SPACe: an open-source, single-cell analysis of Cell Painting data

Area of Science:

• Cellular imaging and high throughput screening
• Computational biology and bioinformatics
• Drug discovery and mechanism of action studies

Background:

• High throughput microscopy, including Cell Painting, generates large datasets for cellular model screening.
• Existing open-source software for analyzing this data often requires substantial computational resources.
• Cell-to-cell variations in response are typically averaged and underutilized in current analyses.

Purpose of the Study:

• To introduce SPACe (Swift Phenotypic Analysis of Cells), an open-source platform for analyzing single-cell, image-based morphological profiles.
• To provide an efficient, accurate, and interpretable tool for phenotypic profiling.
• To demonstrate the utility of analyzing cell-to-cell variations in response to perturbations.

Main Methods:

• Development of the SPACe open-source software platform.
• Analysis of single-cell morphological profiles generated by Cell Painting.
• Application of SPACe to a screening campaign of cell metabolism small-molecule inhibitors across seven cell lines.

Main Results:

• SPAce demonstrates high processing speed and accuracy in mechanism of action recognition.
• The platform ensures reproducibility across biological replicates and is applicable to multiple cell models.
• SPAce effectively captures and interprets variable cell-to-cell responses, enhancing biological interpretability.

Conclusions:

• SPAce offers a powerful, accessible solution for analyzing large-scale phenotypic profiling data.
• The platform's ability to analyze single-cell variations provides deeper biological insights.
• SPAce facilitates a more comprehensive understanding of drug perturbations across diverse cellular contexts.