[en] Automated technologies are attractive for enhancing the robust manufacturing of tissue-engineered products for clinical translation. In this work, we present an automation strategy using a robotics platform for media changes, and imaging of cartilaginous microtissues cultured in static microwell platforms. We use an automated image analysis pipeline to extract microtissue displacements and morphological features as noninvasive quality attributes. As a result, empty microwells were identified with a 96% accuracy, and dice coefficient of 0.84 for segmentation. Design of experiment are used for the optimization of liquid handling parameters to minimize empty microwells during long-term differentiation protocols. We found no significant effect of aspiration or dispension speeds at and beyond manual speed. Instead, repeated media changes and time in culture were the driving force or microtissue displacements. As the ovine model is the preclinical model of choice for large skeletal defects, we used ovine periosteum-derived cells to form cartilage-intermediate microtissues. Increased expression of COL2A1 confirms chondrogenic differentiation and RUNX2 shows no osteogenic specification. Histological analysis shows an increased secretion of cartilaginous extracellular matrix and glycosaminoglycans in larger microtissues. Furthermore, microtissue-based implants are capable of forming mineralized tissues and bone after 4 weeks of ectopic implantation in nude mice. We demonstrate the development of an integrated bioprocess for culturing and manipulation of cartilaginous microtissues and anticipate the progressive substitution of manual operations with automated solutions for the manufacturing of microtissue-based living implants.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Decoene, Isaak ; Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven.
Nasello, Gabriele ; Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Biomechanics Research Unit, GIGA In Silico Medicine, GIGA institute, University ofLiège, Liège, Belgium.
Madeiro de Costa, Rodrigo Furtado ; Department of Development and Regeneration, Stem Cell Biology and Embryology, KU Leuven, Leuven, Belgium.
Nilsson Hall, Gabriella ; Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven.
Pastore, Angela ; Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven.
Van Hoven, Inge ; Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven.
Ribeiro Viseu, Samuel; Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven.
Verfaillie, Catherine ; Department of Development and Regeneration, Stem Cell Biology and Embryology, KU Leuven, Leuven, Belgium.
Geris, Liesbet ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique ; Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven.
Luyten, Frank P ; Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven.
Papantoniou, Ioannis ; Prometheus Division of Skeletal Tissue Engineering, KU Leuven, Leuven, Belgium. ; Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven. ; Institute for Chemical Engineering Sciences, Foundationfor Research and Technology-Hellas, Patras, Greece.
Language :
English
Title :
Robotics-Driven Manufacturing of Cartilaginous Microtissues for Skeletal Tissue Engineering Applications.
Publication date :
13 January 2024
Journal title :
Stem Cells Translational Medicine
ISSN :
2157-6564
eISSN :
2157-6580
Publisher :
Wiley-Blackwell, Gb
Peer reviewed :
Peer Reviewed verified by ORBi
Funding number :
AKUL/13/47/Hercules Foundation/; Research Foundation Flanders/; 12C5923N/NextGenQBio/; STG/20/056/European Union's Horizon/
Zanoni M, Piccinini F, Arienti C, et al. 3D tumor spheroid models for in vitro therapeutic screening: a systematic approach to enhance the biological relevance of data obtained. Sci Rep. 2016;6(1):1-11. http://sourceforge.net/p/revisp/
Kim S, Kim EM, Yamamoto M, Park H, Shin H. Engineering multicellular spheroids for tissue engineering and regenerative medicine. Adv Healthc Mater. 2020;9(23):1-18. https://doi.org/10.1002/adhm.202000608.
Ragelle H, Naba A, Larson BL, et al. Comprehensive proteomic characterization of stem cell-derived extracellular matrices. Biomaterials. 2017;128:147-159. https://doi.org/10.1016/j.biomaterials.2017.03.008
Liu D, Chen S, Win Naing M. A review of manufacturing capabilities of cell spheroid generation technologies and future development [Internet]. Biotechnol Bioeng. 2021;118:542-554. https://onlinelibrary.wiley.com/doi/full/10.1002/bit.27620
Takebe T, Wells JM. Organoids by design. Science. 2019;364:956-959. https://www.science.org/doi/10.1126/science.aaw7567
Burdis R, Chariyev-Prinz F, Browe DC, et al. Spatial patterning of phenotypicallydistinctmicrotissuestoengineerosteochondralgrafts for biological joint resurfacing. Biomaterials. 2022; 289; 121750. https://linkinghub.elsevier.com/retrieve/pii/S0142961222003908
Lipsitz YY, Timmins NE, Zandstra PW. Quality cell therapy manufacturing by design. Nat Biotechnol. 2016;34(4):393-400. https://doi.org/10.1038/nbt.3525
Petry F, Salzig D. Large-scale production of size-adjusted beta-cell spheroids in a fully controlled stirred-tank reactor. Processes. 2022;10(5):861. https://www.mdpi.com/2227-9717/10/5/861/htm
Lim D, Renteria ES, Sime DS, et al. Bioreactor design and validation for manufacturing strategies in tissue engineering. Biodes Manuf. 2021;5(1):43-63. https://doi.org/10.1007/s42242-021-00154-3
Caprio N, Burdick JA. Engineered biomaterials to guide spheroid formation, function, and fabrication into 3D tissue constructs. Acta Biomater. 2022;165:4-18. https://doi.org/10.1016/j.actbio.2022.09.052
Van Loo B, Ten Den SA, Araújo-Gomes N, et al. Mass production of lumenogenic human embryoid bodies and functional cardiospheres using in-air-generated microcapsules. Nat Commun. 2023;14(1):1-15. https://www.nature.com/articles/s41467-023-42297-0
Freeman FE, Pitacco P, van Dommelen LHA, et al. 3D bioprinting spatiotemporally defined patterns of growth factors to tightly control tissue regeneration. Sci Adv. 2020;6(33):1-16.
Rossen NS, Anandakumaran PN, zur Nieden R, et al. Inject-able therapeutic organoids using sacrificial hydrogels. iScience 2020;23(5):101052. https://doi.org/10.1016/j.isci.2020.101052
Lacalle D, Castro-Abril HA, Randelovic T, et al. SpheroidJ: an open-source set of tools for spheroid segmentation. Comput Methods Programs Biomed. 2021;200(1):105837. https://doi.org/10.1016/j.cmpb.2020.105837
Grexa I, Diosdi A, Harmati M, et al. SpheroidPicker for automated 3D cell culture manipulation using deep learning. Sci Rep. 2021;11(1):1-11. https://www.nature.com/articles/s41598-021-94217-1
Deckers T, Hall GN, Papantoniou I, Aerts JM, Bloemen V. A platform for automated and label-free monitoring of morphological features and kinetics of spheroid fusion. Front Bioeng Biotechnol. 2022;10:1441. https://www.frontiersin.org/articles/10.3389/fbioe.2022.946992/full
Shirai K, Kato H, Imai Y, et al. The importance of scoring recognition fitness in spheroid morphological analysis for robust label-free quality evaluation. Regen Ther. 2020;14(6):205-214. https://doi.org/10.1016/j.reth.2020.02.004
Nilsson Hall G, Rutten I, Lammertyn J, et al. Cartilaginous spheroid-assembly design considerations for endochondral ossification: towards robotic-driven biomanufacturing. Biofabrication 2021;13(4):045025. https://doi.org/10.1088/1758-5090/ac2208
Schindelin J, Arganda-Carreras I, Frise E, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676-682. https://doi.org/10.1038/nmeth.2019
Sofroniew N, Lambert T, Evans K, Nunez-Iglesias J, Bokota G, Winston P, et al. napari: a multi-dimensional image viewer for Python. 2022. https://doi.org/10.5281/zenodo.8115575
Wieland F, Schumacher A, Roumans N, van Blitterswijk C, Lapointe V, Rademakers T, et al. Methodological approaches in aggregate formation and microscopic analysis to assess pseudoislet morphology and cellular interactions [version 2; peer review: 2 approved]. 2022. https://doi.org/10.12688/openreseurope.14894.1
Decoene I, Herpelinck T, Geris L, Luyten FP, Papantoniou I. Engineering bone-forming callus organoid implants in a xenogeneicfree differentiation medium. Front Chem Eng. 2022;4(892190):71.
Eintracht J, Harding P, Lima Cunha D, Moosajee M, Ratnayaka JA. Efficient embryoid-based method to improve generation of optic vesicles from human induced pluripotent stem cells. 2022. https://doi.org/10.12688/f1000research.108829.1
Brandenberg N, Hoehnel S, Kuttler F, et al. High-throughput automated organoid culture via stem-cell aggregation in microcavity arrays. Nat Biomed Eng. 2020;4(9):863-874. https://doi.org/10.1038/s41551-020-0565-2
Lenas P, Moos MJ, Luyten FP. Developmental engineering: a new paradigm for the design and manufacturing of cell-based products part I: from three-dimensional cell growth to biomimetics of in vivo development. Tissue Eng Part B Rev. 2009;15(4):381-394. https://doi.org/10.1089/ten.TEB.2008.0575
Lenas P, Luyten FP. An emerging paradigm in tissue engineering: from chemical engineering to developmental engineering for bioartificial tissue formation through a series of unit operations that simulate the in vivo successive developmental stages. Ind Eng Chem Res. 2011;50(2):482-522. https://doi.org/10.1021/ ie100314b
Sheehy EJ, Mesallati T, Kelly L, et al. Tissue engineering whole bones through endochondral ossification: regenerating the distal phalanx. Biores Open Access. 2015;4(1):229-241. https://doi.org/10.1089/biores.2015.0014
Mendes LF, Tam WL, Chai YC, et al. Combinatorial analysis of growth factors reveals the contribution of bone morphogenetic proteins to chondrogenic differentiation of human periosteal cells. Tissue Eng Part C Methods 2016;22(5):473-486. https://doi.org/10.1089/ten.TEC.2015.0436
Nilsson Hall G, Mendes LF, Gklava C, et al. Developmentally engineered callus organoid bioassemblies exhibit predictive in vivo long bone healing. Adv Sci. 2019;7(2):1-16.
Martini L, Fini M, Giavaresi G, Giardino R. Sheep model in orthopedic research: a literature review. Comp Med. 2001;51(4):292-299.
Rousseau CF, Maciulaitis R, Sladowski D, Narayanan G. Cell and gene therapies: European view on challenges in translation and how to address them. Front Med (Lausanne). 2018;5:158. https://doi.org/10.3389/fmed.2018.00158
ten Ham RMT, Hoekman J, Hövels AM, et al. Challenges in advanced therapy medicinal product development: a survey among companies in Europe. Mol Ther Methods Clin Dev. 2018;11(12):121-130. https://doi.org/10.1016/j.omtm.2018.10.003
Silva DN, Chrobok M, Ahlén G, et al. ATMP development and pre-GMP environment in academia: a safety net for early cell and gene therapy development and manufacturing. Immunooncol Technol. 2022;16(100099):1-7. https://doi.org/10.1016/j.iotech.2022.100099
Wilson AJ, Brown N, Rand E, Genever PG. Attitudes towards standardization of mesenchymal stromal cells—a qualitative exploration of expert views. Stem Cells Transl Med 2023;12(11):745-757. https://doi.org/10.1093/stcltm/szad056
Sun W, Starly B, Daly AC, et al. The bioprinting roadmap. Biofabrication. 2020;12(2):022002. https://doi.org/10.1088/17585090/ab5158
Groschl M, Markus A, Leyers S, et al. A liquid handling robot for robust and reproducible preparation of standard and quality control samples in bioanalysis. Adv Robot Autom. 2017;06(01):1-3.
Dettinger P, Kull T, Arekatla G, et al. Open-source personal pipetting robots with live-cell incubation and microscopy compatibility. Nat Commun. 2022;13(1):1-12. https://www.nature.com/articles/s41467-022-30643-7
Mehesz AN, Brown J, Hajdu Z, et al. Scalable robotic biofabrication of tissue spheroids. Biofabrication. 2011;3(2):025002. https://doi.org/10.1088/1758-5082/3/2/025002
Hussain W, Moens N, Veraitch FS, et al. Reproducible culture and differentiation of mouse embryonic stem cells using an automated microwell platform. Biochem Eng J. 2013;77(100):246-257. https://doi.org/10.1016/j.bej.2013.05.008
Ochs J, Biermann F, Piotrowski T, et al. Fully automated cultivation of adipose-derived stem cells in the StemCellDiscovery—a robotic laboratory for small-scale, high-throughput cell production including deep learning-based confluence estimation. Processes. 2021;9(4):575. https://www.mdpi.com/22279717/9/4/575/htm
Ochs J, Hanga MP, Shaw G, et al. Needle to needle robot-assisted manufacture of cell therapy products. Bioeng Transl Med. 2022; 7(3):e10387. https://onlinelibrary.wiley.com/doi/full/10.1002/btm2.10387
Doulgkeroglou MN, Di Nubila A, Niessing B, et al. Automation, monitoring, and standardization of cell product manufacturing. Front Bioeng Biotechnol. 2020;8(7):811. https://doi.org/10.3389/fbioe.2020.00811
Tristan CA, Ormanoglu P, Slamecka J, et al. Robotic high-throughput biomanufacturing and functional differentiation of human pluripotent stem cells. Stem Cell Rep. 2021;16(12):3076-3092. https://doi.org/10.1016/j.stemcr.2021.11.004
Boussaad I, Cruciani G, Bolognin S, et al. Integrated, automated maintenance, expansion and differentiation of 2D and 3D patient-derived cellular models for high throughput drug screening. Sci Rep. 2021;11(1):1439. https://doi.org/10.1038/s41598-021-81129-3
Kanda GN, Tsuzuki T, Terada M, et al. Robotic search for optimal cell culture in regenerative medicine. Elife. 2022;11:e77007. https://doi.org/10.7554/eLife.77007
Ohta A, Kawai S, Pretemer Y, et al. Automated cell culture system for the production of cell aggregates with growth plate-like structure from induced pluripotent stem cells. SLAS Technol. 2023;28(6):433-441. http://slas-technology.org/article/S2472630323000523/fulltext
Krieger J, Nießing B, König N, et al. Implementation of an automated manufacturing platform for engineering of functional osteochondral implants. Procedia CIRP. 2022;110:32-35. https://doi.org/10.1016/j.procir.2022.06.008
Novelli G, Spitalieri P, Murdocca M, Centanini E, Sangiuolo F. Organoid factory: the recent role of the human induced pluripotent stem cells (hiPSCs) in precision medicine. Front Cell Dev Biol. 2023;10:1059579. https://doi.org/10.3389/fcell.2022.1059579
Aguilar IN, Smith LJ, Olivos DJ, et al. Scaffold-free bioprinting of mesenchymal stem cells with the regenova printer: optimization of printing parameters. Bioprinting. 2019;15):e00048.
Shudo Y, MacArthur JW, Kunitomi Y, et al. Three-dimensional multilayered microstructure using needle array bioprinting system. Tissue Eng Part A. 2020;26(5-6):350-357. https://doi.org/10.1089/ten.TEA.2019.0313
Dalton PD, Woodfield TBF, Mironov V, Groll J. Advances in hybrid fabrication toward hierarchical tissue constructs [Internet]. Adv Sci. 2020;7:1902953. Cited June 16, 2021. www.advancedscience. com
Kikuchi T, Kino-oka M, Wada M, et al. A novel, flexible and automated manufacturing facility for cell-based health care products: tissue factory. Regen Ther. 2018;9:89-99. https://doi.org/10.1016/j.reth.2018.08.004
Smiatek J, Jung A, Bluhmki E. Towards a digital bioprocess replica: computational approaches in biopharmaceutical development and manufacturing. Trends Biotechnol. 2020;38(10):1141-1153. https://doi.org/10.1016/j.tibtech.2020.05.008
Lee E, Shah D, Porteus M, Wright JF, Bacchetta R. Design of experiments as a decision tool for cell therapy manufacturing. Cytotherapy. 2022;24(6):590-596. https://doi.org/10.1016/j.jcyt.2022.01.009
Toms D, Deardon R, Ungrin M. Climbing the mountain: experimental design for the efficient optimization of stem cell bioprocessing. J Biol Eng. 2017;11(1):35. https://doi.org/10.1186/ s13036-017-0078-z
Kuterbekov M, Machillot P, Baillet F, et al. Design of experiments to assess the effect of culture parameters on the osteogenic differentiation of human adipose stromal cells. Stem Cell Res Ther. 2019;10(1):256. https://doi.org/10.1186/s13287-019-1333-7
Ladner YD, Armiento AR, Kubosch EJ, Snedeker JG, Stoddart MJ. Optimization of loading protocols for tissue engineering experiments. Sci Rep. 2022;12(1):5094. https://doi.org/10.1038/ s41598-022-08849-y
Yasui R, Sekine K, Taniguchi H. Clever experimental designs: shortcuts for better ipsc differentiation. Cells. 2021;10(12):3540. https://doi.org/10.3390/cells10123540
Bharadwaz A, Dhar S, Jayasuriya AC. Full factorial design of experiment-based and response surface methodology approach for evaluating variation in uniaxial compressive mechanical properties, and biocompatibility of photocurable PEGDMA-based scaffolds. Biomed Mater. 2023;18(2):025019. https://doi.org/10.1088/1748605X/acb7bd
Dellaquila A, Campodoni E, Tampieri A, Sandri M. Overcoming the design challenge in 3D biomimetic hybrid scaffolds for bone and osteochondral regeneration by factorial design. Front Bioeng Biotechnol. 2020;8:743. https://doi.org/10.3389/fbioe.2020.00743
Pisani S, Genta I, Modena T, et al. A proof of concept to define the parameters affecting poly-l-lactide-co-poly-ε-caprolactone shape memory electrospun nanofibers for biomedical applications. Drug Deliv Transl Res. 2023;13(2):593-607. https://doi.org/10.1007/ s13346-022-01218-2
Kuchemüller KB, Pörtner R, Möller J. Design of cell expansion processes for adherent-growing cells with mDOE-workflow. Eng Life Sci. 2023;23(5):e2200059. https://doi.org/10.1002/elsc.202200059
Saucourt C, Vogt S, Merlin A, et al. Design and validation of an automated process for the expansion of peripheral blood-derived CD34+ cells for clinical use after myocardial infarction. Stem Cells Transl Med. 2019;8(8):822-832. https://doi.org/10.1002/sctm.17-0277
Rivera-Ordaz A, Peli V, Manzini P, Barilani M, Lazzari L. Critical analysis of cGMP large-scale expansion process in bioreactors of human induced pluripotent stem cells in the framework of quality by design. Biodrugs. 2021;35(6):693-714. https://doi.org/10.1007/ s40259-021-00503-9
Berg S, Kutra D, Kroeger T, et al. ilastik: interactive machine learning for (bio)image analysis. Nat Methods. 2019;16(12):1226-1232. https://doi.org/10.1038/s41592-019-0582-9
Stirling DR, Swain-Bowden MJ, Lucas AM, et al. CellProfiler 4: improvements in speed, utility and usability. BMC Bioinf. 2021;22(1):1-11. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04344-9
Bradski G. The OpenCV Library. Dr Dobb’s J Softw Tools. 2000;120:122-125.
Edzer JP, Roger SB. Classes and methods for spatial data in R. R News. 2005;5(2):9-13.
Geris L, Papantoniou I. The third era of tissue engineering: reversing the innovation drivers. Tissue Eng Part A. 2019;25(11-12):821-826. https://doi.org/10.1089/ten.TEA.2019.0064
Burdis R, Kelly DJ. Biofabrication and bioprinting using cellular aggregates, microtissues and organoids for the engineering of musculoskeletal tissues. Acta Biomater. 2021;126:1-14. https://doi.org/10.1016/j.actbio.2021.03.016
Lambrechts T, Papantoniou I, Rice B, et al. Large-scale progenitor cell expansion for multiple donors in a monitored hollow fibre bioreactor. Cytotherapy. 2016;18(9):1219-1233. https://doi.org/10.1016/j.jcyt.2016.05.013
Moutsatsou P, Ochs J, Schmitt RH, Hewitt CJ, Hanga MP. Automation in cell and gene therapy manufacturing: from past to future. Biotechnol Lett. 2019;41(11):1245-1253. https://doi.org/10.1007/ s10529-019-02732-z
Rafiq QA, Twomey K, Kulik M, et al. Developing an automated robotic factory for novel stem cell therapy production. Regen Med. 2016;11(4):351-354. https://doi.org/10.2217/rme-2016-0040
McCorry MC, Reardon KF, Black M, et al. Sensor technologies for quality control in engineered tissue manufacturing. Biofabrication. 2022;15(1):012001. https://doi.org/10.1088/1758-5090/ac94a1
Archibald PRT, Chandra A, Thomas D, et al. Comparability of automated human induced pluripotent stem cell culture: a pilot study. Bioprocess Biosyst Eng. 2016;39(12):1847-1858. https://doi.org/10.1007/s00449-016-1659-9
Regent F, Morizur L, Lesueur L, et al. Automation of human pluripotent stem cell differentiation toward retinal pigment epithelial cells for large-scale productions. Sci Rep. 2019;9(1):1-11. https://www.nature.com/articles/s41598-019-47123-6
Kanda GN, Tsuzuki T, Terada M, et al. Robotic search for optimal cell culture in regenerative medicine. Elife. 2022;11(e77007):11. https://doi.org/10.7554/eLife.77007.
Baradez MO, Biziato D, Hassan E, Marshall D. Application of Raman spectroscopy and univariate modelling as a process analytical technology for cell therapy bioprocessing. Front Med (Lausanne). 2018;5:14.
Mercier SM, Rouel PM, Lebrun P. Process analytical technology tools for perfusion cell culture. 2016;25-35.
Caldwell J, Wang W, Zandstra PW. Proportional-integral-derivative (PID) control of secreted factors for blood stem cell culture. PLoS One. 2015;10(9):e0137392. https://doi.org/10.1371/journal.pone.0137392
Chilmonczyk MA, Kottke PA, Stevens HY, Guldberg RE, Fedorov AG. Dynamic mass spectrometry probe for electrospray ionization mass spectrometry monitoring of bioreactors for therapeutic cell manufacturing. Biotechnol Bioeng. 2019;116(1):121-131. https://doi.org/10.1002/bit.26832
Marklein RA, Lam J, Guvendiren M, Sung KE, Bauer SR. Functionally-relevant morphological profiling: a tool to assess cellular heterogeneity. Trends Biotechnol. 2018;36(1):105-118. https://doi.org/10.1016/j.tibtech.2017.10.007
Suyama T, Takemoto Y, Miyauchi H, et al. Morphology-based noninvasive early prediction of serial-passage potency enhances the selection of clone-derived high-potency cell bank from mesenchymal stem cells. Inflamm Regen. 2022;42(1):1-13. https://inflammregen.biomedcentral.com/articles/10.1186/s41232-022-00214-w
Imai Y, Iida M, Kanie K, Katsuno M, Kato R. Label-free morphological sub-population cytometry for sensitive phenotypic screening of heterogenous neural disease model cells. Sci Rep. 2022;12(1):1-13. https://www.nature.com/articles/s41598-022-12250-0
Matsuoka F, Takeuchi I, Agata H, et al. Morphology-based prediction of osteogenic differentiation potential of human mesenchymal stem cells. PLoS One. 2013;8(2):e55082. https://doi.org/10.1371/journal.pone.0055082
Deckers T, Lambrechts T, Viazzi S, et al. High-throughput image-based monitoring of cell aggregation and microspheroid formation. PLoS One. 2018;13(6):e0199092. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0199092
Wuest T, Weimer D, Irgens C, Thoben KD. Machine learning in manufacturing: advantages, challenges, and applications. 2016;4(1):23-45. https://www.tandfonline.com/doi/abs/10.1080/21693277.2016.1192517
Odeh-Couvertier VY, Dwarshuis NJ, Colonna MB, et al. Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform. Bioeng Transl Med. 2022;7(2):e10282. https://doi.org/10.1002/btm2.10282
Srinivasan M, Thangaraj SR, Ramasubramanian K, Thangaraj PP, Ramasubramanian KV. Exploring the current trends of artificial intelligence in stem cell therapy: a systematic review. Cureus. 2021;13(12):e20083. https://pubmed.ncbi.nlm.nih.gov/34873560/
Hort S, Herbst L, Bäckel N, et al. Toward rapid, widely available autologous CAR-T cell therapy – artificial intelligence and automation enabling the smart manufacturing hospital. Front Med (Lausanne). 2022;9(June):1605. https://doi.org/10.3389/fmed.2022.913287.
Watson BM, Kasper K, Mikos - AG, et al. The future of bone regeneration: integrating AI into tissue engineering. Biomed Phys Eng Express. 2021;7(5):052002. https://doi.org/10.1088/2057-1976/ ac154f.
Banerjee D, Singh YP, Datta P, et al. Strategies for 3D bioprinting of spheroids: a comprehensive review. Biomaterials. 2022;291:121881. https://doi.org/10.1016/j.biomaterials.2022.121881
Daly AC, Davidson MD, Burdick JA. 3D bioprinting of high cell-density heterogeneous tissue models through spheroid fusion within self-healing hydrogels. Nat Commun. 2021;12(1):1-13. https://www.nature.com/articles/s41467-021-21029-2
Lammens J, Maréchal M, Geris L, et al. Warning about the use of critical-size defects for the translational study of bone repair: analysis of a sheep tibial model. Tissue Eng Part C Methods 2017;23(11):694-699. https://doi.org/10.1089/ten.TEC.2017.0147
Sparks DS, Saifzadeh S, Savi FM, et al. A preclinical large-animal model for the assessment of critical-size load-bearing bone defect reconstruction. Nat Protoc. 2020;15(3):877-924. https://doi.org/10.1038/s41596-019-0271-2
Grigoryan A, Zacharaki D, Balhuizen A, et al. Engineering human mini-bones for the standardized modeling of healthy hematopoiesis, leukemia, and solid tumor metastasis. Sci Transl Med. 2022;14(666):1-16. https://www.science.org/doi/10.1126/scitranslmed.abm6391
