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A portable bioelectronic sensing system (BESSY) for environmental deployment incorporating differential microbial sensing in miniaturized reactors.

ABSTRACT: Current technologies are lacking in the area of deployable, in situ monitoring of complex chemicals in environmental applications. Microorganisms metabolize various chemical compounds and can be engineered to be analyte-specific making them naturally suited for robust chemical sensing. However, current electrochemical microbial biosensors use large and expensive electrochemistry equipment not suitable for on-site, real-time environmental analysis. Here we demonstrate a miniaturized, autonomous bioelectronic sensing system (BESSY) suitable for deployment for instantaneous and continuous sensing applications. We developed a 2x2 cm footprint, low power, two-channel, three-electrode electrochemical potentiostat which wirelessly transmits data for on-site microbial sensing. Furthermore, we designed a new way of fabricating self-contained, submersible, miniaturized reactors (m-reactors) to encapsulate the bacteria, working, and counter electrodes. We have validated the BESSY's ability to specifically detect a chemical amongst environmental perturbations using differential current measurements. This work paves the way for in situ microbial sensing outside of a controlled laboratory environment.

SUBMITTER: Zhou AY

PROVIDER: S-EPMC5600388 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A portable bioelectronic sensing system (BESSY) for environmental deployment incorporating differential microbial sensing in miniaturized reactors.

Zhou Alyssa Y AY Baruch Moshe M Ajo-Franklin Caroline M CM Maharbiz Michel M MM

PloS one 20170915 9

Current technologies are lacking in the area of deployable, in situ monitoring of complex chemicals in environmental applications. Microorganisms metabolize various chemical compounds and can be engineered to be analyte-specific making them naturally suited for robust chemical sensing. However, current electrochemical microbial biosensors use large and expensive electrochemistry equipment not suitable for on-site, real-time environmental analysis. Here we demonstrate a miniaturized, autonomous b ...[more]

PMID: 28915277

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Project description:BACKGROUND:Recent advances in mobile technologies for sensing human biosignals are empowering researchers to collect real-world data outside of the laboratory, in natural settings where participants can perform their daily activities with minimal disruption. These new sensing opportunities usher a host of challenges and constraints for both researchers and participants. OBJECTIVE:This viewpoint paper aims to provide a comprehensive guide to aid research teams in the selection and management of sensors before beginning and while conducting human behavior studies in the wild. The guide aims to help researchers achieve satisfactory participant compliance and minimize the number of unexpected procedural outcomes. METHODS:This paper presents a collection of challenges, consideration criteria, and potential solutions for enabling researchers to select and manage appropriate sensors for their research studies. It explains a general data collection framework suitable for use with modern consumer sensors, enabling researchers to address many of the described challenges. In addition, it provides a description of the criteria affecting sensor selection, management, and integration that researchers should consider before beginning human behavior studies involving sensors. On the basis of a survey conducted in mid-2018, this paper further illustrates an organized snapshot of consumer-grade human sensing technologies that can be used for human behavior research in natural settings. RESULTS:The research team applied the collection of methods and criteria to a case study aimed at predicting the well-being of nurses and other staff in a hospital. Average daily compliance for sensor usage measured by the presence of data exceeding half the total possible hours each day was about 65%, yielding over 355,000 hours of usable sensor data across 212 participants. A total of 6 notable unexpected events occurred during the data collection period, all of which had minimal impact on the research project. CONCLUSIONS:The satisfactory compliance rates and minimal impact of unexpected events during the case study suggest that the challenges, criteria, methods, and mitigation strategies presented as a guide for researchers are helpful for sensor selection and management in longitudinal human behavior studies in the wild.

Project description:An integrated custom cross-response sensing array has been developed combining the algorithm module's visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n = 288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 105 ~ 108 CFU/mL for Escherichia coli, 102 ~ 107 CFU/mL for E. coli-β, 103 ~ 108 CFU/mL for Staphylococcus aureus, 103 ~ 107 CFU/mL for MRSA, 102 ~ 108 CFU/mL for Pseudomonas aeruginosa, 103 ~ 108 CFU/mL for Enterococcus faecalis, 102 ~ 108 CFU/mL for Klebsiella pneumoniae, and 103 ~ 108 CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification. • A molecular response differential profiling (MRDP) was established based on custom cross-response sensor array for rapid and accurate recognition and phenotyping common pathogenic microorganism. • Differential response profiling of pathogenic microorganism is derived from the competitive response capacity of 6 sensing elements of the sensor array. Each of these sensing elements' performance has competitive reaction with the microorganism. • MRDP was applied to LDA algorithm and resulted in the classification of 8 microorganisms.

Dataset Information

A portable bioelectronic sensing system (BESSY) for environmental deployment incorporating differential microbial sensing in miniaturized reactors.

Publications

A portable bioelectronic sensing system (BESSY) for environmental deployment incorporating differential microbial sensing in miniaturized reactors.

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