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

Abstract

ABSTRACT Macroautophagy is frequently quantified by live imaging of autophagosomes decorated with a marker of fluorescently tagged ATG8 protein (FT-ATG8) in Arabidopsis thaliana . This requires generation of suitable plant material by time-consuming crossing or transformation with FT-ATG8 marker. Autophagosome quantification by image analysis often relies on their counting in individual focal planes. This approach is prone to deliver biased results due to inappropriate sampling of the regions of interest in the Z-direction, as the actual 3D distribution of autophagosomes is usually not taken into account. To overcome such drawbacks, we have developed and tested a workflow consisting of immunofluorescence microscopy of autophagosomes labelled with anti-ATG8 antibody followed by stereological image analysis employing the optical disector and the Cavalieri principle. Our immunolabelling protocol specifically recognized autophagosomes in epidermal cells of A. thaliana root. Higher numbers of immunolabelled autophagosomes were observed when compared with those recognized with FT- At ATG8e marker, suggesting that single At ATG8 isoform markers cannot detect all autophagosomes in a cell. Therefore, immunolabelling provides more precise information as the anti-ATG8 antibody recognizes virtually all At ATG8 isoforms. The number of autophagosomes per tissue volume determined by stereological methods correlated with the intensity of autophagy induction treatment. Compared to autophagosome quantifications in maximum intensity projections, stereological methods detected autophagosomes present in a given volume with higher accuracy. Our novel application of immunolabelling combined with stereological methods constitutes a powerful toolbox for unbiased and reproducible quantification of autophagosomes and offers a convenient alternative to the standard of live imaging using FP-ATG8 marker.

Plant phenotyping relevance

植物のオートファゴソーム状態を対象に、免疫蛍光イメージングと立体解析を組み合わせた定量ワークフローを開発・検証しており、表現型取得法が研究の中心である。

Code and data availability

The supplied blocks describe an immunofluorescence + stereology workflow for autophagosome quantification in Arabidopsis roots, but contain no public phenotype/trait dataset, image deposit, author analysis code, or trained model. Supplement 1 is only a lab protocol; analyses were done manually in Fiji/Prism with no de-

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