An arginine-rich nuclear localization signal (ArgiNLS) strategy for streamlined image segmentation of single cells.

High-throughput volumetric fluorescent microscopy pipelines can spatially integrate whole-brain structure and function at the foundational level of single cells. However, conventional fluorescent protein (FP) modifications used to discriminate single cells possess limited efficacy or are detrimental to cellular health. Here, we introduce a synthetic and nondeleterious nuclear localization signal (NLS) tag strategy, called "Arginine-rich NLS" (ArgiNLS), that optimizes genetic labeling and downstream image segmentation of single cells by restricting FP localization near-exclusively in the nucleus through a poly-arginine mechanism. A single N-terminal ArgiNLS tag provides modular nuclear restriction consistently across spectrally separate FP variants. ArgiNLS performance in vivo displays functional conservation across major cortical cell classes and in response to both local and systemic brain-wide AAV administration. Crucially, the high signal-to-noise ratio afforded by ArgiNLS enhances machine learning-automated segmentation of single cells due to rapid classifier training and enrichment of labeled cell detection within 2D brain sections or 3D volumetric whole-brain image datasets, derived from both staining-amplified and native signal. This genetic strategy provides a simple and flexible basis for precise image segmentation of genetically labeled single cells at scale and paired with behavioral procedures.

An arginine-rich nuclear localization signal (ArgiNLS) strategy for streamlined image segmentation of single-cells.

High-throughput volumetric fluorescent microscopy pipelines can spatially integrate whole-brain structure and function at the foundational level of single-cells. However, conventional fluorescent protein (FP) modifications used to discriminate single-cells possess limited efficacy or are detrimental to cellular health. Here, we introduce a synthetic and non-deleterious nuclear localization signal (NLS) tag strategy, called 'Arginine-rich NLS' (ArgiNLS), that optimizes genetic labeling and downstream image segmentation of single-cells by restricting FP localization near-exclusively in the nucleus through a poly-arginine mechanism. A single N-terminal ArgiNLS tag provides modular nuclear restriction consistently across spectrally separate FP variants. ArgiNLS performance in vivo displays functional conservation across major cortical cell classes, and in response to both local and systemic brain wide AAV administration. Crucially, the high signal-to-noise ratio afforded by ArgiNLS enhances ML-automated segmentation of single-cells due to rapid classifier training and enrichment of labeled cell detection within 2D brain sections or 3D volumetric whole-brain image datasets, derived from both staining-amplified and native signal. This genetic strategy provides a simple and flexible basis for precise image segmentation of genetically labeled single-cells at scale and paired with behavioral procedures.

ASCL1 induces neurogenesis in human Müller glia.

In mammals, loss of retinal cells due to disease or trauma is an irreversible process that can lead to blindness. Interestingly, regeneration of retinal neurons is a well established process in some non-mammalian vertebrates and is driven by the Müller glia (MG), which are able to re-enter the cell cycle and reprogram into neurogenic progenitors upon retinal injury or disease. Progress has been made to restore this mechanism in mammals to promote retinal regeneration: MG can be stimulated to generate new neurons in vivo in the adult mouse retina after the over-expression of the pro-neural transcription factor Ascl1. In this study, we applied the same strategy to reprogram human MG derived from fetal retina and retinal organoids into neurons. Combining single cell RNA sequencing, single cell ATAC sequencing, immunofluorescence, and electrophysiology we demonstrate that human MG can be reprogrammed into neurogenic cells in vitro.