Over the course of the semester, our trainees are reviewing webinars in their given fields and preparing abstracts to help colleagues outside their discipline make an informed choice about watching them. As our program bridges diverse disciplines, these abstracts are beneficial for our own group in helping one another gain key knowledge in each other’s fields. We are happy to share these here for anyone else who may find them helpful.
Iva Kelava, MRC Laboratory for Molecular Biology
July 15, 2021
British Neuroscience Association
In this webinar, Iva Kelava discusses ways to improve the reproducibility of three-dimensional, in vitro models, particularly in the context of neuroscience. In particular, Kelava delves into the utility of organoids and how to augment the reproducibility associated with these systems. This topic is of considerable importance, as reproducibility dictates the reliability of in vitro analyses, thereby determining the usefulness of the associated data. Topics of interest include considerations regarding cell line, cell batch, cell culture substrate, among other cell culture parameters.
Kelava begins this webinar by first introducing the distinction between two-dimensional and three-dimensional culture methods. She proceeds to introduce the importance of reproducibility, as well as methods to preserve aforementioned reproducibility. She then goes on to discuss troubleshooting methods with respect to three-dimensional cell culture, especially in regard to the verification of cell identity. Kelava further emphasizes the analysis of multiple functional parameters when verifying organoid cell identity, as this serves to reveal any potential heterogeneities between cell lines that may confound the reproducibility of neurological data.
She finally concludes the webinar by introducing the concept of commercial organoid kits and communal biobanks, which aid reliability of results by standardizing the organoid cell sources. These considerations will assist the reproducibility of three-dimensional cell culture methods, thereby improving the reliability of data for applications such as drug delivery.