Biology subjects

Zheng, Z.

Publications and source records attributed to Zheng, Z..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Large-scale quantum computing framework enhances drug discovery in multiple stages

Coherent Ising machines (CIMs) excel at solving large-scale combinational optimization problems (COPs), but their insufficient long-term stability has hindered their applications in compute-intensive tasks like computer-aided drug discovery (CADD). By improving fiber vibration isolation and temperature control system, we have implemented a 2000-node CIM named QBoson-CPQC-3Gen achieving stable solutions over one hour on large-scale COPs. Graph-based encoding schemes were further introduced to realize a CIM-based CADD workflow including allosteric site detection, protein-peptide docking and intermolecular similarity calculation. CIM-based methods demonstrated superior speed and accuracy than heuristic algorithms. Especially, QBoson-CPQC-3Gen identified 2 novel druggable sites and bioactive compounds for 6 targets, which were further validated in vitro, in-cell and by crystal structures. Our contributions established a quantum-computing framework for multi-stage drug discovery, representing a significant advancement in both quantum computing applications and pharmaceutical research.

bioinformatics↗

bioRxiv2026

Population genomics of Drosophila pseudoobscura

Drosophila pseudoobscura is an historically important organism in evolutionary genetics, serving as a model system in studies of chromosomal inversions, speciation, sex chromosome evolution, and sex-ratio drive. However, previous population genetics analysis of D. pseudoobscura focused on individual chromosomes or used fragmented genome assemblies as a reference. To address these shortcomings, we generated a D. pseudoobscura population genomics resource consisting of newly sequenced genomes from 60 inbred lines sampled across the species geographic range in North America. Using these data and a chromosome-scale reference genome, we examined patterns of nucleotide diversity and population structure across the chromosomes. We found no strong evidence of population structure on most chromosomes, consistent with prior results. In contrast, we identified population structure on the third chromosome, which we attributed to a well-characterized inversion polymorphism. We assigned individual third chromosome haplotypes to inversion arrangements, demonstrating how tests for population structure can be used to identify polymorphic chromosomal rearrangements. Tajimas D was negative across most of the genome, consistent with a recent population expansion. However, the distribution of genetic variation differed across third chromosome inversion arrangements in ways that were consistent with their hypothesized evolutionary histories, and we identified inter-arrangement genetic differentiation that could be attributed to the inversions suppressing genetic exchange. The population genomic data we have collected is publicly available and will support future research on evolutionary genetics. SummaryThe genomes of 60 isolates of Drosophila pseudoobscura were sequenced and analyzed. This species is a model organism for multiple areas in evolutionary genetics research, including chromosomal rearrangement, sex chromosomes, and speciation. This article presents the largest population genomic data set collected in this species. The analysis of the data demonstrates how population structure detection approaches can be used to identify polymorphic chromosomal inversions. The data presented will be valuable for future work on fundamental questions in population genetics.

genomics↗

bioRxiv2026

Genetic Compensation Restores Embryonic Viability in Fatty Acid Synthase Mutants

Fatty acid synthase (FASN) is a key rate-limited, dimeric multi-enzyme complex in the de novo lipogenesis pathway. Each FASN monomer contains seven catalytic domains, which coordinate the stepwise conversion of acetyl-CoA into fatty acids. While FASN has been extensively studied in cultured cells, particularly for its oncogenic role, its functions in the germline and early embryonic development remain elusive. A major challenge is that the FASN dysfunction typically causes embryonic lethality in animal models, which complicates detailed functional analysis and the identification of compensatory genetic interactors during development. To overcome this limitation and identify novel genetic suppressors of the FASN gene, we utilized a temperature-sensitive allele, fasn-1(g43ts) (A1424T), in the genetically tractable model Caenorhabditis elegans, to conduct unbiased forward genetic screens. We isolated 22 suppressor lines that significantly restored embryonic viability in the fasn-1(g43ts) mutant at the non-permissive temperature. Using a combination of MIP-MAP genomic mapping and a customized bioinformatic pipeline, we identified six missense mutations in the ptr-6 gene, which encodes a protein containing a patched domain associated with the Hedgehog signaling pathway. To validate this genetic suppression, we recreated one of the loss-of-function mutations, ptr-6 (W701*), in the fasn-1(g43ts) background using CRISPR/Cas9 gene editing. Notably, ptr-6(W701*) robustly rescued the embryonic lethality and permeability defects caused by fasn-1 loss-of-function. Taken together, our findings expand the genetic regulatory network of fatty acid synthase during early embryogenesis and highlight ptr-6 and Hedgehog signaling pathway as potential genetic modifiers of FASN-associated developmental and metabolic disorders.

genetics↗

bioRxiv2025

A gastric microbial chromatin remodeler drives gastric cancer progression and immune evasion by reprogramming the host epigenome

Emerging evidence highlights the crucial role of microbial communities in modulating cancer development and progression. In gastric cancer (GC), we identify Streptococcus anginosus (SA) as a tumor-resident oncobacterium that drives both tumor progression and immune evasion. We define a novel mechanism in which SA secretes extracellular vesicles (saEVs) that translocate the bacterial chromatin remodeler saSNF2 into host cell. There, saSNF2 partners with host transcription factor TEAD1--through its ATPase activity and participation in BAF complex assembly--to coordinately activate oncogenic transcription. This transkingdom interaction upregulates the palmitoyltransferase ZDHHC11, which in turn stabilizes PD-L1 via palmitoylation to establish an immunosuppressive niche, while also amplifying other TEAD1 target genes to fuel tumor progression. Functionally, saEVs promote tumor growth and limit CD8+ T-cell infiltration in vivo. Strikingly, pharmacological inhibition of ZDHHC11 reverses immune evasion and synergizes with anti-PD-1 checkpoint blockade. Our results establish SA as a multifaceted driver of GC and reveal the saSNF2-ZDHHC11 axis as a promising target to potentiate immunotherapy.

cancer biology↗

bioRxiv2025

Sampling Function-Related Metastable States of Proteins With DASH

Rational discovery of function-specific protein modulators as well as activity-enhanced engineering proteins underscore the need to identify function-related metastable states (FMSs) of proteins. However, current experimental and computational methods struggle to generate these states directly from their native state (NS), likely because the NS [->] FMS transition is non-spontaneous. To address this challenge, we introduce Deep learning guided Adaptive sampling with seed Selection and Hopping (DASH), integrating both deep learning and physical functions to guide molecular dynamics (MD) simulation towards FMS. DASH successfully sampled NS [->] FMS transitions in 18 cases across two tasks: protein activation and cryptic allosteric site opening. DASH combined with secondary-structure collective variables is further able to sample folding of disordered regions. Compared to existing methods, DASH demonstrates superior performance while requiring comparable simulation time. Crucially, we applied DASH to sample new conformations of proteins, by which revealing new folding states, previously unknown allosteric sites, and potential activators. These predictions have been further verified in wet-lab experiments and crystal structures determinations. Collectively, our framework provides a robust strategy for function-specific pharmacy research and could accelerate future drug discovery efforts.

bioinformatics↗

bioRxiv2025

A K27-linked Ubiquitin Checkpoint Controls NOTCH Homeostasis

Cell surface receptors such as NOTCH1 must be tightly regulated to ensure developmental fidelity and prevent pathological activation. Although the proteolytic steps culminating in nuclear NOTCH1 signaling are established, how cells prevent excessive or uncontrolled activation has remained unresolved. Here we identify the autophagy-related protein UVRAG as a negative regulator of NOTCH1. Upon receptor activation, UVRAG, acting independently of autophagy, recruits and activates the E3-ligase ITCH to catalyze K27-linked ubiquitination of membrane-tethered NOTCH1, thereby licensing ESCRT-dependent lysosomal degradation. Disruption of the UVRAG-ITCH-ESCRT axis stabilizes activated NOTCH1 intermediates and amplifies oncogenic signaling. In T-cell leukemia models driven by constitutive NOTCH1 activity, restoring UVRAG expression reinstates receptor turnover, suppresses disease progression, and improves therapeutic response. These findings define a ubiquitin-directed safeguard circuit that enforces NOTCH1 signaling homeostasis and reveals a tunable axis for intervention in NOTCH1-driven cancers.

molecular biology↗

bioRxiv2025

From labels to latents: revealing state-dependent hippocampal computations with Jump Latent Variable Model

Neural activity is usually interpreted by imposing external labels (e.g., stimuli or position during locomotion) and decoding within that space (e.g. replay). While powerful, such supervision can mask structure in the data that do not correspond to the label. Unsupervised methods, in turn, often assume smooth latent dynamics and miss genuine discontinuities. We introduce a conceptually simple, computationally efficient latent variable model that infers both (i) the latent variables organizing population activity and (ii) whether their dynamics are continuous or fragmented in time. Fitting reduces to an expectation-maximization (EM) procedure that alternates two operations familiar to systems neuroscience--tuning-curve estimation and label decoding--without requiring external labels. Applied to rodent hippocampal spike recordings, the model reveals distinct population patterns at the same physical position that supervised spatial decoding fails to detect. While learned latents exhibit place-field-like tuning, their reactivation patterns are better distinguished by behavioral states. The model further identifies a continuity-fragmentation axis that characterizes population activities across sleep-wake brain states that is modulated by cholinergic inputs. By not relying on externally imposed spatial labels, our approach exposes structure that supervised approaches obscure and provides a powerful tool for datasets lacking behavioral tracking.

neuroscience↗

bioRxiv2025

Thalamic Interictal Epileptic and Non-Epileptic Events during NREM Sleep in Patients with Focal Epilepsy: a Stereo-EEG Study

BackgroundThalamic recordings are increasingly incorporated into stereo-electroencephalography (SEEG) evaluations of drug-resistant focal epilepsy to guide neuromodulation targeting. Human thalamic electrophysiology, however, is poorly defined, limiting the distinction between pathological and physiological activity. Here, we characterized interictal epileptic and non-epileptic events during non-rapid eye movement (NREM) sleep across multiple thalamic nuclei and examined their relationship to seizure outcomes. MethodsWe analyzed NREM sleep SEEG recordings from 64 patients with drug-resistant focal epilepsy. Electrodes sampled four thalamic nuclei: centromedian (CM), pulvinar (Pu), ventral lateral (VL), and ventral posterolateral (VPL). Patients were classified into three outcome groups: favorable, unfavorable, and surgically non-remediable. Rates of thalamic spikes, high-frequency oscillations (HFOs), spike-fast activity, and sleep spindles were analyzed and compared across nuclei and outcomes. FindingsRecordings of the thalamus revealed both pathological and physiological interictal events. Interictal epileptic events were infrequent. Only [~]0.2% of seizure-onset zone spikes propagated to the thalamus. Thalamic spike-fast activity was indicative of unfavorable surgical outcomes (CM: p = 0.047, d = 0.46) or surgically non-remediable epilepsy (VL: p = 0.002, d = 0.84). In contrast, thalamic sleep spindles were ubiquitous but reduced in surgically non-remediable patients (CM: p = 0.031, d = -0.58; VL: p = 0.005, d = -0.79). Finally, unique thalamic SEEG patterns were identified, including spikes concomitant with spindles, isolated spikes, and physiological fast ripples. InterpretationThis study provides a comprehensive characterization of thalamic interictal events during NREM sleep, enriching our understanding of thalamic pathophysiology and highlighting the value of thalamic recordings in presurgical evaluation.

neuroscience↗

bioRxiv2025

Exploring multi-level microbial interactions from individual 3D genomes to community networks

Microbial communities interact with their hosts through complex genomic networks that influence ecosystem stability and disease progression. Here, we present FindMeta3D, a computational framework that simultaneously identifies microbial three-dimensional (3D) genome structures and cross-domain interaction networks. Applying this approach to 528 Hi-C samples, we resolved 3D genome structures of 344 microbial species, revealing five evolutionarily conserved chromatin folding patterns linked to intrinsic sequence features. Further analyses demonstrated distinct microbial-host interaction preferences and identified functional interaction hotspots that are critical for infection. Experimental deletion of such hotspots in the EBV genome resulted in significant infection defects, demonstrating their essential role in viral infectivity. Additionally, we constructed the first Cross-domain Microbial Interaction Network (CMIN), which uncovered pathogen-specific subnetworks and demonstrate dramatic restructuring of gut microbial communities in neutropenic patients, including enhanced Klebsiella-phage interactions. Subnetwork analysis identified potential phage therapy targets, such as Klebsiella phage ST16-OXA48phi5.4. These findings provide fundamental insights into microbial 3D genomics and establish FindMeta3D as a powerful platform for studying microbial genome structure and communities and developing antimicrobial strategies.

microbiology↗

bioRxiv2025

ATRX Loss Predicts Poor Outcomes and Reveals a Therapeutic Vulnerability to TEAD Inhibition in Soft Tissue Sarcomas

ATRX is one of the most frequently altered genes in sarcoma and encodes an ATP-dependent chromatin remodeler implicated in maintaining heterochromatin. However, ATRX alterations have not been leveraged for sarcoma treatment. We observed loss of ATRX protein in 14% of soft tissue leiomyosarcoma (STLMS, n =127), 53% of uterine leiomyosarcoma (ULMS, n = 95), 37% of undifferentiated pleomorphic sarcoma (UPS, n = 82), and 8% of dedifferentiated liposarcoma (DDLPS, n = 84). ATRX loss was associated with significantly worse outcomes in ULMS, UPS, and DDLPS. ATRX knockout in sarcoma cells increased proliferation in cooperation with TP53 deletion. ATRX knockout led to chromatin de-repression and enrichment of PRDM4 and NFIX transcription factor (TF) motifs. PRDM4 and NFIX knockdown in ATRX-mutant sarcoma lines resulted in reduced proliferation and invasion suggesting epistatic relationship. Consistent with the known functional relationship between PRMD4 and YAP1, we observed that ATRX/TP53 KO cells were more sensitive to the TEAD inhibitor VT103 compared to TP53 KO and ATRX WT controls. Overall, our results identify ATRX loss as a prognostic factor of worse outcomes, implicate the ATRX-PRDM4-YAP1 axis as a novel underlying mechanism, and suggest use of TEAD inhibition as a potential therapeutic strategy for ATRX-deficient sarcomas. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC="FIGDIR/small/689992v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@a759ecorg.highwire.dtl.DTLVardef@100c580org.highwire.dtl.DTLVardef@1a675c9org.highwire.dtl.DTLVardef@17f1f91_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

LongPhase-S: purity estimation and variant recalibration with somatic haplotying for long-read sequencing

Accurate detection of somatic variants is crucial for precision oncology, and long-read sequencing offers unprecedented advantages in resolving complex cancer genomes. However, most long-read somatic callers rely on phasing built for a diploid genome, an assumption violated by various contamination, subclonal heterogeneity, and aneuploidy in tumors. We present LongPhase-S, a novel method that jointly reconstructs somatic haplotypes, infers tumor purity, and recalibrates somatic variants in a purity-aware manner for paired tumor-normal long-read sequencing. By anchoring each somatic read to a parental germline lin-eage, LongPhase-S provides a phase-resolved view in which germline and somatic reads are disentangled across the genome. Building on somatic haplotyping, LongPhase-S trains a phase-aware purity estimator that outperformed existing methods. Using eight benchmark datasets comprising six cancer cell lines, including breast, melanoma, and lung cancers, LongPhase-S boosted the accuracy of state-of-the-art somatic callers wuth the estimated purity and somatic haplotypes. Specifically, mean F1 scores increased by 4.5% and 7.1% for single-nucleotide variants and insertions and deletions with ClairS, and by 1.2% and 0.5% with DeepSomatic. Collectively, these results showed that somatic haplotyping is a critical yet missing piece in existing somatic callers, which enables purity-aware and phase-resolved variant interpretation in heterogneous tumors.

bioinformatics↗

bioRxiv2025

Clair-Mosaic: A deep-learning method for long-read mosaic small variant calling

Mosaic variants, defined as postzygotic mutations occurring during an organisms development from zygote to adult, play critical roles in developmental biology, aging, and diseases such as cancer and neurological disorders. However, their accurate detection remains challenging due to low abundance in the genome and low variant allelic fractions (VAF). While current mosaic variant callers are primarily designed for short-read sequencing, no method is available for long-read sequencing, which can generate tens of kilobase-long reads to cover complex genomic regions inaccessible to short reads. To fill the gap, we present Clair-Mosaic, a deep-learning-based method for detecting mosaic small variants from long-read data. Clair-Mosaic was trained on hundreds of millions synthetic variants encompassing diverse read coverages and allelic fractions, enabling it to detect low-VAF mosaic variants with high sensitivity in paired-sample and single-sample modes. In addition to neural network prediction, Clair-Mosaic distinguishes genuine mosaic variants from sequencing artifacts by leveraging their inherent haplotype relationship in phased long reads. Furthermore, a Bayesian mosaic-germline discriminator is introduced to distinguish mosaic variants from germline variants. It also employs multiple post-calling filters, including a mosaic variant database and multiple germline population resources, to tag common germline and mosaic variants. Comprehensive benchmarking on synthetic datasets and real samples demonstrated Clair-Mosaics outstanding performance in ONT and PacBio. Clair-Mosaic is also applicable to short-read data and outperforms methods like MosaicHunter, MosaicForecast, DeepMosaic, and DeepSomatic. Clair-Mosaic is open-source and available at https://github.com/HKU-BAL/Clair-Mosaic.

bioinformatics↗

bioRxiv2025

Deep Disentangled Representation Learning Reveals Neuron Subtype-Specific Nuclear Morphologies Across Aging in Mice and Humans

We present a computational pipeline that links nuclear morphology to mRNA expression-based cell phenotypes under diverse biological conditions, including aging, disease progression, and drug response, using RNAscope imaging. The pipeline consists of three components: nuclear segmentation from RNAscope images, nuclear morphology identification, and downstream statistical analysis. Central to our approach is a novel unsupervised method, based on deep disentangled representation learning, which effectively captures diverse nuclear morphologies in large-scale datasets, as validated on synthetic benchmarks. We applied the full pipeline to RNAscope data targeting dopaminergic and glutamatergic neuron populations in the midbrains of mice and humans. Our analyses uncovered distinct nuclear morphology differences between dopaminergic and non-dopaminergic, as well as glutamatergic and non-glutamatergic neurons, in both species. Moreover, we identified a significant interaction between neurotransmitter identity and healthy aging in mice, reflected in systematic changes in nuclear morphology. These findings position nuclear morphology as a scalable and informative imaging-based readout of cell identity and physiological state.

bioinformatics↗

bioRxiv2025

Noncanonical amino acid incorporation enables minimally disruptive labeling of stress granule and TDP-43 proteinopathy

We report a minimally disruptive labeling strategy for stress granule protein, G3BP Stress Granule Assembly Factor 1 (G3BP1), and ALS-linked protein, TAR DNA-binding protein 43 (TDP-43), using the fluorescent noncanonical amino acid Anap. By integrating genetic code expansion (GCE) with rational site selection, we achieved precise incorporation of Anap that preserves protein structure and function. In live cells and neurons, Anap labeling faithfully recapitulated localization, stress-induced dynamics, and recovery behavior, outperforming conventional fluorescent tags and enabling physiologically relevant visualization of protein pathobiology.

cell biology↗

bioRxiv2025

Enterococcus faecalis delivers Obg GTPase via extracellular vesicles to instigate mTOR activity and promote HCC tumorigenesis

Hepatocellular carcinoma (HCC) is a malignant tumor that has been associated with dysbiosis of the gut microbiota. However, how the gut microbiota plays an oncogenic role in HCC remains largely unknown. Here, we show that Enterococcus faecalis (E. faecalis) is highly enriched in liver tumor tissues and is positively correlated with pathogenesis of HCC. E. faecalis promotes liver cancer cell proliferation, protein translation, cell migration and tumorigenesis. Mechanistically, we found that EF-derived extracellular vesicles (EF-EVs) deliver EF-Obg GTPase to activate host mTOR, thereby promoting liver cancer progression. Intriguingly, the EF-Obg protein exerts its influence on the mTOR pathway via a Ras-like G domain involved in GTP binding. EF-Obg exhibits a striking homology with the G1 site of the G domain of Rheb, a key positive regulator of mTOR. Obg gene is critical for E. faecalis to activate mTOR to promote hepatocarcinogenesis based on an engineered obg knockdown strain by CRISPR interference experiment. Clinically, abundant EF-Obg protein expression is correlated with enhanced activation of mTOR, leading to poor overall survival in HCC patients. Significantly, treatment of mTOR inhibitor Everolimus confers effectiveness in EF-colonized liver cancer orthotopic model, suggesting that Everolimus therapeutic approach can be effective for liver cancer patients with enriched E. faecalis. Taken together, we provide mechanistic and functional evidence to verify a direct causal relationship between tumor-resident E. faecalis enrichment and liver carcinogenesis, revealing that EF-Obg functions as a previously unidentified cross-kingdom activator of mTOR to promote liver tumorigenesis.

cell biology↗

bioRxiv2025

Toxoplasma gondii disrupts vitamin B and K2 biosynthetic pathways in feline gut microbiota: microbial adaptation

Vitamins B and K2 (menaquinone) are critical for microbial and host processes, but their biosynthesis in feline gut microbiota and modulation by Toxoplasma gondii remain unclear. We assembled 1,553 high-quality feline intestinal bacterial genomes from metagenomic and public data to study vitamin B and K2 biosynthesis. We identified 86,829 genes and 159 enzymes, revealing regional differences in gene and species diversity for vitamin production. Among these, 782 genomes supported de novo synthesis of at least one vitamin. T. gondii infection altered synthesis pathways: early infection boosted pyridoxine and riboflavin synthesis in the small intestine, while menadione synthesis declined in both intestines. Actinomycetota and Campylobacterota were key to menaquinone biosynthesis, with T. gondii disrupting canonical pathways and activating alternatives. These findings shed light on the complex interplay between host, parasite, and microbiota in shaping vitamin biosynthesis, offering new perspectives for maintaining feline gut health during parasitic infection.

microbiology↗

bioRxiv2025

Integrative Systems Biology Identifies the AHR Signaling Axis as a Mechanistic Basis for Xiaobi Plaster in Lumbar Disc Degeneration

BackgroundLumbar disc degeneration (LDD) is characterized by chronic inflammation, oxidative stress, and extracellular matrix (ECM) breakdown, yet current therapies provide only symptomatic relief. Xiaobi Plaster (XBG), a traditional Chinese transdermal herbal preparation, has shown clinical benefit, but its molecular mechanisms remain unclear. MethodsWe applied an integrative systems biology framework combining network pharmacology, machine learning, and transcriptomic validation to identify candidate targets of Xiaobi Plaster in LDD. Molecular docking, dynamics simulations, and immune infiltration analysis further elucidated the underlying mechanisms, which were experimentally validated in a rat puncture-induced degeneration model. ResultsThree key targets--AHR, PTPN2, and SOAT1--were identified, with AHR emerging as the central regulator. Differential expression and enrichment analyses highlighted inflammatory and ECM-remodeling pathways, which overlapped with AHR-specific GSEA, underscoring its hub role in integrating immune and redox signaling with matrix turnover. Molecular docking and 100-ns molecular dynamics confirmed rutaecarpine (MOL002662) as the most stable AHR ligand. Transcriptomic data further showed AHR expression correlates with degeneration severity and mast cell infiltration. In vivo qPCR and Western blotting validated upregulation of AHR and supporting roles of PTPN2 and SOAT1 in degenerative discs. ConclusionThis study elucidates the multi-target and multi-pathway mechanisms of XBG in LDD, highlighting AHR as a pivotal therapeutic target modulated by rutaecarpine, with PTPN2 and SOAT1 serving as auxiliary regulators. These findings provide mechanistic insights into the transdermal application of XBG and support the therapeutic relevance of targeting the AHR signaling axis in LDD.

bioinformatics↗

bioRxiv2025

Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution

Formalin-fixed paraffin-embedded (FFPE) tissue-compatible spatial transcriptomics (ST) is transforming biology and histopathology, but achieving high sensitivity, genome-wide coverage, single-cell resolution and researcher-accessibility remains challenging. Here, we present a dendrimeric DNA coordinate-barcoding design for FFPE-adapted spatial RNA sequencing (Decoder-FFPE-seq), by integrating RNA-to-DNA probe conversion with blotting-assisted barcoding. Decoder-FFPE-seq provides single-cell resolution with a 2.54-4.25-fold sensitivity increase in mouse brain transcriptomic profiling over similar assays with an available protocol, successfully mapping rare genes and sparse cell types. It robustly resolves tumor microenvironments (TMEs) in clinical archives preserved 6-20 years. With expanded probe libraries, Decoder-FFPE-seq captures long non-coding and viral RNAs, revealing spatial co-expression networks with mRNAs in oral squamous cell carcinomas (OSCCs). In an OSCC immunotherapy cohort, it delineates TME heterogeneity across pathology response groups and tracks spatial cellular dynamics pre- and post-immuno-chemotherapy. Decoder-FFPE-seq establishes a robust, high-resolution framework for sensitive ST profiling in fundamental and translational studies of archival tissues.

genomics↗