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

Beyond the Gut: The intratumoral microbiome's influence on tumorigenesis and treatment response

The intratumoral microbiome (TM) refers to the microorganisms in the tumor tissues, including bacteria, fungi, viruses, and so on, and is distinct from the gut microbiome and circulating microbiota. TM is strongly associated with tumorigenesis, progression, metastasis, and response to therapy. This paper highlights the current status of TM. Tract sources, adjacent normal tissue, circulatory system, and concomitant tumor co-metastasis are the main origin of TM. The advanced techniques in TM analysis are comprehensively summarized. Besides, TM is involved in tumor progression through several mechanisms, including DNA damage, activation of oncogenic signaling pathways (phosphoinositide 3-kinase [PI3K], signal transducer and activator of transcription [STAT], WNT/β-catenin, and extracellular regulated protein kinases [ERK]), influence of cytokines and induce inflammatory responses, and interaction with the tumor microenvironment (anti-tumor immunity, pro-tumor immunity, and microbial-derived metabolites). Moreover, promising directions of TM in tumor therapy include immunotherapy, chemotherapy, radiotherapy, the application of probiotics/prebiotics/synbiotics, fecal microbiome transplantation, engineered microbiota, phage therapy, and oncolytic virus therapy. The inherent challenges of clinical application are also summarized. This review provides a comprehensive landscape for analyzing TM, especially the TM-related mechanisms and TM-based treatment in cancer.

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.

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.

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.

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.

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.

DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering

Single-cell ribonucleic acid sequencing (scRNA-seq) is a high-throughput genomic technique that is utilized to investigate single-cell transcriptomes. Cluster analysis can effectively reveal the heterogeneity and diversity of cells in scRNA-seq data, but existing clustering algorithms struggle with the inherent high dimensionality, noise, and sparsity of scRNA-seq data. To overcome these limitations, we propose a clustering algorithm: the Dual Correlation Reduction network-based Extreme Learning Machine (DCRELM). First, DCRELM obtains the low-dimensional and dense result features of scRNA-seq data in an extreme learning machine (ELM) random mapping space. Second, the ELM graph distortion module is employed to obtain a dual view of the resulting features, effectively enhancing their robustness. Third, the autoencoder fusion module is employed to learn the attributes and structural information of the resulting features, and merge these two types of information to generate consistent latent representations of these features. Fourth, the dual information reduction network is used to filter the redundant information and noise in the dual consistent latent representations. Last, a triplet self-supervised learning mechanism is utilized to further improve the clustering performance. Extensive experiments show that the DCRELM performs well in terms of clustering performance and robustness. The code is available at https://github.com/gaoqingyun-lucky/awesome-DCRELM .

Motility-activating mutations upstream of flhDC reduce acid shock survival of Escherichia coli

Many neutralophilic bacterial species try to evade acid stress with an escape strategy, which is reflected in the increased expression of genes coding for flagellar components. Extremely acid-tolerant bacteria, such as Escherichia coli, survive the strong acid stress, e.g., in the stomach of vertebrates. Recently, we were able to show that the induction of motility genes in E. coli is strictly dependent on the degree of acid stress, i.e., they are induced under mild acid stress but not under severe acid stress. However, it was not known to what extent fine-tuned expression of motility genes is related to fitness and the ability to survive periods of acid shock. In this study, we demonstrate that the expression of FlhDC, the master regulator of flagellation, is inversely correlated with the acid shock survival of E. coli. We encountered this phenomenon when analyzing mutants from the Keio collection, in which the expression of flhDC was altered by an insertion sequence element. These results suggest a fitness trade-off between acid tolerance and motility.IMPORTANCEEscherichia coli is extremely acid-resistant, which is crucial for survival in the gastrointestinal tract of vertebrates. Recently, we systematically studied the response of E. coli to mild and severe acidic conditions using Ribo-Seq and RNA-Seq. We found that motility genes are induced at pH 5.8 but not at pH 4.4, indicating stress-dependent synthesis of flagellar components. In this study, we demonstrate that motility-activating mutations upstream of flhDC, encoding the master regulator of flagella genes, reduce the ability of E. coli to survive periods of acid shock. Furthermore, we show an inverse correlation between motility and acid survival using a chromosomal isopropyl β-D-thio-galactopyranoside (IPTG)-inducible flhDC promoter and by sampling differentially motile subpopulations from swim agar plates. These results reveal a previously undiscovered trade-off between motility and acid tolerance and suggest a differentiation of E. coli into motile and acid-tolerant subpopulations, driven by the integration of insertion sequence elements.

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.

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.

Exploitation of microbial activities at low pH to enhance planetary health

Awareness is growing that human health cannot be considered in isolation but is inextricably woven with the health of the environment in which we live. It is, however, under-recognized that the sustainability of human activities strongly relies on preserving the equilibrium of the microbial communities living in/on/around us. Microbial metabolic activities are instrumental for production, functionalization, processing, and preservation of food. For circular economy, microbial metabolism would be exploited to produce building blocks for the chemical industry, to achieve effective crop protection, agri-food waste revalorization, or biofuel production, as well as in bioremediation and bioaugmentation of contaminated areas. Low pH is undoubtedly a key physical-chemical parameter that needs to be considered for exploiting the powerful microbial metabolic arsenal. Deviation from optimal pH conditions has profound effects on shaping the microbial communities responsible for carrying out essential processes. Furthermore, novel strategies to combat contaminations and infections by pathogens rely on microbial-derived acidic molecules that suppress/inhibit their growth. Herein, we present the state-of-the-art of the knowledge on the impact of acidic pH in many applied areas and how this knowledge can guide us to use the immense arsenal of microbial metabolic activities for their more impactful exploitation in a Planetary Health perspective.