Summary Cerebral organoids (COs) are rapidly accelerating the rate of translational neuroscience based on their potential to model complex features of the developing human brain. Several studies have examined the electrophysiological and neural network features of COs; however, no study has comprehensively investigated the developmental trajectory of electrophysiological properties in whole-brain COs and correlated these properties with developmentally linked morphological and cellular features. Here, we profiled the neuroelectrical activities of COs over the span of 5 months with a multi-electrode array platform and observed the emergence and maturation of several electrophysiologic properties, including rapid firing rates and network bursting events. To complement these analyses, we characterized the complex molecular and cellular development that gives rise to these mature neuroelectrical properties with immunohistochemical and single-cell transcriptomic analyses. This integrated approach highlights the value of COs as an emerging model system of human brain development and neurological disease.

BackgroundHypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted. With the rise in number of annotation strategies, next-generation sequencing methods have provided further understanding the functions of HPs.ResultsIn our previous work, we developed a six-point classification scoring schema with annotation pertaining to protein family scores, orthology, protein interaction/association studies, bidirectional best BLAST hits, sorting signals, known databases and visualizers which were used to validate protein interactions. In this study, we introduced three more classifiers to our annotation system, viz. pseudogenes linked to HPs, homology modelling and non-coding RNAs associated to HPs. We discuss the challenges and performance of these classifiers using machine learning heuristics with an improved accuracy from Perceptron (81.08 to 97.67), Naive Bayes (54.05 to 96.67), Decision tree J48 (67.57 to 97.00), and SMO_npolyk (59.46 to 96.67).ConclusionWith the introduction of three new classification features, the performance of the nine-point classification scoring schema has an improved accuracy to functionally annotate the HPs.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2554-y) contains supplementary material, which is available to authorized users.