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Multimodal Nanoscale Imaging Reveals Cellular and Metabolic Complexity of Human Breast Milk.
Human milk contains diverse live cells that contribute to infant nutrition, immune protection, and maternal-infant health, yet their cellular biology remains difficult to resolve within this lipid-rich and heterogeneous biofluid. Here, we adapted correlative light, electron, and ion microscopy (CLEIM) for nanoscale imaging and metabolic profiling of human milk cells (HMCs) and integrated these spatial measurements with single-cell transcriptomic analysis. This workflow combines confocal microscopy for cell-type identification, electron microscopy (EM) for ultrastructural mapping, nanoscale secondary ion mass spectrometry (NanoSIMS) for spatial elemental and isotope analysis, and single-cell RNA sequencing (scRNA-seq) for transcriptional interpretation. Using this platform, we generated an ultrastructural atlas of HMCs, including lactocytes, neutrophils, macrophages, dendritic cells, and bacteria-like structures. NanoSIMS revealed sulfur- and iron-enriched neutrophil granules, consistent with antimicrobial functions. Stable isotope tracing further uncovered distinct metabolic specializations among HMCs: 13C-glucose-derived carbon was preferentially incorporated into macrophage lipid droplets, supported by lipid-handling transcriptional features, whereas 15N-amino acid tracing identified metabolically active lactocyte subsets associated with translation and secretory programs. These findings reveal cellular and metabolic heterogeneity in human milk that is not captured by conventional profiling methods. More broadly, this work establishes a spatial multimodal framework for linking cell identity, ultrastructure, metabolic activity, and transcriptional state in complex biological fluids, providing a foundation for future studies of human milk biology and maternal-infant health.
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Ortholog transfer stress test: Regulatory edge conservation across species, tissues, and evolutionary distance.
Researchers routinely take a gene-regulatory network measured in one species-for example, which transcription factor switches which gene on or off in the mouse-and assume the same wiring holds in another species after matching genes by ancestry (orthology). How often this assumption holds quantitatively has not been measured systematically. Here I test it directly. Using single-cell RNA-sequencing of matched organs, I measure, for each transcription factor, how strongly its expression tracks that of every other gene (a co-expression "edge"), compute these edges independently in human and mouse lung, and ask how well they agree. Edges agree strongly when the organ is the same in both species (rank correlation ρ=0.74; the direction of an association-activating versus repressing-matches for 100% of the strongest edges), far beyond chance (z = 112 against a permutation null). Agreement is, however, highly uneven: edges of identity-defining factors such as NKX2-1 (lung), ERG (blood vessel), and RUNX3 (lymphocyte) transfer almost perfectly (ρ>0.80), whereas edges of signal-activated factors such as HIF1A, STAT1, and CTNNB1 barely transfer at all (median ρ=0.14 for the signal-responsive group). Strikingly, changing the organ within one species disrupts edges more than changing the species within one organ: 76% of factors are better conserved across species than across tissues. The conserved signal is not merely a by-product of matching cell-type proportions: after statistically removing cell-type identity as a covariate, cross-species agreement remains substantial (ρ=0.50). Extending the comparison to zebrafish using a real developmental cell atlas (430 million years of divergence), edge agreement falls to weakly positive (ρ=0.15-0.19), and-contrary to a simple expectation-the identity-versus-signaling gap is no longer statistically distinguishable at that distance. Whole-protein sequence identity is a poor predictor of edge transfer at mammalian distances (R2 < 0.01). Finally, benchmarking against a curated database of known regulatory interactions shows that co-expression strength is a weak indicator of direct regulation (area under the ROC curve ≈0.52), so what transfers across species is conserved co-expression structure-largely cell-identity programs-rather than validated direct regulatory wiring. Together these results give practical, quantitative guidance on when cross-species transfer of regulatory information is safe and when it is not.
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Seesaw signatures capture trajectory-like transcriptomic shifts and enable compact tumour cell classification across cancers.
Accurate identification of tumor cells remains a major challenge in single-cell cancer research because malignant and normal cells often differ only subtly and vary greatly across datasets. Here we show that Seesaw pairs, defined by consistent reversals in the relative expression ranks of gene pairs between normal and tumor cells, provide compact and informative markers of malignant transformation. Across 44 single-cell RNA sequencing datasets spanning 22 cancer types, a classifier based on just three Seesaw pairs achieves a median area under the receiver operating characteristic curve of 0.93 in independent test sets and outperforms CellTypist, CTISL, ikarus, and scMalignantFinder. Recurrent Seesaw pairs reveal shared cancer-associated programs across malignancies, and many associated genes are linked to poor prognosis in The Cancer Genome Atlas cohorts. Because the framework is simple and interpretable, it may facilitate practical low-dimensional assays and may also be extendable to other data types in which coordinated rank disruption is informative.
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Tumor reactivity assessment using clonal expression reveals tumor reactive CD8+ T cell heterogeneity across solid tumors.
INTRODUCTION: Tumor infiltrating lymphocytes (TIL) drive the anti-tumor activity of a broad class of immunotherapies. In situ TIL are composed of T cells that recognize tumor antigens (Tumor Reactive T cells, or TRTs) as well as bystander T cells with specificity for other antigens. TRT clonotypes are associated with a unique and tumor-driven exhausted transcriptional state, enabling single-cell RNA sequencing (scRNA-seq)-based predictive models for TRTs using experimentally validated clone labels. METHODS: In this study, a clonotype-level CD8+ TRT classifier (TRACE) was built using an aggregated dataset of validated tumor reactive clonotypes and associated scRNA-seq data from multiple publications that overcomes the limitations of training on a single dataset, donor, or indication. TRACE does not require dataset manipulation for training or prediction, enabling it to be easily applied to new test datasets as they emerge. RESULTS: TRACE exhibited robust performance on held-out TIL and PBMC clones - achieving a mean Matthews correlation coefficient of 0.84 and F1-score of 0.85 - comparable to or outperforming other TRT prediction methods. We experimentally confirmed the reactivity of TRACE-identified TRT clones by co-culturing ex vivo expanded TIL with an autologous melanoma tumor cell line. Finally, we applied TRACE to evaluate the frequency of TRTs across hundreds of patient samples from multiple tumor atlases spanning lung, colorectal, and pancreatic cancer. TRACE scores were observed to be significantly higher in exhausted CD8+ T cells in tumors but not in exhausted cells in normal adjacent or non-cancer samples, suggesting specificity towards identifying tumor-antigen experienced T cells. CONCLUSION: TRACE is a tumor reactivity scoring algorithm released with open model weights that can be applied to tissue or blood single-cell RNAseq datasets. Its application should be of general interest for characterizing the fraction of TRTs in TIL and for establishing correlations with clinical response to immunotherapies.
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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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Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals ubiquitin promotes pulmonary fibrosis in chronic pulmonary diseases.
It is estimated that there are 544.9 million people suffering from chronic respiratory diseases in the world, which is the third largest chronic disease. Although there are various clinical treatment methods, there is no specific drug for chronic pulmonary diseases, including chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and idiopathic pulmonary fibrosis (IPF). Therefore, it is urgent to clarify the pathological mechanism and medication development. Single-cell transcriptome data of human and mouse from GEO database were integrated by "Harmony" algorithm. The data was standardized and normalized by using "Seurat" package, and "SingleR" algorithm was used for cell grouping annotation. The "Findmarker" function is used to find differentially expressed genes (DEGs), which were enriched and analyzed by using "clusterProfiler", and a protein interaction network was constructed for DEGs, and four algorithms are used to find the hub genes. The expression of hub genes were analyzed in independent human and mouse single-cell transcriptome data. Bulk RNA data were used to integrate by the "SVA" function, verify the expression levels of hub genes and build a diagnostic model. The L1000FWD platform was used to screen potential drugs. Through exploring the similarities and differences by integrated single-cell atlas, we found that the lung parenchymal cells showed abnormal oxidative stress, cell matrix adhesion and ubiquitination in COPD, corona virus disease 2019 (COVID-19), ILD and IPF. Meanwhile, the lung resident immune cells showed abnormal Toll-like receptor signals, interferon signals and ubiquitination. However, unlike acute pneumonia (COVID-19), chronic pulmonary disease shows enhanced ubiquitination. This phenomenon was confirmed in independent external human single-cell atlas, but unfortunately, it was not confirmed in mouse single-cell atlas of bleomycin-induced pulmonary fibrosis model and influenza virus-infected mouse model, which means that the model needs to be optimized. In addition, the bulk RNA-Seq data of COVID-19, ILD and IPF was integrated, and we found that the immune infiltration of lung tissue was enhanced, consistent with the single-cell level, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD, and had a strong correlation with the expression of cell matrix adhesion genes. UBA52 and UBB have good diagnostic efficacy, and salermide and SSR-69071 can be used as their candidate drugs. Our study found that the disorder of protein ubiquitination in chronic pulmonary diseases is an important cause of pathological phenotype of pulmonary fibrosis by integrating scRNA-Seq and bulk RNA-Seq, which provides a new horizons for clinicopathology, diagnosis and treatment.
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A single-cell and spatial transcriptomic atlas of human tuberculous constrictive pericardium.
BACKGROUND: Tuberculous pericarditis (TP) can progress to tuberculous constrictive pericarditis (TB-CP), a life-threatening fibrotic syndrome. However, the cellular architecture and intercellular circuits that link granulomatous inflammation to pericardial fibrosis remain poorly defined. METHODS: We performed single-cell RNA sequencing (scRNA-seq) on 81,634 cells from surgically resected pericardium of six patients with TB-CP and three normal controls. Ligand-receptor and trajectory analyses were integrated with spatial transcriptomics, histology, immunohistochemistry, and multiplex immunofluorescence to map cell states, signalling pathways, and immune-stromal niches in situ. FINDINGS: We generated an integrated single-cell and spatial atlas of the human pericardium in TB-CP, delineating 14 major cell types and heterogeneous transcriptional programmes associated with granuloma formation, vascular remodelling, and fibrotic activation. Spatial mapping revealed MMP9+ macrophage-rich granulomatous cores surrounded by infiltrating CCL19+ fibroblasts and T cells. In parallel, S100A4+ endothelial cells displayed endothelial-to-mesenchymal transition-like programmes and trajectories converging on ACTA2+ myofibroblasts, supported by immune cell-derived TGF-β and VEGF signalling. Ligand-receptor analysis and spatial co-localisation suggested macrophages may act as hubs that couple immune activation to endothelial reprogramming, fibroblast activation, and T-cell recruitment within fibrotic lesions. INTERPRETATION: This human atlas defines the cellular landscape and key intercellular circuits underlying the immunopathogenesis of TB-CP. Our findings show that a spatially organised immune-endothelial-fibroblast network is associated with pericardial fibrosis and nominate VEGF/TGF-β pathways and associated cell states as potential biomarkers and therapeutic targets. FUNDING: National Natural Science Foundation of China (82270486 to F.X.); Science and Technology Bureau of Sichuan Province (2021YFS0051, 2024YFFK0209 to Y.W.; 2024NSFC0646 to F.X.).
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The Fibro-Immune Landscape Across Organs: A Single-Cell Comparative Study of Human Fibrotic Diseases.
Fibrosis is a hallmark of the tumor microenvironment in many solid cancers, driving tumor progression, immune evasion, and treatment resistance; however, the molecular and cellular mechanisms underlying fibrogenesis-particularly stromal-immune crosstalk across organs-remain incompletely understood, compounded by organ-specific heterogeneity and a lack of reliable immune-related biomarkers. To address this, we performed an integrative single-cell RNA sequencing (scRNA-seq) analysis of fibrotic tissues from four major organs-liver, lung, heart, and kidney-alongside non-fibrotic controls, applying unsupervised clustering, trajectory inference, cell-cell communication modeling, and gene set variation analysis (GSVA) to map the fibro-immune landscape. Our analysis revealed both conserved and organ-specific features: fibroblasts were the dominant extracellular matrix (ECM)-producing cells in liver and lung, whereas endothelial-derived stromal populations prevailed in heart and kidney. Immune profiling uncovered distinct infiltration patterns-macrophages displayed organ-specific polarization states; T cells were enriched for tissue-resident subsets in lung and mucosal-associated invariant T (MAIT) cells in liver; and B cells exhibited marked heterogeneity, including a pathogenic interferon-responsive subset prominent in pulmonary fibrosis. GSVA further identified divergent signaling programs across organs and lineages, including TGF-β/TNF-α in the heart, NOTCH/mTOR in the kidney, glycolysis/ROS in the lung, and KRAS/interferon pathways in the liver. Cell-cell communication analysis highlighted robust crosstalk between macrophages, T/B cells, and stromal cells mediated by collagen, laminin, and CXCL signaling axes. Together, this cross-organ atlas delineates a highly heterogeneous fibro-immune ecosystem in human fibrotic diseases, revealing shared mechanisms alongside organ-specific regulatory networks, with immediate translational implications for precision anti-fibrotic therapy, immunomodulatory drug repurposing, and the development of context-specific biomarkers for clinical stratification and therapeutic monitoring.
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Deciphering the pulmonary fibrosis niche: A single-cell transcriptomic perspective on cellular crosstalk and microenvironment remodeling.
Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal interstitial lung disease driven by aberrant repair after repetitive alveolar micro-injury. The classical paradigm - that fibrosis is a passive consequence of epithelial injury followed by fibroblast activation - has been substantially reshaped by single-cell RNA sequencing (scRNA-seq) and, more recently, spatial transcriptomics. These technologies have resolved the lung into a constellation of molecularly distinct cell states and exposed a highly structured fibrotic niche in which epithelial, mesenchymal, immune, and endothelial compartments are engaged in active, ligand-receptor-mediated crosstalk. This review synthesizes the emerging single-cell and spatial atlas of human PF/ILD, with a focus on four themes: (i) the ectopic KRT5⁻/KRT17⁺ aberrant basaloid epithelium and the persistence of a KRT8⁺ alveolar differentiation intermediate; (ii) the lipofibroblast-to-myofibroblast trajectory and the collagen-producing fibroblast atlas that explains heterogeneous drug response; (iii) immune remodeling, particularly the metabolic reprogramming of SPP1⁺/MERTK⁺ macrophages and the exhaustion of CD8⁺ T cells; and (iv) the spatial organization of disease-associated niches at the fibroblast-focus edge. We then translate these findings into a framework for molecular subtyping and precision medicine, arguing that the future of IPF therapy lies in disrupting specific niche communication axes rather than broadly antagonizing collagen synthesis. Beyond cataloguing cell types, we emphasize the systems-level principle that emerges from the single-cell literature: IPF is not a disease of one cell type gone wrong, but a disease of the communication network that binds multiple compartments into a self-sustaining, maladaptive ecosystem.
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Single‑cell and spatial transcriptomics in chronic lung disease: cellular heterogeneity and molecular mechanisms in IPF and COPD.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have advanced the study of complex lung diseases by resolving cellular heterogeneity, tissue architecture, and intercellular communication. These technologies are particularly informative in chronic respiratory disorders, where pathological remodeling involves coordinated alterations across epithelial, stromal, immune, and endothelial compartments. Idiopathic pulmonary fibrosis (IPF), a rare fatal fibrotic disorder, and chronic obstructive pulmonary disease (COPD), a prevalent heterogeneous respiratory condition, represent distinct chronic lung diseases shaped by persistent injury, maladaptive repair, and progressive tissue remodeling. However, current understanding of their pathogenesis remains incomplete, and conventional approaches lack the resolution to define disease-associated cell states, spatially restricted niches, or compartment-specific interactions. This review synthesizes recent scRNA-seq and ST studies in IPF and COPD, emphasizing how high-resolution atlases refine established pathobiological frameworks and reveal shared and disease-specific cellular programs. We highlight epithelial dysfunction and impaired progenitor repair as shared mechanistic features of IPF and COPD, while emphasizing alveolar regenerative failure, fibroblast activation, and profibrotic niche formation as central features of IPF, and compartment-specific airway remodeling, inflammatory epithelial states, and emphysema-associated parenchymal injury as prominent features of COPD. We further discuss stromal and immune remodeling, hypoxia-associated vascular remodeling, and the translational implications of these findings for molecular stratification, target prioritization, and mechanism-informed therapeutic development, while noting limitations related to end-stage tissue sampling, platform constraints, and mouse-human correspondence. This review provides an integrated framework for understanding the cellular and spatial mechanisms underlying the pathogenesis and translational relevance of IPF and COPD.