I. INTRODUCTION

Section:

ChooseTop of pageABSTRACTI. INTRODUCTION <<II. BOCIII. BRAIN ORGANOIDIV. BOoC, MERGING OF BOC ...V. FUTURE PERSPECTIVESREFERENCESAs society ages, various geriatric disorders have emerged as social problems.11. H. Arai, Y. Ouchi, M. Yokode, H. Ito, H. Uematsu, F. Eto, S. Oshima, K. Ota, Y. Saito, and H. Sasaki, Geriatr. Gerontol. Int. 12(1), 16–22 (2012). https://doi.org/10.1111/j.1447-0594.2011.00776.x Among them, the most common diseases are neurodegenerative diseases.2–42. M. S. Forman, J. Q. Trojanowski, and V. M. Lee, Nat. Med. 10(10), 1055–1063 (2004). https://doi.org/10.1038/nm11133. I. P. Johnson, “Age-related neurodegenerative disease research needs aging models,” Front.Aging Neurosci. (published online 2022). https://doi.org/10.3389/fnagi.2015.001684. T. Wyss-Coray, Nature 539(7628), 180–186 (2016). https://doi.org/10.1038/nature20411 Annually, the number of patients suffering from neurodegenerative diseases such as Alzheimer's, Parkinson's, and Huntington's diseases are on the rise. Dementia is one of the representative cases of neurodegenerative disease.55. L. Fratiglioni and C. Qiu, Exp. Gerontol. 44(1–2), 46–50 (2009). https://doi.org/10.1016/j.exger.2008.06.006,66. M. Baquero and N. Martín, World J. Clin. Cases 3(8), 682 (2015). https://doi.org/10.12998/wjcc.v3.i8.682 According to the World Health Organization (WHO), 55 million people are affected by dementia, and the number is expected to grow exponentially to 139 million by 2050.77. B. M. Kuehn, JAMA 326(15), 1471 (2021). https://doi.org/10.3389/fnagi.2021.662474 Also, the global cost of dementia is estimated to be USD 1.3 trillion and is expected to reach approximately USD 1.7–2.8 trillion by 2030.88. W. H. Organization, Global Status Report on the Public Health Response to Dementia (World Health Organization, 2021); available at https://www.who.int/publications/i/item/9789240033245. Thus, neurodegenerative diseases have become a major issue associated with significant economic and social costs. The hallmarks of neurodegenerative disease are progressive deterioration in structural and functional properties of the nervous system as a result of neuronal death.9–119. W. W. Chen, X. Zhang, and W. J. Huang, Mol. Med. Rep. 13(4), 3391–3396 (2016). https://doi.org/10.3892/mmr.2016.494810. G.-y. Ou, W.-w. Lin, and W.-j. Zhao, Front. Aging Neurosci. 13, 170 (2021). https://doi.org/10.3389/fnagi.2021.66247411. M. F. Schmidt, Z. Y. Gan, D. Komander, and G. Dewson, Cell Death Differ. 28(2), 570–590 (2021). https://doi.org/10.1038/s41418-020-00706-7 However, the underlying mechanism of these diseases is not entirely understood because of the complex structure and function of the brain.1212. H. Stolp and K. Dziegielewska, Neuropathol. Appl. Neurobiol. 35(2), 132–146 (2009). https://doi.org/10.1111/j.1365-2990.2008.01005.xIn addition to neurodegenerative diseases, neurodevelopmental disorders, including autism spectrum disorders and intellectual disability, are also a significant problem, as the environmental toxicity issues of particulate matter and microplastics are emerging. Approximately, 120 million people worldwide suffer from neurodevelopmental disorders.1313. S. L. James, D. Abate, K. H. Abate, S. M. Abay, C. Abbafati, N. Abbasi, H. Abbastabar, F. Abd-Allah, J. Abdela, and A. Abdelalim, Lancet 392(10159), 1789–1858 (2018). https://doi.org/10.1016/S0140-6736(18)32279-7,1414. P. K. Maulik, M. N. Mascarenhas, C. D. Mathers, T. Dua, and S. Saxena, Res. Dev. Disabil. 32(2), 419–436 (2011). https://doi.org/10.1016/j.ridd.2010.12.018 It is estimated that approximately 1% of children worldwide have autism, with rates increasing over the past few decades.1515. H. Zarafshan, M. Salmanian, S. Aghamohammadi, M. R. Mohammadi, and S.-A. Mostafavi, Basic Clin. Neurosci. 8(2), 95 (2017). https://doi.org/10.18869/nirp.bcn.8.2.95Although many pharmaceutical companies have developed new drugs to treat neurodegenerative diseases, only six of them have been approved by the Food and Drug Administration (FDA), beginning in 1993 with Tacrine.16–1816. J. L. Cummings, T. Morstorf, and K. Zhong, Alzheimer's Res. Ther. 6(4), 37 (2014). https://doi.org/10.1186/alzrt26917. M. L. Crismon, Ann. Pharmacother. 28(6), 744–751 (1994). https://doi.org/10.1177/10600280940280061218. T. Athar, K. Al Balushi, and S. A. Khan, Mol. Biol. Rep. 48(7), 5629–5645 (2021). https://doi.org/10.1007/s11033-021-06512-9 A new drug called aducanumab was recently approved by the FDA about 20 years after memantine was approved in 2003; however, its efficacy remains controversial.19–2219. R. R. Tampi, B. P. Forester, and M. Agronin, Drugs Context 10, 1 (2021). https://doi.org/10.7573/dic.2021-7-320. H. Fillit and A. Green, Nat. Rev. Neurol. 17(3), 129–130 (2021). https://doi.org/10.1038/s41582-020-00454-921. R. Rubin, JAMA 326(6), 469–472 (2021). https://doi.org/10.1001/jama.2021.1155822. F. Tagliavini, P. Tiraboschi, and A. Federico, “Alzheimer's disease: The controversial approval of aducanumab,” Neurol. Sci. (published online 2021). https://doi.org/10.1007/s10072-021-05497-4 Moreover, the current treatment for neurodevelopmental disorder consists of a combination of behavior therapy and some medications to alleviate comorbid symptoms such as anxiety and depression.2323. D. C. Tărlungeanu and G. Novarino, Exp. Mol. Med. 50(8), 1–7 (2018). https://doi.org/10.1038/s12276-018-0129-7 Currently, there are only two FDA-approved drugs that are used to treat autism disorders: riprazole, which is a partial dopamine agonist, and risperidone, which is an antagonist of serotonin and dopamine.2424. N. S. Ashmawi and M. A. Hammoda, Cureus 14(3), e23465 (2022). https://doi.org/10.7759/cureus.23465 However, their use is extremely limited. Since the underlying mechanism of neurodevelopmental disorders has not yet been identified, there is still no fundamental medication for the patient, and the core symptoms remain unsolved. As such, developing new drugs for neurodegenerative diseases and neurodevelopmental disorders is challenging because of the poor understanding of pathogenesis and the absence of an appropriate experimental model.In the preclinical stages of drug development, conventional in vivo animal models play an important role; however, they have drawbacks, including ethical concerns, genetic heterogeneity with humans, and high costs.25–2725. S. J. Mitchell, M. Scheibye-Knudsen, D. L. Longo, and R. de Cabo, Annu. Rev. Anim. Biosci. 3(1), 283–303 (2015). https://doi.org/10.1146/annurev-animal-022114-11082926. J. Kim, B.-K. Koo, and J. A. Knoblich, Nat. Rev. Mol. Cell Biol. 21(10), 571–584 (2020). https://doi.org/10.1038/s41580-020-0259-327. A. Lamas-Paz, F. Hao, L. J. Nelson, M. T. Vázquez, S. Canals, M. G. Del Moral, E. Martínez-Naves, Y. A. Nevzorova, and F. J. Cubero, World J. Gastroenterol. 24(45), 5063 (2018). https://doi.org/10.3748/wjg.v24.i45.5063 As an alternative to in vivo animal models, a variety of in vitro two-dimensional (2D) and three-dimensional (3D) cell culture models that are prepared using human-derived cells have been proposed. 2D-based cell cultures are the most widely used in vitro models. 2D-based cell cultures have numerous advantages, including simplicity, reproducibility, low cost, and versatility of analysis tools.2828. J. Hoarau-Véchot, A. Rafii, C. Touboul, and J. Pasquier, Int. J. Mol. Sci. 19(1), 181 (2018). https://doi.org/10.3390/ijms19010181,2929. M. A. Heinrich, A. M. Mostafa, J. P. Morton, L. J. Hawinkels, and J. Prakash, Adv. Drug Delivery Rev. 174, 265–293 (2021). https://doi.org/10.1016/j.addr.2021.04.018 However, they also have limitations in that they do not reflect the real microenvironment of human tissue, such as mechanical properties, cell–extracellular matrix (ECM) interactions, cell–cell interactions, fluidic conditions, and dynamic organ-level motion, leading to low physiological relevance.3030. M. Kapałczyńska, T. Kolenda, W. Przybyła, M. Zajączkowska, A. Teresiak, V. Filas, M. Ibbs, R. Bliźniak, Ł Łuczewski, and K. Lamperska, Arch. Med. Sci. 14(4), 910–919 (2018). https://doi.org/10.5114/aoms.2016.63743,3131. M. Frühauf, U. Zeitschel, C. Höfling, F. Ullm, F. V. Rabiger, G. Alber, T. Pompe, U. Müller, and S. Roßner, Eur. J. Neurosci. 53(12), 4034–4050 (2021). https://doi.org/10.1111/ejn.14978Cells in the human body exist in a 3D environment and spontaneously interact with other cells. Two techniques have been extensively studied: brain-on-a-chip (BoC) and brain organoids. BoC is fabricated using microfabrication techniques and is capable of constructing sophisticated and complex microstructures for 3D cell cultures.32–3732. D. Huh, G. A. Hamilton, and D. E. Ingber, Trends Cell Biol. 21(12), 745–754 (2011). https://doi.org/10.1016/j.tcb.2011.09.00533. P. Zhuang, A. X. Sun, J. An, C. K. Chua, and S. Y. Chew, Biomaterials 154, 113–133 (2018). https://doi.org/10.1016/j.biomaterials.2017.10.00234. R. Dong, Y. Liu, L. Mou, J. Deng, and X. Jiang, Adv. Mater. 31(45), 1805033 (2019). https://doi.org/10.1002/adma.20180503335. M. Rothbauer, H. Zirath, and P. Ertl, Lab Chip 18(2), 249–270 (2018). https://doi.org/10.1039/C7LC00815E36. O. E. Atat, Z. Farzaneh, M. Pourhamzeh, F. Taki, R. Abi-Habib, M. Vosough, and M. El-Sibai, Human Cell 35 1–14 (2021). https://doi.org/10.1007/s13577-021-00642-937. H. Ryu, M. Chung, J. Song, S. S. Lee, O. Pertz, and N. L. Jeon, Sci. Rep. 8(1), 1–7 (2018). https://doi.org/10.1038/s41598-018-28873-1 The BoC allows the co-culture of heterogeneous cell types, as well as spatial-temporal dynamic stimulation of soluble factors.38–4038. Y.-M. Kook, Y. Jeong, K. Lee, and W.-G. Koh, J. Tissue Eng. 8, 2041731417724640 (2017). https://doi.org/10.1177/204173141772464039. J. Song, H. Choi, S. K. Koh, D. Park, J. Yu, H. Kang, Y. Kim, D. Cho, and N. L. Jeon, Front. Immunol. 12 3848 (2021). https://doi.org/10.3389/fimmu.2021.73331740. H. Wang, P. C. Brown, E. C. Chow, L. Ewart, S. S. Ferguson, S. Fitzpatrick, B. S. Freedman, G. L. Guo, W. Hedrich, and S. Heyward, Clin. Transl. Sci. 14(5), 1659–1680 (2021). https://doi.org/10.1111/cts.13066 However, the BoC cannot replicate the complex features of brain tissue, such as the cortical layer structure formation and the rich diversity of cell types appropriate for the human brain, both of which are characteristic of the brain organoid model.4141. X. Qian, Y. Su, C. D. Adam, A. U. Deutschmann, S. R. Pather, E. M. Goldberg, K. Su, S. Li, L. Lu, and F. Jacob, Cell Stem Cell 26(5), 766–781.e9 (2020). https://doi.org/10.1016/j.stem.2020.02.002,4242. S. Velasco, A. J. Kedaigle, S. K. Simmons, A. Nash, M. Rocha, G. Quadrato, B. Paulsen, L. Nguyen, X. Adiconis, and A. Regev, Nature 570 (7762), 523–527 (2019). https://doi.org/10.1038/s41586-019-1289-x Although BoCs can recapitulate the layer structure by arranging several microchannels in parallel, the thickness of the layers differs from the thickness of individual layers in the cortical layer structure. Moreover, although there have been attempts to co-culture various types of cells on BoCs, the number of co-cultured cell types has not yet reached the diversity of the cell types found in brain organoids. Second, brain organoids are based on mimicry of the spontaneous developmental process of brain tissue. It has great potential for modeling the complex structures of brain tissue. However, brain organoid technology has limitations in mimicking brain-specific microenvironments, such as interstitial flow, brain microvasculature, and the brain immune system. In this regard, a hybrid technology called the brain organoid-on-a-chip (BOoC) has emerged. The BOoC can replicate the structure of the in vivo brain and microenvironmental factors, such as the microvasculature, flow, and diffusion-mediated molecular transport.43–4543. M. Jang, N. Choi, and H. N. Kim, “Hyperglycemic neurovasculature-on-a-chip to study the effect of SIRT1-targeted therapy for the Type 3 Diabetes “Alzheimer's Disease”,” Adv. Sci. (published online 2022). https://doi.org/10.1002/advs.20220188244. C. Hajal, G. S. Offeddu, Y. Shin, S. Zhang, O. Morozova, D. Hickman, C. G. Knutson, and R. D. Kamm, Nat. Protoc. 17(1), 95–128 (2022). https://doi.org/10.1038/s41596-021-00635-w45. Y. Shin, S. H. Choi, E. Kim, E. Bylykbashi, J. A. Kim, S. Chung, D. Y. Kim, R. D. Kamm, and R. E. Tanzi, Adv. Sci. 6(20), 1900962 (2019). https://doi.org/10.1002/advs.201900962 Remarkably, brain organoids are advantageous in the modeling of neurodevelopmental disorders.4646. J. W. Adams, F. R. Cugola, and A. R. Muotri, Physiology 34(5), 365–375 (2019). https://doi.org/10.1152/physiol.00005.2019,4747. X. Lu, J. Yang, and Y. Xiang, Cell Regen. 11(1), 1–13 (2022). https://doi.org/10.1186/s13619-021-00103-6 Brain organoids are composed of newly differentiated cells from stem cells, and are therefore too young to reflect a degenerative process that occurs primarily in the elderly. However, the cells used for BoC are relatively matured, highly differentiated cells, which are ideal for generating an aging model. It would be potentially beneficial if these models were integrated to recapitulate various aspects of neurological diseases.

From this perspective, we summarize the recent advances in BOoC, with a particular focus on the mimicry of structural and functional aspects of in vivo brain tissue. To this end, we first briefly describe the key features of BoCs and brain organoids and their applications in brain disease modeling. Recent advances in BOoC technology are presented. Finally, the remaining issues with the BOoC are described. In the future, it is envisioned that a BOoC that closely mimics human brain tissue will contribute to the understanding of the underlying mechanisms of brain diseases and accelerate the development of new drugs.

IV. BOoC, MERGING OF BOC AND BRAIN ORGANOIDS

Section:

ChooseTop of pageABSTRACTI. INTRODUCTIONII. BOCIII. BRAIN ORGANOIDIV. BOoC, MERGING OF BOC ... <<V. FUTURE PERSPECTIVESREFERENCESAlthough brain organoids have great potential in mimicking the ultrastructures of brain tissue, the BoC is good at reconstructing brain microenvironmental characteristics in an engineered platform. However, each technology also has limitations in the recapitulation of microenvironmental features and structural aspects, implying the need for more in vivo-relevant brain models.111111. J. Song, H. Ryu, M. Chung, Y. Kim, Y. Blum, S. S. Lee, O. Pertz, and N. L. Jeon, Biosens. Bioelectron. 104, 58–64 (2018). https://doi.org/10.1016/j.bios.2017.12.038,112112. S. S. Lee, I. A. Vizcarra, D. H. Huberts, L. P. Lee, and M. Heinemann, Proc. Natl. Acad. Sci. U.S.A. 109(13), 4916–4920 (2012). https://doi.org/10.1073/pnas.1113505109 In this regard, the hybrid platform of brain organoids and BoCs has emerged, termed BOoC, and this model is believed to serve as a next-generation “human brain avatar.” BOoC can be formed by incorporating matured brain organoids into the BoC with hydrogels.Pioneering studies have indicated that the BOoC can present more human brain-relevant physiological and pathological features than those presented by brain organoids and BoCs [Fig. 1(c)]. Even though the brain organoid is expected to significantly contribute to the research on the brain, limitations such as insufficient maturation, heterogeneous differentiation, apoptosis in the core, and insufficient differentiation are observed in cerebral organoids.90,113–11590. M. A. Lancaster, M. Renner, C.-A. Martin, D. Wenzel, L. S. Bicknell, M. E. Hurles, T. Homfray, J. M. Penninger, A. P. Jackson, and J. A. Knoblich, Nature 501(7467), 373–379 (2013). https://doi.org/10.1038/nature12517113. S. H. Choi, Y. H. Kim, L. Quinti, R. E. Tanzi, and D. Y. Kim, Mol. Neurodegener. 11(1), 1–11 (2016). https://doi.org/10.1186/s13024-016-0139-7114. P. Jalink and M. Caiazzo, Biology 10(08), 740 (2021). https://doi.org/10.3390/biology10080740115. D. Hernández, L. A. Rooney, M. Daniszewski, L. Gulluyan, H. H. Liang, A. L. Cook, A. W. Hewitt, and A. Pébay, Stem Cell Rev. Rep. 18 1–14 (2021). https://doi.org/10.1007/s12015-021-10147-5 These limitations can be improved by integrating brain organoids with the BoC. An important advantage of the integration of BoC is its ability to adapt to fluid flows. A continuous flow of fluid supplied by a microchannel to transport nutrients and oxygen and eliminate metabolites from the brain organoid, consequently reducing apoptosis in the core of the brain organoid and improved differentiation.56,11656. J. Park, B. K. Lee, G. S. Jeong, J. K. Hyun, C. J. Lee, and S.-H. Lee, Lab Chip 15(1), 141–150 (2015). https://doi.org/10.1039/C4LC00962B116. E. Berger, C. Magliaro, N. Paczia, A. S. Monzel, P. Antony, C. L. Linster, S. Bolognin, A. Ahluwalia, and J. C. Schwamborn, Lab Chip 18(20), 3172–3183 (2018). https://doi.org/10.1039/C8LC00206A For example, Cho et al. demonstrated that human pluripotent stem cell (hPSC)-derived brain organoids could be cultured in a microfluidic device having a decellularized brain matrix. It has been demonstrated that gravity-driven flow, which mimics the fluid flow in the cerebrospinal and interstitial spaces in the body, can facilitate the delivery of nutrients and oxygen and eliminate waste, thus reducing cell death across organoids.117117. A.-N. Cho, Y. Jin, Y. An, J. Kim, Y. S. Choi, J. S. Lee, J. Kim, W.-Y. Choi, D.-J. Koo, and W. Yu, Nat. Commun. 12(1), 1–23 (2021). https://doi.org/10.1038/s41467-021-24775-5 They also confirmed the increased volume of organoids and improved neurogenesis in cortical development and electrophysiological function. Recently, Spitz et al. also introduced a multi-sensor integrated organoid-on-a-chip to monitor electrochemical signals in the midbrain.118118. S. Spitz, S. Bolognin, K. Brandauer, J. Fuessl, P. Schuller, S. Schobesberger, C. Jordan, B. Schaedl, J. Grillari, and H. D. Wanzenboeck, bioRxiv (2022). By culturing brain organoids under the interstitial fluid flow, they improved brain organogenesis and reduced necrotic core formation. Furthermore, they confirmed functional network maturity by monitoring a time-dependent increase in dopamine signals with significantly higher catecholamine levels.BOoC can similarly generate a variety of stimuli using fluid flow, which allows the evaluation of the effects of various stimuli on the development of a given organoid. For instance, Cui et al. validated the effect of exosomes derived from breast cancer on human-iPSC-derived brain organoids using a microfluidic device with a micropillar array.119119. K. Cui, W. Chen, R. Cao, Y. Xie, P. Wang, Y. Wu, Y. Wang, and J. Qin, Cell Regen. 11(1), 1–12 (2022). https://doi.org/10.1186/s13619-021-00102-7 Brain organoids exposed to breast cancer cell-derived exosomes were shown to exhibit impaired neurodevelopment, indicating that women with breast cancer may be at risk for impaired neurodevelopmental disorders after the birth of their fetus. Furthermore, Wang et al. examined the effects of nicotine on early fetal brain development.120120. Y. Wang, L. Wang, Y. Zhu, and J. Qin, Lab Chip 18(6), 851–860 (2018). https://doi.org/10.1039/C7LC01084B Under continuous fluid flow containing nutrients and nicotine, abnormal neuronal differentiation and migration were observed in BoC-incorporated brain organoids. Additionally, Ao et al. introduced a microfluidic device to validate the effect of prenatal cannabis exposure (PCE) on early human brain development.121121. Z. Ao, H. Cai, D. J. Havert, Z. Wu, Z. Gong, J. M. Beggs, K. Mackie, and F. Guo, Anal. Chem. 92(6), 4630–4638 (2020). https://doi.org/10.1021/acs.analchem.0c00205 They confirmed that fluid flow with PCE resulted in reduced neuronal maturation, downregulation of cannabinoid type 1 receptors, and impaired neurite outgrowth. Using the microfluidic device, they demonstrated sequential processes including embryonic body formation, neuroectoderm induction, ECM embedding, and brain organoid maturation within a single device. Additionally, they used a microfluidic device to create air-liquid interface culture conditions that minimized the heterogeneity of brain organoids and hypoxic core formation. Under static conditions without continuous fluid flow containing PCE, these results cannot be obtained.Meanwhile, BOoC is also useful for studying the underlying mechanisms related to the development of organoids. Karzbrun et al., for the first time, demonstrated the physics of human brain surface wrinkles using BOoC.122122. E. Karzbrun, A. Kshirsagar, S. R. Cohen, J. H. Hanna, and O. Reiner, Nat. Phys. 14(5), 515–522 (2018). https://doi.org/10.1038/s41567-018-0046-7 They revealed that the contraction force at the center of the organoid and cell cycle-dependent nuclear expansion force at the perimeter of the organoids drive the brain wrinkles [Fig. 1(c)-(i)].Furthermore, a brain organoid capable of perfusion was recently proposed by combining vasculature-on-a-chip with a brain organoid. Salmon et al. developed integrated neurovascular organoids using a 3D-printed microfluidic device.123123. I. Salmon, S. Grebenyuk, A. R. Abdel Fattah, G. Rustandi, T. Pilkington, C. Verfaillie, and A. Ranga, Lab Chip 22(8), 1615–1629 (2022). https://doi.org/10.1039/D1LC00535A They co-cultured human pluripotent stem cell (hPSC)-derived cerebral organoids, hPSC-derived pericytes, and endothelial cells on a chip to recapitulate the characteristics of human embryonic development, especially organoid vascularization under spatially and temporally controlled conditions [Fig. 1(c)-(ii)]. They also demonstrated the perfusion and permeability of the vascular networks. FITC-dextran of 40 kDa diffused out of the neurovascular network, whereas microbeads with a 1-mm diameter remained within the microvasculature.

Based on their research, they concluded that vascularized organoids can be used in several applications, including the delivery of small membrane-diffusible molecules to brain organoids via blood vessels.

As shown above, the merging of two independently developed technologies opens a new avenue for studying the mechanisms underlying brain diseases and development.

V. FUTURE PERSPECTIVES

Section:

ChooseTop of pageABSTRACTI. INTRODUCTIONII. BOCIII. BRAIN ORGANOIDIV. BOoC, MERGING OF BOC ...V. FUTURE PERSPECTIVES <<REFERENCESCompared with conventional 2D-based culture models, BOoC has heterogeneous 3D structures within a single organoid and large unit sizes that is difficult to image in high magnification. Therefore, imaging should be performed in series to visualize the height-dependent structures, which is essential for high-content screening (HCS). Furthermore, for high-throughput screening (HTS), multiple organoids should be imaged with an automated imaging system. In both cases, the number of images is too many to identify the readouts in a labor-intensive manner. Therefore, a more robust and scalable analysis technique is required [Fig. 2(a)]. Therefore, the adaptation of machine learning (ML) techniques for data analysis is required for both HCS and HTS. Therefore, a more robust and scalable analysis technique is required [Fig. 2(a)]. Therefore, the adaptation of ML techniques for data analysis is required for both HCS and HTS.In recent years, artificial intelligence has achieved remarkable success, leading to tremendous interest in applying deep-learning technology to analyze biological data.124–127124. S. Min, B. Lee, and S. Yoon, Briefings Bioinf. 18(5), 851–869 (2017). https://doi.org/10.1093/bib/bbw068b125. S. Vatansever, A. Schlessinger, D. Wacker, HÜ Kaniskan, J. Jin, M. M. Zhou, and B. Zhang, Med. Res. Rev. 41(3), 1427–1473 (2021). https://doi.org/10.1002/med.21764126. Z. Dlamini, F. Z. Francies, R. Hull, and R. Marima, Comput. Struct. Biotechnol. J. 18, 2300–2311 (2020). https://doi.org/10.1016/j.csbj.2020.08.019127. A. Barragán-Montero, U. Javaid, G. Valdés, D. Nguyen, P. Desbordes, B. Macq, S. Willems, L. Vandewinckele, M. Holmström, and F. Löfman, Phys. Med. 83, 242–256 (2021). https://doi.org/10.1016/j.ejmp.2021.04.016 Various ML algorithms have been proposed, ranging from supervised learning techniques such as multilayer perception, convolutional neural networks (CNN), and recurrent neural networks to unsupervised learning techniques such as deep generative models128128. I. H. Sarker, SN Comput. Sci. 2(3), 1–21 (2021). https://doi.org/10.1007/s42979-021-00592-x,129129. I. H. Sarker, SN Comput. Sci. 2(6), 1–20 (2021). https://doi.org/10.1007/s42979-021-00815-1 [Fig. 2(b)-(i)]. The types of ML models depend on the types of input data and purpose. Data used in supervised ML are labeled, whereas data used in unsupervised ML are unlabeled.130–132130. M. W. Libbrecht and W. S. Noble, Nat. Rev. Genet. 16(6), 321–332 (2015). https://doi.org/10.1038/nrg3920131. M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, J. Big Data 2(1), 1–21 (2015). https://doi.org/10.1186/s40537-014-0007-7132. K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, and D. I. Fotiadis, Comput. Struct. Biotechnol. J. 13, 8–17 (2015). https://doi.org/10.1016/j.csbj.2014.11.005 These algorithms are capable of clustering, classification, regression, and anomaly detection.128,129,133,134128. I. H. Sarker, SN Comput. Sci. 2(3), 1–21 (2021). https://doi.org/10.1007/s42979-021-00592-x129. I. H. Sarker, SN Comput. Sci. 2(6), 1–20 (2021). https://doi.org/10.1007/s42979-021-00815-1133. A. Patcha and J.-M. Park, Comput. Netw. 51(12), 3448–3470 (2007). https://doi.org/10.1016/j.comnet.2007.02.001134. G. Pang, C. Shen, L. Cao, and A. V. D. Hengel, ACM Comput. Surv. (CSUR) 54(2), 1–38 (2021). https://doi.org/10.1145/3439950 Therefore, it is necessary to choose the most appropriate model according to the type of data and purpose [Fig. 2(b)-(ii)].In particular, as many deep-learning models have been developed using image-based data, such as variational autoencoder and a generative adversarial network, they show outstanding performance when applied to image-based data analysis.135135. J. Song, Y. C. Lee, and J. Lee, “Deep generative model with time series-image encoding for manufacturing fault detection in die casting process,” J. Intell. Manuf. (published online 2022). https://doi.org/10.1007/s10845-022-01981-6 Therefore, the analysis of biological data using deep-learning technology appears to be more attractive, as a large portion of biological experiment data is image-based.Even though the emerging technology of brain organoids and BoC has emerged as a promising technique, the amount of data that can be acquired at a time in the hybrid BooC platform is still insufficient for HTS. In general, the amount of data obtained from a single BoC is usually less than 10. Moreover, the fabrication time for a single BoC is 1–2 h, excluding 4 h for baking.136136. D. Hoelzle, M. Lake, C. Narciso, K. Cowdrick, T. Storey, S. Zhang, and J. Zartman, “Microfluidic device design, fabrication, and testing protocols,” Protocol Exchange (published online 2015). https://doi.org/10.1038/protex.2015.069 Considering the photolithography process for master fabrication, an additional 3–4 h is required. Consequently, the device preparation process for BoCs is labor-intensive and time-consuming. The properties derived from these BoC manufacturing processes make it difficult to introduce high-throughput analysis or ML.Hence, to train the model, approximately 1000 datasets per class are required.137137. A. Olsen, D. A. Konovalov, B. Philippa, P. Ridd, J. C. Wood, J. Johns, W. Banks, B. Girgenti, O. Kenny, and J. Whinney, Sci. Rep. 9(1), 1–12 (2019). https://doi.org/10.1038/s41598-018-38343-3,138138. S. Ravuri and O. Vinyals, Adv. Neural. Inf. Process Syst., arXiv.1905.10887 Therefore, enormous efforts have been made to overcome this gap by increasing the throughput of the experiments. Recently, an injection-molded plastic chip for 3D culture was proposed as a breakthrough for providing a sufficient amount of data.139–142139. Y. Kim, J. Song, Y. Lee, S. Cho, S. Kim, S.-R. Lee, S. Park, Y. Shin, and N. L. Jeon, Lab Chip 21(16), 3150–3158 (2021). https://doi.org/10.1039/D0LC01245A140. S. Lee, J. Lim, J. Yu, J. Ahn, Y. Lee, and N. L. Jeon, Lab Chip 19(12), 2071–2080 (2019). https://doi.org/10.1039/C9LC00148D141. S. Jung, J. Lee, J. Lim, J. Suh, T. Kim, J. Ahn, W. J. Kim, and Y. Kim, Adv. Healthcare Mater. 9(22), 2001633 (2020). https://doi.org/10.1002/adhm.202001633142. A. Malandrino, X. Trepat, R. D. Kamm, and M. Mak, PLoS Comput. Biol. 15(4), e1006684 (2019). https://doi.org/10.1371/journal.pcbi.1006684 The plastic-based microfluidic devices were fabricated using an injection-molding technique. The master mold was usually obtained by milling, and the melted polymer was injected into the mold.143143. K. G. Lee, T. J. Lee, S. W. Jeong, H. W. Choi, N. S. Heo, J. Y. Park, T. J. Park, and S. J. Lee, Sensors 12(8), 10810–10819 (2012). https://doi.org/10.3390/s120810810 Injection molding requires only a few seconds after the mold is made. As the device is ready-made at the factory, it can be immediately used. Scalability is another advantage of injection molding.144144. E. Sharifi, A. Chaudhuri, B. V. Waehrens, L. G. Staal, and S. Davoudabadi Farahani, Sustainability 13(3), 1313 (2021). https://doi.org/10.3390/su13031313 In addition to being easy to use, a large number of plastic-based microfluidic devices in a short period of time at a low cost can be produced. Therefore, injection-molded microfluidic chips are capable of simultaneously testing dozens of experimental sets, greatly reducing the device preparation time and cost. There are also several commercial chips available for 3D culture.145–147145. A. V. Singh, A. Romeo, K. Scott, S. Wagener, L. Leibrock, P. Laux, A. Luch, P. Kerkar, S. Balakrishnan, and S. P. Dakua, Adv. Healthcare Mater. 10(18), 2100633 (2021). https://doi.org/10.1002/adhm.202100633146. Y. Xiao, D. Kim, B. Dura, K. Zhang, R. Yan, H. Li, E. Han, J. Ip, P. Zou, and J. Liu, Adv. Sci. 6(8), 1801531 (2019). https://doi.org/10.1002/advs.201801531147. E. Mavrakis and S. A. Pergantis, Anal. Chim. Acta 1179, 338830 (2021). https://doi.org/10.1016/j.aca.2021.338830 These chips are mostly ready to use, allowing people to easily move from a 2D culture based on a Petri-dish to a 3D culture using hydrogel.Recently, Yu et al. introduced a 384 format 28-well injection-molded plastic array for the generation of angiogenic sprouts and vasculogenic networks with high throughput.148148. J. Yu, S. Lee, J. Song, S.-R. Lee, S. Kim, H. Choi, H. Kang, Y. Hwang, Y.-K. Hong, and N. L. Jeon, Nano Converg. 9(1), 1–11 (2022). https://doi.org/10.1186/s40580-022-00306-w The device was used to identify the optimal conditions for angiogenesis and vasculogenesis because of its ability to process 28 samples at a time without preparation time. As it requires minimal preparation time and labor for devices, a relatively large number of datasets at a time can be provided; obtaining 1000 datasets per condition would be expected to be easier, which is the minimum amount of data required for ML. Moreover, Tung et al. also developed 384 hanging drop array plates that can be used for 3D spheroid culture of a variety of cell types.149149. A. Y. Hsiao, Y. C. Tung, X. Qu, L. R. Patel, K. J. Pienta, and S. Takayama, Biotechnol. Bioeng. 109(5), 1293–1304 (2012). https://doi.org/10.1002/bit.24399 It should be noted that the 384-format microfluidic devices mentioned above are compatible with existing liquid dispensers and plate readers. Compatibility with existing laboratory equipment can accelerate the automation of experiments and allow for the rapid and easy acquisition of large amounts of data. Moreover, Paek et al. developed a wellplate-based high-throughput bone-on-a-chip and analyzed the effect of drugs on osteoporosis using a deep-learning algorithm based on a CNN.150150. K. Paek, S. Kim, S. Tak, M. K. Kim, J. Park, S. Chung, T. H. Park, and J. A. Kim, “A high-throughput biomimetic bone-on-a-chip platform with artificial intelligence-assisted image analysis for osteoporosis drug testing,” Bioeng. Trans. Med. (published online 2022). This platform has overcome the limitations of previous HTS systems, enabling the possibility of obtaining a sufficient amount of data per class for training the model.There are several additional examples of the application of deep learning in OoC if the organ is not limited to the brain.151151. B. P. Jena, D. L. Gatti, S. Arslanturk, S. Pernal, and D. J. Taatjes, Micron 117, 55–59 (2019). https://doi.org/10.1016/j.micron.2018.11.002 Considering that brain science research still largely relies on time-consuming in vivo experiments, this new experimental method can not only provide a large number of useful datasets in a simple and robust manner but also allows for a time-efficient analysis of drug efficacy using state-of-the-art ML techniques.