Highlights d IL-33 is expressed by subsets of hippocampal neurons and is modulated by experience d Microglia drive dendritic spine plasticity and memory precision via neuronal IL-33 d IL-33 gain of function mitigates some age-related decreases in spine plasticity d Neuronal IL-33 induces microglial remodeling of the extracellular matrix

Single-cell sequencing methods have emerged as powerful tools for identification of heterogeneous cell types within defined brain regions. Application of single-cell techniques to study the transcriptome of activated neurons can offer insight into molecular dynamics associated with differential neuronal responses to a given experience. Through evaluation of common whole-cell and single-nuclei RNA-sequencing (snRNA-seq) methods, here we show that snRNA-seq faithfully recapitulates transcriptional patterns associated with experience-driven induction of activity, including immediate early genes (IEGs) such as Fos, Arc and Egr1. SnRNA-seq of mouse dentate granule cells reveals large-scale changes in the activated neuronal transcriptome after brief novel environment exposure, including induction of MAPK pathway genes. In addition, we observe a continuum of activation states, revealing a pseudotemporal pattern of activation from gene expression alone. In summary, snRNA-seq of activated neurons enables the examination of gene expression beyond IEGs, allowing for novel insights into neuronal activation patterns in vivo.

SummaryAstrocyte dysfunction and neuroinflammation are detrimental features in multiple pathologies of the CNS. Therefore, the development of methods that produce functional human astrocytes represents an advance in the study of neurological diseases. Here we report an efficient method for inflammation-responsive astrocyte generation from induced pluripotent stem cells (iPSCs) and embryonic stem cells. This protocol uses an intermediate glial progenitor stage and generates functional astrocytes that show levels of glutamate uptake and calcium activation comparable with those observed in human primary astrocytes. Stimulation of stem cell-derived astrocytes with interleukin-1β or tumor necrosis factor α elicits a strong and rapid pro-inflammatory response. RNA-sequencing transcriptome profiling confirmed that similar gene expression changes occurred in iPSC-derived and primary astrocytes upon stimulation with interleukin-1β. This protocol represents an important tool for modeling in-a-dish neurological diseases with an inflammatory component, allowing for the investigation of the role of diseased astrocytes in neuronal degeneration.

Human neural progenitors derived from pluripotent stem cells develop into electrophysiologically active neurons at heterogeneous rates, which can confound disease-relevant discoveries in neurology and psychiatry. By combining patch clamping, morphological and transcriptome analysis on single human neurons in vitro, we defined a continuum of poor to highly functional electrophysiological states of differentiated neurons. The strong correlations between action potentials, synaptic activity, dendritic complexity and gene expression highlight the importance of methods for isolating functionally comparable neurons for in vitro investigations of brain disorders. While whole-cell electrophysiology is the gold standard for functional evaluation, it often lacks the scalability required for disease modeling studies. Here, we demonstrate a multimodal machine-learning strategy to identify new molecular features that predict the physiological states of single neurons, independently of the time spent in vitro. As further proof of concept, we selected one of the potential neurophysiological biomarkers identified in this study – GDAP1L1 – to isolate highly functional live human neurons in vitro.

Activity-induced remodeling of neuronal circuits is critical for memory formation. This process relies in part on transcription, but neither the rate of activity nor baseline transcription is equal across neuronal cell types. In this study, we isolated mouse hippocampal populations with different activity levels and used single nucleus RNA-seq to compare their transcriptional responses to activation. One hour after novel environment exposure, sparsely active dentate granule (DG) neurons had a much stronger transcriptional response compared to more highly active CA1 pyramidal cells and vasoactive intestinal polypeptide (VIP) interneurons. Activity continued to impact transcription in DG neurons up to 5 h, with increased heterogeneity. By re-exposing the mice to the same environment, we identified a unique transcriptional signature that selects DG neurons for reactivation upon re-exposure to the same environment. These results link transcriptional heterogeneity to functional heterogeneity and identify a transcriptional correlate of memory encoding in individual DG neurons.

Currently, researchers rely on generalized methods to quantify transposable element (TE) RNA expression, such as RT-qPCR and RNA-seq, that do not distinguish between TEs expressed from their own promoter (bona fide) and TEs that are transcribed from a neighboring gene promoter such as within an intron or exon. This distinction is important owing to the differing functional roles of TEs depending on whether they are independently transcribed. Here we report a simple strategy to examine bona fide TE expression, termed BonaFide-TEseq. This approach can be used with any template-switch based library such as Smart-seq2 or the single-cell 5 ′ gene expression kit from 10x, extending its utility to single-cell RNAsequencing. This approach does not require TE-specific enrichment, enabling the simultaneous examination of TEs and protein-coding genes. We show that TEs identified through BonaFide-TEseq are expressed from their own promoter, rather than captured as internal products of genes. We reveal the utility of BonaFide-TEseq in the analysis of single-cell data and show that short-interspersed nuclear elements (SINEs) show cell type-specific expression profiles in the mouse hippocampus. We further show that, in response to a brief exposure of home-cage mice to a novel stimulus, SINEs are activated in dentate granule neurons in a time course that is similar to that of protein-coding immediate early genes. This work provides a simple alternative approach to assess bona fide TE transcription at single-cell resolution and provides a proof-of-concept using this method to identify SINE activation in a context that is relevant for normal learning and memory.

Microglia are critical regulators of brain development that engulf synaptic proteins during postnatal synapse remodeling. However, the mechanisms through which microglia sense the brain environment are not well defined. Here, we characterized the regulatory program downstream of interleukin-33 (IL-33), a cytokine that promotes microglial synapse remodeling. Exposing the developing brain to a supraphysiological dose of IL-33 altered the microglial enhancer landscape and increased binding of stimulus-dependent transcription factors including AP-1/FOS. This induced a gene expression program enriched for the expression of pattern recognition receptors, including the scavenger receptor MARCO. CNS-specific deletion of IL-33 led to increased excitatory/inhibitory synaptic balance, spontaneous absence-like epileptiform activity in juvenile mice, and increased seizure susceptibility in response to chemoconvulsants. We found that MARCO promoted synapse engulfment, and Marco-deficient animals had excess thalamic excitatory synapses and increased seizure susceptibility. Taken together, these data define coordinated epigenetic and functional changes in microglia and uncover pattern recognition receptors as potential regulators of postnatal synaptic refinement.

An incorrect version of Supplementary Data 1, in which normalized counts were analysed instead of raw counts, resulting in a smaller number of differentially expressed genes, was inadvertently published with this article. This version was not used in any of the analyses presented in the paper. The HTML has now been updated to include the correct version of Supplementary Data 1.