Intro

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.

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 less than 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.

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Current Members

Nikos S. Hatzakis

Professor

Min Zhang

Assistant Professor

Errika Voutyritsa

Post-doctoral fellow

Gareth Gerald Doherty

Joint Post-doctoral fellow with Prof. Knud Jørgen Jensen

Artu Breuer

Post-doctoral fellow

Mikhail Baibakov

Post-doctoral fellow

Matias E. Moses

Industrial postdoc, Novozymes

Søren S.-R. Bohr

Ph.D. Fellow

Camilla D. Thorlaksen

Ph.D. fellow, Novo Nordisk STAR programme

Jacob Kæstel-Hansen

Ph.D. fellow

Ge Huang

Ph.D. fellow

Freja S.-R. Bohr

Ph.D. fellow

Frank Høgh Schulz

Ph.D. fellow

Sara Vogt Bleshøy

Ph.D. Fellow

Richard Michael

Ph.D. fellow (Main supervisor Wouter Boomsma)

Emily Winther Sørensen

Ph.D. Fellow

Andreas Malthe Faber

Lead Software Engineer

Annette Juma

Research Assistant

Henrik Pinholt

Master Student

Sabrina Gennis

Master Student

Steen W. Bender

Master Student

Thanos Oikonomou

Master Student

Marcus Winther Dreisler

Master Student

Valia Margaritaki

Master Student

Ina Surkamp

Master Student

Xenia Quaas

Master Student

Emilie Elisabeth Milan Nielsen

Master Student

Kian Kirchof

Master Student

Harris Sideris

Master Student

Guangze Li

Master Student

Alexis Kouvelas

Erasmus+ Fellow

Simos Dagialis

Erasmus+ Fellow

Nikoleta Palioudaki

Erasmus+ Fellow

Despoina Moraiti

Erasmus+ Fellow

Theofannis

Erasmus+ Fellow

Molly Jean Maud Turner

Bachelor Student

Emily She

Bachelor Student

Freya Björk Reinhold

Bachelor Student

Select Publications

Link to full list of publications

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Software

DeepFRET

DeepFRET

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

Extraction of liposome intensity

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

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

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

Funding