Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing

Recent advances in single-cell RNA sequencing of bacteria: Techniques, challenges, and applications

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in complex biological systems. While this technology has been widely applied to eukaryotic cells, its adaptation to bacterial systems has been challenging due to the unique characteristics of bacterial transcripts. This review surveys the recent developments in bacterial scRNA-seq techniques, highlighting the technical challenges, methodological innovations, and emerging applications in microbiology. We discuss the key differences between eukaryotic and bacterial RNA-seq approaches, focusing on the strategies to overcome limitations such as the lack of poly-A tails in bacterial mRNAs and the low RNA content in individual bacterial cells. The review covers various bacterial scRNA-seq methods, including plate-based, split-pool barcoding, and droplet-based techniques, comparing their strengths and limitations in terms of sensitivity, throughput, and applicability to different bacterial species. Furthermore, we explore the biological insights gained from these techniques, such as identifying rare cell states, characterization of antibiotic responses, and analysis of bacterial communities. Finally, we discuss future perspectives and potential applications of bacterial scRNA-seq in understanding microbial physiology, host-pathogen interactions, and complex microbial ecosystems. This comprehensive overview aims to provide researchers with a clear understanding of the current state and future directions of single-cell transcriptomics in bacteria.

Transcriptomic profiling of individual bacteria by MATQ-seq

Bacterial single-cell transcriptomics is revolutionizing our understanding of cell-to-cell variation within bacterial populations and enables gene expression profiling in complex microbial communities. Using the eukaryotic multiple annealing and dC-tailing-based quantitative single-cell RNA-sequencing (scRNA-seq) (MATQ-seq) approach, we have developed a robust bacterial scRNA-seq protocol, which integrates index sorting, random priming and rRNA depletion. This method stands out for its high rate of cell retention and its suitability for experiments with limited input material, offering a reliable method even for small sample sizes. Here we provide a step-by-step protocol covering the entire process of generating single-bacteria transcriptomes, including experimental and computational analysis. It involves (i) single-cell isolation via fluorescence-activated cell sorting (FACS) and cell lysis, (ii) reverse transcription and cDNA amplification using robotic liquid handling, (iii) rRNA depletion, (iv) indexing and sequencing, and (v) data processing steps to start comprehensive data analysis. Using model organisms such as Salmonella enterica, we show that the method achieves a retention rate of 95%, defined as the rate of initially sorted cells converted into effective sequencing libraries. This substantially surpasses other available protocols. The method robustly detects 300-600 genes per cell, highlighting its effectiveness in capturing a broad transcriptomic profile. The entire procedure from FACS-based single-cell isolation to raw data generation spans ~5 d. As MATQ-seq has already been proven robust in several bacterial species, it holds promise for the establishment of a streamlined microbial scRNA-seq platform.

Fast Encapsulation of Microbes into Dissolvable Hydrogel Beads Enables High-Throughput Microbial Single-Cell RNA Sequencing of Clinical Microbiome Samples

Microbial single-cell RNA-seq (mscRNA-seq) can achieve resolution at the cellular level, enhancing the understanding of microbial communities. However, current high-throughput mscRNA-seq methods are limited by multiple centrifugation steps, which can lead to microbial loss and bias. smGel-seq is reported, a high-throughput single-microbe RNA sequencing method for clinical microbiome samples that employs hydrogel beads to encapsulate individual microbes to reduce microbial loss and input requirements. In this method, a novel microchannel array device is implemented for encapsulating single microbe in dissolvable hydrogel beads (smDHBs), along with an optimized automated microfluidic platform to co-encapsulate barcoded beads and smDHBs, enabling high-throughput barcoding of individual microbes. smGel-seq significantly increases the microbial recovery rate in a gut microbiome sample from 8.8% to 91.8%. Furthermore, this method successfully processes clinical microbiome samples with microbial inputs 20 times lower than those required by previous methods. Notably, smGel-seq enables the first mscRNA-seq in a clinical sputum microbiome sample, revealing a specific microbial subpopulation that may play a key role in environmental adaptability, antibiotic resistance, and pathogenicity. These results highlight the compatibility of smGel-seq with clinical microbiome samples and demonstrate its potential for widespread application in diverse clinical and research settings.

Resolving spatiotemporal dynamics in bacterial multicellular populations: approaches and challenges

SUMMARYThe development of multicellularity represents a key evolutionary transition that is crucial for the emergence of complex life forms. Although multicellularity has traditionally been studied in eukaryotes, it originates in prokaryotes. Coordinated aggregation of individual cells within the confines of a colony results in emerging, higher-level functions that benefit the population as a whole. During colony differentiation, an almost infinite number of ecological and physiological population-forming forces are at work, creating complex, intricate colony structures with divergent functions. Understanding the assembly and dynamics of such populations requires resolving individual cells or cell groups within such macroscopic structures. Addressing how each cell contributes to the collective action requires pushing the resolution boundaries of key technologies that will be presented in this review. In particular, single-cell techniques provide powerful tools for studying bacterial multicellularity with unprecedented spatial and temporal resolution. These advancements include novel microscopic techniques, mass spectrometry imaging, flow cytometry, spatial transcriptomics, single-bacteria RNA sequencing, and the integration of spatiotemporal transcriptomics with microscopy, alongside advanced microfluidic cultivation systems. This review encourages exploring the synergistic potential of the new technologies in the study of bacterial multicellularity, with a particular focus on individuals in differentiated bacterial biofilms (colonies). It highlights how resolving population structures at the single-cell level and understanding their respective functions can elucidate the overarching functions of bacterial multicellular populations.

High-throughput single-microbe RNA sequencing reveals adaptive state heterogeneity and host-phage activity associations in human gut microbiome

Microbial communities such as those residing in the human gut are highly diverse and complex, and many with important implications for health and diseases. The effects and functions of these microbial communities are determined not only by their species compositions and diversities but also by the dynamic intra- and inter-cellular states at the transcriptional level. Powerful and scalable technologies capable of acquiring single-microbe-resolution RNA sequencing information in order to achieve a comprehensive understanding of complex microbial communities together with their hosts are therefore utterly needed. Here we report the development and utilization of a droplet-based smRNA-seq (single-microbe RNA sequencing) method capable of identifying large species varieties in human samples, which we name smRandom-seq2. Together with a triple-module computational pipeline designed for the bacteria and bacteriophage sequencing data by smRandom-seq2 in four human gut samples, we established a single-cell level bacterial transcriptional landscape of human gut microbiome, which included 29,742 single microbes and 329 unique species. Distinct adaptive response states among species in Prevotella and Roseburia genera and intrinsic adaptive strategy heterogeneity in Phascolarctobacterium succinatutens were uncovered. Additionally, we identified hundreds of novel host-phage transcriptional activity associations in the human gut microbiome. Our results indicated that smRandom-seq2 is a high-throughput and high-resolution smRNA-seq technique that is highly adaptable to complex microbial communities in real-world situations and promises new perspectives in the understanding of human microbiomes.

Droplet microfluidics: unveiling the hidden complexity of the human microbiome

The human body harbors diverse microbial communities essential for maintaining health and influencing disease processes. Droplet microfluidics, a precise and high-throughput platform for manipulating microscale droplets, has become vital in advancing microbiome research. This review introduces the foundational principles of droplet microfluidics, its operational capabilities, and wide-ranging applications. We emphasize its role in enhancing single-cell sequencing technologies, particularly genome and RNA sequencing, transforming our understanding of microbial diversity, gene expression, and community dynamics. We explore its critical function in isolating and cultivating traditionally unculturable microbes and investigating microbial activity and interactions, facilitating deeper insight into community behavior and metabolic functions. Lastly, we highlight its broader applications in microbial analysis and its potential to revolutionize human health research by driving innovations in diagnostics, therapeutic development, and personalized medicine. This review provides a comprehensive overview of droplet microfluidics' impact on microbiome research, underscoring its potential to transform our understanding of microbial dynamics and their relevance to health and disease.

Intratumoral microbiome: implications for immune modulation and innovative therapeutic strategies in cancer

Recent advancements have revealed the presence of a microbiome within tumor tissues, underscoring the crucial role of the tumor microbiome in the tumor ecosystem. This review delves into the characteristics of the intratumoral microbiome, underscoring its dual role in modulating immune responses and its potential to both suppress and promote tumor growth. We examine state-of-the-art techniques for detecting and analyzing intratumoral bacteria, with a particular focus on their interactions with the immune system and the resulting implications for cancer prognosis and treatment. By elucidating the intricate crosstalk between the intratumoral microbiome and the host immune system, we aim to uncover novel therapeutic strategies that enhance the efficacy of cancer treatments. Additionally, this review addresses the existing challenges and future prospects within this burgeoning field, advocating for the integration of microbiome research into comprehensive cancer therapy frameworks.

SAMPL-seq reveals micron-scale spatial hubs in the human gut microbiome

The local arrangement of microbes can profoundly impact community assembly, function and stability. However, our understanding of the spatial organization of the human gut microbiome at the micron scale is limited. Here we describe a high-throughput and streamlined method called Split-And-pool Metagenomic Plot-sampling sequencing (SAMPL-seq) to capture spatial co-localization in a complex microbial consortium. The method obtains microbial composition of micron-scale subcommunities through split-and-pool barcoding. SAMPL-seq analysis of the healthy human gut microbiome identified bacterial taxa pairs that consistently co-occurred both over time and across multiple individuals. These co-localized microbes organize into spatially distinct groups or 'spatial hubs' dominated by Bacteroidaceae, Ruminococcaceae and Lachnospiraceae families. Using inulin as a dietary perturbation, we observed reversible spatial rearrangement of the gut microbiome where specific taxa form new local partnerships. Spatial metagenomics using SAMPL-seq can unlock insights into microbiomes at the micron scale.

mSphere of Influence: How the single cell contributes to the collective

Gina Lewin works in the field of microbial ecology, with a focus on the human microbiota. In this mSphere of Influence article, she reflects on how two papers describing bacterial single-cell RNA-seq-"Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing" by S. B. Blattman, W. Jiang, P. Oikonomou, and S. Tavazoie (Nat Microbiol 5:1192-1201, 2020, https://doi.org/10.1038/s41564-020-0729-6) and "Microbial single-cell RNA sequencing by split-pool barcoding" by A. Kuchina, L. M. Brettner, L. Paleologu, C. M. Roco, et al. (Science 371:eaba5257, 2021, https://doi.org/10.1126/science.aba5257)-impacted her work by developing a new approach to study how single cells of bacteria contribute to ecosystem-level processes.

Highly multiplexed spatial transcriptomics in bacteria

Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial messenger RNA. To overcome this challenge, we combined 1000-fold volumetric expansion with multiplexed error-robust fluorescence in situ hybridization (MERFISH) to create bacterial-MERFISH. This method enables high-throughput, spatially resolved profiling of thousands of operons within individual bacteria. Using bacterial-MERFISH, we dissected the response of Escherichia coli to carbon starvation, systematically mapped subcellular RNA organization, and charted the adaptation of a gut commensal Bacteroides thetaiotaomicron to micrometer-scale niches in the mammalian colon. We envision that bacterial-MERFISH will be broadly applicable to the study of bacterial single-cell heterogeneity in diverse, spatially structured, and native environments.

Towards improved biofilm models

Biofilms are complex microbial communities that have a critical function in many natural ecosystems, industrial settings as well as in recurrent and chronic infections. Biofilms are highly heterogeneous and dynamic assemblages that display complex responses to varying environmental factors, and those properties present substantial challenges for their study and control. In recent years, there has been a growing interest in developing improved biofilm models to offer more precise and comprehensive representations of these intricate systems. However, an objective assessment for ascertaining the ability of biofilms in model systems to recapitulate those in natural environments has been lacking. In this Perspective, we focus on medical biofilms to delve into the current state-of-the-art in biofilm modelling, emphasizing the advantages and limitations of different approaches and addressing the key challenges and opportunities for future research. We outline a framework for quantitatively assessing model accuracy. Ultimately, this Perspective aims to provide a comprehensive and critical overview of medically focused biofilm models, with the intent of inspiring future research aimed at enhancing the biological relevance of biofilm models.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

An improved bacterial single-cell RNA-seq reveals biofilm heterogeneity

In contrast to mammalian cells, bacterial cells lack mRNA polyadenylated tails, presenting a hurdle in isolating mRNA amidst the prevalent rRNA during single-cell RNA-seq. This study introduces a novel method, ribosomal RNA-derived cDNA depletion (RiboD), seamlessly integrated into the PETRI-seq technique, yielding RiboD-PETRI. This innovative approach offers a cost-effective, equipment-free, and high-throughput solution for bacterial single-cell RNA sequencing (scRNA-seq). By efficiently eliminating rRNA reads and substantially enhancing mRNA detection rates (up to 92%), our method enables precise exploration of bacterial population heterogeneity. Applying RiboD-PETRI to investigate biofilm heterogeneity, distinctive subpopulations marked by unique genes within biofilms were successfully identified. Notably, PdeI, a marker for the cell-surface attachment subpopulation, was observed to elevate cyclic diguanylate (c-di-GMP) levels, promoting persister cell formation. Thus, we address a persistent challenge in bacterial single-cell RNA-seq regarding rRNA abundance, exemplifying the utility of this method in exploring biofilm heterogeneity. Our method effectively tackles a long-standing issue in bacterial scRNA-seq: the overwhelming abundance of rRNA. This advancement significantly enhances our ability to investigate the intricate heterogeneity within biofilms at unprecedented resolution.

scTrends: A living review of commercial single-cell and spatial 'omic technologies

Understanding the rapidly evolving landscape of single-cell and spatial omic technologies is crucial for advancing biomedical research and drug development. We provide a living review of both mature and emerging commercial platforms, highlighting key methodologies and trends shaping the field. This review spans from foundational single-cell technologies such as microfluidics and plate-based methods to newer approaches like combinatorial indexing; on the spatial side, we consider next-generation sequencing and imaging-based spatial transcriptomics. Finally, we highlight emerging methodologies that may fundamentally expand the scope for data generation within pharmaceutical research, creating opportunities to discover and validate novel drug mechanisms. Overall, this review serves as a critical resource for navigating the commercialization and application of single-cell and spatial omic technologies in pharmaceutical and academic research.

Sequencing-based analysis of microbiomes

Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.

Identification and genetic dissection of convergent persister cell states

Persister cells, rare phenotypic variants that survive normally lethal levels of antibiotics, present a major barrier to clearing bacterial infections1. However, understanding the precise physiological state and genetic basis of persister formation has been a longstanding challenge. Here we generated a high-resolution single-cell2 RNA atlas of Escherichia coli growth transitions, which revealed that persisters from diverse genetic and physiological models converge to transcriptional states that are distinct from standard growth phases and instead exhibit a dominant signature of translational deficiency. We then used ultra-dense CRISPR interference3 to determine how every E. coli gene contributes to persister formation across genetic models. Among critical genes with large effects, we found lon, which encodes a highly conserved protease4, and yqgE, a poorly characterized gene whose product strongly modulates the duration of post-starvation dormancy and persistence. Our work reveals key physiologic and genetic factors that underlie starvation-triggered persistence, a critical step towards targeting persisters in recalcitrant bacterial infections.

Unraveling plant-microbe symbioses using single-cell and spatial transcriptomics

Plant-microbe symbioses require intense interaction and genetic coordination to successfully establish in specific cell types of the host and symbiont. Traditional RNA-seq methodologies lack the cellular resolution to fully capture these complexities, but single-cell and spatial transcriptomics (ST) are now allowing scientists to probe symbiotic interactions at an unprecedented level of detail. Here, we discuss the advantages that novel spatial and single-cell transcriptomic technologies provide in studying plant-microbe endosymbioses and highlight key recent studies. Finally, we consider the remaining limitations of applying these approaches to symbiosis research, which are mainly related to the simultaneous capture of both plant and microbial transcripts within the same cells.

Bacterial single-cell RNA sequencing captures biofilm transcriptional heterogeneity and differential responses to immune pressure

Biofilm formation is an important mechanism of survival and persistence for many bacterial pathogens. These multicellular communities contain subpopulations of cells that display metabolic and transcriptional diversity along with recalcitrance to antibiotics and host immune defenses. Here, we present an optimized bacterial single-cell RNA sequencing method, BaSSSh-seq, to study Staphylococcus aureus diversity during biofilm growth and transcriptional adaptations following immune cell exposure. BaSSSh-seq captures extensive transcriptional heterogeneity during biofilm compared to planktonic growth. We quantify and visualize transcriptional regulatory networks across heterogeneous biofilm subpopulations and identify gene sets that are associated with a trajectory from planktonic to biofilm growth. BaSSSh-seq also detects alterations in biofilm metabolism, stress response, and virulence induced by distinct immune cell populations. This work facilitates the exploration of biofilm dynamics at single-cell resolution, unlocking the potential for identifying biofilm adaptations to environmental signals and immune pressure.

Harnessing microbial heterogeneity for improved biosynthesis fueled by synthetic biology

Metabolic engineering-driven microbial cell factories have made great progress in the efficient bioproduction of biochemical and recombinant proteins. However, the low efficiency and robustness of microbial cell factories limit their industrial applications. Harnessing microbial heterogeneity contributes to solving this. In this review, the origins of microbial heterogeneity and its effects on biosynthesis are first summarized. Synthetic biology-driven tools and strategies that can be used to improve biosynthesis by increasing and reducing microbial heterogeneity are then systematically summarized. Next, novel single-cell technologies available for unraveling microbial heterogeneity and facilitating heterogeneity regulation are discussed. Furthermore, a combined workflow of increasing genetic heterogeneity in the strain-building step to help in screening highly productive strains - reducing heterogeneity in the production process to obtain highly robust strains (IHP-RHR) facilitated by single-cell technologies was proposed to obtain highly productive and robust strains by harnessing microbial heterogeneity. Finally, the prospects and future challenges are discussed.

Molecular mechanism and application of emerging technologies in study of bacterial persisters

Since the discovery of antibiotics, they have served as a potent weapon against bacterial infections; however, natural evolution has allowed bacteria to adapt and develop coping mechanisms, ultimately leading to the concerning escalation of multidrug resistance. Bacterial persisters are a subpopulation that can survive briefly under high concentrations of antibiotic treatment and resume growth after lethal stress. Importantly, bacterial persisters are thought to be a significant cause of ineffective antibiotic therapy and recurrent infections in clinical practice and are thought to contribute to the development of antibiotic resistance. Therefore, it is essential to elucidate the molecular mechanisms of persister formation and to develop precise medical strategies to combat persistent infections. However, there are many difficulties in studying persisters due to their small proportion in the microbiota and their non-heritable nature. In this review, we discuss the similarities and differences of antibiotic resistance, tolerance, persistence, and viable but non-culturable cells, summarize the molecular mechanisms that affect the formation of persisters, and outline the emerging technologies in the study of persisters.

Phenotypic Heterogeneity in Pathogens

Pathogen diversity within an infected organism has traditionally been explored through the lens of genetic heterogeneity. Hallmark studies have characterized how genetic diversity within pathogen subpopulations contributes to treatment escape and infectious disease progression. However, recent studies have begun to reveal the mechanisms by which phenotypic heterogeneity is established within genetically identical populations of invading pathogens. Furthermore, exciting new work highlights how these phenotypically heterogeneous subpopulations contribute to a pathogen population better equipped to handle the complex and fluctuating environment of a host organism. In this review, we focus on how bacterial pathogens, including Staphylococcus aureus, Salmonella typhimurium, Pseudomonas aeruginosa, and Mycobacterium tuberculosis, establish and maintain phenotypic heterogeneity, and we explore recent work demonstrating causative links between this heterogeneity and infection outcome.

SAMPL-seq reveals micron-scale spatial hubs in the human gut microbiome

The local arrangement of microbes can profoundly impact community assembly, function, and stability. To date, little is known about the spatial organization of the human gut microbiome. Here, we describe a high-throughput and streamlined method, dubbed SAMPL-seq, that samples microbial composition of micron-scale sub-communities with split-and-pool barcoding to capture spatial colocalization in a complex consortium. SAMPL-seq analysis of the gut microbiome of healthy humans identified bacterial taxa pairs that consistently co-occurred both over time and across multiple individuals. These colocalized microbes organize into spatially distinct groups or "spatial hubs" dominated by Bacteroideceae, Ruminococceae, and Lachnospiraceae families. From a dietary perturbation using inulin, we observed reversible spatial rearrangement of the gut microbiome, where specific taxa form new local partnerships. Spatial metagenomics using SAMPL-seq can unlock new insights to improve the study of microbial communities.

From single cell to spatial multi-omics: unveiling molecular mechanisms in dynamic and heterogeneous systems

Single-cell multi-omics and spatial technology have been widely applied to biomedical studies and recently to environmental studies. The cell size detected by single-cell omics ranges from ∼2 µm (e.g., Bacillus subtilis) to ∼120 µm (e.g., human oocytes). Simultaneous detection of single-cell multi-omics is available to human and plant tissues while limited to microbial samples. Spatial technology enables mapping the detected biomolecules in situ. The recent advances in Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging and Micro/Nanodroplet Processing in One Pot for Trace Samples for the first time allow the application of spatial multi-omics in highly heterogeneous environmental samples composed of plants, fungi, and bacteria. We envision that these technologies will continue to advance our understanding of unique cell types, their developmental trajectory, and the intercellular signaling and interaction within biological samples.

ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets

Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes. Here we provide details for designing large oligonucleotide probe sets for an organism of choice, amplifying probe sets to produce sufficient quantities for repeated experiments, adding unique molecular indexes and poly-A tails to produce finalized probes, in situ probe hybridization and single-cell encapsulation and library preparation. This protocol, from the probe amplification to the library preparation, requires ~7 d to complete. ProBac-seq offers several advantages over other methods by capturing only the desired target sequences and avoiding nondesired transcripts, such as highly abundant ribosomal RNA, thus enriching for signal that better informs on cellular state. The use of multiple probes per gene can detect meaningful single-cell signals from cells expressing transcripts to a lesser degree or those grown in minimal media and other environmentally relevant conditions in which cells are less active. ProBac-seq is also compatible with other organisms that can be profiled by in situ hybridization techniques.

Single-cell phenotypic profiling and backtracing exposes and predicts clinically relevant subpopulations in isogenic Staphylococcus aureus communities

Isogenic bacterial cell populations are phenotypically heterogenous and may include subpopulations of antibiotic tolerant or heteroresistant cells. The reversibility of these phenotypes and lack of biomarkers to differentiate functionally different, but morphologically identical cells is a challenge for research and clinical detection. To overcome this, we present ´Cellular Phenotypic Profiling and backTracing (CPPT)´, a fluorescence-activated cell sorting platform that uses fluorescent probes to visualize and quantify cellular traits and connects this phenotypic profile with a cell´s experimentally determined fate in single cell-derived growth and antibiotic susceptibility analysis. By applying CPPT on Staphylococcus aureus we phenotypically characterized dormant cells, exposed bimodal growth patterns in colony-derived cells and revealed different culturability of single cells on solid compared to liquid media. We demonstrate that a fluorescent vancomycin conjugate marks cellular subpopulations of vancomycin-intermediate S. aureus with increased likelihood to survive antibiotic exposure, showcasing the value of CPPT for discovery of clinically relevant biomarkers.

High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding

Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. The standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R.

An approach to analyze spatiotemporal patterns of gene expression at single-cell resolution in Candida albicans-infected mouse tongues

Microbial gene expression measurements derived from infected organs are invaluable to understand pathogenesis. However, current methods are limited to "bulk" analyses that neglect microbial cell heterogeneity and the lesion's spatial architecture. Here, we report the use of hybridization chain reaction RNA fluorescence in situ hybridization (HCR RNA-FISH) to visualize and quantify Candida albicans transcripts at single-cell resolution in tongues of infected mice. The method is compatible with fixed-frozen and formalin-fixed paraffin-embedded tissues. We document cell-to-cell variation and intriguing spatiotemporal expression patterns for C. albicans mRNAs that encode products implicated in oral candidiasis. The approach provides a spatial dimension to gene expression analyses of host-Candida interactions.

IMPORTANCE

Candida albicans is a fungal pathobiont inhabiting multiple mucosal surfaces of the human body. Immunosuppression, antibiotic-induced microbial dysbiosis, or implanted medical devices can impair mucosal integrity enabling C. albicans to overgrow and disseminate, causing either mucosal diseases such as oropharyngeal candidiasis or life-threatening systemic infections. Profiling fungal genes that are expressed in the infected mucosa or in any other infected organ is paramount to understand pathogenesis. Ideally, these transcript profiling measurements should reveal the expression of any gene at the single-cell level. The resolution typically achieved with current approaches, however, limits most gene expression measurements to cell population averages. The approach described in this report provides a means to dissect fungal gene expression in infected tissues at single-cell resolution.

Unraveling the intricacies of host-pathogen interaction through single-cell genomics

Single-cell genomics provide researchers with tools to assess host-pathogen interactions at a resolution previously inaccessible. Transcriptome analysis, epigenome analysis, and immune profiling techniques allow for a better comprehension of the heterogeneity underlying both the host response and infectious agents. Here, we highlight technological advancements and data analysis workflows that increase our understanding of host-pathogen interactions at the single-cell level. We review various studies that have used these tools to better understand host-pathogen dynamics in a variety of infectious disease contexts, including viral, bacterial, and parasitic diseases. We conclude by discussing how single-cell genomics can advance our understanding of host-pathogen interactions.

Transcriptome analysis: a powerful tool to understand individual microbial behaviors and interactions in ecosystems

Transcriptome analysis is a powerful tool for studying microbial ecology, especially individual microbial functions in an ecosystem and their interactions. With the development of high-throughput sequencing technology, great progress has been made in analytical methods for microbial communities in natural environments. 16S rRNA gene amplicon sequencing (ie microbial community structure analysis) and shotgun metagenome analysis have been widely used to determine the composition and potential metabolic capability of microorganisms in target environments without requiring culture. However, even if the types of microorganisms present and their genes are known, it is difficult to determine what they are doing in an ecosystem. Gene expression analysis (transcriptome analysis; RNA-seq) is a powerful tool to address these issues. The history and basic information of gene expression analysis, as well as examples of studies using this method to analyze microbial ecosystems, are presented.

Single-cell RNA sequencing reveals plasmid constrains bacterial population heterogeneity and identifies a non-conjugating subpopulation

Transcriptional heterogeneity in isogenic bacterial populations can play various roles in bacterial evolution, but its detection remains technically challenging. Here, we use microbial split-pool ligation transcriptomics to study the relationship between bacterial subpopulation formation and plasmid-host interactions at the single-cell level. We find that single-cell transcript abundances are influenced by bacterial growth state and plasmid carriage. Moreover, plasmid carriage constrains the formation of bacterial subpopulations. Plasmid genes, including those with core functions such as replication and maintenance, exhibit transcriptional heterogeneity associated with cell activity. Notably, we identify a cell subpopulation that does not transcribe conjugal plasmid transfer genes, which may help reduce plasmid burden on a subset of cells. Our study advances the understanding of plasmid-mediated subpopulation dynamics and provides insights into the plasmid-bacteria interplay.

Single-cell transcriptomics across 2,534 microbial species reveals functional heterogeneity in the rumen microbiome

Deciphering the activity of individual microbes within complex communities and environments remains a challenge. Here we describe the development of microbiome single-cell transcriptomics using droplet-based single-cell RNA sequencing and pangenome-based computational analysis to characterize the functional heterogeneity of the rumen microbiome. We generated a microbial genome database (the Bovine Gastro Microbial Genome Map) as a functional reference map for the construction of a single-cell transcriptomic atlas of the rumen microbiome. The atlas includes 174,531 microbial cells and 2,534 species, of which 172 are core active species grouped into 12 functional clusters. We detected single-cell-level functional roles, including a key role for Basfia succiniciproducens in the carbohydrate metabolic niche of the rumen microbiome. Furthermore, we explored functional heterogeneity and reveal metabolic niche trajectories driven by biofilm formation pathway genes within B. succiniciproducens. Our results provide a resource for studying the rumen microbiome and illustrate the diverse functions of individual microbial cells that drive their ecological niche stability or adaptation within the ecosystem.

Co-transcriptional gene regulation in eukaryotes and prokaryotes

Many steps of RNA processing occur during transcription by RNA polymerases. Co-transcriptional activities are deemed commonplace in prokaryotes, in which the lack of membrane barriers allows mixing of all gene expression steps, from transcription to translation. In the past decade, an extraordinary level of coordination between transcription and RNA processing has emerged in eukaryotes. In this Review, we discuss recent developments in our understanding of co-transcriptional gene regulation in both eukaryotes and prokaryotes, comparing methodologies and mechanisms, and highlight striking parallels in how RNA polymerases interact with the machineries that act on nascent RNA. The development of RNA sequencing and imaging techniques that detect transient transcription and RNA processing intermediates has facilitated discoveries of transcription coordination with splicing, 3'-end cleavage and dynamic RNA folding and revealed physical contacts between processing machineries and RNA polymerases. Such studies indicate that intron retention in a given nascent transcript can prevent 3'-end cleavage and cause transcriptional readthrough, which is a hallmark of eukaryotic cellular stress responses. We also discuss how coordination between nascent RNA biogenesis and transcription drives fundamental aspects of gene expression in both prokaryotes and eukaryotes.

Advances in single-cell sequencing technology in microbiome research

With the rapid development of histological techniques and the widespread application of single-cell sequencing in eukaryotes, researchers desire to explore individual microbial genotypes and functional expression, which deepens our understanding of microorganisms. In this review, the history of the development of microbial detection technologies was revealed and the difficulties in the application of single-cell sequencing in microorganisms were dissected as well. Moreover, the characteristics of the currently emerging microbial single-cell sequencing (Microbe-seq) technology were summarized, and the prospects of the application of Microbe-seq in microorganisms were distilled based on the current development status. Despite its mature development, the Microbe-seq technology was still in the optimization stage. A retrospective study was conducted, aiming to promote the widespread application of single-cell sequencing in microorganisms and facilitate further improvement in the technology.

Bacterial single-cell RNA sequencing captures biofilm transcriptional heterogeneity and differential responses to immune pressure

Biofilm formation is an important mechanism of survival and persistence for many bacterial pathogens. These multicellular communities contain subpopulations of cells that display vast metabolic and transcriptional diversity along with high recalcitrance to antibiotics and host immune defenses. Investigating the complex heterogeneity within biofilm has been hindered by the lack of a sensitive and high-throughput method to assess stochastic transcriptional activity and regulation between bacterial subpopulations, which requires single-cell resolution. We have developed an optimized bacterial single-cell RNA sequencing method, BaSSSh-seq, to study Staphylococcus aureus diversity during biofilm growth and transcriptional adaptations following immune cell exposure. We validated the ability of BaSSSh-seq to capture extensive transcriptional heterogeneity during biofilm compared to planktonic growth. Application of new computational tools revealed transcriptional regulatory networks across the heterogeneous biofilm subpopulations and identification of gene sets that were associated with a trajectory from planktonic to biofilm growth. BaSSSh-seq also detected alterations in biofilm metabolism, stress response, and virulence that were tailored to distinct immune cell populations. This work provides an innovative platform to explore biofilm dynamics at single-cell resolution, unlocking the potential for identifying biofilm adaptations to environmental signals and immune pressure.

Highly Multiplexed Spatial Transcriptomics in Bacteria

Single-cell decisions made in complex environments underlie many bacterial phenomena. Image-based transcriptomics approaches offer an avenue to study such behaviors, yet these approaches have been hindered by the massive density of bacterial mRNA. To overcome this challenge, we combine 1000-fold volumetric expansion with multiplexed error robust fluorescence in situ hybridization (MERFISH) to create bacterial-MERFISH. This method enables high-throughput, spatially resolved profiling of thousands of operons within individual bacteria. Using bacterial-MERFISH, we dissect the response of E. coli to carbon starvation, systematically map subcellular RNA organization, and chart the adaptation of a gut commensal B. thetaiotaomicron to micron-scale niches in the mammalian colon. We envision bacterial-MERFISH will be broadly applicable to the study of bacterial single-cell heterogeneity in diverse, spatially structured, and native environments.

Single cell genomics based insights into the impact of cell-type specific microbial internalization on disease severity

Host-microbe interactions are complex and ever-changing, especially during infections, which can significantly impact human physiology in both health and disease by influencing metabolic and immune functions. Infections caused by pathogens such as bacteria, viruses, fungi, and parasites are the leading cause of global mortality. Microbes have evolved various immune evasion strategies to survive within their hosts, which presents a multifaceted challenge for detection. Intracellular microbes, in particular, target specific cell types for survival and replication and are influenced by factors such as functional roles, nutrient availability, immune evasion, and replication opportunities. Identifying intracellular microbes can be difficult because of the limitations of traditional culture-based methods. However, advancements in integrated host microbiome single-cell genomics and transcriptomics provide a promising basis for personalized treatment strategies. Understanding host-microbiota interactions at the cellular level may elucidate disease mechanisms and microbial pathogenesis, leading to targeted therapies. This article focuses on how intracellular microbes reside in specific cell types, modulating functions through persistence strategies to evade host immunity and prolong colonization. An improved understanding of the persistent intracellular microbe-induced differential disease outcomes can enhance diagnostics, therapeutics, and preventive measures.

High-Throughput Host-Microbe Single-Cell RNA Sequencing Reveals Ferroptosis-Associated Heterogeneity during Acinetobacter baumannii Infection

Interactions between host and bacterial cells are integral to human physiology. The complexity of host-microbe interactions extends to different cell types, spatial aspects, and phenotypic heterogeneity, requiring high-resolution approaches to capture their full complexity. The latest breakthroughs in single-cell RNA sequencing (scRNA-seq) have opened up a new era of studies in host-pathogen interactions. Here, we first report a high-throughput cross-species dual scRNA-seq technology by using random primers to simultaneously capture both eukaryotic and bacterial RNAs (scRandom-seq). Using reference cells, scRandom-seq can detect individual eukaryotic and bacterial cells with high throughput and high specificity. Acinetobacter baumannii (A.b) is a highly opportunistic and nosocomial pathogen that displays resistance to many antibiotics, posing a significant threat to human health, calling for discoveries and treatment. In the A.b infection model, scRandom-seq witnessed polarization of THP-1 derived-macrophages and the intracellular A.b-induced ferroptosis-stress in host cells. The inhibition of ferroptosis by Ferrostatin-1 (Fer-1) resulted in the improvement of cell vitality and resistance to A.b infection, indicating the potential to resist related infections. scRandom-seq provides a high-throughput cross-species dual single-cell RNA profiling tool that will facilitate future discoveries in unraveling the complex interactions of host-microbe interactions in infection systems and tumor micro-environments.

Machine learning for microbiologists

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

Noncontiguous operon atlas for the Staphylococcus aureus genome

Bacteria synchronize the expression of genes with related functions by organizing genes into operons so that they are cotranscribed together in a single polycistronic messenger RNA. However, some cellular processes may benefit if the simultaneous production of the operon proteins coincides with the inhibition of the expression of an antagonist gene. To coordinate such situations, bacteria have evolved noncontiguous operons (NcOs), a subtype of operons that contain one or more genes that are transcribed in the opposite direction to the other operon genes. This structure results in overlapping transcripts whose expression is mutually repressed. The presence of NcOs cannot be predicted computationally and their identification requires a detailed knowledge of the bacterial transcriptome. In this study, we used direct RNA sequencing methodology to determine the NcOs map in the Staphylococcus aureus genome. We detected the presence of 18 NcOs in the genome of S. aureus and four in the genome of the lysogenic prophage 80α. The identified NcOs comprise genes involved in energy metabolism, metal acquisition and transport, toxin-antitoxin systems, and control of the phage life cycle. Using the menaquinone operon as a proof of concept, we show that disarrangement of the NcO architecture results in a reduction of bacterial fitness due to an increase in menaquinone levels and a decrease in the rate of oxygen consumption. Our study demonstrates the significance of NcO structures in bacterial physiology and emphasizes the importance of combining operon maps with transcriptomic data to uncover previously unnoticed functional relationships between neighbouring genes.

Microbiome single cell atlases generated with a commercial instrument

Single cell sequencing is useful for resolving complex systems into their composite cell types and computationally mining them for unique features that are masked in pooled sequencing. However, while commercial instruments have made single cell analysis widespread for mammalian cells, analogous tools for microbes are limited. Here, we present EASi-seq (Easily Accessible Single microbe sequencing). By adapting the single cell workflow of the commercial Mission Bio Tapestri instrument, this method allows for efficient sequencing of individual microbes' genomes. EASi-seq allows thousands of microbes to be sequenced per run and, as we show, can generate detailed atlases of human and environmental microbiomes. The ability to capture large shotgun genome datasets from thousands of single microbes provides new opportunities in discovering and analyzing species subpopulations. To facilitate this, we develop a companion bioinformatic pipeline that clusters microbes by similarity, improving whole genome assembly, strain identification, taxonomic classification, and gene annotation. In addition, we demonstrate integration of metagenomic contigs with the EASi-seq datasets to reduce capture bias and increase coverage. Overall, EASi-seq enables high quality single cell genomic data for microbiome samples using an accessible workflow that can be run on a commercially available platform.

A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment

Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.

Single-cell analysis reveals that cryptic prophage protease LfgB protects Escherichia coli during oxidative stress by cleaving antitoxin MqsA

Although toxin/antitoxin (TA) systems are ubiquitous, beyond phage inhibition and mobile element stabilization, their role in host metabolism is obscure. One of the best-characterized TA systems is MqsR/MqsA of Escherichia coli, which has been linked previously to protecting gastrointestinal species during the stress it encounters from the bile salt deoxycholate as it colonizes humans. However, some recent whole-population studies have challenged the role of toxins such as MqsR in bacterial physiology since the mqsRA locus is induced over a hundred-fold during stress, but a phenotype was not found upon its deletion. Here, we investigate further the role of MqsR/MqsA by utilizing single cells and demonstrate that upon oxidative stress, the TA system MqsR/MqsA has a heterogeneous effect on the transcriptome of single cells. Furthermore, we discovered that MqsR activation leads to induction of the poorly characterized yfjXY ypjJ yfjZF operon of cryptic prophage CP4-57. Moreover, deletion of yfjY makes the cells sensitive to H2O2, acid, and heat stress, and this phenotype was complemented. Hence, we recommend yfjY be renamed to lfgB (less fatality gene B). Critically, MqsA represses lfgB by binding the operon promoter, and LfgB is a protease that degrades MqsA to derepress rpoS and facilitate the stress response. Therefore, the MqsR/MqsA TA system facilitates the stress response through cryptic phage protease LfgB.IMPORTANCEThe roles of toxin/antitoxin systems in cell physiology are few and include phage inhibition and stabilization of genetic elements; yet, to date, there are no single-transcriptome studies for toxin/antitoxin systems and few insights for prokaryotes from this novel technique. Therefore, our results with this technique are important since we discover and characterize a cryptic prophage protease that is regulated by the MqsR/MqsA toxin/antitoxin system in order to regulate the host response to oxidative stress.

Emerging tools for uncovering genetic and transcriptomic heterogeneities in bacteria

Bacterial communities display an astonishing degree of heterogeneities among their constituent cells across both the genomic and transcriptomic levels, giving rise to diverse social interactions and stress-adaptation strategies indispensable for proliferating in the natural environment (Ackermann in Nat Rev Microbiol 13:497-508, 2015). Our knowledge about bacterial heterogeneities and their physiological ramifications critically depends on our ability to unambiguously resolve the genetic and phenotypic states of the individual cells that make up the population. In this short review, I highlight several recently developed methods for studying bacterial heterogeneities, primarily focusing on single-cell techniques based on advanced sequencing and microscopy technologies. I will discuss the working principle of each technique as well as the types of problems each technique is best positioned to address. With significant improvements in resolution and throughput, these emerging tools together offer unprecedented and complementary views of various types of heterogeneities found within bacterial populations, paving the way for mechanistic dissections and systematic interventions in laboratory and clinical settings.

Exploring the transcriptional landscape of phage-host interactions using novel high-throughput approaches

In the last decade, powerful high-throughput sequencing approaches have emerged to analyse microbial transcriptomes at a global scale. However, to date, applications of these approaches to microbial viruses such as phages remain scarce. Tailoring these techniques to virus-infected bacteria promises to obtain a detailed picture of the underexplored RNA biology and molecular processes during infection. In addition, transcriptome study of stress and perturbations induced by phages in their infected bacterial hosts is likely to reveal new fundamental mechanisms of bacterial metabolism and gene regulation. Here, we provide references and blueprints to implement emerging transcriptomic approaches towards addressing transcriptome architecture, RNA-RNA and RNA-protein interactions, RNA modifications, structures and heterogeneity of transcription profiles in infected cells that will provide guides for future directions in phage-centric therapeutic applications and microbial synthetic biology.

Tools for microbial single-cell genomics for obtaining uncultured microbial genomes

The advent of next-generation sequencing technologies has facilitated the acquisition of large amounts of DNA sequence data at a relatively low cost, leading to numerous breakthroughs in decoding microbial genomes. Among the various genome sequencing activities, metagenomic analysis, which entails the direct analysis of uncultured microbial DNA, has had a profound impact on microbiome research and has emerged as an indispensable technology in this field. Despite its valuable contributions, metagenomic analysis is a "bulk analysis" technique that analyzes samples containing a wide diversity of microbes, such as bacteria, yielding information that is averaged across the entire microbial population. In order to gain a deeper understanding of the heterogeneous nature of the microbial world, there is a growing need for single-cell analysis, similar to its use in human cell biology. With this paradigm shift in mind, comprehensive single-cell genomics technology has become a much-anticipated innovation that is now poised to revolutionize microbiome research. It has the potential to enable the discovery of differences at the strain level and to facilitate a more comprehensive examination of microbial ecosystems. In this review, we summarize the current state-of-the-art in microbial single-cell genomics, highlighting the potential impact of this technology on our understanding of the microbial world. The successful implementation of this technology is expected to have a profound impact in the field, leading to new discoveries and insights into the diversity and evolution of microbes.

Bacterial phenotypic heterogeneity through the lens of single-cell RNA sequencing

Bacterial transcription is not monolithic. Microbes exist in a wide variety of cell states that help them adapt to their environment, acquire and produce essential nutrients, and engage in both competition and cooperation with their neighbors. While we typically think of bacterial adaptation as a group behavior, where all cells respond in unison, there is often a mixture of phenotypic responses within a bacterial population, where distinct cell types arise. A primary phenomenon driving these distinct cell states is transcriptional heterogeneity. Given that bacterial mRNA transcripts are extremely short-lived compared to eukaryotes, their transcriptional state is closely associated with their physiology, and thus the transcriptome of a bacterial cell acts as a snapshot of the behavior of that bacterium. Therefore, the application of single-cell transcriptomics to microbial populations will provide novel insight into cellular differentiation and bacterial ecology. In this review, we provide an overview of transcriptional heterogeneity in microbial systems, discuss the findings already provided by single-cell approaches, and plot new avenues of inquiry in transcriptional regulation, cellular biology, and mechanisms of heterogeneity that are made possible when microbial communities are analyzed at single-cell resolution.

Transcription-replication interactions reveal bacterial genome regulation

Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome1. In bacteria, the relationship between the regulatory architecture of a gene and its expression is well understood for individual model gene circuits2,3. However, a broader perspective of these dynamics at the genome scale is lacking, in part because bacterial transcriptomics has hitherto captured only a static snapshot of expression averaged across millions of cells4. As a result, the full diversity of gene expression dynamics and their relation to regulatory architecture remains unknown. Here we present a novel genome-wide classification of regulatory modes based on the transcriptional response of each gene to its own replication, which we term the transcription-replication interaction profile (TRIP). Analysing single-bacterium RNA-sequencing data, we found that the response to the universal perturbation of chromosomal replication integrates biological regulatory factors with biophysical molecular events on the chromosome to reveal the local regulatory context of a gene. Whereas the TRIPs of many genes conform to a gene dosage-dependent pattern, others diverge in distinct ways, and this is shaped by factors such as intra-operon position and repression state. By revealing the underlying mechanistic drivers of gene expression heterogeneity, this work provides a quantitative, biophysical framework for modelling replication-dependent expression dynamics.

Massively parallel single-cell sequencing of diverse microbial populations

Single-cell genetic heterogeneity is ubiquitous in microbial populations and an important aspect of microbial biology; however, we lack a broadly applicable and accessible method to study this heterogeneity in microbial populations. Here, we show a simple, robust and generalizable method for high-throughput single-cell sequencing of target genetic loci in diverse microbes using simple droplet microfluidics devices (droplet targeted amplicon sequencing; DoTA-seq). DoTA-seq serves as a platform to perform diverse assays for single-cell genetic analysis of microbial populations. Using DoTA-seq, we demonstrate the ability to simultaneously track the prevalence and taxonomic associations of >10 antibiotic-resistance genes and plasmids within human and mouse gut microbial communities. This workflow is a powerful and accessible platform for high-throughput single-cell sequencing of diverse microbial populations.

Bibliometric and visual analysis of single-cell sequencing from 2010 to 2022

Background: Single-cell sequencing (SCS) is a technique used to analyze the genome, transcriptome, epigenome, and other genetic data at the level of a single cell. The procedure is commonly utilized in multiple fields, including neurobiology, immunology, and microbiology, and has emerged as a key focus of life science research. However, a thorough and impartial analysis of the existing state and trends of SCS-related research is lacking. The current study aimed to map the development trends of studies on SCS during the years 2010-2022 through bibliometric software. Methods: Pertinent papers on SCS from 2010 to 2022 were obtained using the Web of Science Core Collection. Research categories, nations/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords were analyzed using VOSviewer, the R package "bibliometric", and CiteSpace. Results: The bibliometric analysis included 9,929 papers published between 2010 and 2022, and showed a consistent increase in the quantity of papers each year. The United States was the source of the highest quantity of articles and citations in this field. The majority of articles were published in the periodical Nature Communications. Butler A was the most frequently quoted author on this topic, and his article "Integrating single-cell transcriptome data across diverse conditions, technologies, and species" has received numerous citations to date. The literature and keyword analysis showed that studies involving single-cell RNA sequencing (scRNA-seq) were prominent in this discipline during the study period. Conclusion: This study utilized bibliometric techniques to visualize research in SCS-related domains, which facilitated the identification of emerging patterns and future directions in the field. Current hot topics in SCS research include COVID-19, tumor microenvironment, scRNA-seq, and neuroscience. Our results are significant for scholars seeking to identify key issues and generate new research ideas.