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Coupled Assays for Monitoring Protein Refolding in Saccharomyces cerevisiae

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Published on: July 9, 2013

Quantifying Population Reversibility of Sensor Performance in Multi-Cycle Single-Sensor Recovery Assay.

Geffen Rosenberg¹, Gili Bisker^1,2,3,4,5,6

¹School of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.

Small (Weinheim an Der Bergstrasse, Germany)

|April 14, 2026

Summary

This summary is machine-generated.

This study developed a workflow to assess individual nanosensor performance over multiple uses. It reveals significant variability in sensor response and recovery, crucial for accurate chemical imaging.

Keywords:

fluorescence sensors near‐infrared sensor recovery sensor reversibility single‐walled carbon nanotubes spatiotemporal sensors

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Area of Science:

Analytical Chemistry
Materials Science
Biotechnology

Background:

Quantitative chemical imaging demands sensors with reliable recovery across repeated exposures.
Single-sensor imaging offers spatiotemporal resolution for biological processes, but requires analysis of sensor variability for calibration.

Purpose of the Study:

To introduce a generic workflow for characterizing individual nanosensor response, recovery, and reversibility under multi-cycle challenges.
To assess sensor variability in quantitative chemical imaging applications.

Main Methods:

An automated microfluidic flow imaging platform was combined with systematic characterization of individual nanosensors.
Three near-infrared fluorescent single-walled carbon nanotube (SWCNT) sensor models targeting dopamine, thiocholine, and serotonin were tested.
A Population Reversibility Score based on Kullback-Leibler Divergence was introduced to quantify performance across cycles.

Main Results:

Single-sensor analysis revealed broad heterogeneity in response magnitude, signal recovery, and reversibility across hundreds of SWCNTs.
While first-cycle ensemble averages matched bulk calibration, individual sensor behavior varied significantly under repeated exposure and wash cycles.
The Population Reversibility Score provided a quantitative metric for cycle- and concentration-dependent performance.

Conclusions:

Nanosensor variability is a critical factor in quantitative chemical imaging that is often masked by ensemble averaging.
The developed workflow and Population Reversibility Score enable detailed analysis of sensor performance, guiding optimization for spatiotemporal analyte mapping.
This framework is applicable to various sensor-analyte systems with transient readouts.