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Massive and parallel expression profiling using microarrayed single-cell sequencing
KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0003-0985-9885
KTH, Centres, Science for Life Laboratory, SciLifeLab.
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2016 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 7, 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. Vol. 7, 13182
National Category
Cell Biology
URN: urn:nbn:se:kth:diva-196395DOI: 10.1038/ncomms13182ISI: 000385549400001ScopusID: 2-s2.0-84991694317OAI: diva2:1050315
Knut and Alice Wallenberg FoundationSwedish Cancer SocietySwedish Foundation for Strategic Research Swedish Research CouncilTorsten Söderbergs stiftelse

QC 20161128

Available from: 2016-11-28 Created: 2016-11-14 Last updated: 2017-01-08Bibliographically approved
In thesis
1. Transcriptome-wide analysis in cells and tissues
Open this publication in new window or tab >>Transcriptome-wide analysis in cells and tissues
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
TRITA-BIO-Report, ISSN 1654-2312 ; 2017:2
RNA-sequencing, single cells, spatially resolved transcriptomics, 3D profiling.
National Category
Research subject
urn:nbn:se:kth:diva-199447 (URN)978-91-7729-259-3 (ISBN)
Public defence
2017-02-10, Air and Fire at Science for Life Laboratory, Tomtebodavägen 23A, Solna, 09:00 (English)

QC 20170109

Available from: 2017-01-09 Created: 2017-01-08 Last updated: 2017-01-09Bibliographically approved

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