Applications of single-cell technology on bacterial analysis

Abstract

Background: Traditionally, scientists studied microbiology through the manner of batch cultures, to conclude the dynamics or outputs by averaging all individuals. However, as the researches go further, the heterogeneities among the individuals have been proven to be crucial for the population dynamics and fates.

Results: Due to the limit of technology, single-cell analysis methods were not widely used to decipher the inherent connections between individual cells and populations. Since the early decades of this century, the rapid development of microfluidics, fluorescent labelling, next-generation sequencing, and high-resolution microscopy have speeded up the development of single-cell technologies and further facilitated the applications of these technologies on bacterial analysis.

Conclusions: In this review, we summarized the recent processes of single-cell technologies applied in bacterial analysis in terms of intracellular characteristics, cell physiology dynamics, and group behaviors, and discussed how single-cell technologies could be more applicable for future bacterial researches.

Graphical abstract

Keywords

single-cell technology / bacterial analysis / fluorescent labelling / next-generation sequencing / microfluidics

INTRODUCTION

Bacteria are abundant everywhere in nature. They play critical roles in our biosphere as cycling elements [¹]. Through studying bacteria, many important principals of life have been revealed. For example, the genetic central dogma [²] that explains how the genetic information flows to biosystems, and the Monod equation [³] that provides an important relationship between bacterial growth rate and culturing conditions. With in-depth studies and the development of quantitative techniques, researchers have found that cell-to-cell variabilities prevalently exist, even if the environmental and genetic variances are controlled as much as possible [⁴]. Actually, recent studies, such as gene expression [⁵,⁶], cell growth and division [⁷], genetic mutation [⁸] and dCas9 target search [⁹], have started to focus on sing-cell snapshots gaining more informative details than bulk averages from the batch culture. The results indeed reflected deeper mechanisms underlying the biological processes and reinforced the sing-cell technologies to dissect the heterogeneity among population samples.

In general, single-cell analysis technologies focus on three levels of the cellular heterogeneity: (i) intracellular components, (ii) individual cell dynamics, and (iii) cellular group behaviors. Cellular heterogeneity arises from fluctuations in gene expression [¹⁰,¹¹], stochastic partition of molecules [¹²] or the random processes within genetic regulatory networks [¹³]. To figure out the mystery of heterogeneity, the first step is to quantify the intracellular components regarding their number and localization. Moreover, time autocorrelation behaviors that are associated with cellular heterogeneities are difficult to study within a population due to lacking global synchronization, such as oscillation [¹⁴,¹⁵] and DNA replication initiation [⁷,¹⁶]. Thus, monitoring individual cells in a high-throughput manner is necessary to depict single-cell dynamics and even population behaviors. In fact, microbial behaviors, like chemotaxis, migration, and communications, have already overstepped our intuition, thus, employing high-quality single-cell data with mathematical models to explore quantitative understanding is becoming more and more important [¹⁷]. In this review, we present recent applications of single-cell technologies on bacterial analysis, organized by the aspects of cellular size, in terms of intracellular, single-cell, and group levels (Figure 1).

CHARACTERIZATION OF INTRACELLULAR COMPONENTS

Intracellular components, including DNA, RNA, and protein, etc., are the essential elements that drive the cell to work orderly. These molecules have unique functions, and they interact with each other forming a complex interaction network. For systematically evaluating how these molecules work, we should concern both their properties (e.g., enzymatic activities) and their properties in the network (e.g., quantities, locations, and interaction relationships). Recently, more and more studies suggest that it is incomprehensive to quantify these properties via batch cultures because their variabilities are averaged out in the population level [¹⁸]. In this section, two categories of single-cell techniques (Figure 2), which are based on fluorescent labelling and next-generation sequencing, will be depicted to show their potential in characterizing intracellular components and uncovering the veil of cellular heterogeneities.

In the bacterial analysis, two kinds of typical fluorescent labelling are frequently quoted and explored, which are oligonucleotide probes and fluorescent proteins (FPs) (Figure 2A). Oligonucleotide probe is normally a short sequence of nucleotides labelled with fluorescent molecules or coupled with reporter molecules, such as fluorescence labels in situ hybridization (FISH) and the intrinsic cellular transcripts known as aptamer. In 1989, Delong et al. [²¹] first used FISH in 16S ribosomal RNA to identify single microbial cells and quantified the changes of ribosome content over time. The variants of oligonucleotide probes for different targets of FISH have increased subsequently such as those for rRNA [²²], mRNA [²³] and genomic DNA [²⁴]. For example, using single-cell mRNA counting FISH, Chong et al. [²⁵] found that the intracellular gyrase concentration is responsible for the transcriptional bursting, which is a major source of gene expression noise. Additionally, FISH combined with flow cytometry could also be applied for sorting and quantifying single cells in mixed populations [²⁶]. However, cells would have to be damaged for permeating oligonucleotides when FISH was deployed. Thus, another kind of nondestructive fluorescent labels, aptamers [²⁷], was proposed and employed for labeling specific RNAs in living cells. These labels were identified in the laboratory through the systematic evolution of ligands by exponential enrichment (SELEX). Among them, the first discovered aptamer, named Spinach [²⁷], which resembled the green fluorescent protein (GFP), can be integrated into 5′-end of a transcript and form a genetic loop binding to the cell-permeable fluorescent molecules, and then emit green fluorescence. Nowadays, RNA labeling toolboxes, including Spinach2 [²⁸], Mango [²⁹] and Broccoli [³⁰], have been provided for RNA quantification in vivo and applied to analyze single bacterial cells successfully [³¹].

FPs are a group of proteins that contain chromophores formed via the interactions among amino acid residues. The most well-known one, GFP that comes from jellyfish Aequorea victoria [³²], is now widely used as a marker for genetic characterizations. Owing to non-specific, small size, noninterference, and cofactor-free, GFP enables us to observe the dynamics of events in living cells [³³]. Up to date, FPs have been common tools for characterizing cell physiology, genetic parts, gene circuits, gene expression noises, and transcriptional levels, etc. Using FPs to titrate the transcription level by inserting into downstream parts of a specific promoter or integrating into a genetic circuit can be used to quantify properties of genetic objects or annotate cell physiology at the single-cell level. Norman et al. [³⁴] utilized two different FPs, GFP and RFP to label different promoters that are regulated via a particular genetic pathway to indicate cell states, and revealed that a simple genetic circuit confers cells a tight timing response ability allowing cells to “cooperate”. Moreover, FPs based ensemble strategy was also used to dissect temporal regulation of diverse genetic effects at the single-cell level, including SOS response [³⁵], hysteretic response [³⁶], stochastic metabolic state shift [³⁷], gene expression noise [⁴,³⁸] and bistability of positive feedback circuits [³⁹], etc. These researches improved our understanding of the correlations between cellular phenotypes/genotypes and intrinsic heterogeneities. Fusing FPs with other functional proteins as a tandem form can not only quantify the expression level of the genes of interest, but also indicate mRNA levels and population structures. For example, when cell tumble bias was measured by controlling the expression of YFP fused CheY protein that is a motility associated protein, a steep response curve of CheY protein versus tumble bias was revealed [⁴⁰]. Similarly, these composite proteins can be applied to indicate DNA damage events [⁴¹], track individual replisomes [¹⁶], quantify specific RNA abundance [¹¹,⁴²,⁴³] at the single-cell level as well.

Another extremely useful tool for characterizing intracellular components is next-generation sequencing technology (Figure 2B). It enhances our ability to decipher the nucleotide sequences. Traditionally, researchers sequence the complex samples that are obtained from the environment directly and then assemble a large pool of sequencing results to dig out unabundant genomic resources hidden in the microbial world [⁴⁴]. Because of the serious imbalance in the distribution of bacterial species and the highly complex sequencing database, it is challenging to recognize the genomes of low-abundance microorganisms. For addressing further insights within complex microbial communities, more researchers are seeking physical separation methods, such as flow cytometry [⁴⁵] and droplet-based microfluidics [⁴⁶], to isolate single cells from the population for downstream DNA amplification and sequencing. This method, so-called single-cell DNA sequencing (scDNA-seq), could bypass the drawbacks of the traditional sequencing methods and provide new information for biological samples. For example, Rinke et al. [⁴⁶] applied scDNA-seq to target and sequence uncultivated bacterial cells and found novel phyla that were missed by the conventional methods. Moreover, their work also found that a new purine synthesis pathway that was considered belonging to archaea presented in bacterial species, which indicated that there are lateral gene transfers between bacteria and archaea. NGS can also be used to quantify the real-time RNA information via counting the sequencing reads of cDNA. This technology, so-called RNA sequencing (RNA-seq), was widely used to analyze physiological states of live cells. For a decade, sing-cell RNA sequencing (scRNA-seq) was broadly applied to explore the physiology and internal gene expression patterns of eukaryotic cells. However, reports of its applications in prokaryotic cells are scarce up to now. One of the reasons is that the small amount of RNA is normally undetectable, as well as lacking polyadenylated tails on mRNA and short half-life time of RNA [⁴⁷]. To our knowledge, the first study of prokaryotic single-cell transcriptome analysis was reported by Kang et al. [²⁰,⁴⁸]. In their work, Burkholderia thailendensis was used for identifying the gene upregulation or downregulation in presence of subinhibitory concentrations of glyphosate. Through acquiring the relative transcription level of mRNAs at the single-cell resolution, physiological states of cells were successfully depicted. Thus, this method is quite ingenious in some scenarios, especially for complex and scarce samples, such as bacterial pathogens that have invaded mammalian cells, for example, Salmonella entrica. These bacteria will exhibit highly heterogeneities in physiology after infecting host cells. Avital et al. [⁴⁹] mapped the gene regulatory patterns of intercellular pathogen Salmonella typhimurium with transcriptomes of the host and found that three different states of pathogens actually behaved in a linear progression. This result generated new insights into the host-pathogen interactions and might open up a new way of antimicrobial treatments [⁴⁹,⁵⁰].

SINGLE-CELL BACTERIAL PHYSIOLOGY

Bacterial physiology is a classic and important topic in microbiology [⁵¹]. It tries to explain all life processes in an individual bacterium throughout the growth and reproduction. Traditionally, the physiological characteristics come from the batch culture, like grow rates, death rates, and size distributions. Mathematically, the Monod equation even provides a quantitative relation between growth rate and the concentrations of growth-limiting substrates [³]; While nutrient growth law emphasizes correlations between balanced growth and cellular chemical composition in any defined medium [⁵²,⁵³]. Scientists mostly validate these relations by population experiments and employ the average values as the fundamental parameters to represent that of single cells. This can result in homogenization for cellular characteristics, and cover up the heterogeneity among single cells [⁵⁴]. Every individual bacterial cell differs from another to some extent, but this information is normally ignored partially due to the insufficient understanding. However, when researchers try to dive deeper into the mechanisms underlying the observations, this heterogeneity or noise from single cells can provide powerful supports. And thus, single-cell technologies become more and more attractive for bacterial physiology in recent years.

Time-lapse microscopy is a powerful method for detecting physiological dynamics at the single-cell level [⁵⁵]. The microscopy platform could be easily coupled with agarose pads for long-term monitoring of single cells (Figure 3A). However, the nutrition quickly depletes due to the fast growth of bacteria, then causing the uneven growth conditions for different partitions on the same pad. To solve this problem, microfluidic chips are comparably ideal, which can provide sustainable nutrition by continuous flow. It can then be used to address an important status of bacterial cells, namely steady state, related to the nutrient growth law [⁵²,⁵³]. Traditionally, the main method to keep this steady state is serial dilution, which is time consuming and labor intensive. In comparison, microfluidics is easier to operate. Briefly, bacteria are loaded into very tiny growing environments on a microfluidic chip, the whole chip is set up with specific culturing conditions and then monitored under a microscope for hours, even a couple of days or weeks depending on the experimental requirements. As reported, there are two main categories of microfluidic designs, which we name “Chamber” and “Mother Machine” (MoMa), besides the other excellent works.

The Chamber design is normally a layout of microfluidic traps that allows bacterial cells growing as a monolayer (Figure 3B). For example, the Elf group designed a typical chamber-like chip inspired by Mather et al. [⁵⁶]. A method combining microfluidics, time-lapsed microscopy, and automated image analysis was developed subsequently [⁵⁷], which can analyze single-cell intracellular dynamics like gene expressions with a high-throughput manner. Campos et al. elaborated homeostasis mechanisms of bacterial cell size using the same design from Elf group [⁵⁸]. Wallden et al. studied the cell size and cell cycle in a further step, and they found that differences in growth rate resulted in cell-to-cell variations for both division timing and cell size; with a similar device, Wehrens et al. [⁵⁹] cultured long filamentous Escherichia coli and argued that divisions are controlled by the Min system and the adder principle, respectively. Walkmoto group [⁶⁰] proposed an empirical growth law that constrains the maximal growth rate of E. coli by analyzing the data from a narrower chamber that contains only a few lines of cells.

For the MoMa chips, it is similar to the above chamber design, but with a much smaller feature that allows only one single line of bacterial cells to grow, as shown in Figure 3C. Better than the chamber, MoMa chips can be used to track one single cell, named as the mother cell, for hundreds of generations. By increasing the number of trapping lines, the throughput of mother cells can be substantially promoted as well. This can then provide a large amount of data point combined with time-lapse monitoring for statistical analysis, and reveal the distribution of single-cell heterogeneities, including cell size, growth rate, death rate, gene expression and so on. For instance, Jun group [⁷] used the MoMa to investigate robust growth of E. coli cells by hundreds of generations for more than 60 hours. It is the first time to make it possible to culture one individual bacterial cell at stable states for a long period. MoMa is regarded as a powerful tool to study bacterial cell-size control as well, due to its high spatiotemporal resolution. Taheri-Araghi et al. [⁶¹] cultured E. coli and Bacillus subtilis in MoMa and put forward an adder principle to quantitatively explain the cell-size control and homeostasis mechanism. Sauls et al. [⁶²] developed the adder principle and proposed a coarse-grained approach. In contrast, Tanouchi et al. [⁶³] proposed a different regulating mechanism, the noisy linear map theory, to explain the origins of cell-size oscillations. MoMa has also been applied to the research of gene expression [⁵⁵], evolution [⁶⁴] and cell cycle [³⁴,⁶⁵]. Besides this, Yang et al. [⁶⁶] systematically characterized the E. coli growth dynamics within different sizes of MoMa and provided further opinions about how to choose or design appropriate MoMa chips. Their data have strongly supported MoMa to be applied in the studies of single-cell bacterial physiology. To facilitate the wide application of Moma, Jun group [⁶⁷] has also concluded a cultivation protocol for MoMa based on their extensive experience, which is helpful to anyone in the field that feels interested in.

GROUP BEHAVIORS

Group behavior is another important aspect of bacterial analysis, which can provide collective information based on single-cell behaviors. Intuitively, the single-cell data are always stochastic and noisy, concluding the populational behavior is quite challenging. However, taking advantage of single-cell technologies, more and more group behaviors have been deciphered by individual cells, and provide fundamentally new theory or answer biological questions from a more statistical angle [⁶⁸]. For example, despite heterogeneity on chemotaxis, E. coli cells could still have spontaneous collective behaviors when migrating, this has been a confusion between single-cell and populational behaviors for a long time. Fu et al. [⁶⁹] resolved this conflict using a microfluidic device consisting of a long channel (Figure 4A), on which cells could perform the traveling bands and be observed precisely (Figure 4B). Except for chemical gradients, thermal gradients may also have strong influences [⁷¹,⁷²], and their interplays have been studied preliminarily as well [⁷³].

Another group behavior, bacterial surface adhesion, is also appealing to many researchers. The characterizing methods include microfluidic devices, cell-tracking programs, atomic force microscopies, etc [⁷⁴], which can all be applied for single-cell analysis. Researchers have ever tried to reduce the amount of bacterium in a group behavior. However, for some studies, it may not be suitable to separate every single cell from each other. For example, Kim et al. cultured bacteria within microfluidic wells at a density of 500–1000 live cells/well. They tested a synthetic community comprising three kinds of bacteria. And the results showed that the microscale spatial structure plays an important role in coexistence [⁷⁵]. Hence, microscale bacteria population seem to be the least unit for studies on co-culture systems, but the single-cell resolution method provided information about how the community communicated and transported.

For the bacterial group analysis, one inevitable topic is antibiotic resistance [⁷⁶,⁷⁷]. The alarming emergence of multidrug resistance (MDR) of bacterial pathogens has been recognized as a global issue. Thus, how to further uncover the mechanism of the generation and prevalence of MDR based on single-cell technologies can help the society control the spread of MDR. One of the most important pathways for resistance dissemination is the bacterial conjugation, which delivers resistant genes from one microorganism to the others. Lopatkin et al. [⁷⁰] developed a novel device that could gain single-cell resolution, and surprisingly, they found that the conjugation efficiency is independent of antibiotic dosing for almost all commercialized types of antibiotics (Figure 4C). Srimani et al. [⁷⁸] utilized the same device to investigate the postantibiotic effect (PAE). PAE refers to the temporary suppression of bacterial growth following transient antibiotic treatment. What they found is that PAE can be explained by the temporal dynamics of drug detoxification in individual cells after an antibiotic is removed from the extracellular environment.

CONCLUSIONS

For a long time, people have to collect bacterial information through macroscale cultivation, commonly based on wells, tubes, flasks, or even larger containers. The results indeed concluded many important biological rules regarding bacterial physiology, gene expression, and regulation etc [³,⁷⁹–⁸¹]. However, deeper mechanisms hidden by a large population have started to be dug out as the rapid development of technologies, and one typical example is the single-cell technologies. For example, the flow cytometry can be applied for the high-throughput detection of single cells [⁸²], but it is not applicable for real-time monitoring. Thus, microscopy can be proposed to supplement the time-scale information. Combining with microfluidics, the growth environments can be well defined, and cultivating medium or stress can also be flexibly controlled [⁸³]. This can mimic more situations in nature and obtain data with much higher resolution than traditional methods. When going deeper into the single cells, the cellular components can also be analyzed to reveal the detailed information for genetic regulations and protein interactions. Herein, we mainly introduced the fluorescent labelling of oligonucleotides and proteins, and next-generation sequencing. These two methods are so far popular assisting tools for single-cell analysis [⁸⁴].

Although the information has been substantially increased from the current single-cell technologies, there are still a lot of uncovered rules and information needed to be figured out. We believed that more and more novel technologies would be integrated for bacterial analysis, for instance, CRISPR [⁸⁵] for intracellular genetic locations and super-resolution microscopy [⁸⁶] for even intracellular structures, to decipher the single cell information [⁸⁷,⁸⁸] and then population behaviors and community interactions. Many studies hereinbefore cited have tried to answer bacterial fundamental questions using single-cell technologies, but their potential is sought to be further explored for expanding our understanding of sophisticated life processes of bacteria.

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