2025-12-04 マックス・プランク研究所
The iSPy pipeline at a glance to quantify nuclear ploidy. Confocal microscopy is performed with nuclear reporters and stains, nuclear segmentation with ilastik is conducted, and a machine learning method is used to classify the ploidy of each nucleus. Colors in the right panel represent different ploidy levels.
<関連情報>
- https://www.mpg.de/25815018/shining-a-spotlight-on-polyploid-cells
- https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(25)00285-1
定量的イメージングを用いた空間倍数性推定 Spatial ploidy inference using quantitative imaging
Nicholas J. Russell ∙ Paulo B. Belato ∙ Lilijana Sarabia Oliver ∙ … ∙ Adrienne H.K. Roeder ∙ Donald T. Fox ∙ Pau Formosa-Jordan
Cell Reports Methods Published:December 4, 2025
DOI:https://doi.org/10.1016/j.crmeth.2025.101249
Motivation
Ploidy, the number of chromosome copies in a nucleus, can vary spatially throughout a tissue, and this variation plays a critical role in eukaryotic organisms during tissue development, following acute stress, and during disease progression. However, common methods to reveal nuclear ploidy destroy the tissue and lose spatial ploidy information. As the importance of ploidy in tissue biology is increasingly recognized, powerful methods are needed to quantify nuclear ploidy throughout a tissue of interest at a given time and position without destroying tissue architecture. Therefore, we developed a high-throughput computational pipeline using quantitative image analysis from microscopy images to infer nuclear ploidy while retaining spatial information.
Highlights
- iSPy is a computational pipeline to generate spatial ploidy maps across a tissue
- iSPy rapidly and accurately quantifies nuclear ploidy in intact tissues
- We apply the method to Arabidopsis, Drosophila, and human nuclei
- The spatial ploidy maps reveal insights into ploidy patterning across a tissue
Summary
Polyploidy (whole-genome duplication) 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 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), an 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.
