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Computational methods for analysis and visualization of spatially resolved transcriptomes
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-4035-5258
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Characterizing the expression level of genes (transcriptome) in cells and tis- sues is essential for understanding the biological processes of multicellular or- ganisms. RNA sequencing (RNA-seq) has gained traction in the last decade as a powerful tool that provides an accurate quantitative representation of the transcriptome in tissues. RNA-seq methods are, however, limited by the fact that they provide an average representation of the transcriptome across the tissue. Single cell RNA sequencing (scRNA-seq) provides quantitative gene expression levels of individual cells. This enables the molecular characteri- zation of cell types in health, disease and developmental tissues. However, scRNA-seq lacks the spatial context needed to understand how cells interact and their microenvironment. Current methods that provide spatially resolved gene expression levels are limited by a low throughput and the fact that the target genes must be known in advance.

Spatial Transcriptomics (ST) is a novel method that combines high-resolution imaging with high-throughput sequencing. ST provides spatially resolved gene expression levels in tissue sections. The first part of the work presented in this thesis (Papers I, II, III and IV) revolves around the ST method and the development of the computational tools required to process, analyse and visualize ST data.

Furthermore, the ST method was utilized to construct a three-dimensional (3D) molecular atlas of the adult mouse brain using 75 consecutive coronal sections (Paper V). We show that the molecular clusters obtained by unsu- pervised clustering of the atlas highly correlates with the Allen Brain Atlas. The molecular clusters provide new insights in the organization of regions like the hippocampus or the amygdala. We show that the molecular atlas can be used to spatially map single cells (scRNA-seq) onto the clusters and that only a handful of genes is required to define the brain regions at a molecular level.

Finally, the hippocampus and the olfactory bulb of transgenic mice mim- icking the Alzheimer’s disease (AD) were spatially characterized using the ST method (Paper VI). Dierential expression analysis revealed genes central in areas highly cited as important in AD including lipid metabolism, cellular bioenergetics, mitochondrial function, stress response and neurotransmission.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. , p. 67
Series
TRITA-CBH-FOU ; 2019:54
Keywords [en]
RNA, RNA-seq, single cell, scRNA-seq, transcriptomics, spatial transcriptomics, brain, 3D, Alzheimer’s disease
National Category
Medical and Health Sciences
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-262828ISBN: 978-91-7873-335-4 (print)OAI: oai:DiVA.org:kth-262828DiVA, id: diva2:1362751
Public defence
2019-11-15, Air and Fire, Tomtebodavägen 23a, Solna, 10:00 (English)
Opponent
Supervisors
Note

QC 2019-10-23

Available from: 2019-10-23 Created: 2019-10-21 Last updated: 2019-10-23Bibliographically 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: 2019-10-23Bibliographically approved
2. ST Pipeline: an automated pipeline for spatial mapping of unique transcripts
Open this publication in new window or tab >>ST Pipeline: an automated pipeline for spatial mapping of unique transcripts
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2017 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, no 16, p. 2591-2593Article in journal (Refereed) Published
Abstract [en]

Motivation: In recent years we have witnessed an increase in novel RNA-seq based techniques for transcriptomics analysis. Spatial transcriptomics is a novel RNA-seq based technique that allows spatial mapping of transcripts in tissue sections. The spatial resolution adds an extra level of complexity, which requires the development of new tools and algorithms for efficient and accurate data processing. Results: Here we present a pipeline to automatically and efficiently process RNA-seq data obtained from spatial transcriptomics experiments to generate datasets for downstream analysis.

Place, publisher, year, edition, pages
Oxford University Press, 2017
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-212596 (URN)10.1093/bioinformatics/btx211 (DOI)000407139800026 ()
Funder
Knut and Alice Wallenberg FoundationSwedish Research CouncilSwedish Foundation for Strategic Research EU, Horizon 2020, 643417Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20170825

Available from: 2017-08-25 Created: 2017-08-25 Last updated: 2019-10-23Bibliographically approved
3. ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets
Open this publication in new window or tab >>ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets
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2018 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 34, no 11, p. 1966-1968Article in journal (Refereed) Published
Abstract [en]

Motiviation: Spatial Transcriptomics (ST) is a method which combines high resolution tissue imaging with high troughput transcriptome sequencing data. This data must be aligned with the images for correct visualization, a process that involves several manual steps. Results: Here we present ST Spot Detector, a web tool that automates and facilitates this alignment through a user friendly interface.

Place, publisher, year, edition, pages
Oxford University Press, 2018
National Category
Chemical Sciences
Identifiers
urn:nbn:se:kth:diva-230824 (URN)10.1093/bioinformatics/bty030 (DOI)000434108800031 ()29360929 (PubMedID)2-s2.0-85048044897 (Scopus ID)
Note

QC 20180619

Available from: 2018-06-19 Created: 2018-06-19 Last updated: 2019-10-23Bibliographically approved
4. ST viewer: a tool for analysis and visualization of spatial transcriptomics datasets
Open this publication in new window or tab >>ST viewer: a tool for analysis and visualization of spatial transcriptomics datasets
2019 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, no 6, p. 1058-1060Article in journal (Refereed) Published
Abstract [en]

Motivation Spatial Transcriptomics (ST) is a technique that combines high-resolution imaging with spatially resolved transcriptome-wide sequencing. This novel type of data opens up many possibilities for analysis and visualization, most of which are either not available with standard tools or too complex for normal users. Results Here, we present a tool, ST Viewer, which allows real-time interaction, analysis and visualization of Spatial Transcriptomics datasets through a seamless and smooth user interface. Availability and implementation The ST Viewer is open source under a MIT license and it is available at https://github.com/SpatialTranscriptomicsResearch/st_viewer. Supplementary information Supplementary data are available at Bioinformatics online.

Place, publisher, year, edition, pages
Oxford University Press, 2019
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-249886 (URN)10.1093/bioinformatics/bty714 (DOI)000462709200023 ()30875427 (PubMedID)2-s2.0-85063013580 (Scopus ID)
Note

QC 20190424

Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-10-23Bibliographically approved
5. Molecular atlas of the adult mouse brain
Open this publication in new window or tab >>Molecular atlas of the adult mouse brain
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Brain maps are essential for integrating information and interpreting the structure-function relationship of circuits and behavior. We aimed to generate a systematic classification of the adult mouse brain organization based on unbiased extraction of spatially-defining features. Applying whole-brain spatial transcriptomics, we captured the gene expression signatures to define the spatial organization of molecularly discrete subregions. We found that the molecular code contained sufficiently detailed information to directly deduce the complex spatial organization of the brain. This unsupervised molecular classification revealed new area- and layer-specific subregions, for example in isocortex and hippocampus, and a new division of striatum. The whole-brain molecular atlas further supports the identification of the spatial origin of single neurons using their gene expression profile, and forms the foundation to define a minimal gene set - a brain palette – that is sufficient to spatially annotate the adult brain. In summary, we have established a new molecular atlas to formally define the identity of brain regions, and a molecular code for mapping and targeting of discrete neuroanatomical domains.

Keywords
spatial transcriptomics, neuroscience, brain, mouse, 3D, atlas, transcriptomics, single cell RNA-seq
National Category
Medical Biotechnology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-262876 (URN)10.1101/784181 (DOI)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20191107

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-11-07Bibliographically approved
6. Spatial Transcriptomics characterization of Alzheimer’s disease in the adult mouse brain
Open this publication in new window or tab >>Spatial Transcriptomics characterization of Alzheimer’s disease in the adult mouse brain
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Alzheimer’s disease (AD) is a devastating neurological disease associated with progressive loss of mental skills, cognitive and physical functions. Here, our goal was to uncover novel and known molecular targets in the structured layers of the hippocampus and olfactory bulbs that may contribute to hippocampal synaptic dysfunction and smelling defects in AD mice. Spatial Transcriptomics was used to identify high confidence genes that were differentially regulated in AD mice relative to controls. A discussion of how these genes may contribute to AD pathology is provided.

Keywords
Alzheimer's disease, Spatial Transcriptomics, single cell RNA-seq
National Category
Medical and Health Sciences
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-262873 (URN)
Note

QC 20191023

Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-10-23Bibliographically approved

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Fernandez Navarro, Jose

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