I am trying to find a quick, easy way to quantify cell morphology. I have two sets of photos, a control and a treatment (brightfield microscopy, cells stained with crystal violet). I want a way to quantify the differences in morphology other than just visually showing that the cells look different. Has anyone found a way to do this? Everything I am finding online requires fluorescence or the use of DAPI and Phalloidin, which would set me back greatly.
Here are a few approaches you could consider:
1. ImageJ Particle Analyzer Plugin: This plugin for ImageJ allows you to automatically measure various particle characteristics, including size, shape, and circularity. You can use it to analyze the morphology of your cells by converting your brightfield images to grayscale and then applying the plugin. The resulting data can be used to compare the morphology of your control and treated cells.
2. CellProfiler: CellProfiler is a free, open-source software designed for large-scale analysis of biological images. It includes a variety of modules for analyzing cell morphology, including one for measuring cell area and another for detecting cell nuclei. You can use it to analyze your brightfield images and generate numerical data comparing the morphology of your control and treated cells.
3. OpenCV: OpenCV is a computer vision library that provides a range of functions for image processing and analysis. You can use it to develop custom algorithms for analyzing cell morphology from brightfield images. For example, you can use OpenCV's edge detection functions to identify cell boundaries and measure cell area or perimeter.
4. MATLAB: MATLAB is a programming environment commonly used for scientific computing and image analysis. You can use it to develop custom algorithms for analyzing cell morphology from brightfield images. For example, you can use MATLAB's image processing tools to segment cells from the background and measure their size and shape.
5. Automated Cell Morphometry: This method uses a combination of image processing techniques to quantify cell morphology. First, the cells are segmented from the background using thresholding or edge detection. Then, features such as cell area, perimeter, and circularity are extracted using shape descriptors. Finally, the data is analyzed using statistical methods to identify significant differences between control and treated cells.
6. Machine Learning: You can also use machine learning algorithms to classify cells based on their morphology. For example, you can train a convolutional neural network (CNN) to recognize control and treated cells based on their brightfield images. Once trained, the CNN can be used to classify new cells and provide a quantitative measurement of their morphology.
All the best
There are multiple ways to do this, depending on your application and research question. If you have two cell morphologies that are quite different from one another doing some simple Calcien-AM stain would be very easy to do. If the difference is smaller and thus more difficult to visualize it might be best do look into depth how to analyse the images.
If you want to look into label-free microscopy, thus independent of markers, you can look into some kind of quantitative phase imaging (QPI)/ digital holographic microscopy (DHM).
Or check out my paper on DHM and cell morphology: Article Deep learning with digital holographic microscopy discrimina...
Kind regards,
Joost Verduijn
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