Details

Autor(en) / Beteiligte

• Rackaityte, E
• Halkias, J
• Fukui, E M
• Mendoza, V F
• Hayzelden, C
• Crawford, E D
• Fujimura, K E
• Burt, T D
• Lynch, S V

Titel

Corroborating evidence refutes batch effect as explanation for fetal bacteria

Ist Teil von

• Microbiome, 2021-01, Vol.9 (1), p.10-10, Article 10

Ort / Verlag

England: BioMed Central Ltd

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• Inclusion of samples with lower sequence reads in a study of very low bacterial burden can artificially inflate false negatives due to inadequate community coverage – this is particularly pertinent since “Batch 2” samples had significantly lower sequence reads per sample than those of “Batch 1” (excluding mock controls; median read depth “Batch 2” = 19,601, “Batch 1” = 39,194; P < 0.0001; specimen read depth available in Supplemental Table 2 of our original manuscript), plausibly explaining the higher rate of Micrococcaceae-negative samples in “Batch 2”. Because our qPCR- and FISH-based analyses of fetal specimens had indicated a sparse, low-burden bacterial presence, the 16S rRNA analysis performed and reported in our study used a multiply rarefied dataset (to ensure that the 16S rRNA profiles were representative) and included only those samples with ≥1000 16S rRNA sequence reads to permit confident detection of bacterial signals. Normalization of read-depth is recommended for analysis of zero-inflated microbiome data and enables clustering of samples according to biological metadata [5], yet this was not performed by De Goffau and colleagues. [...]the batch effect described by De Goffau which the authors claim explain the fetal Micrococcus 16S rRNA signal in Figure 1 and Figure 2, is predicated upon Principal Component Analysis (PCA), an ordination method based on Euclidian distance which assumes linear relationships and a normal data distribution. [...]16S rRNA reads for Micrococcaceae OTU10, found to be enriched in meconium specimens compared to a multitude of technical (n = 48) and biological (n = 35) control samples processed in parallel, were not exclusively detected in “Batch 1”, but existed in both “Batches” (Extended Data Figure 3h of our original manuscript). [...]this approach requires thin-sectioning which dilutes a rare signal. [...]we turned to scanning electron microscopy, which permits the ability to scan the surface of thick sections and obtain high magnification resolution of structures.