Enrichment of gut microbiome strains for cultivation-free genome sequencing using droplet microfluidics

A Guide to Biodetection in Droplets

Droplet-based methods for optical biodetection enable unprecedented high-throughput experimental parameters. The methods, however, remain underused due to the accompanying multidisciplinary and complicated experimental workflows. Here, we provide a tutorial for droplet-based optical biodetection workflows with a focus on the key aspect of label selection. By discussing and guiding readers through recent state-of-the-art studies, we aim to make droplet-based approaches more accessible to the general scientific public.

Microfluidic systems for infectious disease diagnostics

Microorganisms, encompassing both uni- and multicellular entities, exhibit remarkable diversity as omnipresent life forms in nature. They play a pivotal role by supplying essential components for sustaining biological processes across diverse ecosystems, including higher host organisms. The complex interactions within the human gut microbiota are crucial for metabolic functions, immune responses, and biochemical signalling, particularly through the gut-brain axis. Viruses also play important roles in biological processes, for example by increasing genetic diversity through horizontal gene transfer when replicating inside living cells. On the other hand, infection of the human body by microbiological agents may lead to severe physiological disorders and diseases. Infectious diseases pose a significant burden on global healthcare systems, characterized by substantial variations in the epidemiological landscape. Fast spreading antibiotic resistance or uncontrolled outbreaks of communicable diseases are major challenges at present. Furthermore, delivering field-proven point-of-care diagnostic tools to the most severely affected populations in low-resource settings is particularly important and challenging. New paradigms and technological approaches enabling rapid and informed disease management need to be implemented. In this respect, infectious disease diagnostics taking advantage of microfluidic systems combined with integrated biosensor-based pathogen detection offers a host of innovative and promising solutions. In this review, we aim to outline recent activities and progress in the development of microfluidic diagnostic tools. Our literature research mainly covers the last 5 years. We will follow a classification scheme based on the human body systems primarily involved at the clinical level or on specific pathogen transmission modes. Important diseases, such as tuberculosis and malaria, will be addressed more extensively.

Water-in-oil droplet-mediated method for detecting and isolating infectious bacteriophage particles via fluorescent staining

Bacteriophages are the most abundant entities on Earth. In contrast with the number of phages considered to be in existence, current phage isolation and screening methods lack throughput. Droplet microfluidic technology has been established as a platform for high-throughput screening of biological and biochemical components. In this study, we developed a proof-of-concept method for isolating phages using water-in-oil droplets (droplets) as individual chambers for phage propagation and co-cultivating T2 phage and their host cell Escherichia coli within droplets. Liquid cultivation of microbes will facilitate the use of microbes that cannot grow on or degrade agar as host cells, ultimately resulting in the acquisition of phages that infect less known bacterial cells. The compartmentalizing characteristic of droplets and the use of a fluorescent dye to stain phages simultaneously enabled the enumeration and isolation of viable phage particles. We successfully recultivated the phages after simultaneously segregating single phage particles into droplets and inoculating them with their host cells within droplets. By recovering individual droplets into 96-well plates, we were able to isolate phage clones derived from single phage particles. The success rate for phage recovery was 35.7%. This study lays the building foundations for techniques yet to be developed that will involve the isolation and rupturing of droplets and provides a robust method for phage enumeration and isolation.

Exploring the gut microbiota: lifestyle choices, disease associations, and personal genomics

The gut microbiota is a rich and dynamic ecosystem that actively interacts with the human body, playing a significant role in the state of health and disease of the host. Diet, exercise, mental health, and other factors have exhibited the ability to influence the gut bacterial composition, leading to changes that can prevent and improve, or favor and worsen, both intestinal and extra-intestinal conditions. Altered gut microbial states, or 'dysbiosis', associated with conditions and diseases are often characterized by shifts in bacterial abundance and diversity, including an impaired Firmicutes to Bacteroidetes ratio. By understanding the effect of lifestyle on the gut microbiota, personalized advice can be generated to suit each individual profile and foster the adoption of lifestyle changes that can both prevent and ameliorate dysbiosis. The delivery of effective and reliable advice, however, depends not only on the available research and current understanding of the topic, but also on the methods used to assess individuals and to discover the associations, which can introduce bias at multiple stages. The aim of this review is to summarize how human gut microbial variability is defined and what lifestyle choices and diseases have shown association with gut bacterial composition. Furthermore, popular methods to investigate the human gut microbiota are outlined, with a focus on the possible bias caused by the lack of use of standardized methods. Finally, an overview of the current state of personalized advice based on gut microbiota testing is presented, underlining its power and limitations.

Single-cell analysis and spatial resolution of the gut microbiome

Over the past decade it has become clear that various aspects of host physiology, metabolism, and immunity are intimately associated with the microbiome and its interactions with the host. Specifically, the gut microbiome composition and function has been shown to play a critical role in the etiology of different intestinal and extra-intestinal diseases. While attempts to identify a common pattern of microbial dysbiosis linked with these diseases have failed, multiple studies show that bacterial communities in the gut are spatially organized and that disrupted spatial organization of the gut microbiome is often a common underlying feature of disease pathogenesis. As a result, focus over the last few years has shifted from analyzing the diversity of gut microbiome by sequencing of the entire microbial community, towards understanding the gut microbiome in spatial context. Defining the composition and spatial heterogeneity of the microbiome is critical to facilitate further understanding of the gut microbiome ecology. Development in single cell genomics approach has advanced our understanding of microbial community structure, however, limitations in approaches exist. Single cell genomics is a very powerful and rapidly growing field, primarily used to identify the genetic composition of microbes. A major challenge is to isolate single cells for genomic analyses. This review summarizes the different approaches to study microbial genomes at single-cell resolution. We will review new techniques for microbial single cell sequencing and summarize how these techniques can be applied broadly to answer many questions related to the microbiome composition and spatial heterogeneity. These methods can be used to fill the gaps in our understanding of microbial communities.

iSort enables automated complex microfluidic droplet sorting in an effort to democratize technology

Fluorescence-activated droplet sorting (FADS) is a widely used microfluidic technique for high-throughput screening. However, it requires highly trained specialists to determine optimal sorting parameters, and this results in a large combinatorial space that is challenging to optimize systematically. Additionally, it is currently challenging to track every single droplet within a screen, leading to compromised sorting and "hidden" false-positive events. To overcome these limitations, we have developed a setup in which the droplet frequency, spacing, and trajectory at the sorting junction are monitored in real time using impedance analysis. The resulting data are used to continuously optimize all parameters automatically and to counteract perturbations, resulting in higher throughput, higher reproducibility, increased robustness, and a beginner-friendly character. We believe this provides a missing piece for the spreading of phenotypic single-cell analysis methods, similar to what we have seen for single-cell genomics platforms.

Double emulsions as a high-throughput enrichment and isolation platform for slower-growing microbes

Our understanding of in situ microbial physiology is primarily based on physiological characterization of fast-growing and readily-isolatable microbes. Microbial enrichments to obtain novel isolates with slower growth rates or physiologies adapted to low nutrient environments are plagued by intrinsic biases for fastest-growing species when using standard laboratory isolation protocols. New cultivation tools to minimize these biases and enrich for less well-studied taxa are needed. In this study, we developed a high-throughput bacterial enrichment platform based on single cell encapsulation and growth within double emulsions (GrowMiDE). We showed that GrowMiDE can cultivate many different microorganisms and enrich for underrepresented taxa that are never observed in traditional batch enrichments. For example, preventing dominance of the enrichment by fast-growing microbes due to nutrient privatization within the double emulsion droplets allowed cultivation of slower-growing Negativicutes and Methanobacteria from stool samples in rich media enrichment cultures. In competition experiments between growth rate and growth yield specialist strains, GrowMiDE enrichments prevented competition for shared nutrient pools and enriched for slower-growing but more efficient strains. Finally, we demonstrated the compatibility of GrowMiDE with commercial fluorescence-activated cell sorting (FACS) to obtain isolates from GrowMiDE enrichments. Together, GrowMiDE + DE-FACS is a promising new high-throughput enrichment platform that can be easily applied to diverse microbial enrichments or screens.

Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease

The human gut microbiome plays a crucial role in human health and has been a focus of increasing research in recent years. Omics-based methods, such as metagenomics, metatranscriptomics, and metabolomics, are commonly used to study the gut microbiome because they provide high-throughput and high-resolution data. The vast amount of data generated by these methods has led to the development of computational methods for data processing and analysis, with machine learning becoming a powerful and widely used tool in this field. Despite the promising results of machine learning-based approaches for analyzing the association between microbiota and disease, there are several unmet challenges. Small sample sizes, disproportionate label distribution, inconsistent experimental protocols, or a lack of access to relevant metadata can all contribute to a lack of reproducibility and translational application into everyday clinical practice. These pitfalls can lead to false models, resulting in misinterpretation biases for microbe–disease correlations. Recent efforts to address these challenges include the construction of human gut microbiota data repositories, improved data transparency guidelines, and more accessible machine learning frameworks; implementation of these efforts has facilitated a shift in the field from observational association studies to experimental causal inference and clinical intervention.

Recent advances of machine learning applications in human gut microbiota study: from observational analysis toward causal inference and clinical intervention

Statistical methods, especially machine learning, learning(ML), are pivotal for the analyses of large data generated by multiomics human gut microbiota study. These analyses lead to the discovery of microbe-disease associations. Furthermore, recent efforts for more data transparency and accessible analytical tools improved data availability and study reproducibility. Our recent accumulated knowledge on microbe-disease associations brings light to the next questions: what is the role of microbes in disease progression and how can we apply our knowledge of microbiome in clinical settings? Here, we introduce recent studies that implemented ML to answer the questions of causal inference and clinical translation.

Design and construction of a microfluidics workstation for high-throughput multi-wavelength fluorescence and transmittance activated droplet analysis and sorting

Droplet microfluidics has revolutionized quantitative high-throughput bioassays and screening, especially in the field of single-cell analysis where applications include cell characterization, antibody discovery and directed evolution. However, droplet microfluidic platforms capable of phenotypic, fluorescence-based readouts and sorting are still mostly found in specialized labs, because their setup is complex. Complementary to conventional FACS, microfluidic droplet sorters allow the screening of cell libraries for secreted factors, or even for the effects of secreted or surface-displayed factors on a second cell type. Furthermore, they also enable PCR-activated droplet sorting for the isolation of genetic material harboring specific markers. In this protocol, we provide a detailed step-by-step guide for the construction of a high-throughput droplet analyzer and sorter, which can be accomplished in ~45 working hours by nonspecialists. The resulting instrument is equipped with three lasers to excite the fluorophores in droplets and photosensors that acquire fluorescence signals in the blue (425-465 nm), green (505-545 nm) and red (580-630 nm) spectrum. This instrument also allows transmittance-activated droplet sorting by analyzing the brightfield light intensity transmitting through the droplets. The setup is validated by sorting droplets containing fluorescent beads at 200 Hz with 99.4% accuracy. We show results from an experiment where droplets hosting single cells were sorted on the basis of increased matrix metalloprotease activity as an application of our workstation in single-cell molecular biology, e.g., to analyze molecular determinants of cancer metastasis.

The developing infant gut microbiome: A strain-level view

At birth, neonates provide a vast habitat awaiting microbial colonization. Microbiome assembly is a complex process involving microbial seeding and succession driven by ecological forces and subject to environmental conditions. These successional events not only significantly affect the ecology and function of the microbiome, but also impact host health. While the establishment of the infant microbiome has been a point of interest for decades, an integrated view focusing on strain level colonization has been lacking until recently. Technological and computational advancements enabling strain-level analyses of the infant microbiome have demonstrated the immense complexity of this system and allowed for an improved understanding of how strains of the same species spread, colonize, evolve, and affect the host. Here, we review the current knowledge of the establishment and maturation of the infant gut microbiome with particular emphasis on newer discoveries achieved through strain-centric analyses.

The Interplay among Radiation Therapy, Antibiotics and the Microbiota: Impact on Cancer Treatment Outcomes

Radiation therapy has been used for more than a century, either alone or in combination with other therapeutic modalities, to treat most types of cancer. On average, radiation therapy is included in the treatment plans for over 50% of all cancer patients, and it is estimated to contribute to about 40% of curative protocols, a success rate that may reach 90%, or higher, for certain tumor types, particularly on patients diagnosed at early disease stages. A growing body of research provides solid support for the existence of bidirectional interaction between radiation exposure and the human microbiota. Radiation treatment causes quantitative and qualitative changes in the gut microbiota composition, often leading to an increased abundance of potentially hazardous or pathogenic microbes and a concomitant decrease in commensal bacteria. In turn, the resulting dysbiotic microbiota becomes an important contributor to worsen the adverse events caused in patients by the inflammatory process triggered by the radiation treatment and a significant determinant of the radiation therapy anti-tumor effectiveness. Antibiotics, which are frequently included as prophylactic agents in cancer treatment protocols to prevent patient infections, may affect the radiation/microbiota interaction through mechanisms involving both their antimicrobial activity, as a mediator of microbiota imbalances, and their dual capacity to act as pro- or anti-tumorigenic effectors and, consequently, as critical determinants of radiation therapy outcomes. In this scenario, it becomes important to introduce the use of probiotics and/or other agents that may stabilize the healthy microbiota before patients are exposed to radiation. Ultimately, newly developed methodologies may facilitate performing personalized microbiota screenings on patients before radiation therapy as an accurate way to identify which antibiotics may be used, if needed, and to inform the overall treatment planning. This review examines currently available data on these issues from the perspective of improving radiation therapy outcomes.