Leah C. Dorman

Neuronal synapse formation and remodeling are essential to central nervous system (CNS) development and are dysfunctional in neurodevelopmental diseases. Innate immune signals regulate tissue remodeling in the periphery, but how this affects CNS synapses is largely unknown. Here, we show that the interleukin-1 family cytokine interleukin-33 (IL-33) is produced by developing astrocytes and is developmentally required for normal synapse numbers and neural circuit function in the spinal cord and thalamus. We find that IL-33 signals primarily to microglia under physiologic conditions, that it promotes microglial synapse engulfment, and that it can drive microglial-dependent synapse depletion in vivo. These data reveal a cytokine-mediated mechanism required to maintain synapse homeostasis during CNS development.

Hundreds of millions of single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level of single cells. Meta-analyses that span diverse datasets building on recent advances in large language models and other machine-learning approaches pose exciting new directions to model and extract insight from single-cell data. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, the sheer number of datasets, data models and accessibility remains a challenge. Here, we present CZ CELLxGENE Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable single-cell data. Available via a free-to-use online data portal, CZ CELLxGENE hosts a growing corpus of community-contributed data of over 93 million unique cells. Curated, standardized and associated with consistent cell-level metadata, this collection of single-cell transcriptomic data is the largest of its kind and growing rapidly via community contributions. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to explore individual datasets, perform cross-corpus analysis, and run meta-analyses of tens of millions of cells across studies and tissues at the resolution of single cells.

Microglia are brain-resident macrophages that shape neural circuit development and are implicated in neurodevelopmental diseases. Multiple microglial transcriptional states have been defined, but their functional significance is unclear. Here, we identify a type I interferon (IFN-I)-responsive microglial state in the developing somatosensory cortex (postnatal day 5) that is actively engulfing whole neurons. This population expands during cortical remodeling induced by partial whisker deprivation. Global or microglial-specific loss of the IFN-I receptor resulted in microglia with phagolysosomal dysfunction and an accumulation of neurons with nuclear DNA damage. IFN-I gain of function increased neuronal engulfment by microglia in both mouse and zebrafish and restricted the accumulation of DNA-damaged neurons. Finally, IFN-I deficiency resulted in excess cortical excitatory neurons and tactile hypersensitivity. These data define a role for neuron-engulfing microglia during a critical window of brain development and reveal homeostatic functions of a canonical antiviral signaling pathway in the brain.

Abstract Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover ( cellxgene.cziscience.com ), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.