scholarly journals ClusterMap: Comparing analyses across multiple Single Cell RNA-Seq profiles

2018 ◽  
Author(s):  
Xin Gao ◽  
Deqing Hu ◽  
Madelaine Gogol ◽  
Hua Li
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Population Level ◽  
Marker Genes ◽  
Rna Seq ◽  
Matching Problem ◽  
Cut Method ◽  
Underlying Mechanisms

AbstractSingle cell RNA-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. We have developed ClusterMap, a systematic method and workflow to facilitate the comparison of scRNA 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 three case studies, we demonstrate that ClusterMap has the ability to offer us further insight into the different molecular mechanisms of cellular sub-populations across different conditions. ClusterMap is implemented in R and available at https://github.com/xgaoo/ClusterMap.

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 Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies.

2021 ◽  
Author(s):  
Lorenzo Martini ◽  
Roberta Bardini ◽  
Stefano Di Carlo
Keyword(s):  
Single Cell ◽  
Meta Analysis ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Marker Genes ◽  
Inhibitory Interneurons ◽  
Rna Seq ◽  
Circuit Function ◽  
The Brain

The mammalian cortex contains a great variety of neuronal cells. In particular, GABAergic interneurons, which play a major role in neuronal circuit function, exhibit an extraordinary diversity of cell types. In this regard, single-cell RNA-seq analysis is crucial to study cellular heterogeneity. To identify and analyze rare cell types, it is necessary to reliably label cells through known markers. In this way, all the related studies are dependent on the quality of the employed marker genes. Therefore, in this work, we investigate how a set of chosen inhibitory interneurons markers perform. The gene set consists of both immunohistochemistry-derived genes and single-cell RNA-seq taxonomy ones. We employed various human and mouse datasets of the brain cortex, consequently processed with the Monocle3 pipeline. We defined metrics based on the relations between unsupervised cluster results and the marker expression. Specifically, we calculated the specificity, the fraction of cells expressing, and some metrics derived from decision tree analysis like entropy gain and impurity reduction. The results highlighted the strong reliability of some markers but also the low quality of others. More interestingly, though, a correlation emerges between the general performances of the genes set and the experimental quality of the datasets. Therefore, the proposed method allows evaluating the quality of a dataset in relation to its reliability regarding the inhibitory interneurons cellular heterogeneity study.

scholarly journals Prediction of single-cell mechanisms for disease progression in hypertrophic remodelling by a trans-omics approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Momoko Hamano ◽  
Seitaro Nomura ◽  
Midori Iida ◽  
Issei Komuro ◽  
Yoshihiro Yamanishi
Keyword(s):  
Heart Failure ◽  
Single Cell ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Marker Genes ◽  
Specific Gene ◽  
Rna Seq ◽  
Aortic Constriction ◽  
Multiple Risk Factors

AbstractHeart failure is a heterogeneous disease with multiple risk factors and various pathophysiological types, which makes it difficult to understand the molecular mechanisms involved. In this study, we proposed a trans-omics approach for predicting molecular pathological mechanisms of heart failure and identifying marker genes to distinguish heterogeneous phenotypes, by integrating multiple omics data including single-cell RNA-seq, ChIP-seq, and gene interactome data. We detected a significant increase in the expression level of natriuretic peptide A (Nppa), after stress loading with transverse aortic constriction (TAC), and showed that cardiomyocytes with high Nppa expression displayed specific gene expression patterns. Multiple NADH ubiquinone complex family, which are associated with the mitochondrial electron transport system, were negatively correlated with Nppa expression during the early stages of cardiac hypertrophy. Large-scale ChIP-seq data analysis showed that Nkx2-5 and Gtf2b were transcription factors characteristic of high-Nppa-expressing cardiomyocytes. Nppa expression levels may, therefore, represent a useful diagnostic marker for heart failure.

scholarly journals The Application of Single-Cell RNA Sequencing in Mammalian Meiosis Studies

2021 ◽  
Vol 9 ◽  
Author(s):  
Yiheng Peng ◽  
Huanyu Qiao
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Level ◽  
Cellular Division ◽  
Division Process ◽  
Complex Events ◽  
Downstream Analysis

Meiosis is a cellular division process that produces gametes for sexual reproduction. Disruption of complex events throughout meiosis, such as synapsis and homologous recombination, can lead to infertility and aneuploidy. To reveal the molecular mechanisms of these events, transcriptome studies of specific substages must be conducted. However, conventional methods, such as bulk RNA-seq and RT-qPCR, are not able to detect the transcriptional variations effectively and precisely, especially for identifying cell types and stages with subtle differences. In recent years, mammalian meiotic transcriptomes have been intensively studied at the single-cell level by using single-cell RNA-seq (scRNA-seq) approaches, especially through two widely used platforms, Smart-seq2 and Drop-seq. The scRNA-seq protocols along with their downstream analysis enable researchers to accurately identify cell heterogeneities and investigate meiotic transcriptomes at a higher resolution. In this review, we compared bulk RNA-seq and scRNA-seq to show the advantages of the scRNA-seq in meiosis studies; meanwhile, we also pointed out the challenges and limitations of the scRNA-seq. We listed recent findings from mammalian meiosis (male and female) studies where scRNA-seq applied. Next, we summarized the scRNA-seq analysis methods and the meiotic marker genes from spermatocytes and oocytes. Specifically, we emphasized the different features of the two scRNA-seq protocols (Smart-seq2 and Drop-seq) in the context of meiosis studies and discussed their strengths and weaknesses in terms of different research purposes. Finally, we discussed the future applications of scRNA-seq in the meiosis field.

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

PRDM16 Is a Compact Myocardium-Enriched Transcription Factor Required to Maintain Compact Myocardial Cardiomyocyte Identity in Left Ventricle

Circulation ◽  
2021 ◽  
Author(s):  
Tongbin Wu ◽  
Zhengyu Liang ◽  
Zengming Zhang ◽  
Canzhao Liu ◽  
Lunfeng Zhang ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Single Cell ◽  
Rna Sequencing ◽  
Mouse Models ◽  
Molecular Mechanisms ◽  
Left Ventricular ◽  
Ventricular Noncompaction ◽  
Chip Sequencing ◽  
Transcriptional Signature ◽  
Underlying Mechanisms

Background: Left ventricular noncompaction cardiomyopathy (LVNC) was discovered half a century ago as a cardiomyopathy with excessive trabeculation and a thin ventricular wall. In the decades since, numerous studies have demonstrated that LVNC primarily impacts left ventricles (LVs), and is often associated with LV dilation and dysfunction. However, owing in part to the lack of suitable mouse models that faithfully mirror the selective LV vulnerability in patients, mechanisms underlying susceptibility of LV to dilation and dysfunction in LVNC remain unknown. Genetic studies have revealed that deletions and mutations in PRDM16 cause LVNC, but previous conditional Prdm16 knockout mouse models do not mirror the LVNC phenotype in patients, and importantly, the underlying molecular mechanisms by which PRDM16 deficiency causes LVNC are still unclear. Methods: Prdm16 cardiomyocyte (CM)-specific knockout ( Prdm16 cKO ) mice were generated and analyzed for cardiac phenotypes. RNA sequencing and ChIP sequencing were performed to identify direct transcriptional targets of PRDM16 in CMs. Single cell RNA sequencing in combination with Spatial Transcriptomics were employed to determine CM identity at single cell level. Results: CM-specific ablation of Prdm16 in mice caused LV-specific dilation and dysfunction, as well as biventricular noncompaction, which fully recapitulated LVNC in patients. Mechanistically, PRDM16 functioned as a compact myocardium-enriched transcription factor, which activated compact myocardial genes while repressing trabecular myocardial genes in LV compact myocardium. Consequently, Prdm16 cKO LV compact myocardial CMs shifted from their normal transcriptomic identity to a transcriptional signature resembling trabecular myocardial CMs and/or neurons. Chamber-specific transcriptional regulation by PRDM16 was in part due to its cooperation with LV-enriched transcription factors Tbx5 and Hand1. Conclusions: These results demonstrate that disruption of proper specification of compact CM may play a key role in the pathogenesis of LVNC. They also shed light on underlying mechanisms of LV-restricted transcriptional program governing LV chamber growth and maturation, providing a tangible explanation for the susceptibility of LV in a subset of LVNC cardiomyopathies.

scholarly journals JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Latent Space ◽  
Cell Type Specific ◽  
Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

scholarly journals Regeneration of Planarian Auricles and Reestablishment of Chemotactic Ability

2021 ◽  
Vol 9 ◽  
Author(s):  
Eugene Matthew P. Almazan ◽  
Joseph F. Ryan ◽  
Labib Rouhana
Keyword(s):  
Molecular Mechanisms ◽  
Behavioral Experiments ◽  
X Ray ◽  
Preferential Expression ◽  
Chemical Stimuli ◽  
And Function ◽  
Function Models ◽  
Chemotactic Ability

Detection of chemical stimuli is crucial for living systems and also contributes to quality of life in humans. Since loss of olfaction becomes more prevalent with aging, longer life expectancies have fueled interest in understanding the molecular mechanisms behind the development and maintenance of chemical sensing. Planarian flatworms possess an unsurpassed ability for stem cell-driven regeneration that allows them to restore any damaged or removed part of their bodies. This includes anteriorly-positioned lateral flaps known as auricles, which have long been thought to play a central role in chemotaxis. The contribution of auricles to the detection of positive chemical stimuli was tested in this study using Girardia dorotocephala, a North American planarian species known for its morphologically prominent auricles. Behavioral experiments staged under laboratory conditions revealed that removal of auricles by amputation leads to a significant decrease in the ability of planarians to find food. However, full chemotactic capacity is observed as early as 2 days post-amputation, which is days prior from restoration of auricle morphology, but correlative with accumulation of ciliated cells in the position of auricle regeneration. Planarians subjected to x-ray irradiation prior to auricle amputation were unable to restore auricle morphology, but were still able to restore chemotactic capacity. These results indicate that although regeneration of auricle morphology requires stem cells, some restoration of chemotactic ability can still be achieved in the absence of normal auricle morphology, corroborating with the initial observation that chemotactic success is reestablished 2-days post-amputation in our assays. Transcriptome profiles of excised auricles were obtained to facilitate molecular characterization of these structures, as well as the identification of genes that contribute to chemotaxis and auricle development. A significant overlap was found between genes with preferential expression in auricles of G. dorotocephala and genes with reduced expression upon SoxB1 knockdown in Schmidtea mediterranea, suggesting that SoxB1 has a conserved role in regulating auricle development and function. Models that distinguish between possible contributions to chemotactic behavior obtained from cellular composition, as compared to anatomical morphology of the auricles, are discussed.

scholarly journals Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Qingnan Liang ◽  
Rachayata Dharmat ◽  
Leah Owen ◽  
Akbar Shakoor ◽  
Yumei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Retinal Cell ◽  
Peripheral Retina ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Type ◽  
Retinal Tissue ◽  
The Individual

AbstractSingle-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.

scholarly journals Quantitative Single-Cell Transcript Assessment of Biomarkers Supports Cellular Heterogeneity in the Bovine IVD

2019 ◽  
Vol 6 (2) ◽  
pp. 42 ◽  
Author(s):  
Kangning Li ◽  
Devin Kapper ◽  
Sumona Mondal ◽  
Thomas Lufkin ◽  
Petra Kraus
Keyword(s):  
In Situ Hybridization ◽  
Single Cell ◽  
Gaussian Mixture ◽  
Cellular Heterogeneity ◽  
Rna In Situ Hybridization ◽  
Ivd Degeneration

Severe and chronic low back pain is often associated with intervertebral disc (IVD) degeneration. While imposing a considerable socio-economic burden worldwide, IVD degeneration is also severely impacting on the quality of life of affected individuals. Cell-based regenerative medicine approaches have moved into clinical trials, yet IVD cell identities in the mature disc remain to be fully elucidated and tissue heterogeneity exists, requiring a better characterization of IVD cells. The bovine coccygeal IVD is an accepted research model to study IVD mechano-biology and disc homeostasis. Recently, we identified novel IVD biomarkers in the outer annulus fibrosus (AF) and nucleus pulposus (NP) of the mature bovine coccygeal IVD through RNA in situ hybridization (AP-RISH) and z-proportion test. Here we follow up on Lam1, Thy1, Gli1, Gli3, Noto, Ptprc, Scx, Sox2 and Zscan10 with fluorescent RNA in situ hybridization (FL-RISH) and confocal microscopy. This permits sub-cellular transcript localization and the addition of quantitative single-cell derived values of mRNA expression levels to our previous analysis. Lastly, we used a Gaussian mixture modeling approach for the exploratory analysis of IVD cells. This work complements our earlier cell population proportion-based study, confirms the previously proposed biomarkers and indicates even further heterogeneity of cells in the outer AF and NP of a mature IVD.

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