Novel post-acquisition image processing to attenuate red blood cell autofluorescence for quantitative image analysis.

Abstract

Quantitative analysis of microscopy images from samples stained with fluorescent probes necessitates a very low fluorescence background signal. In tissues prepared by immersion in a chemical fixative, followed by conventional processing for paraffin embedding, red blood cell autofluorescence across several imaging channels can be a nuisance. Although many protocols have been proposed to suppress red blood cell autofluorescence prior to microscopy imaging, in many instances they may not prove totally effective. Moreover, in environments such as core facilities where control over tissue processing and staining may not be feasible, methods to address autofluorescence via post-image acquisition processing may be of some advantage. To this end, we have developed an image analysis algorithm using a commercially based software platform to remove contaminating red blood cell autofluorescence during quantitative evaluation of the fluorescence signal from an immunostaining protocol. The method is based upon the low autofluorescence signal of red blood cells exhibited in the blue channel (used to detect DAPI nuclear signal of all cells), which can be subtracted from the total channel signal by increasing the threshold for DAPI signal in the nuclear detection settings during nuclear segmentation. With the contributing signal from the red blood cells eliminated, the specific immunostained signal for the antigen of interest could be determined. We believe that this simple algorithm performed on post-acquisition microscopy images will be of use for quantitative fluorescence analyses whenever red blood cell autofluorescence is present, especially in amounts where creating regions of interest for evaluation is not possible.