Single molecule and cell biophysics for Biomedicine - Hatzakis Lab

The main objective of my lab is to augment our understanding of how the inner working of proteins underlie and control cellular function and response.
We emphasize on decoding how conformational dynamics encode protein function: the acceleration of chemical transformations while maintaining the functional plasticity of accepting structurally diverse substrates and how this relates to cellular responses. Harnessing this knowledge is the founding step to tackle the daunting tasks of a) design novel pharmaceutics in silico b) the efficient delivery of pharmaceuticals across the cell barrier and c) the design of novel biocatalysts with tailor made functionalities.

We employ an eclectic mix of techniques — borrowed from whichever area of experimental science that promises to shed light on the behaviour of biomolecular entities. We emphasize on single molecule studies (functional, FRET, particle tracking) that offer the potential to directly observe the existence, quantify the abundance and dependence on environmental cues, of behaviors that were masked in conventional assays due to averaging a large number of unsynchronized molecules. We employee and works on multiple key biological systems, P450s, Insulin peptides and CRISPR gene editing proteins as well as drug delivery systems for the advanced delivery of biologicals

 

 

 

 

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

 

 

 

 

Selected project

Biased metabolism
We introduced the concept of Biased metabolism, a mechanism akin to biased signaling of GPCRs but for metabolic enzymes like P450 oxidoreductase . we identified a new pharmacological target that of P450 oxidoreductase the omnipotent electron activator of P450 enzymes Using a combination of computational modeling functional assays and single molecule structureal assays we found that ligands bind on POR stabilizes specific conformational states that are linked to distinct downstream metabolic outcomes in cell Biased metabolism may allow designing pathway-specific therapeutics or personalized food suppressing undesired, disease-related, metabolic 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.

Biomolecular recognition by single particle tracking
Lipases are interfacially activated hydrolases, which play an important role in both biological processes as well as industrial applications, catalyzing reactions at the water-lipid interface. Our understanding of lipases function to date primarily relies on averaging techniques reporting the behavior of large ensemble of heterogeneous enzymes, which may disguise heterogenic behaviors. We have developed a single-molecule fluorescence microscopy assay to track thousands of individual Thermomyces Lanuginosus Lipase (TLL) enzymes, on native trimyristin substrate surfaces. Our high- quality data allow us to directly observe distinct patterns of lipase diffusional properties both within the observation time of a single trajectory and after system equilibration. Readouts on multiple lipase mutants allowed us to delve into a deeper understanding of molecular detail governing enzymatic function and provide links of enzyme structure to functional phenotypes. Using Deep-Learning methods, we decipher the subtle mechanistic differences and provide a model for function/inhibition and their relation to structure.

Relevant publications:
Pinholt, H. D. et al. Single Particle Diffusional Fingerprinting A machine learning framework for quantitative analysis of heterogeneous diffusion. PNAS (2021), 31, 118.

Bohr, S.S.-R. et al. Direct observation of Thermomyces lanuginosus lipase diffusional states by Single Particle Tracking and their remodeling by mutations and inhibition. Scientific Reports (2019), 9, 2654.

Bohr, S.S.-R. et al. Label-Free Fluorescence Quantification of Hydrolytic Enzyme Activity on Native Substrates Reveals How Lipase Function Depends on Membrane Curvature. Langmuir (2020), 36, 23, 6473-6481.

Nanocontainers for delivery of biologicals
By employing single particle tracking on novel nano-carriers in drug delivery, we are able to investigate their interactions with biological samples and simultaneously monitor particle mobility and drug release in live human cells. We aim to utilize the methodology to develop and optimize site specific and fast delivery of tailor made pharmaceuticals. To do so, we deploy advanced statistical analysis in combination with machine learning - thus deciphering the mechanistic details that governs interactions at the cellular level.
Ultimo June 2019 our lab will be equipped with a state of the art Olympus super resolution spinning disk microscope, thus extending the method to live 3D tracking.

Relevant publications:
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 (2020), 12, 1, 380-389.

Singh, P. K. et al. Direct Observation of Sophorolipid Micelle Docking in Model Membranes and Cells by Single Particle Studies Reveals Optimal Fusion Conditions. Biomolecules (2020), 10(9), 1291.

Streck, S. et al. Interactions of Cell-Penetrating Peptide-Modified Nanoparticles with Cells Evaluated Using Single Particle Tracking. ACS Appl. Bio Mater. (2021), 4, 4, 3155-3165.

CRISPR structure function
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.

Accelerating biological discoveries by machine learning
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

 

 

Selected publications

Google scholar  profile

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

 

 

 

 

 

 

 

 

 

 

 

 

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