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Reconstruction of the gastric cancer microenvironment after neoadjuvant chemotherapy by longitudinal single-cell sequencing.
BACKGROUND: Little is known on the tumor microenvironment (TME) response after neoadjuvant chemotherapy (NACT) in gastric cancer on the molecular level. METHODS: Here, we profiled 33,589 cell transcriptomes in 14 samples from 11 gastric cancer patients (4 pre-treatment samples, 4 post-treatment samples and 3 pre-post pairs) using single-cell RNA sequencing (scRNA-seq) to generate the cell atlas. The ligand-receptor-based intercellular communication networks of the single cells were also characterized before and after NACT. RESULTS: Compered to pre-treatment samples, CD4+ T cells (P = 0.018) and CD8+ T cells (P = 0.010) of post-treatment samples were significantly decreased, while endothelial cells and fibroblasts were increased (P = 0.034 and P = 0.005, respectively). No significant difference observed with respect to CD4+ Tregs cells, cycling T cells, B cells, plasma cells, macrophages, monocytes, dendritic cells, and mast cells (P > 0.05). In the unsupervised nonnegative matrix factorization (NMF) analysis, we revealed that there were three transcriptional programs (NMF1, NMF2 and NMF3) shared among these samples. Compared to pre-treatment samples, signature score of NMF1 was significantly downregulated after treatment (P = 0.009), while the NMF2 signature was significantly upregulated after treatment (P = 0.013). The downregulated NMF1 and upregulated NMF2 signatures were both associated with improved overall survival outcomes based on The Cancer Genome Atlas (TCGA) database. Additionally, proangiogenic pathways were activated in tumor and endothelial cells after treatment, indicating that NACT triggers vascular remodeling by cancer cells together with stromal cells. CONCLUSIONS: In conclusion, our study provided transcriptional profiles of TME between pre-treatment and post-treatment for in-depth understanding on the mechanisms of NACT in gastric cancer and empowering the development of potential optimized therapy procedures and novel drugs.
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Mapping cells through time and space with moscot.
Single-cell genomic technologies enable the multimodal profiling of millions of cells across temporal and spatial dimensions. However, experimental limitations hinder the comprehensive measurement of cells under native temporal dynamics and in their native spatial tissue niche. Optimal transport has emerged as a powerful tool to address these constraints and has facilitated the recovery of the original cellular context1-4. Yet, most optimal transport applications are unable to incorporate multimodal information or scale to single-cell atlases. Here we introduce multi-omics single-cell optimal transport (moscot), a scalable framework for optimal transport in single-cell genomics that supports multimodality across all applications. We demonstrate the capability of moscot to efficiently reconstruct developmental trajectories of 1.7 million cells from mouse embryos across 20 time points. To illustrate the capability of moscot in space, we enrich spatial transcriptomic datasets by mapping multimodal information from single-cell profiles in a mouse liver sample and align multiple coronal sections of the mouse brain. We present moscot.spatiotemporal, an approach that leverages gene-expression data across both spatial and temporal dimensions to uncover the spatiotemporal dynamics of mouse embryogenesis. We also resolve endocrine-lineage relationships of delta and epsilon cells in a previously unpublished mouse, time-resolved pancreas development dataset using paired measurements of gene expression and chromatin accessibility. Our findings are confirmed through experimental validation of NEUROD2 as a regulator of epsilon progenitor cells in a model of human induced pluripotent stem cell islet cell differentiation. Moscot is available as open-source software, accompanied by extensive documentation.
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Construction of lactate-myeloid-related prognostic risk model and prediction of immune-excluded microenvironment in gastric cancer via integrated scRNA-Seq and bulk RNA-Seq.
BACKGROUND: Gastric cancer (GC) progression entails profound epithelial-stromal remodeling, yet how epithelial metabolic programs are linked to myeloid-dominated immune exclusion and prognostic risk remains unclear. This study aimed to integrate single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic analyses to characterize a lactate-myeloid-related risk axis in GC. METHODS: We integrated six scRNA-seq datasets comprising 116 samples and 355,091 cells spanning paratumor (PT), chronic atrophic gastritis (CAG), moderate intestinal metaplasia (MIM), severe intestinal metaplasia (SIM), early gastric cancer (EGC), and advanced gastric cancer (AGC), and coupled them to bulk cohorts. Epithelial and myeloid states, pseudotime trajectories, ligand-receptor programs, and pathway activities were analyzed. A 19-gene prognostic model anchored on SLC16A3/MCT4 co-expression and macrophage immunoregulatory modules was developed in The Cancer Genome Atlas stomach adenocarcinoma (TCGA-STAD) cohort and externally validated in GSE84437. Mutation burden, tumor immune dysfunction and exclusion (TIDE), and expression-based drug-response predictions were further evaluated. SLC16A3 expression was validated by immunohistochemistry (IHC) in an independent clinical cohort. RESULTS: The integrated atlas showed immune contraction with epithelial expansion in premalignant lesions, followed by enrichment of aberrant epithelium and myeloid lineages in EGC/AGC. Within epithelium, a late-pseudotime SLC16A3+ lactate-export program increased with progression and was associated with extracellular matrix (ECM) remodeling, pro-angiogenic signaling, and myeloid-recruiting chemokines. IHC confirmed marked SLC16A3 enrichment within dense, cohesive neoplastic nests of Lauren intestinal and mixed subtypes. Myeloid decomposition revealed macrophage-centric expansion dominated by SPP1+ and APOE+ tumor-associated macrophage (TAM) states. The model-derived risk score stratified overall survival independently of clinical covariates. Low-risk tumors exhibited higher mutation counts and tumor mutational burden (TMB), whereas high-risk tumors were genomically quieter but TIDE-high. Expression-based analyses nominated differential drug-response patterns across risk groups, which require further validation. CONCLUSIONS: This study identifies an epithelial SLC16A3-associated lactate-myeloid transcriptional program linked to macrophage-rich immune exclusion and poor prognosis in GC. The 19-gene model provides a biologically interpretable framework for risk stratification, while the immune-evasion and drug-response findings should be considered hypothesis-generating and require prospective and functional validation.
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Pan-cancer landscape of basement membrane: multi-omics research and single-cell sequencing validation.
Epithelial carcinoma cells require penetration of the basement membrane (BM) to metastasize. The BM is a thin layer of extracellular matrix beneath epithelial and endothelial tissues. It acts as a structural barrier, preventing cancer cells from invading and undergoing endocytosis and exocytosis. Thus, understanding the relationship between the BM and tumor immunity can lead to new strategies for halting cancer progression and metastasis. Gene expression data of 33 cancers were obtained from the Cancer Genome Atlas database. The study analyzed the correlation between BM regulatory genes, copy number variations, immune-related genes, and tumor immune dysfunction rejection (TIDE). Immunohistochemical methods were used to analyze the expression of regulatory genes. And the BM score was calculated using single-sample gene set enrichment analysis. Single-cell transcriptional sequencing determined the activation status of the BM in the tumor microenvironment. The expression of BM-related genes (BMGs) exhibited significant heterogeneity across different cancer types. Most genes were up-regulated in tumor tissues. Major single nucleotide polymorphisms of BMGs included missense mutations, while major copy number variations were heterozygous deletion and heterozygous amplification. Additionally, the expressions of immune checkpoint molecules CD276, NRP1, and C10orf54 showed positive correlations with BMS. Numerous tumors displayed a significant positive correlation between BMS and TIDE scores. We demonstrate that BM regulatory genes undergo alterations specific to different cancer types, which are associated with the expression of immune checkpoints and immune dysfunction. This indicates that BM remodeling plays an active role in modulating immune resistance, rather than being a passive structural alteration.
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A single-cell atlas of mouse olfactory bulb chromatin accessibility.
Olfaction, the sense of smell, is a fundamental trait crucial to many species. The olfactory bulb (OB) plays pivotal roles in processing and transmitting odor information from the environment to the brain. The cellular heterogeneity of the mouse OB has been studied using single-cell RNA sequencing. However, the epigenetic landscape of the mOB remains mostly unexplored. Herein, we apply single-cell assay for transposase-accessible chromatin sequencing to profile the genome-wide chromatin accessibility of 9,549 single cells from the mOB. Based on single-cell epigenetic signatures, mOB cells are classified into 21 clusters corresponding to 11 cell types. We identify distinct sets of putative regulatory elements specific to each cell cluster from which putative target genes and enriched potential functions are inferred. In addition, the transcription factor motifs enriched in each cell cluster are determined to indicate the developmental fate of each cell lineage. Our study provides a valuable epigenetic data set for the mOB at single-cell resolution, and the results can enhance our understanding of regulatory circuits and the therapeutic capacity of the OB at the single-cell level.
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Targeted single-cell RNA sequencing analysis reveals metabolic reprogramming and the ferroptosis-resistant state in hematologic malignancies.
Hematologic malignancies are the most common hematopoietic diseases and a major public health concern. However, the mechanisms underlying myeloid tumors remain unknown owing to the intricate interplay between mutations and diverse clonal evolution patterns, as evidenced by the analysis of bulk cell-derived omics data. Several single-cell omics techniques have been used to characterize the hierarchies and altered immune microenvironments of hematologic malignancies. The comprehensive single-cell atlas of hematologic malignancies provides novel opportunities for personalized combinatorial targeted treatments, avoiding unwanted chemo-toxicity. In the present study, we performed transcriptome sequencing by combining single-cell RNA sequencing (scRNA-seq) with a targeted oncogenic gene panel for acute myeloid leukemia, overcoming the limitations of scRNA-seq in detecting oncogenic mutations. The distribution of oncogenic IDH1, IDH2, and KRAS mutations in each cell type was identified in the bone marrow (BM) samples of each patient. Our findings suggest that ferroptosis and metabolic reprogramming are involved in the tumorigenesis and chemotherapy resistance of oncogenic mutation-carrying cells. Biological progression via IDH1, IDH2, and KRAS mutations arrests hematopoietic maturation. Our study findings provide a rationale for using primary BM cells for personalized treatment in clinical settings.
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Single-Cell Profiling Across Immune Tissues and Organs Reveals Immunosenescence Signatures in Male Rhesus Monkeys.
Aging gradually impairs immune system function, yet its systemic features across immune organs remain poorly characterized in primates. Here, we perform single-cell transcriptomic profiling of bone marrow, spleen, mesenteric lymph nodes, and peripheral blood mononuclear cells from young and naturally aged male rhesus monkeys. Our study revealed extensive transcriptional remodeling across tissues, particularly the marked upregulation of GZMB expression across multiple cell types in aged monkeys, highlighting it as a candidate biomarker of immunosenescence. Gene regulatory network analysis identifies BHLHE40 as a key transcription factor enriched in multiple CD8+ T cell subtypes during aging, regulating pro-inflammatory and exhaustion-related genes. We also observe an age-associated expansion of CD8+ central memory T cells with increased CCL5 and reduced IL7R expression, consistent with a shift toward a dysfunctional state. In the bone marrow, we discover a distinct naïve B cell population with low PDCD4 expression that declines with age, potentially compromising humoral immunity. These findings offer a comprehensive single-cell atlas of immune aging in a non-human primate model, providing novel insights into cell-type-specific and tissue-dependent features of immunosenescence. Our work establishes a valuable resource for future translational studies and biomarker discovery in human aging.
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Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging.
The bone marrow is the organ responsible for blood production. Diverse non-hematopoietic cells contribute essentially to hematopoiesis. However, these cells and their spatial organization remain largely uncharacterized as they have been technically challenging to study in humans. Here, we used fresh femoral head samples and performed single-cell RNA sequencing (scRNA-Seq) to profile 29,325 enriched non-hematopoietic bone marrow cells and discover nine transcriptionally distinct subtypes. We next employed CO-detection by inDEXing (CODEX) multiplexed imaging of 18 individuals, including both healthy and acute myeloid leukemia (AML) samples, to spatially profile over one million single cells with a novel 53-antibody panel. We discovered a relatively hyperoxygenated arterio-endosteal niche for early myelopoiesis, and an adipocytic, but not endosteal or perivascular, niche for early hematopoietic stem and progenitor cells. We used our atlas to predict cell type labels in new bone marrow images and used these predictions to uncover mesenchymal stromal cell (MSC) expansion and leukemic blast/MSC-enriched spatial neighborhoods in AML patient samples. Our work represents the first comprehensive, spatially-resolved multiomic atlas of human bone marrow and will serve as a reference for future investigation of cellular interactions that drive hematopoiesis.
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Single-cell and transcriptome analyses revealed CTHRC1 a potential therapeutic target mediating invasion and tumor microenvironment in TNBC: experimental validation.
BACKGROUND: Investigating the pivotal role of CTHRC1 in the tumor microenvironment of triple-negative breast cancer (TNBC). METHOD: The RNA transcriptomic data obtained from the Cancer Genome Atlas and single-cell sequencing data from TNBC in Gene Expression Omnibus (GEO) were acquired and subjected to analysis. A comprehensive investigation was conducted with a specific focus on characterizing CTHRC1 in TNBC and its correlation with invasive genes. Furthermore, additional analyses were performed to explore the relationship between CTHRC1, tumor immune cell infiltration, and immunotherapy in TNBC. The expression of CTHRC1 in the tumor microenvironment, cellular differentiation, and cellular communication was systematically analyzed using single-cell data from TNBC. RESULT: The expression of CTHRC1 in patients with TNBC gradually increases concomitantly with the progression of tumor T-stage and N-stage. Simultaneously, there is a concurrent increase in the expression of most invasive gene sets. Furthermore, there is a significant augmentation in both infiltration abundance and activity of M2-type macrophages associated with elevated levels of CTHRC1 expression. Single-cell data reveal an upregulated expression of the invasive gene set in CTHRC1-positive cancer associated fibroblasts (CAFs), thereby modulating their interaction with M2-type macrophages. Multiple immunofluorescence analyses confirmed that CTHRC1 modulates immune cell infiltration and tumor cell invasion through the mediation of CAFs. CONCLUSION: CTHRC1 was a molecule that exhibits characteristic expression in TNBC. CTHRC1 positive CAFs exert regulatory effects within the immunosuppressive microenvironment of TNBC by modulating M2-type macrophages.
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Attention-guided enhanced deconvolution enables reference-free cell type estimation in spatial transcriptomics.
Spatial transcriptomics technologies profile gene expression across tissue sections while retaining spatial information, yet most platforms capture signals from multiple cells per measurement location, requiring computational methods to determine the underlying cellular composition. Current deconvolution approaches either depend on single-cell reference atlases-which may be unavailable for rare tissues or specific disease contexts-or employ unsupervised methods that struggle with complex spatial patterns and typically need manual specification of how many cell types to identify. We developed Attention-Guided Enhanced Deconvolution (AGED), a two-stage framework combining probabilistic modeling with neural attention architectures for reference-free analysis. The first stage uses a Performer-based network with linear-complexity attention to systematically evaluate models with different numbers of cell types, automatically selecting the optimal configuration through composite scoring of reconstruction quality, cluster separation, and topic diversity. The second stage, Attention-Guide, stars with hierarchical probabilistic initialization. It progressively refines cell type features through multiple attention mechanisms: cross-attention based on statistical priors, spatial attention aggregating neighborhood information, and collaborative attention capturing theme-gene associations. A dynamic gating mechanism enables the model to balance global statistical patterns with locally data-driven features, while regularization promotes sparse solutions consistent with biological reality-each position containing only a few dominant cell types. Testing on Mouse Olfactory Bulb (MOB) tissue, AGED automatically identified four anatomical structures and achieved superior reconstruction performance (r=0.86) with characteristic sparsity patterns. When similarly applied to human pancreatic ductal adenocarcinoma (PDAC) and human thymus tissue, it revealed detailed relationships between dissected structures and cell types. The learned cell type distributions aligned well with established neuroanatomical boundaries and known molecular markers, indicating the attention-based refinement maintains biological interpretability throughout training. This framework offers a practical solution for spatial transcriptomics analysis across diverse experimental systems without requiring matched single-cell data.