The gut microbiome
The human gut harbours trillions of microorganisms whose collective genome — the microbiome — encodes metabolic capabilities far exceeding those of the human genome alone.1 Over the past two decades, large-scale sequencing studies have demonstrated that the composition and function of this microbial community are associated with a wide range of health outcomes.2,3 Disruption of a healthy gut microbiome, often called dysbiosis, has been linked to gastrointestinal disorders, autoimmune conditions, cardiometabolic diseases, and neurological conditions.4
Validated sample preservation
protects the accuracy of every result
The moment a sample is collected, the clock starts. Microbial composition shifts fast if preservation isn’t handled correctly — and most collection methods weren’t designed with that in mind.
Microba’s FLOQSwab-ADT was benchmarked head-to-head against the most widely used alternatives.6 It came out on top. Practitioners can be confident the sample that leaves the patient’s home is the sample that gets analysed. No degradation. No compromise.
Best reproducibility
Highest technical (between-replicate) reproducibility and compositional stability relative to flash-frozen controls in a peer-reviewed evaluation
Climate resilient
Stable across −20°C, room temperature, and 50°C for four weeks — suitable for postal collection Australia- wide
100,000 metagenomes processed.
One accredited laboratory. Zero compromises
Most providers are accredited for what happens in the lab. Microba is also accredited for what happens to the data after it leaves the bench. Microba operates an ISO 15189 NATA-accredited laboratory with automated QC from sample receipt to data generation — anything outside predefined thresholds is flagged. 100,000+ metagenomes processed, very result a practitioner receives has been through the same rigorous process with no shortcuts at any stage.
ISO 15189
Internationally recognised standard for medical laboratory processes — covering sample receipt, sequencing, and data generation
ISO 13485
Quality management system for software as a medical device — covering the bioinformatic analysis and interpretation pipeline
Shotgun metagenomics identifies species that other methods miss entirely
Shotgun metagenomics sequences all DNA from a faecal sample — no single gene, no predefined panel. The result is a comprehensive, unbiased view of the entire microbial community at species level, not just at the genus level.7 That distinction matters clinically. Without species-level resolution, you can’t differentiate between species and that difference can be clinically significant.
The Streptococcus example
632 species vs 57
In a direct comparison, shotgun metagenomics identified 632 species in a sample where 16S rRNA gene sequencing detected only 57 — an order-of-magnitude difference in resolution that directly affects clinical utility.
Comparison of microbiome testing methodologies
Species tell you who’s present. Function tells you what they’re doing
Against nine classifiers. MCP outperformed every one.
Generating sequence data is only the first step. The bioinformatic classifier determines what gets identified, what gets missed, and what gets falsely reported. Microba's Community Profiler (MCP) was benchmarked against nine other classifiers across 140 simulated microbial communities in a peer-reviewed study.5 The numbers tell the rest of the story.
Three integrated layers translate microbial
presence into clinical meaning
No single method gives the full picture. Microba Microbiome Explorer combines three layers of information — integrating microbiome profiling with direct markers of gut function and inflammation to give a complete clinical picture
PATHOGEN DETECTION
A panel of 13 common bacterial pathogens and five parasites detected via CE-certified multiplex PCR assays.
HUMAN STOOL MARKERS
Six GI health markers including calprotectin, lactoferrin, faecal occult blood, secretory IgA, pancreatic elastase, and zonulin — assessed using CE-certified immunohistochemistry assays. Faecal pH is also measured as an investigative marker for research use only.
MICROBIOME PROFILING
Species-level profiling of 28,000+ species including microbial diversity, richness, and 16 health-associated functional markers — such as butyrate production, trimethylamine, hexa-acylated lipopolysaccharides, mucin degradation, and oxalate consumption. For research use only.
Every marker reported is clinically relevant and evidence-backed
Not every microbial signal is clinically meaningful. Microba applies a rigorous three-tier evidence framework — only markers that are both evidence-backed and clinically relevant are included in the report.* Practitioners can be confident that everything reported has a reason to be there. Here’s the standard every marker is held to:
Tier 1 Plausible mechanism of action
In-vitro or in-vivo data must demonstrate why the microbial marker is biologically connected to the relevant health category.
Tier 2 Reproducible human associations
At least two peer-reviewed human studies must show a direct or indirect link between the marker and the health outcome.
Tier 3 Significant associations in Microba’s dataset
The marker must show a statistically significant association in Microba’s own database of 19,000+ consented patient profiles, controlled for age, sex, BMI, and bowel habits.
Read the full methodology behind our test.
A rigorously defined reference group of 450+ individuals removes technical bias from every result
Results are only meaningful when compared against the right baseline. Many commercially available tests either provide no details about their reference cohort, use publicly available microbiome data. Microba’s cohort of more than 450 individuals meets strict health inclusion criteria — and all reference samples were collected and processed using exactly the same workflow as patient samples, eliminating a significant source of technical bias.
The Microba’s healthy reference group
Carefully selected to include more than 450 individuals meeting strict inclusion criteria. Critically, all reference samples were collected and processed using exactly the same workflow as patient test samples, eliminating a common source of technical bias.
INCLUSION CRITERIA
No major medical conditions
No or minimal GI symptoms· Mild or lower stress, anxiety, and depression
BMI below 30 Daily fruit and vegetable intake
Low to moderate alcohol consumption
The report tells you what’s there, evidence-graded actions tell you what to do about it.
Microbial markers and gastrointestinal markers are organised into six health categories that map to recognisable clinical concepts. Where a marker falls outside the healthy reference range, the report provides evidence-graded possible actions — reviewed against the available scientific evidence and graded using the NHMRC evidence grading framework. The result is a report that doesn’t just tell you what’s there. It gives you a clinically defensible starting point for what to do next.
Mucin degradation and intestinal
inflammation
When dietary fibre is insufficient, mucin-degrading microbes can consume the protective mucus layer lining the gut, increasing microbial contact with the intestinal epithelium and triggering immune activation. A cross-sectional study of more than 1,000 individuals found a significant positive association between mucin-degrading pathway abundance and faecal calprotectin.8 Elevated mucin degrading pathways have also been observed in colorectal cancer cohorts.9,10 In Microba’s dataset, mucin-degrading species are significantly increased in conditions related to intestinal inflammation
What sets Microba’s approach apart
not 16S or qPCR
benchmarked bioinformatics
evidence curation
reference group
.*The microbiome component of Microba Microbiome Explorer is for research use only and is not a diagnostic tool. Microbiome results should be interpreted by qualified healthcare practitioners in the context of a patient’s clinical history, symptoms, and other diagnostic findings.
- Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016). https://doi.org/10.1056/NEJMra1600266
- Gilbert, J. A., Blaser, M. J., Caporaso, J. G., Jansson, J. K., Lynch, S. V. & Knight, R. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018). https://doi.org/10.1038/nm.4517
- Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021). https://doi.org/10.1038/s41579-020-0433-9
- Zmora, N., Suez, J. & Elinav, E. You are what you eat: diet, health and the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 16, 35–56 (2019). https://doi.org/10.1038/s41575-018-0061-2
- Parks, D. H., Rigato, F., Vera-Wolf, P., Krause, L., Hugenholtz, P., Tyson, G. W. & Wood, D. L. A. Evaluation of the Microba Community Profiler for taxonomic profiling of metagenomic datasets from the human gut microbiome. Front. Microbiol. 12, 643682 (2021). https://doi.org/10.3389/fmicb.2021.643682
- Jovel, J., Patterson, J., Wang, W., Hotte, N., O’Keefe, S., Mitchel, T. et al. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front. Microbiol. 1591-019-0406-6 7, 459 (2016). https://doi.org/10.3389/fmicb.2016.00459
- Sczyrba, A., Hofmann, P., Belmann, P., Koslicki, D., Janssen, S., Dröge, J. et al. Critical assessment of metagenome interpretation — a benchmark of metagenomics software. Nat. Methods 14, 1063–1071 (2017). https://doi.org/10.1038/nmeth.4458
- Hugenholtz, P. & Tyson, G. W. Metagenomics. Nature 455, 481–483 (2008). https://doi.org/10.1038/455481a
- Pribyl, A. L. et al. Assessment of faecal microbiome preservation methods using an active-drying collection device. ISME Commun. 1, 14 (2021). https://doi.org/10.1038/s43705-021-00014-2
- Paone, P. & Cani, P. D. Mucus barrier, mucins and gut microbiota: the expected slimy partners? Gut 69, 2232–2243 (2020). https://doi.org/10.1136/gutjnl-2020-322260
- Cornick, S., Tawiah, A. & Bhatt Bhatt Chadee, K. Roles and regulation of the mucus barrier in the gut. Tissue Barriers 3, e982426 (2015). https://doi.org/10.4161/21688370.2014.982426
- Desai, M. S., Seekatz, A. M., Koropatkin, N. M., Kamada, N., Hickey, C. A., Wolter, M. et al. A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility. Cell 167, 1339–1353 (2016). https://doi.org/10.1016/j.cell.2016.10.043
- Earle, K. A., Billings, G., Sigal, M., Lichtman, J. S., Hansson, G. C., Elias, J. E. et al. Quantitative imaging of gut microbiota spatial organization. Cell Host Microbe 18, 478–488 (2015). https://doi.org/10.1016/j.chom.2015.09.002
- Zhernakova, A., Kurilshikov, A., Bonder, M. J., Tigchelaar, E. F., Schirmer, M., Vatanen, T. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016). https://doi.org/10.1126/science.aad3369
- Thomas, A. M., Manghi, P., Asnicar, F., Pasolli, E., Armanini, F., Zolfo, M. et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25, 667–678 (2019). https://doi.org/10.1038/s41591-019-0405-7
- Wirbel, J., Pyl, P. T., Karber, E., Zych, K., Kashani, A., Milanese, A. et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 25, 679–689 (2019). https://doi.org/10.1038/s4
