Automated Region of interest (ROI) finder based on fluorescent signal of cellular spheroids for image analysis to study cell migration
Description
Automated Region of interest (ROI) finder based on fluorescent signal of cellular spheroids for image analysis to study cell migration
Context and methodology
This repository contains two Fiji macros, that were written in the context of the THIRST project (at TU Wien) for the specific use to analyze the cell migration of green-fluorescent protein (GFP) labeled cells over time in a spheroid doublet fusion experiment. The THIRST project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 772464). The Fiji macros were used for data analysis, for the following publication: "Scaffolded spheroids as building blocks for bottom-up cartilage tissue engineering show enhanced bioassembly dynamics" by O.Kopinski-Grünwald and O.Guillaume et al. (2023), doi: https://doi.org/10.1016/j.actbio.2023.12.001, and are made accessible here.
The provided Fiji macro was written in order to study the cell migration in a spheroid (SPH) doublet fusion experiment. It can be downloaded and adapted or modified to the specific needs of the user, and therefore serves as template for a possible application of Fiji for cell migration analysis. The provided macros can be directly implemented and used in case a similar/comparable experiment is conducted.
Methodology
The "batch_process_ROI_generator.ijm"- file is applied to the dataset of interest first to define the needed regions of interest (ROI), in our case spheroid doublets on day 0, and follows following methodology:
User input to define input and output folder
Generation of a maximum intensity projection of the input z-stacks
Channel separation and application of a blurring filter and masking of the image based on the fluorescent signal
Circle fitting based on the outline of the masked region
The left most pixel of the circle is defined as center of the fusion area (remark: this applies, since in all images of the used dataset the GFP-labeled SPH/S-SPHs was present on the right side of the fusion area)
Automatic definition of three region of interests (ROIs) where one is centered on the identified center of the fusion area, and two neighboring areas covering both sides of the fusion area
Saving the ROIs in a designated output folder.
Using "batch_process_Analysis.ijm"-file to apply the defined ROIs of each sample to the corresponding sample to measure the mean fluorescent intensity of each ROI. The "batch_process_Analysis.ijm" gives the option to define input and output, displays the applied ROI and measures the intended features (here mean intensity fluorescence). The measured data can be saved and used for downstream analysis.
Technical details
The dataset consists of three files:
- README.txt
- batch_process_ROI_generator.ijm
- batch_process_Analysis.ijm
While "batch_process_ROI_generator.ijm"-file is used to define the regions of interest (ROI) depending on the GFP-signal of the LSM-image z-stack the second script uses the defined ROIs (that can be edited before application) to read out the intended parameters for analysis (here the mean fluorescent intensity).
The Fiji macro was written to be applied to z-stack image data taken on an LSM 800 (Zeiss, Germany). File format input: .czi
The code is commented to allow an easy adaptation to other requirements and to facilitate the overall comprehensibility.
Software used and needed for image analysis is Fiji:
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., … Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682. doi:10.1038/nmeth.2019
Language: English
Further details
In case the dataset is used for scientific work/publication please cite the following article:
"Scaffolded spheroids as building blocks for bottom-up cartilage tissue engineering show enhanced bioassembly dynamics" by O.Kopinski-Grünwald and O.Guillaume et al. (2023), doi: https://doi.org/10.1016/j.actbio.2023.12.001