Lena Smirnova

MicroRNA (miRNA) are a newly recognized class of small, noncoding RNA molecules that participate in the developmental control of gene expression. We have studied the regulation of a set of highly expressed neural miRNA during mouse brain development. Temporal control is a characteristic of miRNA regulation in C. elegans and Drosophila, and is also prominent in the embryonic brain. We observed significant differences in the onset and magnitude of induction for individual miRNAs. Comparing expression in cultures of embryonic neurons and astrocytes we found marked lineage specificity for each of the miRNA in our study. Two of the most highly expressed miRNA in adult brain were preferentially expressed in neurons (mir-124, mir-128). In contrast, mir-23, a miRNA previously implicated in neural specification, was restricted to astrocytes. mir-26 and mir-29 were more strongly expressed in astrocytes than neurons, others were more evenly distributed (mir-9, mir-125). Lineage specificity was further explored using reporter constructs for two miRNA of particular interest (mir-125 and mir-128). miRNA-mediated suppression of both reporters was observed after transfection of the reporters into neurons but not astrocytes. miRNA were strongly induced during neural differentiation of embryonic stem cells, suggesting the validity of the stem cell model for studying miRNA regulation in neural development.

The let-7 miRNA regulates developmental timing in C. elegans and is an important paradigm for investigations of miRNA functions in mammalian development. We have examined the role of miRNA precursor processing in the temporal control and lineage specificity of the let-7 miRNA. In situ hybridization (ISH) in E9.5 mouse embryos revealed early induction of let-7 in the developing central nervous system. The expression pattern of three let-7 family members closely resembled that of the brain-enriched miRNAs mir-124, mir-125 and mir-128. Comparison of primary, precursor, and mature let-7 RNA levels during both embryonic brain development and neural differentiation of embryonic stem cells and embryocarcinoma (EC) cells suggest post-transcriptional regulation of let-7 accumulation. Reflecting these results, let-7 sensor constructs were strongly down-regulated during neural differentiation of EC cells and displayed lineage specificity in primary cells. Neural differentiation of EC cells was accompanied by an increase in let-7 precursor processing activity in vitro. Furthermore, undifferentiated and differentiated cells contained distinct precursor RNA binding complexes. A neuron-enhanced binding complex was shown by antibody challenge to contain the miRNA pathway proteins Argonaute1 and FMRP. Developmental regulation of the processing pathway correlates with differential localization of the proteins Argonaute, FMRP, MOV10, and TNRC6B in self-renewing stem cells and neurons.

Recent advances in human stem cell-derived brain organoids promise to replicate critical molecular and cellular aspects of learning and memory and possibly aspects of cognition in vitro . Coining the term “organoid intelligence” (OI) to encompass these developments, we present a collaborative program to implement the vision of a multidisciplinary field of OI. This aims to establish OI as a form of genuine biological computing that harnesses brain organoids using scientific and bioengineering advances in an ethically responsible manner. Standardized, 3D, myelinated brain organoids can now be produced with high cell density and enriched levels of glial cells and gene expression critical for learning. Integrated microfluidic perfusion systems can support scalable and durable culturing, and spatiotemporal chemical signaling. Novel 3D microelectrode arrays permit high-resolution spatiotemporal electrophysiological signaling and recording to explore the capacity of brain organoids to recapitulate the molecular mechanisms of learning and memory formation and, ultimately, their computational potential. Technologies that could enable novel biocomputing models via stimulus-response training and organoid-computer interfaces are in development. We envisage complex, networked interfaces whereby brain organoids are connected with real-world sensors and output devices, and ultimately with each other and with sensory organ organoids (e.g. retinal organoids), and are trained using biofeedback, big-data warehousing, and machine learning methods. In parallel, we emphasize an embedded ethics approach to analyze the ethical aspects raised by OI research in an iterative, collaborative manner involving all relevant stakeholders. The many possible applications of this research urge the strategic development of OI as a scientific discipline. We anticipate OI-based biocomputing systems to allow faster decision-making, continuous learning during tasks, and greater energy and data efficiency. Furthermore, the development of “intelligence-in-a-dish” could help elucidate the pathophysiology of devastating developmental and degenerative diseases (such as dementia), potentially aiding the identification of novel therapeutic approaches to address major global unmet needs.