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利用 DNA 甲基化增强外周血的细胞去卷积,实现高分辨率免疫分析。

Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling.

机构信息

Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.

Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.

出版信息

Nat Commun. 2022 Feb 9;13(1):761. doi: 10.1038/s41467-021-27864-7.

Abstract

DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease.

摘要

DNA 甲基化微阵列可用于研究复杂组织中的细胞类型组成。在这里,我们扩展了基于参考的血液 DNA 甲基化去卷积方法,以包括 12 种白细胞亚型(中性粒细胞、嗜酸性粒细胞、嗜碱性粒细胞、单核细胞、幼稚和记忆 B 细胞、幼稚和记忆 CD4+和 CD8+T 细胞、自然杀伤细胞和 T 调节细胞)。包括衍生变量在内,我们的方法提供了 56 种免疫特征变量。使用 IDOL(识别最佳文库)算法来识别当前和以前平台的 DNA 甲基化数据去卷积所需的文库。使用人工混合物和来自流式细胞术的具有已知细胞组成的全血 DNA 甲基化验证了使用我们增强的文库获得的去卷积估计的准确性。我们将这些文库应用于癌症、衰老和自身免疫性疾病数据集的去卷积。总之,这些文库仅使用 DNA 即可实现血液中免疫细胞谱的详细表示,并促进在人类健康和疾病中对免疫谱进行标准化和全面研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3295/8828780/1ea370c57d59/41467_2021_27864_Fig1_HTML.jpg

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