A novel workflow for unbiased 3D quantification of autophagosomes in Arabidopsis thaliana roots

Abstract

Abstract Macroautophagy is often quantified by live imaging of autophagosomes labeled with fluorescently tagged ATG8 protein (FP–ATG8) in Arabidopsis thaliana. The labeled particles are then counted in single focal planes. This approach may lead to inaccurate results as the actual 3D distribution of autophagosomes is not taken into account and appropriate sampling in the Z-direction is not performed. To overcome this issue, we developed a workflow consisting of immunolabeling of autophagosomes with an anti-ATG8 antibody followed by stereological image analysis using the optical disector and the Cavalieri principle. Our protocol specifically recognized autophagosomes in epidermal cells of Arabidopsis root. Since the anti-ATG8 antibody recognizes multiple AtATG8 isoforms, we were able to detect a higher number of immunolabeled autophagosomes than with the FP–AtATG8e marker, that most probably does not recognize all autophagosomes in a cell. The number of autophagosomes per tissue volume positively correlated with the intensity of autophagy induction. Compared with the quantification of autophagosomes in maximum intensity projections, stereological methods were able to detect the autophagosomes present in a given volume with higher accuracy. Our novel workflow provides a powerful toolkit for unbiased and reproducible quantification of autophagosomes and offers a convenient alternative to the standard of live imaging with FP–ATG8 markers.