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

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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.