ArticleGigaScience2026
Stereo-cell deciphers the spatial and functional heterogeneity of polyploid hepatocytes.
Article in GigaScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Stereo-cell deciphers the spatial and functional heterogeneity of polyploid hepatocytes.GigaScience · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
A characteristic feature of the liver is the presence of numerous polyploid hepatocytes. However, the functional distinctions among diploid, tetraploid, and octoploid hepatocytes remain poorly understood. In this study, we employed the spatially resolved single-cell sequencing technology, Stereo-cell, to dissect the transcriptomic and functional heterogeneity across hepatocyte ploidy subtypes. We detail the development of Stereo-cell imaging-based ploidy identification (SCIPI), a technical pipeline that integrates bright-field cell contour recognition, DAPI-based nuclear area and number quantification, and UMI-barcoded single-cell transcriptomics. This approach enables precise identification of 6 core hepatocyte subtypes: mononucleated diploid (2n×1), mononucleated tetraploid (4n×1), binucleated tetraploid (2n×2), mononucleated octoploid (8n×1), binucleated octoploid (4n×2), and binucleated hexadecaploid (8n×2) hepatocytes. Single-cell transcriptomic analysis based on ploidy annotation revealed that gene expression levels positively with increasing ploidy and nuclear number. Metabolic pathway-associated genes were significantly upregulated in polyploid cells, suggesting that cellular polyploidization enhances the metabolic activity of hepatocytes. Furthermore, this SCIPI strategy is broadly applicable to the study of various polyploid tissues, offering a novel and versatile framework for innovative ploidy-resolved research across diverse biological researches.
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