The enormous cellular diversity in the mammalian brain, which is highly prototypical and organized in a hierarchical manner, is dictated by cell-type–specific gene-regulatory programs at the molecular level. Although prevalent in the brain, the contribution of alternative splicing (AS) to the molecular diversity across neuronal cell types is just starting to emerge. Here, we systematically investigated AS regulation across over 100 transcriptomically defined neuronal types of the adult mouse cortex using deep single-cell RNA-sequencing data. We found distinct splicing programs between glutamatergic and GABAergic neurons and between subclasses within each neuronal class. These programs consist of overlapping sets of alternative exons showing differential splicing at multiple hierarchical levels. Using an integrative approach, our analysis suggests that RNA-binding proteins (RBPs) Celf1/2, Mbnl2, and Khdrbs3 are preferentially expressed and more active in glutamatergic neurons, while Elavl2 and Qk are preferentially expressed and more active in GABAergic neurons. Importantly, these and additional RBPs also contribute to differential splicing between neuronal subclasses at multiple hierarchical levels, and some RBPs contribute to splicing dynamics that do not conform to the hierarchical structure defined by the transcriptional profiles. Thus, our results suggest graded regulation of AS across neuronal cell types, which may provide a molecular mechanism to specify neuronal identity and function that are orthogonal to established classifications based on transcriptional regulation.

The generation of inhibitory interneuron progenitors from human embryonic stem cells (ESCs) is of great interest due to their potential use in transplantation therapies designed to treat central nervous system disorders. The medial ganglionic eminence (MGE) is a transient embryonic structure in the ventral telencephalon that is a major source of cortical GABAergic inhibitory interneuron progenitors. These progenitors migrate tangentially to sites in the cortex and differentiate into a variety of interneuron subtypes, forming local synaptic connections with excitatory projection neurons to modulate activity of the cortical circuitry. The homeobox domain-containing transcription factor NKX2.1 is highly expressed in the MGE and pre-optic area of the ventral subpallium and is essential for specifying cortical interneuron fate. Using a combination of growth factor agonists and antagonists to specify ventral telencephalic fates, we previously optimized a protocol for the efficient generation of NKX2.1-positive MGE-like neural progenitors from human ESCs. To establish their identity, we now characterize the transcriptome of these MGE-like neural progenitors using RNA sequencing and demonstrate the capacity of these cells to differentiate into inhibitory interneurons in vitro using a neuron-astrocyte co-culture system. These data provide information on the potential origin of interneurons in the human brain.

The importance of genomic sequence context in generating transcriptome diversity through RNA splicing is independently unmasked by two studies in this issue (Jaganathan et al., 2019;Baeza-Centurion et al., 2019).Alternative splicing occurs in over 90% of mammalian genes and plays a central role in generation of biological complexity, and the misregulation of splicing is involved in an expanding list of human diseases. Around 10% of human pathogenic mutations reported in Human Gene Mutation Database (HGMD) have been found to affect splicing (Soemedi et al., 2017), and this very likely represents an underestimate. In addition to canonical splice signals such as splice sites recognized by the core spliceosomal components, the great diversity of gene splicing is dramatically affected by a plethora of cis-regulatory elements embedded in the exon or flanking intronic sequences that are recognized by numerous trans-acting splicing factors (such as RNA binding proteins or RBPs). It is the combined effects of these elements that constitute the ''splicing code' ' (Wang and Burge, 2008). Efforts to understand this code have previously entailed extensive modeling of combinations of diverse RNA features (Barash et al., 2010) or an integrated Bayesian model (Zhang et al., 2010), but challenges remain. Experimental approaches, such as splicing reporters based on ''minigenes'' carrying specific mutations, are still the most direct and reliable assessment whether a mutation influences recognition of an exon and potentially results in phenotypic changes. However, these methods are time consuming and not applicable for either massive or novel splicing mutation detection. With the advance of next-generation sequencing technologies and machine learning, a series of in silico tools (Xiong et al., 2015), alone or in combination with experimental tools (Adamson et al., 2018;Cheung et al., 2019;Soemedi et al., 2017), have been designed to help predict the splicing-altering mutations on a genome-wide scale. These studies have shed important insights into the splicing code, especially in evaluating the pathogenic roles of noncoding sequence variants, which were previously of unknown clinical significance. However, the accuracy of the current state-of-the-art splicing predictors remain far from perfect, and how genomic sequence context impacts the effects of mutations is frequently unclear. In this issue of Cell, two studies (Baeza-Centurion et al., 2019;Jaganathan et al., 2019) have made exciting progress toward addressing these challenges.To assess the splicing-altering effects of genomic variants, especially those of noncoding mutations, Jaganathan et al. (2019) have developed a deep residual neural network based on pre-mRNA transcript sequences. Compared to previous efforts in this direction, this method predicts splice sites using long-range (i.e., 5 kb on each side) primary genomic sequence flanking each position as input. Deep learning models automatically extract sequence determinates predictive of splice sites without the requireme...

Directed differentiation of human pluripotent stem cells (hPSCs) has enabled the generation of specific neuronal subtypes that approximate the intended primary mammalian cells on both the RNA and protein levels. These cells offer unique opportunities, including insights into mechanistic understanding of the early driving events in neurodegenerative disease, replacement of degenerating cell populations, and compound identification and evaluation in the context of precision medicine. However, whether the derived neurons indeed recapitulate the physiological features of the desired bona fide neuronal subgroups remains an unanswered question and one important for validating stem cell models as accurate functional representations of the primary cell types. Here, we purified both hPSC-derived and primary mouse spinal motor neurons in parallel and used extracellular multi-electrode array (MEA) recording to compare the pharmacological sensitivity of neuronal excitability and network function. We observed similar effects for most receptor and channel agonists and antagonists, supporting the consistency between human PSC-derived and mouse primary spinal motor neuron models from a physiological perspective.