An exciting future for microbial molecular biology and physiology

ABSTRACT

Continuous advances in technologies ranging from deep sequencing and genetic manipulation to mass spectrometry, single cell imaging, and structural biology have led to previously unimaginable advances in our understanding of microbial physiology and the molecular mechanisms underlying microbial responses in a multitude of environments. Simultaneously, these advances are revealing how much more there is to learn. At the 2024 virtual retreat of the Molecular Biology and Physiology (MBP) Community of the Council on Microbial Sciences (COMS) of the American Society for Microbiology (ASM), eight early-career investigators, along with retreat attendees, discussed some of these astounding advances, as well as the challenges and opportunities the developments raise. Motivated by these discussions, we review the state-of-the-art in molecular microbiology and provide an outlook on this field. Our hope is that the topics discussed here can serve as an inspiration for the development of future technologies, resources, and guidelines and for the training of the next generation of microbiologists.

INTRODUCTION

The first two bacterial genomes were sequenced nearly 30 years ago (1, 2). The cost of one of those sequences, that of Haemophilus influenzae, was over 1,000,000 U.S. dollars and took multiple years (2). Today, a whole bacterial genome can be sequenced for less than 100 U.S. dollars in hours. The exponential decrease in sequencing costs and time has led to an exponential increase in the number of sequenced genomes. As a result, over 1.9 million bacterial genomes are now available in databases such as AllTheBacteria (3), along with vast amounts of information in multi-omic data sets. In addition to the exponential increase of sequencing data and data from other omics techniques, there have been momentous changes in the analyses of single cells and macromolecular structures. This amazing wealth of data and the changing methodologies open new opportunities for understanding microorganisms, microbial communities, and their responses to and impacts on different environments. At the same time, these developments point to new challenges that remain to be solved. It is these opportunities and challenges for research in microbial molecular biology and physiology that we discuss here (see Table S1 for overview of websites in italics and Box 1 for definitions of underlined words).

Box 1. Glossary.

Allelic exchange: a process by which deletions, substitutions or mutations are introduced into a DNA sequence by homologous recombination.

Arrayed (ordered) mutant collection: a large set of pure cultures of distinct mutant strains stored in a format that is compatible with high-throughput liquid handling systems. The identity and location of mutants within the collection are known, facilitating high-throughput tests for genotype-phenotype relationships without the need to simultaneously track the genotype of each culture.

Combinatorial indexing: scRNA-seq technology in which the RNA in each cell is marked by a unique combination of barcodes added during multiple iterations of splitting pools of cells.

CRISPRi-seq: an approach to identify gene function by using CRISPRi to lower the expression of genes and screen or select for altered phenotypes. CRISPRi is a loss-of-function approach. Individual strains are quantified in a pool through sequencing the guide RNA.

Cryo-electron microscopy (cryo-EM): a technique that enables near-atomic resolution three-dimensional structure determination by using an electron microscope to image thin, vitrified samples.

DNA barcode: a short, unique DNA sequence introduced into a strain to facilitate tracking through sequencing.

Dub-seq (dual-barcoded shotgun expression library sequencing): an approach to identify gene function by expressing genomic DNA fragments in a host organism followed by screening or selecting for new phenotypes. This is a gain-of-function mutagenesis approach. Individual expression library fragments are quantified using barcode sequencing.

Functional genomics: a field of study with a broadly defined goal of connecting genotype to phenotype by leveraging information in the complete genome sequence of an organism. One common functional-genomic approach is to associate a phenotype with gene content across a set of related strains to identify the responsible genes. Another functional-genomic approach is to employ genome-scale mutagenesis in a single strain to comprehensively identify all genes associated with a phenotype.

Gain-of-function mutagenesis: mutagenesis approach that results in a new or enhanced function of a gene product.

Genotype: the complete DNA content of an organism, typically defined through the mutations that differentiate a strain from wild type.

Light-sheet microscopy: a fluorescence imaging technique in which a thin sheet of light is used to illuminate a sample. Computational assembly of a stack of light sheet images allows for three-dimensional reconstruction of the image.

Loss-of-function mutagenesis: Mutagenesis approach that reduces or eliminates the function of a gene product.

Model organism: a strain chosen to represent a larger taxonomic group or set of phenotypes. The strain is shared as a common experimental subject among a research community. Model organisms are chosen for ease of manipulation, genetic tractability, and simplicity.

Multicollinearity: a statistical phenomenon where strong correlations exist among predictor variables, such as genes or platforms in multi-platform data. This can complicate joint analyses by inflating variances of estimated coefficients, reducing the reliability and interpretability of the results.

Multi-omic data: paired high-throughput functional readouts, which could include, for example, metagenomic analysis of microbial communities and metabolomic profiling.

Nanoliter droplet barcoding: scRNA-seq technology in which cells are separated into individual droplets and the RNA in each droplet is associated with a different barcode.

Phenotype: any observable trait of an organism.

Pooled mutant collection: A mixed culture comprised of many mutant strains.

RB-TnSeq (randomly barcoded transposon sequencing): an approach to identify gene function through random insertion of transposons across the genome followed by screening or selecting for altered phenotypes. Transposon insertions can lead to both loss-of-function and gain-of-function mutants, but loss-of-function transposon insertion mutations are more common. Individual transposon insertion strains are quantified using barcode sequencing.

Recombineering: a process by which deletions, substitutions or other mutations are introduced into a DNA sequence by homologous recombination, often relying on the phage Lambda Red recombination proteins.

Single-cell RNA-seq (scRNA-seq): sequencing RNA from individual cells.

Strain domestication: the process by which organisms are adapted to the laboratory environment, either through targeted mutagenesis or selection. Domestication often leads to easier manipulation of organisms in the lab (increased growth rate, increased transformability, decreased multi-cellular behaviors) but can be associated with the loss of traits important for survival in the natural environment.

Super-resolution microscopy: a microscopy approach that relies on deconvoluting the individual fluorescent signals to image subcellular dynamics in greater detail than what is achieved by optical microscopy.

DEVELOPMENTS IN THE STANDARDIZATION, INTEGRATION, AND GENERATION OF LARGE DATA SETS

The volumes of genome sequencing data available now provide clearer resolution of the genetic diversity of all microorganisms. These data have created new areas of study, realigned phylogenetic trees, and suggested novel methodologies for the rapid detection of microorganisms. The benefits of whole-genome sequencing are evident in many studies in microbiology. In addition to knowing the genomic sequences of millions of microbes, there is ever-expanding information about the transcriptomic, proteomic, lipidomic, and metabolomic profiles of many bacteria. The aggregated analyses of these multi-omic data sets can provide insights into many fundamental questions in microbial molecular biology and physiology, ranging from how polymicrobial communities respond to antibiotics (4–6) to the mechanistic roles of bacterial metabolism in infection and disease progression (7–9). Although these advances are tremendous, several further developments would allow the information gained from these omics approaches to be realized even more fully.

Standardizing information for large data sets

As the quality, speed, and efficiency of the data generated by whole-genome sequencing and other omics techniques has increased, the ability to keep the resulting data sets current with error correction and updated annotation has fallen behind. Errors in gene annotation plague all genome sequencing data. Some of the problems are due to old information, which, even when updated in new studies, may not yet be reflected in the annotations. Some omissions are due to suppositions about the nature and size of genetic elements. Other errors are due to assumptions based on sequence homology with other organisms. Although sequence homology frequently is a good indication of function, until the appropriate follow-up studies are performed, the annotation needs to be malleable. In all cases, the data underlying different annotations should be made clear.

In the future, it would be optimal to have a centralized, automated system, imposed during the peer-review publication process, which would allow appropriate updating of genome annotation. As published genomes become available in the NCBI GenBank database, an ideal first step would be to continuously update the annotation based on the most recent findings regarding the functions of the various genes. There are enormous benefits to continuous updates to genome annotation, including a more accurate pangenome reference for each species. This would allow for the easy comparison of gene function regardless of the strain used for the initial studies. Such an up-to-date pangenome reference would greatly facilitate genome studies of clinical isolates or environmental samples. A centralized system enabling easy updates to pangenome annotation also would allow finer resolution of genome differences and expand our understanding of the evolution of microorganisms.

Admittedly, substantial bioinformatics work would be required to update annotations to accurately reflect publications prior to the implementation of an automated system. Additionally, there needs to be consideration of the ways to assess and report data quality. Nevertheless, machine learning and artificial intelligence (AI) undoubtedly could aid this process, and—given the importance of annotations for advancing our scientific understanding—this work will be worth the effort.

Improvements to and standardization of metadata—the description of the samples, collection methodologies, and experimental conditions associated with each data set—are also needed. Several data repositories have their own standards, but comparisons of metadata across platforms can be challenging. Looking ahead, there are significant opportunities to deepen the insights derived from multi-omic studies by enhancing the standardization of data collection, annotation, and statistical analysis methods. Numerous reviews have detailed best practices in areas such as sample preparation, optimal read coverage for sequence-based methods, targeted and untargeted metabolite protein profiling, data wrangling (the conversion of raw data into more usable formats), data heterogeneity, power analysis, and the use of bioinformatic tools for various omics approaches (reviewed in references 10–12). Compliance with standards outlined in the Minimum Information for Biological and Biomedical Investigations (MIBBI) project will similarly facilitate consistent collection and storage of experimental metadata.

Integrating multi-omic data

Typically, multi-omic studies employ, at minimum, paired high-throughput functional readouts. This might include, for example, metagenomic analyses of microbial communities with metabolomic profiling. Sequenced-based high-throughput profiling methods and functional-omics approaches, such as metabolomics and proteomics studies, have distinct considerations regarding limitations in data collection, processing, and interpretation (reviewed in references 13, 14). However, these data can all be transformed and aggregated into tabular format, enabling integrated or multi-table statistical analyses (reviewed in references 15, 16). Such analyses have led to exciting insights. For example, metagenomic, meta-transcriptomic, metabolomics, and X-ray crystallography data resulted in the discovery of carbohydrate-active enzymes that the capybara rodent gut microbiome uses to depolymerize otherwise recalcitrant lignocellulose (17). Identification of this enzymatic mechanism is a significant step toward the conversion of lignocellulose into biofuels that can serve as an alternative to fossil fuels. In the area of human health, multi-omic profiling of a human cohort with ulcerative colitis led to the finding of a bacterial strain capable of metabolizing a common immunosuppressive drug and producing immunomodulatory metabolites (18). Going forward, multi-table statistical analyses of multi-omic data will play an important role in increasing our understanding of molecular mechanisms of and predicting the outcomes of all processes involving microbes, including disease.

Multi-omic studies use a wide variety of statistical methods, including univariate, multivariate, and machine-learning techniques. Principal component analysis is a commonly used multivariate method that maximizes variance within a data set. Other multivariate methods, such as canonical correlation analysis and partial least squares, are designed to directly characterize covariance across different tabular omic data sets within a study. For example, canonical correlation analysis was combined with other statistical analysis approaches to identify multi-omic modules linked to insulin resistance (19). As another example, sparse canonical correspondence analysis led to the identification of associations between microbial species and clinical variables of participants in a multifactorial study of well-being (20), exemplifying the value of analyzing count and continuous data (reviewed in reference 15). Network-based methods or multi-block data integration techniques can also identify co-expressed features that are predictive of disease, even if individual features are not informative on their own (21) (reviewed in reference 22). While there is no one-size-fits-all approach, applying appropriate statistical methods tailored to the specific structure and goals of such multi-omic data sets can greatly enhance the robustness and interpretability of findings. Standardizing these methods, incorporating module-level analysis, and addressing cross-omic dependencies, particularly multicollinearity (reviewed in reference 23) will be critical in advancing the field and translating multi-omic data into biological insights that can be tested by other experimental approaches.

Improving the collection of large data sets

Genomics and other omics data additionally will benefit from continued improvements to experimental technology. For example, advances in sequencing read length will provide more reliable genomic sequences, particularly for regions with many repeats. Similarly, improved data collection and analyses will allow more proteins and small molecules to be detected by mass spectrometric approaches. Within metabolomics studies, there have been exciting advancements in both the targeted and untargeted identification of metabolites, especially metabolites identified from host-associated microbial communities (24, 25). However, for bioactive metabolites associated with health status, such as products of aromatic amino acid metabolism, it remains unclear whether they originate from gut microbial pathways, host pathways, or a combination of both (reviewed in reference 26). This uncertainty limits our understanding of the roles microbial production plays in various biological processes. As we will discuss next, another desirable advance is the ability to genetically modify a greater number of microorganisms, which will aid in assessing improved technologies for data collection and help to address ambiguities such as the origin of biologically active metabolites.

DEVELOPMENTS IN THE GENETIC TOOL KIT

One critical challenge in the post-genomic era is extracting functional information from available data sets to answer basic biological questions and provide avenues for improving host and environmental health. An essential step in this process is assigning functions to newly discovered genes, the majority of which remain uncharacterized.

Increasing the ability to genetically manipulate a greater number of organisms

There are a number of established solutions to the problem of assigning function to specific genes, including predictions based on protein sequence homology (reviewed in reference 27), genomic context (28), and, most recently, predicted structural homology (29). While the scale and speed of these approaches are unquestionable advantages, experimental validation remains the gold standard (reviewed in reference 30). Historically, the microbiology field focused on in-depth studies of specific model organism strains from a limited number of species. One of the driving factors behind this trend was the availability of powerful genetic tools that allow for quick and simple genome manipulation. Although genomics opened the door to a variety of non-model organisms and strains, effective genetic tools are desperately needed to functionally characterize unexpected biological phenomena associated with these organisms.

Developing genetic systems to manipulate and characterize the genomes of many newly sequenced bacteria remains a formidable challenge. The most immediate hurdle is that most bacteria are difficult to culture or have not been grown in a laboratory setting (31), making genetic manipulation challenging. Recent approaches have bypassed the need for culture by manipulating bacteria directly within their microbial communities using phage-derived particles, conjugation, or electroporation to deliver editing systems (32, 33). Nevertheless, improving cultivability would greatly accelerate the possibility of manipulating bacterial genomes. An increasing number of innovative approaches are being applied to the growth of difficult-to-culture bacteria (reviewed in reference 34) although these often involve focused efforts on individual taxa or strains.

For bacteria that can be cultured, a variety of methods exist to potentially enable genetic manipulations, including allelic exchange (35, 36), recombineering (37–39), and various CRISPR-based tools (40, 41). These techniques can be very powerful in select organisms but face challenges in many other microbes that prevent their widespread adoption. For example, recombineering using Lambda Red (with components from bacteriophage Lambda) has transformed genome editing in Escherichia coli, but its reliance on host factors has limited its application to other organisms (42, 43). Despite widespread efforts (44, 45), few recombineering systems have matched the effectiveness of Lambda Red. Multiple issues plague other genome editing approaches, including portability, nucleic acid delivery, and toxicity. Innovative solutions are needed to enable efficient, straightforward, and rapid genetic manipulation across a wide range of previously un-editable bacteria. Conceivably, the ever-growing catalog of microbial genomic information will inspire novel ways to address these obstacles.

Improving the ability to connect genotype to phenotype on a larger scale

Expanding beyond the targeted manipulation of individual genes, much can be learned from genome-scale mutagenesis technologies with the potential to connect genotype to phenotype on a massive scale and interrogate entire genomes and even pan-genomes in a single experiment. One of the key drivers of advances in genome-scale genetic analyses is once again the ever-lowering cost and increasing depth of sequencing. Three technologies are particularly useful outside of well-established model organisms: Dub-seq (dual-barcoded shotgun expression library sequencing, an update to expression library screens) (46), RB-TnSeq (randomly-barcoded transposon sequencing, an update to transposon mutagenesis) (47), and CRISPRi-seq (a genome-scale CRISPRi sequencing approach) (reviewed in reference 48). For each of these technologies, large pools of mutants are generated and subjected to growth in an experimental condition of interest. Sequencing of the pools before and after the treatment reveals mutants that are enriched or depleted by the experimental condition (reviewed in reference 49).

Dub-seq (46) is a gain-of-function mutagenesis approach that involves the ectopic expression of foreign genome fragments in a host organism followed by screening for novel phenotypes introduced by the new DNA. Dub-seq operates on a massive scale and overcomes challenges with genetic tractability and cultivability of the target organisms of interest. In one example, Dub-seq was used to identify novel carbohydrate utilization pathways across a pan-genome of species from the Bacteroidales order (50). Although such expression library screens suffer from drawbacks, including false negative results, technologies like Dub-seq allow the rapid identification of genes with defined functions from within complex microbial communities.

RB-TnSeq is a loss-of-function mutagenesis technology based on the random insertion of transposons across the genome of a target organism. DNA barcodes incorporated into the transposons lower the cost and effort of sequencing library preparation and distinguish RB-TnSeq from approaches like TnSeq and INSeq (reviewed in references 51, 52). A tour de force study using RB-TnSeq profiled genome-wide fitness effects in >30 Pseudomonadota across hundreds of growth environments, leading to the identification of novel metabolism and antibiotic resistance genes (53). RB-TnSeq likely will be a powerful first approach in genome-scale studies of new microbes although widespread adoption may be hampered by the need for high-efficiency transformation protocols.

CRISPRi-seq is a genome-scale loss-of-function mutagenesis approach that employs knockdown of gene expression using a catalytically dead CRISPR-Cas9 and a diverse pool of guide RNAs (reviewed in references 48, 54). Once a CRISPRi toolkit has been established in an organism, it provides a tunable and highly adaptable platform for genome-scale loss-of-function studies (55, 56). However, deleterious CRISPRi phenotypes can be unstable due to suppressor mutations, limiting the types of experiments in which these phenotypes can be measured.

All the technologies reviewed above focus on tracking strain abundance in pooled mutant collections using deep sequencing, which offers significant scale and cost benefits. However, pooled collections cannot capture all phenotypes, especially those that do not alter the relative abundance of a strain under the selection conditions. A well-established strategy to overcome this limitation has been to isolate and array a library of single mutant strains from pooled collections (57–61) and individually test the arrayed mutants for specific phenotypes (62–68). Early projects creating genome-scale ordered mutant collections in E. coli and S. cerevisiae (69, 70), in which nearly every non-essential gene in the genome is represented by a mutant located in a defined position in a set of multi-well plates, demonstrated the broad utility of this type of resource. However, the high costs of assembling these collections from scratch (reviewed in reference 71) have limited them to a few model organisms with large research communities. This limitation is being overcome by workflows that enable efficient conversion from pooled to ordered genome-scale mutant collections, thereby reducing the cost and effort required by orders of magnitude (72, 73). This will likely lead to an explosion in the availability of ordered mutant collections.

Arrayed (ordered) mutant collections enable researchers to incorporate functional genomics into the multi-omic approaches that are revolutionizing microbiology. Recent examples of discoveries made possible by ordered collections include the identification of genes responsible for drug metabolism (74) and small molecule production (75) from the human microbiome (functional genomics combined with metabolomics) as well as the discovery of the genetic determinants of cell shape (61, 72, 75) (functional genomics paired with single-cell microscopy) and genetic factors influencing TLR2-dependent innate immune signaling (76) (functional genomics alongside host signaling studies).

As development in this area continues, ongoing innovation will further reduce costs and improve the quality of future ordered mutant collections, making these genetic resources a particularly exciting and promising approach for accelerating advances in microbiology. Combining these genetic approaches with resources such as the forthcoming Microbial Molecular Phenotyping Capability (M2PC) (77), which is being designed to offer an extensive array of automated phenotyping instrumentation, will accelerate our ability to generate functional knowledge about bacteria and their communities.

DEVELOPMENTS IN STUDIES OF NON-MODEL MICROBES AND MICROBIAL COMMUNITIES

Concurrent with the need to genetically manipulate more diverse microorganisms, there is a demand for advances in establishing more species as “model organisms,” studying microbes in their native environments, and examining communities comprised of multiple microbes.

Establishing new model organisms

The criteria for establishing a model organism have not changed substantially over time: ease of cultivation, genetic tractability, and relevance to the biological phenomena we wish to understand. Model organisms have greatly accelerated our mechanistic understanding of important biological processes and served as a focal point for research groups asking related questions. However, the process of establishing a model system can lead to significant differences between the resulting model strain (one that can easily be manipulated in the lab), termed strain domestication, and representative isolates from the natural environment. Changes can include decreased adhesion or reduced biofilm production, as illustrated for Bacillus subtilis (78, 79). Additionally, there are still relatively few model microbes compared to the vast number in ecological systems. The advances outlined in the previous sections raise the intriguing possibility that future model organisms could be established without extensive strain domestication and be based on a foundation of genome-scale genetic toolkits and integrated multi-omic data sets.

A recent effort to establish a model organism for the infant gut microbiome (75) provides a potential preview for this process. Starting with a transformable strain of a Bifidobacterium, which represents the most abundant genus in many healthy infant guts, transformation efficiency was increased to allow the creation of a hyper-saturated transposon-insertion pool. This pool was converted to a genome-scale ordered collection and then interrogated with a multi-omic approach that integrated chemical genomics, mass spectrometry, growth curves, time courses of host colonization, and single-cell imaging, leading to new insights into the establishment and function of the infant gut microbiome (75).

The extent and speed at which these “new” model bacterial systems will be adopted by the larger research community remains to be seen. Historically, scientists establishing their own groups inherit strains from their mentors, and research communities build up over decades as part of professional lineages. Accelerating new model organism adoption will require contemporaneous sharing of resources between labs. For this to happen, the research community needs to overcome both inertia in sharing due to efforts expended on distinct species or strains and logistical challenges for distributing these resources quickly and at scale. Another solution would be to establish a computational framework for automatically translating information on gene function between related species, blurring the distinctions between model and non-model species. Frameworks merging comparative genomics with genome-scale metabolic models (80, 81) and multi-omic data sets (82) are laudable advances towards this goal.

Increasing the ability to study microbial communities

The study of model microorganisms has led to tremendous advances not only in microbiology, but in numerous other fields including molecular biology, biochemistry, and cellular biology (83). However, bacteria are astoundingly diverse (31), with intra- and interspecies variation that is often not well represented by laboratory strains. Moreover, although strains are frequently studied in monoculture in the laboratory, microorganisms usually live in complex and heterogeneous environments where interactions with other microbes profoundly impact their behaviors (reviewed in reference 84). To build on the troves of fundamental knowledge obtained from studies of model organisms, we need to better understand the breadth of related strains and species as well as how they exist within complex microbial communities.

Quorum sensing, a form of bacterial cell-cell signaling, illustrates current challenges and opportunities in efforts to study microbes in communities. The quorum sensing systems of wild bacterial isolates display both sequence-level diversity and altered regulatory networks when compared to those of model laboratory strains (85, 86). Furthermore, it is becoming clear that, in addition to its canonical role in intraspecies communication, quorum sensing can mediate both cooperative and antagonistic interspecies interactions (reviewed in reference 87). A major challenge remains in connecting sequence differences to specific functions, particularly with regard to more detailed molecular function or specificity (beyond broad categorizations into gene or protein families) (reviewed in reference 88). For example, within LuxI/R-type quorum sensing, over 6,000 unique systems were identified by sequencing (89). However, these sequences cannot be used to predict the specific chemical identities of the signals produced by the enzymes encoded by these genes or reveal to which signal(s) a quorum sensing receptor will respond in a mixed community. Although advances in experimental methods facilitate larger-scale experiments to explore these questions, laboratory studies have been limited to exploring only a few dozen systems in detail and roughly 100 systems more shallowly (90, 91). The integration of computational approaches with laboratory experiments enables more rapid and thorough explorations of quorum sensing diversity by helping to link sequences to molecular insights. For instance, covariation analysis (92) was used to identify amino acids responsible for signal selectivity in LuxI/R-type quorum sensing systems (89), advancing efforts to predict signals from sequence and elucidating strain-level genetic diversity that is likely to alter the function of these systems.

The study of microbial communities has yielded important advances in recent years. The combination of metagenomics, isolate libraries, and defined model communities have enabled detailed molecular and chemical studies of bacterial isolates and communities from habitats as diverse as the eastern cottonwood tree (Populus deltoides) (93–95), methane-oxidizing sediment communities (96, 97) (reviewed in reference 98), and the lungs of patients with cystic fibrosis (85, 99). With the vast quantities of genomic and metagenomic data generated for these communities, computational approaches allow the prioritization of gene sequences for study. In the case of quorum sensing, sequence similarity network analysis (100) could be used to group signal synthase genes into clusters expected to produce the same signal (101). When a molecular signal is known for a high-quality reference sequence, it becomes possible to predict what signal is likely produced by the products of related genes in the cluster. The value of this approach is exemplified by a study of LuxI-type signal synthases from methylotrophic bacteria, in which most gene clusters had no reference signal (102). Experimental determination of the signal for the largest gene cluster resulted in the identification of a novel signaling molecule widespread among methylotrophs (102). High-quality reference databases would rapidly improve the utility of such approaches (103), especially as studies expand to metagenomes and large-scale surveys of bacterial communities. In the example of quorum sensing, these studies can result in a catalog of signaling potential for a genus or community and suggest general modes of community interaction.

In recent years, more and more public computational resources have become available, including genomic and metagenomic databases with comprehensive integrated bioinformatics platforms such as Integrated Microbial Genomes & Microbiomes (IMG/M) (104), the Bacterial and Viral Bioinformatics Center (BV-BRC) (105), and the Department of Energy Systems Biology Knowledgebase (KBase) (106). In addition, web resources for generating sequence similarity networks (100) are publicly available and user-friendly, providing graphical user interfaces (GUIs) that allow scientists to submit sequences for analysis without significant computational knowledge. In many cases, the accessibility of these platforms is increased by tutorials and webinars. Looking ahead, greater collaboration between computational biologists and experimental microbiologists will enable increasingly sophisticated and accessible applications of advanced bioinformatic and computational methods. In addition, higher throughput and automated experimental methods, expanding from hundreds to thousands of sequences, strains, or organisms, will enable scientists to rapidly expand insights into the diversity of bacterial metabolism, signaling, and interactions. Developments in establishing reproducible model microbial communities that reflect native-like complexity and avoid the inadvertent domestication of strains will facilitate more mechanistic studies of the complex, heterogeneous, and multifaceted interactions in bacterial communities (107). Finally, advances in single-cell transcriptomics and imaging, as discussed next, will reveal the spatial structure and interactions that take place in natural microbial communities.

DEVELOPMENTS IN STUDIES OF SINGLE CELLS

Even within a single population of microbes of the same species, there is commonly substantial phenotypic variation. A subset of cells may adopt non-growing, stress-tolerant “persister” phenotypes (reviewed in references 108, 109), or cells may diversify into multiple metabolic states (reviewed in references 110, 111). Intrinsic gene expression noise arising from stochastic events further adds to this diversity (112, 113) (reviewed in reference 114). Traditional approaches to capture information about transcripts, proteins, or even small molecules in cells require bulk extraction from millions of cells and, as such, are blind to this variation at the single-cell level. However, the ability to learn about individual cells is changing with advances in single-cell transcriptomics and microscopy.

Advancing single-cell transcriptomics

The sequencing of RNA from individual cells is more and more commonplace for multicellular organisms. However, various technical considerations, chiefly microbes’ tough cell walls, low RNA content, and lack of mRNA polyadenylation (in the case of eubacteria), have been barriers to the adoption of this technology in the microbiology field (reviewed in references 115, 116). This is rapidly changing with the development of several new techniques for single-cell RNA sequencing (scRNA-seq) in both bacteria (117–124) and fungi (125–128). These include methods based on nanoliter droplet barcoding (in which cells are separated into individual droplets and the RNA in each droplet is associated with a different barcode [118, 120, 121, 123, 125, 127, 128]), combinatorial indexing (in which the RNA in each cell is marked by a unique combination of barcodes added during multiple iterations of splitting pools of cells [117–120, 124, 126]), and even spatially resolved, microscopy-based approaches (129, 130). Although the field is still in its infancy, applications already range from understanding the fundamental transcriptional biology of microbes (131, 132) and their mobile genetic elements (such as transposable elements [120], plasmids [133], and bacteriophages [117, 118]), to heterogeneous responses of microbes to antimicrobials and other stresses (118, 120, 123, 125), and diversification within biofilms (119, 129) and within the mammalian host (130). The breadth of these initial efforts indicates that scRNA-seq will be impactful in numerous fields of both basic microbiology and infectious disease going forward.

Making the most of these advances will require the synthesis of microbiological understanding and experimental techniques with computational approaches to handle these large, noisy, and sparse data sets. It can be problematic to adapt existing mammalian frameworks for the analysis of scRNA-seq data to microbes, especially bacteria, given the unique features of these organisms. Bacterial mRNAs are unspliced, polycistronic, and highly unstable compared to eukaryotic transcripts (reviewed in references 116, 134). Physiological events, such as DNA replication and cell cycle progression, also influence transcriptional dynamics in a way that is distinct from eukaryotic cells (131, 135). For this field to progress, individuals conversant across both microbiology and computational analysis are needed. scRNA-seq experiments will also benefit from individuals with diverse experimental backgrounds. These include those who can apply scRNA-seq techniques to more challenging samples such as biofilms or the gut microbiome, or have expertise in quantitative systems biology where much progress has been made in understanding transcriptional noise (reviewed in references 114, 136, 137). Exposing microbiologists early in their training to computational and quantitative sciences would support these efforts in applying scRNA-seq technologies to microbes. Finally, forums to bring researchers with expertise across these diverse areas would accelerate the development of a new single-cell genomics field with tools and applications unique to microbiology.

Advancing single-cell microscopy

An ever-expanding pursuit in the field of microbiology is the investigation of the physiology of single cells and single molecules using microscopic imaging. These approaches enable researchers to explore previously intractable problems, such as how biomolecules function inside living cells (reviewed in reference 138), how microbial communities generate transcriptional heterogeneity in space and time (129, 139, 140), and how the mechanical properties of biofilm communities emerge from cell-cell interactions (141) (reviewed in reference 142). Ongoing advances in imaging-based approaches are making these pursuits commonplace. Many more researchers now have access to confocal microscopes, enabling three-dimensional sectioning of microbial cells and communities. Moreover, the continuous development of cutting-edge imaging techniques, such as super-resolution microscopy and light-sheet microscopy approaches, enables studies at unprecedented spatiotemporal resolution (143) (reviewed in reference 144). New probing techniques allow researchers to examine the entire transcriptomes of individual cells with single-molecule sensitivity in fixed, in-situ communities (129). Furthermore, live-cell reporters for numerous biological processes, such as starvation, c-di-GMP production, and quorum sensing, are becoming increasingly reliable (145–147). Finally, in the age of AI, image analysis software is more effective at improving image quality post-acquisition, and freely available image analysis programs help users extract information from their data sets (148–151). The field of microbiology is now positioned to explore the molecular processes that underpin complex behaviors in unprecedented detail.

What, then, is needed for the future of imaging studies of microbial physiology? Several areas could benefit from technological and experimental developments. One ongoing goal is to examine real-time gene expression in living microbial cells and their communities. Although in situ-based approaches are revolutionizing our ability to investigate transcriptomes with high spatial resolution, current techniques require fixation and, therefore, cannot address the temporal development of transcriptional states in real time. The counterpart to this approach is to examine the activity of one gene at a time, commonly leveraging fluorescent protein-based promoter fusions. This approach is laborious as it involves optimization for each promoter of interest, is indirect, and is subject to the kinetics of fluorescent proteins. Future tools that enable real-time imaging of RNAs in live cells on a transcriptome-wide basis could transform how we study gene expression in microbes.

Another area that is ripe for continued development is live-cell imaging approaches that enable investigators to observe microbes in the communities and hosts in which they live (reviewed in reference 152). Although we know much about the physiology of individual bacteria grown in simple conditions, approaches that expand our ability to examine bacteria in their natural contexts, allowing us to account for intra-species interactions and variability in complex mixtures, will tremendously expand our understanding of bacteria in their native environments. There also is the need to develop additional sensors that faithfully report on the distribution and concentration of diverse cellular molecules. Finally, microbial imaging will benefit from continuous improvements to automation and AI. These techniques would allow us to massively scale up both data acquisition and analysis, creating new possibilities in the process. For instance, imaging entire mutant libraries at the single-cell and population levels could allow us to understand the phenotypic landscapes of model bacteria and the relationship to genomic architecture and physiology (61, 72, 75). Imaging arrayed environmental samples in monoculture and co-culture could allow us to understand how bacteria self-organize and interact in specific environments. Together, the imaging advances described here will complement novel genomic and culturing methods to enable a holistic understanding of microbial physiology, behavior, and ecology in model and natural systems.

DEVELOPMENTS IN DETERMINING THE STRUCTURES OF MICROBIAL MACROMOLECULES AND MACROMOLECULAR COMPLEXES

Finally, thousands of critical insights into molecular biology and physiology have come from the determination of macromolecular structures by nuclear magnetic resonance (NMR), X-ray crystallography, and cryo-electron microscopy (cryo-EM) with continuous advances in technology. Developments in cryo-EM hardware (electron sources, energy filters, and electron detectors) are enabling truly atomic structure determination (153). Recent advances in structure prediction through AlphaFold (29) and RoseTTAFold (154) also are remarkable. Nevertheless, much remains to be learned about the structures of macromolecules and macromolecular complexes to increase our molecular and mechanistic understanding of microbial processes.

Determining and predicting the structures of several types of macromolecules, including multi-component, nucleic acid-bound assemblies, and large integral membrane complexes, continue to be challenging. The predictions of these complexes are limited, in part, because the corpus of these structures in the Protein Data Bank (PDB), given the barriers to solving the structures, is not as comprehensive as that of protein-only folds. For example, processes such as transcription, DNA repair, and transposition rely on the coordinated assembly of large macromolecular complexes at specific DNA locations. An emerging theme from these studies is the role of DNA distortions in priming complex assemblies. The integration of the bacterial RNA-guided transposon V-K CAST (CRISPR-associated transposon) requires a large megadalton complex of transposon-encoded proteins assembled on a specific DNA distortion, created by the RNA-guided CRISPR-like effector, to recruit the transposase to the target site (155). Target DNA distortions are also observed in other integration complex assemblies (156, 157) although the specific structure of the distortion varies. These super-complexes can be very difficult to reconstitute. DNA-binding proteins can be labile and hard to express in large quantities, and the quantity becomes increasingly limiting with each additional component. Finally, since cryo-EM relies on averaging a large number of particle images to increase the signal in inherently noisy images, high sample concentrations are needed to obtain a sufficient number of images (reviewed in reference 158). If samples are heterogeneous, then orders of magnitude more data may be required.

To circumvent yield issues, EM grids can be prepared using a thin layer of graphene, which provides an atomically thin, essentially transparent carbon surface to which complexes often adhere better compared to the air-water interface, thus reducing sample concentration requirements by an order of magnitude (159, 160). In another approach, functionalized affinity grids specifically enrich for the molecule of interest (161), also reducing sample concentration requirements. Further improvements come from engineering components of the complexes. For instance, the design of a suitable DNA substrate facilitates complex formation by bypassing slow, rate-limiting assembly kinetics (162, 163). Increases in yield, which can lead to the solution of multiple states, additionally will help address the issue that many structures only represent one static form of a complex that cycles through unexpected conformations not represented by known or predicted structures. Increases in data collection speed and throughput will also help resolve multiple states within a conformationally heterogeneous data set, further adding structural and mechanistic insights.

Excitingly, cryo-EM validation practices are continuously evolving. However, it is important to note that critical metrics such as resolution estimates can be biased by choices during image processing (reviewed in reference 164). As the use of cryo-EM becomes increasingly widespread, automated, and broadly adopted, it remains critical that the resulting maps are carefully inspected and subjected to rigorous validation.

Finally, regardless of how they were solved, structures may differ from those found in cells given that the biochemical preparations may not represent the conditions in a cell or be missing a component. This problem is especially acute if the complexes are purified from heterologous organisms, the processes being studied are incompletely characterized, or additional cellular factors are required for complex assembly and stabilization. For example, S15, an abundant bacterial protein that normally functions in the ribosome, is repurposed by the V-K CAST system from Scytonema hoffmmanni (ShCAST) to stabilize its integration complex (165). S15 is not absolutely required for in vitro activity of ShCAST (166) and was initially thought to be a contaminant (165). However, S15 stimulates ShCAST activity by an order of magnitude (155, 165). Therefore, the development of robust methods to systematically identify required factors would be beneficial for studies of assemblies. Conducting genome-wide screens, as described above, is one avenue for identifying missing factors (167), whereas host factors known for applicable activities, such as the bacterial integration host factor (IHF) known to induce DNA distortions, can also be implicated by studying sequence preferences (168). More recently, macromolecular assemblies have been visualized by directly imaging cellular extracts (169, 170), and one can envision a hybrid approach that enriches for desired target assemblies in cell-free extracts as well as cellular lysates. Ever-increasing developments in structure determination and prediction, cross-fertilized by the multi-omic, genetic, single cell, and computational advances discussed in earlier sections, hopefully will ultimately lead to structural and mechanistic understanding of nearly all microbial macromolecules and macromolecular complexes.

OVERARCHING CHALLENGES AND OPPORTUNITIES

Several interconnected themes reoccur in the approaches and methodologies described above. These include the need to improve effective sharing of protocols, computational pipelines and programs, resources, and data sets. Addressing this need will require international discussions on how these sharing platforms are best developed and sustainably maintained. For the most effective sharing, improvements to data standardization and updates (for instance, of gene sequence annotations) also need to be continuous. Increased accessibility to cutting-edge approaches and up-to-date data sets will benefit scientists worldwide. Simultaneously, there are opportunities to continue to improve technology at all levels, from increasing the ability to monitor more molecules at diverse spatial scales, as well as the activities of individual cells, to determining the atomic-scale structures of macromolecules, all in conjunction with improving our ability to culture and genetically manipulate a broader range of organisms and to compare data sets across these systems. Attainment of these goals will be facilitated by advances in automation and the application of advanced computing and AI. Additionally, and perhaps most importantly, scientists at all levels need to be trained to have expertise across disciplines to be able to take advantage of and critically evaluate the diverse array of exponentially expanding data sets. With all these new technologies and approaches in the hands of the next generation of scientists, the future of microbial molecular biology and physiology is tremendously exciting and will undoubtedly lead to the discovery of completely unexpected microbial mechanisms and solutions for improving health and the environment worldwide.

SUPPLEMENTAL MATERIAL

The following material is available online at https://doi.org/10.1128/mbio.00694-25.

Table S1. mbio.00694-25-s0001.xlsx.

Table of useful websites.

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