Litterman, A. | bioRxiv Science

bioRxiv2025

SAMHD1 Knockout iPSC model enables high lenti-viral transduction in myeloid cell types

Recent advances in functional genomics tools have ushered in a new era of genetic editing to identify molecular pathways relevant to developmental and disease biology. However, limited model systems are available that adequately mimic cell states and phenotypes associated with human disease pathways. Here, we quantitatively analyzed the founder population bottleneck effect and demonstrated how the population changes from induced pluripotent stem cells (iPSCs) to hematopoietic stem cells and to the final induced macrophage population. We then engineered SAMHD1 knockout (KO) iPSC and characterized the iPSC line with RNA Seq, and induced macrophages from two distinct protocols with functional analysis. We then generated SAMHD1 KO CRISPR-dCAS9 KRAB iPSC through lenti-viral transduction aiming to increase the efficiency of lentiviral mediated gene transfer. We demonstrated increased lenti-viral transduction efficiency in induced macrophage, as well as microglia induced with two distinct protocols. This model allows for efficient gene knock down, as well as large-scale functional genomics screens in mature iPSC-derived macrophages or microglia with applications in innate immunity and chronic inflammatory disease biology. These experiments highlight the broad applicability of this platform for disease-relevant target identification and may improve our ability to run large-scale screens in iPSC-derived myeloid model systems.

cell biology↗

bioRxiv2025

Network-aware self-supervised learning enables high-content phenotypic screening for genetic modifiers of neuronal activity dynamics

High-throughput phenotypic screening has historically relied on manually selected features, limiting our ability to capture complex cellular processes, particularly neuronal activity dynamics. While recent advances in self-supervised learning have revolutionized the ability to study cellular morphology and transcriptomics, dynamic cellular processes have remained challenging to phenotypically profile. To address this limitation, we developed Plexus, a self-supervised model specifically designed to capture and quantify network-level neuronal activity dynamics. Unlike existing phenotyping tools that focus on static readouts, Plexus leverages a network-level cell encoding method, which enables it to efficiently encode dynamic neuronal activity data into rich representational embeddings. In turn, Plexus achieves state of the art performance in detecting phenotypic changes in neuronal activity. We validated Plexus using a comprehensive GCaMP6m simulation framework and demonstrated its enhanced ability to classify distinct neuronal activity phenotypes compared to traditional signal-processing approaches. To enable practical application, we integrated Plexus with a scalable experimental system utilizing human iPSC-derived neurons equipped with the GCaMP6m calcium indicator and CRISPR interference machinery. This integrated platform successfully identified nearly seventeen times as many distinct phenotypic changes in response to genetic perturbations compared to conventional signal processing methods, as demonstrated in a 52-gene CRISPRi screen across multiple iPSC lines. Using this framework, we identified potential genetic modifiers of aberrant neuronal activity in frontotemporal dementia, illustrating its utility for understanding complex neurological disorders.

bioinformatics↗