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.

Single-cell RNA-sequencing (scRNA-seq) has become an essential tool for characterizing multicelled 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 gramnegative 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.

Yeasts are naturally diverse, genetically tractable, and easy to grow in a myriad of experimental conditions such that researchers have the ability to investigate any number of genotypes, strains, environments, or the interaction thereof. However, studies of variation in the yeast transcriptome have been limited by the processing capabilities of available RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform for yeasts. 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 we use 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 samples 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 report transcriptionally distinct cell states related to cell cycle, growth rate, metabolic strategies, stress responses, etc. 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.

BackgroundA hub protein is one that interacts with many functional partners. The annotation of hub proteins, or more generally the protein-protein interaction “degree” of each gene, requires quality genome-wide data. Data obtained using yeast two-hybrid methods contain many false positive interactions between proteins that rarely encounter each other in living cells, and such data have fallen out of favor.ResultsWe find that protein “stickiness”, measured as network degree in ostensibly low quality yeast two-hybrid data, is a more predictive genomic metric than the number of functional protein-protein interactions, as assessed by supposedly higher quality high throughput affinity capture mass spectrometry data. In the yeast Saccharomyces cerevisiae, a protein’s high stickiness, but not its high number of functional interactions, predicts low stochastic noise in gene expression, low plasticity of gene expression across different environments, and high probability of forming a homo-oligomer. Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential. Once the higher stickiness of homo-oligomers is controlled for, we find that homo-oligomers have noisier and more plastic gene expression than other proteins, consistent with a role for homo-oligomerization in mediating robustness.ConclusionsOur work validates use of the number of yeast two-hybrid interactions as a metric for protein stickiness. Sticky proteins exhibit low stochastic noise in gene expression, and low plasticity in expression across different environments.

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Stochastic differentiation and programmed cell death are common developmental processes in microbes, driving diverse altruistic behaviors that promote cooperation. Utilizing cell death in developmental programs requires control over the rate of differentiation to balance cell proliferation against the utility of sacrifice. However, the regulatory networks that control these behaviors are often complex and have yet to be successfully harnessed as biotechnology. Here, we engineered a synthetic developmental gene network that couples stochastic differentiation with programmed cell death to implement a two-member division of labor. Progenitor cellobiose consumer cells were engineered to grow on cellobiose and differentiate at a controlled rate into self-destructive altruists that release an otherwise sequestered cellulase enzyme payload through autolysis to form a developmental Escherichia coli consortium that utilizes cellulose for growth. We used an experimentally parameterized model of task switching, payload delivery and cellulose conversion to nutrients to set key parameters to achieve overall population growth supported by cellulase release, liberating 14-23% of the available carbon. An inevitable consequence of engineering altruistic developmental behaviors is the emergence of cheaters that undermine cooperation. We observed cheater phenotypes for consumers and altruists, identified mutational hotspots and constructed a predictive model of circuit longeivity based on mutation rate estimates for each mode of evolutionary escape. This work introduces the altruistic developmental program as a new tool for synthetic biology, demonstrates the utility of population dynamics models to engineer complex phenotypes and provides a testbed for probing the evolutionary biology of selfdestructive altruism.

In the past decade, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) mass spectrometry (MS) has become a timely and cost-effective alternative to bacterial identification. The MALDI-ToF MS technique analyzes the total protein of culturable microorganisms at the species level and produces a mass spectra based on peptides which is compared to a database of identified profiles. Consequently, unique signatures of each microorganism are produced allowing identification at the species and, more importantly, strain level. Our present study proposes that the MALDI-ToF MS can be further used to screen functional and metabolic differences. While other studies applied the MALDI-ToF technique to identify subgroups within species, we investigated how various environmental factors could alter the unique bacterial signatures. We found that genetic and phenotypic differences between microorganisms belonging to the same species can be reflected in peptide-mass fingerprints generated by MALDI-ToF MS.These results suggest that MALDI-ToF MS can screen intra-species phenotypic differences of several microorganisms.

Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) provides a fast and easy means to identify culturable microorganisms to the species level. The sample preparation of microbial colonies for MALDI‐TOF analysis requires a suitable protein extraction method. While standard MALDI‐TOF sample preparation methods are well suited for the identification of and the discrimination between microorganisms belonging to different species, they are not disruptive enough to allow the discrimination between different strains of the same microorganism. More disruptive protein extraction methods lead to better discrimination power because they allow a better breakdown of bacterial cell membrane and a more efficient extraction of conserved microbial proteins that are specific to each species and strain. Here we describe how to extract proteins from single microbial colonies using formic acid and acetonitrile to disrupt cells prior to placing them on a target plate for MALDI‐TOF MS analysis. Contrary to other sample preparation methods for MALDI‐TOF MS, this approach allows the discrimination between different strains of microorganisms of the same species. Our approach also provides the groundwork data for building algorithms that allow the detection of specific microbial strains of interest, with a great potential for diagnostic applications in clinical settings. © 2021 Wiley Periodicals LLC. Basic Protocol: Protein extraction and MALDI‐TOF bio‐typing of phenotypically distinct bacterial species