Lehrer, J. | bioRxiv Science

bioRxiv2024

Fate plasticity of interneuron specification

The generation of neuronal subtypes in the mammalian central nervous system is driven by competing genetic programs. The medial ganglionic eminence (MGE) gives rise to two major cortical interneuron (cIN) populations, marked by Somatostatin (Sst) and Parvalbumin (Pvalb), which develop on different timelines. The extent to which external signals influence these identities remains poorly understood. Pvalb-positive cINs are particularly important for regulating cortical circuits through strong perisomatic inhibition, yet they have been difficult to model in vitro. Here we investigated the role of the environment in shaping and maintaining Pvalb cINs. We grafted mouse MGE progenitors into a variety of 2D and 3D co-culture models, including mouse and human cortical, MGE, and thalamic systems with dissociated cells, organoids, organotypic cultures, and conditioned media. Across models, we observed distinct proportions of Sst- and Pvalb-positive cIN descendants. Strikingly, grafting MGE progenitors into 3D human, but not mouse, corticogenesis models led to efficient, non-autonomous differentiation of Pvalb-positive cINs. This differentiation was characterized by upregulation of Pvalb maturation markers, downregulation of Sst-specific markers, and the formation of perineuronal nets. Furthermore, lineage-traced postmitotic Sst-positive cINs, when grafted onto human cortical models, also upregulated Pvalb expression. These results reveal an unexpected level of fate plasticity in MGE-derived cINs, demonstrating that their identities can be dynamically shaped by the surrounding environment.

neuroscience↗

bioRxiv2023

A data-efficient deep learning tool for scRNA-Seq label transfer in neuroscience

Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Ma-chine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably high accuracy. This accuracy is maintained in trans-sample label transfers of the adult hu-man cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal sub-types, even in periods of fate refinement, shedding light on genetic changes affecting specific cell types across development. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities and unveil genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. When cell types are obscured by stress signals, label transfer from primary tissue improves the accuracy of cortical organoid annotations, serving as a reliable ground truth. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

bioinformatics↗