# SAMHD1 knockout hiPSC model enables high lentiviral transduction efficiency in myeloid cell types

## TL;DR

A new hiPSC model with a gene knockout improves lentiviral transduction in myeloid cells, enabling better studies of immune-related diseases.

## Contribution

Development of a SAMHD1 knockout hiPSC model that enhances lentiviral transduction efficiency in macrophages and microglia.

## Key findings

- SAMHD1 knockout increases lentiviral transduction efficiency in macrophages and microglia.
- The model supports efficient gene knockdown and large-scale functional genomics screens in myeloid cells.
- The platform is applicable to innate immunity and chronic inflammatory disease research.

## Abstract

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 human induced pluripotent stem cells (hiPSCs) to hematopoietic stem cells and to the final induced macrophage population. We then engineered a key gene encoding an enzyme in the myeloid cell antiviral pathway-SAMHD1-knockout (KO) hiPSCs and characterized the hiPSC line with RNA-Seq and induced macrophages from two distinct protocols with functional analysis. We then generated SAMHD1 KO CRISPR-dCAS9 KRAB hiPSCs through lentiviral transduction aiming to increase the efficiency of lentiviral mediated gene transfer. We demonstrated increased lentiviral transduction efficiency in induced macrophages, as well as microglia induced with two distinct protocols. This model allows for efficient gene knockdown, as well as large-scale functional genomics screens in mature hiPSC-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 hiPSC-derived myeloid model systems.