Using Big Data to Determine Developmental Changes in Newborns


The first week of a newborn’s life is a time of the most rapid biological change as the baby adapts to living outside the womb, suddenly exposed to new challenges such as bacteria and viruses. Yet surprisingly little is known about these early changes. An international research study, co-led by University of British Columbia and Boston Children’s Hospital, has pioneered a technique to get huge amounts of data from a tiny amount of newborn blood, allowing for the most comprehensive data analysis to date.

“We found thousands of changes over the first week of life, including changes in gene expression and parts of the immune defense pathways,” said Casey Shannon, co-lead author and a computational biologist at the Prevention of Organ Failure (PROOF) Centre of Excellence at St. Paul’s Hospital.

Published today in Nature Communications, this study revealed key molecular changes in the first week of newborns’ life, establishing a common developmental pathway to further our understanding of newborn health and the impact of vaccines. This study showcases important contributions from researchers at the Centre for Heart Lung Innovation (HLI) and PROOF Centre, with Casey Shannon as a co-lead author, Daniel He and Dr. Amrit Singh as co-authors, and Dr. Scott Tebbutt as a senior author. One of the key computational approaches that was used for this study was developed by Dr. Singh during his Ph.D.

The researchers piloted their technique with a group of newborns from The Gambia in West Africa after first obtaining permission from village elders and informed consent from mothers in local languages. Importantly, they then validated the approach with a second group of newborns from Papua New Guinea (Australasia). What they found was that the two independent groups of babies shared a common, highly dynamic developmental trajectory—suggesting that the molecular changes do not occur at random, but instead follow an age-specific pathway which may be used to monitor the impact of life-saving interventions.