Rapid phenotypic antimicrobial susceptibility testing using nanoliter arrays

Abstract

Antibiotic resistance is a major global health concern that requires action across all sectors of society. In particular, to allow conservative and effective use of antibiotics clinical settings require better diagnostic tools that provide rapid determination of antimicrobial susceptibility. We present a method for rapid and scalable antimicrobial susceptibility testing using stationary nanoliter droplet arrays that is capable of delivering results in approximately half the time of conventional methods, allowing its results to be used the same working day. In addition, we present an algorithm for automated data analysis and a multiplexing system promoting practicality and translatability for clinical settings. We test the efficacy of our approach on numerous clinical isolates and demonstrate a 2-d reduction in diagnostic time when testing bacteria isolated directly from urine samples.

Keywords: antibiotic resistance; antibiotic susceptibility testing; microfluidics; nanoliter wells; resazurin.

Fig. 1.

Device design and principle of operation. (A) Illustration of SNDA–AST device on a conventional microscope slide being loaded with a conventional 10-µL pipette. The SNDA device consists of two rows of 8-nL wells connected by a main delivery channel. Each well contains ∼3-µm restrictions that allow the air to escape to two surrounding channels, thus facilitating simple capillary based filling. (Inset) 1, 200 µm; 2, 400 µm; 3, 100 µm; and 4, 2–5 µm. (B) Step 1: The SNDA-based AST device is loaded with a single-step injection of a two-plug formulation of the bacterial suspension with 10% resazurin followed by a plug of FC-40 oil. The purpose of the oil is to discretize the sample by isolating the wells while delivering oxygen and preventing evaporation. Step 2: The well fluorescence, indicating the level of metabolic activity occurring in the culture, is measured every 30 min and is proportional to the amount of bacteria/metabolism in the well. Bacteria are not drawn to scale.

Fig. S1.

A table summarizing calculations made using the Poisson distribution for different levels of acceptable error and confidence and a mean of $\mathrm{\lambda }=4$ bacteria per well. For our system, we chose to settle at an acceptable error of 10% in the concentration and be 95% confident that we have that concentration. This leads us to the conclusion that we have to average the results of ∼100 wells per treatment to achieve the desired level of accuracy.

Fig. 2.

Time to S/R determination. (A) First, the fluorescent profile of each well is normalized to the average profile of the negative control by subtraction. Here, data for Klebsiella pneumoniae are used where treatments 1 and 2 were assigned resistant and susceptible determinations, respectively. Averages of each treatment are shown. Next, the normalized profile of each well is fit to both a linear or exponential model with an offset however only the ‟best” model is kept, using the root mean square error as a determiner. (B) Slope distributions are then obtained from the fits created for every time point. This is done iteratively after every data point collected using all data points collected up to that time point for the fits. Here the slopes of the fits created at the last time point are shown. (C and D) Finally, determinations are made for each treatment by categorizing the average slopes of the treatments using fixed fractional thresholds of that of the normalized positive control. We define the time to results as the time point at which the algorithm has presented a decisive determination (one that does not change for the remainder of the experiment). (E) A summary of AST times obtained for the clinical isolates studied in this work using the SNDA–AST system compared with the time to results obtained in the clinic for the same isolate sample using the VITEK 2 AST system. Blue circles represent susceptible determinations, green circles represent resistant determinations, and orange circles marked with a “V” present the results obtained by the VITEK 2 AST system in the clinic. A red dashed line illustrates the ending of a typical 8-h work day.

Fig. 3.

Antibiotic incorporation via lyophilization and multiplexing. (A) Workflow illustration for antibiotic loading and lyophilization. (B) Comparison of the standard “wet” and dried gentamicin at the breakpoint concentration using E. coli with 8 mg/L ampicillin showing that the dried antibiotic has efficacy similar to that of the standard “wet” counterpart. (C) Schematic of parallel SNDA–AST device used for multiplexing. A bacterial sample can be loaded into the device and tested against two different types of antibiotics simultaneously. Inlet/outlet reservoirs are indicated by gray and yellow circles. Yellow circles specifically indicate oil reservoirs. (D) Demonstration of sensitivity testing of two different dried antibiotics simultaneously at breakpoint concentrations using the parallel SNDA–AST device, confirming that this isolate of E. coli is resistant to ampicillin and susceptible to gentamicin. Error bars in all graphs present 95% CIs on the mean.

Fig. 4.

Same-day AST for UTIs. (A) Timeline comparing the current clinical workflow (Top) to the SNDA–AST workflow (Bottom). In the current workflow, lengthy plating and AST steps cause the next step to be initiated only the next working day (shaded regions), causing AST results (asterisk) to be delivered in 2 d. With the SNDA–AST setup, the entire workflow can be performed and results (asterisk) can be delivered the same day. (B) Illustrated schematic of bacteria isolation protocol developed for clinical urine samples. (1) The urine sample is directed through a 5-µm filter to trap WBC and large debris followed by a 0.22-µm filter to trap bacteria. (2) Growth medium is directed through the 0.22-µm filter in the opposite direction to flush out trapped bacteria and the suspension is collected in a separate vial. (3) The bacterial concentration is adjusted via microscope observation. (4) The sample is then supplemented with 10% resazurin and loaded into the SNDA–AST device for analysis. (C) A summary of AST times obtained for the clinical urine samples studied in this work using the SNDA–AST system compared with the time to results obtained in the clinic for the same isolate sample using the VITEK 2 AST system. Blue circles represent susceptible determinations, green circles represent resistant determinations, and orange circles marked with a “V” present the results obtained by the VITEK 2 AST system in the hospital. A red dashed line indicates the assumed 8-h work day, showing that, unlike results from the VITEK 2, results from the SNDA–AST system can be used the same day.

Fig. S2.

(A) An image of the filtration setup used to isolate the bacteria from clinical urine samples where the sample is drawn through the needle and through the series of filters from left to right. (B) A microscope image of bacteria in a single well of the SNDA as seen during the counting step required for adjusting the cell concentration for subsequent AST.

Fig. S3.

Retrieval efficiency of the filtration process. Here we quantify the retrieval efficiency of the filtration process used to produce the data in Fig. 4 on clinical urine samples. A laboratory-grown culture of E. coli was used at an initial concentration of 5 × 10⁵ cells per mL as determined by optical density and a standard curve. n = 4 repetitions for each measurement. Error bars represent 95% CIs on the mean. Results were quantified by plating the bacterial suspensions before and after the respective filtering steps using the Drigalski technique. Percent values are calculated with respect to the bacterial concentration before each specific step.

Fig. S4.

Bulk colorimetric assay performed in parallel with the same sample used for AST with the SNDA–AST. This example is from the E. coli tested in Table 2 in Exp. 4. From left to right: Positive control, negative control, ampicillin 8 mg/L, and ciprofloxacin 0.5 mg/L. The result with ampicillin determined using this bulk colorimetric assay matches that of the VITEK 2 on the same isolate performed in the clinic, although does not match the determination produced by the SNDA–AST. We hypothesize that this is most likely due to a pronounced lag phase associated with the bacteria that were extracted directly from clinical urine samples that were refrigerated overnight before testing.

Fig. S5.

Data from two discrepancies in the urine analysis studies summarized in Table 2. Evidence of a slight signal rise compared with the negative control (red arrows) can be seen for the treatments that were incorrectly determined susceptible [ciprofloxacin (CIP) in Exp. 2 and ampicillin (AMP) in Exp. 4]. This rise may be due to metabolism from live bacteria that are not undergoing significant proliferation due to a lag phase. These data suggest that an improvement to the analysis algorithm that uses absolute fluorescence intensity rather than slope to make determinations can be of value. R.F.U., relative fluorescence units.