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. 2022 Dec 5;16(6):061101. doi: 10.1063/5.0131733

I-LIFT (image-based laser-induced forward transfer) platform for manipulating encoded microparticles

Sumin Lee 1, Wooseok Lee 1, Amos Chungwon Lee 2, Juhong Nam 1, JinYoung Lee 3, Hamin Kim 4, Yunjin Jeong 2, Huiran Yeom 2, Namphil Kim 1, Seo Woo Song 2,a), Sunghoon Kwon 1,2,4,1,2,4,1,2,4,a)
PMCID: PMC9726220  PMID: 36483021

Abstract

Encoded microparticles have great potential in small-volume multiplexed assays. It is important to link the micro-level assays to the macro-level by indexing and manipulating the microparticles to enhance their versatility. There are technologies to actively manipulate the encoded microparticles, but none is capable of directly manipulating the encoded microparticles with homogeneous physical properties. Here, we report the image-based laser-induced forward transfer system for active manipulation of the graphically encoded microparticles. By demonstrating the direct retrieval of the microparticles of interest, we show that this system has the potential to expand the usage of encoded microparticles.

Encoded microparticles have shown great potential in biological and chemical applications including disease diagnostics,1–4 drug delivery,5,6 and other small-volume reactions,7–11 owing to the high multiplexity and miniaturization. These properties make encoded microparticles an ideal substitute for the enormous chemical libraries needed for multiplex assays.12–14 An example of using encoded microparticles for multiplexed assays is high throughput screening in early-stage drug discovery. High throughput screening in early-stage drug discovery is essential as it enables the finding of effective hits through unbiased large-scale screening.15 The desire for miniaturization of the high-throughput screening increased due to the cost savings in reaction and sample volume.16–18 Due to the aforementioned reasons, a microparticle-based approach was adopted to small-volume high throughput screening to use the microparticle as a microcarrier capable of controlled loading and releasing of the predetermined substances.19

To increase the versatility of the encoded microparticles, it is important to link the micro-level assays to macro-level post-processing via indexing and sorting.20–23 Technologies to actively manipulate or sort encoded microparticles are being developed, but active manipulation of the microparticles with similar physical, chemical, and electromagnetic properties remains challenging.22–24 Some examples of active microparticle manipulation include acoustophoresis,25–27 dielectrophoresis,28–32 magnetophoresis,33,34 and optical methods.35–37 Among these methods, optical methods provide advantages in that they can manipulate the encoded microparticles with similar sizes and electromagnetic properties, which widens the spectrum of encoding methods. However, optical methods such as optical tweezers require complex excitation and a strong optical field intensity to manipulate the encoded microparticles. To overcome such technical limitations, a high-throughput optical manipulation needs to be developed for the practical application of using encoded microparticles in multiplexed assays. The laser-induced forward transfer (LIFT) system has the advantage of transferring the micro-sized particles owing to the precise controllability, but it has not been yet applied to selecting the specific encoded microparticles among the mixed libraries.38–41

Here, we developed the image-based laser-induced forward transfer (I-LIFT) platform for connecting the micro-scale to a macro-scale interface that can transfer the chemical-laden graphically encoded microparticles with the specific codes to the desired position (Fig. 1). Graphically encoded microparticles hold a number of advantages in that it has a wide range of encoding variety with high decoding accuracy, regardless of the types of loaded chemicals. By connecting the deep learning-driven decoding of the encoded microparticles and laser-induced forward transfer system, we could retrieve the particles of interest from the mixed pool of the encoded particle libraries. First, the mixed library of the microparticles is dispersed to the indium tin oxide (ITO)-coated glass slides for the imaging of the graphical codes. In the trained neural net (NN), the microparticle features are extracted from the acquired images, and the identities of the codes on the microparticles and their coordinates are returned. After decoding, the coordinates of the microparticle containing the target chemicals are fed as an input for the isolation by the laser-induced forward transfer process. ITO coated on the normal glass slide works as a sacrificial layer to push out the attached particles when irradiated by the near-infrared (1064 nm) nano-second pulsed laser through vaporization. We used the nano-second pulse laser to apply the laser ablation to the irradiated regions with less damage to the placed microparticles. Also, we attached the motorized stage in the bottom part of the device for automatically selecting the position of the retrieval.

FIG. 1.

FIG. 1.

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I-LIFT system for manipulating encoded microparticles for multiplexed assay. (a) Graphically encoded microparticles can be manipulated by deep learning-driven decoding and laser-induced forward transferring systems. Each code refers to the corresponding chemicals, so by indexing the particle codes from the mixed pool, it is able to access the specific chemical substance. (b) Encoded microparticles can be mass-produced via photomask lithography, where photomask contains the graphical encodings. When the ultraviolet (UV) beam is induced to the prepolymers mixed with cross-linkers, it is able to polymerize the desired microparticles with desired size, shape, and engraved graphical codes. (c) After the fabrication of the empty microparticles with homogeneous encodings, the loading and releasing of the chemicals in microparticles can be controlled. When freeze-drying the chemical substances with the hydrogel particles, chemical substances are absorbed into the porous hydrogel matrix. Freeze-drying inhibits the coffee-ring effect, which can induce the non-uniform loading of the chemical substances to the microparticles. Each homogeneous encoded microparticles containing the corresponding chemicals can be stored in silicone oil and mixed to form heterogeneous chemical libraries without cross-contamination. When the microparticles encounter the solvent, it starts releasing the absorbed chemical substances according to the initial loading amount. (d) Schematic of the overall process of the I-lift system.

I-LIFT utilizes graphically encoded microparticles for the encoding scheme, providing a limitless number of encodings. We introduced the graphical codes that contain the long and short geometry lines to guarantee alignment even in inverted and rotated images, and the presence of the code circles refers to the barcodes. Neural net (NN)-based decoding method was utilized for automatic classifications of the encodings. We first prepared the training datasets with each code label by cropping the boundaries of the particles from the image of the glass slide on which the microparticles with single codes are dispersed. The datasets of different labels are merged and used for training the two-layer feed-forward neural net using MATLAB for image processing and decoding. As the number of hidden layers in the neural network increases, the parameters of weight and bias learned from the neural network could extract the code-specific features from the images, reporting higher accuracy of decoding. We increased datasets using data augmentation by flipping and rotating the microparticle images and could reach 98.9% accuracy in decoding the encoded microparticles [Fig. 1(c)]. The chemical releasing ratio of the microparticles has a linear correlation with the initial loading amount, therefore, the delivery of the chemicals can be controlled [Fig. 1(d)].

After extracting the coordinates of the desired microparticles from the deep learning-based decoding pipeline, we then tested if the I-LIFT system could successfully transfer the target microparticles from the mixed library of ten varying encoded microparticles (Fig. 2). Particles were detected from the image of a spread mixed pool of the libraries, and initial objects were obtained through circle detection. By calculating the code of the microparticles by the trained neural net, it was able to gain the probability vector of the corresponding codes. The microparticle image demonstrated in Fig. 2(c) had 99% probability of code 7, which corresponds to the designed code. It was able to get the coordinates of the target particles, and when illuminated by the laser beam, the sacrificial layer vaporized, further pushing the particles forward. Then, using the LIFT system, we were able to retrieve the specific microparticles that were dispersed on the ITO-coated glass slide. By adding the motor stage to the retrieval system, it was able to designate the specific position of the retrieval at a one-target-per-second speed. We demonstrated the chemical delivery by loading the red, blue, and green color food dyes into the microparticles. Food dyes were dissolved in the dimethyl sulfoxide (DMSO) solution for uniform delivery and were loaded to the microparticles through freeze-drying.

FIG. 2.

FIG. 2.

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The efficiency of the microparticle decoding and laser-induced forward transfer of the selected microparticles. (a) Design of the graphical code for encoded microparticles is determined by the presence of the code circles and long and short codes are for the alignment. (b) The concept of the neural net (NN)-based decoding pipeline for the pattern recognizer of the code. Microparticles are detected in the image and are modified into the array for the input. The pipeline solves the pattern-recognition problems with a two-layer feed-forward neural network. (c) The Receiver Operating Characteristic (ROC) curve was generated to evaluate the true positive rate vs false positive rate. Over 100 000 images per each code were used to train the neural net, and 70% of the images were used as the training set, 15% for the validation set, and 15% for the test set. The accuracy of the NN-based particle decoding increases according to the number of hidden layers. Data augmentation was performed by rotation and flipping of the particles to increase the training performance, and the final performance of the NN-based data augmentation was 98.9%. (d) The releasing ratio of the chemical-laden microparticles has a linear correlation with the initial loading amount. (e) I-LIFT system could actively transfer the target microparticles after decoding the particles of interest. The red box refers to the target microparticles to be isolated. (f) Food dye was loaded to the microparticles and delivered to the 96-well plate to validate that the I-LIFT system was able to deliver the particles to the desired position.

We have developed the I-LIFT system for connecting the micro-to-macro interface by active manipulation of the graphically encoded microparticles to the targeted positions. We validated the graphically encoded microparticle decoding process through an NN-based pipeline and showed that their coordinates could be accessed through the LIFT system in a fully automatic manner. By accessing the target coordinates of the corresponding microparticles using the optical transfer method, we could meticulously access the microparticles of interest from a mixed pool of various particles. Graphically encoded microparticles have a wide spectrum in terms of encoding capability regardless of the types of loaded chemical substance, broadening the application of small-volume multiplexed assays. Although we have reached the decoding accuracy of 98.9% in this paper, decoding accuracy can be enhanced by increasing the training sets even more or by introducing other neural nets such as convolutional neural nets. Manipulation of the desired microparticles has been demonstrated in a multiplicity of ten variations. In the current state, our design has encoding capacity of 256 graphical codes, but the multiplicity could be increased when opting for other graphical designs. Also, we demonstrated the retrieval of graphically encoded microparticles to a 96-well plate, but it can be further applied to the 384-well or 1536-well plates using the motorized stage. We envision that the described system could further expand the potential of encoded microparticles by actively manipulating the microparticles of interest in small-volume multiplexed assays.

SUPPLEMENTARY MATERIAL

See the supplementary material for the LIFT system, graphical codes used in the experiment, generating training sets for training the neural net, and the manipulation of microparticles.

ACKNOWLEDGMENTS

This work was supported by the Ministry of Science and ICT (MSIT) of the Republic of Korea and the National Research Foundation of Korea (No. NRF-2020R1A3B3079653 to S.K), (No. 2020R1C1C1007665 to S.W.S.), (No. 2021R1I1A1A01045372 to A.C.L.), by Global Ph.D. Fellowship Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. 2019H1A2A1076304 to S.L.), by Hyundai Motor Chung Mong-Koo Foundation (to W.L.), and by the Brain Korea 21 Plus Project in 2022.

AUTHOR DECLARATIONS

Conflict of Interest

The authors have no conflicts to disclose.

Author Contributions

S. Lee and W. Lee contributed equally to the manuscript.

Sumin Lee: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Funding acquisition (equal); Investigation (equal); Methodology (equal); Project administration (equal); Resources (equal); Software (equal); Supervision (equal); Validation (equal); Visualization (equal); Writing – original draft (equal); Writing – review & editing (equal). Wooseok Lee: Conceptualization (equal); Data curation (equal); Funding acquisition (equal); Investigation (equal); Resources (equal); Software (equal); Visualization (equal); Writing – original draft (equal). Amos Chungwon Lee: Conceptualization (equal); Funding acquisition (equal); Supervision (equal); Visualization (equal); Writing – original draft (equal); Writing – review & editing (equal). Juhong Nam: Data curation (equal); Investigation (equal); Writing – review & editing (equal). JinYoung Lee: Data curation (equal); Investigation (equal); Writing – review & editing (equal). Hamin Kim: Validation (equal); Writing – review & editing (equal). Yunjin Jeong: Investigation (equal); Writing – review & editing (equal). Huiran Yeom: Conceptualization (equal); Writing – review & editing (equal). Namphil Kim: Conceptualization (equal); Formal analysis (equal); Writing – review & editing (equal). Seo Woo Song: Conceptualization (equal); Formal analysis (equal); Funding acquisition (equal); Supervision (equal); Validation (equal); Writing – review & editing (equal). Sunghoon Kwon: Conceptualization (equal); Funding acquisition (equal).

DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

See the supplementary material for the LIFT system, graphical codes used in the experiment, generating training sets for training the neural net, and the manipulation of microparticles.

Data Availability Statement

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

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