Bacterial droplet-based single-cell RNA-seq reveals antibiotic-associated heterogeneous cellular states

CRISPRi functional genomics in bacteria and its application to medical and industrial research

SUMMARYFunctional genomics is the use of systematic gene perturbation approaches to determine the contributions of genes under conditions of interest. Although functional genomic strategies have been used in bacteria for decades, recent studies have taken advantage of CRISPR (clustered regularly interspaced short palindromic repeats) technologies, such as CRISPRi (CRISPR interference), that are capable of precisely modulating expression of all genes in the genome. Here, we discuss and review the use of CRISPRi and related technologies for bacterial functional genomics. We discuss the strengths and weaknesses of CRISPRi as well as design considerations for CRISPRi genetic screens. We also review examples of how CRISPRi screens have defined relevant genetic targets for medical and industrial applications. Finally, we outline a few of the many possible directions that could be pursued using CRISPR-based functional genomics in bacteria. Our view is that the most exciting screens and discoveries are yet to come.

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.

Vancomycin heteroresistance caused by unstable tandem amplifications of the vanM gene cluster on linear conjugative plasmids in a clinical Enterococcus faecium

Vancomycin heteroresistance is prone to missed detection and poses a risk of clinical treatment failure. We encountered one clinical Enterococcus faecium strain, SRR12, that carried a complete vanM gene cluster but was determined as susceptible to vancomycin using the broth microdilution method. However, distinct subcolonies appeared within the clear zone of inhibition in the E-test assay, one of which, named SRR12-v1, showed high-level resistance to vancomycin. SRR12 was confirmed as heteroresistant to vancomycin using population analysis profiling and displayed "revive" growth curves with a lengthy lag phase of over 13 hours when exposed to 2-32 mg/L vancomycin. The resistant subcolony SRR12-v1 was found to carry an identical vanM gene cluster to that of SRR12 but a significantly increased vanM copy number in the genome. Long-read whole genome sequencing revealed that a one-copy vanM gene cluster was located on a pELF1-like linear plasmid in SRR12. In comparison, tandem amplification of the vanM gene cluster jointed with IS1216E was seated on a linear plasmid in the genome of SRR12-v1. These amplifications of the vanM gene cluster were demonstrated as unstable and would decrease accompanied by fitness reversion after serial passaging for 50 generations under increasing vancomycin pressure or without antibiotic pressure but were relatively stable under constant vancomycin pressure. Further, vanM resistance in resistant variants was verified to be carried by conjugative plasmids with variable sizes using conjugation assays and S1-pulsed field gel electrophoresis blotting, suggesting the instability/flexibility of vanM cluster amplification in the genome and an increased risk of vanM resistance dissemination.

Microbiome and infectious disease: diagnostics to therapeutics

Over 300 years of research on the microbial world has revealed their importance in human health and disease. This review explores the impact and potential of microbial-based detection methods and therapeutic interventions, integrating research of early microbiologists, current findings, and future perspectives.

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.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals immune suppression subtypes and establishes a novel signature for determining the prognosis in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with lower survival rates. Recent advancements in targeted therapies and immunotherapies targeting immune checkpoints have achieved remarkable success, there is still a large percentage of LUAD that lacks available therapeutic options. Due to tumor heterogeneity, the diagnosis and treatment of LUAD are challenging. Exploring the biology of LUAD and identifying new biomarker and therapeutic targets options are essential.

METHOD

We performed single-cell RNA sequencing (scRNA-seq) of 6 paired primary and adjacent LUAD tissues, and integrative omics analysis of the scRNA-seq, bulk RNA-seq and whole-exome sequencing data revealed molecular subtype characteristics. Our experimental results confirm that CDC25C gene can serve as a potential marker for poor prognosis in LUAD.

RESULTS

We investigated aberrant gene expression in diverse cell types in LUAD via the scRNA-seq data. Moreover, multi-omics clustering revealed four subgroups defined by transcriptional profile and molecular subtype 4 (MS4) with poor survival probability, and immune cell infiltration signatures revealed that MS4 tended to be the immunosuppressive subtype. Our study revealed that the CDC25C gene can be a distinct prognostic biomarker that indicates immune infiltration levels and response to immunotherapy in LUAD patients. Our experimental results concluded that CDC25C expression affects lung cancer cell invasion and migration, might play a key role in regulating Epithelial-Mesenchymal Transition (EMT) pathways.

CONCLUSIONS

Our multi-omics result revealed a comprehensive set of molecular attributes associated with prognosis-related genes in LUAD at the cellular and tissue level. Identification of a subtype of immunosuppressive TME and prognostic signature for LUAD. We identified the cell cycle regulation gene CDC25C affects lung cancer cell invasion and migration, which can be used as a potential biomarker for LUAD.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

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.

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.

Engineered Bio-Heterojunction Confers Extra- and Intracellular Bacterial Ferroptosis and Hunger-Triggered Cell Protection for Diabetic Wound Repair

Nanomaterial-mediated ferroptosis has garnered considerable interest in the antibacterial field, as it invokes the disequilibrium of ion homeostasis and boosts lipid peroxidation in extra- and intracellular bacteria. However, current ferroptosis-associated antibacterial strategies indiscriminately pose damage to healthy cells, ultimately compromising their biocompatibility. To address this daunting issue, this work has designed a precise ferroptosis bio-heterojunction (F-bio-HJ) consisting of Fe2 O3 , Ti3 C2 -MXene, and glucose oxidase (GOx) to induce extra-intracellular bacteria-targeted ferroptosis for infected diabetic cutaneous regeneration. Fe2 O3 /Ti3 C2 -MXene@GOx (FMG) catalytically generates a considerable amount of ROS which assaults the membrane of extracellular bacteria, facilitating the permeation of synchronously generated Fe2+ /Fe3+ into bacteria under near-infrared (NIR) irradiation, causing planktonic bacterial death via ferroptosis, Fe2+ overload, and lipid peroxidation. Additionally, FMG facilitates intracellular bacterial ferroptosis by transporting Fe2+ into intracellular bacteria via inward ferroportin (FPN). With GOx consuming glucose, FMG creates hunger protection which helps macrophages escape cell ferroptosis by activating the adenosine 5'-monophosphate (AMP) activated protein kinase (AMPK) pathway. In vivo results authenticate that FMG boosts diabetic infectious cutaneous regeneration without triggering ferroptosis in normal cells. As envisaged, the proposed tactic provides a promising approach to combat intractable infections by precisely terminating extra-intracellular infection via steerable ferroptosis, thereby markedly elevating the biocompatibility of therapeutic ferroptosis-mediated strategies.

Development and future of droplet microfluidics

Over the past two decades, advances in droplet-based microfluidics have facilitated new approaches to process and analyze samples with unprecedented levels of precision and throughput. A wide variety of applications has been inspired across multiple disciplines ranging from materials science to biology. Understanding the dynamics of droplets enables optimization of microfluidic operations and design of new techniques tailored to emerging demands. In this review, we discuss the underlying physics behind high-throughput generation and manipulation of droplets. We also summarize the applications in droplet-derived materials and droplet-based lab-on-a-chip biotechnology. In addition, we offer perspectives on future directions to realize wider use of droplet microfluidics in industrial production and biomedical analyses.

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.

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.

Antimicrobial resistance crisis: could artificial intelligence be the solution?

Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.

Involvement of S100A6/S100A11 in T-Cell Immune Regulatory in HCC Revealed by Single Cell RNA-seq

Background: Immunotherapy plays a significant role in the treatment of hepatocellular carcinoma (HCC). Members of the S100 protein family (S100s) have been widely implicated in the pathogenesis and progression of tumors. However, the exact mechanism by which S100s contribute to tumor immunity remains unclear. Methods: To explore the role of S100s in HCC immune cells, we collected and comparatively analyzed single-cell RNA sequencing (scRNA-seq) data of HCC and hepatitis B virus-associated HCC. By mapping cell classification and searching for S100s binding targets and downstream targets. Results: S100A6/S100A11 was differentially expressed in tumor T cells and involved in the nuclear factor (NF) κB pathway. Further investigation of the TCGA dataset revealed that patients with low S100A6/S100A11 expression had a better prognosis. Temporal cell trajectory analysis showed that the activation of the NF-κB pathway is at a critical stage and has an important impact on the tumor microenvironment. Conclusion: Our study revealed that S100A6/S100A11 could be involved in regulating the differentiation and cellular activity of T-cell subpopulations in HCC, and its low expression was positively correlated with prognosis. It may provide a new direction for immunotherapy of HCC and a theoretical basis for future clinical applications.

Targeted rRNA depletion enables efficient mRNA sequencing in diverse bacterial species and complex co-cultures

IMPORTANCE

Microbes present one of the most diverse sources of biochemistry in nature, and mRNA sequencing provides a comprehensive view of this biological activity by quantitatively measuring microbial transcriptomes. However, efficient mRNA capture for sequencing presents significant challenges in prokaryotes as mRNAs are not poly-adenylated and typically make up less than 5% of total RNA compared with rRNAs that exceed 80%. Recently developed methods for sequencing bacterial mRNA typically rely on depleting rRNA by tiling large probe sets against rRNAs; however, such approaches are expensive, time-consuming, and challenging to scale to varied bacterial species and complex microbial communities. Therefore, we developed EMBR-seq+, a method that requires fewer than 10 short oligonucleotides per rRNA to achieve up to 99% rRNA depletion in diverse bacterial species. Finally, EMBR-seq+ resulted in a deeper view of the transcriptome, enabling systematic quantification of how microbial interactions result in altering the transcriptional state of bacteria within co-cultures.

Organoid assessment technologies

Despite enormous advances in the generation of organoids, robust and stable protocols of organoids are still a major challenge to researchers. Research for assessing structures of organoids and the evaluations of their functions on in vitro or in vivo is often limited by precision strategies. A growing interest in assessing organoids has arisen, aimed at standardizing the process of obtaining organoids to accurately resemble human-derived tissue. The complex microenvironment of organoids, intricate cellular crosstalk, organ-specific architectures and further complicate functions urgently quest for high-through schemes. By utilizing multi-omics analysis and single-cell analysis, cell-cell interaction mechanisms can be deciphered, and their structures can be investigated in a detailed view by histological analysis. In this review, we will conclude the novel approaches to study the molecular mechanism and cell heterogeneity of organoids and discuss the histological and morphological similarity of organoids in comparison to the human body. Future perspectives on functional analysis will be developed and the organoids will become mature models.

Single-cell patterning and characterisation of antibiotic persistent bacteria using bio-sCAPA

In microbiology, accessing single-cell information within large populations is pivotal. Here we introduce bio-sCAPA, a technique for patterning bacterial cells in defined geometric arrangements and monitoring their growth in various nutrient environments. We demonstrate bio-sCAPA with a study of subpopulations of antibiotic-tolerant bacteria, known as persister cells, which can survive exposure to high doses of antibiotics despite lacking any genetic resistance to the drug. Persister cells are associated with chronic and relapsing infections, yet are difficult to study due in part to a lack of scalable, single-cell characterisation methods. As >105 cells can be patterned on each template, and multiple templates can be patterned in parallel, bio-sCAPA allows for very rare population phenotypes to be monitored with single-cell precision across various environmental conditions. Using bio-sCAPA, we analysed the phenotypic characteristics of single Staphylococcus aureus cells tolerant to flucloxacillin and rifampicin killing. We find that antibiotic-tolerant S. aureus cells do not display significant heterogeneity in growth rate and are instead characterised by prolonged lag-time phenotypes alone.

Spatial host-microbiome sequencing reveals niches in the mouse gut

Mucosal and barrier tissues, such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host-microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for understanding homeostasis and disease. Here we present spatial host-microbiome sequencing (SHM-seq), an all-sequencing-based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from a tissue by modifying spatially barcoded glass surfaces to enable simultaneous capture of host transcripts and hypervariable regions of the 16S bacterial ribosomal RNA. We applied our approach to the mouse gut as a model system, used a deep learning approach for data mapping and detected spatial niches defined by cellular composition and microbial geography. We show that subpopulations of gut cells express specific gene programs in different microenvironments characteristic of regional commensal bacteria and impact host-bacteria interactions. SHM-seq should enhance the study of native host-microbe interactions in health and disease.

Collaborating single-cell and bulk RNA sequencing for comprehensive characterization of the intratumor heterogeneity and prognostic model development for bladder cancer

INTRODUCTION

Gaining a deeper insight into the single-cell RNA sequencing (scRNA-seq) results of bladder cancer (BLCA) provides a transcriptomic profiling of individual cancer cells, which may disclose the molecular mechanisms involved in BLCA carcinogenesis.

METHODS

scRNA data were obtained from GSE169379 dataset. We used the InferCNV software to determine the copy number variant (CNV) with normal epithelial cells serving as the reference, and performed the pseudo-timing analysis on subsets of epithelial cell using Monocle3 software. Transcription factor analysis was conducted using the Dorothea software. Intercellular communication analysis was performed using the Liana software. Cox analysis and LASSO regression were applied to establish a prognostic model.

RESULTS

We investigated the heterogeneity of tumors in four distinct cell types of BLCA cancer, namely immune cells, endothelial cells, epithelial cells, and fibroblasts. We evaluated the transcription factor activity of different immune cells in BLCA and identified significant enrichment of TCF7 and TBX21 in CD8+ T cells. Additionally, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs), namely iCAFs and myoCAFs, which exhibited distinct communication patterns. Using sub-cluster and cell trajectory analyses, we identified different states of normal-to-malignant cell transformation in epithelial cells. TF analysis further revealed high activation of MYC and SOX2 in tumor cells. Finally, we identified five model genes (SLCO3A1, ANXA1, TENM3, EHBP1, LSAMP) for the development of a prognostic model, which demonstrated high effectiveness in stratifying patients across seven different cohorts.

CONCLUSIONS

We have developed a prognostic model that has demonstrated significant efficacy in stratifying patients with BLCA.

Uncovering the temporal dynamics and regulatory networks of thermal stress response in a hyperthermophile using transcriptomics and proteomics

Facing rapid fluctuations in their natural environment, extremophiles, like the hyperthermophilic archaeon Pyrococcus furiosus, exhibit remarkable adaptability to extreme conditions. However, our understanding of their dynamic cellular responses remains limited. This study integrates RNA-sequencing and mass spectrometry data, thereby elucidating transcriptomic and proteomic responses to heat and cold shock stress in P. furiosus. Our results reveal rapid and dynamic changes in gene and protein expression following these stress responses. Heat shock triggers extensive transcriptome reprogramming, orchestrated by the transcriptional regulator Phr, targeting a broader gene repertoire than previously demonstrated. For heat shock signature genes, RNA levels swiftly return to baseline upon recovery, while protein levels remain persistently upregulated, reflecting a rapid but sustained response. Intriguingly, cold shock at 4°C elicits distinct short- and long-term responses at both RNA and protein levels. Cluster analysis identified gene sets with either congruent or contrasting trends in RNA and protein changes, representing well-separated arCOG groups tailored to their individual cellular responses. Particularly, upregulation of ribosomal proteins and significant enrichment of 5'-leadered sequences in cold-shock responsive genes suggest that translation regulation is important during cold shock adaption. Further investigating transcriptomic features, we reveal that thermal stress genes are equipped with basal sequence elements, such as strong promoter and poly(U)-terminators, facilitating a regulated response of the respective transcription units. Our study provides a comprehensive overview of the cellular response to temperature stress, advancing our understanding of stress response mechanisms in hyperthermophilic archaea and providing valuable insights into the molecular adaptations that facilitate life in extreme environments.IMPORTANCEExtreme environments provide unique challenges for life, and the study of extremophiles can shed light on the mechanisms of adaptation to such conditions. Pyrococcus furiosus, a hyperthermophilic archaeon, is a model organism for studying thermal stress response mechanisms. In this study, we used an integrated analysis of RNA-sequencing and mass spectrometry data to investigate the transcriptomic and proteomic responses of P. furiosus to heat and cold shock stress and recovery. Our results reveal the rapid and dynamic changes in gene and protein expression patterns associated with these stress responses, as well as the coordinated regulation of different gene sets in response to different stressors. These findings provide valuable insights into the molecular adaptations that facilitate life in extreme environments and advance our understanding of stress response mechanisms in hyperthermophilic archaea.

Single-cell analysis and spatial resolution of the gut microbiome

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

Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection

Bacterial populations are highly adaptive. They can respond to stress and survive in shifting environments. How the behaviours of individual bacteria vary during stress, however, is poorly understood. To identify and characterize rare bacterial subpopulations, technologies for single-cell transcriptional profiling have been developed. Existing approaches show some degree of limitation, for example, in terms of number of cells or transcripts that can be profiled. Due in part to these limitations, few conditions have been studied with these tools. Here we develop massively-parallel, multiplexed, microbial sequencing (M3-seq)-a single-cell RNA-sequencing platform for bacteria that pairs combinatorial cell indexing with post hoc rRNA depletion. We show that M3-seq can profile bacterial cells from different species under a range of conditions in single experiments. We then apply M3-seq to hundreds of thousands of cells, revealing rare populations and insights into bet-hedging associated with stress responses and characterizing phage infection.

Bacterial acid stress response: from cellular changes to antibiotic tolerance and phenotypic heterogeneity

Most bacteria are neutralophiles but can survive fluctuations in pH in their environment. Herein, we provide an overview of the adaptation of several human, soil, and food bacteria to acid stress, mainly based on next-generation sequencing studies, highlighting common and specific strategies. We also discuss the interplay between acid stress response and antibiotic tolerance, as well as the response of individual cells.

Recent Advances in Bacterial Persistence Mechanisms

The recurrence of bacterial infectious diseases is closely associated with bacterial persisters. This subpopulation of bacteria can escape antibiotic treatment by entering a metabolic status of low activity through various mechanisms, for example, biofilm, toxin-antitoxin modules, the stringent response, and the SOS response. Correspondingly, multiple new treatments are being developed. However, due to their spontaneous low abundance in populations and the lack of research on in vivo interactions between persisters and the host's immune system, microfluidics, high-throughput sequencing, and microscopy techniques are combined innovatively to explore the mechanisms of persister formation and maintenance at the single-cell level. Here, we outline the main mechanisms of persister formation, and describe the cutting-edge technology for further research. Despite the significant progress regarding study techniques, some challenges remain to be tackled.

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.

Multidimensional specialization and generalization are pervasive in soil prokaryotes

Habitat specialization underpins biological processes from species distributions to speciation. However, organisms are often described as specialists or generalists based on a single niche axis, despite facing complex, multidimensional environments. Here, we analysed 236 environmental soil microbiomes across the United States and demonstrate that 90% of >1,200 prokaryotes followed one of two trajectories: specialization on all niche axes (multidimensional specialization) or generalization on all axes (multidimensional generalization). We then documented that this pervasive multidimensional specialization/generalization had many ecological and evolutionary consequences. First, multidimensional specialization and generalization are highly conserved with very few transitions between these two trajectories. Second, multidimensional generalists dominated communities because they were 73 times more abundant than specialists. Lastly, multidimensional specialists played important roles in community structure with ~220% more connections in microbiome networks. These results indicate that multidimensional generalization and specialization are evolutionarily stable with multidimensional generalists supporting larger populations and multidimensional specialists playing important roles within communities, probably stemming from their overrepresentation among pollutant detoxifiers and nutrient cyclers. Taken together, we demonstrate that the vast majority of soil prokaryotes are restricted to one of two multidimensional niche trajectories, multidimensional specialization or multidimensional generalization, which then has far-reaching consequences for evolutionary transitions, microbial dominance and community roles.

Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq

Bacteria colonize almost all parts of the human body and can differ significantly. However, the population level transcriptomics measurements can only describe the average bacteria population behaviors, ignoring the heterogeneity among bacteria. Here, we report a droplet-based high-throughput single-microbe RNA-seq assay (smRandom-seq), using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion for mRNA enrichment. smRandom-seq showed a high species specificity (99%), a minor doublet rate (1.6%), a reduced rRNA percentage (32%), and a sensitive gene detection (a median of ~1000 genes per single E. coli). Furthermore, smRandom-seq successfully captured transcriptome changes of thousands of individual E. coli and discovered a few antibiotic resistant subpopulations displaying distinct gene expression patterns of SOS response and metabolic pathways in E. coli population upon antibiotic stress. smRandom-seq provides a high-throughput single-microbe transcriptome profiling tool that will facilitate future discoveries in microbial resistance, persistence, microbe-host interaction, and microbiome research.

scDemultiplex: An iterative beta-binomial model-based method for accurate demultiplexing with hashtag oligos

Single-cell sequencing have been widely used to characterize cellular heterogeneity. Sample multiplexing where multiple samples are pooled together for single-cell experiments, attracts wide attention due to its benefits of increasing capacity, reducing costs, and minimizing batch effects. To analyze multiplexed data, the first crucial step is to demultiplex, the process of assigning cells to individual samples. Inaccurate demultiplexing will create false cell types and result in misleading characterization. We propose scDemultiplex, which models hashtag oligo (HTO) counts with beta-binomial distribution and uses an iterative strategy for further refinement. Compared with seven existing demultiplexing approaches, scDemultiplex achieved great performance in both high-quality and low-quality data. Additionally, scDemultiplex can be combined with other approaches to improve their performance.

High-throughput functional genomics: A (myco)bacterial perspective

Advances in sequencing technologies have enabled unprecedented insights into bacterial genome composition and dynamics. However, the disconnect between the rapid acquisition of genomic data and the (much slower) confirmation of inferred genetic function threatens to widen unless techniques for fast, high-throughput functional validation can be applied at scale. This applies equally to Mycobacterium tuberculosis, the leading infectious cause of death globally and a pathogen whose genome, despite being among the first to be sequenced two decades ago, still contains many genes of unknown function. Here, we summarize the evolution of bacterial high-throughput functional genomics, focusing primarily on transposon (Tn)-based mutagenesis and the construction of arrayed mutant libraries in diverse bacterial systems. We also consider the contributions of CRISPR interference as a transformative technique for probing bacterial gene function at scale. Throughout, we situate our analysis within the context of functional genomics of mycobacteria, focusing specifically on the potential to yield insights into M. tuberculosis pathogenicity and vulnerabilities for new drug and regimen development. Finally, we offer suggestions for future approaches that might be usefully applied in elucidating the complex cellular biology of this major human pathogen.

Enhancing the sensitivity of bacterial single-cell RNA sequencing using RamDA-seq and Cas9-based rRNA depletion

Bacterial populations exhibit heterogeneity in gene expression, which facilitates their survival and adaptation to unstable and unpredictable environments through the bet-hedging strategy. However, unraveling the rare subpopulations and heterogeneity in gene expression using population-level gene expression analysis remains a challenging task. Single-cell RNA sequencing (scRNA-seq) has the potential to identify rare subpopulations and capture heterogeneity in bacterial populations, but standard methods for scRNA-seq in bacteria are still under development, mainly due to differences in mRNA abundance and structure between eukaryotic and prokaryotic organisms. In this study, we present a hybrid approach that combines random displacement amplification sequencing (RamDA-seq) with Cas9-based rRNA depletion for scRNA-seq in bacteria. This approach allows cDNA amplification and subsequent sequencing library preparation from low-abundance bacterial RNAs. We evaluated its sequenced read proportion, gene detection sensitivity, and gene expression patterns from the dilution series of total RNA or the sorted single Escherichia coli cells. Our results demonstrated the detection of more than 1000 genes, about 24% of the genes in the E. coli genome, from single cells with less sequencing effort compared to conventional methods. We observed gene expression clusters between different cellular proliferation states or heat shock treatment. The approach demonstrated high detection sensitivity in gene expression analysis compared to current bacterial scRNA-seq methods and proved to be an invaluable tool for understanding the ecology of bacterial populations and capturing the heterogeneity of bacterial gene expression.

Conditioning of the immune system by the microbiome

The human intestinal microbiome has coevolved with its host to establish a stable homeostatic relationship with hallmark features of mutualistic symbioses, yet the mechanistic underpinnings of host-microbiome interactions are incompletely understood. Thus, it is an opportune time to conceive a common framework for microbiome-mediated regulation of immune function. We propose the term conditioned immunity to describe the multifaceted mechanisms by which the microbiome modulates immunity. In this regard, microbial colonization is a conditioning exposure that has durable effects on immune function through the action of secondary metabolites, foreign molecular patterns, and antigens. Here, we discuss how spatial niches impact host exposure to microbial products at the level of dose and timing, which elicit diverse conditioned responses.

Single-cell transcriptomics and data analyses for prokaryotes-Past, present and future concepts

Transcriptomics, or more specifically mRNA sequencing, is a powerful tool to study gene expression at the single-cell level (scRNA-seq) which enables new insights into a plethora of biological processes. While methods for single-cell RNA-seq in eukaryotes are well established, application to prokaryotes is still challenging. Reasons for that are rigid and diverse cell wall structures hampering lysis, the lack of polyadenylated transcripts impeding mRNA enrichment, and minute amounts of RNA requiring amplification steps before sequencing. Despite those obstacles, several promising scRNA-seq approaches for bacteria have been published recently, albeit difficulties in the experimental workflow and data processing and analysis remain. In particular, bias is often introduced by amplification which makes it difficult to distinguish between technical noise and biological variation. Future optimization of experimental procedures and data analysis algorithms are needed for the improvement of scRNA-seq but also to aid in the emergence of prokaryotic single-cell multi-omics. to help address 21st century challenges in the biotechnology and health sector.

Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling

The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs' efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.

Propionate primes the DC pump in neonates

The protective benefits of breastmilk are well-appreciated, yet lack mechanistic detail. In this issue of Immunity, Sikder et al. reveal how breastmilk-microbiota-derived propionate induces Flt3L expression, dendritic cell maturation, regulatory T cell recruitment, and antiviral immunity in the lung.

The Blood Microbiome and Health: Current Evidence, Controversies, and Challenges

Blood is conventionally thought to be sterile. However, emerging evidence on the blood microbiome has started to challenge this notion. Recent reports have revealed the presence of genetic materials of microbes or pathogens in the blood circulation, leading to the conceptualization of a blood microbiome that is vital for physical wellbeing. Dysbiosis of the blood microbial profile has been implicated in a wide range of health conditions. Our review aims to consolidate recent findings about the blood microbiome in human health and to highlight the existing controversies, prospects, and challenges around this topic. Current evidence does not seem to support the presence of a core healthy blood microbiome. Common microbial taxa have been identified in some diseases, for instance, Legionella and Devosia in kidney impairment, Bacteroides in cirrhosis, Escherichia/Shigella and Staphylococcus in inflammatory diseases, and Janthinobacterium in mood disorders. While the presence of culturable blood microbes remains debatable, their genetic materials in the blood could potentially be exploited to improve precision medicine for cancers, pregnancy-related complications, and asthma by augmenting patient stratification. Key controversies in blood microbiome research are the susceptibility of low-biomass samples to exogenous contamination and undetermined microbial viability from NGS-based microbial profiling, however, ongoing initiatives are attempting to mitigate these issues. We also envisage future blood microbiome research to adopt more robust and standardized approaches, to delve into the origins of these multibiome genetic materials and to focus on host–microbe interactions through the elaboration of causative and mechanistic relationships with the aid of more accurate and powerful analytical tools.

Advancing massive transcriptional profiling of single bacteria

Single-bacteria RNA-seq is becoming a transformative technology for elucidating bacterial diversity in ecology, microbial communities, and pathogenicity. In a recent report by Ma et al. published in Cell, the authors present BacDrop, a versatile, high-throughput method for capturing the transcriptomes of millions of bacteria.