scholarly journals Morphometric reconstructions atlas shows insult-driven plasticity in cortical VIP/ChAT interneurons

2020 ◽  
Author(s):  
Nadav Yayon ◽  
Oren Amsalem ◽  
Amir Dudai ◽  
Or Yakov ◽  
Gil Adam ◽  
...  
Keyword(s):  
Steady State ◽  
Structure Function ◽  
Single Cell ◽  
Cognitive Functions ◽  
Spatial Organization ◽  
Rna Seq ◽  
Neuronal Structure ◽  
Experimental Conditions ◽  
Neuronal Populations ◽  
Local Modulation

We developed an automatic morphometric reconstruction pipeline, Pop-Rec, and used it to study the morphologies of cortical cholinergic VIP/ChAT interneurons (VChIs). Cholinergic networks control high cognitive functions, but their local modulation and stress-driven plasticity patterns remained elusive. Reconstructing thousands of local VChIs registered to their exact coordinates in multiple cleared murine cortices highlighted distinct populations of bipolar and multipolar VChIs which differed in their dendritic spatial organization. Following mild unilateral whisker deprivation, Pop-Rec found both ipsi-and contra-lateral VChI dendritic arborization changes. Furthermore, RNA-seq of FACS-sorted VChIs showed differentially expressed dendritic, synapse and axon-modulating transcripts in whisker-deprived mice. Indicating novel steady-state morphological roles, those genes also clustered distinctly in naïve single cell VChIs. This VChIs “morpheome” atlas is the first example of unbiased analysis of neuronal populations and holds the possibility to compare neuronal structure-function relationships across experimental conditions.

scholarly journals Ocular macrophage origin and heterogeneity during steady state and experimental choroidal neovascularization

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Steven Droho ◽  
Benjamin R. Thomson ◽  
Hadijat M. Makinde ◽  
Carla M. Cuda ◽  
Harris Perlman ◽  
...  
Keyword(s):  
Steady State ◽  
Single Cell ◽  
Choroidal Neovascularization ◽  
Differential Expression Analysis ◽  
Age Related Macular Degeneration ◽  
Rna Seq ◽  
Laser Injury ◽  
Classical Monocytes ◽  
Macrophage Subtypes ◽  
Macrophage Subsets

Abstract Background Neovascular age-related macular degeneration (nAMD) commonly causes vision loss from aberrant angiogenesis, termed choroidal neovascularization (CNV). Macrophages are heterogeneous cells that are necessary for experimental CNV, present in human CNV samples, and can display diverse functions, which are dependent upon both their origin and tissue microenvironment. Despite these associations, choroidal macrophage heterogeneity remains unexplored. Methods We performed multi-parameter flow cytometry on wildtype (WT) and Ccr2−/− mice after laser injury to identify macrophage subtypes, and determine which subsets originate from classical monocytes. To fate map tissue resident macrophages at steady state and after laser injury, we used the Cx3cr1CreER/+ ; Rosa26zsGFP/+ mouse model. We reanalyzed previously published single-cell RNA-seq of human choroid samples from healthy and nAMD patients to investigate human macrophage heterogeneity, disease association, and function. Results We identified 4 macrophage subsets in mice: microglia, MHCII+CD11c−, MHCII+CD11c+, and MHCII−. Microglia are tissue resident macrophages at steady state and unaffected by laser injury. At steady state, MHCII− macrophages are long lived, tissue resident macrophages, while MHCII+CD11c− and MHCII+CD11c+ macrophages are partially replenished from blood monocytes. After laser injury, MHCII+CD11c− macrophages are entirely derived from classical monocytes, MHCII− macrophages originate from classical monocytes (90%) and an expansion of tissue resident macrophages (10%), and MHCII+CD11c+ macrophages are derived from classical monocytes (70%), non-classical monocytes (10%), and an expansion of tissue resident macrophages (20%). Single-cell RNA-seq analysis of human choroid found 5 macrophage subsets: two MHCII+CD11C− and three MHCII+CD11C+ populations. One MHCII+CD11C+ subset was 78% derived from a patient with nAMD. Differential expression analysis identified up-regulation of pro-angiogenic gene expression in one MHCII+CD11C− and two MHCII+CD11C+ subsets, including the disease-associated cluster. The upregulated MHCII+CD11C− pro-angiogenic genes were unique compared to the increased MHCII+CD11C+ angiogenesis genes. Conclusions Macrophage origin impacts heterogeneity at steady state and after laser injury in mice. Both mice and human patients demonstrate similar macrophage subtypes. Two discrete pro-angiogenic macrophage populations exist in the human choroid. Targeting specific, pro-angiogenic macrophage subsets is a potential novel therapeutic for nAMD.

scholarly journals Assessment of statistical methods from single cell, bulk RNA-seq and metagenomics applied to microbiome data

2020 ◽  
Author(s):  
Matteo Calgaro ◽  
Chiara Romualdi ◽  
Levi Waldron ◽  
Davide Risso ◽  
Nicola Vitulo
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Differential Expression Analysis ◽  
Informed Choice ◽  
Correct Identification ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Exploratory Data ◽  
Microbiome Data ◽  
False Discoveries

AbstractBackgroundThe correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking.ResultsHere, we compare methods developed for single cell, bulk RNA-seq, and microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, and power. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing.ConclusionsThe multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

scholarly journals A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples

2021 ◽  
Author(s):  
Wenpin Hou ◽  
Zhicheng Ji ◽  
Zeyu Chen ◽  
E John Wherry ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Biological Processes ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Computational Framework ◽  
Statistical Framework ◽  
Gene Regulatory ◽  
Multiple Samples ◽  
False Discoveries

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many computational methods have been developed to infer the pseudo-temporal trajectories of cells within a biological sample, methods that compare pseudo-temporal patterns with multiple samples (or replicates) across different experimental conditions are lacking. Lamian is a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. It can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions, and also to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both simulations and real scRNA-seq data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

scholarly journals Cellular taxonomy and spatial organization of the ventral posterior hypothalamus reveals neuroanatomical parcellation of the mammillary bodies

2020 ◽  
Author(s):  
Laura E. Mickelsen ◽  
William F. Flynn ◽  
Kristen Springer ◽  
Lydia Wilson ◽  
Eric J. Beltrami ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Single Cells ◽  
Neuronal Cell ◽  
Cell Types ◽  
Posterior Hypothalamus ◽  
Neuronal Populations ◽  
Mammillary Bodies ◽  
Distinct Cell ◽  
And Behavior

ABSTRACTThe ventral posterior hypothalamus (VPH) is an anatomically complex brain region implicated in arousal, reproduction, energy balance and memory processing. However, neuronal cell type diversity within the VPH is poorly understood, an impediment to deconstructing the roles of distinct VPH circuits in physiology and behavior. To address this question, we employed a droplet-based single cell RNA sequencing (scRNA-seq) approach to systematically classify molecularly distinct cell types in the mouse VPH. Analysis of >16,000 single cells revealed 20 neuronal and 18 non-neuronal cell populations, defined by suites of discriminatory markers. We validated differentially expressed genes in a selection of neuronal populations through fluorescence in situ hybridization (FISH). Focusing on the mammillary bodies (MB), we discovered transcriptionally-distinct clusters that exhibit a surprising degree of segregation within neuroanatomical subdivisions of the MB, while genetically-defined MB cell types project topographically to the anterior thalamus. This single cell transcriptomic atlas of cell types in the VPH provides a detailed resource for interrogating the circuit-level mechanisms underlying the diverse functions of VPH circuits in health and disease.

scholarly journals Discrete Distributional Differential Expression (D3E) - A Tool for Gene Expression Analysis of Single-cell RNA-seq Data

2015 ◽  
Author(s):  
Mihails Delmans ◽  
Martin Hemberg
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Burst Size ◽  
Synthetic Data ◽  
Driving Mechanism ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Distributional Method ◽  
Average Burst Size ◽  
Differential Gene

The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed (DE) between two experimental conditions. Here, we present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. D3E is based on an analytically tractable stochastic model, and thus it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.

scholarly journals Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

2019 ◽  
Author(s):  
Jovan Tanevski ◽  
Thin Nguyen ◽  
Buu Truong ◽  
Nikos Karaiskos ◽  
Mehmet Eren Ahsen ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Gene Selection ◽  
Spatial Information ◽  
Spatial Clustering ◽  
Spatial Location ◽  
Drosophila Embryo ◽  
Rna Seq ◽  
Hybridization Data ◽  
Transcriptomics Data

AbstractSingle-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

scholarly journals An improved zebrafish transcriptome annotation for sensitive and comprehensive detection of cell type-specific genes

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Nathan D Lawson ◽  
Rui Li ◽  
Masahiro Shin ◽  
Ann Grosse ◽  
Onur Yukselen ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Human Disease ◽  
Untranslated Regions ◽  
Rna Seq ◽  
Cell Type ◽  
Experimental Conditions ◽  
Cell Clustering ◽  
A Genome ◽  
Cell Type Specific

The zebrafish is ideal for studying embryogenesis and is increasingly applied to model human disease. In these contexts, RNA-sequencing (RNA-seq) provides mechanistic insights by identifying transcriptome changes between experimental conditions. Application of RNA-seq relies on accurate transcript annotation for a genome of interest. Here, we find discrepancies in analysis from RNA-seq datasets quantified using Ensembl and RefSeq zebrafish annotations. These issues were due, in part, to variably annotated 3' untranslated regions and thousands of gene models missing from each annotation. Since these discrepancies could compromise downstream analyses and biological reproducibility, we built a more comprehensive zebrafish transcriptome annotation that addresses these deficiencies. Our annotation improves detection of cell type-specific genes in both bulk and single cell RNA-seq datasets, where it also improves resolution of cell clustering. Thus, we demonstrate that our new transcriptome annotation can outperform existing annotations, providing an important resource for zebrafish researchers.

scholarly journals Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations

2019 ◽  
Vol 5 (5) ◽  
pp. eaav2249 ◽  
Author(s):  
Dongju Shin ◽  
Wookjae Lee ◽  
Ji Hyun Lee ◽  
Duhee Bang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cost Effective ◽  
Specific Gene ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Cost Effective Method ◽  
Treatment Experiment ◽  
Single Cell Profiling ◽  
Multiple Samples

The development of high-throughput single-cell RNA sequencing (scRNA-seq) has enabled access to information about gene expression in individual cells and insights into new biological areas. Although the interest in scRNA-seq has rapidly grown in recent years, the existing methods are plagued by many challenges when performing scRNA-seq on multiple samples. To simultaneously analyze multiple samples with scRNA-seq, we developed a universal sample barcoding method through transient transfection with short barcode oligonucleotides. By conducting a species-mixing experiment, we have validated the accuracy of our method and confirmed the ability to identify multiplets and negatives. Samples from a 48-plex drug treatment experiment were pooled and analyzed by a single run of Drop-Seq. This revealed unique transcriptome responses for each drug and target-specific gene expression signatures at the single-cell level. Our cost-effective method is widely applicable for the single-cell profiling of multiple experimental conditions, enabling the widespread adoption of scRNA-seq for various applications.

ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions

2019 ◽  
Vol 35 (17) ◽  
pp. 3038-3045 ◽  
Author(s):  
Xin Gao ◽  
Deqing Hu ◽  
Madelaine Gogol ◽  
Hua Li
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Population Level ◽  
Supplementary Information ◽  
Marker Genes ◽  
Rna Seq ◽  
Matching Problem ◽  
Experimental Conditions ◽  
Cut Method ◽  
Underlying Mechanisms

Abstract Motivation Single cell RNA-Seq (scRNA-Seq) facilitates the characterization of cell type heterogeneity and developmental processes. Further study of single cell profiles across different conditions enables the understanding of biological processes and underlying mechanisms at the sub-population level. However, developing proper methodology to compare multiple scRNA-Seq datasets remains challenging. Results We have developed ClusterMap, a systematic method and workflow to facilitate the comparison of scRNA-seq profiles across distinct biological contexts. Using hierarchical clustering of the marker genes of each sub-group, ClusterMap matches the sub-types of cells across different samples and provides ‘similarity’ as a metric to quantify the quality of the match. We introduce a purity tree cut method designed specifically for this matching problem. We use Circos plot and regrouping method to visualize the results concisely. Furthermore, we propose a new metric ‘separability’ to summarize sub-population changes among all sample pairs. In the case studies, we demonstrate that ClusterMap has the ability to provide us further insight into the different molecular mechanisms of cellular sub-populations across different conditions. Availability and implementation ClusterMap is implemented in R and available at https://github.com/xgaoo/ClusterMap. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space

2021 ◽  
Author(s):  
Lei Xiong ◽  
Kang Tian ◽  
Yuzhe Li ◽  
Qiangfeng Zhang
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Experimental Conditions ◽  
Multiple Data ◽  
Mouse Tissues ◽  
Heterogeneous Datasets ◽  
Cell Data ◽  
Human And Mouse ◽  
Biological Differences

Abstract Single-cell RNA-seq and ATAC-seq analyses have been widely applied to decipher cell-type and regulation complexities. However, experimental conditions often confound biological variations when comparing data from different samples. For integrative single-cell data analysis, we have developed SCALEX, a deep generative framework that maps cells into a generalized, batch-invariant cell-embedding space. We demonstrate that SCALEX accurately and efficiently integrates heterogenous single-cell data using multiple benchmarks. It outperforms competing methods, especially for datasets with partial overlaps, accurately aligning similar cell populations while r,etaining true biological differences. We demonstrate the advantages of SCALEX by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19, which were assembled from multiple data sources and can keep growing through the inclusion of new incoming data. Analyses based on these atlases revealed the complex cellular landscapes of human and mouse tissues and identified multiple peripheral immune subtypes associated with COVID-19 disease severity.

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