Biology subjects

Ren, P.

Publications and source records attributed to Ren, P..

At least 19 recordsLinked to original sources

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

Transmembrane coupling of protein condensates via membrane-mediated interactions: A simulation study

Recent experiments show that protein condensates sitting on opposite surfaces of a flat lipid membrane move together and prefer to overlap, even though they cannot touch each other. This points to an indirect, membrane-mediated interaction. Two mechanisms could be responsible: a curvature-induced interaction, which is energetic in origin, and a fluctuation-induced interaction, which is entropic. Here we study both with coarse-grained molecular dynamics simulations, using Cookes implicit-solvent lipid model together with a generic bead-spring polymer model for the condensate. We compute the potential of mean force between two condensates across the membrane. For condensates of the same size, full overlap is unfavorable, and the pair instead settles into a partially overlapping state that bends the membrane into an S-like shape. When the two condensates differ strongly in size, full overlap becomes favorable. We explain this with a simple geometric picture. The condensate wets the membrane as a thin film and imposes curvature only along its rim, while membrane tension flattens the membrane under its interior. The resulting ring of curvature can trap a smaller condensate on the opposite side. We also compare the bending undulations and the effective bending modulus of a bare membrane, a membrane with one condensate, and a membrane with condensates on both sides. A wetting condensate suppresses the undulation modes and stiffens the membrane, but whether this makes overlap entropically favorable remains inconclusive. Our results indicate that the coupling is driven mainly by curvature, and that it depends on the wetting mechanism and on the membrane tension.

biophysics↗

bioRxiv2026

Simulations show increased lipid interdigitation in transmembrane coupling of protein condensates

Liquid-liquid phase separation of proteins has been observed to occur on biological membranes, where it is thought to play a role in diverse cellular behaviors. Recent work has demonstrated colocalization between protein condensates on opposing leaflets of the bilayer, suggesting that protein phase separation may be coupled across the bilayer. However, the mechanism behind this coupling phenomenon remains poorly understood. Here we seek to understand the protein-protein and protein-membrane interactions that give rise to transbilayer coupling of protein condensates. We perform coarse-grained molecular dynamics simulations of a bilayer with a disordered protein condensate tethered to each leaflet surface. In this system, we observe stable, coupled diffusion of the condensates across the membrane. We find that increasing the protein-protein interaction strength leads to decoupling, driven by competing membrane curvatures induced by each condensate. However, by applying membrane tension we suppress curvature and restore coupling even at higher protein interaction strengths. Under coupling conditions, we find that lipid entropy is reduced upon direct contact with proteins, but this effect is not transferred to the opposing leaflet. Interestingly, further analysis reveals increased transverse lipid packing (interdigitation) beneath the condensates relative to protein-free regions. Based on these observations, we propose that enhanced lipid interdigitation mediates interleaflet communication and serves as the primary mechanism driving transbilayer coupling of condensates in this system. This work provides insight into a potential physical mechanism for transmembrane communication in cellular contexts and suggests directions for future investigation. Significance StatementLiquid-like condensates are active participants at cellular membranes, where they act as organizers and catalysts for various cellular processes. Recent work has demonstrated that protein condensates can couple across the bilayer; however, the molecular mechanism of this transbilayer coupling remained unknown. Here, we investigate the molecular basis of transmembrane condensate coupling through detailed analysis and propose a mechanism for the phenomenon. This work advances our understanding of how information is transmitted across the bilayer, with implications in cellular requiring coordination across the membrane, such as signaling, and more broadly in the field of membrane biophysics.

biophysics↗

bioRxiv2026

Internalized Components of Membrane Attack Complexes Disrupt Proteostasis and Acquire Alarmin-Like Properties

Immune effects of membrane attack complexes (MAC) have been widely attributed to their abilities to cause cell death. Here, we show that the MAC component, C9, forms non-cytolytic aggregates with pro-inflammatory effects. Intracellular aggregates of C9 are detected within inflamed tissues of patients in association with endothelial cell (EC) activation but not increased cell death. We identify NUMBL as a Rab35 effector that directly binds surface-bound C9 to promote C9 internalization and entry into the endolysosomal pathway. Within acidified endolysosomes, C9 forms insoluble aggregates that are targeted for degradative aggrephagy in a process that activates NF-{kappa}B. For C9 aggrephagy to occur, ZFYVE21, a Rab5 effector, complexes with RNF34 to bridge C9 aggregates to LC3B+ aggresome membranes. We detect C9 aggregates in vivo, and we show that a ZFYVE21-RNF34 signaling axis is required for C9 aggrephagy and NF-{kappa}B -dependent EC activation in three separate mouse models. Mice with conditional loss of ZFYVE21 in ECs show reduced aggregraphy, resulting in attenuated systemic inflammation and reduced tissue injury following skin transplantation. Our data show that the C9 component of MACs forms intracellular aggregates with alarmin-like properties.

biochemistry↗

bioRxiv2026

Integrating Histology with Spatial Molecular Programs Using a Multimodal Foundation Model

Histopathological assessment remains central to cancer diagnosis and stratification, yet its mechanistic interpretation remains limited without molecular context. To address this, we developed SQUALL, a multimodal foundation model integrating histology with spatial molecular programs. For pretraining, we assembled histMol, a large-scale corpus of 1.76 billion paired histology-spatial transcriptomics spots/bins across 33 tissues and 12 platforms from 3,446 tissue sections. Following pretraining, SQUALL enables transcriptome-wide virtual biomarker profiling, prognostically relevant spatial niches discovery, and integrative disease progression modeling. Leveraging its multimodal embeddings, SQUALL identifies niches associated with tertiary lymphoid structure (TLS) maturation and ovarian cancer relapse, reconstructs molecular trajectories of breast cancer invasion across 325,112 spots, and uncovers underlying transcriptional programs. Applied to whole-slide images from 898 patients, SQUALL outperforms existing pathology foundation models in outcome prediction while enabling interpretable risk stratification. Together, these results establish spatially aligned multimodal pretraining as a new paradigm for extending molecular insights into pathology images.

bioinformatics↗

bioRxiv2026

STID: A Standardized Spatial Transcriptomic Analysis Framework for Infectious Diseases

Spatial transcriptomic studies of infectious diseases still rely on fragmented data analysis processes. Here, we developed STID, a standardized framework for spatial transcriptomic analysis of infectious diseases that leverages the Seurat ecosystem and incorporates Python-based modules. STID provides an extensible infection-specific data structure and supports a full suite of analyses, such as pathogen background correction, infection-associated spot and niche identification, single-sample niche characterization, and multi-sample comparative and temporal analyses. Moreover, STID is broadly applicable to spatial transcriptomic data from infectious diseases caused by bacteria, viruses, and parasites, and enables systematic characterization of the structural features, cellular composition, molecular functions, and host-pathogen interactions within pathogen-infected and/or host-responsive niches. Overall, STID provides an accessible, reproducible, and extensible framework for analyzing infection-associated spatial transcriptomic data and for dissecting host-pathogen interactions in their native spatial microenvironments. MotivationSpatial transcriptomics technologies have emerged as powerful approaches for dissecting the structural and functional features of spatial microenvironments. However, the current general-purpose tools remain fundamentally inadequate for resolving the spatial heterogeneity of infectious disease samples, where the intricacies of host-pathogen interactions render spatial microenvironments both challenging to dissect and largely inaccessible. Tools tailored to infectious diseases are critically lacking, including those for reducing pathogen-derived background noise, identifying and isolating infection{square}associated spots or niches, dissecting host-pathogen interactions, and supporting systematic multi-sample analyses. We therefore developed STID, a unified framework that integrates standardized workflows and addresses the analytical bottlenecks in spatial transcriptomic analysis of infectious diseases. HighlightsO_LISTID standardizes spatial transcriptomic analysis in infectious diseases C_LIO_LISTID improves pathogen-infected spot detection by correcting pathogen background C_LIO_LISTID distinguishes pathogen-infected and host-responsive niches C_LIO_LISTID supports multi-sample comparative and temporal analyses of niches C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/727492v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@167d351org.highwire.dtl.DTLVardef@1628848org.highwire.dtl.DTLVardef@1e157aforg.highwire.dtl.DTLVardef@143ca1b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

A High-Throughput Platform for Rapidly Adapting DNA Aptamers to SARS-CoV-2 Evolution

Rapid pathogen evolution threatens public health by eroding the effectiveness of vaccines, therapeutics, and diagnostic tools. Although spike protein targeting monoclonal antibodies (mAbs) were developed within 10-12 months of the initial outbreak to serve as key theranostic agents, their redesign has struggled to keep pace with viral evolution, rendering many neutralizing antibodies ineffective. Here we demonstrate a novel platform that combines a random-rational hybrid library diversification with high-throughput MiSeq screening to rapidly reprogram aptamers against emerging SARS-CoV-2 spike variants. Interactions between 3 different spike proteins and 11,806 unique aptamer variant designs were profiled within a few days. Starting from a 40-nt aptamer originally selected against wild-type (WT) spike protein, our screen identified a Delta-binding mutant with a 4-fold affinity improvement and an Omicron-binding mutant that converted undetectable binding into nanomolar affinity. We also identified a WT-selective mutant with substantially reduced affinity for Delta, as well as previously unrecognized bases that critically contribute to spike recognition. Integrating high-throughput binding data with molecular dynamics simulations further revealed sequence-dependent structural features underlying variant-specific aptamer-spike interactions. Finally, we developed a sensor based on the identified WT-selective aptamer mutant, enabling highly specific detection of the WT spike protein with robust performance. Together, this work establishes a rapid and adaptable aptamer engineering platform for rapid adaptation of aptamers to evolving pathogens in future pandemics.

bioengineering↗

bioRxiv2026

Lifetime-based multiplexed detection of viral RNA using fluorogenic aptamers

Fluorogenic aptamers (FAPs) are emerging molecular probes for viral RNA and DNA sensing. However, their use in multiplexed nucleic acid sensing has been hindered by cross-reactivity and overlapping emission spectra. Here we address these limitations by introducing a fluorescence-lifetime-based multiplexed detection strategy using variants of the DNA fluorogenic aptamer Lettuce that exhibits distinct fluorescence lifetimes when complexed with the fluorogen TO1-biotin. To effectively evolve Lettuce for diverse lifetimes, we developed a large-scale screening platform, termed FAP-FLIM-NGS (fluorogenic aptamer-based fluorescence lifetime imaging microscopy on next-generation sequencing chips), which measures the fluorescence lifetimes of [~]104 Lettuce/TO1-biotin complexes directly on an Illumina MiSeq flow cell. Using this approach, three variants with markedly different lifetimes were identified: a single mutant (smC14T, 6.0 ns) and two double mutants (dmA5T/C14T, 5.2 ns, and dmA5T/T22A, 4.4 ns). To demonstrate the utility of these Lettuce variants in multiplexed detection, a set of split Lettuce probes targeting viral RNA fragments derived from SARS-CoV-2, MERS-CoV, and influenza A were designed and tested. Phasor plot analysis confirmed that these probes can robustly distinguish individual targets as well as mixtures containing any two or all three targets purely based on distinct fluorescence lifetimes of probes, thereby overcoming the challenges of cross-reactivity and spectral overlap. Beyond this proof of concept, our findings establish a generalizable strategy for engineering FAPs with customized photophysical properties, opening new avenues for next-generation diagnostics and molecular sensing technologies.

bioengineering↗

bioRxiv2026

Estimating chronological and brain age using risk-taking behavior under uncertainty

Risky decision-making under uncertainty reflects complex cognitive processes supported by distributed brain networks that are vulnerable to aging. However, it remains unclear whether risk-taking behavior can serve as a behavioral marker of brain aging. In the present study, we combined behavioral tasks, computational modeling, and structural magnetic resonance imaging to investigate the relationship between risky decision-making, chronological age, and brain age. A total of 55 young adults and 112 healthy older adults completed the Iowa Gambling Task (IGT) and the Balloon Analogue Risk Task (BART), along with neuropsychological assessments and neuroimaging scanning. Decision processes were quantified using computational models, including the Value-Plus-Perseveration model and Exponential-Weight Mean-Variance. Brain age was estimated from gray matter volume. The results showed significant age-related alterations in parameters reflecting feedback sensitivity, learning rate, and loss aversion in both tasks. Within older adults, several decision parameters were significantly associated with both chronological age and brain age. Regression analyses further showed that computational parameters significantly predicted chronological age and brain age, whereas traditional cognitive screening measures did not show significant predictive effect. Structural brain analyses indicated that IGT-related parameters were primarily associated with the basal ganglia, while BART-related parameters were linked to a broader network including prefrontal, cingulate, and temporal regions. These findings suggest that computational markers of risk-taking behavior capture subtle age-related changes in cognitive processes and brain deterioration. Therefore, risk-taking parameters may serve as reliable functional markers of brain aging, providing critical insights into the mechanisms underlying successful aging.

neuroscience↗

bioRxiv2026

Transcranial direct current stimulation combined cognitive training modulates risk-taking behavior in older adults

Risk-taking behavior is a complex cognitive function that often declines with aging, contributing to impaired decision-making and reduced quality of life. While transcranial direct current stimulation (tDCS) has shown promising effects in modulating cognitive function, its influence on ecologically valid, complex behaviors like risky decision-making in older adults remains poorly understood. We investigated whether a single-session stimulation over the medial orbitofrontal cortex (MOFC) combined with cognitive training could enhance decision-making in healthy older adults. In a randomized, sham-controlled study, bilateral MOFC stimulation (left/anode, right/cathode) was applied during a training task based on the Iowa Gambling Task. Pre- and post-intervention assessments utilized conventional behavioral measures and the Values-Plus-Perseveration computational model, alongside task-related fMRI to examine MOFC network changes. Compared to sham, tDCS significantly enhanced the ability to distinguish advantageous from disadvantageous options. Modeling analysis revealed stimulation-induced changes in multiple latent components, such as learning rate, loss aversion, and perseveration decay. Generalized Psychophysiological Interaction analysis showed that tDCS reconfigured the MOFC network by reducing fronto-frontal hyper-connectivity and enhancing fronto-striatal connectivity. These behavioral improvements were specifically associated with the left MOFC network targeted by anodal stimulation. Our results provide causal evidence that tDCS can mitigate age-related impairments in risk-taking by reconfiguring MOFC-related networks. These findings advance the mechanistic understanding of tDCS in aging and highlight its potential as an intervention to prevent age-related decline in decision making.

neuroscience↗

bioRxiv2026

Functional and Structural Basis of Omicron BA.3.2.1 Spike

SARS-CoV-2 Omicron descendant BA.3.2, derived from BA.3 and carrying 39 additional spike substitutions, prompts concerns about altered fitness and antigenicity. Using BA.3.2.1 as a representative sublineage, we have engineered live-attenuated mNeonGreen SARS-CoV-2 encoding spikes from BA.3.2.1 and the JN.1-derived LP.8.1 and XEC, and benchmarked them against BA.3 and KP.3 in Calu-3 cells and primary human airway epithelium. BA.3.2.1 outcompetes BA.3 yet replicates more slowly than JN.1-descendants. Despite increased RBD-hACE2 affinity, BA.3.2.1 spike binds hACE2 less efficiently than LP.8.1, and shows the greatest resistance to neutralization by sera from KP.2/KP.3 infections. Cryo-EM reveals a predominantly closed, compact, asymmetric trimeric spike with a remodeled N-terminal domain, a distinct fusion-peptide-proximal region and altered N-glycosylation. BA.3.2.1 spike adopts a fine-tuned receptor-binding interface and shifted monoclonal-antibody footprints. These findings identify a trade-off between immune evasion and replication fitness driven by closed spike conformation, rationalizing BA.3.2s limited prevalence and underscoring the necessity for continued variant surveillance.

microbiology↗

bioRxiv2026

CAR-T-drug conjugate against solid tumor

Chimeric antigen receptor (CAR)-T cell therapy has been clinically successful in hematologic cancers but faces challenges in solid tumors, primarily due to limited tumor infiltration, immunosuppressive tumor microenvironments (TME), and antigen heterogeneity. While combining CAR-T cell therapy with chemotherapy can enhance antitumor activity, this often leads to substantial systemic toxicity. In this study, we introduce CAR-T-drug conjugate (CAR-T-D-C), a new class of dual-functional therapeutics that effectively addresses these obstacles by integrating click chemistry for precise conjugation of cytotoxic agents onto antigen-specific CAR-T cells. By transforming the hostile TME into an ally, this approach facilitates localized delivery of cytotoxic payload directly to the tumor site, enhancing the overall effectiveness of CAR-T therapy in solid tumors. CAR-T-D-Cs with different CAR-T cell binders exhibit robust antitumor activity across diverse solid tumor models, including both human tumor xenografts and syngeneic models. Spatial transcriptomic studies reveals that CAR-T-D-C achieves improved CAR-T tumor infiltration and functional activation within the TME. Compared to conventional CAR-T therapy, CAR-T-D-C markedly enhances immune cell infiltration, augments effector functions, promotes antigen spreading, amplifies systemic immune responses, and improves overall anti-tumor immunity. CAR-T-D-C represents a versatile therapeutic concept that combines the potency of small molecule drugs and the specificity of CAR-T cells as a 2-in-1 immunochemotherapy for treatment of solid tumors.

immunology↗

bioRxiv2025

Integrative spatial multi-omics reveals prognostic tumor niches in female genital tumors

Female genital tumors (FGTs), including ovarian, endometrial, and cervical cancers, pose a major global health challenge, yet their spatial and molecular determinants of progression and therapy resistance remain poorly understood. Here we present a large-scale integrative spatial and multi-omics atlas of FGTs, combining exome, transcriptome, methylome, and miRNA sequencing with high-resolution Visium HD spatial transcriptomics, profiling over 62 million cells from 100 tumors. We show that HPV-driven suppression of TGF{beta} signaling in cervical cancer disrupts myofibroblast (myCAF) formation and reshapes immune infiltration, while serous tumors exhibit extensive oncogene amplifications that activate MYC and PI3K pathways, linking chromosomal instability (CIN) to proliferative, immune-cold phenotypes. In high-grade serous ovarian cancer, we identify a previously unrecognized malignant domain, the Proliferating Core Desert (PCD) niche, defined by CIN-high epithelial enrichment, immune exclusion, stromal protection, and chemotherapy resistance, maintained through crosstalk with macrophages and fibroblasts. Finally, we develop POWER, a pathology foundation model that predicts PCD niche abundance from routine H&E slides, enabling robust stratification of prognosis and anti-angiogenic therapy response.

genomics↗

bioRxiv2025

A machine-learning-guided hydrogen-bonded organic framework for long-term, ultrasound-triggered pain therapy

Effective treatment of chronic pain remains hindered by the lack of drug delivery systems that simultaneously achieve long-term stability, high spatial precision, and non-invasiveness1,2,3. Here, we utilize a programmable, ultrasound-responsive drug delivery platform based on hydrogen-bonded organic framework (HOF) nanoparticles, enabling on-demand anesthetic release with long-term and durable analgesic efficacy. A machine learning (ML)-guided screening pipeline was developed to evaluate about 250 FDA-approved drugs, spanning both hydrophilic and lipophilic agents, and identified bupivacaine (lipophilic) and lidocaine hydrochloride (hydrophilic) as optimal candidates. These agents were efficiently encapsulated into HOF nanoparticles via diffusion and double-solvent methods. Ultrasound-triggered drug release in vitro transiently suppressed calcium signaling in adeno-associated virus (AAV)-transfected neurons. In vivo, ultrasound-activated release of bupivacaine or lidocaine HCl at the sciatic nerve site significantly elevated mechanical nociceptive thresholds for up to seven days, reduced autotomy (self-mutilaion) behavior, and improved motor function in a rat model of chronic pain. Notably, the machine learning-identified top candidates not only exhibited high loading efficiency, but also demonstrated superior therapeutic outcomes in vivo, establishing a direct link between computational prediction and biological efficacy. This non-invasive, programmable HOF-based system provides a clinically translatable platform for on-demand, spatiotemporally precise pain management.

bioengineering↗

bioRxiv2025

Visualising sub-second dynamics of nanoparticle extravasation in vivo

Altered vascular integrity is a hallmark for many diseases. Monitoring vascular permeability, including its extent and underlying transport pathways, is thus important for advancing our understanding of disease mechanisms towards innovative diagnosis and treatment. Here we show real-time fluorescence imaging using lanthanide-based upconversion nanoparticles as contrast agents to visualise subtle changes to vascular permeability in vivo. Based on simultaneous confocal imaging of the vasculature alongside single particle tracking in the wide-field at video rate, we performed high-throughput surveillance across live zebrafish larvae to pinpoint locations of potential nanoparticle extravasation, achieving superior sensitivity and specificity over conventional fluorescent dyes. Further analyses of the sub-second dynamics of individual nanoparticle extravasation events unveiled distinct characteristics to distinguish between transcellular and paracellular transport. We applied the technique to evaluate the blood-brain barrier (BBB) in zebrafish larvae at different developmental stages, and potential BBB perturbation strategy via nanoparticle functionalisation with polysorbates. Significantly increased BBB penetration by 36.5 folds was shown for the functionalised particles compared to phospholipid-coated particles, attributed to enhanced transcellular crossing. The technique is readily applicable to monitoring vascular integrity and investigating endothelial transport for improved understanding of vascular biology, facilitating advanced research in disease diagnostics and drug delivery towards translation.

biophysics↗

bioRxiv2025

Unraveling Tyrosine-Kinase Inhibitor Resistance in NSCLC Cells via Same-cell Measurement of RNA, Protein, and Morphological Responses

Non-small cell lung cancer (NSCLC) frequently develops resistance to tyrosine kinase inhibitors (TKIs), limiting the long-term success of targeted therapies. A deeper understanding of resistance mechanisms at the molecular and cellular levels may enable the development of more effective treatment strategies. Here, we applied the Teton detection assay on the AVITI24 platform to measure concurrently RNA, protein, and cellular morphology in NSCLC cell lines treated with the TKIs gefitinib and osimertinib. This single-cell, multiomic analysis revealed distinct expression and morphological profiles between drug-sensitive and resistant cells, including differences in MAPK-related pathway activity. Stratifying responses at the single-cell level uncovered subtle responses not detectable in bulk measurements. We identified CDK4/6 activity as a route of cell survival under TKI treatment and demonstrated that co-treatment with the CDK4/6 inhibitor palbociclib enhanced TKI efficacy. The ability to measure multiomics and cellular morphology in the same cells opens new avenues for future studies aimed at improving personalized treatment strategies in NSCLC and overcoming the obstacles posed by drug resistance.

genomics↗

bioRxiv2025

Loading causes molecular damage in fibrin fibers

Blood clotting is the bodys natural reaction in wound healing and is also the cause of many pathologies. Fibrin - the main protein in the clotting process provides clots mechanical strength by forming a scaffold of complex fibrin fibers. Fibrin fibers exhibit high extensibility and primarily elastic properties under static loading, which differ from in vivo dynamic forces. In many biological materials, the mechanical response changes under repeated loading/unloading (cyclic loading). Using lateral force microscopy, we show fibrin fibers possess viscoelastic behavior and experience irreversible damage under cyclic loading. Cross-linking results in a more rigid structure with permanent damage occurring mostly at larger strains, which is corroborated by computational modeling of fibrin extension using a hyperelastic model. Molecular spectroscopy analysis with broadband coherent anti-Stokes Raman scattering spectroscopy in addition to molecular dynamic simulations allow identification of the source of damage, the unfolding pattern, and inter and intramolecular changes in fibrin. The results show partial recovery of proteins secondary and tertiary structures under load, providing deeper understanding of fibrins unique behavior in wound healing or pathologies like stroke and embolism.

biophysics↗

bioRxiv2025

High-Throughput Multiomics Profiling of Model Systems Using the AVITI24 Platform

We present a multiomics platform comprising Teton, a detection assay system, and AVITI24, a dual-flowcell instrument that performs both cellular imaging and sequencing readout. Teton integrates a compartmentalized flowcell for cell culture with methods to measure morphology, RNA, and protein at subcellular resolution. The platform quantifies morphological features through cell painting of 6 cellular components, RNA expression of up to 350 transcripts via sequencing of oligonucleotides hybridized to mRNA, and protein expression of up to 200 targets using antibody-linked oligonucleotide sequencing. The flow cell accommodates >1 million cells in a 10 cm squared open-well format or can be subdivided into 12 or 48 wells to support experiments with multiple conditions or time points. We describe and validate the detection methods of the platform and showcase its capabilities by co-culturing three cancer cell lines and elucidating the cellular pathways triggered by various drug treatments as a function of time. Using multiple time points enables us to capture the dynamics of cellular processes including receptor activation and signaling cascades. The results demonstrate how different cancer cells evade TNF-induced apoptosis by activating compensatory signaling programs that maintain survival despite pro-apoptotic cues. Our model system replicates previously published results and highlights the versatility of the platform in enabling rapid, high-throughput analysis of complex cellular responses in varied biological contexts.

genomics↗