Partaker

Partaker (Python-based Analyzer for Real-Time Assessment of Kinetics and Expression in Real-time) is a GUI application for single-cell bacterial image analysis in microfluidic time-lapse microscopy. It integrates deep learning segmentation, multi-channel fluorescence quantification with Relative Promoter Unit (RPU) calibration, and morphological analysis in a unified interface.

Partaker allows you to create plots like this:

Features

• Multi-model segmentation: Six selectable deep learning models for cell segmentation
• Multi-channel fluorescence: Per-cell fluorescence quantification with background subtraction and RPU calibration
• Morphological analysis: Automated cell classification (rod, coccoid, artifact) with eight morphological descriptors
• ND2 and TIFF support: Native reading of Nikon ND2 files and standard TIFF stacks
• Multi-file stitching: Automatic concatenation of sequential acquisitions on the time axis
• Session management: Save and resume analysis sessions through HDF5

Segmentation Models

Partaker integrates the following pretrained deep learning backends:

Model	Family	Best For
omnipose_bact_phase	Omnipose	Phase-contrast, dense monolayers
omnipose_bact_fluo	Omnipose	Fluorescence images
bact_phase_cp3	Cellpose 3	Phase-contrast, general bacteria
bact_fluor_cp3	Cellpose 3	Fluorescence images
cellpose_deepbacs	DeepBacs/Cellpose	Phase-contrast bacteria
unet	Custom U-Net	User-trained binary segmentation

Users can compare model outputs on their data and select the best-performing model for their imaging conditions.

Requirements

• Python >= 3.10, < 3.12
• macOS (Apple Silicon or Intel), Windows, or Linux
• uv package manager (recommended)

Installation

1. Install uv if you do not have it:

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone the repository:

git clone https://github.com/SamOliveiraLab/partaker.git
cd partaker

3. Launch Partaker:

uv run gui

This will automatically download and install all dependencies and open the main GUI window.

Quick Start

1. Load data: Go to File > Open and select your ND2 or TIFF file
2. Navigate: Use the T (time), P (position), and C (channel) sliders to browse your data
3. Segment: Select a segmentation model from the dropdown (e.g., omnipose_bact_phase) and switch Display Mode to "Labeled Segmentation" or "Overlay with Outlines"
4. Batch segment: Use the Segmentation tab on the right panel to select positions, time range, and model, then click "Segment Selected"
5. Analyze fluorescence: Switch to the Population tab to quantify fluorescence intensity per cell across channels
6. Morphology: Use the Morphology tab to extract cell shape descriptors and classify cell types

Display Modes

• Normal: Raw microscopy image
• Overlay with Outlines: Segmentation contours overlaid on the original image
• Labeled Segmentation: Color-coded instance labels showing individual cells

Custom U-Net Weights

To use a custom-trained U-Net model, set the environment variable before launching:

export PARTAKER_UNET_WEIGHTS="/path/to/your/unet_weights.pt"
uv run gui

The weights file should be a PyTorch state-dict (.pt). A conversion script (convert.py) is provided for converting Keras .h5 weights to PyTorch format.

Example Dataset

The validation dataset used in the manuscript (two-strain E. coli co-culture in monolayer microfluidic chambers) is available on the BioImage Archive.

Demo

A video walkthrough of Partaker is available on YouTube: Partaker Demo

Documentation

Full documentation is available at: https://samoliveiralab.github.io/partaker/

Contributing

Contributions are welcome. Please use conventional commits and contact the maintainers before starting major changes.