Biology subjects

Cao, H.

Publications and source records attributed to Cao, H..

At least 19 recordsLinked to original sources

bioRxiv2026

SubcellPert: Benchmarking Subcellular Perturbation Prediction

Perturbations change both RNA abundance and intracellular organization, but gene-count predictors omit the positions needed to model spatial responses. To address this limitation, we introduce \textbf{\textit{SubcellPert}}, a benchmark for perturbation-response prediction at single-RNA-molecule resolution. It comprises 14 imaging datasets with 13.88 million cells and 3.65 billion RNA molecules. Given source observations and a directed condition change, models generate target RNA populations in normalized two-dimensional coordinates. Two independently trained tasks separate abundance from localization: \textit{Oracle} predicts coordinates given exact target counts; \textit{Joint} predicts both. Eight baselines span source resampling, classical transport, and frozen foundation-model adapters. Fixed perturbation and cellular-context holdouts evaluate abundance error, target-gene coverage, and conditional spatial error. Through within-dataset analyses, we find context-dependent gains from spatial features in condition decoding. Interpreting spatial responses requires distinguishing changes within spatial clusters from population composition, and RNA fractions from enrichment relative to compartment geometry. Through comprehensive experiments, we find that Gaussian transport achieves the lowest Oracle spatial error on eight of eleven cellular-context OOD datasets. Audits show that better centroid accuracy can accompany worse radial distributions, and that 19 of 190 Joint adapter fits retain source counts after validation. SubcellPert opens a spatial frontier for virtual cell research, laying the groundwork for predictive models that capture both the molecular composition and intracellular organization of cellular responses. The code is available at \url{https://github.com/king159/SubcellPert}.

bioinformatics↗

bioRxiv2026

Median Preoptic Brs3-Expressing Neurons Regulate Heat-Evoked Defensive Escalation

Animals confronted with increasing heat must switch from exploratory behavior to vigorous actions that limit further exposure, but the forebrain circuits controlling this transition are poorly understood. We exposed male mice to progressively stronger floor heating while providing continuous access to a climbing mesh separated from the heating plate. Higher plate settings shifted behavior from rearing toward wall-directed digging and climbing, with jumping at the highest setting, and increased c-Fos labeling in the median preoptic nucleus. Neurons expressing bombesin receptor subtype 3 showed greater c-Fos recruitment under strong heating, and their population calcium signals increased when mice entered the plate-controlled zone and decreased when they left. Optogenetic activation without heating produced place avoidance, vigorous actions, and increased locomotion. Inhibition reduced locomotion and, during strong heating, shifted behavior from climbing and jumping toward rearing and weakened avoidance of a localized heat source. These findings identify median preoptic Brs3-expressing neurons as regulators of behavioral escalation and heat avoidance.

neuroscience↗

bioRxiv2026

Explainable t-SNE for Single-Cell RNA-seq Data Analysis: A Cell-DrivenInformation Fusion Framework

Single-cell RNA sequencing fundamentally depends on defining cell-to-cell similarity to resolve distinct cell states. However, conventional dimensional-reduction frameworks such as t-SNE and UMAP inherently rely on Euclidean geometry, a construct ill-suited for high-dimensional, sparse count matrices, and produce cluster boundaries that lack biological interpretability. Here, we introduce cell-driven information fusion, an end-to-end framework that constructs cell affinity directly from raw counts by integrating three biological signals: gene detection patterns, expression magnitude of dominant transcripts, and global profile correlation. A sparsity-adaptive operator merges these signals into a unified affinity matrix, while an entry-usage diagnostic traces which biological component governs local cluster boundaries. The framework comprises two complementary implementations: c-TSNE for low-to-moderate sparsity (below 80 percent), which consistently outperforms Euclidean baselines across six benchmarks, and c-UMAP for high-sparsity regimes. Evaluated across 29 datasets and two protocols against 11 baseline methods, c-UMAP achieved the highest mean normalized and adjusted mutual information (NMI and AMI) in every setting, with all improvements remaining significant after Bonferroni correction. Because a single fixed weight vector was applied across all 29 datasets per protocol, these gains reflect intrinsic algorithmic robustness rather than dataset-specific hyperparameter tuning. Furthermore, c-UMAP scales seamlessly to 1.14 million cells, running up to 22-fold faster than the leading baseline. Crucially, by operating directly on raw counts without variance-based gene selection, our approach avoids the median 46.9 percent loss of top differentially expressed genes per cell class, preserving essential lineage markers such as IAPP and FOXP3. Together, these results demonstrate that atlas-scale efficiency, raw-data preservation, and mechanistic interpretability are fully compatible.

bioinformatics↗

bioRxiv2026

Engineered CTLA-4 targeting chimeras for targeted protein degradation and immune synapse reprogramming

Despite the success of immune checkpoint blockade in cancer therapy, overcoming the immunosuppressive tumor microenvironment remains a major barrier to durable responses. Here, we introduce cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4)-targeting chimeras (cTACs), a class of synthetic molecules that harness CTLA-4-mediated trans-endocytosis (TE) for targeted protein degradation across cellular boundaries. The lead candidate, cTAC4.0, is composed of the extracellular domain of CD80 fused to an anti-programmed death-ligand 1 (PD-L1) fragment via a human immunoglobulin G4 (IgG4) heavy chain. Unlike existing protein degraders that function within cancer cells via house-keeping pathways, cTAC4.0 operates in trans between immune and tumor cells. It redirects the natural CD80-CTLA-4 internalization to capture and degrade PD-L1 from tumor cells into T cells, while simultaneously converting PD-L1-mediated suppression into CD28-driven co-stimulation, depending on the relative availability of CTLA-4 or CD28. Mechanistically, we demonstrate that cTAC4.0 promotes CTLA-4-dependent internalization and lysosomal degradation of both membrane-bound and soluble PD-L1, reduces surface PD-L1 levels on target cells, and activates T cells in a CD28-dependent manner. Functionally, cTAC4.0 enhances the cytotoxicity of both bispecific T cell engagers and T cell receptor (TCR)-engineered T cells in vitro, and when combined with these engineered T cells, it suppresses tumor growth in vivo in a glioblastoma xenograft model. By coupling immune checkpoint degradation with T cell activation in an immune synapse-enriched manner, cTACs establish a paradigm for intercellular protein degradation systems that simultaneously deplete suppressive ligands and enhance T cell function to overcome resistance in cancer immunotherapy.

immunology↗

bioRxiv2026

ctDNA Release Heterogeneity in Luminal Breast Cancer Revealed by Single-Cell Multi-Omics Spatial Analysis

Backgroud Breast cancer is a leading cause of cancer-related mortality, and HR+/HER2-; is the predominant subtype. Molecular pathology, including PIK3CA and ESR1 mutation, informs prognosis and treatment selection. Circulating tumor DNA (ctDNA) offers a non-invasive approach for cancer monitoring, but its clinical utility is limited by tissue plasma discordance, reflecting heterogeneous ctDNA release. The cellular and spatial determinants of this release remain poorly understood, particularly in HR+/HER2-; subtype, where heterogeneity across subclones complicates liquid biopsy interpretation. Results To identify the cellular and spatial determinants of ctDNA release, we developed scMASTER, integrating single-cell transcriptomes and mutations, spatial transcriptomics and matched tumor and plasma DNA sequencing. In ten patients with non-metastatic HR+/HER2- breast cancer, eight recurrent malignant epithelial states showed distinct ctDNA release potential. Antigen-presentation, stress, mitosis and mesenchymal-like states showed high release. PIK3CA and ESR1 mutations were linked to distinct functional states and altered ctDNA detectability. Three spatial architectures, EMT-enriched regions, intraductal luminal progenitors and avascular hypoxic zones, showed high release. Tumor-microenvironment interactions were bidirectional: anti-tumoral immunity correlated with reduced release, whereas immunomodulatory components correlated with increased release. A stratification model based on these features identified high-risk, immunosuppressed patients with poor prognosis, validated across external cohorts. Conclusions Our study establishes a single-cell spatial framework for decoding ctDNA release heterogeneity, linking tumor states, somatic mutations, spatial architecture, and immune interactions to ctDNA release. These findings provide mechanistic insights into tissue-plasma discordance, offer a biological rationale for interpreting ctDNA dynamics, refine patient risk stratification and liquid biopsy-based monitoring in HR+/HER2-; breast cancer.

cancer biology↗

bioRxiv2026

EM3DFold: accurate de novo protein and nucleic acid model building for cryo-EM maps using language model-powered deep learning

Cryo-electron microscopy (cryo-EM) has become one of the most powerful techniques for macromolecular structure determination. However, accurate model building from cryo-EM maps remains challenging, particularly for nucleic acids. Here, we present EM3DFold, a unified de novo model-building framework for accurate structure determination of proteins, nucleic acids, and protein-nucleic acid complexes from cryo-EM maps using a density-aware, large language model-powered three-track attention (TTA) network. The TTA network effectively integrates sequence, density, and structural information to enable accurate all-atom model building. EM3DFold was extensively evaluated on independent benchmarks of 298 experimental cryo-EM maps at < 4.0 [A] resolutions, and achieves an unprecedentedly high median accuracy of 75% completeness (88% coverage and 94% sequence accuracy) for 178 nucleic acid maps, 90% completeness (95% coverage and 96% sequence accuracy) for 124 protein-nucleic acid complexes, and 95% completeness (97% coverage and 98% sequence accuracy) for 120 protein-only targets, substantially outperforming state-of-the-art methods including ModelAngelo, EM2NA, CryoREAD, and EMProt. In addition, EM3DFold also produces models with superior model-to-map fit and stereochemical quality, providing a robust and reliable solution for automated cryo-EM model building. The EM3DFold package is freely available at https://github.com/huang-laboratory/EM3DFold.

bioinformatics↗

bioRxiv2026

LNP-CpG: deploy the self-adjuvant role of mRNA vaccines

With the rapid advancement of mRNA vaccines, lipid nanoparticles (LNPs) have emerged as pivotal carriers and adjuvants for non-mRNA vaccine modalities, driven by their superior nucleic acid delivery efficiency and intrinsic self-adjuvanting properties. In this study, we systematically evaluated various formulation strategies combining LNPs and the CpG adjuvant within a varicella-zoster virus glycoprotein E (VZV-gE) subunit vaccine framework. We demonstrated that uniform nanoparticles formed by LNP-encapsulated CpG (LNP-CpG), when simply admixed with the gE antigen, elicited superior immunogenicity compared to alternative encapsulation configurations. Intramuscular administration of a two-dose (LNP-CpG)+gE regimen significantly augmented both humoral and cellular immune responses in mice, markedly outperforming the commercial vaccine Shingrix (administered at a 1/10 human dose). Crucially, the identical regimen induced robust, comparable immune profiles to a full human dose of Shingrix in rhesus macaques. Furthermore, LNP-CpG displayed broad-spectrum utility across diverse vaccine platforms, demonstrating efficacy against both respiratory (RSV) and neurotropic (HSV) pathogens, compatibility with multiple modalities, including subunit (VZV-gE, RSV-Pre-F), live-attenuated (LA-HSV), and inactivated (i-HSV) vaccines; and versatile implementation in a combined VZV+RSV formulation. Collectively, our findings position LNP-CpG as a versatile, safe, highly efficacious adjuvant platform with substantial clinical translational potential, offering a compelling paradigm for next-generation vaccine development.

immunology↗

bioRxiv2026

Base-and-Sugar Dual-Frame Flow Matching for RNA Co-Design

Current frame-based RNA generative models represent each nucleotide with a single rigid coordinate frame. However, our analysis of seven nucleotide frame constructions across 11,497 static RNA chains and 31,432 multi-state relation groups reveals a clear trade-off: base-centered frames better preserve base-mediated relations, whereas sugar-centered frames better support sugar-phosphate backbone reconstruction. Motivated by this complementarity, we introduce DuetRNA, a joint sequence-structure generation model for RNA co-design that performs SE(3) flow matching over coupled base-and-sugar frames. Empirically, we evaluate DuetRNA under both the inverse-folded (IF) backbone-design protocol and generated-sequence (GS) co-design protocol. DuetRNA improves scTM-validity over the strongest matched baselines by 7.67 and 4.33 % under IF and GS, respectively. Controlled ablations further support the necessity of the dual-frame modeling in DuetRNA. The official DuetRNA repository is https://github.com/XjunLi/DuetRNA.

bioinformatics↗

bioRxiv2026

Peptide structural plasticity is predictable from sequence and environment

Many peptides often do not have a single dominant structure. Instead, many remain disordered in water and fold when they encounter membranes or other chemical environments, a property that underlies diverse biological functions but is difficult to predict. Here we introduce ApexFold, a machine-learning frame-work that predicts how peptide secondary structure change across environments. ApexFold uses peptide sequence and features together with physicochemical descriptors of the surrounding medium to estimate the fractions of helical, {beta}-like and disordered structure expected in each condition. Trained on circular-dichroism measurements from 1,187 peptides assayed in water, co-solvents and membrane-mimicking micelles, ApexFold predicted solvent-induced structural shifts in independent peptide panels and outperformed static structure predictors that return a single conformation. These results show that peptide structural plasticity can be learned from sequence and environment, providing a way to prioritize peptides and experimental conditions before synthesis and structural characterization.

bioengineering↗

bioRxiv2026

ABC1K7: A 350-Myr chloroplast rheostat fine-tuned during coconut domestication

Perennial crops follow different domestication trajectories from annuals, yet the molecular basis of slow-variable domestication--subtle tuning of conserved regulatory hubs--remains poorly characterized. We reconstructed the evolutionary history of the chloroplast kinase ABC1K7 across 9 seed plant species spanning [~]350 Myr, employing PAML codon models, IQ-TREE robust codon models, and protein-level phylogenetic inference, with AlphaFold2 structural modeling. ABC1K7 was under extreme purifying selection ({omega} = 0.073-0.104) across all seed plants. In coconut, a single Y[->]F substitution at residue 652--located >30 [A] from the catalytic core in a predicted intrinsically disordered region--represents the only non-synonymous change differentiating coconut from 7 of 8 angiosperm orthologs, and exhibits perfect co-segregation with domestication traits across a 17-year breeding panel (n = 327). These findings provide population-level evidence consistent with the slow-variable domestication model, identifying ABC1K orthologs as targets for perennial crop improvement.

genomics↗

bioRxiv2026

Predicting Macroscopic Axon Topology from Microscopic Kinematics: An Interactive Tracking and Random Walk Pipeline for Substrate-Dependent Cortical Neurospheres

The cortical neuron is a fundamental building block of the mammalian brain, and the morphology of its axonal projections is central to how functional circuits assemble. The trajectory along which an axon grows is a key determinant of connectivity, yet the kinematics of cortical axon outgrowth remain poorly quantified. Characterizing these dynamics is most tractable in vitro, where axonal growth can be measured directly and under controlled, reproducible conditions. Even in culture, however, this remains challenging because cortical neurons require dense plating for viability, and their soma is motile, so growth behavior is highly sensitive to local density and population context, complicating reproducible measurement of intrinsic dynamics. To overcome these limitations, we used size-controlled cortical neurospheres, which provide a fixed spatial origin and a reproducible environment, together with a custom semi-automated tracking pipeline to quantify single-axon kinematics across two functionalized substrates and two developmental phases. This approach revealed a substrate-dependent divergence in outgrowth: during the later developmental phase, axons on poly-D-lysine with laminin (PDL-LA) substrate grew faster than those on PDL, with a mean step size of 0.436 versus 0.339 {micro}m /min. Decomposing trajectories into Katz dynamic states, we built a generative biased random walk model that reproduces axonal behavior at both microscopic (single-axon) and macroscopic (network topology) scales. This open, reproducible framework links single-axon kinematics to network architecture, enabling the structural connectivity of neurospherebased circuits in vitro to be predicted from measurable growth dynamics, a necessary foundation for future studies linking circuit structure to emergent function.

bioengineering↗

bioRxiv2026

A missing PEPC1 exaptation restricts C₄ evolution in palms (Arecaceae)

O_LIC4 and CAM photosynthesis evolved repeatedly across angiosperms, yet the four palm species examined here -- representing three of five Arecaceae subfamilies ([~]2,600 species total) -- all lack both. We hypothesized that this reflects the absence of PEPC1, a phosphoenolpyruvate carboxylase isoform required for C4 carbon fixation initiation and present in commelinids (Poaceae + Bromeliaceae) but whose distribution across monocots is unknown. C_LIO_LIWe surveyed six core C4 enzyme families by HMMER profiling across nine plant genomes -- four palms (Cocos nucifera, Elaeis guineensis, Phoenix dactylifera, Nypa fruticans), two grasses, one bromeliad, one basal monocot, and one fern. We constructed maximum-likelihood PEPC phylogenies and performed codon-based branch-site likelihood ratio tests using PAML. C_LIO_LIAll six enzyme families were present in palms. PEPC1 was detected in all commelinids (rice, maize, pineapple) but absent from all four palm species and outgroups. No palm PEPC sequence fell within the PPC-1 clade. Molecular dating indicates the Arecaceae-commelinid divergence ([~]120 Mya) predates PEPC1 origin ([~]105 Mya). Branch-site tests on the maize C4-PEPC1 branch were marginally significant (2{Delta}l = 3.03, p {approx} 0.082). C_LIO_LIThe absence of PEPC1 represents an ancestral molecular barrier that likely precluded C4 evolution in palms. This identifies a lineage-specific gene duplication as a constraint on photosynthetic pathway evolution in a major tropical plant lineage. C_LI

plant biology↗

bioRxiv2026

PEPC Gene Family Evolution Across Arecaceae: Complete Five-Subfamily Evidence for a Two-Lock Model of Irreversible C4 Exclusion

BackgroundC4 photosynthesis has evolved more than sixty times across flowering plants, but never in palms. Over 2,500 palm species have spent more than 100 million years in high-light, water-stressed habitats where C4 physiology would be advantageous, yet none have taken this path. Phosphoenolpyruvate carboxylase (PEPC) is the enzyme that gates entry into the C4 pathway. In every C4 grass, a commelinid-specific paralog called PEPC1 drives the CO2-concentrating mechanism that defines the syndrome. Until this study, one of the five palm subfamilies -- Ceroxyloideae -- had never been examined for PEPC gene content. ResultsWe present a complete PEPC census across 13 palm species spanning all five subfamilies (Calamoideae, Nypoideae, Coryphoideae, Ceroxyloideae, Arecoideae). Ceroxyloideae was characterized for the first time, using deep tBLASTn screening of ten million whole-genome shotgun reads. PEPC1 is absent from every palm species examined. Copy numbers are tightly constrained to 5-7 per genome, in stark contrast with the 6-25 copies found in grasses. A phylogenetic analysis of 337 PEPC sequences places all 55 palm loci at basal positions relative to the PEPC1 clade, consistent with the palm lineage having diverged approximately 120 million years ago -- roughly 15 million years before the PEPC1 duplication that occurred within the commelinids. Birth-death modeling reveals long-term gene family stasis in palms. Codon substitution analyses yield a genome-wide {omega} of 0.0582, indicating pervasive purifying selection and no signature of C4-specific adaptation. Coconut PEPC promoters uniformly lack the mesophyll-specific MESP-1/GT-1/DOF regulatory module that is required for C4-type gene expression. ConclusionsWe propose a Two-Lock Model of irreversible C4 exclusion in palms. The first is a Gene Lock: PEPC1 is phylogenetically absent from all five subfamilies. The second is a Regulatory Lock: the ancestral promoter architecture of palm PEPC genes lacks the cis-elements needed for mesophyll-specific expression. Together, these two locks -- one phylogenetic, one regulatory -- explain why C4 photosynthesis has never arisen in one of the largest C4-absent plant families. The model provides a testable framework for investigating other lineages that have remained closed to the C4 path.

evolutionary biology↗

bioRxiv2026

Muscle March5 restrains an ATF4-GDF15 endocrine stress axis that modulates feeding and body composition

Skeletal muscle can release endocrine stress signals during aging and wasting, but the upstream mechanisms that restrain this response remain incompletely defined. Here we identify March5 as a muscle proteostatic checkpoint that limits ATF4-dependent GDF15 production. March5 expression declined in aged and atrophic muscle, whereas muscle-specific March5 deletion induced ATF4 accumulation, marked GDF15 elevation, reduced food intake and progressive loss of body, muscle and bone mass. Restoration of feeding, GDF15 neutralization or muscle Atf4 deletion substantially attenuated the wasting phenotype. Mechanistically, March5 interacted with ATF4 and promoted its ubiquitination at K92, thereby limiting ATF4 stability and Gdf15 expression. Conversely, muscle March5 gain-of-function or pharmacological attenuation of ATF4 signaling improved feeding, body composition and physical performance in aged mice. These findings define a March5-ATF4-GDF15 endocrine stress axis linking muscle proteostatic control to feeding suppression and systemic body-composition remodeling.

physiology↗

bioRxiv2026

Seeing the world in a grain of sand: Fine global and local representations in foveal and parafoveal vision

Foveal vision enables primates to perceive small objects with remarkable precision by resolving both fine-global form and fine-local detail. Yet current coarse-to-fine and local-to-global frameworks, derived largely from parafoveal recordings and large-object representations, posit that V1 encodes local detail whereas IT represents global form, leaving the neuronal basis of foveal object representation unresolved. Here we show that fine-global form and fine-local detail are encoded parallelly as early as V1 and preserved across V2, V4 and IT at both foveal (1{degrees}-2{degrees}) and parafoveal (2{degrees}-6{degrees}) retinal eccentricities. Coarse-global features dominate parafoveal vision and higher cortical areas. Response latencies revealed a rapid processing sequence, with 2-6 ms separating coarse-global, fine-local, and fine-global processing within parafoveal V1, V2 and V4. These findings establish a cortical neuronal framework to resolve fine-global form of small objects in foveal vision along primate ventral visual stream, supporting fine-scale perception during reading and fine object recognition. Highlights O_LIFoveal two-photon imaging across V1, V2, V4, and IT C_LIO_LIGlobal and local processing examined across eccentricity C_LIO_LIFine and coarse global representations emerge as early as V1 C_LIO_LIFine global and local representations are preserved from V1 up to IT C_LIO_LITightly timed coarse-to-fine-to-fine processing within each cortex C_LIO_LIFast coarse-global dominates parafoveal and higher cortical areas C_LI

neuroscience↗

bioRxiv2026

EMAlign: accurate alignment of cryo-EM maps through main-chain probability using deep learning

Accurate alignment of cryo-EM density maps is essential for comparing conformational states, searching map libraries, and guiding atomic model building, but remains challenging for noisy ex-perimental maps and partially overlapping structures. Existing alignment methods are often based on raw maps, which may result in reduced accuracy due to the density noise, or require manual intervention for local alignment, which suffers from limited general applicability. Addressing the limitations, we present EMAlign, an automatic global and local cryo-EM map alignment with predicted main-chain probability using deep learning. First, EMAlign predicts main-chain prob-ability maps from raw cryo-EM density maps using a BiMCUNet network. Then, a fast Fourier transform (FFT)-based search strategy is used to globally search the accurate alignment between cryo-EM maps based on predicted main-chain probability maps. As such, the main-chain prob-ability map overcomes the noisy raw map problem, and the FFT-based exhaustive global search ensures the general applicability of alignment. EMAlign is evaluated on 64 global map pairs, 195 local map pairs, and 60 structure-to-map pairs at 3-10 [A] resolution and compared with gm-fit, fitmap, VESPER, and CryoAlign. It is shown that EMAlign outperforms the other methods in both global and local alignment, achieving mean RMSDs of 1.03 [A] (global), 2.56 [A] (lo-cal), and 0.82 [A] (structure-to-map), with success rates of 100.0%, 100.0%, and 98.3% under the criterion of RMSD < 10 [A] . The EMAlign package is freely available at https://github.com/huang-laboratory/EMAlign/.

bioinformatics↗

bioRxiv2026

CHOP promotes the transition to chronic integrated stress response signaling with suppression of hepatocyte identity

The transcription factor CHOP promotes cell death during ER stress, but it is strongly induced even by moderate stresses that do not result in appreciable cell death. Its role during less severe stresses--especially in intact tissues in vivo--is poorly understood. Here, we both deleted and restored CHOP specifically in hepatocytes and challenged animals with ER stress in vivo. We found that CHOP influenced stress-dependent hepatocyte gene expression through two previously unappreciated mechanisms. It directly suppressed the expression of transcriptional master regulators of hepatocyte identity and metabolism. And more broadly, it exacerbated ER stress through the promotion of protein synthesis, which led to persistent activation of the integrated stress response (ISR) despite dephosphorylation of eIF2. This shift to second-phase ISR signaling was phenocopied by deletion of the protective UPR sensor ATF6, suggesting that it reflects a transition from an acute stress response to a chronic one. Our findings show that CHOP augments the capacity of the ISR and UPR to continue to mount a protective response even after eIF2 phosphorylation has been suppressed. In vivo, where ISR signaling intersects with hepatocyte gene regulatory networks, this transition favors lipid dysregulation, highlighting a pathway through which CHOP impacts tissue function independent of cell death.

cell biology↗

bioRxiv2026

Single-molecule imaging and tracking on clinical liquid biopsies reveals cancer biomarkers nanoscale organization and heterogeneity

Single-molecule imaging and tracking have revealed fundamental biological mechanisms at the molecular scale, yet their application to clinical research remains limited by technical complexity and sample preparation incompatible with patient-derived specimens. As a result, we lack information with molecular-scale resolution of clinically relevant biomarkers. Here, we develop a workflow enabling Points Accumulation in Nanoscale Topography (PAINT) combined with single-particle tracking (SPT) on clinical liquid biopsies, allowing analysis of biomarker nanoscale organization at the single-molecule level in cancer patients. Our approach features a sample preparation tailored to liquid biopsies and requires no fixation, covalent labelling, or genetic modification, making single-molecule imaging compatible with hospital clinical workflows. We demonstrate the methods diversity by imaging liquid biopsies from blood, bone marrow aspirates, and pleural effusions across different cancer types. PAINT-SPT captures both the expression and mobility of clinically relevant membrane receptor biomarkers, revealing pronounced inter- and intra-patient heterogeneity at the molecular and cellular levels. We discover that individual patients exhibit distinct molecular mobility fingerprints that reflect biomarker interaction states and correlate with clinical diagnostic readouts. Furthermore, these fingerprints distinguish healthy from cancer cells, enabling the development of a classifier that accurately identifies cancer cells based on their single-molecule behaviour. Together, our results establish a route to investigate patient-derived clinical samples at the single-molecule level and open new opportunities to understand cancer biology and biomarker function beyond ensemble-averaged measurements.

biophysics↗