The threat of antibiotic resistance is the most pressing global crisis that faces humanity today. Accelerated by misuse and over-prescription, natural selection for resistant strains can be slowed down if antibiotics are prescribed more responsibly, that is, prescribed only to treat susceptible bacteria. Part of the challenge of minimising the spread of resistant bacteria is the limited ability to perform antibiotic susceptibility tests (ABT) in regions that do not have access to laboratories or trained professionals to interpret results.
Currently, the gold standard for antibiotic susceptibility testing is the broth microdilution method. This involves suspending bacteria isolated from the patient in liquid growth media, diluting them and dispensing them in a 96-well microtiter plate (MTP). The wells in the plates contain varying amounts of antibiotics and are incubated overnight before microbiologists observe the wells for growth.
Growth is quantified using turbidity, or how cloudy the solution appears. A more turbid sample indicates more growth, while a less turbid sample indicates less growth. According to the US Food and Drug Administration (FDA) and Clinical Laboratory Standards Institute (CLSI) standards, a bacteria/drug combination is categorised as either ‘susceptible’ or ‘resistant’. The lowest concentration of antibiotic required to prohibit growth is called the ‘minimum inhibitory concentration’ (MIC), a useful way to numerically quantify resistance. When a strain requires a greater MIC, it is more resistant to that particular antibiotic. By tracking MIC, researchers can monitor the antibiotic resistance of different bacteria.
The challenges presented by this method include the years of microbiology training necessary to interpret results, as well as the tedious, time-consuming analysis of the well plates.
In a new study from UCLA, researchers introduce a cost-effective system to read and rapidly interpret turbidity results automatically. The system comprises a smartphone and a processing server, like a laptop, attached to a custom portable 96-well microtiter plate reader (see figure below). Rather than a microbiologist or a physician studying and interpreting results, a lab technician can insert the well plates into the attachment, where an array of LEDs shine on the samples. The phone’s camera is used to photograph the transmitted light from each well in the MTP at multiple exposures, and these images are uploaded to the server to quantify well turbidity and determine MIC within one minute.
Figure 2 from Feng et al. 2016
The system uses these results to categorise the bacteria/drug combination as either susceptible or resistant. Researchers tested 78 patient isolate test-plates and found a turbidity detection accuracy of 98.21 per cent, MIC accuracy of 95.12 per cent and a drug susceptibility interpretation accuracy of 99.23 per cent.
The current limitations of this attachment lie in the preparation of the 96-well microtiter plate. To prepare a plate, a large machine is used to deposit specific drug concentrations in each well and fill each of them with the microbes to be tested.
The widespread use of this technology would allow for easy tracking of drug resistant bacteria and increase communication between microbiologists worldwide. Ideally, a database of all results could be created through the use of wireless Internet and cloud capability.
One of the co-authors of this study is Aydogan Ozcan, who is a co-founder of the start-up company Cellmic LLC, which aims to commercialise computational microscopy and diagnostic tools. Company products include rapid diagnostic test readers, lens free holographic microscope, and handheld analysers for blood count, allergens, and mercury contamination. As the attachment is produced via 3D printing, it is probable that commercialisation is just around the corner.
With new inventions such as this, increased access to lab-based technologies in limited resource environments can lead to closing the healthcare gap. Furthermore, increased automation of lab procedures eliminate inefficiencies and accelerate diagnosis and treatment, allowing maximal quality treatment for the patient.
Chin, Matthew (December 15, 2016): UCLA researchers combat antimicrobial resistance using smartphones. Retrieved January 3, 2017;
Editors (December 23, 2016): Portable Antibiotic Resistance Detector Developed. Retrieved January 3, 2017;
Feng S, Tseng D, Di Carlo D, Garner, OB, Ozcan A (December 15, 2016): High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader. Retrieved January 3, 2017.
Thumb picture: False coloured light micrograph of MRSA colonies. Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterium which is difficult to treat due to its resistance to common forms of antibiotics (Credit Derren Ready, Wellcome Images)