Rasmussen M, Reddy M, Nolan R, Camunas-Soler J, Khodursky A, Scheller NM, Cantonwine DE, Engelbrechtsen L, Mi JD, Dutta A, Brundage T, Siddiqui F, Thao M, Gee EPS, La J, Baruch-Gravett C, Santillan MK, Deb S, Ame SM, Ali SM, Adkins M, DePristo MA, Lee M, Namsaraev E, Gybel-Brask DJ, Skibsted L, Litch JA, Santillan DA, Sazawal S, Tribe RM, Roberts JM, Jain M, Høgdall E, Holzman C, Quake SR, Elovitz MA, McElrath TF. (2022) RNA profiles reveal signatures of future health and disease in pregnancy. Nature [Epub ahead of print]. [article]
A study of pregnant women’s blood RNA has found specific molecular profiles that identify women at risk of pre-eclampsia. These insights can identify complications before a woman experiences symptoms.
The study, published today in Nature, involved researchers from King’s and Guy’s and St Thomas’ NHS Foundation Trust in partnership with Mirvie. The study examines genetic material found in blood samples that can predict pregnancy complications such as pre-eclampsia. The study examines genetic material found in blood samples that can predict pregnancy complications such as pre-eclampsia.
Pre-eclampsia effects up to 1 in 12 pregnancies and is a significant cause of maternal morbidity. It is also a cause of a higher risk of cardiovascular disease. Most cases of pre-eclampsia are diagnosed when the mother experiences symptoms in the third trimester. This study could widen the window of detection and lead to quicker intervention.
Researchers took 2500 blood samples from eight prospectively collected cohorts that included multiple ethnicities, nationalities, socioeconomic contexts and geographic locations. They then examined the anonymised cfRNA profiles – signals from the fetus and pregnant mother’s tissues – that reflect fetal development and healthy pregnancy progression. This provided a non-invasive window into maternal and fetal health.
In this study, researchers show the cfRNA signals which deviate from those of a healthy pregnancy. One single blood sample could reliably identify women at risk of developing preeclampsia months prior to the presentation of the disease. Using machine learning to analyse tens of thousands of RNA messages from the mother, baby and placenta, the Mirvie RNA platform can identify 75% of women who go on to develop preeclampsia. Researchers hope this test can be widened to investigate other pregnancy complications, such as preterm birth.
Source – King’s College London
Rasmussen M, Reddy M, Nolan R, Camunas-Soler J, Khodursky A, Scheller NM, Cantonwine DE, Engelbrechtsen L, Mi JD, Dutta A, Brundage T, Siddiqui F, Thao M, Gee EPS, La J, Baruch-Gravett C, Santillan MK, Deb S, Ame SM, Ali SM, Adkins M, DePristo MA, Lee M, Namsaraev E, Gybel-Brask DJ, Skibsted L, Litch JA, Santillan DA, Sazawal S, Tribe RM, Roberts JM, Jain M, Høgdall E, Holzman C, Quake SR, Elovitz MA, McElrath TF. (2022) RNA profiles reveal signatures of future health and disease in pregnancy. Nature [Epub ahead of print]. [article]
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A study of pregnant women’s blood RNA has found specific molecular profiles that identify women at risk of pre-eclampsia. These insights can identify complications before a woman experiences symptoms.
The study, published today in Nature, involved researchers from King’s and Guy’s and St Thomas’ NHS Foundation Trust in partnership with Mirvie. The study examines genetic material found in blood samples that can predict pregnancy complications such as pre-eclampsia. The study examines genetic material found in blood samples that can predict pregnancy complications such as pre-eclampsia.
Pre-eclampsia effects up to 1 in 12 pregnancies and is a significant cause of maternal morbidity. It is also a cause of a higher risk of cardiovascular disease. Most cases of pre-eclampsia are diagnosed when the mother experiences symptoms in the third trimester. This study could widen the window of detection and lead to quicker intervention.
Researchers took 2500 blood samples from eight prospectively collected cohorts that included multiple ethnicities, nationalities, socioeconomic contexts and geographic locations. They then examined the anonymised cfRNA profiles – signals from the fetus and pregnant mother’s tissues – that reflect fetal development and healthy pregnancy progression. This provided a non-invasive window into maternal and fetal health.
In this study, researchers show the cfRNA signals which deviate from those of a healthy pregnancy. One single blood sample could reliably identify women at risk of developing preeclampsia months prior to the presentation of the disease. Using machine learning to analyse tens of thousands of RNA messages from the mother, baby and placenta, the Mirvie RNA platform can identify 75% of women who go on to develop preeclampsia. Researchers hope this test can be widened to investigate other pregnancy complications, such as preterm birth.
Source – King’s College London
Rasmussen M, Reddy M, Nolan R, Camunas-Soler J, Khodursky A, Scheller NM, Cantonwine DE, Engelbrechtsen L, Mi JD, Dutta A, Brundage T, Siddiqui F, Thao M, Gee EPS, La J, Baruch-Gravett C, Santillan MK, Deb S, Ame SM, Ali SM, Adkins M, DePristo MA, Lee M, Namsaraev E, Gybel-Brask DJ, Skibsted L, Litch JA, Santillan DA, Sazawal S, Tribe RM, Roberts JM, Jain M, Høgdall E, Holzman C, Quake SR, Elovitz MA, McElrath TF. (2022) RNA profiles reveal signatures of future health and disease in pregnancy. Nature [Epub ahead of print]. [article]
Related Posts
Single-cell and single-embryo RNA sequencing
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Deep learning improves microRNA target prediction from sequence
Atlas of the brain’s striatum could guide researchers to new drug treatments
scLS – a computationally efficient differentially expressed gene detection algorithm
Spatial mapping of RNA turnover kinetics in the mouse brain
Immune cells offer insights on billion-dollar virus
SPIDER improves spatial transcriptomics data using single-cell RNA sequencing
Ultrafast and reference-free sequence discovery in single-cell data
ARCADIA combines RNA sequencing and spatial proteomics to reveal how tissue location shapes cell behavior
An end-to-end computational framework for “Record-seq” transcriptional recording data
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
ExoShorkie – predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data
MiRQuery – a user-friendly web app for the interactive analysis and visualization of microRNA sequencing data
RNA sequencing resolves cryptic pathogenic variants in mitochondrial disease
Unlocking the past – new method helps gain insights into old tissue
New RNA sequencing model improves sequencing depth planning for UMI transcriptomics
Combining RNA sequencing and pathology images identifies glioblastoma subgroups linked to survival
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