Hatzakis Group - Single molecule and cell biophysics for Biomedicine

The Hatzakis Group combines single-molecule and live-cell imaging with machine learning to study biomolecular dynamics, protein function and cellular processes, with applications in biomedicine and drug delivery.

Research focus

The primary focus of our lab is providing the missing understanding of how the spatiotemporal localization of biomoleculas in cells underlies and controls biological events at the molecular cellular and organismal level.

Harnessing this knowledge is the key step to tackle the daunting tasks of design and efficiently deliver novel pharmaceutics. To succeed in this our lab employees state of the art fluorescent microscopes to image individual molecules, cells and tissues.

To extract quantitative information of the multidimensional multiterrabyte data we generate, we develop and utilize machine learning toolboxes.

Group leader

Nikos Hatzakis
Professor
Office: Frederiksberg Campus, Thorvaldsensvej 40, T-554

Projects and collaborations

Our projects are in tight collaboration with Industrial partners, medical doctors, or/and scientists across disciplines expanding the horizons of chemical synthesis.

Advanced microscopic techniques produce vast amounts of unstructured data the analysis of which by conventional methodologies is tedious, time consuming and may be biased by unconscious biases. We have been developing agnostic quantitative and automated analysis methodologies based on machine learning to treat classify and annotate biological behaviors. The toolboxes offer rapid analysis often accelerated by 3- 6 orders of magnitude, help eliminating potential human biases and provide statistical insights on biological parameters that underlie and control protein function and cellular responses.

Relevant publications

Jacob Kæstel-Hansen et al. Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function. Nat Methods 22, 1091–1100 (2025).
Steen W. B. Bender et al. SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis. Nat Commun 15, 1763 (2024).
Malle, M. G., et al. Single-particle combinatorial multiplexed liposome fusion mediated by DNA. Nature Chemistry (2022) 14, 558-565
Pinholt, H. D. et al. Single Particle Diffusional Fingerprinting A machine learning framework for quantitative analysis of heterogeneous diffusion.PNAS (2021), 31, 118.
Thomsen, J., et al. DeepFRET, a software for rapid and automated single- molecule FRET data classification using deep learning. eLife (2020), 9.

Pushing the biomolecular recognition detection to new frontiers

Screening of biomolecular recognition often suffers from challenges such as long running time, high person power as well as excessive materials cost. To surpass these challenges have developed miniaturized assays for ultra-sensitive and high-throughput screening of biomolecular interactions and to explore:

DNA-DNA recognition and sub-attolitter cargo delivery,
Transporter function and
How membrane properties affect protein function.

Rapid and reliably analyis and classification of the multidimensional multi terabyte data is achieved by our deep learning analytic tools.

Relevant publications

Malle, M. G., et al. Single-particle combinatorial multiplexed liposome fusion mediated by DNA. Nature Chemistry (2022) 14, 558-565
Schmidt, S. G., et al. The dopamine transporter antiports potassium to increase the uptake of dopamine. Nature Communications(2023) 13, 2446
Thomsen, S. P., et al. A large size-selective DNA nanopore with sensing applications. Nature Communications (2019) 175, 5655

Biased P450 metabolism by smart ligands targeting protein dynamics and targeting metabolic diseases

POR is a central molecular hub activating a plethora of metabolic pathways by donating electrons to more than 50 different cytochrome P450 enzymes (CYPs). Point mutations in POR cause severe metabolic disorder due to altered POR-CYP interactions.

In collaboration with Paediatric Endocrinology at the University Hospital Bern, Switzerland, and Department of Plant Biology, University of Copenhagen, we study these interactions all the way from clinical phenotype down to the fundamental limit of individual proteins.

Combining single molecule FRET and single turnover studies with cell studies and docking simulations we advance our understanding on the intricate role of conformational dynamics to activity and specificity and eventually how pathogenic mutations and small molecule ligand interactions control metabolic disorder and biosynthetic pathways.

Relevant publications

Jensen, S.B. et al. Biased cytochrome P450-mediated metabolism via small-molecule ligands binding P450 oxidoreductase. Nature Communications(2021), 12, 2260.
Laursen, T. et al. Characterization of a Dynamic Metabolon Producing the Defense Compound Dhurrin in Sorghum. Science (2017), 354, 890-893.
Bavishi, K. et al. Direct Observation of Multiple Conformational States in Cytochrome P450 Oxidoreductase and their Modulation by Membrane Environment. Scientific Reports (2018), 8, 1-9.
Laursen, T. et al. Single Molecule Activity Measurements of Cytochrome P450 Oxidoreductase Reveal the Existence of Two Discrete Functional States. ACS Chem. Biol. (2014), 9, 630-634.

Insights into single particle of proteins, viruses, and pharmaceutics nanocarriers

We have pioneered the development of powerful methodologies to track the spatiotemporal localization in live cells of individual biomolecules,(proteins organelles viruses and nanocarriers) and quantify their interaction with membranes cell entry pathways and utilized this information to tailor their targeted delivery directly.

To analyse the complex, multidimensional, multiterabyte data we acquire, we have employed novel methodologies based on machine learning that offer rapid precise and automated transition from raw microcopy images to quantitative biomedicine insights accelerating discoveries often by 104 times.

Relevant publications

Jacob Kæstel-Hansen et al. Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function. Nat Methods 22, 1091–1100 (2025).
Anwesha Sanyal et al. Neuronal constitutive endolysosomal perforations enable α-synuclein aggregation by internalized PFFs. J Cell Biol (2025) 224 (2): e202401136.
Pinholt, H. D. et al. Single Particle Diffusional Fingerprinting A machine learning framework for quantitative analysis of heterogeneous diffusion.PNAS (2021), 31, 118.
Moses E. M.,et al., ACS Applied Materials & Interfaces (2021) 13 (28), 33704- 33712
Wan, F., et al. Ultrasmall TPGS–PLGA Hybrid Nanoparticles for Site-Specific Delivery of Antibiotics into Pseudomonas aeruginosa Biofilms in Lungs. ACS Appl. Mater. Interfaces (2019) 12, 1, 380–389

Adaptive immunity in bacteria is accomplished by the CRISPR system, and CRISPR- associated proteins (Cas). Proteins coupled with RNA are guided by this system to recognize and cleave foreign genetic material.

As such, it’s also a powerful method for genome editing, and is receiving lot of bio technical and medical attention currently.

By using single molecule FRET we can study this system in great detail, and obtain a wealth of structural and kinetic information, when combining with other techniques. Read how we did this in Stella et al. (2018), published in Cell.

Relevant publications

Stella, S., et. al. Conformational Activation Promotes CRISPR-Cas12a Catalysis and Resetting of the Endonuclease Activity. Cell (2018), 175, 1856–1871.e21
Thomsen, J., et al. DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning. eLife (2020), 9

Technologies and facilities

Novel new imaging technologies

We have therefore pioneered the development of novel new imaging technologies - with an emphasis on single particle and live cell microscopy - that promises to shed light on the interplays between the behaviour (dynamics, function and localisation) of biomolecules and high throughput single particle screening methodologies to decipher oligonucleotide interactions with membranes.

By tracking the spatiotemporal displacement and localization of one protein - or an assembly - in real time we revealed internalization pathways and cell fate of nanoparticles and viruses.

Our functional and FRET studies at the fundamental limit of individual catalytic cycle, on the other hand have helped deciphering protein structure and function dependence on temporal cell localization and the design of novel ligand biasing aberrant biological function.

Recognizing that 4D imaging generates terabytes of data sets, that are prohibitively hard to be quantitatively evaluated by current semi-manual analysis, we have developed toolboxes and softwares based on machine learning to rapidly, reliably and free of human cognitive biases, analyze the wealth of novel microscopy data we, and others, produce.

Our all-inclusive softwares for windows and macs, offer transition from raw data to quantitative analysis and data classification with <5 clicks, accelerating the automatic analysis by ~6 orders of magnitude.

These combined methodologies bridge 4D imaging with sophisticate image analysis required for delving into the era of 4D cell and tissue imaging.

Facilities

The Hatzakis Group labs are equipped with everything needed to perform state of the art experiments on a variety of protein, enzyme and related experiments. Most experiments in the group are build around fluorescent microscopy, performed on one (or both) of two microscopes:

An IX81 Olympus confocal microscope
An Olympus Total Internal Reflection Fluorescence microscope (TIRFm) set up for super resolution microscopy
State of the art Olympus SpinSR10 Confocal Super Resolution Microscope
GMO facilities: for cell culture and protein expression and purification.

Publications

Research output

See a list of our publications via Nikos' UCPH research profile.

Google scholar

See a list of our publications in Google Scholar

Software

Rapid and automated single molecule FRET data classification using deep learning.

Link to DeepFRET software

Python based script for extraction and analysis of .tif formatted image files. The script can extract the intensity from individual liposomes based in a changeable ROI size given initially and subtract local background.

Link to Extraction of liposome intensity software

Python based script for extraction and analysis of .tif formatted image files. The script will identify cells on a image based on set thresholds and parameters. From this a mask will be created to extract the intensity from up to three channels, here corresponding to signal from membrane stain (blue), yellow fluorescent protein (YFP, green) and Cy5/Atto655 (red).

Link to Cell analyzer HEK293/PYY/eYFP software

Python Script for analysing .tif movies of lipases diffusing on a surface. Code used to make data for Bohr, S. S.-R. et al. Scientific Reports, 2019.

Link to Single Particle Tracking of Lipases software

An all inclusive tool for SPT data analysis, processing, and classification. The method uses machine learning for dissecting the features that underlie diffusional behavior and establishing molecular identity, regardless of the underlying diffusion type. See our recent paper in PNAS 2021.

Link to Diffusional fingerprinting software