4. Quantitative Microbiome Profiling and Dynamap – Towards Absolute Values
Relative ratios can mislead; quantitative profiling and Dynamap measure absolute cell counts — a real advance, though they still don't resolve the question of interpretation.
A shared weakness of conventional 16S and shotgun analyses is that they produce relative compositional data [18], [23]. If all bacterial taxa decrease equally (for example under antibiotic treatment), the relative proportions remain unchanged – while the total bacterial biomass shrinks to a fraction of its former level. This compositional bias fundamentally complicates clinical interpretation [18].
The methodological response to this is quantitative microbiome profiling (QMP): by adding internal standards (e.g. known quantities of synthetic DNA spikes), relative values can be converted to absolute cell counts. This in principle makes it possible to distinguish true growth and decline from mere ratio shifts [23].
The Dynamap – referenced in domestic clinical practice – offers an approach based on this principle [23], using full DNA molecule identification. The technology is promising, but the clinical experience base remains limited. The fact that it aims to examine the entire DNA content (not just the 16S amplicon region) in principle reduces some of the biases of amplicon sequencing. Absolute quantification is also an advance [23].
Nevertheless, the fundamental interpretive questions – what a given composition means for the particular patient, and what the clinician should do about it – are not automatically resolved by this technology either [23]. The value of a more precise measurement tool is determined by the clinical context and the evidence-based interpretive framework.
What does this mean for the patient?
Quantitative microbiome profiling and Dynamap technology represent a genuine methodological advance over conventional relative analysis. In a patient who has undergone a course of antibiotics, conventional 16S analysis might show that the microbiome has "remained stable" – while the total bacterial count has dropped to a fraction, with only the proportions remaining similar. Absolute quantification can capture this difference [23].
It is important, however, to see clearly that more precise measurement does not in itself resolve every interpretive question. QMP shows whether a taxon has genuinely increased or decreased – but does not say whether this is good or bad, desirable or undesirable. For that, knowledge of the particular patient's clinical context, symptoms, exposome profile and treatment goal is required [23].
In the case of Dynamap, it must also be taken into account that domestic availability and the clinical experience base are currently limited. The gap between a promising methodological approach and routinely available, validated clinical applicability typically spans years – and this gap currently exists. The development direction of the technology is sound, but realistic expectations are needed from both clinician and patient [23].
Overall: if forced to choose between the three methods today, the QMP/Dynamap approach provides the most information in principle, and involves the fewest compositional biases [18], [23]. However, the validated reference databases and interpretive protocols required for clinical decision support are still under development for all three methods.
References
[18] Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 2017. Link
Microbiome datasets generated by high-throughput sequencing of 16S rRNA amplicons, metagenomes or metatranscriptomes are inherently compositional because the instrument imposes an arbitrary total. The review explains the pathologies that arise when compositional data are analysed with non-compositional methods and provides guidance for applying compositional data analysis throughout microbiome study workflows. The compositional framework is presented as essential, not optional, for valid inference.
[23] Vandeputte D, Kathagen G, D'hoe K et al. Quantitative Microbiome Profiling Links Gut Community Variation to Microbial Load. Nature. 2017. Link
Conventional sequencing-based faecal microbiota analyses provide only relative abundances, hampering the link between microbiome features and quantitative host parameters when microbial load varies between samples. The authors argue that relative profiling can mask altered total microbiota abundance as a key disease-associated signal and call for quantitative microbiome profiling that pairs relative composition with cell-density counts to enable genuine characterisation of host-microbiota interactions.