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ANNUAL
REVIEWS

*Annual Review of Microbiology*

# Mutagenesis: Insights from Single-Cell, Real-Time Approaches

Marina Elez, Chiara Enrico Bena, and Lydia Robert

Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France;
email: marina.elez@inrae.fr

Annu. Rev. Microbiol. 2025. 79:35.1–35.21

The *Annual Review of Microbiology* is online at
micro.annualreviews.org

https://doi.org/10.1146/annurev-micro-050224-093623

Copyright © 2025 by the author(s).
All rights reserved

## Keywords

mutation, single cells, microfluidics, heterogeneity, bacteria, evolution

## Abstract

All genetic variation fueling evolution depends on mutations. Although mutations have been extensively studied for almost a century, until a decade ago the investigation of mutations was limited to population-level analysis. This constraint has hampered the exploration of cellular heterogeneity in mutation processes and its evolutionary implications. To overcome these limitations, quantitative visualization methods for studying mutations in the bacterium *Escherichia coli* at the single-cell level have been developed. These approaches offer the possibility of accessing a major source of mutations, i.e., DNA polymerase errors, and their fate, i.e., repair versus conversion to mutation. In addition, such methods allow for quantitative characterization of the effects of mutations on cell fitness. This article discusses insights into the mutation process derived from these new single-cell mutagenesis approaches.

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## Contents

1. INTRODUCTION .................................................................................. 35.2

2. POPULATION APPROACHES FOR STUDYING MUTAGENESIS .................... 35.3

2.1. The Luria and Delbrück Fluctuation Test........................................ 35.3

2.2. The Mutation Accumulation Experiment........................................ 35.6

2.3. Methods Relying on Error-Corrected Next-Generation Sequencing ........ 35.7

3. STUDYING MUTAGENESIS DIRECTLY IN LIVE *ESCHERICHIA COLI* CELLS .................................................................................. 35.7

3.1. Exploiting the Mismatch Repair System to See Replication Errors in *Escherichia coli* ........................................................................ 35.8

3.2. The Mutation Visualization Assay: A Quantitative Single-Cell Mutagenesis Tool.................................................................................. 35.9

3.3. Comparing Results from Different Methods ........................................ 35.9

4. NEW INSIGHTS INTO SPONTANEOUS MUTAGENESIS FROM VISUALIZATION STUDIES AT THE SINGLE-CELL LEVEL ........................ 35.10

4.1. Dynamics of Replication Errors and Mutations .................................... 35.11

4.2. Cell Cycle and Replication Error Rate.................................................. 35.11

4.3. Mutation Origin.................................................................................. 35.12

5. SINGLE-CELL STUDIES OF MUTAGENESIS DURING EXOGENOUS AND ENDOGENOUS STRESS.................................................. 35.13

5.1. Alkylation DNA Damage .................................................................. 35.13

5.2. H₂O₂ Oxidative DNA Damage ................................................................ 35.14

5.3. Endogenous Stress ............................................................................ 35.14

6. ESTIMATION OF THE DISTRIBUTION OF MUTATION FITNESS EFFECTS USING SINGLE-CELL STUDIES........................................ 35.15

7. PERSPECTIVES .................................................................................... 35.15

## 1. INTRODUCTION

Mutations are a fundamental driver of evolution, shaping its speed and direction through two key parameters: their dynamics and their impact on fitness. The dynamics of mutations determines how quickly genetic variation arises within a population, as well as the pattern with which mutations occur over time, for instance, in bursts versus at a constant rate. This dynamics affects the likelihood of simultaneous mutation occurrences within a genome, which can be crucial for adaptation, such as when multiple drugs are applied in combination, potentially explaining the emergence of drug resistance. The distribution of fitness effects (DFE) of mutations, which spans from beneficial to lethal, determines how much of this variation persists over time and is a key to explaining important evolutionary phenomena such as the evolution of sexual reproduction and mutation rates (50, 102, 108). Together, mutation dynamics and DFE influence how rapidly populations adapt to environmental changes.

Both factors—particularly mutation dynamics—have historically been difficult to study experimentally, despite their importance. Until recently, mutational studies were limited by methods that could not track mutations at the single-cell level. This limitation reinforced the assumption that mutations occur independently and uniformly (at a constant rate) across isogenic populations, following Poisson dynamics (20, 66, 67). This assumption was first put into question by a 1991 theoretical study that proposed that any large bacterial populations harbors subpopulations with

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elevated mutation rates (78). These subpopulations could result from two mechanisms: (a) muta-
tions leading to a permanent decrease in DNA replication fidelity or repair capacity or (b) the
transient inactivation of proteins involved in DNA replication or repair as a result of transcrip-
tion or translation errors (78). A decade later, sequencing studies provided indirect evidence of this
mutation rate heterogeneity, showing more instances of multiple mutations than expected under
the constant-rate model (21, 40).

Over the past decade, methodological advances have significantly improved our ability to detect mutation rate variability and to characterize mutation dynamics. These advances include: (a) the development of single-cell techniques for visualizing replication errors, a major source of mutation in growing cells (23, 88, 114); (b) the detection of mutations arising from these replication errors (26); and (c) the use of microfluidic devices that enable long-term tracking of single cells under controlled conditions (2, 83, 109, 117). The first direct evidence of heterogeneous replication error rates in isogenic populations came from a study using one of these visualization techniques. This study showed that subpopulations of cells temporarily exhibit higher rates of replication errors in response to DNA-damaging agents (114). However, fully characterizing these mutator episodes—including their intensity and duration and the size of the subpopulations—remained challenging until the integration of microfluidic devices with replication error visualization tools. This integration enabled continuous, long-term monitoring of replication errors and mutations in individual cells (26, 79, 87, 88, 112). Microfluidics offers precise control over environmental conditions, allowing for differentiation between stochastic fluctuations in single-cell composition and physiology and environmental factors driving mutation rate variation. Additionally, these combined approaches have provided new insights into the DFE of spontaneously arising mutations, which traditional population-level studies could not capture.

In this review, we provide an overview of the experimental methods used to study mutagenesis, contrasting classical population-level techniques with recent real-time, single-cell approaches. We discuss the strengths and limitations of each method, with a particular focus on the advances facilitated by single-cell studies.

## 2. POPULATION APPROACHES FOR STUDYING MUTAGENESIS

In this section, we discuss the methodologies that enable studying mutations at the population
level: the classic fluctuation test of Luria and Delbrück (32, 66, 96, 121), the increasingly popular
mutation accumulation (MA) experiment followed by conventional next-generation sequencing
(NGS) of whole genome (MA-WGS) (49, 68, 96), and the more recent approaches of duplex
sequencing (DupSeq) relying on error-corrected NGS (8, 92, 94) and maximum-depth sequencing
(MDS) (47, 81).

These methods allow different estimates to be obtained: mutation rate (expressed per genera-
tion and per cell or per genome) or mutant frequency and, in some cases, the mutation spectrum,
which provides insight into the types, frequencies, and localization of different mutations.

2.1. The Luria and Delbrück Fluctuation Test

The fluctuation test, developed in the 1940s (66), is a widely used method for estimating mutation
rates (20, 96). In this approach, mutations are not observed directly but are inferred through their
effects, such as the ability to grow in the presence of an antibiotic. Since many mutations do not
produce detectable phenotypic effects, the test is limited to studying a small subset of mutations.
In practice, multiple parallel cultures are inoculated with a small number of cells, grown under
nonselective conditions until saturation, and then plated on selective media to count the mutants
that have acquired selectable mutations (Figure 1a). On the basis of these data, the mutation

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Population approaches

Fluctuation test

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Single-cell approaches

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DNA mismatch created
by replication error

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Fixed mutation (new DNA sequence)

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Replication fork leading to one-quarter
of mutants in the progeny

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Replication fork leading to one-eighth
of mutants in the progeny

(Caption for Figure 1 appears on following page)

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<caption>**Figure 1** (*Figure appears on preceding page*)</caption>

Experimental approaches for studying mutagenesis. (a) Fluctuation test schematic. (b) Schematic of the MA assay coupled with whole-genome sequencing. (c) Single-cell, real-time methods for studying mutagenesis. (i) Blue fluorescence images of microcolonies (white borders) of *Escherichia coli* carrying a transposable element with inducible excision. The fluorescent reporter mCerulean is expressed only when the transposable element is excised. Panel adapted with permission from Reference 53. (ii) Kymograph of *E. coli* growing in the mother machine device and expressing a fluorescent reporter (CFP) (*cyan*) for tracking gene copy number variation. Fluorescence intensity is proportional to CFP gene copy number. Panel adapted with permission from Reference 110; copyright 2025 Springer Nature. (iii) Micrograph of mismatch repair-deficient *E. coli* cells (*mutH*) expressing a fluorescently tagged MutL protein. Point mutations and small indels are detected as fluorescent MutL foci. Panel adapted with permission from Reference 23; copyright 2025 Elsevier. (iv) Overlay of *E. coli* cells (brightfield image) and the tracks of diffusing (*cyan*) and DNA-bound (*red*) MutS protein monitored through a single-molecule technique based on photoactivation. Panel adapted with permission from Reference 114; copyright 2025 AAAS. (d) The mother machine microfluidics device. Panel reproduced from Reference 26 (CC BY 4.0). (e) Schematic illustrating the fates of replication error, with the lifetimes of YFP-MutL foci indicating successful or unsuccessful repair outcomes. Panel adapted from References 22 and 26 (CC BY 4.0). (f) Kymograph showing *E. coli* (in *red*) growing in the mother machine device and displaying short- and long-lived YFP-MutL foci (*yellow dots*). To detect all short-lived foci (lasting ~40 s), imaging is typically performed every 7–15 s, and for long-lived foci (with a lifetime of ~20 min), images are acquired every 2 min. (g) Illustration of chromosome segregation and mutant distribution among progeny of a cell with a replication error. Panel adapted with permission from Reference 88; copyright 2025 AAAS. Abbreviations: CFP, cyan fluorescent protein; MA, mutation accumulation; PDMS, polydimethylsiloxane; YFP, yellow fluorescent protein.

rate for the selected phenotype can be estimated using statistical models that link the observed number of mutants to the number of mutation events (32, 90, 121). Typically, the distribution of mutants in microbial populations is modeled using the Luria-Delbrück distribution (LD). This distribution has no simple explicit expression, and phenotypic mutation rate estimates are usually derived using approximations or numerical methods. Among these, the most accurate method is maximum likelihood, which finds the mean number of mutation events per culture that best fits the LD distribution to the data.

However, the fluctuation test has several limitations that impact its precision and accuracy. One major issue is that the Luria-Delbrück model relies on assumptions such as exponential cell growth, no cell death, immediate mutation expression, and a constant mutation rate between single cells and through time. In practice, these assumptions are not always, if ever, valid. For instance, treatments like antibiotics can cause significant cell death, which leads to an overestimation of mutation rates when using existing methods to calculate mutation rates from the fluctuation test (35). Additionally, phenotypic delay, where several generations are needed for mutant proteins to accumulate to sufficient levels (104), has been proposed as a reason why mutation rates from the fluctuation test are much lower than those obtained from methods that do not rely on phenotypic expression (47, 60, 118). Over the past decades, various extensions of the Luria-Delbrück model have been developed to relax some of these assumptions and improve its applicability (58, 59, 120, 121). However, even with these generalizations, the model may not always fit biological reality (35), and the data needed to inform which parameters should be adjusted are often unavailable.

A second major limitation is that the fluctuation test is based on the detection of a limited number of mutations conferring a specific phenotype, which may not reflect the global, genome-wide mutation rate shaped by natural selection (69). Therefore, accurately characterizing the mutation process requires genome-wide mutation rate estimates. These estimates can be extrapolated from phenotypic mutation rates through a two-step process (20). First, the phenotypic mutation rate is converted into a per-base mutation rate by determining how many base changes lead to the observed phenotype, typically through sequencing numerous (hundreds of) independent mutants displaying the phenotype (37, 56). However, base pair substitution rates vary significantly depending on the surrounding sequence context (60, 95, 105, 106), and the mutations giving the selectable phenotype may not have the same rate of occurrence. Therefore, estimating the per-base mutation rate requires accounting for this effect (56). Once the per-base mutation rate has been determined,

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it can be extrapolated to estimate the genome-wide mutation rate. However, substantial variation in mutation rates across different regions of the genome has been observed (19, 33, 56, 57, 63, 105) and can reduce the precision and accuracy of genome-wide mutation rate estimates derived from the fluctuation test.

Some studies, instead of reporting mutation rates, provide mutant frequencies, i.e., the average number of mutants observed for a given number of cells. However, mutant frequencies are not informative about the underlying mutation process and are difficult to compare from one study to another, as they are highly sensitive to the number of generations of growth, which can vary greatly from one protocol to another. Therefore, mutant frequencies are mainly useful for relative comparisons within a single study, such as evaluating whether a particular repair pathway or gene has a significant effect on mutation occurrence. Their precision is, however, limited by their high sensitivity to early occurring mutations.

Fluctuation tests can be used to study the mutation spectrum by sequencing the reporter gene in mutants that survive selection. However, since they sample only a small genomic region and focus on mutations that confer a survival advantage, the resulting spectrum may not accurately reflect genome-wide mutation spectrum.

## 2.2. The Mutation Accumulation Experiment

MA experiments, first introduced by Muller (76) in the 1920s and refined by Mukai (75) in the 1960s, involve growing isogenic cells or inbred organisms and then reducing the population to a single, randomly selected individual to reinitiate growth (49) (Figure 1b). The repeated bottlenecking minimizes the effects of natural selection, allowing spontaneous genomic mutations to accumulate largely independently of their fitness effects. As a result, the MA approach allows researchers to detect and study mutations that would normally be eliminated by selection in natural populations.

In the pregenomic era, MA experiments allowed for the inference of some key mutation parameters from phenotypic data collected from MA lines (6, 27, 38, 39). Fitness changes were tracked over time over multiple independent MA lines, and researchers often observed a decline in fitness due to the accumulation of deleterious mutations (3, 52). From these data, estimates of deleterious mutation rates and of the average mutation effect on fitness were derived. However, these two parameters cannot be dissociated without further assumptions, as a few mutations with strong effects can lead to the same phenotypic trajectories as do many mutations with weak effects. This confusion persisted until independent mutation rate estimates became available (27). The introduction of NGS in the early 2000s greatly advanced the characterization of mutations through MA experiments. The sequencing of whole genomes of MA lines has provided direct access to the full spectrum of mutations, leading to a better understanding of mutation origins and their links to environmental factors or DNA repair deficiencies (34, 49, 65). By counting the accumulated mutations and calculating the number of generations over which they occurred, mutation rates could be estimated at the whole-genome level. This integration of MA experiments with WGS has been used in numerous studies on various species, making it the preferred method for characterizing mutations and estimating their rates (69).

Despite the advantages, the MA-WGS approach has some limitations. It cannot detect mutations with strong negative fitness effects, such as lethal or highly deleterious mutations. For organisms with low mutation rates or small genomes, such as bacteria and bacteriophages, given the limited number of lines that can reasonably be handled, it can take several months to accumulate enough mutations for reliable estimates (60). The temporal resolution is also relatively low. Most MA-WGS studies rely on Illumina's short-read sequencing (100–150-bp reads), which, when combined with computational tools like breseq (18), can accurately predict various mutation types,

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including single-nucleotide mutations, small indels (insertions or deletions of a few nucleotides), and structural variants (such as transpositions, duplications, and deletions) (107, 115). However, short-read technologies struggle to detect mutations in repetitive regions. Long-read sequencing technologies, such as those of PacBio and Oxford Nanopore Technologies, offer a promising alternative for detecting mutations in these regions and resolving complex structural variants (64, 122).

## 2.3. Methods Relying on Error-Corrected Next-Generation Sequencing

NGS technologies have become indispensable in mutation studies, fundamentally transforming the field of mutagenesis. Although NGS has a relatively high error rate (~0.1%), it allows researchers to distinguish genuine variants from errors by using redundant sequencing of numerous identical DNA fragments from different cells. However, this approach is limited to detecting only prevalent mutations within a population, those present in more than 1% of the DNA molecules. NGS is not effective in capturing rare variants, such as de novo mutations, in real time, before these mutations expand and become prevalent within a population.

To overcome these limitations, various experimental and computational strategies have been developed to reduce sequencing errors. One of the most effective approaches is tag-based error correction, also known as molecular barcoding, which involves adding a unique molecular barcode to each DNA fragment during library preparation. Among these techniques, DupSeq has emerged as the most accurate (92, 94).

DupSeq stands out by uniquely barcoding each strand of a DNA molecule and allowing sequences from both strands to be linked. The two strands are sequenced independently, generating multiple reads that are used to construct single-strand consensus sequences, which are then compared. A mutation is confirmed only if it is detected on both strands, as random errors typically appear on just one. This dramatically reduces the error rate approximately 10<sup>5</sup>-fold but requires deeper sequencing coverage, which increases costs, making DupSeq highly effective for sequencing small genomic regions or genomes but not practical for entire large genomes (8). It quantifies mutation frequency by calculating the proportion of mutated bases relative to the total number of sequenced bases. Only variants occurring at frequencies above the background error rate are considered true mutations. This method allows for relative comparisons of mutation frequencies within a study.

Researchers use the MDS approach (47) to introduce barcodes directly to genomic DNA fragments and to sequence single strands with extremely high coverage, generating a vast number of reads from each fragment to increase the chances of detecting rare variants. Consistent detection of the same mutation across multiple reads is considered strong evidence of a true mutation. MDS significantly accelerates mutation rate estimation in microorganisms compared to traditional methods like MA-WGS, for example, delivering results for *Escherichia coli* within 2 weeks (<120 generations) instead of several months (thousands of generations). However, MDS has its limitations: It is not suitable for whole-genome analysis and is typically restricted to examining multiple regions of a few hundred nucleotides per experiment. Additionally, many mutations originate as mismatches during DNA replication, but only a small fraction of these are converted into mutations, as most are efficiently repaired. MDS detects these mismatches indiscriminately, requiring further correction to accurately distinguish true mutations.

## 3. STUDYING MUTAGENESIS DIRECTLY IN LIVE *ESCHERICHIA COLI* CELLS

In the last decade, several methods have been developed to detect DNA mutations in live single cells, including those targeting a single specific large-scale mutation as well as a single approach

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that enables genome-wide detection of most point mutations and small indels. Tracking a specific mutation has become feasible by incorporating it into a DNA fragment that either includes or controls a fluorescent reporter enabling visualization by microscopy (Figure 1c). This approach has enabled, for instance, real-time monitoring of transposable element excisions (53), gene copy number variations (110), and DNA fragment acquisition during transformation (74). While these methods offer valuable insights into mechanisms of evolution such as transposition and gene duplication, they are restricted to tracking a single genetic change at a specific genome location. In the following sections, we focus on the approach allowing detection of small-scale mutations, genome wide, in real time in single cells. Point mutations are a common type of spontaneous mutations (107). These mutations can arise due to errors in DNA replication or from DNA damage, which may be caused by endogenous factors such as oxidative stress, alkylation, depurination, and deamination, as well as exogenous factors like chemical exposure and radiation (15, 96). Although cells have evolved repair mechanisms to correct many DNA lesions and replication errors, these systems are not fully efficient, which results in mutations. Oxidative stress, a by-product of aerobic respiration and cellular metabolism, is a major source of endogenous DNA damage (34) and a key driver of mutagenesis and evolution (14). In unstressed, growing bacteria, the inability to prevent oxidative damage leads to a sharp (approximately 150-fold) increase in mutation rates, similar to the increase caused by the inability to repair replication errors. Interestingly, inactivation of other major DNA repair pathways has no substantial impact on mutagenesis, underscoring the prominent role of oxidative damage and replication errors as the primary sources of mutations in actively growing, unstressed cells (34, 60).

## 3.1. Exploiting the Mismatch Repair System to See Replication Errors in *Escherichia coli*

The evolutionarily conserved DNA mismatch repair (MMR) system repairs ~99% of replication errors in *E. coli* (34, 60). It involves two main players: (a) MutS, which detects errors, i.e., DNA mismatches, and (b) MutL, which coordinates mismatch detection with downstream repair steps (30, 48, 73). One of the crucial features of this pathway is its ability to distinguish the new strand from the old, ensuring that repair is directed to the new strand, which contains the error. In the case of the MMR system typical of most organisms, the strand discrimination signal has not been conclusively identified but is generally assumed to be replication-associated strand discontinuities and nicks (73, 86). In the case of the methyl-directed MMR, on which *E. coli* relies, this task is performed by the MutH endonuclease, which cleaves unmethylated GATC sites (73) persisting on the newly synthesized strand for a few minutes after replication (12, 13).

The study of fluorescently tagged MMR proteins MutS and MutL in live *E. coli* and *Bacillus subtilis* cells and of their homologs in *Saccharomyces cerevisiae* by wide-field microscopy over the past two decades has revealed the presence of fluorescent foci on replication errors (11, 22–24, 42, 54, 61, 88, 93, 98, 101, 112, 119). This led in 2010 to the idea of using fluorescence microscopy, namely fluorescent MutL foci, as a tool for detecting and quantifying, in live cells, replication errors and/or mutations arising from these errors (23). Later, Uphoff et al. (114) tracked single molecules of photoactivated fluorescent MutS to distinguish DNA-bound from mobile MutS, interpreting DNA-bound MutS as a marker for mutations (Figure 1c). Elez et al. (23, 25) preferred labeled MutL to MutS, as functional MutL foci on replication errors are easier to detect in wide-field microscopy (Figure 1c) and can be easily differentiated from nonfunctional aggregates by their dependence on the presence of MutS. Later work by Uphoff and colleagues also relied on MutL foci imaged by wide-field microscopy (55, 112, 116). In *E. coli* cells inactivated for MMR through *mutH* inactivation, where all errors are converted into mutations, detection of MutL foci allows nascent mutations to be seen (23, 88). In cells that are MMR proficient, only 1% of

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replication errors are converted into mutations (60). Interpretations of whether MutL foci in the repair-proficient context detect all replication errors or only repaired or unrepaired errors have differed across studies (23, 42, 112, 119). To disentangle these differing results, fluorescent MutL foci were further characterized using the combination of time-lapse fluorescence microscopy and microfluidics, ultimately resulting in a quantitative tool for studying mutation occurrence and error repair efficiency at the single-cell level (26, 88).

## 3.2. The Mutation Visualization Assay: A Quantitative Single-Cell Mutagenesis Tool

Monitoring the emergence of fluorescent MutL foci in individual cells over time allows for the characterization of their dynamics of occurrence and the estimation of their rate. This rate can then be compared with estimates of replication error and/or mutation rate obtained through independent methods. Such a comparative analysis is crucial to assess the accuracy of detection and therefore to validate the assay as a quantitative tool. Achieving this requires monitoring the cells by microscopy under stable, controlled conditions over a long period of time, which was made possible by the microfluidic device mother machine (2, 83, 109, 117) (Figure 1d). In this device, bacteria are grown in single lines in microchannels that are slightly larger than the width of the bacteria and that are open only at one end onto a main trench through which the growth medium flows. In this way, cells at the closed end of each growth microchannel can be continuously monitored, while their daughter cells are pushed toward the open ends of the channels, as a result of cell growth in the microchannel, and are washed away by the flowing medium. Several hundred individual cells per experiment can be monitored for 2-3 days, corresponding to hundreds of generations, with a high temporal resolution on the order of a minute (2, 83, 109, 117).

In MMR-deficient *mutH* cells, where all replication errors transform into mutations, fluorescent MutL foci emerge at a rate mirroring the mutation rate (88). These foci persist until a new replication cycle converts the error into a mutation, i.e., on average a doubling time, which is ~20 min in *E. coli* growing in rich medium (88). Inhibiting new replication cycles prolongs their presence to more than 200 min (23). Conversely, in cells with efficient MMR (either fully or partially active), two distinct categories of MutL foci are observed, with average lifetimes of approximately 40 s and 20 min in rich growth medium (Figure 1e,f). Comparing their rates with expected error and mutation rates across different strains reveals that they represent successful and unsuccessful repair events, i.e., nascent mutations (26). These demonstrations validated the imaging approach as an accurate tool for detecting and quantifying both repaired and unrepaired replication errors, i.e., mutations.

Finally, in this approach detection is independent of selection, enabling even lethal mutations to be recorded.

## 3.3. Comparing Results from Different Methods

In mutation visualization (MV) assays, long-lived yellow fluorescent protein (YFP)-MutL foci represent unrepaired replication errors that will be converted into mutations, and their rate of occurrence can therefore be used to infer the mutation rate. How can this rate be compared to the mutation rates obtained using traditional population approaches, in particular the fluctuation test and the MA-WGS approach? In addition to having different technical advantages and limitations, these two classical techniques provide estimates that are not identical from a conceptual point of view. In particular, fast-growing bacteria can undergo multifork replication and be effectively polyploid, thus increasing the influx of mutation, but the impact of polyploidy on mutation rate estimation is different for MA-WGS and the fluctuation test (104).

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In the MA-WGS approach, the increased mutation influx from polyploidy is compensated by a lower probability of mutation fixation in the MA line due to segregation of DNA copies among progeny during propagation, so the mutation rate is estimated per genome copy or replicated base pair, rather than per cell. By contrast, fluctuation test estimates are typically expressed per cell. However, as Sun et al. (104) pointed out, this is not coherent with the effect of polyploidy in this assay. In particular, when mutations are recessive, which is often the case in fluctuation tests based on antibiotic resistance, the mutation rate estimate is closer to the rate per genome than per cell (104), due to segregation lag, i.e., phenotype expression delayed by nonmutated gene copies. Sun et al. (104) also showed that, for dominant mutations, the fluctuation test estimate cannot be interpreted easily and lies between the per-cell and per-genome rates. Note that these results were obtained in the case of double-stranded mutations and that the common case of single-stranded mutations, such as those created by replication errors, is more complex and depends not only on dominance/recessivity but also on whether the change occurs on the coding or noncoding strand (104). Comparing the results of MA-WGS and fluctuation tests is therefore not straightforward.

During MV experiments, fluorescent MutL foci are quantified in single cells, and the estimated mutation rate is therefore naturally expressed as a number of mutations per cell (and per generation or time unit). To compare the results obtained by MV with the results obtained by MA-WGS, the estimated mutation rate must be normalized by the proportion of mutant cells in the progeny of a cell containing a DNA mismatch (i.e., a fluorescent MutL focus), which corresponds to the probability of fixation in an MA line. The probability of observing a MutL focus is a priori proportional to the number of replication forks in the cell. However, during multifork replication, the proportion of descendants that will carry the mutation can be different for different forks, since the ploidy of the replicated locus varies (Figure 1g). Taking this into account, one can calculate the average proportion of mutant cells in the progeny of a cell containing a mismatch (for further details, see the supplemental materials of Reference 88). The mutation rate per genome copy is then obtained by the product between the measured rate of long-lived foci and the estimated proportion of mutant cells in the progeny of a cell containing a mismatch.

In investigations of molecular mechanisms of mutagenesis and in particular spontaneous point mutations, our opinion is that the most useful measure of mutation rate is the number of mutations per nucleotide or equivalently per genome copy, which directly reflects replication fidelity. In contrast, the rate per cell also reflects ploidy and can therefore vary through time during a cell cycle and change with the environment, potentially obscuring the investigation of replication fidelity. However, as Sun et al. (104) discuss, the rate per cell can be an interesting parameter for evolution and adaptation, since it estimates the total mutational influx in a population. Polyploidy increases this influx and leads to masking of recessive mutations, potentially impacting the standing genetic variation and mutational load and influencing adaptation potential. Interestingly, single-cell methods such as MV assays are to our knowledge the only methods that directly give the mutation rate per cell.

## 4. NEW INSIGHTS INTO SPONTANEOUS MUTAGENESIS FROM VISUALIZATION STUDIES AT THE SINGLE-CELL LEVEL

Many spontaneous mutations result from replication errors that escaped repair by MMR (60). This section reviews the new findings that single-cell studies have provided concerning these mutations, analyzing their origin and rate variations within isogenic populations and throughout the cell cycle.

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## 4.1. Dynamics of Replication Errors and Mutations

It is generally assumed that spontaneous mutations occur at a constant rate within a population under a constant environment, according to Poisson dynamics, and the estimation of the mutation rate by classical population methods is founded on this assumption (90). However, in the 1990s, a theoretical study by Ninio (78) proposed that mutation rate might be subject to substantial transient variations, through time and between isogenic cells. In this study, Ninio estimated that, for an *E. coli* population evolving in a constant environment, transient mutation rate increases should produce a much larger fraction of the arising mutations than would true genetic mutators, thus pointing at the evolutionary significance of transient increases in mutation rates. Later, mutation bursts caused by transient rate increases were observed in several organisms (21, 40). Bursts may arise as a consequence of fluctuations in intracellular composition, such as transient imbalances in the nucleotide pool, fluctuating concentration of repair enzymes, random DNA damage, or errors in transcription and translation producing mutator polymerases (21). Recently, MV assays based on fluorescent MutL, time-lapse microscopy, and microfluidics allowed for characterization of the dynamics of spontaneous mutations resulting from replication errors in *E. coli*. The appearance of such mutations depends on the production of DNA replication errors, followed by the failure of MMR to repair these errors. In MMR-deficient *mutH* cells, MutL foci detect all replication errors, allowing for the study of the dynamics of error production (88). Repair was studied in MMR-proficient cells, in which MutL foci allow for the detection of all errors and differentiation between repaired and unrepaired ones on the basis of foci lifetimes (26). Time intervals between successive YFP-MutL appearances (hereafter termed waiting times) were calculated and analyzed for all errors as well as for unrepaired errors (Figure 2a). A Poisson process should result in an exponential distribution of these waiting times, which was indeed observed for all errors (88). In contrast, for errors on which repair failed, short waiting times were overrepresented (26) (Figure 2b). This study demonstrated that the production of errors occurs at a constant rate, while repair efficiency fluctuates through time and between isogenic cells, leading to transient increases in mutation rate, i.e., mutation bursts, over several generations. Mutation bursts could be significant for evolution, as they could facilitate the acquisition of mutation combinations essential for the evolution of complex traits such as virulence and antibiotic resistance (1, 62, 85).

## 4.2. Cell Cycle and Replication Error Rate

Population-based methods lack the temporal resolution needed to study variations in the rate of replication errors over a cell cycle, but the real-time single-cell mutagenesis tool opened up this possibility. The replication error rate per cell depends on the rate of replication, i.e., the number of bases synthesized per unit of time. When the growth medium is rich, bacteria grow rapidly, with a generation time up to half that required for genome replication (40 min). To sustain this, cells undergo overlapping replication cycles (a new DNA replication cycle is initiated before the previous one is completed). Consequently, under these conditions, the number of replication forks, and thus the amount of DNA replication per time unit, varies across the cell cycle. Variations in error production rate within a single cell cycle are therefore expected. MV assay using YFP-MutL, time-lapse microscopy, and microfluidics allowed this possibility to be investigated experimentally. During rapid growth, the replication error rate fluctuated up to threefold over different cell cycle stages (88).

In addition to variations in the number of replication forks, the accuracy of DNA replication may fluctuate throughout the cell cycle due to variations in the speed of the replisome, which in *E. coli* exhibits wavelike oscillations along the genome (7). These oscillations correlate with

<figure><img src="image_23.png" /></figure>
<figure><img src="image_24.png" /><figcaption>**Figure 2**</figcaption></figure>

Single-cell, real-time study of point mutations and small indels. (a) Cell length measurements over time (cyan) for three representative mother cell lineages grown in the mother machine. Short-lived yellow fluorescent protein-MutL foci (black lines) indicate successfully repaired replication errors, and long-lived foci (red lines) represent mutations. Time is normalized by the average doubling time. Panel adapted from Reference 26 (CC BY 4.0). (b) Sketch comparing the cumulative distribution function of waiting times for Poisson dynamics (black) and a model with an overrepresentation of short waiting times (red). (c) Schematic showing simultaneous monitoring of fluorescent reporters for stress response (cyan) and replication errors (magenta dots) in cells growing in the mother machine. (d) Sketch of the temporal evolution of the stress response reporter (cyan) and replication error events (magenta lines) in single cells subjected to stress.

the variations in the mutation rate along the genome observed in several bacterial species with defective MMR (19, 33), suggesting a potential trade-off between replication speed and accuracy.

4.3. Mutation Origin

In *E. coli*, ~1% of replication errors evade correction by MMR and are thus converted into mutations. The underlying cause of this escape—whether limited efficiency of error detection by MMR, failure of repair after detection, or a combination of both—has long remained unknown (28, 119). The YFP-MutL-based MV assay was applied to an MMR-proficient strain to

<figure><img src="image_25.png" /></figure>
investigate this cause, on the premise that, if errors escape correction because MMR does not
recognize them, they should not be detected with YFP-MutL. Conversely, if escape results from
MMR failures at the repair stage, these errors should be visible as long-lived YFP-MutL foci.
Quantifying foci revealed that the proportion of long-lived MutL foci reflects the efficiency
of MMR; i.e., it is equal to the proportion of errors leading to mutations in MMR-proficient
cells. Additionally, their absolute rate corresponds to the mutation rate of MMR-proficient cells
estimated by classical population methods (26). These findings demonstrated that failures to
repair detected errors account for many spontaneous mutations.

Studies of these foci also provided insights into molecular mechanisms underlying MMR fail-
ures. In *E. coli*, MMR repair occurs shortly after DNA replication when the newly synthesized
strand remains unmethylated, as MutH cannot cut fully methylated DNA (73). Long-lived YFP-
MutL foci, indicating MMR failures, were quantified in cells with varying levels of MutH and
Dam methylase, the enzyme responsible for methylation of GATC sites in *E. coli*. These experi-
ments revealed that the success or failure of error repair is determined by a competition between
MutH and Dam, which determines the time window during which repair can occur (26). While
*E. coli* relies on GATC methylation for strand discrimination during MMR, most other organisms
use replication-generated strand discontinuities and nicks as the guiding signal (73, 86). Studies
in *S. cerevisiae* indicate that, similarly to methyl-directed MMR, non-methyl-directed MMR oper-
ates within a limited time window determined by a transient strand discrimination signal and that
shortening this window reduces MMR efficiency (86). Overall, these findings suggest an evolu-
tionarily conserved time constraint for MMR, potentially explaining the origin of a large fraction
of spontaneous mutations across organisms.

5. SINGLE-CELL STUDIES OF MUTAGENESIS DURING EXOGENOUS AND ENDOGENOUS STRESS

Environmental assaults, whether physical, chemical or biological, can disrupt normal cell function-
ing and induce stress. In addition, toxic metabolic by-products, spontaneous chemical alterations
of cellular components, or fluctuations in protein abundance can cause endogenous stress. In re-
sponse to stress, cells rapidly activate specific pathways to minimize damage and restore cellular
homeostasis. This rapid reaction increases an organism's chances of survival and reproduction in
the face of sudden environmental changes. However, in extreme stress conditions, this immediate
response may not be sufficient, and the survival of the population may depend on adaptive mu-
tations. Stress could influence this long-term genetic adaptation by increasing mutagenesis (29,
31, 36, 55, 84, 112, 116). This section provides an overview of research allowed by single-cell mu-
tation visualization, exploring the link between stress-induced mutagenesis, triggered by external
or internal stress, and stress responses, in particular the timing of their activation and cell-to-cell
heterogeneity in response activation.

5.1. Alkylation DNA Damage

The Uphoff group studied the mutagenic potential of exogenous alkylation damage by simul-
taneously monitoring MutL fluorescent foci (or tracking single molecules of fluorescent MutS)
and expression from the promoter of Ada, the transcriptional activator of the alkylation stress
adaptive response, in single *E. coli* cells (112, 114, 116). Alkylating agents cause mutagenic DNA
lesions leading to replication errors, as well as toxic lesions that block DNA replication and lead
to cell death if they are not repaired (97). In *E. coli*, six genes are dedicated to protecting DNA
from alkylation damage, and two are constitutively expressed and contribute to the cell's basal
repair capacity, while the other four, including *ada*, are part of the adaptive response induced

<figure><img src="image_26.png" /></figure>
upon alkylation stress (97). The basal level of the methyltransferase Ada varies significantly in an isogenic population of cells, and a subset of cells lacks any Ada molecules (114). Cells without Ada are unresponsive to alkylation stress until the random expression of at least one Ada molecule, which can take multiple generations (113, 114).

By combining microfluidics with fluorescent MutL or MutS assays, studies showed that cells with delayed induction of the adaptive response undergo a transient increase in the rate of repli-cation errors (112, 114) (Figure 2c,d). However, these studies did not differentiate between errors that were subsequently repaired and those that were converted into mutations, and thus the overall impact on mutation rate was uncertain. Subsequent investigations using batch cultures, however, revealed that this subpopulation also exhibits a higher frequency of mutant cells (116). Together, these results demonstrate that, under exogenous alkylation stress, cell-to-cell variability in the timing of the activation of the adaptive response leads to heterogeneous mutation rates.

## 5.2. H₂O₂ Oxidative DNA Damage

An excess of ROS damages cellular components, including DNA. The purine nucleobase guanine is particularly prone to oxidation, and oxidized guanine can cause mismatches during DNA replication and can lead to mutations if these mismatches are left unrepaired (9, 44). Cells react to excess ROS by activating the oxidative stress response, a set of defense mechanisms for neutralizing ROS and repairing oxidative damage (43, 45). As a primary defense, many bacteria rely on ROS-scavenging enzymes such as catalase and peroxiredoxins, regulated by oxidation-sensitive transcription factors such as OxyR (16, 43, 45). Although this defense mechanism provides effective resistance against H₂O₂ toxicity, sublethal concentrations of H₂O₂ are highly mutagenic (89). The Uphoff group studied at the single-cell level how the timing of phenotypic adaptation to H₂O₂ affects H₂O₂-induced mutagenesis (55). To do so, these researchers simultaneously monitored the appearance of fluorescent MutL foci, to measure replication error levels, and expression from transcriptional reporters for OxyR-regulated katG catalase and glutaredoxin 1. The study shows that (a) H₂O₂ treatment results in a burst of replication errors shortly after the start of treatment (at 4.5 min), (b) the replication error level starts to decrease as soon as the OxyR reporter signal increases, and (c) a constitutive OxyR response prevents this burst. Uphoff and colleagues also showed that, in treated batch cultures, the frequency of mutants increased significantly during 5 min of treatment but that there was no further increase with additional incubation with H₂O₂. These results suggest that some of the H₂O₂-induced replication errors are converted into mutations and that the increase in mutagenesis is limited to a brief interval after treatment. Thus, the burst of mutations coincides with a delay in the response to oxidative stress, highlighting the control of mutagenesis by the timing of phenotypic adaptation. As these researchers observed similar results for alkylation stress (see Section 5.1), these findings indicate that mutation bursts during stress are a common phenomenon associated with adaptation delays.

## 5.3. Endogenous Stress

Even in the absence of any exogenous stress, a population may contain a fraction of cells that undergo transient endogenous stresses. Among the most common endogenous stresses are (a) oxidative stress resulting from ROS, which are naturally produced as by-products of cellular metabolism; (b) proteotoxic stress, caused by protein misfolding occurring due to errors in protein synthesis; and (c) genotoxic stress, resulting from DNA damage. Cells react to these endogenous aggressions by inducing stress responses: (a) the oxidative stress response, (b) the heat shock response, and (c) the SOS response, respectively (103). Spontaneous genotoxic stress, i.e., DNA double-strand breaks, for example, affects up to 1% of *E. coli* cells growing in the absence of

<figure><img src="image_27.png" /></figure>
exogenous stress, as evidenced by spontaneous induction of the SOS response (46, 72, 80). Woo et al. (119) examined the generation of replication errors in a population of *E. coli* growing without exogenous stress and compared cells undergoing endogenous stress or those with impaired translational fidelity with the remaining population. Replication errors in these cells could be increased because (a) the SOS stress response leads to the induction of error-prone DNA polymerases (4, 99) with an increased error rate compared to replicative DNA polymerases (91); (b) translation errors can produce erroneous proteins involved in DNA replication or repair with mutagenic potential (e.g., proteins lacking proofreading and having a dominant negative effect) (5, 10, 71, 100); and (c) ROS can also alter the function of enzymes that are involved in DNA replication and repair and can modify DNA bases, which, if left unrepaired, can lead to replication errors (9, 44).

To study this, Woo et al. (119) used the fluorescent MutL assay in MMR-deficient cells (*mutH*) and visualized MutL foci alongside stress response reporters or a translation fidelity reporter in cells plated on agarose pads. They found that MutL foci were slightly more frequent in cells harboring higher fluorescence of stress reporters (SOS, heat shock, ROS, or translation errors). In contrast, Robert et al. (88) found no significant difference in replication error production between (a) filamenting cells or slowly growing cells, phenotypes that are hallmarks of stress in bacteria, and (b) the rest of the population. The discrepancies between these studies could stem from differences in the experimental protocols or data analyses. In our opinion, assessing replication fidelity with the fluorescent MutL assay requires growing and imaging bacteria in finely controlled conditions, which requires microfluidics. It also requires normalization of the MutL foci occurrence rate by the number of replication forks, which depends on both cell size and growth rate and cannot be estimated from cell size only, as performed by Woo et al. (119).

# 6. ESTIMATION OF THE DISTRIBUTION OF MUTATION FITNESS EFFECTS USING SINGLE-CELL STUDIES

Several approaches, each with its own limitations, have been developed to study the DFE of muta-
tions. These methods involve either directly measuring the fitness of collections of mutants,
generated spontaneously or through induction, or monitoring fitness changes in lineages during
MA or laboratory evolution (LA) experiments (6, 27, 38, 51). Induced mutations allow for genome-
wide analysis but often result in gene inactivation or functional alterations, limiting their ability
to capture the full spectrum of mutation effects (17, 51). In contrast, studies using spontaneous
mutants generally focus on specific regions of the genome and suffer from low throughput (6, 38).
While both LA and MA experiments enable genome-wide analysis of spontaneous mutations, both
are subject to sampling bias. The LA experiment focuses on beneficial mutations that arise under
selective pressure (6, 38), whereas the MA experiment minimizes selection, allowing access to a
broader range of effects. However, MA cannot capture lethal and highly deleterious mutations
(27, 38, 39), and both approaches have low throughput, limiting the precision of DFE estimation.
Recent methodological advances have addressed these challenges through the development of a
high-throughput, microfluidic version of MA, relying on the mother machine device (88). In par-
ticular, the device design effectively eliminates natural selection, allowing for unbiased sampling
of mutations, including lethal ones. This approach was used in *E. coli* to show that, under optimal
growth conditions, 1% of spontaneous mutations are lethal, while most nonlethal mutations are
quasi-neutral, with an average effect much smaller than previously estimated (52, 82, 111).

7. PERSPECTIVES

This review explores the advancements that novel, single-cell, real-time tools have brought to our
understanding of mutagenesis. Single-cell approaches have already demonstrated their strength in

<figure><img src="image_28.png" /></figure>
many other fields of microbiology such as gene expression, shedding light on its stochastic nature and the physiological and evolutionary consequences of the resulting phenotypic variability, or cell cycle control, allowing links between growth, division, and DNA replication to be revisited. In the field of mutagenesis, single-cell approaches have started to emerge more recently and are in their infancy, but recent studies showed their great potential, and several key directions are emerging for the future.

Developing single-cell tools that would enable characterization and quantification of the full range of mutagenesis sources beyond DNA replication errors would be of great help in understanding spontaneous and stress-induced mutagenesis. In particular, clarifying the contributions of different mutation sources in different environments would be useful. For example, while replication errors and oxidative DNA damage are considered primary drivers of spontaneous point mutations and small indels in the absence of exogenous stress, their exact relative contributions to spontaneous mutagenesis are still unknown.

Real-time, single-cell mutagenesis approaches hold great promise for studying antibiotic-induced mutations and the dynamics of resistance. Resistance development depends on mutation supply, which is driven by mutation occurrence and population dynamics. Unlike conventional methods, these approaches allow for the simultaneous observation of both factors, enabling quantitative assessments of how antibiotics affect mutation supply. They also open avenues for exploring the mechanistic basis of antibiotic-induced mutagenesis by enabling direct studies of the roles of MMR regulation and error-prone polymerases.

Microfluidics and imaging-based mutagenesis methods also offer powerful tools for exploring the complexity of the DFE. By integrating high-throughput data acquisition with controlled environmental conditions, these methods could help elucidate the impacts of genetic and environmental factors on the DFE, building on recent findings (17, 29, 41, 70, 77). These approaches are particularly well-suited to studying fluctuating environments, uniquely enabling investigation of conditions similar to those faced by natural populations.

## DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

## ACKNOWLEDGMENTS

This work was supported by the Agence Nationale de la Recherche grant ANR-20-CE12-0008 (MUMI) (to M.E.) and by the Impulscience program of the Fondation Bettencourt Schueller (to L.R.).

## LITERATURE CITED

1. Alexander HK, Mayer SI, Bonhoeffer S. 2017. Population heterogeneity in mutation rate increases the frequency of higher-order mutants and reduces long-term mutational load. *Mol. Biol. Evol.* 34(2):419–36

2. Allard P, Papazotos F, Potvin-Trottier L. 2022. Microfluidics for long-term single-cell time-lapse microscopy: advances and applications. *Front. Bioeng. Biotechnol.* 10:968342

3. Andersson DI, Hughes D. 1996. Muller's ratchet decreases fitness of a DNA-based microbe. *PNAS* 93(2):906–7

4. Baharoglu Z, Mazel D. 2014. SOS, the formidable strategy of bacteria against aggressions. *FEMS Microbiol. Rev.* 38(6):1126–45

5. Balashov S, Humayun MZ. 2003. *Escherichia coli* cells bearing a ribosomal ambiguity mutation in *rpsD* have a mutator phenotype that correlates with increased mistranslation. *J. Bacteriol.* 185(16):5015–18

<figure><img src="image_29.png" /></figure>
6. Bataillon T, Bailey SF. 2014. Effects of new mutations on fitness: insights from models and data. *Ann. N. Y. Acad. Sci.* 1320(1):76–92

7. Bhat D, Hauf S, Plessy C, Yokobayashi Y, Pigolotti S. 2022. Speed variations of bacterial replisomes. *eLife* 11:e75884

8. Bhawsinghka N, Burkholder A, Schaaper RM. 2023. Detection of DNA replication errors and 8-oxodGTP-mediated mutations in *E. coli* by duplex DNA sequencing. *DNA Repair* 123:103462

9. Bjelland S. 2003. Mutagenicity, toxicity and repair of DNA base damage induced by oxidation. *Mutat. Res.* 531(1–2):37–80

10. Boe L. 1992. Translational errors as the cause of mutations in *Escherichia coli*. *Molec. Gen. Genet.* 231(3):469–71

11. Campbell CS, Hombauer H, Srivatsan A, Bowen N, Gries K, et al. 2014. Mlh2 is an accessory factor for DNA mismatch repair in *Saccharomyces cerevisiae*. *PLOS Genet.* 10(5):e1004327

12. Campbell JL, Kleckner N. 1988. The rate of Dam-mediated DNA adenine methylation in *Escherichia coli*. *Gene* 74(1):189–90

13. Campbell JL, Kleckner N. 1990. *E. coli oriC* and the *dnaA* gene promoter are sequestered from *dam* methyltransferase following the passage of the chromosomal replication fork. *Cell* 62(5):967–79

14. Carvajal-Garcia J, Samadpour AN, Hernandez Viera AJ, Merrikh H. 2023. Oxidative stress drives mutagenesis through transcription-coupled repair in bacteria. *PNAS* 120(27):e2300761120

15. Chatterjee N, Walker GC. 2017. Mechanisms of DNA damage, repair, and mutagenesis. *Environ. Mol. Mutagen*. 58(5):235–63

16. Christman MF, Morgan RW, Jacobson FS, Ames BN. 1985. Positive control of a regulon for defenses against oxidative stress and some heat-shock proteins in *Salmonella typhimurium*. *Cell* 41(3):753–62

17. Couce A, Limdi A, Magnan M, Owen SV, Herren CM, et al. 2024. Changing fitness effects of mutations through long-term bacterial evolution. *Science* 383(6681):eadd1417

18. Deatherage DE, Barrick JE. 2014. Identification of mutations in laboratory evolved microbes from next-generation sequencing data using breseq. *Methods Mol. Biol.* 1151:165–88

19. Dillon MM, Sung W, Lynch M, Cooper VS. 2018. Periodic variation of mutation rates in bacterial genomes associated with replication timing. *mBio* 9(4):e01371-18

20. Drake JW. 1991. A constant rate of spontaneous mutation in DNA-based microbes. *PNAS* 88(16):7160–64

21. Drake JW. 2007. Too many mutants with multiple mutations. *Crit. Rev. Biochem. Mol. Biol.* 42(4):247–58

22. Elez M. 2021. Mismatch repair: from preserving genome stability to enabling mutation studies in real-time single cells. *Cells* 10(6):1535

23. Elez M, Murray AW, Bi L-J, Zhang X-E, Matic I, Radman M. 2010. Seeing mutations in living cells. *Curr. Biol.* 20(16):1432–37

24. Elez M, Radman M, Matic I. 2012. Stoichiometry of MutS and MutL at unrepaired mismatches in vivo suggests a mechanism of repair. *Nucleic Acids Res.* 40(9):3929–38

25. Elez M, Robert L, Matic I. 2018. Method for detecting and studying genome-wide mutations in single living cells in real time. *Methods Mol. Biol.* 1736:29–39

26. Enrico Bena C, Ollion J, De Paepe M, Ventroux M, Robert L, Elez M. 2024. Real-time monitoring of replication errors' fate reveals the origin and dynamics of spontaneous mutations. *Nat. Commun.* 15:2702

27. Eyre-Walker A, Keightley PD. 2007. The distribution of fitness effects of new mutations. *Nat. Rev. Genet.* 8(8):610–18

28. Feng G, Tsui HC, Winkler ME. 1996. Depletion of the cellular amounts of the MutS and MutH methyl-directed mismatch repair proteins in stationary-phase *Escherichia coli* K-12 cells. *J. Bacteriol.* 178(8):2388–96

29. Ferenci T. 2019. Irregularities in genetic variation and mutation rates with environmental stresses. *Environ. Microbiol.* 21(11):3979–88

30. Fishel R. 2015. Mismatch repair. *J. Biol. Chem.* 290(44):26395–403

31. Fitzgerald DM, Rosenberg SM. 2019. What is mutation? A chapter in the series: how microbes “jeopardize” the modern synthesis. *PLOS Genet.* 15(4):e1007995

32. Foster PL. 2006. Methods for determining spontaneous mutation rates. *Methods Enzymol.* 409:195–213

<figure><img src="image_30.png" /></figure>
33. Foster PL, Hanson AJ, Lee H, Popodi EM, Tang H. 2013. On the mutational topology of the bacterial genome. *G3* 3(3):399-407

34. Foster PL, Lee H, Popodi E, Townes JP, Tang H. 2015. Determinants of spontaneous mutation in the bacterium *Escherichia coli* as revealed by whole-genome sequencing. *PNAS* 112(44):E5990-99

35. Frenoy A, Bonhoeffer S. 2018. Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria. *PLOS Biol*. 16(5):e2005056

36. Galhardo RS, Hastings PJ, Rosenberg SM. 2007. Mutation as a stress response and the regulation of evolvability. *Crit. Rev. Biochem. Mol. Biol*. 42(5):399-435

37. Garibyan L. 2003. Use of the *rpoB* gene to determine the specificity of base substitution mutations on the *Escherichia coli* chromosome. *DNA Repair* 2(5):593-608

38. Gordo I, Perfeito L, Sousa A. 2012. Fitness effects of mutations in bacteria. *J. Mol. Microbiol. Biotechnol.* 21(1-2):20-35

39. Halligan DL, Keightley PD. 2009. Spontaneous mutation accumulation studies in evolutionary genetics. *Annu. Rev. Ecol. Evol. Syst.* 40:151-72

40. Heasley LR, Sampaio NMV, Argueso JL. 2021. Systemic and rapid restructuring of the genome: a new perspective on punctuated equilibrium. *Curr. Genet.* 67(1):57-63

41. Hietpas RT, Bank C, Jensen JD, Bolon DNA. 2013. Shifting fitness landscapes in response to altered environments: fitness landscapes in altered environments. *Evolution* 67(12):3512-22

42. Hombauer H, Campbell CS, Smith CE, Desai A, Kolodner RD. 2011. Visualization of eukaryotic DNA mismatch repair reveals distinct recognition and repair intermediates. *Cell* 147(5):1040-53

43. Imlay JA. 2008. Cellular defenses against superoxide and hydrogen peroxide. *Annu. Rev. Biochem.* 77:755-76

44. Imlay JA. 2013. The molecular mechanisms and physiological consequences of oxidative stress: lessons from a model bacterium. *Nat. Rev. Microbiol.* 11(7):443-54

45. Imlay JA. 2015. Transcription factors that defend bacteria against reactive oxygen species. *Annu. Rev. Microbiol.* 69:93-108

46. Jaramillo-Riveri S, Broughton J, McVey A, Pilizota T, Scott M, El Karoui M. 2022. Growth-dependent heterogeneity in the DNA damage response in *Escherichia coli*. *Mol. Syst. Biol.* 18(5):e10441

47. Jee J, Rasouly A, Shamovsky I, Akivis Y, R. Steinman S, et al. 2016. Rates and mechanisms of bacterial mutagenesis from maximum-depth sequencing. *Nature* 534(7609):693-96

48. Jiricny J. 2013. Postreplicative mismatch repair. *Cold Spring Harb. Perspect. Biol.* 5(4):a012633

49. Katju V, Bergthorsson U. 2019. Old trade, new tricks: insights into the spontaneous mutation process from the partnering of classical mutation accumulation experiments with high-throughput genomic approaches. *Genome Biol. Evol.* 11(1):136-65

50. Keightley PD, Otto SP. 2006. Interference among deleterious mutations favours sex and recombination in finite populations. *Nature* 443(7107):89-92

51. Kemble H, Nghe P, Tenaillon O. 2019. Recent insights into the genotype-phenotype relationship from massively parallel genetic assays. *Evol. Appl.* 12(9):1721-42

52. Kibota TT, Lynch M. 1996. Estimate of the genomic mutation rate deleterious to overall fitness in *E. coli*. *Nature* 381(6584):694-96

53. Kim NH, Lee G, Sherer NA, Martini KM, Goldenfeld N, Kuhlman TE. 2016. Real-time transposable element activity in individual live cells. *PNAS* 113(26):7278-83

54. Klocko AD, Schroeder JW, Walsh BW, Lenhart JS, Evans ML, Simmons LA. 2011. Mismatch repair causes the dynamic release of an essential DNA polymerase from the replication fork. *Mol. Microbiol.* 82(3):648-63

55. Lagage V, Chen V, Uphoff S. 2023. Adaptation delay causes a burst of mutations in bacteria responding to oxidative stress. *EMBO Rep.* 24(1):e55640

56. Lang GI, Murray AW. 2008. Estimating the per-base-pair mutation rate in the yeast *Saccharomyces cerevisiae*. *Genetics* 178(1):67-82

57. Lang GI, Murray AW. 2011. Mutation rates across budding yeast chromosome VI are correlated with replication timing. *Genome Biol. Evol.* 3:799-811

58. Lansch-Justen L, El Karoui M, Alexander HK. 2024. Estimating mutation rates under heterogeneous stress responses. *PLOS Comput. Biol.* 20(5):e1012146

<figure><img src="image_31.png" /></figure>
59. Łazowski K. 2023. Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur). *Mutat. Res.* 826:111816

60. Lee H, Popodi E, Tang H, Foster PL. 2012. Rate and molecular spectrum of spontaneous mutations in the bacterium *Escherichia coli* as determined by whole-genome sequencing. *PNAS* 109(41):E2774-83

61. Lenhart JS, Pillon MC, Guarné A, Simmons LA. 2013. Trapping and visualizing intermediate steps in the mismatch repair pathway in vivo. *Mol. Microbiol.* 90(4):680-98

62. Lobinska G, Pilpel Y, Ram Y. 2023. Phenotype switching of the mutation rate facilitates adaptive evolution. *Genetics* 225(1):iyad111

63. Long H, Sung W, Miller SF, Ackerman MS, Doak TG, Lynch M. 2015. Mutation rate, spectrum, topology, and context-dependency in the DNA mismatch repair-deficient *Pseudomonas fluorescens* ATCC948. *Genome Biol. Evol.* 7(1):262-71

64. López-Cortegano E, Craig RJ, Chebib J, Balogun EJ, Keightley PD. 2023. Rates and spectra of de novo structural mutations in *Chlamydomonas reinhardtii*. *Genome Res.* 33(1):45-60

65. Lujan SA, Kunkel TA. 2021. Stability across the whole nuclear genome in the presence and absence of DNA mismatch repair. *Cells* 10(5):1224

66. Luria SE, Delbrück M. 1943. Mutations of bacteria from virus sensitivity to virus resistance. *Genetics* 28(6):491-511

67. Lynch M. 2010. Evolution of the mutation rate. *Trends Genet.* 26(8):345-52

68. Lynch M, Ackerman MS, Gout J-F, Long H, Sung W, et al. 2016. Genetic drift, selection and the evolution of the mutation rate. *Nat. Rev. Genet.* 17(11):704-14

69. Lynch M, Ali F, Lin T, Wang Y, Ni J, Long H. 2023. The divergence of mutation rates and spectra across the Tree of Life. *EMBO Rep.* 24(10):e57561

70. Maharjan RP, Ferenci T. 2017. A shifting mutational landscape in 6 nutritional states: stress-induced mutagenesis as a series of distinct stress input-mutation output relationships. *PLOS Biol.* 15(6):e2001477

71. Mamun AAMA, Marians KJ, Humayun MZ. 2002. DNA polymerase III from *Escherichia coli* cells expressing *mutA* mistranslator tRNA is error-prone. *J. Biol. Chem.* 277(48):46319-27

72. Massoni SC, Leeson MC, Long JE, Gemme K, Mui A, Sandler SJ. 2012. Factors limiting SOS expression in log-phase cells of *Escherichia coli*. *J. Bacteriol.* 194(19):5325-33

73. Modrich P. 2016. Mechanisms in *E. coli* and human mismatch repair (Nobel Lecture). *Angew. Chem. Int. Ed.* 55(30):8490-501

74. Mortier-Barrière I, Polard P, Campo N. 2020. Direct visualization of horizontal gene transfer by transformation in live pneumococcal cells using microfluidics. *Genes* 11(6):675

75. Mukai T. 1964. The genetic structure of natural populations of *Drosophila melanogaster*. *I. Spontaneous mutation rate of polygenes controlling viability*. *Genetics* 50(1):1-19

76. Muller HJ. 1928. The measurement of gene mutation rate in *Drosophila*, its high variability, and its dependence upon temperature. *Genetics* 13(4):279-357

77. Nichols RJ, Sen S, Choo YJ, Beltrao P, Zietek M, et al. 2011. Phenotypic landscape of a bacterial cell. *Cell* 144(1):143-56

78. Ninio J. 1991. Transient mutators: a semiquantitative analysis of the influence of translation and transcription errors on mutation rates. *Genetics* 129(3):957-62

79. Ollion J, Elez M, Robert L. 2019. High-throughput detection and tracking of cells and intracellular spots in mother machine experiments. *Nat. Protoc.* 14(11):3144-61

80. Pennington JM, Rosenberg SM. 2007. Spontaneous DNA breakage in single living *Escherichia coli* cells. *Nat. Genet.* 39(6):797-802

81. Penno C, Motherway MO, Fu Y, Sharma V, Crispie F, et al. 2022. Maximum depth sequencing reveals an ON/OFF replication slippage switch and apparent in vivo selection for bifidobacterial pilus expression. *Sci. Rep.* 12(1):9576

82. Perfeito L, Sousa A, Bataillon T, Gordo I. 2014. Rates of fitness decline and rebound suggest pervasive epistasis. *Evolution* 68(1):150-62

83. Potvin-Trottier L, Luro S, Paulsson J. 2018. Microfluidics and single-cell microscopy to study stochastic processes in bacteria. *Curr. Opin. Microbiol.* 43:186-92

84. Pribis JP, Zhai Y, Hastings PJ, Rosenberg SM. 2022. Stress-induced mutagenesis, gambler cells, and stealth targeting antibiotic-induced evolution. *mBio* 13(3):e01074-22

<figure><img src="image_32.png" /></figure>
85. Ram Y, Hadany L. 2014. Stress-induced mutagenesis and complex adaptation. *Proc. R. Soc. B* 281(1792):20141025

86. Reyes GX, Kolodziejczak A, Devakumar LJPS, Kubota T, Kolodner RD, et al. 2021. Ligation of newly replicated DNA controls the timing of DNA mismatch repair. *Curr. Biol.* 31(6):1268–76.e6

87. Robert L, Ollion J, Elez M. 2019. Real-time visualization of mutations and their fitness effects in single bacteria. *Nat. Protoc.* 14(11):3126–43

88. Robert L, Ollion J, Robert J, Song X, Matic I, Elez M. 2018. Mutation dynamics and fitness effects followed in single cells. *Science* 359(6381):1283–86

89. Rodríguez-Rojas A, Kim JJ, Johnston PR, Makarova O, Eravci M, et al. 2020. Non-lethal exposure to H₂O₂ boosts bacterial survival and evolvability against oxidative stress. *PLOS Genet.* 16(3):e1008649

90. Rosche WA, Foster PL. 2000. Determining mutation rates in bacterial populations. *Methods* 20(1):4–17

91. Sale JE, Lehmann AR, Woodgate R. 2012. Y-family DNA polymerases and their role in tolerance of cellular DNA damage. *Nat. Rev. Mol. Cell Biol.* 13(3):141–52

92. Salk JJ, Schmitt MW, Loeb LA. 2018. Enhancing the accuracy of next-generation sequencing for detecting rare and subclonal mutations. *Nat. Rev. Genet.* 19(5):269–85

93. Schmidt TT, Hombauer H. 2016. Visualization of mismatch repair complexes using fluorescence microscopy. *DNA Repair* 38:58–67

94. Schmitt MW, Kennedy SR, Salk JJ, Fox EJ, Hiatt JB, Loeb LA. 2012. Detection of ultra-rare mutations by next-generation sequencing. *PNAS* 109(36):14508–13

95. Schroeder JW, Hirst WG, Szewczyk GA, Simmons LA. 2016. The effect of local sequence context on mutational bias of genes encoded on the leading and lagging strands. *Curr. Biol.* 26(5):692–97

96. Schroeder JW, Yeesin P, Simmons LA, Wang JD. 2018. Sources of spontaneous mutagenesis in bacteria. *Crit. Rev. Biochem. Mol. Biol.* 53(1):29–48

97. Sedgwick B. 2004. Repairing DNA-methylation damage. *Nat. Rev. Mol. Cell Biol.* 5(2):148–57

98. Simmons LA, Davies BW, Grossman AD, Walker GC. 2008. β clamp directs localization of mismatch repair in *Bacillus subtilis*. *Mol. Cell* 29(3):291–301

99. Simmons LA, Foti JJ, Cohen SE, Walker GC. 2008. The SOS regulatory network. *EcoSal Plus* 3(1):10.1128/ecosalplus.5.4.3

100. Slupska MM, Baikalov C, Lloyd R, Miller JH. 1996. Mutator tRNAs are encoded by the *Escherichia coli* mutator genes *mutA* and *mutC*: a novel pathway for mutagenesis. *PNAS* 93(9):4380–85

101. Smith BT, Grossman AD, Walker GC. 2001. Visualization of mismatch repair in bacterial cells. *Mol. Cell* 8(6):1197–206

102. Sniegowski PD, Gerrish PJ, Lenski RE. 1997. Evolution of high mutation rates in experimental populations of *E. coli*. *Nature* 387(6634):703–5

103. Storz G, Hengge R. 2010. *Bacterial Stress Responses*. American Society for Microbiology Press. 1,167 pp.

104. Sun L, Alexander HK, Bogos B, Kiviet DJ, Ackermann M, Bonhoeffer S. 2018. Effective polyploidy causes phenotypic delay and influences bacterial evolvability. *PLOS Biol.* 16(2):e2004644

105. Sung W, Ackerman MS, Gout J-F, Miller SF, Williams E, et al. 2015. Asymmetric context-dependent mutation patterns revealed through mutation-accumulation experiments. *Mol. Biol. Evol.* 32(7):1672–83

106. Tanneur I, Dervyn E, Guérin C, King GKK, Jules M, Nicolas P. 2024. The mutational landscape of *Bacillus subtilis* conditional hypermutators shows how proofreading skews DNA polymerase error rates. *bioRxiv*. https://doi.org/10.1101/2023.12.29.573609

107. Tenaillon O, Barrick JE, Ribeck N, Deatherage DE, Blanchard JL, et al. 2016. Tempo and mode of genome evolution in a 50,000-generation experiment. *Nature* 536(7615):165–70

108. Tenaillon O, Toupance B, Le Nagard H, Taddei F, Godelle B. 1999. Mutators, population size, adaptive landscape and the adaptation of asexual populations of bacteria. *Genetics* 152(2):485–93

109. Thiermann R, Sandler M, Ahir G, Sauls JT, Schroeder J, et al. 2024. Tools and methods for high-throughput single-cell imaging with the mother machine. *eLife* 12:RP88463

110. Tomanek I, Grah R, Lagator M, Andersson AMC, Bollback JP, et al. 2020. Gene amplification as a form of population-level gene expression regulation. *Nat. Ecol. Evol.* 4(4):612–25

111. Trindade S, Perfeito L, Gordo I. 2010. Rate and effects of spontaneous mutations that affect fitness in mutator *Escherichia coli*. *Philos. Trans. R. Soc. B* 365(1544):1177–86

<figure><img src="image_33.png" /></figure>
112. Uphoff S. 2018. Real-time dynamics of mutagenesis reveal the chronology of DNA repair and damage tolerance responses in single cells. *PNAS* 115(28):E6516–25

113. Uphoff S. 2019. A quantitative model explains single-cell dynamics of the adaptive response in *Escherichia coli*. *Biophys. J.* 117(6):1156–65

114. Uphoff S, Lord ND, Okumus B, Potvin-Trottier L, Sherratt DJ, Paulsson J. 2016. Stochastic activation of a DNA damage response causes cell-to-cell mutation rate variation. *Science* 351(6277):1094–97

115. Villalba De La Peña M, Summanen PAM, Liukkonen M, Kronholm I. 2023. Chromatin structure influences rate and spectrum of spontaneous mutations in *Neurospora crassa*. *Genome Res.* 33(4):599–611

116. Vincent MS, Uphoff S. 2021. Cellular heterogeneity in DNA alkylation repair increases population genetic plasticity. *Nucleic Acids Res.* 49(21):12320–31

117. Wang P, Robert L, Pelletier J, Dang WL, Taddei F, et al. 2010. Robust growth of *Escherichia coli*. *Curr. Biol.* 20(12):1099–103

118. Williams AB. 2014. Spontaneous mutation rates come into focus in *Escherichia coli*. *DNA Repair* 24:73–79

119. Woo AC, Faure L, Dapa T, Matic I. 2018. Heterogeneity of spontaneous DNA replication errors in single isogenic *Escherichia coli* cells. *Sci. Adv.* 4(6):eaat1608

120. Ycart B. 2014. Fluctuation analysis with cell deaths. *J. Appl. Probab. Stat.* 9(1):13

121. Zheng Q. 2015. A new practical guide to the Luria-Delbrück protocol. *Mutat. Res.* 781:7–13

122. Zhou X, Pan J, Wang Y, Lynch M, Long H, Zhang Y. 2023. De novo structural variations of *Escherichia coli* detected by nanopore long-read sequencing. *Genome Biol. Evol.* 15(6):evad106

<figure><img src="image_34.png" /></figure>