Martin M.A.,University of California at Santa Barbara |
Lassek W.D.,University of Pittsburgh |
Gaulin S.J.,University of California at Santa Barbara |
Evans R.W.,University of Pittsburgh |
And 6 more authors.
Maternal and Child Nutrition | Year: 2012
Breast milk fatty acid (FA) composition varies greatly among individual women, including in percentages of the long-chain polyunsaturated FAs (LCPUFA) 20:4n-6 (arachidonic acid, AA) and 22:6n-3 (docosahexaenoic acid, DHA), which are important for infant neurological development. It has been suggested that owing to wide variation in milk LCPUFA and low DHA in Western diets, standards of milk FA composition should be derived from populations consuming traditional diets. We collected breast milk samples from Tsimane women at varying lactational stages (6-82 weeks). The Tsimane are an indigenous, natural fertility, subsistence-level population living in Amazonia Bolivia. Tsimane samples were matched by lactational stage to samples from a US milk bank, and analysed concurrently for FA composition by gas-liquid chromatography. We compared milk FA composition between Tsimane (n=35) and US (n=35) mothers, focusing on differences in LCPUFA percentages that may be due to population-typical dietary patterns. Per total FAs, the percentages of AA, DHA, total n-3 and total n-6 LCPUFA were significantly higher among Tsimane mothers. Mean percentages of 18:2n-6 (linoleic acid) and trans FAs were significantly higher among US mothers. Tsimane mothers' higher milk n-3 and n-6 LCPUFA percentages may be due to their regular consumption of wild game and freshwater fish, as well as comparatively lower intakes of processed foods and oils that may interfere with LCPUFA synthesis. © 2012 Blackwell Publishing Ltd. Source
Guo M.,The Perinatal Institute |
Guo M.,University of Cincinnati |
Wang H.,The Perinatal Institute |
Potter S.S.,Cincinnati Childrens Hospital Medical Center |
And 3 more authors.
PLoS Computational Biology | Year: 2015
A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html. © 2015 Guo et al. Source