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Integration of RNA and protein expression profiles to study human cells
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. Human Protein Atlas. (Cell Profiling)ORCID iD: 0000-0002-7692-1100
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cellular life is highly complex. In order to expand our understanding of the workings of human cells, in particular in the context of health and disease, detailed knowledge about the underlying molecular systems is needed. The unifying theme of this thesis concerns the use of data derived from sequencing of RNA, both within the field of transcriptomics itself and as a guide for further studies at the level of protein expression. In paper I, we showed that publicly available RNA-seq datasets are consistent across different studies, requiring only light processing for the data to cluster according to biological, rather than technical characteristics. This suggests that RNA-seq has developed into a reliable and highly reproducible technology, and that the increasing amount of publicly available RNA-seq data constitutes a valuable resource for meta-analyses. In paper II, we explored the ability to extrapolate protein concentrations by the use of RNA expression levels. We showed that mRNA and corresponding steady-state protein concentrations correlate well by introducing a gene-specific RNA-to-protein conversion factor that is stable across various cell types and tissues. The results from this study indicate the utility of RNA-seq also within the field of proteomics.

The second part of the thesis starts with a paper in which we used transcriptomics to guide subsequent protein studies of the molecular mechanisms underlying malignant transformation. In paper III, we applied a transcriptomics approach to a cell model for defined steps of malignant transformation, and identified several genes with interesting expression patterns whose corresponding proteins were further analyzed with subcellular spatial resolution. Several of these proteins were further studied in clinical tumor samples, confirming that this cell model provides a relevant system for studying cancer mechanisms. In paper IV, we continued to explore the transcriptional landscape in the same cell model under moderate hypoxic conditions.

To conclude, this thesis demonstrates the usefulness of RNA-seq data, from a transcriptomics perspective and beyond; to guide in analyses of protein expression, with the ultimate goal to unravel the complexity of the human cell, from a holistic point of view.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. , 54 p.
Series
TRITA-BIO-Report, ISSN 1654-2312
Keyword [en]
RNA-seq, Transcriptomics, Proteomics, Malignant transformation, Cancer, Functional enrichment
National Category
Biological Sciences
Research subject
Biotechnology; Medical Technology
Identifiers
URN: urn:nbn:se:kth:diva-196700ISBN: 978-91-7729-209-8OAI: oai:DiVA.org:kth-196700DiVA: diva2:1048144
Public defence
2016-12-16, Rockefeller, Nobels väg 11, Solna, 13:00 (English)
Opponent
Supervisors
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20161121

Available from: 2016-11-21 Created: 2016-11-18 Last updated: 2016-11-21Bibliographically approved
List of papers
1. Assessing the consistency of public human tissue RNA-seq data sets
Open this publication in new window or tab >>Assessing the consistency of public human tissue RNA-seq data sets
2015 (English)In: Briefings in Bioinformatics, ISSN 1467-5463, E-ISSN 1477-4054, Vol. 16, no 6, 941-949 p.Article in journal (Refereed) Published
Abstract [en]

Sequencing-based gene expression methods like RNA-sequencing (RNA-seq) have become increasingly common, but it is often claimed that results obtained in different studies are not comparable owing to the influence of laboratory batch effects, differences in RNA extraction and sequencing library preparation methods and bioinformatics processing pipelines. It would be unfortunate if different experiments were in fact incomparable, as there is great promise in data fusion and meta-analysis applied to sequencing data sets. We therefore compared reported gene expression measurements for ostensibly similar samples (specifically, human brain, heart and kidney samples) in several different RNA-seq studies to assess their overall consistency and to examine the factors contributing most to systematic differences. The same comparisons were also performed after preprocessing all data in a consistent way, eliminating potential bias from bioinformatics pipelines. We conclude that published human tissue RNA-seq expression measurements appear relatively consistent in the sense that samples cluster by tissue rather than laboratory of origin given simple preprocessing transformations. The article is supplemented by a detailed walkthrough with embedded R code and figures.

Place, publisher, year, edition, pages
Oxford University Press, 2015
Keyword
RNA-seq, public data, meta-analysis, gene expression, clustering
National Category
Biochemistry and Molecular Biology Computational Mathematics
Identifiers
urn:nbn:se:kth:diva-180145 (URN)10.1093/bib/bbv017 (DOI)000365708700005 ()25829468 (PubMedID)
Note

QC 20160113

Available from: 2016-01-13 Created: 2016-01-07 Last updated: 2016-11-20Bibliographically approved
2. Gene specific correlation of RNA and protein levels in human cells and tissues
Open this publication in new window or tab >>Gene specific correlation of RNA and protein levels in human cells and tissues
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2016 (English)In: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292Article in journal (Refereed) In press
Abstract [en]

An important issue for molecular biology is to establish if transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non-secreted proteins based on Parallel Reaction Monitoring to measure, at steady-state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene-specific RNA-to-protein (RTP) conversion factor independent of the tissue-type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP-ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands protein copies per mRNA molecule for others. In conclusion, our data suggests that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics. 

National Category
Biochemistry and Molecular Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-193966 (URN)
Note

QC 20161013

Available from: 2016-10-13 Created: 2016-10-13 Last updated: 2016-11-20Bibliographically approved
3. Majority of differentially expressed genes are down-regulated during malignant transformation in a four-stage model
Open this publication in new window or tab >>Majority of differentially expressed genes are down-regulated during malignant transformation in a four-stage model
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2013 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 110, no 17, 6853-6858 p.Article in journal (Refereed) Published
Abstract [en]

The transformation of normal cells to malignant, metastatic tumor cells is a multistep process caused by the sequential acquirement of genetic changes. To identify these changes, we compared the transcriptomes and levels and distribution of proteins in a four-stage cell model of isogenically matched normal, immortalized, transformed, and metastatic human cells, using deep transcriptome sequencing and immunofluorescence microscopy. The data show that similar to 6% (n = 1,357) of the human protein-coding genes are differentially expressed across the stages in the model. Interestingly, the majority of these genes are down-regulated, linking malignant transformation to dedifferentiation. The up-regulated genes are mainly components that control cellular proliferation, whereas the down-regulated genes consist of proteins exposed on or secreted from the cell surface. As many of the identified gene products control basic cellular functions that are defective in cancers, the data provide candidates for follow-up studies to investigate their functional roles in tumor formation. When we further compared the expression levels of four of the identified proteins in clinical cancer cohorts, similar differences were observed between benign and cancer cells, as in the cell model. This shows that this comprehensive demonstration of the molecular changes underlying malignant transformation is a relevant model to study the process of tumor formation.

Keyword
Breast-Cancer, Annexin A1, Cell-Migration, Tumor-Growth, T-Antigen, Carcinogenesis, Metastasis, Hallmarks, Carcinoma, Oncogenes
National Category
Biological Sciences Medical and Health Sciences
Identifiers
urn:nbn:se:kth:diva-123630 (URN)10.1073/pnas.1216436110 (DOI)000318677300059 ()2-s2.0-84876846983 (ScopusID)
Funder
Knut and Alice Wallenberg FoundationScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20130614

Available from: 2013-06-14 Created: 2013-06-13 Last updated: 2016-11-20Bibliographically approved
4. Transcriptome profiling of a cell line model for malignant transformation in response to moderate hypoxia
Open this publication in new window or tab >>Transcriptome profiling of a cell line model for malignant transformation in response to moderate hypoxia
(English)Manuscript (preprint) (Other academic)
National Category
Natural Sciences
Identifiers
urn:nbn:se:kth:diva-196741 (URN)
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20161121

Available from: 2016-11-21 Created: 2016-11-21 Last updated: 2016-11-21Bibliographically approved

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