Global Stem Cell Array Team, N.

bioRxiv2024

A repository of Ogden syndrome (NAA10) and NAA15 patient derived iPSC lines and isogenic pairs by X-chromosome screening and genome-editing.

Amino-terminal (Nt-) acetylation (NTA) is a common protein modification, affecting 80% of cytosolic proteins in humans. The human essential gene, NAA10, encodes the enzyme NAA10, as the catalytic subunit for the N-terminal acetyltransferase A (NatA) complex, including the accessory protein, NAA15. The first human disease directly involving NAA10 was discovered in 2011, and it was named Ogden syndrome (OS), after the location of the first affected family residing in Ogden, Utah, USA. Since that time, other variants have been found in NAA10 and NAA15. Here we describe the generation of 31 iPSC lines, with 16 from females and 15 from males. This cohort includes CRISPR-mediated correction to the wild-type genotype in 4 male lines, along with editing one female line to generate homozygous wild-type or mutant clones. Following the monoclonalizaiton and screening for X-chromosome activation status in female lines, 3 additional pairs of female lines, in which either the wild type allele is on the active X chromosome (Xa) or the pathogenic variant allele is on Xa, have been generated. Subsets of this cohort have been successfully used to make cardiomyocytes and neural progenitor cells (NPCs). These cell lines are made available to the community via the NYSCF Repository.

genetics↗

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↗