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Label-free quantitative nanoscopy for molecular specific identification at depth in pristine living biological tissues

Periodic Reporting for period 2 - SPECIPHIC (Label-free quantitative nanoscopy for molecular specific identification at depth in pristine living biological tissues)

Reporting period: 2021-08-01 to 2023-01-31

Studying the presence, organization and interaction of multiple proteins within intact biological tissue is a challenging but highly rewarding field of fundamental research and medical diagnosis. Indeed, in situ molecular knowledge unlocks the capability to understand the nature and function of living matter. Working with pristine living tissues guaranties the reliability of the conclusions since the 3D structuration, microenvironment and fluid circulation are preserved. For accurate biological conclusions, it would thus be key to identify multiple kind of molecules simultaneously while preserving the thick sample absolutely unspoiled and alive.
Optical imaging is the best compromise to have a molecular sensitivity while ensuring spatiotemporal resolution and limited sample invasiveness. Reaching a molecular identification requires to optically harvest specific signature from the targeted molecules. This means that (a) the probed signal should vary with different targeted molecules; (b) the spatial resolution should be high enough to isolate specific region containing the specific pools of molecules.

SPECIPHIC key objective is thus to simultaneously identify multiple bio-molecules deep into living unlabeled biological tissue. To achieve this objective, the proposed paradigm consists in: 1) performing with a super-resolution label-free measurement, and 2) deciphering molecular signatures within this enhanced signal with machine learning. This should unlock unprecedented possibilities for accurate studies in unmodified samples in a complete stealthy manner with applications target in fundamental biology as well as medicine.

This novel paradigm has never been considered and the scientific impact of the SPECIPHIC project will be chiefly in biomedical imaging. It will have numerous direct applications since non-invasive molecular studies will be unlocked at depth in living pristine biological samples. It impacts biological questions that can only be addressed if the sample remains unmodified and alive. This includes fundamental biology for understanding the process in living matter (e.g. stem cells differentiation), up to diagnosis and prognostics in oncology and preimplantation tissue or embryo characterizations.
During this 30 months reporting period, we have built two homemade microscopes to have a full control over the parameters.
One is dedicated to widefield imaging of cells and nanoparticles (including virus) and can be easily moved from one lab to another. It allows us to bring him directly inside a biosafety cabinet of a high-confinement biological lab (L3) to perform imaging.
The second one is dedicated to point scanning imaging with super-resolution imaging capability. We were able to obtain and publish the first actual label-free super-resolution images (down to 86-nm resolution) of living biological samples.
We have improved our label-free sensing using the phase information of the light to gain a factor 10 in speed and sensitivity as compared to the beginning of the project.
We have reached -for the first time to our knowledge- super-resolution without modifying the sample (Aguilar et all. Open Research Science, 2020, patent WO2022122981A1): a major milestone to envision the targeted objective of the SPECIPHIC project. We expect now to improve the speed and the applicability of the method both for material sciences and living thick sample.

To our knowledge, we have developed the most sensitive single shot quantitative phase imaging device compatible with living sample imaging (i.e. with 1/2 atom high variation resolution). This essential step is key to sense tiny molecular variations inside a complex environment (manuscript to be submitted). We will now apply this sensor to sense the presence of acute and small organelles in living pristine biological samples.

Along with this resolution and sensitivity enhancement capability, we have demonstrated that our imaging method can be coupled with machine learning in a very efficient manner to identify sub-cellular structures (mitochondria, endoplasmic reticulum) on pristine living cells (manuscript in preparation): this is the first step towards molecular signature identification. We will now focus on transposing our results performed on single adherent cell to actual biological tissues. Combined with super-resolution imaging we expect unprecedented label-free imaging with the capability to uncover specific sub-cellular structures inside a complex and alive sample.
Machine learning based specificity from label-free images (Live MEF cells)
Label-free super resolution imaging (Diatom Shell)