Capturing the heterogeneous phenotypes of microbial populations at relevant spatiotemporal scales is highly challenging. Here, we present par-seqFISH (parallel sequential fluorescence in situ hybridization), a transcriptome-imaging approach that records gene expression and spatial context within microscale assemblies at a single-cell and molecule resolution. We applied this approach to the opportunistic pathogen Pseudomonas aeruginosa, analyzing about 600,000 individuals across dozens of conditions in planktonic and biofilm cultures. We identified numerous metabolic- and virulence-related transcriptional states that emerged dynamically during planktonic growth, as well as highly spatially resolved metabolic heterogeneity in sessile populations. Our data reveal that distinct physiological states can coexist within the same biofilm just several micrometers away, underscoring the importance of the microenvironment. Our results illustrate the complex dynamics of microbial populations and present a new way of studying them at high resolution.

/pdf/spatial-transcriptomics-of-planktonic-and-sessile-bacterial-594h9rw1ja.pdf

Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution

Despite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. Although single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems1-13, the application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance14-16, lack of mRNA polyadenylation and thick cell walls17. Here, we present prokaryotic expression profiling by tagging RNA in situ and sequencing (PETRI-seq)-a low-cost, high-throughput prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing11,12,18 to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single-cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates of more than 200 mRNAs per cell for exponentially growing Escherichia coli. These characteristics enable robust discrimination of cell states corresponding to different phases of growth. When applied to wild-type Staphylococcus aureus, PETRI-seq revealed a rare subpopulation of cells undergoing prophage induction. We anticipate that PETRI-seq will have broad utility in defining single-cell states and their dynamics in complex microbial communities.

Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing.

The human body is constantly exposed to microorganisms, which entails manifold interactions between human cells and diverse commensal or pathogenic bacteria. The cellular states of the interacting cells are decisive for the outcome of these encounters such as whether bacterial virulence programmes and host defence or tolerance mechanisms are induced. This Review summarizes how next-generation RNA sequencing (RNA-seq) has become a primary technology to study host–microbe interactions with high resolution, improving our understanding of the physiological consequences and the mechanisms at play. We illustrate how the discriminatory power and sensitivity of RNA-seq helps to dissect increasingly complex cellular interactions in time and space down to the single-cell level. We also outline how future transcriptomics may answer currently open questions in host–microbe interactions and inform treatment schemes for microbial disorders. Interactions between microorganisms and their hosts are highly context dependent and contribute to both normal tissue function and infectious disease pathology. In this Review, Westermann and Vogel describe how advances in RNA sequencing techniques are providing molecular insights into host–microbe interactions, including advances in cross-species and single-cell transcriptomics.

Cross-species RNA-seq for deciphering host–microbe interactions

Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

/pdf/best-practices-on-the-differential-expression-analysis-of-2dwbpddofy.pdf

Best practices on the differential expression analysis of multi-species RNA-seq.

Biofilms are a ubiquitous mode of microbial life and display an increased tolerance to different stresses. Inside biofilms, cells may experience both externally applied stresses and internal stresses that emerge as a result of growth in spatially structured communities. In this review, we discuss the spatial scales of different stresses in the context of biofilms, and if cells in biofilms respond to these stresses as a collection of individual cells, or if there are multicellular properties associated with the response. Understanding the organizational level of stress responses in microbial communities can help to clarify multicellular functions of biofilms.

/pdf/multicellular-and-unicellular-responses-of-microbial-566omzolnq.pdf

Multicellular and unicellular responses of microbial biofilms to stress

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

https://science.sciencemag.org/content/sci/early/2020/12/16/science.aba5257.full.pdf

Microbial single-cell RNA sequencing by split-pool barcoding

Single-cell RNA-sequencing (scRNA-seq) has become an essential tool for characterizing multi-celled eukaryotic systems but current methods are not compatible with bacteria. Here, we introduce microSPLiT, a low cost and high-throughput scRNA-seq method that works for gram-negative and gram-positive bacteria and can resolve transcriptional states that remain hidden at a population level. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled from different growth stages, creating a detailed atlas of changes in metabolism and lifestyle. We not only retrieve detailed gene expression profiles associated with known but rare states such as competence and PBSX prophage induction, but also identify novel and unexpected gene expression states including heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. microSPLiT empowers high-throughput analysis of gene expression in complex bacterial communities.

https://www.biorxiv.org/content/biorxiv/early/2019/12/10/869248.full.pdf

Abstract Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine 1,2 . Here we reduced this complexity by focusing on cellular organization—a key readout and driver of cell behaviour 3,4 —at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the ‘wiring’ of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring. 

/pdf/integrated-intracellular-organization-and-its-variations-in-10tl6067.pdf

Luana Paleologu

Papers

Microbial single-cell RNA sequencing by split-pool barcoding

Microbial single-cell RNA sequencing by split-pool barcoding

Integrated intracellular organization and its variations in human iPS cells