Multiplexed Analyte Detection
Multiplexed Analyte Detection - Related Videos
02:02
Detection of a Target Analyte Using a Bacterial Biosensor
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2025
Source: Barber, A. E., et al. Preparation and Application of a New Bacterial Biosensor for the Presumptive Detection of Gunshot Residue. J. Vis. Exp. (2019).This video demonstrates the detection of specific analytes using a bacterial biosensor. A bacterial culture carrying a plasmid with a red fluorescent protein (RFP) gene under an analyte-sensitive promoter is taken. After thoroughly wiping the test surface, a small section of the wipe is immersed in the culture and incubated. The analyte...
08:01
Tuning a Parallel Segmented Flow Column and Enabling Multiplexed Detection
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Cited by 1 •
2015
Here, we present a protocol for the operation and tuning of parallel segmented flow chromatography columns to enable multiplexed detection.
05:25
Simultaneous Detection of Different Antibody Classes in a Multiplexed Serological Test
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2023
A three-channel dual-reporter fluorescence flow analysis system was used to develop a bead-based multiplex immunoassay that simultaneously evaluates serum samples for IgG and IgM elicited against multiple antigens of different Borrelia species that cause Lyme borreliosis in Europe and North America.
05:13
Biolayer Interferometry Technology to Detect Interactions between Ligand and Analyte
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2025
In this video, we demonstrate biolayer interferometry (BLI) technology to detect the intermolecular interactions between ligands and target analytes in real-time. BLI measures changes in the interference pattern of white light reflected off a layer of immobilized ligands on a biosensor surface as they interact with analytes in solution.
06:12
Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets
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Cited by 6 •
2023
This paper proposes a new generation of multiparametric analytical platforms with increased throughput for the characterization of extracellular vesicle subsets. The method is based on a combination of multiplexed biosensing methods with metrological and morphomechanical analyses by atomic force microscopy, coupled with Raman spectroscopy, to qualify vesicular targets trapped on a microarray biochip.
FAQs
Recognition elements provide selectivity by pairing each target with a compatible antibody, nucleic-acid probe, or engineered binding molecule. Multiplexed analyte detection depends on keeping these pairings distinguishable when they occur in the same sample. The quality of those molecular matches affects whether measured signals can be assigned confidently to the intended analytes.
Different analytes need signals that can be resolved rather than interpreted as one combined response. Systems therefore distinguish interactions through signal characteristics, physical location, or an assigned code. This separation allows several measurements to remain attributable to individual targets, which is central to reliable quantification in a multiplexed assay.
Panel design determines which targets are measured together and how their recognition and signal assignments coexist. A well-organized panel supports broader profiling without losing analyte identity during resolution. In bioengineering, this matters when the goal is to examine biomarkers, pathogens, cells, or other features of a complex biological system in one coordinated analysis.
A typical workflow begins by selecting analytes and matching each with a selective recognition element. The assay then associates each interaction with a distinguishable signal, location, or code. After multiple targets are processed in the same sample, the resulting signals are resolved and quantified together, producing a coordinated analyte profile rather than separate measurements.
Researchers may choose this approach when they need information about several targets while conserving sample volume and time. Supported uses include biomarker profiling, pathogen characterization, cell analysis, and monitoring complex biological systems. The combined readout can reveal a broader pattern of analytes than a single-target assay, making it useful for bioengineering studies of multifactorial samples.
Computational analysis helps organize and interpret the multiple signals produced by the assay alongside the panel design and signal-separation strategy. Its role becomes especially important when many analytes are measured together and their results must remain associated with the correct targets. Advances in computational analysis are expanding the method’s value in diagnostics and research.
In bioengineering, the method connects molecular recognition, engineered binding molecules, signal design, and computational analysis within one measurement strategy. That combination supports analysis of biological systems whose behavior cannot be represented by one target alone. Continued advances in panel design, signal separation, and computational analysis are expanding its value in diagnostics and research.