CFReT-screen-mAP-analysis

About

Cardiac fibrosis, a condition associated with significant morbidity and mortality, contributes to the global burden of cardiovascular diseases, which remain the leading cause of death worldwide, accounting for approximately 31% of all deaths. This pathological process is characterized by the excessive deposition of extracellular matrix (ECM) components, especially collagen, resulting in the formation of scar tissue. While ECM production is a vital part of the body’s response to injury, its overproduction disrupts tissue homeostasis and leads to structural changes that impair organ function. In the heart, for example, excessive ECM accumulation causes stiffening of the myocardial tissue, compromising its ability to contract and relax efficiently. This can lead to reduced cardiac output, impaired electrical conductivity, and an increased risk of heart failure or fatal arrhythmias. Given its global impact and contribution to high mortality rates, there is an urgent need to develop advanced diagnostic tools and targeted therapeutic strategies to better understand, manage, and treat cardiac fibrosis.

Recently, we applied image-based profiling with the CellPainting(CP) assay to investigate whether single-cell image-based profiles could be leveraged to identify failing heart cells (cardiac fibrosis), healthy heart cells, and heart cells treated with drug_x. Our study demonstrated that by using image-based profiles with machine learning models, we could accurately distinguish between single cells derived from either heart failure or non-heart failure patients. Additionally, we identified morphological features that were critical for the model’s ability to differentiate between heart states. These findings suggest that specific cellular structures are strong indicators of heart cell health or failure. The processing methods and results of this analysis can be accessed here.

Building upon these results, we advanced our study to the next level by leveraging high-content screening (HCS) with CP assay. Specifically, we applied a library of 550 small-molecule compounds to identify those with the potential to reverse the effects of cardiac fibrosis. This approach not only aims to pinpoint candidate compounds for therapeutic intervention but also seeks to understand the biological mechanisms underlying the reversal of fibrosis.

Analytical approach

In this notebook, we utilize a statistical framework called Mean Average Precision (mAP) to evaluate and analyze image-based profiles generated through high-content screening (HCS) using the CP assay. mAP is a robust and versatile metric designed to quantify the ability of a query profile to retrieve related profiles based on phenotypic similarity.

This framework enables us to assess two key aspects:

• Phenotypic activity: How distinct perturbations (e.g., compounds or treatments) are from controls.
• Phenotypic consistency: How similar profiles are within biologically related groups (e.g., compounds with similar mechanisms of action).

By leveraging mAP, we aim to identify potential compounds that can reverse the effects of cardiac fibroblasts, which are central to the pathology of cardiac fibrosis. Beyond identifying these candidate compounds, mAP also helps us uncover the biological mechanisms driving this reversal, providing insights into the cellular and molecular processes that contribute to phenotypic recovery.

Installation guide

This document outlines the steps to install and set up the repository on your local machine. The repository uses Poetry to manage all dependencies. Follow these steps to ensure a smooth installation process.

Prerequisites

Before starting, ensure that:

1. Poetry is installed on your local machine.
   • Follow the official Poetry installation guide to install Poetry on your operating system.
2. You have a Python environment manager, such as conda, installed.

Installation Steps

1. Create a Python Environment

It is recommended to create a virtual environment to avoid dependency conflicts. Use conda or another Python environment manager to create a new environment:

conda create -n CFReT-screen-mAP python=3.9
conda activate CFReT-screen-mAP

2. Clone the Repository

Clone the repository to your local machine and navigate into it:

git clone https://github.com/WayScience/CFReT-screen-mAP-analysis.git
cd CFReT-screen-mAP-analysis

3. Install Dependencies with Poetry

Use Poetry to install all the dependencies required for the project:

poetry install

4. (Optional) Install Development Tools

If you are a contributor and need to make changes to the project, install the development dependencies:

poetry install --with dev

Troubleshooting

• Poetry not found: Ensure that Poetry is installed and added to your system's PATH. Verify the installation with:

  poetry --version

• Dependency issues: If you encounter dependency conflicts, try updating Poetry and clearing its cache:

  poetry self update
  poetry cache clear --all .

Additional Notes

• Always activate your Python environment before working on the project:

  conda activate CFReT-screen-mAP

• For more information about using Poetry, visit the official documentation.