Molecular images and machine learning to extract placental function from maternal cfDNA

摘要 循环游离DNA(CfDNA)使产前诊断发生了革命性的变化，但这只是 冰山，因为cfDNA片段化模式嵌入了表观遗传足迹，表明细胞起源、细胞 功能和病理状态。CfDNA是片段化的，大小集中在145bp和166bp左右， 大约是包裹在一个核小体和一个核小体加上它的连接子上的DNA的长度， 分别进行了分析。也存在较短的片段(30-100bp)，并具有10bp的明显周期性，对应于 围绕核心组蛋白的DNA螺旋的转折。最近的报告表明，碎片化 CfDNA的大小是组织特有的，它是不同的核小体间距的产物，这是 单个组织的功能。当这些单独的片段与现有的表观遗传学数据进行比较时 组织，它们可以简单地根据它们是否揭示核小体的位置而被归入起源细胞 起源组织的信息。孕妇胎盘来源cfDNA片段的鉴定 血液循环将提供一种评估人类胎盘功能的非侵入性手段。 怀孕了。 几个主要障碍阻碍了cfDNA作为一种非侵入性检查胎盘功能的方法：1) 能够准确识别构成调节性短链160bpcfDNA片段的胎盘来源 信息(父亲的SNPs发生频率约为1/2000bp)。2)能够使用这些 以拼凑出胎盘的精确表观遗传状态。3)深度全基因组的成本 传统上需要用来解开混合细胞起源的表观遗传学图谱的测序。 我们的目标是通过利用最先进的基因组学和机器学习来克服这些障碍 从cfdna中提取有关人类胎盘功能的精确信息的技术。我们将首先编译 稳健和准确的核小体信息，包括表观遗传和转录因子的占有率，来自 然后我们将建立机器学习平台来阐明胎盘cfDNA来自 低成本的母体循环。该项目的成功将使对高风险的早期干预成为可能 并便于对怀孕进行纵向、非侵入性的实时监测 进展，从而为适应性治疗决策提供信息。

Abstract Circulating cell free DNA (cfDNA) has revolutionized prenatal diagnostics, but this is the tip of the iceberg, as cfDNA fragmentation patterns embed epigenetic footprints indicative of cell of origin, cellular function and pathological state. cfDNA is fragmented with sizes centered around 145bp and 166bp which is approximately the length of DNA wrapped around a nucleosome, and a nucleosome plus its linker, respectively. Shorter fragments (30-100bp) also exist and have a clear periodicity of 10bp, corresponding to a turn of the DNA helix wrapped around the core histone. Recent reports have shown that the fragmentation sizes of cfDNA are tissue specific, which is a product of distinct nucleosome spacing that is inherent in the function of individual tissues. When these individual fragments are compared with existing epigenetic data from tissues, they can be binned into cell of origin simply based on whether they reveal the nucleosome positioning information of the originating tissue. Identifying cfDNA fragments of placental origin from maternal circulation would provide a non-invasive means of assessing placental function during human pregnancy. Several major barriers inhibit cfDNA as a non-invasive method for examining placental function: 1) the ability to accurately identify the placental origin of the short <160bp cfDNA fragments that constitute regulatory information (paternal SNPs occur at frequency of approximately 1/2000bp). 2) the ability to use these fragments to piece together precise epigenetic states of the placenta. 3) the cost of deep whole genome sequencing that has traditionally been required to deconvolute epigenetic profiles of admixed cellular origins. Our goal is to overcome each of these barriers by exploiting state-of-the-art genomics and machine learning techniques to extract precise information about human placental function from cfDNA. We will first compile robust and accurate nucleosome information, including epigenetic and transcription factor occupancy, from the human placenta and then we will establish machine-learning platforms to elucidate placental cfDNA from maternal circulation at low cost. Success in this project will enable earlier intervention for high-risk pregnancies and facilitate the longitudinal, non-invasive real-time monitoring of pregnancy progression, thereby informing adaptive treatment decision-making.