| Literature DB >> 28638468 |
Karan Syal1, Manni Mo1,2, Hui Yu1, Rafael Iriya1,2, Wenwen Jing1, Sui Guodong3, Shaopeng Wang1,4, Thomas E Grys5, Shelley E Haydel6,7, Nongjian Tao1,4,2.
Abstract
Infectious diseases caused by bacterial pathogens are a worldwide burden. Serious bacterial infection-related complications, such as sepsis, affect over a million people every year with mortality rates ranging from 30% to 50%. Crucial clinical microbiology laboratory responsibilities associated with patient management and treatment include isolating and identifying the causative bacterium and performing antibiotic susceptibility tests (ASTs), which are labor-intensive, complex, imprecise, and slow (taking days, depending on the growth rate of the pathogen). Considering the life-threatening condition of a septic patient and the increasing prevalence of antibiotic-resistant bacteria in hospitals, rapid and automated diagnostic tools are needed. This review summarizes the existing commercial AST methods and discusses some of the promising emerging AST tools that will empower humans to win the evolutionary war between microbial genes and human wits.Entities:
Keywords: AST methods; antibiotic susceptibility tests
Mesh:
Substances:
Year: 2017 PMID: 28638468 PMCID: PMC5479269 DOI: 10.7150/thno.19217
Source DB: PubMed Journal: Theranostics ISSN: 1838-7640 Impact factor: 11.556
Figure 1Evolution of agar dilution methods for determining antibiotic susceptibility from the discovery of antibiotics (a) to currently used disk diffusion (b) and Etest (c) assays. a) Photograph showing lack of staphylococcal colonies in the vicinity of the Penicillium mold adapted from Alexander Fleming's original research paper on the discovery of penicillin. b) E-Test uses gradient antibiotic concentrations to determine MIC of antibiotics. c) Disk diffusion assays involve placing multiple antibiotic-impregnated disks onto an agar surface inoculated with bacteria and measuring the diameter of zones of inhibition to qualitatively determine antibiotic susceptibility. Figure 1a Adapted from - Alexander Fleming. On the Antibacterial Action of Cultures of a Penicillum, with Special Reference to their use in the isolation of B. Influenze. Br J ExpPathol. 1929 Jun; 10(3): 226-236Disk diffusion assay image produced by John Popovich, Haydel Lab, ASU.E-test image produced by Rachael Liesman.
Figure 2Rapid AST using an emerging imaging based tool. a) Schematic comparison of traditional AST using broth microdilution and imaging-based AST demonstrates how tracking single cell divisions can produce rapid results compared to traditional optical density (OD) tools which are limited by their sensitivity to measure only higher bacterial concentrations. b) Setup of a 96-well plate modified into a microfluidic agarose chip for concurrent addition of bacteria and antibiotics followed by microscopic imaging. c) Schematic of steps involved in adding bacteria and antibiotics and imaging a localized area to observe changes. From Choi J, Yoo J, Lee M, Kim E-G, Lee JS, Lee S, et al. A rapid antimicrobial susceptibility test based on single-cell morphological analysis. Sci Transl Med 2014; 6:267ra174 Reprinted with permission from AAAS.
Figure 3Future technologies measuring bacterial nano-motion as a measure of bacterial metabolism to perform antibiotic susceptibility. a) Snapshots of bacteria z-micro-motion. Panels a1-a4 show time differential images captured at various time points which show contrast of the bacteria versus the background. The observation of the small contrast is due to micro-motions of the live bacterial cells. b) Z-displacement vs. time - The positions of minimum contrast (a1 and a3) correspond to bacterial z-position farther away from the surface. The position of maximum contrast (a2) corresponds to z-position closest to the surface. c) z-displacement plot of a dead bacterial cell (no motion) showing a standard deviation of 0.15 nm. Reprinted with permission from Syal K, Iriya R, Yang Y, Yu H, Wang S, Haydel SE, et al. Antimicrobial Susceptibility Test with Plasmonic Imaging and Tracking of Single Bacterial Motions on Nanometer Scale. ACS Nano 2015; 10:845-852 Copyright 2017 American Chemical Society.
Summary of AST Technologies
| AST Technologies | Summary of Method | Time of AST | Direct on patient sample | Real MIC | FDA Approved | Reference |
|---|---|---|---|---|---|---|
| 1. Agar Dilution Assay | Bacteria inoculated on agar plates with antibiotic discs of different concentrations | 16-24 Hours | No | Yes/No | Yes | [7] |
| 2. Disk Diffusion | Bacteria inoculated on agar plates with a single antibiotic disk | 16-24 Hours | No | Yes/No | Yes | [7, 12] |
| 3. E-test | Bacteria inoculated on agar plates with a graded antibiotic concentration strips | 16-24 Hours | No | Yes | Yes | [7, 12] |
| 1. Broth Dilution Assay | Bacteria inoculated in liquid media with different antibiotics to monitor growth | 12-24 Hours | No | Yes | Yes | [7, 10] |
| 2. Automated Instruments | ||||||
| a) MicroScanWalkAway | Measure bacterial growth in the presence of antibiotics by recording bacterial turbidity using a photometer | 4.5-18 Hours | No | Yes | Yes | [8, 16] |
| b) Vitek-1/Vitek-2 | Measure bacterial growth in the presence of antibiotics by recording bacterial turbidity using a photometer | 6-11 Hours | No | Yes | Yes | [17 ,70] |
| c) BD Phoenix | Record bacterial growth in the presence of antibiotics by recording bacterial turbidity and colorimetric changes | 9-15 Hours | No | Yes | Yes | [71] |
| d) Sensititre | Record bacterial growth with antibiotics by measuring fluorescence | 18-24 Hours | No | Yes | Yes | [7] |
| 1. Multiplexed automated digital microscopy (MADM) | Image single bacteria growing into colonies with antibiotics and quantify growth rates | 3-5 Hours | Yes (Urine, Blood) | Yes | Yes | [31, 72, 73] |
| 2. Single-cell morphological analysis (SCMA) | Image single bacterial cell's morphology changes on antibiotic action | 3-4 Hours | Yes (Urine) | Yes | No | [32, 74] |
| 3. oCelloscope | Measure growth of bacterial cells using low resolution optical system | 1-4 Hours | Yes (Urine) | Yes | No | [33] |
| 1. BacterioScan FLLS | Measures bacterial numbers and sizes on antibiotic action | 3-10 Hours | Yes (Urine) | Yes | No | [25, 43] |
| 2. LifeScaleMicochannel Resonator | Count bacterial cells and morphology changes on single cells post antibiotic action | > 3 Hours | No | Yes | No | [44] |
| 3. Genefluidics | Count 16s RNA increase as a proxy to bacterial growth | 4 Hours | Yes (Urine) | Yes/No | No | [45, 75] |
| 4. Smarticles | Bacteriophages which express luciferase on growing cells | - | - | - | No | [30] |
| 1. AFM Cantilever | Measure cantilever fluctuations originating from bacterial motion as a proxy for metabolism | < 2 Hours | No | Yes | No | [22] |
| 2. PIT | Image and Quantify sub-nanometer motion of bacterial cells | < 2 Hours | Yes | Unknown | No | [59] |
| 3. Flow Cytometry | Count viable bacterial cells using dyes | 2-3 hours | No | Yes | No | [63] |
| 4. IMC | Heat signature of growing cells | 3-14 Hours | Yes | Yes | No | [68] |