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Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
KTH, School of Biotechnology (BIO), Gene Technology.ORCID iD: 0000-0002-2207-7370
KTH, School of Biotechnology (BIO), Gene Technology.
KTH, School of Biotechnology (BIO), Gene Technology.ORCID iD: 0000-0003-0985-9885
KTH, School of Biotechnology (BIO), Gene Technology.
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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
Resource type
Text
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. Vol. 353, no 6294, p. 78-82
National Category
Genetics
Identifiers
URN: urn:nbn:se:kth:diva-189924DOI: 10.1126/science.aaf2403ISI: 000378816200040PubMedID: 27365449Scopus ID: 2-s2.0-84976875145OAI: oai:DiVA.org:kth-189924DiVA, id: diva2:950353
Note

QC 20160729

Available from: 2016-07-29 Created: 2016-07-25 Last updated: 2018-10-01Bibliographically 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
Series
TRITA-BIO-Report, ISSN 1654-2312 ; 2017:2
Keywords
RNA-sequencing, single cells, spatially resolved transcriptomics, 3D profiling.
National Category
Genetics
Research subject
Biotechnology
Identifiers
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)
Opponent
Supervisors
Note

QC 20170109

Available from: 2017-01-09 Created: 2017-01-08 Last updated: 2017-01-23Bibliographically 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. p. 56
Series
TRITA-BIO-Report, ISSN 1654-2312 ; 2017:5
Keywords
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
3. Spatially Resolved Gene Expression Analysis
Open this publication in new window or tab >>Spatially Resolved Gene Expression Analysis
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Spatially resolved transcriptomics has greatly expanded our knowledge of complex multicellular biological systems. To date, several technologies have been developed that combine gene expression data with information about its spatial tissue context. There is as yet no single spatial method superior to all others, and the existing methods have jointly contributed to progress in this field of technology. Some challenges presented by existing protocols include having a limited number of targets, being labor extensive, being tissue-type dependent and having low throughput or limited resolution. Within the scope of this thesis, many aspects of these challenges have been taken into consideration, resulting in a detailed evaluation of a recently developed spatial transcriptome-wide method. This method, termed Spatial Transcriptomics (ST), enables the spatial location of gene activity to be preserved and visually links it to its histological position and anatomical context. Paper I describes all the details of the experimental protocol, which starts when intact tissue sections are placed on barcoded microarrays and finishes with high throughput sequencing. Here, spatially resolved transcriptome-wide data are obtained from both mouse olfactory bulb and breast cancer samples, demonstrating the broad tissue applicability and robustness of the approach. In Paper II, the ST technology is applied to samples of human adult heart, a tissue type that contains large proportions of fibrous tissue and thus makes RNA extraction substantially more challenging. New protocol strategies are optimized in order to generate spatially resolved transcriptome data from heart failure patients. This demonstrates the advantage of using the technology for the identification of lowly expressed biomarkers that have previously been seen to correlate with disease progression in patients suffering heart failure. Paper III shows that, although the ST technology has limited resolution compared to other techniques, it can be combined with single-cell RNA-sequencing and hence allow the spatial positions of individual cells to be recovered. The combined approach is applied to developing human heart tissue and reveals cellular heterogeneity of distinct compartments within the complete organ. Since the ST technology is based on the sequencing of mRNA tags, Paper IV describes a new version of the method, in which spatially resolved analysis of full-length transcripts is being developed. Exploring the spatial distribution of full-length transcripts in tissues enables further insights into alternative splicing and fusion transcripts and possible discoveries of new genes.  

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 40
Series
TRITA-CBH-FOU ; 2018:43
Keywords
RNA, RNA-sequencing, transcriptomics, spatial transcriptomics, single cells
National Category
Engineering and Technology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-235652 (URN)978-91-7729-965-3 (ISBN)
Public defence
2018-10-26, Gardaulan, Nobels väg 18, Folkhälsomyndigheten, Solna, 10:00 (English)
Opponent
Supervisors
Note

QC 20181002

Available from: 2018-10-02 Created: 2018-10-01 Last updated: 2018-10-02Bibliographically approved

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