Biology subjects

Hunter, C. J.

Publications and source records attributed to Hunter, C. J..

3 recordsLinked to original sources

bioRxiv2021

Modeling gene x environment interactions in PTSD using glucocorticoid-induced transcriptomics in human neurons

Post-traumatic stress disorder (PTSD) results from severe trauma exposure, but the extent to which genetic and epigenetic risk factors impact individual clinical outcomes is unknown. We assessed the impact of genomic differences following glucocorticoid administration by examining the transcriptional profile of human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons and live cultured peripheral blood mononuclear cells from combat veterans with PTSD (n=5) and without PTSD (n=5). This parallel examination in baseline and glucocorticoid-treated conditions resolves cell-type specific and diagnosis-dependent elements of stress response, and permits discrimination of gene expression signals associated with PTSD risk from those induced by stress. Computational analyses revealed neuron-specific glucocorticoid-response expression patterns that were enriched for transcriptomic patterns observed in clinical PTSD samples. PTSD-specific signatures, albeit underpowered, accurately stratify veterans with PTSD relative to combat-exposed controls. Overall, in vitro PTSD and glucocorticoid response signatures in blood and brain cells represent exciting new platforms with which to test the genetic and epigenetic mechanisms underlying PTSD, identify biomarkers of PTSD risk and onset, and conduct drug-screening to identify novel therapeutics to prevent or ameliorate clinical phenotypes.

pathology↗

bioRxiv2021

Hydrogels Containing Gradients in Vascular Density Reveal Dose-Dependent Role of Angiocrine Cues on Stem Cell Behavior

Biomaterials that replicate patterns of microenvironmental signals from the stem cell niche offer the potential to refine platforms to regulate stem cell behavior. While significant emphasis has been placed on understanding the effects of biophysical and biochemical cues on stem cell fate, vascular-derived or angiocrine cues offer an important alternative signaling axis for biomaterial-based stem cell platforms. Elucidating dose-dependent relationships between angiocrine cues and stem cell fate are largely intractable in animal models and two-dimensional cell culture. In this study, we leverage microfluidic mixing devices to generate three-dimensional hydrogels containing lateral gradients in vascular density alongside murine hematopoietic stem cells (HSCs). Regional differences in vascular density can be generated via embossed gradients in cell, matrix, or growth factor density. HSCs co-cultured alongside vascular gradients reveal spatial patterns of HSC phenotype in response to angiocrine signals. Notably, decreased Akt signaling in high vessel density regions led to increased expansion of lineage-positive hematopoietic cells. This approach offers a combinatorial tool to rapidly screen a continuum of microenvironments with varying vascular, biophysical, and biochemical cues to reveal the influence of local angiocrine signals on HSC fate.

bioengineering↗

bioRxiv2020

Deep learning and automated Cell Painting reveal Parkinson's disease-specific signatures in primary patient fibroblasts

Drug discovery for diseases such as Parkinsons disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinsons disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platforms robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinsons disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.

cell biology↗