
==== Front
J Pathol Clin Res
J Pathol Clin Res
10.1002/(ISSN)2056-4538
CJP2
The Journal of Pathology: Clinical Research
2056-4538
John Wiley & Sons, Inc. Hoboken, USA

10.1002/2056-4538.70002
CJP270002
Review
Review
Challenges for pathologists in implementing clinical microbiome diagnostic testing
Pathologist and clinical microbiome testing
Y Gerasimova et al
Gerasimova Yulia 1
Ali Haroon 2
Nadeem Urooba https://orcid.org/0000-0002-1182-4262
3 urooba.nadeem@utsouthwestern.edu

1 Department of Infectious Diseases University of Texas Southwestern Medical Center Dallas TX USA
2 Department of Medicine Woodland Heights Medical Center Lufkin TX USA
3 Department of Pathology University of Texas Southwestern Medical Center Dallas TX USA
* Correspondence to: Urooba Nadeem, Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA. E‐mail: urooba.nadeem@utsouthwestern.edu

17 9 2024
9 2024
10 5 10.1002/cjp2.v10.5 e7000211 8 2024
21 12 2023
26 8 2024
© 2024 The Author(s). The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Abstract

Recent research has established that the microbiome plays potential roles in the pathogenesis of numerous chronic diseases, including carcinomas. This discovery has led to significant interest in clinical microbiome testing among physicians, translational investigators, and the lay public. As novel, inexpensive methodologies to interrogate the microbiota become available, research labs and commercial vendors have offered microbial assays. However, these tests still have not infiltrated the clinical laboratory space. Here, we provide an overview of the challenges of implementing microbiome testing in clinical pathology. We discuss challenges associated with preanalytical and analytic sample handling and collection that can influence results, choosing the appropriate testing methodology for the clinical context, establishing reference ranges, interpreting the data generated by testing and its value in making patient care decisions, regulation, and cost considerations of testing. Additionally, we suggest potential solutions for these problems to expedite the establishment of microbiome testing in the clinical laboratory.

microbiome
microbiome testing
pathologist
pathologist and microbiome
source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:17.09.2024
No conflicts of interest were declared.
==== Body
pmcIntroduction

Microbiome refers to the entire community of microorganisms that colonize individual sites in the human body [1, 2]. The notion that the microbiome influences human health is gaining credence in both scientific and lay communities. Studies demonstrate that microbes are neither harmful to us nor are innocent bystanders but participate in host homeostasis, metabolism, and immune regulation [3, 4, 5, 6, 7, 8].

Over recent years, sequencing technology and computational biology breakthroughs have allowed us to study complex microbial communities inexpensively. Proposed applications of clinical microbial testing are as diverse as infectious disease diagnostics, oncology screening, predicting outcomes after transplantation, and serving as a companion diagnostic for cancer immunotherapy [9, 10, 11, 12, 13, 14, 15].

Despite microbiome science's recent successes, clinical testing has lagged far behind the investigative progress [15, 16]. The pathology laboratory, for the most part, remains the home for testing that drives medical management. We are now rapidly approaching a crossroads – the growing need for microbiome testing and interpretation will require innovation in pathology laboratories, much like the genomic revolution called for two decades ago [17]. Here, we address the major challenges associated with moving microbial testing to the clinical laboratory to deliver on the microbiome and its potential. These challenges include but are not limited to the appropriate choice of tissue and other biospecimens, preanalytical and analytic variables that can influence testing, establishing reference ranges, mitigating sample contamination, interpreting and reporting results, bioinformatics challenges, and regulatory and cost considerations (Figure 1). We discuss how the pathologist's expertise and oversight will be pivotal in defining and maintaining reliable and accurate testing procedures and universal standards to ensure patient safety and effective healthcare outcomes.

Figure 1 Challenges of implementing microbiome testing in clinical pathology laboratories and proposed solutions. Created with BioRender.com.

Preanalytical variables and analytic considerations

For all patient biospecimens, significant preanalytical variation and contamination may result from events occurring even before the specimen arrives at the pathology laboratory [1, 18]. At all collection points, sample collectors, physicians, and technicians can introduce microbial DNA into a sample. To overcome this, mirroring the techniques used in ultra‐clean labs where personnel must wear protective clothing and equipment to cover all exposed human surfaces (i.e. full disposable body suits, shoe covers, face masks, sleeves, a plastic visor, and multiple pairs of gloves) can serve to minimize the introduction of human and bacterial DNA into the sample [19, 20, 21]. This consideration is especially important in tissues with low microbial biomass, as they are highly vulnerable to such contamination and the presence of human DNA can overwhelm the smaller concentrations of microbial DNA [19, 20, 21]. One study demonstrates that adding benzonase to the testing sample can selectively lyse human cells and provide a more accurate microbial sequencing depth [22]. Additionally, specimens need to be preserved under appropriate conditions [21]. While immediate freezing at −80 °C is the gold standard, this is often not reasonable for self‐collected samples or at smaller collection centers. In this case, storage buffers and preservatives can stabilize microbial structures at −20 °C and even at room temperature. Furthermore, the freeze–thaw cycle may alter the microbial composition during transport, and appropriate preservatives are perhaps a more reliable way of sample storage and transport for mass clinical testing [23, 24]. Sample processing should be performed in an isolated, low‐contaminant, and controlled environment (e.g. still‐air cabinet or laminar‐flow hood). Studies suggest that treating all tools with a ≥3% sodium hypochlorite solution and ultraviolet (UV) radiation can minimize interference from background equipment DNA [25]. Recently, the preanalytical CEN/TS standard for microbiome diagnostics (CEN/TS 17626:2021) put forth preanalytical standards for human microbiome testing – adopting and proposing similar guidelines will be indispensable for developing improved methodologies [26].

During the testing process, microbial contamination of biospecimens could occur from DNA present in the reagents [27] and equipment utilized during DNA extractions and library preparation. Consumables labeled “DNA‐free” usually contain a significant amount of microbial DNA [28]; therefore, treating hard surfaces, such as plastic tubes and pipettes, with ethylene oxide can decrease microbial signal from these external sources [28]. Reagents can be decontaminated by UV treatment but UV irradiation can destroy enzyme function, so each reagent has to be treated based on its unique composition and function [29]. One study demonstrates that extraction with Mo Bio PowerMag with ClearMag beads consistently provides accurate results for low biomass samples [30]. Furthermore, physically isolated workstations allocated only for microbiome testing should be used for aliquoting and handling reagents to limit contamination [20, 28].

The US Food and Drug Administration (FDA) recommends that multiple control types, external (positive and negative) and internal controls, be run in parallel with the testing samples to ascertain specimen and nucleic acid quality, and the accuracy of the test [20, 25]. Moreover, preemptive use of additional controls is suggested in certain cases if specific external interference is suspected or deviation from set guidelines occurs in the collection/testing process.

Another consideration is the laboratory and environmental microbiota, which represent a significant source of sample contamination [31]. The presence of unique microbial profiles across different buildings and outdoor environments has a significant effect on microbiota profiles. Additionally, even within a given laboratory, contaminant profiles can vary seasonally, yearly, or with personnel changes. Indoor environments, such as hospitals, have an individualized microbial profile similar to that of the individual(s) [32, 33]. Nonetheless, the mechanisms and distinct microbiota responsible for environmental contamination are poorly understood. However, these environmental microbes can be accounted for by collecting air samples from the collection room, any material used during the sampling procedure, gauze, drills, needles; and any preservatives/reagents used in the storage and transport of the solution. These collected control sampling blank controls should be run with the biologic specimens and can later be flagged/filtered during the downstream subtractive analysis.

Yet another contaminant source is cross‐contamination from other samples. Cross‐contamination can create a “batch effect.” This typically occurs during sample processing and includes the transfer of primary sample DNA, barcodes, or amplicons from neighboring wells or tubes. Paradoxically, using robotics for sample processing did not decrease the contamination risk; instead, automated robots contribute to well‐to‐well cross‐contamination across samples, leading to specific batch and robot‐related contamination [34]. Solutions to minimize cross‐contamination include physically separating pre‐polymerase chain reaction (PCR) work from post‐PCR work to avoid contamination from highly amplified products; and using appropriate tools for sample handling, such as filter tips with barriers and low‐aerosol pipettes to reduce sample carryover [35]. For more accurate results, distinct dual indexing not only reduces index swapping during sequencing but can also reduce costs [36]. Cleaning and disinfecting sequencers by performing maintenance washes between sequencing runs can decrease run‐to‐run cross‐contamination [37]. However, neither contaminant DNA nor cross‐contamination can be eliminated; instead, operating procedures need to account for these interferences to ensure robust results.

Moreover, the ideal specimen source for diagnosing diseases using microbial studies is neither intuitive nor well‐established [38, 39]. Whole blood, plasma, fecal samples, urine, saliva, and tissue can be used for microbial testing. Each specimen source has differing diagnostic benefits and challenges for clinical microbial testing. For instance, more than 50% of whole blood is composed of plasma and, therefore, contains genetic material from microbes present in all blood components, including those in plasma. This might suggest that whole blood is a better choice than plasma for microbial testing as it would have more comprehensive microbial material. However, whole blood also has a higher ratio of human DNA compared with plasma. In clinical microbial assays, human DNA competes with microbe DNA in a relationship such that abundant human reads can dilute pathogen reads to an undetectable level [40]. A suggested solution – depleting human genomic material – can potentially improve the detection of microbial DNA [40, 41]. Removing the cell component from blood will also result in the removal of intracellular and phagocytosed microbes. Another ubiquitous specimen source available for testing is the formalin‐fixed paraffin‐embedded (FFPE) tissue acquired during surgical procedures [42]. However, numerous different parameters hinder the modification of FFPE for microbial testing purposes. Currently, the method of tissue stabilization, the type of fixative, and the time in fixative that is optimal for microbiome test processing are unknown. Additionally, when tissue is frozen or embedded in paraffin, it can become contaminated with environmental microbes [43]. However, microbiome scientists have proposed standards and methodologies to combat this problem such as the development of Protoblocks [44] (Figure 1).

Methods of microbial testing

The methods to study microbiota are rapidly evolving [6, 45] (Figure 2). All the presently available methods have their strengths and shortcomings [6, 45]. A short description of each technique and its advantages and limitations are discussed in Table 1. Here, we briefly describe the current landscape of microbial testing focusing on their diagnostic relevance in the clinic.

Figure 2 Currently available methods for studying microbiome‐host interactions in the context of health and disease states. Created with BioRender.com.

Table 1 Summary of microbial testing methods mentioned in the article, including their advantages and limitations

Technique	Description	Advantages	Limitations	
Culture	Cultivation of microorganisms on various media under controlled conditions	Inexpensive

Quantification of the number of viable microorganisms in a sample

Provide reference genomes and functional data to aid the bioinformatics database for sequencing‐based testing

	Risk of contamination

Bias in isolation

Cannot identify rarer microbes

Labor intensive

Low throughput

	
Amplicon Sequencing (16S, 18S rRNA, Internal Transcribed Spacer)	Isolation, amplification, and sequencing of the conserved 16S rRNA gene presented in bacteria and archaea and 18S for eukaryotes, e.g. fungi	Inexpensive and quick sample preparation and analysis

Cultivation‐independent

Can be used in samples host‐contaminated and low‐biomass samples

	PCR amplification and primer bias

Low taxonomic resolution (cannot distinguish between closely related species/strains)

Inability to identify nonbacterial microorganisms

No direct information about the functional capabilities of the microbial community

Cannot distinguish between alive and dead organisms

	
Metagenomics	Extraction, sequencing, and analysis of the collective DNA in a sample	High sensitivity

Comprehensive insight into all DNA present in the sample

Identification of novel genes

	Time‐consuming

Lack of standardized protocols

High cost

Contamination by host DNA can obstruct microbial detection

Poorly available databases with limited information about viruses and plasmids

Complex data interpretation

Gene function identification difficulties due to incomplete databases

Cannot distinguish between alive and dead organisms

	
Metatranscriptomics	Extraction, sequencing, and analysis of the collective RNA in a sample	High throughput

High sensitivity

Comprehensive insight into all RNA present in the sample

Can discriminate between the number of dead versus alive organisms

True functional analysis of microbial communities

Discovery of novel genes and pathways

	Lack of standardized protocols

Low stability and degradation of microbial RNA

Multiple purification and amplification steps needed

High cost

	
Metabolomics	Identification, quantification, and analysis of microbial metabolites	Functional analysis of microbial communities

Identification of biomarkers associated with specific physiological conditions, diseases, or responses to treatments

	Lack of standardized protocols

Complex data interpretation

Metabolite identification difficulties due to incomplete databases

Contamination by host metabolites

Low stability of metabolites

	
Metaproteomics	Identification, quantification, and analysis of microbial proteins	Functional analysis of microbial communities

Identification of specific microbial species and strains that are actively contributing to the community's functions

	Lack of standardized protocols

Complex data interpretation

Protein identification difficulties due to incomplete databases

	
Fluorescence in situ hybridization (FISH)	Detection and location of specific DNA sequences with fluorescently labeled oligonucleotide probes	High sensitivity

Visualization and the spatial distribution of microbes

Quantification of microbes

	High cost

Labor intensive

Limited throughput

Limited resolution

Incapable of characterizing unknown species

	

Traditionally, culture‐dependent approaches were the cornerstone of diagnostic techniques in the microbiology laboratory (Table 1) [46]. Despite the evolution of culture‐based techniques to comprehensively detect a larger number of organisms from the human microbiome, including anaerobes and nonbacterial members, they remain too onerous and labor‐intensive to investigate hundreds of microbes simultaneously. Although it is the culture‐independent molecular approaches that have revolutionized the testing methodology for microbiota, even today a full understanding of the physiology and role of these microbes in host health and their characteristics must be achieved through laboratory cultivation. The critical role of culture in establishing clinical testing is to provide reference genomes and functional data to aid the bioinformatics database curation for sequencing‐based testing.

At the most basic level, identification and quantification of the microorganisms present in a sample can be performed by amplicon sequencing, using either 16S rRNA (bacteria) or 18S rRNA (eukaryotes) [47, 48]. Most microbiome studies employ amplicon sequencing to compare individuals with and without a specific disease. Amplicon‐based investigations offer notable advantages over culture methodologies, for example, obviating the need for preculturing, and can identify nonviable organisms from FFPE tissue or fastidious organisms from direct specimens. Additionally, they can aid in the direct identification of organisms from patient specimens within highly intricate polymicrobial bacterial communities [19]. Although bioinformatic tools can infer or predict the functionality of distinct microbial communities, this analysis alone cannot provide direct insights into the functional aspects of the microbiota. For instance, when conducting a 16S rRNA analysis, observing Escherichia coli can represent different possibilities ranging from a probiotic strain, a benign indigenous E. coli, or even the pathogenic E. coli O157:H7 [6].

In contrast to amplicon‐based sequencing, which can only identify a limited number of microbes using specific primers or probes, metagenomics collectively sequences the entire DNA present in a sample, including the viral and eukaryotic DNA [1]. This technique is also fundamentally different from whole genome sequencing which explores the entire genetic information from a single organism. In metagenomics, DNA is extracted from all cells (microbial or human) in a community, and instead of targeting a specific genomic locus for amplification, all DNA is sheared into fragments and independently sequenced by a shotgun sequencing approach. The resulting reads are aligned to various genomic locations for all genomes present in the sample, including nonmicrobes. Using powerful computational tools, this technique then assembles the strain and functional information from the DNA sequence fragments into complete genomes and separates the host DNA from microbial genetic material [1]. Thus, the advantage of metagenomics is that it can infer a comprehensive list of microbial strains present in a sample, including the fungi and viruses overlooked by 16S rRNA amplicon analysis; but it also provides quantitative information about how abundant each of those strains are. However, it is important to realize that sequencing depth is a critical determinant of the inferred microbial communities. Today, studies demonstrating the practical diagnostic applications of microbiome metagenomic testing in diverse applications, including antimicrobial resistance and oncology, are emerging [1]. Its use as an actionable clinical diagnostic test is hampered by expensive and difficult sample preparation, the need for deep sequencing depths (>20 million reads per sample) compared to other methods, and the lack of standardization of the analysis pipeline. Furthermore, contamination from human DNA may mask microbial DNA detection from rare organisms. Although computational biology tools can help infer the potential functionality of the microbiota from metagenomics data, this inference still represents only a list of potential functions and not a direct determination of the actual role of a microbial community in a specific host [1, 6, 16, 49, 50].

The next group of investigative tests can directly measure the impact of microbial community output. Most of these are in their nascent stage and can serve as promising diagnostic biomarkers. These include total RNA (metatranscriptomics), metabolic products (metabolomics), and proteins (proteomics). Metatranscriptomics, in stark comparison to amplicon and metagenomic sequencing, can assist in discriminating alive/transcribing organisms versus dead/dormant microbes [42], though it is biased toward organisms with higher rates of transcription. Coupled metagenome and metatranscriptome analysis demonstrates that metatranscriptomics can identify important species in both normal controls and disease subjects that are not identified by the earlier methods. However, not only are metatranscriptomic assays cost‐prohibitive but the sample preparation is difficult and requires multiple steps to remove host RNA [51, 52, 53]. Meanwhile, proteomics and metabolomics employ advanced mass spectroscopy to measure the relative abundance of proteins and metabolites (such as peptides, oligosaccharides, and lipids) that arise from the metabolism of the microbiota and can alter the host's physiology [53, 54, 55]. These are powerful techniques for understanding the impact of microbiota on host physiology. Nonetheless, these measures are underdeveloped not only in terms of methodology but also in terms of the need for extensive scientific work to define differentiation between host‐derived and microbially derived molecules before application to clinical diagnostics seems feasible. Finally, although investigations have focused on sequencing‐based methods to understand the microbiomes, direct observation of the microbiome will be another essential tool in clinical space. For instance, fluorescence in situ hybridization (FISH) can be used to evaluate the microbe's precise cellular location and taxonomy in FFPE tissue [45, 46, 56]. FISH testing is rapid, provides limited but specific information, and is cost‐effective because sample preparation is easy compared to molecular methods [45, 46].

Although various existing techniques in different sections of the pathology laboratory – microbiology, chemistry, therapeutic drug monitoring, histopathology, and immunohistochemistry – can be customized to investigate diverse aspects of the microbiome, there are limiting factors that prohibit the implementation of widespread clinical testing [45, 57]. This includes the inability to perform different types of microbiome testing on a single sample collection because all the above‐mentioned techniques require different biospecimens, conditions of collection and preservation, etc. for optimum results. Since the testing methodology is not yet adapted for clinical use, there is an absence of standardized clinical protocols to handle and prepare biospecimens for different microbiome tests. Advancement in testing techniques will inevitably result in the emergence of specialty laboratories for specialized clinical microbial testing.

Establishing reference ranges

The first step in transitioning microbiome testing from research to the clinical laboratory involves the establishment of a reference range [58, 59, 60]. As the single most used criterion for the interpretation of numerical pathology reports [58, 59], the quality of the reference intervals plays a substantial role in any test result interpretation [61]. In contrast to research laboratories, clinical laboratories focus on assessing individual cases (“n of 1”) compared with a reference population [62], whereas in the research domain, results are compared from healthy controls and disease subjects by group averages using statistical tests [63]. In the absence of universally accepted reference ranges, testing performed between different laboratories cannot be compared, and neither can the accuracy of testing be commented upon. Consequently, the need for standardized reference organisms to facilitate such comparisons and define optimal analysis methods is critical before instituting clinical testing.

Numerous unique problems arise in establishing a reference range for clinical microbiome testing. First, there is no distinct microbiome profile that is predictive of a particular disease. The “one microbe – one disease” paradigm is overly simplistic; microbiota coexist as a community and demonstrate a high degree of mutual interdependence to ensure optimal functioning [64, 65]. Whether certain microbe species or genera are advantageous or harmful depends on the presence of other microbe communities, their density, and how these organisms interact with each other [66, 67, 68]. For example, Clostridium difficile is a harmful pathogen, yet several healthy hosts can be colonized by the microbe, without any disease manifestation, as its pathogenic potential depends on the state of the other intestinal microbes that compete with it for nutrients [69, 70]. Second, and perhaps more challenging, is the interindividual variability in the microbial composition among healthy individuals. These differences are partly attributed to external exposures, including diseases, medications, and dietary patterns [71, 72, 73, 74, 75], but are also a result of the nonstandardized methods that were used to perform these investigations. Hence, it is necessary to consider differences among patient cohorts when analyzing the microbiota in individual patients.

Nonetheless, the conventional methods of establishing reference ranges by analyzing hundreds of individual microbial profiles to determine mean and standard deviation values for each detected organism might not prove useful here. A potential solution is using mock communities or custom mixtures consisting of a pool of diverse microbial strains microorganisms or their nucleic acids. These mixtures can be developed and utilized as external controls to establish and define diagnostic criteria for microbiome testing of various specific diseases. The presence of such controls will make it possible to compare assays between different laboratories. These communities will be critical for developing standardized reference organisms to define optimum detection values of microbes for different diseases.

While microbial analysis will, at least for some time, generate unexpected and novel findings, the field of pathology is familiar with handling ancillary data within clinical contexts. These findings are of the same nature, though of possibly larger dimensionality, as whole genome analysis data [76]. Additionally, this testing will also identify novel microorganisms that have not previously been identified. This data will need to be independently confirmed through additional testing and will require an assessment of the clinical significance, considering their potential pathogenicity and treatment options [77, 78, 79].

Interpreting test results and bioinformatics

Today, there is no consensus on how to communicate the complex and multidimensional data generated from 16S rRNA sequencing or metagenomic studies for patient care purposes. 16S rRNA sequencing results are generally reported as relative abundance. While this does not impact analysis in the research realm, relative values are counterintuitive for clinical use, since the relative values fail to reflect true changes among microbiota when absolute quantities change but proportions remain unchanged [80]. Additionally, quantitation may be important if a certain microbe describes a diagnostic phenotype, such as the presence of Fusobacterium in colorectal cancer [81].

Furthermore, a difference in community structure does not provide information about the microbiomes' functional output. Different communities of microbes can execute the same functions [82]. Microbial communities should be viewed as functional networks, and their functionality is not a mere summation of individual microorganisms. Unlike host‐genome sequencing studies, there are no easy answers; microbes vary their behavior based on the context provided by the activity of other microbes and the host, as illustrated by the C. difficile example discussed earlier. Moreover, microbiome analysis of one biospecimen may not accurately reflect the microbiome composition at the tissue level. Thus, the importance of these tests in the context of making patient care decisions is not yet known.

Accurate classification of microbial sequencing relies on a robust database that has accurate and diverse microbe representation [83, 84]. The existing microbial sequence databases have been built through an ad hoc process designed to support research activities. Publicly deposited sequences such as the National Center of Biotechnology Information and nucleotide database are comprehensive but have misannotations and errors from voluntary submitters. Other regulatory‐grade reference datasets such as FDA‐ARGOS [85] or those meant for specific human pathogens (fluDB) [86] are more valuable as they are less error‐prone, but the data were compiled for specific applications. Therefore, they fall short of what is required for the delivery of accurate, safe, and effective patient care. A customized bioinformatics pipeline for analysis of clinical microbiome data [87, 88, 89, 90] requires a clinical‐grade microbiome database that incorporates data from multiple sources to increase the specificity and sensitivity of identified microorganisms. This dataset will need comparison with controls and patients to determine the value and accuracy of the final result. It will also need to be maintained and validated by a consortium of physicians, highly trained programming staff, and ongoing national regulatory oversight.

Moreover, unlike human genome sequencing, which aligns sequences to established references, for many microbial organisms, reliable references are lacking, so hypothesis‐driven predictions will need to be performed. Furthermore, to ensure reliable analyses, maintaining a comprehensive database of the environmental normal flora of the laboratory and typical contaminants is essential [27, 91]. Like the wet lab workflow, analysis software and reference databases should ideally be predetermined before validation for clinical use.

These discoveries can then be integrated with other critical pieces of medical data to determine decisions within a clinical timeframe. As data collections are built, the efficiency of analyses will increase. Such datasets should also include standard histopathology and prediction data from digital slide scanning and image analysis technologies – a field that is developing rapidly and holds tremendous potential for rapid and clinically accurate disease characterization.

However, microbiome datasets will have the same limitations as any other sample‐dependent dataset. A single fecal or blood sample is only a snapshot of the person's microbiome profile at the time and location that the sample was collected. The way the sample was collected, stored, and analyzed may have a significant impact on the analysis. Therefore, developing predictive patterns will likely require analyses of millions of samples linked to highly granular clinical metadata.

The pathology laboratory can either host computational servers locally or move the bioinformatics analysis and data storage to cloud platforms. In either case, hardware and software setups can be complex, and adequate measures must be in place to protect confidential patient sequence data and information, especially in the cloud environment. Storage requirements for sequencing data can quickly become quite large, and the clinical laboratory must decide on the quantity, location, and duration of data storage. Performance validation and verification for bioinformatics analysis constitute a time‐consuming endeavor and include analysis of control and patient datasets and comparisons with clinical testing to determine the accuracy of the result [92]. In our opinion, similar to the analysis for molecular studies, the bioinformatics for microbiome testing should be performed by pathologists. By enlisting new professionals trained in pathology, microbiome medicine, and bioinformatics, a collaborative effort needs to be started to establish a microbiome database to aggregate information about the microbes found in clinical samples and their clinical significance.

Affiliated laboratories developing microbiome approaches for clinical diagnostics could enroll in ongoing consortium‐led studies to collaboratively contribute sequencing data and de‐identified patient metadata into a central repository available to members. This approach could generate adequately powered studies spanning multiple institutions to address questions less amenable to publicly funded research, as well as provide avenues for replication and confirmation of impactful findings. Furthermore, the consortium could partner with agencies developing calibration standards to ensure the differing methods reach the same key results and use these calibration standards to assess the analytic performance of differing approaches. These calibration standards would need to assess the performance of all steps in a clinical metagenomic workflow, including contrived specimens to assess the laboratory steps and contrived sequencing data files to assess the bioinformatic processes. The goal of this consortium would be to develop clinical testing that can provide quick yes/no type answers for clinical decision‐making.

While every novel test represents a healthcare innovation, it is the interpretation of the test results that adds value to the test. The goal of every interpretation is to improve or influence clinical decision‐making, and clinical microbial testing is no different. Like other standard molecular diagnostics, these tests have little utility without proper clinical context [93]. Several commercial companies are tapping into the intense consumer interest in microbiome‐based diagnostics and treatments by directly marketing tests to patients or outsourcing them directly to providers. Physicians, particularly gastroenterologists and pulmonologists, are increasingly being asked by their patients to help interpret such test reports. Traditionally, ensuring the correct interpretation of diagnostic test results falls in the realm of pathology. Pathologists have been performing microbial analysis on tissue and other biospecimens for decades, such as in situ hybridization studies for viruses or PCR to determine the presence of microbes in different biospecimens; therefore, integration of the impact of the microbiome in an appropriate clinical context is not new.

The analysis of microbial functional data adds a volume of scale and complexity that demands a new kind of sophistication and knowledge. Scaling up from standard molecular diagnostics to microbiome testing in the pathology laboratory is contingent on having an interpretation system in place that enables rapid assessment within a clinical time window. This last step is the most crucial and involves novel close coupling between clinicians and biomedical informatics specialists [94, 95].

Regulations

Clinical microbiome testing will eventually require regulatory oversight like any other clinical testing. Therefore, the College of American Pathologists and other international/national pathology organizations should take the initiative to develop global standards and regulations that will govern microbiome testing. Clinically actionable findings that qualify for medical insurance coverage or state‐funded health services should be explicitly defined. These criteria will ensure reproducibility and measurable quality metrics for the testing process itself. Furthermore, they will facilitate the dialogue between medical insurance, government agencies, and medical practices to enable faster resolution of reimbursement‐type questions.

Conclusions

The prospect that microbiome‐based tests will be incorporated into diagnostic algorithms and influence therapeutic decisions is exciting. Currently, we are at the nascent stages of comprehending the role of a distinct microbiome profile in predisposition to certain diseases and responses to different treatment regimens. Additionally, testing is complex and dependent on several external factors, and the data produced is difficult to translate into utility for patients. To succeed at translating the information gained from microbial testing into actionable data, the pathology department must respond swiftly to adapt the novel microbiome testing produced in the research world for clinical purposes. A discussion spearheaded by the pathology community and involving multiple stakeholders (treating physicians, scientists, commercial test developers, hospital systems, insurance payers, and governmental agencies) is urgently needed to conclude the clinical feasibility and application of the results of microbiome‐based tests for appropriate decision‐making in real‐time.

Ultimately, it is the patients who are impacted by this, and clinical microbiome testing must demonstrate clear clinical benefit field to advance, and the primary method of determining value is with studies investigating outcomes that follow the clinical use of these approaches.

Author Contributions

UN and HA conceptualised the review. YG, HA and UN wrote the original manuscript. UN and YG critically reviewed and edited all versions of the manuscript.
==== Refs
References

1 Chiu CY , Miller SA . Clinical metagenomics. Nat Rev Genet 2018; 20 : 341–355.
2 Gilbert JA , Blaser MJ , Caporaso JG , et al. Current understanding of the human microbiome. Nat Med 2018; 24 : 392–400.29634682
3 Integrative HMP (iHMP) Research Network Consortium . The integrative human microbiome project: dynamic analysis of microbiome‐host omics profiles during periods of human health and disease. Cell Host Microbe 2014; 16 : 276–289.25211071
4 Manor O , Levy R , Borenstein E . Mapping the inner workings of the microbiome: genomic‐ and metagenomic‐based study of metabolism and metabolic interactions in the human microbiome. Cell Metab 2014; 20 : 742–752.25176148
5 Tremaroli V , Backhed F . Functional interactions between the gut microbiota and host metabolism. Nature 2012; 489 : 242–249.22972297
6 Young VB . The role of the microbiome in human health and disease: an introduction for clinicians. BMJ 2017; 356 : j831.28298355
7 Gholizadeh P , Mahallei M , Pormohammad A , et al. Microbial balance in the intestinal microbiota and its association with diabetes, obesity and allergic disease. Microb Pathog 2019; 127 : 48–55.30503960
8 Bull MJ , Plummer NT . Part 1: the human gut microbiome in health and disease. Integr Med (Encinitas) 2014; 13 : 17–22.26770121
9 Kostic AD , Chun E , Robertson L , et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor‐immune microenvironment. Cell Host Microbe 2013; 14 : 207–215.23954159
10 Parhi L , Alon‐Maimon T , Sol A , et al. Breast cancer colonization by Fusobacterium nucleatum accelerates tumor growth and metastatic progression. Nat Commun 2020; 11 : 3259.32591509
11 Riquelme E , Zhang Y , Zhang L , et al. Tumor microbiome diversity and composition influence pancreatic cancer outcomes. Cell 2019; 178 : 795–806.e12.31398337
12 Viaud S , Saccheri F , Mignot G , et al. The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 2013; 342 : 971–976.24264990
13 Iida N , Dzutsev A , Stewart CA , et al. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 2013; 342 : 967–970.24264989
14 Vetizou M , Pitt JM , Daillere R , et al. Anticancer immunotherapy by CTLA‐4 blockade relies on the gut microbiota. Science 2015; 350 : 1079–1084.26541610
15 Staley C , Kaiser T , Khoruts A . Clinician guide to microbiome testing. Dig Dis Sci 2018; 63 : 3167–3177.30267172
16 Damhorst GL , Adelman MW , Woodworth MH , et al. Current capabilities of gut microbiome‐based diagnostics and the promise of clinical application. J Infect Dis 2021; 223 : S270–S275.33330938
17 Wall DP , Tonellato PJ . The future of genomics in pathology. F1000 Med Rep 2012; 4 : 14.22802873
18 Compton CC , Robb JA , Anderson MW , et al. Preanalytics and precision pathology: pathology practices to ensure molecular integrity of cancer patient biospecimens for precision medicine. Arch Pathol Lab Med 2019; 143 : 1346–1363.31329478
19 Selway CA , Eisenhofer R , Weyrich LS . Microbiome applications for pathology: challenges of low microbial biomass samples during diagnostic testing. J Pathol Clin Res 2020; 6 : 97–106.31944633
20 Eisenhofer R , Minich JJ , Marotz C , et al. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol 2019; 27 : 105–117.30497919
21 Robert S . Microbiome diagnostics. Clin Chem 2020; 66 : 68–76.31843867
22 Nelson MT , Pope CE , Marsh RL , et al. Human and extracellular DNA depletion for metagenomic analysis of complex clinical infection samples yields optimized viable microbiome profiles. Cell Rep 2019; 26 : 2227–2240.30784601
23 Bundgaard‐Nielsen C , Hagstrom S , Sorensen S . Interpersonal variations in gut microbiota profiles supersedes the effects of differing fecal storage conditions. Sci Rep 2018; 8 : 17367.30478355
24 Poulsen CS , Kaas RS , Aarestrup FM , et al. Standard sample storage conditions have an impact on inferred microbiome composition and antimicrobial resistance patterns. Microbiol Spectr 2021; 9 : e0138721.34612701
25 Champlot S , Berthelot C , Pruvost M , et al. An efficient multistrategy DNA decontamination procedure of PCR reagents for hypersensitive PCR applications. PLoS One 2010; 5 : e13042.20927390
26 Stumptner C , Stadlbauer V , O'Neil D , et al. The pre‐analytical CEN/TS standard for microbiome diagnostics‐how can research and development benefit? Nutrients 2022; 14 : 1976.35565946
27 Salter SJ , Cox MJ , Turek EM , et al. Reagent and laboratory contamination can critically impact sequence‐based microbiome analyses. BMC Biol 2014; 12 : 87.25387460
28 Shen H , Rogelj S , Kieft TL . Sensitive, real‐time PCR detects low‐levels of contamination by Legionella pneumophila in commercial reagents. Mol Cell Probes 2006; 20 : 147–153.16632318
29 Llamas B , Valverde G , Fehren‐Schmitz L , et al. From the field to the laboratory: controlling DNA contamination in human ancient DNA research in the high‐throughput sequencing era. STAR 2016; 3 : 1–14.
30 Minich JJ , Zhu Q , Janssen S , et al. KatharoSeq enables high‐throughput microbiome analysis from low‐biomass samples. mSystems 2018; 3 : e00218‐17.29577086
31 Ng LS , Teh WT , Ng SK , et al. Bacterial contamination of hands and the environment in a microbiology laboratory. J Hosp Infect 2011; 78 : 231–233.21481970
32 Kelley ST , Gilbert JA . Studying the microbiology of the indoor environment. Genome Biol 2013; 14 : 202.23514020
33 Cabo Verde S , Almeida SM , Matos J , et al. Microbiological assessment of indoor air quality at different hospital sites. Res Microbiol 2015; 166 : 557–563.25869221
34 Minich JJ , Sanders JG , Amir A , et al. Quantifying and understanding well‐to‐well contamination in microbiome research. mSystems 2019; 4 : e00186‐19.
35 Le Rouzic E . Contamination‐pipetting: relative efficiency of filter tips compared to microman positive displacement pipette. Nat Methods 2006; 3 : III–IV.
36 Costello M , Fleharty M , Abreu J , et al. Characterization and remediation of sample index swaps by non‐redundant dual indexing on massively parallel sequencing platforms. BMC Genomics 2018; 19 : 332.29739332
37 Nilsson M , De Maeyer H , Allen M . Evaluation of different cleaning strategies for removal of contaminating DNA molecules. Genes (Basel) 2022; 13 : 162.35052502
38 Gaston DC . Clinical metagenomics for infectious diseases: progress toward operational value. J Clin Microbiol 2023; 61 : e0126722.36728425
39 Flurin L , Wolf MJ , Fisher CR , et al. Pathogen detection in infective endocarditis using targeted metagenomics on whole blood and plasma: a prospective pilot study. J Clin Microbiol 2022; 60 : e0062122.36040200
40 Shi Y , Wang G , Lau HC , et al. Metagenomic sequencing for microbial DNA in human samples: emerging technological advances. Int J Mol Sci 2022; 23 : 2181.35216302
41 Cheng WY , Liu WX , Ding Y , et al. High sensitivity of shotgun metagenomic sequencing in colon tissue biopsy by host DNA depletion. Genomics Proteomics Bioinformatics 2023; 21 : 1195–1205.
42 El Tekle G , Garrett WS . Bacteria in cancer initiation, promotion and progression. Nat Rev Cancer 2023; 23 : 600–618.37400581
43 Cruz‐Flores R , López‐Carvallo JA , Cáceres‐Martínez J , et al. Microbiome analysis from formalin‐fixed paraffin‐embedded tissues: current challenges and future perspectives. J Microbiol Methods 2022; 196 : 106476.35490989
44 Bueso FY , Walker SP , Hogan G , et al. Protoblock – a biological standard for formalin fixed samples. Microbiome 2020; 8 : 122.32828122
45 National Academies of Sciences, Engineering, and Medicine . Environmental Chemicals, the Human Microbiome, and Health Risk: A Research Strategy. National Academies Press: Washington, DC, 2017. 10.17226/24960.
46 Hamady M , Knight R . Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res 2009; 19 : 1141–1152.19383763
47 Davidson RM , Epperson LE . Microbiome sequencing methods for studying human diseases. Methods Mol Biol 2018; 1706 : 77–90.29423794
48 Popovic A , Parkinson J . Characterization of eukaryotic microbiome using 18S amplicon sequencing. Methods Mol Biol 2018; 1849 : 29–48.30298246
49 Knight R , Vrbanac A , Taylor BC , et al. Best practices for analysing microbiomes. Nat Rev Microbiol 2018; 16 : 410–422.29795328
50 Shakya M , Lo CC , Chain PSG . Advances and challenges in metatranscriptomic analysis. Front Genet 2019; 10 : 904.31608125
51 Bashiardes S , Zilberman‐Schapira G , Elinav E . Use of metatranscriptomics in microbiome research. Bioinform Biol Insights 2016; 10 : 19–25.27127406
52 Moran MA . Metatranscriptomics: eavesdropping on complex microbial communities. Microbiome 2009; 4 : 329–334.
53 Aguiar‐Pulido V , Huang W , Suarez‐Ulloa V , et al. Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis. Evol Bioinform Online 2016; 12 : 5–16.27199545
54 Smirnov KS , Maier TV , Walker A , et al. Challenges of metabolomics in human gut microbiota research. Int J Med Microbiol 2016; 306 : 266–279.27012595
55 Kimball AB , Grant RA , Wang F , et al. Beyond the blot: cutting edge tools for genomics, proteomics and metabolomics analyses and previous successes. Br J Dermatol 2012; 166 : 1–8.
56 Frickmann H , Zautner AE , Moter A , et al. Fluorescence in situ hybridization (FISH) in the microbiological diagnostic routine laboratory: a review. Crit Rev Microbiol 2017; 43 : 263–293.28129707
57 Karen Titus , Microbiome swims into our ken, August 2019. Available from: https://www.captodayonline.com/microbiome-swims-into-our-ken/
58 Horowitz GL . Reference intervals: practical aspects. EJIFCC 2008; 19 : 95–105.27683304
59 Jones G , Barker A . Reference intervals. Clin Biochem Rev 2008; 29 Suppl 1 : S93–S97.18852866
60 CLSI . Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline (3rd edn). Clinical and Laboratory Standards Institute: Wayne, PA, 2008, Report No.: CLSI document C28‐A3.
61 Allaband C , McDonald D , Vázquez‐Baeza Y , et al. Microbiome 101: studying, analyzing, and interpreting gut microbiome data for clinicians. Clin Gastroenterol Hepatol 2019; 17 : 218–230.30240894
62 Katayev A , Balciza C , Seccombe DW . Establishing reference intervals for clinical laboratory test results: is there a better way? Am J Clin Pathol 2010; 133 : 180–186.20093226
63 Thomas SV , Suresh K , Suresh G . Design and data analysis case‐controlled study in clinical research. Ann Indian Acad Neurol 2013; 16 : 483–487.24339564
64 Rakoff‐Nahoum S , Foster KR , Comstock LE . The evolution of cooperation within the gut microbiota. Nature 2016; 533 : 255–259.27111508
65 Faust K , SathirapongsasutiJF IJ , et al. Microbial co‐occurrence relationships in the human microbiome. PLoS Comput Biol 2012; 8 : e1002606.22807668
66 Kinross JM , Darzi AW , Nicholson JK . Gut microbiome‐host interactions in health and disease. Genome Med 2011; 3 : 14.21392406
67 Contijoch EJ , Britton GJ , Yang C , et al. Gut microbiota density influences host physiology and is shaped by host and microbial factors. Elife 2019; 8 : e40553.30666957
68 Khosravi A , Mazmanian SK . Disruption of the gut microbiome as a risk factor for microbial infections. Curr Opin Microbiol 2013; 16 : 221–227.23597788
69 Bäumler AJ , Sperandio V . Interactions between the microbiota and pathogenic bacteria in the gut. Nature 2016; 535 : 85–93.27383983
70 Sassone‐Corsi M , Raffatellu M . No vacancy: how beneficial microbes cooperate with immunity to provide colonization resistance to pathogens. J Immunol 2015; 194 : 4081–4087.25888704
71 Human Microbiome Project Consortium . Structure, function and diversity of the healthy human microbiome. Nature 2012; 486 : 207–214.22699609
72 Matson V , Gajewski TF . Dietary modulation of the gut microbiome as an immunoregulatory intervention. Cancer Cell 2022; 40 : 246–248.35290783
73 Qin J , Li R , Raes J , et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010; 464 : 59–65.20203603
74 Shreiner AB , Kao JY , Young VB . The gut microbiome in health and in disease. Curr Opin Gastroenterol 2015; 31 : 69–75.25394236
75 Spencer CN , McQuade JL , Gopalakrishnan V , et al. Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response. Science 2021; 374 : 1632–1640.34941392
76 Jiang X , Hu X . Data analysis for gut microbiota and health. Adv Exp Med Biol 2017; 1028 : 79–87.29058217
77 Lagier JC , Khelaifia S , Alou MT , et al. Culture of previously uncultured members of the human gut microbiota by culturomics. Nat Microbiol 2016; 1 : 16203.27819657
78 Vanni C , Schechter MS , Acinas SG , et al. Unifying the known and unknown microbial coding sequence space. Elife 2022; 11 : e67667.35356891
79 Nayfach S , Shi ZJ , Seshadri R , et al. New insights from uncultivated genomes of the global human gut microbiome. Nature 2019; 568 : 505–510.30867587
80 Gloor GB , Macklaim JM , Pawlowsky‐Glahn V , et al. Microbiome datasets are compositional: and this is not optional. Front Microbiol 2017; 8 : 1–6.28197127
81 Lloyd‐Price J , Arze C , Ananthakrishnan AN , et al. Multi‐omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 2019; 569 : 655–662.31142855
82 Bosi E , Bacci G , Mengoni A , et al. Perspectives and challenges in microbial communities metabolic modeling. Front Genet 2017; 8 : 88.28680442
83 Forster SC , Kumar N , Anonye BO , et al. A human gut bacterial genome and culture collection for improved metagenomic analyses. Nat Biotechnol 2019; 37 : 186–192.30718869
84 Almeida A , Nayfach S , Boland M , et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol 2021; 39 : 105–114.32690973
85 Sichtig H , Minogue T , Yan Y , et al. FDA‐ARGOS: a public quality‐controlled genome database resource for infectious disease sequencing diagnostics and regulatory science research. Nat Commun 2019; 10 : 3313.31346170
86 Zhang Y , Aevermann BD , Anderson TK , et al. Influenza research database: an integrated bioinformatics resource for influenza virus research. Nucleic Acids Res 2017; 45 : D466–D474.27679478
87 Naccache SN , Federman S , Veeraraghavan N , et al. A cloud‐compatible bioinformatics pipeline for ultrarapid pathogen identification from next‐generation sequencing of clinical samples. Genome Res 2014; 24 : 1180–1192.24899342
88 Flygare S , Simmon K , Miller C , et al. Taxonomer: an interactive metagenomics analysis portal for universal pathogen detection and host mRNA expression profiling. Genome Biol 2016; 17 : 111.27224977
89 Kim D , Song L , Breitwieser FP , et al. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res 2016; 26 : 1721–1729.27852649
90 Wood DE , Salzberg SL . Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014; 15 : R46.24580807
91 Li R , Tun HM , Jahan M , et al. Comparison of DNA‐, PMA‐, and RNA‐based 16S rRNA Illumina sequencing for detection of live bacteria in water. Sci Rep 2017; 7 : 5752.28720878
92 Miller S , Naccache SN , Samayoa E , et al. Laboratory validation of a clinical metagenomic sequencing assay for pathogen detection in cerebrospinal fluid. Genome Res 2019; 29 : 831–842.30992304
93 Burd EM . Validation of laboratory‐developed molecular assays for infectious diseases. Clin Microbiol Rev 2010; 23 : 550–576.20610823
94 Kedia S , Ahuja V . Human gut microbiome: a primer for the clinician. JGH Open 2023; 7 : 337–350.37265934
95 Hopson LM , Singleton SS , David JA , et al. Bioinformatics and machine learning in gastrointestinal microbiome research and clinical application. Prog Mol Biol Transl Sci 2020; 176 : 141–178.33814114
