Abstract
The introduction of internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell-derived cortical organoids in two different settings: Using microscopy to monitor organoid growth in an introductory tissue culture course and using high density multielectrode arrays to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training.
Significance Statement
The use of stem cell-derived cortical organoid models in academia and biotechnology has drastically increased in recent years. Given these trends, there is a critical need for students to be trained in organoid culture, differentiation, and analysis. To date, education curricula that focus on organoids are theoretical. Taking advantage of cloud technologies, such as internet-connected microscopes and multielectrode arrays, we propose approaches to introduce students to cortical organoids using live experiments. We show that these approaches develop interest in the field and prospects in students in biology and other STEM disciplines, such as mathematics and computer science.
Footnotes
M.A.M.-R. is a cofounder of Paika, a company for remote people-to-people interactions. The authors declare no other conflicts of interests.
This work was supported by Schmidt Futures (SF857) to M.T. and D.H.; National Human Genome Research Institute (1RM1HG011543) to M.T. and D.H.; National Science Foundation (NSF2134955) to M.T. and D.H. (NSF2034037) to M.T.; the National Institute of Mental Health (1U24MH132628) to D.H. and M.A.M.-R. H.E.S. is a National Science Foundation Graduate Student Research Fellowship grantee. K.V. was supported by the ARCS Foundation and grant T32HG012344 from the National Human Genome Research Institute (NHGRI). J.L.S. was supported by the University of California Santa Cruz Chancellor’s Postdoctoral Fellowship, the NIH K12GM139185 (through NIGMS to UCSC IBSC), and LRP0000018281 (NICHD). We are thankful to the Pacific Research Platform, supported by the National Science Foundation under Award Numbers CNS-1730158, ACI-1540112, ACI-1541349, OAC-1826967, the University of California Office of the President, and the University of California San Diego's California Institute for Telecommunications and Information Technology/Qualcomm Institute. Thanks to CENIC for the 100 Gbps networks. This research received software engineering support from the University of Washington’s Scientific Software Engineering Center (SSEC) supported by Schmidt Futures, as part of the Virtual Institute for Scientific Software (VISS).
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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