The ScanDrop ​Platform, Cloud-Enabled Microscopy

Researchers have developed an all-in-one platform – ScanDrop – for the rapid and specific capture, detection, and identification of bacteria in drinking water

The ScanDrop platform integrates droplet microfluidics, a portable imaging system, and cloud-based control software and data storage. The cloud-based control software and data storage enables robotic image acquisition, remote image processing, and rapid data sharing. These features form a “cloud” network for water quality monitoring. The entire water quality diagnostic process required 8 hours from sample collection to online-accessible results compared with 2–4 days for other currently available standard detection methods.

Introduction

Worldwide water-associated infectious diseases are a major cause of morbidity and mortality. It is estimated that 4.0% of global deaths and 5.7% of the global disease burden are caused by waterborne diseases. Common waterborne diseases include diarrhea (bacterial, viral and parasitic), schistosomiasis, trachoma, ascariasis, and trichuriasis. Low income countries are particularly vulnerable to waterborne diseases because of their under-developed infrastructure and poor water management. Water and sewage distribution systems in high income societies also require pollutant and microorganism monitoring.

Escherichia coli , found in mammalian feces, has been a biological indicator for water quality since the 19th century. Testing for the presence of  E. coli  is obligatory for current water management systems. Herein, we report a comprehensive system – ScanDrop – for the rapid and specific identification of  E. coli  in drinking water.

The identification of bacteria in a water sample includes two major steps: 1) the capture of target bacteria from the water sample, and 2) the identification of the captured bacteria. Traditional methods for  E. coli  detection include culture, fermentation, enzyme-linked immunosorbent (ELISA), and polymerase chain reaction (PCR) assays. These traditional methods have disadvantages including long identification times (2–4 days), and/or high labor and reagent costs. Despite high costs, rapid tests are necessary to enable quick responses to putative contamination threats. Recently, novel sensors and assays for rapid pathogen detection have been developed, including the capture of whole pathogen cells or molecular fragments for further amplification and identification, with detection methods utilizing a variety of transducing technologies (optical, electrochemical, surface plasmon resonance and piezoelectric). Many of these newer methods remain expensive and/or require sophisticated instrumentation, and most have yet to reach the market place. Therefore, there remains a need for alternative platforms for the detection of bacteria in water samples.

It remains challenging to inexpensively perform water quality control testing at multiple locations along a distribution system, and to rapidly process and share the test results. To address these challenges, we have developed the ScanDrop platform. ScanDrop is a self-contained detection platform that enables the online control of water testing at multiple locations along the distribution system. ScanDrop integrates live-bacteria capturing and detection, droplet microfluidics, automated fluorescence microscopy, and cloud-based data management and sharing. Droplet microfluidics, applied in ScanDrop, is an emerging application of microelectromechanical systems (MEMS) technology, where assay reagents and biological sample are confined to the pico-liter reactors, composed of water in oil emulsion. Small volumes, rapid reagent mixing and non-complex droplet control make droplet microfluidics an attractive choice for the next-generation of high-throughput assays and herein detection of bacteria in water samples.

In this work, we demonstrate ScanDrop's capability to detect live  E. coli  in water samples. Magnetic beads, conjugated with specific antibodies, were used to quickly and effectively capture  E. coli  from contaminated water. The captured bacteria were then encapsulated into pico-liter droplets containing fluorescently labeled antibodies, for subsequent detection using a proprietary automated optical fluorescence signal registration system. Imaging system control was facilitated by leveraging a cloud-based laboratory automation system, coined Programing a Robot, PR-PR. We envision that multiple ScanDrop systems could be dispatched at multiple locations to form a cloud-enabled water quality assessment network. Each system could be managed in real-time from a remote control center. Such a network could potentially reduce the infrastructure, management, and labor costs required to perform multiple sample analysis and rapidly share results.

Accesss the research paper here:  http://journals.plos.org

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Taxonomy

• Standards & Quality
• Quality
• Research
• Quality Maintenance
• Water Quality Monitoring