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Abstract

Living organisms are striking in their complexity at all levels of organization. No one single analytical method can capture all the information necessary to provide comprehensive knowledge about the cellular processes. The rapid development of new technologies for biological imaging and single cell analysis gives an opportunity to measure different aspects of the living systems at single-cell resolution. Combining these diverse data types can lead to an insight that would not have been possible if each modality were to be considered separately. However, there is no general recipe for a successful multimodal analysis project and it remains challenging to both keep high data quality and establish a reliable way to find correspondence between the two data types. In this thesis I show two different examples of combining different imaging modalities.

Firstly, I demonstrate how, making use of the stereotypical development of the marine worm Platynereis dumerilii, I developed fully automated deep-learning based registration pipeline that allowed to map multiple 3D smFISH datasets to the EM image stack of a whole body 6-days post fertilization larva of the animal with near single-cell accuracy. Automated registration enables systematic study of the connection between cell-type specific gene expression and cell phenotype.

In the second project I combined imaging mass spectrometry for spatially-resolved detection of 13C6-glucose-derived fatty acids in cellular lipids with microscopy and computational methods for data integration and analysis. I validated this method on a spatially-heterogeneous normoxia-hypoxia model of liver cancer cells. I demonstrated the single-cell heterogeneity of acetyl-CoA pool labelling degree upon ACLY knockdown that would be impossible to detect with bulk analysis.

Segmentation of matching objects in different modalities is a crucial step in multimodal image analysis. Transferable and easy to train segmentation of large biological images with neural networks remains challenging. In the final part of my thesis I show how feature normalization inside the neural network can lead to tiling artifacts or suboptimal performance, and propose a normalization strategy for successfully eliminating the artifacts while keeping high segmentation accuracy.