Biology subjects

Wirtz, D.

Publications and source records attributed to Wirtz, D..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Accelerated Discovery of Cell Migration Regulators Using Label-Free Deep Learning-Based Automated Tracking

Cell migration plays a key role in normal developmental programs and in disease, including immune responses, tissue repair, and metastasis. Unlike other cell functions, such as proliferation which can be studied using high-throughput assays, cell migration requires more sophisticated instruments and analysis, which decreases throughput and has led to more limited mechanistic advances in our understanding of cell migration. Current assays either preclude single-cell level analysis, require tedious manual tracking, or use fluorescently labeled cells, which greatly limit the number of extracellular conditions and molecular manipulations that can be studied in a reasonable amount of time. Using the migration of cancer cells as a testbed, we established a workflow that images large numbers of cells in real time, using a 96-well plate format. We developed and validated a machine-vision and deep-learning analysis method, DeepBIT, to automatically detect and track the migration of individual cells from time-lapsed videos without cell labeling and user bias. We demonstrate that our assay can examine cancer cell motility behavior in many conditions, using different small-molecule inhibitors of known and potential regulators of migration, different extracellular conditions such as different contents in extracellular matrix and growth factors, and different CRISPR-mediated knockouts. About 1500 cells per well were tracked in 840 different conditions, for a total of ~1.3M tracked cells, in 70h (5 min per condition). Manual tracking of these cells by a trained user would take ~5.5 years. This demonstration reveals previously unidentified molecular regulators of cancer cell migration and suggests that collagen content can change the sign of how cytoskeletal molecules can regulate cell migration.

cell biology↗

bioRxiv2025

Geometric Protein Optimization

The vastness of the space of possible protein variations is often regarded an insurmountable challenge for effective optimization. Traditional approaches focus primarily on substitutions near the binding pocket but are frequently hindered by epistatic effects and non-convex optimization landscapes. Current AI-aided methods can accelerate laboratory procedures but largely target the same substitutions. Here, we propose a complementary approach, Geometric Protein Optimization (GPO), an AI-native framework that fine-tunes the global geometry of the protein by combining a large number of substitutions from diverse locations along the sequence. GPO leverages the strong inverse power-law dependence of electrostatic forces where small adjustments can have large effects on binding affinity. We make the surprising discovery that the inclusion of distal substitutions leads to a smoother and approximately separable optimization landscape. Our empirical investigations reveal three stylized facts about this landscape that we use as a guide to develop BuildUp, a baseline algorithm for GPO. Results show that it is able to navigate this landscape much more effectively and achieve significant improvements in in silico binding affinity (Kd) across diverse protein-ligand complexes. Evaluations of the derived variants through protein-ligand interaction profiling, docking simulations, and molecular dynamics simulations confirm that GPO can achieve beneficial effects even with a sequence-based scoring function.

biochemistry↗

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

3D histology reveals that immune response to pancreatic precancers is heterogeneous and depends on global pancreas structure

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer for which few effective therapies exist. Immunotherapies specifically are ineffective in pancreatic cancer, in part due to its unique stromal and immune microenvironment. Pancreatic intraepithelial neoplasia, or PanIN, is the main precursor lesion to PDAC. Recently it was discovered that PanINs are remarkably abundant in the grossly normal pancreas, suggesting that the vast majority will never progress to cancer. Here, through construction of 48 samples of cm3-sized human pancreas tissue, we profiled the immune microenvironment of 1,476 PanINs in 3D and at single-cell resolution to better understand the early evolution of the pancreatic tumor microenvironment and to determine how inflammation may play a role in cancer progression. We found that bulk pancreatic inflammation strongly correlates to PanIN cell fraction. We found that the immune response around PanINs is highly heterogeneous, with distinct immune hotspots and cold spots that appear and disappear in a span of tens of microns. Immune hotspots generally mark locations of higher grade of dysplasia or locations near acinar atrophy. The immune composition at these hotspots is dominated by naive, cytotoxic, and regulatory T cells, cancer associated fibroblasts, and tumor associated macrophages, with little similarity to the immune composition around less-inflamed PanINs. By mapping FOXP3+ cells in 3D, we found that regulatory T cells are present at higher density in larger PanIN lesions compared to smaller PanINs, suggesting that the early initiation of PanINs may not exhibit an immunosuppressive response. This analysis demonstrates that while PanINs are common in the pancreases of most individuals, inflammation may play a pivotal role, both at the bulk and the microscopic scale, in demarcating regions of significance in cancer progression.

cancer biology↗

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

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

A 3D in vitro assay to study combined immune cell infiltration and cytotoxicity

Immune cell-mediated killing of cancer cells in a solid tumor is prefaced by a multi-step infiltration cascade of invasion, directed migration, and cytotoxic activities. In particular, immune cells must invade and migrate through a series of different extracellular matrix (ECM) boundaries and domains before reaching and killing their target tumor cells. These infiltration events are a central challenge to the clinical success of CAR T cells against solid tumors. The current standard in vitro cell killing assays measure cell cytotoxicity in an obstacle-free, two-dimensional (2D) microenvironment, which precludes the study of 3D immune cell-ECM interactions. Here, we present a 3D combined infiltration/cytotoxicity assay based on an oil-in-water microtechnology. This assay measures stromal invasion following extravasation, migration through the stromal matrix, and invasion of the solid tumor in addition to cell killing. We compare this 3D cytotoxicity assay to the benchmark 2D assay through tumor assembloid cocultures with immune cells and engineered immune cells. This assay is amenable to an array of imaging techniques, which allows direct observation and quantification of each stage of infiltration in different immune and oncological contexts. We establish the 3D infiltration/cytotoxicity assay as an important tool for the mechanistic study of immune cell interactions with the tumor microenvironment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/586980v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@6a161dorg.highwire.dtl.DTLVardef@d1e5d3org.highwire.dtl.DTLVardef@47b87corg.highwire.dtl.DTLVardef@a0e051_HPS_FORMAT_FIGEXP M_FIG The 3D combined infiltration/cytotoxicity assay captures three important steps of immune cell infiltration into the solid tumor microenvironment: (1) circulating immune cells extravasate and invade the stromal matrix, (2) immune cells migrate through the stromal matrix to reach the tumor core, and (3) immune cells that successfully navigate the stroma must cross a basement membrane boundary secreted by the cancer cells to contact and kill the cancer cells within a solid tumor. C_FIG

cancer biology↗

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↗

bioRxiv2024

A TBX5-dependent compartment boundary patterns the cardiac interventricular septum

Failure of septation of the interventricular septum (IVS) is the most common congenital heart defect (CHD), but mechanisms for patterning the IVS are largely unknown. Here, we show that a Tbx5+/Mef2cAHF+ progenitor lineage forms a compartment boundary bisecting the IVS. This coordinated population originates at a first- and second heart field interface. Ablation of Tbx5+/Mef2cAHF+ progenitors cause IVS disorganization, right ventricular hypoplasia and mixing of IVS lineages. Reduced dosage of the CHD transcription factor TBX5 disrupts boundary position and integrity, resulting in ventricular septation defects (VSDs) and patterning defects, including misexpression of Slit2 and Ntn1, which encode guidance cues. Reducing NTN1 dosage partly rescues cardiac defects in Tbx5 mutant embryos. Loss of Slit2 or Ntn1 causes VSDs and perturbed septal lineage distributions. Thus, we identify Tbx5 as a candidate selector gene, directing progenitors and regulating essential cues, to pattern a compartment boundary for proper cardiac septation, revealing mechanisms for cardiac birth defects.

developmental biology↗

bioRxiv2023

Engineering self-propelled tumor-infiltrating CAR T cells using synthetic velocity receptors

Chimeric antigen receptor (CAR) T cells express antigen-specific synthetic receptors, which upon binding to cancer cells, elicit T cell anti-tumor responses. CAR T cell therapy has enjoyed success in the clinic for hematological cancer indications, giving rise to decade-long remissions in some cases. However, CAR T therapy for patients with solid tumors has not seen similar success. Solid tumors constitute 90% of adult human cancers, representing an enormous unmet clinical need. Current approaches do not solve the central problem of limited ability of therapeutic cells to migrate through the stromal matrix. We discover that T cells at low and high density display low- and high-migration phenotypes, respectively. The highly migratory phenotype is mediated by a paracrine pathway from a group of self-produced cytokines that include IL5, TNF, IFN{gamma}, and IL8. We exploit this finding to "lock-in" a highly migratory phenotype by developing and expressing receptors, which we call velocity receptors (VRs). VRs target these cytokines and signal through these cytokines cognate receptors to increase T cell motility and infiltrate lung, ovarian, and pancreatic tumors in large numbers and at doses for which control CAR T cells remain confined to the tumor periphery. In contrast to CAR therapy alone, VR-CAR T cells significantly attenuate tumor growth and extend overall survival. This work suggests that approaches to the design of immune cell receptors that focus on migration signaling will help current and future CAR cellular therapies to infiltrate deep into solid tumors.

immunology↗

bioRxiv2023

Three-dimensional reconstruction of fetal rhesus macaque kidneys at single-cell resolution reveals complex inter-relation of structures

Kidneys are among the most structurally complex organs in the body. Their architecture is critical to ensure proper function and is often impacted by diseases such as diabetes and hypertension. Understanding the spatial interplay between the different structures of the nephron and renal vasculature is crucial. Recent efforts have demonstrated the value of three-dimensional (3D) imaging in revealing new insights into the various components of the kidney; however, these studies used antibodies or autofluorescence to detect structures and so were limited in their ability to compare the many subtle structures of the kidney at once. Here, through 3D reconstruction of fetal rhesus macaque kidneys at cellular resolution, we demonstrate the power of deep learning in exhaustively labelling seventeen microstructures of the kidney. Using these tissue maps, we interrogate the spatial distribution and spatial correlation of the glomeruli, renal arteries, and the nephron. This work demonstrates the power of deep learning applied to 3D tissue images to improve our ability to compare many microanatomical structures at once, paving the way for further works investigating renal pathologies.

developmental biology↗

bioRxiv2023

Three-dimensional assessments are necessary to determine the true spatial tissue composition of diseased tissues

Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter-and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts.

cancer biology↗

bioRxiv2023

Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings

Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence reveals that pancreatic intraepithelial neoplasms (PanINs), the microscopic precursor lesions in the pancreatic ducts that can give rise to invasive pancreatic cancer, are significantly larger and more prevalent than previously believed. Better understanding of the growth law dynamics of PanINs may improve our ability to understand how a miniscule fraction of these lesions makes the transition to invasive cancer. Here, using artificial intelligence (AI)-based three-dimensional (3D) tissue mapping method, we measured the volumes of >1,000 PanIN and found that lesion size is distributed according to a power law with a fitted exponent of -1.7 over > 3 orders of magnitude. Our data also suggest that PanIN growth is not very sensitive to the pancreatic microenvironment or an individuals age, family history, and lifestyle, and is rather shaped by general growth behavior. We analyze several models of PanIN growth and fit the predicted size distributions to the observed data. The best fitting models suggest that both intraductal spread of PanIN lesions and fusing of multiple lesions into large, highly branched structures drive PanIN growth patterns. This work lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, genomic, and transcriptomic features to understand pancreas tumorigenesis, and demonstrates the utility of combining experimental measurement of human tissues with dynamic modeling for understanding cancer tumorigenesis.

biophysics↗

bioRxiv2023

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework--a cell behavior hypothesis grammar--that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

systems biology↗

bioRxiv2023

Mechano-induced homotypic patterned domain formation by monocytes

Matrix stiffness and corresponding mechano-signaling play indispensable roles in cellular phenotypes and functions. How tissue stiffness influences the behavior of monocytes, a major circulating leukocyte of the innate system, and how it may promote the emergence of collective cell behavior is less understood. Here, using tunable collagen-coated hydrogels of physiological stiffness, we show that human primary monocytes undergo a dynamic local phase separation to form highly patterned multicellular multi-layered domains on soft matrix. Local activation of the {beta}2 integrin initiates inter-cellular adhesion, while global soluble inhibitory factors maintain the steady-state domain pattern over days. Patterned domain formation generated by monocytes is unique among other key immune cells, including macrophages, B cells, T cells, and NK cells. While inhibiting their phagocytic capability, domain formation promotes monocytes survival. We develop a computational model based on the Cahn-Hilliard equation, which includes combined local activation and global inhibition mechanisms of intercellular adhesion suggested by our experiments, and provides experimentally validated predictions of the role of seeding density and both chemotactic and random cell migration on pattern formation.

biophysics↗

bioRxiv2023

Small extracellular vesicles promote stiffness-mediated metastasis

Tissue stiffness is a critical prognostic factor in breast cancer and is associated with metastatic progression. Here we show an alternative and complementary hypothesis of tumor progression whereby physiological matrix stiffness affects the quantity and protein cargo of small EVs produced by cancer cells, which in turn drive their metastasis. Primary patient breast tissue produces significantly more EVs from stiff tumor tissue than soft tumor adjacent tissue. EVs released by cancer cells on matrices that model human breast tumors (25 kPa; stiff EVs) feature increased adhesion molecule presentation (ITG2{beta}1, ITG6{beta}4, ITG6{beta}1, CD44) compared to EVs from softer normal tissue (0.5 kPa; soft EVs), which facilitates their binding to extracellular matrix (ECM) protein collagen IV, and a 3-fold increase in homing ability to distant organs in mice. In a zebrafish xenograft model, stiff EVs aid cancer cell dissemination through enhanced chemotaxis. Moreover, normal, resident lung fibroblasts treated with stiff and soft EVs change their gene expression profiles to adopt a cancer associated fibroblast (CAF) phenotype. These findings show that EV quantity, cargo, and function depend heavily on the mechanical properties of the extracellular microenvironment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/545937v3_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1be1dbdorg.highwire.dtl.DTLVardef@928710org.highwire.dtl.DTLVardef@1e14e9borg.highwire.dtl.DTLVardef@efdb7f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗