Biology subjects

Chang, T.

Publications and source records attributed to Chang, T..

At least 19 recordsLinked to original sources

bioRxiv2026

ESX-1 substrates ESAT-6 and CFP-10 limit cGAS-STING activation via DNA and Zn2+ restriction

ESX-1-mediated phagosomal permeabilization exposes mycobacterial DNA to cytosolic cGAS-STING surveillance, yet the response is not overwhelming, suggesting additional layers of control exist. Here we show that the ESX-1 substrates ESAT-6 and CFP-10 constrain cGAS activation through complementary biochemical mechanisms. Purified Mycobacterium tuberculosis CFP-10 is a metal-dependent, 5'-3' exonuclease-like enzyme that preferentially degrades linear DNA, limiting the availability of stimulatory ligand. By contrast, purified M. tuberculosis ESAT-6 binds Zn2+ and limits the Zn2+-dependent conditions required for cGAS activation. During M. marinum infection, loss of these substrates augmented interferon-associated responses. Wild-type alleles restored immune suppression, whereas DNA-processing-defective CFP-10 or Zn2+-binding-defective ESAT-6 variants did not. These genotype-dependent differences were largely abolished in cGas-/- and Sting-/- macrophages. The double mutant was attenuated in zebrafish and mice, and its enhanced immune activation and attenuation were markedly diminished in cGas-/- mice. Prior exposure to the mutant improved zebrafish survival following wild-type challenge. Thus, beyond enabling mycobacterial DNA access to the cytosol, ESX-1 deploys its substrates to constrain cGAS-STING signaling by controlling activating-ligand availability and the metal-dependent environment required for sensor activation.

microbiology↗

bioRxiv2026

Global, single-cell-resolution of antiviral defenses in marine prokaryoplankton

Background Growing evidence suggests that variation in resistance to viral infections by marine prokaryoplankton enables the coexistence of both viral and host populations. Recent experimental work on model organisms has revealed dozens of novel antiviral defense mechanisms. This diversity has prompted the hypothesis of the pan-immunity model, in which diverse defenses are a shared resource among closely related individuals to broaden the population's resistance and minimize an individual's burden. The composition and abundance of such defense pools in natural microbial communities, however, remain largely unknown. Here, we begin to parameterize resistance in marine prokaryotes and define their defense repertoire by quantifying antiviral defenses across thousands of randomized single-amplified genomes (SAGs) from a global collection of seawater samples. Results Prokaryoplankton SAGs contained an average of 1.1 defenses, with dark ocean (200 m - 11 km) prokaryotes having a slightly higher genomic load of defenses as compared to the sunlit, surface ocean. The number of defenses per cell was taxon-specific, with little or no relationship to depth, taxon abundance, estimated maximal growth rate, or viral infection rate. The most numerous and taxonomically widespread defenses were restriction modification systems, followed by dGTPase (mostly in Pelagibacterales) in the sunlit ocean and AbiU (mostly in Nitrososphaerales) in the dark ocean. Most other defense types were rare (< 0.1% SAGs) yet distributed across distant taxa (> 3 phyla). We found evidence for the cross-domain exchange of the most common defense system, RM II, between archaea and bacteria, which improves our understanding of the biology of this prevalent defense and could be consequential in predicting target motifs for connecting viruses with potential hosts in epigenetic studies. Conclusions Collectively, these results suggest lineages restrict genomic real estate for defenses yet enable extensive lateral transfer, potentially for the maintenance of defense variation at the community level rather than only among closely related individuals. This study provides a quantitative atlas of prokaryoplankton immunity toward grounding our understanding of virus-microbe interactions in the Earth's largest biome, the open ocean.

microbiology↗

bioRxiv2026

Mitochondrial perturbation adapts the proteome from early to advanced immune responses

Dysregulated immunity, a hallmark of many human diseases, co-occurs with mitochondrial dysfunction and is commonly associated with misprimed primary immune signaling. While transcriptionally well-characterized, the impact of mitochondria on the host response at the protein level is less clear. Using in vitro and in vivo approaches including proteotranscriptomics, our data suggest that OXPHOS promotes expression of early, cell autonomous immune proteins whereas mitochondrial perturbation favors mediators of cell extrinsic responses like inflammation. This response is independent of immune cues, time-dependent, conserved, and occurs across tissues in mouse models of mitochondrial dysfunction. These data illustrate unappreciated roles for mitochondrial state in adapting host responses at the protein level, which have implications for complex disease etiology and the ancestral origins for eukaryotic immune sensing.

immunology↗

bioRxiv2026

Integrated Proteo-Metabolomics of Urinary Extracellular Vesicles Reveals Early Molecular Divergence and Temporal Pathogenesis of Sepsis-Associated AKI

BackgroundSepsis-associated acute kidney injury (S-AKI) is a major contributor to morbidity and mortality in critically ill patients. However, the molecular mechanisms underlying its temporal progression remain poorly understood because conventional biomarkers primarily reflect renal dysfunction rather than disease pathogenesis. Urinary extracellular vesicles (uEVs), which carry kidney-derived molecular cargo, provide a promising platform for monitoring renal-specific biological alterations during disease progression. MethodsWe conducted a longitudinal multi-omics study of uEVs collected from 81 patients with sepsis, including 48 patients with S-AKI and 33 sepsis-only controls. Patients were randomly assigned to a discovery cohort (n = 52) and an independent validation cohort (n = 29). Urine samples were collected at Day 1, Day 4, and Day 8 after AKI diagnosis. High-resolution proteomic and metabolomic profiling was performed to characterize temporal molecular alterations. Enriched pathways identified in the discovery cohort were evaluated in the validation cohort using pathway-level concordance analysis. ResultsComparative analysis between S-AKI and sepsis-only patients identified distinct stage-specific molecular alterations throughout disease progression. At the early stage (Day 1), validated pathways included complement and coagulation cascades, ferroptosis, HIF-1 signaling, sphingolipid metabolism, and arachidonic acid metabolism, highlighting coordinated inflammatory, hypoxic, and lipid metabolic responses. During the mid-stage (Day 4), persistent activation of complement and coagulation cascades, ferroptosis, and HIF-1 signaling was accompanied by metabolic reprogramming involving alanine, aspartate and glutamate metabolism and tyrosine metabolism. Although limited sample availability reduced statistical power at Day 8, phenylalanine metabolism remained validated in the metabolomic analysis, suggesting persistent metabolic dysregulation during late-stage disease progression. ConclusionsThis study provides the first longitudinal, independently validated multi-omics characterization of human uEVs in S-AKI. By integrating proteomic and metabolomic profiling, we reveal the temporal evolution of renal-specific molecular pathways from early inflammatory and hypoxic responses to subsequent metabolic reprogramming. These findings establish uEV-based multi-omics as a promising strategy for molecular phenotyping of S-AKI beyond conventional clinical biomarkers and provide a valuable resource for future biomarker discovery and therapeutic target identification.

biochemistry↗

bioRxiv2026

Multi-Omics Characterization of Plasma and Urine Extracellular Vesicles Identifies Non-Invasive Biomarkers for IgA Nephropathy

BackgroundIgA nephropathy (IgAN) is increasingly recognized as a systemic immune-mediated disease characterized by aberrant IgA1 glycosylation, circulating immune complex formation, complement activation, and emerging metabolic perturbations. However, clinical diagnosis still relies on invasive renal biopsy, and non-invasive biomarkers capable of capturing both systemic immune activation and kidney-specific alterations remain lacking. Extracellular vesicles (EVs), as biologically active carriers of proteins and metabolites, provide a unique opportunity to interrogate compartment-specific molecular signatures underlying IgAN pathophysiology. MethodsWe performed an integrated, untargeted multi-omics analysis of plasma- and urine-derived EVs from 60 individuals (24 IgAN, 21 chronic kidney disease [CKD], and 15 controls). Differentially expressed proteins (DEPs) and metabolite features (DEFs) discriminating IgAN from CKD and controls were identified using Venn diagram analysis, followed by pathway enrichment and receiver operating characteristic (ROC) evaluation. ResultsVenn analysis identified 22 and 3 candidate DEPs in plasma EVs (pEVs) and urinary EVs (uEVs), respectively, revealing broader systemic proteomic alterations relative to renal EV cargo. Notably, complement and coagulation regulators, including C4b-binding protein alpha chain (C4BPA) and vitamin K-dependent protein S (PROS1), demonstrated strong discriminatory performance between IgAN and CKD (AUC = 0.826 and 0.795), suggesting EV-associated complement-coagulation crosstalk in IgAN. Metabolomic profiling revealed 1,006 and 540 candidate DEFs in pEVs and uEVs, respectively. Enrichment analyses highlighted steroid biosynthesis and fatty acid metabolism pathways in both compartments, indicating immune-metabolic reprogramming. Three metabolite features (C27H44O, C30H50O, and C28H46O) distinguished IgAN from CKD with high accuracy (AUC = 0.942-0.877). ConclusionsThis study provides the first compartment-resolved, plasma- and urine-derived EV multi-omics landscape of IgAN. Our findings suggest that EV cargo reflects coordinated complement dysregulation and metabolic alterations, extending current understanding of IgAN beyond glomerular immune complex deposition. These EV-associated proteins and metabolites offer a mechanistically informed framework for non-invasive biomarker development and for exploring immune-metabolic pathways involved in IgAN progression.

biochemistry↗

bioRxiv2026

Infection-Induced Dynamin-2 Repurposing Drives Podosome Maturation and Mechanical Immunity

Macrophages are the vanguard of innate immunity, relying on phagocytosis to clear invading pathogens. Podosomes in macrophages have recently emerged as mechanical structures that assemble at phagocytic cups to facilitate pathogen clearance. However, how these unique actin structures are regulated to execute antimicrobial functions remains poorly understood. Here, we reveal that fungal infection drives podosome maturation to strengthen macrophage mechanical immunity. Exposure to the opportunistic fungus Candida albicans upregulates the scaffold protein Tks5 and activates Src kinase, which repurposes dynamin-2 from a membrane fission enzyme into an actin-bundling protein. This functional switch stabilizes the podosome actin core, enhancing the adhesion and mechanical force generation required to efficiently engulf, fold, and trap invading fungi. Ultimately, our findings define a mechanobiological paradigm in which infection-induced molecular repurposing drives macrophage antifungal immunity.

cell biology↗

bioRxiv2026

Synteny-aware microbial pangenome graphs reveal blueprints of genomic variation

Pangenomics quantifies the conserved and variable gene repertoire among genomes, but popular implementations ignore gene synteny. Graph-based approaches incorporate both gene homology and synteny, but become difficult to interpret due to pervasive rearrangements. Here we present network-pruning and graph-layout algorithms that enable interactive, synteny-aware quantification and visualization of gene conservation and variability. Applied to 29 genomes of the marine genus Undatipelagibacter (formerly SAR11 subclade Ia.3.VI), we find that genomic variability forms not a few hypervariable islands against a static backbone but a structured continuum, whose variable regions differ in scale, topology, function, and evolutionary character. Genome variation spans from ancient, specialized regions of hundreds of genes whose propensity to vary is conserved across genera, to single hypervariable genes shaped by epistatic co-selection with partners dispersed genome-wide, and shows that chromosomal context carries evolutionary information synteny-unaware pangenomics cannot capture, and some evolutionary processes act on entire functional subsystems throughout a pangenome.

microbiology↗

bioRxiv2026

Fibulin-2 transduces a matrix-to-metabolism signal in kidney fibrosis

Fibrotic extracellular matrix (ECM) is not merely a structural scaffold but an instructive signaling interface that shapes epithelial cell state. However, the molecular cues by which matrix remodeling controls tubular metabolism during kidney fibrosis remain poorly defined. Here, we identify Fibulin-2 (FBLN2) as a fibroblast-derived matrix cue that transduces fibrotic ECM remodeling into tubular mitochondrial metabolic reprogramming. Using fibroblast-selective deletion of Smoothened (Smo) across distinct fibroblast subpopulations, we found that loss of fibroblast Smo preserved kidney function and attenuated fibrosis in mouse models of chronic kidney injury. Multi-omics profiling revealed coordinated remodeling of the fibrotic matrisome, highlighted by suppression of FBLN2, an ECM glycoprotein genetically linked to kidney function in humans. Mechanistically, FBLN2 engaged EGFR in tubular epithelial cells and activated EGFR-AKT signaling in a non-canonical ligand-like manner. This signaling axis suppressed acetyl-CoA acetyltransferase 1 (ACAT1), a mitochondrial regulator of fatty acid oxidation and amino acid metabolism. Disruption of fibroblast Smo-FBLN2 signaling restored ACAT1-dependent oxidative metabolism and reduced tubular fibrotic activation. Spatial lipidomics revealed compartment-specific lipid remodeling associated with altered mitochondrial fatty acid metabolism, including acylcarnitine and phospholipid changes linked to reduced fibrotic injury. Together, these findings define a Fibulin-2-EGFR-ACAT1 matrix-to-metabolism signaling axis that couples fibrotic ECM remodeling to tubular mitochondrial metabolism during kidney fibrosis.

cell biology↗

bioRxiv2026

Spatiotemporal Dynamics of Highly Pathogenic Avian Influenza H5 Virus Introductions and Regional Spread in the Republic of Korea

Highly pathogenic avian influenza (HPAI) viruses from clade 2.3.4.4 have caused recurrent outbreaks in poultry since 2014. In the Republic of Korea, clade 2.3.4.4b viruses have driven five epidemic waves, yet the factors underlying HPAI introduction and farm-to-farm spread remain poorly understood. We compiled hemagglutinin gene sequences of clade 2.3.4.4b viruses from wild birds and poultry in the Republic of Korea (October 2016-March 2024) and reconstructed dispersal dynamics using Bayesian phylogeography. Dispersal patterns suggest that domestic duck farms in the western provinces likely form a key interface for spillover from wild birds into poultry. Mixed-effects generalized linear models showed that both wild-to-poultry and farm-to-farm transition rates were positively associated with the number of poultry farms in the destination province, while wild-to-poultry rates were further associated with higher avian influenza virus infection probability among wild birds. Wild-to-poultry transition rates were lower in 2020-2024 than in 2016-2018, which may reflect strengthened interventions. These findings suggest that poultry farm abundance and introduction pressure from wild birds jointly shape the spatial dynamics of HPAI introduction and spread. More broadly, these factors may provide operational indicators to guide risk-based surveillance and control strategies. Author SummaryHighly pathogenic avian influenza (HPAI) H5 viruses continue to cause major losses in poultry and pose recurring risks at the wildlife-livestock interface. Effective control depends on identifying where viruses are most likely to enter poultry populations and how they spread between farms. Using viral genomic data from wild birds and poultry in the Republic of Korea, this study suggests that domestic duck farms in western provinces likely form a key interface for introductions from wild birds into poultry. We also found that regions with more poultry farms were more likely to receive and spread the virus, while introduction risk was further elevated where infection pressure from wild birds was higher. By linking viral genomic patterns with ecological and epidemiological information, our study helps identify where HPAI viruses are most likely to enter poultry populations and spread between farms. These findings can guide targeted surveillance and early control in regions at greatest risk.

evolutionary biology↗

bioRxiv2026

Generalizable Finger Movement Decoding from Intracranial Recordings Across Static and Dynamic Actions

Reliable decoding of finger movements requires brain-computer interfaces (BCIs) that generalize across the diverse static and dynamic actions performed in daily life. Static postures and dynamic movement sequences rely on partially distinct neural representations, and the balance between these components varies widely across tasks. Yet most BCI decoding pipelines are optimized for a narrow task domain, limiting their ability to transfer to new scenarios. Here we show that standard intracranial decoding approaches show notable performance drops when applied to previously unseen finger tasks with different static-dynamic compositions, and that generalization critically depends on multiple elements of the decoding pipeline. High gamma features and short temporal windows yield the most robust cross task transfer. Although nonlinear models perform best when examples of all tasks are represented during training, linear decoders generalize better to novel, particularly dynamic, actions. We identify a consistent static-dynamic structure across tasks that may provide a useful basis for improving generalization. Finally, we show that anatomical heterogeneity between tasks also constrains generalization. Together, these results establish design principles for BCIs that generalize reliably across the rich repertoire of human finger movements.

neuroscience↗

bioRxiv2026

A rapid, sensitive, and quantitative high plex biomarker digital detection platform enabled by Hypercoding

Low-cost, multiplexed, and automated assays are needed to make omic technologies more broadly accessible in clinical, research, and commercial settings. We present Hypercoding, a scalable technology for detection and quantitation of multi-omic targets. Drawing from data reliability methods in the telecommunications field, Hypercoding uses fluorescent signals from hybridization with an error-correcting code to enable detection of high-plexity targets from biological samples, such as human DNA. In the presence of the target, a linear DNA construct is circularized, immobilized, and amplified to enable single-molecule detection of a target via rapid readout cycles within a 96-well plate. We demonstrate capability for >10,000 code plexity and accurate (98.7%) genotyping of 209 pharmacogenomic variants. Furthermore, we show computation of copy number variation with whole chromosome and sub-gene resolution, as well as quantitation of target abundance down to 10 fM sensitivity with a dynamic range of up to 10 logs.

genomics↗

bioRxiv2025

Tissue-guided multi-omics profiling identifies extracellular vesicle biomarkers indicative of lung pathology in acute respiratory distress syndrome

BackgroundAcute respiratory distress syndrome (ARDS) remains a lethal inflammatory lung condition lacking reliable biomarkers that reflect lung-specific pathology. Extracellular vesicles (EVs) circulate systemically and may carry molecular signals from injured organs, but the correspondence between EV cargo and lung tissue alterations remains unclear. MethodsWe established aspiration-, lipopolysaccharide (LPS)-, and COVID-19-induced murine ARDS models and applied a tissue-guided multiomics framework integrating proteomic and metabolomic analyses of lung tissue and plasma-derived EVs to identify lung-originating circulating biomarkers. ResultsFour proteins--haptoglobin (HP), inter-alpha-trypsin inhibitor heavy chains 3 and 4 (ITIH3, ITIH4), and clusterin (CLU)--were consistently upregulated in both lung tissue and plasma EVs across all ARDS etiologies. Metabolomic integration revealed dysregulation of arachidonic acid metabolism as a unifying inflammatory axis. Multiomics network analysis further distinguished etiology-specific molecular programs, including glycolytic activation in aspiration-induced, platelet aggregation in LPS-induced, and vascular smooth muscle dysregulation in COVID-19-induced ARDS. ConclusionsThis study establishes a tissue-informed EV profiling framework that links local lung pathology to systemic molecular signatures, revealing HP, ITIH3, ITIH4, CLU, and arachidonic-acid-related metabolites as potential diagnostic markers for ARDS. These findings provide a foundation for developing clinically translatable, EV-based biomarker assays for early detection and molecular subtyping of lung injury.

biochemistry↗

bioRxiv2025

AI-based supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study

Precision oncology aims to guide treatment decisions using biomarkers. While DNA-based panels are increasingly applied, RNA transcriptomics remain underused due to limited datasets and the absence of robust models. We assembled the largest transcriptomic resource for drug response prediction to date, spanning 91 cohorts, 5,675 patients, nine cancer types, and six frontline therapies: anti-PD-1/PD-L1 immune-checkpoint inhibitors, trastuzumab, bevacizumab, BRAF inhibitors, paclitaxel, and FAC/FEC (Fluorouracil-Adriamycin-Cyclophosphamide/Fluorouracil-Epirubicin-Cyclophosphamide) chemotherapy. We developed EXPRESSO (EXpression-Profile-RESponSe-Optimizer), a supervised machine-learning framework that predicts treatment response from pre-treatment transcriptomes by integrating drug targets and context-specific biomarkers. EXPRESSO achieves mean ROC-AUCs of 0.62-0.73 and median odds ratios of 2.4-4.6 across therapies, outperforming 20 published transcriptomic signatures and other machine learning methods. Prospective validation on 22 independent cohorts confirms that performance generalizes beyond cross-validation. The EXPRESSO signature additionally stratifies progression-free survival in immune checkpoint blockade-treated cohorts, demonstrating prognostic value beyond binary response prediction. Robustness analysis reveals that predictive performance plateaued for some therapies with increasing training cohorts but continued to improve for others. These findings suggest inherent limits of supervised brute-force learning for certain treatments, but additional data and deeper mechanistic modeling may further enhance transcriptomics-based predictors. SIGNIFICANCEEXPRESSO forms the next step in studying the feasibility of harnessing bulk transcriptomic data to inform therapeutic decision-making, advancing the role of transcriptomics from exploratory biomarker discovery to actionable predictive modeling.

cancer biology↗

bioRxiv2025

Arylsulfatase I is a novel lysosomal chondroitin sulfatase regulating endochondral ossification

During endochondral ossification, chondrocytes undergo maturation and biochemically modify the collagenous extracellular matrix of cartilage. Similar modifications to cartilage proteoglycans (PGs), which are predominantly chondroitin sulfate, have not been characterized. Using synchrotron X-ray fluorescence imaging, we demonstrated that PG sulfation significantly decreased during cartilage maturation of chick embryos. Laser-capture microdissection and RNAseq revealed upregulation of Arylsulfatase I (Arsi) in mature cartilage of mouse. ARSI protein also increased in mature cartilage of mouse and chick in vivo and during maturation of ATDC5 chondrocytes in vitro, whereas expression of the two known chondroitin sulfate PG sulfatases (ARSB and GALNS) was not specific to mature cartilage. Colocalization studies suggested that ARSI is lysosomal, and functional assays revealed that ARSI impacts lysosome homeostasis in chondrocytes. Biochemical analyses of ARSI gain and loss of function cell lines and isolated cell-free systems revealed that ARSI is a novel chondroitin sulfatase, specifically desulfating GalNAc4S at the nonreducing terminal of CS/DS. Finally, Arsi knockout in RCS chondrocytes caused increased expression of maturation genes, such as Col10a1 and Mmp13. In total, these data identify ARSI as a novel PG sulfatase regulating endochondral ossification.

cell biology↗

bioRxiv2025

Serotonin shapes the temporal window for associative fear learning

Fear learning is a critical adaptive mechanism that enables the association of an environmental cue (the conditioned stimulus, CS) with a potential threat (the unconditioned stimulus, US), even when these events are temporally discontiguous. When associations are instead formed between unrelated cues and threats, the maladaptive learning process can give rise to neurological disorders. Therefore, maintaining accurate temporal boundaries for forming such associations is crucial for distinguishing real threats from irrelevant stimuli. Despite its importance, the mechanisms defining this precise time window remain poorly understood. Here, we report that serotonin plays a crucial role in modulating the associable interval in trace fear conditioning. Specifically, we show that serotonergic projections from the dorsal raphe nuclei (DRN) to the CA1 region of the ventral hippocampus (vCA1) dynamically regulate the association of discontiguous CS and US. Mechanistically, we demonstrate that sustained serotonin release in the vCA1 maintains the ensemble activity of 5-HT2C receptor-expressing pyramidal neurons, facilitating the association between temporally separated events. In contrast, when the trace interval exceeds the associable window, transient serotonin release fails to drive vCA1 neuronal ensemble activity, thus preventing maladaptive learning. These findings highlight a key role for serotonin in regulating the temporal precision of fear learning and reveal the DRN-vCA1 serotonergic pathway as a potential therapeutic target for related disorders.

neuroscience↗

bioRxiv2025

Pan-cancer prediction of tumor immune activation and response to immune checkpoint blockade from tumor transcriptomics and histopathology

Accurately predicting which patients will respond to immune checkpoint blockade (ICB) remains a major challenge. Here, we present TIME_ACT, an unsupervised 66-gene transcriptomic signature of tumor immune activation derived from TCGA (The Cancer Genome Atlas) melanoma data. First, we demonstrate that TIME_ACT scores accurately identify tumors with activated immune microenvironments across different cancer types. Further, analysis of spatial features reveals that tumor microenvironment regions with dense lymphocyte infiltration near tumor cells have high TIME_ACT scores, successfully marking localized immune activation. Second, across 25 transcriptomic ICB cohorts encompassing nine cancer types, TIME_ACT achieves a mean AUC of 0.76 and a mean odds ratio of 5.77, significantly outperforming 30 established transcriptomic signatures and prediction methods for ICB response, including a recently developed foundation model for immunotherapy response prediction. Third, we show that TIME_ACT scores can be accurately inferred from routine tumor histopathology slides and that slide-inferred TIME_ACT scores predict ICB response across nine new independent patient cohorts spanning eight cancer types, achieving a mean AUC of 0.72 and a mean odds ratio of 4.99. These findings establish TIME_ACT as a robust, pan-cancer biomarker that enables accurate, low-cost, and clinically scalable prediction of ICB response from routine histopathology.

cancer biology↗

bioRxiv2025

Deepening imaging-based spatial proteomics at high spatial resolution through controlled tissue resizing and in-situ bottom-up mass spectrometry

Understanding the spatial organization of proteins in tissues is essential for elucidating biological function, but current proteomic imaging methods face limitations in resolution, sensitivity, and multiplexing capability. Here, we present IMPACT (Imaging Mass Spectrometry for Proteome Analysis on Compressed Tissue), a hydrogel-based workflow that integrates controlled vertical tissue compression following expansion to enhance protein detection while improving spatial resolution in mass spectrometry imaging (MSI). Through a 6-fold lateral expansion and 100-fold thickness reduction, analytes were concentrated at the surface to improve detection and enable analysis of thick specimen. Coupled with optimized in-gel digestion using hybrid enzymes, IMPACT increased protein identifications 3-fold compared to conventional MSI while maintaining spatial fidelity. We demonstrate IMPACTs utility in mapping low-abundance proteins and resolving fine anatomical structures. Orthogonal validation with immunofluorescence and integration with spatial transcriptomics further reveal protein-RNA distribution discrepancies, highlighting the need for direct proteomic imaging. IMPACT bridges critical gaps in spatial proteomics by enabling high-resolution, high-sensitivity, and multiplexed untargeted protein imaging, opening new avenues for tissue biology and biomarker discovery.

biophysics↗

bioRxiv2025

A synthetic biology toolkit for interrogating plasmid-dependent methylotrophy and enhancing methanol-based biosynthesis of Bacillus methanolicus

Bacillus methanolicus represents a thermophilic methylotroph whose methanol utilization depends on plasmid-encoded genes. It serves as a unique model for deciphering plasmid-dependent methylotrophy and an ideal chassis for low-carbon biomanufacturing using CO2-derived C1 substrates. Despite its evolutionary uniqueness and industrial potential, the lack of synthetic biology tools has hindered both mechanistic understanding and strain engineering. Here, we present a comprehensive synthetic biology platform comprising a high-efficiency electroporation protocol, a CRISPR method enabling robust and multiplex genome editing, diverse neutral loci for gene integration and overexpression, and a cloud-based genome-scale metabolic model iBM822 for user-friendly biodesign. Leveraging this toolkit, we systematically dissected plasmid-dependent methylotrophy, host restriction-modification systems, and functional significance of the chromosomal methylotrophic genes through targeted deletion. To address plasmid loss-induced strain degeneration, we integrated the large endogenous plasmid pBM19 into the chromosome for stable and intact methylotrophic growth. Finally, by integrating metabolic modeling with CRISPR editing, we engineered L-arginine feedback regulation to achieve the first L-arginine biosynthesis from methanol. This study establishes a synthetic biology framework for B. methanolicus, promoting mechanistic exploration of methylotrophy and low-carbon biomanufacturing.

synthetic biology↗