High-resolution, high-throughput detection of hidden antibiotic resistance with the dilution-and-delay (DnD) susceptibility assay

Domain: chemistry
Published: 2026-03-07
Editorial outcome: minor_revision

Abstract
Rising rates of antibiotic treatment failure highlight the complexity of resistance mechanisms. While genetically encoded resistance is well established, recent clinical studies have uncovered noncanonical mechanisms driven by phenotypic heterogeneity, such as heteroresistance, persistence, or adaptive resistance. Yet, standard susceptibility tests lack the resolution or throughput to detect these diverse phenotypic mechanisms. To address these limitations, we develop a scalable, high-resolution assay—Dilution-and-Delay (DnD)—by implementing two basic principles of bacterial growth. DnD detects rare drug-insensitive cells at frequencies as low as 1 in 100 million, while also reporting bulk population inhibition as the conventional MIC. We demonstrate this capability across synthetic communities, heteroresistance, persistence, and evolutionary progression. These high-resolution data expose how traditional susceptibility assays are constrained by statistical detection limits. Scaling up DnD for high-throughput application to ~120 clinical isolates reveals that under-the-radar multidrug resistance or tolerance is common. By combining resolution with scalability, DnD provides an advanced platform for antibiotic susceptibility testing with broad impact on basic research, clinical practice, and epidemiological surveillance. It supports a new framework for measuring, defining, and understanding antibiotic resistance.

Introduction
Antibiotics are foundational to modern medicine—significantly improving quality of life, extending human lifespans, and enabling complex medical procedures. However, treatment failure due to inappropriate antibiotic use remains common, worsening patient outcomes and increasing the burden on healthcare systems 1,2. Antibiotic susceptibility testing plays a critical role in combating this threat by guiding clinical decisions and ensuring treatments are tailored to the infecting pathogen.

Concerns about antibiotic failure date back to the very beginning of the antibiotic era, when Alexander Fleming warned that improper use of penicillin could drive the emergence of resistance—a prediction that proved prescient. Conventional antibiotic resistance arises by the acquisition of genetically encoded determinants through mutations or horizontal gene transfer 3, which confer population-scale advantages under antibiotic pressure.

However, recent studies have shed new light on diverse mechanisms by which bacteria evade antibiotic treatments. Bacteria can survive through tolerance, where cells withstand drug exposure by decreasing their growth or entering a dormant state 4,5. Importantly, both resistance and tolerance can manifest heterogeneously within populations. In heteroresistance, only a minority exhibits resistance in a population, while the majority of cells remain susceptible 6,7. In persistence, a subpopulation transiently enters an antibiotic-tolerant state, surviving lethal antibiotic exposure and resuming growth once treatment ends 8,9. Adaptive resistance, induced by sublethal antibiotic exposure, can further enable small subpopulations to gradually withstand higher drug concentrations 10,11. These traits can propagate through lineages and be stably maintained in the population 12,13.

Importantly, these resistant or tolerant subpopulations (hereafter referred to as antibiotic-insensitive) are often part of a broader evolutionary landscape. Heteroresistance or persistence often emerges early during the evolution under antibiotic treatments 14-16. Once such subpopulations are present, inappropriate treatments can accelerate their acquisition of genetic resistance—providing an alternate accelerated route to population-wide resistance 14-17. However, the true magnitude of this problem remains elusive because it routinely goes undetected in the clinic and research labs 18,19. A major obstacle is the limited resolution of conventional antibiotic susceptibility testing. The current gold-standard assay measure population growth in antibiotic-containing microbroth, reporting the minimum inhibitory concentration (MIC) 20,21—a single numerical value that summarizes the response of the entire population 22. Disk diffusion and E-tests are alternative methods 23. Disk diffusion measure inhibition zone diameters around antibiotic-impregnated disks on an agar plate, interpreting the MIC using zone diameter breakpoints. E-tests employ antibiotic gradient strips to provide an MIC estimate based on the point of growth inhibition. In all cases, the MIC obtained is compared with the pre-defined clinical breakpoint to classify the population as susceptible or resistant 24. While simple and widely adopted, these current tests average over the population, masking underlying heterogeneity. As a result, they fail to inform non-canonical antibiotic insensitivity embedded within a susceptible population 25-27. This, in turn, results in inappropriate therapy and “unexplained” treatment failures. More concerningly, when inappropriate therapy drives the evolutionary progression of minority variants to full-scale resistance 14-17, current tests remain blind to this process—detecting it only after resistant clones have swept through and become fixed in the population, by which point it is too late to adjust therapy and reverse the course. These limitations underscore the urgent need for high-resolution susceptibility testing capable of detecting antibiotic-insensitive cells even when they occur at very low frequencies within susceptible populations.

The ideal solution would be single-cell-level detection of bacterial survival. Leveraging advances in microscopy and microfluidics, we and others have measured antibiotic susceptibility at single-cell resolution, detecting survival frequencies as low as 1 in 10,000 (10⁻⁴) 28,29. While promising, this range is not sufficient to reliably detect non-canonical mechanisms of antibiotic insensitivity such as heteroresistance or persistence, which can occur at frequencies far below this range (e.g., 10-7 for heteroresistance) 25-27,30,31. Moreover, these approaches depend on specialized instrumentation and technical expertise, making them inaccessible to most laboratories.

Historically, agar-based population analysis profiling (PAP) has been the method of choice for detecting heteroresistance 25-27. In PAP, bacterial cultures are plated across a range of antibiotic concentrations, and colonies that grow at high concentrations are counted to quantify resistant cells. Although highly sensitive, PAP is labor-intensive and time-consuming—requiring agar preparation and manual CFU counting—making it impractical for routine diagnostics. This diagnostic blind spot obscures the diverse mechanisms underlying treatment failure and evidence-based clinical guidelines.

To address this challenge, we investigated two fundamental principles of bacterial growth, dilution-to-extinction and delay-to-growth, thereby developing a practical, scalable, high-resolution strategy for antibiotic susceptibility testing. This assay retains the operational efficiency of conventional susceptibility tests, making it readily adoptable. Yet, it detects rare antibiotic-insensitive cells at frequencies as low as 1 in 100 million, while simultaneously reporting the bulk response as the standard MIC. We demonstrated its utility across synthetic communities, heterogeneous strains, and persistent populations, and by tracking the evolutionary dynamics early from the onset. Scaling it to high throughput, we uncovered a widespread prevalence of multidrug insensitivity in clinical isolates. Our analysis also shows that in heterogeneous populations, the MIC largely reflects the statistical sampling limits of conventional assays rather than a true population-wide inhibitory dose. These findings establish antibiotic susceptibility as a quantitative spectrum rather than a binary trait, providing a new framework for measuring, defining, and understanding bacterial response to antibiotics.

Results
The gold standard for antibiotic susceptibility testing is broth-based culture, standardized by international organizations, including the Clinical and Laboratory Standards Institute (CLSI) 20 or the European Committee on Antimicrobial Susceptibility Testing (EUCAST) 21. These protocols specify inoculating a fixed number of bacterial cells (N, typically ~5105) into antibiotic-containing media and visually observing the turbidity after a defined period (T, typically ~18 hours) to determine the MIC of the sample. However, N and T are procedural parameters—not intrinsic biological properties. We hypothesized that systematically varying these parameters could reveal resistant cells hidden in a susceptible population.

Dilution-to-extinction quantifies rare resistant cells through serial dilution.
Consider a bacterial culture in which resistant cells occur at a frequency of 10⁻⁷. If the standard inoculum N  5105 is used, it will likely contain no resistant cells and will be sterilized by antibiotic treatment. In contrast, a culture of N  10⁹ cells would almost certainly include resistant cells, which will grow in the presence of antibiotics to turn the culture turbid. Therefore, by systematically varying N, we can infer the abundance of resistant subpopulations based on turbidity.

We formalized this concept into a dilution-to-extinction assay (Fig. 1a). Starting from a high-density culture, we performed serial dilutions and incubated them in antibiotic-containing media. At low dilutions, resistant cells are likely present and grow, driving turbidity. As the dilution increases, the probability of including resistant cells decreases, and eventually, a dilution contains none—resulting in no growth. The first dilution in the series that lacks turbidity defines the extinction point (Fig. 1a). For example, if the original population contains a few tens of resistant cells, the first 10-fold dilution would still turn turbid, while the second would not. In our analysis, we determined the number of dilutions required to reach the extinction point to estimate the number of resistant cells in the original sample.

To validate this approach, we spiked a susceptible strain with known fractions of a fully resistant strain, preparing defined mixtures. We applied the dilution-to-extinction protocol in the presence of antibiotics to estimate resistant cells, and in parallel, performed the same dilution series without antibiotics to estimate the total number of viable cells (Fig. 1a). Comparing the extinction points across the two conditions enabled us to compute the resistance frequency (RF) in each mixture (Fig. 1a).

Across samples with resistant fractions spanning several orders of magnitude, the measured RF values closely recapitulated the known input frequencies (Fig. 1b). Replicate measurements, indicated by three different symbols, exhibited ~10-fold variation, consistent with the resolution limit imposed by 10-fold serial dilution. Nonetheless, the data showed a strong linear relationship, with a slope of 0.95 and an R² of 0.98 (Fig. 1b). Notably, the method reliably detected resistant cells at frequencies as low as ~10⁻⁸— below a conventional heteroresistance threshold of ~10⁻⁷ 27. This test indicates that the dilution-to-extinction assay enables unbiased detection of resistant subpopulations across a broad dynamic range.

Delay-to-Growth Captures Rare Survivors Through Density Monitoring
The other fixed parameter in standard antibiotic susceptibility testing is the observation time (T), typically ~18 hours 20,21. This constraint assumes that antibiotic-sensitive and -resistant populations will fully diverge within that period. However, when resistant cells are rare, their outgrowth is delayed. The less resistant cells are present, the longer this delay. The core idea behind the delay-to-growth assay is to analyze this delay under antibiotic treatment to infer the abundance of resistant cells.

We tested this principle using the defined mixtures of susceptible and resistant strains. Optical density (OD₆₀₀) was measured over time using a spectrophotometer to monitor culture growth. In the absence of antibiotics, the culture exhibited immediate exponential growth without lag (Fig. 1c, grey). In contrast, when antibiotics are present, the OD₆₀₀ initially declined due to killing of susceptible cells—often dropping below the detection limit of a spectrophotometer (Fig. 1c, cyan). This was followed by delayed increase in OD₆₀₀, corresponding to the outgrowth of surviving resistant cells that become detectable.

To estimate the number of resistant cells in the original culture, we fit the exponential phase of the recovery curve, extrapolating it back to time zero (dashed line, Fig. 1c). This yielded an OD₆₀₀ of resistant subpopulations at t = 0, denoted OD₆₀₀ᴿ (Fig. 1c). To calculate its frequency relative to the total population, we compared it with the total OD of the culture (OD₆₀₀ᴾ in Fig. 1c). The resulting ratio, OD₆₀₀ᴿ / OD₆₀₀ᴾ, provides an estimate of RF in the original sample.

To assess the quantitative accuracy and dynamic range of the method, we varied the abundance of resistant cells in the defined mixture over several orders of magnitude and measured the RFs. The measured RF values closely matched the known input ratio, yielding a linear regression with a slope of 0.95 with R2 of 0.996 (Fig. 1d). Therefore, the delay-to-growth approach accurately captures resistant subpopulations.

Integrating Two Axes of Resolution: The Dilution-and-Delay (DnD) Framework
To improve both quantitative accuracy and operational efficiency, we integrated the dilution-to-extinction and delay-to-growth assays into a unified framework. Since both assays are performed in broth and rely on turbidity as the readout—whether visual inspection or optical density reading—they are naturally compatible. Moreover, turbidity can be monitored automatically via OD₆₀₀ measurements using standard plate readers, enabling seamless integration into high-throughput workflows. Importantly, the two assays are based on distinct underlying principles, yielding orthogonal estimates of RF. Cross-validating these independent measurements provides an internal consistency check, thereby increasing the robustness and reliability of RF quantification.

We implemented this combined strategy as the Dilution-and-Delay (DnD) assay. Following the dilution-to-extinction protocol, we prepared serial dilutions in a 96-well microtiter plate, then continuously monitored OD₆₀₀ in each well over time using a plate reader (Fig. 1e). Wells that failed to show an OD₆₀₀ increase were used to define the extinction point as described in the dilution-to-extinction method (Fig. 1a). The wells that exhibited growth were used to estimate RF values, based on the delay-to-growth method (Fig. 1c). The dilution series also served as technical replicates (Fig. 1e); RF values from individual wells were averaged to improve statistical precision, yielding the final RF for that experiment.

We validated the DnD assay using defined mixtures of fully resistant and susceptible strains. Across all input ratios, the averaged RF was within the ~10-fold uncertainty inherent to the dilution-to-extinction method alone, showing an internal check of consistency (Supplementary Fig. 1). Linear regression of input versus the RFs showed a slope closest to 1 and the highest R² (0.999), exhibiting near-perfect agreement (Fig. 1f).

Validation Against Heteroresistant Clinical Isolates
We next applied our assays to a panel of five clinical isolates previously characterized as heteroresistant against various antibiotics 17,18,32. Unlike the defined artificial mixtures used above, these strains endogenously harbor resistant subpopulations with variable frequencies and resistance levels. Each strain had been previously evaluated using population analysis profiling (PAP)—a well-established method for identifying heteroresistance—revealing a gradual decline in CFUs with increasing antibiotic concentrations (red symbols in Fig. 2a-c for three strains; Supplementary Fig. 2 for the remaining two strains). This gradual decrease reflects a heterogeneous distribution of resistance within each population. By comparing CFUs at each antibiotic concentration to those in antibiotic-free conditions, we obtained RF_PAP (red in Fig. 2). Three replicate measurements were averaged to calculate the mean RF_PAP: ⟨RF⟩_PAP.

Although PAP is accurate, its labor- and time-intensive nature limits its utility in clinical and high-throughput settings. We therefore evaluated our DnD (green), dilution-to-extinction (grey), and delay-to-growth (yellow color) assays on the same strains. Measurements were repeated three times for each condition. All three assays produced RFs that closely matched one another and recapitulated the gradual decline observed in PAP (Fig. 2a-c and Supplementary Fig. 2). For quantitative comparison, we characterized how our assay results deviate from that of the PAP assay by normalizing their RFs by ⟨RF⟩_PAP, thereby quantifying their deviation by RF / ⟨RF⟩_PAP for each strain and antibiotic concentration. Aggregating these values (open symbols, Fig. 2d) revealed that RF values from each of our assays were centered around the ⟨RF⟩_PAP, indicating their strong agreement. Dilution-to-extinction exhibited the most variance, about 10-fold, as expected. The DnD assay exhibited the lowest variance.

We further performed statistical comparison using a one-way ANOVA test between RF values from each assay and those from PAP. All tests yielded p > 0.05, indicating that our assay results and PAP results are not statistically different (Fig. 2d). This finding aligns with earlier comparisons using defined mixtures, where each assay accurately captured the input frequency (Fig. 1). Notably, among the three assays, the DnD assay produced the highest p-value (approaching 1), suggesting its measurements are quantitatively indistinguishable from PAP. Together, these tests demonstrate that all three assays faithfully quantify antibiotic-resistant subpopulations, with the DnD assay providing the greatest accuracy and reproducibility.

The DnD Assay Reveals That the Sampling Limit Determines MIC.
The MIC is widely used as a measure of antibiotic resistance, traditionally defined as the lowest drug concentration that prevents population-level growth 22. While the DnD assay was designed to quantify rare antibiotic-insensitive subpopulations, its serial dilution format includes wells containing the conventional inoculum size (~5105 cells 20,21), enabling simultaneous determination of MIC by standard criteria.

To clarify the meaning of MIC in heterogeneous populations, we measured the MIC of artificial mixtures containing defined ratios of fully susceptible and fully resistant cells (Fig. 1). At low resistant frequencies, the MIC matched that of the susceptible strain (1 µg/mL), whereas at high frequencies it matched that of the resistant strain (1,024 µg/mL); see Supplementary Fig. 3. A sharp transition occurred when the resistant frequency crossed ~310-6 (vertical dashed line). Following conventional classification based on the breakpoint (4 µg/mL, horizontal red line), the population would therefore flip from “susceptible” to “resistant” at this threshold, illustrating the flaws of binary interpretation in heterogeneous populations.

This threshold was also evident in endogenously heteroresistant strains (Fig. 2). We determined the MIC of these strains by visually inspecting wells inoculated with the standard inoculum size (Supplementary Fig. 4a). We then plotted the resistance frequency (RF) at the MIC for each strain (Supplementary Fig. 4b). Across all strains, the MIC values corresponded to the first antibiotic concentration at which RF dropped below ~310-6 (dashed line). This threshold can be understood based on the standard inoculum size (~510⁵ cells); at this inoculum size, a population with an RF below ~310-6 is unlikely to contain resistant cells, resulting in complete clearance and thus reporting the MIC.

These results demonstrate that MIC marks the point at which resistant survivors fall below detection, representing the statistical sampling limit of conventional assays. Consequently, resistant subpopulation below this threshold escape detection, underscoring the need for high-resolution approaches such as the DnD assay.

The DnD Enables Detecting Early Stages of Evolution and its Dynamics.
The evolution of antibiotic resistance is often tracked by monitoring changes in MIC 33-35. Our above analysis reveals that such changes become apparent only when resistant subpopulations expand beyond the sampling threshold of conventional assays, leaving the earliest stages of reduced susceptibility hidden. However, early detection is critical both clinically, where interventions are more effective before resistant mutants become established 35,36, and scientifically, where direct observation can shed light into the selective dynamics driving their establishment 36. With its ability to quantify resistant minorities with high resolution, the DnD is ideally suited to track the early stages of resistance evolution.

To demonstrate this, we evolved a susceptible E. cloacae strain under colistin exposure by serially passaging the culture for several days. The DnD assay captured the initial appearance of resistant mutants, as well as their gradual expansion over time (Supplementary Fig. 5a). Only when this subpopulation crossed the sampling threshold, it triggered a measurable increase in MIC (Supplementary Fig. 5b). This experiment demonstrates that DnD can identify the emergence of resistance early in evolution.

High-Throughput Screening of Clinical Isolates Uncovers Widespread Multi-drug Heteroresistance.
Population heterogeneity—particularly the presence of minority resistant cells within seemingly susceptible populations—challenges binary classification. To clarify how antibiotic susceptibility should be measured and quantified, we next examined how often clinical isolates harbor such minorities and how broadly their resistance frequencies (RF) are distributed.

In addition to quantitative accuracy, another key advantage of the DnD assay is its compatibility with automated, high-throughput screening. Because the assay is implemented in microtiter plates and utilizes optical density as a readout, it can be fully automated using a standard plate reader. We applied the DnD assay to a panel of ~120 previously uncharacterized clinical isolates of Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter cloacae, and Escherichia coli. These strains were obtained from the Multi-site Gram-negative Surveillance Initiative (MuGSI), CDC. Each isolate was tested against five antibiotics: meropenem and piperacillin/tazobactam (β-lactams), tobramycin (aminoglycosides), ciprofloxacin (fluoroquinolones), and colistin (polymyxins).

For each strain and drug, we determined the RF at the clinical breakpoint concentration. Comparison with independently performed PAP assays showed strong quantitative agreement: RF_DnD versus RF_PAP yielded a linear relationship with slope = 0.95 and R² = 0.95 (Fig. 3a; Supplementary Fig. 6 shows individual antibiotics).

We next plotted the histogram of RF_DnD, which revealed a trimodal distribution (Fig. 3b). Two sharp peaks on the left and right reflected conventional binary categories: susceptible (RF = 0) and resistant (RF ≈ 1). Strikingly, a broad plateau between these extremes spans several orders of magnitude, containing 18.8% of test results. The absence of a single peak in this intermediate region highlights that resistance is not a binary trait, but instead varies continuously across a quantitative spectrum.

Next, we asked how different strains and antibiotics are represented within this intermediate region. For this analysis, we labeled this broad region collectively as heteroresistance (HR); see Supplementary Table 1. The prevalence of HR varied widely by both antibiotics (Fig. 3c) and bacterial species (Fig. 3d). For example, HR was frequent for β-lactams (meropenem and piperacillin–tazobactam), but less common for tobramycin (aminoglycoside) and ciprofloxacin (fluoroquinolone). Notably, multi-drug HR was also observed: ~21% of isolates displayed HR to two antibiotics P(HR_2), and ~6% to three or more P(HR_3) and P(HR_4). Multi-drug HR was particularly enriched in E. coli and P. aeruginosa.

An important question is whether heteroresistance to one antibiotic promotes heteroresistance to others, or whether they are independent. To address this, we measured the probabilities of multi-drug HR: P(HR_2), P(HR_3), and P(HR_4). We then compared these values to the expectations if HR events occurred independently, (P(HR_1)², P(HR_1)³, P(HR_1)⁴). Remarkably, the observed and expected values aligned along the y=x line (Fig. 3e). This alignment suggests that acquisitions of multi-drug HRs are independent events. However, we note that these estimates carry large error bars due to the limited sample size. Thus, additional studies with larger cohorts will be required to confirm this trend.

The DnD Assay Reveals Widespread Persistence.
Persistence refers to a phenomenon in which a subpopulation of bacterial cells enters a non-growing or abnormally slow-growing state, allowing them to tolerate otherwise lethal antibiotic treatment 31. This tolerant state is transient: once the antibiotic is removed, these persister cells can exhibit normal growth.

Persistence is typically measured using a CFU-based time-kill assay, which exploit this transiency 31. In this assay, a bacterial culture was exposed to high concentrations of a bactericidal antibiotic to eliminate actively growing cells. Samples are collected at different time points, washed, and plated on antibiotic-free agar; CFUs reflects the number of surviving persisters. However, like PAP tests, this approach requires manual plating and colony counting, making it labor-intensive and time-consuming.

We therefore evaluated whether our dilution-to-extinction, delay-to-growth, and DnD assays could also quantify persister cells. While these assays were previously performed in the presence of antibiotics to measure resistant cells that grow despite antibiotic exposure, persisters are not resistant and can grow only after the antibiotic is removed. For this reason, traditional persistence assays are conducted in antibiotic-free media following antibiotic exposure. We adopted the same strategy here, performing our assays in antibiotic-free broth after transient antibiotic treatment.

For rigorous tests, we first focused on well-characterized incidences. Previous work, including by our group, has shown that exponentially growing E. coli cultures contain negligible levels of persisters, whereas overnight stationary-phase cultures exhibit dramatically increased persistence 8,9. Another model of enhanced persistence involves deletion of bfmS in Acinetobacter baumannii 37. We applied our assays to both conditions, alongside CFU-based assays for comparison.

Across all cases, our assays accurately quantified persistence frequencies and reproduced the expected biological patterns (Fig. 4a-b). When we determine the deviation of each assay from the CFU-based assay (as done in Fig. 2f), we again found that DnD offers the greatest accuracy and reproducibility (Fig. 4c).

We then applied the DnD assay to a panel of ~120 clinical isolates to quantify their persistence frequencies (PF). We compared it with independently performed CFU assays: PF_DnD versus PF_CFU yielded a linear relationship with slope = 0.93 and R² = 0.98 (Fig. 4d), further demonstrating that the DnD can accurately quantify persistence. We next plotted the histogram of PF_DnD (Fig. 4e). A large fraction of strains (38.1 %) contained persister cells, with their frequency varying over 5 orders of magnitude (red bars).

Methods
Bacterial strains and culture conditions
Colistin-susceptible and resistant Enterobacter cloacae (AMK105 and AMK104 in Fig. 1) were obtained from David Weiss’ lab. Previously characterized heteroresistant strains (AMK107, AMK117, AMK118, AMK120, KMK4 in Fig. 2 and Supplementary Fig. 2) 17,18,32 were obtained from David Weiss’ and Dan Andersson’s lab. Clinical isolates (Supplementary Table 1) were obtained through the Georgia Emerging Infections Program, as part of the CDC’s Multi-site Gram-negative Surveillance Initiative (MuGSI) in Georgia, USA. Wild-type Acinetobacter baumannii ATCC7978 and its ∆bfmS derivative (KMPK111 and KMK112 in Fig. 4) were obtained from Isberg lab at Tufts University 37. Escherichia coli K-12 strain NCM3722 (referred to as NMK1) was obtained from the Kim Lab bacterial collection 13.

Mueller-Hinton broth (MHB; BD Difco #275730) were used. Bacterial cultures were grown in MHB at 37°C with shaking at 250 rpm, in 20 x 150mm borosilicate glass culture tubes (Fisher Scientific #1496133). Exponentially growing cultures, typically at the OD of 0.1, were used for the assay.

Antibiotics
Colistin sulfate (#1264-72-8), tobramycin (#32986-56-4), gentamicin sulfate (#1405-41-0) and carbenicillin disodium (#4800-94-6) were obtained from Sigma-Aldrich. Meropenem trihydrate (#119478-56-7) was purchased from Research Product Industry. Tetracycline hydrochloride (#64-75-5) and ampicillin (#69-52-3) were obtained from Bio Basic.

Population analysis profile (PAP)
PAP was performed as previously described 32. Briefly, a single colony of a given strain, corresponding to a clonal population derived from one viable cell recovered from −80 °C glycerol stock, was inoculated into 2 mL MHB and grown with shaking at 250 rpm. After overnight growth (~16 hours), cultures were diluted to OD = 0.001 and grown until OD reached ~ 0.1 before being serially diluted in MHB in a 96-well plate (Corning #3598). From each dilution, 5 µL was plated on Mueller-Hinton agar (MHA; BD Difco #225250) containing antibiotics at indicated concentrations. Colonies were enumerated after 24 hours. Resistance frequency (RF) at indicated antibiotic concentration was calculated by dividing the number of colonies on antibiotic-free MHA by the number of surviving colonies on MHA containing antibiotics.

Dilution-to-Extinction Assay
For resistance frequency (RF) measurements, bacterial cultures were adjusted to approximately 10⁹ CFU/mL based on OD₆₀₀ readings and subjected to 10-fold serial dilutions in Mueller-Hinton Broth (MHB) up to 10⁻⁸. Dilutions were performed across rows in a 96-well microtiter plate (Corning #3596), with each well containing 200 μL of the diluted culture. For each dilution, parallel wells were prepared in MHB with or without indicated concentrations of antibiotics. Plates were incubated at 37 °C for ~18 hours. After incubation, wells were visually inspected for turbidity. The first dilution in the series that lacks turbidity defines the extinction point. RF was estimated as the difference in dilution factor between the extinction points in antibiotic-free versus antibiotic-containing conditions.

For persistence frequency measurements, exponential-phase (OD₆₀₀ ~ 0.1) and 3 days stationary-phase (OD₆₀₀ ~ 4.0) cultures of E. coli NMK1, as well as exponential phase cultures of wild-type and ∆bfmS A. baumannii, were exposed to the indicated concentrations of antibiotics for 6 or 24 hours. Following treatment, cultures were washed twice with phosphate-buffered saline (PBS) to remove residual antibiotics and then subjected to 10-fold serial dilutions in 200 μL MHB across rows of 96-well plates, as described above. Plates were incubated at 37 °C for ~24 hours. After incubation, wells were visually inspected for turbidity. Persistence frequency was calculated by comparing the extinction point of the treated culture to that of the untreated control.

Delay-to-Growth Assay
For resistance frequency measurements, exponential-phase bacterial cultures (OD₆₀₀ ~ 0.1) were inoculated into 200 μL Mueller-Hinton Broth (MHB) in a 96-well microtiter plate, with wells containing MHB with and without the indicated concentrations of antibiotics. Plates were incubated at 37 °C with shaking at 250 rpm for 18 to 24 hours. OD₆₀₀ was measured and recorded at 10-minute intervals using a spectrophotometer.

To estimate the number of resistant cells in the original population, the exponential phase of the recovery growth curve in antibiotic-containing wells was fitted. Exponential growth phases were identified based on both OD range and temporal continuity. Specifically, data points were required to fall within an OD600 range of 0.05 – 0.4, a regime in which optical density scales linearly with cell number under our experimental conditions. In addition, at least five consecutive time points were required to determine the log-linear behavior. Linear regression was then performed on the semi-log-transformed data, and only fits with a coefficient of determination (R2)  0.97 were accepted for subsequent extrapolation and frequency estimation. The extrapolated OD600 at t = 0 (OD₆₀₀ᴿ) represents the contribution of the resistant subpopulation. This value was normalized to the initial OD₆₀₀ of the total population (OD₆₀₀ᴾ), yielding the resistance frequency (RF): OD₆₀₀ᴿ / OD₆₀₀ᴾ.

For persistence frequency measurements, E. coli NMK1 and A. baumannii (wild-type and ∆bfmS) were treated with the indicated concentrations of antibiotics for 6 or 24 hours. After treatment, cells were washed twice with PBS to remove residual antibiotics, then re-inoculated into 200 μL of fresh MHB in a 96-well microtiter plate. Cultures were incubated at 37 °C with shaking at 250 rpm for ~24 hours, with OD₆₀₀ monitored at 10-minute intervals.

The OD₆₀₀ of the persister subpopulation (OD₆₀₀PS) was estimated by extrapolating the exponential phase of the post-treatment growth curve back to time zero. Persistence frequency was calculated as the ratio of OD₆₀₀PS to the OD₆₀₀ of the total population (OD₆₀₀P): PF = OD₆₀₀PS / OD₆₀₀P.

Dilution-and-Delay (DnD) Assay
Exponential-phase (OD ~0.1) bacterial cultures were subjected to 10-fold serial dilutions in MHB across rows of a 96-well microtiter plate, with each well containing 200 μL of culture supplemented with or without the indicated concentrations of antibiotics. Plates were incubated at 37 °C with shaking at 250 rpm for approximately 18 hours. OD₆₀₀ was recorded at 10-minute intervals using the microplate reader.

For wells that exhibited detectable growth, the exponential phase of the recovery curve was fitted and extrapolated back to time zero as discussed above to determine RFs. The final RF value for a given replicate was obtained by averaging the individual RF values across all growing wells in the dilution series (See the flowchart in Supplementary Fig. 8 and the data analysis example in Source Data file).

For persistence frequency measurements, exponential-phase (OD₆₀₀ ~ 0.1) and 3 days stationary-phase (OD₆₀₀ ~ 4) cultures of E. coli NMK1, A. baumannii (wild-type and ∆bfmS), and ~120 clinical isolates (OD₆₀₀ ~ 0.1) were exposed to the indicated concentrations of antibiotics for 6 or 24 hours at 37 °C with shaking. After treatment, cultures were washed twice with fresh MHB to remove residual antibiotics, then serially diluted in 200 μL fresh MHB across rows of a 96-well plate as described above. Plates were incubated at 37 °C with shaking at 250 rpm, and OD₆₀₀ was recorded every 10 minutes for ~24 hours.

CFU-based Time-kill Assay
Exponential-phase (OD₆₀₀ ~ 0.1) and 3 days stationary-phase (OD₆₀₀ ~ 4) cultures of E. coli NMK1, A. baumannii (wild-type and ∆bfmS), and ~120 clinical isolates (OD₆₀₀ ~ 0.1) were exposed to the indicated concentrations of antibiotics for 6 or 24 hours at 37 °C with shaking. After treatment, 100 μL aliquots were removed from the cultures, washed twice with PBS to remove residual antibiotics, serially diluted in PBS, and plated on MHA. Plates were incubated at 37 °C for ~24 hours, and colony-forming units (CFU) were enumerated to determine viable cell counts. To calculate persistence frequency, the number of surviving cells at 6 or 24 hours was divided by the initial CFU count (t = 0) prior to antibiotic exposure.

Evolution experiment under colistin exposure
To track early-stage evolutionary dynamics of antibiotic insensitivity, a colistin-susceptible E. cloacae strain (AMK105) was evolved under continuous colistin exposure by serial passaging. Unless otherwise noted, cultures were grown in MHB at 37 °C with shaking at 250 rpm.

At time 0, an AMK105 culture (OD ≈ 0.1) was inoculated into fresh MHB containing colistin at 0.5 or 1 μg/mL (corresponding to 0.5× and 1× the ancestral MIC). Colistin concentration was increased stepwise over successive passages based on the emergence of resistant mutants in the evolving population, in order to maintain sustained selective pressure. Cultures were propagated for several days by serial passaging every 12 h, with each passage diluted to OD ≈ 0.1 into fresh MHB containing the appropriate colistin concentration.

At indicated time points, aliquots were collected for phenotypic characterization. Resistance frequency (RF) at the selection concentration was quantified using the DnD assay as described above, and the emergence of resistant mutants was operationally defined as a sustained increase in RF relative to the ancestral population (Supplementary Fig. 5a). In parallel, MIC was measured at selected time points using a standard broth microdilution readout embedded within the DnD dilution series (wells inoculated at ~5 × 105 cells), following CLSI/EUCAST criteria.

Statistical analysis
Statistical analysis was conducted using GraphPad Prism version 10 (GraphPad Software). Details of biological replicates and statistical tests are provided in the corresponding figure legends. All data sets were tested for normality using the Shapiro-Wilk test and were confirmed to meet the normality criteria. Statistical analyses were performed as appropriate based on the experimental design.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Ma and Kim report on antibiotic susceptibility testing with the Dilution and Delay (DnD) assay.\nOverall, the concept is very interesting and the study is well performed.\nCurrently, heteroresistance, persistence is not detected well with the methods in use, in particular with semi-automated\nsusceptibility testing systems.\nTherefore, DnD could improve our understanding of these phenomenons in the treatment of infections.\nI have only few suggestions to further improve the paper:\n- The manuscript would profit from adding the clinical perspective: When are heteroresistance and persistence typically\nobserved, when is it important to early detect them (e.g. this is probably not the case in urinary tract infections that are rapidly\ncleared despite resistant subpopulations).\nAdditionally, phenotypic heterogeneity is more common in some species (e.g. Mycobacteria, Enterobacter cloacae & others).\n- Do we have enough data when DnD (or other method) would be useful for the correct treatment and prognosis? Is there\nany animal data to support the clinical importance/improved treatment?\nIf not, this should be discussed in the limitations.\nMinor comments:\nREF 20 and 21 should be corrected\n- Laboratories operate with tight budgets and often a lack of personnel, therefore automated methods are very common.\nCould the authors calculate the additional costs for DnD susceptibility testing if 15-20 antibiotics are tested (which is the\ncase for most infections).", "reviewer_2": "The manuscript submitted by Muqing Ma and Minsu Kim introduces the Dilution-and-Delay (DnD) assay, a novel high-\nthroughput method for detecting and quantifying antibiotic heteroresistance - rare subpopulations of resistant bacteria that\nescape detection by standard Minimum Inhibitory Concentration (MIC) testing. By integrating two orthogonal quantification\nprinciples into a standard 96-well microplate format, the DnD assay achieves the sensitivity of Population Analysis Profiling\n(PAP) with the throughput of broth microdilution. The work provides both a potential mechanistic explanation for\n\"unexplained\" treatment failures and a practical tool for clinical and research applications.\n8. FINAL RECOMMENDATION\n8.1 Recommendation: ACCEPT WITH MINOR REVISIONS\nThis is a high-quality, technically sound manuscript that introduces a valuable methodological advance to the field of\nantimicrobial susceptibility testing. The conclusions are well-supported by rigorous experimental evidence, and the work\naddresses a genuine clinical need.\n8.2 Specific Revisions Requested\n1. Introduction\no Contrary to ETEST® gradient MIC strips, the disk diffusion method described by Matuschek et al. (2014) does not use the\ndiameter of the inhibition zone to infer the MIC but provide “a reading guide on how to read zone diameters on individual\nspecies-agent combinations and zone diameter breakpoints calibrated to the EUCAST clinical MIC breakpoints”.\no Explain the selection of a threshold at 1 in 100 million.\n2. Expand Methods—Data Analysis Section:\no Line 519-520, precise what a single colony from ¬80°C glycerol stock corresponds to.\no For Figure 1, indicate the tested concentration of colistin\no Provide the Rf value of the susceptible strain (AMK105) and the fully resistant strain (AMK104)\no Provide explicit criteria for selecting the exponential growth phase (e.g., \"OD between 0.05 and 0.5 with ≥ 5 consecutive\ntime points\").\no State acceptance thresholds for curve fits (e.g., \"R² > 0.95 for log-linear regression\").\no Add a representative example showing raw kinetic data, the fitted line, and intercept annotation.\no For figure 3.c, provide the percentage of resistant isolates for each antibiotic-species combination\no For figure 4, provide the percentage of clinical isolates with a PF > 0 for each of the five species.\no The protocol for the detection of early stages of evolution is missing. What is the concentration of colistin, what are the\nincubation conditions, …? As the MIC for the mutants are =< 2mg.l-1, below the resistance breakpoint, they may not be\nclassified as “resistant” but having “reduced susceptibility”\n3. Supplementary Materials:\no Provide pseudocode or a flowchart for the full analysis pipeline.\no Include a sample Excel or CSV file showing the analysis steps for one representative sample.\no Consider releasing an R or Python script on GitHub for transparency and reproducibility.\no Add MIC values of the clinical isolates in Supplementary Table 1\n4. Discussion—Limitations:\no Add a paragraph addressing potential confounds:\n  Post-antibiotic effects and their impact on Delay-based back-extrapolation.\n  Antibiotic stability during extended incubations.\n  Edge cases (e.g., slow-growing resistant variants with severe fitness costs).\no Discuss how users should verify results (e.g., \"We recommend confirming Delay-based RF estimates with the Dilution\nmethod for routine use\").\no Regulatory Implications: Explicitly discuss the challenge of integrating quantitative resistance frequencies into current\nbinary (S/R) reporting structures. Acknowledge that new breakpoints will likely be needed to interpret DnD results clinically.\n5. Discussion—Future Directions:\no Explicitly state the need for prospective clinical outcome studies to link DnD-detected heteroresistance to treatment failure\nrates.\no Suggest mechanistic follow-up (genomic/transcriptomic characterization of heteroresistant subpopulations).\n8.3 Why Publication is Warranted\n• Methodological Innovation: The DnD assay combines two orthogonal principles in a novel way, achieving a favorable\nbalance of sensitivity, throughput, and practicality.\n• Clinical Impact: Heteroresistance is increasingly recognized as a barrier to successful antibiotic therapy. A scalable\nmethod to detect it fills a genuine diagnostic gap.\n• Robustness: The validation against defined mixtures and equivalence to PAP provide strong evidence for technical\nsoundness.\n• Accessibility: Unlike single-cell microfluidics or specialized equipment, DnD uses standard lab infrastructure, maximizing\nadoption potential.\n10. CONCLUSION\nThis manuscript presents a significant methodological contribution to clinical microbiology. The DnD assay is scientifically\nrigorous, clinically relevant, and immediately applicable in research and diagnostic settings. With minor revisions to\nenhance reproducibility and discuss limitations, this work is well-suited for publication and will likely become a standard tool\nfor heteroresistance detection in the field.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "Response to Reviewers\nReviewer #1\nWe thank the reviewer for the positive assessment of the conceptual advance and technical rigor\nof this work. We appreciate the emphasis on clinical relevance and agree that clarifying the\nappropriate clinical contexts and limitations of DnD will further strengthen the manuscript.\nComment 1\nThe manuscript would profit from adding the clinical perspective: when heteroresistance and\npersistence are typically observed, and when early detection is important. Phenotypic\nheterogeneity is also more common in some species.\nResponse:\nWe agree and have addressed this comment by adding a clinical perspective to the Discussion. In\nparticular, we now note that heteroresistance and persistence are often recognized\nretrospectively—when standard susceptibility testing indicates apparent susceptibility, yet\nantibiotic therapy fails to clear infection (e.g., delayed clearance, breakthrough growth, or\nrelapse). We further emphasize why early detection is clinically important. Finally, we added a\nbrief statement that our high-throughput implementation of DnD suggests organism- and drug-\ndependent variation in the prevalence of heteroresistance and persistence; we explicitly frame\nthis as a hypothesis generated by our dataset that will require systematic validation in future\nclinical studies. This discussion has been added to Discussion, lines 427 to 435.\nComment 2\nDo we have enough data to show when DnD would be useful for correct treatment and\nprognosis? Is there animal data? If not, this should be discussed as a limitation.\nResponse:\nWe acknowledge this important point. At present, our study does not include prospective clinical\noutcome data or animal models linking DnD-detected heteroresistance directly to treatment\nefficacy or prognosis. We have therefore explicitly discussed this as a limitation in the\nDiscussion. We additionally clarified that what is still unclear in the field is how to best treat\nheteroresistant and persistent infections. Yet the important first step to develop optimal treatment\nstrategy is to reliably identify and quantify these types of infections, which DnD addresses. This\ndiscussion is added to Discussion, lines 436 to 453.\nMinor comments\nComment 3\nREF 20 and 21 should be corrected.\nResponse:\nThank you for pointing this out. The two references have been revised in the manuscript to:\n20. Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial\nSusceptibility Testing, 35th edn. CLSI supplement M100 (Clinical and Laboratory Standards\nInstitute, Wayne, PA, 2025).\n21. European Committee on Antimicrobial Susceptibility Testing. Determination of minimum\ninhibitory concentrations (MICs) of antibacterial agents by broth dilution. Clin. Microbiol.\nInfect. 9, ix–xv (2003). https://doi.org/10.1046/j.1469-0691.2003.00790.x\nComment 4\nLaboratories operate with tight budgets and often a lack of personnel. Could the authors\nestimate additional costs for DnD if 15–20 antibiotics are tested?\nResponse:\nWe appreciate the reviewer’s question regarding cost considerations when testing multiple\nantibiotics. DnD is designed to run on standard 96-well microtiter plates using routine\nconsumables. The incremental costs are dominated by plasticware (plates and tips), media, and\nantibiotics. Importantly, because DnD operates in microliter-scale volumes, media and antibiotic\nconsumption per condition is reduced by an order of magnitude relative to agar-based methods\nsuch as PAP. Using standard pricing, this corresponds to a cost on the order of a few dollars to\nlow tens of dollars per isolate per 15–20-drug panel, with the exact value varying by supplier and\nassay layout. This discussion has been added to the Discussion section (Discussion, lines 398 to\n403).\nReviewer #2 and #3\nWe thank the reviewer for the detailed, constructive review. We were encouraged by the\nrecommendation of acceptance with minor revisions. Below we address each specific request.\n1. Introduction\nComment 1a\nClarify disk diffusion versus ETEST® methodology.\nResponse:\nThanks for pointing this out. We have revised the text to clarify the methodological distinction\nbetween disk diffusion and E-test assays, including how inhibition zones and antibiotic gradients\nare used to derive susceptibility readouts. The revised text now follows the EUCAST framework\nas described by Matuschek et al. (2014) (Introduction, lines 69 to 72).\nComment 1b\nExplain the selection of the threshold at 1 in 100 million.\nResponse:\nWe have clarified the rationale for selecting the 1 in 100 million threshold by emphasizing that it\nreflects the practical sampling and observation limits of the DnD assay, rather than a biological\ncutoff. We further note that this frequency scale captures the lowest-frequency range in the\noperational definition of heteroresistance. This explanation has been added to the Discussion\n(Discussion, lines 348 to 351).\n2. Methods—Data Analysis\nComment 2a\nLine 519-520, precise what a single colony from ¬80°C glycerol stock corresponds to.\nResponse:\nFollowing your suggestion, we have clarified what a single colony from a −80 °C glycerol stock\ncorresponds to (Methods, lines 573 to 574).\nComment 2b\nFor Figure 1, indicate the tested concentration of colistin.\nResponse:\nThank you for your suggestion. We have added the tested colistin concentration for Fig. 1 (Fig. 1\ncaption, line 478).\nComment 2c\nProvide the Rf value of the susceptible strain (AMK105) and the fully resistant strain (AMK104)\nResponse:\nBased on your suggestion, we have stated RF values for the fully susceptible (AMK105) and\nfully resistant (AMK104) strains and made necessary revisions (Fig. 1 caption, lines 476 to 480).\nComment 2d\nProvide explicit criteria for selecting the exponential growth phase (e.g., \"OD between 0.05 and\n0.5 with ≥ 5 consecutive time points\").\nResponse:\nFollowing your suggestion, we have explicitly defined objective criteria for selecting exponential\ngrowth phases (Methods, lines 608 to 611).\nComment 2e\nState acceptance thresholds for curve fits (e.g., \"R² > 0.95 for log-linear regression\").\nResponse:\nThanks for your suggestion. We have added the acceptance thresholds for curve fitting (R² >\n0.97) (Methods, lines 611 to 613).\nComment 2f\nAdd a representative example showing raw kinetic data, the fitted line, and intercept annotation.\nResponse:\nBased on your suggestion, we have added a representative example showing raw OD₆₀₀ data,\nfitted line, and intercept annotation (the 11th sheet of Source data Excel file). This is then\nmentioned and used as a quantitative example in the newly added full analysis flowchart\n(Supplementary Fig. 8).\nComment 2g\nFor figure 3.c, provide the percentage of resistant isolates for each antibiotic-species\ncombination.\nResponse:\nThank you for your suggestion. The percentage of resistant isolates for each antibiotic-species\ncombination has been provided in the 3rd sheet of Source data Excel file.\nComment 2h\nFor figure 4, provide the percentage of clinical isolates with a PF > 0 for each of the five\nspecies.\nResponse:\nThank you for your suggestion. The percentage of clinical isolates with a PF > 0 for each of the\nfive species has been provided in the 4th sheet of Source data Excel file.\nComment 2i\nThe protocol for the detection of early stages of evolution is missing. What is the concentration\nof colistin, what are the incubation conditions, …? As the MIC for the mutants are =< 2mg.l-1,\nbelow the resistance breakpoint, they may not be classified as “resistant” but having “reduced\nsusceptibility”\nResponse:\nFollowing your suggestion, we have added full protocol details for the evolution experiments,\nincluding colistin concentrations and incubation conditions (Methods, lines 653 to 668), and used\nthe suggested “reduced susceptibility” terminology (Results, Line 257).\n3. Supplementary Materials\nComment 3a\nProvide pseudocode or a flowchart for the full analysis pipeline.\nResponse:\nThanks for this helpful suggestion. To clarify the full analysis workflow, we have added a\nschematic flowchart illustrating the complete data processing and analysis pipeline. This\nflowchart is now included as Supplementary Fig. 8, and outlines each major step from raw\nexperimental measurements through data normalization, model fitting, and final output metrics.\nWe believe this addition improves transparency and reproducibility of the analysis.\nComment 3b\nInclude a sample Excel or CSV file showing the analysis steps for one representative sample.\nResponse:\nWe appreciate this suggestion. To provide a concrete example of the analysis workflow, we have\nincluded a step-by-step implementation for one representative sample in the Source Data Excel\nfile (Sheet 11). This sheet shows the raw input values, intermediate calculations, and final\noutputs used in the analysis, thereby illustrating how the full pipeline is applied to an individual\ndataset.\nComment 3c\nConsider releasing an R or Python script on GitHub for transparency and reproducibility.\nResponse:\nWe appreciate this suggestion. At present, the analysis workflow is provided in Supplementary\nFig. 8 to maximize accessibility and ease of use for experimental and clinical laboratories. We\ndid not develop a computer program or script for this research, although we do believe it could\nfacilitate the analysis.\nComment 3d\nAdd MIC values of the clinical isolates in Supplementary Table 1\nResponse:\nThank you for your suggestion. We have added the MIC values of the clinical isolates to the 3rd\nsheet of Source data Excel file. Because of the large number of isolates and antibiotics analyzed,\nincluding these values directly in Supplementary Table 1 would substantially increase its size\nand reduce readability. We therefore provide the complete MIC dataset in the Source Data to\nensure transparency while maintaining a concise Supplementary Table.\n4. Discussion—Limitations\nComment 4a\nAdd a paragraph addressing potential confounds:\n• Post-antibiotic effects and their impact on Delay-based back-extrapolation.\n• Antibiotic stability during extended incubations.\n• Edge cases (e.g., slow-growing resistant variants with severe fitness costs).\nResponse:\nFollowing your suggestion, we have added a paragraph about these factors, including post-\nantibiotic effects, antibiotic stability during extended incubation, and edge cases such as slow-\ngrowing resistant variants. This section has been added to the Discussion (lines 404 to 411).\nComment 4b\nDiscuss how users should verify results (e.g., \"We recommend confirming Delay-based RF\nestimates with the Dilution method for routine use\").\nResponse:\nFollowing your suggestion, we now recommend cross-validating delay-based RF estimates with\ndilution-based measurements for routine use. See Discussion (line 411 to 416).\nComment 4c\nRegulatory Implications: Explicitly discuss the challenge of integrating quantitative resistance\nfrequencies into current binary (S/R) reporting structures. Acknowledge that new breakpoints\nwill likely be needed to interpret DnD results clinically.\nResponse:\nThank you for your suggestion. We have expanded the Discussion to explicitly address the\nregulatory implications of DnD. We now discuss the challenge of integrating quantitative\nresistance-frequency measurements into the current binary (S/R) susceptibility reporting\nframework and note that interpreting such outputs will require new interpretive schemes,\npotentially including an additional category (e.g., heteroresistant) rather than direct replacement\nof existing MIC-based breakpoints. We further emphasize that outcome-linked clinical validation\nand consensus standardization will be required to establish how DnD results should be used in\nroutine clinical reporting. See Discussion (lines 436 to 453).\n5. Discussion—Future Directions\nComment 5a\nExplicitly state the need for prospective clinical outcome studies to link DnD-detected\nheteroresistance to treatment failure rates.\nResponse:\nWe appreciate this suggestion. We have explicitly addressed this point in the Discussion by\nstating that, while DnD enables sensitive quantification of heteroresistant subpopulations, linking\nDnD-detected heteroresistance to treatment failure rates will require prospective clinical outcome\nstudies. We emphasize that such outcome-linked validation is a critical next step to determine\nhow resistance-frequency measurements should inform clinical decision-making. Please see\nDiscussion (Lines 436 to 453).\nComment 5b\nSuggest mechanistic follow-up (genomic/transcriptomic characterization of heteroresistant\nsubpopulations).\nResponse:\nWe agree with the reviewer that mechanistic follow-up studies will be important for elucidating\nthe origins of heteroresistant subpopulations. We have therefore expanded the Discussion (Lines\n424 to 426) to explicitly note that genomic and transcriptomic characterization of DnD-identified\nresistant minorities represents a natural next step to link quantitative resistance-frequency\nmeasurements with underlying molecular mechanisms."}]

Decision letter