Plasmonic Scattering Microscopy for Label‐Free Imaging of Molecular Binding Kinetics: From Single Molecules to Single Cells

Zhang, Pengfei; Zhou, Xinyu; Wang, Shaopeng

doi:10.1002/cmtd.202200066

Cited by 5 publications

(3 citation statements)

References 70 publications

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“…P-EIM is a label-free imaging technique that leverages surface plasmon resonance (SPR) imaging to identify variations in the electric fields within the sensing medium (Figure ). − Action potentials (APs) in neuron cells are triggered via a patch-clamp setup. This stimulation leads to an ion flux that changes the local electric field, which affects the surface charge density on the gold sensing surface.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Enhanced Label-Free Action Potential Detection Using Plasmonic-Based Electrochemical Impedance Microscopy

Haji Najafi Chemerkouh,

Zhou,

Yang

et al. 2024

Anal. Chem.

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Measuring neuronal electrical activity, such as action potential propagation in cells, requires the sensitive detection of the weak electrical signal with high spatial and temporal resolution. None of the existing tools can fulfill this need. Recently, plasmonic-based electrochemical impedance microscopy (P-EIM) was demonstrated for the label-free mapping of the ignition and propagation of action potentials in neuron cells with subcellular resolution. However, limited by the signal-to-noise ratio in the high-speed P-EIM video, action potential mapping was achieved by averaging 90 cycles of signals. Such extensive averaging is not desired and may not always be feasible due to factors such as neuronal desensitization. In this study, we utilized advanced signal processing techniques to detect action potentials in P-EIM extracted signals with fewer averaged cycles. Matched filtering successfully detected action potential signals with as few as averaging five cycles of signals. Long short-term memory (LSTM) recurrent neural network achieved the best performance and was able to detect single-cycle stimulated action potential successfully [satisfactory area under the receiver operating characteristic curve (AUC) equal to 0.855]. Therefore, we show that deep learning-based signal processing can dramatically improve the usability of P-EIM mapping of neuronal electrical signals.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Enhanced Label-Free Action Potential Detection Using Plasmonic-Based Electrochemical Impedance Microscopy

Haji Najafi Chemerkouh,

Zhou,

Yang

et al. 2024

Anal. Chem.

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“…4,5 Overexpression of EGFR receptors and excessive levels of EGFR antigen have been associated with cancer; therefore increasing research studies have been conducted to detect EGFR in cancer patients' serum samples. 6,7 Enzymelinked immunosorbent assay, 8 PCR, 9 electron paramagnetic resonance spectrometry, 10 hydrophilic interaction liquid chromatography/mass spectrometry, 11 surface plasmon reso-nance, 12,13 and electrochemical voltammetry methods 6 are some of the analytical techniques currently used to detect EGFR. However, these techniques have limited specificity and selectivity; therefore the development of trustworthy and affordable analytical approaches for EGFR biomarker identification is of utmost importance.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the associated risk and improved means of ensuring a humane healthcare system, cancer biomarker detection has recently had a noteworthy impact on cancer research. Several biomarkers, EGFR, carcinoembryonic antigen, HER-2, CA-125, CD44, etc., have been categorized as valid biomarkers associated with breast cancer. , In this context, a noteworthy biomarker over cancer tumorigenic cells is the epidermal growth factor receptor (EGFR) for which altered expression and downstream signaling cascades have been reported to be associated with the worsening of the cancerous condition. , Overexpression of EGFR receptors and excessive levels of EGFR antigen have been associated with cancer; therefore increasing research studies have been conducted to detect EGFR in cancer patients’ serum samples. , Enzyme-linked immunosorbent assay, PCR, electron paramagnetic resonance spectrometry, hydrophilic interaction liquid chromatography/mass spectrometry, surface plasmon resonance, , and electrochemical voltammetry methods are some of the analytical techniques currently used to detect EGFR. However, these techniques have limited specificity and selectivity; therefore the development of trustworthy and affordable analytical approaches for EGFR biomarker identification is of utmost importance.…”

Section: Introductionmentioning

confidence: 99%

Yttrium Functionalized Reduced Graphene Oxide Nanocomposite-Based Aptasensor for Ultrasensitive Detection of a Breast Cancer Biomarker

Parihar,

Khan

2023

ACS Appl. Nano Mater.

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Higher cancer mortality rate can be prevented by the early detection of cancer-associated biomarkers and appropriate therapeutic intervention. Recently, electrochemical biosensors have drawn a great deal of interest because of their extremely high sensitivity, selectivity, and affordability in early cancer detection. Herein, we fabricated an aptasensor for the inexpensive and label-free detection of the epidermal growth factor receptor (EGFR) antigen, a biomarker linked to breast cancer. The reduced graphene oxide–yttrium nanocomposite was synthesized and characterized using FTIR, XRD, TEM, SEM, Raman spectroscopy, and UV–vis spectroscopy, which confirmed the synthesis of the nanocomposite. The synthesized material was used for the fabrication of electrochemical aptasensor for the detection of epidermal growth factor receptor antigen. The developed sensor (Y2O3–rGO/Apt/BSA) showed a wide linear detection range (10 fg mL–1 to 100 ng mL–1), a low detection limit of 0.251 fg mL–1, and an excellent sensitivity of 51.96 μA fM–1·cm–2. The aptasensor also provides a straightforward and quick approach to EGFR antigen detection in biological serum samples. The simple and facile synthesis method reported in this work led to the production of a high-purity, water-dispersible yttrium functionalized reduced graphene oxide nanocomposite for cost-effective, faster, and ultrasensitive detection of breast cancer biomarkers using electrochemical aptasensors.

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Enhanced plasmonic scattering imaging via deep learning–based super-resolution reconstruction for exosome imaging

Huo,

Chen,

Wang

et al. 2024

Anal Bioanal Chem

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Plasmonic Scattering Microscopy for Label‐Free Imaging of Molecular Binding Kinetics: From Single Molecules to Single Cells

Cited by 5 publications

References 70 publications

Deep Learning Enhanced Label-Free Action Potential Detection Using Plasmonic-Based Electrochemical Impedance Microscopy

Deep Learning Enhanced Label-Free Action Potential Detection Using Plasmonic-Based Electrochemical Impedance Microscopy

Yttrium Functionalized Reduced Graphene Oxide Nanocomposite-Based Aptasensor for Ultrasensitive Detection of a Breast Cancer Biomarker

Enhanced plasmonic scattering imaging via deep learning–based super-resolution reconstruction for exosome imaging

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