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Wang et al. Microbiome Res Rep. 2025;4:23 https://dx.doi.org/10.20517/mrr.2024.94 Page 11 of 19

This highlights that in relative abundance analysis, samples with low microbial loads significantly influence
the ranking of the top 10 genera. This effect arises from the assumption that all samples contribute equally
to 100%. In contrast, in absolute quantification analysis, groups with high microbial loads exert considerable
influence on the ranking of the top 10 genera across two or more groups, potentially obscuring true gut
microbiota dynamics. Therefore, using absolute quantification methods to separately calculate the top 10 or
even top 20 genera by group enables a more precise analysis of gut microbiota.

Taxonomic differences of top 10 by relative abundance and absolute abundance
To assess the differential abundance of the top 10 genera based on relative and absolute quantification with
spike-in, we conducted a differential abundance analysis using both methodologies [Figure 5]. In the
relative abundance analysis, significant differences were observed between the mother and infant groups for
the genera Blautia, Faecalibacterium, Veillonella, and Ruminococcus. However, after applying the spike-in
absolute quantification method, significant differences were identified for the genera Blautia,
enhancing the standard relative composition analysis.
Faecalibacterium, Ruminococcus, Coprococcus, Subdoligranulum, and Anaerostipes.

α and β diversity
Figure 6 presents the α- and β-diversity among the mother and infant pairs. The α-diversity, as measured by
the Shannon index and Chao1 index, shows no difference between the relative analysis and the spike-in
absolute quantification method [Figure 6A]. However, the indices indicate that the richness and evenness of
the microbiome in the mother group are significantly higher than those in the infant group.

The Principal Coordinates Analysis (PCoA) plots illustrate the β-diversity found among the mother and
infant pairs. In the PCoA plot derived from the relative abundance analysis [Figure 6B], the analysis shows a
statistically significant separation between the mother and infant groups (P = 0.02, F = 6.6). Similarly, the
PCoA plot from the absolute abundance analysis using spike-in [Figure 6C] shows a statistically significant
difference between the groups (P = 0.02, F = 6.3). Despite the slight differences in the variance explained by
PC1 and PC2, both PCoA plots consistently demonstrate significant separation between the mother and
infant microbiomes.

DISCUSSION
The purpose of this study was to evaluate different methods for absolute quantitation of bacterial loads in
microbiomes with a view to proposing a method that would allow for analyzing large sample sets in our
[37]
ongoing studies . We propose using spike-in bacterial DNA to calibrate intestinal microbiome profiles to
actual microbial loads. Pseudoalteromonas sp. APC 3896 and Planococcus sp. APC 3900 were employed as
spike-in bacteria, demonstrating their suitability for comprehensive gut microbiome profiling. These two
bacteria are typically absent in mammalian intestinal microbiomes, making them effective reporters of true
microbial load. Incorporating spike-in bacteria offers a novel perspective to gut microbiome profiling,

Although this study utilized only two spike-in strains and relied on 16S rRNA gene sequencing, the
complete absence of Pseudoalteromonas and Planococcus genera in human gut microbiomes justifies the use
of genus-level abundance as a reliable proxy for species-level quantification. This rationale is supported by
both our current cohort data and ongoing analyses of large-scale metagenomic datasets. Moreover, when
applying this method in metagenomic sequencing, it becomes possible to perform a dual-level validation:
the abundance of Pseudoalteromonas and Planococcus can be cross-verified at both the genus and species
levels, further confirming the accuracy and consistency of the spike-in approach.