Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Gene specific correlation of RNA and protein levels in human cells and tissues
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab. (Uhlen)ORCID iD: 0000-0002-0017-7987
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-7692-1100
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0001-5689-9797
Show others and affiliations
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. 

Place, publisher, year, edition, pages
2016.
National Category
Biochemistry and Molecular Biology
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-193966OAI: oai:DiVA.org:kth-193966DiVA: diva2:1035374
Note

QC 20161013

Available from: 2016-10-13 Created: 2016-10-13 Last updated: 2017-11-29Bibliographically approved
In thesis
1. Targeted proteomics methods for protein quantification of human cells, tissues and blood
Open this publication in new window or tab >>Targeted proteomics methods for protein quantification of human cells, tissues and blood
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The common concept in this thesis was to adapt and develop quantitative mass spectrometric assays focusing on reagents originating from the Human Protein Atlas project to quantify proteins in human cell lines, tissues and blood. The work is based around stable isotope labeled protein fragment standards that each represent a small part of a human protein-coding gene. This thesis shows how they can be used in various formats to describe the protein landscape and be used to standardize mass spectrometry experiments. The first part of the thesis describes the use of antibodies in combination with heavy stable isotope labeled antigens to establish a semi-automated protocol for protein quantification of complex samples with fast analysis time  (Paper~I). Paper II introduces a semi-automated cloning protocol that can be used to selectively clone variants of recombinant proteins, and highlights the automation process that is necessary for large-scale proteomics endeavors. This paper also describes the technology that was used to clone all protein standards that are used in all of the included papers.

                     

The second part of the thesis includes papers that focus on the generation and application of antibody-free targeted mass spectrometry methods. Here, absolute protein copy numbers were determined across human cell lines and tissues (Paper III) and the protein data was correlated against transcriptomics data. Proteins were quantified to validate antibodies in a novel method that evaluates antibodies based on differential protein expression across multiple cell lines (Paper IV). Finally, a large-scale study was performed to generate targeted proteomics assays (Paper V) based on protein fragments. Here, assay coordinates were mapped for more than 10,000 human protein-coding genes and a subset of peptides was thereafter used to determine absolute protein levels of 49 proteins in human serum.

                     

In conclusion, this thesis describes the development of methods for protein quantification by targeted mass spectrometry and the use of recombinant protein fragment standards as the common denominator.

Place, publisher, year, edition, pages
Stockholm: Kungliga Tekniska högskolan, 2016. 90 p.
Series
TRITA-BIO-Report, ISSN 1654-2312 ; 2016:16
Keyword
proteomics, mass spectrometry, protein quantification, stable isotope standard, parallel reaction monitoring, immuno-enrichment
National Category
Biochemistry and Molecular Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-193951 (URN)978-91-7729-153-4 (ISBN)
Public defence
2016-11-11, Gard-aulan, Folkhälsomyndigheten, Nobels väg 18, Solna, 10:00 (English)
Opponent
Supervisors
Note

QC 20161013

Available from: 2016-10-13 Created: 2016-10-13 Last updated: 2016-10-14Bibliographically approved
2. Integration of RNA and protein expression profiles to study human cells
Open this publication in new window or tab >>Integration of RNA and protein expression profiles to study human cells
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
RNA-seq, Transcriptomics, Proteomics, Malignant transformation, Cancer, Functional enrichment
National Category
Biological Sciences
Research subject
Biotechnology; Medical Technology
Identifiers
urn:nbn:se:kth:diva-196700 (URN)978-91-7729-209-8 (ISBN)
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

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Edfors, FredrikDanielsson, FridaHallström, BjörnKäll, LukasLundberg, EmmaForsström, BjörnUhlén, Mathias
By organisation
Proteomics and NanobiotechnologyScience for Life Laboratory, SciLifeLab
In the same journal
Molecular Systems Biology
Biochemistry and Molecular Biology

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 67 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf