Author Correction: Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis.

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Ultra-high throughput single-cell analysis of proteins and RNAs by split-pool synthesis.

Single-cell omics provide insight into cellular heterogeneity and function. Recent technological advances have accelerated single-cell analyses, but workflows remain expensive and complex. We present a method enabling simultaneous, ultra-high throughput single-cell barcoding of millions of cells for targeted analysis of proteins and RNAs. Quantum barcoding (QBC) avoids isolation of single cells by building cell-specific oligo barcodes dynamically within each cell. With minimal instrumentation (four 96-well plates and a multichannel pipette), cell-specific codes are added to each tagged molecule within cells through sequential rounds of classical split-pool synthesis. Here we show the utility of this technology in mouse and human model systems for as many as 50 antibodies to targeted proteins and, separately, >70 targeted RNA regions. We demonstrate that this method can be applied to multi-modal protein and RNA analyses. It can be scaled by expansion of the split-pool process and effectively renders sequencing instruments as versatile multi-parameter flow cytometers.

Nucleoid and cytoplasmic localization of small RNAs in Escherichia coli.

Bacterial small RNAs (sRNAs) regulate protein production by binding to mRNAs and altering their translation and degradation. sRNAs are smaller than most mRNAs but larger than many proteins. Therefore it is uncertain whether sRNAs can enter the nucleoid to target nascent mRNAs. Here, we investigate the intracellular localization of sRNAs transcribed from plasmids in Escherichia coli using RNA fluorescent in-situ hybridization. We found that sRNAs (GlmZ, OxyS, RyhB and SgrS) have equal preference for the nucleoid and cytoplasm, and no preferential localization at the cell membrane. We show using the gfp mRNA (encoding green fluorescent protein) that non-sRNAs can be engineered to have different proportions of nucleoid and cytoplasmic localization by altering their length and/or translation. The same localization as sRNAs was achieved by decreasing gfp mRNA length and translation, which suggests that sRNAs and other RNAs may enter the densely packed DNA of the nucleoid if they are sufficiently small. We also found that the Hfq protein, which binds sRNAs, minimally affects sRNA localization. Important implications of our findings for engineering synthetic circuits are: (i) sRNAs can potentially bind nascent mRNAs in the nucleoid, and (ii) localization patterns and distribution volumes of sRNAs can differ from some larger RNAs.

Paradoxical suppression of small RNA activity at high Hfq concentrations due to random-order binding.

Small RNAs (sRNAs) are important regulators of gene expression during bacterial stress and pathogenesis. sRNAs act by forming duplexes with mRNAs to alter their translation and degradation. In some bacteria, duplex formation is mediated by the Hfq protein, which can bind the sRNA and mRNA in each pair in a random order. Here we investigate the consequences of this random-order binding and experimentally demonstrate that it can counterintuitively cause high Hfq concentrations to suppress rather than promote sRNA activity in Escherichia coli. As a result, maximum sRNA activity occurs when the Hfq concentration is neither too low nor too high relative to the sRNA and mRNA concentrations ('Hfq set-point'). We further show with models and experiments that random-order binding combined with the formation of a dead-end mRNA-Hfq complex causes high concentrations of an mRNA to inhibit its own duplex formation by sequestering Hfq. In such cases, maximum sRNA activity requires an optimal mRNA concentration ('mRNA set-point') as well as an optimal Hfq concentration. The Hfq and mRNA set-points generate novel regulatory properties that can be harnessed by native and synthetic gene circuits to provide greater control over sRNA activity, generate non-monotonic responses and enhance the robustness of expression.

Quantitative characterization of gene regulation by Rho dependent transcription termination.

Rho factor dependent transcription termination (RTT) is common within the coding sequences of bacterial genes and it acts to couple transcription and translation levels. Despite the importance of RTT for gene regulation, its effects on mRNA and protein concentrations have not been quantitatively characterized. Here we demonstrate that the exogenous cfp gene encoding the cyan fluorescent protein can serve as a model for gene regulation by RTT. This was confirmed by showing that Psu and bicyclomycin decrease RTT and increase full length cfp mRNAs (but remarkably they have little effect on protein production). We then use cfp to characterize the relationship between its protein and full length mRNA concentrations when the translation initiation rate is varied by sequence modifications of the translation initiation region (TIR). These experiments reveal that the fold change in protein concentration (RP) and the fold change in full length mRNA concentration (Rm) have the relationship RP≈Rm(b), where b is a constant. The average value of b was determined from three separate data sets to be ~3.6. We demonstrate that the above power law function can predict how altering the translation initiation rate of a gene in an operon will affect the mRNA concentrations of downstream genes and specify a lower bound for the associated changes in protein concentrations. In summary, this study defines a simple phenomenological model to help program expression from single genes and operons that are regulated by RTT, and to guide molecular models of RTT.

Modulating the frequency and bias of stochastic switching to control phenotypic variation.

Mechanisms that control cell-to-cell variation in gene expression ('phenotypic variation') can determine a population's growth rate, robustness, adaptability and capacity for complex behaviours. Here we describe a general strategy (termed FABMOS) for tuning the phenotypic variation and mean expression of cell populations by modulating the frequency and bias of stochastic transitions between 'OFF' and 'ON' expression states of a genetic switch. We validated the strategy experimentally using a synthetic fim switch in Escherichia coli. Modulating the frequency of switching can generate a bimodal (low frequency) or a unimodal (high frequency) population distribution with the same mean expression. Modulating the bias as well as the frequency of switching can generate a spectrum of bimodal and unimodal distributions with the same mean expression. This remarkable control over phenotypic variation, which cannot be easily achieved with standard gene regulatory mechanisms, has many potential applications for synthetic biology, engineered microbial ecosystems and experimental evolution.

Minimal effect of gene clustering on expression in Escherichia coli.

Genes that interact or function together are often clustered in bacterial genomes, and it has been proposed that this clustering may affect gene expression. In this study, we directly compared gene expression in nonclustered arrangements and in three common clustered arrangements (codirectional, divergent, and operon) using synthetic circuits in Escherichia coli. We found that gene clustering had minimal effects on gene expression. Specifically, gene clustering did not alter constitutive expression levels or stochastic fluctuations in expression ("expression noise"). Remarkably, the expression of two genes that share the same chromosome position with the same promoter (operon) or with separate promoters (codirectional and divergent arrangements) was not significantly more correlated than genes at different chromosome positions (nonclustered arrangements). The only observed effect of clustering was increased transcription factor binding in codirectional and divergent gene arrangements due to DNA looping, but this is not a specific feature of clustering. In summary, we demonstrate that gene clustering is not a general modulator of gene expression, and therefore any effects of clustering are likely to occur only with specific genes or under certain conditions.

Regulatory consequences of gene translocation in bacteria.

Gene translocations play an important role in the plasticity and evolution of bacterial genomes. In this study, we investigated the impact on gene regulation of three genome organizational features that can be altered by translocations: (i) chromosome position; (ii) gene orientation; and (iii) the distance between a target gene and its transcription factor gene ('target-TF distance'). Specifically, we quantified the effect of these features on constitutive expression, transcription factor binding and/or gene expression noise using a synthetic network in Escherichia coli composed of a transcription factor (LacI repressor) and its target gene (yfp). Here we show that gene regulation is generally robust to changes in chromosome position, gene orientation and target-TF distance. The only demonstrable effect was that chromosome position alters constitutive expression, due to changes in gene copy number and local sequence effects, and that this determines maximum and minimum expression levels. The results were incorporated into a mathematical model which was used to quantitatively predict the responses of a simple gene network to gene translocations; the predictions were confirmed experimentally. In summary, gene translocation can modulate constitutive gene expression levels due to changes in chromosome position but it has minimal impact on other facets of gene regulation.

Direct comparison of small RNA and transcription factor signaling.

Small RNAs (sRNAs) and proteins acting as transcription factors (TFs) are the principal components of gene networks. These two classes of signaling molecules have distinct mechanisms of action; sRNAs control mRNA translation, whereas TFs control mRNA transcription. Here, we directly compare the properties of sRNA and TF signaling using mathematical models and synthetic gene circuits in Escherichia coli. We show the abilities of sRNAs to act on existing target mRNAs (as opposed to TFs, which alter the production of future target mRNAs) and, without needing to be first translated, have surprisingly little impact on the dynamics. Instead, the dynamics are primarily determined by the clearance rates, steady-state concentrations and response curves of the sRNAs and TFs; these factors determine the time delay before a target gene's expression can maximally respond to changes in sRNA and TF transcription. The findings are broadly applicable to the analysis of signaling in gene networks, and we demonstrate that they can be used to rationally reprogram the dynamics of synthetic circuits.

Fundamental relationship between operon organization and gene expression.

Half a century has passed since the discovery of operons (groups of genes that are transcribed together as a single mRNA). Despite the importance of operons in bacterial gene networks, the relationship between their organization and gene expression remains poorly understood. Here we show using synthetic operons in Escherichia coli that the expression of a given gene increases with the length of the operon and as its position moves farther from the end of the operon. These findings can be explained by a common mechanism; increasing the distance from the start of a gene to the end of the operon (termed the "transcription distance") provides more time for translation to occur during transcription, resulting in increased expression. We confirmed experimentally that the increased expression is indeed due to increased translation. Furthermore our analysis indicates the translation initiation rate for an mRNA is sixfold greater during transcription than after its release, which amplifies the impact of the transcription distance on gene expression. As a result of these mechanisms, gene expression increases by ∼40% for each 1,000 nucleotides of transcription distance. In summary, we demonstrate that a fundamental relationship exists between gene expression and the number, length, and order of the genes in an operon. This relationship has important implications for understanding the functional basis of genome organization and practical applications for synthetic biology.

Disruption of small RNA signaling caused by competition for Hfq.

Small RNAs (sRNAs) regulate diverse pathways, including stress responses, virulence, and metabolism in Escherichia coli. At the center of this large sRNA regulatory network is the Hfq protein. Hfq mediates the binding of sRNAs to their target mRNAs; without Hfq, most sRNAs cannot efficiently regulate target mRNA expression. Here, we show in vivo that Hfq can be a limiting factor for sRNA activity and that it can be easily depleted, causing disruption of the sRNA network. Depletion of the available Hfq can occur when sRNAs and target mRNAs are transcribed at high levels without their partners, resulting in the sequestration of Hfq into sRNA-Hfq and target mRNA-Hfq complexes. This can be avoided by coordinating the transcription of sRNAs with their target mRNAs so that they are turned on and off together to maximize duplex formation and minimize Hfq sequestration. Therefore, the limited availability of Hfq results in a highly interdependent sRNA network, wherein the activity of each sRNA depends on the activity of the other sRNAs and target mRNAs in the network.