Ratiometric Sers Tracking
Ratiometric Sers Tracking - Related Videos
13:05
Ratiometric Imaging of Extracellular pH in Dental Biofilms
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Cited by 24 •
2016
A pH-sensitive ratiometric dye is used in combination with confocal laser scanning microscopy and digital image analysis to monitor extracellular pH in dental biofilms in real-time.
08:12
A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions
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2017
We present a software solution for semi-automated tracking of relative protein concentration along the length of dynamic cellular protrusions.
14:39
Measuring Phagosome pH by Ratiometric Fluorescence Microscopy
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Cited by 26 •
2015
Phagosomal pH influences phagosome maturation, oxidant production, phagosomal killing as well as antigen presentation. Here we describe a ratiometric method for measuring time-course and endpoint pH changes in individual phagosomes in living phagocytes using fluorescence microscopy.
05:55
Multiple Object Tracking
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University In a staggeringly complex and engaging world, it is crucial to selectively process some stimuli at the expense of others. Experimental psychologists call this ability attention. Specifically, visual attention refers to the ability to selectively process aspects of a visual scene. Many paradigms used to study visual attention involve brief, punctuated, and repeated trials. However, everyday situations often place sustained demands...
12:42
Imaging the Neutrophil Phagosome and Cytoplasm Using a Ratiometric pH Indicator
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Cited by 13 •
2017
This manuscript describes a simple method to measure the phagosomal pH and area as well as the cytoplasmic pH of human and mouse neutrophils using the ratiometric indicator seminaphthorhodafluor (SNARF)-1, or S-1. This is achieved using live-cell confocal fluorescence microscopy and image analysis.
FAQs
Ratiometric SERS tracking calculates a ratio between a target-responsive Raman signal and a reference signal rather than interpreting the target signal alone. Because both signals are evaluated within the measurement, the ratio can reduce effects caused by variation in probe concentration, illumination, substrate enhancement, or signal collection. This supports more consistent quantitative monitoring across biological measurements.
The reference signal acts as a comparison point for the target-responsive signal. Its value allows changes in the target-related response to be expressed relative to another Raman measurement, rather than as an isolated intensity. That comparison is important when experimental conditions differ, because the resulting ratio can compensate for several measurement-related differences that would otherwise affect interpretation.
Compared with interpreting a single Raman intensity, the ratio provides a relative readout that is less dependent on the absolute strength of one signal. This distinction matters when probe concentration, illumination, substrate enhancement, or signal collection varies. By incorporating a reference measurement, the approach is better suited to quantitative tracking in biological settings where measurement conditions may not remain identical.
An engineered nanoprobe can incorporate Raman reporters together with recognition elements. The recognition components connect the probe to the biological target or interaction of interest, while the Raman measurements provide target-responsive and reference signals for ratio calculation. This design lets the same bioengineering platform support tracking of molecular interactions, cellular processes, or delivery behavior.
The method can be used to investigate molecular interactions, follow cellular processes, and monitor delivery behavior. These applications use engineered nanoprobes to connect molecular recognition with Raman-based measurement. Because the output is quantitative, researchers can use the signal ratio to assess biological events while reducing variability associated with the measurement environment.
Spatially resolved analysis shows where a biological target or process is associated with the measured Raman response, rather than providing only an overall measurement. When combined with ratiometric correction, this capability supports more reliable observation in complex biological environments. The approach is also non-destructive, making it useful for monitoring biological behavior without requiring destructive analysis.
It provides a quantitative Raman-based readout that is designed to remain more reliable despite differences in probe concentration, illumination, substrate enhancement, and signal collection. This is particularly relevant to complex biological environments, where such measurement factors can vary. The resulting data can support non-destructive, spatially resolved analysis of engineered-probe behavior and biological processes.