ArticlePlant physiology2026
Systems-level proteomic models of cotton fiber development: a high-resolution data resource to analyze cell dynamics and trait engineering.
Article in Plant physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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10 authors.
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Abstract
The shapes and material properties of cotton (Gossypium spp.) seed coat trichoblasts form the basis of a multibillion-dollar natural fiber industry. As such, these highly specialized cells are low-hanging fruit for intentional trait engineering. However, broad success will require more mechanistic knowledge of their systems-level cellular controls. This time-series study integrates daily measurements of purified fiber transcriptomes and proteomes with multiscale fiber phenotyping datasets that span the same developmental interval. Abundance profiles of the subcellular proteomes are the foundation of the analyses. This resource article provides direct information about which homoeologs operate and offers informative depictions of how compartmentalized cellular systems change during developmental transitions. Prediction accuracy was partially validated by analyzing protein expression group 11, which contained multiple known secondary cell wall (CW) cellulose synthases together with dozens of unknown proteins, and displayed an averaged expression profile that strongly correlated with a sharp state transition in cellulose microfibril alignment and increased cellulose content. The dataset as a whole can serve as a hypothesis-generating tool to guide future experiments related to CW glycome remodeling, morphogenesis, reversible tissue formation, and growth rate control. Integration of mRNA and protein abundance revealed widespread evidence of post-transcriptional control. In addition, there were hundreds of transcriptionally controlled genes with different time points of transition. This latter gene set can be used to more reliably analyze transcriptional control networks and to generate collections of gene expression drivers for cotton fiber research. The protein and transcript abundance profiles are organized into user-friendly tables and a web interface that can be searched using any plant ortholog of interest based on developmental time, abundance, annotations, or phenotypic association.
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