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longitudinally collect subjective symptoms, and monitor DED-contributory lifestyle factors on an individual level may play an important role in real-izing the principles of P4 medicine in DED manage-ment.CL is a well-established, effective tool in improving one’s visual acuity and quality, with the global user population nearing 140 million people31). However, the discomfort derived from its use, ranging from ocular to non-ocular symptoms, causes discontinu-ation of CL in 12-58% of its users14). A significant portion of the discomfort experienced amongst CL users is currently correlated with the development of DED, which is broadly termed “CL-associated DED (CLADE)”32). As with classical DED, CLADE is highly heterogeneous in presentation, and multi-factorial with influence from the environmental, lifestyle, and host-related factors, which created obstacles in comprehensively investigating its pathogenesis31, 33-35). To overcome these obstacles, we stratified the collected subjective symptoms of CLADE through dimension reduction and clus-tering techniques14). This strategy revealed 14 distinct clusters of CLADE (Figure 2B), with subsequent hierarchical clustering elucidating indi-vidual characteristics of each cluster (Figure 2C). Such stratification methods to group various symp-toms of a seemingly singular disease may hold a crucial position in establishing digital phenotyping protocols in the field of mHealth, as well as genom-ics-integrated comprehensive analysis of lesser understood diseases.Multi-omics approach for the ocular immunologyThe immune system comprises numerous cell types and subtypes, with three major cell types being T cell, B cell, and macrophage36). Both DED and corneal transplantation are deeply connected to the inflammatory and immune dynamics of the anterior ocular surface37-42). Foreign objects and eye inflammation promote antigen-presenting cells. It is followed by the initiation of Th1 dominant immune reaction in nearby cervical lymph nodes, disrupting various ocular tissue function43-46). Regu-latory T cells (Tregs) -have received attention as a potential therapeutic target for this mecha-nism47-49). They were found in 1995 for their role in suppressing effector T cells and maintaining toler-ance to self-antigens. Therefore, artificially prolif-524erating and homing Treg activity in the cornea appeared promising in maintaining immune toler-ance in corneal allograft recipients and suppressing inflammation in DED patients50, 51). Our recent find-ings revealed that Tregs possess a significant degree of plasticity and readily respond to their environments and inflammatory signals47, 52, 53). This led to decreased immunosuppressive activity of Tregs, evidenced by the loss of Foxp3 transcription factor expression and redifferentiation into helper T cells53). Additionally, the population of Tregs in an average individual constituted both Tregs with stable Foxp3 expression and plasticity of Tregs (exTreg). The latter lost the expression of Foxp3, reflecting their nonuniform cell differentiation, and immunosuppressive molecules52). Separation of immune cells by the cluster of differentiation (CD) antigens through flow cytometry may be an option54-56), but the practical ceiling on the number of antigens that a single operation can simultane-ously measure limits the use of flow cytometry to reliably isolate stable Tregs. Through such methods, the isolated cell population will inevitably contain cells undergoing different stages of differentiation with different functions. This will limit its applica-tion for research and therapeutic purposes. There-fore, in elucidating the mechanism behind Tregs plasticity, an approach to accurately determine the characteristics and differentiation pathway of indi-vidual Tregs in an unevenly distributed cell popu-lation is required. One promising solution to this puzzle is multi-omics data collection with subse-quent AI analysis of the resultant big data, unveiling fundamental molecular mechanisms behind Treg plasticity and heterogeneity2).The multi-omics analysis yields a comprehen-sive, cross-sectional snapshot on various levels of cellular function and their interactions. These inter-actions reflect one’s physiologic status, including genome, epigenome, transcriptome, proteome, and metabolome. This status may act as a cornerstone in elucidating complex physiologic phenomenon and disease mechanisms57, 58). Single cell RNA sequencing (RNAseq) may help investigate the heterogeneity in the immune cell population59). This technique provides insight into minute details on intracellular dynamics, including transcription factor networks. These networks can help identify molecular expla-nations for the variability and plasticity of immune