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Spatially resolved and single cell transcriptomics
KTH, School of Biotechnology (BIO), Gene Technology.ORCID iD: 0000-0001-8728-3709
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In recent years, massive parallel sequencing has revolutionized the field of biology and has provided us with a vast number of new discoveries in fields such as neurology, developmental biology and cancer research. A significant area is deciphering gene expression patterns, as well as other aspects of transcriptome information, such as the impact of splice variants and mutations on biological functions and disease development. By applying RNA-sequencing, one can extract this type of information in a large-scale manner. The most recent approaches include high-resolution techniques such as single cell sequencing and in situ methods in order to circumvent the problems with gene expression averaging in homogenized samples, and loss of spatial information.

The research in this thesis is focused on the development of a novel genome-wide spatial transcriptomics method. The technique is used for analysis of intact tissue sections as well as single cells from solution, with the aim to combine gene expression and morphological information. In Paper I, the method is described in detail, and it is shown that the method is able to generate spatial high quality data from mouse olfactory bulb tissue sections (a part of the forebrain) as well as from tissue sections from breast cancer samples. In Paper III, we adapt the library preparation method in order to be able to execute it on a robotic workstation, thus increasing the reproducibility and the throughput, and decreasing the hands-on time. In Paper IV, we generate 3D-data from breast cancer samples by serial sectioning. We show that the gene expression can be highly variable along all three axes of a tumor, and we track pathways with specific spatial activity, as well as perform subtype classification with three-dimensional resolution. In Paper II, we present a high-throughput method for single cell transcriptomics of cells in solution. The method is based on the same type of solid surface capture as the tissue protocol described in Papers I, III and IV. Again, we show that we can generate high-quality gene expression data, and connect this to morphological characteristics of the analyzed single cells; both using cultured cells and samples from patients with leukemia.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. , p. 56
Series
TRITA-BIO-Report, ISSN 1654-2312 ; 2017:5
Keyword [en]
spatial, transcriptomics, single cell, 3D, RNA-sequencing
National Category
Biochemistry and Molecular Biology
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-200364ISBN: 978-91-7729-272-2 (print)OAI: oai:DiVA.org:kth-200364DiVA, id: diva2:1068517
Public defence
2017-02-24, Air & Fire, Tomtebodavägen 23 a., Solna, 09:00 (English)
Opponent
Supervisors
Funder
Knut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research Swedish Research CouncilScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20170125

Available from: 2017-01-25 Created: 2017-01-25 Last updated: 2017-01-25Bibliographically approved
List of papers
1. 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
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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
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
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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. An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries.
Open this publication in new window or tab >>An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries.
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2016 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 6, article id 37137Article in journal (Refereed) Published
Abstract [en]

Sequencing the nucleic acid content of individual cells or specific biological samples is becoming increasingly common. This drives the need for robust, scalable and automated library preparation protocols. Furthermore, an increased understanding of tissue heterogeneity has lead to the development of several unique sequencing protocols that aim to retain or infer spatial context. In this study, a protocol for retaining spatial information of transcripts has been adapted to run on a robotic workstation. The method spatial transcriptomics is evaluated in terms of robustness and variability through the preparation of reference RNA, as well as through preparation and sequencing of six replicate sections of a gingival tissue biopsy from a patient with periodontitis. The results are reduced technical variability between replicates and a higher throughput, processing four times more samples with less than a third of the hands on time, compared to the standard protocol.

Place, publisher, year, edition, pages
Nature Publishing Group, 2016
National Category
Biological Sciences
Identifiers
urn:nbn:se:kth:diva-196766 (URN)10.1038/srep37137 (DOI)000388080900001 ()27849009 (PubMedID)2-s2.0-84995665687 (Scopus ID)
Funder
Knut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research Swedish Research Council
Note

QC 20161124

Available from: 2016-11-21 Created: 2016-11-21 Last updated: 2017-11-29Bibliographically approved
4. Three-dimensional spatial transcriptomics analysis for classification of breast cancer
Open this publication in new window or tab >>Three-dimensional spatial transcriptomics analysis for classification of breast cancer
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(English)Manuscript (preprint) (Other academic)
National Category
Biochemistry and Molecular Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-200367 (URN)
Projects
Spatially resolved and single cell transcriptomics
Note

QC 20170125

Available from: 2017-01-25 Created: 2017-01-25 Last updated: 2017-01-25Bibliographically approved

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