Brain organoids offer immense potential for studying brain development, neurological disorders, and evaluating drug responses. However, their exploitation is hindered by several challenges, including the lack of a label-free and non-destructive monitoring technique for studying their development and response to the external perturbations. To address this, we propose a non-invasive Raman Spectroscopy approach, enabling label-free discrimination of maturation stages without disruption. Using a custom high-throughput multi-modal Raman microscope equipped with a 785 nm laser source, we collected Raman Spectroscopy (RS) data from cortical organoids at various developmental stages and employing machine learning techniques, we extracted crucial features linked to each stage. This label-free methodology facilitates observing dynamic changes in organoids without compromising their growth, enabling longitudinal studies for deeper insights into their development and drug responses.
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