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PubMed2026

A murine model of sepsis induces age- and sex-specific chromatin remodeling in myeloid-derived suppressor cells.

INTRODUCTION: Sepsis survivors frequently develop long-term immune dysfunction, but the epigenetic mechanisms underlying persistent myeloid suppression remain unclear. Myeloid-derived suppressor cells (MDSCs), whose function is shaped by host age and sex, are key contributors to post-sepsis immune dysregulation. METHODS: Here, we present a high-resolution epigenetic map targeting gene promoters of MDSCs after sepsis and daily chronic stress using MAPit-FENGC, a single-molecule assay that simultaneously profiles DNA methylation and chromatin accessibility. In a clinically relevant murine model, including young and older adult male and female mice, splenic MDSCs were isolated for MAPit-FENGC and single-cell RNA sequencing. RESULTS: Unsupervised clustering identified nine promoter classes reflecting chromatin dynamics: age- and sex-dependent sepsis-induced opening (Classes 1-4), persistent closure with varying levels of DNA methylation (Classes 5-7), and constitutive openness post-sepsis (Classes 8, 9). Transcriptomic profiling corroborated these promoter states, linking accessibility with gene expression. CONCLUSIONS: These findings define promoter-level epigenetic classes across a targeted locus panel in splenic CD11b+Gr1+ cells within this murine sepsis model and generate mechanistic hypotheses regarding age- and sex-associated chromatin states.

Animals↗
PubMed2026

Lactylation-related immune-metabolic dysregulation defines prognostic and therapeutic stratification in lung adenocarcinoma.

BACKGROUND: Lactylation links lactate metabolism with inflammatory signaling and immune regulation in tumors. However, its cellular distribution and translational value in lung adenocarcinoma (LUAD) remain unclear. METHODS: Single-cell RNA-sequencing datasets GSE189357 and GSE171145 were integrated to characterize lactylation-related activity, intercellular communication, and malignant epithelial cell states in LUAD. Single-cell-derived lactylation-related differentially expressed genes were mapped to TCGA-LUAD and multiple GEO cohorts. Univariate Cox regression and machine learning algorithms were used to construct a lactylation-related prognostic signature (LRPS). The associations of LRPS with prognosis, immunotherapy response, drug sensitivity, genomic alterations, immune infiltration, and inflammation- and metabolism-related pathways were evaluated. KRT7 was further validated using virtual knockout analysis, spatial transcriptomics, and in vitro and in vivo experiments. RESULTS: lactylation-related transcriptional activity showed heterogeneous distribution across LUAD cell populations and was associated with altered cell-cell communication. In malignant epithelial cells, LRTS-high and LRTS-low states exhibited distinct metabolic, inflammatory, and tumor-related pathway activities. LRPS showed stable prognostic performance in TCGA-LUAD and multiple GEO cohorts and remained an independent prognostic factor. Low LRPS was associated with greater potential benefit from immunotherapy, whereas different LRPS groups displayed distinct drug sensitivity, genomic alteration, and immune microenvironment patterns. KRT7 was highly expressed in LUAD and associated with poor prognosis. KRT7 knockdown suppressed LUAD cell proliferation, migration, invasion, colony formation, and tumor growth in vivo. CONCLUSIONS: This study identifies lactylation-related immune-metabolic dysregulation as a clinically relevant feature of LUAD and develops a single-cell-guided LRPS for prognosis and therapeutic stratification. KRT7 emerged as an LRPS-related functional candidate with experimentally supported roles in malignant LUAD phenotypes.

Immunotherapy↗
PubMed2025

Bundle sheath cell-specific expression of chloroplast genes encoding subunits of the NADH dehydrogenase-like complex in maize.

C4 photosynthesis alleviates the limitation caused by the oxygenase activity of Rubisco by partitioning photosynthetic functions between two distinct cell types: bundle sheath cells (BSCs) and mesophyll cells (MCs). These cell types perform different steps of photosynthesis using specialized machinery, accompanied by differential expression of chloroplast genes. To uncover the underlying molecular mechanisms for this differentiation, we isolated BSCs and MCs and compared their chloroplast transcriptomes, focusing on the chloroplast NADH dehydrogenase-like (NDH) complex, which is enriched in BSCs. To investigate whether RNA stabilization contributes to differential gene expression, we analyzed RNA footprints that reflect the binding of pentatricopeptide repeat (PPR) proteins to their RNA targets. We could not detect cell-type-specific accumulation of footprint RNAs. We then focused on transcriptional regulation, specifically on an operon that starts with the rps15 gene. The operon includes six ndh genes and the psaC gene encoding a photosystem I subunit. Transcript levels of all genes in this operon were higher in BSCs than in MCs, suggesting coordinated regulation as a transcriptional unit. Based on the genomic location of the rps15 gene within inverted repeats near the junctions on both sides of the small single copy region, we demonstrated that rps15, through two distinct promoters, is sufficient to drive preferential accumulation of downstream transcripts in BSCs.

Zea mays↗
PubMed2026

Decoding regional keratinization in human oral mucosa through high-resolution spatial transcriptomics.

Oral mucosa exhibits region-specific keratinization, essential for periodontal health, yet the spatial and molecular mechanisms driving these differences remain poorly understood. This study aimed to generate a high-resolution spatial transcriptomic atlas of the human oral mucosa around the mucogingival junction, to reveal stromal-epithelial interactions, that distinguish keratinized from non-keratinized programs. Formalin-fixed paraffin-embedded specimens from the mucogingival junction area of two healthy donors were analyzed with the 10 × Genomics Visium HD platform, yielding two keratinized and two non-keratinized regions. Spatial clustering, pseudotime trajectory inference, cell-type integration with a single-cell reference, and ligand-receptor network analysis were applied to delineate epithelial and stromal compartments. Sixteen reproducible clusters, recapitulating tissue architecture, were identified and revealed distinct transcriptional signatures, distinguishing gingiva from lining mucosa. Pseudotime analysis revealed bifurcating epithelial lineages, originating from a shared basal progenitor layer into keratinized and non-keratinized programs. Gingival keratinization was driven by stromal collagen ligands (COL1A1, COL1A2, COL6A1, COL6A2) engaging epithelial receptors (CD44, SDC1), further reinforced within the epithelium by desmosomal adhesion via DSG1-DSC2/3. Gingival keratinization emerges from integrated stromal collagen signaling and epithelial adhesion. This spatially resolved framework advances understanding of oral mucosal specialization and provides a foundation for biologically guided regenerative therapies.

Humans↗
PubMed2025

Integrating multi-omics approaches in acute myeloid leukemia (AML): Advancements and clinical implications.

Acute myeloid leukemia (AML) is a highly heterogeneous and aggressive hematologic malignancy characterized by clonal proliferation of myeloid precursors. Despite significant advancements in genomic profiling and targeted therapies, patient outcomes remain suboptimal due to disease complexity, resistance mechanisms, and high relapse rates. The integration of multi-omics approaches-spanning genomics, epigenomics, transcriptomics, proteomics, and metabolomics-has revolutionized AML research, offering a comprehensive understanding of leukemogenesis, tumor heterogeneity, and therapeutic vulnerabilities. Recent studies leveraging high-throughput sequencing, mass spectrometry, and advanced computational tools have uncovered novel biomarkers, clonal evolution dynamics, and microenvironmental interactions that drive AML progression and resistance. For instance, single-cell multi-omics has revealed chemotherapy-resistant leukemic stem cell populations, while proteogenomic analyses have identified actionable targets such as MCL1 and metabolic dependencies like OXPHOS. Clinically, integrated omics platforms are refining risk stratification, minimal residual disease (MRD) monitoring, and personalized therapy selection. However, challenges such as data integration complexity, cost barriers, and ethical considerations remain. This review highlights the transformative potential of multi-omics in AML, emphasizing recent advancements in technology, biomarker discovery, and therapeutic innovation. By bridging the gap between molecular insights and clinical practice, multi-omics integration promises to redefine AML management, paving the way for precision oncology and improved patient outcomes.

Humans↗
PubMed2026

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics↗
PubMed2026

Wound healing in Atlantic spiny dogfish sharks.

Field observations and limited experimental studies indicate that elasmobranchs can repair substantial skin injuries, but the temporal course and cellular composition of wound healing in Atlantic spiny dogfish remain poorly characterized. We conducted an exploratory laboratory study in 20 female Atlantic spiny dogfish (Squalus acanthias) using standardized full-thickness skin wounds monitored by serial photography for 35 days, histological analysis at defined post-injury time points, and pooled single-nucleus RNA sequencing of intact and wounded skin. A continuous neoepithelial layer covered all examined wound beds by Day 1, whereas macroscopic wound area decreased progressively over 35 days and dermal denticles remained absent from the repaired surface. Histological examination showed progressive neoepidermal maturation, basement-membrane reformation, collagen deposition, and granulation-tissue organization, indicating that epithelial coverage preceded restoration of normal skin architecture. Single-nucleus RNA sequencing identified epithelial, stromal, vascular, pigment, neural, and immune-cell populations. T and B cells were detected in intact skin, and their relative abundance, together with that of several other leukocyte populations, increased at Day 1 and generally declined by Day 14. Because samples were pooled by time point, these transcriptomic changes are descriptive. These findings characterize rapid early reepithelialization followed by slower tissue remodeling in Atlantic spiny dogfish and provide a foundation for future comparative studies of elasmobranch skin repair.

Animals↗
PubMed2025

Perplexity as a Metric for Isoform Diversity in the Human Transcriptome.

Long-read sequencing (LRS) has revealed a far greater diversity of RNA isoforms than earlier technologies, increasing the critical need to determine which, and how many, isoforms per gene are biologically meaningful. To define the space of relevant isoforms from LRS, many existing analysis pipelines rely on arbitrary expression cutoffs, but a single threshold cannot accommodate the broad variability in isoform complexity across genes, cell-types, and disease states captured by LRS. To address this, we propose using perplexity-an interpretable measure derived from entropy-that quantifies the effective number of isoforms per gene based on the full, unfiltered isoform ratio distribution. Calculating perplexity for 124 ENCODE4 PacBio LRS datasets spanning 55 human cell types, we show that it provides intuitive assessments of isoform diversity and captures uncertainty across genes with varying complexity. Perplexity can be calculated at multiple gene regulatory levels-from transcript to protein-to compare how isoform diversity is reduced across stages of gene expression. On average, genes have an ORF-level perplexity of 2.1, indicating production of two distinct protein isoforms. We extended this analysis to evaluate expression variation across tissues and identified 4,593 ORFs across 3,102 genes with moderate to extreme tissue-specificity. We propose perplexity as a consistent, quantitative metric for interpreting isoform diversity across genes, cell types, and disease states. All results are compiled into a community resource to enable cross-study comparisons of novel isoforms.

Journal Article↗
PubMed2026

Convergent mitochondrial impairment and apoptosis driven by simultaneous down-regulation of multiple genes at 11p11.2 in Alzheimer's disease.

Genome-wide association studies (GWAS) and multi-omics analyses have identified numerous risk loci and thousands of potential causal genes associated with Alzheimer's disease (AD). However, the synergistic pathogenic contributions of multiple low-risk causal genes within a single locus remain poorly understood. Polygenic synergism at the 11p11.2 locus was systematically examined in AD pathogenesis. Three causal genes (MTCH2, NDUFS3, and PSMC3) exhibited coordinated down-regulation in both AD patients and AD mouse models. Individual knockdown in cultured cells altered mitochondrial function and disrupted AD-associated pathways, as revealed by transcriptomic profiling. Integrated RNA-seq analysis and experimental validation demonstrated that the concurrent down-regulation of all three genes synergistically enhanced mitochondrial reactive oxygen species (ROS) generation and activated the caspase-7-mediated apoptotic pathway. Notably, pharmacological caspase inhibition with Q-VD-OPh attenuated neuronal apoptosis, ameliorated memory deficits, and reduced Aβ plaque deposition in APP/PS1 mice. Simultaneous down-regulation of multiple genes at the 11p11.2 locus contributed to mitochondrial dysfunction and apoptosis in AD, highlighting polygenic synergism as a key pathogenic mechanism.

Animals↗
PubMed2026

Integrated transcriptome analysis and machine learning to construct a homeostatic model of acetylation for bladder cancer and validate the key gene CES1.

BACKGROUND: Bladder cancer (BLCA) is one of the most common malignant tumors of the urinary system. Protein acetylation (PA) plays a critical role in regulating multiple biological processes (BPs), cellular homeostasis, and cancer-related signaling pathways. This study aimed to construct a homeostatic model of acetylation for BLCA using integrated transcriptome analysis and machine learning and to validate the key gene CES1. METHODS: RNA sequencing (RNA-seq) and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Acetylation-related differentially expressed genes (DEGs) in BLCA were screened using differential expression analysis (DEA). An acetylation homeostatic model was constructed via univariate, machine learning-based least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, followed by validation in multiple cohorts. Single-cell RNA-seq analysis was used to explore gene expression patterns in diverse cell types. Enrichment analysis (EA), immune infiltration, and drug sensitivity analysis (DSA) were performed to characterize molecular features of different risk groups. Finally, the biological function of CES1 as the key gene was verified by in vitro knockdown experiments. RESULTS: We established a robust acetylation homeostatic model consisting of five genes, which effectively predicted overall survival (OS) and served as an independent prognostic factor in BLCA. High-risk patients showed significantly poorer prognosis, distinct immune infiltration profiles, and differential drug sensitivity. CES1 was identified and validated as the key gene in this model, which was highly expressed in BLCA and associated with poor prognosis. Knockdown of CES1 markedly suppressed cell proliferation, invasion, and migration, and reduced intracellular coenzyme A (CoA) levels, thereby regulating PA homeostasis. CONCLUSIONS: We developed and validated a novel acetylation homeostatic model for survival stratification and personalized treatment guidance in BLCA, based on integrated transcriptome analysis and machine learning. CES1 is closely associated with intracellular CoA levels and the malignant progression of BLCA. Its potential association with PA homeostasis requires further mechanistic validation, and it may act as a candidate therapeutic biomarker for BLCA.

Bladder cancer (BLCA)↗
PubMed2002

Primary effect of chemotherapy on the transcription profile of AIDS-related Kaposi's sarcoma.

BACKGROUND: Drugs & used in anticancer chemotherapy have severe effects upon the cellular transcription and replication machinery. From in vitro studies it has become clear that these drugs can affect specific genes, as well as have an effect upon the total transcriptome. METHODS: Total mRNA from two skin lesions from a single AIDS-KS patient was analyzed with the SAGE (Serial Analysis of Gene Expression) technique to assess changes in the transcriptome induced by chemotherapy. SAGE libraries were constructed from material obtained 24 (KS-24) and 48 (KS-48) hrs after combination therapy with bleomycin, doxorubicin and vincristine. KS-24 and KS-48 were compared to SAGE libraries of untreated AIDS-KS, and to libraries generated from normal skin and from isolated CD4+ T-cells, using the programs USAGE and HTM. SAGE libraries were also compared with the SAGEmap database. RESULTS: In order to assess the primary response of AIDS-related Kaposi's sarcoma (AIDS-KS) to chemotherapy in vivo, we analyzed the transcriptome of AIDS-KS skin lesions from a HIV-1 seropositive patient at two time points after therapy. The mRNA profile was found to have changed dramatically within 24 hours after drug treatment. There was an almost complete absence of transcripts highly expressed in AIDS-KS, probably due to a transcription block. Analysis of KS-24 suggested that mRNA pool used in its construction originated from poly(A) binding protein (PABP) mRNP complexes, which are probably located in nuclear structures known as interchromatin granule clusters (IGCs). IGCs are known to fuse after transcription inhibition, probably affecting poly(A)+RNA distribution.Forty-eight hours after chemotherapy, mRNA isolated from the lesion was largely derived from infiltrating lymphocytes, confirming the transcriptional block in the AIDS-KS tissue. CONCLUSIONS: These in vivo findings indicate that the effect of anti-cancer drugs is likely to be more global than up- or downregulation of specific genes, at least in this single patient with AIDS-KS. The SAGE results obtained 24 hrs after chemotherapy can be most plausibly explained by the isolation of a fraction of more stable poly(A)+RNA.

Acquired Immunodeficiency Syndrome↗
PubMed2026

PYCR1 promotes glutamine metabolism and the progression of lung adenocarcinoma by regulating the expression of OPLAH.

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Glutamine plays a critical role in the progression of LUAD. However, the function of pyrroline-5-carboxylate reductase 1 (PYCR1) and its regulatory role in glutamine metabolism remain unclear. Transcriptomic and clinical data for LUAD were obtained from The Cancer Genome Atlas (TCGA) and validated using Gene Expression Omnibus (GEO) datasets (GSE19188, GSE13213). Glutamine metabolism-related genes were analyzed for differential expression and prognostic significance. Functional enrichment was performed via gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses. Single-cell RNA-seq data (GSE117570) were processed using Seurat, and cell-cell communication was inferred with CellChat. In vitro, lentiviral overexpression, Western blotting, EdU, CCK-8, and glutamine uptake assays were conducted. An orthotopic xenograft model was established in nude mice to assess tumor growth in vivo. Six glutamine-metabolism-related genes were found significantly overexpressed in LUAD tissues and associated with poor overall survival. Single-cell sequencing revealed predominant PYCR1 expression in malignant cells. Functional assays demonstrated that PYCR1 overexpression enhanced glutamine uptake, proliferation, and inhibited apoptosis in LUAD cells, effects mediated via suppression of the P53 pathway. PYCR1 promoted tumor growth in a xenograft model and was found to transcriptionally upregulate 5-oxoprolinase (OPLAH), which augmented its oncogenic effects. Our findings identify the PYCR1/OPLAH axis as a key driver of LUAD progression via p53 signaling, revealing a promising therapeutic target.

Pyrroline Carboxylate Reductases↗
PubMed2026

Ten quick tips for spatial transcriptomics analysis.

Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery.

Spatial Transcriptomics↗
PubMed2026

Identification of CD55 as a downstream factor of EP4 receptor signaling in colorectal cancer cells.

Prostaglandin E2 (PGE2) signaling through the E-type prostanoid 4 (EP4) receptor has been implicated in the pathophysiology of colorectal cancer (CRC). We herein identified decay-accelerating factor, also known as CD55, as a novel CRC-associated downstream factor of the EP4 receptor. The integration of transcriptomic profiling of PGE2-stimulated HCA-7 human colon cancer cells with analyses of cancer genomic databases predicted CD55 as a potential EP4 receptor-regulated target. Inhibitor-based experiments showed the induction of CD55 after a PGE2 stimulation required the EP4 receptor and Gi protein in HCA-7 cells, whereas protein kinase A signaling was dispensable. In combination with a toxicogenomic database analysis, p38 mitogen-activated protein kinase (MAPK) was identified as the predominant effector connecting the EP4 receptor to CD55 upregulation. A single-cell RNA-seq re-analysis of human CRC tissues revealed CD55 upregulation and p38 MAPK-related gene set enrichment in epithelial cells expressing the EP4 receptor, suggesting that this induction mechanism may operate in a subset of epithelial cells in clinical specimens. Collectively, these results delineate a PGE2/EP4 receptor/Gi protein/p38 MAPK signaling axis that induces CD55 expression in HCA-7 cells and epithelial tumor cells, provide new mechanistic clues for understanding the regulation of complement regulatory molecule CD55 expression by prostaglandin signaling.

Humans↗
PubMed2026

NFS1 activates PI3K/AKT/mTOR signaling to upregulate GPX4 expression and enhance ferroptosis resistance in osteosarcoma.

Osteosarcoma continues to exhibit poor survival outcomes due to chemoresistance and metastasis, with metabolic reprogramming and ferroptosis resistance being key features of tumor heterogeneity, yet their upstream regulators remain poorly defined. NFS1, a cysteine desulfurase essential for iron-sulfur cluster biogenesis, protects multiple cancers from ferroptosis, but its role in osteosarcoma is unknown. In this study, we performed a transcriptomic meta-analysis and found that NFS1 expression was significantly upregulated in osteosarcoma tissues, with further elevation in metastatic lesions, and high NFS1 expression correlated with poor overall survival. Genome‑wide CRISPR screening data revealed a marked NFS1 dependency in osteosarcoma cell lines. Functionally, NFS1 promoted cell proliferation, migration, and invasion, whereas its knockdown suppressed these phenotypes. Using single‑cell RNA sequencing data from 27 osteosarcoma specimens, we applied a multi‑algorithm glycolytic scoring framework and observed NFS1 enrichment in highly glycolytic malignant cells, along with an association with PI3K/AKT/mTOR pathway activation. Mechanistically, NFS1 selectively enhanced PI3K, AKT, and mTOR phosphorylation without altering total protein levels, and upregulated GPX4, a central ferroptosis suppressor, leading to elevated ferroptosis resistance scores in NFS1‑high malignant cells. Collectively, these findings identify a previously unrecognized NFS1-PI3K/AKT/mTOR-GPX4 regulatory axis in osteosarcoma, linking metabolic reprogramming to ferroptosis resistance, and suggest that NFS1 functions as an oncogenic driver, as well as a promising prognostic biomarker and therapeutic target in osteosarcoma.

Humans↗
PubMed2026

Proteomics-based approaches to neutrophil biology.

INTRODUCTION: Neutrophils are central effectors of innate immunity and key contributors to inflammation, host defense, and tissue injury across a wide range of physiological and pathological contexts. Due to their short lifespan, rapid activation, and extensive post-translational regulation, comprehensive molecular characterization of neutrophil function requires approaches that go beyond transcriptomics or marker-based analyses. AREAS COVERED: This review summarizes how proteomic technologies have advanced the understanding of neutrophil biology by enabling unbiased, system-wide profiling of protein abundance, subcellular organization, post-translational modifications, and functional heterogeneity. We discuss global and subcellular proteomics, PTM-centric analyses, and emerging low-input and single-cell proteomic strategies, highlighting recent studies of infection, cancer, metabolic disorders, aging, autoimmune disease, and inflammation. The literature covered includes current large-scale quantitative proteomics, targeted PTMs, and integrative multi-omics studies in both human samples and relevant experimental models. EXPERT OPINION: Proteomics has established neutrophils as highly plastic and context-dependent cells whose functions are governed by coordinated remodeling of signaling, metabolism, and effector pathways. Future progress will depend on expanding neutrophil-specific PTM maps, improving low-input workflows, and integrating single-cell and spatial proteomics. Together, these advances are expected to redefine neutrophil functional states and accelerate translation toward clinically meaningful biomarkers and therapeutic strategies.

Humans↗
PubMed2026

Integrative Long-Read Multi-Omics of a Patient With GPI Deficiency: A Molecular Case Study of a Candidate Dual-Effect GPI Variant.

The molecular determinants of phenotypic severity in red cell enzymopathies are often obscured by the disconnect between coding sequence variants and their regulatory landscapes. Here we present a single-patient molecular case study that uses an integrative multi-omic approach-combining short-read WGS, PacBio HiFi long-read sequencing, native CpG methylation profiling, and Iso-Seq full-length transcriptomics-to characterize a severe, transfusion-dependent hemolytic anaemia. We identified a compound heterozygous state in the glucose-6-phosphate isomerase (GPI) gene, with no wild-type allele present. One allele (Haplotype 1) carried a missense variant (p.His191Arg); the other (Haplotype 2) carried a distinct missense variant, c.1414C>T (p.Arg472Cys), previously reported as biochemically unstable. Long-read phasing placed the two variants in trans. Allele-resolved transcript counts showed a directionally consistent but statistically non-significant trend toward higher expression of Haplotype 2 across two Iso-Seq replicates. Notably, the c.1414C>T transition abolishes a local CpG dinucleotide; in a small number of haplotype-2 reads spanning this position, the corresponding cytosine on the wild-type/Haplotype-1 background was methylated. We did not measure GPI protein abundance, enzymatic activity, or stability in this patient, and we do not establish that methylation at this site regulates GPI transcription. On the basis of these correlative observations in a single patient, we propose-as a hypothesis for future testing-that a coding variant might simultaneously perturb protein stability and disrupt a local epigenetic mark, and we outline the experiments required to test whether such a dual effect contributes to disease. This case illustrates the value of integrative long-read multi-omics for generating mechanistic hypotheses about variants of uncertain significance, while underscoring that causal claims require dedicated functional validation.

Humans↗
PubMedDate not supplied

Large-scale analysis of gene expression: methods and application to the kidney.

Characterization of tissue-specific gene expression profiles, or transcriptomes, may serve two purposes: a) establishing relationships between cell transcriptomes and functions (i.e. molecular and physiological phenotypes) under physiological and pathophysiological conditions serves to elucidate gene functions, and b) determination of the totality of genes expressed in a cell seems a prerequisite for understanding cell functions, because the properties of proteins vary with their environment. Sophisticated methods are now available for transcriptome analysis. They are based on serial, partial sequencing of cDNAs (sequencing of expressed sequenced tags (ESTs) and serial analysis of gene expression (SAGE)), or on parallel hybridization of labeled cDNAs to specific probes immobilized on a grid (macro- and microarrays and DNA chips). Some methods were designed specifically to compare gene expression under different conditions (substractive hybridization, glass microarrays). However, all these methods require several microg of mRNA as starting material, making impossible, in most tissues, to analyse gene expression in homogeneous cell populations. To get around this limitation, we developed a scaled-down SAGE method (SAGE adaptation to downsized extracts: SADE) in our laboratory. SAGE is based on the following: a) each cDNA is characterized by a 10-bp informative sequence called tag, b) the information from several transcripts is condensed into a single DNA molecule by concatenation of several tags, c) sequencing of individual clones from the library of concatemers, computer analysis of sequences and interrogation of sequence databases allow quantitative gene expression profiling. Applied to microdissected mouse nephron segments, SADE made it possible to determine segment-specific transcriptomes.

Animals↗