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Autofluorescence Imaging to Evaluate Cellular Metabolism
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Novel post-acquisition image processing to attenuate red blood cell autofluorescence for quantitative image analysis
Nicole A Bouffard1,2, Kyra Lee1,2, Nicole M DeLance1,2
1Microscopy Imaging Center, Larner College of Medicine, University of Vermont, Burlington, VT, 05405, USA.
Histochemistry and Cell Biology
|October 19, 2022
Summary
Red blood cell autofluorescence in microscopy images can hinder analysis. This study presents a simple image analysis algorithm to effectively remove this background noise, improving quantitative fluorescence measurements.
Area of Science:
- Biomedical Imaging
- Cell Biology
- Quantitative Microscopy
Background:
- Quantitative analysis of fluorescent microscopy images requires minimal background signal.
- Red blood cell autofluorescence is a common issue in immunostained tissue samples, complicating accurate signal quantification.
- Existing methods to suppress autofluorescence are not always effective or feasible in all laboratory settings.
Purpose of the Study:
- To develop and validate an image analysis algorithm for removing red blood cell autofluorescence from microscopy images.
- To enable accurate quantitative fluorescence analysis in the presence of significant red blood cell autofluorescence.
- To provide a post-acquisition solution for autofluorescence challenges in core facilities.
Main Methods:
- Developed an image analysis algorithm utilizing commercially available software.
- Leveraged the low autofluorescence of red blood cells in the blue channel (DAPI) for signal subtraction.
- Applied threshold adjustments in nuclear detection settings during image segmentation.
- Eliminated red blood cell signal contribution to allow specific antigen signal determination.
Main Results:
- Successfully removed contaminating red blood cell autofluorescence from microscopy images.
- Enabled precise quantification of specific immunostained signals.
- Demonstrated the algorithm's utility even when red blood cells obscure regions of interest.
Conclusions:
- The developed image analysis algorithm offers a simple and effective post-acquisition method to eliminate red blood cell autofluorescence.
- This approach is valuable for quantitative fluorescence analyses in diverse biological samples, particularly when traditional autofluorescence suppression methods are insufficient.
- The algorithm enhances the reliability of quantitative microscopy, especially in core facility settings with variable sample processing.
