Professor Nikos S Hatzakis, Department of Chemistry, Faculty of Science, University of Copenhagen

Bridging the Dimensions: Unraveling 4D Cellular Dynamics, using Single Particle Imaging, and Machine Learning

Biological motion is highly heterogeneous and displays a variety of diffusion types that may vary drastically across both systems and time and are dependent on regulatory cues or spatial localization Single-particle tracking (SPT) has enabled the quantitative analysis of dynamic biological processes with nanometer spatial and millisecond temporal resolution, revealing dynamic behaviors such as cell entry pathways and trafficking of biologicals previously masked in ensemble averaging. This heterogeneity while important for biology, imposes considerable analytic challenges.

We have developed and applied powerful methodologies to track the spatiotemporal localization of biomolecular entities (1-3), their interaction with membranes(1,4,6), and cell entry pathways of biologicals(2,3) and utilized this information to design and optimize their targeted delivery directly or with nanocontainers(1,3). To analyse the complex, multidimensional, multiterabyte data we acquire, we have employed novel methodologies based on machine learning(1-7)  that offer rapid precise and automated transition from raw microcopy images to quantitative biomedicine insights accelerating discoveries often by 1000 times.

Here I will focus on 3D imaging of live cells for prolonged periods that combined with our deep learning  toolboxes offering “fingerprinting” of biomolecular motion that allow a) the differentiation of endolysosomal identity based solely on diffusion consequently paving the way for label-free colocalization analysis and b) accelerate cell virus entry detection and genetic material release based exclusively on diffusional behavior 

Recent lab publication
1. Malle, Nat. Chem. 2022
2. Pinholt, PNAS 2021
3. Wan,  ACS appl mat 2019
4. Jensen,  Nat. Commun. 2021
5. Thomsen,  Elife 2020
6. Thomsen Nat. commun 2019
7. Stella, Cell 2018