Spatial analysis of tissue transcriptomes in health and disease

QC 2024-10-15

Abstract

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.

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