Prof. José A. Ribeiro Profile

Prof. José A. Ribeiro

at Univ de Porto

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Publications (2)

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Proceedings Article | 20 June 2024 Presentation + Paper

Automation of optical tweezers: an enabler for single cell analysis and diagnostic

Pedro Jorge, Joana Teixeira, Vicente Rocha, José Ribeiro, Nuno Silva

Proceedings Volume 13008, 130080E (2024) https://doi.org/10.1117/12.3022229

KEYWORDS: Particles, Optical tweezers, Diagnostics, Automation, Random forests, Data modeling, Data acquisition

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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.

Proceedings Article | 10 June 2024 Presentation + Paper

Probing molecular affinity with optical tweezers

Joana Teixeira, José Ribeiro, Pedro A. Jorge, Nuno Silva

Proceedings Volume 12991, 1299105 (2024) https://doi.org/10.1117/12.3022142

KEYWORDS: Particles, Optical tweezers, Proteins, Antibodies, Random forests, Molecular interactions, Data modeling

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Recent advances in optical trapping have opened new opportunities for manipulating micro and nanoparticles, establishing Optical Tweezers (OT) as a powerful tool for single-cell analysis. Furthermore, intelligent systems have been developed to characterize these particles, as information about their size and composition can be extracted from the scattered radiation signal. In this manuscript, we aim to explore the potential of optical tweezers for the characterization of sub-micron size variations in microparticles. We devised a case study, aiming to assess the limits of the size discrimination ability of an optical tweezer system, using transparent 4.8 μm PMMA particles, functionalized with streptavidin. We focused on the heavily studied streptavidin-biotin system, with streptavidin-functionalized PMMA particles targeting biotinylated bovine serum albumin. This binding process results in an added molecular layer to the particle’s surface, increasing its radius by approximately 7 nm. An automatic OT system was used to trap the particles and acquire their forward-scattered signals. Then, the signals’ frequency components were analyzed using the power spectral density method followed by a dimensionality reduction via the Uniform Manifold Approximation and Projection algorithm. Finally, a Random Forest Classifier achieved a mean accuracy of 94% for the distinction of particles with or without the added molecular layer. Our findings demonstrate the ability of our technique to discriminate between particles that are or are not bound to the biotin protein, by detecting nanoscale changes in the size of the microparticles. This indicates the possibility of coupling shape-changing bioaffinity tools (such as APTMERS, Molecular Imprinted Polymers, or antibodies) with optical trapping systems to enable optical tweezers with analytical capability.

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