Biology subjects

Luo, S.

Publications and source records attributed to Luo, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Matrix metalloproteinases proteolyze RAB proteins and contribute to cisplatin-induced ototoxicity

Matrix metalloproteinases (MMPs) are rapidly expressed and activated in response to oxidative stress and contribute to various pathological conditions. Cisplatin is a highly effective chemotherapeutic agent; however, its clinical use is limited by its associated permanent hearing loss (ototoxicity). While cispwlatin-induced oxidative stress and inner ear cell death are well-established, the contribution of MMPs remains unclear. In this study, we demonstrate that cisplatin exposure triggers activation of MMP-2 and MMP-9 and expression of an intracellular N-terminal-truncated isoform of MMP-2 in mouse inner ear hair cells. Pharmacological inhibition of MMP-2 and genetic knockdown of Mmp-9 enhanced hair cell survival and attenuated cisplatin-induced inflammation and cytotoxicity. Furthermore, proteomic analysis revealed that proteins involved in intracellular trafficking, including RAB proteins, may serve as potential substrates of intracellular MMP-2 upon cisplatin exposure, pointing to a previously unrecognized mechanism of cisplatin-induced hair cell injury. In vitro analysis confirmed that MMP-2 cleaves RAB9A in response to cisplatin, and in silico analyses predicted MMP-2-preferred cleavage sites on RAB9A. Collectively, our findings identify MMP-2 as a promising therapeutic target for mitigating cisplatin-induced ototoxicity.

pharmacology and toxicology↗

bioRxiv2026

Hierarchy in neuronal representations of multiple tasks in prefrontal cortex

The ability to perform multiple tasks is one of the fundamental hallmarks of general intelligence in the brain. However, the underlying neural mechanisms remain largely unknown. We trained macaque monkeys to perform four tasks with identical spatial layouts but different cognitive demands, and recorded activities of thousands of prefrontal neurons using two-photon calcium imaging. Within the same neuronal population, we identified a multitask neural geometry composed of multiple low-dimensional subtask spaces, each encoding spatial information within a specific subtask. These subtask spaces shared a ring-like representational structure, forming a generalized spatial code across different subtasks. Task separation arose from separable bases and offsets of subtask spaces, modulated primarily by meta-task features. Thus, the neural geometry is organized by a three-level hierarchical structure: location codes are nested within subtask spaces, which are grouped as different meta-tasks. This hierarchy supported generalization across both locations and tasks, and explained monkeys error behaviors. Together, the hierarchically organized neural geometry underlies flexible multitask behavior.

neuroscience↗

bioRxiv2026

eScreen: a deep learning framework for functionally decoding the regulatory genome at single-nucleotide resolution

The human genome is densely populated with cis-regulatory elements (CREs), yet deciphering their functional regulatory syntax and combinational logic remains a fundamental challenge. Here, we integrate 379 genome-scale CRISPR screen experiments, encompassing 21 million perturbations across 23 cell types, to construct a compendium of 41,239 high-confidence functional CREs from 530,527 candidates. Leveraging this resource, we develop eScreen, a deep learning model built on the StripedHyena2 architecture to functionally decode the regulatory genome at single-nucleotide resolution. eScreen achieves three primary functions: (1) predicts genome-wide cell-type-specific CRE functional activity with high accuracy, outperforming existing models; (2) provides mechanistic interpretation of regulatory syntax at single-nucleotide resolution; (3) dissects the functional organization of enhancer clusters through in silico perturbation analysis. We perform multiple independent CRISPR knockout, CRISPR interference (CRISPRi), and base editing screens to validate these functions of eScreen both at scale and on individual cases. Furthermore, we provide an interactive web server (https://escreen.huanglabxmu.com/) for the community to access the integrated CRISPR screen resources and eScreen functions. Collectively, our work establishes a highly precise and convenient tool to decode the causal effects of the regulatory genome.

genomics↗

bioRxiv2026

Pan-genome and multi-omics analyses reveal the genomic basis of symbiotic evolution in the mycoheterotrophic medicinal orchid Gastrodia elata

Mycoheterotrophy represents an extreme evolutionary strategy in which plants abandon photosynthesis and become obligately dependent on fungal partners for carbon and nutrients. Gastrodia elata, a medicinal orchid forming long-term symbioses with Armillaria and Mycena, provides a compelling system to investigate the genomic basis of obligate plant-fungus mutualism. Here, we generate a chromosome-level genome assembly (1.09 Gb) of a dark-red G. elata accession and construct a pan-genome from 12 Gastrodia accessions to dissect the evolutionary consequences of mycoheterotrophy. Phylogenomic analyses reveal pervasive hybridization across cultivated germplasm and demonstrate that tuber morphology more accurately reflects intraspecific relationships than stem color, challenging the traditional color-based taxonomy. Comparative genomic analyses reveal a pronounced degeneration of photosynthetic capacity. We detect no intact nuclear-encoded rbcS loci, which would preclude assembly of the Rubisco holoenzyme and thus canonical Calvin-Benson carbon fixation. Notably, a subset of photosynthesis-related genes is retained and transcriptionally active, suggesting their co-option into non-photosynthetic regulatory roles. Despite millions of years of intimate association with fungal partners, we detect no evidence of fungal-derived horizontal gene transfer based on a multi-step phylogenomic validation pipeline, indicating that G. elata-fungus mutualism is sustained through metabolic exchange rather than genetic integration. We further identify the Gastrodia antifungal protein (GAFP) gene family as a key molecular innovation underlying symbiotic homeostasis. The recently derived Class 1 GAFP lineage exhibits distinct domain architecture, tandem expansion, and elevated expression, consistent with enhanced regulation of fungal overgrowth. Finally, we delineate a specialized nutrient-acquisition framework adapted to obligate heterotrophy, in which fungal trehalose hydrolysis supplies hexose carbon via an expanded sugar transporter protein (STP) repertoire, while organic nitrogen is assimilated through amino acid and oligopeptide transporters coupled with urease-mediated conversion, compensating for the loss of nitrate uptake and reduction pathways. Together, these results establish a pan-genomic blueprint for symbiotic adaptation and reductive genome evolution in G. elata.

genomics↗

bioRxiv2026

Combining evolution and machine learning-guided pathway optimization to engineer a novel methylsuccinate module for synthetic C1 metabolism in vivo

De novo metabolic pathways open possibilities for sustainable biotransformations in microbes. However, the in vivo-implementation of such new-to-nature pathways is highly challenging and heavily relies on adaptive laboratory evolution (ALE) of the hosts native metabolic network. Here, we assess how much this need for host-centric ALE can be overcome and/or complemented through the informed design of the newly introduced pathway. Exemplifying for a synthetic CO2-fixation module via methyl-succinate, we established methylsuccinate-dependent growth of Escherichia coli over six months by ALE of E. colis native metabolism. In parallel, we developed a machine-learning guided workflow (MEVIS) for the automated engineering of the synthetic pathway, resulting in methylsuccinate-dependent growth within three weeks. Critically, performing MEVIS in the background of the ALE-evolved strain is necessary to further approach wild-type like growth, demonstrating how ALE in combination with machine-learning-guided lab automation holds great potential to accelerate and improve design-build-test-learn cycles in contemporary metabolic engineering.

bioengineering↗

bioRxiv2026

Subchondral H-type Blood Vessel Formation Aggravates Articular Cartilage Degeneration through LEP-LEPR Axis

BackgroundOsteoarthritis (OA) is a chronic degenerative joint disease characterized by cartilage deterioration that has resulted in severe physical and economic costs for society. Risk factors for osteoarthritis encompass genetic predisposition, mechanical influences, obesity, inflammation, and metabolic problems. Recent reports indicate that the morphological alterations of subchondral bone evolve dynamically with the advancement of osteoarthritis. A significant alteration in subchondral bone is the development of aberrant blood vessels, particularly H-type vessels. H-type arteries are essential for preserving subchondral bone homeostasis. They support nutrients and oxygen to bone tissue and eliminate waste, thereby supporting the metabolic activities of osteoblasts and osteoclasts. However, the upstream factors governing H-type arteries are yet unknown. MethodsThe whole transcriptome analysis was performed by informatics which is further verified in clinical samples. Using LEP and LEPR knockout mice, the correlation between abnormal subchondral bone metabolism and LEP, LEPR, and H-type vessel formation were uncovered. ConclusionsThis study reveals that the lipid metabolism pathway was highly enriched on the OA lesion, and LEP and LEPR were markedly underexpressed in the subchondral bone on the side with severe OA lesions. The causal relationship of the LEP-LEPR-CD31 signalling pathway in aging and osteoarthritis models is revealed at the overall and molecular levels. This project will provide a new theoretical basis for the pathogenesis of early OA and new ideas and targets for the clinical treatment of OA by exploring the LEP-LEPR-CD31 signalling in osteoarthritis.

pathology↗

bioRxiv2025

Spatially resolved translational dysregulation in Grin2a+/- mouse model of schizophrenia

Loss-of-function (LoF) mutations of GRIN2A, encoding the GluN2A subunit of N-methyl-D-aspartate receptor (NMDAR), confer a high risk for schizophrenia (SCZ)1-3, yet how they affect diverse brain cell types remains poorly understood. Here, we combined subcellular-resolution spatial omics technologies, STARmap4 and RIBOmap5, to jointly resolve single-cell transcriptomes and translatomes for 3,447 genes in the brains of Grin2a+/- mice and their wild-type littermates across 538,188 cells. Translational dysregulation was markedly more prominent than transcriptional changes in neurons. Across neuronal subtypes, a set of genes including Camk2a, Arc, Egr1, Egr3, Chmp2b, and Pja2 exhibited translational reduction in a Grin2a gene dose-dependent fashion, suggesting a connection between NMDAR hypofunction and reduced protein synthesis of downstream synaptic plasticity effectors. In interneurons (particularly parvalbumin interneurons), a strong reduction of Gad2 translation implies loss of inhibitory function in cortical microcircuits, which has long been hypothesized for SCZ pathophysiology. Non-neuronal cell types including astrocytes, oligodendrocytes, and vascular cells also exhibited region-specific translational changes in neurotransmitter transport, lipid synthesis, myelination, and stress response pathways, some of which co-varied with regional neuron state. Together, our study reveals brain-wide translation dysregulation as a critical mechanism underlying SCZ pathophysiology.

neuroscience↗

bioRxiv2025

IGHV3-53 antibody abundance drives divergent SARS-CoV-2 immune imprinting

The mechanisms driving divergent SARS-CoV-2 immune imprinting in populations with distinct SARS-CoV-2 exposure histories remain unclear. However, conventional wild-type mouse models fail to recapitulate severe imprinting, even after potent ancestral-strain mRNA immunization, hindering imprinting-related mechanistic investigation and vaccine evaluation. Here, we surprisingly found that V(D)J-humanized mice could faithfully recapitulate human severe imprinting phenotypes. Comprehensive antibody repertoire analysis and deep mutational scanning-based epitope mapping of 583 monoclonal antibodies from these models revealed that the pre-existing IGHV3-53/66 antibody abundance determine imprinting severity through antibody-mediated masking of Omicron-specific epitopes. Both passive IGHV3-53/66 antibody transfer and IGHV3-53 knock-in were sufficient to induce severe imprinting in wild-type mice. Together, these findings demonstrate that the V(D)J germline repertoire--even a single germline-encoded antibody response--can profoundly shape humoral imprinting severity. Accordingly, we established an IGHV3-53 knock-in mouse model that accurately recapitulates the human antibody landscape, providing a valuable tool for guiding future COVID-19 vaccine updates.

immunology↗

bioRxiv2025

STUltra: scalable and accurate integration for subcellular-level spatial omics data

High-resolution spatial transcriptomics (ST) is generating massive datasets involving multiple tissue sections, continuous developmental stages, or various disease conditions. Yet, their scale, biological heterogeneity, and substantial batch effects hinder data integration for further analysis, particularly the temporal interpretation of disease-associated spatial domains within tissue organization landscapes. Here, we present STUltra, a scalable hypergraph framework that integrates multi-sample ST data for precise domain detection and genome-wide association study (GWAS)-based disease trait mapping. From ST datasets, STUltra first constructs interval-sampled integrative hypergraphs, in which hyperedges capture tissue neighborhoods within the slices as well as shared biological contexts across the slices. It then combines a robust graph autoencoder with contrastive learning to learn batch-corrected, spatially informed embeddings for identifying spatial domains across the tissue sections. These embeddings are then coupled with GWAS statistics to map trait-associated signals onto the integrated tissue sub-structure landscapes spanning different sections, developmental stages, and disease conditions. STUltra substantially outperforms competing methods, especially being much faster, on diverse integration benchmarks, enabling efficient processing of datasets containing millions of spots. As a case study on a mouse embryo dataset, STUltra recovered continuous developmental programs and mapped congenital heart disease signals beyond cardiac regions to broader programs including vascular and mesenchymal domains. On a mouse Alzheimer's disease dataset, STUltra prioritized localized AD-associated genetic signals to microglia and a disease-expanded C1QA/HEXB-high astrocyte state. Together, STUltra provides a scalable framework to bridge the gap between spatiotemporal tissue structures and complex disease traits, facilitating discoveries from previously unrecognized molecular and cellular architectures across diverse contexts.

bioinformatics↗

bioRxiv2025

From Tasks to Topology: Dorsal and Ventral Streams Emerge in Optimized Neural Networks

The primate visual system is organized into dorsal and ventral pathways, classically linked to visuomotor control and perception. A long-standing question is whether this division reflects intrinsic architectural priors or emerges from task demands. We trained a single convolutional network to perform classification and grasp prediction of 3D objects, without imposing modular structure. Dual-stream topology - functionally distinct visuomotor and perceptual pathways - emerged spontaneously with rich cross-communication. Shapley value analyses revealed that action- and perception-selective features developed progressively across depth, reflecting task-driven hierarchical specialization. Time-resolved EEG showed that model activity mapped onto dissociable temporal components in human cortex: ventral-aligned signals emerged early and late, where dorsal- and ventral-aligned responses coincided in the intervening interval. These results demonstrate that task optimization alone can explain core features of dorsal-ventral organization, and that distinct temporal roles for perception and action arise naturally atop a shared feedforward scaffold, without requiring architectural hard-coding or recurrence.

neuroscience↗

bioRxiv2025

Olfactory biopsy analysis of Alzheimer's pathobiology across disease stages

Alzheimers Disease (AD) is a neurodegenerative condition affecting millions worldwide. Defining early pathobiological events remains challenging, in part due to inaccessibility of neural tissue. Because olfactory neurons are accessible, and olfactory loss is prevalent in AD, we evaluated olfactory brush biopsies from controls, individuals with cerebrospinal fluid (CSF) biomarker-confirmed AD, and cognitively typical individuals whose positive biomarkers signal a pre-clinical AD stage. We define via single cell RNA-sequencing (n=22 subjects) conserved neuroinflammatory T cell, myeloid, and olfactory neuron changes detectable even in pre-clinical AD subjects. Activated memory T cell states were a hallmark of pre-clinical AD, paralleling CSF T cell phenotypes seen in advanced disease, accompanied by both microglia-like inflammatory programs and olfactory neuron inflammatory injury. Together, our findings establish a novel platform permitting analysis of neural tissue in AD at its earliest stages.

neuroscience↗

bioRxiv2025

Benchmarking Perturbation Tools for the Noncoding Genome

Deciphering the functionality of the noncoding genome which includes important cis-regulatory elements (CREs) and transcribed noncoding RNA genes remains technically challenging. Here, using massively parallel genetic screening, we systematically benchmark the performance of five representative loss-of-function perturbation tools, including single guide RNA (gRNA) mediated SpCas9 cleavage or CRISPR interference, and paired gRNA (pgRNA) involved dual-SpCas9, Big Papi (paired SpCas9 and SaCas9) or dual-enAsCas12a fragment deletion methods, in decoding the roles of the noncoding genome. For targeting CREs such as enhancer, dual-SpCas9 outperforms other methods with superior efficiency of destroying functional genomic regions. For perturbing noncoding RNA genes, in addition to dual-SpCas9, other RNA-targeting methods such as RNA interference are recommended to discriminate transcript-dependent or -independent roles. A deep learning model DeepDC with associated web server is built to facilitate optimal dual-SpCas9 pgRNA design for efficiently deleting a genomic fragment. Together, our work provides practical guidance on selecting appropriate loss-of-function tools to resolve the functional complexity of the noncoding genome.

genetics↗

bioRxiv2025

A Covalent PFKL Activator Suppresses Tumor Growth

Glycolysis fuels vital cellular functions and its dysregulation is implicated in cancer, neurodegeneration, antibiotic resistance and diabetes. The glycolytic dependency of cancer, known as the Warburg effect, presents a key vulnerability for developing targeted anticancer agents but remains challenging due to metabolic heterogeneity and resistance. Here, we developed a first-in-class covalent phosphofructokinase-1 liver type (PFKL) activator that induces metabolic imbalance coupled to delivery of a cytotoxic payload to cancer cells in vitro and in vivo. The electrophile-drug conjugate (EDC) site-specifically and proteome-wide selectively modifies K677 in the allosteric effector site to stabilize the R-state tetramer of PFKL and destabilize cell metabolism. We introduce EDCs as a new delivery mechanism analogous to antibody-drug conjugates but differentiated by selective covalent targeting of intracellular proteins.

biochemistry↗

bioRxiv2025

Computational method for mapping mass signatures along developmental gradients reveals a novel role for a monosaccharide tetrose in maize salt-stress response.

Metabolic processes are essential for regulating and maintaining developmental transitions, from stem cell quiescence through differentiation. However, the distinct metabolite-driven mechanisms that are critical for development remain poorly characterized due to inherent challenges in measuring their production, localization, and function in situ. We employed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to map metabolites in the developing maize root, which has a well-characterized longitudinal gradient that encompasses developmental transitions from quiescence through proliferation and maturation. DESI-MSI enables in situ analysis of the chemical composition of tissue sections with high spatial resolution ([~]50-100 {micro}m). To identify metabolites with specific developmental enrichment patterns, we developed a computational approach called Developmental Imaging Mass Spectrometry Pipeline for Linear Evaluation (DIMPLE). DIMPLE processes mass signatures along linear gradients, generating clusters of metabolites with specific developmental enrichment patterns in the maize root. We employed this method to compare developmental enrichment of metabolites in a salt-resilient maize variety, Oaxacan Green, and a salt-susceptible variety, B73. DIMPLE identifies specific differences in the mass signatures and the overall enrichment patterns between these varieties. DIMPLE also revealed a metabolite, D-erythrose, that had different localization patterns in these varieties. We found that in salt-sensitive maize varieties, treatment with D-erythrose improves stress tolerance by increasing primary root length. Overall, DIMPLE enables comprehensive and rapid analysis of metabolite patterns along a linear gradient, revealing new biology in plant growth and stress response.

plant biology↗

bioRxiv2025

Causal necessity of human hippocampus for structure-based inference in learning

When meeting new individuals or encountering known individuals in new circumstances, we intuitively map out their relationships - not merely by direct experience, but by quickly inferring new connections based on prior relational knowledge. Using a novel task, we demonstrated that participants indeed employ knowledge of relational structures to facilitate learning of new relationships in a changing environment. Computational modelling revealed that participants leveraged relational knowledge to support inference, thus facilitating learning. Whole brain neuroimaging identified a uniquely robust representation of relational structure in the hippocampus. Neural networks trained on similar tasks demonstrated the emergence of relational structure representations, resembling those found in hippocampus. Lesioning network units sustaining such representations disrupted structure-based inference and predicted hippocampuss essential role. Transcranial ultrasound stimulation of human hippocampus, transiently modulating its activity without affecting overlying tissue, produced similar disruption effects, empirically confirming the causal necessity of hippocampal representations for structure-based inference in learning.

neuroscience↗

bioRxiv2025

A Paracrine-to-Autocrine Shunt of GREM1 Fuels Colorectal Cancer Metastasis via ACVR1C

Tumor cells typically rely on paracrine stromal signals to guide malignant behaviors, but whether they acquire signaling autonomy to support metastasis remains unclear. We elucidate this in colorectal cancer (CRC) by uncovering a paracrine-to-autocrine shunt of Gremlin1 (GREM1), a canonical stromal-secreted antagonist of bone morphogenetic proteins (BMPs). We demonstrate that while GREM1 remains restricted to stromal cells in earlier-stage (I-III) CRC, its ectopic expression in tumor epithelium increases markedly in stage IV. Mechanistically, we identify ACVR1C as a novel, high-affinity epithelial receptor for GREM1. Their interaction activates SMAD2/3 signaling, which upregulates SNAI1 and GREM1, establishing a feedback loop that amplifies epithelial-mesenchymal transition (EMT). Disrupting this loop impairs CRC metastasis in vivo. Clinically, epithelial GREM1 or ACVR1C expression predicts metastasis and poor survival. These findings define a paradigm in which tumor cells hijack stromal GREM1 to establish a GREM1-ACVR1C autocrine loop that sustains EMT and metastasis, marking a shift toward signaling autonomy and revealing a targetable vulnerability in advanced CRC.

cancer biology↗

bioRxiv2025

APPLE-MS: A affinity purification-mass spectrometry method assisted by PafA-mediated proximity labeling

While affinity purification-mass spectrometry (AP-MS) has significantly advanced protein-protein interaction (PPI) studies, its limitations in detecting weak, transient, and membrane-associated interactions remain. To address these challenges, we introduced an innovative proteomic method termed Affinity Purification coupled Proximity LabEling-Mass Spectrometry (APPLE-MS), which combines the high specificity of Twin-Strep-tag enrichment with PafA-mediated proximity labeling. This method achieves unprecedented sensitivity while maintaining high specificity (4.07-fold over AP-MS). APPLE-MS also revealed the dynamic mitochondrial interactome of SARS-CoV-2 ORF9B during antiviral responses, while endogenous PIN1 profiling uncovered novel roles in DNA replication. Notably, APPLE-MS enabled in situ mapping of GLP-1 receptor complexes, demonstrating its unique capabilities for membrane PPI studies. This versatile method advances interactome research by providing comprehensive, physiologically relevant PPI networks, opening new opportunities for mechanistic discovery and therapeutic targeting. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=184 HEIGHT=200 SRC="FIGDIR/small/652320v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@aaf55borg.highwire.dtl.DTLVardef@1d25af1org.highwire.dtl.DTLVardef@150c1acorg.highwire.dtl.DTLVardef@db0d1c_HPS_FORMAT_FIGEXP M_FIG C_FIG MOTIVATION - Cell Reports Methods onlyAffinity purification-mass spectrometry (AP-MS) has become a widely used method for capturing high-affinity protein-protein interactions (PPIs). However, it still faces several limitations, including high levels of non-specific binding, challenges in detecting weak interactions, and limited ability to identify PPIs at the cell surface in situ. To overcome these challenges, we introduce a modular AP-MS method enhanced by PafA-mediated proximity labeling, which improves both the specificity and sensitivity of PPI detection in a single, streamlined workflow. SIGNIFICANCE - Cell Chemical Biology onlyProximity labeling is a powerful tool in chemical biology, providing critical support for studying protein function, cell signaling, and disease mechanisms by chemically labeling and capturing biomolecular interactions. In this study, we introduce a modular affinity purification-mass spectrometry (AP-MS) method that integrates proximity labeling. This method leverages the proximity-dependent enzymatic activity of PafA to covalently attach PupE to nearby proteins, in combination with the high-affinity interaction between Twin-Strep-tag and streptavidin to achieve efficient binding. This innovative approach enables precise identification of protein-protein interactions (PPIs), offering a powerful tool for capturing weak and transient interactions that are often missed by traditional AP-MS techniques. By enhancing both the specificity and efficiency of protein labeling, our work provides a robust chemical biology strategy to elucidate complex protein interactions, which are essential for understanding cellular processes and developing targeted therapeutic strategies, offering new insights into protein function and interaction dynamics in both health and disease.

biochemistry↗

bioRxiv2025

PASTA: Versatile Tyramine-oligonucleotide Amplification for Multi-modal Spatial Biology

Spatial proteomics techniques have revolutionized our understanding of tissue architecture, but are frequently limited by detection sensitivity, bioconjugation limitations, multiplexing capacity, and multi-modal integration. Here we present Protein and nucleic Acid Serial Tyramine Amplification (PASTA), a novel signal amplification approach that significantly enhances detection sensitivity while maintaining compatibility with diverse spatial profiling methodologies. PASTA utilizes horseradish peroxidase (HRP) recruitment pathways to generate tyramine radicals that deposit oligonucleotides, enabling adaptable signal amplification across multiple biomarkers at high-plex via cyclical imaging using complementary fluorophore-labeled oligonucleotides. We demonstrate that PASTA achieves up to 100-fold signal enhancement for markers with minimal background in blank controls. The method is compatible with in situ hybridization for DNA/RNA detection, proximity ligation assays for protein-protein interactions, sequential antibody staining protocols, or any modular combination thereof. PASTA enables antibody rescue of markers with suboptimal signal-to-noise ratios and is versatile in its applications to unconjugated antibodies, and multi-round probe-based RNA detection systems beyond current capabilities. This technique addresses key limitations in spatial-omics by enhancing sensitivity for challenging targets while maintaining compatibility with established multiplexing strategies, providing a versatile, cost-efficient, and valuable tool for comprehensive spatial tissue analysis in both research and clinical applications.

molecular biology↗