Biology subjects

Stansbury, C.

Publications and source records attributed to Stansbury, C..

6 recordsLinked to original sources

bioRxiv2026

An agent-based model of wound healing in fibroblast scratch assays

Wound healing assays are widely used to study fibroblast migration and proliferation in the lab. Although traditional scratch assays provide valuable insight, they can be time-intensive, inflexible, and difficult to reproduce. Computational models provide an efficient and reproducible way to study fibroblast behavior in silico. We developed an agent-based simulation of fibroblast scratch assays that models migration, proliferation, contact inhibition, cell-cell mechanical interactions, and the extracellular matrix (ECM) through a particle representation of fibroblasts and the ECM. To validate our model, we performed two tests: a leave-one-out test, where model parameters were calibrated to 6 imaging time series and tested on the remaining one, and a prediction test, where model parameters were calibrated to the first 24 hours of data and the remaining 66 hours of data were predicted. In our leave-one-out test, our model achieved a mean normalized root mean squared error (mean NRMSE) of 3.34%, and a Pearsons R of > 0.98 across all tested wound geometries time series. In the prediction test, the model predicted the remaining 66 hours of wound closure with an NRMSE of 2.08%. These results demonstrate that experimentally observed wound healing behavior of fibroblasts across different geometries can be modeled by local, agent-based rules. Our model provides an efficient platform for the experimentation and in silico study of fibroblast behavior.

systems biology↗

bioRxiv2026

ARCH3D: A foundation model for global genome architecture

Biological foundation models are transforming scientific discovery by creating information-rich representations that enable inference in low-data settings. Progress on these models has mainly been achieved by increasing input contextual information, e.g., base pairs or genes. Most work, however, focuses on DNA, RNA, and protein, leaving genome architecture, a fundamental component regulating processes like the cell cycle and cell-fate determination, underexplored. Here, we introduce ARCH3D: a foundation model for global genome architecture. ARCH3D uses a novel masked locus modeling task that increases input contextual information to include genome-wide contact profiles of loci spread across the entirety of the genome. We demonstrate this strategy captures global genome structure by showing ARCH3D embeddings preserve genomic spatial structure, reconstruct interchromosomal interactions under extreme sparsity, and enable identification of multi-way interactions. Ultimately, ARCH3D provides a potential structural foundation for building the virtual genome, an artificial intelligence-based model capable of simulating genome behavior and dynamics.

bioinformatics↗

bioRxiv2024

Data-guided direct reprogramming of human fibroblasts into the hematopoietic lineage

Direct reprogramming of human fibroblasts into hematopoietic stem cells (HSCs) offers a promising strategy for generating autologous cells to treat blood and immune disorders. Current protocols are limited by low efficiency and insufficient tools for evaluating reprogramming outcomes. Although functional assays are the standard for confirming cell identity, they require fully reprogrammed cells, limiting their utility during protocol development. To address this, we assembled a single-cell transcriptomic reference atlas of hematopoietic reprogramming and tested an algorithmically-predicted transcription factor recipe for HSC induction. Long-read single-cell RNA sequencing of CD34+ reprogrammed cells revealed progressive loss of fibroblast identity alongside induction of early hematopoietic and endothelial programs, with reference-atlas benchmarking placing reprogrammed cells in an intermediate transcriptomic state between fibroblasts, endothelial cells, and HSCs. Isoform-level analysis further revealed transcriptional remodeling not captured by gene-level analyses. This experimental-computational framework offers a generalizable strategy for characterizing partially reprogrammed states and guiding optimization of reprogramming protocols.

bioinformatics↗

bioRxiv2023

Automated In Vitro Wound Healing Assay

Restoring the epidermal barrier after injury requires spatial and temporal orchestration of migration, proliferation, and signaling across many cell types. The mechanisms that coordinate this complex process are incompletely understood. In vitro wound assays are common model systems for examining these mechanisms in wound healing. In the scratch assay, a cell-free gap is created by mechanical removal of cells from a monolayer, followed by monitoring cell migration into the gap over time. While simple and low-cost, manual scratch assays are limited by low reproducibility and low throughput. Here, we have designed a robotics-assisted automated wound healing (AWH) assay that increases reproducibility and throughput while integrating automated live-cell imaging and analysis. Wounds are designed as computer-aided design (CAD) models and recreated in confluent cell layers by the BioAssemblyBot (BAB) 3D-bioprinting platform. The dynamics of migration and proliferation in individual cells are evaluated over the course of wound closure using live-cell fluorescence microscopy and our high-performance image processing pipeline. The AWH assay outperforms the standard scratch assay with enhanced consistency in wound geometry. Our ability to create diverse wound shapes in any multi-well plate with the BAB not only allows for multiple experimental conditions to be analyzed in parallel but also offers versatility in the design of wound healing experiments. Our method emerges as a valuable tool for the automated completion and analysis of high-throughput, reproducible, and adaptable in vitro wound healing assays.

bioengineering↗

bioRxiv2021

Deciphering Multi-way Interactions in the Human Genome

Chromatin architecture, a key regulator of gene expression, can be inferred using chromatin contact data from chromosome conformation capture, or Hi-C. However, classical Hi-C does not preserve multi-way contacts. Here we use long sequencing reads to map genome-wide multi-way contacts and investigate higher order chromatin organization in the human genome. We use hypergraph theory for data representation and analysis, and quantify higher order structures in neonatal fibroblasts, biopsied adult fibroblasts, and B lymphocytes. By integrating multi-way contacts with chromatin accessibility, gene expression, and transcription factor binding, we introduce a data-driven method to identify cell type-specific transcription clusters. We provide transcription factor-mediated functional building blocks for cell identity that serve as a global signature for cell types.

bioinformatics↗

bioRxiv2019

Predicting 30-day Hospital Readmissions Using Artificial Neural Networks with Medical Code Embedding

Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural network. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models significantly improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.

bioinformatics↗