Single molecule and cell biophysics for Biomedicine - Hatzakis Lab

The main objective of my group is to augment our understanding on the molecular mechanisms that underlie and control vital cellular functions. We approach this challenge by deciphering the dynamic interplay between the function and spatiotemporal localization of biomolecules (virus, dug nanocarrier, oligonucleotides or protein assemblies) and how this correlate to cellular and organismal response.

 

 

 

 

 

 

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 

lab setup

 

 

 

 

Accelerating biological discoveries by machine learning and quantitative single particle microscopy

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.

High throughput single-nanocontainer readouts and deep learning: 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:

  1. DNA-DNA recognition and sub-attolitter cargo delivery,
  2. Transporter function and 
  3. 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

Metabolic pathways redefined: 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.

Deciphering cellular choreography: 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

Bridging structure and function of CRISPR-Cas12a with smFRET and Cryo-EM

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

 

 

 

 

 

 

 

 

  • NanoPANS project funded by the Lundbeck Foundation (2025)
  • NovoNordisk foundation Challenge Center for Optimized oligo Escape and control of disease (2024)
  • The 4D Cellular Dynamics (2023)

  • 2014: Villum Foundation, Young Investigator Fellowship 

Villum Foundation logo


  • 2016: Novozymes A/S & The Henning Holck-Larsen Foundation, Guest Post-doctoral Fellowship 


  • 2016: Novo Scholarship Programme

Novo Nordisk and Novozymes logos


  • 2017: Carlsberg Foundation, Most Distinguished Associate Professor Fellowship 

Carlsberg Foundation logo


  • 2017: Marie Curie, Post Doc Fellowship

Marie Curie Fellowship logo


  • 2017: Lundbeck fonden, Post Doc Fellowship

Lundbeck foundation logo


  • 2017: Velux foundation Center: Advanced Biomolecular Engineering

Velux Foundation logo


  • 2018: Innovation Foundation Denmark, Industrial Post-doc

Innovation Foundation logo

 

 

 

 

 

 

DeepFRET

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

Extraction of liposome intensity

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.

Cell analyzer HEK293/PYY/eYFP

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).

Single Particle Tracking of Lipases

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.

Diffusional  fingerprinting

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. 

https://github.com/hatzakislab/Diffusional-Fingerprinting

 

 

Staff

Name Title Phone E-mail
Aimilia Nousi Special Consultant +4535333053 E-mail
Artu Breuer Postdoc +4535326895 E-mail
Athanasios Oikonomou PhD Fellow E-mail
Emily Claire Winther Sørensen PhD Fellow +4535331488 E-mail
Frank Høgh Schulz PhD Fellow +4535323121 E-mail
Freja Schmidt-Rasmussen Bohr PhD Fellow E-mail
Gareth Gerald Doherty Guest Researcher +4535321193 E-mail
Georgios Bolis PhD Fellow +4535328401 E-mail
Georgios Kyriakakis PhD Fellow E-mail
Janni Støvring Mortensen Postdoc E-mail
Konstantinos Tsolakidis Academic Staff +4535328863 E-mail
Marcus Winther Dreisler PhD Fellow E-mail
Min Zhang Assistant Professor E-mail
Nikos Hatzakis Professor +4535334502 E-mail
Richard Michael PhD Fellow E-mail
Sara Vogt Bleshøy PhD Fellow +4535326121 E-mail
Stavroula Margaritaki PhD Fellow +4535321061 E-mail
Steen Wielandt Barfod Bender PhD Fellow E-mail
Tania Sabina Darphorn Special Consultant +4593565147 E-mail
Victoria Jade Kladny PhD Fellow +4535324958 E-mail

Master Student

Name Title Phone E-mail
Emilie Elisabeth Milan Nielsen Master Student
Kterina Vougiatzi
Master Student
Freya Rerihold
Master Student
Sascha Valetin Brown
Master Student
Athanasia Vapori
Master Student

Bachelor Student

Name Title Phone E-mail
Molly Jean Maud Turner Bachelor Student
Julie Bernt Frederiksen
Bachelor Student
Josefine Bjørcklind
Bachelor Student

Contact


Nikos Hatzakis
Nikos Hatzakis

Professor, group leader

Nano Science Center
Department of Chemistry
Office: T554
Phone: +45 50 20 29 51
E-mail: hatzakis@chem.ku.dk 

Google scholar 

Orcid
https://orcid.org/0000-0003-4202-0328

Center for Optimized Oligo Escape and Control of Disease (COE)