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Spatially Resolved Gene Expression Analysis
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.ORCID iD: 0000-0001-5941-7220
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 [en]
RNA, RNA-sequencing, transcriptomics, spatial transcriptomics, single cells
National Category
Engineering and Technology
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-235652ISBN: 978-91-7729-965-3 (print)OAI: oai:DiVA.org:kth-235652DiVA, id: diva2:1252472
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
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: 2018-10-01Bibliographically approved
2. Spatial detection of fetal marker genes expressed at low level in adult human heart tissue
Open this publication in new window or tab >>Spatial detection of fetal marker genes expressed at low level in adult human heart tissue
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 12941Article in journal (Refereed) Published
Abstract [en]

Heart failure is a major health problem linked to poor quality of life and high mortality rates. Hence, novel biomarkers, such as fetal marker genes with low expression levels, could potentially differentiate disease states in order to improve therapy. In many studies on heart failure, cardiac biopsies have been analyzed as uniform pieces of tissue with bulk techniques, but this homogenization approach can mask medically relevant phenotypes occurring only in isolated parts of the tissue. This study examines such spatial variations within and between regions of cardiac biopsies. In contrast to standard RNA sequencing, this approach provides a spatially resolved transcriptome- and tissue-wide perspective of the adult human heart, and enables detection of fetal marker genes expressed by minor subpopulations of cells within the tissue. Analysis of patients with heart failure, with preserved ejection fraction, demonstrated spatially divergent expression of fetal genes in cardiac biopsies.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2017
National Category
Genetics
Identifiers
urn:nbn:se:kth:diva-217038 (URN)10.1038/s41598-017-13462-5 (DOI)000412781300009 ()29021611 (PubMedID)2-s2.0-85031126431 (Scopus ID)
Note

QC 20171101

Available from: 2017-11-01 Created: 2017-11-01 Last updated: 2018-10-01Bibliographically approved
3. An organ‐wide gene expression atlas of the developing human heart
Open this publication in new window or tab >>An organ‐wide gene expression atlas of the developing human heart
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

The human developing heart holds a greater proportion of stem-cell-like cells than the adult heart. However, it is not completely understood how these stem cells differentiate into various cardiac cell types. We have performed an organ-wide transcriptional landscape analysis of the developing heart to advance our understanding of cardiac morphogenesis in humans. Comprehensive spatial gene expression analyses identified distinct profiles that correspond not only to individual chamber compartments, but also distinctive regions within the outflow tract. Furthermore, the generated spatial expression reference maps facilitated the assignment of 3,787 human embryonic cardiac cells obtained from single-cell RNA-sequencing to an in situlocation. Through this approach we reveal that the outflow tract contains a wider range of cell types than the chambers, and that the epicardium expression profile can be traced to several cell types that are activated at different stages of development. We also provide a 3D spatial model of human embryonic cardiac cells to enable further studies of the developing human heart. 

National Category
Developmental Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-235648 (URN)
Note

QC 20181002

Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-02Bibliographically approved
4. Spatial Isoform Profiling within Individual Tissue Sections
Open this publication in new window or tab >>Spatial Isoform Profiling within Individual Tissue Sections
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Spatial Transcriptomics has been shown to be a persuasive RNA sequencing

technology for analyzing cellular heterogeneity within tissue sections. The

technology efficiently captures and barcodes 3’ tags of all polyadenylated

transcripts from a tissue section, and thus provides a powerful platform when

performing quantitative spatial gene expression studies. However, the current

protocol does not recover the full-length information of transcripts, and

consequently lack information regarding alternative splice variants. Here, we

introduce a novel protocol for spatial isoform profiling, using Spatial

Transcriptomics barcoded arrays.

National Category
Biological Sciences
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-235650 (URN)
Note

QC 20181002

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

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