Spatiotemporal coordination of cellular and molecular events is crucial for cell fate commitment during mouse gastrulation. However, the high-precision mechanisms governing the timing and spatial dynamics remain poorly understood. Here, we present a time-series single-cell multi-omic dataset of the gastrulating mouse embryos and construct a hierarchical gene regulatory landscape. Integrating this with real three-dimensional transcriptomic coordinate, we created ST-MAGIC and ST-MAGIC (+) atlas, dissecting the spatiotemporal logics of regulatory networks and signaling responsiveness underpinning the lineage commitment at gastrulation. Specifically, we delineated the multi-omic basis for left-right symmetry breaking events in the gastrula and also revealed the spatiotemporal molecular relay for axial mesendoderm lineage, where early and intermediate transcription factors first open the chromatin regions and setup the responsiveness to signaling, followed by terminal factors to consolidate the transcriptomic architecture. In summary, our study presents a spatiotemporal regulatory logic framework of mouse gastrulation for advancing our understanding of mammalian embryogenesis. Spatiotemporal coordination of cellular and molecular events is crucial for cell fate commitment. Here, Yang et al. describe how transcription factors, signaling pathways, and epigenomic states regulate left-right patterning and axial mesendoderm lineage commitment during mouse gastrulation.

Spatially resolved transcriptomic technologies have emerged as pivotal tools for elucidating molecular regulation and cellular interplay within the intricate tissue microenvironment, but hampered by insufficient gene recovery or challenges in achieving intact single-cell resolution. Here, we develop Cellular Mapping of Attributes with Position (CMAP), a method that efficiently maps large-scale individual cells to their precise spatial locations by integrating single-cell and spatial data through a divide--and--conquer strategy. Analysis of both simulated and real datasets shows that CMAP performs effectively and is adaptable across diverse data types and sequencing platforms. Particularly, CMAP handles scenarios well where discrepancies exist between single-cell and spatial transcriptomics data. Our findings underscore CMAP's capacity to endow single-cells with exact spatial coordinates, facilitating the dissection of nuanced spatial-organ-specific endothelial cell heterogeneity, as well as the intricate cancer immune microenvironments that elude conventional single-cell or spatial data analysis.

Introduction Adenomyosis is a prevalent gynaecological disorder affecting women of reproductive age, yet the underlying etiology and cellular-molecular mechanisms driving its pathogenesis remain poorly understood. Gonadotropin-releasing hormone agonists (GnRHa) are commonly used to improve clinical pregnancy rates in affected patients; however, their effects on specific cellular niches within the diseased uterus have not been fully elucidated. Objective This study aimed to profile adenomyosis at both the single-cell and spatial transcriptomic levels to examine various cell populations and systematically evaluate the effects of GnRHa treatment. Methods To characterize in depth the cellular and molecular landscape of adenomyosis, we employed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (Geo-seq) to profile 15 participants, including 11 patients with adenomyosis (three untreated and eight treated with GnRHa) and four control participants. For validation using immunofluorescence and immunohistochemistry, we included an additional two control subjects and 11 patients with adenomyosis. Furthermore, publicly available scRNA-seq datasets from samples collected during the proliferative phase of the menstrual cycle were incorporated for comparison. Results Our analysis revealed that ectopic endometrial glands in adenomyosis were enriched with ciliated epithelial cells, a feature that persisted following GnRHa treatment, suggesting a potential role of these cells in disease pathogenesis. Immune-inflammatory signatures, including elevated CD4+ T cells and LYVE1+ macrophages, were prominent in untreated adenomyosis, contributing to a pro-angiogenic microenvironment. This inflammatory and angiogenic activity was partially mitigated by GnRHa therapy. Notably, mast cells were found to be concentrated in the junctional zone, implicating this region as a potential site of disease initiation. Conclusion Our findings provide evidence for the invagination theory, highlighting the involvement of ciliated epithelial cells and immune-angiogenic crosstalk in adenomyosis pathogenesis. Moreover, this study demonstrates that GnRHa exerts therapeutic effects through the normalization of immune cell composition and restoration of epithelial-stromal interaction, offering novel mechanistic insights into its mode of action.

A fundamental principle of immunotherapy is that T cells are capable of detecting tumor epitopes presented on cancer cell surfaces. Immunopeptidomic strategies empowered by liquid chromatography-tandem mass spectrometry have transformed tumor epitopes identification and provided novel insights into tumor immunology. It enables in-depth profiling of major histocompatibility complex (MHC) presented ligands, thereby offering valuable perspectives on the molecular dialog among tumor and T cells. Here, we developed an immune-ligand identification and analysis pipeline from large-scale immunopeptidomics data. Through an extensive collection and processing of 5821 immunopeptidomic samples, which amounted to 305.7 million MS2 spectra, we identified 24 380 595 peptide-spectrum matches from these samples and further detected a total of 1 017 731 unique MHC immune ligands. These ligands were deconvolved and classified to specific HLA alleles. In total, we detected 582 852 HLA-I peptides and 434 879 HLA-II peptides that can bind to 292 HLA alleles, thereby greatly expanding the cancer immunopeptidome. Additionally, we identified and annotated 372 720 tumor-associated post-translational modification (PTM) peptides, revealing the comprehensive landscape of PTM antigens. All ligands and annotations were aggregated into Ligand.MHC Atlas, a comprehensive repository dedicated to tumor-derived HLA-presented ligands across 26 major human cancers (54 subtypes). Overall, our study uniquely integrates batch-effect correction, leverages the optimized software with novel deconvolution approach for immunopeptidomics analysis and ligand identification, and provides a public web portal with a comprehensive HLA ligand repository. Ligand.MHC Atlas functions as an invaluable resource, offering crucial understandings into immunology investigations. It will accelerate the advancement of cancer vaccines and immunotherapies. Ligand.MHC Atlas is available at http://modinfor.com/Ligand.MHC-Atlas/.

The functional development of the mammalian lung is a complex process that relies on the spatial and temporal organization of multiple cell types and their states. However, a comprehensive spatiotemporal transcriptome atlas of the developing lung has not yet been reported. Here we apply high-throughput spatial transcriptomics to allow for a comprehensive assessment of mouse lung development comprised of two critical developmental events: branching morphogenesis and alveologenesis. We firstly generate a spatial molecular atlas of mouse lung development spanning from E12.5 to P0 based on the integration of published single cell RNA-sequencing data and identify 10 spatial domains critical for functional lung organization. Furthermore, we create a lineage trajectory connecting spatial clusters from adjacent time points in E12.5-P0 lungs and explore TF (transcription factor) regulatory networks for each lineage specification. We observe the establishment of pulmonary airways within the developing lung, accompanied by the proximal-distal patterning with distinct characteristics of gene expression, signaling landscape and transcription factors enrichment. We characterize the alveolar niche heterogeneity with maturation state differences during the later developmental stage around birth and demonstrate differentially expressed genes, such as Angpt2 and Epha3, which may perform a critical role during alveologenesis. In addition, multiple signaling pathways, including ANGPT, VEGF and EPHA, exhibit increased levels in more maturing alveolar niche. Collectively, by integrating the spatial transcriptome with corresponding single-cell transcriptome data, we provide a comprehensive molecular atlas of mouse lung development with detailed molecular domain annotation and communication, which would pave the way for understanding human lung development and respiratory regeneration medicine.

The clinical application of checkpoint inhibitors in lung cancer has been impeded by the emergence of immune-related adverse effects, such as pneumonitis. To date, the precise immune pathogenesis of immune checkpoint inhibitor-related pneumonitis (CIP) remains elusive. Here, we perform comprehensive single-cell analysis, specifically the combination of scRNA-seq and scTCR/BCR-seq, to profile molecular and cellular changes in CIP tissues and matched noncancerous adjacent tissues from lung cancer patients. CIP patients exhibit disrupted immune homeostasis, marked by expansion of the CD8⁺ tissue-resident memory T cell population, elevated IFNG expression and increased TCR clone sharing with other CD8⁺ T cells. We also identify increased IL-17A levels, robust IgG isotype class switching in B cells and GSDME-mediated macrophage pyroptosis as potential mechanisms involved in CIP. These findings provide valuable insights into the mechanisms underlying CIP and inform potential strategies for further intervention.

T cell receptors (TCRs) play a vital role in immune recognition by binding specific epitopes. Accurate prediction of TCR–epitope interactions is fundamental for advancing immunology research. Although numerous computational methods have been developed, a comprehensive evaluation of their performance remains lacking. Here we assessed 50 state-of-the-art TCR–epitope prediction models using 21 datasets covering 762 epitopes and hundreds of thousands binding TCRs. Our analysis revealed that the source of negative TCRs substantially impacts model accuracy, with external negatives potentially introducing uncontrolled confounders. Model performance generally improved with more TCRs per epitope, highlighting the importance of large and diverse datasets. Models incorporating multiple features typically outperformed those using only complementarity-determining region 3β information, yet all struggle to generalize to unseen epitopes. The use of independent test sets proved crucial for unbiased assessment on both seen and unseen epitopes. These insights will guide the development of more accurate and generalizable TCR–epitope prediction models for real-world applications. This Analysis benchmarks 50 state-of-the-art TCR–epitope binding prediction methods and evaluates key factors that influence predictive performance.

Background The lung is a vital organ for mammalian life, as it is intricately connected to the cardiovascular system for supporting gas exchange. The functional development of the mammalian lung is a complex process that relies on the spatial and temporal organization of multiple cell types and their states. However, a comprehensive spatiotemporal transcriptome atlas of the developing lung has not yet been reported. Results Here we apply high-throughput spatial transcriptomics to allow for a comprehensive assessment of mouse lung development comprised of two critical developmental events: branching morphogenesis and alveologenesis. We firstly generate a spatial molecular atlas of mouse lung development spanning from E12.5 to P0 based on the integration of published single cell RNA-sequencing data and identify 10 spatial domains critical for functional lung organization. Furthermore, we create a lineages map connecting spatial clusters from adjacent time points in E12.5-P0 lungs and explore TF regulatory networks for each lineage specification. We observe the establishment of pulmonary airways within the developing lung, accompanied by the proximal-distal patterning by distinct characteristics of gene expression, signaling landscape and transcription factors enrichment. We characterize the alveolar niche heterogeneity with maturation state differences during the later developmental stage around birth and demonstrate differentially expressed genes, such as Angpt2 and Epha3, which may perform a critical role during alveologenesis. In addition, multiple signaling pathways, including ANGPT, VEGF and EPHA, exhibit increased levels in more maturing alveolar niche. Conclusion By integrating the spatial transcriptome with corresponding single-cell transcriptome data, we provide a comprehensive molecular atlas of mouse lung development with detailed molecular domain annotation and communication, which would pave the way for understanding human lung development and respiratory regeneration medicine.

Duchenne muscular dystrophy (DMD) is a progressive muscle-wasting disease caused by mutations in the DMD gene. Muscle fibers rely on the coordination of multiple cell types for repair and regenerative capacity. To elucidate the cellular and molecular changes in these cell types under pathologic conditions, we generated a rhesus monkey model for DMD that displays progressive muscle deterioration and impaired motor function, mirroring human conditions. By leveraging these DMD monkeys, we analyzed freshly isolated muscle tissues using single-cell RNA sequencing (scRNA-seq). Our analysis revealed changes in immune cell landscape, a reversion of lineage progressing directions in fibrotic fibro-adipogenic progenitors (FAPs), and TGF-β resistance in FAPs and muscle stem cells (MuSCs). Furthermore, MuSCs displayed cell-intrinsic defects, leading to differentiation deficiencies. Our study provides important insights into the pathogenesis of DMD, offering a valuable model and dataset for further exploration of the underlying mechanisms, and serves as a suitable platform for developing and evaluating therapeutic interventions.

Spatiotemporal coordination of cellular and molecular events is crucial for cell fate commitment during mouse gastrulation. However, the high-precision mechanisms governing the timing and spatial dynamics remain poorly understood. Here, we present a time-series single-cell multi-omic dataset from the mouse gastrulating embryos and construct a hierarchical gene regulatory landscape. Integrating this with real three-dimensional transcriptomic coordinate, we created ST-MAGIC and ST-MAGIC (+) atlas, dissecting the spatiotemporal logics of regulatory networks and signaling responsiveness underpinning the lineage commitment at gastrulation. Specifically, we delineated the multi-omic basis for left-right symmetry breaking events in the gastrula and also revealed the spatiotemporal molecular relay for axial mesendoderm lineage, where early and intermediate transcription factors first open the chromatin regions and setup the responsiveness to signaling, followed by terminal factors to consolidate the transcriptomic architecture. In summary, our study presents a spatiotemporal regulatory logic framework of mouse gastrulation, that advances our understanding of mammalian embryogenesis. ### Competing Interest Statement The authors have declared no competing interest.

In this Journal Club, Shengbao Suo highlights two publications that underscore the importance of genetic regulation of endogenous retrovirus expression in tumours.

Cellular immunity is orchestrated by T cells through their immense T-cell receptors (TCRs) repertoire, which interact with antigenic peptides presented by major histocompatibility complex (pMHC) molecules, but the specificities of the T cell response is largely undetermined because of the huge variety of TCRs. Here, we present DeepTR, a one-stop collection of unsupervised and supervised deep learning approaches for pan peptide-MHC class I binding prediction, TCR featurization, and accurate T cell response prediction. DeepTR yields higher predictive performance and more efficient feature representation for peptide binding to MHC and enables superior antigen-specific TCR featurization than current state-of-the-art approaches. Through a transfer learning strategy, DeepTR provides accurate prediction of T cell activation achieved by mimicking crucial steps of the antigen presentation pathway. DeepTR also enables the discovery of specific TCR groups with a new regulatory mechanism and characterizes important contact residues that mediate TCR-antigen binding specificity. DeepTR may advance our understanding of the mechanisms of T cell-mediated immunity and yield new insight in both personalized immune treatment and development of targeted vaccines. DeepTR is freely available at https://bioinfo.uth.edu/DeepTR.

Adult skeletal muscle regeneration is mainly driven by muscle stem cells (MuSCs), which are highly heterogeneous. Although recent studies have started to characterize the heterogeneity of MuSCs, whether a subset of cells with distinct exists within MuSCs remains unanswered. Here, we find that a population of MuSCs, marked by Gli1 expression, is required for muscle regeneration. The Gli1+ MuSC population displays advantages in proliferation and differentiation both in vitro and in vivo. Depletion of this population leads to delayed muscle regeneration, while transplanted Gli1+ MuSCs support muscle regeneration more effectively than Gli1- MuSCs. Further analysis reveals that even in the uninjured muscle, Gli1+ MuSCs have elevated mTOR signaling activity, increased cell size and mitochondrial numbers compared to Gli1- MuSCs, indicating Gli1+ MuSCs are displaying the features of primed MuSCs. Moreover, Gli1+ MuSCs greatly contribute to the formation of GAlert cells after muscle injury. Collectively, our findings demonstrate that Gli1+ MuSCs represents a distinct MuSC population which is more active in the homeostatic muscle and enters the cell cycle shortly after injury. This population functions as the tissue-resident sentinel that rapidly responds to injury and initiates muscle regeneration.

Combined 7 supplemental figures and 1 supplemental table

Supplementary Figures 1-8 with Legends included

Supplementary Table 1. Summary of proposed mechanisms of MDSC-mediated suppression.

Individual cells are basic units of life. Despite extensive efforts to characterize the cellular heterogeneity of different organisms, cross-species comparisons of landscape dynamics have not been achieved. Here, we applied single-cell RNA sequencing (scRNA-seq) to map organism-level cell landscapes at multiple life stages for mice, zebrafish and Drosophila. By integrating the comprehensive dataset of > 2.6 million single cells, we constructed a cross-species cell landscape and identified signatures and common pathways that changed throughout the life span. We identified structural inflammation and mitochondrial dysfunction as the most common hallmarks of organism aging, and found that pharmacological activation of mitochondrial metabolism alleviated aging phenotypes in mice. The cross-species cell landscape with other published datasets were stored in an integrated online portal-Cell Landscape. Our work provides a valuable resource for studying lineage development, maturation and aging.

Mammalian embryos have sophisticated cell organizations that are orchestrated by molecular regulation at cellular and tissue level. It has recently been appreciated that the cells that make up the animal body themselves harbor significant heterogeneity in the context of both cellular and particularly spatial dimension. However, current spatial transcriptomics profiling of embryonic tissues either lack three-dimensional representation or are restricted to limited depth and organs. Here, we reported a holistic spatial transcriptome atlas of all major organs at embryonic day 13.5 of mouse embryo and delineated a 3D rendering of the molecular regulation of embryonic patterning. By integrating with corresponding single-cell transcriptome data, the spatial organogenesis atlas provides rich molecular annotation of the dynamic organ nature, spatial cellular interaction, embryonic axes and divergence of cell fates underlying mammalian development, which would pave the way for precise organ-engineering and stem-cell based regenerative medicine. ### Competing Interest Statement The authors have declared no competing interest.

Neoadjuvant immune checkpoint blockade has shown promising clinical activity. Here, we characterized early kinetics in tumor-infiltrating and circulating immune cells in oral cancer patients treated with neoadjuvant anti-PD-1 or anti-PD-1/CTLA-4 in a clinical trial (NCT02919683). Tumor-infiltrating CD8 T cells that clonally expanded during immunotherapy expressed elevated tissue-resident memory and cytotoxicity programs, which were already active prior to therapy, supporting the capacity for rapid response. Systematic target discovery revealed that treatment-expanded tumor T cell clones in responding patients recognized several self-antigens, including the cancer-specific antigen MAGEA1 Treatment also induced a systemic immune response characterized by expansion of activated T cells enriched for tumor-infiltrating T cell clonotypes, including both pre-existing and emergent clonotypes undetectable prior to therapy. The frequency of activated blood CD8 T cells, notably pre-treatment PD-1-positive KLRG1-negative T cells, was strongly associated with intra-tumoral pathological response. These results demonstrate how neoadjuvant checkpoint blockade induces local and systemic tumor immunity.