[en] Midbrain organoids are advanced in vitro cellular models for disease modeling. They have been used successfully over the past decade for Parkinson's disease (PD) research and drug development. The three-dimensional structure and multicellular composition allow disease research under more physiological conditions than is possible with conventional 2D cellular models. However, there are concerns in the field regarding the organoid batch-to-batch variability and thus the reproducibility of the results. In this manuscript, we generate multiple independent midbrain organoid batches derived from healthy individuals or glucocerebrosidase (GBA)-N370S mutation-carrying PD patients to evaluate the reproducibility of the GBA-N370S mutation-associated PD transcriptomic and metabolic signature as well as selected protein abundance. Our analysis shows that GBA-PD-associated phenotypes are reproducible across organoid generation batches and time points. This proves that midbrain organoids are not only suitable for PD in vitro modeling but also represent robust and highly reproducible cellular models.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
ZUCCOLI, Elisa ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Developmental and Cellular Biology
Al Sawaf, Haya; Developmental and Cellular Biology, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, Avenue du Swing, 4367 Belvaux, Luxembourg
Tuzza, Mona; Developmental and Cellular Biology, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, Avenue du Swing, 4367 Belvaux, Luxembourg
Nickels, Sarah L; Developmental and Cellular Biology, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6, Avenue du Swing, 4367 Belvaux, Luxembourg
ZAGARE, Alise ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine > Developmental and Cellular Biology > Team Jens Christian SCHWAMBORN
SCHWAMBORN, Jens Christian ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Developmental and Cellular Biology
External co-authors :
yes
Language :
English
Title :
Reproducibility of PD patient-specific midbrain organoid data for in vitro disease modeling.
Fonds National de la Recherche Luxembourg Fonds National de la Recherche Luxembourg Fonds National de la Recherche Luxembourg
Funding text :
The work with iPSCs has been approved by the Ethics Review Panel (ERP) of the University of Luxembourg and the national Luxembourgish Research Ethics Committee (CNER, Comit\u00E9 National d'Ethique de Recherche) under CNER No. 201901/01 (ivPD) and No. 202406/03 (AdvanceOrg).This project has received funding from the National Centre of Excellence in Research on Parkinson's Disease (NCER-PD), which is funded by the Luxembourg National Research Fund (FNR) (FNR/NCER13/BM/11264123). Further, FNR funding as part of the i2TRON Doctoral Training Unit (PRIDE19/14254520) and the CORE program (CORE 22_BM_17193204_MidStriPD) is acknowledged.This research was funded in whole by the FNR Luxembourg. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.This project has received funding from the National Centre of Excellence in Research on Parkinson\u2019s Disease (NCER-PD), which is funded by the Luxembourg National Research Fund ( FNR ) ( FNR/NCER13/BM/11264123 ). Further, FNR funding as part of the i2TRON Doctoral Training Unit ( PRIDE19/14254520 ) and the CORE program ( CORE 22_BM_17193204_MidStriPD ) is acknowledged.
Kelava, I., Lancaster, M.A., Dishing out mini-brains: Current progress and future prospects in brain organoid research. Dev. Biol. 420 (2016), 199–209, 10.1016/j.ydbio.2016.06.037.
Dovonou, A., Bolduc, C., Soto Linan, V., Gora, C., Peralta III, M.R., Lévesque, M., Animal models of Parkinson's disease: Bridging the gap between disease hallmarks and research questions. Transl. Neurodegener., 12, 2023, 36, 10.1186/s40035-023-00368-8.
Monzel, A.S., Smits, L.M., Hemmer, K., Hachi, S., Moreno, E.L., van Wuellen, T., Jarazo, J., Walter, J., Brüggemann, I., Boussaad, I., et al. Derivation of human midbrain-specific organoids from neuroepithelial stem cells. Stem Cell Rep. 8 (2017), 1144–1154, 10.1016/j.stemcr.2017.03.010.
Nickels, S.L., Modamio, J., Mendes-Pinheiro, B., Monzel, A.S., Betsou, F., Schwamborn, J.C., Reproducible generation of human midbrain organoids for in vitro modeling of Parkinson's disease. Stem Cell Res., 46, 2020, 101870, 10.1016/j.scr.2020.101870.
Smith, L., Schapira, A.H.V., GBA variants and Parkinson disease: Mechanisms and treatments. Cells, 11, 2022, 1261, 10.3390/cells11081261.
Melo dos Santos, L.S., Trombetta-Lima, M., Eggen, B.J.L., Demaria, M., Cellular senescence in brain aging and neurodegeneration. Ageing Res. Rev., 93, 2024, 102141, 10.1016/j.arr.2023.102141.
Wang, Y., Kuca, K., You, L., Nepovimova, E., Heger, Z., Valko, M., Adam, V., Wu, Q., Jomova, K., The role of cellular senescence in neurodegenerative diseases. Arch. Toxicol. 98 (2024), 2393–2408, 10.1007/s00204-024-03768-5.
Rosety, I., Zagare, A., Saraiva, C., Nickels, S., Antony, P., Almeida, C., Glaab, E., Halder, R., Velychko, S., Rauen, T., et al. Impaired neuron differentiation in GBA-associated Parkinson's disease is linked to cell cycle defects in organoids. NPJ Parkinson's Dis., 9, 2023, 166, 10.1038/s41531-023-00616-8.
Zagare, A., Hemedan, A., Almeida, C., Frangenberg, D., Gomez-Giro, G., Antony, P., Halder, R., Krüger, R., Glaab, E., Ostaszewski, M., et al. Insulin resistance is a modifying factor for Parkinson's disease. Mov. Disord. 40 (2025), 67–76, 10.1002/mds.30039.
Hernandez-Segura, A., Nehme, J., Demaria, M., Hallmarks of cellular senescence. Trends Cell Biol. 28 (2018), 436–453, 10.1016/j.tcb.2018.02.001.
Brunner, J.W., Lammertse, H.C.A., van Berkel, A.A., Koopmans, F., Li, K.W., Smit, A.B., Toonen, R.F., Verhage, M., van der Sluis, S., Power and optimal study design in iPSC-based brain disease modelling. Mol. Psychiatr. 28 (2023), 1545–1556, 10.1038/s41380-022-01866-3.
Serdar, C.C., Cihan, M., Yücel, D., Serdar, M.A., Sample size, power and effect size revisited: Simplified and practical approaches in pre-clinical, clinical and laboratory studies. Biochem. Med., 31, 2020, 010502, 10.11613/BM.2021.010502.
Jensen, K.B., Little, M.H., Organoids are not organs: sources of variation and misinformation in organoid biology. Stem Cell Rep. 18 (2023), 1255–1270, 10.1016/j.stemcr.2023.05.009.
Reinhardt, P., Glatza, M., Hemmer, K., Tsytsyura, Y., Thiel, C.S., Höing, S., Moritz, S., Parga, J.A., Wagner, L., Bruder, J.M., et al. Derivation and expansion using only small molecules of human neural progenitors for neurodegenerative disease modeling. PLoS One, 8, 2013, e59252, 10.1371/journal.pone.0059252.
Bolognin, S., Fossépré, M., Qing, X., Jarazo, J., Ščančar, J., Moreno, E.L., Nickels, S.L., Wasner, K., Ouzren, N., Walter, J., et al. 3D cultures of Parkinson's disease-specific dopaminergic neurons for high content phenotyping and drug testing. Adv. Sci., 6, 2019, 1800927, 10.1002/advs.201800927.
Hiltemann, S., Rasche, H., Gladman, S., Hotz, H.R., Larivière, D., Blankenberg, D., Jagtap, P.D., Wollmann, T., Bretaudeau, A., Goué, N., et al. Galaxy Training: A powerful framework for teaching. PLoS Comput. Biol., 19, 2023, e1010752, 10.1371/journal.pcbi.1010752.
Doyle, M., Phipson, B., Dashnow, H., 1: RNA-Seq reads to counts. (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-reads-to-counts/tutorial.html.
Love, M.I., Huber, W., Anders, S., Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol., 15, 2014, 550, 10.1186/s13059-014-0550-8.
Benjamini, Y., Hochberg, Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B 57 (1995), 289–300.
Chen, E.Y., Tan, C.M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G.V., Clark, N.R., Ma'ayan, A., Ma'ayan, A., Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinf., 14, 2013, 128, 10.1186/1471-2105-14-128.
Kuleshov, M.V., Jones, M.R., Rouillard, A.D., Fernandez, N.F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S.L., Jagodnik, K.M., Lachmann, A., et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44 (2016), W90–W97, 10.1093/nar/gkw377.
Milacic, M., Beavers, D., Conley, P., Gong, C., Gillespie, M., Griss, J., Haw, R., Jassal, B., Matthews, L., May, B., et al. The reactome pathway knowledgebase 2024. Nucleic Acids Res. 52 (2024), D672–D678, 10.1093/nar/gkad1025.
Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25 (2000), 25–29, 10.1038/75556.
Gene Ontology Consortium, Aleksander, S.A., Balhoff, J., Carbon, S., Cherry, J.M., Drabkin, H.J., Ebert, D., Feuermann, M., Gaudet, P., Harris, N.L., et al. The gene ontology knowledgebase in 2023. Genetics, 224, 2023, iyad031, 10.1093/genetics/iyad031.
Shirkhorshidi, A.S., Aghabozorgi, S., Wah, T.Y., A comparison study on similarity and dissimilarity measures in clustering continuous data. PLoS One, 10, 2015, e0144059, 10.1371/journal.pone.0144059.
Kim, T., Chen, I.R., Lin, Y., Wang, A.Y.Y., Yang, J.Y.H., Yang, P., Impact of similarity metrics on single-cell RNA-seq data clustering. Brief. Bioinform. 20 (2019), 2316–2326, 10.1093/bib/bby076.
Rohart, F., Gautier, B., Singh, A., Lê Cao, K.A., mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol., 13, 2017, e1005752, 10.1371/journal.pcbi.1005752.
Singh, A., Shannon, C.P., Gautier, B., Rohart, F., Vacher, M., Tebbutt, S.J., Lê Cao, K.A., DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 35 (2019), 3055–3062, 10.1093/bioinformatics/bty1054.
