kth.sePublications KTH
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Mapping Transcriptomes in Tissues
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. (Genomics)ORCID iD: 0000-0003-4209-2911
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the past few decades, the advent of pioneering biotechnological methods has allowed scientists to analyze the molecular components of multicellular organisms with remarkable precision. The field of transcriptomics has witnessed a rapid development of technologies for gene expression profiling of biological samples. These gene expression profiles offer crucial insights into biological processes that underlie organism development, homeostasis, and disease-causing dysregulation. Modern transcriptomics technologies can profile samples at various degrees of precision and resolution, and when combined, they contribute to a comprehensive understanding of the complex molecular mechanisms that shape entire organisms. Some of these molecular mechanisms occur at the microscopic scale, controlled by communication between nearby cells. Other mechanisms depend on coordinated efforts between large networks of cells organized into tissues and organs. Cells, tissues and organs represent hierarchical levels of structural organization, and each level plays a vital role in the proper functioning of the organism. Gene expression profiling technologies yield comprehensive data that can be harnessed to explore and characterize biological phenomena within and across these structural levels. The central theme of this thesis revolves around the use of experimental technologies and computational methods in the field of transcriptomics to enhance our understanding of multicellular life. Particular attention is directed at a technology known as Visium, which has held an important position in the field in recent years. The research articles included in this thesis demonstrate the applications of Visium and related technologies in biological research.

In article I, we present a computational toolbox for processing, analyzing, and visualizing Visium data, assembled into an open-source package written in the R programming language. The package facilitates the characterization of gene expression profiles in tissue sections and seamlessly integrates expression data with corresponding histological images. This computational framework was used extensively for the data analyses presented in articles II, III and IV and the articles listed in the extended list of publications.

In article II, we report one of the first spatiotemporal, transcriptomics atlases of the developing human heart. The atlas encompasses three developmental time points during the first trimester, and is constructed from gene expression data from isolated cells and intact tissue sections. Joint analysis of this data enabled characterization of the transcriptomic profiles and the cellular composition of anatomical domains within the heart, illuminating biological processes that underlie cardiac morphogenesis in humans.

Article III constitutes a study of the transcriptomic landscape of the murine colon generated using spatially resolved transcriptomics. By folding the organ into a roll, we successfully obtained tissue sections covering the entire colon, enabling organ-wide transcriptomic profiling. Sections were acquired from a healthy colon and a colon recovering from damage due to treatment with a tissue-damaging substance. Data-driven analysis of the healthy colon unveiled a previously undiscovered molecular regionalization from the proximal to distal parts. In the recovering colon, we observed dramatic alterations in the distal tissues, while the proximal parts remained more similar to the healthy colon. In the injured distal colon, we mapped multiple gene expression programs associated with distinct biological responses to tissue injury.

In article IV, we introduce an experimental protocol that makes the Visium method compatible with fresh frozen tissue samples with low RNA quality. The protocol was tested on human prostate cancer, lung, colon, small intestine and pediatric brain tumor tissue samples, as well as mouse brain and cartilage tissue samples. Together, these tissue samples represented a wide selection of specimens with varying composition and RNA quality. Through comparative analyses, we demonstrated that the proposed experimental protocol surpassed the standard Visium protocol in performance for samples with low to moderate RNA integrity.

Finally, in article V, we present an updated R package for Visium data analysis. This R package builds upon the work presented in article I, but offers a more versatile and efficient computational framework. The package features web-based tools for interactive data exploration, image processing methods and methods to map cell types in tissue sections. Additionally, it includes several spatially aware analysis methods that incorporate information about distances between measurements to investigate biological phenomena that exhibit spatial patterns.

Abstract [sv]

Under de senaste decennierna har tillkomsten av banbrytande bioteknologiska metoder gjort det möjligt för forskare att analysera de molekylära komponenterna i flercelliga organismer med anmärkningsvärd precision. Forskningsfältet transkriptomik har bevittnat en snabb utveckling av teknologier som har utökat möjligheterna att erhålla omfattande genuttrycksprofiler från biologiska prover. Dessa genuttrycksprofiler ger avgörande insikter i biologiska processer som ligger till grund för organismers utveckling, homeostas och sjukdomsframkallande dysreglering. Moderna teknologier kan användas för att utforska prover i olika grader av precision och upplösning, och när de kombineras bidrar de till en holistisk bild av de invecklade molekylära mekanismerna som formar flercelliga organismer. Vissa av dessa molekylära mekanismer förekommer i mikroskopisk skala och styrs genom kommunikation mellan närliggande celler. Andra mekanismer är beroende av samordnade processer inom stora nätverk av celler organiserade i vävnader och organ. Celler, vävnader och organ bildar en hierarki av strukturella nivåer, och varje nivå spelar en viktig roll för att organismen ska fungera korrekt. Experimentella teknologier inom fältet transkriptomik ger omfattande data som kan användas för att utforska och karaktärisera biologiska fenomen inom och mellan dessa strukturella nivåer. Det centrala temat för denna avhandling kretsar kring användningen av experimentella teknologier och beräkningsmetoder inom biologisk forskning. Här undersöks hur dessa verktyg kan användas för att förbättra vår förståelse av flercelligt liv. Särskild uppmärksamhet riktas mot en teknologi känd som Visium, vilken haft en viktig position inom fältet de senaste åren. Forskningsartiklarna som ingår i denna avhandling visar tillämpningarna av Visium-teknologin och relaterade teknologier inom biologisk forskning. 

I artikel I beskriver vi beräkningsverktyg för bearbetning, analys och visualisering av Visium-data, sammansatt till ett paket skrivet i programmeringsspråket R. Verktygen möjliggör karaktärisering av genuttrycksprofiler i vävnadssnitt och integrering av genuttrycksdata med histologiska bilder i en interaktiv programmeringsmiljö. Denna mjukvara användes i stor utsträckning för de dataanalyser som presenteras i artiklarna II, III och IV och analyser gjorda i artiklarna listade i den utökade publikationslistan. 

I artikel II presenterar vi ett atlas för det mänskliga hjärtat under utveckling, baserat på data framställd med transkriptomiska metoder. Atlasen omfattar tre tidpunkter under den första trimestern, konstruerad med hjälp av genuttrycksdata från celler och vävnadssnitt. Integrerad analys av dessa data möjliggjorde karakterisering av genuttrycksprofiler och den cellulära sammansättningen av anatomiska domäner i hjärtat, vilket belyser de biologiska processer som ligger till grund för hjärtats morfogenes hos människor. 

Artikel III utgör en studie av genuttryckslandskapet i tjocktarmen hos möss, genererad med spatial transkriptomik. Genom att vika organet till en rulle kunde vi erhålla vävnadssnitt som täcker hela tjocktarmen, vilket möjliggjorde transkriptomisk profilering av hela organet i ett enda experiment. Vävnadssnitt togs från en frisk tjocktarm och en tjocktarm som återhämtade sig från skada inducerad med en vävnadsskadande substans (DSS-inducerad kolit). Datadriven analys av den friska tjocktarmen avslöjade en tidigare oupptäckt molekylär regionalisering från de proximala till distala delarna. I den skadade tjocktarmen fann vi dramatiska förändringar i de distala vävnaderna, medan de proximala delarna var mer jämförbara med den friska tjocktarmen. I den skadade distala tjocktarmen kartlade vi flera genuttrycksprogram associerade med distinkta biologiska svar på vävnadsskada. 

I artikel IV introducerar vi ett experimentellt protokoll som utökar tillämpningarna av Visium-metoden för att studera vävnadsprover med låg RNA-kvalitet. Protokollet testades på vävnadsprover från prostatacancer, lunga, tjocktarm, tunntarm och pediatrisk hjärntumör från människa, samt vävnadsprover från hjärna och brosk från mus. Tillsammans representerade dessa prover ett brett urval av vävnader med varierande sammansättning och RNA-kvalitet. Genom jämförande analyser visade vi att denna experimentella metod överträffade det standardiserade Visium-protokollet för prover med låg till måttlig RNA-kvalitet. 

Slutligen, i artikel V, presenterar vi en uppdaterad mjukvara (R-paket) för analys av Visium-data. Detta R-paket bygger på det arbete som presenterades i artikel I, men erbjuder mer mångsidiga och effektiva verktyg för bearbetning, analys och visualisering av data. Paketet inkluderar bland annat webbaserade verktyg för interaktiv utforskning av data, bildbehandlingsmetoder och metoder för att kartlägga celltyper i vävnadssnitt. Vidare innehåller paketet ett antal analysmetoder som inkorporerar information om avstånd mellan mätningar, vilket möjliggör undersökning av biologiska fenomen som uppvisar spatiala mönster.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2023. , p. 67
Series
TRITA-CBH-FOU ; 2023:17
Keywords [en]
spatially resolved transcriptomics, spatial transcriptomics, transcriptomics, data analysis
Keywords [sv]
rumsligt upplöst transkriptomik, rumslig transkriptomik, transkriptomik, dataanalys
National Category
Biochemistry Molecular Biology Bioinformatics and Computational Biology
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-326142ISBN: 978-91-8040-554-6 (print)OAI: oai:DiVA.org:kth-326142DiVA, id: diva2:1752949
Public defence
2023-05-26, Air&Fire, Tomtebodavägen 23A, 17165, via Zoom: https://kth-se.zoom.us/j/69983443965, Solna, 10:00 (English)
Opponent
Supervisors
Note

QC 2023-04-25

Available from: 2023-04-25 Created: 2023-04-25 Last updated: 2025-02-20Bibliographically approved
List of papers
1. Seamless integration of image and molecular analysis for spatial transcriptomics workflows
Open this publication in new window or tab >>Seamless integration of image and molecular analysis for spatial transcriptomics workflows
2020 (English)In: BMC Genomics, E-ISSN 1471-2164, Vol. 21, no 1, article id 482Article in journal (Refereed) Published
Abstract [en]

Background: Recent advancements in in situ gene expression technologies constitute a new and rapidly evolving field of transcriptomics. With the recent launch of the 10x Genomics Visium platform, such methods have started to become widely adopted. The experimental protocol is conducted on individual tissue sections collected from a larger tissue sample. The two-dimensional nature of this data requires multiple consecutive sections to be collected from the sample in order to construct a comprehensive three-dimensional map of the tissue. However, there is currently no software available that lets the user process the images, align stacked experiments, and finally visualize them together in 3D to create a holistic view of the tissue. Results: We have developed an R package named STUtility that takes 10x Genomics Visium data as input and provides features to perform standardized data transformations, alignment of multiple tissue sections, regional annotation, and visualizations of the combined data in a 3D model framework. Conclusions: STUtility lets the user process, analyze and visualize multiple samples of spatially resolved RNA sequencing and image data from the 10x Genomics Visium platform. The package builds on the Seurat framework and uses familiar APIs and well-proven analysis methods. An introduction to the software package is available at https://ludvigla.github.io/STUtility_web_site/.

Place, publisher, year, edition, pages
BioMed Central, 2020
Keywords
Spatial transcriptomics, Transcriptomics, Genomics, Software, Visualization, Image processing, Data analysis, R-package, 3D
National Category
Medical Imaging
Identifiers
urn:nbn:se:kth:diva-279176 (URN)10.1186/s12864-020-06832-3 (DOI)000553139000001 ()32664861 (PubMedID)2-s2.0-85088007267 (Scopus ID)
Note

QC 20200907

Available from: 2020-09-07 Created: 2020-09-07 Last updated: 2025-02-09Bibliographically approved
2. A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart
Open this publication in new window or tab >>A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart
Show others...
2019 (English)In: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 179, no 7, p. 1647-+Article in journal (Refereed) Published
Abstract [en]

The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.

Place, publisher, year, edition, pages
CELL PRESS, 2019
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-266427 (URN)10.1016/j.cell.2019.11.025 (DOI)000502546200020 ()31835037 (PubMedID)2-s2.0-85076165520 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20200117

Available from: 2020-01-17 Created: 2020-01-17 Last updated: 2023-04-25Bibliographically approved
3. The spatial transcriptomic landscape of the healing mouse intestine following damage
Open this publication in new window or tab >>The spatial transcriptomic landscape of the healing mouse intestine following damage
Show others...
2022 (English)In: Nature Communications, E-ISSN 2041-1723, Vol. 13, no 1, article id 828Article in journal (Refereed) Published
Abstract [en]

The colon is comprised of specialized cells that interact with each other to function, however, the molecular regionalization of the colon is incompletely understood. Here, the authors use spatial transcriptomics to generate a publicly available resource defining the transcriptomic regionalization of the colon during steady state and mucosal healing. The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics to define how the transcriptomic landscape is spatially organized in the steady state and healing murine colon. At steady state conditions, we demonstrate a previously unappreciated molecular regionalization of the colon, which dramatically changes during mucosal healing. Here, we identified spatially-organized transcriptional programs defining compartmentalized mucosal healing, and regions with dominant wired pathways. Furthermore, we showed that decreased p53 activation defined areas with increased presence of proliferating epithelial stem cells. Finally, we mapped transcriptomics modules associated with human diseases demonstrating the translational potential of our dataset. Overall, we provide a publicly available resource defining principles of transcriptomic regionalization of the colon during mucosal healing and a framework to develop and progress further hypotheses.

Place, publisher, year, edition, pages
Springer Nature, 2022
National Category
Cell and Molecular Biology Cancer and Oncology Biochemistry Molecular Biology
Identifiers
urn:nbn:se:kth:diva-309824 (URN)10.1038/s41467-022-28497-0 (DOI)000754315500019 ()35149721 (PubMedID)2-s2.0-85124578152 (Scopus ID)
Note

QC 20220315

Available from: 2022-03-15 Created: 2022-03-15 Last updated: 2025-02-20Bibliographically approved
4. Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples
Open this publication in new window or tab >>Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples
Show others...
2023 (English)In: Nature Communications, E-ISSN 2041-1723, Vol. 14, no 1Article in journal (Refereed) Published
Abstract [en]

Spatially resolved transcriptomics has enabled precise genome-wide mRNA expression profiling within tissue sections. The performance of methods targeting the polyA tails of mRNA relies on the availability of specimens with high RNA quality. Moreover, the high cost of currently available spatial resolved transcriptomics assays requires a careful sample screening process to increase the chance of obtaining high-quality data. Indeed, the upfront analysis of RNA quality can show considerable variability due to sample handling, storage, and/or intrinsic factors. We present RNA-Rescue Spatial Transcriptomics (RRST), a workflow designed to improve mRNA recovery from fresh frozen specimens with moderate to low RNA quality. First, we provide a benchmark of RRST against the standard Visium spatial gene expression protocol on high RNA quality samples represented by mouse brain and prostate cancer samples. Then, we test the RRST protocol on tissue sections collected from five challenging tissue types, including human lung, colon, small intestine, pediatric brain tumor, and mouse bone/cartilage. In total, we analyze 52 tissue sections and demonstrate that RRST is a versatile, powerful, and reproducible protocol for fresh frozen specimens of different qualities and origins. 

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Biochemistry Molecular Biology
Identifiers
urn:nbn:se:kth:diva-326141 (URN)10.1038/s41467-023-36071-5 (DOI)001026236800009 ()36720873 (PubMedID)2-s2.0-85147171092 (Scopus ID)
Note

QC 20230426

Available from: 2023-04-25 Created: 2023-04-25 Last updated: 2025-02-20Bibliographically approved
5. Semla: A versatile toolkit for spatially resolved transcriptomics analysis and visualization
Open this publication in new window or tab >>Semla: A versatile toolkit for spatially resolved transcriptomics analysis and visualization
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Spatially resolved transcriptomics technologies generate gene expression data with retained positional information from a tissue section, often accompanied by a corresponding histological image. Computational tools should make it effortless to incorporate spatial information into data analyses and present analysis results in their histological context. Here, we present semla, an R package for processing, analysis, and visualization of spatially resolved transcriptomics data, which includes interactive web applications for data exploration and tissue annotation.

Keywords
spatially resolved transcriptomics, Visium, data analysis, data visualization, R package
National Category
Bioinformatics and Computational Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-326088 (URN)
Funder
Swedish Foundation for Strategic ResearchEU, European Research CouncilSwedish Cancer SocietyEU, Horizon 2020
Note

QC 20230424

Available from: 2023-04-24 Created: 2023-04-24 Last updated: 2025-02-07Bibliographically approved

Open Access in DiVA

kappa(7780 kB)935 downloads
File information
File name FULLTEXT01.pdfFile size 7780 kBChecksum SHA-512
daa26e251ef7a5ecfb5131bde68b3eaf6c74918f8ff8a501b0646fc74aa0280016ee35b57f759a4be5f3ecfff37f791c3d5ad6274937558f8879402c623d89b4
Type fulltextMimetype application/pdf

Authority records

Larsson, Ludvig

Search in DiVA

By author/editor
Larsson, Ludvig
By organisation
Gene TechnologyScience for Life Laboratory, SciLifeLab
BiochemistryMolecular BiologyBioinformatics and Computational Biology

Search outside of DiVA

GoogleGoogle Scholar
Total: 937 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 2358 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf