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T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing.
BACKGROUND: Hepatocellular carcinoma (HCC), the third most prevalent cause of cancer-related death, is a frequent primary liver cancer with a high rate of morbidity and mortality. T-cell depletion (TEX) is a progressive decline in T-cell function due to continuous stimulation of the TCR in the presence of sustained antigen exposure. Numerous studies have shown that TEX plays an essential role in the antitumor immune process and is significantly associated with patient prognosis. Hence, it is important to gain insight into the potential role of T cell depletion in the tumor microenvironment. The purpose of this study was to develop a trustworthy TEX-based signature using single-cell RNA-seq (scRNA-seq) and high-throughput RNA sequencing, opening up new avenues for evaluating the prognosis and immunotherapeutic response of HCC patients. METHODS: The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases were used to download RNA-seq information for HCC patients. The 10x scRNA-seq. data of HCC were downloaded from GSE166635, and UMAP was used for clustering descending, and subgroup identification. TEX-related genes were identified by gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Afterward, we established a prognostic TEX signature using LASSO-Cox analysis. External validation was performed in the ICGC cohort. Immunotherapy response was assessed by the IMvigor210, GSE78220, GSE79671, and GSE91061cohorts. In addition, differences in mutational landscape and chemotherapy sensitivity between different risk groups were investigated. Finally, the differential expression of TEX genes was verified by qRT-PCR. RESULT: 11 TEX genes were thought to be highly predictive of the prognosis of HCC and substantially related to HCC prognosis. Patients in the low-risk group had a greater overall survival rate than those in the high-risk group, according to multivariate analysis, which also revealed that the model was an independent predictor of HCC. The predictive efficacy of columnar maps created from clinical features and risk scores was strong. CONCLUSION: TEX signature and column line plots showed good predictive performance, providing a new perspective for assessing pre-immune efficacy, which will be useful for future precision immuno-oncology studies.
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Spatiotemporal transcriptome atlas of human embryos after gastrulation.
The comprehensive spatiotemporal atlas of gene expression during early human embryonic development is critical for insights into embryogenesis1, organogenesis2 and disease origins3,4. Here, leveraging Stereo-seq technology, we generated spatial transcriptomic profiles across 77 sagittal sections of 13 whole-human embryos ranging from Carnegie stage 12 to 23, integrated with single-nucleus RNA sequencing to elucidate gene expression patterns within defined cellular contexts, revealing the cellular heterogeneity that drives organ-specific differentiation. Our study has established a regulatory profile for the development of 50 organs and 198 substructures, and identified potential tissue-identity regulators. Of note, it uncovered previously uncharacterized gene functions in cardiac and brain development. The atlas not only substantiates and refines the current understanding of human organ development but also highlights key organs susceptible to genetic disorders. Furthermore, we characterized the allelic gene expression within specific organs at different developmental stages. This work presents a comprehensive compilation of genome-wide gene expression profiles for each spatially defined cell population, which can be visualized as a spatial display of the embryonic transcriptional landscape. These results offer the most thorough delineation to data of the spatiotemporal transcriptomic dynamics of human organogenesis.
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Dynamic evolution of venetoclax resistance in acute myeloid leukemia unveiled by longitudinal single-cell RNA-seq.
Despite extensive investigation into venetoclax resistance mechanisms in acute myeloid leukemia (AML), the dynamics of bone marrow (BM) microenvironment remodeling during venetoclax-based therapies remain poorly characterized at single-cell resolution. Using paired single-cell RNA sequencing of BM specimens from AML patients undergoing DAV therapy (venetoclax/decitabine/cytarabine; pre- vs post-treatment), we systematically mapped therapy-induced transcriptional reprogramming, regulatory network alterations, and niche crosstalk across clinical response subgroups. Our analysis revealed two pivotal mechanisms governing therapeutic outcomes: First, pre-existing immune-activating niches marked by elevated HLA class I presentation synergized with therapy-enhanced CD8+ T cell cytotoxicity and reduced tumor-promoting stroma-leukemia interactions to facilitate favorable responses. Second, responder leukemic cells exhibited transposable element (TE)-associated type I interferon signaling upregulation. Primitive leukemic clones displayed IMPDH2-high states linked to BCL2 inhibitor sensitivity, while resistant monocytic populations upregulated glycolysis and MCL1 to bypass BCL2 dependence. Leveraging these insights, we established a prognostic signature predicting patient responses to venetoclax-based therapies, validated in independent cohorts (Tumor Profiler, BeatAML2). High-risk patients identified by this signature demonstrated heightened sensitivity to IGF-1R inhibition. Functional validation in an established resistant cell line model confirmed that IGF-1R inhibition synergized with DAV by suppressing glucose uptake and differentiation. This study provides a comprehensive single-cell atlas of BM microenvironment evolution during venetoclax-based therapy, proposes a prognostic biomarker, and identifies a clinically actionable strategy to overcome therapeutic resistance in AML.
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A single-cell, spatial transcriptomic atlas of the Arabidopsis life cycle.
Arabidopsis has been pivotal in uncovering fundamental principles of plant biology, yet a comprehensive, high-resolution understanding of its cellular identities throughout the entire life cycle remains incomplete. Here we present a single-nucleus and spatial transcriptomic atlas spanning ten developmental stages, encompassing over 400,000 nuclei from all organ systems and tissues-from seeds to developing siliques. Leveraging paired single-nucleus and spatial transcriptomic datasets, we annotate 75% of identified cell clusters, revealing striking molecular diversity in cell types and states across development. Our integrated approach identified conserved transcriptional signatures among recurrent cell types, organ-specific heterogeneity and previously uncharacterized cell-type-specific markers validated spatially. Moreover, we uncover dynamic transcriptional programs governing secondary metabolite production and differential growth patterns, exemplified by detailed spatial profiling of the compact yet complex apical hook structure; this profiling revealed transient cellular states linked to developmental progression and hormonal regulation, highlighting the hidden complexity underlying plant morphogenesis. Functional validation of genes uniquely expressed within specific cell contexts confirmed their essential developmental roles, underscoring the predictive power of our atlas. Collectively, this comprehensive resource provides an invaluable foundation for exploring cellular differentiation, environmental responses and genetic perturbations at high resolution, advancing our understanding of plant biology.
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Spatial and single-cell transcriptomic atlas of human suprachiasmatic nucleus.
The suprachiasmatic nucleus (SCN) is considered the master pacemaker of the circadian clock in mammals, but our current knowledge of the SCN is mostly based on rodent studies. Here, we report a comprehensive molecular and cellular atlas for the adult human SCN by spatial transcriptomics, single-nucleus RNA sequencing, and deep-learning-based histological analysis. We identified seven human SCN neuron subtypes with specific transcriptomes and spatial distributions. Comparison of humans, mice, and non-human primates revealed the conserved functional segregation within the SCN regulated by LIM homeobox 1 (LHX1) and RAR-related orphan receptor B (RORB). Furthermore, our results suggested that the human SCN has undergone marked reorganization of its neuropeptide signaling network. Finally, integrative analysis of human SCN transcriptomes and genome-wide association studies (GWASs) identified arginine vasopressin (AVP)/neuromedin S (NMS) subtype as the potential neuronal correlate for morningness chronotype. Thus, our spatial and single-cell transcriptomic atlas of the human SCN provided a basis for the understanding of neural and molecular mechanisms of the human circadian clock.
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scRNA-seq of colorectal cancer shows regional immune atlas with the function of CD20+ B cells.
Colorectal cancer (CRC) from different regions exhibits different histological, genetic characteristics, and molecular subtypes, even in response to conventional chemotherapies and immunotherapies. To characterize the immune landscape in different regions of CRC and search for potential therapeutic targets, we analyzed 39,484 single-cell transcription data from 19 samples of CRC and paired normal tissues from four regions to identify the immune characteristics of CRC among anatomic locations, especially in B cells. We discovered that immune cell infiltration in tumors significantly varied among different regions of CRC. B cells from right- and left-sided CRC had different development trajectories, but both had extensive interactions with myeloid cells and T cells. Survival analysis suggested that CD20+ B cells correlated with good prognosis in CRC patients, especially on the right side. Furthermore, the depletion of CD20+ B cells demonstrated that anti-CD20 promoted tumor growth progression and reversed the tumor-killing activity of anti-PD-1 treatment in vivo and in vitro. Our results highlight the characterization of the immune landscape of CRC in different regions. CD20+ B-cell infiltration has been associated with CRC patient prognosis and may promote the tumor-killing role of PD-1 antibodies.
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Integrated single-cell and spatial transcriptomic profiling decodes lineage plasticity and immune microenvironment remodeling in prostate cancer progression.
This study presents a comprehensive single-cell and spatial transcriptomic atlas of prostate cancer progression, integrating 127 single-cell RNA sequencing samples and 9 spatial transcriptomics profiles spanning the disease continuum from healthy prostate to neuroendocrine carcinoma. Our analysis defines four evolutionarily connected malignant epithelial subtypes: luminal-identity (sub1), stress-adaptive luminal (sub2), neuroendocrine (sub3), and a double-negative basal-like state (sub4). We identify FOSL1 as a key driver of lineage plasticity through direct transcriptional regulation of HMGA1, promoting treatment resistance via enhanced proliferation, EMT and stemness. The tumor microenvironment undergoes coordinated reprogramming during progression, with neoadjuvant hormone therapy inducing distinct cellular responses: FOLR2 + and CX3CR1 + TAMs upregulate TGF-β signaling to establish immunosuppressive niches, while CXCL12 + iCAFs and ACTA2 + myCAFs maintain spatial co-localization and facilitate immune cell recruitment. Spatial analyses reveal enhanced chemokine signaling post-therapy, particularly in specific TAM subsets, driving increased but functionally impaired lymphoid infiltration characterized by T-cell exhaustion and regulatory T-cell expansion. This integrated analysis establishes a unified paradigm connecting epithelial plasticity with microenvironmental reprogramming, revealing FOSL1-HMGA1 signaling and macrophage-driven immunosuppression as promising therapeutic targets for advanced prostate cancer.
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ProjectSVR: mapping single-cell RNA-seq data to reference atlases by supported vector regression.
Mapping the query cells onto a well-constructed reference atlas, known as reference mapping, enables robust, reproducible interpretation of new single-cell RNA-seq data in the context of curated and annotated cell subtypes and states. However, existing methods often rely on complex integration frameworks or require re-access to raw data, limiting their applicability and reproducibility. To address this, we introduce ProjectSVR, a machine learning-based framework that formulates reference mapping as a multi-target regression task. By leveraging ensemble support vector regression (SVR) to learn the relationship between gene set activity scores and low-dimensional reference embeddings, ProjectSVR enables platform-agnostic and integration-independent mapping. Benchmarking across diverse biological contexts-including immune responses, developmental trajectories, and disease states-demonstrates that ProjectSVR achieves comparable accuracy and robustness to state-of-the-art methods, with reduced dependence on data-specific preprocessing. Our findings demonstrate that ProjectSVR is a valuable tool for reference mapping, considerably simplifying the analysis of scRNA-seq data when well-constructed reference atlases are available.
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A human embryonic limb cell atlas resolved in space and time.
Human limbs emerge during the fourth post-conception week as mesenchymal buds, which develop into fully formed limbs over the subsequent months1. This process is orchestrated by numerous temporally and spatially restricted gene expression programmes, making congenital alterations in phenotype common2. Decades of work with model organisms have defined the fundamental mechanisms underlying vertebrate limb development, but an in-depth characterization of this process in humans has yet to be performed. Here we detail human embryonic limb development across space and time using single-cell and spatial transcriptomics. We demonstrate extensive diversification of cells from a few multipotent progenitors to myriad differentiated cell states, including several novel cell populations. We uncover two waves of human muscle development, each characterized by different cell states regulated by separate gene expression programmes, and identify musculin (MSC) as a key transcriptional repressor maintaining muscle stem cell identity. Through assembly of multiple anatomically continuous spatial transcriptomic samples using VisiumStitcher, we map cells across a sagittal section of a whole fetal hindlimb. We reveal a clear anatomical segregation between genes linked to brachydactyly and polysyndactyly, and uncover transcriptionally and spatially distinct populations of the mesenchyme in the autopod. Finally, we perform single-cell RNA sequencing on mouse embryonic limbs to facilitate cross-species developmental comparison, finding substantial homology between the two species.
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Atlas of the aging mouse brain reveals white matter as vulnerable foci.
Aging is the key risk factor for cognitive decline, yet the molecular changes underlying brain aging remain poorly understood. Here, we conducted spatiotemporal RNA sequencing of the mouse brain, profiling 1,076 samples from 15 regions across 7 ages and 2 rejuvenation interventions. Our analysis identified a brain-wide gene signature of aging in glial cells, which exhibited spatially defined changes in magnitude. By integrating spatial and single-nucleus transcriptomics, we found that glial aging was particularly accelerated in white matter compared with cortical regions, whereas specialized neuronal populations showed region-specific expression changes. Rejuvenation interventions, including young plasma injection and dietary restriction, exhibited distinct effects on gene expression in specific brain regions. Furthermore, we discovered differential gene expression patterns associated with three human neurodegenerative diseases, highlighting the importance of regional aging as a potential modulator of disease. Our findings identify molecular foci of brain aging, providing a foundation to target age-related cognitive decline.