Spatial ploidy inference using quantitative imaging

Abstract

Polyploidy (whole-genome multiplication) is a common yet under-surveyed property of tissues across multicellular organisms. Polyploidy plays a critical role during tissue development, following acute stress, and during disease progression. Common methods to reveal polyploidy involve either destroying tissue architecture by cell isolation or by tedious identification of individual nuclei in intact tissue. Therefore, there is a critical need for rapid and high-throughput ploidy quantification using images of nuclei in intact tissues. Here, we present iSPy (Inferring Spatial Ploidy), a new unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest. We demonstrate the use of iSPy in Arabidopsis, Drosophila, and human tissue. iSPy can be adapted for a variety of tissue preparations, including whole mount and sectioned. This high-throughput pipeline will facilitate rapid and sensitive identification of nuclear ploidy in diverse biological contexts and organisms.

Plant phenotyping relevance

Arabidopsisを含む組織の核倍数性を画像から空間的に推定する新規計算パイプラインを開発しており、植物の状態計測手法が研究の中心である。

Code and data availability

The paper's Data Availability Statement explicitly points to a public OSF data repository (containing the paper's imaging/phenotyping data) and a public GitLab repository for the iSPy analysis code, both with authors' URLs.