Parallel CRISPR-Cas9 screens identify mechanisms of PLIN2 and lipid droplet regulation.

Despite the key roles of perilipin-2 (PLIN2) in governing lipid droplet (LD) metabolism, the mechanisms that regulate PLIN2 levels remain incompletely understood. Here, we leverage a set of genome-edited human PLIN2 reporter cell lines in a series of CRISPR-Cas9 loss-of-function screens, identifying genetic modifiers that influence PLIN2 expression and post-translational stability under different metabolic conditions and in different cell types. These regulators include canonical genes that control lipid metabolism as well as genes involved in ubiquitination, transcription, and mitochondrial function.

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression.

A CRISPRi/a platform in human iPSC-derived microglia uncovers regulators of disease states.

Microglia are emerging as key drivers of neurological diseases. However, we lack a systematic understanding of the underlying mechanisms. Here, we present a screening platform to systematically elucidate functional consequences of genetic perturbations in human induced pluripotent stem cell-derived microglia.

Multi-modality machine learning predicting Parkinson's disease.

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort.

Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis.

Single-cell transcriptomics provide a systematic map of gene expression in different human cell types. The next challenge is to systematically understand cell-type-specific gene function. The integration of CRISPR-based functional genomics and stem cell technology enables the scalable interrogation of gene function in differentiated human cells.