Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( 🔒 ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Need Help

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

Award Number: R01HD094513
ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 03/20/2018
PERIOD OF PERFORMANCE END DATE: 02/29/2024

Group Awards By:

View Award Description

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = -$1,272 )
2024	2022	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	000	5	6/18/2024	NON-COMPETING CONTINUATION	-$1,272
														Subtotal = -$1,272

Issue Date FY: 2022 ( Subtotal = $591,753 )
2022	2022	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	001	5	5/19/2022	NON-COMPETING CONTINUATION	$59,176
2022	2022	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	000	5	3/1/2022	NON-COMPETING CONTINUATION	$532,577
														Subtotal = $591,753

Issue Date FY: 2021 ( Subtotal = $591,753 )
2021	2021	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	001	4	4/6/2021	NON-COMPETING CONTINUATION	$0
2021	2021	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	000	4	2/24/2021	NON-COMPETING CONTINUATION	$591,753
														Subtotal = $591,753

Issue Date FY: 2020 ( Subtotal = $603,830 )
2020	2020	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	000	3	2/25/2020	NON-COMPETING CONTINUATION	$603,830
														Subtotal = $603,830

Issue Date FY: 2019 ( Subtotal = $603,830 )
2019	2019	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 SERRA MALL	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	000	2	1/29/2019	NON-COMPETING CONTINUATION	$603,830
														Subtotal = $603,830

Issue Date FY: 2018 ( Subtotal = $737,430 )
2018	2018	LELAND STANFORD JUNIOR UNIVERSITY, THE	450 SERRA MALL	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	000	1	3/20/2018	NEW	$737,430
														Subtotal = $737,430

Grand Total All Awards = $3,127,324

Top

All Categories

About

Search

Reports

Data Submission

Award Information

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

Award Number: R01HD094513

ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 03/20/2018

PERIOD OF PERFORMANCE END DATE: 02/29/2024

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer