Robust self-supervised denoising of voltage imaging data using CellMincer.

Voltage imaging enables high-throughput investigation of neuronal activity, yet its utility is often constrained by a low signal-to-noise ratio (SNR). Conventional denoising algorithms, such as those based on matrix factorization, impose limiting assumptions about the noise process and the spatiotemporal structure of the signal. While deep learning based denoising techniques offer greater adaptability, existing approaches fail to fully exploit the fast temporal dynamics and unique short- and long-range dependencies within voltage imaging datasets. Here, we introduce CellMincer, a novel self-supervised deep learning method designed specifically for denoising voltage imaging datasets. CellMincer operates on the principle of masking and predicting sparse sets of pixels across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without the need for large temporal denoising contexts. We develop and utilize a physics-based simulation framework to generate realistic datasets for rigorous hyperparameter optimization and ablation studies, highlighting the key role of conditioning the denoiser on precomputed spatiotemporal auto-correlations to achieve 3-fold further reduction in noise. Comprehensive benchmarking on both simulated and real voltage imaging datasets, including those with paired patch-clamp electrophysiology (EP) as ground truth, demonstrates CellMincer's state-of-the-art performance. It achieves substantial noise reduction across the entire frequency spectrum, enhanced detection of subthreshold events, and superior cross-correlation with ground-truth EP recordings. Finally, we demonstrate how CellMincer's addition to a typical voltage imaging data analysis workflow improves neuronal segmentation, peak detection, and ultimately leads to significantly enhanced separation of functional phenotypes.

Robust induction of functional astrocytes using NGN2 expression in human pluripotent stem cells.

Emerging evidence of species divergent features of astrocytes coupled with the relative inaccessibility of human brain tissue underscore the utility of human pluripotent stem cell (hPSC) technologies for the generation and study of human astrocytes. However, existing approaches for hPSC-astrocyte generation are typically lengthy or require intermediate purification steps. Here, we establish a rapid and highly scalable method for generating functional human induced astrocytes (hiAs). These hiAs express canonical astrocyte markers, respond to pro-inflammatory stimuli, exhibit ATP-induced calcium transients and support neuronal network development. Moreover, single-cell transcriptomic analyses reveal the generation of highly reproducible cell populations across individual donors, mostly resembling human fetal astrocytes. Finally, hiAs generated from a trisomy 21 disease model identify expected alterations in cell-cell adhesion and synaptic signaling, supporting their utility for disease modeling applications. Thus, hiAs provide a valuable and practical resource for the study of basic human astrocyte function and dysfunction in disease.