Experimental and Computational Methods for the Study of Cerebral Organoids: A Review

Cerebral (or brain) organoids derived from human cells have enormous potential as physiologically relevant downscaled in vitro models of the human brain. In fact, these stem cell-derived neural aggregates resemble the three-dimensional (3D) cytoarchitectural arrangement of the brain overcoming not only the unrealistic somatic flatness but also the planar neuritic outgrowth of the two-dimensional (2D) in vitro cultures. Despite the growing use of cerebral organoids in scientific research, a more critical evaluation of their reliability and reproducibility in terms of cellular diversity, mature traits, and neuronal dynamics is still required. Specifically, a quantitative framework for generating and investigating these in vitro models of the human brain is lacking. To this end, the aim of this review is to inspire new computational and technology driven ideas for methodological improvements and novel applications of brain organoids. After an overview of the organoid generation protocols described in the literature, we review the computational models employed to assess their formation, organization and resource uptake. The experimental approaches currently provided to structurally and functionally characterize brain organoid networks for studying single neuron morphology and their connections at cellular and sub-cellular resolution are also discussed. Well-established techniques based on current/voltage clamp, optogenetics, calcium imaging, and Micro-Electrode Arrays (MEAs) are proposed for monitoring intra- and extra-cellular responses underlying neuronal dynamics and functional connections. Finally, we consider critical aspects of the established procedures and the physiological limitations of these models, suggesting how a complement of engineering tools could improve the current approaches and their applications

Neural activity classification with machine learning models trained on interspike interval series data

The flow of information through the brain is reflected by the activity patterns of neural cells. Indeed, these firing patterns are widely used as input data to predictive models that relate stimuli and animal behavior to the activity of a population of neurons. However, relatively little attention was paid to single neuron spike trains as predictors of cell or network properties in the brain. In this work, we introduce an approach to neuronal spike train data mining which enables effective classification and clustering of neuron types and network activity states based on single-cell spiking patterns. This approach is centered around applying state-of-the-art time series classification/clustering methods to sequences of interspike intervals recorded from single neurons. We demonstrate good performance of these methods in tasks involving classification of neuron type (e.g. excitatory vs. inhibitory cells) and/or neural circuit activity state (e.g. awake vs. REM sleep vs. nonREM sleep states) on an open-access cortical spiking activity dataset

Development of scanning ion conductance microscopy for biomedical applications

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Illuminating cAMP dynamics at the synapse with multiphoton FLIM-FRET Imaging

The study of signalling pathways within mammalian physiology has long been hindered by the size of the players involved, being far beyond the realms of the conventional light microscope. The advent of advanced fluorescent imaging techniques has revolutionised our capabilities to probe biological processes. The work in this thesis particularly utilised Förster resonance energy transfer (FRET), a fluorescence-based technique that can provide functional readouts of the processes underlying cellular function. Specifically I worked to develop and optimise a fluorescence imaging system for investigating the dynamics and function of cyclic adenosine monophosphate (cAMP), a ubiquitous second messenger. The neuroscientific study of how the brain can learn and recall memories is a rapidly advancing field. The current challenges of tackling dementias, such as Alzheimer’s disease, and preventing memory loss can only be addressed through better understanding of how memories can be stored. It is now believed that neurons retain memories within their synapses, the femtolitre structures that determine the strength of these connections. cAMP has been shown to play a distinctive role in orchestrating the retention of long term memory at the synaptic level. However, its spatial and temporal activation profiles are still not fully understood. To address this, my PhD project combined FRET readouts with cutting edge imaging techniques applied to synapses in neuronal cultures that provide reasonably convenient optical access. By examining the structure of these synapses, along with the measurement of cAMP concentration in different neuronal regions, this project uncovered the highly compartmentalised nature of this signalling molecule, seen to act directly at the sites of strengthening synapses. Through the optimisation of a FRET imaging system for studying activity in neuronal tissues, this project establishes a method for the future investigation of a plethora of pathways underlying the healthy functioning of the mammalian brain.Open Acces

Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

A combined experimental and computational approach to investigate emergent network dynamics based on large-scale neuronal recordings

Sviluppo di un approccio integrato computazionale-sperimentale per lo studio di reti neuronali mediante registrazioni elettrofisiologich

A novel Three-Dimensional Micro-Electrode Array for in-vitro electrophysiological applications

Microelectrode arrays (MEAs) represent a powerful and popular tool to study in vitro neuronal networks and acute brain slices. The research standard for MEAs is planar or 2D-MEAs, which have been in existence for over 30 years and used for extracellular recording and stimulation from cultured neuronal cells and tissue slices. However, planar MEAs suffer from rapid data attenuation in the z-direction when stimulating/recording from 3D in-vitro neuronal cultures or brain slices. The existing proposed 3D in-vitro neuronal models allow to record the electrophysiological activity of the 3D network only from the bottom layer (i.e. the one directly coupled to the planar MEAs). Thus, to further develop and optimize such 3D neuronal network systems and to study and understand how the 3D neuronal network dynamics changes in different layers of the 3D structure, new three-dimensional microelectrodes arrays (3D-MEAs) are required. Early attempts in this field resulted in interesting integrated approaches toward protruding or spiked 3D-MEAs. Although these first prototypes could be successfully employed with brain slices, the limited heights of the electrodes (up to max 70 \u3bcm) and the peculiar shape of the recording areas made them not an ideal solution for 3D neuronal cultures. Moreover, a convenient and versatile method for the fabrication of multilevel 3D microelectrode arrays has yet to be obtained, due to the usually complicated and expensive designs and a lack of a full compatibility with standard MEAs both in terms of materials and recording area dimensions. To overcome the afore-mentioned challenges, in this work, I present the design, microfabrication, and characterization of a new 3D-MEA composed of pillar-shaped gold 3D structures with heights of more than 100 \u3bcm that can be used, in principle, on every kind of MEA, both custom-made and commercial. I successfully demonstrate the capability and ability of such 3D-MEA to record electrophysiological spontaneous activity from 3D engineered in-vitro neuronal networks and both 4-AP-induced epileptiform-like and electrically-evoked activity from mouse acute brain slices. I also demonstrate how the developed 3D-MEA allows better recording and stimulating conditions while interfacing with acute brain slices as compared to planar electrode arrays and previously reported 3D MEA technologies