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A novel RNA sequencing data analysis method for cell line authentication
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology.ORCID iD: 0000-0003-0492-9960
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2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 2, article id e0171435Article in journal (Refereed) Published
Abstract [en]

We have developed a novel analysis method that can interrogate the authenticity of biological samples used for generation of transcriptome profiles in public data repositories. The method uses RNA sequencing information to reveal mutations in expressed transcripts and subsequently confirms the identity of analysed cells by comparison with publicly available cell-specific mutational profiles. Cell lines constitute key model systems widely used within cancer research, but their identity needs to be confirmed in order to minimise the influence of cell contaminations and genetic drift on the analysis. Using both public and novel data, we demonstrate the use of RNA-sequencing data analysis for cell line authentication by examining the validity of COLO205, DLD1, HCT15, HCT116, HKE3, HT29 and RKO colorectal cancer cell lines. We successfully authenticate the studied cell lines and validate previous reports indicating that DLD1 and HCT15 are synonymous. We also show that the analysed HKE3 cells harbour an unexpected KRAS-G13D mutation and confirm that this cell line is a genuine KRAS dosage mutant, rather than a true isogenic derivative of HCT116 expressing only the wild type KRAS. This authentication method could be used to revisit the numerous cell line based RNA sequencing experiments available in public data repositories, analyse new experiments where whole genome sequencing is not available, as well as facilitate comparisons of data from different experiments, platforms and laboratories.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE , 2017. Vol. 12, no 2, article id e0171435
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:kth:diva-204084DOI: 10.1371/journal.pone.0171435ISI: 000394423800024PubMedID: 28192450Scopus ID: 2-s2.0-85012231859OAI: oai:DiVA.org:kth-204084DiVA, id: diva2:1085453
Note

QC 20170329

Available from: 2017-03-29 Created: 2017-03-29 Last updated: 2018-09-05Bibliographically approved
In thesis
1. Exploring genetic heterogeneity in cancer using high-throughput DNA and RNA sequencing
Open this publication in new window or tab >>Exploring genetic heterogeneity in cancer using high-throughput DNA and RNA sequencing
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

High-throughput sequencing (HTS) technology has revolutionised the biomedical sciences, where it is used to analyse the genetic makeup and gene expression patterns of both primary patient tissue samples and models cultivated in vitro. This makes it especially useful for research on cancer, a disease that is characterised by its deadliness and genetic heterogeneity. This inherent genetic variation is an important aspect that warrants exploration, and the depth and breadth that HTS possesses makes it well-suited to investigate this facet of cancer.

The types of analyses that may be accomplished with HTS technologies are many, but they may be divided into two groups: those that analyse the DNA of the sample in question, and those that work on the RNA. While DNA-based methods give information regarding the genetic landscape of the sample, RNA-based analyses yield data regarding gene expression patterns; both of these methods have already been used to investigate the heterogeneity present in cancer. While RNA-based methods are traditionally used exclusively for expression analyses, the data they yield may also be utilised to investigate the genetic variation present in the samples. This type of RNA-based analysis is seldom performed, however, and valuable information is thus ignored.

The aim of this thesis is the development and application of DNA- and RNA- based HTS methods for analysing genetic heterogeneity within the context of cancer. The present investigation demonstrates that not only may RNA-based sequencing be used to successfully differentiate different in vitro cancer models through their genetic makeup, but that this may also be done for primary patient data. A pipeline for these types of analyses is established and evaluated, showing it to be both robust to several technical parameters as well as possess a broad scope of analytical possibilities. Genetic variation within cancer models in public databases are evaluated and demonstrated to affect gene expression in several cases. Both inter- and intra-patient genetic heterogeneity is shown using the established pipeline, in addition to demonstrating that cancerous cells are more heterogeneous than their normal neighbours. Finally, two bioinformatic open source software packages are presented.

The results presented herein demonstrate that genetic analyses using RNA-based methods represent excellent complements to already existing DNA-based techniques, and further increase the already large scope of how HTS technologies may be utilised.

Place, publisher, year, edition, pages
Stockholm: Kungliga tekniska högskolan, 2018. p. 83
Series
TRITA-CBH-FOU ; 2018:31
Keywords
Biotechnology, bioinformatics, RNA-seq, WGS, WES, systems biology, variant analysis, single nucleotide variant, gene expression, machine learning, clustering, open source, R, bioconductor, Python
National Category
Medical Biotechnology Bioinformatics and Systems Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-234265 (URN)978-91-7729-918-9 (ISBN)
Public defence
2018-10-05, FR4, Oskar Klein's Auditorium, Albanova, Stockholm, 10:00 (English)
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Supervisors
Note

QC 20180906

Available from: 2018-09-06 Created: 2018-09-05 Last updated: 2018-09-06Bibliographically approved

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Uhlén, MathiasAl-Khalili Szigyarto, Cristina

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