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SCAN: Spatiotemporal Cloud Atlas for Neural cells.
The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.
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ScRNA-seq links dental fibroblasts heterogeneity with mechanoresponsiveness.
BACKGROUND AND OBJECTIVE: Periodontal ligament (PDL) and dental pulp (DP) share a common origin but have distinct biological and mechanical functions. To what extent the mechanoresponsive property of PDL can be attributed to its unique transcriptional profiles of cellular heterogeneity is unclear. This study aims to decipher cellular heterogeneity and distinct mechanoresponsive characteristics of odontogenic soft tissues and their underlying molecular mechanisms. MATERIALS AND METHODS: A single-cell comparison of digested human periodontal ligament (PDL) and dental pulp (DP) was performed using scRNA-seq. An in vitro loading model was constructed to measure mechanoresponsive ability. Dual-luciferase assay, overexpression, and shRNA knockdown were used to investigate the molecular mechanism. RESULTS: Our results demonstrate striking fibroblast heterogeneity across and within human PDL and DP. We demonstrated that a tissue-specific subset of fibroblasts existed in PDL exhibiting high expression of mechanoresponsive extracellular matrix (ECM) genes, which was verified by an in vitro loading model. ScRNA-seq analysis indicated a particularly enriched regulator in PDL-specific fibroblast subtype, Jun Dimerization Protein 2 (JDP2). Overexpression and knockdown of JDP2 extensively regulated the downstream mechanoresponsive ECM genes in human PDL cells. The force loading model demonstrated that JDP2 responded to tension and that knockdown of JDP2 effectively inhibited the mechanical force-induced ECM remodeling. CONCLUSIONS: Our study constructed the PDL and DP ScRNA-seq atlas to demonstrate PDL and DP fibroblast cellular heterogeneity and identify a PDL-specific mechanoresponsive fibroblast subtype and its underlying mechanism.
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Single-cell sequencing and network pharmacology coupled with molecular docking and experimental validation reveal the effects of YPFS on macrophages in stage I non-small cell lung cancer.
BACKGROUND: Non-small cell lung cancer (NSCLC), the most common subtype of lung cancer, presents major clinical challenges owing to its highly complex tumor microenvironment and substantial cellular heterogeneity. A comprehensive understanding of the immune landscape-particularly the phenotypic diversity and functional roles of macrophage subpopulations-is critical for identifying novel therapeutic targets and developing more effective treatment strategies. METHODS: We constructed a comprehensive single-cell atlas of Stage I NSCLC using single-cell RNA sequencing (scRNA-seq) to characterize immune heterogeneity, with particular emphasis on macrophage subsets. Network pharmacology was employed to investigate the interactions between AHSA1 and bioactive compounds derived from the traditional Chinese medicine formulation Yu Ping Feng San (YPFS). To further evaluate these interactions, molecular docking simulations were performed to assess binding affinities and elucidate the potential regulatory effects on macrophage polarization. RESULTS: ScRNA-seq analysis revealed heterogeneous macrophage subtypes, each exhibiting distinct roles in tumor progression. AHSA1 was identified as a key regulator of macrophage polarization, with higher expression levels associated with unfavorable prognosis. Through network pharmacology and molecular docking, strong binding affinities were observed between AHSA1 and Yu Ping Feng San (YPFS)-derived compounds, including wogonin, kaempferol, and quercetin. Notably, quercetin displayed the highest binding affinity and exerted the strongest inhibitory effect on M2 macrophage polarization. CONCLUSION: These findings identify AHSA1 as a pivotal regulator of macrophage polarization in NSCLC and position quercetin as a promising macrophage-targeted therapeutic candidate. Furthermore, the results highlight the potential of Yu Ping Feng San (YPFS) as a complementary therapeutic strategy for NSCLC, offering a bridge between traditional Chinese medicine and modern oncology.
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Single-cell transcriptome analysis reveals SOX9 mutation-driven tumor stemness and microenvironment remodeling through the COL1A1-CD44 axis in colorectal cancer.
Colorectal cancer (CRC) exhibits substantial interpatient heterogeneity and is driven by intricate crosstalk between tumor-intrinsic mutations and the tumor microenvironment (TME), yet the mechanisms by which specific genetic alterations remodel the TME and regulate tumor stemness remain elusive. The SRY-box transcription factor 9 (SOX9) is a key regulator of intestinal homeostasis, with mutations implicated in CRC progression, but its single-cell level effects on TME dynamics have not been fully elucidated. To dissect the heterogeneity and cell-cell interactions underlying SOX9 mutation-driven CRC progression, we integrated single-cell RNA sequencing (scRNA-seq) data of 23 CRC tumor samples with bulk sequencing and mutation data from The Cancer Genome Atlas (TCGA) using the Scissor algorithm. We identified a malignant epithelial subpopulation (Scissor+) specifically associated with SOX9 mutations, which exhibits enhanced stemness signatures. Further, we predicted a significant upregulation of the COL1A1-CD44 axis in SOX9-mutant tumors via CellChat, which was supported by immunohistochemical analyses in patient tissues and mouse xenograft models. By integrating stemness-related genes and fibroblast-derived ligands, we established a 172-gene fibroblast-stemness signature that was associated with overall survival, recurrence-free survival, and disease-specific survival across multiple independent CRC cohorts. Functional validation via CD44 knockdown and in vivo xenograft assays confirmed that the COL1A1-CD44 axis mediates SOX9 mutation-driven stemness maintenance and tumor progression. Collectively, our data enhance understanding of how SOX9 mutations remodel the TME, pinpoint the COL1A1-CD44 axis as a promising therapeutic candidate for SOX9-mutant CRC, and support precise patient stratification and targeted interventions.
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Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states.
Single-cell RNA sequencing (scRNA-seq) is a prominent tool for studying human disease biology. The availability of massive scRNA-seq datasets and advanced machine learning has driven the development of single-cell foundation models that provide versatile cell representations based on expression. To understand disease states, we need to account for entire tissue ecosystems, simultaneously considering many different interacting cells. Here, we tackle this challenge by generating patient-level representations derived from multi-cellular expression contexts measured with scRNA-seq of tissues. We develop PaSCient, a machine learning model that employs a multi-level representation learning paradigm and provides importance scores at the cell and gene levels for fine-grained analysis of disease characteristics. We apply PaSCient to a large-scale scRNA-seq atlas of 12.5 million cells from over 2,700 patients. Comprehensive benchmarking demonstrates the superiority of PaSCient in disease classification and multiple downstream applications, including dimensionality reduction, feature prioritization, and patient subgroup discovery.
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Single-cell and spatial transcriptomics inform mechanistic physiology in non-model animals.
Comparative physiology increasingly requires cellular resolution at the level of cell types, tissue microenvironments, and regulatory programs that associate genotype with function under environmental variation. Bulk transcriptomics and tissue-level assays have revealed pathways associated with stress responses, metabolic shifts, and developmental transitions; however, they average signals across heterogeneous cell populations and frequently obscure which cells contribute to physiological phenotypes. Single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq), and spatial transcriptomics (ST) address complementary aspects of this limitation by resolving transcriptional heterogeneity and, for spatial approaches, preserving tissue context. These technologies are extending beyond classical biomedical models to non-model animals, enabling discovery of cell states, comparison of cell-type evolution, and spatially informed hypotheses concerning ionoregulation, respiration, immune defense, endocrine signaling, regeneration, and symbiosis. This review provides a physiology-centered blueprint for applying these approaches to non-model species and critically evaluates dissociation and preservation bias, genome annotation, seasonal and ecological variation, biological replication, pseudoreplication, cross-species integration, and spatial resolution. We distinguish descriptive mapping, replicate-aware association, mechanistic hypothesis generation, and causal validation because expression patterns, colocalization, trajectories, and ligand-receptor predictions do not by themselves demonstrate mechanism. Practical reporting and validation standards are proposed to improve reproducibility and connect cellular maps with independent physiological measurements and perturbation experiments.
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Single-cell transcriptome analyses reveal cellular and molecular responses to low nitrogen in burley tobacco leaves.
Tobacco (Nicotiana tabacum) is cultivated and consumed worldwide. It requires great amounts of nitrogen (N) to achieve the best yield and quality. With a view to sustainable and environmentally friendly agriculture, developing new genotypes with high productivity under low N conditions is an important approach. It is unclear how genes in tobacco are expressed at the cellular level and the precise mechanisms by which cells respond to environmental stress, especially in the case of low N. Here, we characterized the transcriptomes in tobacco leaves grown in normal and low-N conditions by performing scRNA-seq. We identified 10 cell types with 17 transcriptionally distinct cell clusters with the assistance of marker genes and constructed the first single-cell atlas of tobacco leaves. Distinct gene expression patterns of cell clusters were observed under low-N conditions, and the mesophyll cells were the most important responsive cell type and displayed heterogene responses among its three subtypes. Pseudo-time trajectory analysis revealed low-N stress decelerates the differentiation towards mesophyll cells. In combination with scRNA-seq, WGCNA, and bulk RNA-seq results, we found that genes involved in porphyrin metabolism, nitrogen metabolism, carbon fixation, photosynthesis, and photosynthesis-antenna pathway play an essential role in response to low N. Moreover, we identified COL16, GATA24, MYB73, and GLK1 as key TFs in the regulation of N-responsive genes. Collectively, our findings are the first observation of the cellular and molecular responses of tobacco leaves under low N stress and lay the cornerstone for future tobacco scRNA-seq investigations.
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Single-cell analysis identifies endothelial venous thromboembolism signatures as predictors of immunotherapy resistance and survival across cancers.
BACKGROUND: Venous thromboembolism (VTE) is a major complication in cancer patients, yet the transcriptional mechanisms by which tumor-associated endothelial cells drive thrombogenesis and immune evasion remain poorly understood. OBJECTIVES: This study characterized VTE-related transcriptional signatures in the tumor microenvironment (TME), identified endothelial-specific risk genes, and developed a prognostic model linking hypercoagulation, immune dysfunction, and tumor progression. METHODS: We systematically analyzed 38 single-cell RNA-sequencing (scRNA-seq) datasets from diverse tumor types to characterize VTE-related gene expression across the TME. Endothelial cells were identified as the primary carriers of the transcriptional signatures of VTE. A VTE score was computed at the single-cell level to describe high-risk endothelial cell subsets. Functional annotation, intercellular communication analysis, and immune profiling were performed to assess the phenotypic and immunologic roles of these genes. Based on bulk RNA-seq datasets from The Cancer Genome Atlas and Gene Expression Omnibus, an endothelial-derived VTE molecular model (Endo.VTM) was constructed. Clustered regularly interspaced short palindromic repeats-associated protein 9 dependency screening was also performed. RESULTS: Endothelial cells with high-VTE scores showed transcriptional programs enriched for coagulation, immune suppression, and altered cell-cell signaling. The Endo.VTM, comprising 12 core genes, demonstrated robust prognostic value across multiple cancer types and was significantly associated with immune checkpoint expression, tumor immune dysfunction, and response to immunotherapy. Clustered regularly interspaced short palindromic repeats-associated protein 9 dependency screening highlights therapeutic vulnerabilities among model genes. CONCLUSION: We revealed that endothelial cells serve as central regulators of thrombosis-related transcription in the TME and revealed a novel axis linking VTE, immune evasion, and tumor progression. The Endo.VTM offers a clinically actionable framework for prognostic stratification.
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Clonal Metamorphosis: Deconstructing MPN Evolution with Single-Cell and Spatial Multi-Omics.
Myeloproliferative neoplasms (MPNs) present a fundamental paradox: despite sharing a small set of canonical driver mutations in JAK2, CALR, or MPL, patients exhibit striking heterogeneity in disease latency, clinical presentation, and evolutionary trajectories to myelofibrosis or secondary acute myeloid leukemia. This review synthesizes recent advances in single-cell and spatial multi-omic technologies that are resolving this paradox by moving analysis from bulk averages to individual cells and their microenvironmental ecosystems. We examine how targeted single-cell DNA sequencing reconstructs clonal architectures and phylogenies, revealing that driver mutations arise within complex mosaics where mutation order, co-mutation context, and cellular ancestry determine phenotypic outcomes. Integrated single-cell transcriptomic and epigenomic profiling exposes within-clone heterogeneity, lineage biases, and functional states that explain variable penetrance and therapy responses. Spatial transcriptomics, especially when integrated with single-cell transcriptomics, histopathology, and multiplex proteomics, further demonstrates that malignant hematopoietic stem and progenitor cells actively remodel bone marrow niches, creating localized inflammatory and fibrotic microenvironments that select for aggressive subclones. Together, these approaches support a new ecological model of MPN pathogenesis in which early epigenetic hits create permissive stem cell reservoirs, clonal competition and cooperation shape disease progression, and non-cell-autonomous niche and immune signals drive malignant metamorphosis. We discuss how this framework refines prognostication, informs rational combination therapies targeting both malignant cells and their ecosystem, and enables real-time monitoring of clonal dynamics, ultimately charting a course from descriptive atlases to actionable clinical strategies.
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Integrated single-cell and machine learning analysis identifies PMAIP1 as a novel biomarker for predicting prognosis and immunotherapy response in colorectal cancer.
Metastasis in colorectal cancer (CRC) is the primary driver of its high mortality rate, with dysregulation of apoptosis and remodeling of the tumor microenvironment (TME) serving as key biological events driving disease progression. However, the intrinsic molecular connections between these processes—particularly the dynamic changes during the progression from primary tumors to liver metastases—remain to be systematically elucidated. This study aims to identify core genes regulating the crosstalk between apoptosis and immunity in CRC using multidimensional computational biology approaches and to explore their clinical value as prognostic biomarkers and potential therapeutic targets. This study integrated single-cell RNA sequencing (scRNA-seq) data with bulk transcriptomic data from The Cancer Genome Atlas (TCGA) database. We first utilized scRNA-seq to dissect cellular heterogeneity and communication networks in CRC primary tumors and liver metastases. Subsequently, weighted gene co-expression network analysis (WGCNA) and differential expression analysis were employed, combined with machine learning algorithms (LASSO, Random Forest, XGBoost), to screen features from apoptosis- and immunity-related gene sets and pinpoint key candidate genes. Finally, the biological functions and clinical significance of the core gene were systematically validated through pan-cancer analysis, immune infiltration analysis, mutation profile analysis, prognostic model construction, and validation via real-time quantitative PCR (RT-qPCR) in clinical samples. Single-cell profiling revealed that colorectal cancer (CRC) liver metastases exhibit a distinct pro-inflammatory immune microenvironment characterized by the enrichment of CD8⁺ effector memory T cells and inflammatory monocytes, along with extensive remodeling of cell-cell communication networks centered on the MIF and CD99 signaling pathways. Through multi-level bioinformatics analysis, we identified the apoptosis-related gene PMAIP1 as a central regulatory hub mediating these processes. Clinical cohort and pan-cancer analyses demonstrated that PMAIP1 is significantly overexpressed across multiple tumor types, particularly in CRC, where its elevated expression serves as an independent predictor of poor prognosis (HR = 0.39, p = 0.028). Further investigation uncovered a critical biological paradox: On the one hand, the high expression of PMAIP1 was positively correlated with the infiltration of core effector immune cells, such as CD8⁺T cells. CD8⁺T cells are the key immune cells mediating tumor cell killing, and their enrichment is expected to form an effective anti-tumor immune barrier, which is consistent with the characteristics of the “pro-inflammatory” immune microenvironment. On the other hand, the high expression of PMAIP1 is also significantly associated with the up-regulation of multiple immune checkpoint molecules, such as PD-1 and PD-L1. The activation of immune checkpoint molecules directly induces the functional exhaustion of infiltrating CD8⁺T cells, which is manifested as decreased proliferation ability, decreased secretion of killer cytokines, and loss of anti-tumor activity. In short, PMAIP1 both “recruits” effector immune cells with antitumor potential and “weakens” these cells by upregulating immune checkpoint molecules, resulting in the coexistence of two contradictory phenotypes of “increased immune cell infiltration” and “decreased immune response efficiency”. Finally, it still promotes the progress of colorectal cancer and is related to the poor prognosis of patients. A nomogram model incorporating PMAIP1 demonstrated strong performance in predicting patient survival, and its overexpression in clinical CRC samples was validated by RT-qPCR. This study systematically delineates the immune landscape of CRC primary tumors and liver metastases, and for the first time reveals the apoptosis gene PMAIP1 as a critical regulatory node linking cellular apoptosis to immune microenvironment remodeling. PMAIP1 not only emerges as a potent, novel biomarker for poor prognosis in CRC but also, through its dual functions in regulating immune infiltration and checkpoints, represents a highly promising target for guiding combined immunotherapy strategies.