Behavioral state coding by molecularly defined paraventricular hypothalamic cell type ensembles

Science ◽  
2020 ◽  
Vol 370 (6514) ◽  
pp. eabb2494 ◽  
Author(s):  
Shengjin Xu ◽  
Hui Yang ◽  
Vilas Menon ◽  
Andrew L. Lemire ◽  
Lihua Wang ◽  
...  

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.

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Tianyuan Lu ◽  
Jessica C. Mar

Abstract Background It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level. Methods Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism. Results For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF. Conclusions We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.


Science ◽  
2020 ◽  
Vol 370 (6518) ◽  
pp. eaba7612 ◽  
Author(s):  
Silvia Domcke ◽  
Andrew J. Hill ◽  
Riza M. Daza ◽  
Junyue Cao ◽  
Diana R. O’Day ◽  
...  

The chromatin landscape underlying the specification of human cell types is of fundamental interest. We generated human cell atlases of chromatin accessibility and gene expression in fetal tissues. For chromatin accessibility, we devised a three-level combinatorial indexing assay and applied it to 53 samples representing 15 organs, profiling ~800,000 single cells. We leveraged cell types defined by gene expression to annotate these data and cataloged hundreds of thousands of candidate regulatory elements that exhibit cell type–specific chromatin accessibility. We investigated the properties of lineage-specific transcription factors (such as POU2F1 in neurons), organ-specific specializations of broadly distributed cell types (such as blood and endothelial), and cell type–specific enrichments of complex trait heritability. These data represent a rich resource for the exploration of in vivo human gene regulation in diverse tissues and cell types.


2019 ◽  
Author(s):  
Kelly M. Bakulski ◽  
John F. Dou ◽  
Robert C. Thompson ◽  
Christopher Lee ◽  
Lauren Y. Middleton ◽  
...  

AbstractBackgroundLead (Pb) exposure is ubiquitous and has permanent developmental effects on childhood intelligence and behavior and adulthood risk of dementia. The hippocampus is a key brain region involved in learning and memory, and its cellular composition is highly heterogeneous. Pb acts on the hippocampus by altering gene expression, but the cell type-specific responses are unknown.ObjectiveExamine the effects of perinatal Pb treatment on adult hippocampus gene expression, at the level of individual cells, in mice.MethodsIn mice perinatally exposed to control water (n=4) or a human physiologically-relevant level (32 ppm in maternal drinking water) of Pb (n=4), two weeks prior to mating through weaning, we tested for gene expression and cellular differences in the hippocampus at 5-months of age. Analysis was performed using single cell RNA-sequencing of 5,258 cells from the hippocampus by 10x Genomics Chromium to 1) test for gene expression differences averaged across all cells by treatment; 2) compare cell cluster composition by treatment; and 3) test for gene expression and pathway differences within cell clusters by treatment.ResultsGene expression patterns revealed 12 cell clusters in the hippocampus, mapping to major expected cell types (e.g. microglia, astrocytes, neurons, oligodendrocytes). Perinatal Pb treatment was associated with 12.4% more oligodendrocytes (P=4.4×10−21) in adult mice. Across all cells, differential gene expression analysis by Pb treatment revealed cluster marker genes. Within cell clusters, differential gene expression with Pb treatment (q<0.05) was observed in endothelial, microglial, pericyte, and astrocyte cells. Pathways up-regulated with Pb treatment were protein folding in microglia (P=3.4×10−9) and stress response in oligodendrocytes (P=3.2×10−5).ConclusionBulk tissue analysis may be confounded by changes in cell type composition and may obscure effects within vulnerable cell types. This study serves as a biological reference for future single cell studies of toxicant or neuronal complications, to ultimately characterize the molecular basis by which Pb influences cognition and behavior.


Author(s):  
Isabella N. Grabski ◽  
Rafael A. Irizarry

AbstractSingle-cell RNA sequencing (scRNA-seq) quantifies gene expression for individual cells in a sample, which allows distinct cell-type populations to be identified and characterized. An important step in many scRNA-seq analysis pipelines is the annotation of cells into known cell-types. While this can be achieved using experimental techniques, such as fluorescence-activated cell sorting, these approaches are impractical for large numbers of cells. This motivates the development of data-driven cell-type annotation methods. We find limitations with current approaches due to the reliance on known marker genes or from overfitting because of systematic differences between studies or batch effects. Here, we present a statistical approach that leverages public datasets to combine information across thousands of genes, uses a latent variable model to define cell-type-specific barcodes and account for batch effect variation, and probabilistically annotates cell-type identity. The barcoding approach also provides a new way to discover marker genes. Using a range of datasets, including those generated to represent imperfect real-world reference data, we demonstrate that our approach substantially outperforms current reference-based methods, in particular when predicting across studies. Our approach also demonstrates that current approaches based on unsupervised clustering lead to false discoveries related to novel cell-types.


2020 ◽  
Vol 176 (2) ◽  
pp. 396-409
Author(s):  
Kelly M Bakulski ◽  
John F Dou ◽  
Robert C Thompson ◽  
Christopher Lee ◽  
Lauren Y Middleton ◽  
...  

Abstract Lead (Pb) exposure is ubiquitous with permanent neurodevelopmental effects. The hippocampus brain region is involved in learning and memory with heterogeneous cellular composition. The hippocampus cell type-specific responses to Pb are unknown. The objective of this study is to examine perinatal Pb treatment effects on adult hippocampus gene expression, at the level of individual cells. In mice perinatally exposed to control water or a human physiologically relevant level (32 ppm in maternal drinking water) of Pb, 2 weeks prior to mating through weaning, we tested for hippocampus gene expression and cellular differences at 5 months of age. We sequenced RNA from 5258 hippocampal cells to (1) test for treatment gene expression differences averaged across all cells, (2) compare cell cluster composition by treatment, and (3) test for treatment gene expression and pathway differences within cell clusters. Gene expression patterns revealed 12 hippocampus cell clusters, mapping to major expected cell types (eg, microglia, astrocytes, neurons, and oligodendrocytes). Perinatal Pb treatment was associated with 12.4% more oligodendrocytes (p = 4.4 × 10−21) in adult mice. Across all cells, Pb treatment was associated with expression of cell cluster marker genes. Within cell clusters, Pb treatment (q &lt; 0.05) caused differential gene expression in endothelial, microglial, pericyte, and astrocyte cells. Pb treatment upregulated protein folding pathways in microglia (p = 3.4 × 10−9) and stress response in oligodendrocytes (p = 3.2 × 10−5). Bulk tissue analysis may be influenced by changes in cell type composition, obscuring effects within vulnerable cell types. This study serves as a biological reference for future single-cell toxicant studies, to ultimately characterize molecular effects on cognition and behavior.


2021 ◽  
Author(s):  
Hao-Shan Chen ◽  
Xiao-Long Zhang ◽  
Rong-Rong Yang ◽  
Guang-Ling Wang ◽  
Xin-Yue Zhu ◽  
...  

The complexity of brain circuitry is manifested by numerous cell types based on genetic marker, location and neural connectivity. Cell-type specific recording and manipulation is essential to disentangle causal neural mechanisms in physiology and behavior; however, many current approaches are largely limited by number of intersectional features, incompatibility of common effectors and insufficient gene expression. To tackle these limitations, we devise an intein-based intersectional synthesis of transactivator (IBIST) to selectively control gene expression of common effectors in specific cell types defined by a combination of multiple features. We validate the specificity and sufficiency of IBIST to control common effectors including fluorophores, optogenetic opsins and Ca2+ indicators in various intersectional conditions in vivo. Using IBIST-based Ca2+ imaging, we show that the IBIST can intersect up to five features, and that hippocampal cells tune differently to distinct emotional valences depending on the pattern of projection targets. Collectively, the IBIST multiplexes the capability to intersect cell-type features and is compatible with common effectors to effectively control gene expression, monitor and manipulate neural activities.


F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1522 ◽  
Author(s):  
Brendan T. Innes ◽  
Gary D. Bader

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3250-3250
Author(s):  
Eleni-Dimitra Papanagnou ◽  
Tina Bagratuni ◽  
Efstathios Kastritis ◽  
Issidora Papassideri ◽  
Evangelos Terpos ◽  
...  

Abstract Organisms require efficient surveillance of proteome functionality to prevent disruption of proteostasis. Central to the proteostasis ensuring network is the proteasome, which degrades both normal short-lived ubiquitinated proteins and damaged or mutated proteins. Over-activation of the proteasome seems to represent a hallmark of advanced tumors and thus, its selective inhibition provides a strategy for the development of novel anti-tumor therapies. This approach is applied in multiple myeloma (MM) that represents the second most common hematological malignancy. Specifically, proteasome inhibitors have demonstrated clinical efficacy in the treatment of MM and mantle cell lymphoma and are evaluated for the treatment of other malignancies. Nevertheless, the impact of proteasome dysfunction in normal human tissues (which relates to side effects in the clinic) remains poorly understood. By using the fruit fly Drosophila melanogaster as an in vivo experimental platform to study proteasome physiology we found that proteasome functionality is sex-, tissue- and age-dependent. Oral administration of proteasome inhibitors (e.g. Bortezomib or Carfilzomib) in young flies suppressed proteasome activities in the somatic tissues; reduced motor function (recapitulating peripheral neuropathy of Bortezomib treatment in the clinic) and caused premature aging. It also increased oxidative stress and activated an Nrf2-dependent feedback regulatory circuit that upregulated proteasome genes in order to restore normal proteasome functionality. Moreover, in line with observations in the clinic, Carfilzomib was found to cause milder (as compared to Bortezomib) neuromusculatory toxicity and reduction of flies' lifespan. To address the question whether these findings can be translated in humans we started characterizing proteasome physiology in both healthy donors, as well as in MM patients treated with therapeutic proteasome inhibitors. For our studies we used isolated red blood cells (RBCs; represent an anucleate relatively "long-lived" proteome) and peripheral blood mononucleated cells (PBMCs; represent cell lineages with active genomic responses). Our analyses in healthy donors of different ages revealed significant variability of basal proteasome peptidase activities in both cell types. PBMCs expressed (as compared to RBCs) higher basal proteasome activities and RBCs from females had higher chymotrypsin-like activity as compared to RBCs from males of similar age. Furthermore, as in the flies' somatic tissues, proteasome activities were found (independently of sex and cell type) to decline during aging. Studies in RBCs and PBMCs isolated from MM patients treated with Bortezomib revealed donor-, cell type- and drug-specific readouts. In most (but not all) cases proteasome activities were suppressed in both cell types at 24-hrs post-drug administration. RBCs were particularly sensitive to the inhibitor and their proteasome activities remained low during the entire course of treatment. On the contrary, PBMCs were characterized by phases of rebound proteasome activities during the periods of no drug administration; these phases correlated with upregulation of proteasome genes expression, indicating that the feedback regulatory circuit which functions to restore proteasome activities in flies is also operational in humans. Additional gene expression analyses in PBMCs showed that proteasome inhibition also triggers the induction of genes involved in chaperon, autophagy, unfolded protein- and antioxidant-responses pathways; while, as in the fly model, the intensity of genes induction seems to decline during aging. Interestingly, in those patients who (despite treatment) showed no reduction of proteasome activities we found marginal gene expression alterations, suggesting that the observed gene induction largely depends on proteasome loss of function. Importantly, at the clinical level we observed a positive correlation between the degree of proteasome inhibition (in PBMCs or RBCS) and the depth of disease responses. The similarities between the Drosophila pharmacological model and the MM patients indicate that the molecular responses to proteasome malfunction are largely conserved in higher metazoans. We foresee that our ongoing studies will support a more personalized clinical therapeutic approach in hematological malignancies. Disclosures Terpos: Amgen: Honoraria, Other: Travel expenses, Research Funding; Takeda: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses; Novartis: Honoraria; Celgene: Honoraria, Other: Travel expenses. Dimopoulos:Celgene: Honoraria; Onyx: Honoraria; Novartis: Honoraria; Genesis: Honoraria; Janssen-Cilag: Honoraria; Janssen: Honoraria; Amgen: Honoraria.


Development ◽  
1994 ◽  
Vol 120 (7) ◽  
pp. 1983-1995 ◽  
Author(s):  
J. Castelli-Gair ◽  
S. Greig ◽  
G. Micklem ◽  
M. Akam

Homeotic genes confer identity to the different segments of Drosophila. These genes are expressed in many cell types over long periods of time. To determine when the homeotic genes are required for specific developmental events we have expressed the Ultrabithorax, abdominal-A and Abdominal-Bm proteins at different times during development using the GAL4 targeting technique. We find that early transient homeotic gene expression has no lasting effects on the differentiation of the larval epidermis, but it switches the fate of other cell types irreversibly (e.g. the spiracle primordia). We describe one cell type in the peripheral nervous system that makes sequential, independent responses to homeotic gene expression. We also provide evidence that supports the hypothesis of in vivo competition between the bithorax complex proteins for the regulation of their down-stream targets.


F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1522 ◽  
Author(s):  
Brendan T. Innes ◽  
Gary D. Bader

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.


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