scholarly journals EPEN-05. MUTATIONAL ANALYSIS OF THE C11ORF95 DOMAIN AND SINGLE-CELL RNA-SEQ PROFILE OF A MOUSE MODEL OF SUPRATENTORIAL EPENDYMOMA

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i14-i14
Author(s):  
Kevin Truong ◽  
James He ◽  
Gavin Birdsall ◽  
Ericka Randazzo ◽  
Jesse Dunnack ◽  
...  
Keyword(s):  
Mouse Model ◽  
Single Cell ◽  
Tumor Cells ◽  
Cell Fate ◽  
Transcriptional Activation ◽  
Mutational Analysis ◽  
Cell Types ◽  
Marker Genes ◽  
Rna Seq ◽  
Loss Of Function

Abstract We used a recently developed mouse model to better understand the cellular and molecular determinants of tumors driven by the oncogenic fusion protein C11orf95-RELA. Our approach makes use of in utero electroporation and a binary transposase system to introduce human C11orf95-RELA sequence, wild type and mutant forms, into neural progenitors. We used single cell RNA-seq to profile the cellular constituents within the resulting tumors in mice. We find that approximately 70% of the cells in the tumors do not express the oncogene C11orf95-RELA and these non-oncogene expressing cells are a combination of different non-tumor cell cell-types, including significant numbers of T-cells, and macrophages. The C11orf95-RELA expressing tumor cells have a unique transcriptomic profile that includes both astrocytic and neural progenitor marker genes, and is distinct from glioblastoma transcriptomic profiles. Since C11orf95-RELA is believed to function through a combination of both activation of NF-κB response genes by constitutive activation of RELA, and genes not activated by NF-κB, we assessed the expression of NF-κB response genes across the populations of cells in the tumor. Interestingly, when tumor cells highly expressing C11orf95-RELA were analyzed further, the subclusters identified were distinguished by upregulation of non-NF-kB pathways involved in cell proliferation, cell fate determination, and immune activation. We hypothesized that the C11orf95 domain may function to bring RELA transcriptional activation to inappropriate non-NF-κB targets, and we therefore performed a point mutation analysis of the C11orf95 domain. We found that mutations in either of the cysteines or histidines that make up a possible zinc finger domain in C11orf95 eliminate the ability of the fusion to induce tumors. In cell lines, these loss-of-function point mutants still trafficked to nuclei, and activated NF-κB pathways. We are currently using RNAseq and CRISPR loss-of function to identify genes downstream of C11orf95-RELA that are required for tumorigenesis.

scholarly journals TMOD-32. MUTATIONAL ANALYSIS OF THE C11orf95 DOMAIN AND SINGLE CELL RNASEQ PROFILE OF A MOUSE MODEL OF SUPRATENTORIAL EPENDYMOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii234-ii235
Author(s):  
Kevin Troung ◽  
James He ◽  
Ericka Randazzo ◽  
Jesse Dunnack ◽  
Joseph LoTurco
Keyword(s):  
Mouse Model ◽  
Tumor Cell ◽  
Single Cell ◽  
Transcriptional Activation ◽  
Mutational Analysis ◽  
Cell Types ◽  
Marker Genes ◽  
Loss Of Function ◽  
Function Point ◽  
Mutant Forms

Abstract We used a recently developed mouse model to better understand the cellular and molecular determinants of tumors driven by the oncogenic fusion protein C11orf95-RELA. Our approach makes use of in utero electroporation and a binary transposase system to introduce human C11orf95-RELA sequence, wild type and mutant forms, into neural progenitors. We used single cell RNA-seq to profile the cellular constituents within the resulting tumors in mice. We find that approximately 70% of the cells in the tumors do not express the oncogene C11orf95-RELA and these non-oncogene expressing cells are a combination of different non-tumor cell cell-types, including significant numbers of T-cells, and macrophages. The C11orf95-RELA expressing tumor cells have a unique transcriptomic profile that includes both astrocytic and neural progenitor marker genes, and is distinct from glioblastoma transcriptomic profiles. Since C11orf95-RELA is believed to function through a combination of both activation of NF-κB response genes by constitutive activation of RELA, and genes not activated by NF-κB, we assessed the expression of NF-κB response genes across the populations of cells in the tumor. Interestingly, the genes that best defined the oncogene expressing tumor cell population were not enriched for NF-κB response genes relative. We hypothesized that the C110rf95 domain may function to bring RELA transcriptional activation to inappropriate non-NF-κB targets, and we therefore performed a point mutation analysis of the C110rf95 domain. We found that mutations in either of the cysteines or histidines that make up a possible zinc finger domain in C11orf95 eliminate the ability of the fusion to induce tumors. In cell lines, these loss-of-function point mutants still trafficked to nuclei, and activated NF-κB pathways. We are currently using RNAseq and CRISPR loss-of function to identify genes downstream of C11orf95-RELA that are required for tumorigenesis.

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 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 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 LRcell: detecting the source of differential expression at the sub-cell type level from bulk RNA-seq data

2021 ◽  
Author(s):  
Wenjing Ma ◽  
Sumeet Sharma ◽  
Peng Jin ◽  
Shannon L Gourley ◽  
Zhaohui Qin
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Cell Type ◽  
Reference Dataset ◽  
Cell Type Composition ◽  
Type Composition ◽  
Differential Gene

The rapid proliferation of single-cell RNA-sequencing (scRNA-seq) datasets have revealed cell heterogeneity at unprecedented scales. Several deconvolution methods have been developed to decompose bulk experiments to reveal cell type contributions. However, these methods lack power in identifying the accurate cell type composition when having a considerable amount of sub-cell types in the reference dataset. Here, we present LRcell, a R Bioconductor package (http://bioconductor.org/packages/release/bioc/html/LRcell.html) aiming to identify specific sub-cell type(s) that drives the changes observed in a bulk RNA-seq differential gene expression experiment. In addition, LRcell provides pre-embedded marker genes computed from putative single-cell RNA-seq experiments as options to execute the analyses.

scholarly journals IKAP - Identifying K mAjor cell Population groups in single-cell RNA-seq analysis

2019 ◽  
Author(s):  
Yun-Ching Chen ◽  
Abhilash Suresh ◽  
Chingiz Underbayev ◽  
Clare Sun ◽  
Komudi Singh ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Cell Types ◽  
Marker Genes ◽  
Rna Seq ◽  
Population Groups ◽  
Tuning Parameters ◽  
Multiple Datasets ◽  
Cell Groups ◽  
Cell Ontology

AbstractIn single-cell RNA-seq analysis, clustering cells into groups and differentiating cell groups by marker genes are two separate steps for investigating cell identity. However, results in clustering greatly affect the ability to differentiate between cell groups. We develop IKAP – an algorithm identifying major cell groups that improves differentiating by tuning parameters for clustering. Using multiple datasets, we demonstrate IKAP improves identification of major cell types and facilitates cell ontology curation.

scholarly journals SC3 - consensus clustering of single-cell RNA-Seq data

2016 ◽  
Author(s):  
Vladimir Yu. Kiselev ◽  
Kristina Kirschner ◽  
Michael T. Schaub ◽  
Tallulah Andrews ◽  
Andrew Yiu ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Marker Genes ◽  
Consensus Clustering ◽  
Rna Seq ◽  
Large Dataset ◽  
Wide Audience ◽  
Large Variability ◽  
Biological Interpretation

AbstractUsing single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative cell-type characterisation based on expression profiles. However, due to the large variability in gene expression, identifying cell types based on the transcriptome remains challenging. We present Single-Cell Consensus Clustering (SC3), a tool for unsupervised clustering of scRNA-seq data. SC3 achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach. Tests on twelve published datasets show that SC3 outperforms five existing methods while remaining scalable, as shown by the analysis of a large dataset containing 44,808 cells. Moreover, an interactive graphical implementation makes SC3 accessible to a wide audience of users, and SC3 aids biological interpretation by identifying marker genes, differentially expressed genes and outlier cells. We illustrate the capabilities of SC3 by characterising newly obtained transcriptomes from subclones of neoplastic cells collected from patients.

ColorCells: a database of expression, classification and functions of lncRNAs in single cells

2020 ◽  
Author(s):  
Ling-Ling Zheng ◽  
Jing-Hua Xiong ◽  
Wu-Jian Zheng ◽  
Jun-Hao Wang ◽  
Zi-Liang Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Tissue Specificity ◽  
Single Cells ◽  
Noncoding Rnas ◽  
Cell Types ◽  
Marker Genes ◽  
Rna Seq ◽  
Expression Variability ◽  
Expression Classification

Abstract Although long noncoding RNAs (lncRNAs) have significant tissue specificity, their expression and variability in single cells remain unclear. Here, we developed ColorCells (http://rna.sysu.edu.cn/colorcells/), a resource for comparative analysis of lncRNAs expression, classification and functions in single-cell RNA-Seq data. ColorCells was applied to 167 913 publicly available scRNA-Seq datasets from six species, and identified a batch of cell-specific lncRNAs. These lncRNAs show surprising levels of expression variability between different cell clusters, and has the comparable cell classification ability as known marker genes. Cell-specific lncRNAs have been identified and further validated by in vitro experiments. We found that lncRNAs are typically co-expressed with the mRNAs in the same cell cluster, which can be used to uncover lncRNAs’ functions. Our study emphasizes the need to uncover lncRNAs in all cell types and shows the power of lncRNAs as novel marker genes at single cell resolution.

scholarly journals Identification of transcriptional signatures for cell types from single-cell RNA-Seq

2018 ◽  
Author(s):  
Vasilis Ntranos ◽  
Lynn Yi ◽  
Páll Melsted ◽  
Lior Pachter
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Accurate Method ◽  
Marker Genes ◽  
Differential Analysis ◽  
Rna Seq ◽  
Large Numbers

AbstractSingle-cell RNA-Seq makes it possible to characterize the transcriptomes of cell types and identify their transcriptional signatures via differential analysis. We present a fast and accurate method for discriminating cell types that takes advantage of the large numbers of cells that are assayed. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3’ single-cell RNA-Seq that can identify previously undetectable marker genes.

scholarly journals FEATS: Feature selection based clustering of single-cell RNA-seq data

2020 ◽  
Author(s):  
Edwin Vans ◽  
Ashwini Patil ◽  
Alok Sharma
Keyword(s):  
Feature Selection ◽  
Single Cell ◽  
Outlier Detection ◽  
Cell Types ◽  
Superior Performance ◽  
Adjusted Rand Index ◽  
Marker Genes ◽  
Rna Seq ◽  
Number Of Clusters ◽  
Computational Performance

ABSTRACTAdvances in next-generation sequencing (NGS) have made it possible to carry out transcriptomic studies at single-cell resolution and generate vast amounts of single-cell RNA-seq data rapidly. Thus, tools to analyze this data need to evolve as well to improve accuracy and efficiency. We present FEATS, a python software package that performs clustering on single-cell RNA-seq data. FEATS is capable of performing multiple tasks such as estimating the number of clusters, conducting outlier detection, and integrating data from various experiments. We develop a univariate feature selection based approach for clustering, which involves the selection of top informative features to improve clustering performance. This is motivated by the fact that cell types are often manually determined using the expression of only a few known marker genes. On a variety of single-cell RNA-seq datasets, FEATS gives superior performance compared to the current tools, in terms of adjusted rand index (ARI) and estimating the number of clusters. In addition to cluster estimation, FEATS also performs outlier detection and data integration while giving an excellent computational performance. Thus, FEATS is a comprehensive clustering tool capable of addressing the challenges during the clustering of single-cell RNA-seq data. The installation instructions and documentation of FEATS is available at https://edwinv87.github.io/feats/.

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