Comparison of metabolite profiles of NS0 cells following sampling with MxP^® FastQuench (FQ) versus centrifugation (C) without washing.

<p>A: PCA of all samples showing very high difference in metabolite levels between both sampling methods. B: Volcano-plot of ANOVA comparison (all C samples versus all FQ, corrected for culture duration) demonstrating that most metabolites had significantly higher levels in centrifuged samples than in filtered. Pluses mark supplemented metabolites and crosses essential supplemented metabolites.</p

Fast Filtration of Bacterial or Mammalian Suspension Cell Cultures for Optimal Metabolomics Results - Fig 5

<p>A. Comparison of metabolite levels for centrifuged (no washing) in contrast to MxP® FastQuench (with washing) samples of NS0 cells. Impact of sampling on metabolite levels for selected metabolites with especially misleading results for centrifuged (no washing) in contrast to MxP^® FastQuench (with washing) samples. B Extracellular levels of glucose and lactate as well as cell viability and total cell number for comparisons</p

Impact of vacuum strength on glutamate, ATP and AMP levels in CHO cells, in filtrate or in washing solution was low (5 replicates, all scales are logarithmic).

<p>All other quantified metabolites (GSH, NAD, ADP, G6P) exhibited similar curves and are not shown for the sake of clarity. The example metabolites were selected due to their biological interest, size difference, cell culture medium content (e.g. glutamate) or in literature described possible leakage [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159389#pone.0159389.ref025" target="_blank">25</a>]. Metabolite level changes compared to 35 mbar were not statistically significant (Student’s t-test, all p values above 0.05).</p

Overview of the steps from sample to peak naming the most critical factors for each step

<p>Overview of the steps from sample to peak naming the most critical factors for each step</p

Additional file 10 of Buffy coat signatures of breast cancer risk in a prospective cohort study

Additional file 10: Fig. S3. ROC curves for the tested classifiers. Individual ROC curves are shown for each cross-validation fold. SVM: support vector machines; PLR: penalized logistic regression; NNET: neural network; RF: random forests; LogitBoost: boosted logistic regressison; KNN: k-nearest neighbours; PAM: Prediction Analysis for Microarrays; RPART: classification and regression tre

Additional file 6 of Buffy coat signatures of breast cancer risk in a prospective cohort study

Additional file 6. Supplementary Table 6. Top GO Biological Processes, GO Cellular Components, GO Molecular Functions, Reactome, and KEGG Pathways enriched from significantly differentially methylated regions (FDR 0.075) identified when overlapping DMRs identified in the main analysis with DMRs identified when samples were limited to participants aged 50 and above at recruitment

Additional file 7 of Buffy coat signatures of breast cancer risk in a prospective cohort study

Additional file 7: Fig. S2. Relative proportions of hypermethylated, hypomethylated and all regions of the dataset when annotated by Enhancer status as annotated in the FANTOM5 enhancer atlas for the GM12878 human lymphoblastoid cell line; andgenic annotations

Additional file 1 of Buffy coat signatures of breast cancer risk in a prospective cohort study

Additional file 1. Supplementary Methods, Supplementary Tables and References

Additional file 2 of Buffy coat signatures of breast cancer risk in a prospective cohort study

Additional file 2: Fig. S1. Distribution of participants within the model development and held-out sample sets byage at recruitment,body mass index,exit age, and proportion-of-whole graphs illustrating the distribution of participants bytumour subtype,menopausal status at recruitment,hormonal contraceptive use,hormone therapy use, andpregnancy history

Additional file 9 of Buffy coat signatures of breast cancer risk in a prospective cohort study

Additional file 9. Supplementary Table 9. Genomic regions utilized by the PAM prediction model