Biology subjects

Paull, D.

Publications and source records attributed to Paull, D..

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↗

bioRxiv2020

Modular deep learning enables automated identification of monoclonal cell lines

Monoclonalization refers to the isolation and expansion of a single cell derived from a cultured population. This is a valuable step in cell culture so as to minimize a cell lines technical variability downstream of cell-altering events, such as reprogramming or gene editing, as well as for processes such as monoclonal antibody development. However, traditional methods for verifying clonality do not scale well, posing a critical obstacle to studies involving large cohorts. Without automated, standardized methods for assessing clonality post-hoc, methods involving monoclonalization cannot be reliably upscaled without exacerbating the technical variability of cell lines. We report the design of a deep learning workflow that automatically detects colony presence and identifies clonality from cellular imaging. The workflow, termed Monoqlo, integrates multiple convolutional neural networks and, critically, leverages the chronological directionality of the cell culturing process. Our algorithm design provides a fully scalable, highly interpretable framework, capable of analyzing industrial data volumes in under an hour using commodity hardware. In the present study, we focus on monoclonalization of human induced pluripotent stem cells (HiPSCs) as a case example. Monoqlo standardizes the monoclonalization process, enabling colony selection protocols to be infinitely upscaled while minimizing technical variability.

cell biology↗

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↗