Biology subjects

Wu, P.-H.

Publications and source records attributed to Wu, P.-H..

At least 19 recordsLinked to original sources

bioRxiv2026

Reprogramming CAR-T cell function through synthetic cytokine receptors suppresses metastatic progression of TNBC

Patient T cells modified with chimeric antigen receptors (CARs) have shown remarkable therapeutic success in previously incurable cancers. However, despite its success in hematological malignancies, CAR-T therapy has shown only modest efficacy in solid tumors. The medical field has focused on developing new molecular designs that enhance CAR-T persistence, prevent exhaustion in the tumor microenvironment (TME), and increase the specificity of the CAR-T cell to various tumor antigens. However, much less effort has been placed on enhancing CAR-T cells' invasion into and migration through the stromal matrix of the TME, where they can elicit tumor-directed cytotoxicity. Here, we engineered CAR-T cells with synthetic cytokine receptors targeting inflammatory cytokines produced by the cells themselves, which increase their speed through collagen I matrices. We show that this enhanced motility is driven by increased CAR-T cell translocation through confined microchannels via cellular deformation, together with elevated migratory speeds in proximity to neighboring cells, suggesting improved responsiveness to local cues. These migration-enhanced CAR-T cells significantly reduce both primary tumor size and distant organ metastasis in triple-negative breast cancer (TNBC) mouse models compared to standard CAR-T cells. These results support molecular design strategies that exploit mechanisms to improve cellular therapies for metastatic solid tumors.

bioengineering↗

bioRxiv2026

De novo design of CR2 binder as vaccine scaffold

Efficient B cell activation during vaccine-induced humoral immunity relies on both B cell receptor (BCR) antigen recognition and synergistic signaling from co-receptors. Complement receptor 2 (CR2), the primary BCR co-receptor on B cells, lowers the activation threshold and amplifies downstream kinase signaling by orders of magnitude when engaged by complement fragment C3d decorated antigens. Targeting CR2 therefore represents a rational vaccine enhancement strategy, yet native C3d suffers from low affinity, poor stability, and manufacturing challenges. Here, we report the de novo design of a highly stable, high-affinity CR2 binder using deep learning driving protein design methods. Biophysical characterization, high-resolution cryoEM structural determination, and functional assays in vitro and in vivo confirm that the designed binder matches computational design models and specifically engages CR2 to boost B cell activation. When fused to antigen as a vaccine scaffold, the trimeric CR2 binder elicits robust humoral immune responses comparable to nanoparticle vaccines, while retaining the simplicity of single-chain protein production. Our work establishes a modular CR2 targeting vaccine scaffold platform with broad translational potential for next-generation protein vaccines.

immunology↗

bioRxiv2026

VITAL-3D: Volumetric Single-Cell Quantification Reveals Microenvironment-Dependent Drug Responses in Breast Cancer

Preclinical drug evaluation relies heavily on two-dimensional (2D) monolayer assays, which fail to recapitulate the structural and functional complexity of the tumor microenvironment and may therefore misrepresent therapeutic efficacy. Here, we present VITAL (Volumetric Imaging-based Toxicity and Live Analysis), a high-throughput imaging platform that enables direct single-cell quantification of proliferation and cell death in both 2D and three-dimensional (3D) extracellular matrix (ECM) cultures using a 96-well format. By combining volumetric imaging with automated single-cell analysis, VITAL enables dynamic assessment of drug responses beyond conventional viability assays and EC measurements. Using breast cancer cell lines treated with anticancer agents, we systematically compared drug responses between 2D and 3D microenvironments. Although EC values were often comparable between culture formats, growth kinetics and concentrations required to induce complete growth arrest or net cell loss differed substantially in 3D cultures. In particular, drug concentrations required to induce net cell loss were consistently higher in 3D, revealing microenvironment-dependent survival responses that were not captured by EC alone. Furthermore, clinically expected subtype-specific responses, including tamoxifen sensitivity in ER-positive cells and olaparib sensitivity in BRCA1-mutant cells, were more accurately resolved under 3D culture conditions and extended treatment durations. Together, these findings demonstrate that growth-based, single-cell quantification provides a more comprehensive assessment of therapeutic efficacy than conventional endpoint measurements and establish VITAL as a scalable platform for physiologically relevant preclinical drug screening.

cancer biology↗

bioRxiv2026

Senescent cell networks link matrix remodeling and vascular dysfunction in human fibroids

Uterine fibroids (leiomyomas) are highly prevalent benign tumors defined by excessive extracellular matrix (ECM) deposition, altered vascular structure, and progressive tissue stiffening, yet the cellular programs that coordinate these features remain poorly understood. Cellular senescence has been implicated in fibroid biology, but whether senescence represents a uniform state or distinct, functionally specialized cell identities within fibroids is unknown. Here, we identify the distinct heterogeneous populations of senescent cells ("senotypes") present in human fibroids and characterize their role in shaping the fibroid microenvironment. Using single-cell RNA sequencing (scRNA-seq) integrated with a senescence gene signature and protein-level validation, we identify senescent cells (SnC) distributed across fibroblast, mural, and endothelial compartments, each exhibiting distinct transcriptional programs. SnC endothelial cells (ECs) are enriched in fibroids relative to matched myometrium and activate TEAD4-associated mechanosensing, angiogenic, and immune signaling pathways, despite being associated with impaired vessel maturation in situ. In parallel, SnC fibroblast and mural populations in fibroids upregulated SRF-associated cytoskeletal and ECM programs, accompanied by increased COL6A3 expression and collagen VI deposition, consistent with tissue stiffening. Ligand-receptor and spatial analyses reveal that these SnC populations function as interconnected signaling hubs, coordinating immune cell recruitment and stromal remodeling. Importantly, analysis of human fibroids treated with collagenase demonstrated a reduction in both ECM density and SnC burden, supporting a reinforcing relationship between matrix mechanics and senescence. Together, these findings establish senescence in fibroids as a heterogeneous, mechanically reinforced, and network-driven process that links vascular dysfunction, immune signaling, and fibrosis, highlighting distinct SnC states as potential translational targets for non-surgical therapies.

cell biology↗

bioRxiv2026

Molecular factors shaping small RNA content in mouse sperm

In mammalian reproduction, sperm and oocytes fuse to generate embryos. In contrast to maternally inherited small RNAs, sperm-borne small RNAs have been implicated in embryo viability but remain incompletely understood. Unanswered questions about molecular forces shaping small RNA content in sperm and sources of reporting discrepancies hinder a deeper understanding of their post-fertilization roles. Here, we investigate the dynamics and protein association of small RNAs in C57BL/6 mice during spermiogenesis and sperm capacitation. Using unique molecular identifiers and spike-ins to minimize technical biases in low-input sequencing, we demonstrate a selective sperm small RNA repertoire derived from a subset of spermatogenic small RNAs that remains malleable during capacitation. Retained PIWI-interacting RNAs persist despite widespread RNA decay and are insensitive to capacitation, which may be partly attributed to their association with MIWI proteins. Our data reveal RNA species susceptible to PCR duplicate-related abundance overestimation and uncover biological and technical factors shaping experimentally observed sperm small RNA profiles. These findings refine our understanding of sperm-borne RNAs and inform future functional studies and medically assisted reproduction.

molecular biology↗

bioRxiv2026

A molecular and cellular mechanism for bitter taste in the mosquito Aedes aegypti

The yellow fever mosquito, Aedes aegypti, is a vector of Zika, Dengue, Chikungunya and yellow fever, disease-causing viruses impacting millions of people annually across the globe. Female mosquitoes transmit pathogens through serial blood-feeding, while both male and female mosquitoes feed on plant resources. Contact chemosensation (taste) guides these two feeding modes in both positive ways, feeding readily on sugar in nectar or ATP in blood, and negative ways, demonstrating aversion to chemically diverse bitter compounds in blood or sugar meals. Here, we identify a receptor, AaegGr14, that is expressed in neurons in the labellum and cibarium of the mosquito. Activation of Gr14-expressing neurons causes reduced feeding, while Gr14 and Gr14-expressing GRNs are required for aversion to bitter compounds during nectar- but not blood-feeding. This work provides a molecular and cellular on-ramp to understand bitter taste in mosquitoes and establishes that there are feeding context-dependent differences in how taste mechanisms influence blood- and nectar-feeding. Understanding the molecular and cellular basis of bitter taste in mosquitoes during nectar- and blood-feeding will help inform vector control strategies and elucidate shared principles and unique aspects of insect taste systems.

neuroscience↗

bioRxiv2026

Distinct senescent β-cell senotypes differentially drive islet aging and dysfunction

Biological aging greatly impacts the bodys ability to handle glucose, and represents a major risk factor the development and progression of type 2 diabetes (T2D). Nonetheless, despite advances in cellular senescence research and the development of new senolytic therapies, the heterogeneity of cellular senescence in the human endocrine pancreas, as well as its roles in normal aging, remains to be elucidated at the single-cell level. Here, we performed single-cell-resolved spatial proteomics and transcriptomics on intact pancreas from 26 donors (ages 20-80) and multiplexed single-cell RNA sequencing and functional assays on dispersed islets from 14 donors (ages 34-69). We identify two discrete SnC subpopulations distinguished by relative expression of CDKN1A and CDKN2A. CDKN1A senescent cells (SnCs) exhibit loss of {beta}-cell identity, impaired insulin secretion, and a proinflammatory SASP associated with increased islet immune infiltration. In contrast, CDKN2A SnCs retain transcriptional identity and functional competence, with lower inflammatory signaling. Together, these findings identify heterogeneous and functionally divergent senotypes in the human pancreas, distinguishing an adaptive (CDKN2A) from a maladaptive (CDKN1A) senescence program, thus providing a mechanism-guided framework for senescence-targeted therapies in T2D.

cell biology↗

bioRxiv2026

Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer

Resistance to systemic therapy is a major unmet challenge in pancreatic cancer. To identify potential mechanisms of resistance, we developed a novel 3D pipeline in clinical samples that uses deep learning to classify sensitive and persistent tumor cell populations based on morphological features, enabling subsequent molecular characterization of intratumoral heterogeneity. We applied this automated 3D pipeline to a cohort of human pancreatic cancer samples treated with neoadjuvant chemotherapy, identifying heterogeneity in response to therapy both between and within tumors. Application of spatial proteomics to these sensitive and persistent regions identified enhanced epithelial-to-mesenchymal transition and non-classical cell states in persistent cells, confirming our morphological classification. Integration of spatial transcriptomics in multiple pancreatic cancer cohorts associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy. Our validated 3D multi-omic pipeline is now poised for application to clinical trials, enabling discovery of resistance mechanisms and design of new therapeutic combinations to circumvent resistance. Statement of significanceWe developed a novel 3D multi-omic pipeline to identify mechanisms of resistance to chemotherapy in clinical samples. This approach associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy and is poised for broader application in neoadjuvant clinical trials.

cancer biology↗

bioRxiv2025

Senescence-Linked Fibrosis in the Aging Human Ovary Revealed by p16-Based Histological Profiling and Spatial Transcriptomics

Cellular senescence is implicated as a driver of ovarian aging, but senescent cells in the human postmenopausal ovary remain poorly defined. Using spatially resolved p16INK4a protein expression, a canonical senescence marker, we identified and mapped senescent cells in postmenopausal ovaries. We integrated p16 immunohistochemistry, multiplexed immunofluorescence, spatial transcriptomics, and AI-guided digital pathology to map senescent microenvironments. p16-positive cells formed discrete stromal, vascular, and cyst-associated clusters that increased with age and were enriched for macrophages and myofibroblast-like cells. Wholetranscriptome profiling of 92 spatial regions uncovered a 32-gene p16-associated signature, BuckSenOvary, that distinguished p16-positive regions across cortex and medulla. BuckSenOvary is characterized by suppression of cell-cycle regulators and activation of inflammatory and extracellular-matrix remodelling genes. AI-based collagen matrix analysis confirmed that p16-positive regions exhibit more architecturally complex collagen, demonstrating that focal senescent microenvironments are fibro-inflammatory. These findings position senescent ovarian niches as therapeutic targets to preserve ovarian function.

cell biology↗

bioRxiv2025

NLTD 2.0: A Nonlinear Framework for Robust and Customizable Color Deconvolution in Histopathology

Advancements in computational approaches have enabled robust utilization of histological tissue data. A crucial step in the development of computational tools for the objective and quantitative analysis of tissue sections has been color deconvolution. Color deconvolution functions by separating the absorption of colors corresponding to stained molecular or tissue compartments. The most widely used color deconvolution method in digital pathology, linear color deconvolution as described in Ruifrok 2001 et al., decomposes color images according to the absorbance values for individual stains. However, linear color deconvolution assumes that stains are linearly decomposable, and it relies heavily upon identifying optimal color vectors of stains, which is often challenging. Furthermore, linear deconvolution methods cannot deconvolve the image with more than three stains, further limiting their broader applicability. To combat the limitations of previous methods, we developed an intuitive and robust color deconvolution method that effectively and accurately separates more than three stain signals, does not rely on predetermined color vectors, and doesnt rely on identifying optimal stain vectors. The proposed method, NLTD 2.0, presents a robust and efficient solution to tackle color variations in histopathology images, enhancing the reliability and precision of computational pathology. Additionally, incorporating the method as an ImageJ plugin amplifies accessibility and usability, enabling researchers and pathologists to leverage its capabilities without specialized programming skills. The intuitive interface streamlines the application, fostering broader acceptance within the computational pathology community.

bioinformatics↗

bioRxiv2025

3D multi-omic mapping of whole nondiseased human fallopian tubes at cellular resolution reveals a large incidence of ovarian cancer precursors

Uncovering the spatial and molecular landscape of precancerous lesions is essential for developing meaningful cancer prevention and early detection strategies. High-Grade Serous Carcinoma (HGSC), the most lethal gynecologic malignancy, often originates from Serous Tubal Intraepithelial Carcinomas (STICs) in the fallopian tubes, yet their minute size and our historical reliance on standard 2D histology contribute to their underreporting. Here, we present a spatially resolved, multi-omics framework that integrates whole-organ 3D imaging at cellular resolution with targeted proteomic, metabolomic, and transcriptomic profiling to detect and characterize microscopic tubal lesions. Using this platform, we identified a total of 99 STICs and their presumed precursors that harbor TP53 mutations in morphologically unremarkable tubal epithelium in all five specimens obtained from cancer-free organ donors with average-risk of developing ovarian cancer. Although these lesions comprised only 0.2% of the epithelial compartment, they displayed geographic diversity, immune exclusion, metabolic rewiring, and DNA copy number changes among lesions and normal fallopian tube epithelium discovered alterations in STIC-associated genes and the pathways they control. In sum, this platform provides a comprehensive 3D atlas of early neoplastic transformation, yielding mechanistic insights into tumor initiation and informing clinical screening strategies for detecting cancer precursors in whole organs at cellular resolution.

bioinformatics↗

bioRxiv2024

Solving the "Blind men and the elephant problem": Additive deep learning of complex high dimensional models from partial faceted datasets

Biological systems are complex networks involving tens of thousands of interacting molecular components, and measurable biological functions are emerging properties of these complex networks. Many quantitative studies in biology attempt to connect biological function with molecular components and genes, in the process developing mechanistic understanding. However, it is challenging to quantify the contribution of all components to the biological function simultaneously, especially at the single cell level. Instead, in typical experiments, only a subset of the variables (or facet) is measured. This makes it difficult to obtain a complete and unbiased understanding of the network and how different components of the network cooperatively contribute to the biological function. In this paper, we explore a machine learning approach to combine different facets of data and obtain a complete picture of the biological system based on conditional distributions from faceted data subsets. Both a polynomial regression approach and a neural network approach are developed and examined with two set of concrete examples: A mechanical spring network system deforming under external forces and a small (8-dimensions) biological network including the cellular senescence marker P53. In the later example, single cell data is collected to validate the machine learning approach. We find that the full system is successfully reconstructed from faceted data in both examples. We further discuss the additive property of the model, where the model predictive accuracy increases with increasing number of simultaneously measured variables (dimension of subsets). Our model provides a systematic and novel approach to integrate different pieces of experimental information to reconstruct complex high dimensional systems, arriving at an unbiased and wholistic model of biological function.

bioinformatics↗

bioRxiv2024

High-motility pro-tumorigenic monocytes drive macrophage enrichment in the tumor microenvironment

Enrichment of tumor-associated macrophages (TAM{Phi}s) in the tumor microenvironment correlates with worse clinical outcomes in triple-negative breast cancer (TNBC) patients, prompting the development of therapies to inhibit TAM{Phi} infiltration. However, the lackluster efficacy of CCL2-based chemotaxis blockade in clinical trials suggests that a new understanding of monocyte/macrophage infiltration may be necessary. Here we demonstrate that random migration, and not only chemotaxis, drives macrophage tumor infiltration. We identified tumor- associated monocytes (TAMos) that display a dramatically enhanced migration capability, induced rapidly by the tumor microenvironment, that drives effective tumor infiltration, in contrast to low-motility differentiated macrophages. TAMo, not TAM{Phi}, promotes cancer cell proliferation through activation of the MAPK pathway. IL-6 secreted both by cancer cells and TAMo themselves enhances TAMo migration by increasing dendritic protrusion dynamics and myosin- based contractility via the JAK2/STAT3 signaling pathway. Independent from CCL2 mediated chemotaxis, IL-6 driven enhanced migration and pro-proliferative effect of TAMo were validated in a syngeneic TNBC mouse model. Depletion of IL-6 in cancer cells significantly attenuated monocyte infiltration and reversed TAMo-induced cancer cell proliferation. This work reveals the critical role random migration plays in monocyte driven TAM{Phi} enrichment in a tumor and pinpoints IL-6 as a potential therapeutic target in combination with CCL2 to ameliorate current strategies against TAM{Phi} infiltration.

immunology↗

bioRxiv2024

Senescent cell heterogeneity and responses to senolytic treatment are related to cell cycle status during cell growth arrest.

Cellular senescence has been strongly linked to aging and age-related diseases. It is well established that the phenotype of senescent cells is highly heterogeneous and influenced by their cell type and senescence-inducing stimulus. Recent single-cell RNA-sequencing studies identified heterogeneity within senescent cell populations. However, proof of functional differences between such subpopulations is lacking. To identify functionally distinct senescent cell subpopulations, we employed high-content image analysis to measure senescence marker expression in primary human endothelial cells and fibroblasts. We found that G2-arrested senescent cells feature higher senescence marker expression than G1-arrested senescent cells. To investigate functional differences, we compared IL-6 secretion and response to ABT263 senolytic treatment in G1 and G2 senescent cells. We determined that G2-arrested senescent cells secrete more IL-6 and are more sensitive to ABT263 than G1-arrested cells. We hypothesize that cell cycle dependent DNA content is a key contributor to the heterogeneity within senescent cell populations. This study demonstrates the existence of functionally distinct senescent subpopulations even in culture. This data provides the first evidence of selective cell response to senolytic treatment among senescent cell subpopulations. Overall, this study emphasizes the importance of considering the senescent cell heterogeneity in the development of future senolytic therapies.

cell biology↗

bioRxiv2024

Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts

Cellular senescence is an established driver of aging, exhibiting context-dependent phenotypes across multiple biological length-scales. Despite its mechanistic importance, profiling senescence within cell populations is challenging. This is in part due to the limitations of current biomarkers to robustly identify senescent cells across biological settings, and the heterogeneous, non-binary phenotypes exhibited by senescent cells. Using a panel of primary dermal fibroblasts, we combined live single-cell imaging, machine learning, multiple senescence induction conditions, and multiple protein-based senescence biomarkers to show the emergence of functional subtypes of senescence. Leveraging single-cell morphologies, we defined eleven distinct morphology clusters, with the abundance of cells in each cluster being dependent on the mode of senescence induction, the time post-induction, and the age of the donor. Of these eleven clusters, we identified three bona-fide senescence subtypes (C7, C10, C11), with C10 showing the strongest age-dependence across a cohort of fifty aging individuals. To determine the functional significance of these senescence subtypes, we profiled their responses to senotherapies, specifically focusing on Dasatinib + Quercetin (D+Q). Results indicated subtype-dependent responses, with senescent cells in C7 being most responsive to D+Q. Altogether, we provide a robust single-cell framework to identify and classify functional senescence subtypes with applications for next-generation senotherapy screens, and the potential to explain heterogeneous senescence phenotypes across biological settings based on the presence and abundance of distinct senescence subtypes.

bioengineering↗

bioRxiv2024

The microanatomy of human skin in aging

Aging is a major driver of diseases in humans. Identifying features associated with aging is essential for designing robust intervention strategies and discovering novel biomarkers of aging. Extensive studies at both the molecular and organ/whole-body physiological scales have helped determined features associated with aging. However, the lack of meso-scale studies, particularly at the tissue level, limits the ability to translate findings made at molecular scale to impaired tissue functions associated with aging. In this work, we established a tissue image analysis workflow - quantitative micro-anatomical phenotyping (qMAP) - that leverages deep learning and machine vision to fully label tissue and cellular compartments in tissue sections. The fully mapped tissue images address the challenges of finding an interpretable feature set to quantitatively profile age-related microanatomic changes. We optimized qMAP for skin tissues and applied it to a cohort of 99 donors aged 14 to 92. We extracted 914 microanatomic features and found that a broad spectrum of these features, represented by 10 cores processes, are strongly associated with aging. Our analysis shows that microanatomical features of the skin can predict aging with a mean absolute error (MAE) of 7.7 years, comparable to state-of-the-art epigenetic clocks. Our study demonstrates that tissue-level architectural changes are strongly associated with aging and represent a novel category of aging biomarkers that complement molecular markers. Our results highlight the complex and underexplored multi-scale relationship between molecular and tissue microanatomic scales.

pathology↗

bioRxiv2024

Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping

The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 {micro}m, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.

bioinformatics↗

bioRxiv2024

Substrate stiffness modulates the emergence and magnitude of senescence phenotypes

Cellular senescence is a major driver of aging and disease. Here we show that substrate stiffness modulates the emergence and magnitude of senescence phenotypes after exposure to senescence inducers. Using a primary dermal fibroblast model, we show that decreased substrate stiffness accelerates senescence-associated cell-cycle arrest and regulates the expression of conventional protein-based biomarkers of senescence. We found that the expression of these senescence biomarkers, namely p21WAF1/CIP1 and p16INK4a are mechanosensitive and are in-part regulated by myosin contractility through focal adhesion kinase (FAK)-ROCK signaling. Interestingly, at the protein level senescence-induced dermal fibroblasts on soft substrates (0.5 kPa) do not express p21WAF1/CIP1 and p16INK4a at comparable levels to induced cells on stiff substrates (4GPa). However, cells express CDKN1a, CDKN2a, and IL6 at the RNA level across both stiff and soft substrates. Moreover, when cells are transferred from soft to stiff substrates, senescent cells recover an elevated expression of p21WAF1/CIP1 and p16INK4a at levels comparable to senescence cells on stiff substrates, pointing to a mechanosensitive regulation of the senescence phenotype. Together, our results indicate that the emergent senescence phenotype depends critically on the local mechanical environments of cells and that senescent cells actively respond to changing mechanical cues.

molecular biology↗