本研究分析了染色體三維結構對T細胞衰竭機制的影響，使用一種以原位高通量染色體構象捕獲(in situ Hi-C)結合染色質免疫沉澱(ChIP)的技術HiChIP作為生物實驗方法，並利用資料分析和機器學習建立模型比較與解析染色體三維結構變化和與T細胞衰竭的關係。我們使用早期和晚期腫瘤來觀察 T細胞衰竭並比較染色質環(Chromatin loop)，拓樸結構域(TAD)和A/B區室(A/B Compartments)在T細胞衰竭標誌基因上的不同強度變化。最終在衰竭的T細胞中確定了調控T細胞衰竭的特定基因發生的染色質三維結構重塑，以及其可能的調控上游與原理。

This study analyzes the effects of three-dimensional chromosome regulation mechanisms on T cell exhaustion using data analysis and machine learning on in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) data. We can compare the relationship between three-dimensional structural changes and T exhaustion and predict it with machine learning models.We use early and late-stage tumors for observing T exhaustion and comparing the different intensities of the interaction loop. As a result, using chromosome conformation capturing techniques such as HiChIP to profile the 3D chromosome architecture in exhausted T cells, we observed chromosome re-organization as T cells become exhausted.

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