.g., advanced stage), elevated PD-1/PD-L1, higher genomic instability, and immunosuppressive microenvironments. Single-cell analysis revealed altered T-cell interactions in high-risk cases. Mechanistically, YTHDC1 was shown to bind and stabilize POU5F1 (OCT4) mRNA, thereby inhibiting proliferation and migration in BLCA cell lines (T24, 5637). This anti-tumor effect was dependent on POU5F1. CONCLUSION: The machine learning-derived epigenetic signature is a robust indicator of BLCA heterogeneity across multiple dimensions. YTHDC1, a core component, inhibits cancer progression by stabilizing POU5F1 mRNA, highlighting a novel regulatory axis.
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Exploring the Influence of T Cell Marker Gene Expression on the Pathobiology and Clinical Prognostic Outcomes in Intestinal-Type Gastric Carcinoma.
BACKGROUND: Gastric cancer (GC) poses a significant global health challenge. This study is aimed at elucidating the role of the immune system, particularly T cells and their subtypes, in the pathogenesis and progression of intestinal-type gastric carcinoma (GC), and at evaluating the predictive utility of a T cell marker gene-based risk score for overall survival. METHODS: We performed an extensive analysis using single-cell RNA sequencing data to map the diversity of immune cells and identify specific T cell marker genes within GC. Pseudotime trajectory analysis was employed to observe the expression patterns of tumor-related pathways and transcription factors (TFs) at various disease stages. We developed a risk score using data from The Cancer Genome Atlas (TCGA) as a training set and validated it with the GSE15459 dataset. RESULTS: Our analysis revealed distinct patterns of T cell marker gene expression associated with different stages of GC. The risk score, based on these markers, successfully stratified patients into high-risk and low-risk groups with significantly different overall survival prospects. High-risk patients exhibited poorer survival outcomes compared to low-risk patients (p < 0.05). Additionally, the risk score was capable of identifying patients across a spectrum from chronic atrophic gastritis to early GC. CONCLUSION: The findings enhance the understanding of the tumor immune microenvironment in GC and propose new immunotherapeutic targets. The T cell marker gene-based risk score offers a potential tool for gastroenterologists to tailor treatment plans more precisely according to the cancer's severity.