北京大学何爱彬小组在研究中取得进展。他们发现了无细胞染色质状态追踪揭示疾病起源和治疗反应。该项研究成果发表在2026年3月4日出版的《自然》上。

在这里，小组开发了cf-EpiTracing，这是一种高度敏感的自动化平台，可以分析低至50μl人血浆。通过将多模态染色质状态与机器学习相结合，cf-EpiTracing能够准确地反卷积细胞类型的起源。研究小组从125名健康个体和549名炎症性肠病、结直肠癌、冠心病或淋巴瘤患者的血浆中生成了2417个cf-EpiTracing谱。cf-EpiTracing能够不偏不倚地识别原发病变组织和其他器官受累，对具有不同遗传和表观遗传基础的B细胞淋巴瘤亚型进行分层，并检测早期疾病或病变。研究动态的表观遗传特征揭示了疾病从滤泡性淋巴瘤到异位大B细胞淋巴瘤的转变。

此外，cf-EpiTracing揭示了套细胞淋巴瘤患者的基因组易位和表观遗传改变。值得注意的是，他们的研究利用整体表观遗传特征，独立于基因转录知识，准确报告复发风险和治疗反应。总之，这些发现确立了cf-EpiTracing作为一种自动化、非侵入性、以表观基因组为中心的框架，在早期诊断、分子分型和预后预测方面具有广泛的应用。

据了解，血液中的无细胞DNA来源于健康和病变组织中垂死细胞释放的碎片化染色质。这些片段携带丰富的分子形态，可以揭示原始组织的病理改变。

附：英文原文

Title: Cell-free chromatin state tracing reveals disease origin and therapy responses

Author: Chen, Xubin, Meng, Xiaoxuan, Zhang, Weilong, Zhang, Xiawei, Zhang, Yaping, Yang, Ping, Liu, Yan, Bao, Fang, Li, Sen, Wang, Jing, Yan, Changjian, Li, Chunyuan, Zhang, Lingke, Hao, Xiaoyu, Liu, Jia, Sun, Jing, Wang, Zhengting, Tian, Yu, Zhu, Liqing, Hou, Yan, Liu, Zongchao, Li, Wenqing, Mi, Lan, Qi, Xinyu, Yue, Yanzhu, Du, Peng, Chen, Guoqiang, Zheng, Junke, Dou, Liping, Jing, Hongmei, He, Aibin

Issue&Volume: 2026-03-04

Abstract: Cell-free DNA in blood originates from fragmented chromatin released by dying cells from both healthy and diseased tissues1,2. These fragments carry rich molecular modalities that can reveal pathological alterations in tissues of origin3,4,5,6,7,8,9,10. Here we develop cf-EpiTracing, a highly sensitive automated platform that profiles histone modifications in cell-free DNA from as little as 50μl of human plasma. By integrating multimodal chromatin states with machine learning, cf-EpiTracing enables accurate deconvolution of cell types of origin. We generated 2,417 cf-EpiTracing profiles from plasma of 125 healthy individuals and 549 patients with inflammatory bowel disease, colorectal cancer, coronary heart disease or lymphoma. cf-EpiTracing enabled unbiased identification of primary diseased tissues and other organ involvement, stratification of B cell lymphoma subtypes with different genetic and epigenetic underpinnings, and detection of early-stage diseases or lesions. Surveying dynamics of epigenetic signatures uncovered disease transformation from follicular lymphoma to diffuse large B cell lymphoma. Further, cf-EpiTracing revealed genomic translocations and epigenetic alterations in patients with mantle cell lymphoma. Of note, our study leverages holistic epigenetic signatures, independently of knowledge of gene transcription, to accurately report recurrence risk and therapeutic response. Together, these findings establish cf-EpiTracing as an automated, non-invasive, epigenome-centric framework with broad applications in early diagnosis, molecular subtyping and prognostic prediction.

DOI: 10.1038/s41586-026-10224-0

Source: https://www.nature.com/articles/s41586-026-10224-0