[](https://github.com/HenriquesLab/PhotoFiTT/blob/main/LICENSE.txt)
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[](https://github.com/HenriquesLab/PhotoFiTT/)
[](https://github.com/HenriquesLab/PhotoFiTT/)
<img src="https://github.com/HenriquesLab/PhotoFiTT/blob/main/docs/logo/photofitt-logo.png" align="right" width="300"/>
# PhotoFiTT: Phototoxicity Fitness Time Trial
A Quantitative Framework for Assessing Phototoxicity in Live-Cell
# General description of the workflow
PhotoFiTT was designed to quantitatively analyse the impact that fluorescence light excitation has in cell behaviour.
PhotoFiTT focuses on three different measurements: (1) Identified pre-mitotic cells, (2) Cell size dynamics and (3) Cell activity.
These are the steps to follow to replicate the analysis:
### Deep learning based analysis
Follow these steps to detect cells and pre-mitotic rounding events in the data.
1. Cell Detection and Quantification (deep learning-based image analysis: This processing is only applied to the first time point of each video.
- Virtual Staining: Use ZeroCostDL4Mic/DL4MicEverywhere Pix2Pix notebook to train a virtual staining model that infers cell nuclei. Analyse the first frame of each video.
- Nuclei Segmentation: Use ZeroCostDL4Mic/DL4MicEverywhere 2D StarDist notebook to apply the pretrained StarDist-versatile model to segment individual nuclei in the virtually stained images.
- Initial Cell Quantification: Count the number of detected nuclei (Use notebook `XXXXX.ipnynb` to generate a CSV file with the counts). The number of detected nuclei serves as the baseline cell count for each field of view, enabling tracking of population dynamics over time.
2. Pre-mitotic Cell Identification (deep learning-based image analysis):
- For CHO cells imaged with brightfield, you can use our trained StarDist model. Otherwise, manually annotate a representative image set and train a new StarDist model using the corresponding ZeroCostDL4Mic/DL4MicEverywhere notebooks.
### Image data Analysis
1. Cell Size Analysis and Classification `XXXXX.ipnynb`
2. Quantification of Cellular Activity `XXXXX.ipnynb`
## Data structure
1. The masks and the raw input, should be equally organised by folders, each folder for each condition to be analysed in a hierarchical manner.
For example:
```
-Raw-images (folder)
|
|--Biological-replica-date-1 (folder) [Subcaegory-00]
|
|--Cell density / UV Ligth / WL 475 light [Subcategory-01]
|
|-- control-condition (folder) [Subcategory-02]
| | file1.tif
| | file2.tif
| | ...
|
|-- condition1 (folder) [Subcategory-02]
| | file1.tif
| | file2.tif
| | ...
|
|-- condition2 (folder) [Subcategory-02]
| | file1.tif
| | file2.tif
| | ...
|
|--Cell density / UV Ligth / WL 475 light [Subcategory-01]
...
-Masks (folder)
|
|--Biological-replica-date-1 (folder) [Subcaegory-00]
|
|--Cell density / UV Ligth / WL 475 light [Subcategory-01]
|
|-- control-condition (folder) [Subcategory-02]
| | file1.tif
| | file2.tif
| | ...
|
|-- condition1 (folder) [Subcategory-02]
| | file1.tif
| | file2.tif
| | ...
|
|-- condition2 (folder) [Subcategory-02]
| | file1.tif
| | file2.tif
| | ...
|
|--Cell density / UV Ligth / WL 475 light [Subcategory-01]
...
```
# Package installation
- The code provides an `environment.yaml` file to create a conda environment with all the dependencies needed.
Place your terminal in the `photofitt` folder. Use either conda or mamba:
```
git clone https://github.com/HenriquesLab/photofitt.git
cd photofitt
mamba env create -f environment.yml
mamba activate photofitt
```
- **ONCE PUBLISHED** You can now install the package using pip install or conda as follows:
- ```
pip install photofitt
```
or
-
```
conda install photofitt
```
- **Meanwhile**:
- ```
git clone https://github.com/HenriquesLab/photofitt.git
cd photofitt
python setup.py
```
or
- ```
git clone https://github.com/HenriquesLab/photofitt.git
cd photofitt
pip install .
```
or
- ```
git clone https://github.com/HenriquesLab/photofitt.git
cd photofitt
conda build conda-recipe/meta.yaml
```
## Common error messages
- Error messages with `lxml`.
Most probably you need to update developers tools in your system. Before anything, run in Mac M1:
-
```
xcode-select --install
```
- If you were in Linux, you can run
- ```
sudo apt-get update
sudo apt-get install libxml2-dev libxslt-dev python-dev
```
Raw data
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