Self-organizing human cardiac microchambers mediated by geometric confinement.
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ABSTRACT:
SUBMITTER: Ma Z
PROVIDER: S-EPMC4503387 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNeurons are dynamically coupled with each other through neurite-mediated adhesion during development. Understanding the collective behavior of neurons in circuits is important for understanding neural development. While a number of genetic and activity-dependent factors regulating neuronal migration have been discovered on single cell level, systematic study of collective neuronal migration has been lacking. Various biological systems are shown to be self-organized, and it is not known if neural circuit assembly is self-organized. Besides, many of the molecular factors take effect through spatial patterns, and coupled biological systems exhibit emergent property in response to geometric constraints. How geometric constraints of the patterns regulate neuronal migration and circuit assembly of neurons within the patterns remains unexplored.Methodology/principal findingsWe established a two-dimensional model for studying collective neuronal migration of a circuit, with hippocampal neurons from embryonic rats on Matrigel-coated self-assembled monolayers (SAMs). When the neural circuit is subject to geometric constraints of a critical scale, we found that the collective behavior of neuronal migration is spatiotemporally coordinated. Neuronal somata that are evenly distributed upon adhesion tend to aggregate at the geometric center of the circuit, forming mono-clusters. Clustering formation is geometry-dependent, within a critical scale from 200 µm to approximately 500 µm. Finally, somata clustering is neuron-type specific, and glutamatergic and GABAergic neurons tend to aggregate homo-philically.Conclusions/significanceWe demonstrate self-organization of neural circuits in response to geometric constraints through spatiotemporally coordinated neuronal migration, possibly via mechanical coupling. We found that such collective neuronal migration leads to somata clustering, and mono-cluster appears when the geometric constraints fall within a critical scale. The discovery of geometry-dependent collective neuronal migration and the formation of somata clustering in vitro shed light on neural development in vivo.
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Project description:Polymer microstructures are widely used in optics, flexible electronics, and so forth. We demonstrate a cost-effective bottom-up manner for patterning polymer microstructures by evaporative self-assembly under a flexible geometric confinement at a high temperature. Two-parallel-plates confinement would become curve-to-flat shape geometric confinement as the polydimethylsiloxane (PDMS) cover plate deformed during solvent swelling. We found that a flexible cover plate would be favorable for the formation of gradient microstructures, with various periodicities and widths obtained at varied heights of clearance. After thermal annealing, the edge of the PMMA (Poly-methylmethacrylate) microstructures would become smooth, while the RR-P3HT (regioregular-poly(3-hexylthiophene)) might generate nanocrystals. The morphologies of RR-P3HT structures included thick films, straight lines, hierarchical stripes, incomplete stripes, and regular dots. Finally, a simple field-effect transistor (FET) device was demonstrated with the RR-P3HT micropattern as an active layer.
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Project description:Here, we used agarose microwells to control embryos body (EB) shapes and generated whole brain human cerebral organoids (hCO) from these EBs to investigate the effects of early spatial regulation on transcriptomic changes during brain region specification using bulk RNA sequencing. Our results showed that gross morphological and transcriptional changes related to hCO differentiation were similar between 96-well and microwell hCOs, indicating that the microwell growth environment did not significantly affect the overall neural differentiation or viability of the hCOs. However, investigating individual developmental time points and comparing across 96-well and microwell conditions more closely revealed notable differences in genes associated with pluripotency, mesoderm, MGE, retina and cortical development, and epithelial and mesenchymal fates, implying that the microwell growth environment may bias lineage commitment in neurodevelopment. Furthermore, we found that the direction and intensity of this transcriptional bias was associated with the starting EB shapes. The butterfly shape showed the most noticeable differences, followed closely by the peanut shape. Among other notable differences, round microwells exhibited a potential delay in the kinetics of neurodevelopment, and our results also demonstrated that non-spherical shapes may favor the development of MGE-associated brain regions and cell types over cortical regions.
2021-02-23 | GSE167208 | GEO
Project description:Neuromuscular networks assemble during early human embryonic development and are essential for the control of body movement. Previous neuromuscular junction modeling efforts using human pluripotent stem cells (hPSCs) generated either spinal cord neurons or skeletal muscles in monolayer culture. Here, we use hPSC-derived axial stem cells, the building blocks of the posterior body, to simultaneously generate spinal cord neurons and skeletal muscle cells that self-organize to generate human neuromuscular organoids (NMOs) that can be maintained in 3D for several months. Single-cell RNA-sequencing of individual organoids revealed reproducibility across experiments and enabled the tracking of the neural and mesodermal differentiation trajectories as organoids developed and matured. NMOs contain functional neuromuscular junctions supported by terminal Schwann cells. They contract and develop central pattern generator-like neuronal circuits. Finally, we successfully use NMOs to recapitulate key aspects of myasthenia gravis pathology, thus highlighting the significant potential of NMOs for modeling neuromuscular diseases in the future.
2020-01-20 | GSE128357 | GEO
Project description:BackgroundParallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.ResultsThe method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.ConclusionsThe SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.
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Project description:Structural connectivity in the brain is typically studied by reducing its observation to a single spatial resolution. However, the brain possesses a rich architecture organized over multiple scales linked to one another. We explored the multiscale organization of human connectomes using datasets of healthy subjects reconstructed at five different resolutions. We found that the structure of the human brain remains self-similar when the resolution of observation is progressively decreased by hierarchical coarse-graining of the anatomical regions. Strikingly, a geometric network model, where distances are not Euclidean, predicts the multiscale properties of connectomes, including self-similarity. The model relies on the application of a geometric renormalization protocol which decreases the resolution by coarse-graining and averaging over short similarity distances. Our results suggest that simple organizing principles underlie the multiscale architecture of human structural brain networks, where the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions. The implications are varied and can be substantial for fundamental debates, such as whether the brain is working near a critical point, as well as for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.
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