Transcriptomic studies have attempted to classify glioblastoma (GB) into subtypes that predict survival and have different therapeutic vulnerabilities1, 2-3. Here we identified three metabolic subtypes: glycolytic, oxidative and a mix of glycolytic and oxidative, using mass spectrometry imaging of rapidly excised tumour sections from two patients with GB who were infused with [U-13C]glucose and from spatial transcriptomic analysis of contiguous sections. The phenotypes are not correlated with microenvironmental features, including proliferation rate, immune cell infiltration and vascularization, are retained when patient-derived cells are grown in vitro or as orthotopically implanted xenografts and are robust to changes in oxygen concentration, demonstrating their cell-intrinsic nature. The spatial extent of the regions occupied by cells displaying these distinct metabolic phenotypes is large enough to be detected using clinically applicable metabolic imaging techniques. A limitation of the study is that it is based on only two patient tumours, albeit on multiple sections, and therefore represents a proof-of-concept study.

Spatial transcriptomics technologies aim to spatially map gene expression in tissues and typically use oligonucleotide array surfaces that have undergone spatial indexing. These arrays are used to capture nucleic acids diffusing from adjacently placed tissues, allowing subsequent sequencing to reveal both gene and position. Slide-tags is a recently developed method by Russell et al. that inverts this principle. Instead of capturing molecules released from the tissue, probes are detached from a pre-decoded bead array and diffused into tissues, tagging nuclei with spatial barcodes. In this work we reanalyze this data and discover a latent, spatially informative cell-bead network formed incidentally from barcode diffusion and the biophysical properties of the tissue. This allows us to treat Slide-tags as a network-based imaging-by-sequencing approach. By optimizing spatial constraints encoded in the cell-bead network structure, we can achieve unassisted tissue reconstruction, a fundamental shift from classical spatial technologies based on pre-indexed arrays.

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.

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.

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.

While triple-negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigate TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We perform an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures leads to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identifies nine TNBC spatial archetypes, further validated in external datasets. Several spatial archetypes are associated with disease outcome and characterized by potentially actionable features. In this work, we provide a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy.

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/.

Spatial transcriptomics (ST) maps RNA level patterns within a tissue. This technology has not been previously applied to human placental tissue. We demonstrate analysis of human placental samples with ST. Unsupervised clustering revealed that distinct RNA patterns were found corresponding to different morphological structures. Additionally, when focusing upon terminal villi and hemoglobin associated structures, RNA levels differed between placentas from full term healthy pregnancies and those complicated by preeclampsia. The results from this study can provide a benchmark for future ST studies in placenta.

Idiopathic pulmonary fibrosis (IPF) is a devastating disease characterized by progressive and irreversible scarring of the lung tissue. Development of new efficacious and safe treatments is hampered by limited understanding of disease pathogenesis, lack of predictive preclinical models, and narrow therapeutic index of candidate drugs targeting complex biologies. Here, we tackle these aspects by generating spatially resolved transcriptomic maps of fibrotic lungs from clinical samples and a preclinical mouse model. We utilized the Visium platform to study parenchyma biopsies from four healthy lungs and regions of varying fibrotic severity from four IPF patient lungs. By mapping single cell RNA-seq data spatially, we were able to detect distinct fibroblast populations in different regions of the lesioned IPF lung, as well as the presence of various immune cell populations. To study lung fibrosis preclinically in vivo, the bleomycin mouse model is the most widely used alternative, although its translatability to human disease is disputed. Visium data from mouse lungs collected at two time points following bleomycin administration were generated, which allowed us to characterize the fibrotic lesions and inflammatory areas in their spatiotemporal context. In addition, mass spectrometry imaging was performed on adjacent tissue sections to provide paired spatial metabolomics. Herein, we have generated spatial maps of the lung fibrosis transcriptome from IPF lung biopsies and bleomycin-injured mouse lungs, providing an extensive resource to probe disease pathogenesis and animal model translatability.

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.