The continued and accelerated emergence of anti-microbial resistance is a global threat and antimicrobial-resistant infections caused by Mycobacterium tuberculosis (Mtb) are particularly concerning. In 2018, approximately 484,000 people developed multidrug-resistant tuberculosis and an estimated 214,000 people died from rifampicin-resistant or multidrug-resistant tuberculosis (Geneva: World Health Organization, 2019). Approximately 10% of all multidrug resistant tuberculosis infections are resistant to at least four commonly used anti-TB drugs (Dorman et al., 2018).

Rapid, reliable diagnosis is a key to the prevention and effective treatment of antimicrobial resistant infections (Boolchandani et al., 2019). In the case of Mtb, culture-based methods such as common agar plates or more advanced indicator tubes with fluorescent growth reporters are gold standards for identifying sensitivity to any antibiotic, but these testing methods require one to four weeks to produce results (Kim, 2005; Lawson et al., 2013; Kontos et al., 2004; Garrigó et al., 2007). Biomarker methods, such as the PCR-based Gene-Xpert assay, can rapidly identify antimicrobial resistant Mtb infections but require knowledge of specific genetic markers associated with resistance and are thus insensitive to unknown resistance mechanisms (Dorman et al., 2018). Detecting antimicrobial-resistant Mtb infections is especially challenging when bacterial sub-populations are present that possess differing levels of antimicrobial sensitivity (El-Halfawy and Valvano, 2015). If present in relatively low abundance, these heteroresistant populations may evade detection until after treatment begins. Failure to rapidly identify and treat an antimicrobial-resistant or heteroresistant Mtb infection contributes to disease progression, treatment failure and tuberculosis relapse (Shin et al., 2018).

We developed ODELAM (One-cell Doubling Evaluation of Living Arrays of Mycobacterium), a time-lapse microscopy-based method designed to quantify growth phenotypes of populations of individual Mtb cells and colony forming units. Generally imaging platforms used to study mycobacteria use either microfluidic techniques or growth on solid media. Microfluidic platforms provide high resolution phenotypic analysis of mycobacterial cell biology but are limited in the number of cells that can be readily observed (Aldridge et al., 2012; Golchin et al., 2012; Wakamoto et al., 2013). Conventional assays on solid media provide crude growth analysis, including antibiotic sensitivity, on large populations of cells but require up to 8 weeks for observations whereas specialized microfluidic methods require approximately 9 days (Choi et al., 2014; Barr et al., 2016).

ODELAM bridges these disparate methods by assaying growth temporally at a mesoscale, on up to ~100,000 colony forming units (CFUs) in a single experiment. Originally designed for yeast, the adaptation offers improvements on phenotypic analysis and diagnostics for detecting and characterizing populations of drug resistant Mtb (Herricks et al., 2017). While commercial time-lapse and laboratory based genomic screening tools are available (Lee et al., 2019; Baryshnikova et al., 2010; Bean et al., 2014; Zackrisson et al., 2016; Golchin et al., 2012), ODELAM uniquely enables rapid quantitative measurements of growth kinetics of homogeneous and heterogeneous cultured clinical isolates of Mtb in as little as 30 hrs and under a variety of environmental conditions, including antimicrobial drug pressures.

We demonstrate ODELAM by analyzing ofloxacin (OFX) resistance in cultured Mtb clinical isolates. ODELAM was first benchmarked with the laboratory strain H37Rv and then two cultured clinical isolates were tested, an OFX-resistant isolate and an isolate with OFX heteroresistance (Eilertson et al., 2016). Microscopy-based kinetic growth analysis of these strains under increasing drug pressures revealed characteristic growth and drug sensitivity phenotypes of each strain and specifically demonstrate the ability to rapidly detect heteroresistant sub-populations in a cultured clinical isolate. ODELAM is a powerful new tool that can enable the timely identification of Mtb antimicrobial resistant phenotypes in a laboratory setting.

ODELAM is engineered to quantitatively assess growth kinetics of large populations of individual Mtb CFUs. In each region of interest, ODELAM can observe up to 1500 CFUs and quantify their morphological and multiparameter growth phenotypes. A typical experiment involves 80 or 96 regions of interest. Single bacilli and small clusters containing two or more bacilli are observed over time as they grow into microscopically observable colonies (Figure 1A). In these experiments, Mtb bacilli were observed to grow immediately after spotting on media. Mtb CFUs were recorded for a total of 96 hrs and growth curves were extracted from the projected area of the individual colonies over time and fitted to the Gompertz function (Figure 1B; Herricks et al., 2017). The kinetic parameters doubling time (Td), lag time (T-Lag), time in exponential phase (T-Exp) and number of doublings (Num Dbl) were derived from the Gompertz function and plotted as normalized frequency histograms which revealed population distributions (Figure 1C). Summary non-parametric statistics for the isolates measured are presented as interquartile range (IQR) with respect to the median (Table 1). For example, H37Rv, an Mtb strain commonly used in research laboratories, had a median lag time of 0.5 s and grew with a doubling time median of 22.9 hrs (IQR 7.8). H37Rv bacilli grew in exponential phase for a median of 52.5 hrs (IQR 33.8) and during this time underwent a median of 3.1 doublings (IQR 1.6). These data are consistent with bulk laboratory measurements (Peñuelas-Urquides et al., 2013) and demonstrate the innate heterogeneity of the genetically homogeneous clonal population.

Figure 1

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Growth phenotype of Mtb strain H37Rv exposed to ofloxacin

One of the main advantages of ODELAM compared to standard bulk culture experiments is its ability to assess population responses on a single-CFU level. We therefore used ODELAM to reveal growth parameter values for individual CFUs in response to increasing drug pressures and compared these results to standard dose-response measurements (Figure 2). We first focused on H37Rv. H37Rv is sensitive to ofloxacin with minimum inhibitory concentration (MIC) of 0.5 μg/ml as measured by normalized population growth in a standard dose-response curve (Figure 2B). By ODELAM, we observed H37Rv to initially grow at all concentrations of OFX indicating that OFX’s toxicity does not alter growth kinetics over the first 24 hrs (Figure 2A). This is consistent with OFX’s mechanism of action on the enzyme target, DNA gyrase, which leads to the accumulation of DNA damage (Manjunatha et al., 2002; Gore et al., 2006; Willmott et al., 1994). Above the MIC, toxicity led to cessation of growth, detected as an exit from exponential phase. This is reported as and a reduction in time spent in exponential phase (T-Exp) and in the number of doublings (Num Dbl) as shown by shifts of the population histograms (Figure 2C). Narrowing of the population histograms reflects a more uniform population. The population distribution at the MIC remained relatively broad but was narrower than at lower drug concentrations or in controls. Notably, the doubling time distributions did not appreciably change; cells maintained similar doubling rates prior to growth cessation. Overall, the primary effect of OFX as detected by ODELAM was to arrest H37Rv growth after 2–3 doublings at the MIC and by the first doubling at high drug concentrations, while not appreciably affecting the rate at which H37Rv doubles prior to growth arrest (Table 1).

Figure 2

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Growth phenotype of a resistant strain TRS10 exposed to ofloxacin

We observed a cultured clinical isolate TRS10, which contains a missense mutation in the gene encoding gyrase A (D94G) that increases the isolate’s MIC to OFX (Eilertson et al., 2016; Hooper, 2001). In the absence of drug pressure, the median doubling time of TRS10 was approximately 20% longer than that of H37Rv (Table 1). The MIC of TRS10 to OFX was 16 μg/ml as measured by the normalized population growth in a standard dose-response curve (Figure 3B). By contrast to H37Rv, which ceased to grow at its MIC, TRS10 slowed its doubling time as OFX concentration increased up to, and including, its MIC (Figure 3A). Further, TRS10’s doubling time slowed dramatically from ~30 to~50 hrs as drug concentration shifted from 2 to 4 μg/ml (Table 1).

Figure 3

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Total growth (Num Dbl) is influenced by all three growth parameters (Td, T-Lag and T-Exp). Therefore, to discern the contribution of these parameters to total growth we determined the population effect size, which quantifies the magnitude of effect for each parameter (Td, T-Lag, T-Exp, Num Dbl). In this case, the Kolmogorov-Smirnov (K-S) statistic was used as a score to evaluate the effect size and plotted against the concentration of OFX for each strain (Figure 4; Massey, 1951). As a non-parametric test, the K-S statistic allows the comparison of the distributions without presumption of their shape or parameters that underlie them. For H37Rv, the effect size for time in exponential phase tracked with the number of doublings as the concentration of OFX increased above 0.5 μg/ml while doubling time and lag time effect sizes did not (Figure 4A). In contrast, for TRS10 the effect size for doubling time tracked with the number of doublings as the concentration of OFX increased (Figure 4B). This OFX effect on TRS10’s doubling time as opposed to the cessation of growth observed for H37Rv (T-Exp) likely reflects the reduced binding of OFX to the mutant gyrase (Maruri et al., 2012; Willmott et al., 1994). Together these results show the ability of ODELAM and this analysis to reveal strain-dependent responses to antibiotics that would be missed by standard MIC assays. The examples of H37Rv and TRS10 responding to OFX (Figures 2 and 3), as assayed by ODELAM, discerns these differing growth phenotypes and can inform on potential mechanisms of drug action.

Figure 4

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Detecting heteroresistance

Heteroresistance in cultured clinical isolates is prevalent and confounds diagnosis and treatment of Mtb (Sebastian et al., 2017). Therefore, we tested the ability of ODELAM to rapidly detect heteroresistance. TRS10 was mixed with H37Rv to generate strain ratios ranging from 1:1 to 1:4166. Mixed cultures were assayed by standard dose-response batch culture MIC assay (Figure 5A) and with ODELAM (Figure 5B and C) at a concentration of 2 μg/ml OFX which discriminates OFX-sensitive and OFX-resistant CFUs.

Figure 5

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To evaluate the sensitivity of the MIC assay to heteroresistant cultures, the MICs of the dilution series were plotted (Figure 5A). The observed MIC varied across the dilution series, increasing with the proportion of resistant cells. While the assay is sufficiently sensitive to detect shifts in the MIC at very low relative concentrations of resistant cells, it does not distinguish between a monoculture with a specific resistance phenotype and a mixed population with heteroresistance. Thus, the standard dose-response batch culture MIC assay yielded inaccurate results and was unable to reflect the MIC of the constituent population.

By contrast ODELAM directly observed OFX resistant CFUs in the mixed culture (Figure 5B,C). Resistant and sensitive CFUs were distinguished by their number of doublings and their growth in exponential phase. In this ODELAM assay, a single OFX resistant CFU was detected from the 0.04% TRS10 mixture (Figure 5C). Accordingly, there was an increase in the proportion of OFX-resistant CFUs detected as the percentage of TRS10 increased (Figure 5B,C). The 50% mix detected 200 resistant CFUs out a total of 721 CFUs. Similarly, 28 of 970 CFUs were detected in the 6.25% TRS10 mix, and 12 of 1144 were detected in the 1.56% TRS10 mix (Figure 5C). A summary of the results from 12 dilution mixes and 8 replicates are plotted (Figure 5B). The relationship between dilution and detection was linear from 50% to 0.2%. Below 0.2% the expected number of TRS10 CFUs fall to around 1 to 5 in 1000 individuals and because each region of interest observes about 1000 CFUs, the measurements become dominated by chance. ODELAM consistently observed fewer resistant TRS10 CFUs from the mixture than expected from the OD₆₀₀ measurements used to generate the mixture (Figure 5B) and likely reflects the vagaries associated with using OD₆₀₀ to predict CFUs, which is known to vary between strains (Peñuelas-Urquides et al., 2013).

Detecting heteroresistance in cultured clinical isolate ADB42

Clinical isolate ADB42 was previously observed to be OFX resistant with MIC ranging between 8 and 256 μg/ml of OFX (Eilertson et al., 2016; Eilertson et al., 2014). By whole genome sequence analysis, the canonical OFX resistance conferring gyrase-A D94G SNP was not present at a frequency sufficient to be associated with the observed OFX resistance; other mechanisms were attributed to the observed phenotype (Eilertson et al., 2016). In our hands, the standard dose-response assay yielded a MIC of 16 μg/ml (Figure 6A). By ODELAM population measurements, the histograms revealed features indicative of phenotypic heterogeneity. Prominent tails were observed on the right-hand side of the exponential phase and number of doublings population distributions for OFX concentrations at and above 0.5 μg/ml (Figure 6B), suggesting the presence of at least two populations. These populations could be segregated by a simple threshold of 2 doublings as detected by ODELAM on 2 μg/ml OFX (Figure 7). The CFU growth curves corresponding to these populations showed either OFX sensitivity (Figure 7A) or OFX resistance (Figure 7B) similar to those observed for the experimentally mixed populations of H37Rv and TRS10 (Figure 5C).

Figure 6

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Figure 7

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ADB42 was grown in culture and subjected to transcriptome analysis by RNA-sequencing, which revealed the presence of a gyrase-A D94G SNP at a frequency of 15% (Figure 8A). Across the experiments reported here, ODELAM measured roughly 24% of the ADB42 CFUs were resistant to OFX, which is within a 95% confidence interval of random sampling modeled by a binomial distribution (Table 2). Ninety-six individual clones of ADB42 were isolated and their growth on 2 μg/ml OFX was measured in a spot assay (Figure 8B and C). These clones were also measured with ODELAM to evaluate their growth kinetics and OFX resistance (Figure 9). Thirty-three clones exhibited OFX resistance at 2 μg/ml OFX in both assays. In contrast to the parental cultured clinical isolate, these individual clones did not display phenotypic heterogeneity by ODELAM (data not shown). In total, these data indicate that cultured clinical isolate ADB42 contains two distinct populations one of which is resistant to ofloxacin and likely contains a D94G gyrase-A mutation that confers OFX resistance.

Figure 8

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Figure 9

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Table 2

Total number of sample reads (number of trials)	Number of D94G SNPs detected (number of success) p=r	Ratio of resistant cells to sensitive cells (probability of trial success)	95% Confidence interval [Low]-[High]
62	9	0.09–0.24	[2, 9 - 9, 21]
Total number of colonies picked (number of trials)	Number of resistant colonies (number of success) p=r
96	33	0.28–0.44	[17,33 - 33,61]
	One or two cells contribute to colony p=1-(1 r)2	0.14–0.26	[17,33 - 33,61]
Total number of colonies tracked	Number that fit resistance criterion	Ratio of resistant cells to sensitive cells
963	237	0.2461
473	114	0.2410
1476	273	0.1850
1043	244	0.2339
359	101	0.2557
561	157	0.2799
573	146	0.2548
459	123	0.2680
	Mean resistance ratio	0.2456

In further support of these findings, we sequenced the quinolone-resistance-determining region (QRDR) of gyrA in fourteen of these ADB42 clones, 10 OFX-resistant and four OFX-sensitive. All 10 resistant isolates contained a SNP A7582G in gyrA, which corresponds to the canonical D94G mutations that confers OFX resistance (Willmott et al., 1994). The four sensitive clones did not contain this SNP (Table 3). Additional gyrase mutations were present in the QRDR of gyrA but these mutations were not associated with OFX sensitivity or resistance (Table 3).

Table 3

		QRDR	QRDR
Position in the gyrase protein	21	94	95	247
H37Rv	E	D	S	G
TRS10	Q	G	T	S
ADB42 OFX Sensitive	Q	D	T	G
ADB42 OFX Resistant	Q	G	T	G

Observation of OFX induced cell lysis

ODELAM directly observes cells and therefore informs on additional cellular phenotypes. After Mtb growth arrest on OFX, some cells appear to lyse as detected by a change in contrast (Figure 10A), which we interpret as a consequence of cytotoxicity (Figure 10A). This phenotype is observed as a sharp reduction in a colony’s size in its growth curve (Figure 10B). The percentage of colonies that have this phenotype increased with OFX concentration and became apparent at concentrations greater than 0.5 μg/ml OFX. The frequency of lysis scales with the sensitivity of the strain to OFX. Here H37Rv showed as much as 95% of the tracked colonies exhibiting cell lysis, where as ADB42 and TRS10 showed 65% and 30%, respectively (Figure 10C). These observations by ODELAM highlight the complex transition from a cytostatic to a cytolytic phase of the drug-organism interaction.

Figure 10

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Rate of observation time convergence

Rapid detection of drug sensitivity improves in the successful treatment of tuberculosis. To establish the minimum time required by ODELAM to reliably identify anti-microbial resistance and sensitivity, we determined the length of time necessary to estimate a colony’s set of 4 kinetic growth parameters. Each growth curve is fit using an algorithm optimized for ODELAM data. Ideal growth curves were generated in silico varying the kinetic parameters with random noise added. The Gompertz fit algorithm then estimated growth parameters for each noisy growth curve (Figure 11A–C). To simulate observing growth data over time, an increasing number of time points were included into the fit algorithm. As more time points from simulated growth curves were added, we evaluated if the estimated parameters converged to stable values (Figure 12A). Stable estimates were defined by how many time points were needed to be included for the differences in sequential estimates to differ by a precision of less than 2.5% or 5% (Figure 12B–C). Using this routine, we calculated that the time required to reliably determine the set of kinetic parameters for a growth curve must be ~1.5 times longer than the time needed for a CFU to exit exponential phase. In the case of H37Rv, which, in the presence of OFX, exits exponential phase after ~20 hrs, ODELAM can detect drug sensitivity in ~30 hrs (Figure 2).

Figure 11

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Figure 12

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As the rates of antimicrobial-resistant infections rise, the need for fast and unbiased assays to detect and characterize antimicrobial-resistant bacterial infections are required. ODELAM is a time-lapse, microscopy-based assay that detects Mtb drug sensitivity within 30 hrs, offers an unparalleled view into the population dynamics of Mtb growth kinetics and can directly observe phenotypic heterogeneity. While commercial multiplexed automated digital microscope-based techniques are available to rapidly screen for antibiotic resistance (Chantell, 2015), here, using ODELAM we demonstrate the value of measuring growth kinetics on a single CFU level. With ODELAM. we observed dramatically different effects of OFX on the growth parameters of drug sensitive and drug resistant cultured clinical isolates of Mtb. These effects may offer insight into differing mechanisms of action for antimicrobials on Mtb or other organisms. ODELAM successfully discriminates between drug sensitive and drug resistant colonies in a mixed population. ODELAM also directly observes bacteriolysis and prolonged growth arrest, providing insight into the mechanisms of drug action. Based on this proof of principle, we suggest the investigation of additional cultured clinical isolates, additional Mtb drug classes, and advances in this technology to improve throughput and feasibility in resource limited settings.

MATLAB data *.mat files and MATLAB *.m files utilized for generating figures in this submission are posted at Dryad. Additional source code has been made available at https://github.com/AitchisonLab/.

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Author details

1. Thurston Herricks

   Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Magdalena Donczew

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0247-7967

2. Magdalena Donczew

   Department of Microbiology, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Thurston Herricks

   Competing interests

   No competing interests declared

3. Fred D Mast

   Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, United States

   Contribution

   Conceptualization, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2177-6647

4. Tige Rustad

   Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Robert Morrison

   Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, United States

   Contribution

   Software, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

6. Timothy R Sterling

   Division of Infectious Disease, Department of Medicine, Vanderbilt University Medical Center, Nashville, United States

   Contribution

   Conceptualization, Resources, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

7. David R Sherman

   Department of Microbiology, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   dsherman@uw.edu

   Competing interests

   No competing interests declared

8. John D Aitchison

   1. Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, United States
   2. Department of Pediatrics, University of Washington, Seattle, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   John.Aitchison@seattlechildrens.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9153-6497

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