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. Credit: Nicholas J. Russell

A collaborative effort by the Formosa-Jordan lab from the Max Planck Institute for Plant Breeding Research in Cologne, Germany, the Fox lab from Duke University, U.S., and the Roeder lab from Cornell University, U.S., developed a new computational pipeline that enables the high-throughput quantification of ploidy, i.e., the copy number of chromosomes, across tissues from microscopy images.

The study is published in Cell Reports Methods.

Understanding endopolyploidy and its significance

Scientists have long known that some cells in a given tissue undergo a process of duplicating their entire genome without dividing—a process called endopolyploidy, where cellular ploidy is the copy number of chromosomes in a cell, and a polyploid cell is a cell that has more than two copies of each chromosome.

Instead of splitting into two new cells after copying their entire genetic material, these specialized cells retain the extra DNA within a single, enlarged cell.

Endopolyploidy is widespread across nature, appearing in plant and animal tissues as well as human tissue. This natural strategy is essential for tissues to develop properly or regenerate when something goes wrong. However, it is also associated with diseases such as cancer.

Despite the importance of endopolyploidy, there is still a lack of understanding of what exactly triggers it, which cells adopt this state, and how its emergence is spatially controlled within tissues.

Challenges in quantifying endopolyploidy

Pinpointing in which cells endopolyploidy arises and where these cells are located within the tissue is crucial for uncovering the fundamentals of growth, regeneration, and disease.

But until now, endopolyploidy quantification in a tissue of interest at a given time and position across a tissue has remained a major challenge, as it relies on techniques that destroy the tissue architecture, or on tedious, manual examination of each nucleus.

The iSPy pipeline and its applications

The multidisciplinary international team now overcomes these longstanding limitations with their new pipeline named iSPy (Inferring Spatial Ploidy), which allows scientists to visualize and study polyploid cells directly in intact, living tissue.

iSPy is a high-throughput, automated pipeline that combines experimental methods with advanced image software analysis. Starting with microscopy images, a segmentation software program identifies cell nuclei and calculates certain nuclear characteristics, such as nuclear volume.

Building on these segmented images, iSPy then identifies nuclei that are polyploid throughout a tissue and creates detailed maps of their spatial organization. Excitingly, the researchers could show that iSPy can be used in situations such as developmental programmed endopolyploidy in Arabidopsis leaf development and human cardiomyocytes to tracking regeneration-induced polyploidy in the fruit fly.

Implications for research and future discoveries

Thus, iSPy is a powerful and easy-to-use tool for identifying and analyzing polyploid cells in different tissues in diverse organisms. For the first time, scientists are now able to identify and track these specific cells over time and analyze their pattern within the architecture of the specific tissue in a high-throughput manner.

The lead author of the work, Nicholas Russell, said, "Identifying polyploid cells without destroying a tissue has been a wish for the community for a long time, and I hope that this pipeline can be used and adapted over the years to identify previously unknown spatial and temporal ploidy patterns in many organisms."

Pau Formosa-Jordan added, "Many differentiated tissues exhibit spatial patterns of ploidy across organisms, and we have very little knowledge about it. Our pipeline will hopefully help to understand how living tissues develop, age, or are repaired upon injury, and might open new avenues in understanding certain diseases such as cancer."

More information: Russell, Nicholas J. et al. Spatial ploidy inference using quantitative imaging, Cell Reports Methods (2025). DOI: 10.1016/j.crmeth.2025.101249. www.cell.com/cell-reports-meth … 2667-2375(25)00285-1

Citation: Shining a spotlight on polyploid cells: New tool uncovers spatial patterns of DNA content across tissues (2025, December 8) retrieved 8 December 2025 from https://phys.org/news/2025-12-spotlight-polyploid-cells-tool-uncovers.html

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