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Updated: Feb 14, 2026

Quantification and Size-profiling of Extracellular Vesicles Using Tunable Resistive Pulse Sensing
Published on: October 19, 2014
Submicron Protein Particle Characterization using Resistive Pulse Sensing and Conventional Light Scattering Based
Gregory V Barnett1, Julia M Perhacs1, Tapan K Das1
1Biologics Characterization and Analytical Development, Bristol-Myers Squibb, Hopewell, New Jersey, USA.
Resistive pulse sensing (RPS) effectively characterizes submicron protein particles, offering high size resolution. This technique provides valuable insights into particle formation during biologics drug development.
Area of Science:
- Biopharmaceutical analysis
- Nanoparticle characterization
- Protein formulation science
Background:
- Characterizing submicron protein particles (0.1-1μm) is difficult due to limitations in current instrumentation.
- Monitoring particle size and concentration across a broad range presents significant analytical challenges in biopharmaceutical development.
Purpose of the Study:
- To report for the first time the characterization of submicron protein particles using resistive pulse sensing (RPS).
- To establish a method for monitoring submicron protein particles in pharmaceutical formulations.
Main Methods:
- Resistive pulse sensing (RPS) was employed alongside dynamic light scattering (DLS) and size-exclusion chromatography with multi-angle light scattering (SEC-MALS).
- Analyses were conducted on protein and placebo formulations, size standards, and stressed protein samples.
Main Results:
- A robust RPS method was developed, suitable for particle concentrations ranging from 4 × 10⁷ to 1 × 10¹¹ particles/mL.
- RPS particle size distributions demonstrated high resolution and consistency with DLS and SEC-MALS data.
- RPS provided accurate particle counts and superior size resolution compared to light scattering techniques.
Conclusions:
- Resistive pulse sensing is a viable technique for characterizing highly polydisperse submicron particles in protein formulations.
- RPS-derived particle distribution data offer critical insights into particle formation mechanisms under various stress conditions relevant to biologics development.
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