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Comparative cellular analysis of motor cortex in human, marmoset and mouse.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

Animals↗

Integrative quantum and systems biology of cancer: From molecular fluctuations to ecological outcomes.

This review treats cancer as a multiscale adaptive system, asks what the framework must predict to be worth adopting, and separates at each scale what the evidence establishes from what is proposed. It is an expert narrative synthesis, not a systematic review, and states the limits of that design. Proton transfer and tautomeric shifts contribute to spontaneous mispairing but do not license claims of directed or non-random mutation: replication timing, three-dimensional chromatin organization, sequence context and known mutagenic processes explain most mutational heterogeneity, leaving any quantum contribution as a residual against that baseline. The Waddington quasi-potential is bounded: outside detailed balance the dynamics are not gradient-derivable and require a probability-flux term. Hysteresis, rate-limited bimodality and return to state after perturbation distinguish an attractor from a transcriptomic cluster. Single-cell karyotype and live-imaging evidence supports whole-genome doubling as an unstable intermediate of heterogeneous origin and context-dependent consequence, not a uniform adaptive strategy. Systems and synthetic biology, virtual cells and digital twins are assessed against benchmarks, not promise. Tissue-scale ecology is reported with the spatial measurements now quantifying it, including evidence that stromal niche construction is not uniformly tumor-supporting. RNA modification is a layer in its own right, showing that the interpretation of a regulatory signal, not its magnitude, is biologically decisive. A dedicated section states the framework's commitments, the observable and evidence at each scale, and what would falsify them, asking what this adds to somatic mutation theory with clonal evolution and plasticity.

Neoplasms↗

The hepatic transcriptome in human liver disease.

The transcriptome is the mRNA transcript pool in a cell, organ or tissue with the liver transcriptome being amongst the most complex of any organ. Functional genomics methodologies are now being widely utilized to study transcriptomes including the hepatic transcriptome. This review outlines commonly used methods of transcriptome analysis, especially gene array analysis, focusing on publications utilizing these methods to understand human liver disease. Additionally, we have outlined the relationship between transcript and protein expressions as well as summarizing what is known about the variability of the transcriptome in non-diseased liver tissue. The approaches covered include gene array analysis, serial analysis of gene expression, subtractive hybridization and differential display. The discussion focuses on primate whole organ studies and in-vitro cell culture systems utilized. It is now clear that there are a vast number research opportunities for transcriptome analysis of human liver disease as we attempt to better understand both non-diseased and disease hepatic mRNA expression. We conclude that hepatic transcriptome analysis has already made significant contributions to the understanding of human liver pathobiology.

Journal Article↗

The genetic architecture of odor-guided behavior in Drosophila: epistasis and the transcriptome.

We combined transcriptional profiling and quantitative genetic analysis to elucidate the genetic architecture of olfactory behavior in Drosophila melanogaster. We applied whole-genome expression analysis to five coisogenic smell-impaired (smi) mutant lines and their control. We used analysis of variance to partition variation in transcript abundance between males and females and between smi genotypes and to determine the genotype-by-sex interaction. A total of 666 genes showed sexual dimorphism in transcript abundance, and 530 genes were coregulated in response to one or more smi mutations, showing considerable epistasis at the level of the transcriptome in response to single mutations. Quantitative complementation tests of mutations at these coregulated genes with the smi mutations showed that in most cases (67%) epistatic interactions for olfactory behavior mirrored epistasis at the level of transcription, thus identifying new candidate genes regulating olfactory behavior.

Animals↗

Glial degeneration and reactive gliosis in alpha-synucleinopathies: the emerging concept of primary gliodegeneration.

The concept of gliodegenerative diseases has not been widely established although there is accumulating evidence that glial cells may represent a primary target of degenerative disease processes. In the central nervous system (CNS), examples that provide a "proof of concept" include at least one alpha-synucleinopathy, multiple system atrophy (MSA), but this disease is conventionally discussed under the heading of "neurodegeneration". Additional evidence in support of primary glial affection has been reported in neurodegenerative disorders such as Parkinson's disease, Alzheimer's disease and transmissible spongiform encephalopathies. Based on biochemical, genetic and transcriptomic studies it is also becoming increasingly clear that the molecular changes measured in whole tissue extracts, e.g. obtained from Parkinson's disease brain, are not based on a purely neuronal contribution. This important evidence has been missed in cell culture or laser capture work focusing on the neuronal cell population. Studies of animal and in vitro models of disease pathogenesis additionally suggest glial accountability for some CNS degenerative processes. This review provides a critical analysis of the evidence available to date in support of the concept of gliodegeneration, which we propose to represent an essential although largely disregarded component of the spectrum of classical "neurodegeneration". Examples from the spectrum of alpha-synucleinopathies are presented.

Animals↗

Repeated emergence and fitness heterogeneity of KPC-33 in ST11 Klebsiella pneumoniae under ceftazidime-avibactam pressure.

Ceftazidime-avibactam (CZA) is an important therapeutic option for infections caused by Klebsiella pneumoniae carbapenemase (KPC)-producing Klebsiella pneumoniae. However, CZA exposure also selects for emergent KPC variants. Their in vivo evolutionary patterns, fitness consequences, and underlying molecular mechanisms remain unclear. We performed a longitudinal multiomics analysis of 35 clonally related ST11 KPC-producing K. pneumoniae isolates collected from eight hospitalized patients during clinical follow-up, most of whom had received CZA therapy. Whole-genome sequencing, antimicrobial susceptibility testing, in vitro competition assays, enzyme kinetic analysis, and transcriptomic sequencing were used to systematically characterize the within-host evolutionary dynamics of KPC variants and the fitness heterogeneity of KPC-33. Multiple KPC variants were identified during longitudinal follow-up, among which KPC-33 was the most frequently detected. Among the seven patients who received CZA treatment, KPC-33 was detected in longitudinal isolates from four patients. It was also identified in patient P3, who had not received CZA, whereas other variants were only sporadically identified. Biochemical analysis showed that KPC-33 exhibited an altered kinetic profile relative to KPC-2, characterized by reduced catalytic turnover and altered substrate affinity. KPC-33 did not exhibit a uniform and pronounced fitness defect but instead showed marked strain-dependent heterogeneity. Strains with higher competitive fitness generally showed only limited transcriptional changes, whereas those with lower fitness were accompanied by broader transcriptional remodeling. In this longitudinal cohort, KPC-33 was repeatedly detected, predominantly under CZA-associated selective conditions. Its fitness consequences were clearly strain background dependent and may be associated with the extent of transcriptional remodeling. These findings provide new evidence for understanding the in vivo evolution of CZA resistance.

KPC-33↗

Structural and functional gastrointestinal abnormalities in ACTA2 R179H mice modeling multisystemic smooth muscle dysfunction syndrome.

Multisystemic smooth muscle dysfunction syndrome (MSMDS) is a rare disorder caused by ACTA2 mutations, including the R179H variant, which alters actin filament stability and dynamics and smooth muscle contractility. Cardiovascular complications dominate its clinical presentation, but gastrointestinal (GI) dysfunction significantly affects quality of life. To investigate the structural, functional, and cellular basis of gut dysmotility in MSMDS, we reviewed clinical data from 24 patients with MSMDS and studied the ACTA2 R179H mouse model. Patients exhibited severe gut dysmotility, with 75% requiring medication for chronic constipation. ACTA2 mutant mice displayed cecal and colonic dilatation, reduced intestinal length, and disrupted colonic migrating motor complexes. Delayed whole-gut transit and impaired contractile responses to electrical and pharmacological stimulation were observed. Transcriptomic analysis revealed significant actin cytoskeleton-related gene changes in smooth muscle cells, and immune profiling identified increased lymphocytic infiltration. Despite functional abnormalities, there were no obvious changes in the enteric nervous system. These findings establish ACTA2 mice as a robust model for studying GI pathology in MSMDS, elucidating the role of smooth muscle dysfunction in gut dysmotility. This model provides a foundation for developing targeted therapies aimed at restoring intestinal motility by directly addressing actin cytoskeletal disruptions in smooth muscle cells.

Animals↗

Identification of arthritis-related gene clusters by microarray analysis of two independent mouse models for rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease affecting approximately 1% of the population worldwide. Previously, we showed that human T-cell leukemia virus type I-transgenic mice and interleukin-1 receptor antagonist-knockout mice develop autoimmunity and joint-specific inflammation that resembles human RA. To identify genes involved in the pathogenesis of arthritis, we analyzed the gene expression profiles of these animal models by using high-density oligonucleotide arrays. We found 1,467 genes that were differentially expressed from the normal control mice by greater than threefold in one of these animal models. The gene expression profiles of the two models correlated well. We extracted 554 genes whose expression significantly changed in both models, assuming that pathogenically important genes at the effector phase would change in both models. Then, each of these commonly changed genes was mapped into the whole genome in a scale of the 1-megabase pairs. We found that the transcriptome map of these genes did not distribute evenly on the chromosome but formed clusters. These identified gene clusters include the major histocompatibility complex class I and class II genes, complement genes, and chemokine genes, which are well known to be involved in the pathogenesis of RA at the effector phase. The activation of these gene clusters suggests that antigen presentation and lymphocyte chemotaxis are important for the development of arthritis. Moreover, by searching for such clusters, we could detect genes with marginal expression changes. These gene clusters include schlafen and membrane-spanning four-domains subfamily A genes whose function in arthritis has not yet been determined. Thus, by combining two etiologically different RA models, we succeeded in efficiently extracting genes functioning in the development of arthritis at the effector phase. Furthermore, we demonstrated that identification of gene clusters by transcriptome mapping is a useful way to find potentially pathogenic genes among genes whose expression change is only marginal.

Animals↗

Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson's disease.

We have used brain tissue from clinically well-documented and neuropathologically confirmed cases of sporadic Parkinson's disease to establish the transcriptomic expression profile of the medial and lateral substantia nigra. In addition, the superior frontal cortex was analyzed in a subset of the same cases. DNA oligonucleotide microarrays were employed, which provide whole human genome coverage. A total of 570 genes were found to be differentially regulated at a high level of significance. A large number of differentially regulated expressed sequence tags were also identified. Levels of mRNA sequences encoded by genes of key interest were validated by means of quantitative real-time polymerase chain reaction (PCR). Comparing three different normalization procedures, results based on the recently published GeneChip Robust Multi Array algorithm were found to be the most accurate predictor of real-time PCR results. Several new candidate genes which map to PARK loci are reported. In addition, the DNAJ family of chaperones is discussed in the context of Parkinson's disease pathogenesis.

Aged↗

Precision-Based Filtering Facilitates Cross-Referencing of Conventional and Single-Nucleus Transcriptomes to Identify Time- and Temperature-Sensitive Cell Populations.

Transcriptome analysis via RNA sequencing (RNAseq) has become a ubiquitous method of molecular characterization from whole organisms, dissected tissues, and single cells. These experiments continue to provide an extraordinary volume of data describing molecular states and responses to many conditions. However, standard approaches to RNAseq analysis commonly use expression level filters that eliminate potentially useful data in the service of decreasing noise. Here we describe the implementation of a coefficient of variation-based filter for RNAseq gene expression data. This filter prioritizes consistent data across replicates, allowing lowly-expressed genes with low-variation measurements to be retained for downstream analysis. We show, using two independent Arabidopsis RNAseq datasets, that this filter allows for the inclusion of many more transcription factors than even a low-stringency expression level filter. This effect is independent of sequencing depth. We find that these lowly-expressed genes mark specific cell clusters in our single-nucleus (sn)RNAseq dataset and may facilitate future characterization of currently unknown cell types or states. We further characterize communities of co-expressed genes, sampled across the day at two growth temperatures, in relation to snRNAseq cell clusters, finding evidence for a highly photosynthetic cell population, and a cell state marked by high cell division and translation. These methods can be expanded to RNAseq analysis in many systems, facilitating the construction of more detailed models of tissue-specific gene regulatory networks.

Transcriptome analysis↗

Genomic characterization of a hypervirulent Aeromonas veronii NN0115 from Nile tilapia and head kidney transcriptome of infected fish reveals B-cell-dominated immune response with specific immunoglobulin downregulation.

Aeromonas veronii is a pathogen of multiple fish species, yet systematic understanding of its infection in Nile tilapia (Oreochromis niloticus) remains limited. A dominant strain, NN0115, was isolated from a natural outbreak and identified as A. veronii by 16S rRNA and whole-genome average nucleotide identity (ANI, 96.33%). Experimental infection revealed high virulence (LD50 = 3.41 × 106 CFU/mL, equivalent to 8.53 × 104 CFU/fish). The genome is 4.58 Mb (58.57% GC) and encodes 4216 proteins. Virulence factor analysis identified 1253 genes, dominated by motility-related (264) and immune modulation (208) factors. Genomic island GI2 harbors 7 virulence genes and two dual-function resistance-virulence genes. The strain is resistant to 9 of 25 agents tested but carries three RND efflux pump genes whose predicted resistance was not phenotypically observed. The head kidney transcriptome of tilapia at 24 h post-bacterial infection identified 773 differentially expressed genes; among them, 57 were immunoglobulin (Ig) genes, and 56 were down-regulated. Integration of published single-cell transcriptomic data showed that non-Ig B-cell marker genes were down-regulated by 32%, whereas Ig genes were reduced by 63%, indicating selective transcriptional suppression of Ig genes rather than a general decrease in B-cell transcriptional activity. Together, this study provides a comprehensive characterization of a highly virulent A. veronii from Nile tilapia and reveals that selective downregulation of B-cell Ig genes is the dominant transcriptional feature of the host head kidney response.

Animals↗

Multiomics Integration Identifies a Molecular Subtype of Intrahepatic Cholangiocarcinoma With Enhanced Benefit From Adjuvant Therapy.

Intrahepatic cholangiocarcinoma (iCCA) is a molecularly heterogeneous liver cancer with a poor prognosis. Improved stratification is needed to guide postoperative therapy. In this study, we applied integrative multiomics analysis to classify iCCA and identify biomarkers predictive of adjuvant treatment benefit. Using publicly available datasets (including whole exome sequencing, RNA sequencing, proteomics, and phosphoproteomics from FU-iCCA cohort and a transcriptomic cohort GSE244807), we defined 3 robust molecular subtypes of iCCA. These subtypes exhibited distinct genomic alterations, pathway activation, and immune microenvironments, with significant differences in overall survival (OS). Through protein-protein interaction network analysis and consensus feature selection using 10 clustering algorithms, we prioritized 8 marker genes distinguishing the subtypes. A Cox proportional-hazards model constructed from these markers stratified patients into high- and low-risk groups. High-risk iCCA, characterized by elevated expression of markers such as CLDN18, MUC1, and MUC5AC, had significantly worse OS in the absence of adjuvant therapy. Notably, in an independent validation of 174 patients with iCCA who underwent resection (single-center cohort), high expression of any of these 3 markers were associated with markedly prolonged OS in patients who received adjuvant chemotherapy or chemoembolization, compared with those who did not. In contrast, marker-negative patients showed no clear benefit from adjuvant therapy. In conclusion, our multiomics approach identified a high-risk, mucin-enriched subtype of iCCA. CLDN18, MUC1, and MUC5AC emerge as candidate predictive biomarkers for adjuvant chemotherapy benefit in iCCA, warranting prospective validation to improve personalized postoperative management.

Humans↗

Genomic subtypes of non-muscle-invasive bladder cancer: guiding immunotherapy decision-making for patients exposed to aristolochic acid.

BACKGROUND: The limited genomic data on non-muscle-invasive bladder cancer (NMIBC) hampers our understanding of its carcinogenesis and development. Specifically, Aristolochic acid (AA), a potent human carcinogenic compound from aristolochia plants and commonly found in Chinese herbal medicine, has been extensively documented as being closely associated with the onset and progression of bladder cancer. However, the field of AA-induced NMIBC remains largely unexplored in terms of its genomic and molecular characteristics, as well as clinical therapeutic strategies. METHODS: To bridge this knowledge gap, we conducted a comprehensive study using a cohort of 81 NMIBC samples. We performed whole-exome sequencing (WES) and RNA sequencing (RNA-seq) to obtain detailed genomic and transcriptomic data. We subjected these datasets to genomic analysis and subtype analysis to gain valuable insights into NMIBC. RESULTS: By temporally dissecting mutations in NMIBC specimens, we identified a comprehensive mutational landscape of NMIBC and the associations of these mutations with recurrence-free survival. Additionally, we discerned four genomic subtypes of NMIBC: AA-like, FGFR3/HRAS, FGFR3 & chr9Del, and genome instability (GI). The AA-like subtype presented a high frequency of gene mutations along with a pronounced AA mutagenesis signature of SBS22 (Fisher test: P-value 3.5e-4, OR 25.25) even after temporal dissection. The FGFR3/HRAS subtype exhibited FGFR3 or HRAS mutations with few copy number alterations (CNAs). The FGFR3 & chr9Del subtype was characterized by the co-occurrence of chr9p and chr9q deletions as well as FGFR3 mutations, while the GI subtype showed a high frequency of CNAs. Notably, the AA-like and GI subtypes demonstrated better outcomes after immunotherapy, whereas the FGFR3/HRAS subtype showed poorer outcomes. CONCLUSIONS: Our findings provide novel perspectives on the genomics of NMIBC, unveiling four prominent genomic subtypes, each showing different outcomes following immunotherapy. TRIAL REGISTRATION: No. 2019PHB268-01 (retrospectively registered on February 14, 2020).

Humans↗

Gain-of-function PPM1D mutations attenuate ischemic stroke.

Identification of genetic aberrations in stroke, the second leading cause of death worldwide, is of paramount importance for understanding the disease pathogenesis and generating new therapies. Whole-genome sequencing from 10,241 ischemic stroke patients identified eight patients carrying gain-of-function mutations on coding variants in the protein phosphatase magnesium-dependent 1 δ (PPM1D) gene. Patients carrying PPM1D mutations exhibit better stroke-related clinical phenotypes, including improvements in peripheral inflammation, fibrinogen, low-density lipoprotein, cholesterol and plateletcrit level. Experimental brain ischemia in Ppm1d-deficient (Ppm1d-/-) mice resulted in enlarged lesions and pronounced neurological impairments. Spatial transcriptomics revealed a distinct Ppm1d-associated gene expression pattern, indicating disrupted endothelial homeostasis during ischemic brain injury. Proteomic analysis demonstrated that differentially expressed proteins in primary brain endothelial cells from Ppm1d-/- mice were significantly enriched in the peroxisome proliferator-activated receptors (PPARs)-mediated metabolic signaling. Mechanistically, Ppm1d deficiency promoted aberrant fatty acid β-oxidation and increased oxidative stress, which impaired endothelial cell function through the PPARα pathway. A small molecule, T2755, was identified to engage Trp427 and stabilize PPM1D, thereby mitigating ischemic brain injury in mice. Collectively, we find that PPM1D protects against ischemic brain injury and validates its pharmacological stabilizer T2755 as a promising therapy for ischemic stroke. Gain-of-function PPM1D mutations attenuate ischemic cerebral injury. Whole-genome sequencing data of 10,241 ischemic stroke patients from the Third Chinese National Stroke Registry (CNSR-III) identified eight patients with gain-of-function mutations in the protein phosphatase magnesium-dependent 1 δ (PPM1D) gene (17q23.2). These mutation carriers displayed improved peripheral inflammation, decreased fibrinogen, low-density lipoprotein, cholesterol and plateletcrit level. Ppm1d-deficient (Ppm1d-/-) mice exhibited exacerbated stroke outcomes, characterized by enlarged infarct volumes, disrupted cerebrovascular architecture, and enhanced neuro-inflammation. Mechanistically, Ppm1d deficiency induced the disturbance of endothelial fatty acid metabolism involving the PPARα pathway. Through integrated computational modeling, virtual screening, and in vitro validation, T2755 was identified as a small molecule PPM1D stabilizer. Pharmacological PPM1D stabilization with T2755 significantly attenuated ischemic brain injury in murine models.

Aged↗

A De Novo 16p13.3 Triplication Underlying Early-Onset Complex Neurodegeneration.

BACKGROUND: Neurodegenerative disorders are clinically and genetically heterogeneous, characterized by progressive neuronal loss and multidomain functional decline. Despite a presumed genetic etiology, a substantial proportion of cases remain molecularly undiagnosed. OBJECTIVE: The aim was to identify the genetic cause of an early-onset neurodegenerative disorder presenting with ataxia and cognitive impairment. METHODS: Rare copy-number variants were detected via short-read whole-genome sequencing (WGS), with candidate structural models inferred using long-read WGS. We performed transcriptomic profiling of peripheral blood leukocytes by RNA sequencing, with validation using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: We identified a de novo copy-number gain at 16p13.3. Combined copy-number profiling and long-read WGS suggested a candidate model comprising a triplicated segment in tandem with a proximal duplication, joined to a distal duplication via an inverted junction. Transcriptomic analysis demonstrated significant upregulation of ATP6V0C, AMDHD2, and PDPK1. CONCLUSIONS: These findings support a role for structural variation in early-onset neurodegeneration and highlight the value of combining short-read copy-number profiling with long-read WGS to detect and characterize complex genomic rearrangements. © 2026 International Parkinson and Movement Disorder Society.

16p13.3↗

Global physiological understanding and metabolic engineering of microorganisms based on omics studies.

Through metabolic engineering, scientists seek to modify the metabolic pathways of living organisms to facilitate optimized, efficient production of target biomolecules. During the past decade, we have seen notable improvements in biotechnology, many of which have been based on metabolically engineered microorganisms. Recent developments in the fields of functional genomics, transcriptomics, proteomics, and metabolomics have changed metabolic engineering strategies from the local pathway level to the whole system level. This article focuses on recent advances in the field of metabolic engineering, which have been powered by the combined approaches of the various "omics" that allow us to understand the microbial metabolism at a global scale and to develop more effectively redesigned metabolic pathways for the enhanced production of target bioproducts.

Biomedical Engineering↗

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↗

Attribution of PM2.5-Induced Transcriptomic Perturbation to Toxic Components.

Ambient fine particulate matter (PM2.5) is a chemically complex mixture whose health impacts are not fully captured by particle mass. Here, we developed an interpretable chemotranscriptomic framework to attribute PM2.5-induced molecular perturbations to toxicity-relevant components. PM2.5 collected from urban roadside and coastal environments was separated into whole, extractable, and unextractable fractions, characterized by LC/GC × GC-HRMS-based nontarget analysis and inductively coupled plasma mass spectrometry (ICP-MS), and evaluated using cytotoxicity testing and transcriptomic profiling in human bronchial epithelial cells. Urban PM2.5 exhibited greater cytotoxic potency per unit mass than coastal PM2.5, with extractable fractions accounting for most cytotoxic and pathway-level responses. Transcriptomics revealed distinct site-specific modes of action: urban PM2.5 preferentially induced oxidative stress, xenobiotic metabolism, and cell cycle suppression, consistent with acute, nonapoptotic injury, whereas coastal PM2.5 elicited weaker cytotoxicity but stronger interferon-mediated immune and apoptosis-related signaling. Integrating chemical abundance with pathway activity using random forest regression, SHAP interpretation, and mechanistic corroboration reduced 5,033 detected features to 444 pathway-linked candidate drivers. Fewer than 5% of features explained ∼95% of cumulative model contribution. Standard-confirmed contributors included plasticizer-related compounds, aromatic and heteroaromatic combustion products, and copper for urban PM2.5 and secondary/aged organics and nickel for coastal PM2.5. These findings support mechanism-informed prioritization of hazardous PM2.5 components beyond mass-based assessment.

Particulate Matter↗