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.
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Three-dimensional intact-tissue sequencing of single-cell transcriptional states.
The mammalian brain consists of an intricate tapestry of cell types, with diversity crucial for function that arises from both differential gene expression and circuit-specific anatomy. Yet, retrieving high-content gene-expression information while retaining 3D positional anatomy at cellular resolution has been difficult, limiting integrative understanding of brain structure and function. Here we introduce and apply a technology for 3D intact-tissue RNA sequencing, termed STARmap (Spatially-resolved Transcript Amplicon Readout Mapping), which integrates highly-specific signal amplification, novel hydrogel-tissue chemistry, and an error-reduction sequencing process. The capabilities of STARmap were tested by mapping from 160 to 1,020 distinct genes simultaneously in sections of mouse brain at single-cell resolution with unprecedented efficiency, accuracy and reproducibility. These experiments led to the discovery of multiple new neocortical cell types, with gene markers and spatial patterns of organization not previously described, by comparison of the molecularly-defined architectures of sensory versus cognitive neocortex, and by quantification of expression of activity-regulated genes as a function of stimulation condition, spatial position, and cell typology. By adapting STARmap to thick tissue blocks, we observed and confirmed a novel molecularly-defined gradient distribution of excitatory neuron subtypes across cubic millimeter-scale volumes (>30,000 cells), and discovered a short-range 3D pattern of self-clustering shared by many inhibitory neuron subtypes that was accurately identifiable with a 3D STARmap approach.
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- Award ID(s):
- 1707261
- NSF-PAR ID:
- 10063217
- Date Published:
- Journal Name:
- Science
- ISSN:
- 1440-0502
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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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Fernandez-Valverde, Selene L. (Ed.)Both the composition of cell types and their spatial distribution in a tissue play a critical role in cellular function, organ development, and disease progression. For example, intratumor heterogeneity and the distribution of transcriptional and genetic events in single cells drive the genesis and development of cancer. However, it can be challenging to fully characterize the molecular profile of cells in a tissue with high spatial resolution because microscopy has limited ability to extract comprehensive genomic information, and the spatial resolution of genomic techniques tends to be limited by dissection. There is a growing need for tools that can be used to explore the relationship between histological features, gene expression patterns, and spatially correlated genomic alterations in healthy and diseased tissue samples. Here, we present a technique that combines label-free histology with spatially resolved multiomics in unfixed and unstained tissue sections. This approach leverages stimulated Raman scattering microscopy to provide chemical contrast that reveals histological tissue architecture, allowing for high-resolution in situ laser microdissection of regions of interests. These microtissue samples are then processed for DNA and RNA sequencing to identify unique genetic profiles that correspond to distinct anatomical regions. We demonstrate the capabilities of this technique by mapping gene expression and copy number alterations to histologically defined regions in human oral squamous cell carcinoma (OSCC). Our approach provides complementary insights in tumorigenesis and offers an integrative tool for macroscale cancer tissues with spatial multiomics assessments.more » « less
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Abstract Objective. Intracortical brain interfaces are an ever evolving technology with growing potential for clinical and research applications. The chronic tissue response to these devices traditionally has been characterized by glial scarring, inflammation, oxidative stress, neuronal loss, and blood-brain barrier disruptions. The full complexity of the tissue response to implanted devices is still under investigation.Approach. In this study, we have utilized RNA-sequencing to identify the spatiotemporal gene expression patterns in interfacial (within 100µ m) and distal (500µ m from implant) brain tissue around implanted silicon microelectrode arrays. Naïve, unimplanted tissue served as a control.Main results. The data revealed significant overall differential expression (DE) in contrasts comparing interfacial tissue vs naïve (157 DE genes), interfacial vs distal (94 DE genes), and distal vs naïve tissues (21 DE genes). Our results captured previously characterized mechanisms of the foreign body response, such as astroglial encapsulation, as well as novel mechanisms which have not yet been characterized in the context of indwelling neurotechnologies. In particular, we have observed perturbations in multiple neuron-associated genes which potentially impact the intrinsic function and structure of neurons at the device interface. In addition to neuron-associated genes, the results presented in this study identified significant DE in genes which are associated with oligodendrocyte, microglia, and astrocyte involvement in the chronic tissue response.Significance . The results of this study increase the fundamental understanding of the complexity of tissue response in the brain and provide an expanded toolkit for future investigation into the bio-integration of implanted electronics with tissues in the central nervous system. -
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that impedes patients’ cognition, social, speech and communication skills. ASD is highly heterogeneous with a variety of etiologies and clinical manifestations. The prevalence rate of ASD increased steadily in recent years. Presently, molecular mechanisms underlying ASD occurrence and development remain to be elucidated. Here, we integrated multi-layer genomics data to investigate the transcriptome and pathway dysregulations in ASD development. The RNA sequencing (RNA-seq) expression profiles of induced pluripotent stem cells (iPSCs), neural progenitor cells (NPCs) and neuron cells from ASD and normal samples were compared in our study. We found that substantially more genes were differentially expressed in the NPCs than the iPSCs. Consistently, gene set variation analysis revealed that the activity of the known ASD pathways in NPCs and neural cells were significantly different from the iPSCs, suggesting that ASD occurred at the early stage of neural system development. We further constructed comprehensive brain- and neural-specific regulatory networks by incorporating transcription factor (TF) and gene interactions with long 5 non-coding RNA(lncRNA) and protein interactions. We then overlaid the transcriptomes of different cell types on the regulatory networks to infer the regulatory cascades. The variations of the regulatory cascades between ASD and normal samples uncovered a set of novel disease-associated genes and gene interactions, particularly highlighting the functional roles of ELF3 and the interaction between STAT1 and lncRNA ELF3-AS 1 in the disease development. These new findings extend our understanding of ASD and offer putative new therapeutic targets for further studies.more » « less
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