In vitro antimicrobial susceptibility testing methods: agar dilution to 3D tissue-engineered models
- 2.6k Downloads
In the field of orthopaedic surgery, bacterial invasion of implants and the resulting periprosthetic infections are a common and unresolved problem. Antimicrobial susceptibility testing methods help to define the optimal treatment and identify antimicrobial resistance. This review discusses proven gold-standard techniques and recently developed models for antimicrobial susceptibility testing, while also providing a future outlook. Conventional, gold-standard methods, such as broth microdilution, are still widely applied in clinical settings. Although recently developed methods based on microfluidics and microdroplets have shown advantages over conventional methods in terms of testing speed, safety and the potential to provide a deeper insight into resistance mechanisms, extensive validation is required to translate this research to clinical practice. Recent optical and mechanical methods are complex and expensive and, therefore, not immediately clinically applicable. Novel osteoblast infection and tissue models best resemble infections in vivo. However, the integration of biomaterials into these models remains challenging and they require a long tissue culture, making their rapid clinical implementation unlikely. A method applicable for both clinical and research environments is difficult to realise. With a continuous increase in antimicrobial resistance, there is an urgent need for methods that analyse recurrent infections to identify the optimal treatment approaches.
The risk of infection of a total joint replacement and in trauma surgery was described as early as in 1987 by Gristina , emphasising the likelihood of bacteria to adhere to implanted biomaterials, eventually resulting in infections. Yet, decades later, infections remain a complex and consequential problem for patients and surgeons. With a growing ageing population worldwide, the number of total joint replacements has also increased, leading to a concomitant increase in infection incidence. Additionally, treatment is getting increasingly difficult due to developing bacterial resistances. Periprosthetic infections, if not properly treated, eventually become chronic and engender osteomyelitis [2, 3, 4]. Different types of bacteria can trigger an infection, including those from the Enterobacteriaceae, Pseudomonas, Enterococcus and Streptococcus genera. Once introduced in the surrounding tissue during trauma injury or surgery, the bacteria can migrate towards the bloodstream and cause temporary bacteraemia, as well as towards foreign material (i.e. implants) to form a biofilm on the implant surface. Staphylococcus aureus, in combination with other bacteria such as Staphylococcus epidermis, has been found to be present and to trigger osteomyelitis in the majority of implant infections . These bacteria not only form colonies on the implant surface, but also anchor within the host bone matrix and invade host osteoblasts . A large percentage (25%) of S. aureus is able to undergo a change in phenotype to produce small colony variants (SCVs) . These SCVs express more adhesins to facilitate uptake into non-phagocytic host cells, such as osteoblasts. The SCVs then persist in host cells by reducing their toxicity, reducing immune response to keep the host cells alive [4, 5]. Subsequently, they can reverse, exit the host cells and induce recurrent, chronic infection by becoming highly virulent again and infecting new host cells [4, 5, 6, 7, 8]. Bacteria are categorised by their respiratory pathway: anaerobic (survival and replication in oxygen-deprived environments), aerobic (survival and replication in oxygen-rich environments) and facultative anaerobic (bacteria, such as S. aureus, can grow in the presence and absence of oxygen) [9, 10, 11].
First introduced in 1929, in vitro antimicrobial susceptibility testing (AST) methods are still considered to be the most valuable in determining the efficacy of antibiotics or antimicrobial compounds against various microorganisms . In general, in vitro AST methods combine one or more antimicrobial agents or materials with bacteria to assess bacterial growth. The efficacy of an agent to kill the bacteria over time can be measured in various ways, which has previously been discussed in the review of van Belkum and Dunne . However, this review does not provide detailed information on the functional principle and validity of these methods. Moreover, since then, research has been published towards models that attempt to resemble in vivo functionalities. The current review, therefore, provides researchers and clinicians with an updated and more detailed insight into in vitro and in vivo-like AST possibilities to help them choose the best method for their own laboratories and research. It gives an overview of the different types of AST methods that are currently being used and discusses promising novel model developments, in particular those to study bacterial infections in osteoblasts and bone tissue. We outline the overall capabilities and efficacies of the tests, as well as their advantages and disadvantages. Furthermore, details about the ease of performance, cost, automation and commercial availability of different AST methods are provided, which should enable clinicians and researchers to choose the optimal model for specific applications (see Table 1 in the Appendix).
In this review, various AST methods are categorised based on their chronological development: 1. current gold-standard clinical methods, 2. mechanical methods, 3. optical methods, 4. microfluidics and microdroplets methods, 5. models of in vivo infection. Studies that implement bacterial biofilms in their models are excluded from this review, as in vivo-like biofilm models are much more complex and require different experimental setups and time scales.
Gold-standard clinical antimicrobial susceptibility testing (AST) methods
Simple clinical AST methods were developed and standardised between 1920 and 1980. The gold-standard methods focus on the determination of the minimum inhibitory concentration (MIC) or the qualitative growth curve using a simple readout method for which the unaided eye is generally sufficient. Gold-standard methods are standardised by various organisations, such as the Clinical and Laboratory Standards Institute (CLSI) and the International Organization for Standardization (ISO) [12, 14, 15, 16]. The interpretation of test results is, among others, standardised by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) . Although the tests are reliable, they require extensive manual laboratory work and the results are normally not obtained within the same day, limiting their application. Furthermore, these tests do not offer insights about mechanisms of action.
Agar dilution method
The agar dilution technique and the broth dilution technique had their debut in 1929. Their final versions were introduced in the 1940s and are still being used today . The agar dilution method is used for the determination of the MIC of antimicrobial compounds [18, 19]. Following standardised guidelines, anaerobic  or aerobic  bacteria are seeded on nutrient agar medium, which is supplemented with different concentrations of antimicrobial agents. The colony forming units (CFU) are then counted after 48 h of incubation. This method has a simple and cost-effective readout and was found to be reliable in determining MICs . However, the culture time to ensure CFU formation is long. An upgraded version is the chromogenic agar medium, which enables faster CFU detection due to an early visible colour response within 18–24 h for susceptible strains. Furthermore, it is suitable for cultures containing multiple bacteria types, as it is possible to stain only resistant bacteria, and differentiate between strains and species. The readout remains the same, but only the CFU appear coloured [22, 23]. The benefits of the agar dilution method are its simplicity and well-understood parameters. More recent AST methods, described in this review, provide greater possibilities.
Broth macrodilution method
One of the oldest AST methods, also known as the tube dilution method, makes use of tubes containing two-fold serial dilutions of antibiotic agents in liquid growth medium [15, 24]. Subsequently, a known quantity of suspended bacteria is added to each solution. After 24 h of incubation, the bacterial growth is measured by the turbidity within the tubes, which gives an MIC value. The primary advantage of this method is the ability to obtain quantitative MIC values , as well as the minimum bactericidal concentration (MBC), which is the lowest amount of drug at which 99.9% of the bacteria is killed (bactericidal endpoint). This method is standardised by the CLSI for aerobically growing bacteria . Standardised modifications of the method for fastidious organisms are provided in the same document.
Another analytical technique for broth macrodilution is the time–kill methodology [26, 27]. The inoculum can be multiplied in order to analyse the rate of bacterial killing for different concentrations. Viability is determined by colony counting on agar plates at regular time points over 24 h. Bacterial activity is then defined as a concentration reduction by time of ≥ 3 log10 CFU/mL compared to the initial inoculum. It is a standardised method described by the CLSI . Time–kill curves can be drawn to visualise the antimicrobial activity of an agent, by plotting time or concentration versus the CFU/mL. By providing dynamic information between drug and bacteria, the time–kill method is especially useful for determining the bactericidal effect.
Broth macrodilution is a reliable and standardised method for diagnostic purposes. A major disadvantage is that each antibiotic solution has to be prepared by hand. Although this provides a certain degree of flexibility in drug choice and concentration, it is a very time-consuming process, especially for larger experimental investigations. Faster methods that are more suitable for screening have now replaced broth macrodilution. Additionally, a considerable amount of reagents is needed for each and every dilution. Due to the latter reason, microdilution, a downscaled version of macrodilution, has been developed.
Disc diffusion/Kirby–Bauer method
In the same decade as the agar dilution method, diffusion techniques were discovered, which led to the introduction of the Kirby–Bauer method in 1959. Approved since 1975, this method makes use of a paper disc impregnated with antibiotics to determine drug resistance [16, 27]. Agar plates are enriched with bacterial inoculum and incubated for 18–24 h. These colonies are then resuspended in broth or saline to a standardised concentration and applied with a cotton swab on an agar plate. Subsequently, the paper discs are placed and the plates are incubated overnight. An inhibitory zone arises where the concentration of the diffused antibiotic particles is high enough to inhibit bacterial growth. Qualitative results are provided, categorising bacteria as susceptible, intermediate and resistant strains, depending on the size of the inhibitory zone. Standardised readouts are available for the assessment of the inhibitory zone of many bacteria types and strains, as well as for drug interactions by introducing multiple discs containing different drugs on the same agar plate . Disc diffusion is standardised for rapidly growing aerobic pathogens as well as several fastidious organisms, such as Haemophilus influenzae, Neisseria gonorrhoeae, N. meningitidis, S. pneumoniae, and beta-haemolytic and viridans group streptococci . Modifications of the method may enable the testing of other fastidious bacteria as well . The handling of the test is simple, straightforward and does not require any special equipment. The possibility to change the type of antibiotic by simply changing a single disc provides a certain degree of added flexibility. Currently, this AST method has the lowest costs . A significant disadvantage is its unsuitability for slow and anaerobically growing microorganisms [27, 29]. Furthermore, this test is dependent on appropriate diffusion. As such, the molecular weight of the drug molecules is a crucial factor when using this method. In addition, imperfections and unevenness of the agar plates can affect diffusion and lead to false results. Another drawback is that the test only provides qualitative results and no quantitative MIC values . More complex approaches, as described in the following paragraphs, have attempted to address some of these disadvantages.
Another technique with comparable functionality is the spiral gradient endpoint technique, where the gradient is produced by a spiral plater that deposits a stock solution on the agar plate. The distance from the centre of the plate to the endpoint of growth enables the determination of the MIC , as previously reviewed by Jorgensen and Ferraro . This method was found to be an effective method for the detection of heterogeneous vancomycin-non-susceptible S. aureus (hVISA) and vancomycin-intermediate S. aureus (VISA) .
The Etest has several advantages over the disc diffusion test. First, because the gradient is stable for up to 20 h, it is suitable for a variety of pathogens, ranging from rapid-growing aerobic and anaerobic bacteria to slow-growing fastidious bacteria . In addition, the test can be used for finding heterogeneous resistance, or a bacterial strain that forms both susceptible and resistant colonies . It can provide an exact MIC value, which allows fine-tuning of the antibiotic treatment regimens of patients. However, this method is more expensive than the paper disc diffusion method [15, 27]. Jorgensen and Ferraro [15, 33] also found a systematic bias for smaller and larger MIC values for specific bacteria–drug combinations compared to broth dilution, demonstrating a limitation of the test. In general, the Etest is a convenient AST method that provides exact quantitative MICs at a higher cost.
Microdilution was pioneered in 1977. Microdilution attempts to increase the throughput of broth macrodilution by conducting one test using 12 different antibiotic agents in a 96-well plate. After the manual or automated addition of a small amount of bacteria to each well, the plate is incubated overnight, producing an observable colour change, along with bacterial growth. An automatic or manual viewing device can be used to determine the MIC by fluorescence intensity or turbidity measurements . The well plates can be purchased containing different concentrations of the dry agent pre-prepared in each well, improving standardisation and ease of use. Microdilution is standardised by the CLSI for both anaerobic  and aerobic bacteria . The most obvious benefit is the reduction in workspace, reagents and time. However, a major drawback is that drug selection is limited to commercially available panels. With a higher degree of automation, microdilution is now a gold-standard technique preferred over broth macrodilution.
Polymerase chain reaction (PCR)-based techniques
Polymerase chain reaction (PCR)-based techniques for AST are commercially available in the form of assays and automated machines, and are highly favoured in clinical settings. PCR, both real-time and conventional, are used to amplify DNA sequences that are specific to a particular pathogen and its susceptibility or resistance to a drug. An example includes the mecA gene that encodes the penicillin-binding protein PBP2a, which reduces the affinity for beta-lactam antibiotics [41, 42]. This gene is commonly found in MRSA and it is highly advantageous in that it can be quickly identified in clinics . Furthermore, the extent and intensity of gene expression and not just its presence are important parameters, as some genes need a high expression in order to produce resistance .
One major advantage of PCR-based methods is that they can be conducted and evaluated rapidly. Results can be obtained with high specificity and sensitivity within 2 h. Furthermore, samples do not always have to be purified, but can be primary, non-sterile, clinical samples, and, thus, can contain bacterial mixtures [45, 46, 47, 48]. Real-time PCR can detect differences between susceptible and resistant strains with very short incubation time and has been applied for a variety of bacterial types, particularly MRSA and vancomycin-resistant MRSA (VMRSA) [49, 50, 51, 52, 53]. It can also distinguish between live and dead bacteria by using, for example, ethidium monoazide or propidium monoazide [54, 55]. On the other hand, PCR techniques cannot provide information about mechanisms of resistance . Furthermore, the number of genes and mechanisms involved in resistance can be quite complex depending on the strain, which can lead to misinterpretation of data [56, 57, 58]. Knowing which genes to analyse is a prerequisite to minimise the possibility of detecting false resistance, and this remains one of the major drawbacks of this method. Nevertheless, PCR-based AST methods are a safe, efficient and reliable screening tool in clinical settings.
Since the turn of the century, mechanical methods have been developed for AST. To date, they primarily serve in research applications and are not affiliated with clinical diagnostics. Magnets, electrodes or cantilevers are introduced in the bacterial culture to measure variables such as rotational and resonance frequency, as well as impedance to assess bacterial growth, even for small amounts of bacteria. Their setup is usually complex and the readout is microscope- or computer-aided and, therefore, largely automated.
Asynchronous magnetic bead rotation sensor
Single-cell AMBR biosensor
Surface acoustic wave sensor
The surface acoustic wave (SAW) sensor is a method to determine the amount of cells in a culture by placing electrodes in the culture solution, which measure frequency changes, caused by an impedance change of microbial metabolism. Conductive electrodes are placed in the culture solution of interest and the time is measured until frequency changes are detected . To detect E. coli growth from an initial solution of 100 cells/mL, only a 7-h measurement was required. This is faster than most gold-standard AST methods. No pre-culture is required, as long as the starting amount of cells is very low. As an additional advantage, the system operates wirelessly and, therefore, offers the opportunity for isolated, safe bacterial work with highly virulent strains. To date, SAW has been applied only to E. coli; therefore, testing and validation with other bacteria are necessary to determine its potential applications. Unfortunately, measurements are prone to building up noise and are sensitive to frequency changes caused by protein adhesion to electrodes, temperature changes and the presence of amino acids with acidic or basic residues. Thus, the pH value of the assay must remain constant [62, 63], which makes longer bacterial culture or even co-culture difficult to pursue. The SAW method is used for cells in suspension, which means that clinical samples would need a pre-treatment in order to be analysed. Even though a clear number of cells/mL can be determined, this method does not provide data about resistance and mechanism(s) of resistance [62, 63].
Microbial cell weighting with vibrating cantilevers
Another quantitative method involves vibrating cantilevers that make use of the buoyant mass principle . A bacterial suspension flows through a channel within a resonating cantilever. The position of bacterial cells on the cantilever changes the total cantilever mass. This leads to a change in resonance frequency, which can be directly related to the buoyant mass of the cell. The cellular, buoyant density usually differs per cell species. As this method allows single-cell analysis of volume, mass and density, a precise growth rate can be determined as well as bacterial adhesion to cells [64, 65, 66]. Vibrating cantilevers have already been used in antimicrobial susceptibility research and various other fields. In addition to cancer cells, viruses, and mouse and human T cells, research using this method has been performed for yeast, E. coli, E. faecalis and Citrobacter rodentium [67, 68, 69, 70, 71, 72]. One benefit is that the overall growth rate and cell type can be determined with a notably small amount of cells. More research is necessary, however, for the application of this method for AST purposes to ensure reliable detection of various resistant strains and bacteria types. Furthermore, this is a rapid method, with results obtainable in 20 min to 2 h, as no pre-culture is required [70, 72]. Microchannel cantilevers also have the potential of high-throughput measurements and are, therefore, suitable for screening . It still needs to be validated if cultures of multiple bacteria and co-cultures with human cells are possible.
Since their introduction in the mid-1970s, optical methods have developed into sophisticated tools over the years. Generally, light beams are used to detect, stimulate or trap bacterial cells. Common variables measured are bacterial motion, molecular vibrations, bacterial density and fluorescence intensity. Depending on the method, bacterial strains can be identified and a bacterial growth curve as well as quantitative MICs can be determined. Because they rely on expensive and complex spectroscopic readout methods, optical methods are mostly applied in research settings.
Optical density (OD) measurement
Biomimetic polymer sensor
The biomimetic polymer sensor is a fast, automated assay based on the principles of agar plate CFU counting . Membrane-active compounds such as peptides and toxins that are secreted by bacteria interact with agar-embedded nanoparticles, made of phospholipids and chromatic polymer polydiacetylene (PDA). During interaction with these compounds, PDA undergoes a transition from blue to red and starts to emit intense fluorescence at 560 nm and 640 nm, which can be detected by common high-throughput screening instruments, such as multi-well fluorescence plate readers. The initial fluorescence signals indicating the colour change are detectable in less than 6 h, faster than the bacterial colony formation visible to the naked eye. Therefore, it is a much faster and easier method to determine bacterial growth than the common agar plate counting. In addition, it is possible to incorporate antimicrobial agents into the chromatic matrix, which enables the simultaneous detection of antibiotic susceptibility while monitoring bacterial growth. Such a measurement can also be performed at the single-cell level, as a single cell can be fluorescently detected. This method has been proven suitable for various E. coli strains, Bacillus cereus and S. enterica, and is potentially applicable to a wide range of bacteria, including Gram-positive and -negative strains. One major disadvantage of this technique is that it does not differentiate between bacterial species and is not suitable for screening unknown pathogens. Bacterial mixtures, common for clinical samples, cannot be analysed by this method. Within the study of Silbert et al. , the variability of colour responses between experiments was reported to be 10–15%, which may limit the suitability of the method for precise MIC determination. Furthermore, no official standardised protocol is available. Despite these disadvantages that require more research, the PDA sensor method is a fast screening alternative to the gold-standard agar plate counting method.
Flow cytometry and fluorescence-activated cell sorting (FACS)
Fluorescence-activated cell sorting (FACS), a method of flow cytometry, is used to distinguish between cell types and to determine their viability and count by light scattering and fluorescent activity. Bacterial viability and antibiotic susceptibility can be determined by combining bacteria, antibiotics and relevant fluorescent stains . After 2 h of incubation of cells with fluorescent stains, the sample can be analysed by flow cytometry. Standardised protocols and standardised data interpretation are not available for AST and vary for each pathogen and antimicrobial agent. So far, flow cytometry in AST has primarily been used for MRSA [83, 84], MSSA [83, 84], E. coli  and S. enterica . As certain fluorescent stains can bind to nucleic acids within the bacterial cells, permeabilised cells give stronger fluorescent emission than non-permeabilised cells, and completely lysed cells give an even greater amount of fluorescence. For example, MRSA were found to show a greater population of viable cells than MSSA after incubation with oxacillin . However, the amount of fluorescence activity is not necessarily in direct correlation with the viability of the cells, due to the fact that flow cytometry inadequately distinguishes single cells from cell aggregates. Second, the amount of fluorescence is dependent on size and metabolic characteristics that vary per bacterial strain, and are also dependent on the antibiotic used. Third, some antibiotics can alter the extent of permeability of bacteria by inducing a membrane potential . Cell lysis often occurs due to exposure to β-lactams and results in brighter fluorescent emission without a change in the number of cells . Flow cytometric results and images taken with an epifluorescence microscope should, therefore, be interpreted carefully. Shrestha et al.  as well as Sánchez-Romero and Casadesús  found that antibiotic resistance could be determined and distinguished from susceptible strains after 2 h with both side and forward scatter using flow cytometry. Another benefit of this technique is that it can be used for prokaryotic and eukaryotic cells. Within a co-culture of bacteria and osteoblasts to study infection, both cell types can be distinguished and analysed by their viability over time, even in the presence of antimicrobial compounds. With its fast performance and wide potential, flow cytometry could find powerful application in research and in clinical screening. However, extensive research is needed to improve its validity.
Image analysis software for quantification of bacterial growth
Bacterial growth can also be analysed by taking images of different growth stages and quantifying them with an image analysis program. There are different types of software available, such as CellProfiler , Colony Imager , YeastXtract  and HT Colony Analyser . Colonyzer, for example, is a software program that automatically quantifies the size, granularity, colour and location of microbial organisms on solid agar plates . It includes a collection of image analysis algorithms that can detect low-density organisms from plate micrographs that are barely visible to the human eye. Most software programs for quantifications are open source, and when using only agar colonisation, this method is relatively cheap, with easy handling and is applicable for a variety of pathogens. So-called digital plate reading systems are available and already thoroughly discussed . Due to their high sensitivity, image analysis software and digital plate reading systems are able to detect early growth of pathogens. MICs can, therefore, be determined much faster. Furthermore, the exposure to pathogens is decreased when closed systems are used and common laboratory work, such as plate labelling, is not necessary due to automation. Some disadvantages of image analysis software or digital plate reading systems are that co-culture with eukaryotic cells is not possible with agar plates, and antimicrobial release from a substrate other than solid agar is not available. In order to determine the exact MIC, many consecutive images have to be taken and analysed, which can still be time-consuming, depending on the software, amount of samples and extent of automation. In summary, the image analysis software available is a reliable method for AST under appropriate conditions.
In a proof-of-principle study, Samadi et al.  showed that a single bacterium can be trapped in an optical tweezer, a 1064-nm laser beam, in order to assess the effect of chemical agents. The highly diluted sample is mounted on a piezoelectric transducer. The light scattered forward from the trapped cell is collected and sent to a position-sensitive detector. With statistical analysis of the position signal at different time points, the viability can be determined. This measurement is sensitive to such an extent that flagellar motion can be distinguished from the Brownian motion. Depending on the concentration of the chemical agent and bacteria type, the single-cell response can be determined within a couple of minutes, which is a major advantage. The single-cell trapping makes it a safe application for research and offers the opportunity for in-depth analysis of bacterial viability and the precise “killing time” upon antimicrobial exposure. Precise MICs can be determined without growing colonies or pre-culturing the cells. However, the system is only applicable for motile bacteria. For AST applications, this method has been demonstrated only for E. coli and, therefore, has no standardised protocols. With further research and validation, this technique could be promising due to its high-sensitivity capabilities.
Raman spectroscopy has also been demonstrated to be a precise technique to measure the susceptibility of bacteria to antimicrobial compounds [93, 94, 95]. Using a frequency-doubled argon ion laser at an excitation wavelength of 244 nm, Raman scattering of aromatic amino acids and nucleic acid bases is induced within bacterial cells and can be detected by vibrational bands. The total cell mass can be determined over time and plotted, giving the growth curve for the different bacterial growth phases, starting with the log phase, followed by the exponential growth phase, retardation phase and stationary phase prior to cell death. Every single phase can be seen within Raman spectra. Common analysis techniques include hierarchical cluster analysis (HCA) and principal component analysis (PCA). Raman spectroscopy can provide information about the underlying mechanism by which a drug affects the bacterial cells with the help of statistical methods. A different Raman technique, called the laser tweezers Raman spectroscopy (LTRS), allows single-cell analysis. In contrast to the common Raman technique, LTRS is applicable to low bacterial concentrations and distinguishes cellular drug response from normal growth of E. coli after 4.5 h [96, 97].
Raman spectroscopy has the advantage that any type of microbe and antimicrobial compound can be analysed, as long as nucleic acids and proteins are present to a sufficient extent, and size and migration characteristics are taken into account. Additionally, Raman spectroscopy is highly sensitive regarding which bands of nucleic acids and which building blocks of proteins are excited. This makes it possible to assess the actual mechanism of antimicrobial compounds to kill microbes. One disadvantage is the high costs of Raman spectroscopes, although running costs are relatively low. Even though Raman spectroscopy has been used in clinics as a diagnostic tool for cancer , it has not yet been applied as AST method.
Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS)
Rapid antimicrobial susceptibility testing (RAST) methods
Beginning in the mid-1970s, research efforts have focused on improving these gold-standard AST methods, in order to reduce costs, time and effort. Rapid antimicrobial susceptibility testing (RAST) methods are optical detection systems that facilitate the observation of small changes in bacterial growth and were assessed with gold-standard methods such as Kirby–Bauer or agar dilution as the control. The Autobac 1 system  was introduced in 1975 as an RAST method. Other machines, such the Vitek2, BAXTER MicroScan Walkaway, BD Phoenix Automated Microbiology System and Sensititre ARIS 2X, followed and are FDA-approved for clinical application [14, 107, 108, 109, 110]. All use similar techniques, including microdilution trays or small cards that are inoculated with a known concentration of bacteria. Subsequently, multiple antibiotic tests can be performed on the same tray/card. Readout is done by photometers or fluorometers, which monitor reactions in each well and report changes within 3 to 24 h. The underlying software is able to integrate information from multiple reactions, to identify microorganisms and to comment on mechanisms of resistance patterns. Depending on the system, it is possible to use untreated clinical samples. Various studies compared these systems with gold-standard techniques and found that automated systems accelerated patient treatment, decreased the mortality rate and were cost-saving [107, 108, 110]. Newer methods were found to deliver even more reliable results, showing that even well-established automated systems have their constraints . However, a detailed discussion on the reliability and constraints of RAST methods is beyond the scope of this review. To summarise, automated systems show, due to their speed and resulting decrease in mortality rate and cost-savings, clear benefits as diagnostic tools in the clinic.
Microfluidics and microdroplets methods
Developed in the last decade, microfluidics and microdroplets methods enable the building of complex in vitro systems on one chip. By the use of small amounts of fluids through channels in the micrometre range, chips can be designed on which several functionalities, such as bacterial culture, nucleic acid hybridisation and amplification, and cell lysis, can be combined. These hold the major advantages of “temporal and special control over fluids and physical parameters, and integration of sensors to obtain direct and in situ read-out” . So far, detection within microfluidic AST methods is generally electrochemical or optical/microcalorimetric. These methods provide rapid results and show the potential for high throughput, due to a high surface-to-volume ratio. Currently, microfluidic AST methods have been used only for antimicrobial research, but hold great potential for clinical application for several reasons, such as a reduction in workload and materials . Besides the following methods, interested readers are also referred to more research highlights within antimicrobial microfluidics .
Microfluidic agarose channels (MAC) tracking single cell growth
This method enables single-cell analysis in a closed system, which creates a safer work environment. Data (MIC values) can be obtained within 3–4 h, which is highly advantageous compared to standard clinical AST methods for fast patient diagnosis and treatment. One disadvantage is that clinical samples need several preparation steps before the analysis can be performed. The MAC system is an improvement regarding MIC determination, compared to gold-standard methods. Whether conclusions on resistance can also be drawn still needs to be investigated.
Microfluidic pH sensor
The microfluidic pH sensor has the major advantage in that a bacterial growth curve can be assessed within 2 h. As a major disadvantage, this method cannot be used with co-cultures, as eukaryotic cells would produce metabolites and, thereby, induce a pH change, independent of the bacterial growth. It remains to be determined whether this method can be used to assess antimicrobial resistance.
Self-loading microfluidics device
This self-loading device requires only a single pipetting step for testing several antibiotic concentrations simultaneously, which minimises the workload, the amount of reagents and equipment for readout. Furthermore, the chip enables testing of a variety of bacteria ranging from Gram-positive to Gram-negative ones. Its working is confirmed for E. faecalis, Proteus mirabilis, Klebsiella pneumoniae and E. coli. However, a major drawback is that the commercial test will offer only a standard number of antibiotics at certain concentrations, thereby leaving no experimental flexibility. Moreover, the determination of resistance is only done by measuring extreme concentrations compared to wild-type susceptible strains and does not take into account a method that could detect the mechanism of resistance. Despite the downsides, this and similar tools provide a new direction and opportunities in AST.
The major advantages of microdroplets are the possibility to fine-tune the antibiotic concentrations , the outstanding reproducibility of conditions within the droplets and the opportunity for high-throughput studies [111, 118, 119]. Furthermore, the single-cell method was found to be highly specific, as the metabolism of single cells can be observed. Long-term bacterial growth can be determined by scanning the same droplet array repeatedly. This technique may be limited when applied to bacterial species or strains that do not produce detectable osmotic changes or are unable to express a fluorescent reporter gene. The MDA disadvantageously depends on fluorescence, which requires special handling, always entails the risk of bleaching and can be affected by cellular lysis by antibiotics, as previously described . While promising, this method requires further development and refinement to be reliably applied for AST.
Osteoblast infection models
Osteoblast infection models are in vivo-like models which make use of co-cultures of bacteria and eukaryotic cells and have been developed in the past decade. Currently, such models are predominantly applied in research and no accepted clinical applications yet exist. By ensuring a certain degree of eukaryotic cell infection, the models can test the extra- and intracellular antimicrobial activity of compounds and assess the long-term effect of bacteria and antimicrobial compounds on human cells. This is necessary as various bacterial strains are likely to invade human cells. Readout generally requires fluorescence and microscopic tools, by which fluorescent intensity is related to cellular viability. Although these models are slow, they provide qualitative results on bacterial growth, offer insight into antimicrobial resistance mechanisms and provide the opportunity to test novel materials, such as bone graft substitutes.
Human osteoblast infection model
Kreis et al.  set up a human osteoblast infection model in order to determine the activity of tigecycline against intracellular S. aureus, which is capable of invading human cells. Overnight bacterial cultures were added to a washed osteoblast culture and incubated for 30 min to ensure a multiplicity of infection (MOI) of 100, with an optical density of 1. Different antibiotic agents were then separately added to the cultures at a dosage that mimics the high concentration of intravenous administration. After 20 h and 40 h, the cells were washed, which destroys the osteoblasts and leaves behind only the intracellular bacteria. The bacteria were subsequently diluted and seeded on agar plates for CFU counting . One major advantage of this model is that it resembles more closely an in vivo situation compared to other AST methods, as it includes the effect of antibiotics on internalised bacteria. However, it is difficult to assess whether this model can be extended to in vivo scenarios, as it has only been evaluated for S. aureus, which easily develops resistances to antibiotics . Additionally, this model is time-consuming and requires sufficient cell and bacterial culture knowledge to properly interpret the results.
Microfluidic titanium (Ti) alloy model
The experimental setup of this microfluidics-based infection model—featuring osteoblasts, serum-based medium and a constant perfusion flow rate—mimics the in vivo situation representing infections related to bone implants. It has not yet been used for testing the effect of antibiotics, so its AST capabilities remain to be determined. This might be a challenge as with only the co-culture, osteoblasts were difficult to be kept alive after 25 h. Furthermore, the model does not support the analysis of intracellular bacteria, taken up by osteoblasts, which are usually responsible for recurrent infections. Nevertheless, its separated channels offer the possibility to test several antimicrobial agents or concentrations simultaneously, provided the inlets are connected to different sources. This interesting model will need improvement to give a more realistic and in vivo-like competition between osteoblasts and bacteria in “race for the surface”.
Microfluidic 3D bone tissue model
This 3D bone tissue model has the major advantage in that it creates a more in vivo-like environment for bacterial susceptibility testing. The infection of osteoblasts and subsequent antibiotic treatment were shown to be successful in this model, as determined by osteoblast proliferation and survival. However, this model utilised osteoblasts and a single S. epidermis strain, and investigations with primary human cells as well as multiple bacterial species and strains are required to evaluate the model’s capabilities. Another disadvantage is that alkaline phosphatase and osteocalcin markers were difficult to measure due to the small amount of cells and effluents. Real-time monitoring of biomineralisation and bone tissue density could be a further improvement, as they would give much more reliable data on the efficacy and quality of 3D tissue culture . While 3D models are considered more representative of in vivo systems, much more development is needed in order for such methods to be accepted as suitable replacements to traditional AST techniques.
Bacterial infections accompanying trauma surgery and total joint replacements have been an unresolved problem for decades. In order to determine optimal treatment and detect possible resistance of bacteria against antibiotics, AST methods have been developed and refined over the last century.
The first antimicrobial susceptibility methods on the market were various agar or broth dilution and disc diffusion techniques, which have become the gold-standard tests to which all other AST methods are currently compared during development, identification, validation and clinical trials . Despite their simplicity, gold-standard techniques are still clinically applied with almost no changes since the first development, as they possess the major benefits of being cheap and not requiring any expensive equipment. Furthermore, as these techniques have been applied extensively, they are found to be very reliable in determining the antimicrobial effectivity and have been validated for a variety of bacterial strains. However, such techniques fail to provide insight into the mechanism of antimicrobial resistance, are relatively time-consuming and require knowledge of working with the specific bacteria.
Newer techniques, including mechanical, optical and microfluidics methods, were found to be suitable for single-cell analysis, subsequent quantification and for determining underlying resistance mechanisms. In addition, they provide much faster MIC values and depiction of antimicrobial resistance, even at low bacterial concentrations. Another major benefit is that these systems are isolated, which creates a much safer work environment. Microfluidics and microdroplets methods qualify as high-throughput systems, as they enable the fine-tuning of antibiotic concentrations and other complex conditions in a reproducible manner. However, most of these methods have only been proven to function for one type of bacteria and, therefore, need validation for other strains, as well as the ability to analyse bacterial mixtures. Furthermore, all systems require preparation of clinical samples or depend on fluorescence, which augments the workload due to special handling.
Compared to microfluidics methods, the osteoblast infection models extend the possibility to create a more in vivo-like infective situation. This allows for a more in-depth research of the bacterial–human cell interaction, internalisation of bacteria into host cells and, furthermore, the mechanism of resistance for an antimicrobial compound, as actual tissue is grown. Unfortunately, these models are currently only suitable for research rather than clinical application due to their intensive mode of use. Precise control of the bacteria–cell–antibiotic ratio and further culture conditions are required in order to mimic a naturally occurring infection. Furthermore, little to no validation is performed of these models for AST and such tissue models require weeks of culture. However, they show great promise in becoming of important diagnostic value in implant-related infections.
Although not discussed within this review, biofilm models feature prominently in AST. Planktonic and biofilm bacteria were found to be affected differently by antibiotics and, therefore, need different models as well . Actual biofilm models provide better information on resistance mechanism in bacterial cell aggregations. One step further in designing realistic infection models would then be the implementation of biomaterials such as bone graft materials into biofilm models to imitate host tissue infection. These models would be extremely useful as, first, more realistic infection models would be created and, second, novel, smart bone graft substitutes  and implant coatings could be tested for their antimicrobial characteristics.
In conclusion, this review of the existing antimicrobial susceptibility testing (AST) methods has shown that it is difficult to develop a system that combines the advantages of being precise, versatile, inexpensive and easy to perform. The selection of one AST method over another depends on the application: clinical needs for analysing patient material are different from what is required to answer important fundamental research questions. A method applicable to both environments is not very likely to be developed. Even if state-of-the-art models such as osteoblast infection or 3D tissue models can mimic in vivo-like situations to a certain extent, their current clinical value is low. This is due to their time-consuming process, requirement of sufficient cell and bacterial knowledge, and the fact that their application is limited to determining the microbial and antimicrobial coating and implant interaction. Osteoblast infection models have to be expanded towards multiple bacteria and strains, the introduction of biomaterials, as well as reduce their time consumption to be of sufficient clinical value. An exception are microfluidics methods, which show great potential for both research and clinical application. With easy readouts and high-throughput, they can be useful in the clinic, while offering the possibility to perform in-depth mechanistic study, relevant to fundamental research. Continued validation and optimisation of these new methods should offer robust AST options. In particular, with recent reports of increased antimicrobial resistance, there is an urgency to develop and implement methods that analyse recurrent infections induced by intracellular bacteria. These could be biofilm models including biomaterials in the future.
We would like to thank Hang Nguyen (the Maastricht Multimodal Molecular Imaging Institute, Maastricht University) for the comments on a preliminary version of the manuscript.
This article did not require any funding.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent is not applicable, as this article does not contain any studies with human participants.
- 4.Tuchscherr L, Medina E, Hussain M, Völker W, Heitmann V, Niemann S, Holzinger D, Roth J, Proctor RA, Becker K, Peters G, Löffler B (2011) Staphylococcus aureus phenotype switching: an effective bacterial strategy to escape host immune response and establish a chronic infection. EMBO Mol Med 3(3):129–141. https://doi.org/10.1002/emmm.201000115 PubMedPubMedCentralCrossRefGoogle Scholar
- 5.Proctor RA, Kriegeskorte A, Kahl BC, Becker K, Löffler B, Peters G (2014) Staphylococcus aureus Small Colony Variants (SCVs): a road map for the metabolic pathways involved in persistent infections. Front Cell Infect Microbiol 4:99. https://doi.org/10.3389/fcimb.2014.00099 PubMedPubMedCentralCrossRefGoogle Scholar
- 8.Garcia LG, Lemaire S, Kahl BC, Becker K, Proctor RA, Denis O, Tulkens PM, Van Bambeke F (2013) Antibiotic activity against small-colony variants of Staphylococcus aureus: review of in vitro, animal and clinical data. J Antimicrob Chemother 68(7):1455–1464. https://doi.org/10.1093/jac/dkt072 PubMedCrossRefGoogle Scholar
- 9.Unden G, Müllner M, Reinhart F (2009) Chapter 16. Sensing of oxygen by bacteria. In: Krämer R, Jung K (eds) Bacterial signaling, 1st edn. Wiley, WeinheimGoogle Scholar
- 10.Rote NS, Huether SE (2014) Chapter 10. Infection. In: McCance KL, Huether SE (eds) Pathophysiology, 7th edn. Elsevier Health Sciences, St. LouisGoogle Scholar
- 11.Murray PR, Witebsky FG (2014) The clinician and the microbiology laboratory. In: Bennett JE, Dolin R, Blaser MJ (eds) Principles and practice of infectious diseases, 8th edn. Elsevier Health Sciences, St. LouisGoogle Scholar
- 12.Poupard JA, Rittenhouse SF, Walsh LR (2013) The evolution of antimicrobial susceptibility testing methods. Advances in experimental medicine and biology, vol 349. Springer, USAGoogle Scholar
- 14.Jorgensen JH, Turnidge JD (2015) Chapter 71. Susceptibility test methods: dilution and disk diffusion methods*. In: Jorgensen J, Pfaller M, Carroll K, Funke G, Landry M, Richter S, Warnock D (ed) Manual of clinical microbiology, 11th edn. ASM Press, Washington, DC, pp 1253–1273. https://doi.org/10.1128/9781555817381.ch71
- 17.Leclercq R, Cantón R, Brown DFJ, Giske CG, Heisig P, MacGowan AP, Mouton JW, Nordmann P, Rodloff AC, Rossolini GM, Soussy CJ, Steinbakk M, Winstanley TG, Kahlmeter G (2013) EUCAST expert rules in antimicrobial susceptibility testing. Clin Microbiol Infect 19(2):141–160. https://doi.org/10.1111/j.1469-0691.2011.03703.x PubMedCrossRefGoogle Scholar
- 20.Clinical and Laboratory Standards Institute (CLSI) (2012) Methods for antimicrobial susceptibility testing of anaerobic bacteria; Approved standard—Eighth edition. CLSI document M11-A8. http://shop.clsi.org/microbiology-documents/M11.html
- 21.Clinical and Laboratory Standards Institute (CLSI) (2012) Methods for dilution antimicrobial susceptibility tests for bacteria that grow aerobically; Approved standard—Ninth edition. CLSI document M07-A9Google Scholar
- 23.Malhotra-Kumar S, Haccuria K, Michiels M, Ieven M, Poyart C, Hryniewicz W, Goossens H; MOSAR WP2 Study Team (2008) Current trends in rapid diagnostics for methicillin-resistant Staphylococcus aureus and glycopeptide-resistant enterococcus species. J Clin Microbiol 46(5):1577–1587. https://doi.org/10.1128/JCM.00326-08 PubMedPubMedCentralCrossRefGoogle Scholar
- 24.Ericsson HM, Sherris JC (1971) Antibiotic sensitivity testing. Report of an international collaborative study. Acta Pathol Microbiol Scand B Microbiol Immunol 217(Suppl 217):1+Google Scholar
- 25.Clinical and Laboratory Standards Institute (CLSI) (1999) Methods for determining bactericidal activity of antimicrobial agents; Approved standard. CLSI document M26-A. http://shop.clsi.org/microbiology-documents/M26.html
- 26.Credito K, Lin G, Appelbaum PC (2007) Activity of daptomycin alone and in combination with rifampin and gentamicin against Staphylococcus aureus assessed by time–kill methodology. Antimicrob Agents Chemother 51(4):1504–1507. https://doi.org/10.1128/AAC.01455-06 PubMedPubMedCentralCrossRefGoogle Scholar
- 28.Clinical and Laboratory Standards Institute (CLSI) (2015) Performance standards for antimicrobial disk susceptibility tests; Approved standard—Twelfth Edition. CLSI document M02-A12. http://shop.clsi.org/microbiology-documents/M02-M100-PK.html
- 29.Clinical and Laboratory Standards Institute (CLSI) (2012) Performance standards for antimicrobial disk susceptibility tests; Approved standard—Eleventh edition. CLSI document M02-A11Google Scholar
- 30.Clinical and Laboratory Standards Institute (CLSI) (2016) Methods for antimicrobial dilution and disk susceptibility testing of infrequently isolated or fastidious bacteria, 3rd edn. CLSI document M45. http://shop.clsi.org/microbiology-documents/M45.html
- 34.Clinical and Laboratory Standards Institute (CLSI) (2007) Methods for antimicrobial susceptibility testing of anaerobic bacteria; Approved standard—Seventh edition. CLSI document M11-A7Google Scholar
- 40.Braissant O, Müller G, Egli A, Widmer A, Frei R, Halla A, Wirz D, Gasser TC, Bachmann A, Wagenlehner F, Bonkat G (2014) Seven hours to adequate antimicrobial therapy in urosepsis using isothermal microcalorimetry. J Clin Microbiol 52(2):624–626. https://doi.org/10.1128/JCM.02374-13 PubMedPubMedCentralCrossRefGoogle Scholar
- 41.Liu X, Pai PJ, Zhang W, Hu Y, Dong X, Qian PY, Chen D, Lam H (2016) Proteomic response of methicillin-resistant S. aureus to a synergistic antibacterial drug combination: a novel erythromycin derivative and oxacillin. Sci Rep 6:19841. https://doi.org/10.1038/srep19841 PubMedPubMedCentralCrossRefGoogle Scholar
- 42.York MK, Gibbs L, Chehab F, Brooks GF (1996) Comparison of PCR detection of mecA with standard susceptibility testing methods to determine methicillin resistance in coagulase-negative staphylococci. J Clin Microbiol 34(2):249–253 http://jcm.asm.org/content/34/2/249.abstract PubMedPubMedCentralGoogle Scholar
- 43.Carver PL, Lin SW, DePestel DD, Newton DW (2008) Impact of mecA gene testing and intervention by infectious disease clinical pharmacists on time to optimal antimicrobial therapy for Staphylococcus aureus bacteremia at a university hospital. J Clin Microbiol 46(7):2381–2383. https://doi.org/10.1128/JCM.00801-08 PubMedPubMedCentralCrossRefGoogle Scholar
- 44.Figueiredo S, Bonnin RA, Poirel L, Duranteau J, Nordmann P (2012) Identification of the naturally occurring genes encoding carbapenem-hydrolysing oxacillinases from Acinetobacter haemolyticus, Acinetobacter johnsonii, and Acinetobacter calcoaceticus. Clin Microbiol Infect 18(9):907–913. https://doi.org/10.1111/j.1469-0691.2011.03708.x PubMedCrossRefGoogle Scholar
- 46.Gröbner S, Dion M, Plante M, Kempf VA (2009) Evaluation of the BD GeneOhm StaphSR assay for detection of methicillin-resistant and methicillin-susceptible Staphylococcus aureus isolates from spiked positive blood culture bottles. J Clin Microbiol 47(6):1689–1694. https://doi.org/10.1128/JCM.02179-08 PubMedPubMedCentralCrossRefGoogle Scholar
- 47.Mehta MS, McClure JT, Mangold K, Peterson LR (2015) Performance of 3 real-time PCR assays for direct detection of Staphylococcus aureus and MRSA from clinical samples. Diagn Microbiol Infect Dis 83(3):211–215. https://doi.org/10.1016/j.diagmicrobio.2014.06.005 PubMedCrossRefGoogle Scholar
- 49.Cuny C, Witte W (2005) PCR for the identification of methicillin-resistant Staphylococcus aureus (MRSA) strains using a single primer pair specific for SCCmec elements and the neighbouring chromosome-borne orfX. Clin Microbiol Infect 11(10):834–837. https://doi.org/10.1111/j.1469-0691.2005.01236.x PubMedCrossRefGoogle Scholar
- 50.Jones CH, Tuckman M, Howe AY, Orlowski M, Mullen S, Chan K, Bradford PA (2006) Diagnostic PCR analysis of the occurrence of methicillin and tetracycline resistance genes among Staphylococcus aureus isolates from phase 3 clinical trials of tigecycline for complicated skin and skin structure infections. Antimicrob Agents Chemother 50(2):505–510. https://doi.org/10.1128/AAC.50.2.505-510.2006 PubMedPubMedCentralCrossRefGoogle Scholar
- 51.Sinsimer D, Leekha S, Park S, Marras SA, Koreen L, Willey B, Naidich S, Musser KA, Kreiswirth BN (2005) Use of a multiplex molecular beacon platform for rapid detection of methicillin and vancomycin resistance in Staphylococcus aureus. J Clin Microbiol 43(9):4585–4591. https://doi.org/10.1128/JCM.43.9.4585-4591.2005 PubMedPubMedCentralCrossRefGoogle Scholar
- 52.Zhang K, McClure JA, Elsayed S, Louie T, Conly JM (2005) Novel multiplex PCR assay for characterization and concomitant subtyping of staphylococcal cassette chromosome mec types I to V in methicillin-resistant Staphylococcus aureus. J Clin Microbiol 43(10):5026–5033. https://doi.org/10.1128/JCM.43.10.5026-5033.2005 PubMedPubMedCentralCrossRefGoogle Scholar
- 53.Huletsky A, Giroux R, Rossbach V, Gagnon M, Vaillancourt M, Bernier M, Gagnon F, Truchon K, Bastien M, Picard FJ, van Belkum A, Ouellette M, Roy PH, Bergeron MG (2004) New real-time PCR assay for rapid detection of methicillin-resistant Staphylococcus aureus directly from specimens containing a mixture of staphylococci. J Clin Microbiol 42(5):1875–1884. https://doi.org/10.1128/JCM.42.5.1875-1884.2004 PubMedPubMedCentralCrossRefGoogle Scholar
- 57.Kunze N, Moerer O, Steinmetz N, Schulze MH, Quintel M, Perl T (2015) Point-of-care multiplex PCR promises short turnaround times for microbial testing in hospital-acquired pneumonia—an observational pilot study in critical ill patients. Ann Clin Microbiol Antimicrob 14(1):33. https://doi.org/10.1186/s12941-015-0091-3 PubMedPubMedCentralCrossRefGoogle Scholar
- 58.Jamal W, Al Roomi E, Abdul Aziz LR, Rotimi VO (2014) Evaluation of Curetis Unyvero, a multiplex PCR-based testing system, for rapid detection of bacteria and antibiotic resistance and impact of the assay on management of severe nosocomial pneumonia. J Clin Microbiol 52(7):2487–2492. https://doi.org/10.1128/JCM.00325-14 PubMedPubMedCentralCrossRefGoogle Scholar
- 60.Sinn I, Albertson T, Kinnunen P, Breslauer DN, McNaughton BH, Burns MA, Kopelman R (2012) Asynchronous magnetic bead rotation microviscometer for rapid, sensitive, and label-free studies of bacterial growth and drug sensitivity. Anal Chem 84(12):5250–5256. https://doi.org/10.1021/ac300128p PubMedPubMedCentralCrossRefGoogle Scholar
- 61.Kinnunen P, Sinn I, McNaughton BH, Newton DW, Burns MA, Kopelman R (2011) Monitoring the growth and drug susceptibility of individual bacteria using asynchronous magnetic bead rotation sensors. Biosens Bioelectron 26(5):2751–2755. https://doi.org/10.1016/j.bios.2010.10.010 PubMedCrossRefGoogle Scholar
- 66.Cermak N, Olcum S, Delgado FF, Wasserman SC, Payer KR, Murakami AM, Knudsen SM, Kimmerling RJ, Stevens MM, Kikuchi Y, Sandikci A, Ogawa M, Agache V, Baléras F, Weinstock DM, Manalis SR (2016) High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays. Nat Biotechnol 34(10):1052–1059. https://doi.org/10.1038/nbt.3666 PubMedPubMedCentralCrossRefGoogle Scholar
- 71.Stevens MM, Maire CL, Chou N, Murakami MA, Knoff DS, Kikuchi Y, Kimmerling RJ, Liu H, Haidar S, Calistri NL, Cermak N, Olcum S, Cordero NA, Idbaih A, Wen PY, Weinstock DM, Ligon KL, Manalis SR (2016) Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate. Nat Biotechnol 34(11):1161–1167. https://doi.org/10.1038/nbt.3697 PubMedPubMedCentralCrossRefGoogle Scholar
- 74.Kilian O, Hossain H, Flesch I, Sommer U, Nolting H, Chakraborty T, Schnettler R (2009) Elution kinetics, antimicrobial efficacy, and degradation and microvasculature of a new gentamicin-loaded collagen fleece. J Biomed Mater Res B Appl Biomater 90(1):210–222. https://doi.org/10.1002/jbm.b.31275 PubMedGoogle Scholar
- 83.Shrestha NK, Scalera NM, Wilson DA, Procop GW (2011) Rapid differentiation of methicillin-resistant and methicillin-susceptible Staphylococcus aureus by flow cytometry after brief antibiotic exposure. J Clin Microbiol 49(6):2116–2120. https://doi.org/10.1128/JCM.02548-10 PubMedPubMedCentralCrossRefGoogle Scholar
- 85.Sánchez-Romero MA, Casadesús J (2014) Contribution of phenotypic heterogeneity to adaptive antibiotic resistance. Proc Natl Acad Sci U S A 111(1):355–360. https://doi.org/10.1073/pnas.1316084111
- 86.Kohanski MA, Dwyer DJ, Collins JJ (2010) How antibiotics kill bacteria: from targets to networks. Nat Rev Microbiol 8(6):423–435. https://doi.org/10.1038/nrmicro2333
- 89.Shah NA, Laws RJ, Wardman B, Zhao LP, Hartman JLT 4th (2007) Accurate, precise modeling of cell proliferation kinetics from time-lapse imaging and automated image analysis of agar yeast culture arrays. BMC Syst Biol 1:3. https://doi.org/10.1186/1752-0509-1-3 PubMedPubMedCentralCrossRefGoogle Scholar
- 93.Neugebauer U, Schmid U, Baumann K, Holzgrabe U, Ziebuhr W, Kozitskaya S, Kiefer W, Schmitt M, Popp J (2006) Characterization of bacterial growth and the influence of antibiotics by means of UV resonance Raman spectroscopy. Biopolymers 82(4):306–311. https://doi.org/10.1002/bip.20447 PubMedCrossRefGoogle Scholar
- 101.Dubois D, Leyssene D, Chacornac JP, Kostrzewa M, Schmit PO, Talon R, Bonnet R, Delmas J (2010) Identification of a variety of Staphylococcus species by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J Clin Microbiol 48(3):941–945. https://doi.org/10.1128/JCM.00413-09 PubMedCrossRefGoogle Scholar
- 102.Clark AE, Kaleta EJ, Arora A, Wolk DM (2013) Matrix-assisted laser desorption ionization-time of flight mass spectrometry: a fundamental shift in the routine practice of clinical microbiology. Clin Microbiol Rev 26(3):547–603. https://doi.org/10.1128/CMR.00072-12 PubMedPubMedCentralCrossRefGoogle Scholar
- 106.Thornsberry C, Gavan TL, Sherris JC, Balows A, Matsen JM, Sabath LD, Schoenknecht F, Thrupp LD, Washington JA (1975) Laboratory evaluation of a rapid, automated susceptibility testing system: report of a collaborative study. Antimicrob Agents Chemother 7(4):466–480. https://doi.org/10.1128/aac.7.4.466 PubMedPubMedCentralCrossRefGoogle Scholar
- 109.Carroll KC, Borek AP, Burger C, Glanz B, Bhally H, Henciak S, Flayhart DC (2006) Evaluation of the BD Phoenix automated microbiology system for identification and antimicrobial susceptibility testing of staphylococci and enterococci. J Clin Microbiol 44(6):2072–2077. https://doi.org/10.1128/JCM.02636-05 PubMedPubMedCentralCrossRefGoogle Scholar
- 111.Jiang L, Boitard L, Broyer P, Chareire AC, Bourne-Branchu P, Mahé P, Tournoud M, Franceschi C, Zambardi G, Baudry J, Bibette J (2016) Digital antimicrobial susceptibility testing using the MilliDrop technology. Eur J Clin Microbiol Infect Dis 35(3):415–422. https://doi.org/10.1007/s10096-015-2554-z PubMedCrossRefGoogle Scholar
- 123.Lee JH, Gu Y, Wang H, Lee WY (2012) Microfluidic 3D bone tissue model for high-throughput evaluation of wound-healing and infection-preventing biomaterials. Biomaterials 33(4):999–1006. https://doi.org/10.1016/j.biomaterials.2011.10.036 PubMedCrossRefGoogle Scholar
- 125.Zaatreh S, Wegner K, Strauß M, Pasold J, Mittelmeier W, Podbielski A, Kreikemeyer B, Bader R (2016) Co-culture of S. epidermidis and human osteoblasts on implant surfaces: an advanced in vitro model for implant-associated infections. PLoS One 11(3):e0151534. https://doi.org/10.1371/journal.pone.0151534 PubMedPubMedCentralCrossRefGoogle Scholar
- 127.Yuan H, Fernandes H, Habibovic P, de Boer J, Barradas AMC, de Ruiter A, Walsh WR, van Blitterswijk CA, de Bruijn JD (2010) Osteoinductive ceramics as a synthetic alternative to autologous bone grafting. Proc Natl Acad Sci U S A 107(31):13614–13619. https://doi.org/10.1073/pnas.1003600107 PubMedPubMedCentralCrossRefGoogle Scholar
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.