Digital electrical impedance analysis for single bacterium sensing and antimicrobial susceptibility testing

Scherer, Brian; Surrette, Christine; Li, Hui; Torab, Peter; Kvam, Erik; Galligan, Craig; Go, Steven; Grossmann, Greg; Hammond, Tyler C.; Johnson, Tammy; St-Pierre, R. G.; Nelson, John; Potyrailo, Radislav A.; Khire, Tejas; Hsieh, Kuangwen; Wang, Tza Huei; Wong, Pak Kin; Puleo, Chris

doi:10.1039/d0lc00937g

Cited by 19 publications

(22 citation statements)

References 31 publications

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“…[19][20][21] Different microfluidic systems have been demonstrated to set up antibiotic concentration gradients to screen antibiotic susceptibility. 22,23 With microfluidic devices, AST can be achieved through single cell level analysis, [24][25][26] Raman metabolic imaging, 27,28 and electrokinetic measurements. 29,30 In addition, rapid AST can be implemented by the digital nucleic acid quantification of bacteria after exposure to antibiotics.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial screening SlipChip for rapid phenotypic antimicrobial susceptibility testing

Liu

et al. 2022

Lab Chip

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Section: Introductionmentioning

confidence: 99%

Combinatorial screening SlipChip for rapid phenotypic antimicrobial susceptibility testing

Liu

et al. 2022

Lab Chip

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“…(25) The minute change in the bacterial morphology could be indicative of the bacterial response to antimicrobials before cell replication. While promising examples of bacterial analysis at the single-cell level have emerged, (26)(27)(28) the potential of single-cell morphological analysis has not been realised for rapid AST and MIC determination due to a lack of effective strategy for analysing the dynamic, multiparametric morphological features of bacteria.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Workflow for Single-Cell Antimicrobial Susceptibility Testing ofKlebsiella pneumoniaeto Meropenem in Sub-Doubling Time

Tjandra

Ram-Mohan

Roshardt

et al. 2022

Preprint

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Background: Multidrug-resistant bacteria are among the most urgent global public health threats. Rapid determination of antimicrobial resistance in a single bacterium is a major clinical unmet need in the diagnosis of bacterial infections. Methods: By capturing dynamic single-cell morphological features of over twenty-eight thousand cells, we evaluated strategies based on time and concentration differentials for classifying the susceptibility of Klebsiella pneumoniae to meropenem and predicting their minimum inhibitory concentrations (MIC). Findings: The classifiers achieved as high as 97% accuracy in 20 minutes (two-fifths of the doubling time) and reached over 99% accuracy within 50 minutes (one doubling time) in predicting the antimicrobial response. A regression model based on the concentration differential of individual cells predicted the MIC with >97% categorical agreement and 100% essential agreement. When tested against cells from an unseen strain, the regressor achieved a categorical agreement of 91.9% with a very major error of 0.1%. Interpretation: We report morphometric antimicrobial susceptibility testing (MorphoAST), an image-based machine learning workflow, for rapid determination of antimicrobial susceptibility by single-cell morphological analysis in a sub-doubling time. Our approach has the ability to predict bacterial antimicrobial responsiveness in a fraction of the organisms doubling time. This innovation will have significant implications for the future management of bacterial infections. Funding: This work was supported in part by NIH NIAID (R01AI153133).

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“…7 In response, many microfluidic-based methods were developed to accelerate the process of AST. Among them, the strategies to indicate the cell resistance to antibiotics include the identification of resistant genes, [8][9][10] the cell growth monitoring [11][12][13][14][15] and the metabolic activity sensing. [16][17][18][19][20][21] The detection of metabolic activity of cells exposed to antibiotics is widely applied to perform fast AST in microfluidic devices.…”

Section: Introductionmentioning

confidence: 99%

A portable and high-integrated 3D microfluidic chip for bacterial quantification and antibiotic susceptibility testing

Cai

Liu

2021

Preprint

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On-site single-cell antibiotic susceptibility testing (sc-AST) provides unprecedented technical potential to improve the treatment of bacterial infections and study heterogeneous resistance to antibiotics. Herein, we developed a portable and high-integrated 3D polydimethylsiloxane (PDMS) chip to perform fast and on-site bacteria quantification and sc-AST. The 3D arrangement of the chambers significantly improved the integration of reaction units (∼10000/cm2) and widened the dynamic range to 5 orders of magnitude. A capillary valve-based flow distributor was adopted for flow equidistribution in 64 parallel channels and uniform sample loading in as short as 2 s. The degassed PDMS enabled this device to independently dispense the sample into 3D chamber array with almost 100% efficiency. The quantification of Escherichia coli (E. coli) strains with various activity was accomplished in 0.5-2 h, shortened by 20 h in comparison to the traditional plate counting. The functionality of our platform was demonstrated with several effective antibiotics by determining minimum inhibitory concentrations at single-cell level. Furthermore, we utilized the lyophilization of test reagents and needle-mediated reagents rehydration to realize one-step on-site sc-AST. The results indicate that the proposed sc-AST platform is portable, highly sensitive, fast, accurate and user-friendly, thus it has the potential to facilitate precise therapy in time and monitor the treatment. Meanwhile, it could serve as an approach for investigating the mechanisms of heteroresistance at single-cell resolution.

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Digital electrical impedance analysis for single bacterium sensing and antimicrobial susceptibility testing

Abstract: We report on a hand-held multiplexed impedance sensor system and show evidence for impedance-based monitoring of the growth of a single bacterium.

Cited by 19 publications

References 31 publications

Combinatorial screening SlipChip for rapid phenotypic antimicrobial susceptibility testing

Combinatorial screening SlipChip for rapid phenotypic antimicrobial susceptibility testing

Machine Learning Workflow for Single-Cell Antimicrobial Susceptibility Testing ofKlebsiella pneumoniaeto Meropenem in Sub-Doubling Time

A portable and high-integrated 3D microfluidic chip for bacterial quantification and antibiotic susceptibility testing

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