Rapid bacterial detection and antibiotic susceptibility testing in whole blood using one-step, high throughput blood digital PCR

Abstract

Sepsis due to antimicrobial resistant pathogens is a major health problem worldwide. The inability to rapidly detect and thus treat bacteria with appropriate agents in the early stages of infections leads to excess morbidity, mortality, and healthcare costs. Here we report a rapid diagnostic platform that integrates a novel one-step blood droplet digital PCR assay and a high throughput 3D particle counter system with potential to perform bacterial identification and antibiotic susceptibility profiling directly from whole blood specimens, without requiring culture and sample processing steps. Using CTX-M-9 family ESBLs as a model system, we demonstrated that our technology can simultaneously achieve unprecedented high sensitivity (10 CFU per ml) and rapid sample-to-answer assay time (one hour). In head-to-head studies, by contrast, real time PCR and BioRad ddPCR only exhibited a limit of detection of 1000 CFU per ml and 50-100 CFU per ml, respectively. In a blinded test inoculating clinical isolates into whole blood, we demonstrated 100% sensitivity and specificity in identifying pathogens carrying a particular resistance gene. We further demonstrated that our technology can be broadly applicable for targeted detection of a wide range of antibiotic resistant genes found in both Gram-positive (vanA, nuc, and mecA) and Gram-negative bacteria, including ESBLs (bla_CTX-M-1 and bla_CTX-M-2 families) and CREs (bla_OXA-48 and bla_KPC), as well as bacterial speciation (E. coli and Klebsiella spp.) and pan-bacterial detection, without requiring blood culture or sample processing. Our rapid diagnostic technology holds great potential in directing early, appropriate therapy and improved antibiotic stewardship in combating bloodstream infections and antibiotic resistance.

Figure 1.

IC3D Workflow for rapid bacterial ID/AST. a) raw blood sample and PCR reagents are mixed and then encapsulated in picoliter-sized droplets. b) PCR produces a fluorescence signal in the droplets that contain bacterial targets. c-d) Droplets are collected in a cuvette and analyzed using our high throughput 3D particle counter.

Figure 2.

High throughput droplet generation chip design and characterization. a) Schematic of PDMS microfluidic droplet generator employing flow-focusing principle across 4 parallel nozzles. b) Key design features are identified including i) micropillar sample filter, ii) droplet generation nozzle, and iii) droplet collection chamber. (scale bar = 1 mm) c) Photograph of droplets containing 10% whole blood in cuvette before 3D scanning. Droplet layer (approximately 1 ml total volume) floats on top of a layer of HFE-2000 oil due to lower density. d) 2D monolayer of droplets for brightfield microscopy to assess droplet uniformity (average droplet diameter = 85 μm, %CV = <5%) (Scale bar = 500μm).

Figure 3.

Blood droplet thermal stability optimization. a) Addition of Tween-20 to PCR mixture prior to encapsulation (i: 0%; ii: 0.01%; iii: 0.2%). Addition of Tween-20 was not found to significantly affect droplet thermal stability. b) Optimization of surfactant concentration for oil in PCR tubes. Emulsions of 2% surfactant with PCR mixtures containing 10% whole blood were loaded into PCR tubes containing preloaded various percentage of surfactant in HFE7500 (i: 2%; ii: 5%, iii: 10%). Droplets before and after PCR thermocycling were imaged (BF, 4x) and quantified for droplet CV. 5% surfactant oil in PCR tubes was found to be optimal. c) Optimization of NP-40 concentration in PCR buffer mixture prior to encapsulation (i: 0%; ii: 0.2%; iii: 0.4%). Final concentration of 0.2% NP-40 was found to result in improved droplet thermal stability. d) Addition of mineral oil overlay during 40 cycles of thermocycling (i: without mineral oil, ii: with 70 μl mineral oil overlay). Mineral oil overlay was found to significantly improve droplet thermal stability and yield. e) Final condition combining optimal parameters found in a-d. A portion of 10% whole blood droplets created with 2% PFPE surfactant oil were continuously mixed with 5% (w/w) surfactant in HFE7500 oil for 30 mins, before loading into PCR tubes. For PCR thermocycling, 70 μl of droplets (without mixing condition (i) or with mixing (ii)) were transferred into PCR tubes preloaded with 70 μl of mineral oil and 30 μl of 5% (w/w) surfactant in HFE7500 oil. The addition of a mixing step was not found to significantly improve droplet uniformity or thermal stability.

Figure 4.

Microscope images of negative (a,b) and positive (c,d) droplet samples encapsulating an engineered E. coli strain containing the synthetic bla_CTX-M-9 target gene before (a,c) and after (b,d) thermocycling. Cy5 fluorescence images were recorded to measure droplet SNR for different replicates. (Same intensity range for all panels, scaled to visualize trace fluorescent signal in negative control droplets. Scalebar = 500 μm). e) Quantitative intensity measurements for droplets in panels B (negative control) and D (positive control), where overall SNR averages 2.87 (defined as 90th % positive droplet intensity divided by average negative droplet intensity), (n = 473 individual droplets). The optimal conditions used in this experiment were 10% whole blood, 40 PCR cycles, 2:1 primer-to-probe ratio (1μM primer, 500 nM probe), 5mg/ml BSA, 0.2% NP-40, and a 70 μl mineral oil overlay over droplets during thermocycling.

Figure 5.

Single-digit sensitivity using the IC3D blood ddPCR system. a) Representative fluorescence time trace data from (i) high concentration sample (10⁴ CFU/ml), (ii) low concentration sample (100 CFU/ml) and (iii) negative control. E. coli strain containing the synthetic bla_CTX-M-9 target gene were used as a model target. b) Representative peaks identified by the shape fitting algorithm based on variable peak amplitude, fixed width, and fixed statistical significance. i) High amplitude and ii) lower amplitude peaks both fit acceptance criteria (10⁴ CFU/ml sample), iii) low amplitude peak identified in low concentration sample (100 CFU/ml), iv) false positive rate = 0% for negative control. c) Demonstration of IC3D sensitivity in detecting low concentrations of bacteria spiked into whole blood. Y-axis = log (events per 60s), error bars = relative error (for symmetrical display on log-scale), defined as ±0.434*stDev/y) across 3 independent sample replicates. CFU/ml (x axis) represents the number of spiked bacteria in the final measurement volume.

Figure 6.

Clinical isolate specificity demonstration with IC3D blood ddPCR raw fluorescent time trace. Each box represents a 200 ms sample of data from a particular clinical sample (see Table S5). The numbers displayed in the box in the upper right corner of each panel are the number of confirmed hits in a 60s scan. (X-axis = acquisition time in seconds, y-axis = offset signal amplitude in mV).

Figure 7.

Demonstration of wide applicability of IC3D blood ddPCR technology for the rapid diagnosis and mangement of BSI and antibiotic resistance. a) Pan-bacteria detection with 16s gene panel. b) Species-identification panel including E. coli- (uidA) and Klebsiella- (khe) specific genetic markers. c) Representative example of bla_OXA-48 as a model using internal negative control and positive control (engineered bacteria) along with clinical isolate. Inset panels for engineered control and clinical isolates demonstrate representative peaks identified with shape-fitting algorithm (X-axis = 6ms of data at 64kHz, Y-axis = raw signal intensity in mV). d) Antibiotic resistance genetic panel including bla_CTX-M-1, bla_CTX-M-2, vanA, and bla_KPC, and MRSA (nuc, mecA). Each panel is a representative portion of the raw fluorescence signal recorded by the particle counter instrument. X-axis = 200ms of data at 64kHz, Y-axis = offset signal amplitude in mV). Orange highlighted peaks denote signal fluctuations that match the stringent shape-fitting criteria consistent with positive, fluorescent target-containing droplets.