Shayn Peirce-Cottler

Objective: Structural adaptations of capillary networks, through angiogenesis, arterialization, and regression, are implicated in many diseases, and gaining a better understanding of the cell-cell interactions that underpin these adaptations may lead to novel therapeutic discoveries for disease management. Endothelial cells and pericytes are the two cell types that comprise capillary networks. Experimental model systems have been developed to study the dynamic interactions between endothelial cells and pericytes, providing valuable insights into capillary development, cell-to-cell communication, and responses to growth factors and therapeutic agents. Methods: In this study, we present a novel and simple co-culture system that uses commercially available primary human endothelial cells and pericytes, does not require microfluidic perfusion, and allows simultaneous observation of cell morphologies and interactions over time in 60 samples, enabling high-throughput analysis of multiple culture conditions with replicates. Results: Image analysis pipelines were created to quantify microvascular adaptations, including one to measure colocalization between endothelial cells and pericytes, capturing dynamic coupling and uncoupling associated with capillary stability, angiogenesis, and regression. We validated the ability of our co-culture system to reproducibly represent the effects of fibrotic and angiogenic activation signals, including an FDA-approved drug, on endothelial cells, pericytes, and their coupling. Conclusion: This novel, high-throughput microvascular screening assay enables quantification of microvascular dynamics in response to disease-relevant stimuli and therapeutics in a repeatable, real-time manner.

Fibroblasts maintain the extracellular matrix (ECM) to support tissue homeostasis and wound healing. In fibrotic diseases, fibroblasts are a primary driver of disease progression through excess collagen secretion and enhanced contractility. Replacing native tissue with a collagen rich fibrotic scar leads to a decline in tissue function. Recent research into idiopathic pulmonary fibrosis (IPF) has identified fibroblast subpopulations that may be primed for the hyper-activation that leads to increased progression of fibrotic disease. Understanding the contribution of these subpopulations to disease progression requires integrating experimental and computational techniques to understand their dynamic contributions to tissue phenotype. Herein, we introduce a framework for modeling subpopulations using a multiscale mechanistic computational model to understand differences within subpopulations, at the intracellular level and how these differences contribute to cell-and tissue-level pathology. We build and validate this framework using two well-defined subpopulations of fibroblasts in IPF. The subpopulations are defined by the presence or absence of Thy-1, a cell-surface protein that regulates fibroblast mechanosensing. We first developed a logic-based network model of a fibroblast. We then applied this model to identify sub-networks that regulate myofibroblast marker expression in the two subpopulations. Coupling this with an agent-based model (ABM) of the lung microenvironment, we observed how different rules regulating cell fate decisions in each subpopulation affected collagen content. Computational image outputs were analyzed with the open-source biological image analysis software QuPath to quantify how changes in subpopulation dynamics change model-predicted foci characteristics such as size and collagen density. We find that the ability for Thy-1 ⁺ fibroblasts to transition to Thy-1 ^- fibroblasts significantly increases total collagen content, as well as influences fibrotic foci characteristics. Overall, we present a combined experimental and computational framework for studying how dynamic changes in fibroblast subpopulations lead to tissue-level disease phenotypes. Author Summary: In wound healing fibroblasts are responsible for rebuilding the scaffolding, called the extracellular matrix (ECM), of the damaged tissue to aid in regeneration. In fibrosis, the normal wound healing processes are hijacked leading to overproduction of ECM proteins, such as collagen, by fibroblasts leading to fibrosis. In fibrotic diseases with no known cause, such as idiopathic pulmonary fibrosis (IPF), subpopulations of fibroblasts have been identified as possible drivers of disease. Herein, we use a multiscale computational model that represents intracellular, cellular, and tissue level signaling to study how the presence or absence of a single protein on a fibroblast’s surface, Thy-1, affects the formation of fibrotic scar in IPF. Thy-1 regulates how a fibroblast senses the stiffness of the microenvironment. The absence of Thy-1 impacted intracellular signaling and when combined across many cells led to fibrosis at the tissue level. Additionally, if normal Thy-1 ⁺ fibroblasts could dynamically lose Thy-1 expression the amount of collagen they produced correlated with that in end stage IPF lungs. This model presents a framework for studying different fibroblast subpopulations using multiscale computational modeling to understand how changes in a single protein in one cell can, over an entire population, affect the dynamics of disease progression.

Sensors that employ and interface with cell-laden hydrogels can be used to identify novel cell signal transduction mechanisms, facilitate the detection of novel analytes, and test the effects of environmental perturbations on cell behavior, such as microgravity and drug exposure. Here, we report a novel cellularized hydrogel-interfaced cantilever (CHIC) biosensor for continuously monitoring the mechanical properties of 3D cell co-cultures in hydrogels. CHIC biosensors interfaced with 3D microporous annealed particle hydrogels laden with co-cultured fibroblasts and endothelial cells enabled monitoring of changes in the culture stiffness and the response to TGF-β1 stimulation over time via a mechanobiological transduction mechanism. 3D cell culture responses to the growth factor TGF-β1 (10 ng/mL) were associated with a change in the biosensor phase angle at resonance (ϕ). Long-term studies using CHIC biosensor arrays in a 3D co-culture assay over three weeks showed that TGF-β1 stimulation-induced changes in biosensor ϕ were consistent with changes in compressive modulus measured by an independent destructive testing method. The biosensor response and results of independent destructive mechanical characterization were corroborated by gene and protein expression analysis. The decrease in ϕ following TGF-β1 stimulation was correlated with upregulation of ACTA2, IL6, COL4A1, CXCL10, downregulation of MMP1, IL1R1, and CXCL8 in fibroblasts and endothelial cells. Meanwhile, expressions of PDGF-BB and IFN-γ remained elevated compared to the unstimulated control throughout the time course. This work establishes a new biosensor that enables mechanical property sensing of hydrogel-based 3D cell co-culture models.

Pericytes are critical components of the neurovascular unit (NVU), regulating endothelial cell stability, blood-brain barrier (BBB) integrity, and neuroimmune signaling. Their role in Alzheimer's Disease (AD), particularly regarding sex differences and brain region specificity, remains poorly defined. Using single-nucleus RNA sequencing analysis and cell-cell communication tools (LIANA, Tensor-cell2cell), we characterize pericyte transcriptional and signaling changes across the middle temporal gyrus (MTG) and dorsolateral prefrontal cortex (DLPFC) of AD and non-AD donors stratified by sex. We identify a pericyte-endothelial TGFβ signaling program upregulated in female AD donors specifically in the MTG, and a downregulated estrogen pathway involving pericyte-astrocyte ligand-receptor interactions, supported by increased pericyte-astrocyte separation in spatial transcriptomics. We also identify a microglia-to-pericyte program enriched for hypoxia and p53 pathways that is elevated in both sexes with regional specificity. Our findings implicate pericyte-driven communication as a mechanistic contributor to female-biased AD vulnerability, and support sex- and region-aware approaches in neurodegeneration research.

Agent-based modeling (ABM) is a computational method for predicting the emergent outcomes of interacting, autonomous individuals in a complex system. Here, ABM is used to simulate interactions between fibroblast and myofibroblast cells during idiopathic pulmonary fibrosis (IPF) in alveolar tissue microenvironments. These microenvironments are derived from histology of a healthy human lung sample and moderate- and severe-IPF lung samples. Fibroblast differentiation, cell migration, and collagen secretion in response to the spatial distribution of the cytokine transforming growth factor-beta are captured in the ABM using NetLogo software. Results are presented from one simulated year without treatment and with mechanisms representing treatment by pirfenidone and pentoxifylline, alone and in combination. A total of 180 in silico experiments are run, analyzed, and compared in a high-throughput workflow. The effects of the initial number of fibroblasts and treatment scenarios on various metrics related to collagen accumulation and collagen invasion into alveolar regions are determined. The ABM and the analysis files are shared to facilitate model reuse. By integrating computational modeling of IPF and therapeutics, this research aims to improve understanding of fibrosis progression and assess the efficacy of novel and existing treatments targeting different mechanisms to inform decision-making for IPF treatment.

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.

Objective: Synthetic hydrogels that support 3D cell culture are widely used as platforms for modeling disease, such as tissue fibrosis, which leads to mechanical stiffening of the extracellular matrix (ECM). To interrogate how mechanical stiffness of the ECM affects microvascular remodeling, we developed a bioactive poly(ethylene glycol) diacrylate (PEGDA) hydrogel model with tunable stiffness that permits microvascular sprouting. Methods: Lung explants harvested from healthy and fibrotic mice were cultured ex vivo on PEGDA hydrogels for 7 days. Capillary sprouting from lung segments was evaluated via imaging and secreted angiogenic markers. Results: Healthy lung explants had decreased sprout formation and length on stiffer hydrogels. The sprouts from fibrotic lung explants, however, were not impacted by hydrogel stiffness. This difference was associated with higher expression of angiogenic markers and matrix remodeling enzymes in the fibrotic lung explants. Conclusions: Our results suggest a compensation in vasculature derived from fibrotic tissue to matrix mechanics in promoting angiogenic sprouting.

Microvascular impairments are a major issue in many diseases of the brain. In fact, they are often considered the earliest pathological phenomena in neurodegenerative diseases like Alzheimer's Disease or the locus of pathology as in stroke. Still, little is known about the mechanistic and cellular level events that contribute to these impairments. Given the importance of the neurovascular unit (NVU) in maintaining functional brain tissue, alterations to NVU cell types are important to study in the context of disease progression. With the use of two‐photon microscopy, microvessels and cells can be imaged and evaluated throughout disease progression. Herein we aim to provide a comprehensive protocol for setting up and using two‐photon microscopy for serial imaging of neurovascular unit cell types (i.e., pericytes, astrocytes, and microglia). We also describe interpreting results from cell and vessel changes based on disease or functional tests through multiple timepoints. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Mouse set up for serial, live 2‐photon imagingBasic Protocol 2: Field of view acquisitions over multiple timepointsBasic Protocol 3: Line scan acquisitions for acquiring red blood cell metricsBasic Protocol 4: Quantitative measurements of vessel and cell changesBasic Protocol 5: Documenting changes in astrocyte morphology following induction of transient focal photothrombotic stroke using Rose Bengal