Decoding Cellular Mysteries: Cell Painting Image Analysis

Cell painting image analysis is a transformative technique in the world of life sciences, offering a multidimensional window into the intricate landscapes of cellular biology.

What is Cell Painting?

At its core, cell painting is a high-throughput, phenotypic screening method that uses fluorescent dyes to stain various cellular components and structures. These dyes illuminate specific aspects of the cell, such as the nucleus, cytoskeleton, or mitochondria, making them visible under a microscope. By systematically labeling and imaging cells in this way, scientists generate a treasure trove of data that provides insights into cell health, behavior, and responses to external stimuli.

Scientists at Visikol perform cell painting assays with various treatments on cell lines and generate large sets of data. Then, they develop the workflow of cell painting image analysis based on client’s requirements. This involves a series of steps to process and analyze images obtained through high-throughput microscopy after cells have been stained with fluorescent dyes. This workflow is critical in various fields, including drug discovery, toxicology, and cell biology research.

Here’s a detailed breakdown process.

1. Cell Culture and Treatment:
   • Cultivate the cells of interest in a suitable cell culture medium.
   • Treat the cells with different conditions or compounds, such as drug candidates or experimental treatments. Each condition is often tested in multiple wells.
2. Staining:
   • Apply a set of fluorescent dyes or probes to the cells. These dyes target specific cellular components, such as the nucleus, cytoskeleton, endoplasmic reticulum, and mitochondria.
   • Incubate the cells to allow the dyes to bind and label the cellular structures.
3. Image Acquisition:
   • Use high-throughput microscopy to capture images of the stained cells.
   • Typically, multiple images are taken for each well of a multi-well plate, resulting in a large dataset.
4. Image Preprocessing:
   • Correct for variations in lighting, remove noise, and enhance image quality. Common preprocessing steps include background subtraction, flat-field correction, and noise reduction. This is called Illumination correction.
   • Ensure that the images are in a standardized format and that no artifacts are addressed.
5. Feature Extraction:
   • Employ image analysis software to extract quantitative features from the images. These features may include:
     • Size: Measurements of cell and organelle size.
     • Shape: Geometric properties of cells and cellular components.
     • Texture: Patterns and variations within the cell.
     • Intensity: Fluorescence intensity values for different channels.
     • Spatial distribution: Information on the location and distribution of cellular structures.
6. Data Normalization:
   • Normalize the extracted features to account for variations between different images, wells, or experiments. This step ensures that data from different conditions can be compared accurately.
7. Data Analysis:
   • Apply statistical and machine learning techniques to analyze the normalized data. Common analysis tasks include:
     • Phenotype identification: Classifying cells into different phenotypic categories based on their features.
     • Hit identification: Identifying compounds or conditions that induce specific cellular responses.
     • Pathway analysis: Exploring how specific cellular pathways or processes are affected by treatments.
     • Visualization: Create plots, heatmaps, or other visual representations to interpret and communicate the results.
8. Validation and Further Experiments:
   • Validate the findings through additional experiments, such as follow-up assays or validation screens.
   • Explore the mechanisms behind observed cellular responses, if necessary.
9. Data Interpretation and Reporting:
   • Interpret the results in the context of the research objectives.
   • Prepare reports or presentations to communicate findings to colleagues, stakeholders, or for publication in scientific journals.
10. Iterative Analysis:
    • Depending on the research goals, the analysis may be iterative, with researchers refining their approach and conducting additional experiments as needed.

Visikol has successfully developed this image analysis pipeline and analyzed various cell painting images which aids in screening various drugs in pre-clinical settings. The workflow of image analysis also helps to uncover cellular responses to various treatments and conditions. It plays a crucial role in advancing our understanding of cell biology, drug discovery, and toxicology studies by providing quantitative insights into the effects of different interventions on cellular behavior.

Cell painting image analysis is more than just a technique; it’s an artistic journey into the cellular cosmos. It empowers scientists to decode the intricate language of cells, shedding light on their inner workings and responses. As technology advances and datasets grow larger, the potential for groundbreaking discoveries in fields like drug development and disease understanding is limitless. In the world of cellular exploration, cell painting is the brush that paints the path to discovery. To learn more the cell painting assay, please reach out to a member of our team today!