Advanced 3D Models of Human Brain Tissue Using Neural Cell Lines: State-of-the-Art and Future Prospects

Fabbri, Rachele; Cacopardo, Ludovica; Ahluwalia, Arti; Magliaro, Chiara

doi:10.3390/cells12081181

Cited by 19 publications

(10 citation statements)

References 121 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As previously mentioned, preclinical model recapitulation of non‐diseased tissue for non‐cancer contexts (e.g., rare diseases) is likely more informative than tumor recapitulation, due to multiple genomic abnormalities in malignancies. 44 , 45 When subsetting only GBM tumor‐derived cell lines and PDXs, we found PDX models and cell lines both correlate highest to GBM‐specific tumor tissue, followed by all other brain tumor tissue, and then non‐diseased brain tissue (Figure 2B ). While PDX model profiles correlated significantly higher to brain tissue regardless of disease context ( p < .0001, W = 410–455), the range of correlations for specific models overlapped between PDX and cell lines (rho .77–.89).…”

Section: Discussionmentioning

confidence: 98%

“…However, we suggest that the comparison of cell lines to non‐diseased tissues is important for the selection of preclinical models that may better recapitulate non‐cancer disease contexts, considering genetic drift and other abnormalities of tumors. 44 , 45 We found that by using subsets of the most varying genes (i.e., 100, 1000, 5000, and 10 000), cell lines generally correlated less to their tissue of origin (both tumor and non‐diseased), and yet that the full gene sets correlated higher than any varying gene subset (median correlation rho = .82) (Figure 1A ).…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, others have demonstrated the challenge of modeling the tissue context accurately with cell lines in these tissues, leading to recommendations for patient primary‐derived cells and organoids for in vitro studies. 27 , 44 , 45 , 47 , 48 , 49 …”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating cancer cell line and patient‐derived xenograft recapitulation of tumor and non‐diseased tissue gene expression profiles in silico

et al. 2023

View full text Add to dashboard Cite

BackgroundPreclinical models like cancer cell lines and patient‐derived xenografts (PDXs) are vital for studying disease mechanisms and evaluating treatment options. It is essential that they accurately recapitulate the disease state of interest to generate results that will translate in the clinic. Prior studies have demonstrated that preclinical models do not recapitulate all biological aspects of human tissues, particularly with respect to the tissue of origin gene expression signatures. Therefore, it is critical to assess how well preclinical model gene expression profiles correlate with human cancer tissues to inform preclinical model selection and data analysis decisions.AimsHere we evaluated how well preclinical models recapitulate human cancer and non‐diseased tissue gene expression patterns in silico with respect to the full gene expression profile as well as subsetting by the most variable genes, genes significantly correlated with tumor purity, and tissue‐specific genes.MethodsBy using publicly available gene expression profiles across multiple sources, we evaluated cancer cell line and patient‐derived xenograft recapitulation of tumor and non‐diseased tissue gene expression profiles in silico.ResultsWe found that using the full gene set improves correlations between preclinical model and tissue global gene expression profiles, confirmed that glioblastoma (GBM) PDX global gene expression correlation to GBM tumor global gene expression outperforms GBM cell line to GBM tumor global gene expression correlations, and demonstrated that preclinical models in our study often failed to reproduce tissue‐specific expression. While including additional genes for global gene expression comparison between cell lines and tissues decreases the overall correlation, it improves the relative rank between a cell line and its tissue of origin compared to other tissues. Our findings underscore the importance of using the full gene expression set measured when comparing preclinical models and tissues and confirm that tissue‐specific patterns are better preserved in GBM PDX models than in GBM cell lines.ConclusionFuture studies can build on these findings to determine the specific pathways and gene sets recapitulated by particular preclinical models to facilitate model selection for a given study design or goal.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating cancer cell line and patient‐derived xenograft recapitulation of tumor and non‐diseased tissue gene expression profiles in silico

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The human SK-N-AS neural cell line was used to determine the biocompatibility of the different scaffolds, since they are related to the nerve tissue and share surface proteins and other biomarkers with neural cells [64]. Moreover, they have a high in vitro proliferation capacity.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Printable Biomaterials for Use in Neural Tissue Engineering: An In Vitro Characterization and In Vivo Biocompatibility Assessment

Etayo-Escanilla,

Campillo,

Ávila-Fernández

et al. 2024

Polymers

View full text Add to dashboard Cite

Nervous system traumatic injuries are prevalent in our society, with a significant socioeconomic impact. Due to the highly complex structure of the neural tissue, the treatment of these injuries is still a challenge. Recently, 3D printing has emerged as a promising alternative for producing biomimetic scaffolds, which can lead to the restoration of neural tissue function. The objective of this work was to compare different biomaterials for generating 3D-printed scaffolds for use in neural tissue engineering. For this purpose, four thermoplastic biomaterials, ((polylactic acid) (PLA), polycaprolactone (PCL), Filaflex (FF) (assessed here for the first time for biomedical purposes), and Flexdym (FD)) and gelatin methacrylate (GelMA) hydrogel were subjected to printability and mechanical tests, in vitro cell–biomaterial interaction analyses, and in vivo biocompatibility assessment. The thermoplastics showed superior printing results in terms of resolution and shape fidelity, whereas FD and GelMA revealed great viscoelastic properties. GelMA demonstrated a greater cell viability index after 7 days of in vitro cell culture. Moreover, all groups displayed connective tissue encapsulation, with some inflammatory cells around the scaffolds after 10 days of in vivo implantation. Future studies will determine the usefulness and in vivo therapeutic efficacy of novel neural substitutes based on the use of these 3D-printed scaffolds.

show abstract

“…In vitro models of neural tissues are emerging as a promising, more ethical and accessible way to study brain pathophysiology with respect to animal models. Indeed, their high controllability and observability have allowed the investigation of neurotoxicity, neuroprotection, drug screening and therapeutic assessment for different neuropathies [1][2][3]. The electrophysiological behaviour of cultured neuronal networks can be studied via patch-clamp, calcium imaging and microelectrode arrays (MEAs) [4].…”

Section: Introductionmentioning

confidence: 99%

Digitoids: a novel computational platform for mimicking oxygen-dependent firing dynamics inin vitroneuronal networks

Fabbri,

Ahluwalia,

Magliaro

2023

Preprint

Self Cite

View full text Add to dashboard Cite

In vitromodels of neural tissues are crucial to gain new insights on the pathophysiology of the brain. However, cell cultures are associated with many drawbacks and difficulties, e.g., technical complexities, ethical problems and high cost. Computational model-based solutions could represent an important tool to support the study of neuron function. In this work we present a novel computational platform where digital neuronal networks, i.e., Digitoids, can be developed with different size and layouts. The Digitoids rely on a novel firing model where the dependence on oxygen concentration is introduced, since it is a crucial limiting factor in cell cultures. To validate the performance of the platform, Digitoids were developed with the same morphological arrangement as observed in neuron monolayersin vitro. The comparison between the functional output of the Digitoids and the experimental data are not statistically different. The platform delivers a flexible digital tool that can easily be adapted for mimickingin vitromodels with increasing complexity and can be exploited to optimize the laboratory experiments involving neuron cultures.Author SummaryThe use of cell models within laboratories is crucial to gain new understandings of the functioning of neuronal assemblies. However, culturing cells requires highly skilled personnel, a huge quantity of disposable materials and is thus associated with elevated costs. To overcome these limitations, the use of computer-based systems that reproduce the electrical behaviour of neurons can be employed. Sincein vitromodels are vessel-free, we present a novel computational model of neuron electrophysiology, where we introduce the dependence on local oxygen concentration Indeed, oxygen affects the metabolism and the function of cultured neurons. Thanks to our model and platform, we are able to reproduce the morphology of the cultured networks of neurons and build the so-calledDigitoids. We then simulate theDigitoidsand obtain their electrophysiological output. We compared theDigitoids’output with experimental data from neurons cultured on microelectrode arrays. We did not find any differences between the electrophysiological output of theDigitoidsand the experimental data, therefore we conclude that our novel oxygen-dependent model can be used to develop more physiologically relevant tools for simulating the activity of cultured neurons.

show abstract

Advanced 3D Models of Human Brain Tissue Using Neural Cell Lines: State-of-the-Art and Future Prospects

Cited by 19 publications

References 121 publications

Evaluating cancer cell line and patient‐derived xenograft recapitulation of tumor and non‐diseased tissue gene expression profiles in silico

Evaluating cancer cell line and patient‐derived xenograft recapitulation of tumor and non‐diseased tissue gene expression profiles in silico

Comparison of Printable Biomaterials for Use in Neural Tissue Engineering: An In Vitro Characterization and In Vivo Biocompatibility Assessment

Digitoids: a novel computational platform for mimicking oxygen-dependent firing dynamics inin vitroneuronal networks

Contact Info

Product

Resources

About