Histograms and 2D plot profiling for quantification of numerous immunofluorescent signals on entire panoramic photomicrographs: a new method description
- Resource Type
- Conference
- Authors
- Duplancic, Roko; Kero, Darko
- Source
- 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech) Smart and Sustainable Technologies (SpliTech), 2021 6th International Conference on. :1-6 Sep, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Signal Processing and Analysis
Histograms
Protocols
Thresholding (Imaging)
Software
Data models
Photomicrography
Immune system
quantification
immunofluorescence
signal
- Language
We report a new method for quantifying and colocalizing immunofluorescence (IF) signals of several markers from panoramic high-resolution photomicrographs of histological sections using basic immunofluorescent staining protocols and software for image processing and analysis that is commercially available. Human gum tissue samples were stained with CD45 leukocyte antigen primary antibodies, and several factors associated to heparan sulphate glycosaminoglycans (HS GAG). Quantification of IF signals spatial gradients and expression domains was done using histograms and 2D plot profiles. Elaborate of the significance of tissue histo-morphometric profiling and IF signal thresholding is presented. Our approach makes use of pixel (px) counts and comparison of px grey value (GV) to quantify the IF staining. This is the sole base for determining the histological section's cellular substance - counting the total and/or the number of cells bound to the antibody was completely omitted. Since whole histological sections were used there was no need for determining or analysing any “regions of interest” (ROIs). With this method it is possible to colocalize several markers from a histological sample even if the basic IF staining protocol is used. Other way to achieve this is to perform a series of repeated staining cycles on the same histological section and those techniques require advance laboratory protocols and a cutting-edge equipment setup that may not be available for small laboratories that are employed in academic research so our method is presented as a viable second option. Furthermore, if combined with ontological bases data, this method of quantifying IF signals can be used for development of in silico disease models.