Advancements in microscopy techniques permit us to acquire endless datasets of images. A major bottleneck in cell imaging is how to analyze petabytes of data in an effective, reliable, objective, and effortless way. Quantitative imaging is becoming crucial to disentangle the complexity of many biological and pathological processes. For instance, cell shape is a summary readout of a myriad of cellular processes. Changes in cell shape use to reflect changes in growth, migration mode (including speed and persistence), differentiation stage, apoptosis, or gene expression, serving to predict health or disease. However, in certain contexts, e.g., tissues or tumors, cells are tightly packed together, and measurement of individual cellular shapes can be challenging and laborious. Bioinformatics solutions like automated computational image methods provide a blind and efficient analysis of large image datasets. Here we describe a detailed and friendly step-by-step protocol to extract various cellular shape parameters quickly and accurately from colorectal cancer cells forming either monolayers or spheroids. We envision those similar settings could be extended to other cell lines, colorectal and beyond, either label/unlabeled or in 2D/3D environments.
|Title of host publication||Intestinal Differentiated Cells. Methods in Molecular Biology|
|Number of pages||11|
|Publication status||Published - 14 Jun 2023|
|Name||Methods in Molecular Biology|
Bibliographical noteFunding Information:
Tiff images of HCT116 cell monolayers and 3D spheroids were kindly provided by Lautaro Baro and Dr. Asifa Islam, respectively, both members of our lab. Images have been deposited in Zenodo repository (doi: 10.52181/zenodo.7679086). This work was sup-ported by grants from the Academy of Medical Science/the Well-come Trust/the Government Department of Business, Energy and Industrial Strategy/the British Heart Foundation/Diabetes AMS Springboard Award UK [SBF006\1070], and the CIDEGENT Excellent Research Program from the Valencian regional govern-ment CIDEGENT/2021/026 to M.A.J
© 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.