Cleavage and polyadenylation are essential processes that can impact many aspects of mRNA fate. Most eukaryotic genes have alternative polyadenylation (APA) events. While the heterogeneity of mRNA polyadenylation isoform choice has been studied in specific tissues, less attention has been paid to the neuronal heterogeneity of APA selection at single-nucleus resolution. APA is highly controlled during development and neuronal activation, however, to what extent APA events vary in a specific neuronal cell population and the regulatory mechanisms are still unclear. In this paper, we investigated dynamic APA usage in different cell types using snRNA-seq data of 1424 human brain cells generated by single-cell 3′ RNA sequencing. We found that distal APA sites are not only favored by global neuronal cells, but that their usage also varies between the principal types of neuronal cell populations (excitatory neurons and inhibitory neurons). A motif analysis and a gene functional analysis indicated the enrichment of RNA-binding protein (RBP) binding sites and neuronal functions for the set of genes with neuron-enhanced distal PAS usage. Our results revealed the extensive involvement of APA regulation in neuronal populations at the single-nucleus level, providing new insights into roles for APA in specific neuronal cell populations, as well as utility in future functional studies.

Background: Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer.Methods: An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis.Results: Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR < 0.05). We then found that these 436 regulatory regions were associated with 74 potential target genes. After functional enrichment analysis, we observed that these potential target genes were enriched in cancer-associated pathways. We further demonstrated that using the gene expression signals and the epithelial tissue proportion extracted from this integration framework could stratify patient prognoses more accurately, outperforming predictions based on only omics or image features.Conclusion: This integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets.

Existing studies have demonstrated that the integration analysis of transcriptomic and epigenomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, such investigations on large-scale sequencing data remain challenging for researchers or clinicians with limited bioinformatics knowledge. To facilitate the interpretation of the gene regulatory landscape, we developed an R Shiny application [Linking of atac-seq to gene expression data (Linkage)] for exploring and visualizing potential cis-regulatory elements (CREs) of genes based on ATAC-seq and RNA-seq data. Linkage offers six modules to identify, annotate, and interpret potential gene regulatory elements from the whole genome step by step. Linkage currently supports the analysis of human and mouse datasets. Linkage can provide interactive visualization for the correlation between chromatin accessibility and gene expression. More than that, Linkage identifies transcription factors (TFs) that potentially drive the chromatin changes through identifying TF binding motifs within the CREs and constructing trans-regulatory networks of the target gene set. This powerful tool enables researchers to conduct extensive multi-omics integration analysis and generate visually appealing visualizations that effectively highlight the relationship between genes and corresponding regulatory elements. With Linkage, users can obtain publishable results and gain deeper insights into the gene regulatory landscape. ‘Linkage’ is freely available as a Shiny web application https://xulabgdpu.org.cn/linkage . The source code and instructions for Linkage can be accessed via Github https://github.com/luoyyyy/Linkage .

The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer’s disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important “subregion gene pairs”. The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD.

Cell states within cancer have garnered significant attention, yet the mechanisms through which malignant cells assert dominance in pan-cancer commonalities remain elusive. In this study, we employed label-free multiplexed single-cell RNA sequencing (scRNA-seq) to analyze cell states in 159,372 cells across 245 cell lines spanning 14 tissue types, integrating both public and proprietary datasets. We identified 21 meta-programs (MPs) representing shared characteristics across pan-cancer landscapes, encompassing 16 biological processes. Subsequently, we developed a deep learning model StateNet to generate cell-state fingerprints for delineating the individuality of each cell line based on these MPs. Leveraging StateNet, we pinpointed ACAT2 as a potential mediator bridging hypoxia and the lipid metabolism pathway, and we also showcased that epithelial–mesenchymal transition programs are vital for classifying cell lines through perturbation experiments. StateNet not only elucidates the overarching manifold structure of scRNA-seq data but also furnishes cell-state fingerprints of cell clusters, unveiling prognosis-related programs and distinguishing between patients with varying survival outcomes. Utilizing these prognosis-related programs on 3210 cancer samples, we constructed Cox models and identified risk-associated programs and genes responsible for different cancer types. StateNet thus emerges as a novel and efficient tool for cancer profiling, unraveling the shared commonalities and distinct individualities of pan-cancer cells across expansive datasets.

Background: Spatial transcriptomics technologies measure gene expression at different spatial resolutions and generate diverse data outputs. Existing workflows support a range of platforms and analyses, but combining methods and extending analyses to selected populations or tissue regions can require additional scripts and repeated data preparation. Findings: We developed Spatialsnake, a flexible workflow built on SpatialData and Snakemake for analysing processed outputs from Visium, Visium HD, Stereo-seq, Xenium and MERFISH. A common command-line interface supports single- and multi-sample analysis, with adjustable parameters and saved intermediate results. Users can select populations, tissue regions or distance-based neighborhoods, repeat clustering and annotation, and map refined labels back to the full dataset. Additional modules support cell-type composition estimation, spatial-domain identification, cell– cell communication inference, regulon analysis and sample-level comparisons. We demonstrate these functions using mouse-brain Visium and colorectal cancer Visium HD datasets. Joint analysis of three colorectal cancer sections retained 1,527,987 bins and enabled further analysis of 223,632 epithelial bins, linking epithelial subpopulation annotations to their locations within the complete tissue. Conclusions: Spatialsnake provides a unified workflow for spatial transcriptomics analysis across sequencing- and imaging-based platforms. Its flexible analysis modules support both whole-tissue analysis and further investigation of selected cell populations and tissue regions.