Cellular and transcriptional diversity over the course of human lactation
Name
pnas.2121720119.pdf
Description
Published version
Size
2.28 MB
Format
Adobe PDF
Checksum (MD5)
97a936e531ceb78d82d21bb28cd9ee2e
Author(s)
Nyquist, Sarah K
Gao, Patricia
Haining, Tessa KJ
Retchin, Michael R
Golan, Yarden
Drake, Riley S
Kolb, Kellie
Mead, Benjamin E
Ahituv, Nadav
Martinez, Micaela E
Date Issued
2022
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
Proceedings of the National Academy of Sciences
Citation
Nyquist, Sarah K, Gao, Patricia, Haining, Tessa KJ, Retchin, Michael R, Golan, Yarden et al. 2022. "Cellular and transcriptional diversity over the course of human lactation." Proceedings of the National Academy of Sciences of the United States of America, 119 (15).
Version
Final published version
Abstract
Significance
Human breast milk is the nutritional food source evolved specifically to meet the needs of infants, but much remains to be learned about its composition and changes over the course of lactation. Our description of the cellular components of breast milk, their associations with maternal–infant dyad metadata, and quantification of alterations at the gene and pathway levels provide a longitudinal picture of human breast milk cells across lactational time. These results pave the way for improved therapeutic support of healthy lactation and milk production.
Human breast milk is the nutritional food source evolved specifically to meet the needs of infants, but much remains to be learned about its composition and changes over the course of lactation. Our description of the cellular components of breast milk, their associations with maternal–infant dyad metadata, and quantification of alterations at the gene and pathway levels provide a longitudinal picture of human breast milk cells across lactational time. These results pave the way for improved therapeutic support of healthy lactation and milk production.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Ragon Institute of MGH, MIT and Harvard
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1073/PNAS.2121720119
