Genomic DNA is constantly under threat from intracellular and environmental factors that damage its chemical structure. Uncorrected DNA damage may impede cellular propagation or even result in cell death, making it critical to restore genomic integrity. Decades of research have revealed a wide range of mechanisms through which repair factors recognize damage and co-ordinate repair processes. In recent years, single-molecule live-cell imaging methods have further enriched our understanding of how repair factors operate in the crowded intracellular environment. The ability to follow individual biochemical events, as they occur in live cells, makes single-molecule techniques tremendously powerful to uncover the spatial organization and temporal regulation of repair factors during DNA–repair reactions. In this review, we will cover practical aspects of single-molecule live-cell imaging and highlight recent advances accomplished by the application of these experimental approaches to the study of DNA–repair processes in prokaryotes.

Introduction

Maintaining genomic integrity is critical for the perpetuation of life. Chromosomal DNA that carries the genetic information required for cellular proliferation can be damaged upon exposure to DNA-damaging agents generated as by-products of cellular metabolism, or upon exposure to environmental factors [1]. In response, organisms have evolved mechanisms that repair this damage and maintain genetic integrity. In the absence of DNA–repair mechanisms, uncorrected lesions can interfere with biological pathways that involve DNA metabolism, leading to mutations in daughter cells or resulting in cell death [2]. Alongside DNA–repair mechanisms, organisms have also evolved strategies to tolerate DNA damage and allow repair processes a greater chance to detect DNA damage. One such strategy involves synthesizing DNA across damaged DNA templates with the use of translesion synthesis (TLS) polymerases [3,4]. In this process, one of the daughter strands inherits the lesion, whereas the other inherits undamaged DNA. The DNA damage may then be repaired by repair proteins at a later stage, while allowing uninterrupted DNA replication. Resumption of replication of DNA across damaged templates may also be aided by factors involved in DNA recombination that enable restart of stalled replication forks [5]. Several decades of genetic, biochemical, structural and biophysical investigations have revealed a rich diversity in DNA–repair and damage tolerance mechanisms that have evolved to resolve a multitude of DNA lesions [1].

Completion of repair often involves the co-ordinated action of several repair factors. Broadly, repair reactions may be considered to consist of the following steps: damage recognition, damage verification, damage removal and repair synthesis. How various proteins acting as DNA-damage sensors detect damage in a vast excess of undamaged DNA continues to be an area of extensive investigation [6]. Single-molecule real-time imaging experiments of fluorescently labelled repair factors using purified proteins and stretched DNA show that damage sensors may employ a combination of 3D diffusion and 1D sliding modes along the length of the DNA during lesion search (uracil DNA glycosylase [7], Fpg, Nei and Nth [8], UvrAB [9] and MutS [10,11]). These single-molecule methods have been extremely successful in using reconstituted reactions based on purified proteins to directly observe interactions between DNA and repair factors. Using the ability to remove ensemble averaging and extract kinetic information at the single-molecule level, fundamental mechanisms have been uncovered that describe how protein–DNA interactions support specific DNA-damage recognition. However, the behaviour of these DNA–repair factors in the intracellular milieu has remained elusive until recently.

With recent advances in imaging approaches that allow the visualization of single proteins within live cells, quantitative information can be obtained about expression levels, properties of diffusion, binding kinetics, and heterogeneities in the population and behaviour [1219]. Single-molecule localization microscopy methods have yielded access to spatial localization of repair factors in individual cells, as well as the temporal behaviour of molecular parameters through the various steps of DNA–repair processes [1418]. Dynamic monitoring of changes in the network of interactions that maintain genomic integrity has the potential to reveal how cells make decisions that tilt the balance between error-free repair and error-prone repair leading to mutagenesis. In this review, we will focus on the applications of single-molecule live-cell imaging to study DNA repair in bacterial systems. We first discuss experimental designs and practical considerations and then highlight the kinds of information that can be gained using single-molecule live-cell imaging.

Experimental methods

Choice of model organism

Bacteria have served as model organisms in the field of DNA repair due to their ease of culture, fast growth rates and the availability of a wide range of tools that enable genetic manipulation. As a result, repair mechanisms are well characterized genetically, biochemically and structurally in both the gram-negative Escherichia coli (E. coli) and gram-positive Bacillus subtilis (B. subtilis) model systems. Investigations of DNA repair in bacteria are important for two reasons. First, repair mechanisms are functionally conserved from prokaryotes to eukaryotes, even if repair factors are not always structurally conserved. Repair pathways in prokaryotes are generally simpler, consisting of fewer interacting partners. These properties make them easier to study and yield powerful insights into repair mechanisms. As a result, insights gained from bacteria enable similar investigations in eukaryotes. Second, large population sizes and short doubling times of bacteria enable the investigation of the heterogeneity of the responses to stress, allowing researchers to study factors that influence the choice of repair pathway. Such efforts have direct relevance to the understanding and tackling of the emerging problem of antibiotic resistance [20,21].

Labelling

Fluorescence imaging has revolutionized our understanding of biology by revealing intracellular spatial organization and kinetics of protein–substrate interactions [22,23]. This approach requires the protein of interest to be tagged to a fluorescent probe. There are two major classes of fluorescent probes for live-cell imaging: fluorescent proteins and organic dyes [24]. Probes with high photo-stability and high brightness are desired, since they permit observation on longer timescales at high signal-to-background ratios. Whereas organic dyes possess superior photophysical properties, fluorescent proteins remain the most commonly used probes for bacterial imaging. This is because stoichiometric labelling, cell permeability, toxicity and the requirement for harsh labelling conditions pose significant challenges that limit the utility of organic dyes in live-cell imaging applications. Genetically expressible fluorescent proteins (FPs) represent a convenient approach to overcome these limitations, albeit at the expense of less optimal photophysical properties. Since the fluorescent probe is fused to the protein of interest via a flexible peptide linker [25], stoichiometric labelling is generally assured. An explosion in the variety of FPs has provided a large number of options for multi-colour and super-resolution imaging [24,26].

Lacking precedent in the literature, the decision to tag the protein on the N- or the C-terminus may be made by considering structural and biochemical evidence. C-terminal fusions have the added advantage that the presence of the fluorescence signal, in general, corresponds to the expression of the full-length construct. The fusion protein may be expressed from a chromosomal locus or from a plasmid. Although time-consuming, creating a chromosomal fusion of the tagged construct under the native promoter is preferable as the expression level of the repair protein will be comparable with that of the wild-type system and homogenous expression is typically maintained across the cell population. The replacement of the native gene with the tagged version is accomplished using strategies involving phage-encoded recombinases such as λ Red [27] and Cre/lox [28]. Whereas λ Red recombination has been the standard workhorse for recombineering in E. coli, its limitation in incorporating long DNA fragments and the requirement for an antibiotic resistance marker to be simultaneously recombined pose a challenge to the types of mutations that can be introduced into the chromosome for structure–function investigations. To overcome these limitations, CRISPR–Cas9-assisted λ Red may be used [29,30]. Alternatively, protein fusions encoded by long DNA fragments can be expressed at transposon insertion sites using the Tn7 system [31]. On the other hand, plasmid-based expression represents a convenient approach for the investigation of structural mutants on the behaviour of the repair factors in cells. Additionally, expression from low-copy plasmids allows introduction of inducible promoters that are responsive to ultraviolet light (UV) [32,33], arabinose [34] or xylose [35].

Care must be taken to validate the fusion constructs prior to interpreting the results. Fusion proteins may present several types of artefacts, including aggregation at high copy number [36], mislocalization at low-copy number [37], mislocalization due to inter-fluorophore interactions [38], fluorescent signals that are indistinguishable from cellular autofluorescence [39] and loss of enzymatic functions [40,41]. Artefacts introduced by fluorophores can be caught by performing control studies conducted with different tags or by visualizing mutants. Therefore, it is critical to ensure that the tagged proteins retain properties of wild-type repair factors. For assaying enzymatic function, survival or mutagenesis assays are frequently used to monitor the cellular response to genotoxic stresses [1418]. Changes to fluorescent proteins or linker sequences and lengths can be introduced in case the label is found to perturb the enzymatic function [25].

In addition to the practical considerations mentioned above, maturation time and folding efficiency [38] of fluorescent proteins are important considerations when studying the dynamics of protein expression in live cells. The expected copy number of the target protein should also guide the choice of fluorophore: photoactivatable fluorescent proteins may be used to visualize localizations of single molecules of proteins that are expressed at high copy numbers, using photoactivatable light microscopy-type imaging approaches to only activate a small sub-population at any given moment [42]. While photoactivatable mCherry (PAmCherry) [43] has been widely used for single-molecule imaging in bacterial cells, new fluorescent proteins with enhanced photophysical properties continue to be developed [38]. Alternately, single-molecule imaging may be performed with variants of GFP (green fluorescent protein) or YFP (yellow fluorescent protein) using photobleaching-assisted or reactivation localization [44].

In addition to fluorescent proteins, protein fusions may be made with the HaloTag [45] or SNAP-tag [46], enabling subsequent conjugation to organic dyes. Organic dyes offer superior photophysical properties compared with genetically expressible fluorescent proteins [47]. However, labelling involves extended periods of incubation and extensive washing to reduce background signals from unbound dyes — conditions that may be incompatible with live-cell imaging. Additionally, intracellular dye concentrations are diluted as bacteria continue to divide, resulting in lower labelling efficiencies and limiting time-lapse imaging of cells. Signal-to-background ratios can be improved using fluorogenic probes: molecules that are fluorescent upon non-covalently binding to a scaffold protein that stabilizes the dye in its fluorescent conformation [48]. A recently developed tag, dL5 combined with the fluorogen malachite green, has been demonstrated to enable super-resolution imaging in live Caulobacter crescentus [49].

Single-molecule imaging setups

The visualization of the single-molecule fluorescence is performed with a fluorescence microscope equipped with appropriate optical configurations, high numerical-aperture objectives, sensitive cameras (EM-CCD or sCMOS) (Figure 1A) and a heated stage. Unlike conventional fluorescence microscopes, single-molecule imaging in single cells is performed under near-TIRF conditions: the laser excitation beams are inclined at the coverslip-sample interface (Highly Inclined Laminated Optical sheet) to reduce background signals [50]. This geometry allows for penetration into the cell, enabling imaging with a depth of 0.5–1 µm into the flow-cell, with a high signal-to-background ratio. Exposure to laser light can cause photodamage in live cells. By potentially creating alternate substrates for DNA–repair proteins, photodamage can interfere with the study of DNA–repair and damage tolerance mechanisms. This makes it challenging to follow the same cells over extended periods of time. In practice, time-lapsed imaging protocols involve using the lowest excitation intensities that enable observation of foci with a good signal-to-background ratio.

Experimental setups for single-molecule live-cell imaging.

Figure 1.
Experimental setups for single-molecule live-cell imaging.

(A) Schematic of single-molecule fluorescence microscope equipped with high-intensity laser sources, a high numerical-aperture lens and a sensitive camera. Green and yellow illustrate excitation and emission light paths, respectively. Grey paths indicate excitation light of other laser sources at different wavelengths. (B–D) The schematics describe three approaches to immobilize cells for imaging: (B) agarose pad, (C) microfluidic channels and (D) flow-cell device made with an APTES-treated coverslip. Panel C is reproduced from ref. [53] with permission.

Figure 1.
Experimental setups for single-molecule live-cell imaging.

(A) Schematic of single-molecule fluorescence microscope equipped with high-intensity laser sources, a high numerical-aperture lens and a sensitive camera. Green and yellow illustrate excitation and emission light paths, respectively. Grey paths indicate excitation light of other laser sources at different wavelengths. (B–D) The schematics describe three approaches to immobilize cells for imaging: (B) agarose pad, (C) microfluidic channels and (D) flow-cell device made with an APTES-treated coverslip. Panel C is reproduced from ref. [53] with permission.

Foci corresponding to the signal from fluorescently tagged DNA-bound DNA–repair proteins may be blurred due to underlying dynamics of the nucleoid, cell growth or improper immobilization of cells on the substrate. To track single-molecule signals accurately and reliably interpret the data, it is critical that cells are immobilized during the duration of imaging. Depending on the nature of the acquisition protocol, cells may be imaged for several minutes to observe binding of DNA–repair proteins or to monitor intracellular diffusion. Immobilization can be accomplished using physical confinement: agarose pads [51] or microfluidic channels [52,53], or using positively charged surfaces created with APTES [16] (Figure 1B–D). Agarose pads have been widely used due to the ease of setup, although nutrient depletion limits its uses in long-term imaging where imaging is performed under flow. On the other hand, microfluidic and flow-cell devices offer the advantages of controlled environments, enabling imaging for extended periods and with higher throughput.

Cell growth and manipulation

Cells grown in Luria–Bertani media exhibit high autofluorescence that can confound signals originating from fluorescent proteins. Therefore, the use of media such as M9 [54] or EZ-rich defined media (EZRDM, Teknova) in which cells exhibit low autofluorescence is preferable for imaging. Additionally, the M9 minimal medium allows single rounds of DNA replication at a time, resulting in slow growth, whereas the much richer EZRDM supports multiple instances of origin firing, resulting in fast growth. The choice of medium therefore allows flexibility in terms of investigation of pathways that may exhibit a dependence on the metabolic state of the cells.

In the absence of genotoxic agents, imaging of cells in growth medium enables investigation of basal levels of repair activities. Insights into repair mechanisms may be gained by manipulating factors that affect repair processes. The most obvious factor is the concentration of substrates. Substrates for specific DNA–repair pathways can be created by exposing cells to DNA-damaging agents that inflict specific types of DNA damage (Tables 1 and 2). For instance, substrates for nucleotide excision repair (NER) and TLS may be created by exposure to UV irradiation [16,17]. Similarly, substrates for double-strand break repair (DSBR) may be created by treatment with norfloxacin [55]. These chemical and physical reagents offer easy ways to inflict DNA damage throughout the genome in a dose-dependent manner. However, a significant limitation of such approaches in imaging applications is that the sites of DNA damage are invisible. To investigate site-specific interactions, both the repair factor and the site of damage need to be visualized. Efforts in this direction have been made in the study of DSBR mechanisms by labelling sites in the vicinity of an I-SceI recognition site and treatment with I-SceI that inflicts a double-strand break [56]. Without the ability to label lesions or induce site-specific damage, discerning causal interactions between repair protein localization and the presence of DNA damage remains a challenge.

Table 1
DNA-damaging agents employed in single-molecule live-cell imaging for the investigation of repair and damage tolerance pathways
Pathway studied with live-cell imaging DNA-damaging agent Type of induced damage/protein target Organism Reference 
Base excision repair MMS Alkylation E. coli [14
Mismatch repair 2-aminopurine (2-AP) Mismatch B. subtilis [15
TLS Ultraviolet (254 nm) light Cyclobutane pyrimidine dimer, (6-4) photoproduct E. coli [16
NER Ultraviolet (254 nm) light Cyclobutane pyrimidine dimer, (6-4) photoproduct E. coli [17
Mismatch repair MMS Mismatch E. coli [18
Direct reversal MMS Alkylation E. coli [18
DNA recombination Norfloxacin DNA gyrase inhibitor E. coli [55
DNA recombination I-SceI endonuclease Double-strand break E. coli [56
DNA recombination Mitomycin C, HO endonuclease Interstrand cross-link, double-strand break B. subtilis [63,64
DNA recombination Mitomycin C Interstrand cross-link E. coli [65
Pathway studied with live-cell imaging DNA-damaging agent Type of induced damage/protein target Organism Reference 
Base excision repair MMS Alkylation E. coli [14
Mismatch repair 2-aminopurine (2-AP) Mismatch B. subtilis [15
TLS Ultraviolet (254 nm) light Cyclobutane pyrimidine dimer, (6-4) photoproduct E. coli [16
NER Ultraviolet (254 nm) light Cyclobutane pyrimidine dimer, (6-4) photoproduct E. coli [17
Mismatch repair MMS Mismatch E. coli [18
Direct reversal MMS Alkylation E. coli [18
DNA recombination Norfloxacin DNA gyrase inhibitor E. coli [55
DNA recombination I-SceI endonuclease Double-strand break E. coli [56
DNA recombination Mitomycin C, HO endonuclease Interstrand cross-link, double-strand break B. subtilis [63,64
DNA recombination Mitomycin C Interstrand cross-link E. coli [65
Table 2
DNA-damaging agents employed in other assays for the investigation of repair and damage tolerance pathways
Pathway studied with other assays DNA-damaging agent Type of induced damage/protein target Organism Reference 
Base excision repair X-ray Radiation-induced DNA damage E. coli [66
NER Cisplatin Intrastrand cross-link
Interstrand cross-link 
E. coli [67
NER Nitrogen mustard Interstrand cross-link E. coli [68
NER N-ethyl-N-nitrosourea Alkylation E. coli [69
NER 4-nitroquinoline-1-oxide Monoadduct E. coli [70
Transcription-coupled repair Nitrofurazone Monoadduct E. coli [71
TLS Nitrofurazone, 4-nitroquinoline-1-oxide Monoadduct E. coli [7274
DNA recombination Ciprofloxacin DNA gyrase inhibitor E. coli [75
Interstrand cross-link repair Psoralen and ultraviolet A Interstrand cross-link E. coli [76
Pathway studied with other assays DNA-damaging agent Type of induced damage/protein target Organism Reference 
Base excision repair X-ray Radiation-induced DNA damage E. coli [66
NER Cisplatin Intrastrand cross-link
Interstrand cross-link 
E. coli [67
NER Nitrogen mustard Interstrand cross-link E. coli [68
NER N-ethyl-N-nitrosourea Alkylation E. coli [69
NER 4-nitroquinoline-1-oxide Monoadduct E. coli [70
Transcription-coupled repair Nitrofurazone Monoadduct E. coli [71
TLS Nitrofurazone, 4-nitroquinoline-1-oxide Monoadduct E. coli [7274
DNA recombination Ciprofloxacin DNA gyrase inhibitor E. coli [75
Interstrand cross-link repair Psoralen and ultraviolet A Interstrand cross-link E. coli [76

Besides substrates, concentrations of upstream or downstream repair factors may also influence repair [57]. In extreme cases, repair reactions can be abolished by deleting genes encoding repair proteins or by replacing wild-type copies with mutants that are deficient in catalyzing repair. On the other hand, concentrations of proteins can be increased using plasmids that overexpress wild-type, labelled or mutant proteins [17]. Metabolic processes such as DNA replication, transcription and translation can also influence repair. These metabolic processes can be modulated using well-characterized antibiotics [15,58,59] or site-specific roadblock repressors such as the Lac [60] or Tet repressor [61], and more recently dCas9 [62].

Mobility and dissociation kinetics

Understanding kinetics of intracellular interactions is crucial to determine the rate-limiting steps and overall rates of repair. This information enables contextualization of how repair reactions are conducted in cells. For example, if the repair of a single lesion occurs over a timescale of minutes [57], is the basal copy number of repair proteins sufficient in events of genotoxic stresses? Are there competing pathways that repair the same lesion, and to what extent does each pathway contribute to the repair of damaged DNA? Answering these questions clarifies how cells choose between DNA–repair, damage tolerance and recombinational-repair pathways in response to DNA damage.

Repair proteins can freely diffuse or exhibit DNA binding on multiple different timescales corresponding to the various specific and non-specific interactions with their substrates [9,11,57,77,78]. Hence, experimental characterization of residence times can inform on enzymatic function. In a landmark study involving the observation of fluorescently labelled Polymerase I (Pol I) and DNA ligase (LigA) in live E. coli, Uphoff et al. [14] demonstrated that in vivo, these DNA–repair proteins are either DNA bound (immobile) or freely diffusive (mobile) (Figure 2A–D). Furthermore, when single-nucleotide gap substrates were produced following methyl methanesulfonate (MMS) treatment, these investigators observed a five-fold increase in the DNA-bound population in cells (Figure 2D). Residence times of Pol I and LigA were found to be ∼2 s in both undamaged and MMS-treated cells (Figure 2E), suggesting the repair rates are limited by upstream processes [14]. In this work, measurements of protein dissociation kinetics were made by measuring the lifetime of individual foci formed by fluorescently labelled proteins bound to their substrates. The cumulative residence time distributions were fit to mathematical models to yield effective off-rates. It should be noted that such analyses could represent underestimates of off-rates, as measurements of the cumulative residence time distribution were limited by the poor photo-stability of the fluorophores. To correct for contribution of photobleaching events to residence time distributions, the investigators determined the photobleaching rate constant by imaging a fusion protein that binds DNA on timescales longer than photobleaching timescales (MukBEF-PAmCherry). This approach has been successfully used to uncover single off-rates of protein–DNA interactions.

Visualizing fluorescently labelled Pol I and LigA in live E. coli.

Figure 2.
Visualizing fluorescently labelled Pol I and LigA in live E. coli.

(A) Localization of Pol I-PAmCherry visualized with a conventional fluorescence microscope. (B) Localization of single Pol I-PAmCherry. (C) Spatial tracking of single molecules through time showed that Pol I is either diffusive (blue) or immobile (red). (D) Distributions of the apparent diffusion constants (D*) of Pol I and Lig in untreated (left) and MMS-treated (right) cells. Red bars represent the DNA-bound fractions of Pol I and Lig. (E) Bar plots display residence time distribution of Pol I (upper) and Lig (lower). Solid lines represent fits to exponential decay model. These are corrected with the photobleaching rate constant to obtain off-rate constants (dash lines). (F) Schematic of interval imaging technique. Time-series acquisitions are collected with dark frames of varying length inserted between consecutive integration frames (yellow bars). Scale bars, 1 µm. Panels A–E are reproduced from ref. [14] with permission.

Figure 2.
Visualizing fluorescently labelled Pol I and LigA in live E. coli.

(A) Localization of Pol I-PAmCherry visualized with a conventional fluorescence microscope. (B) Localization of single Pol I-PAmCherry. (C) Spatial tracking of single molecules through time showed that Pol I is either diffusive (blue) or immobile (red). (D) Distributions of the apparent diffusion constants (D*) of Pol I and Lig in untreated (left) and MMS-treated (right) cells. Red bars represent the DNA-bound fractions of Pol I and Lig. (E) Bar plots display residence time distribution of Pol I (upper) and Lig (lower). Solid lines represent fits to exponential decay model. These are corrected with the photobleaching rate constant to obtain off-rate constants (dash lines). (F) Schematic of interval imaging technique. Time-series acquisitions are collected with dark frames of varying length inserted between consecutive integration frames (yellow bars). Scale bars, 1 µm. Panels A–E are reproduced from ref. [14] with permission.

Recently, another powerful approach termed ‘interval imaging’ has been introduced that enables detection of multiple off-rates of protein–DNA complexes (Figure 2F) [19,79,80]. In this method, cells are imaged by introducing a waiting time interval between acquisitions. A series of movies are acquired in which the duration of the waiting time interval is increased, while maintaining the total number of images (and hence photon load) taken in a movie constant. This strategy enables observation of molecules bound to DNA on timescales that vary over several orders of magnitude, while keeping the photobleaching rate constant. From the movies, the lifetimes of individual bound molecules are measured and a cumulative residence time distribution is generated. This distribution is then fit to a sum of the photobleaching rate and multiple off-rates. This strategy enables quantitation of multiple specific or non-specific interactions of repair proteins with DNA. In combination with functional mutants, this approach has the potential to provide a comprehensive description of the kinetics of interactions that govern DNA repair in cells.

Spatial organization

Intracellular spatial organization, as revealed by fluorescence microscopy, is an important experimental observable that can provide significant new insights into how proteins function in cells. Knowing the localization of a protein in relation to other interacting partners can reveal important regulatory features. In a study involving fluorescently labelled MutS and DnaX in B. subtilis, Liao et al. [15] found that the mismatch sensor MutS explores the entire nucleoid, but slows down as it approaches the replisome (Figure 3A,B). By combining localizations from many cells to generate probability density maps, strong co-localizations of MutS and the replisome were found, independent of mismatch formation following 2-aminopurine treatment (Figure 3C,D). Moreover, this co-localization was maintained even when wild-type MutS was replaced with a MutS mutant deficient in binding mismatch (Figure 3E), while a MutS mutant deficient in interacting with the replisome distributed more evenly across cells (Figure 3F). These observations suggested that the replisome serves as a scaffold to enable MutS-targeted search.

Visualizing fluorescently labelled MutS and the replisome in B. subtilis.

Figure 3.
Visualizing fluorescently labelled MutS and the replisome in B. subtilis.

(A) Localizations of MutS-PAmCherry (magenta) and the clamp loader DnaX-mCitrine (green). Scale bar, 1 µm. (B) Correlation of the distance from the replisome and speed of the MutS-PAmCherry trajectory shown in (A, bottom panel). (C–F) Localization probability density maps of DnaX (upper) and wild-type MutS and mutants (lower). Wild-type MutS in (C) untreated and (D) 2-aminopurine (2-AP)-treated cells. (E) MutS[F30A] and (F) MutS800 mutants in untreated cells. N: the number of cells. Images are reproduced from ref. [15] with permission.

Figure 3.
Visualizing fluorescently labelled MutS and the replisome in B. subtilis.

(A) Localizations of MutS-PAmCherry (magenta) and the clamp loader DnaX-mCitrine (green). Scale bar, 1 µm. (B) Correlation of the distance from the replisome and speed of the MutS-PAmCherry trajectory shown in (A, bottom panel). (C–F) Localization probability density maps of DnaX (upper) and wild-type MutS and mutants (lower). Wild-type MutS in (C) untreated and (D) 2-aminopurine (2-AP)-treated cells. (E) MutS[F30A] and (F) MutS800 mutants in untreated cells. N: the number of cells. Images are reproduced from ref. [15] with permission.

In another example, the sensitivity of single-molecule fluorescence imaging allowed the spatial characterization of proteins that are present at low-copy numbers, such as polymerase V (Pol V) [16]. Pol V is one of three specialized TLS polymerases in E. coli. While allowing DNA synthesis to continue across damaged DNA templates, Pol V is highly mutagenic and multiple mechanisms of regulation have been identified that regulate its activity [81]. The transcription of genes encoding UmuC and UmuD, precursors of Pol V, is strongly repressed in normal growth conditions. In response to extreme genetic instability resulting in the triggering of the SOS response, the umuDC promoter is derepressed leading to the expression of UmuC and UmuD. Furthermore, cleavage of the umuD gene product is a prerequisite for the formation of catalytically active Pol V. Additionally, it has been demonstrated that an active mutasome is permitted only single rounds of nucleotide incorporation by the RecA protein [82]. By monitoring the localization of fluorescently labelled UmuC in live E. coli, Robinson et al. [16] demonstrated that UmuC was sequestered at the cell membrane during the first 45 min following UV treatment (Figure 4A). The release of UmuC into the cytosol was shown to depend on the cleavage of UmuD to UmuD′ (Figure 4B). This spatial sequestering was proposed to act as an additional level of regulation that limits the access of Pol V to DNA. Furthermore, the authors showed that Pol V in UV-irradiated cells rarely co-localized with the replisome (Figure 4C). This observation suggests that Pol V mainly acts at single-stranded gaps that are skipped by the replisome.

Visualizing fluorescently labelled Pol V in E. coli.

Figure 4.
Visualizing fluorescently labelled Pol V in E. coli.

(A and B) Left: localizations of UmuC-mKate2 in (A) wild-type cells or (B) cells expressing non-cleavable UmuD(K97A) following UV treatment. Right: 2D contour plots display the autocorrelation of fluorescence signal across the short axis of cells. Membrane-associated molecules contributed to the increase in autocorrelation measurements within the 0.5–1 µm region. (C) Localizations of Pol V (magenta) and the replisome (green) following UV treatment. Images are reproduced from ref. [16] with permission.

Figure 4.
Visualizing fluorescently labelled Pol V in E. coli.

(A and B) Left: localizations of UmuC-mKate2 in (A) wild-type cells or (B) cells expressing non-cleavable UmuD(K97A) following UV treatment. Right: 2D contour plots display the autocorrelation of fluorescence signal across the short axis of cells. Membrane-associated molecules contributed to the increase in autocorrelation measurements within the 0.5–1 µm region. (C) Localizations of Pol V (magenta) and the replisome (green) following UV treatment. Images are reproduced from ref. [16] with permission.

Cell-to-cell heterogeneity

Under stressful conditions such as during antibiotic treatment, failure to repair DNA can be beneficial as the increased mutagenesis increases the chance of acquiring resistance. The mechanisms that lead to the acquisition of antibiotic resistance may be invisible in traditional mutagenesis experiments due to the transient nature of these phenomena or due to the averaging nature of bulk measurements. Single-cell imaging experiments have the power to reveal this heterogeneity in the DNA-damage response with high temporal resolution, across a population of cells, observed over multiple generations. In a study monitoring alkylation damage repair by Ada in E. coli, Uphoff et al. [18] demonstrated a mechanism through which a sub-population of cells can acquire a higher rate of mutagenesis. By monitoring the expression of fluorescently labelled Ada, the authors found that the expression level was so low (∼1.4 molecules per cell) that stochastic variation could be used to explain the absence of Ada in 20–30% of undamaged cells (Figure 5A). As Ada regulates its own expression, cells without Ada were found to exhibit a delay (one generation time) in Ada expression following MMS treatment. Strikingly, cells with delayed Ada expression also exhibited an increase in the binding fraction of fluorescently labelled MutS (Figure 5B,C), supposedly the consequence of the increased formation of mismatches as uncorrected MMS-induced O6MeG base-pairs with T instead of C. This observation highlights the ability of cells to compensate for repair functions using alternate pathways.

Visualizing fluorescently labelled Ada in E. coli.

Figure 5.
Visualizing fluorescently labelled Ada in E. coli.

(A) Schematic explains the heterogeneity of Ada expression. As Ada autoregulates its own expression, cells without Ada exhibited a delay in Ada up-regulation following MMS treatment. (B and C) Visualizing Ada-YPet (yellow) and MutS-PAmCherry (red: bound MutS-PAmCherry; blue: diffusing MutS-PAmCherry) following MMS treatment. Scale bars, 2 µm. Images are reproduced from ref. [18] with permission.

Figure 5.
Visualizing fluorescently labelled Ada in E. coli.

(A) Schematic explains the heterogeneity of Ada expression. As Ada autoregulates its own expression, cells without Ada exhibited a delay in Ada up-regulation following MMS treatment. (B and C) Visualizing Ada-YPet (yellow) and MutS-PAmCherry (red: bound MutS-PAmCherry; blue: diffusing MutS-PAmCherry) following MMS treatment. Scale bars, 2 µm. Images are reproduced from ref. [18] with permission.

Perspectives

Well suited to dissect intracellular kinetics, spatial organization and heterogeneity in expression levels of repair factors, single-molecule live-cell imaging has yielded unexpected insights into the inner workings of the DNA–repair machineries in bacteria. These approaches have revealed how various steps of DNA repair are co-ordinated in live cells and their consequences on the fates of cells. To this end, the development of superior fluorescent probes that facilitate multi-colour and long-timescale imaging will enable researchers to build a comprehensive picture of how the DNA–repair response evolves in real time. Novel in vivo manipulation tools that enable site-specific perturbations will additionally allow the mapping of check points during repair. As single-molecule experimental setups become increasingly accessible to non-experts, we anticipate that the applications of single-molecule imaging to probe DNA metabolism will continue to expand to other bacteria and higher organisms. Coupled with genetic, biochemical, structural, in vitro assays and modelling, single-molecule live-cell imaging will tremendously contribute to a mechanistic understanding of how biological systems maintain their genomic integrity and the processes leading to mutagenesis and carcinogenesis [83].

Abbreviations

     
  • DSBR

    double-strand break repair

  •  
  • EM-CCD

    electron-multiplying charge-coupled device

  •  
  • EZRDM

    EZ-rich defined media

  •  
  • FPs

    fluorescent proteins

  •  
  • MMS

    methyl methanesulfonate

  •  
  • NER

    nucleotide excision repair

  •  
  • PAmCherry

    photoactivatable mCherry

  •  
  • Pol I

    polymerase I

  •  
  • Pol V

    polymerase V

  •  
  • sCMOS

    scientific complementary metal-oxide semiconductor

  •  
  • TIRF

    total internal reflection fluorescence

  •  
  • TLS

    translesion synthesis

  •  
  • UV

    ultraviolet light

Funding

A.M.v.O. acknowledges the support by the Australian Research Council [DP150100956 and FL140100027].

Competing Interests

The Authors declare that there are no competing interests associated with the manuscript.

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Author notes

*

These authors contributed equally to this work.