scholarly journals Joint single cell DNA-Seq and RNA-Seq of cancer reveals subclonal signatures of genomic instability and gene expression

2018 ◽  
Author(s):  
Noemi Andor ◽  
Billy T. Lau ◽  
Claudia Catalanotti ◽  
Vijay Kumar ◽  
Anuja Sathe ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Copy Number ◽  
Gastric Cancer Cell ◽  
Tumor Evolution ◽  
Rna Seq ◽  
Cancer Heterogeneity ◽  
Gastric Cancer Cell Lines ◽  
Joint Study ◽  
Copy Number Changes

ABSTRACTSequencing the genomes of individual cancer cells provides the highest resolution of intratumoral heterogeneity. To enable high throughput single cell DNA-Seq across thousands of individual cells per sample, we developed a droplet-based, automated partitioning technology for whole genome sequencing. We applied this approach on a set of gastric cancer cell lines and a primary gastric tumor. In parallel, we conducted a separate single cell RNA-Seq analysis on these same cancers and used copy number to compare results. This joint study, covering thousands of single cell genomes and transcriptomes, revealed extensive cellular diversity based on distinct copy number changes, numerous subclonal populations and in the case of the primary tumor, subclonal gene expression signatures. We found genomic evidence of positive selection – where the percentage of replicating cells per clone is higher than expected – indicating ongoing tumor evolution. Our study demonstrates that joining single cell genomic DNA and transcriptomic features provides novel insights into cancer heterogeneity and biology.SIGNIFICANCEWe conducted a massively parallel DNA sequencing analysis on a set of gastric cancer cell lines and a primary gastric tumor in combination with a joint single cell RNA-Seq analysis. This joint study, covering thousands of single cell genomes and transcriptomes, revealed extensive cellular diversity based on distinct copy number changes, numerous subclonal populations and in the case of the primary tumor, subclonal gene expression signatures. We found genomic evidence of positive selection where the percentage of replicating cells per clone is higher than expected indicating ongoing tumor evolution. Our study demonstrates that combining single cell genomic DNA and transcriptomic features provides novel insights into cancer heterogeneity and biology.

scholarly journals Mapping single-cell transcriptomes to copy number evolutionary trees

2021 ◽  
Author(s):  
Pedro F Ferreira ◽  
Jack Kuipers ◽  
Niko Beerenwinkel
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Copy Number ◽  
Evolutionary Dynamics ◽  
Real Data ◽  
Selective Advantage ◽  
Tumor Evolution ◽  
Clustering Methods ◽  
Gene Expression Heterogeneity ◽  
Cell Gene Expression

Cancer arises and evolves by the accumulation of somatic mutations that provide a selective advantage. The interplay of mutations and their functional consequences shape the evolutionary dynamics of tumors and contribute to different clinical outcomes. In the absence of scalable methods to jointly assay genomic and transcriptomic profiles of the same individual cell, the two data modalities are usually measured separately and need to be integrated computationally. Here, we introduce SCATrEx, a statistical model to map single-cell gene expression data onto the evolutionary history of copy number alterations of the tumor. SCATrEx jointly assigns cancer cells assayed with scRNA-seq to copy number profiles arranged in a copy number aberration tree and augments the tree with clone-specific clusters. Our simulations show that SCATrEx improves over both state-of-the-art unsupervised clustering methods and cell-to-clone assignment methods. In an application to real data, we observe that SCATrEx finds inter-clone and intra-clone gene expression heterogeneity not detectable using other integration methods. SCATrEx will allow for a better understanding of tumor evolution by jointly analysing the genomic and transcriptomic changes that drive it.

scholarly journals An Integrated Approach for RNA-seq Data Normalization

2016 ◽  
Vol 15 ◽  
pp. CIN.S39781 ◽  
Author(s):  
Shengping Yang ◽  
Donald E. Mercante ◽  
Kun Zhang ◽  
Zhide Fang
Keyword(s):  
Gene Expression ◽  
Dna Sequences ◽  
Copy Number ◽  
Differentially Expressed Gene ◽  
Integrated Approach ◽  
Gene Profiling ◽  
Data Normalization ◽  
Rna Seq ◽  
Dna Copy Number ◽  
Copy Number Changes

Background DNA copy number alteration is common in many cancers. Studies have shown that insertion or deletion of DNA sequences can directly alter gene expression, and significant correlation exists between DNA copy number and gene expression. Data normalization is a critical step in the analysis of gene expression generated by RNA-seq technology. Successful normalization reduces/removes unwanted nonbiological variations in the data, while keeping meaningful information intact. However, as far as we know, no attempt has been made to adjust for the variation due to DNA copy number changes in RNA-seq data normalization. Results In this article, we propose an integrated approach for RNA-seq data normalization. Comparisons show that the proposed normalization can improve power for downstream differentially expressed gene detection and generate more biologically meaningful results in gene profiling. In addition, our findings show that due to the effects of copy number changes, some housekeeping genes are not always suitable internal controls for studying gene expression. Conclusions Using information from DNA copy number, integrated approach is successful in reducing noises due to both biological and nonbiological causes in RNA-seq data, thus increasing the accuracy of gene profiling.

Transcriptional profiling of iPSC-derived neurons with reciprocal monogenic CNV in 3p26.3 region

2020 ◽  
pp. 10-11
Author(s):  
М.Е. Лопаткина ◽  
В.С. Фишман ◽  
М.М. Гридина ◽  
Н.А. Скрябин ◽  
Т.В. Никитина ◽  
...  
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
System Development ◽  
Transcriptional Profiling ◽  
Global Gene Expression ◽  
Nervous System Development ◽  
Number Variation ◽  
The Central Nervous System ◽  
Cntn6 Gene ◽  
Copy Number Changes

Проведен анализ генной экспрессии в нейронах, дифференцированных из индуцированных плюрипотентных стволовых клеток пациентов с идиопатическими интеллектуальными нарушениями и реципрокными хромосомными мутациями в регионе 3p26.3, затрагивающими единственный ген CNTN6. Для нейронов с различным типом хромосомных аберраций была показана глобальная дисрегуляция генной экспрессии. В нейронах с вариациями числа копий гена CNTN6 была снижена экспрессия генов, продукты которых вовлечены в процессы развития центральной нервной системы. The gene expression analysis of iPSC-derived neurons, obtained from patients with idiopathic intellectual disability and reciprocal microdeletion and microduplication in 3p26.3 region affecting the single CNTN6 gene was performed. The global gene expression dysregulation was demonstrated for cells with CNTN6 copy number variation. Gene expression in neurons with CNTN6 copy number changes was downregulated for genes, whose products are involved in the central nervous system development.

scholarly journals Comprehensive analysis of the gene expression profiles in human gastric cancer cell lines

Oncogene ◽  
2002 ◽  
Vol 21 (42) ◽  
pp. 6549-6556 ◽  
Author(s):  
Jiafu Ji ◽  
Xin Chen ◽  
Suet Yi Leung ◽  
Jen-Tsan A Chi ◽  
Kent Man Chu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comprehensive Analysis ◽  
Human Gastric Cancer ◽  
Gastric Cancer Cell Lines

scholarly journals Gastric Cancer Cell Lines Have DifferentMYC-Regulated Expression Patterns but Share a Common Core of Altered Genes

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Jersey Heitor da S. Maués ◽  
Helem Ferreira Ribeiro ◽  
Giovanny R. Pinto ◽  
Luana de Oliveira Lopes ◽  
Letícia M. Lamarão ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Common Core ◽  
Gastric Cancer Cell ◽  
Histological Subtypes ◽  
Gene Sets ◽  
Regulated Expression ◽  
Gastric Cancer Cell Lines

MYCis an oncogene responsible for excessive cell growth in cancer, enabling transcriptional activation of genes involved in cell cycle regulation, metabolism, and apoptosis, and is usually overexpressed in gastric cancer (GC). By using siRNA and Next-Generation Sequencing (NGS), we identifiedMYC-regulated differentially expressed Genes (DEGs) in three Brazilian gastric cancer cell lines representing the histological subtypes of GC (diffuse, intestinal, and metastasis). The DEGs were picked usingSailfishsoftware, followed by Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using KEGG. We found 11 significantly enriched gene sets by using enrichment score (ES), False Discovery Rate (FDR), and nominal P-values. We identified a total of 5.471 DEGs with correlation over (80%). In diffuse-type and in metastatic GC cell lines,MYC-silencing caused DEGs downregulation, while the intestinal-type GC cells presented overall DEGs upregulation afterMYCsiRNA depletion. We were able to detect 11 significant gene sets when comparing our samples to the hallmark collection of gene expression, enriched mostly for the following hallmarks: proliferation, pathway, signaling, metabolic, and DNA damage response. When we analyzed our DEGs considering KEGG metabolic pathways, we found 12 common branches covering a wide range of biological functions, and three of them were common to all three cell lines: ubiquitin-mediated proteolysis, ribosomes, and system and epithelial cell signaling inHelicobacter pyloriinfection. The GC cell lines used in this study share 14MYC-regulated genes, but their gene expression profile is different for each histological subtype of GC. Our results present a computational analysis ofMYC-related signatures in GC, and we present evidence that GC cell lines representing distinct histological subtypes of this disease have differentMYC-regulated expression profiles but share a common core of altered genes. This is an important step towards the understanding ofMYC’s role in gastric carcinogenesis and an indication of probable new drug targets in stomach cancer.

scholarly journals Single cell RNA sequencing of calvarial and long bone endocortical cells

2019 ◽  
Author(s):  
Ugur M. Ayturk ◽  
Joseph P. Scollan ◽  
Alexander Vesprey ◽  
Christina M. Jacobsen ◽  
Paola Divieti Pajevic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cultured Cells ◽  
Neutralizing Antibody ◽  
Long Bone ◽  
Appendicular Skeleton ◽  
Rna Seq ◽  
Isolated Cells

ABSTRACTSingle cell RNA-seq (scRNA-seq) is emerging as a powerful technology to examine transcriptomes of individual cells. We determined whether scRNA-seq could be used to detect the effect of environmental and pharmacologic perturbations on osteoblasts. We began with a commonly used in vitro system in which freshly isolated neonatal mouse calvarial cells are expanded and induced to produce a mineralized matrix. We used scRNA-seq to compare the relative cell type abundances and the transcriptomes of freshly isolated cells to those that had been cultured for 12 days in vitro. We observed that the percentage of macrophage-like cells increased from 6% in freshly isolated calvarial cells to 34% in cultured cells. We also found that Bglap transcripts were abundant in freshly isolated osteoblasts but nearly undetectable in the cultured calvarial cells. Thus, scRNA-seq revealed significant differences between heterogeneity of cells in vivo and in vitro. We next performed scRNA-seq on freshly recovered long bone endocortical cells from mice that received either vehicle or Sclerostin-neutralizing antibody for 1 week. Bone anabolism-associated transcripts were also not significantly increased in immature and mature osteoblasts recovered from Sclerostin-neutralizing antibody treated mice; this is likely a consequence of being underpowered to detect modest changes in gene expression, since only 7% of the sequenced endocortical cells were osteoblasts, and a limited portion of their transcriptomes were sampled. We conclude that scRNA-seq can detect changes in cell abundance, identity, and gene expression in skeletally derived cells. In order to detect modest changes in osteoblast gene expression at the single cell level in the appendicular skeleton, larger numbers of osteoblasts from endocortical bone are required.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

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