Spatial Transcriptomics across kingdoms
Time: Thu 2025-03-27 13.30
Location: Air&Fire, Tomtebodavägen 23B, Solna
Video link: https://kth-se.zoom.us/j/65605519312
Language: English
Subject area: Biotechnology
Doctoral student: Sami Saarenpää , Genteknologi, Science for Life Laboratory, SciLifeLab
Opponent: professor Matias Kirst,
Supervisor: Universitetslektor Stefania Giacomello, Science for Life Laboratory, SciLifeLab, Genteknologi; Professor Joakim Lundeberg, Science for Life Laboratory, SciLifeLab, Genteknologi
QC 2025-02-27
Abstract
Understanding how organisms develop and function requires decoding the complex interactions between cellular heterogeneity and gene expression. In multicellular organisms, specialized cell types differentiate from a single origin, responding to genetic regulatory programs and environmental cues to form tissues and organs. In contrast, unicellular organisms exhibit striking functional diversity at the single-cell level and can adapt to dynamic environments through regulated gene expression programs. Recent advances in spatially resolved transcriptomics, particularly in the Spatial Transcriptomics (ST) technology, have provided critical insights into these processes, characterizing gene expression patterns and regulatory networks in unicellular and multicellular systems.
This thesis leverages the ST technology to introduce methodological advancements for studying gene expression patterns across kingdoms, especially in plant development, microbial diversity, and host-microbe interactions.
In Article A, we presented an automated approach for ST library preparation, improving the efficiency and reproducibility of spatial gene expression profiling. It improved ST’s scalability and robustness for large-scale studies, facilitating the analysis of spatial organization in tissue sections.
For Article B, we generated a comprehensive spatiotemporal gene expression atlas for Picea abies shoot primordia, combining morphological and gene expression analysis. Through the atlas, we revealed gene expression patterns for previously unknown genes and provided initial annotations for several key developmental genes, while previously, annotations were based mainly on data from other species. Overall, this study advances our understanding of the molecular mechanisms that govern the reproductive development of conifers.
In Article C, we introduced a multimodal ST approach that simultaneously captures the host transcriptome and microbial abundance. Applying this method to Arabidopsis thaliana leaves, we identified distinct microbial hotspots and examined their spatial interactions with the host, providing insights into the complex dynamics of the host-microbiome relationships.
We applied the ST technology to various plankton species in Article D and generated parallel imaging and transcriptomic data. The findings offer new perspectives on these microorganisms’ cellular diversity and ecological roles and highlight the potential of array-based approaches in the study of microbial communities.
In conclusion, these studies advance the spatially resolved transcriptomics field and answer questions related to gene regulation in unicellular and multicellular organisms. As such, they expand our knowledge of orchestrated gene expression patterns that underlie life at diverse biological levels. They also provide a resource for applying sustainable development goals to improve crop resilience, forestry, and disease risk.