Cotton Genomics and Genetics 2025, Vol.16, No.6, 278-289 http://cropscipublisher.com/index.php/cgg 281 After successful extraction, the study once relied on two-dimensional gel electrophoresis (2-DE) and two-dimensional differential gel electrophoresis (2D-DIGE) to observe the protein differences between infected and healthy tissues. However, these methods were gradually replaced by gel-free techniques later on. Nowadays, LC-MS/MS systems such as Orbitrap, Q-TOF or MALDI-TOF/TOF are more common, and they all have obvious advantages in sensitivity, coverage and reproducibility (Yang et al., 2019). Quantitative proteomics has also undergone a similar transformation. Labeling techniques such as iTRAQ and TMT, as well as label-free quantification (LFQ) methods, have become mainstream. They enable researchers to precisely identify the key differential proteins involved in cell wall recombination, stress signaling and secondary metabolism during V. dahliae infection. The obtained results were then functionally annotated with the help of UniProt, NCBI or CottonGen databases, and the biological roles of these proteins were understood through GO and KEGG pathway mapping. 3.2 Advances in mass spectrometry and data analysis The innovation of mass spectrometry technology has almost changed the research pace of plant proteomics. Low-abundance proteins that were difficult to detect in the past can now be reliably identified when cotton is under pathogen stress with the help of technologies such as DIA and SWATH-MS (Lu et al., 2022). These methods significantly broaden the dynamic range, and the results are more stable and closer to physiological reality. In terms of data analysis tools, MaxQuant, Proteome Discoverer and Perseus remain the main players. They combine protein abundance with annotation information such as GO and KEGG, enabling researchers to sort out key biological processes such as oxidative stress, signal transduction, and secondary metabolism from complex data. However, numbers and lists alone cannot explain all the issues. Thus, network analysis tools such as STRING or Cytoscape began to come into play. They can transform the interactions between proteins into network maps, from which the "core nodes" of defense responses can be identified. Such an integrated approach enables people to understand the defense signal mechanism of cotton against the Trichoderma lucidum from a systematic perspective and also provides a direction for the search of new disease-resistant proteins. 3.3 Limitations and opportunities Although the application of proteomics has shown us many breakthroughs, the cotton-pathogen system remains a thorny issue. The protein types of cotton are complex and the genomic annotation is incomplete. Many low-abundance or regulated proteins are difficult to be stably detected (Bawa et al., 2022). Even if the technology is mature, minor differences in sample pretreatment, protein digestion, and mass spectrometry acquisition methods may still make it difficult to fully compare the results among studies. However, these imperfections are precisely the breakthrough points for innovation. Nowadays, an increasing number of studies are attempting to integrate proteomics with transcriptomics, metabolomics, and even phosphorylomics to construct a systematic "defense panorama". In the future, data analysis involving single-cell proteomics, targeted validation techniques (SRM/MRM), and machine learning may make the quantification and prediction of disease-resistant proteins more accurate. In other words, the limitations still exist, but the direction is clear. 4 Case Study 4.1 Proteomic response of Gossypium hirsutumcultivar CRI 12 to Verticillium dahliae In cotton research, CRI 12 (Gossypium hirsutum L. Terrestrial cotton) is often regarded as a representative of "moderate resistance". Its agronomic traits are stable. Although it is not completely immune to the Variegata officinalis, it can maintain relative tolerance. Therefore, it is widely used in the study of the resistance mechanism of Fusarium wilt. Researchers conducted a comparative proteomic analysis of leaf samples at different time points after inoculation with healthy controls. The results showed that there were significant fluctuations in the leaf protein composition of CRI 12, whether in terms of the initiation of defense responses or metabolic adjustments.
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