Biological noise is ubiquitous in living systems; yet, it is often neglected in cell-based experiments, potentially biasing data interpretation. We provide a quantitative characterization of single-cell equivalent diameter distributions in six human cell types using cell counting. By analyzing thousands of cells over passage number, we show that cell size is phenotype dependent and varies significantly with passage, with most cell types exhibiting a progressive reduction in median diameter. This variability is structured: When diameters are converted to masses, the distributions obey scaling laws, revealing conserved statistical properties of human cells in culture. Because key physiological processes such as metabolism scale with cell mass, passage-dependent shifts in diameter distributions can propagate into functional readouts. We show that changes in cell size are sufficient to bias estimates of construct-level metabolic rate, potentially confounding the interpretation of size-normalized assays and treatment effects. Our results highlight that biological noise in vitro is a source of statistical structure that enables scaling analyses and a dynamic property that, if ignored, can lead to systematic misinterpretation of experimental outcomes. Accounting for size distributions and their evolution over passages may therefore improve experimental design, data interpretation, and, ultimately, the translatability of in vitro models.

Background Domestication appears to modify the morphology and physiology of the central nervous system. Owing to the limited availability of proteomic and cytoarchitectural data for comparisons between wild and domesticated species, we conducted a comparative analysis of the cytoarchitecture and protein profile of the primary motor cortex (M1), the key region controlling motor activity, in pigs and wild boars to assess the effects of domestication. Methods M1 samples used in this study were obtained from brains previously analyzed in an earlier investigation. From six pig and wild boar brains fixed in paraformaldehyde, the right and left M1 regions were isolated. Paraffin sections, 5 µm thick, were prepared for histological and immunohistochemical analyses, and 10 µm sections were used for proteomic analysis. In M1, cortical thickness, cell density, and the density of parvalbumin-positive neurons were quantified, while proteomic analysis was performed to characterize the M1 protein profile. Results Our results revealed a lower density of parvalbumin-expressing interneurons compared with wild boars. Moreover, proteomic analyses showed an overexpression in wild boars of proteins involved in oxidative stress protection and synaptic plasticity. Conclusions These findings indicate that domestication may have influenced the cytoarchitecture of the pig M1. The reduced number of parvalbumin-expressing interneurons may reflect a modification in neuronal network properties in pigs compared with wild boars. In contrast, the proteomic profile of wild boars revealed an enrichment of proteins associated with oxidative stress protection and synaptic plasticity, supporting structural distinctions in the M1. Collectively, these results suggest that the wild boar may exhibit more finely tuned regulation of motor control, supported by enhanced mechanisms to sustain neuronal activity and viability.

BackgroundAccurate cellular models are critical for understanding tissue function and accelerating drug discovery. While in vitro systems like spheroids and organoids are widely used, they are costly, low-throughput, and often lack reproducibility. Here, we introduce Evolvoid, a computational pipeline based on genetic algorithms that generates virtual 3D spheroid-like cell constructs with optimized morphologies by simulating key biophysical principles - such as thermodynamic principles, nutrient transport and their uptake by cells.ResultsBy integrating finite element simulations with evolutionary principles, our in silico platform evolves populations of randomly generated shapes, iteratively selecting individuals that best satisfy a biophysically informed fitness function. The fitness function encodes selection pressures based on general biophysical constraints - namely, the minimization of surface energy and the maintenance of cell viability under variable oxygen conditions. The Shannon entropy is used to track genome complexity over generations. In consistence with evolutionary dynamics, the complexity increases progressively. Moreover, albeit based on basic biophysical optimization rules, the fittest individuals generated by Evolvoid closely resemble the shape and size of cellular spheroids obtained in vitro.ConclusionEvolvoid is modular, scalable, and tunable to different construct sizes, cell types, or culture conditions, and thus provides a versatile platform for designing and optimizing 3D cellular models entirely in silico. It offers a foundation for developing high-fidelity, cost-effective digital shape twins of biological systems, supporting the advancement of lab-on-a-laptop technologies and new approach methodologies, thereby reducing the reliance on in vitro and animal models.

Understanding how neuronal circuits generate complex activity patterns and perform computations remains a significant challenge in neuroscience. In vitro neuronal models provide controlled environments to investigate brain microcircuits, their responses to stimuli, and dysfunctions in pathological conditions. While invaluable for direct observation and manipulation, these experiments are also resource-intensive and raise ethical concerns, particularly when involving human-derived neurons. In silico models offer a cost-effective, scalable complementary alternative. They integrate multi-scale data, enabling high-throughput investigations and the exploration of mechanisms that may be beyond the reach of experimental methods. These computational approaches support hypothesis generation, data interpretation, and theoretical insight. When combined with in vitro studies, they create a synergistic framework that advances our understanding of neuronal function and dysfunction in ways neither method could achieve alone. This review examines computational models developed since 2000 to support in vitro neuronal investigations, with a focus on their contributions to understanding network dynamics. This includes topics such as neuronal activity, stem-cell-derived neurons, network topology, and metabolism. We highlight key applications, from predicting mechanisms of neuropathy to exploring network learning and memory. We offer an overview of a corner problem for the development of computational models, that is parameter estimation, and discuss implementation strategies emphasizing accessibility through public repositories. By synthesizing these developments, this review aims to inspire new approaches in computational neuroscience, advancing the study of brain function and dysfunction.

The human brain originates from the neural tube that detaches from the ectodermal layer and gradually develops into a mature structure through highly regulated molecular and cellular processes. Here, stem cell technology is combined with 4D bioprinting, a fabrication process that utilizes additive manufacturing, to generate a 4D-neural tube (4D-NT). This consists of a scaffold that can self-fold over time, which is then populated with iPSC-derived neuroprogenitors, mimicking neural tube cellular architecture. The scaffold's "smart" self-folding behavior is driven by the differential swelling properties of bilayer films, which create a deformation gradient upon hydration. Cellular analyses reveal a highly efficient induction of neuroprogenitors on 4D-NTs, demonstrating the ability of this model to mimic the spatial and structural complexity of the developing human neural tube. Furthermore, 4D-NTs seeded with iPSCs with a mutation in WDR62, associated with autosomal recessive primary microcephaly (MCPH), recapitulate the earlier observations obtained in 2D/3D neural cultures, thereby validating the newly developed 4D-NT platform and suggesting it represents a tool that can facilitate understanding of human neural development and disease.

Objective. Neurons exhibit deterministic behavior influenced by stochastic cellular or extracellular components. Estimating this random component is challenging due to unknown underlying deterministic dynamics. In this study, we aim to estimate the neural random component, termed intrinsic dynamic neural noise, from experimental time series without prior assumptions on the underlying neural model.Approach. The method relies on the nonlinear approximate entropy profile and was evaluated using synthetic data from Izhikevich's models and simulated calcium dynamics driven by dynamical noise. We then applied the method to experimental time series from calcium imaging in mice and zebrafish brain regions, as well as electrophysiological data from a 128-channel cortical probe in anesthetized rats.Main results. The results show region-specific behavior, with higher dynamic neural noise in the somatosensory cortex of mice and anterior telencephalic area of zebrafish. Furthermore, neuronal stochasticity is greater in genetically encodedCa2+indicators than inCa2+dyes, and neural noise increases with recording depth.Significance. These findings offer insights into neural dynamics and suggest dynamic noise as a key biomarker.

Introduction:Computational models are valuable tools for understanding and studying a wide range of characteristics and mechanisms of the brain. Furthermore, they can also be exploited to explore biological neural networks from neuronal cultures. However, few of the current in silico approaches consider the energetic demand of neurons to sustain their electrophysiological functions, specifically their well-known oxygen-dependent firing. Methods:In this work, we introduce Digitoids, a computational platform which integrates a Hodgkin-Huxley-like model to describe the time-dependent oscillations of the neuronal membrane potential with oxygen dynamics in the culture environment. In Digitoids, neurons are connected to each other according to Small-World topologies observed in cell cultures, and oxygen consumption by cells is modeled as limited by diffusion through the culture medium. The oxygen consumed is used to fuel their basal metabolism and the activity of Na+-K+-ATP membrane pumps, thus it modulates neuronal firing. Results:Our simulations show that the characteristics of neuronal firing predicted throughout the network are related to oxygen availability. In addition, the average firing rate predicted by Digitoids is statistically similar to that measured in neuronal networks in vitro, further proving the relevance of this platform. Dicussion:Digitoids paves the way for a new generation of in silico models of neuronal networks, establishing the oxygen dependence of electrophysiological dynamics as a fundamental requirement to improve their physiological relevance.

Accurate descriptions of the variability in single-cell oxygen consumption and its size-dependency are key to establishing more robust tissue models. By combining microfabricated devices with multiparameter identification algorithms, we demonstrate that single human hepatocytes exhibit an oxygen level-dependent consumption rate and that their maximal oxygen consumption rate is significantly lower than that of typical hepatic cell cultures. Moreover, we found that clusters of two or more cells competing for a limited oxygen supply reduced their maximal consumption rate, highlighting their ability to adapt to local resource availability and the presence of nearby cells. We used our approach to characterize the covariance of size and oxygen consumption rate within a cell population, showing that size matters, since oxygen metabolism covaries lognormally with cell size. Our study paves the way for linking the metabolic activity of single human hepatocytes to their tissue- or organ-level metabolism and describing its size-related variability through scaling laws.

The development of robust tools for segmenting cellular and sub-cellular neuronal structures lags behind the massive production of high-resolution 3D images of neurons in brain tissue. The challenges are principally related to high neuronal density and low signal-to-noise characteristics in thick samples, as well as the heterogeneity of data acquired with different imaging methods. To address this issue, we design a framework which includes sample preparation for high resolution imaging and image analysis. Specifically, we set up a method for labeling thick samples and develop SENPAI, a scalable algorithm for segmenting neurons at cellular and sub-cellular scales in conventional and super-resolution STimulated Emission Depletion (STED) microscopy images of brain tissues. Further, we propose a validation paradigm for testing segmentation performance when a manual ground-truth may not exhaustively describe neuronal arborization. We show that SENPAI provides accurate multi-scale segmentation, from entire neurons down to spines, outperforming state-of-the-art tools. The framework will empower image processing of complex neuronal circuitries. Tools to segment cellular and sub-cellular neuronal structures can be hindered by high neuronal density and low signal-to-noise in thick samples. Here, the authors present SENPAI, a framework for imaging and segmenting neurons from conventional and super-resolution microscopy of clarified brain tissues.

In 2013, M. Lancaster described the first protocol to obtain human brain organoids. These organoids, usually generated from human-induced pluripotent stem cells, can mimic the three-dimensional structure of the human brain. While they recapitulate the salient developmental stages of the human brain, their use to investigate the onset and mechanisms of neurodegenerative diseases still faces crucial limitations. In this review, we aim to highlight these limitations, which hinder brain organoids from becoming reliable models to study neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). Specifically, we will describe structural and biological impediments, including the lack of an aging footprint, angiogenesis, myelination, and the inclusion of functional and immunocompetent microglia—all important factors in the onset of neurodegeneration in AD, PD, and ALS. Additionally, we will discuss technical limitations for monitoring the microanatomy and electrophysiology of these organoids. In parallel, we will propose solutions to overcome the current limitations, thereby making human brain organoids a more reliable tool to model neurodegeneration.

Citation: Magliaro C and Ahluwalia A. To brain or not to brain organoids. Front Sci (2023) 1:1148873. doi: 10.3389/fsci.2023.1148873

Accurately modeling oxygen transport and consumption is crucial to predict metabolic dynamics in cell cultures and optimize the design of tissue and organ models. We present a methodology to characterize the Michaelis-Menten oxygen consumption parameters in vitro, integrating novel experimental techniques and computational tools. The parameters were derived for hepatic cell cultures with different dimensionality (i.e., 2D and 3D) and with different surface and volumetric densities. To quantify cell packing regardless of the dimensionality of cultures, we devised an image-based metric, referred to as the proximity index. The Michaelis-Menten parameters were related to the proximity index through an uptake coefficient, analogous to a diffusion constant, enabling the quantitative analysis of oxygen dynamics across dimensions. Our results show that Michaelis-Menten parameters are not constant for a given cell type but change with dimensionality and cell density. The maximum consumption rate per cell decreases significantly with cell surface and volumetric density, while the Michaelis-Menten constant tends to increase. In addition, the dependency of the uptake coefficient on the proximity index suggests that the oxygen consumption rate of hepatic cells is superadaptive, as they modulate their oxygen utilization according to its local availability and to the proximity of other cells. We describe, for the first time, how cells consume oxygen as a function of cell proximity, through a quantitative index, which combines cell density and dimensionality. This study enhances our understanding of how cell-cell interaction affects oxygen dynamics and enables better prediction of aerobic metabolism in tissue models, improving their translational value.

Human-relevant three-dimensional (3D) models of cerebral tissue can be invaluable tools to boost our understanding of the cellular mechanisms underlying brain pathophysiology. Nowadays, the accessibility, isolation and harvesting of human neural cells represents a bottleneck for obtaining reproducible and accurate models and gaining insights in the fields of oncology, neurodegenerative diseases and toxicology. In this scenario, given their low cost, ease of culture and reproducibility, neural cell lines constitute a key tool for developing usable and reliable models of the human brain. Here, we review the most recent advances in 3D constructs laden with neural cell lines, highlighting their advantages and limitations and their possible future applications.

In vitro models of neural tissues are crucial to gain new insights on the pathophysiology of the brain. However, cell cultures are associated with many drawbacks and difficulties, e.g., technical complexities, ethical problems and high cost. Computational model-based solutions could represent an important tool to support the study of neuron function. In this work we present a novel computational platform where digital neuronal networks, i.e., Digitoids, can be developed with different size and layouts. The Digitoids rely on a novel firing model where the dependence on oxygen concentration is introduced, since it is a crucial limiting factor in cell cultures. To validate the performance of the platform, Digitoids were developed with the same morphological arrangement as observed in neuron monolayers in vitro . The comparison between the functional output of the Digitoids and the experimental data are not statistically different. The platform delivers a flexible digital tool that can easily be adapted for mimicking in vitro models with increasing complexity and can be exploited to optimize the laboratory experiments involving neuron cultures. Author Summary The use of cell models within laboratories is crucial to gain new understandings of the functioning of neuronal assemblies. However, culturing cells requires highly skilled personnel, a huge quantity of disposable materials and is thus associated with elevated costs. To overcome these limitations, the use of computer-based systems that reproduce the electrical behaviour of neurons can be employed. Since in vitro models are vessel-free, we present a novel computational model of neuron electrophysiology, where we introduce the dependence on local oxygen concentration Indeed, oxygen affects the metabolism and the function of cultured neurons. Thanks to our model and platform, we are able to reproduce the morphology of the cultured networks of neurons and build the so-called Digitoids . We then simulate the Digitoids and obtain their electrophysiological output. We compared the Digitoids’ output with experimental data from neurons cultured on microelectrode arrays. We did not find any differences between the electrophysiological output of the Digitoids and the experimental data, therefore we conclude that our novel oxygen-dependent model can be used to develop more physiologically relevant tools for simulating the activity of cultured neurons.