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PubMed2026

Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer.

Advances in spatially resolved technologies enable the simultaneous acquisition of diverse data modalities within a tissue slice while preserving critical spatial context, which presents unprecedented opportunities to decipher intricate tissue heterogeneity. However, existing computational approaches lack the intrinsic flexibility to universally process both spatial multi-modal and multi-omics data. Here, we introduce STransformer, a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short-range cellular interactions and tissue-wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity. Systematic evaluations across diverse species, tissue types, and data modalities highlight its profound versatility. For spatial multi-modal data, STransformer delineates intricate anatomical structures in the human cortex, uncovers pathological mechanisms in Alzheimer's disease, and characterizes dynamic spatiotemporal developmental trajectories during chicken cardiogenesis. Scaling to spatial multi-omics data, STransformer synergizes spatial transcriptomic and proteomic profiles to decipher intricate immune microenvironments within the human tonsil, and jointly analyzes spatial epigenomic and transcriptomic data to infer regulatory mechanisms in the mouse embryonic brain. Consequently, STransformer serves as a highly versatile and robust analytical framework for advancing our understanding of tissue heterogeneity and disease pathogenesis.

Multiomics↗
PubMed2026

Spatial Multiomics Reveal Insights Into ADC Efficacy.

Antibody-drug conjugates (ADCs) have transformed the therapeutic landscape of solid tumors; however, responses remain heterogeneous and complex to predict. In addition, a growing number of multiple ADC targets are either approved or in late-stage clinical development, such as NECTIN-4, HER2, or TROP2 for metastatic urothelial cancer. Spatial multiomics-representing next-generation methods that couple high-plex RNA sequencing and multiplex protein imaging with precise x-y-z coordinates within tissues-offer a direct way to correlate (ADC) antigen expression, cell state information, and micro-anatomical context with patient treatment outcomes. In this review, we highlight suitability and technological advancements in current spatial transcriptomics and proteomics approaches to decode modes of action and resistance to ADCs and extract biological insights, particularly in metastatic urothelial cancer-and propose an integrative framework that combines spatial readouts with machine and/or deep learning-driven analytics to stratify patients, forecast on- and off-target toxicities, and guide next-generation linker-payload designs or combination therapies.

Humans↗
PubMed2026

Maternal immune activation perturbs the brain epitranscriptome.

Maternal immune activation (MIA) results in abnormal fetal neurodevelopment and an increased risk of neurodevelopmental disorders. Altered RNA translation has been implicated in the pathophysiology of MIA-associated neurodevelopmental deficits, but more precise mechanisms underlying disruption in RNA metabolism are lacking. Here, we characterize key components of the RNA epitranscriptomic machinery, which refers to the set of reversible chemical modifications on RNA molecules that influence RNA function, including translation, stability, splicing, and localization. Using spatial transcriptomics, we define cell type- and brain region-specific distribution of epitranscriptome regulators in the developing mouse brain. We also use direct RNA sequencing to define how MIA changes the brain epitranscriptome landscape. We identify the demethylase FTO as being notably perturbed in the context of MIA. Using pharmacological and genetic approaches, we target FTO to ameliorate behavioral phenotypes in MIA offspring. In total, this work expands upon mechanisms of translational misregulation in MIA and identifies new targets for therapeutic manipulation.

Animals↗
PubMed2026

Interpretable data integration for single-cell and spatial multi-omics.

Integrating single-cell or spatial transcriptomic and epigenomic data enables scrutinizing the transcriptional regulatory mechanisms controlling cell fate. Current integration methods usually align multi-omics data into a shared latent space but fail to reveal the underlying connections between genes and regulatory elements. The correlation- or regression-based regulatory inference methods cannot dissect different transcriptional regulation codes for cells under different spatial and temporal states. To address both problems, we develop a feature-guided optimal transport (FGOT) method, which simultaneously uncovers cellular heterogeneity and their associated transcriptional regulatory links. FGOT also provides post hoc interpretability for existing integration methods. FGOT is applicable for paired/unpaired single-cell multi-omics data and paired spatial multi-omics data. Benchmarking and validating via histone modification data or three-dimensional (3D) genomics data show good robustness and accuracy in integration and inference of regulatory links. The method allows systematic screening of cell-state and spatial-location-specific regulatory elements in diseases at the single-cell level. A record of this paper's transparent peer review process is included in the supplemental information.

Single-Cell Analysis↗
PubMed2025

Multi-omics analysis reveals distinct spatial compartmentalization of lung repair niches in pediatric ARDS.

BACKGROUND: Pediatric acute respiratory distress syndrome (PARDS), often triggered by viral infections, is a life-threatening condition. Despite its severity, children demonstrate significantly better survival rates and superior lung repair compared to adults. However, the mechanisms underlying this age-specific advantage remain incompletely understood. PATIENTS AND METHODS: We conducted a pilot multi-omics study of influenza-associated PARDS integrating single-cell RNA sequencing (scRNA-seq) of pediatric lung tissue and bronchoalveolar lavage fluid (BALF), spatial transcriptomics, and plasma proteomics. Analyses were harmonized with the Human Lung Cell Atlas (HLCA) reference, reanalysis of public pediatric PARDS airway scRNA-seq, and contextual comparisons to adult lethal COVID-19 lung. RESULTS: Tissue scRNA-seq and spatial data indicated outcome-linked divergence in PARDS. Survivor showed spatially restricted repair with preserved alveolar type II (AT2) cells, AT2-to-alveolar type I (AT1) differentiation signatures, and higher KRT17, whereas fatal case and adults exhibited diffuse immune activation with pro-fibrotic and pro-apoptotic signaling. In BALF, KRT17-positive airway stress–repair epithelial cells (hillock-like) increased from the acute to recovery phase, and plasma proteomics showed higher circulating KRT17 in survivors. HLCA-based label transfer strengthened cell-type definitions and enabled pediatric–adult comparisons suggesting biological and developmental differences; the adult lethal COVID-19 atlas provided a benchmark with attenuated epithelial repair and prominent collagen CTHRC1-pathologic fibroblasts. Fibroblast programs were regionally compartmentalized, with injury-enriched CTHRC1+ states versus alveolar fibroblasts in preserved areas, and showed stronger injury–homeostasis anti-correlation in fatalities. Myeloid remodeling included BALF transitions from FCN1-high inflammatory states toward FABP4-positive resident-like states, consistent with public pediatric datasets showing reduced inflammatory and interferon-stimulated gene (ISG) modules and severity-linked increases in aged neutrophils. CONCLUSIONS: This pilot multi-omics case series outlines putative pediatric lung repair niches in influenza-associated PARDS. KRT17-positive transitional epithelium, preserved AT2 differentiation, and restoration of resident-like macrophages may align with recovery, whereas diffuse immune activation and CTHRC1-enriched fibroblast programs may accompany worse outcomes. HLCA-guided annotations and adult benchmarks indicate possible age-related differences, warranting validation in larger multi-center cohorts.

Humans↗
PubMed2025

Multi-omics analyses reveal DjTcf4 critical for proper timing of differentiation in planarian regeneration.

The blastema is key to forming complete tissues in regenerating Dugesia japonica (D. japonica). However, the dynamic changes in cellular compositions and transcription landscapes in blastema during regeneration are understudied. Here, through genome reannotation, 3D spatial transcriptome construction, single-cell RNA sequencing (scRNA-seq), and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) analyses of changes in gene expression and chromatin structures, we delineate key transcription factors regulating the developmental trajectories of major cell clusters in the regenerating head. Importantly, we find that the T cell factor 4 (DjTcf4)-positive cells highly accumulate at wound areas, and its gene network is critical for the proper timing of development during regeneration in multiple progenitor cells. Depletion of DjTcf4 and its target genes leads to singular eye and/or dull tail phenotypes and delays regeneration. Taken together, we build multi-omics atlases in D. japonica and reveal the noncanonical function of the DjTcf4 network in developmental pattern formation, laying a foundation for studies of regeneration in D. japonica.

Animals↗
PubMed2026

Mapping antibody sequences and effector functions across spatial niches.

Antibodies are fundamental to human health but can also drive pathology. Each antibody has a molecular specificity, encoded by their clonally heritable B cell receptor (BCR). Recent advances in spatial transcriptomics coupled with repertoire sequencing have enabled capturing antibody-secreting cells (ASCs) and their clonal BCR within their tissue microenvironment. However, our understanding of antibody production niches remains limited. Furthermore, where antibodies are produced can be distinct from where antibodies exert their effector function. Here, we propose a conceptual spatial framework to distinguish between 'antibody production niches', defined by the ASC, BCR, and niche composition, versus 'antibody functional niches', composed of the antibody, antigen, and effector landscape. We then examine the possibilities and challenges to map and link antibody-encoding sequences and antibody effector functions using current and emerging technologies. Combined, we argue that integrating spatial sequence data with the antibody functional context is essential to decode the architecture of antibody-mediated immunity.

Humans↗
PubMed2026

Machine learning and multi-omics clustering to map cellular rewiring and immune evasion in ccRCC.

Immune checkpoint blockade (ICB) efficacy in clear cell renal cell carcinoma (ccRCC) is limited by tumor microenvironment (TME) heterogeneity. Because traditional bulk-derived models lack spatial resolution, we developed an integrated framework connecting macroscopic survival risks to microscopic TME structures. We applied ten algorithms to establish multi-omics subtypes and evaluated 101 machine-learning combinations across three independent cohorts to generate a Consensus Machine Learning-driven Signature (CMLS). The signature's spatial and cellular origins were decoded using spatial transcriptomics (ST) and a 140,000-cell scRNA-seq atlas. Expression of key genes was experimentally validated via RT-qPCR in 17 paired ccRCC clinical tissues. We identified two molecular subtypes with distinct clinical and epigenetic profiles. SuperPC optimization yielded a 24-gene CMLS serving as an independent prognostic factor. scRNA-seq and ST deconvolution revealed these signals predominantly originate from cancer-associated fibroblasts (CAFs) and malignant epithelial cells, which collaborate to drive spatial immune exclusion. RT-qPCR confirmed significant overexpression of five core CMLS genes in ccRCC versus adjacent normal tissues. Low CMLS scores correlated with enhanced ICB responsiveness, whereas high-CMLS tumors demonstrated specific vulnerability to dasatinib and dabrafenib. The CMLS translates spatial immune-exclusion dynamics into a quantifiable metric, outperforming tumor mutational burden in predicting ICB benefits, providing a robust tool for patient stratification in ccRCC.

Humans↗
PubMed2025

OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.

Cell deconvolution estimates cell type proportions from bulk omics data, enabling insights into tissue microenvironments and disease. However, practical applications are often hindered by batch effects between bulk data and referenced single-cell data, a challenge that is frequently overlooked. To address this discrepancy, we developed OmicsTweezer, a distribution-independent cell deconvolution model. By integrating optimal transport with deep learning, OmicsTweezer aligns simulated and real data in a shared latent space, effectively mitigating data shifts and inter-omics distribution differences. OmicsTweezer is versatile, capable of deconvolving bulk RNA-seq, bulk proteomics, and spatial transcriptomics. Extensive evaluations on simulated and real-world datasets demonstrate its robustness and accuracy. Furthermore, applications in prostate and colon cancer showcase OmicsTweezer's ability to identify biologically meaningful cell types. As a unified deconvolution framework for multi-omics data, OmicsTweezer offers an efficient and powerful tool for studying disease microenvironments.

Humans↗
PubMed2025

HMGA2 links morphological evolution and microenvironment dynamics to systemic therapy response in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) exhibits significant heterogeneity due to morphological changes and tumor microenvironment dynamics, influencing systemic therapy responses. While the role of high-mobility group AT-hook 2 (HMGA2) in tumor progression has been implicated in other cancers, its significance in ccRCC remains unclear. This study investigates the role of HMGA2 in these processes and its clinical impact. METHODS: Spatial transcriptomics (ST) was performed on primary ccRCC samples to investigate expression trajectories associated with HMGA2 expression and morphological evolution. In metastatic ccRCC cohorts treated with systemic therapy, immunohistochemistry and bulk RNA sequencing data were analyzed to evaluate molecular and clinical features in relation to HMGA2. Single-cell RNA sequencing (scRNA-seq) data were used to explore immune cell populations and their interactions. Based on these findings, multiplex immunohistochemistry (mIHC) assessed spatial distribution, cell-cell interactions, and pathological responses of key immune populations. RESULTS: HMGA2 expression was associated with aggressive morphological patterns, such as solid sheets and rhabdoid/sarcomatoid. ST revealed a progressive increase in HMGA2 expression along the morphological trajectory, marked by a shift from clear to eosinophilic cytoplasm, with eccentric nuclei and prominent nucleoli, and loss of vascular architecture. HMGA2-high tumors exhibited aggressive phenotypes driven by cell cycle, epithelial-mesenchymal transition, and inflammatory signaling pathways. Clinically, patients with high HMGA2 had worse progression-free survival but responded better to immune checkpoint inhibitor combination (Combo-ICI) therapy than to tyrosine kinase inhibitor monotherapy. To assess the immune landscape, scRNA-seq data revealed that HMGA2-high tumors were enriched with progenitor exhausted CD8+ T cells (Tpex), along with increased frequencies of conventional dendritic cell type 1 (cDC1) and inflammatory cDC type 2, which were found to interact with Tpex via ICAM-1. mIHC confirmed that Tpex were enriched among Combo-ICI responders in HMGA2-high tumors, with higher densities and closer proximity to ICAM-1+ cDC1. CONCLUSIONS: These findings suggest that dynamic HMGA2 expression contributes to morphological evolution and modulates immune responses through enhanced Tpex-cDCs engagement, serving as a potential marker for systemic therapy response in ccRCC. However, additional experimental studies are required to validate these mechanisms.

Humans↗
PubMed2026

Genetic evidence links hypertension to accelerated brain aging.

Hypertension affects one-third of adults and is a major comorbidity of neurocognitive disorders. The causal relationship, shared genetic architecture, and upstream mechanisms linking hypertension to brain aging remain unclear. Hypertension GWAS datasets from MVP and FinnGen R12 were meta-analyzed as the exposure, and a European-ancestry brain age gap (BAG) GWAS derived from the UK Biobank and LIFE-Adult cohorts was used as the outcome. MR and GSMR assessed causality. LDSC, HDL, and S-LDSC estimated genetic correlation. Four TWAS methods (MAGMA, FUSION, JTI-PrediXcan, FOCUS) mapped associations to genes, followed by SMR for causal validation and PoPS for prioritization. GSMAP with spatial transcriptomics characterized regional and cell-type enrichment. Hypertension and brain aging were genetically correlated, and MR and GSMR analyses suggested a causal effect of hypertension on increased brain age gap. TWAS identified 15 shared Hypertension-BAG genes, 10 supported by SMR. PoPS prioritized TRIM47 as the core gene. Shared signals were enriched in meninges, fiber tracts, cortical layer 1, and CA1 stratum lacunosum/radiatum, with cell-type enrichment in meninges, smooth muscle cells, oligodendrocytes, and astrocyte subtypes. Hypertension is genetically correlated with, and shows evidence of a causal effect on, accelerated brain aging. TRIM47 is a core gene bridging hypertension and BAG. GSMAP-based spatial enrichment provides a hypothesis-generating framework for understanding vascular, meningeal, and myelin-related pathways linking hypertension to increased brain age gap.

Humans↗
PubMed2026

Malignant epithelial states drive immune dysfunction in ampulla of Vater carcinoma.

BACKGROUND: Ampulla of Vater (AoV) carcinoma is a rare malignancy arising at the junction of intestinal and pancreatobiliary epithelium. Its heterogeneous clinical behavior and histological diversity have hindered therapeutic advances, and the cellular basis of this heterogeneity remains unclear. We aimed to construct a single-cell transcriptomic atlas of AoV carcinoma, with a focus on identifying epithelial subtypes and their interactions with the tumor microenvironment (TME). METHODS: We performed single-cell RNA sequencing on eight primary AoV tumors and four matched normal tissues. Comprehensive clustering and transcriptomic analyses identified cell-type composition, epithelial heterogeneity, and tumor-immune interactions. Findings were validated using deconvolution of bulk RNA-seq data from 62 AoV carcinoma patients. Results Malignant epithelial cells were categorized into four distinct subtypes: Int-Wnt, PB-KRAS, Int-Hypoxia, and Cycling stage. PB-KRAS cells exhibited stem-like transcriptional programs and high genomic instability. Deconvolution analysis of bulk RNA-seq data from the independent AoV cohort revealed that enrichment of the PB-KRAS subtype correlated with tumor recurrence and poor survival. Our immune profiling analysis discovered a significant association between PB-KRAS subtype and GZMK+ CD8+ T cells, which are in a pre-dysfunctional state, alongside SPP1+ macrophages exhibiting immunosuppressive traits. Spatial transcriptome data further supports the immunosuppressive natures of TME around PB-KRAS subtype malignant epithelial cells in AoV carcinoma. CONCLUSIONS: Our study presents a single-cell atlas of AoV carcinoma, highlighting the molecular diversity of malignant epithelium and its association with the immune microenvironment. The PB-KRAS subtype emerges as a stem-like, immunosuppressive tumor state associated with poor prognosis, providing insights for future therapeutic targeting.

Ampulla of Vater carcinoma↗
PubMed2026

Essence: A benchmarking-validated transformer framework for early diagnosis of Parkinson's disease using cerebrospinal fluid protein biomarkers.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms. The lack of objective molecular biomarkers limits early diagnosis and personalized treatment. Here, we propose Essence, a benchmarking-validated framework integrating cerebrospinal fluid (CSF) proteomics with traditional and deep learning models to identify robust protein signatures for PD. Using data from two independent cohorts, 1266 high-confidence proteins are quantified, among which 178 exhibit differential abundance between PD and healthy controls (HC). Through systematic benchmarking of ten machine learning algorithms and four neural architectures, the Transformer model consistently outperforms alternatives across multiple feature selection strategies, achieving an area under the receiver operating characteristic curve (AUC) of 1.0000 with only 35 features. Functional analyses of the top-ranked 35 proteins reveal enrichment in neuroinflammatory, synaptic, and oxidative stress-related pathways. Importantly, spatial transcriptomic profiling based on the Allen Brain Atlas shows region-specific expression of these biomarkers in PD-relevant brain structures, including the striatum, subthalamic nucleus, hippocampus, and white matter tracts. This anatomical alignment supports the functional relevance of the identified markers and highlights their potential utility in early-stage diagnosis and mechanistic understanding of PD.

Benchmarking↗
PubMed2025

Dissecting Sex-Specific Pathology in K18-hACE2 Transgenic Mice Infected With Different SARS-CoV-2 Variants.

Sex-biased differences in COVID-19 outcomes in relation to individual SARS-CoV-2 variants are not well understood. In this study, lungs and nasal cavities of age-matched female and male K18-hACE2 transgenic mice were collected for dissecting sex-specific differences in pathology after infection of SARS-CoV-2 614 G, Delta, or Omicron variant. Overall, Delta infection induced the most severe inflammation and pathology in nasal cavity and lung followed by the 614 G, then Omicron variant. Sex differences in host responses to SARS-CoV-2 infection were variant-specific. Delta-infected males showed increased pulmonary infiltration of CD163+ "M2" macrophages, Ly6G+ neutrophils, and NKR-P1C + NK cells during early onset of infection, and elevated lung inflammatory cytokines such as IL-10, IL-6, and IP-10 than Delta-infected females. Conversely, females had increased lung CD4 + T cell recruitment after Omicron infection and significantly elevated lung MCP-1 secretion after Delta infection than males. Lung spatial transcriptomics data revealed that Delta-infected females had enriched gene pathways related to humoral immune response and interferon signaling, while males had enriched pathways associated with extracellular matrix production, chemokine signaling, and cell chemotaxis. Taken together, this study highlights the complex infection dynamics with respect to individual SARS-CoV-2 variants and underscores the importance of sex as a confounding factor for COVID-19 pathology.

Animals↗
PubMed2026

Odon: an ultra-fast viewer for spatial proteomics.

MOTIVATION: Multiplexed spatial proteomics and spatial transcriptomics generate large, high-dimensional imaging datasets that are challenging to visualize efficiently, particularly at whole-slide and cohort scale. Visualization is an essential step for rapid detection of staining artefacts, such as protein aggregates or non-specific staining. RESULTS: Here, we present Odon, a native Rust desktop viewer designed for rapid, interactive exploration of multiplex imaging data on a standard laptop. Odon is primarily built around the OME-Zarr imaging format, and supports annotations via GeoJSON and GeoParquet, with secondary support for SpatialData, Xenium containers, and TIFF. Data can be stored locally or streamed directly from HTTP or S3-compatible object storage using viewport-driven tile loading. Odon incorporates a highly optimized rendering engine designed for viewport-driven tile loading and GPU-based compositing. In scripted benchmarks using synthetic multiplex OME-Zarr datasets, Odon showed lower peak memory use, lower affine-derived zoom-step error, and faster warm-start image loading than napari and QuPath under the tested conditions. Its GPU-based compositing pipeline also enables smooth rendering and interaction with >1 000 000 segmented cells. Odon further supports integrated visual analytics, including live thresholding and cell selection, and a mosaic mode for simultaneous viewing of hundreds of regions of interest in cohort and tissue microarray studies. Together, these features establish Odon as a high-performance platform for scalable visualization of spatial proteomics data. AVAILABILITY AND IMPLEMENTATION: Source code and compiled installers are available at https://github.com/alexcoulton/odon.

Proteomics↗
PubMed2026

Spatially guided in vivo single-cell functional genomics of postnatal heart.

Understanding how spatial organization and cell-cell interactions shape gene regulatory programs is central to decoding tissue development and function. The transition at birth, marked by increased circulatory demands and rapid tissue growth, requires precise spatiotemporal coordination of cardiac maturation. In this study, we generated a high-resolution spatial and temporal atlas of the postnatal mouse heart by integrating single-nucleus RNA sequencing with image-based spatial transcriptomics. This framework revealed dynamic cellular interactions, niche-specific signaling and transcriptional programs guiding cardiomyocyte maturation. To functionally test prioritized regulators in vivo and at scale, we developed PIP-seq (probe-based indel-detectable Perturb-seq), a high-throughput platform that detects single guide RNA identity, infers gene editing and profiles transcription from fixed nuclei. Applying PIP-seq to the developing postnatal heart, we identified 21 previously uncharacterized regulators of cardiomyocyte maturation, including genes essential for sarcomere assembly, metabolic reprogramming and electrophysiological transitions. Together, our findings define how microenvironmental signals and intrinsic gene programs cooperate to guide heart maturation and establish a broadly applicable framework for functional genomics in complex tissues.

Animals↗
PubMed2026

Genomic and transcriptomic features of HBV integration in treatment-naïve, HBeAg-positive children with chronic HBV infection.

BACKGROUND: Hepatitis B virus (HBV) integration represents a major obstacle to curing HBV; however, the landscape of HBV integration and local immune response to transcriptionally active viral integration in children with chronic HBV infection remain unclear. Herein, we aimed to elucidate this landscape in this population. METHODS: Genomic analyses using a probe-based capture strategy were performed on 18 children and 28 adults with chronic HBV infection. Spatial transcriptomics (ST) was performed on 12 children from our cohort and 3 adults from a public database. FINDINGS: All patients were hepatitis B e antigen (HBeAg)-positive and treatment-naïve. Genomically, children exhibited significantly lower clonal expansion level of HBV-integrated hepatocytes than adults, despite comparable unique breakpoint counts. After adjusting for confounding variables, age was identified as an independent risk factor for total frequency of unique integration breakpoints (b = 3.22, P = 0.005). Spatially, ST revealed that spots with transcriptionally active viral integration exhibited a sparse distribution and accounted for a low proportion of all spots in children. Notably, at these spots, children showed reduced adaptive immune cells (e.g., CD8+ T cells) but increased innate components (myeloid cells, Kupffer cells, activated dendritic cells) and APC co-stimulation, whereas adults exhibited a uniform reduction of immune cell populations. INTERPRETATION: Compared with adults, children exhibit lower clonal expansion of HBV-integrated hepatocytes and distinct immune profiles in response to transcriptionally active viral integration, offering new insights into their differing clinical course. FUNDING: Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education).

Humans↗
PubMed2026

Mycosis Fungoides-Like Atopic Dermatitis Represents a Th22-Dominant Inflammatory Endotype.

BACKGROUND: Early-stage mycosis fungoides (MF) often presents diagnostic challenges because of its clinical overlap with atopic dermatitis (AD). In clinical practice, we encountered a subset of patients with severe AD who fulfilled the MF diagnostic criteria yet remained clinically indistinguishable from AD and presented refractoriness to advanced therapies. We termed this ambiguous entity "mycosis fungoides-like AD" (mfAD) and sought to determine whether it represents malignant transformation or a distinct inflammatory endotype of AD. METHODS: Skin biopsies were obtained from 7 patients with AD and 11 patients with mfAD. We performed paired single-cell RNA sequencing and single-cell T-cell receptor sequencing analyses. Publicly available MF and AD datasets were integrated for comparative analysis. Spatial transcriptomic profiling was used to contextualize single-cell findings within the tissue architecture. RESULTS: Comparative transcriptomic analysis revealed that T cells in mfAD were aligned with those in AD and lacked genomic instability. High-resolution profiling showed that mfAD was characterized by oligoclonal Th22 expansion rather than a single dominant malignant clone. Notably, all patients with mfAD achieved rapid clinical remission with selective JAK1 inhibition, indicating the therapeutic response characteristics of inflammatory dermatoses. CONCLUSION: Our findings demonstrate that mfAD is not a true malignancy, but rather a Th22-driven inflammatory endotype of AD. These results redefine mfAD as an inflammatory subtype within the AD spectrum, providing a mechanistic explanation for both the "pseudo-monoclonality" that leads to MF misdiagnosis and the failure of dupilumab. This study establishes a rationale for the use of JAK inhibitors in precision medicine for this patient population.

JAK inhibitor↗