Biology subjects

Fogg, K. C.

Publications and source records attributed to Fogg, K. C..

6 recordsLinked to original sources

bioRxiv2024

Empowering high-throughput high-content analysis of 3D tumor models: Open-source software for automated non-confocal image analysis

PurposeThe primary aim of this study was to develop an open-source Python-based software for the automated analysis of dynamic cell behaviors in three-dimensional tumor models using non-confocal microscopy. This research seeks to address the existing gap in accessible tools for high-throughput analysis of cancer and endothelial cell dynamics in vitro, facilitating the rapid assessment of drug sensitivity. MethodsOur approach involved annotating over 1000 2 mm Z-stacks of cancer and endothelial cell co-culture model and training machine learning models to automatically calculate cell coverage, cancer invasion depth, and microvessel dynamics. Specifically, cell coverage area was computed using focus stacking and Gaussian mixture models to generate thresholded Z-projections. Cancer invasion depth was determined using a deep neural network binary classification model, measuring the distance between Z-planes with invaded cells. Lastly, microvessel dynamics were assessed through a U-Net Xception-style deep learning model for segmentation, a disperse algorithm for network graph representation, then persistent homology to quantify microvessel length and connectivity. Finally, we reanalyzed an image set from a high-throughput drug screen involving a chemotherapy agent on a 3D cervical and endothelial co-culture model. ResultsThe software accurately measured cell coverage, cancer invasion, and microvessel length, yielding drug sensitivity IC50 values with a 95% confidence level compared to manual calculations. Additionally, it significantly reduced the image processing time from weeks down to hours. ConclusionsOur free and open source software offers an automated solution for quantifying 3D cell behavior in tumor models using non-confocal microscopy, providing the broader Cellular and Molecular Bioengineering community with an alternative to standard confocal microscopy paired with proprietary software.

bioengineering↗

bioRxiv2024

Engineering a three-dimensional multilayer multicellular model of endometrial cancer for high throughput drug screening and novel treatment methods

Endometrial cancer is one of the most common gynecological cancers in the world, with an estimated 382,000 new cases and 90,000 deaths each year. There is no specific treatment, as the underlying causes of endometrial cancer neoplasia are poorly understood. This study focuses on the development and validation of a three-dimensional (3D) in vitro multilayer, multicellularhydrogel that facilitates drug screening analysis. We hypothesized that a specific combination of natural (collagen type I and IV, fibrinogen, fibronectin, Laminin) and synthetic (GELMA, PEGDA) polymers would maximize microvessel formation and cell invasion. The 3D model incorporates human microvascular endothelial cells (hMVEC) and endometrial cancer cells (HEC-1A) atop hydrogel formulations mimicking cell-specific extracellular matrix components. Using a D-optimal experimental design, 45 hydrogel combinations were generated. The predicted hydrogel formulation to maximize all cell responses enhanced higher microvessel formation and cancer invasion compared to the gold standard Matrigel. Subsequent validation emphasizes the importance of a disease-specific model and cell crosstalk in maximizing microvessel formation and cancer invasion. The optimized 3D model adeptly captures variances in cell responses among endometrial cancer cell lines from distinct stages. Finally, the platform is employed to compare cell viability, microvessel formation, and cancer invasion across Ishikawa, KLE, and HEC-1A cells after Paclitaxel exposure, delivered both as a free drug and loaded in poly(caprolactone) (PCL) nanoparticles. Overall, this study provides a valuable tool for exploring intricate interactions within the tumor microenvironment, offering a holistic understanding of cell responses and fostering the development of targeted therapeutic strategies for endometrial cancer.

bioengineering↗

bioRxiv2024

Development of a 3D in vitro human-sized model of cervical dysplasia to evaluate the delivery of ethyl cellulose-ethanol injection for the treatment of cervical dysplasia ablation

Cervical cancer, the second leading cause of cancer-related death for women worldwide, remains a preventable yet persistent disease that disproportionately affects women in low and middle-income countries (LMICs). While existing therapies for treating cervical dysplasia are effective, they are often inaccessible in LMICs. Ethanol ablation is an alternative low-cost, accessible therapy that we previously enhanced into an ethyl cellulose (EC)-ethanol gel formulation to improve efficacy. When seeking to evaluate EC-ethanol for cervical dysplasia, we found a paucity of relevant animal models. Thus, in this study, we developed a 3D in vitro model of cervical dysplasia featuring a central lesion of cervical cancer cells surrounded by fibroblasts and keratinocytes to enable the evaluation of EC-ethanol and other novel therapeutics. Our GelMA-based 3D model successfully captured the architectural complexity of cervical dysplasia, showcasing cell response and high viability. The GelMA hydrogel formulation (8.7% w/v) exhibited viscoelastic properties akin to human cervical tissue. Using micro-CT imaging, we assessed EC-ethanol injection deposition in the hydrogel, revealing retention of virtually the entire injected volume near the injection site. Finally, we evaluated the EC-ethanol injections efficacy in eliminating cervical cancer cells. The EC-ethanol injection led to a significant decrease in cancer cell viability while preserving healthy cells in the 3D in vitro model. Taken together, our in vitro model mirrored the architecture of cervical dysplasia and demonstrated the potential of EC-ethanol for localized treatment of cervical dysplasia.

bioengineering↗

bioRxiv2023

Statistical Optimization of Hydrazone-Crosslinked Hyaluronic Acid Hydrogels for Protein Delivery

Hydrazone-crosslinked hydrogels are attractive protein delivery vehicles for regenerative medicine. However, each regenerative medicine application requires unique hydrogel properties to achieve an ideal outcome. The properties of a hydrogel can be impacted by numerous factors involved in its fabrication. We used design of experiments (DoE) statistical modeling to efficiently optimize the physicochemical properties of a hyaluronic acid (HA) hydrazone-crosslinked hydrogel for protein delivery for bone regeneration. We modified HA with either adipic acid dihydrazide (HA-ADH) or aldehyde (HA-Ox) functional groups and used DoE to evaluate the interactions of three input variables, the molecular weight of HA (40 or 100 kDa), the concentration of HA-ADH (1-3% w/v), and the concentration of HA-Ox (1-3% w/v), on three output responses, gelation time, compressive modulus, and hydrogel stability over time. We identified 100 kDa HA-ADH3.0HA-Ox2.33 as an optimal hydrogel that met all of our design criteria, including displaying a gelation time of 3.7 minutes, compressive modulus of 62.1 Pa, and minimal mass change over 28 days. For protein delivery, we conjugated affinity proteins called affibodies that were specific to the osteogenic protein bone morphogenetic protein-2 (BMP-2) to HA hydrogels and demonstrated that our platform could control the release of BMP-2 over 28 days. Ultimately, our approach demonstrates the utility of DoE for optimizing hydrazone-crosslinked HA hydrogels for protein delivery. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/549125v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@bd1a4org.highwire.dtl.DTLVardef@1f0766borg.highwire.dtl.DTLVardef@1eea01corg.highwire.dtl.DTLVardef@151bd6b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Characterizing the extracellular matrix transcriptome of endometriosis

In recent years, the matrisome, a set of proteins that make up the extracellular matrix (ECM) or are closely involved in ECM behavior, has been shown to have great importance for characterizing and understanding disease pathogenesis and progression. The matrisome is especially critical for examining diseases characterized by extensive tissue remodeling. Endometriosis is characterized by the extrauterine growth of endometrial tissue, making it an ideal condition to study through the lens of matrisome gene expression. While large gene expression datasets have become more available, and gene dysregulation in endometriosis has been the target of several studies, the gene expression profile of the matrisome specifically in endometriosis has not been well characterized. In our study, we explored three Gene Expression Omnibus (GEO) DNA microarray datasets containing endometriosis and healthy samples of eutopic endometrium. We established that matrisome gene expression alone can stratify healthy and endometriosis samples and identified the matrisome genes and gene networks that hold inferential significance for the onset and progression of endometriosis. Furthermore, we found that menstrual cycle phase accounted for over a third of the variance of matrisome gene expression within the samples. Taken together, these findings may aid in developing future in vitro models of disease and identifying novel treatment strategies for this underserved patient population.

bioinformatics↗

bioRxiv2022

Engineering High Throughput Screening Platforms of Cervical Cancer

There is a critical need for complex multicellular three-dimensional physiomimetic models of cancer that can interface with high throughput drug screening methods to assess anti-metastatic and anti-angiogenic drug efficacy in a rapid yet high content manner. We report a multilayer multicellular platform of human cervical cancer cell lines and primary human microvascular endothelial cells that incorporates critical biophysical and extracellular matrix cues, interfaces with standard high throughput drug screening methods, and can evaluate cervical cancer invasion and endothelial microvessel formation over time. Through the use of Design of Experiments statistical optimization, we identified the specific concentrations of collagen I, fibrinogen, fibronectin, GelMA, and PEGDA in each hydrogel layer that maximized cervical cancer invasion and endothelial microvessel length simultaneously. We then validated the optimized platform and assessed the viscoelastic properties of the composite hydrogels as well as their individual constituents. Finally, using this optimized platform, we conducted a targeted drug screen of four clinically relevant drugs on two cervical cancer cell lines. From these data we identified each of the cervical cancer cell lines (SiHa and Ca Ski) as either responsive or refractive to Paclitaxel, Dasitinib, Dovitinib, or Pazopanib. Overall, we developed a phenotypic drug screening platform of cervical cancer that captures cell behavior present in the cervical cancer tumor microenvironment, captures patient to patient variability, and integrates with standard high throughput high content drug screening methods. This work provides a valuable platform that can be used to screen large compound libraries for mechanistic studies, drug discovery, and precision oncology for cervical cancer patients.

bioengineering↗