scholarly journals Platform-integrated mRNA isoform quantification

2019 ◽  
Vol 36 (8) ◽  
pp. 2466-2473 ◽  
Author(s):  
Jiao Sun ◽  
Jae-Woong Chang ◽  
Teng Zhang ◽  
Jeongsik Yong ◽  
Rui Kuang ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Cancer Cell Lines ◽  
Supplementary Information ◽  
Rna Seq ◽  
Mrna Quantification ◽  
Transcript Quantification ◽  
Isoform Expression ◽  
Isoform Quantification

Abstract Motivation Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction. Results In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString’s nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available. Availability and implementation Source code is available at: https://github.com/CompbioLabUcf/IntMTQ. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A large-scale comparative study of isoform expressions measured on four platforms

2019 ◽  
Author(s):  
Wei Zhang ◽  
Raphael Petegrosso ◽  
Jae Woong Chang ◽  
Jiao Sun ◽  
Jeongsik Yong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Study ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Exon Array ◽  
Cancer Cell Lines ◽  
Rna Seq ◽  
Gene Expressions ◽  
Isoform Quantification

Abstract Background: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA-sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 403 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion: In the comparisons of the platforms for evaluating expressions at both isoform and gene levels, we found that (1) the degree of agreement across platforms on quantifying isoform expressions is lower than gene expressions; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array quantification results on isoform-level compared to NanoString; (4) different RNA-seq isoform quantification algorithms showed inconsistent results, and two isoform quantification methods Net-RSTQ and eXpress are more consistent across the platforms in the comparison; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

scholarly journals A large-scale comparative study of isoform expressions measured on four platforms

2019 ◽  
Author(s):  
Wei Zhang ◽  
Raphael Petegrosso ◽  
Jae Woong Chang ◽  
Jeongsik Yong ◽  
Jeremy Chien ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Study ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Exon Array ◽  
Cancer Cell Lines ◽  
Rna Seq ◽  
Eukaryotic Genes ◽  
Isoform Quantification

Abstract Background: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles and thus, it is necessary to accurately measure isoform expressions as well as the genes'. While previous studies have demonstrated the strong agreement between mRNA-sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based and RT-qPCR platforms using 46 cancer cell lines across different cancer types to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 403 custom-designed probes for measuring the expressions of 405 isoforms in 155 genes and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines, and then combined the data with the matched RNA-seq, Exon-array and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion: In the comparisons of the platforms for evaluating expressions at both isoform and gene levels, we found that (1) the degree of agreement across platforms on quantifying isoform expressions is lower than gene expressions; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq quantification results on isoform-level compared to NanoString and Exon-array; (4) different RNA-seq isoform quantification algorithms showed inconsistent results, and two isoform quantification methods Net-RSTQ and eXpress are more consistent across the platforms in the comparison; (5) RNA-seq has the best overall consistent with the other platforms on gene expression quantification.

scholarly journals A large-scale comparative study of isoform expressions measured on four platforms

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Wei Zhang ◽  
Raphael Petegrosso ◽  
Jae-Woong Chang ◽  
Jiao Sun ◽  
Jeongsik Yong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Study ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Exon Array ◽  
Cancer Cell Lines ◽  
Rna Seq ◽  
Gene Expressions ◽  
Isoform Quantification

Abstract Background Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles, and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 404 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes, and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array, and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion In the comparisons of the platforms for measuring the expressions at both isoform and gene levels, we found that (1) the agreement on isoform expressions is lower than the agreement on gene expressions across the four platforms; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array than NanoString in isoform quantification; (4) different RNA-seq isoform quantification methods show varying estimation results, and among the methods, Net-RSTQ and eXpress are more consistent across the platforms; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

scholarly journals A large-scale comparative study of isoform expressions measured on four platforms

2020 ◽  
Author(s):  
Wei Zhang ◽  
Raphael Petegrosso ◽  
Jae Woong Chang ◽  
Jiao Sun ◽  
Jeongsik Yong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Study ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Exon Array ◽  
Cancer Cell Lines ◽  
Rna Seq ◽  
Gene Expressions ◽  
Isoform Quantification

Abstract Background: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles, and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA-sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with {\color{red}404} custom-designed probes for measuring the expressions of 478 isoforms in 155 genes, and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array, and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion: In the comparisons of the platforms for measuring the expressions at both isoform and gene levels, we found that (1) the agreement on isoform expressions is lower than the agreement on gene expressions across the four platforms; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array than NanoString in isoform quantification; (4) different RNA-seq isoform quantification methods show varying estimation results, and among the methods, Net-RSTQ and eXpress are more consistent across the platforms; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

scholarly journals The alternative splicing repressors hnRNP A1/A2 and PTB influence pyruvate kinase isoform expression and cell metabolism

2010 ◽  
Vol 107 (5) ◽  
pp. 1894-1899 ◽  
Author(s):  
Cynthia V. Clower ◽  
Deblina Chatterjee ◽  
Zhenxun Wang ◽  
Lewis C. Cantley ◽  
Matthew G. Vander Heiden ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Pyruvate Kinase ◽  
Cell Lines ◽  
Cancer Cell ◽  
Aerobic Glycolysis ◽  
Lactate Production ◽  
Cancer Cell Lines ◽  
Protein Isoforms ◽  
Hnrnp A1 ◽  
Isoform Expression

Cancer cells preferentially metabolize glucose by aerobic glycolysis, characterized by increased lactate production. This distinctive metabolism involves expression of the embryonic M2 isozyme of pyruvate kinase, in contrast to the M1 isozyme normally expressed in differentiated cells, and it confers a proliferative advantage to tumor cells. The M1 and M2 pyruvate-kinase isozymes are expressed from a single gene through alternative splicing of a pair of mutually exclusive exons. We measured the expression of M1 and M2 mRNA and protein isoforms in mouse tissues, tumor cell lines, and during terminal differentiation of muscle cells, and show that alternative splicing regulation is sufficient to account for the levels of expressed protein isoforms. We further show that the M1-specific exon is actively repressed in cancer-cell lines—although some M1 mRNA is expressed in cell lines derived from brain tumors—and demonstrate that the related splicing repressors hnRNP A1 and A2, as well as the polypyrimidine-tract-binding protein PTB, contribute to this control. Downregulation of these splicing repressors in cancer-cell lines using shRNAs rescues M1 isoform expression and decreases the extent of lactate production. These findings extend the links between alternative splicing and cancer, and begin to define some of the factors responsible for the switch to aerobic glycolysis.

scholarly journals Large-scale screening identifies a novel microRNA, miR-15a-3p, which induces apoptosis in human cancer cell lines

RNA Biology ◽  
2013 ◽  
Vol 10 (2) ◽  
pp. 287-300 ◽  
Author(s):  
Aliaksandr Druz ◽  
Yu-Chi Chen ◽  
Rajarshi Guha ◽  
Michael Betenbaugh ◽  
Scott E. Martin ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines ◽  
Large Scale Screening

scholarly journals Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

2019 ◽  
Author(s):  
Gabriela S. Kinker ◽  
Alissa C. Greenwald ◽  
Rotem Tal ◽  
Zhanna Orlova ◽  
Michael S. Cuoco ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Human Tumors ◽  
Model Systems ◽  
Cellular Heterogeneity ◽  
Specific Cell ◽  
Rna Seq ◽  
Mesenchymal Transition

AbstractCultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors, given the absence of a native tumor microenvironment. Here, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines. We uncovered expression programs that are recurrently heterogeneous within many cancer cell lines and are largely independent of observed genetic diversity. These programs of heterogeneity are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-to-mesenchymal transition, and protein maturation and degradation. Notably, some of these recurrent programs recapitulate those seen in human tumors, suggesting a prominent role of intrinsic plasticity in generating intra-tumoral heterogeneity. Moreover, the data allowed us to prioritize specific cell lines as model systems of cellular plasticity. We used two such models to demonstrate the dynamics, regulation and drug sensitivities associated with a cancer senescence program also observed in human tumors. Our work describes the landscape of cellular heterogeneity in diverse cancer cell lines, and identifies recurrent patterns of expression heterogeneity that are shared between tumors and specific cell lines and can thus be further explored in follow up studies.

Advances of proteomics technologies for multidrug-resistant mechanisms

2019 ◽  
Vol 11 (19) ◽  
pp. 2573-2593 ◽  
Author(s):  
Tiantian Zhang ◽  
Qijuan Yuan ◽  
Zhipeng Gu ◽  
Changhu Xue
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Cancer Cell Lines ◽  
Multidrug Resistant ◽  
Drug Induced ◽  
Dna Repair Mechanisms ◽  
Repair Mechanisms ◽  
Recent Developments ◽  
Induced Apoptosis

Multidrug resistance (MDR) is a vital issue in cancer treatment. Drug resistance can be developed through a variety of mechanisms, including increased drug efflux, activation of detoxifying systems and DNA repair mechanisms, and escape of drug-induced apoptosis. Identifying the exact mechanism related in a particular case is a difficult task. Proteomics is the large-scale study of proteins, particularly their expression, structures and functions. In recent years, comparative proteomic methods have been performed to analyze MDR mechanisms in drug-selected model cancer cell lines. In this paper, we review the recent developments and progresses by comparative proteomic approaches to identify potential MDR mechanisms in drug-selected model cancer cell lines, which may help understand and design chemical sensitizers.

scholarly journals Discovery and Functional Characterization of Pro-growth Enhancers in Human Cancer Cells

2021 ◽  
Author(s):  
Poshen B. Chen ◽  
Patrick C. Fiaux ◽  
Bin Li ◽  
Kai Zhang ◽  
Naoki Kubo ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Human Cancer ◽  
Functional Characterization ◽  
Transcriptional Regulators ◽  
Cancer Cell Lines ◽  
Colorectal Cancer Cell Line

AbstractPrecision medicine depends critically on developing treatment strategies that can selectively target cancer cells with minimal adverse effects. Identifying unique transcriptional regulators of oncogenic signaling, and targeting cancer-cell-specific enhancers that may be active only in specific tumor cell lineages, could provide the necessary high specificity, but a scarcity of functionally validated enhancers in cancer cells presents a significant hurdle to this strategy. We address this limitation by carrying out large-scale functional screens for pro-growth enhancers using highly multiplexed CRISPR-based perturbation and sequencing in multiple cancer cell lines. We used this strategy to identify 488 pro-growth enhancers in a colorectal cancer cell line and 22 functional enhancers for the MYC and MYB key oncogenes in an additional nine cancer cell lines. The majority of pro-growth enhancers are accessible and presumably active only in cancer cells but not in normal tissues, and are enriched for elements associated with poor prognosis in colorectal cancer. We further identify master transcriptional regulators and demonstrate that the cancer pro-growth enhancers are modulated by lineage-specific transcription factors acting downstream of growth signaling pathways. Our results uncover context-specific, potentially actionable pro-growth enhancers from cancer cells, yielding insight into altered oncogenic transcription and revealing potential therapeutic targets for cancer treatment.

scholarly journals Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

2020 ◽  
Author(s):  
Andrew Jones ◽  
Aviad Tsherniak ◽  
James M. McFarland
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Cancer Cell Line ◽  
Cellular Responses ◽  
Cancer Cell Lines ◽  
Transcriptional Responses ◽  
Specific Expression ◽  
Transcriptional Signature

AbstractWhile chemical and genetic viability screens in cancer cell lines have identified many promising cancer vulnerabilities, simple univariate readouts of cell proliferation fail to capture the complex cellular responses to perturbations. Complementarily, gene expression profiling offers an information-rich measure of cell state that can provide a more detailed account of cellular responses to perturbations. Relatively little is known, however, about the relationship between transcriptional responses to per-turbations and the long-term cell viability effects of those perturbations. To address this question, we integrated thousands of post-perturbational transcriptional profiles from the Connectivity Map with large-scale screens of cancer cell lines’ viability response to genetic and chemical perturbations. This analysis revealed a generalized transcriptional signature associated with reduced viability across perturbations, which was consistent across post-perturbation time-points, perturbation types, and viability datasets. At a more granular level, we lay out the landscape of treatment-specific expression-viability relationships across a broad panel of drugs and genetic reagents, and we demonstrate that these post-perturbational expression signatures can be used to infer long-term viability. Together, these results help unmask the transcriptional changes that are associated with perturbation-induced viability loss in cancer cell lines.

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