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Spatial analysis of tissue transcriptomes in health and disease
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Safety Sciences, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden.ORCID iD: 0000-0003-3755-718X
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The human body consists of complex tissue structures, and their integrity and functions are critical for our well-being. By studying the gene expression within our tissues, we can generate an enhanced understanding of the mechanisms at play in healthy and diseased states. Through novel innovations within the biotechnology field, the scale and resolution of transcriptomic approaches have drastically improved. Moving from the traditional bulk-level analysis, we are currently able to study transcriptome-wide gene expression in individual cells as well as in thin tissue sections where the spatial origins of the transcripts are preserved. One of the leading technologies for obtaining spatially resolved transcriptomics data is Visium, which enables sequencing-based global transcriptomics analysis with high spatial resolution coupled with a microscopy image of the tissue histology. This powerful technique can be applied to generate molecular maps of heterogeneous tissues for in-depth characterization of cellular niches and dynamics associated with responses to exogenous substances and/or pathology. Thus, the application of spatially resolved transcriptomics, as demonstrated in this thesis, has the potential to aid our understanding of diseases and guide the development of better treatments.

Firstly, to be able to extract biologically relevant knowledge from the rich datasets generated by the Visium platform there needs to be well-functioning and accessible bioinformatics tools. As presented in article I, we have developed a new computational toolkit called semla, written in the widely used programming language R, for the analysis and visualization of Visium data. Building on top of previous R packages, semla brings several new functionalities for performing and exploring spatial analyses of tissue gene expression data, with an emphasis on versatility and accessibility.

Article II presents the first-ever spatially resolved transcriptomics data generated and analyzed for human white adipose tissue, collected from donors of normal to obese weight ranges. By characterizing adipocytes in situ, we were able to distinguish three distinct adipocyte subtypes and describe their profiles in terms of transcriptional signatures, spatial characteristics, and association with obesity. Furthermore, samples from human donors subjected to insulin treatment were analyzed and revealed that only one of the three adipocyte subtypes appeared to elicit a response to the presence of insulin.

For article III, we studied the devastating disease idiopathic pulmonary fibrosis using Visium. Here, we present a comprehensive map of the transcriptome within the fibrotic niches in affected lung tissues and use computational approaches to detangle disease-associated mechanisms. In addition, there is a critical need for suitable preclinical models of this disease to develop new highly sought-after therapeutics. Therefore, we investigated the spatial landscape of the lungs of the most widely used mouse model for idiopathic pulmonary fibrosis and could perform translational comparisons of the fibrotic disease manifestations in the two respective settings.

From our lung fibrosis mouse model samples, we moreover processed serial tissue sections with mass spectrometry imaging to generate matched spatial multimodal data. Driven by the need to integrate the spatial omics data, we developed a new computational pipeline for joint spatial multimodal processing. Presented in article IV is our computational framework, MAGPIE, designed to align Visium and mass spectrometry imaging data into a shared coordinate system through a flexible and streamlined pipeline that outputs files readily readable by downstream analysis toolkits such as semla. We demonstrate and benchmark the utility of MAGPIE using various datasets and showcase the strength of having spatial multi-omics data for studying disease mechanisms and local responses to pharmaceutical substances.

Abstract [sv]

Människokroppen består av komplexa vävnadsstrukturer, och deras integritet och funktion är avgörande för vårt välbefinnande. Genom att studera genuttrycket inom våra vävnader kan vi få en fördjupad förståelse för de mekanismer som är verksamma både när vi är friska och sjuka. Med nya framsteg inom bioteknik så har omfattningen och upplösningen hos metoderna för att analysera transkriptomet drastiskt förbättras. Från traditionell analys på bulk-nivå kan vi nu studera hela transkriptomets genuttryck hos enskilda celler samt inom tunna vävnadssnitt där ursprunget av transkriptens position har bevarats. En av de ledande teknologierna för att erhålla spatiellt upplöst transkriptomikdata är Visium, där sekvenseringsbaserad global analys av transkriptomet kan utföras med hög rumslig upplösning kopplat till en mikroskopibild av vävnadens histologi. Denna kraftfulla teknik kan tillämpas för att skapa molekylära kartor över heterogena vävnader för en djupgående karaktärisering av cellulära nischer och dynamik som är förknippade med svar på exogena substanser och/eller patologier. Således har tillämpningen av transkriptomik med spatiell upplösning potential att hjälpa vår sjukdomsförståelse och bidra till utvecklingen av bättre behandlingsmetoder.

För att kunna extrahera biologiskt relevant kunskap från de omfattande dataset som genereras via Visium-plattformen, behövs välfungerande och tillgängliga bio-informatiska verktyg. I artikel I har vi skapat ett nytt verktyg vid namn semla. Det är skrivet i det brett använda programmeringsspråket R, för analys och visualisering av Visium-data. Genom att bygga vidare på tidigare R-paket tillför semla flera nya funktioner för att utföra spatiella analyser av genuttrycksdata i vävnader, med fokus på mångsidighet och tillgänglighet. 

Artikel II presenterar den första spatiellt upplösta transkriptomikdatan genererad för human vit fettvävnad, som är insamlad från donatorer med normalvikt till fetma. Genom att karakterisera adipocyter in situ kunde vi urskilja tre distinkta adipocyt-subtyper och beskriva deras profiler utifrån transkriptionella signaturer, spatiella kännetecken och koppling till fetma. Vidare analyserades prover från humana donatorer som fått insulintillförsel, vilket visade att endast en av de tre adipocyt-subtyperna verkade uppvisa ett svar på närvaron av insulin.

För artikel III studerade vi den förödande sjukdomen idiopatisk lungfibros med hjälp av Visium. Här presenterar vi en omfattande karta över transkriptomet inom de fibrotiska nischerna i sjuk lungvävnad och använder bioinformatiska metoder för att reda ut sjukdomsassocierade mekanismer. Dessutom finns det ett kritiskt behov av lämpligare prekliniska modeller för denna sjukdom för att kunna utveckla nya, bättre behandlingar. Därför undersökte vi det spatiella landskapet i lungor från den mest använda musmodellen för idiopatisk lungfibros och kunde genomföra translationella jämförelser av de fibrotiska sjukdomsyttringarna i de två modellerna.

Med prover från lungfibrosmusmodellen processade vi dessutom seriella vävnadssnitt med rumsbunden masspektrometri (MSI) för att generera matchad spatiell multimodal data. Med målet att integrera den spatiella omik-datan utvecklade vi en ny pipeline för sammanslagning av den spatiella multimodala datan. I artikel IV presenteras vår lösning, MAGPIE, som är utformad för att sätta Visium- och MSI-data i ett gemensamt koordinatsystem genom en flexibel och effektiv pipeline som genererar filer som lätt kan bearbetas av efterföljande analysverktyg som exempelvis semla. Vi demonstrerar och utvärderar nyttan av MAGPIE med hjälp av olika dataset och visar på styrkan med att använda spatiell multi-omikdata för att studera sjukdoms-mekanismer och lokala vävnadsresponser från läkemedelssubstanser.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2024. , p. 81
Series
TRITA-CBH-FOU ; 2024:39
Keywords [en]
spatially resolved transcriptomics, spatial transcriptomics, transcriptomics, spatial analysis, disease biology, adipose, lung, bioinformatics
National Category
Cell and Molecular Biology Bioinformatics and Systems Biology Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-354759ISBN: 978-91-8106-079-9 (print)OAI: oai:DiVA.org:kth-354759DiVA, id: diva2:1905206
Public defence
2024-11-22, Air&Fire, Tomtebodavägen 23a, via Zoom: https://kth-se.zoom.us/j/64393893293, Solna, 10:00 (English)
Opponent
Supervisors
Note

QC 2024-10-15

Available from: 2024-10-15 Created: 2024-10-11 Last updated: 2024-10-21Bibliographically approved
List of papers
1. 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
2023 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 39, no 10Article in journal (Refereed) Published
Abstract [en]

SUMMARY: 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 generated by the Visium platform, that includes interactive web applications for data exploration and tissue annotation. AVAILABILITY AND IMPLEMENTATION: The R package semla is available on GitHub (https://github.com/ludvigla/semla), under the MIT License, and deposited on Zenodo (https://doi.org/10.5281/zenodo.8321645). Documentation and tutorials with detailed descriptions of usage can be found at https://ludvigla.github.io/semla/.

Place, publisher, year, edition, pages
Oxford University Press (OUP), 2023
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:kth:diva-339513 (URN)10.1093/bioinformatics/btad626 (DOI)001088393600007 ()37846051 (PubMedID)2-s2.0-85175270209 (Scopus ID)
Note

Not duplicate with DiVA 1752550

QC 20231114

Available from: 2023-11-14 Created: 2023-11-14 Last updated: 2024-10-11Bibliographically approved
2. Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin
Open this publication in new window or tab >>Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin
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2021 (English)In: Cell Metabolism, ISSN 1550-4131, E-ISSN 1932-7420, Vol. 33, no 9, p. 1869-+Article in journal (Refereed) Published
Abstract [en]

The contribution of cellular heterogeneity and architecture to white adipose tissue (WAT) function is poorly understood. Herein, we combined spatially resolved transcriptional profiling with single-cell RNA sequencing and image analyses to map human WAT composition and structure. This identified 18 cell classes with unique propensities to form spatially organized homo-and heterotypic clusters. Of these, three constituted mature adipocytes that were similar in size, but distinct in their spatial arrangements and transcriptional profiles. Based on marker genes, we termed these Adipo(LEP), Adipo(PLIN), and Adipo(SAA). We confirmed, in independent datasets, that their respective gene profiles associated differently with both adipocyte and whole-body insulin sensitivity. Corroborating our observations, insulin stimulation in vivo by hyperinsulinemic-euglycemic clamp showed that only Adipo(PLIN) displayed a transcriptional response to insulin. Altogether, by mining this multimodal resource we identify that human WAT is composed of three classes of mature adipocytes, only one of which is insulin responsive.

Place, publisher, year, edition, pages
Elsevier BV, 2021
National Category
Endocrinology and Diabetes Cell and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-303062 (URN)10.1016/j.cmet.2021.07.018 (DOI)000696568500003 ()34380013 (PubMedID)2-s2.0-85114341893 (Scopus ID)
Note

QC 20211005

Available from: 2021-10-05 Created: 2021-10-05 Last updated: 2024-10-11Bibliographically approved
3. Mapping spatially resolved transcriptomes in human and mouse pulmonary fibrosis
Open this publication in new window or tab >>Mapping spatially resolved transcriptomes in human and mouse pulmonary fibrosis
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2024 (English)In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 56, no 8, p. 1725-1736Article in journal (Other academic) Published
Abstract [en]

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis and limited treatment options. Efforts to identify effective treatments are thwarted by limited understanding of IPF pathogenesis and poor translatability of available preclinical models. Here we generated spatially resolved transcriptome maps of human IPF (n = 4) and bleomycin-induced mouse pulmonary fibrosis (n = 6) to address these limitations. We uncovered distinct fibrotic niches in the IPF lung, characterized by aberrant alveolar epithelial cells in a microenvironment dominated by transforming growth factor beta signaling alongside predicted regulators, such as TP53 and APOE. We also identified a clear divergence between the arrested alveolar regeneration in the IPF fibrotic niches and the active tissue repair in the acutely fibrotic mouse lung. Our study offers in-depth insights into the IPF transcriptional landscape and proposes alveolar regeneration as a promising therapeutic strategy for IPF.

Place, publisher, year, edition, pages
Springer Nature, 2024
National Category
Cell and Molecular Biology Respiratory Medicine and Allergy
Identifiers
urn:nbn:se:kth:diva-354758 (URN)10.1038/s41588-024-01819-2 (DOI)001260455900001 ()38951642 (PubMedID)2-s2.0-85197617751 (Scopus ID)
Funder
Swedish Foundation for Strategic Research, ID18-0094AstraZeneca
Note

QC 20241016

Available from: 2024-10-11 Created: 2024-10-11 Last updated: 2024-10-16Bibliographically approved
4. Integrative analysis of spatial transcriptomics, metabolomics, and histologic changes illustrated in tissue injury studies
Open this publication in new window or tab >>Integrative analysis of spatial transcriptomics, metabolomics, and histologic changes illustrated in tissue injury studies
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Recent developments in spatially resolved omics have expanded studies linking gene expression, epigenetic alterations, protein levels, and metabolite intensity to tissue histology. The integration of multiple spatial measurements can offer new insights into alterations propagating across modalities, however, it also presents experimental and computational challenges. 

To set the multimodal data into a shared coordinate system for enhanced integration and analysis, we propose MAGPIE, a framework for co-registering spatially resolved transcriptomics and spatial metabolomics measurements on the same or consecutive tissue sections, present within their existing histological context. Further, we showcase the utility of the MAGPIE framework on spatial multi-omics data from lung tissue, an inherently heterogeneous tissue type with integrity challenges and for which we developed an experimental sampling strategy to allow multimodal data generation. In these case studies, we were able to link pharmaceutical co-detection with endogenous responses in rat lung tissue following inhalation of a small molecule, which had previously been stopped during preclinical development with findings of lung irritation, and to characterise the metabolic and transcriptomic landscape in a mouse model of drug-induced pulmonary fibrosis in conjunction with histopathology annotations.

The generalisability and scalability of the MAGPIE framework were further benchmarked on public datasets from multiple species and tissue types, demonstrating applicability to both DESI and MALDI mass spectrometry imaging together with Visium-enabled transcriptomic assessment. MAGPIE highlights the refined resolution and increased interpretability of spatial multimodal analyses in studying tissue injury, particularly in a pharmacological context, and offers a modular, accessible computational workflow for data integration.

Keywords
Spatially resolved transcriptomics, Visium, Mass Spectrometry Imaging, Histology, Multi-omics, Pipeline, Data analysis, Data integration
National Category
Bioinformatics (Computational Biology) Cell and Molecular Biology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-354731 (URN)
Funder
AstraZenecaSwedish Foundation for Strategic Research
Note

QC 20241016

Available from: 2024-10-11 Created: 2024-10-11 Last updated: 2024-10-16Bibliographically approved

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Franzén, Lovisa

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67891011129 of 18
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