Hook effect detection and detection-range-controllable one-step immunosensor for inflammation monitoring

Shin, Sung‐Chul; Choi, Min-Joo; Shim, Jeoyoung; Park, Sungha

doi:10.1016/j.snb.2019.127408

Cited by 8 publications

(7 citation statements)

References 40 publications

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“…At higher concentrations, the signals reach a plateau and start to decline. This phenomenon has been previously observed in several assays regarding the bioelectrical identification of molecules through membrane engineering [13,20,21]. It could be attributed to the saturation of the system when the concentration of the analyte exceeds a certain concentration.…”

Section: Optimizaton Of Membrane-engineered Sk-n-sh-s1 Protein Cells ...supporting

confidence: 53%

Ultra-Fast and Sensitive Screening for Antibodies against the SARS-CoV-2 S1 Spike Antigen with a Portable Bioelectric Biosensor

et al. 2022

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As a consequence of the progress of the global vaccination against the COVID-19 disease, fast, accurate and affordable assays are needed for monitoring the efficiency of developing immunity against the coronavirus at the population level. In this context, we herewith report the proof-of-concept development of an innovative bioelectric biosensor for the ultra-detection (in less than three minutes) of IgG antibodies against the SARS-CoV-2 S1 spike antigen. The biosensor comprises a disposable set of screen-printed electrodes upon which are immobilized cells engineered to bear the S1 protein on their surface. When anti-S1 antibodies are presented to the engineered cell population, a rapid, specific, and selective change of the cell membrane potential occurs; this is in turn recorded by a bespoke portable potentiometer. End results are communicated via Bluetooth to a smartphone equipped with a customized user interface. By using the novel biosensor, anti-S1 antibodies could be detected at concentrations as low as 5 ng/mL. In a preliminary clinical trial, positive results were derived from patients vaccinated or previously infected by the virus. Selectivity over other respiratory viruses was demonstrated by the lack of cross-reactivity to antibodies against rhinovirus. After further clinical validation and extension to also screen IgM, IgA and possible neutralizing antibodies, our approach is intended to facilitate the mass and reliable detection of antibodies in the early stages following vaccination and to monitor the duration and level of acquired immunity both in a clinical and self-testing environment.

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Section: Optimizaton Of Membrane-engineered Sk-n-sh-s1 Protein Cells ...supporting

confidence: 53%

Ultra-Fast and Sensitive Screening for Antibodies against the SARS-CoV-2 S1 Spike Antigen with a Portable Bioelectric Biosensor

et al. 2022

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“…In contrast, when 2250 Units/mL of ACE2 were used for increasing cell specification the results obtained displayed a decrease in the cell membrane potential. This effect, i.e., the decline of the biosensor response above low target analyte concentrations when the biorecognition element has been electroinserted at low or high concentrations (750/2250 Units/mL) is a frequent, yet not fully investigated phenomenon in molecular recognition through membrane engineering [37,38]. It is possibly due to either low level of responses at low ACE2 concentrations (even at higher S1 concentrations) or due to regional ion fluctuations of the cell membranes during the interaction with the analyte [39].…”

Section: Membrane Engineering Of Sk-n-sh Cells With Ace2 Triggers Distinct Bioelectric Alterations After Interaction With the Sars-cov-2 mentioning

confidence: 99%

Angiotensin-Converting Enzyme 2 (ACE2) As a Novel Biorecognition Element in A Cell-Based Biosensor for the Ultra-Rapid, Ultra-Sensitive Detection of the SARS-CoV-2 S1 Spike Protein Antigen

et al. 2021

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Antigen screening for the SARS-CoV-2 S1 spike protein is among the most promising tools for the mass monitoring of asymptomatic carriers of the virus, especially in limited resource environments. Herewith, we report on the possible use of the angiotensin-converting enzyme 2 (ACE2), the natural receptor and entry point of the virus, as a biorecognition element for the detection of the S1 antigen combined with an established bioelectric biosensor based on membrane-engineered cells. The working principle of our approach is based on the measurable change of the electric potential of membrane-engineered mammalian cells bearing ACE2 after attachment of the respective viral protein. We demonstrate that sensitive and selective detection of the S1 antigen is feasible in just three min, with a limit of detection of 20 fg/mL. In a preliminary clinical application, positive patient-derived samples were identified with a 87.9% score compared to RT-PCR. No cross-reactivity was observed against a wide range of nucleocapsid protein concentrations. The novel biosensor is embedded in a commercially ready-to-use testing platform, complete with the consumable immobilized cell–electrode interface and a portable read-out device operable through smartphone or tablet. In addition, the possible application of the system for the high throughput screening of potential pharmacological inhibitors of the ACE2 receptor-S1 RBD interaction is discussed.

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“…11 To overcome the prozone effect, strategies such as diluting the sample prior to use 12 or computational modeling of test and control lines using real-time kinetics 13 A regression analysis was used to compare the known analyte concentration vs wavelength response alongside other statistical modeling to investigate linearity, prozone effect, and precision. 15 In particular, many previous works have demonstrated the presence of the prozone effect in C-reactive protein (CRP) as a case study. CRP is a well-investigated inflammatory biomarker, that is, an acute-phase protein produced by the liver, and rises rapidly in the instance of inflammation.…”

Section: ■ Introductionmentioning

confidence: 99%

Combating Prozone Effects and Predicting the Dynamic Range of Naked-Eye Nanoplasmonic Biosensors through Capture Bioentity Optimization

Bradley,

Bhalla

2024

ACS Meas. Sci. Au

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Accurately quantifying high analyte concentrations poses a challenge due to the common occurrence of the prozone or hook effect within sandwich assays utilized in plasmonic nanoparticle-based lateral flow devices (LFDs). As a result, LFDs are often underestimated compared to other biosensors with concerns surrounding their specificity and sensitivity toward the target analyte. To address this limitation, here we develop an analytical model capable of predicting the prozone effect and subsequently the dynamic range of the biosensor based on the concentration of the capture antibody. To support our model, we conduct a sandwich immunoassay to detect C-reactive protein (CRP) in a phosphate-buffered saline (PBS) buffer using an LFD. Within the experiment, we investigate the relationship between the CRP dynamic range and the prozone effect as a function of the capture antibody concentration, which is increased from 0.1 to 2 mg/mL. The experimental results, while supporting the developed analytical model, show that increasing the capture antibody concentration increases the dynamic range. The developed model therefore holds the potential to expand the measurable range and reduce costs associated with quantifying biomarkers in diverse diagnostic assays. This will ultimately allow LFDs to have better clinical significance before the prozone effect becomes dominant.

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Hook effect detection and detection-range-controllable one-step immunosensor for inflammation monitoring

Cited by 8 publications

References 40 publications

Ultra-Fast and Sensitive Screening for Antibodies against the SARS-CoV-2 S1 Spike Antigen with a Portable Bioelectric Biosensor

Ultra-Fast and Sensitive Screening for Antibodies against the SARS-CoV-2 S1 Spike Antigen with a Portable Bioelectric Biosensor

Angiotensin-Converting Enzyme 2 (ACE2) As a Novel Biorecognition Element in A Cell-Based Biosensor for the Ultra-Rapid, Ultra-Sensitive Detection of the SARS-CoV-2 S1 Spike Protein Antigen

Combating Prozone Effects and Predicting the Dynamic Range of Naked-Eye Nanoplasmonic Biosensors through Capture Bioentity Optimization

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