The brain is an organ of immense complexity. Next-generation RNA sequencing (RNA-seq) is becoming increasingly popular in the deconstruction of this complexity into distinct classes of ‘cell types’. ...Notably, in addition to revealing the organization of this distinct cell-type landscape, the technology has also begun to illustrate that continuous variation can be found within narrowly defined cell types. Here we summarize the evidence for graded transcriptomic heterogeneity being present, widespread, and functionally relevant in the nervous system. We explain how these graded differences can map onto higher-order organizational features and how they may reframe existing interpretations of higher-order heterogeneity. Ultimately, a multimodal approach incorporating continuously variable cell types will facilitate an accurate reductionist interpretation of the nervous system.
RNA-seq is becoming a popular tool for classifying cells in the nervous system.
Classically, neurons have been thought to exist in discrete classes, where cells in a given class are generally homogeneous.
Several recent studies using RNA-seq have challenged this conventional notion, having found that narrowly defined neuronal cell classes can exhibit prominent heterogeneity.
This heterogeneity seems to conform to continuous variation within cell types rather than to further reduction to additional discrete cell types.
These findings suggest that the assumptions underlying the definition of a cell type, and the corresponding functional implications, may need to be reconsidered.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Proprioceptive feedback mainly derives from groups Ia and II muscle spindle (MS) afferents and group Ib Golgi tendon organ (GTO) afferents, but the molecular correlates of these three afferent ...subtypes remain unknown. We performed single cell RNA sequencing of genetically identified adult proprioceptors and uncovered five molecularly distinct neuronal clusters. Validation of cluster-specific transcripts in dorsal root ganglia and skeletal muscle demonstrates that two of these clusters correspond to group Ia MS afferents and group Ib GTO afferent proprioceptors, respectively, and suggest that the remaining clusters could represent group II MS afferents. Lineage analysis between proprioceptor transcriptomes at different developmental stages provides evidence that proprioceptor subtype identities emerge late in development. Together, our data provide comprehensive molecular signatures for groups Ia and II MS afferents and group Ib GTO afferents, enabling genetic interrogation of the role of individual proprioceptor subtypes in regulating motor output.
The extent of microglial heterogeneity in humans remains a central yet poorly explored question in light of the development of therapies targeting this cell type. Here, we investigate the population ...structure of live microglia purified from human cerebral cortex samples obtained at autopsy and during neurosurgical procedures. Using single cell RNA sequencing, we find that some subsets are enriched for disease-related genes and RNA signatures. We confirm the presence of four of these microglial subpopulations histologically and illustrate the utility of our data by characterizing further microglial cluster 7, enriched for genes depleted in the cortex of individuals with Alzheimer's disease (AD). Histologically, these cluster 7 microglia are reduced in frequency in AD tissue, and we validate this observation in an independent set of single nucleus data. Thus, our live human microglia identify a range of subtypes, and we prioritize one of these as being altered in AD.
Aging is characterized by an increased vulnerability to infection and the development of inflammatory diseases, such as atherosclerosis, frailty, cancer and neurodegeneration. Here, we find that ...aging is associated with the loss of diurnally rhythmic innate immune responses, including monocyte trafficking from bone marrow to blood, response to lipopolysaccharide and phagocytosis. This decline in homeostatic immune responses was associated with a striking disappearance of circadian gene transcription in aged compared to young tissue macrophages. Chromatin accessibility was significantly greater in young macrophages than in aged macrophages; however, this difference did not explain the loss of rhythmic gene transcription in aged macrophages. Rather, diurnal expression of Kruppel-like factor 4 (Klf4), a transcription factor (TF) well established in regulating cell differentiation and reprogramming, was selectively diminished in aged macrophages. Ablation of Klf4 expression abolished diurnal rhythms in phagocytic activity, recapitulating the effect of aging on macrophage phagocytosis. Examination of individuals harboring genetic variants of KLF4 revealed an association with age-dependent susceptibility to death caused by bacterial infection. Our results indicate that loss of rhythmic Klf4 expression in aged macrophages is associated with disruption of circadian innate immune homeostasis, a mechanism that may underlie age-associated loss of protective immune responses.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
How neuron types encode behavioral states
What is the contribution of molecularly defined cell types to neural coding of stimuli and states? Xu
et al.
aimed to evaluate neural representation of ...multiple behavioral states in the mouse paraventricular hypothalamus. To achieve this goal, they combined deep-brain two-photon imaging with post hoc validation of gene expression in the imaged cells. The behavioral states could be well predicted by the neural response of multiple neuronal clusters. Some clusters were broadly tuned and contributed strongly to the decoding of multiple behavioral states, whereas others were more specifically tuned to certain behaviors or specific time windows of a behavioral state.
Science
, this issue p.
eabb2494
An imaging method can merge molecular and systems neuroscience to reveal combinatorial cell type coding of essential survival behaviors.
INTRODUCTION
Brain function is often compared to an orchestral ensemble, where subgroups of neurons that have similar activity are analogous to different types of instruments playing a musical score. Brains are composed of specialized neuronal subtypes that can be efficiently classified by gene expression profiles measured by single-cell RNA sequencing (scRNA-seq). Are these molecularly defined cell types the “instruments” in the neural ensemble? To address this question, we examined the neural ensemble dynamics of the hypothalamic paraventricular nucleus (PVH), a small brain region that is important for behavior states such as hunger, thirst, and stress. Past work has emphasized specialized behavioral state–setting roles for different PVH cell types, but it is not clear whether the dynamics of the PVH ensemble support this view.
RATIONALE
We considered three possibilities for how PVH neurons could be involved in encoding behavioral states: (i) PVH neurons of a molecularly defined cell type may respond similarly and be specialized for a behavioral state as a “labeled-line,” (ii) molecularly defined cell types may show unrelated activity patterns and be irrelevant to behavioral state coding, and (iii) molecularly defined neurons may respond similarly within a type, but behavioral state may be encoded by combinations of cell types. To evaluate the role of molecularly defined cell types in the neural ensemble, it is important to monitor activity in many individual neurons with subsecond temporal resolution along with quantitative gene expression information about each cell. For this, we developed the CaRMA (calcium and RNA multiplexed activity) imaging platform in which deep-brain two-photon calcium imaging of neuron activity is performed in mice during multiple behavioral tasks. This is followed by ex vivo multiplexed RNA fluorescent in situ hybridization to measure gene expression information in the in vivo–imaged neurons.
RESULTS
We simultaneously imaged calcium activity in hundreds of PVH neurons from 10 cell types across 11 behavioral states. Within a molecularly defined cell type, neurons often showed similar activity patterns such that we could predict functional responses of individual neurons solely from their quantitative gene expression information. Behavioral states could be decoded with high accuracy based on combinatorial assemblies of PVH cell types, which we called “grouped-ensemble coding.” Labeled-line coding was not observed. The neuromodulatory receptor gene
neuropeptide receptor neuropeptide Y receptor type 1
(
Npy1r
) was usually the most predictive gene for neuron functional response and was expressed in multiple cell types, analogous to the “conductor” of the PVH neural ensemble.
CONCLUSION
Our results validated molecularly defined neurons as important information processing units in the PVH. We found correspondence between the gene expression hierarchies used for molecularly defined cell type classification and functional activity hierarchies involving coordination by neuromodulation. CaRMA imaging offers a solution to the problem of how to rapidly evaluate the function of the panoply of cell types being uncovered with scRNA-seq. CaRMA imaging bridges a gap between the abstract digital elements typically described in systems neuroscience with the “wetware” associated with traditional molecular neuroscience. Merging these two areas is essential to understanding the relationships of gene expression, brain function, behavior, and ultimately neurological diseases.
CaRMA imaging reveals combinatorial cell type coding of behavior states.
CaRMA imaging records calcium dynamics of PVH neurons across multiple behavioral states followed by gene expression profiling. Combinatorial assemblies of PVH cell types encoded behavioral states. The PVH neural activity ensemble was split by Npy1r expression into two main cell classes that were subdivided into cell types. Thus, neuromodulation coordinates cell types for grouped-ensemble coding to represent different survival behaviors such as eating, drinking, and stress.
Brains encode behaviors using neurons amenable to systematic classification by gene expression. The contribution of molecular identity to neural coding is not understood because of the challenges involved with measuring neural dynamics and molecular information from the same cells. We developed CaRMA (calcium and RNA multiplexed activity) imaging based on recording in vivo single-neuron calcium dynamics followed by gene expression analysis. We simultaneously monitored activity in hundreds of neurons in mouse paraventricular hypothalamus (PVH). Combinations of cell-type marker genes had predictive power for neuronal responses across 11 behavioral states. The PVH uses combinatorial assemblies of molecularly defined neuron populations for grouped-ensemble coding of survival behaviors. The neuropeptide receptor neuropeptide Y receptor type 1 (Npy1r) amalgamated multiple cell types with similar responses. Our results show that molecularly defined neurons are important processing units for brain function.
Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types ...with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems.
Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple "robustness score" for each of these clusters, facilitating the assessment of cluster quality.
chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
There is a high diversity of neuronal types in the mammalian neocortex. To facilitate construction of system models with multiple cell types, we generate a database of point models associated with ...the Allen Cell Types Database. We construct a set of generalized leaky integrate-and-fire (GLIF) models of increasing complexity to reproduce the spiking behaviors of 645 recorded neurons from 16 transgenic lines. The more complex models have an increased capacity to predict spiking behavior of hold-out stimuli. We use unsupervised methods to classify cell types, and find that high level GLIF model parameters are able to differentiate transgenic lines comparable to electrophysiological features. The more complex model parameters also have an increased ability to differentiate between transgenic lines. Thus, creating simple models is an effective dimensionality reduction technique that enables the differentiation of cell types from electrophysiological responses without the need for a priori-defined features. This database will provide a set of simplified models of multiple cell types for the community to use in network models.
Single-cell RNA sequencing data can unveil the molecular diversity of cell types. Cell type atlases of the mouse spinal cord have been published in recent years but have not been integrated together. ...Here, we generate an atlas of spinal cell types based on single-cell transcriptomic data, unifying the available datasets into a common reference framework. We report a hierarchical structure of postnatal cell type relationships, with location providing the highest level of organization, then neurotransmitter status, family, and finally, dozens of refined populations. We validate a combinatorial marker code for each neuronal cell type and map their spatial distributions in the adult spinal cord. We also show complex lineage relationships among postnatal cell types. Additionally, we develop an open-source cell type classifier, SeqSeek, to facilitate the standardization of cell type identification. This work provides an integrated view of spinal cell types, their gene expression signatures, and their molecular organization.
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.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Huntington Disease (HD) is an inherited movement disorder caused by expanded CAG repeats in the Huntingtin gene. We have used single nucleus RNASeq (snRNASeq) to uncover cellular phenotypes that ...change in the disease, investigating single cell gene expression in cingulate cortex of patients with HD and comparing the gene expression to that of patients with no neurological disease. In this study, we focused on astrocytes, although we found significant gene expression differences in neurons, oligodendrocytes, and microglia as well. In particular, the gene expression profiles of astrocytes in HD showed multiple signatures, varying in phenotype from cells that had markedly upregulated metallothionein and heat shock genes, but had not completely lost the expression of genes associated with normal protoplasmic astrocytes, to astrocytes that had substantially upregulated glial fibrillary acidic protein (GFAP) and had lost expression of many normal protoplasmic astrocyte genes as well as metallothionein genes. When compared to astrocytes in control samples, astrocyte signatures in HD also showed downregulated expression of a number of genes, including several associated with protoplasmic astrocyte function and lipid synthesis. Thus, HD astrocytes appeared in variable transcriptional phenotypes, and could be divided into several different "states", defined by patterns of gene expression. Ultimately, this study begins to fill the knowledge gap of single cell gene expression in HD and provide a more detailed understanding of the variation in changes in gene expression during astrocyte "reactions" to the disease.
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