Desi Shang

Cell–cell communication (CCC) is central to the organization, function, and plasticity of multicellular life. Advancing experimental technologies and growing insights into complex multicellular systems and disease microenvironments are driving the demand for experimentally validated CCCs that are systematically and manually curated across diverse tissues, phenotypes, and signaling modalities. Here, we present CCCdb (http://www.licpathway.net/cccdb/index.php), a comprehensive, manually curated database of experimentally validated CCCs for human and mouse. A total of 8467 entries were extracted from thousands of publications, each annotated with standardized information on cell types, tissues, and phenotypes. These entries cover 98 tissues, 1132 cell types, and 548 phenotypes, mediated through communication via direct contact, autocrine, paracrine, and endocrine signaling. CCCdb curates experiment-supported CCCs across cellular subtypes, tissue interfaces, and physiological or pathological states. To enhance accessibility and biological interpretability, CCCdb integrates a ReAct-based AI assistant that enables intelligent natural-language queries and intuitive navigation through biological information. We believe that CCCdb will serve as a foundational resource for elucidating the mechanisms by which cells maintain tissue homeostasis and drive disease progression.

Spatially resolved transcriptomics technologies potentially provide the extra spatial position information and tissue image to better infer spatial cell–cell interactions (CCIs) in processes such as tissue homeostasis, development, and disease progression. However, methods for effectively integrating spatial multimodal data to infer CCIs are still lacking. Here, the authors propose a deep learning method for integrating features through co‐convolution, called SpaGraphCCI, to effectively integrate data from different modalities of SRT by projecting gene expression and image feature into a low‐dimensional space. SpaGraphCCI can achieve significant performance on datasets from multiple platforms including single‐cell resolution datasets (AUC reaches 0.860–0.907) and spot resolution datasets (AUC ranges from 0.880 to 0.965). SpaGraphCCI shows better performance by comparing with the existing deep learning‐based spatial cell communication inference methods. SpaGraphCCI is robust to high noise and can effectively improve the inference of CCIs. We test on a human breast cancer dataset and show that SpaGraphCCI can not only identify proximal cell communication but also infer new distal interactions. In summary, SpaGraphCCI provides a practical tool that enables researchers to decipher spatially resolved cell–cell communication based on spatial transcriptome data.

Single-cell RNA sequencing has revolutionized the high-resolution characterization of cellular heterogeneity within the tumor microenvironment. It is now widely recognized that cell heterogeneity plays a major role in determining variations in key clinical phenotypes, including patient survival, tumor size, lymph node involvement, metastasis, and responses to anticancer therapies. However, a critical gap remains in the lack of comprehensive databases that systematically link single-cell data to these clinically relevant phenotypes. To address this, we developed CPRCSdb (http://www.yzbio.top/CPRCSdb/), a comprehensive database that integrates clinical phenotypes with single-cell transcriptomic heterogeneity. CPRCSdb includes over 4.05 million cells from 1053 manually curated single-cell samples and 101 integrated datasets, along with 11,032 bulk RNA-seq samples, covering 29 cancer types. Our database spans 5 disease phenotype categories and the therapy response phenotypes of 30 different drugs. We analyzed 6197 associations between bulk-level phenotypes and single-cell heterogeneity, identifying over 1.13 million clinically relevant cells. Rigorous quality control measures were applied to ensure data accuracy. For each association, CPRCSdb supports downstream analyses, including differential expression, functional enrichment, and cell–cell communication inference. We anticipate that CPRCSdb will serve as a valuable and integrative resource for investigating the mechanisms underlying tumorigenesis and cancer progression.

Spatial omics technologies have enabled the creation of intricate spatial maps that capture molecular features and tissue morphology, providing valuable insights into the spatial associations and functional organization of tissues. Accurate annotation of spot or domain types is essential for downstream spatial omics analyses, but this remains challenging. Therefore, this study aimed to develop a manually curated spatial omics database (SpatialRef, https://bio.liclab.net/spatialref/), to provide comprehensive and high-quality spatial omics data with known spot labels across multiple species. The current version of SpatialRef aggregates >9 million manually annotated spots across 17 Human, Mouse and Drosophila tissue types through extensive review and strict quality control, covering multiple spatial sequencing technologies and >400 spot/domain types from original studies. Furthermore, SpatialRef supports various spatial omics analyses about known spot types, including differentially expressed genes, spatially variable genes, Gene Ontology (GO)/KEGG annotation, spatial communication and spatial trajectories. With a user-friendly interface, SpatialRef facilitates querying, browsing and visualizing, thereby aiding in elucidating the functional relevance of spatial domains within the tissue and uncovering potential biological effects.

Colorectal cancer (CRC) is a relatively common malignancy clinically and the second leading cause of cancer‐related deaths. Recent studies have identified T‐cell exhaustion as playing a crucial role in the pathogenesis of CRC. A long‐standing challenge in the clinical management of CRC is to understand how T cells function during its progression and metastasis, and whether potential therapeutic targets for CRC treatment can be predicted through T cells. Here, we propose DeepTEX, a multi‐omics deep learning approach that integrates cross‐model data to investigate the heterogeneity of T‐cell exhaustion in CRC. DeepTEX uses a domain adaptation model to align the data distributions from two different modalities and applies a cross‐modal knowledge distillation model to predict the heterogeneity of T‐cell exhaustion across diverse patients, identifying key functional pathways and genes. DeepTEX offers valuable insights into the application of deep learning in multi‐omics, providing crucial data for exploring the stages of T‐cell exhaustion associated with CRC and relevant therapeutic targets.

The advent of single cell transposase-accessible chromatin sequencing (scATAC-seq) technology enables us to explore the genomic characteristics and chromatin accessibility of blood cells at the single-cell level. To fully make sense of the roles and regulatory complexities of blood cells, it is critical to collect and analyze these rapidly accumulating scATAC-seq datasets at a system level. Here, we present scBlood ( https://bio.liclab.net/scBlood/ ), a comprehensive single-cell accessible chromatin database of blood cells. The current version of scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from ∼400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types. All data hosted on scBlood have undergone preprocessing from raw fastq files and multiple standards of quality control. Furthermore, we conducted comprehensive downstream analyses, including multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor (TF) enrichment analysis. In summary, scBlood provides a user-friendly interface for searching, browsing, analyzing, visualizing, and downloading scATAC-seq data of interest. This platform facilitates insights into the functions and regulatory mechanisms of blood cells, as well as their involvement in blood-related diseases.

Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway‐Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function‐based robust drug responsiveness prediction model. PBAC has four layers: gene‐pathway layer, attention layer, convolution layer and fully connected layer. PBAC improves the performance of predicting drug responsiveness by focusing on important pathways, helping us understand the mechanism of drug action in diseases. We validated the PBAC model using data from four chemotherapy drugs (Bortezomib, Cisplatin, Docetaxel and Paclitaxel) and 11 immunotherapy datasets. In the majority of datasets, PBAC exhibits superior performance compared to traditional machine learning methods and other research approaches (area under curve = 0.81, the area under the precision‐recall curve = 0.73). Using PBAC attention layer output, we identified some pathways as potential core cancer regulators, providing good interpretability for drug treatment prediction. In summary, we presented PBAC, a powerful tool to predict drug responsiveness based on the biology pathway information and explore the potential cancer‐driving pathways.

Long non-coding RNAs (lncRNAs) possess a wide range of biological functions, and research has demonstrated their significance in regulating major biological processes such as development, differentiation, and immune response. The accelerating accumulation of lncRNA research has greatly expanded our understanding of lncRNA functions. Here, we introduce LncSEA 2.0 (http://bio.liclab.net/LncSEA/index.php), aiming to provide a more comprehensive set of functional lncRNAs and enhanced enrichment analysis capabilities. Compared with LncSEA 1.0, we have made the following improvements: (i) We updated the lncRNA sets for 11 categories and extremely expanded the lncRNA scopes for each set. (ii) We newly introduced 15 functional lncRNA categories from multiple resources. This update not only included a significant amount of downstream regulatory data for lncRNAs, but also covered numerous epigenetic regulatory data sets, including lncRNA-related transcription co-factor binding, chromatin regulator binding, and chromatin interaction data. (iii) We incorporated two new lncRNA set enrichment analysis functions based on GSEA and GSVA. (iv) We adopted the snakemake analysis pipeline to track data processing and analysis. In summary, LncSEA 2.0 offers a more comprehensive collection of lncRNA sets and a greater variety of enrichment analysis modules, assisting researchers in a more comprehensive study of the functional mechanisms of lncRNAs.

Background: Hepatocellular carcinoma (HCC) is one of the major concerns regarding public health globally. Cancer-associated fibroblasts (CAFs) play a vital role in HCC progression. The identification of CAF-associated HCC subtypes and the development of CAF-related HCC precise treatment strategies are unmet needs. Methods: A total of 288 CAF signatures were obtained from previous studies. Consensus clustering analysis was employed to identify the CAF-related subtypes in HCC. Enrichment analysis, CIBERSORT, and ESTIMATE were applied to comprehensively evaluate heterogeneity across the HCC subtypes. Four machine-learning methods, including Least Absolute Shrinkage and Selector Operation regression, Elastic Net, survival Support Vector Machine, and Neural Network, were used to construct the prognostic model (HC score). The immunotherapy cohort was enrolled to explore the potential of the HC score in predicting immunotherapy responsiveness. Results: Based on the CAF signatures, we identified two HCC subtypes: HCf-inactive and HCf-active subtypes. The two HCC subtypes had significantly different immune features, fibrosis features, and prognoses. Furthermore, we constructed a CAF-related gene prognostic model HC score based on the integration of four machine-learning methods. The HC score predicted the outcomes in patients with HCC compared with traditional clinicopathological features. Moreover, the HC score could also effectively predict the microenvironment characteristics of HCC. The immunotherapy cohort analysis indicated that the HC score had great potential to help identify the immunotherapy candidates. Conclusions: We identified two CAF-related HCC subtypes: HCf-inactive and HCf-active subtypes. A gene prognostic model was developed that could well predict HCC prognosis, microenvironment status, and immunotherapy responsiveness.

Atherosclerosis (AS) is a major contributor to morbidity and mortality worldwide. However, the molecular mechanisms and mediator molecules involved remain largely unknown. Copper, which plays an essential role in cardiovascular disease, has been suggested as a potential risk factor. Copper homeostasis is closely related to the occurrence and development of AS. Recently, a new cell death pathway called cuproptosis has been discovered, which is driven by intracellular copper excess. However, no previous studies have reported a relationship between cuproptosis and AS. In this study, we integrated bulk and single-cell sequencing data to screen and identify key cuproptosis-related genes in AS. We used correlation analysis, enrichment analysis, random forest, and other bioinformatics methods to reveal their relationships. Our findings report, for the first time, the involvement of cuproptosis-related genes FDX1, SLC31A1, and GLS in atherogenesis. FDX1 and SLC31A1 were upregulated, while GLS was downregulated in atherosclerotic plaque. Receiver operating characteristic curves demonstrate their potential diagnostic value for AS. Additionally, we confirm that GLS is mainly expressed in vascular smooth muscle cells, and SLC31A1 is mainly localized in macrophages of atherosclerotic lesions in experiments. These findings shed light on the cuproptosis landscape and potential diagnostic biomarkers for AS, providing further evidence about the vital role of cuproptosis in atherosclerosis progression.