Standardizing a method for functional assessment of neural networks in brain organoids.

During the last decade brain organoids have emerged as an attractive model system, allowing stem cells to be differentiated into complex 3D models, recapitulating many aspects of human brain development. Whilst many studies have analysed anatomical and cytoarchitectural characteristics of organoids, their functional characterisation has been limited, and highly variable between studies. Standardised, consistent methods for recording functional activity are critical to providing a functional understanding of neuronal networks at the synaptic and network level that can yield useful information about functional network phenotypes in disease and healthy states. In this study we outline a detailed methodology for calcium imaging and Multi-Electrode Array (MEA) recordings in brain organoids. To illustrate the utility of these functional interrogation techniques in uncovering induced differences in neural network activity we applied various stimulating media protocols. We demonstrate overlapping information from the two modalities, with comparable numbers of active cells in the four treatment groups and an increase in synchronous behaviour in BrainPhys treated groups. Further development of analysis pipelines to reveal network level changes in brain organoids will enrich our understanding of network formation and perturbation in these structures, and aid in the future development of drugs that target neurological disorders at the network level.

Assessment of Normal Tissue Radiosensitivity by Evaluating DNA Damage and Repair Kinetics in Human Brain Organoids.

DNA-double strand break (DSB), detected by immunostaining of key proteins orchestrating repair, like γH2AX and 53BP1, is well established as a surrogate for tissue radiosensitivity. We hypothesized that the generation of normal brain 3D organoids ("mini-brains") from human induced pluripotent stem cells (hiPSC) combined with detection of DNA damage repair (DDR) may hold the promise towards developing personalized models for the determination of normal tissue radiosensitivity. In this study, cerebral organoids, an in vitro model that stands in its complexity between 2D cellular system and an organ, have been used. To quantify radiation-induced response, immunofluorescent staining with γH2AX and 53BP1 were applied at early (30 min, initial damage), and late time points (18 and 72 h, residual damage), following clinical standard 2 Gy irradiation. Based on our findings, assessment of DDR kinetics as a surrogate for radiosensitivity in hiPSC derived cerebral organoids is feasible. Further development of mini-brains recapitulating mature adult neuronal tissue and implementation of additional signaling and toxicity surrogates may pave the way towards development of next-generation personalized assessment of radiosensitivity in healthy neuronal tissue.

Estimating the dimensionality of the manifold underlying multi-electrode neural recordings.

It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms' accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the "Joint Autoencoder", which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.

Assessment of neurodegeneration and neuronal loss in aged 5XFAD mice.

Neuronal loss resulting from progressive neurodegeneration is a major pathological feature of Alzheimer's disease (AD). Here, we present a protocol to detect neurodegeneration, neuronal apoptosis, and neuronal loss in 5XFAD mouse strain, which is a well-established model for interrogating the molecular mechanism of neuronal death in AD. This protocol describes the use of the neurodegenerative marker Fluro-Jade C, cleaved caspase-3 immunofluorescent staining and Nissl staining for the analysis of neurodegeneration and neuronal loss in 5XFAD mice. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2021).

Mapping complex cell morphology in the grey matter with double diffusion encoding MR: A simulation study.

This paper investigates the impact of cell body (namely soma) size and branching of cellular projections on diffusion MR imaging (dMRI) and spectroscopy (dMRS) signals for both standard single diffusion encoding (SDE) and more advanced double diffusion encoding (DDE) measurements using numerical simulations. The aim is to investigate the ability of dMRI/dMRS to characterize the complex morphology of brain cells focusing on these two distinctive features of brain grey matter. To this end, we employ a recently developed computational framework to create three dimensional meshes of neuron-like structures for Monte Carlo simulations, using diffusion coefficients typical of water and brain metabolites. Modelling the cellular structure as realistically connected spherical soma and cylindrical cellular projections, we cover a wide range of combinations of sphere radii and branching order of cellular projections, characteristic of various grey matter cells. We assess the impact of spherical soma size and branching order on the b-value dependence of the SDE signal as well as the time dependence of the mean diffusivity (MD) and mean kurtosis (MK). Moreover, we also assess the impact of spherical soma size and branching order on the angular modulation of DDE signal at different mixing times, together with the mixing time dependence of the apparent microscopic anisotropy (µA), a promising contrast derived from DDE measurements. The SDE results show that spherical soma size has a measurable impact on both the b-value dependence of the SDE signal and the MD and MK diffusion time dependence for both water and metabolites. On the other hand, we show that branching order has little impact on either, especially for water. In contrast, the DDE results show that spherical soma size has a measurable impact on the DDE signal's angular modulation at short mixing times and the branching order of cellular projections significantly impacts the mixing time dependence of the DDE signal's angular modulation as well as of the derived µA, for both water and metabolites. Our results confirm that SDE based techniques may be sensitive to spherical soma size, and most importantly, show for the first time that DDE measurements may be more sensitive to the dendritic tree complexity (as parametrized by the branching order of cellular projections), paving the way for new ways of characterizing grey matter morphology, non-invasively using dMRS and potentially dMRI.

Assessment of the Effects of Drugs on Mitochondrial Respiration.

Mitochondria are targets of newly synthesized drugs and being tested for the treatment of various diseases caused or accompanied by disruption of cellular bioenergetics. In drug development, it is necessary to test for drug-induced changes in mitochondrial enzyme activity that may be related to therapeutic or adverse drug effects. Measurement of drug effect on mitochondrial oxygen consumption kinetics and/or protective effects of drugs against calcium-induced inhibition of the mitochondrial respiration can be used for the study mitochondrial toxicity and neuroprotective effects of drugs. Supposing that the drug-induced inhibition of the mitochondrial respiratory rate and/or individual mitochondrial complexes is associated with adverse drug effects, the effects of drugs on mitochondrial respiration in isolated mitochondria allow selection of novel molecules that are relatively safe for mitochondrial toxicity.

Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids.

In the last decade, different research groups in the academic setting have developed induced pluripotent stem cell-based protocols to generate three-dimensional, multicellular, neural organoids. Their use to model brain biology, early neural development, and human diseases has provided new insights into the pathophysiology of neuropsychiatric and neurological disorders, including microcephaly, autism, Parkinson's disease, and Alzheimer's disease. However, the adoption of organoid technology for large-scale drug screening in the industry has been hampered by challenges with reproducibility, scalability, and translatability to human disease. Potential technical solutions to expand their use in drug discovery pipelines include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) to create isogenic models, single-cell RNA sequencing to characterize the model at a cellular level, and machine learning to analyze complex data sets. In addition, high-content imaging, automated liquid handling, and standardized assays represent other valuable tools toward this goal. Though several open issues still hamper the full implementation of the organoid technology outside academia, rapid progress in this field will help to prompt its translation toward large-scale drug screening for neurological disorders.

Human Brain Organoids as an In Vitro Model System of Viral Infectious Diseases.

Brain organoids, or brainoids, have shown great promise in the study of central nervous system (CNS) infection. Modeling Zika virus (ZIKV) infection in brain organoids may help elucidate the relationship between ZIKV infection and microcephaly. Brain organoids have been used to study the pathogenesis of SARS-CoV-2, human immunodeficiency virus (HIV), HSV-1, and other viral infections of the CNS. In this review, we summarize the advances in the development of viral infection models in brain organoids and their potential application for exploring mechanisms of viral infections of the CNS and in new drug development. The existing limitations are further discussed and the prospects for the development and application of brain organs are prospected.

Sources of widefield fluorescence from the brain.

Widefield fluorescence microscopy is used to monitor the spiking of populations of neurons in the brain. Widefield fluorescence can originate from indicator molecules at all depths in cortex and the relative contributions from somata, dendrites, and axons are often unknown. Here, I simulate widefield illumination and fluorescence collection and determine the main sources of fluorescence for several GCaMP mouse lines. Scattering strongly affects illumination and collection. One consequence is that illumination intensity is greatest ~300-400 µm below the pia, not at the brain surface. Another is that fluorescence from a source deep in cortex may extend across a diameter of 3-4 mm at the brain surface, severely limiting lateral resolution. In many mouse lines, the volume of tissue contributing to fluorescence extends through the full depth of cortex and fluorescence at most surface locations is a weighted average across multiple cortical columns and often more than one cortical area.

Evaluation of the diffusion MRI white matter tract integrity model using myelin histology and Monte-Carlo simulations.

Quantitative evaluation of brain myelination has drawn considerable attention. Conventional diffusion-based magnetic resonance imaging models, including diffusion tensor imaging and diffusion kurtosis imaging (DKI),1 have been used to infer the microstructure and its changes in neurological diseases. White matter tract integrity (WMTI) was proposed as a biophysical model to relate the DKI-derived metrics to the underlying microstructure. Although the model has been validated on ex vivo animal brains, it was not well evaluated with ex vivo human brains. In this study, histological samples (namely corpus callosum) from postmortem human brains have been investigated based on WMTI analyses on a clinical 3T scanner and comparisons with gold standard myelin staining in proteolipid protein and Luxol fast blue. In addition, Monte Carlo simulations were conducted to link changes from ex vivo to in vivo conditions based on the microscale parameters of water diffusivity and permeability. The results show that WMTI metrics, including axonal water fraction AWF, radial extra-axonal diffusivity De⊥, and intra-axonal diffusivity Dawere needed to characterize myelin content alterations. Thus, WMTI model metrics are shown to be promising candidates as sensitive biomarkers of demyelination.

Collection of Frozen Rodent Brain Regions for Downstream Analyses.

As our understanding of neurobiology has progressed, molecular analyses are often performed on small brain areas such as the medial prefrontal cortex (mPFC) or nucleus accumbens. The challenge in this work is to dissect the correct area while preserving the microenvironment to be examined. In this paper, we describe a simple, low-cost method using resources readily available in most labs. This method preserves nucleic acid and proteins by keeping the tissue frozen throughout the process. Brains are cut into 0.5-1.0 mm sections using a brain matrix and arranged on a frozen glass plate. Landmarks within each section are compared to a reference, such as the Allen Mouse Brain Atlas, and regions are dissected using a cold scalpel or biopsy punch. Tissue is then stored at -80 °C until use. Through this process rat and mouse mPFC, nucleus accumbens, dorsal and ventral hippocampus and other regions have been successfully analyzed using qRT-PCR and Western assays. This method is limited to brain regions that can be identified by clear landmarks.

Transcriptomic evidence that von Economo neurons are regionally specialized extratelencephalic-projecting excitatory neurons.

von Economo neurons (VENs) are bipolar, spindle-shaped neurons restricted to layer 5 of human frontoinsula and anterior cingulate cortex that appear to be selectively vulnerable to neuropsychiatric and neurodegenerative diseases, although little is known about other VEN cellular phenotypes. Single nucleus RNA-sequencing of frontoinsula layer 5 identifies a transcriptomically-defined cell cluster that contained VENs, but also fork cells and a subset of pyramidal neurons. Cross-species alignment of this cell cluster with a well-annotated mouse classification shows strong homology to extratelencephalic (ET) excitatory neurons that project to subcerebral targets. This cluster also shows strong homology to a putative ET cluster in human temporal cortex, but with a strikingly specific regional signature. Together these results suggest that VENs are a regionally distinctive type of ET neuron. Additionally, we describe the first patch clamp recordings of VENs from neurosurgically-resected tissue that show distinctive intrinsic membrane properties relative to neighboring pyramidal neurons.

Quantitative analysis of 1300-nm three-photon calcium imaging in the mouse brain.

1300 nm three-photon calcium imaging has emerged as a useful technique to allow calcium imaging in deep brain regions. Application to large-scale neural activity imaging entails a careful balance between recording fidelity and perturbation to the sample. We calculated and experimentally verified the excitation pulse energy to achieve the minimum photon count required for the detection of calcium transients in GCaMP6s-expressing neurons for 920 nm two-photon and 1320 nm three-photon excitation. By considering the combined effects of in-focus signal attenuation and out-of-focus background generation, we quantified the cross-over depth beyond which three-photon microscopy outpeforms two-photon microscopy in recording fidelity. Brain tissue heating by continuous three-photon imaging was simulated with Monte Carlo method and experimentally validated with immunohistochemistry. Increased immunoreactivity was observed with 150 mW excitation power at 1 and 1.2 mm imaging depths. Our analysis presents a translatable model for the optimization of three-photon calcium imaging based on experimentally tractable parameters.

Reduction of Dimensionality in Monte Carlo Simulation of Diffusion in Extracellular Space Surrounding Cubic Cells.

The real-time iontophoretic method has measured volume fraction and tortuosity of the interstitial component of extracellular space in many regions and under different conditions. To interpret these data computer models of the interstitial space (ISS) of the brain are constructed by representing cells as Basic Cellular Structures (BCS) surrounded by a layer of ISS and replicating this combination to make a 3D ensemble that approximates brain tissue with a specified volume fraction. Tortuosity in such models is measured by releasing molecules of zero size into the ISS and allowing them to execute random walks in the ISS of the ensemble using a Monte Carlo algorithm. The required computational resources for such simulations may be high and here we show that in many situations the 3D problem may be reduced to a quasi-1D problem with consequent reduction in resources. We take the simplest BCS in the form of cubes and use MCell software to perform the Monte Carlo simulations but the analysis described here may be extended in principle to more complex BCS and an ISS that has a defined viscosity and an extracellular matrix that interacts with diffusing molecules. In the course of this study we found that the original analytical description of the relation between volume fraction and tortuosity for an ensemble of cubes may require a small correction.

An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome.

Simultaneous profiling of transcriptome and chromatin accessibility within single cells is a powerful approach to dissect gene regulatory programs in complex tissues. However, current tools are limited by modest throughput. We now describe an ultra high-throughput method, Paired-seq, for parallel analysis of transcriptome and accessible chromatin in millions of single cells. We demonstrate the utility of Paired-seq for analyzing the dynamic and cell-type-specific gene regulatory programs in complex tissues by applying it to mouse adult cerebral cortex and fetal forebrain. The joint profiles of a large number of single cells allowed us to deconvolute the transcriptome and open chromatin landscapes in the major cell types within these brain tissues, infer putative target genes of candidate enhancers, and reconstruct the trajectory of cellular lineages within the developing forebrain.

The physiological experiments of Constantin von Economo on the central pathways of mastication and deglutition.

The first study of Constantin von Economo on the mammalian brain was published in 1902. Experiments were carried out in rabbits at the Physiological Institute headed by Siegmund von Exner-Ewarten in Vienna to investigate the central pathways of chewing and swallowing. After placing cortical lesions, Economo applied cortical and subcortical electrical stimulation to obtain masticatory movements, and tracked degenerated fibers by means of the Marchi method. He traced fibers through the internal capsule, ventral nucleus of the thalamus, subthalamic nucleus, substantia nigra and its connections with the motor nucleus of the trigeminal nerve, and nucleus solitarius. He suggested that the substantia nigra is responsible for coordinating alimentation movements, with the involvement of cranial nerves V, VII, IX and X as well. We discuss these findings in a historical and a modern perspective, including the concept of a central pattern generator in the pontine reticular formation and its interaction with the nucleus solitarius. Today we understand that mastication is a voluntary action controlled by motor cortical areas, by motoneurons of the trigeminal, and by a neural pattern generator in the pons. On the other hand, deglutition comprises 'reflex swallowing' triggered by sensory fibers of cranial nerves V, IX and X, and 'voluntary swallowing' which may be controlled by both cortical fields and subcortical areas, such as the internal capsule, the hypothalamus and the mesencephalic reticular formation.

In situ estimation of optical properties of rat and monkey brains using femtosecond time-resolved measurements.

An accurate knowledge of tissue optical properties (absorption coefficients, µa, and reduced scattering coefficients, µs') is critical for precise modeling of light propagation in biological tissue, essential for developing diagnostic and therapeutic optical techniques that utilize diffusive photons. A great number of studies have explored the optical properties of various tissue, and these values are not known in detail due to difficulties in the experimental determination and significant variations in tissue constitution. Especially, in situ estimates of the optical properties of brain tissue, a common measurement target in optical imaging, is a challenge because of its layer structure (where the thin gray matter covers the white matter). Here, we report an approach to in situ estimates of the µa and µs' of the gray and white matter in living rat and monkey brains by using femtosecond time-resolved measurements and Monte Carlo simulation. The results demonstrate that the µa of the gray matter is larger than that of the white matter, while there was no significant difference in the µs' between the gray and white matter. The optical properties of the rat brain were very similar to those of the monkey brain except for the µa of the gray matter here.

Three-dimensional GPU-accelerated active contours for automated localization of cells in large images.

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. Successful cell segmentation algorithms rely identifying seed points, and are highly sensitive to variablility in cell size. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional contour evolution that extends previous work on fast two-dimensional snakes. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell localization tasks when compared to existing methods on large 3D brain images.