Objective
The goal of biological research is to understand how life works. Although progress is fast, there seems to be an infinity of things we do not understand. When it comes to understanding tissue from the bottom up, our knowledge leaves much to desire. Feynman claimed that “It is very easy to answer many of these fundamental biological questions; you just look at the thing!” well the problem is that looking at the thing is the problem. Microscopy might never give us the possibility to directly see DNA- or RNA-sequence. For this, the community has evolved extraordinarily powerful sequencers. Today one man can routinely read millions of sequences on a weekly basis. And likely soon, we will read billions of sequences daily in small labs. But this, in itself, will not allow us to just look at the thing. We argue in this proposal, that by using the sequencer itself as a microscope, we will get that much closer to actually see what is going on in biological systems.
Researchers have started in this direction by coupling microscopy- and sequencing-data from the same sample, but that is a temporary solution. Here, we propose a technology for inferring images using sequencing data alone, bypassing the need for advanced microscopy and leveraging the potential of the exponential growth of sequencing technology.
We use DNA seeds and perform a reaction in-situ that allow these seeds to copy themselves locally. This is analogous to phylogenetic reconstruction, but instead of inferring ancestry, we infer relations of amplicons to spatial locations in tissue. By using a unique approach, we derive spatial information connected to RNA transcript information directly in-situ, allowing for a non-targeted spatial transcriptomics technique that is as simple as running a PCR. When successful, this approach will then enable us, and others, to learn the inner secrets of biological system at a significantly faster rate.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesgeneticsDNA
- natural sciencesphysical sciencesopticsmicroscopy
- natural sciencesbiological sciencesgeneticsRNA
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
17177 Stockholm
Sweden