Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Transcriptome-wide analysis in cells and tissues
KTH, School of Biotechnology (BIO), Gene Technology.ORCID iD: 0000-0003-0985-9885
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

High-throughput sequencing has greatly influenced the amount of data produced and biological questions asked and answered. Sequencing approaches have also enabled rapid development of related technological fields such as single-cell and spatially resolved expression profiling. The introductory parts of this thesis give an overview of the basic molecular and technological apparatus needed to analyse the transcriptome in cells and tissues. This is succeeded by a summary of present investigations that report recent advancements in RNA profiling.

RNA integrity needs to be preserved for accurate gene expression analysis. A method providing a low-cost alternative for RNA preservation was reported. Namely, a low concentration of buffered formaldehyde was used for fixation of human cell lines and peripheral blood cells (Paper I). The results from bulk RNA sequencing confirmed gene expression was not negatively impacted with the preservation procedure (r2>0.88) and that long-term storage of such samples was possible (r2=0.95). However, it is important to note that a small population of cells overexpressing a limited amount of genes can skew bulk gene expression analyses making them sufficient only in carefully designed studies. Therefore, gene expression should be investigated at the single cell resolution when possible. A method for high-throughput single cell expression profiling termed microarrayed single-cell sequencing was developed (Paper II). The method incorporated fluorescence-activated cell sorting, sample deposition and profiling of thousands of barcoded single cells in one reaction. After sample attachment to a barcoded array, a high-resolution image was taken which linked the position of each array barcode sequence to each individual deposited cell. The cDNA synthesis efficiency was estimated at 17.3% while detecting 27,427 transcripts per cell on average. Additionally, spatially resolved analysis is important in cell differentiation, organ development and pathological changes. Current methods are limited in terms of throughput, cost and time. For that reason, the spatial transcriptomics method was developed (Paper III). Here, the barcoded microarray was used to obtain spatially resolved expression profiles from tissue sections using the same imaging principle. The mouse olfactory bulb was profiled on a whole-transcriptome scale and the results showed that the expression correlated well (r2=0.94-0.97) as compared to bulk RNA sequencing. The method was 6.9% efficient, reported signal diffusion at ~2 μm and accurately deconvoluted layer-specific transcripts in an unbiased manner. Lastly, the spatial transcriptomics concept was applied to profile human breast tumours in three dimensions (Paper IV). Unbiased clustering revealed previously un-annotated regions and classified them as parts of the immune system, providing a detailed view into complex interactions and crosstalk in the whole tissue volume. Spatial tumour classification divulged that certain parts of the tumour clearly classified as other subtypes as compared to bulk analysis providing useful data for current practice diagnostics.

The last part of the thesis discusses a look towards the future, how the presented methods could be used, improved upon or combined in translational research.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017.
Series
TRITA-BIO-Report, ISSN 1654-2312 ; 2017:2
Keyword [en]
RNA-sequencing, single cells, spatially resolved transcriptomics, 3D profiling.
National Category
Genetics
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-199447ISBN: 978-91-7729-259-3 (print)OAI: oai:DiVA.org:kth-199447DiVA, id: diva2:1062732
Public defence
2017-02-10, Air and Fire at Science for Life Laboratory, Tomtebodavägen 23A, Solna, 09:00 (English)
Opponent
Supervisors
Note

QC 20170109

Available from: 2017-01-09 Created: 2017-01-08 Last updated: 2017-01-23Bibliographically approved
List of papers
1. Toward Rare Blood Cell Preservation for RNA Sequencing
Open this publication in new window or tab >>Toward Rare Blood Cell Preservation for RNA Sequencing
2015 (English)In: Journal of Molecular Diagnostics, ISSN 1525-1578, E-ISSN 1943-7811, Vol. 17, no 4, p. 352-359Article in journal (Refereed) Published
Abstract [en]

Cancer is driven by various events Leading to cell differentiation and disease progression. Molecular tools are powerful approaches for describing how and why these events occur. With the growing field of next-generation DNA sequencing, there is an increasing need for high-quality nucleic acids derived from human cells and tissues a prerequisite for successful cell profiting. Although advances in RNA preservation have been made, some of the largest biobanks still do not employ RNA blood preservation as standard because of Limitations in low blood-input volume and RNA stability over the whole gene body. Therefore, we have developed a robust protocol for blood preservation and tong-term storage while maintaining RNA integrity. Furthermore, we explored the possibility of using the protocol for preserving rare cell samples, such as circulating tumor cells. The results of our study confirmed that gene expression was not impacted by the preservation procedure (r(2) > 0.88) or by Long-term storage (r(2) = 0.95), with RNA integrity number values averaging over 8. Similarly, cell surface antigens were still available for antibody selection (r(2) = 0.95). Lastly, data mining for fusion events showed that it was possible to detect rare tumor cells among a background of other cells present in blood irrespective of fixation. Thus, the developed protocol would be suitable for rare blood cell preservation followed by RNA sequencing analysis.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:kth:diva-171274 (URN)10.1016/j.jmoldx.2015.03.009 (DOI)000357441600003 ()25989392 (PubMedID)2-s2.0-84931316023 (Scopus ID)
Note

QC 20150728

Available from: 2015-07-28 Created: 2015-07-27 Last updated: 2017-12-04Bibliographically approved
2. Massive and parallel expression profiling using microarrayed single-cell sequencing
Open this publication in new window or tab >>Massive and parallel expression profiling using microarrayed single-cell sequencing
Show others...
2016 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 7, article id 13182Article in journal (Refereed) Published
Abstract [en]

Single-cell transcriptome analysis overcomes problems inherently associated with averaging gene expression measurements in bulk analysis. However, single-cell analysis is currently challenging in terms of cost, throughput and robustness. Here, we present a method enabling massive microarray-based barcoding of expression patterns in single cells, termed MASC-seq. This technology enables both imaging and high-throughput single-cell analysis, characterizing thousands of single-cell transcriptomes per day at a low cost (0.13 USD/cell), which is two orders of magnitude less than commercially available systems. Our novel approach provides data in a rapid and simple way. Therefore, MASC-seq has the potential to accelerate the study of subtle clonal dynamics and help provide critical insights into disease development and other biological processes.

Place, publisher, year, edition, pages
Nature Publishing Group, 2016
National Category
Cell Biology
Identifiers
urn:nbn:se:kth:diva-196395 (URN)10.1038/ncomms13182 (DOI)000385549400001 ()2-s2.0-84991694317 (Scopus ID)
Funder
Knut and Alice Wallenberg FoundationSwedish Cancer SocietySwedish Foundation for Strategic Research Swedish Research CouncilTorsten Söderbergs stiftelse
Note

QC 20161128

Available from: 2016-11-28 Created: 2016-11-14 Last updated: 2017-11-29Bibliographically approved
3. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Open this publication in new window or tab >>Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Show others...
2016 (English)In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 353, no 6294, p. 78-82Article in journal (Refereed) Published
Abstract [en]

Analysis of the pattern of proteins or messenger RNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.

Place, publisher, year, edition, pages
AMER ASSOC ADVANCEMENT SCIENCE, 2016
National Category
Genetics
Identifiers
urn:nbn:se:kth:diva-189924 (URN)10.1126/science.aaf2403 (DOI)000378816200040 ()27365449 (PubMedID)2-s2.0-84976875145 (Scopus ID)
Note

QC 20160729

Available from: 2016-07-29 Created: 2016-07-25 Last updated: 2017-11-28Bibliographically approved
4. Three-dimensional whole transcriptome analysis of tissue for classification of breast cancer. Submitted manuscript.
Open this publication in new window or tab >>Three-dimensional whole transcriptome analysis of tissue for classification of breast cancer. Submitted manuscript.
2017 (English)Manuscript (preprint) (Other academic)
National Category
Genetics
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-199446 (URN)
Note

QC 20170109

Available from: 2017-01-08 Created: 2017-01-08 Last updated: 2017-01-23Bibliographically approved

Open Access in DiVA

fulltext(16566 kB)146 downloads
File information
File name FULLTEXT02.pdfFile size 16566 kBChecksum SHA-512
463b734f96c797b6c080cddb17030306d9c978f9d5e327ee946f321159021fed78796f72a596e0eff4a8ba7f5b64e4bbc9edc34c4d84dc8fac4a33a4c145a2d9
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Vickovic, Sanja
By organisation
Gene Technology
Genetics

Search outside of DiVA

GoogleGoogle Scholar
Total: 165 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 958 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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