How to Systematically Explore the Morphology of Labeled Cells

How do You Determine Cell Morphology?

Determining cell morphology is typically done using optical microscopy methods, though electron microscopy methods have also been used for their excellent spatial resolutions.2

Many biological samples require some degree of sample preparation for the evaluation of cell morphology. This may be adding some kind of fluorescent marker to make labeled cells for the microscopy method or to highly particular regions of the cell of interest. For particular experiment types, such as high-content screening, both genetic and fluorescent labels may be added during the preparation.3

Once the sample is prepared and the microscopy images are collected, the images can then be evaluated to assess the cell morphology. Notably, there are approaches to quantify the cell morphology so that different results and measurements can be compared. Cells are often evaluated on their area, optical volume, thickness and an irregularity parameter to describe the shape.

High Content Imaging

Image-based high content screening is a particularly powerful tool for systematically explored labelled cell morphology.4 The advantage of high content screening methods is that multiple species can be visualized in a single microscopy experiment and with the right instrumentation and data analysis tools, there can be both automation of the microscopy experiment and the data analysis.

Using an automated, high content image analysis software, morphological parameters are automatically quantified on a cell-per-cell basis, for thousands and millions of cells within minutes.  Such morphological parameters include area, perimeter, axial ratio , elongation ratio, Convex Hull (CH) area, solidity, and many more. This quantitative, meaningful information extracted from microscopy images, allows cell researchers to investigate what biological phenomenon occurred in their sample, and measure the extent of the phenotypic implications of that phenomenon in a subjective, unbiased manner. Tera bites of data is produced within minutes in a fully automated fashion, enabling statistically significant results and insights.

One way of achieving quantitative cell morphology analysis is to use IDEA Bio’s WiScan Hermes and Athena platforms. IDEA Bio-Medical are experts in high content imaging and automated image analysis and the WiScan Hermes allows you to achieve high throughput imaging for a wide range of sample types, covering all scales of biology from whole organisms down to sub-nuclear features.

With seven fluorescent colors, a large range of magnifications including water and oil immersion media, and compatibility with bright field imaging, the WiScan Hermes is a highly flexible instrument. There are a number of data analysis packages available for automated processing of cell morphology data for achieving quicker assay analysis.

Contact IDEA Bio-Medical today to find out how the WiScan Hermes can help you accelerate your analysis of cell morphology, whether that is for the identification of cellular processes such as endocytosis and cell adherence, or for the evaluation of nuclei structures for disease screening and identification.

References

1. Brancato, V., Oliveira, J. M., Correlo, V. M., Reis, R. L., & Kundu, S. C. (2020). Could 3D models of cancer enhance drug screening? Biomaterials, 232, 119744. https://doi.org/10.1016/j.biomaterials.2019.119744
2. Koster, A. J., & Klumperman, J. (2003). Electron microscopy in cell biology : integrating structure and function. Nature Cell Biology, 4, SS6–SS9. https:// doi:10.1038/nrm1194
3. Mattiazzi Usaj, M., Styles, E. B., Verster, A. J., Friesen, H., Boone, C., & Andrews, B. J. (2016). High-Content Screening for Quantitative Cell Biology. Trends in Cell Biology, 26(8), 598–611. https://doi.org/10.1016/j.tcb.2016.03.008
4. Mattiazzi Usaj, M., Styles, E. B., Verster, A. J., Friesen, H., Boone, C., & Andrews, B. J. (2016). High-Content Screening for Quantitative Cell Biology. Trends in Cell Biology, 26(8), 598–611. https://doi.org/10.1016/j.tcb.2016.03.008