Imaging: various bacteria types were imaged using a home built SMLM as well as commercial SR set ups, unravelling their cell shape at the nanoscale as well as the relocation of key protein markers’ organisation along the BCC.
Analysis tools:
1: both segmentation and structural characterisation tools have been developed. For segmentation two approaches were followed. Firstly, an SMLM specific approach relying on relative assessment of the local density of points for the segmentation of individual bacteria from the initial field of view (FOV), followed by tessellation to extract the underlying skeleton of the cell shape. The second approach attempts at providing a generalisable solution to segmentation with intensity thresholding and connectivity. Both techniques were thoroughfully validated with simulated and experimental data sets.
2: I relied on generative models, a subgroup of unsupervised machine learning, for the extraction of dynamic information from fixed cells (i.e. static) images. It provides the considerable advantage to require no a priori knowledge or pre-defined model, and, hence, is truly compatible with multi cell types studies. I developed for the most part, data specific auto encoders. They take the segmented images as input and learn how to replicate these images. The architecture allows in the process to reduce inputted images to a very low dimensional space from which dynamic information, and more, can be easily extracted.
Dissemination:
1;List the conferences attended (all of which included reference to EU funding):
- SMLMS (2020) - online - organization of a track
- SMLMS (2021) – Switzerland - in person - organization of a discussion & challenge around clustering for SMLM
- LS2 webinar series (over 2021) - online - organization & board member
- MiFoBio (2022) - France - in person - track organization/chair & invited speaker
- COMPARE seminar series (2022) - UK - in person - invited speaker
- Theory of Living Matter Group seminar (2022) - UK - in person - invited speaker
- SciLifeLab annual meeting (2022)- Sweden - in person - invited speaker
2: List of publications over the duration of my MSCA fellowship:
In preparation
- J. Griffié et al. An unsupervised approach to extract dynamic information from static super-resolved images. To be submitted in December 2022 to Nature Machine intelligence.
Submitted work
- J. Griffié, T. Pham, C. Sieben, R. Lang, V. Cevher, S. Holden, M. Unser, S. Manley, D. Sage. Virtual-SMLM, a virtual environment for real-time interactive SMLM acquisition. BioRxiv (2020), revisions for submission at Nature Communications.
Accepted & published work
- M. Lelek, M.T. Gyparaki, G. Beliu, F. Schueder, J. Griffié, S. Manley, R. Jungmann, M. Sauer, M. Lakadamyali, C. Zimmer. Single-molecule localization microscopy. Nature Reviews Methods Primers(2022).
- D. Mahecic, W.L. Stepp, C. Zhang, J. Griffié, M. Weigert, S. Manley. Event-driven acquisition for content-enriched microscopy. Nature Methods (2022).
- D.J. Nieves, J.A. Pike, F. Levet, J. Griffié, D. Sage, E.A.K. Cohen, J.B. Sibarita, M. Heilemann, D.M. Owen. A framework for evaluating the performance of SMLM cluster analysis algorithms. Nature Methods (2022).
- C. Zhang, L. Reymond, O. Rutschmann, M.A. Meyer, J. Denereaz, J. Qiao, F. Ryckebusch, J. Griffié, W.L. Stepp, S. Manley. Fluorescent d-Amino Acids for Super-resolution Microscopy of the Bacterial Cell Wall. ACS Chemical Biology (2022).
- L.G. Jensen, T.Y. Hoh, D.J. Williamson, J. Griffié, D. Sage, P. Rubin-Delanchy, D.M. Owen. Correction of multiple-blinking artefacts in photoactivated localisation microscopy. Accepted at Nature Methods (2022).
All accepted & submitted work have been made accessible on bioArchive (open access) prior to their publication or were published in open access journals.
Exploitation:
I provided a detailed data management plan which explained where: the raw data, the models and the meta data are made available. All are open access.
