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Bioengineered models of Parkinson’s disease using patient-derived dopaminergic neurons exhibit distinct biological profiles in a 3D microenvironment

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Abstract

Three-dimensional (3D) in vitro culture systems using human induced pluripotent stem cells (hiPSCs) are useful tools to model neurodegenerative disease biology in physiologically relevant microenvironments. Though many successful biomaterials-based 3D model systems have been established for other neurogenerative diseases, such as Alzheimer’s disease, relatively few exist for Parkinson’s disease (PD) research. We employed tissue engineering approaches to construct a 3D silk scaffold-based platform for the culture of hiPSC-dopaminergic (DA) neurons derived from healthy individuals and PD patients harboring LRRK2 G2019S or GBA N370S mutations. We then compared results from protein, gene expression, and metabolic analyses obtained from two-dimensional (2D) and 3D culture systems. The 3D platform enabled the formation of dense dopamine neuronal network architectures and developed biological profiles both similar and distinct from 2D culture systems in healthy and PD disease lines. PD cultures developed in 3D platforms showed elevated levels of α-synuclein and alterations in purine metabolite profiles. Furthermore, computational network analysis of transcriptomic networks nominated several novel molecular interactions occurring in neurons from patients with mutations in LRRK2 and GBA. We conclude that the brain-like 3D system presented here is a realistic platform to interrogate molecular mechanisms underlying PD biology.

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Fig. 1

adapted from Kim et al. [59]

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Availability of data and material

The datasets used and analyzed for this study can be obtained from corresponding authors upon reasonable request. Raw sequencing data (consented hiPSC donors only) and normalized read counts can be freely accessed through the Gene Expression Omnibus (GSE172409).

Abbreviations

2D:

Two dimensional

3D:

Three dimensional

ANOVA:

Analysis of variance

BP:

Biological processes

CCK:

Cholecystokinin

CCKBR:

Cholecystokinin B receptor

CDG:

Candidate disease gene

CM:

Conditioned media

Cyclic AMP:

Cyclic 3´,5´-cyclic adenosine monophosphate

DA:

Dopaminergic

DAPI:

4′, 6-Diamidino-2-phenylindole

DEG:

Differentially expressed gene

DGKG:

Diacylglycerol Kinase Gamma

DPBS:

Dulbecco’s phosphate buffered saline

DRD1:

Dopamine receptor D1

dsDNA:

Double-stranded Deoxyribonucleic acid

ENPEP:

Glutamyl Aminopeptidase

EXTL1:

Exostosin Like Glycosyltransferase 1

FPPs:

Floor plate progenitors

GBA:

Glucocerebrosidase

GLIDE:

Global and local integrated diffusion embedding

GO:

Gene ontology

GRK5:

G Protein-Coupled Receptor Kinase 5

HES1:

Hes Family BHLH Transcription Factor 1

hiPSCs:

Human induced pluripotent stem cells

IDA:

Information-dependent acquisition

IGFBP5:

Insulin Like Growth Factor Binding Protein 5

KLK6:

Kallikrein Related Peptidase 6

LATS2:

Large Tumor Suppressor Kinase 2

LC–MS:

Liquid chromatography tandem mass spectrometry

LRRK2:

Leucine-rich repeat kinase 2

LYN:

LYN Proto-Oncogene, Src Family Tyrosine Kinase

MAOB:

Monoamine oxidase B

MAPT:

Microtubule associated protein tau

miRNA:

Micro ribonucleic acid

mRNA:

Messenger ribonucleic acid

MS2:

Fragmentation spectra

PCA:

Principle component analysis

PD:

Parkinson’s Disease

PDK4:

Pyruvate Dehydrogenase Kinase 4

PITX3:

Paired Like Homeodomain 3

PLCB2:

Phospholipase C Beta 2

PLD1:

Phospholipase D1

PLK1:

Polo like kinase 1

PSMD4:

Proteasome 26S Subunit, Non-ATPase 4

PTER:

Phosphotriesterase Related

SNCA:

α-Synuclein (gene)

SNCAIP:

α-Synuclein interacting protein

SPON1:

Spondin 1

TCF12:

Transcription Factor 12

TEAD1:

TEA domain transcription factor 1

TEAD2:

TEA domain transcription factor 2

TEAD3:

TEA domain transcription factor 3

TH:

Tyrosine hydroxylase

TUJ1:

β3-Tubulin

USP9X:

Ubiquitin specific peptidase 9 X-linked

YAS1:

Yes1 associated transcriptional regulator

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Acknowledgements

We thank the National Institutes of Health and the National Science Foundation for financial support of this project, and the New York Stem Cell Foundation’s Global Stem Cell Array® team for generating and providing the hiPSC lines. The authors acknowledge the Tufts University High Performance Compute Cluster (https://it.tufts.edu/high-performance-computing) and the confocal microscopy lab core (National Institutes of Health S10 OD021624) which were utilized for the research reported in this paper. Additional support from the National Science Foundation came through the Tufts T-Tripods Institute from the Harnessing the Data Revolution “Big Idea” effort. We also thank Dr. Volha Liaudanskaya and Dr. Mattia Bonzanni for experimental and intellectual feedback, as well as Sydney Peters and Emily Kim for technical support.

Funding

NIH (P41EB027062) to DLK, NSF (1934553) to LJC, NSF (1337760) to KL.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Tufts University, Medford, MA, USA

    Nicholas J. Fiore, Thomas J. F. Nieland & David L. Kaplan

  2. The New York Stem Cell Foundation Research Institute, New York, NY, USA

    Yosif M. Ganat, Gist Croft & Scott A. Noggle

  3. Department of Computer Science, Tufts University, Medford, MA, USA

    Kapil Devkota & Lenore J. Cowen

  4. Research Technology, Tufts University, Medford, MA, USA

    Rebecca Batorsky

  5. Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA

    Ming Lei & Kyongbum Lee

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  1. Nicholas J. Fiore
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Contributions

Conceptualization: NJF, TJFN, DLK; Methodology: NJF, YMG, YG, LJC; Formal analysis: NJF, RB, KD, LJC; Investigation: NJF, TJFN, DLK; Resources: ML, KL, YMG, GC, SN, DLK; Data curation: NJF, KD, RB, ML, KL; Original manuscript writing: NJF, DLK, TJFN; Supervision: TJFN, DLK; Funding acquisition: DLK, LJC. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Thomas J. F. Nieland or David L. Kaplan.

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The authors declare no competing interests.

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All work was approved by the Tufts Institutional Biosafety Committee.

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The skin biopsies used for the generation of hiPSCs were donated from consenting participants under a protocol covering use in research, approved by the Western Institutional Review Board (WIRB).

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Supplementary Information

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18_2021_4047_MOESM1_ESM.tiff

Supplemental File S1 Representative fragmentation spectra generated from untargeted liquid chromatography tandem mass spectrometry analysis. Mass/charge (m/z) peaks were compared between experimental samples (top panels, representative samples shown) and purified standards (bottom panels), which confirmed the identities of (A) Adenosine, (B) adenine, and (C) adenosine 3',5'-cyclic monophosphate metabolites. All results were acquired in positive ionization mode

18_2021_4047_MOESM2_ESM.tiff

Supplemental File S2 Principle component analysis of transcriptomic data of control, GBA and LRRK2 DA neurons. (A) 3D culture is consistently shifted along the PC1 and PC2 axes compared to 2D, indicating a culture condition dependent shift in global gene expression. Panel (B) highlights the results for the individual groups

18_2021_4047_MOESM3_ESM.xlsx

Supplemental File S3 Differential expression results using the DESeq2 R package, sorted by PD comparison in 2D and 3D culture format. A differential expression cutoff of abs(Log2(Fold change) > 0.585 in combination with q < 0.01 was used to determine significance

18_2021_4047_MOESM4_ESM.xlsx

Supplemental File S4 Over-representation analysis of biological processes when comparing heathy hiPSC-DA neurons cultured in 3D versus 2D conditions (3D CTRL versus 2D CTRL). Upregulated and downregulated GO terms were determined using differentially expressed genes (q < 0.01) with positive and negative fold change, respectively

18_2021_4047_MOESM5_ESM.xlsx

Supplemental File S5 Lists of the top 100 differentially expressed genes (HGNC symbols) from each Parkinson’s disease comparison that also existed in the DREAM3 molecular interaction network. Each list was utilized as the initial seed network for candidate disease gene prioritization and subsequent link prediction analysis

18_2021_4047_MOESM6_ESM.xlsx

Supplemental File S6 List of genes and molecular interactions for each link prediction network generated from PD genotype comparisons. The top 100 differentially expressed genes (DEGs), that also existed in the molecular interaction network DREAM3 (Supplemental file S5), were utilized to generate individual link prediction networks from all disease comparisons made in 2D and 3D culture formats: LRRK2 vs CTRL, GBA_PD vs CTRL, and GBA_PD vs GBA_CTRL. Application of Kohler’s random walk with restart nominated candidate disease genes (CDGs) within the neighborhood of the DEGs. Likely molecular interactions between DEGs and CDGs were computationally predicted by using global and local integrated diffusion embedding (GLIDE)

18_2021_4047_MOESM7_ESM.tiff

Supplemental File S7 α-synuclein protein measurements in 2D and 3D cultures (with reference to Fig. 3). α-synuclein protein levels (ELISA) in (A) 2D and (B) 3D culture conditions were determined from individual cell donor lines. (C) Fold change of α-synuclein protein levels was normalized to results from healthy individual cultures indicating increased expression in 3D cultures relative to the 2D culture. Statistical significance for (D) 2D and (E) 3D cultures were determined with one-way ANOVA followed by Tukey’s multiple comparison testing. (F) Fold change of α-synuclein protein levels (C) were compared across 2D and 3D culture systems by multiple t-tests with the two-stage step-up procedure of Benjamini, Krieger, and Yekutieli. Six or eight individual cultures were collected from two independent experiments, depending on the sample. A false discovery rate (q value or adjusted p value) of less than 0.05 was considered significant

18_2021_4047_MOESM8_ESM.tiff

Supplemental File S8 Neuronal network quantifications of TH+ and TUJ1+ signal ratios from immunofluorescent images suggested similar dopamine neuron content across individual donor lines and culture conditions. Statistical testing was performed using a two-way ANOVA with a significance cutoff of p = 0.05. Data displayed as the mean ± SEM from n = 4 replicate cultures per group

18_2021_4047_MOESM9_ESM.tiff

Supplemental File S9 Representative immunofluorescent images of 3D construct seeded with patient derived dopaminergic neurons. Wide-field images show homogeneously attached cells stained with DAPI (nuclei, blue) onto the scaffolding and neuronal processes (β3-tubulin, green) extending along the scaffolding and into the surrounding collagen hydrogel. To account for the autofluorescence of the silk scaffolding [147], digitally expanded images provide a clarified view of individual nuclei and neurites. Scale bars represent 500 μm

18_2021_4047_MOESM10_ESM.pdf

Supplemental File S10 Gene expression levels for neuronal, glial, and pluripotency markers. The low expression levels of markers for astrocytes, microglia, oligodendrocytes, and hiPSCs suggested that each groups’ culture composition were primarily neuronal

18_2021_4047_MOESM11_ESM.pdf

Supplemental File S11 3D CTRL versus 2D CTRL differential expression datasets for human dopamine neuron gene sets (hDA0, hDA1, and hDA2, characterized from developing human brains [93]). The 3D system upregulated a higher number of dopamine neuron genes, suggesting a more in vivo-like phenotype compared to 2D culture. Genes were highlighted blue if upregulated in 3D systems (log2(Fold Change) > 0.585 and p.adj < 0.05) or red if upregulated in 2D systems (log2(Fold Change) < 0.585 and p.adj < 0.05). Genes unaffected by culture condition were colored gray

18_2021_4047_MOESM12_ESM.xlsx

Supplemental File S12 Differentially expressed genes that are unique or shared between 2D and 3D cultures for each of the LRRK2 and GBA genotype comparisons (with reference to Fig. 6A, 6B). Differential expression analysis was performed with the DESeq2 R package and statistical significance was determined with a cutoff of abs(Log2(Fold change) > 0.585 in combination with q < 0.01

18_2021_4047_MOESM13_ESM.pdf

Supplemental File S13 Log2(Fold Change) of differentially expressed genes with a STRING association score to α-synuclein (with reference to Fig. 6C, Fig. 6D). Differential expression analysis was performed with the DESeq2 R package and statistical significance was determined with a cutoff of abs(Log2(Fold change) > 0.585 in combination with q < 0.01

18_2021_4047_MOESM14_ESM.pdf

Supplemental File S14 Log2(Fold Change) of differentially expressed genes in link prediction networks (with reference to Fig. 7). Differential expression analysis was performed with the DESeq2 R package and statistical significance was determined with a cutoff of abs(Log2(Fold change) > 0.585 in combination with q < 0.01

18_2021_4047_MOESM15_ESM.pdf

Supplemental File S15 Link prediction networks unique to 2D or 3D culture systems generated using differential expression data. The top 100 differentially expressed genes (DEGs), that also existed in the molecular interaction network DREAM3 (Supplemental file S5), were utilized to generate individual link prediction networks from all disease comparisons made in 2D and 3D culture formats: LRRK2 vs CTRL, GBA_PD vs CTRL, and GBA_PD vs GBA_CTRL. Application of Kohler’s random walk with restart nominated candidate disease genes (CDGs) within the neighborhood of the DEGs. Molecular interactions between DEGs and CDGs were computationally predicted using global and local integrated diffusion embedding (GLIDE). Networks unique to each culture condition were extracted for GBA or LRRK2 genotype comparisons. Genes with a green frame were commonly observed across GBA and LRRK2 genotype comparisons for the given culture condition

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Fiore, N.J., Ganat, Y.M., Devkota, K. et al. Bioengineered models of Parkinson’s disease using patient-derived dopaminergic neurons exhibit distinct biological profiles in a 3D microenvironment. Cell. Mol. Life Sci. 79, 78 (2022). https://doi.org/10.1007/s00018-021-04047-7

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