Archive Meta Data

Author(s)

Sara Vogt Blæshøy, Jacob Kæstel-Hansen, Nikos Hatzakis

Title

Insulin internalization and colocalization

Description

Frozen archive of data and models to reproduce 

"Diverse Intracellular Trafficking of Insulin Analogs by Machine Learning-based Colocalization and Diffusion Analysis"
Sara Vogt Bleshøy,  Jacob Kæstel-Hansen,  Annette Juma Nielsen,  Narendra Kumar Mishra,  Knud J. Jensen,  Nikos S. Hatzakis
doi: https://doi.org/10.1101/2024.12.12.628100

bioarxiv: https://www.biorxiv.org/content/10.1101/2024.12.12.628100v1.abstract
Will also exist in a peer-reviewed published version.

The code used to analyze this data is available here: https://github.com/JKaestelHansen/Colocalizational_Fingerprinting


# Colocalizational_Fingerprinting
Code for Colocalizational Fingerprinting developed by Jacob Kaestel-Hansen and Sara Vogt Bleshøy during their PhD work in the lab of Prof. Nikos Hatzakis.

### Citing:
#### Diverse Intracellular Trafficking of Insulin Analogs by Machine Learning-based Colocalization and Diffusion Analysis
Sara Vogt Bleshøy, Jacob Kæstel-Hansen, Annette Juma Nielsen, Narendra Kumar Mishra, Knud J. Jensen, Nikos S. Hatzakis

![image](Image_model.png)

We developed a combined approach integrating Colocalization Fingerprinting, a machine learning framework for reliable, time-resolved colocalization analysis, together with our recently developed deep learning-assisted single-particle diffusional analysis (DeepSPT). Our analysis revealed subtle, yet significant differences in intracellular behavior between IAsp655 and HI655, particularly in diffusional behavior and lysosomal colocalization, highlighting the potential of our approach to decipher subtle differences in intracellular trafficking and sorting characteristics. In addition to contributing to a more detailed understanding of biology of insulin analogs and intracellular sorting, we provide a reliable machine-learning methodology to study intricate cellular processes.

### Installation:

Colocalizational_Fingerprinting's installation guide utilize conda environment setup, therefore either miniconda or anaconda is required to follow the bellow installation guide.
 - Anaconda install guide: [here](https://www.anaconda.com/download)
 - Mini conda install guide: [here](https://docs.conda.io/en/latest/miniconda.html)

Colocalizational_Fingerprinting is most easily setup in a new conda environment with dependecies, versions, and channels found in ColocFP_simple.yml - Open Terminal / Commando prompt at wished location of Colocalizational_Fingerprinting and run the bash commands below, which creates the environemnt, downloades and installs packages, typically in less than <15 minutes. The code has been tested both on MacOS and Linux operating systems.

```bash:
Especially if running this on an Apple Macbook - M1/M2/M3 processor:

git clone git@github.com:JKaestelHansen/Colocalizational_Fingerprinting.git OR git clone https://github.com/JKaestelHansen/Colocalizational_Fingerprinting (potentially substitute JKaestelHansen with hatzakislab
cd Colocalizational_Fingerprinting

conda env create -f ColocFP_simple.yml 
conda activate ColocFP_simple

python -m pip install "setuptools<65" --no-cache-dir
python -m pip install --no-cache-dir "probfit==1.2.0"
python -m pip install --no-cache-dir "iminuit==2.11.0"

(A thanks to Konstantinos Tsolakidis for contributing to this approach)

Python version: 3.8.18
```

Colocalizational_Fingerprinting utilizes and relies on code written for https://github.com/JKaestelHansen/DeepSPT/, thus users need to install DeepSPT following instructions through the link and copy all folders into Colocalizational_Fingerprinting. Specifically, the folder "deepspt_src" and the folder "mlruns" (make sure it is not an empty "mlruns" folder". Place these folders in the Colocalizational_Fingerprinting directory

### Usage:
All scripts to reproduce the results of the paper appear in this repo.
In addition, two usage example scripts are provided to showcase how to run Colocalizational_Fingerprinting. usage_example.py shows how to run coloc analysis using the model trained in this paper. usage_example2.py contains code to retrain a model. Paths need to be changed to fit your data paths. The HMM used for fingerprinting is in 'colloc_data/hmm'

For DeepSPT usage we refer to https://github.com/JKaestelHansen/DeepSPT/


### Contact:

Jacob Kæstel-hansen, Postdoctoral fellow\
MIT, Biological Engineering\
jkh@chem.ku.dk

Sara Vogt Bleshøy, PhD fellow\
UCPH Department of Chemistry\
svb@chem.ku.dk

Nikos Hatzakis, Professor
UCPH, Department of Chemistry
hatzakis@chem.ku.dk