Presentation + Paper
20 June 2024 Automation of optical tweezers: an enabler for single cell analysis and diagnostic
Author Affiliations +
Abstract
Sensing at the single cell level can provide insights into its dynamics and heterogeneity, yielding information otherwise unattainable with traditional biological methods where average population behavior is observed. In this context, optical tweezers provide the ability to select, separate, manipulate and identify single cells or other types of microparticles, potentially enabling single cell diagnostics. Forward or backscatter analysis of the light interacting with the trapped cells can provide valuable insights on the cell optical, geometrical and mechanical properties. In particular, the combination of tweezers systems with advanced machine learning algorithms can enable single cell identification capabilities. However, typical processing pipelines require a training stage which often struggles when trying to generalize to new sets of data. In this context, fully automated tweezers system can provide mechanisms to obtain much larger datasets with minimum effort form the users, while eliminating procedural variability. In this work, a pipeline for full automation of optical tweezers systems is discussed. A performance comparison between manually operated and fully automated tweezers systems is presented, clearly showing advantages of the latter. A case study demonstrating the ability of the system to discriminate molecular binding events on microparticles is presented.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pedro Jorge, Joana Teixeira, Vicente Rocha, José Ribeiro, and Nuno Silva "Automation of optical tweezers: an enabler for single cell analysis and diagnostic", Proc. SPIE 13008, Biophotonics in Point-of-Care III, 130080E (20 June 2024); https://doi.org/10.1117/12.3022229
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KEYWORDS
Particles

Optical tweezers

Automation

Diagnostics

Data acquisition

Data modeling

Random forests

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