Indexing graphically encoded microparticles links micro-level assays with macro post-processing

Because of their high multiplexity and miniaturization, encoded microparticles represent enormous potential in biological and chemical applications, particularly in medical contexts such as disease diagnostics, high-throughput screening in early-stage drug discovery, and drug delivery. To increase their versatility, linking micro-level assays to macro-level post-processing is an imperative.

But indexing microparticles based on physical, chemical, and electromagnetic properties in a deliberate and efficient way is no small feat.

To address this challenge, Lee et al. introduced a new system for categorizing and manipulating graphically encoded microparticles that promises to improve and expand the practical use and applicability of such particles.

“Graphical encoding has an advantage over color-barcoding microparticles because of the wide range of encodability,” said author Sumin Lee. “But the isolation and transfer of particles with homogeneous physical properties have been limited thus far.”

The system Lee and colleagues developed helps bridge the gap, connecting the micro-scale to a macro-scale interface. The Image-based Laser-Induced Forward Transfer (I-LIFT) system integrates an image recognition deep learning method with a laser-induced transferring technology to actively decode chemical-laden graphically encoded microparticles.

“The I-LIFT is capable of retrieving particles of interest from a mixed pool of encoded particle libraries and effectively transferring them to a desired position,” said Lee.

The system achieved a decoding accuracy of 98.9% with a capacity of 256 graphical codes, and Lee reports that accuracy and capacity may be further enhanced with the introduction of other decoding methods and graphical designs.

“We hope this will help expand the application of microparticles towards solving real-world problems,” said Lee.

Source: ““I-LIFT (Image-based Laser-induced Forward Transfer) platform for manipulating encoded microparticles,” by Sumin Lee, Wooseok Lee, Amos Chungwon Lee, Juhong Nam, JinYoung Lee, Hamin Kim, Yunjin Jeong, Huiran Yeom, Namphil Kim, Seo Woo Song, and Sunghoon Kwon, Biomicrofluidics (2022). The article can be accessed at https://doi.org/10.1063/5.0131733 .