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

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,
1–41. H. Lee, J. Kim, H. Kim, J. Kim, and S. Kwon, “Colour-barcoded magnetic microparticles for multiplexed bioassays,” Nat. Mater. 9, 745–749 (2010). https://doi.org/10.1038/nmat28152. G. Svedberg et al., “Towards encoded particles for highly multiplexed colorimetric point of care autoantibody detection,” Lab Chip 17, 549–556 (2017). https://doi.org/10.1039/C6LC01358A3. J. Kim, S. Bae, S. Song, K. Chung, and S. Kwon, “Fiber composite slices for multiplexed immunoassays,” Biomicrofluidics 9, 044109 (2015). https://doi.org/10.1063/1.49275904. J. F. Djoba Siawaya et al., “An evaluation of commercial fluorescent bead-based luminex cytokine assays,” PLoS One 3, e2535 (2008). https://doi.org/10.1371/journal.pone.0002535 drug delivery,55. S. W. Song et al., “One-step generation of a drug-releasing hydrogel microarray-on-a-chip for large-scale sequential drug combination screening,” Adv. Sci. 6, 1801380 (2019). https://doi.org/10.1002/advs.201801380,66. S. Eun Chung et al., “One-step pipetting and assembly of encoded chemical-laden microparticles for high-throughput multiplexed bioassays,” Nat. Commun. 5, 3468 (2014). https://doi.org/10.1038/ncomms4468 and other small-volume reactions,7–117. Y. Song et al., “Liquid-capped encoded microcapsules for multiplex assays,” Lab Chip 17, 429–437 (2017). https://doi.org/10.1039/C6LC01268J8. H. E. Muñoz, J. Che, J. E. Kong, and D. Di Carlo, “Advances in the production and handling of encoded microparticles,” Lab Chip 14, 2212–2216 (2014). https://doi.org/10.1039/c4lc90042a9. D. Zhang, J. De, Y. Lei, and H. Fu, “Organic multicomponent microparticle libraries,” Nat. Commun. 12, 1–10 (2021). https://doi.org/10.1038/s41467-021-22060-z10. A. Subramanian et al., “A next generation connectivity map: L1000 platform and the first 1,000,000 profiles,” Cell 171, 1437–1452.e17 (2017). https://doi.org/10.1016/j.cell.2017.10.04911. Y. Choi et al., “High information capacity DNA-based data storage with augmented encoding characters using degenerate bases,” Sci. Rep. 9, 1–7 (2019). https://doi.org/10.1038/s41598-019-43105-w owing to the high multiplexity and miniaturization. These properties make encoded microparticles an ideal substitute for the enormous chemical libraries needed for multiplex assays.12–1412. E. M. Payne, D. A. Holland-Moritz, S. Sun, and R. T. Kennedy, “High-throughput screening by droplet microfluidics: Perspective into key challenges and future prospects,” Lab Chip 20, 2247–2262 (2020). https://doi.org/10.1039/D0LC00347F13. Y. Feng, A. K. White, J. B. Hein, E. A. Appel, and P. M. Fordyce, “MRBLES 2.0: High-throughput generation of chemically functionalized spectrally and magnetically encoded hydrogel beads using a simple single-layer microfluidic device,” Microsyst. Nanoeng. 6, 1–13 (2020). https://doi.org/10.1038/s41378-020-00220-314. T. H. Linz, W. Hampton Henley, and J. Michael Ramsey, “Photobleaching kinetics-based bead encoding for multiplexed bioassays,” Lab Chip 17, 1076–1082 (2017). https://doi.org/10.1039/C6LC01415A 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.1515. R. MacArron et al., “Impact of high-throughput screening in biomedical research,” Nat. Rev. Drug Discovery 10, 188–195 (2011). https://doi.org/10.1038/nrd3368 The desire for miniaturization of the high-throughput screening increased due to the cost savings in reaction and sample volume.16–1816. C. Ye et al., “DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery,” Nat. Commun. 91(9), 1–9 (2018). https://doi.org/10.1038/s41467-018-06500-x17. T. Tronser, A. A. Popova, and P. A. Levkin, “Miniaturized platform for high-throughput screening of stem cells,” Curr. Opin. Biotechnol. 46, 141–149 (2017). https://doi.org/10.1016/j.copbio.2017.03.00518. W. Lei et al., “Droplet-microarray: Miniaturized platform for high-throughput screening of antimicrobial compounds,” Adv. Biosyst. 4, 2000073 (2020). https://doi.org/10.1002/adbi.202000073 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.1919. S. W. Song et al., “Uniform drug loading into prefabricated microparticles by freeze-drying,” Part. Part. Syst. Charact. 34, 1600427 (2017). https://doi.org/10.1002/ppsc.201600427To 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.20–2320. J. Zhou, P. Mukherjee, H. Gao, Q. Luan, and I. Papautsky, “Label-free microfluidic sorting of microparticles,” APL Bioeng. 3, 041504 (2019). https://doi.org/10.1063/1.512050121. K. Yiannacou and V. Sariola, “Controlled manipulation and active sorting of particles inside microfluidic chips using bulk acoustic waves and machine learning,” Langmuir 37, 4192–4199 (2021). https://doi.org/10.1021/acs.langmuir.1c0006322. Y. Zhang et al., “From passive to active sorting in microfluidics: A review,” Rev. Adv. Mater. Sci. 60, 313–324 (2021). https://doi.org/10.1515/rams-2020-004423. W. Wu et al., “Precise sorting of gold nanoparticles in a flowing system,” ACS Photonics 3, 2497–2504 (2016). https://doi.org/10.1021/acsphotonics.6b00737 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.22–2422. Y. Zhang et al., “From passive to active sorting in microfluidics: A review,” Rev. Adv. Mater. Sci. 60, 313–324 (2021). https://doi.org/10.1515/rams-2020-004424. P. Sajeesh and A. K. Sen, “Particle separation and sorting in microfluidic devices: A review,” Microfluid. Nanofluidics 17, 1–52 (2014). https://doi.org/10.1007/s10404-013-1291-9 Some examples of active microparticle manipulation include acoustophoresis,25–2725. D. J. Collins, A. Neild, and Y. Ai, “Highly focused high-frequency travelling surface acoustic waves (SAW) for rapid single-particle sorting,” Lab Chip 16, 471–479 (2016). https://doi.org/10.1039/C5LC01335F26. J. D. Adams and H. T. Soh, “Tunable acoustophoretic band-pass particle sorter,” Appl. Phys. Lett. 97, 064103 (2010). https://doi.org/10.1063/1.346725927. A. H. J. Yang and H. T. Soh, “Acoustophoretic sorting of viable mammalian cells in a microfluidic device,” Anal. Chem. 84, 10756–10762 (2012). https://doi.org/10.1021/ac3026674 dielectrophoresis,28–3228. S. Li et al., “High-throughput particle manipulation by hydrodynamic, electrokinetic, and dielectrophoretic effects in an integrated microfluidic chip,” Biomicrofluidics 7, 024106 (2013). https://doi.org/10.1063/1.479585629. M. Alshareef et al., “Separation of tumor cells with dielectrophoresis-based microfluidic chip,” Biomicrofluidics 7, 011803 (2013). https://doi.org/10.1063/1.477431230. L. Mazutis et al., “Single-cell analysis and sorting using droplet-based microfluidics,” Nat. Protoc. 8, 870–891 (2013). https://doi.org/10.1038/nprot.2013.04631. M. Hajari, A. Ebadi, M. J. Farshchi Heydari, M. Fathipour, and M. Soltani, “Dielectrophoresis-based microfluidic platform to sort micro-particles in continuous flow,” Microsyst. Technol. 26, 751–763 (2020). https://doi.org/10.1007/s00542-019-04629-332. S. Fiedler, S. G. Shirley, T. Schnelle, and G. Fuhr, “Dielectrophoretic sorting of particles and cells in a microsystem,” Anal. Chem. 70, 1909–1915 (1998). https://doi.org/10.1021/ac971063b magnetophoresis,3333. Y. Jo, F. Shen, Y. K. Hahn, J. H. Park, and J. K. Park, “Magnetophoretic sorting of single cell-containing microdroplets,” Micromachines 7, 56 (2016). https://doi.org/10.3390/mi7040056,3434. M. Hejazian, W. Li, and N. T. Nguyen, “Lab on a chip for continuous-flow magnetic cell separation,” Lab Chip 15, 959–970 (2015). https://doi.org/10.1039/C4LC01422G and optical methods.35–3735. A. Mocciaro et al., “Light-activated cell identification and sorting (LACIS) for selection of edited clones on a nanofluidic device,” Commun. Biol. 1, 1–8 (2018). https://doi.org/10.1038/s42003-018-0034-636. I. Ricárdez-Vargas, P. Rodríguez-Montero, R. Ramos-García, and K. Volke-Sepúlveda, “Modulated optical sieve for sorting of polydisperse microparticles,” Appl. Phys. Lett. 88, 121116 (2006). https://doi.org/10.1063/1.218335737. J. Marcos Fernández-Pradas and P. Serra, “Laser-induced forward transfer: A method for printing functional inks,” Crystals 10, 651 (2020). https://doi.org/10.3390/cryst10080651 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.38–4138. A. Palla-Papavlu et al., “Microfabrication of polystyrene microbead arrays by laser induced forward transfer,” J. Appl. Phys. 108, 033111 (2010). https://doi.org/10.1063/1.346674639. A. I. Kuznetsov, R. Kiyan, and B. N. Chichkov, “Laser fabrication of 2D and 3D metal nanoparticle structures and arrays,” Opt. Express 18(20), 21198–21203 (2010). https://doi.org/10.1364/OE.18.02119840. J. Marcos Fernández-Pradas and P. Serra, “Laser-induced forward transfer: A method for printing functional inks,” Crystals 10, 651 (2020). https://doi.org/10.3390/cryst1008065141. J. Luo et al., “Printing functional 3D microdevices by laser-induced forward transfer,” Small 13, 1602553 (2017). https://doi.org/10.1002/smll.201602553Here, 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.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.

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

Section:

ChooseTop of pageABSTRACTSUPPLEMENTARY MATERIAL <<REFERENCESPrevious sectionNext sectionSee 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.

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).

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

REFERENCES

Section:

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Sariola, “Controlled manipulation and active sorting of particles inside microfluidic chips using bulk acoustic waves and machine learning,” Langmuir 37, 4192–4199 (2021). https://doi.org/10.1021/acs.langmuir.1c00063, Google ScholarCrossref22. Y. Zhang et al., “From passive to active sorting in microfluidics: A review,” Rev. Adv. Mater. Sci. 60, 313–324 (2021). https://doi.org/10.1515/rams-2020-0044, Google ScholarCrossref23. W. Wu et al., “Precise sorting of gold nanoparticles in a flowing system,” ACS Photonics 3, 2497–2504 (2016). https://doi.org/10.1021/acsphotonics.6b00737, Google ScholarCrossref24. P. Sajeesh and A. K. Sen, “Particle separation and sorting in microfluidic devices: A review,” Microfluid. Nanofluidics 17, 1–52 (2014). https://doi.org/10.1007/s10404-013-1291-9, Google ScholarCrossref25. D. J. Collins, A. Neild, and Y. Ai, “Highly focused high-frequency travelling surface acoustic waves (SAW) for rapid single-particle sorting,” Lab Chip 16, 471–479 (2016). https://doi.org/10.1039/C5LC01335F, Google ScholarCrossref26. J. D. Adams and H. T. Soh, “Tunable acoustophoretic band-pass particle sorter,” Appl. Phys. Lett. 97, 064103 (2010). https://doi.org/10.1063/1.3467259, Google ScholarScitation, ISI27. A. H. J. Yang and H. T. Soh, “Acoustophoretic sorting of viable mammalian cells in a microfluidic device,” Anal. Chem. 84, 10756–10762 (2012). https://doi.org/10.1021/ac3026674, Google ScholarCrossref28. S. Li et al., “High-throughput particle manipulation by hydrodynamic, electrokinetic, and dielectrophoretic effects in an integrated microfluidic chip,” Biomicrofluidics 7, 024106 (2013). https://doi.org/10.1063/1.4795856, Google ScholarScitation, ISI29. M. Alshareef et al., “Separation of tumor cells with dielectrophoresis-based microfluidic chip,” Biomicrofluidics 7, 011803 (2013). https://doi.org/10.1063/1.4774312, Google ScholarScitation, ISI30. L. Mazutis et al., “Single-cell analysis and sorting using droplet-based microfluidics,” Nat. Protoc. 8, 870–891 (2013). https://doi.org/10.1038/nprot.2013.046, Google ScholarCrossref31. M. Hajari, A. Ebadi, M. J. Farshchi Heydari, M. Fathipour, and M. Soltani, “Dielectrophoresis-based microfluidic platform to sort micro-particles in continuous flow,” Microsyst. Technol. 26, 751–763 (2020). https://doi.org/10.1007/s00542-019-04629-3, Google ScholarCrossref32. S. Fiedler, S. G. Shirley, T. Schnelle, and G. Fuhr, “Dielectrophoretic sorting of particles and cells in a microsystem,” Anal. Chem. 70, 1909–1915 (1998). https://doi.org/10.1021/ac971063b, Google Scholar

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