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The claustrum is a functionally and structurally complex brain region, whose very spatial extent remains debated. Histochemical-based approaches typically treat the claustrum as a relatively narrow anatomical region that primarily projects to the neocortex, whereas circuit-based approaches can suggest a broader claustrum region containing projections to the neocortex and other regions. Here, in the mouse, we took a bottom-up and cell-type-specific approach to complement and possibly unite these seemingly disparate conclusions. Using single-cell RNA-sequencing, we found that the claustrum comprises two excitatory neuron subtypes that are differentiable from the surrounding cortex. Multicolor retrograde tracing in conjunction with 12-channel multiplexed in situ hybridization revealed a core-shell spatial arrangement of these subtypes, as well as differential downstream targets. Thus, the claustrum comprises excitatory neuron subtypes with distinct molecular and projection properties, whose spatial patterns reflect the narrower and broader claustral extents debated in previous research. This subtype-specific heterogeneity likely shapes the functional complexity of the claustrum.

The basolateral amygdala complex (BLA), extensively connected with both local amygdalar nuclei as well as long-range circuits, is involved in a diverse array of functional roles. Understanding the mechanisms of such functional diversity will be greatly informed by understanding the cell-type-specific landscape of the BLA. Here, beginning with single-cell RNA sequencing, we identified both discrete and graded continuous gene-expression differences within the mouse BLA. Via in situ hybridization, we next mapped this discrete transcriptomic heterogeneity onto a sharp spatial border between the basal and lateral amygdala nuclei, and identified continuous spatial gene-expression gradients within each of these regions. These discrete and continuous spatial transformations of transcriptomic cell-type identity were recapitulated by local morphology as well as long-range connectivity. Thus, BLA excitatory neurons are a highly heterogenous collection of neurons that spatially covary in molecular, cellular, and circuit properties. This heterogeneity likely drives pronounced spatial variation in BLA computation and function.

Understanding the principles governing neuronal diversity is a fundamental goal for neuroscience. Here, we provide an anatomical and transcriptomic database of nearly 200 genetically identified cell populations. By separately analyzing the robustness and pattern of expression differences across these cell populations, we identify two gene classes contributing distinctly to neuronal diversity. Short homeobox transcription factors distinguish neuronal populations combinatorially, and exhibit extremely low transcriptional noise, enabling highly robust expression differences. Long neuronal effector genes, such as channels and cell adhesion molecules, contribute disproportionately to neuronal diversity, based on their patterns rather than robustness of expression differences. By linking transcriptional identity to genetic strains and anatomical atlases, we provide an extensive resource for further investigation of mouse neuronal cell types.

Inflammation often induces regeneration to repair the tissue damage. However, chronic inflammation can transform temporary hyperplasia into a fertile ground for tumorigenesis. Here, we demonstrate that the microRNA miR-34a acts as a central safeguard to protect the inflammatory stem cell niche and reparative regeneration. Although playing little role in regular homeostasis, miR-34a deficiency leads to colon tumorigenesis after Citrobacter rodentium infection. miR-34a targets both immune and epithelial cells to restrain inflammation-induced stem cell proliferation. miR-34a targets Interleukin six receptor (IL-6R) and Interleukin 23 receptor (IL-23R) to suppress T helper 17 (Th17) cell differentiation and expansion, targets chemokine CCL22 to hinder Th17 cell recruitment to the colon epithelium, and targets an orphan receptor Interleukin 17 receptor D (IL-17RD) to inhibit IL-17-induced stem cell proliferation. Our study highlights the importance of microRNAs in protecting the stem cell niche during inflammation despite their lack of function in regular tissue homeostasis.

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.

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. DOI: http://dx.doi.org/10.7554/eLife.14997.001, eLife digest Both mouse and human brains are made up of many millions of cells called neurons that are interconnected to form circuits. These neurons are not all the same, because different classes of neurons express different complements of genes. Neurons that express similar genes tend to look and act alike, whereas neurons that express different genes tend to be dissimilar. Cembrowski et al. have used a technique called next-generation RNA sequencing (RNA-seq) to determine which genes are expressed in groups of neurons that represent the main cell types found in a part of the brain called the hippocampus. This brain region is important for memory, and was chosen because the location and appearance of the main cell types in the hippocampus were already well understood. The approach revealed that the main types of neurons in the mouse hippocampus are all very different from each other in terms of gene expression, and that even neurons of the same type can exhibit large differences across the hippocampus. Cembrowski et al. created a website that will allow other researchers to easily navigate, analyze, and visualize gene expression data in these populations of neurons. Future work could next make use of recent technological advances to analyze gene expression in individual neurons, rather than groups of cells, to provide an even more detailed picture. It is also hoped that understanding the differences in gene expression will guide examination of how the hippocampus contributes to memory and what goes wrong in diseases that affect this region of the brain. DOI: http://dx.doi.org/10.7554/eLife.14997.002

The roles of long non-coding RNAs (lncRNAs) in regulating cancer and stem cells are being increasingly appreciated. Its diverse mechanisms provide the regulatory network with a bigger repertoire to increase complexity. Here we report a novel LncRNA, Lnc34a, that is enriched in colon cancer stem cells (CCSCs) and initiates asymmetric division by directly targeting the microRNA miR-34a to cause its spatial imbalance. Lnc34a recruits Dnmt3a via PHB2 and HDAC1 to methylate and deacetylate the miR-34a promoter simultaneously, hence epigenetically silencing miR-34a expression independent of its upstream regulator, p53. Lnc34a levels affect CCSC self-renewal and colorectal cancer (CRC) growth in xenograft models. Lnc34a is upregulated in late-stage CRCs, contributing to epigenetic miR-34a silencing and CRC proliferation. The fact that lncRNA targets microRNA highlights the regulatory complexity of non-coding RNAs (ncRNAs), which occupy the bulk of the genome. DOI: http://dx.doi.org/10.7554/eLife.14620.001, eLife digest Tumors are made of millions of cells that are not all the same. A type of cancer cell known as cancer stem cells (or CSCs for short) are often better at dividing to produce new cells and moving to new sites in the body than other types of cancer cell. Very small molecules called micro ribonucleic acids (or microRNAs for short) can influence how CSCs grow and divide by regulating the activity of specific genes. For example, a microRNA molecule called miR-34a suppresses the activity of several genes – which slows the growth of various tumors, including lung and bowel cancers. This miR-34a is often missing from some types of cells in advanced tumors. Genes encode the instructions to produce RNAs, and Wang, Bu et al. wanted to find out what stops miR-34a being produced in certain bowel cancer cells. The experiments revealed a new, very long RNA molecule – named long non-coding RNA 34 (or Lnc34a) – that binds to the gene that encodes miR-34a. Lnc34a recruits proteins that modify the gene and switch off the production of miR-34a. Furthermore, microscopy experiments revealed that when colon cancer cells divide, Lnc34a is distributed unevenly so that it blocks the production of miR-34a in one daughter cell but not the other. Lastly, Wang, Bu et al. confirmed that Lnc34a is found in higher levels in CSCs than in other cancer cells, which helps them to grow and divide more rapidly. Future experiments will try to find out what controls the production of Lnc34a and search for drugs that can block this process in cancer cells. DOI: http://dx.doi.org/10.7554/eLife.14620.002