Development of MAST: A Microscopy-Based Antimicrobial Susceptibility Testing Platform

Abstract

Antibiotic resistance is compromising our ability to treat bacterial infections. Clinical microbiology laboratories guide appropriate treatment through antimicrobial susceptibility testing (AST) of patient bacterial isolates. However, increasingly, pathogens are developing resistance to a broad range of antimicrobials, requiring AST of alternative agents for which no commercially available testing methods are available. Therefore, there exists a significant AST testing gap in which current methodologies cannot adequately address the need for rapid results in the face of unpredictable susceptibility profiles. To address this gap, we developed a multicomponent, microscopy-based AST (MAST) platform capable of AST determinations after only a 2 h incubation. MAST consists of a solid-phase microwell growth surface in a 384-well plate format, inkjet printing-based application of both antimicrobials and bacteria at any desired concentrations, automated microscopic imaging of bacterial replication, and a deep learning approach for automated image classification and determination of antimicrobial minimal inhibitory concentrations (MICs). In evaluating a susceptible strain set, 95.8% were within ±1 and 99.4% were within ±2, twofold dilutions, respectively, of reference broth microdilution MIC values. Most (98.3%) of the results were in categorical agreement. We conclude that MAST offers promise for rapid, accurate, and flexible AST to help address the antimicrobial testing gap.

Keywords: antimicrobials; inkjet printing; machine learning; susceptibility testing.

Figure 1. The microscopy-based antimicrobial susceptibility testing (MAST) assay

The HP D300 digital dispenser (A) and disposable small volume T8+ (B, top) or large volume D4+ (B, bottom) cassettes are used for antibiotic and cell dispensing. (C) Solid surfaces are prepared in single wells of a 384-well plate using CAMHB solidified with poloxamer 407 (CAMHB-P), and varying size droplets of antimicrobial are digitally dispensed into each well creating (D) a doubling dilution series. (E) Bacteria are dispensed on top of the antibiotic-containing well surfaces and incubated at 35±2°C for 2 hours. (F) Images of cells are collected by automated microscopy and (G) classified as “growth” or “inhibition” using deep learning. Areas defined by the ConvNet as showing inhibition are highlighted with red overlay. The minimal inhibitory concentration (MIC) of antimicrobial is defined as the lowest concentration resulting in bacterial growth inhibition (F, third well from right, corresponding to G, third image from right).

Figure 2. Digital dispensing of bacteria suspensions

Standardized suspensions of bacteria were dispensed into liquid media using the HP D300. The number of bacteria dispensed was quantified by plate count (A–E). Each point is the mean of three independent experiments with error bars representing one standard deviation from the mean. There was a linear relationship between dispense volume and CFU dispensed for all organisms with an average R² = 0.96 (dotted lines).

Figure 3. Representative cell densities immediately after digital dispensing

Standardized suspensions of (A) E. coli ATCC 25922 (average of 164 cells/field or 1.6 cells/1000 μm²) or (B) E. cloacae ATCC 13047 (average of 264 cells/field or 2.58 cells/1000 μm²) were dispensed onto solid microwell surfaces using the HP D300 digital dispensing system and visualized using a Zeiss Cell Observer microscope. Arrows indicate individual cells. (C) Number of cells visible in the central field of 12 representative wells was quantified on 3 separate days. Error bars indicate one standard deviation from the mean.

Figure 4. Representative morphologies of inhibited E. coli ATCC 25922

Antibiotics and E. coli ATCC25922 were dispensed into microwells and automatically imaged with a Zeiss Cell Observer microscope after <4 hour incubation. Panels represent the central field of a microwell containing (A) ciprofloxacin, (B) cefepime, (C) gentamicin, (D) meropenem at the MIC. Insets A–C show close-up views of an individual cell. Inset D shows a close-up of two cells.

Figure 5. Graphical representation of MAST MIC assay output for E. coli ATCC 25922

Each point in (A–D) represents ConvNet output (fraction of image crops with inhibition probability >0.5) from the adjacent image at the indicated antibiotic concentration. Red overlay in images indicates areas where the ConvNet algorithm detected bacterial inhibition. Solid blue line represents a sigmoid fit to the ConvNet data. Dashed line represents the threshold delineating growth (black points) and inhibition (red points).