The human microbiome is a set of microorganisms (bacteria, viruses, protozoa, and fungi) residing in our body in different types of commensal relationships. The gut microbiota is the most important and consequential of them, with approximately 1,000 species (1) and 100 trillion microorganisms, which is ten times more than the number of eukaryotic cells that make up our body (2). It plays a role in regulating nutrient intake, intestinal motility, and metabolic and immunological development (3). However, how the newborn gut microbiota forms remains an open question. The combination of neonatal (gestational age, genetic history), maternal (delivery mode, diet), and environmental (e.g., antibiotic exposures) variables are considered to impact microbial colonization (4, 5), although the precise processes remain unknown. Several studies have shown that the infant gut microbiome can be influenced by different factors, leading to many diseases in the infant or child, including necrotizing enterocolitis, obesity, and inflammatory diseases (6, 7). Thus it is critical to understand the mechanism of microbiome colonization in newborns and how various factors may influence this process.

With the development, sophistication, and sensitivity of metagenomics and culturomics, identifying fecal microbes is improving our current knowledge about gut microbiota development, particularly during the early days' post-delivery (8, 9). Two methods have been widely used to examine microbial diversity: 16S rRNA gene amplicon sequencing and whole-genome shotgun metagenomics sequencing (WGS). WGS metagenomics sequencing allows the characterization of microbial whole genomes, while 16S rRNA gene amplicon gives a depth description of the diversity of certain taxonomic groups (10, 11).

The purpose of this review is to summarize and discuss the available amplicon-based and WGS metagenomics methodologies and tools used to profile the newborn microbiome and study the in utero transmission of microorganisms from mother to newborn.

Conflicting results from various studies have unsettled the notion of the in utero gut microbiome. The “sterile uterus” theory maintains that the embryo grows in a sterile environment in utero, and except for intrauterine infections during pregnancy, microbial colonization begins after birth (9, 12). However, this theory of the sterile uterus has been called into question by recent studies using both metagenomics and culture techniques that revealed the presence of microbial community in the meconium, placenta, blood umbilical cord, and amniotic fluid (13, 14). In addition, other researchers have reported the presence of a unique microbiota in the placenta and amniotic fluid, as well as in healthy women at the time of elective cesarean section, associated with low diversity and a prevalence of Proteobacteria (15). Likewise, other studies have discovered microbes in the umbilical cord blood and amniotic fluid in healthy women and those with pregnancy complications (16, 17). Several studies have found microorganisms in the meconium, lending credence to the in utero colonization of the baby's gut theory. Staphylococcus was reported as the most frequent bacterium in meconium samples, followed by Enterobacteriaceae family, Enterococcus, Lactobacillus, and Bifidobacterium genera (18).

In contrast, supporters of the sterile uterus theory attribute the identified microbial components to external contamination (19) because there is no indication of bacterial colony survival (20). Likewise, attempts to grow viable bacteria from placental samples in healthy pregnancies have thus far been unsuccessful (21). Perez-Muñoz et al. (12), on the other hand, support both the “sterile uterus” theory and “in utero colonization” as a methodology-associated artifact (22). They concluded that the data stating a sterile uterine environment was more robust. They claim that methodological techniques in which contamination is fairly easy are responsible for the observed in utero colonization (12). In well-controlled studies, oral, vaginal, and placental samples were compared to contaminated controls. They determined that while there are unique microbial profiles in vaginal and oral samples, they did not discover a distinct placental microbiome, supporting the sterile environment theory (23). Therefore, firm conclusions remain elusive, and further research in this field is still required.

Several studies have shown that the gut microbiota has a role in the programming and development of the fetal immune system, metabolic programming, and preventing pathogen colonization of the gut, all of which have long-term consequences in infancy, early childhood, and adulthood (24–26). Colonization of the gut microbiome is a complicated process controlled by many variables (Figure 1) (27). Despite recent research on in utero colonization, the birth canal is still considered the baby's first bacterial encounter (28). The neonatal microbiota is dominated by Enterobacteria, Escherichia, and Shigella throughout the first few days (9, 29, 30). According to a new study (9), while the Firmicutes phylum dominates the meconium, Proteobacteria species dominate fecal samples from newborns throughout their 1st months of life. The newborn gut microbiota shows more significant fluctuations and minor variations in the early days of life. Nevertheless, as the infant ages, the bacterial communities increase in diversity and stability. It has been proven that by the age of two, the neonatal microbiota has stabilized to a level equivalent to that of adults (31). However, other scientists suggest that this process might take up to 5 years (32).

Prior to the microbiome era, bacterial characterization was primarily focused on identifying pathogenic species. However, the revelation of the importance of the microbiome-host interaction in human health has resulted in the creation of novel microbe investigation technologies. These so-called metagenomics techniques have helped researchers better understand the microbial diversity contained in a sample. This approach directly uses the genetic material existing in an environmental sample without the requirement for culture. Table 1 lists the most commonly used methodologies for studying the microbiome, as well as its benefits and limitations.

The most common methods to explore the human microbiome are 16S rRNA gene amplicon sequencing and shotgun metagenomics sequencing (WGS) (10). The 16S rRNA genes consist of both highly conserved and variable regions used for taxonomic classification. At the same time, WGS presents all genome sequences found in a given sample (10, 11). Meta-omic studies have been increasingly used to study the gut microbiome community to better understand taxonomic classification, metabolic pathways, and the essential proteins and metabolites implicated in a specific host phenotype (64). One of these methods is metabolomics, which is used to profile the metabolites generated by the gut microbiome and define metabolites and biochemical pathways (62). Metatranscriptomics is another method for identifying and measuring gut microbial mRNA that reveals which genes and pathways are active and play essential roles in health and diseases (62). Another powerful approach to identifying and quantifying proteins from microbial communities is metaproteomics (63). Using a single omics approach to explore the gut microbiota has its own set of limitations (62), which can be addressed by combining various omics techniques. Consequently, researchers may better understand the relationship between the microbiome and diseases (65, 66).

There are several bioinformatic tools known to analyze 16S rRNA gene amplicon sequencing data, including QIIME 2 (67), Mothur (68), amplicon sequence variants (ASV)-based DADA2 (69), MED (70), and UNOISE (71). Operational taxonomic unit (OTU)-based methods such as QIIME eliminate sequencing errors by clustering the sequences in OTUs using a similarity threshold (usually 97%) (72, 73). On the other hand, ASV-based tools predict and correct sequencing errors (denoising) before forming clusters, allowing resolving sequences differing by a single nucleotide (69).

Previous studies have contrasted these two techniques and concluded that OTUs give poorer taxonomic resolution than ASVs and that choosing between the two can have an influence on alpha diversity estimations (73, 74). Nevertheless, data quality and PCR errors have a significant impact on ASV approaches, resulting in the loss of a significant quantity of relevant information. As a result, when the data quality isn't good enough, an OTU-based strategy is required (75).

Prodan et al. compared the most common bioinformatics pipelines for 16S rRNA gene amplicon sequencing (including MOTHUR, QIIME-UCLUST, USEARCH-UPARSE, DADA2, USEARCH-UNOISE3, Qiime2, and Deblur) (76). They found that DADA2 had the best resolution and sensitivity, but USEARCH-UNOISE3 had the best overall performance, combining high sensitivity with excellent specificity. However, to produce more robust data, research in this field needs to move toward improved methods (77). Another study by the Almeida group found that when comparing QIIME, QIIME 2, MAPseq, and Mothur, QIIME 2 was the best tool for composition prediction, while MAPseq was more precise, with few genera being misallocated (78).

The reference database utilized, in addition to selecting the appropriate bioinformatics tool, is a crucial aspect in ensuring the most significant classification performance. The Ribosomal Database Project (RDP), SILVA, and Greengenes are the most important 16S databases (79). However, SILVA is updated more regularly than Greengenes, which was last updated in May 2013. In addition, SILVA contains rRNA sequences of different species, including eukaryotic organisms, archaea, and bacterial species (79).

The main metagenomic techniques and pipelines that have been used in neonatal microbiome studies are listed in Table 2.

Most microbiome research has focused on the gut, leading to the development of technologies that are better suited to samples with high microbial biomass (101). Samples from other body sites or, in comparison to the gut, have lower microbial biomass and are technically more difficult to explore. The main challenge with these samples is the amount of DNA, which comes from the environment. Microbial contamination has been presented as a real problem due to a lack of vigilance and understanding of technical problems while working with low microbial biomass samples (101–103). The publication of placental microbiota is an excellent illustration of this (101). This resulted in a false-positive finding, and unfortunately, other researchers have followed the same path.

Microbiome analysis has previously had problems due to a lack of controls. Initial research on the placental microbiota, for example, aimed to solve many issues about baby development and premature birth during pregnancy (13). Aagaard et al., on the other hand, did not take the essential procedures to monitor and reduce contamination. During the extractions, non-template controls were used, but only a fraction was sequenced. The sequences obtained in the negative controls were not compared to those found in the biological samples in any way. Furthermore, no environmental controls were collected or examined, preventing environmental contaminants from being detected.

Finally, the detection limit was not specified, prohibiting the researchers from determining whether a credible signal could be identified. Contaminant microbial species could not be reliably identified or analyzed in placenta samples, making it impossible to verify whether the placental microbial signature was genuinely endogenous. Lauder et al. (23) discovered that the microbial placenta profiles were similar to those of extraction blank control (EBC) and air samples, indicating that the placenta was likely sterile and lacked a varied microbial signature. Many other well-controlled investigations have now shown the absence of a placental microbiome (19–21). These studies emphasize the necessity of having solid processes and controls in place to avoid biased results.

As awareness of the aforementioned challenges of low microbial biomass sample examination, more researchers are now including controls in their data analysis. Nevertheless, this practice is still not standardized across the board, and researchers urgently need to learn from these past errors to improve microbiome research in the future, in particular for newborns. More researchers are increasingly integrating controls from sampling to data analysis as knowledge of the limitations of low microbial biomass sample inspection has increased. Nonetheless, this method is not uniform across the board, and researchers must learn from previous mistakes if they are to enhance microbiome research in the future.

Common microbiome procedures are not optimal for low microbial biomass samples, even in today's common microbiome research. Low biomass laboratory and analytical techniques have improved as a result of recent research, but there is always a potential for improvement (103, 104). To prevent contamination and biases, greater knowledge of when and how they arise is needed. DNA contamination and biases can occur at any step during the sample preparation and analysis process. Therefore, it is recommended to include controls from the samples of the professionals who collect and process samples, the collection room, the laboratory environment, equipment, and reagents. Moreover, all sample collectors and laboratory technicians should wear clothes that cover exposed skin, such as gloves and face masks (102, 103). Additionally, ultraviolet radiation can be used to minimize reagent and equipment contamination (101). It is also recommended that low biomass samples should be sequenced at a higher depth to capture a sufficient number of unique sequences (105).

Several bioinformatic approaches have been developed to monitor and remove contaminated DNA that affects samples with low microbial biomass. First, as previously mentioned, the limit of detection can be achieved using positive and negative controls (101–103). Individual contaminated species may also be traced using their specific sequence from their source (i.e., equipment, reagents, environmental controls, etc.) and then eliminated or identified using publicly accessible software such as SourceTracker and Decontam (101, 106). While programs can be used to eliminate or track contaminants, they do not replace the need to monitor and investigate contaminants throughout the sampling and laboratory processes.

As the number of investigations into neonatal microbiome samples grows, it is critical that new protocols and methods are developed to reduce the impact of contamination and biases on these samples, as well as to fully understand the limitations when developing diagnostic tests based on the findings (101–103). To reduce some of these difficulties and better detect disease causes and consequences, new methods based on sensitive, high-throughput techniques that explore undiscovered microbes and microbial populations as a whole are necessary.

With the advent of metagenomics, we can now define the structure and function of the microbiome community at the pre- and post-delivery stages with high precision. Due to the metagenomics' potential, dogmas such as the “sterile womb” hypothesis are being challenged by the discovery of microbes in previously sterile tissues. However, if these bacteria are rucked as real occurrences and not just experimental artifacts/contaminants, it is unknown if they colonize the embryo or are only present for the purpose of priming the fetal immune system.

Our review of maternal and environmental factors influencing intestinal microbiota highlights the need to focus on these aspects during pregnancy. The gut microbiome plays a significant role in human growth and development, as well as in the establishment of immunological responses, indicating that balanced gut microbiota is essential for good health. Emerging data suggesting early-life gut microbiome establishment as a protective factor against gut dysbiosis–related diseases later in life supports the need for targeted therapies to restore the gut microbiome in early-life.

To keep up with the latest sequencing technology and Metagenomic methods, new bioinformatics tools are constantly being developed and updated. The most difficult issue for the user is not being confused on offered alternatives. Indeed, comparing the outcomes obtained through several Bioinformatic tools remains the best strategy. Furthermore, using frequent comparison studies and reviews to help the user is beneficial and should be read and well analyzed before making a final decision on which tool to use. Therefore, to understand the neonatal microbiome and the newborn-health link, it is necessary to choose efficient computational tools and methodologies to analyze the neonatal microbiome.

Even though microbiological techniques have advanced in the last years, certain aspects still need to be enhanced such as reducing contamination, which is a critical issue in microbiome studies, particularly for samples with low microbial mass, as well as the differences in sample collection, storage, DNA extraction protocols, sequencing methods, and bioinformatics tools, which could all be responsible for introducing biases into the results and variability between microbiome studies. Additionally, other sections of the infant gut microbiota composed of fungi and viruses require additional investigation. Furthermore, using fecal samples to study neonatal gut microbiota has limitations. It is not always representative of the gut microbiome and leaves out gut-adherent bacteria that affect colon epithelial physiology and functions.

Although dysbiosis has been associated with several diseases, such as inflammatory diseases, atopic diseases, and NEC, other conditions have also been linked to disruptions in the microbiome. Nevertheless, in most cases causal correlations have yet to be demonstrated. Moreover, is there a critical period of microbiome development that, if disturbed, leads to a disease state in cases where illness states have been connected to microbiome alterations? Are there some microorganisms that can protect you from diseases? What human host variables and/or environmental interactions are crucial for the development of a healthy newborn microbiome? Scientists working to determine the likely microbial etiologies of infant diseases must address these and other problems. By obtaining these responses, potential protective and therapeutic strategies can be created to modulate initial microbial colonization and decrease the risk of adverse health outcomes associated with unbalanced microbiota evolution.

HG, ZB, and ND contributed to conceptualization and writing. SS, SE, and NA contributed to writing. SH, HB, NA, ML-P, SA, and CN reviewed the manuscript. All authors read and approved the submitted version.

The Article Processing Charge (APC) paid by Mohammed VI University of Health Sciences (UM6SS).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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