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Stem cells and organoids have emerged as pivotal biological tools for biologically relevant models. Together, these in vitro models realistically recapitulate structural and functional elements of the in vivo organ, allowing for studies of cellular, molecular, and genetic features that underpin various diseases that are difficult to observe in low-biomass tissues. Stem cells, and more recently organoids, have been applied in vivo as regenerative therapies. The emergence of the microbiome as an occupant throughout different body locales requires new approaches to understand the complex cellular interactions with the host tissue at each site. The success of regenerative medicine strategies and therapeutic development is intricately linked to this understanding and management of host–microbe dynamics. Interactions with the host microbiome and infections can both significantly impair tissue regeneration and compromise the function of stem cell–derived therapies. Therefore, a comprehensive understanding of how pathogens and the microbiome interact with stem cells and organoids is relevant for developing safe and effective regenerative medicine interventions. This review explores the evolving landscape of organoid technology, including a discussion on the importance of stem cell studies and considerations for organoid development that are important for use as models to study microbiome interactions. Additionally, this work describes the pivotal role of cell culture models in advancing host–microbe interaction studies in understudied low-biomass organs such as the stomach and reproductive tract. Through this assessment, we aim to shed light on the potential of these models to transform the approach to studying and managing infectious diseases within the context of regenerative medicine.

Background Recent studies have suggested that bacteria associated with the placenta—a “placental microbiome”—may be important in reproductive health and disease. However, a challenge in working with specimens with low bacterial biomass, such as placental samples, is that some or all of the bacterial DNA may derive from contamination in dust or commercial reagents. To investigate this, we compared placental samples from healthy deliveries to a matched set of contamination controls, as well as to oral and vaginal samples from the same women. Results We quantified total 16S rRNA gene copies using quantitative PCR and found that placental samples and negative controls contained low and indistinguishable copy numbers. Oral and vaginal swab samples, in contrast, showed higher copy numbers. We carried out 16S rRNA gene sequencing and community analysis and found no separation between communities from placental samples and contamination controls, though oral and vaginal samples showed characteristic, distinctive composition. Two different DNA purification methods were compared with similar conclusions, though the composition of the contamination background differed. Authentically present microbiota should yield mostly similar results regardless of the purification method used—this was seen for oral samples, but no placental bacterial lineages were (1) shared between extraction methods, (2) present at >1 % of the total, and (3) present at greater abundance in placental samples than contamination controls. Conclusions We conclude that for this sample set, using the methods described, we could not distinguish between placental samples and contamination introduced during DNA purification.

Microbiome research has uncovered magnificent biological and chemical stories across nearly all areas of life science, at times creating controversy when findings reveal fantastic descriptions of microbes living and even thriving in what were once thought to be sterile environments. Scientists have refuted many of these claims because of contamination, which has led to robust requirements, including the use of controls, for validating accurate portrayals of microbial communities. In this study, we describe a previously undocumented form of contamination, well-to-well contamination, and show that this sort of contamination primarily occurs during DNA extraction rather than PCR, is highest with plate-based methods compared to single-tube extraction, and occurs at a higher frequency in low-biomass samples. This finding has profound importance in the field, as many current techniques to “decontaminate” a data set simply rely on an assumption that microbial reads found in blanks are contaminants from “outside,” namely, the reagents or consumables. Microbial sequences inferred as belonging to one sample may not have originated from that sample. Such contamination may arise from laboratory or reagent sources or from physical exchange between samples. This study seeks to rigorously assess the behavior of this often-neglected between-sample contamination. Using unique bacteria, each assigned a particular well in a plate, we assess the frequency at which sequences from each source appear in other wells. We evaluate the effects of different DNA extraction methods performed in two laboratories using a consistent plate layout, including blanks and low-biomass and high-biomass samples. Well-to-well contamination occurred primarily during DNA extraction and, to a lesser extent, in library preparation, while barcode leakage was negligible. Laboratories differed in the levels of contamination. Extraction methods differed in their occurrences and levels of well-to-well contamination, with plate methods having more well-to-well contamination and single-tube methods having higher levels of background contaminants. Well-to-well contamination occurred primarily in neighboring samples, with rare events up to 10 wells apart. This effect was greatest in samples with lower biomass and negatively impacted metrics of alpha and beta diversity. Our work emphasizes that sample contamination is a combination of cross talk from nearby wells and background contaminants. To reduce well-to-well effects, samples should be randomized across plates, samples of similar biomasses should be processed together, and manual single-tube extractions or hybrid plate-based cleanups should be employed. Researchers should avoid simplistic removals of taxa or operational taxonomic units (OTUs) appearing in negative controls, as many will be microbes from other samples rather than reagent contaminants. IMPORTANCE Microbiome research has uncovered magnificent biological and chemical stories across nearly all areas of life science, at times creating controversy when findings reveal fantastic descriptions of microbes living and even thriving in what were once thought to be sterile environments. Scientists have refuted many of these claims because of contamination, which has led to robust requirements, including the use of controls, for validating accurate portrayals of microbial communities. In this study, we describe a previously undocumented form of contamination, well-to-well contamination, and show that this sort of contamination primarily occurs during DNA extraction rather than PCR, is highest with plate-based methods compared to single-tube extraction, and occurs at a higher frequency in low-biomass samples. This finding has profound importance in the field, as many current techniques to “decontaminate” a data set simply rely on an assumption that microbial reads found in blanks are contaminants from “outside,” namely, the reagents or consumables.

Background Studies on the airway microbiome have been performed using a wide range of laboratory protocols for high-throughput sequencing of the bacterial 16S ribosomal RNA (16S rRNA) gene. We sought to determine the impact of number of polymerase chain reaction (PCR) steps (1- or 2- steps) and choice of target marker gene region (V3 V4 and V4) on the presentation of the upper and lower airway microbiome. Our analyses included lllumina MiSeq sequencing following three setups: Setup 1 (2-step PCR; V3 V4 region), Setup 2 (2-step PCR; V4 region), Setup 3 (1-step PCR; V4 region). Samples included oral wash, protected specimen brushes and protected bronchoalveolar lavage (healthy and obstructive lung disease), and negative controls. Results The number of sequences and amplicon sequence variants (ASV) decreased in order setup1 > setup2 > setup3. This trend appeared to be associated with an increased taxonomic resolution when sequencing the V3 V4 region (setup 1) and an increased number of small ASVs in setups 1 and 2. The latter was considered a result of contamination in the two-step PCR protocols as well as sequencing across multiple runs (setup 1). Although genera Streptococcus , Prevotella , Veillonella and Rothia dominated, differences in relative abundance were observed across all setups. Analyses of beta-diversity revealed that while oral wash samples (high biomass) clustered together regardless of number of PCR steps, samples from the lungs (low biomass) separated. The removal of contaminants identified using the Decontam package in R, did not resolve differences in results between sequencing setups. Conclusions Differences in number of PCR steps will have an impact of final bacterial community descriptions, and more so for samples of low bacterial load. Our findings could not be explained by differences in contamination levels alone, and more research is needed to understand how variations in PCR-setups and reagents may be contributing to the observed protocol bias.

Background Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. Results We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (10 8 –10 3 cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 10 6 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera. Conclusions MicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.

Changes in the milk microbiota during the course of mastitis are due to the nature of a sporadic occurring disease difficult to study. In this study we experimentally induced mastitis by infusion of Escherichia coli endotoxins in one udder quarter each of nine healthy lactating dairy cows and assessed the bacteriological dynamics and the milk microbiota at four time points before and eight time points after infusion. As control, saline was infused in one udder quarter each of additionally nine healthy cows that followed the same sampling protocol. The milk microbiota was assessed by sequencing of the 16 S rRNA gene and a range of positive and negative controls were included for methodological evaluation. Two different data filtration models were used to identify and cure data from contaminating taxa. Endotoxin infused quarters responded with transient clinical signs of inflammation and increased SCC while no response was observed in the control cows. In the milk microbiota data no response to inflammation was identified. The data analysis of the milk microbiota was largely hampered by laboratory and reagent contamination. Application of the filtration models caused a marked reduction in data but did not reveal any associations with the inflammatory reaction. Our results indicate that the microbiota in milk from healthy cows is unaffected by inflammation.

Background: Profiling the microbiome of low-biomass samples is challenging for metagenomics since these samples are prone to contain DNA from other sources (e.g. host or environment). The usual approach is sequencing short regions of the 16S rRNA gene, which fails to assign taxonomy to genus and species level. To achieve an increased taxonomic resolution, we aim to develop long-amplicon PCR-based approaches using Nanopore sequencing. We assessed two different genetic markers: the full-length 16S rRNA (~1,500 bp) and the 16S-ITS-23S region from the rrn operon (4,300 bp). Methods: We sequenced a clinical isolate of Staphylococcus pseudintermedius, two mock communities and two pools of low-biomass samples (dog skin). Nanopore sequencing was performed on MinION™ using the 1D PCR barcoding kit. Sequences were pre-processed, and data were analyzed using EPI2ME or Minimap2 with rrn database. Consensus sequences of the 16S-ITS-23S genetic marker were obtained using canu. Results: The full-length 16S rRNA and the 16S-ITS-23S region of the rrn operon were used to retrieve the microbiota composition of the samples at the genus and species level. For the Staphylococcus pseudintermedius isolate, the amplicons were assigned to the correct bacterial species in ~98% of the cases with the16S-ITS-23S genetic marker, and in ~68%, with the 16S rRNA gene when using EPI2ME. Using mock communities, we found that the full-length 16S rRNA gene represented better the abundances of a microbial community; whereas, 16S-ITS-23S obtained better resolution at the species level. Finally, we characterized low-biomass skin microbiota samples and detected species with an environmental origin. Conclusions: Both full-length 16S rRNA and the 16S-ITS-23S of the rrn operon retrieved the microbiota composition of simple and complex microbial communities, even from the low-biomass samples such as dog skin. For an increased resolution at the species level, targeting the 16S-ITS-23S of the rrn operon would be the best choice.

The atmosphere may be Earth’s largest microbial ecosystem. It is connected to all of Earth’s surface ecosystems and plays an important role in microbial dispersal on local to global scales. Despite this grand scale, surprisingly little is understood about the atmosphere itself as a habitat. A key question remains unresolved: does the atmosphere simply transport microorganisms from one location to another, or does it harbour adapted, resident, and active microbial communities that overcome the physiological stressors and selection pressures the atmosphere poses to life? Advances in extreme microbiology and astrobiology continue to push our understanding of the limits of life towards ever greater extremes of temperature, pressure, salinity, irradiance, pH, and water availability. Earth’s atmosphere stands as a challenging, but potentially surmountable, extreme environment to harbour living, active, resident microorganisms. Here, we confront the current understanding of the atmosphere as a microbial habitat, highlighting key advances and limitations. We pose major ecological and mechanistic questions about microbial life in the atmosphere that remain unresolved and frame the problems and technical pitfalls that have largely hindered recent developments in this space, providing evidence-based insights to drive future research in this field. New innovations supported by rigorous technical standards are needed to enable progress in understanding atmospheric microorganisms and their influence on global processes of weather, climate, nutrient cycling, biodiversity, and microbial connectivity, especially in the context of rapid global change.

Background The composition of the skin microbiome is predicted to play a role in the development of conditions such as atopic eczema and psoriasis. 16S rRNA gene sequencing allows the investigation of bacterial microbiota. A significant challenge in this field is development of cost effective high throughput methodologies for the robust interrogation of the skin microbiota, where biomass is low. Here we describe validation of methodologies for 16S rRNA (ribosomal ribonucleic acid) gene sequencing from the skin microbiome, using the Illumina MiSeq platform, the selection of primer to amplify regions for sequencing and we compare results with the current standard protocols.. Methods DNA was obtained from two low density mock communities of 11 diverse bacterial strains (with and without human DNA supplementation) and from swabs taken from the skin of healthy volunteers. This was amplified using primer pairs covering hypervariable regions of the 16S rRNA gene: primers 63F and 519R (V1-V3); and 347F and 803R (V3-V4). The resultant libraries were indexed for the MiSeq and Roche454 and sequenced. Both data sets were denoised, cleaned of chimeras and analysed using QIIME. Results There was no significant difference in the diversity indices at the phylum and the genus level observed between the platforms. The capture of diversity using the low density mock community samples demonstrated that the primer pair spanning the V3-V4 hypervariable region had better capture when compared to the primer pair for the V1-V3 region and was robust to spiking with human DNA. The pilot data generated using the V3-V4 region from the skin of healthy volunteers was consistent with these results, even at the genus level (Staphylococcus, Propionibacterium, Corynebacterium, Paracoccus, Micrococcus, Enhydrobacter and Deinococcus identified at similar abundances on both platforms). Conclusions The results suggest that the bacterial community diversity captured using the V3-V4 16S rRNA hypervariable region from sequencing using the MiSeq platform is comparable to the Roche454 GS Junior platform. These findings provide evidence that the optimised method can be used in human clinical samples of low bacterial biomass such as the investigation of the skin microbiota.

Background The gut microbiome has emerged as a clear player in health and disease, in part by mediating host response to environment and lifestyle. The urobiome (microbiota of the urinary tract) likely functions similarly. However, efforts to characterize the urobiome and assess its functional potential have been limited due to technical challenges including low microbial biomass and high host cell shedding in urine. Here, to begin addressing these challenges, we evaluate urine sample volume (100 ml–5 mL) and host DNA depletion methods and their effects on urobiome profiles in healthy dogs, which are a robust large animal model for the human urobiome. We collected urine from seven dogs and fractionated samples into aliquots. One set of samples was spiked with host (canine) cells to model a biologically relevant host cell burden in urine. Samples then underwent DNA extraction followed by 16S rRNA gene and shotgun metagenomic sequencing. We then assembled metagenome-assembled genomes (MAGs) and compared microbial composition and diversity across groups. We tested six methods of DNA extraction: QIAamp BiOstic Bacteremia (no host depletion), QIAamp DNA Microbiome, Molzym MolYsis, NEBNext Microbiome DNA Enrichment, Zymo HostZERO, and propidium monoazide. Results In relation to urine sample volume, ≥ 3.0 mL resulted in the most consistent urobiome profiling. In relation to host depletion, individual (dog) but not extraction method drove overall differences in microbial composition. DNA Microbiome yielded the greatest microbial diversity in 16S rRNA sequencing data and shotgun metagenomic sequencing data and maximized MAG recovery while effectively depleting host DNA in host-spiked urine samples. As proof-of-principle, we then mined MAGs for select metabolic functions including central metabolism pathways and environmental chemical degradation. Conclusions Our findings provide guidelines for studying the urobiome in relation to sample volume and host depletion and lay the foundation for future evaluation of urobiome function in relation to health and disease. 8CHKbex1v4sZa8qvvHUnQG Video Abstract