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PubMed2026

Four-dimensional molecular mapping from a spatial snapshot reveals the dynamics of hair follicle organogenesis.

Understanding organ formation requires capturing molecular information simultaneously in three-dimensional (3D) space and across developmental time. To this end, we developed 3D DNase-Enhanced Expression Profiling (3DEEP), a tissue-clearing approach that removes genomic DNA to extend spatial transcriptomic profiling hundreds of microns into intact tissues. We applied 3DEEP to neonatal mouse skin, capturing hundreds of developing hair follicles across their organogenesis trajectory. Ordering follicles by molecularly inferred developmental age transformed this single spatial snapshot into a four-dimensional (3D + time) molecular map of organogenesis. This map revealed developmental dynamics spanning stem cell compartment stratification, emergence of new cell subtypes within the follicle, and cascading structural transformations leading to hair canal formation. Comparative analysis of Foxn1-deficient nude mice, a hairlessness model, revealed organ-wide changes in developmental dynamics, including delayed molecular progression, reduced coordination, and increased developmental instability, preceding overt structural defects. This work demonstrates how deep-tissue spatial transcriptomics can uncover hidden dynamics of organ formation.

Animals↗
PubMed2026

Emerging Principles in Spatial Functional Genomics.

Spatial transcriptomic and proteomic atlases have enabled mapping of gene programs within intact tissues, but these measurements remain largely descriptive and do not define the mechanisms controlling tissue biology. Pooled CRISPR screening provides scalable causal interrogation of gene function but remains largely confined to dissociated systems that lack spatial context. In vivo spatial functional genomics (SFG) bridges these approaches by integrating genetic perturbations with in situ transcriptomic and proteomic readouts to measure gene function within intact tissue ecosystems. By preserving spatial organization, SFG enables interpretation of perturbations through effects on cell-cell interactions, diffusible signals, multicellular niches, and tissue architecture. Here, we outline key design axes of SFG: perturbation strategy, barcoding strategy, and phenotypic readout. We discuss computational challenges, including spatial autocorrelation, neighborhood dependence, and context-aware null modeling, and highlight how SFG reveals non-cell-autonomous, architecture-dependent mechanisms of gene function, advancing toward predictive models of tissue organization and gene function.

Genomics↗
PubMed2026

Multi-omics analysis reveals stage-associated differences in gut immunity and microbiota between juvenile and adult common carp (Cyprinus carpio).

In vertebrates, the development of intestinal immunity is closely associated with dynamic changes in the gut microbiota. However, stage-associated differences in intestinal immunity and gut microbial communities remain poorly characterized in teleost fish. In this study, transcriptomic analysis combined with 16S rRNA gene sequencing was employed to characterize intestinal immunity and gut microbial communities in juvenile and adult common carp (Cyprinus carpio). Transcriptomic profiling revealed marked developmental differences in intestinal immune function. Juvenile carp exhibited a predominantly innate immune phenotype, characterized by elevated expression of pro-inflammatory cytokines, antimicrobial peptides, and lysozyme-related genes. This immune profile was accompanied by enhanced mucosal barrier function and a relatively pro-inflammatory intestinal environment. In contrast, adult carp displayed increased expression of genes associated with adaptive immunity, suggesting that adult common carp exhibit relatively stronger adaptive immune characteristics than juvenile fish. Gut microbiota analysis demonstrated significant stage-dependent differences in microbial diversity and community composition. Juvenile fish were enriched with bacterial taxa potentially associated with innate immune activation, whereas adult fish harbored distinct microbial communities linked to intestinal homeostasis and barrier maintenance. Furthermore, correlation analyses identified significant associations between specific microbial taxa and innate immune-related gene expression, suggesting a close association between gut microbiota composition and intestinal immune characteristics in juvenile and adult common carp. Collectively, these findings reveal stage-associated differences in intestinal immunity and gut microbial communities between juvenile and adult common carp, thereby providing insights into intestinal immune characteristics at different developmental stages in teleost fish.

Animals↗
PubMed2025

RNA sequencing offers new diagnostic opportunities in neurodevelopmental disorders: A systematic review.

PURPOSE: Transcriptomics by way of RNA sequencing (RNAseq) has emerged as a means to increase the diagnostic yield in genetic conditions. In this systematic review, we focus on the contribution of transcriptomics to improve the diagnostic yield in neurodevelopmental disorders. METHODS: We performed a systematic literature search in PubMed until January 2024, including articles describing diagnostic RNAseq on at least 1 individual with a primary neurodevelopmental phenotype. We extracted data on cohort size, phenotype, sample tissue, previously used diagnostic methods, added diagnostic yield of RNAseq, the use of control samples, and technical aspects of the RNA sequencing methodology. RESULTS: A total of 17 articles were eligible for inclusion in the systematic review. We found an average added diagnostic yield of 15.5% through RNA sequencing for individuals with neurodevelopmental disorders. There is heterogeneity in the tissue type, reported quality measures, and the computational pipeline. CONCLUSION: The significantly increased diagnostic yield demonstrates the value of this novel tool in the diagnostic setting of neurodevelopmental disorders. Our results offer an overview of common methodologies for RNAseq and allow us to formulate recommendations for genetic labs and clinicians when implementing RNAseq as a diagnostic tool. Lastly, we provide recommendations for future publications to increase transparency and reproducibility.

Humans↗
PubMed2026

A multi-modal survival prediction framework with group-based batch training and structural consistency alignment.

OBJECTIVE: Integrating whole-slide images (WSIs) with transcriptomic profiles is pivotal for enhancing cancer survival prediction. However, the intrinsic gigapixel resolution and variable sequence lengths of WSIs create a fundamental trade-off between training efficiency and the preservation of data heterogeneity in existing frameworks. Furthermore, substantial statistical and structural discrepancies between histological and genomic modalities often impede effective cross-modal alignment and fusion, thereby limiting prognostic accuracy. METHODS: We propose PRISM, an efficient multi-modal learning framework for integrating WSIs with transcriptomic profiles. To reconcile training efficiency with full data heterogeneity, PRISM first stochastically partitions variable-length WSI sequences into a main subset and a complementary residual subset, both of which are packed into fixed-length groups for batch training. The main subset is processed in the main branch, utilizing isolation masking to maintain intra-group sequence independence. Simultaneously, the residual subset is consolidated into "hyperslides" within a residual branch that leverages tailored supervision, effectively capturing inter-slide correlations. Furthermore, PRISM integrates an Informative Token Aggregation (ITA) module to reduce redundancy in WSIs and employs Cross-batch Structural Consistency Alignment (CBSCA) mechanism to enhance inter-modal structural connectivity. Finally, efficient cross-modal feature interaction is achieved through a Low-rank Bilinear Gated Fusion (LBGF) module. Code is available at https://github.com/Alisa2080/PRISM. RESULTS: Compared with existing methods, PRISM achieves the best overall C-index across five TCGA cohorts. On the larger TCGA-BRCA dataset, PRISM requires only 6 hours of training time, substantially reducing computational cost relative to strong multimodal baselines. Furthermore, comprehensive evaluations demonstrate that PRISM achieves the best overall IBS ranking and favorable time-dependent AUC performance at 1, 3, and 5 years, thereby delivering a more favorable trade-off between prognostic performance and computational efficiency. CONCLUSION: PRISM provides a favorable balance between predictive performance, calibration quality, and computational efficiency, highlighting its potential for practical deployment in multimodal survival modeling for computational pathology.

Humans↗
PubMed2025

Validation of breast cancer as a risk factor for anxiety and depression: Insights from Mendelian randomization analysis.

This study employed Mendelian randomization (MR) analysis to confirm the association between breast cancer and the risk of anxiety and depression, and to explore the molecular mechanisms by which lipid nanoparticles of ketamine (LNP@Ket) modulate these behaviors in a mouse model of breast cancer. Through single-cell transcriptomic analysis, the study aimed to clarify nuclear factor erythroid 2-related factor 2 (Nrf2)'s role in the development of anxiety and depression in these mice. Analysis of patient data from genome-wide association study (GWAS) databases supported the link between breast cancer, anxiety, and depression. In vivo experiments demonstrated that treating breast cancer mice with LNP@Ket significantly reduced anxiety and depression behaviors. The synthesis of LNP@Ket and its subsequent analysis highlighted its inhibitory effects on these behaviors. Single-cell transcriptomic sequencing identified key cells and genes affected by LNP@Ket treatment, particularly emphasizing Nrf2. Upregulation of Nrf2 in astrocytes increased the expression of antioxidant enzymes and reduced pro-inflammatory cytokines, alleviating anxiety and depression symptoms by inhibiting neuroinflammation and neurodegeneration. This comprehensive study highlights the pivotal role of Nrf2 in the therapeutic efficacy of LNP@Ket for treating anxiety and depression in breast cancer mice.

Anxiety and depression behaviors↗
PubMed2026

Uncovering hidden complexity in the Apis mellifera mitotranscriptome: a polyadenylation-centered perspective.

Mitochondrial transcription is gaining increasing attention as researchers seek to better understand the full coding potential of mitochondrial DNA (mtDNA). Emerging evidence suggests that mtDNA may encode additional elements beyond classical oxidative phosphorylation genes, pointing to a more complex transcriptional architecture than previously recognized. In this study, we explored the mitochondrial transcriptome of Apis mellifera (Insecta: Hymenoptera), with a particular focus on polyadenylation-associated features. Our analysis revealed that both sense and antisense transcripts undergo polyadenylation, although transcript abundance and poly(A) tail lengths varied markedly across mitochondrial genes. Several transcripts exhibited alternative isoforms, either extended or truncated, frequently including intergenic regions. These regions may represent functional non-coding elements or structural variants rather than conventional untranslated regions (UTRs). Interestingly, some transcripts also contained non-templated nucleotide additions particularly cytosine residues immediately upstream of the poly(A) tails. Monocistronic units that included portions of downstream intergenic regions were among the most abundantly represented, suggesting a possible regulatory role for these sequences. To experimentally validate our in silico findings, we performed RT-qPCR to assess relative gene expression and applied 3' RACE-PCR to define transcript boundaries. These approaches confirmed the presence of multiple transcript isoforms and supported the involvement of polyadenylation in shaping mitochondrial RNA diversity. Together, our findings reveal a previously underappreciated level of complexity in the A. mellifera mitochondrial transcriptome and highlight the potential regulatory significance of polyadenylation dynamics and intergenic region transcription.

Animals↗
PubMed2026

Comparative phylogenomics and transcriptional regulatory networks of AQPs, HSPs, and LEA proteins in salt-stressed Portulaca oleracea.

Soil salinization severely threatens global food security, necessitating systematic investigations of halophytes like Portulaca oleracea to decode the molecular mechanisms of environmental resilience. Utilizing an integrated framework of deep learning-based genome annotation (58,817 predicted genes; 96.5% BUSCO completeness), multi-tissue RNA-Seq, phylogenomics, and gene regulatory network (GRN) inference, the synergistic orchestration of 78 aquaporins (AQPs), 525 heat shock proteins (HSPs), and 119 late embryogenesis abundant (LEA) proteins was elucidated. The active transcriptome, encompassing 39,065 expressed loci, revealed a systemic growth-defense trade-off. Tissues displayed distinct adaptive mechanisms: leaves modulated intracellular water balance via specialized AQPs, whereas adult roots maintained proteostasis through robust HSP20/HSP70 induction. Phylogenomic clustering across 154 species demonstrated that salinity tolerance constitutes an evolutionary mosaic, identifying 81 halophyte-exclusive orthogroups and 1129 species-specific clusters. Comparative topology across six independent GRNs (4.2M-5.3 M edges) unmasked a highly modular transcriptional reprogramming strategy governed by a core apparatus of 22 stress-exclusive regulators, with functional enrichment heavily prioritizing protein dimerization and chromatin remodeling. Theoretically, the distinct convergence of Trihelix transcription factors with guard cell differentiation pathways offers a candidate transcriptomic framework to explain the plant's characteristic C4-CAM photosynthetic plasticity under severe osmotic pressure. Practically, these evolutionary blueprints and specific master switches transcend single-gene transgenic limitations. Utilizing these root-sustained and stress-inducible targets under localized promoters provides a naturally optimized, network-level precision engineering roadmap to transfer robust, compartmentalized halotolerance to sensitive glycophytic crops.

Gene Regulatory Networks↗
PubMed2024

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis.

Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene-expression-based patient subgroups (sepsis response signatures [SRS]) informative for outcome and underlying pathophysiology. Here, we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-sequencing data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection.

Humans↗
PubMed2026

Identification and genetic validation of potential therapeutic targets for pulmonary hypertension through multi-omics causal inference.

Pulmonary hypertension (PH) underscores the urgent need for novel therapeutic targets. This study aimed to employ a proteome-wide Mendelian randomization (MR) approach to systematically identify circulating proteins causally associated with PH, thereby providing genetically validated candidate targets for drug development. We adopted a 2-sample MR design, integrating large-scale plasma proteomic quantitative trait loci (pQTL) data (encompassing 4148 proteins) and summary statistics from a large-scale PH genome-wide association study (2047 cases, 8301 controls). Candidate targets were screened through a multilayered analytical pipeline comprising proteomic MR, transcriptomic MR, and summary-data-based Mendelian randomization. The ultimately identified MR-Identified Causal Candidate Targets (MR-ICTs) underwent rigorous Bayesian colocalization analysis, followed by biological characterization through functional enrichment analysis, single-cell transcriptomics, and phenome-wide association studies. Through robust genetic causal inference, this study provides that circulating proteins such as LYZ, GREM2, NID1, and PF4V1 play causal roles in PH pathogenesis. These findings offer a set of rigorously genetically validated, high-priority therapeutic targets for developing novel PH treatments, specifically addressing key pathological mechanisms such as innate immunity, BMP signaling pathway dysregulation, and platelet activation. Our multi-dimensional analysis ultimately identified 6 MR-ICTs causally associated with PH. Notably, the causal associations for lysozyme C (LYZ), gremlin-2 (GREM2), nidogen-1 (NID1), and platelet factor 4 variant 1 (PF4V1) were stringently validated by Bayesian colocalization analysis (posterior probability for hypothesis 4 [PPH4], indicating a shared causal variant, > 0.99). Functional enrichment analysis revealed significant involvement of these targets in immune response and TGF-β signaling pathways. Single-cell analysis further elucidated their cell-type-specific expression, with LYZ predominantly expressed in monocytes and PF4V1 almost exclusively in platelets.

Hypertension, Pulmonary↗
PubMed2025

Integrated multi-omics analyses provide new insights into genomic variation landscape and regulatory network candidate genes associated with walnut endocarp.

Persian walnut (Juglans regia) is an economically important nut oil tree; the fruit has a hard endocarp/shell to protect seeds, thus playing a key role in its evolution, and the shell thickness is an important trait for walnut breeding. However, the genomic landscape and the gene regulatory networks associated with walnut shell development remain to be systematically elucidated. Here, we report a high-quality genome assembly of the walnut cultivar 'Xiangling' and construct a graphic structure pan-genome of eight Juglans species to reveal the genetic variations at the genome level. We re-sequence 285 accessions to characterize the genomic variation landscape. Through genome-wide association studies (GWAS), we identified 19 loci associated with more than 268 loci that underwent selection during walnut domestication and improvement. Multi-omics analyses, including transcriptomics, metabolomics, DNA methylation, and spatial transcriptomics across eleven developmental stages, revealed several candidate genes related to secondary cell biosynthesis and lignin accumulation. This integrated multi-omics approach revealed several candidate genes associated with secondary cell biosynthesis and lignin accumulation, such as UGP, MYB308, MYB83, NAC043, NAC073, CCoAOMT1, CCoAOMT7, CHS2, CESA7, LAC7, COBL4, and IRX12. Overexpression of JrUGP and JrMYB308 in Arabidopsis thaliana confirmed their roles in lignin biosynthesis and cell wall thickening. Consequently, our comprehensive multi-omics findings offer novel insights into walnut genetic variation and network regulation of endocarp development and shell thickness, which enable further genome-informed breeding strategies for walnut cultivar improvement.

Juglans↗
PubMed2025

Multi-omics analyses provide insights into the molecular basis for salt tolerance of Phyla nodiflora.

The perennial herbaceous plant, Phyla nodiflora (Verbenaceae), which possesses natural resistance to multiple abiotic stresses, is widely used as a pioneer species in island ecological restoration. Due to the lack of information about its genome, the mechanism underlying its tolerance to environmental stresses, such as salinity, is almost entirely unknown. Here, we report on the high-quality genome of P. nodiflora that is 403.07 Mb in size, and which was assembled and anchored onto 18 pseudo-chromosomes. Genomic synteny revealed that P. nodiflora underwent two whole genome duplication events, which promoted the expansion of genes related to environmental adaptation and the biosynthesis of secondary metabolites. An integrated genomic and transcriptomic analysis suggested that salt stress tolerance in P. nodiflora is associated with the expansion and activated expression of genes related to abscisic acid (ABA) homeostasis and signaling. The expansion of ZEP family genes may contribute to the consistent increase in ABA levels under salt stress. Lysine acetylomic analysis revealed that exposure to salt led to widespread protein deacetylation, with these proteins primarily involved in signal transduction, carbohydrate transport and metabolism, and transcription regulation. Deacetylation of glutathione S-transferase increased enzymatic activities in response to salt-induced oxidative stress. Collectively, the genomic, transcriptomic, and lysine acetylomic analyses provide profound insight into the molecular basis of the adaptation of P. nodiflora to salt stress, and will be helpful to engineer salt-tolerant plants for ecological restoration.

Salt Tolerance↗
PubMed2025

EucaMOD: a comprehensive multi-omics database for functional genomics research and molecular breeding of fast-growing eucalyptus trees.

Eucalyptus, one of the most widely planted plantation tree species globally, is primarily found in tropical and subtropical regions and contributes significantly to economic and social benefits. With advances in sequencing technologies, there is an increasing demand for the systematic analysis of multi-omics data among Eucalyptus species to enhance genetic breeding efforts. Although several early genomic databases have been established for eucalyptus, they have not been updated in a timely manner and lack recent multi-omics data, rendering them insufficient for current research needs. To address this gap, we developed the eucalyptus multi-omics database (EucaMOD, http://eucalyptusggd.net/eucamod), a comprehensive resource for cross-omics studies. In this study, we functionally annotated 45 eucalyptus genomes and structurally annotated 15, conducting comparative genomics and pan-proteomics analyses across all genomes. Additionally, we analyzed eucalyptus transcriptome, epigenome, and variome data through standardized workflows, enabling the in-depth mining and reanalysis of multi-omics datasets. EucaMOD is the most comprehensive multi-omics database for eucalyptus to date and includes data from 45 genomes (39 species), 870 mRNA-seq samples, 17 miRNA-seq samples, 52 epigenomic datasets (histone modifications and transcription factor binding), and genetic variation data from 1219 samples. To support functional genomics and molecular breeding research, the database is organized into the following 11 modules: Home, Species, Genomics, Comparative genomics, Pan-proteomics, Transcriptomics, Epigenetics, Variomics, Tools, Download, and Help. EucaMOD also offers online analysis tools for data mining, providing free public services to aid eucalyptus gene function and genetic engineering studies.

Eucalyptus↗
PubMed2025

Interaction of host gene-gut microbiota in male grading of Macrobrachium rosenbergii.

UNLABELLED: The giant freshwater prawn (GFP; Macrobrachium rosenbergii), a crustacean of high nutritional and economic value, is crucial for aquaculture. During the same growth cycle, male GFPs develop into three distinct forms: small males, orange claw males, and blue claw males. These morphotypes display varying social behaviors, which severely constrain their industrial development. To address this, this study collected male GFP samples at critical developmental time points (100, 110, and 120 days post-hatching) for phenotypic trait measurement and analysis to obtain external morphological data. Through gut microbiota diversity analysis, we identified key gut bacteria (Lactococcus garvieae and Lactobacillus taiwanensis) influencing male morphotype differentiation. Transcriptomic analysis revealed host Kyoto Encyclopedia of Gene and Genome pathways and key genes (Wnt-6, CTSB, CTSL, PPAE, and TP53) associated with morphotype differentiation. The interactions among phenotypic traits, gut microbiota, and key genes were systematically studied through association analysis. Weighted gene co-expression network analysis was employed to construct co-expression modules, from which critical gene modules influencing phenotypic variation were identified. Through association network analysis, we established an "Achromobacter-CD-TRINITY_DN93139_c0_g2 (calpain clp-1)" interaction model. Our findings provide novel insights into the genetic enhancement of GFPs and offer guidelines for future research regarding gut symbiotic bacteria and breeding initiatives. IMPORTANCE: Male Macrobrachium rosenbergii (giant freshwater prawn [GFP]) in the same growth cycle will develop into small males, orange claw males, and blue claw males. This individual heterogeneity in growth significantly impacts the benefits of aquaculture. However, the factors influencing the differentiation of male GFP morphotype remain unclear. This study analyzed the phenotypic data of various GFP levels, the structure of the intestinal microbiota, and the differential genes within the gonadal transcriptome at critical time points of male GFP-level type differentiation. The aim was to explore the potential role of intestinal microbiota and differential genes in this phenomenon. This study offers new insights into the research on the phenomenon of male GFP-level type differentiation.

Animals↗
PubMed2026

From immature to mature epithelium: unveiling structural dynamics and transcriptional programs in rainbow trout intestinal barrier.

The intestinal epithelium is crucial for nutrient absorption, immune defense, and barrier function in farmed fish. However, the molecular mechanisms underlying its development and maturation in salmonids remain poorly characterized, hindering our ability to address pervasive gut health challenges in aquaculture. In this study, we use the RTgutGC cell line to implement an epithelial maturation model with the aim of characterizing the global transcriptional program in rainbow trout (Oncorhynchus mykiss). We evaluated in vitro culture conditions to generate a polarized epithelial barrier with high transepithelial electrical resistance (TEER = 75.8 Ω × cm2), low permeability (6.2 × 10-6 cm/s), and well-defined apical specializations, including microvilli-like structures and clusters of these structures (brush border). Comparative transcriptomic profiling between immature (7 days post-seeding, dps) and mature (28 dps) epithelia revealed 3,817 differentially expressed genes (DEGs). Functional enrichment analysis showed that maturation was characterized by the downregulation of proliferative and ribosomal pathways and the concerted upregulation of processes critical for barrier function, including transmembrane transport, proteolysis, cell adhesion, extracellular matrix organization, and tight junction assembly. We identified a core set of 60 genes indicators of epithelial maturation, encompassing solute transporters (slc26a6, slc43a2), tight junction proteins (tjp1, cldn1, cldn3, cldn5, cldn18, among others), and structural components essential for microvilli formation and polarization (cdhr5b, pard6a). By integrating ultrastructural, functional, and transcriptomic data, this study established a framework for future mechanistic investigations into gut development and maturation in vitro. This set of mature epithelium indicators has diverse applications, such as the design of nutritional and pharmacological interventions to improve gut health and resilience in farmed fish.

Animals↗
PubMed2026

A regulatory network underlying idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease in which genetic susceptibility interacts with epithelial, immune, and mesenchymal remodeling. Although the chromosome 11p15.5 locus contains established IPF susceptibility signals near MUC5B and TOLLIP, the broader regulatory architecture of this region remains incompletely resolved. METHODS: We integrated IPF genome-wide association study summary statistics with methylation, expression, and protein quantitative trait loci using summary-data-based Mendelian randomization (SMR). SMR-prioritized candidates were evaluated in independent transcriptomic and methylation cohorts and further contextualized using microRNA, transcription-factor, protein-interaction, machine-learning, single-cell, and spatial transcriptomic analyses. Fibrosis-associated expression patterns were assessed in a bleomycin-induced pulmonary fibrosis rat model. RESULTS: The analyses recovered the established MUC5B and TOLLIP signals and prioritized BRSK2 as a comparatively underexplored candidate supported by eQTL-based SMR and independent molecular evidence. The BRSK2 pQTL association did not pass the HEIDI test and was therefore not interpreted as convergent protein-level genetic evidence. Network analyses linked BRSK2 to cell-cycle, metabolic-stress, and senescence-related programs, while cross-cohort machine learning prioritized FOXA2, CDC25B, and NFE2 as informative network features. Single-cell and spatial analyses localized BRSK2 preferentially to fibroblast and myofibroblast compartments and to regions with greater histological fibrosis severity. In fibrotic rat lungs, BRSK2 expression increased, whereas FOXA2 and CDC25B decreased at the transcript and protein levels. CONCLUSIONS: These findings refine the molecular landscape of the chromosome 11p15.5 IPF susceptibility locus and prioritize BRSK2 as a candidate component of an IPF-associated profibrotic fibroblast state. Its causal contribution, direct regulatory relationships, and therapeutic tractability require targeted mechanistic validation.

Idiopathic Pulmonary Fibrosis↗
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↗
PubMed2027

Systematic Analysis of Tumor Microenvironment Using IOBR.

The Immuno-Oncology Biological Research (IOBR) package is an R-based analysis tool for exploring the tumor microenvironment (TME) and its influence on anti-tumor immunity. Built for high-throughput data-spanning both transcriptomic and genomic profiles-IOBR integrates six analytical modules, including transcriptomic data preprocessing, TME profiling, TME pattern identification, ligand-receptor interaction analysis, genome-TME interaction assessment, and visualization. In this chapter, we walk through a multi-omics workflow using example datasets, illustrating data preparation, distribution analyses, result interpretation, and graphical output. IOBR is open source and is available at https://github.com/IOBR/IOBR and a detailed GitBook ( https://iobr.github.io/book/ ) offers a complete manual and analysis guide for each function.

Tumor Microenvironment↗