We present a spatial omics approach that combines histology, mass spectrometry imaging and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular-weight metabolites across tissue regions. The workflow is compatible with commercially available Visium glass slides. We demonstrate the potential of our method using mouse and human brain samples in the context of dopamine and Parkinson’s disease.

Capture array-based spatial transcriptomics methods have been widely used to resolve gene expression in tissues; however, their spatial resolution is limited by the density of the array. Here we present expansion spatial transcriptomics to overcome this limitation by clearing and expanding tissue prior to capturing the entire polyadenylated transcriptome with an enhanced protocol. This approach enables us to achieve higher spatial resolution while retaining high library quality, which we demonstrate using mouse brain samples. 

Cardiomyocytes play key roles during cardiogenesis, but have poorly understood features, especially in prenatal stages. Here, we characterized human prenatal cardiomyocytes, 6.5-7 weeks post-conception, by integrating single-cell RNA sequencing, spatial transcriptomics, and ligand-receptor interaction information. Using a computational workflow developed to dissect cell type heterogeneity, localize cell types, and explore their molecular interactions, we identified eight types of developing cardiomyocyte, more than double compared to the ones identified in the Human Developmental Cell Atlas. These have high variability in cell cycle activity, mitochondrial content, and connexin gene expression, and are differentially distributed in the ventricles, including outflow tract, and atria, including sinoatrial node. Moreover, cardiomyocyte ligand-receptor crosstalk is mainly with non-cardiomyocyte cell types, encompassing cardiogenesis-related pathways. Thus, early prenatal human cardiomyocytes are highly heterogeneous and develop unique location-dependent properties, with complex ligand-receptor crosstalk. Further elucidation of their developmental dynamics may give rise to new therapies.

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.

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.

The authors created a comprehensive developmental cell atlas for spatiotemporal gene expression of the human spinal cord, revealed species-specific regulation during development and used the atlas to infer novel markers for pediatric ependymomas. The spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. In this study, by performing integrated analysis of single-cell and spatial multi-omics data, we used 16 prenatal human samples to create a comprehensive developmental cell atlas of the spinal cord during post-conceptional weeks 5-12. This revealed how the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. We identified unique events in human spinal cord development relative to rodents, including earlier quiescence of active neural stem cells, differential regulation of cell differentiation and distinct spatiotemporal genetic regulation of cell fate choices. In addition, by integrating our atlas with pediatric ependymomas data, we identified specific molecular signatures and lineage-specific genes of cancer stem cells during progression. Thus, we delineate spatiotemporal genetic regulation of human spinal cord development and leverage these data to gain disease insight.

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/.

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. 

Therapeutic promotion of intestinal regeneration holds great promise, but defining the cellular mechanisms that influence tissue regeneration remains an unmet challenge. To gain insight into the process of mucosal healing, we longitudinally examined the immune cell composition during intestinal damage and regeneration. B cells were the dominant cell type in the healing colon, and single-cell RNA sequencing (scRNA-seq) re-vealed expansion of an IFN-induced B cell subset during experimental mucosal healing that predominantly located in damaged areas and associated with colitis severity. B cell depletion accelerated recovery upon injury, decreased epithelial ulceration, and enhanced gene expression programs associated with tissue re-modeling. scRNA-seq from the epithelial and stromal compartments combined with spatial transcriptomics and multiplex immunostaining showed that B cells decreased interactions between stromal and epithelial cells during mucosal healing. Activated B cells disrupted the epithelial-stromal cross talk required for orga-noid survival. Thus, B cell expansion during injury impairs epithelial-stromal cell interactions required for mucosal healing, with implications for the treatment of IBD.

The mammalian brain contains many specialized cells that develop from a thin sheet of neuroepithelial progenitor cells. Single-cell transcriptomics revealed hundreds of molecularly diverse cell types in the nervous system, but the lineage relationships between mature cell types and progenitor cells are not well understood. Here we show in vivo barcoding of early progenitors to simultaneously profile cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. By reconstructing thousands of clones, we discovered fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. We combined spatial transcriptomics with clonal barcoding and disentangled migration patterns of clonally related cells in densely labeled tissue sections. Our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.