Session 2

Invited talk by Carolina Wählby
Uppsala Universitet and SciLifeLab, Uppsala, Sweden

Spatial (transcript)omics – a joint mapping of tissue function and architecture

Optical microscopy combined with specific staining techniques has been crucial for much of the understanding we have today of both developmental processes, and disease, such as cancer. Via single cell transcriptomics, we have learnt that mRNA sequencing is an efficient way to identify different cell types and their function. By doing the sequencing directly in the tissue, via in situ sequencing, we can now map function to subcellular tissue architecture, and not only know which cells and functions are present, but also where, and in what context, they are present. This information can further be combined with detection of specific interactions between e.g., a drug and drug target. The techniques rely on computational methods, specifically addressing transcript detection, gene decoding, visualization, and finally quantification and interpretation. Visual assessment of microscopy image data becomes limiting as dataset size and complexity grows. It can also be difficult to draw confident conclusions if the observed processes are subtle and the samples are heterogenous. In the past ten years, AI, and particularly learning-based approaches relying on deep convolutional neural networks, have gained enormous popularity in all fields of image-based science. The methods have great potential, but they must also be used with care, not to fool us in our findings. We apply AI to map tissue morphology in relation to spatially resolved gene expression in tissue, with applications in cancer and development.

Biosketch

Carolina Wählby is professor in Quantitative Microscopy at the Dept. of Information Technology, Uppsala University, Sweden, and Scientific Director of the SciLifeLab Bioimage Informatics Unit. Her research is focused on developing computational approaches for extracting information from image data with applications in life science; primarily at the microscopy scale. Methods include traditional image analysis and computer vision technology as well as AI and deep learning. The goal of the analysis ranges from predicting and understanding dynamics of cancer cells to fast antibiotics susceptibility testing and ‘functional pathology’ combining tissue morphology with spatial transcriptomics. The research is primarily funded by the ERC and the Swedish Foundation for Strategic research. She received the SBI2 President’s innovation award in 2014 for her pioneering work on in situ sequencing, the Thuréus prize in 2015 for development of open tools for image analysis, and is a member of the Royal Swedish Academy of Engineering Sciences. She has a MSc in molecular biotechnology and a PhD in digital image analysis, and carried out postdoc research within genetics and pathology. She worked at the Imaging Platform of the Broad Institute of Harvard and MIT in 2009-2015 and became full professor at Uppsala University in 2014. She is a member of the steering group of a 300M€ effort on Data Driven Life Science, funded by the Knut and Alice Wallenberg Foundation, with the ambitious goal of training the next generation of life scientists.