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An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries.
KTH, School of Biotechnology (BIO), Gene Technology.ORCID iD: 0000-0002-2219-0197
KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
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2016 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 6, 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. Vol. 6, 37137
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:kth:diva-196766DOI: 10.1038/srep37137ISI: 000388080900001PubMedID: 27849009Scopus ID: 2-s2.0-84995665687OAI: oai:DiVA.org:kth-196766DiVA: diva2:1048438
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: 2017-01-25Bibliographically approved
In thesis
1. Library Preparation for High Throughput DNA Sequencing
Open this publication in new window or tab >>Library Preparation for High Throughput DNA Sequencing
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Order 3 billion base pairs of DNA in the correct order and you get the blueprint of a human, the genome. Before the introduction of massively parallel sequencing a little more than a decade ago it would cost around $10 million to get this blueprint. Since then, sequencing throughput and cost have plummeted and now that figure is around $1000, and large sequencing centres such as the National Genomics Infrastructure in Stockholm is sequencing the equivalent of 25 human genomes per hour. The papers that form the basis of this thesis cover different aspects of the rapidly expanding DNA sequencing field.

 

Paper I describes a model system that employ massively parallel sequencing to characterize the behaviour of type IIS restriction enzymes. Enzymes are biological macromolecules that catalyse chemical reactions in the cell. All commercially available sequencing systems use enzymes to prepare the nucleic acids before they are loaded on the machine. Thus, intimate knowledge of enzymes is vital not only when designing new sequencing protocols, but also for understanding the limitations of current protocols. Paper II covers the automation of a library preparation protocol for spatially resolved transcriptome sequencing. Automation increases the sample throughput and also minimises the risk of human errors that can introduce technical noise in the data. In paper III, the power of massively parallel sequencing is employed to describe the RNA content of the endometrium at two different time points during the menstrual cycle. Finally, paper IV covers the sequencing of highly degraded nucleic acids from formalin fixed, paraffin embedded samples. These samples often have a rich clinical background, making them extremely valuable for researchers. However, it is challenging to sequence these samples and this study looks at the impact that different preparation kits have on the quality of the sequencing data. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. 56 p.
Series
TRITA-BIO-Report, ISSN 1654-2312 ; 2017:1
Keyword
DNA, RNA, sequencing, massively parallel sequencing, library preparation, automation, genome, transcriptome
National Category
Biological Sciences
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-196560 (URN)978-91-7729-212-8 (ISBN)
Public defence
2017-01-13, Air & Fire, Science for Life Laboratory, Tomtebodavägen 23, Solna, 13:00 (English)
Opponent
Supervisors
Note

QC 20161124

Available from: 2016-11-24 Created: 2016-11-16 Last updated: 2016-11-24Bibliographically approved
2. Spatially resolved and single cell transcriptomics
Open this publication in new window or tab >>Spatially resolved and single cell transcriptomics
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. 56 p.
Series
TRITA-BIO-Report, ISSN 1654-2312 ; 2017:5
Keyword
spatial, transcriptomics, single cell, 3D, RNA-sequencing
National Category
Biochemistry and Molecular Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-200364 (URN)978-91-7729-272-2 (ISBN)
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

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