Gene and protein expressions are key determinants of cellular function. Neurons are the building blocks of brain circuits, yet the relationship between their molecular identity and the spatial distribution of their dendritic inputs and axonal outputs remains incompletely understood. The open‐source knowledge base
- Award ID(s):
- 1707316
- NSF-PAR ID:
- 10250970
- Editor(s):
- Klausberger, Thomas
- Date Published:
- Journal Name:
- PLOS Biology
- Volume:
- 19
- Issue:
- 5
- ISSN:
- 1545-7885
- Page Range / eLocation ID:
- e3001213
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Hippocampome.org amasses such transcriptomic data from the scientific literature for morphologically defined neuron types in the rodent hippocampal formation: dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex. Positive, negative, or mixed expression reports were initially obtained from published articles directly connecting molecular evidence to neurons with known axonal and dendritic patterns across hippocampal layers. Here, we supplement this information by collating, formalizing, and leveraging relational expression inferences that link a gene or protein expression or lack thereof to that of another molecule or to an anatomical location. With these additional interpretations, we freely release online a comprehensive human‐ and machine‐readable molecular profile for more than 100 neuron types inHippocampome.org . Analysis of these data ascertains the ability to distinguish unequivocally most neuron types in each of the major subdivisions of the hippocampus based on currently known biochemical markers. Moreover, grouping neuron types by expression similarity reveals eight superfamilies characterized by a few defining molecules. -
INTRODUCTION Neurons are by far the most diverse of all cell types in animals, to the extent that “cell types” in mammalian brains are still mostly heterogeneous groups, and there is no consensus definition of the term. The Drosophila optic lobes, with approximately 200 well-defined cell types, provides a tractable system with which to address the genetic basis of neuronal type diversity. We previously characterized the distinct developmental gene expression program of each of these types using single-cell RNA sequencing (scRNA-seq), with one-to-one correspondence to the known morphological types. RATIONALE The identity of fly neurons is determined by temporal and spatial patterning mechanisms in stem cell progenitors, but it remained unclear how these cell fate decisions are implemented and maintained in postmitotic neurons. It was proposed in Caenorhabditis elegans that unique combinations of terminal selector transcription factors (TFs) that are continuously expressed in each neuron control nearly all of its type-specific gene expression. This model implies that it should be possible to engineer predictable and complete switches of identity between different neurons just by modifying these sustained TFs. We aimed to test this prediction in the Drosophila visual system. RESULTS Here, we used our developmental scRNA-seq atlases to identify the potential terminal selector genes in all optic lobe neurons. We found unique combinations of, on average, 10 differentially expressed and stably maintained (across all stages of development) TFs in each neuron. Through genetic gain- and loss-of-function experiments in postmitotic neurons, we showed that modifications of these selector codes are sufficient to induce predictable switches of identity between various cell types. Combinations of terminal selectors jointly control both developmental (e.g., morphology) and functional (e.g., neurotransmitters and their receptors) features of neurons. The closely related Transmedullary 1 (Tm1), Tm2, Tm4, and Tm6 neurons (see the figure) share a similar code of terminal selectors, but can be distinguished from each other by three TFs that are continuously and specifically expressed in one of these cell types: Drgx in Tm1, Pdm3 in Tm2, and SoxN in Tm6. We showed that the removal of each of these selectors in these cell types reprograms them to the default Tm4 fate. We validated these conversions using both morphological features and molecular markers. In addition, we performed scRNA-seq to show that ectopic expression of pdm3 in Tm4 and Tm6 neurons converts them to neurons with transcriptomes that are nearly indistinguishable from that of wild-type Tm2 neurons. We also show that Drgx expression in Tm1 neurons is regulated by Klumpfuss, a TF expressed in stem cells that instructs this fate in progenitors, establishing a link between the regulatory programs that specify neuronal fates and those that implement them. We identified an intronic enhancer in the Drgx locus whose chromatin is specifically accessible in Tm1 neurons and in which Klu motifs are enriched. Genomic deletion of this region knocked down Drgx expression specifically in Tm1 neurons, leaving it intact in the other cell types that normally express it. We further validated this concept by demonstrating that ectopic expression of Vsx (visual system homeobox) genes in Mi15 neurons not only converts them morphologically to Dm2 neurons, but also leads to the loss of their aminergic identity. Our results suggest that selector combinations can be further sculpted by receptor tyrosine kinase signaling after neurogenesis, providing a potential mechanism for postmitotic plasticity of neuronal fates. Finally, we combined our transcriptomic datasets with previously generated chromatin accessibility datasets to understand the mechanisms that control brain wiring downstream of terminal selectors. We built predictive computational models of gene regulatory networks using the Inferelator framework. Experimental validations of these networks revealed how selectors interact with ecdysone-responsive TFs to activate a large and specific repertoire of cell surface proteins and other effectors in each neuron at the onset of synapse formation. We showed that these network models can be used to identify downstream effectors that mediate specific cellular decisions during circuit formation. For instance, reduced levels of cut expression in Tm2 neurons, because of its negative regulation by pdm3 , controls the synaptic layer targeting of their axons. Knockdown of cut in Tm1 neurons is sufficient to redirect their axons to the Tm2 layer in the lobula neuropil without affecting other morphological features. CONCLUSION Our results support a model in which neuronal type identity is primarily determined by a relatively simple code of continuously expressed terminal selector TFs in each cell type throughout development. Our results provide a unified framework of how specific fates are initiated and maintained in postmitotic neurons and open new avenues to understanding synaptic specificity through gene regulatory networks. The conservation of this regulatory logic in both C. elegans and Drosophila makes it likely that the terminal selector concept will also be useful in understanding and manipulating the neuronal diversity of mammalian brains. Terminal selectors enable predictive cell fate reprogramming. Tm1, Tm2, Tm4, and Tm6 neurons of the Drosophila visual system share a core set of TFs continuously expressed by each cell type (simplified). The default Tm4 fate is overridden by the expression of a single additional terminal selector to generate Tm1 ( Drgx ), Tm2 ( pdm3 ), or Tm6 ( SoxN ) fates.more » « less
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INTRODUCTION Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons. RATIONALE Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types. RESULTS To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of >400,000 candidate enhancers in each of 222 mammals and their associations with the phenotypes we investigated. CONCLUSION TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution. Tissue-Aware Conservation Inference Toolkit (TACIT) associates genetic differences between species with phenotypes. TACIT works by generating open chromatin data from a few species in a tissue related to a phenotype, using the sequences underlying open and closed chromatin regions to train a machine learning model for predicting tissue-specific open chromatin and associating open chromatin predictions across dozens of mammals with the phenotype. [Species silhouettes are from PhyloPic]more » « less
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Abstract MOV10 is an RNA helicase that associates with the RNA‐induced silencing complex component Argonaute (AGO), likely resolving RNA secondary structures. MOV10 also binds the Fragile X mental retardation protein to block AGO2 binding at some sites and associates with UPF1, a principal component of the nonsense‐mediated RNA decay pathway. MOV10 is widely expressed and has a key role in the cellular response to viral infection and in suppressing retrotransposition. Posttranslational modifications of MOV10 include ubiquitination, which leads to stimulation‐dependent degradation, and phosphorylation, which has an unknown function. MOV10 localizes to the nucleus and/or cytoplasm in a cell type‐specific and developmental stage‐specific manner. Knockout of
Mov10 leads to embryonic lethality, underscoring an important role in development where it is required for the completion of gastrulation. MOV10 is expressed throughout the organism; however, most studies have focused on germline cells and neurons. In the testes, the knockdown ofMov10 disrupts proliferation of spermatogonial progenitor cells. In brain, MOV10 is significantly elevated postnatally and binds mRNAs encoding cytoskeleton and neuron projection proteins, suggesting an important role in neuronal architecture. HeterozygousMov10 mutant mice are hyperactive and anxious and their cultured hippocampal neurons have reduced dendritic arborization. Zygotic knockdown ofMov10 inXenopus laevis causes abnormal head and eye development and mislocalization of neuronal precursors in the brain. Thus, MOV10 plays a vital role during development, defense against viral infection and in neuronal development and function: its many roles and regulation are only beginning to be unraveled.This article is categorized under:
RNA Interactions with Proteins and Other Molecules > RNA‐Protein Complexes
RNA Interactions with Proteins and Other Molecules > Protein‐RNA Interactions: Functional Implications
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Abstract Single‐neuron gene expression studies may be especially important for understanding nervous system structure and function because of the neuron‐specific functionality and plasticity that defines functional neural circuits. Cellular dissociation is a prerequisite technical manipulation for single‐cell and single cell‐population studies, but the extent to which the cellular dissociation process affects neural gene expression has not been determined. This information is necessary for interpreting the results of experimental manipulations that affect neural function such as learning and memory. The goal of this research was to determine the impact of cellular dissociation on brain transcriptomes. We compared gene expression of microdissected samples from the dentate gyrus (DG), CA3, and CA1 subfields of the mouse hippocampus either prepared by a standard tissue homogenization protocol or subjected to enzymatic digestion used to dissociate cells within tissues. We report that compared to homogenization, enzymatic dissociation alters about 350 genes or 2% of the hippocampal transcriptome. While only a few genes canonically implicated in long‐term potentiation and fear memory change expression levels in response to the dissociation procedure, these data indicate that sample preparation can affect gene expression profiles, which might confound interpretation of results depending on the research question. This study is important for the investigation of any complex tissues as research effort moves from subfield level analysis to single cell analysis of gene expression.
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- Journal Article:
- https://doi.org/10.1371/journal.pbio.3001213
