Cells regulate function by synthesizing and degrading proteins. This turnover ranges from minutes to weeks, as it varies across proteins, cellular compartments, cell types, and tissues. Current methods for tracking protein turnover lack the spatial and temporal resolution needed to investigate these processes, especially in the intact brain, which presents unique challenges. We describe a pulse-chase method (DELTA) for measuring protein turnover with high spatial and temporal resolution throughout the body, including the brain. DELTA relies on rapid covalent capture by HaloTag of fluorophores that were optimized for bioavailability in vivo. The nuclear protein MeCP2 showed brain region- and cell type-specific turnover. The synaptic protein PSD95 was destabilized in specific brain regions by behavioral enrichment. A novel variant of expansion microscopy further facilitated turnover measurements at individual synapses. DELTA enables studies of adaptive and maladaptive plasticity in brain-wide neural circuits.

In the hippocampus, the classical pyramidal cell type of the subiculum acts as a primary output, conveying hippocampal signals to a diverse suite of downstream regions. Accumulating evidence suggests that the subiculum pyramidal cell population may actually be comprised of discrete subclasses. Here, we investigated the extent and organizational principles governing pyramidal cell heterogeneity throughout the mouse subiculum. Using single-cell RNA-seq, we find that the subiculum pyramidal cell population can be deconstructed into eight separable subclasses. These subclasses were mapped onto abutting spatial domains, ultimately producing a complex laminar and columnar organization with heterogeneity across classical dorsal-ventral, proximal-distal, and superficial-deep axes. We further show that these transcriptomically defined subclasses correspond to differential protein products and can be associated with specific projection targets. This work deconstructs the complex landscape of subiculum pyramidal cells into spatially segregated subclasses that may be observed, controlled, and interpreted in future experiments.

The mammalian hippocampus, comprised of serially connected subfields, participates in diverse behavioral and cognitive functions. It has been postulated that parallel circuitry embedded within hippocampal subfields may underlie such functional diversity. We sought to identify, delineate, and manipulate this putatively parallel architecture in the dorsal subiculum, the primary output subfield of the dorsal hippocampus. Population and single-cell RNA-seq revealed that the subiculum can be divided into two spatially adjacent subregions associated with prominent differences in pyramidal cell gene expression. Pyramidal cells occupying these two regions differed in their long-range inputs, local wiring, projection targets, and electrophysiological properties. Leveraging gene-expression differences across these regions, we use genetically restricted neuronal silencing to show that these regions differentially contribute to spatial working memory. This work provides a coherent molecular-, cellular-, circuit-, and behavioral-level demonstration that the hippocampus embeds structurally and functionally dissociable streams within its serial architecture.

Clarifying gene expression in narrowly defined neuronal populations can provide insight into cellular identity, computation, and functionality. Here, we used next-generation RNA sequencing (RNA-seq) to produce a quantitative, whole genome characterization of gene expression for the major excitatory neuronal classes of the hippocampus; namely, granule cells and mossy cells of the dentate gyrus, and pyramidal cells of areas CA3, CA2, and CA1. Moreover, for the canonical cell classes of the trisynaptic loop, we profiled transcriptomes at both dorsal and ventral poles, producing a cell-class- and region-specific transcriptional description for these populations. This dataset clarifies the transcriptional properties and identities of lesser-known cell classes, and moreover reveals unexpected variation in the trisynaptic loop across the dorsal-ventral axis. We have created a public resource, Hipposeq (http://hipposeq.janelia.org), which provides analysis and visualization of these data and will act as a roadmap relating molecules to cells, circuits, and computation in the hippocampus.

Tissue and organ function has been conventionally understood in terms of the interactions among discrete and homogeneous cell types. This approach has proven difficult in neuroscience due to the marked diversity across different neuron classes, but it may be further hampered by prominent within-class variability. Here, we considered a well-defined canonical neuronal population-hippocampal CA1 pyramidal cells (CA1 PCs)-and systematically examined the extent and spatial rules of transcriptional heterogeneity. Using next-generation RNA sequencing, we identified striking variability in CA1 PCs, such that the differences within CA1 along the dorsal-ventral axis rivaled differences across distinct pyramidal neuron classes. This variability emerged from a spectrum of continuous gene-expression gradients, producing a transcriptional profile consistent with a multifarious continuum of cells. This work reveals an unexpected amount of variability within a canonical and narrowly defined neuronal population and suggests that continuous, within-class heterogeneity may be an important feature of neural circuits.