Author Corrections: Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly.
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ABSTRACT:
SUBMITTER: Ren X
PROVIDER: S-EPMC8648747 | biostudies-literature | 2021 Dec
REPOSITORIES: biostudies-literature
Publications
Author Corrections: Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly.
Ren Xianwen X Zhong Guojie G Zhang Qiming Q Zhang Lei L Sun Yujie Y Zhang Zemin Z
Cell research 20211201 12
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Project description:Single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic studies by providing unprecedented cellular and molecular throughputs, but spatial information of individual cells is lost during tissue dissociation. While imaging-based technologies such as in situ sequencing show great promise, technical difficulties currently limit their wide usage. Here we hypothesize that cellular spatial organization is inherently encoded by cell identity and can be reconstructed, at least in part, by ligand-receptor interactions, and we present CSOmap, a computational tool to infer cellular interaction de novo from scRNA-seq. We show that CSOmap can successfully recapitulate the spatial organization of multiple organs of human and mouse including tumor microenvironments for multiple cancers in pseudo-space, and reveal molecular determinants of cellular interactions. Further, CSOmap readily simulates perturbation of genes or cell types to gain novel biological insights, especially into how immune cells interact in the tumor microenvironment. CSOmap can be a widely applicable tool to interrogate cellular organizations based on scRNA-seq data for various tissues in diverse systems.
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Project description:The self-organization of multicomponent supramolecular systems involving a variety of two-dimensional (2 D) polygons and three-dimensional (3 D) cages is presented. Nine self-organizing systems, SS(1)-SS(9), have been studied. Each involves the simultaneous mixing of organoplatinum acceptors and pyridyl donors of varying geometry and their selective self-assembly into three to four specific 2 D (rectangular, triangular, and rhomboid) and/or 3 D (triangular prism and distorted and nondistorted trigonal bipyramidal) supramolecules. The formation of these discrete structures is characterized using NMR spectroscopy and electrospray ionization mass spectrometry (ESI-MS). In all cases, the self-organization process is directed by: 1) the geometric information encoded within the molecular subunits and 2) a thermodynamically driven dynamic self-correction process. The result is the selective self-assembly of multiple discrete products from a randomly formed complex. The influence of key experimental variables--temperature and solvent--on the self-correction process and the fidelity of the resulting self-organization systems is also described.
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Project description:MotivationSingle-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive diseases. However, spatial information is often lost during tissue dissociation. Spatial transcriptomic (ST) technologies can provide precise spatial gene expression atlas, while their practicality is constrained by the number of genes they can assay or the associated costs at a larger scale and the fine-grained cell-type annotation. By transferring knowledge between scRNA-seq and ST data through cell correspondence learning, it is possible to recover the spatial properties inherent in scRNA-seq datasets.ResultsIn this study, we introduce COME, a COntrastive Mapping lEarning approach that learns mapping between ST and scRNA-seq data to recover the spatial information of scRNA-seq data. Extensive experiments demonstrate that the proposed COME method effectively captures precise cell-spot relationships and outperforms previous methods in recovering spatial location for scRNA-seq data. More importantly, our method is capable of precisely identifying biologically meaningful information within the data, such as the spatial structure of missing genes, spatial hierarchical patterns, and the cell-type compositions for each spot. These results indicate that the proposed COME method can help to understand the heterogeneity and activities among cells within tissue environments.Availability and implementationThe COME is freely available in GitHub (https://github.com/cindyway/COME).
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Project description:Ample research in visual working memory (VWM) has demonstrated that the memorized items are maintained in integrated spatial configurations, even when the spatial context is task irrelevant. These insights were obtained in studies in which participants were provided with the information they memorized. However, the encoding of provided information is only one aspect of memory. In everyday life, individuals often construct their own memory representations, an aspect of memory we have previously termed self-initiated (SI) working memory. In this study, we employed a SI VWM task in which participants selected the visual targets they memorized. The spatial locations of the targets were task irrelevant. Nevertheless, we were interested to see whether participants would construct spatially structured memory representations, which would suggest that they intended to maintain the visual targets as integrated spatial configurations. The results of two experiments demonstrated that participants constructed spatially structured configurations relative to random displays. Specifically, participants selected visual targets in close spatial proximity and constructed spatial sequences with short distances and fewer path crossings. When asked to construct configurations for a hypothetical competitor in a memory contest, participants disrupted the spatial structure by selecting visual targets that were further apart and by increasing the distances between them, which suggests that these characteristics were under their control. At the end of each experiment, participants provided verbal descriptions of the strategies they used to construct the memory displays. While the spatial structure of the SI memory representations was robust, it was absent from the participants' explicit descriptions, which focused on non-spatial strategies. Participants reported selecting items based, most frequently, on semantic categories and visual features. Taken together, these results demonstrated that participants had access to the metacognitive knowledge on the spatial structure of VWM representations, knowledge they manipulated to construct memory representations that enhanced or disrupted memory performance. While having a profound impact on behavior, this metacognitive knowledge on spatial structure remained implicit, as it was absent from the participants' verbal reports. Viewed from a larger perspective, this study explores how individuals interact with the world by actively structuring their surroundings to maximize cognitive performance.
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Project description:Heterotypic cooperation-two populations exchanging distinct benefits that are costly to produce-is widespread. Cheaters, exploiting benefits while evading contribution, can undermine cooperation. Two mechanisms can stabilize heterotypic cooperation. In 'partner choice', cooperators recognize and choose cooperating over cheating partners; in 'partner fidelity feedback', fitness-feedback from repeated interactions ensures that aiding your partner helps yourself. How might a spatial environment, which facilitates repeated interactions, promote fitness-feedback? We examined this process through mathematical models and engineered Saccharomyces cerevisiae strains incapable of recognition. Here, cooperators and their heterotypic cooperative partners (partners) exchanged distinct essential metabolites. Cheaters exploited partner-produced metabolites without reciprocating, and were competitively superior to cooperators. Despite initially random spatial distributions, cooperators gained more partner neighbors than cheaters did. The less a cheater contributed, the more it was excluded and disfavored. This self-organization, driven by asymmetric fitness effects of cooperators and cheaters on partners during cell growth into open space, achieves assortment. DOI: http://dx.doi.org/10.7554/eLife.00960.001.
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Project description:Ligand binding induces extensive spatial reorganization and clustering of the EphA2 receptor at the cell membrane. It has previously been shown that the nanoscale spatial distribution of ligands modulates EphA2 receptor reorganization, activation and the invasive properties of cancer cells. However, intracellular signaling downstream of EphA2 receptor activation by nanoscale spatially distributed ligands has not been elucidated. Here, we used DNA origami nanostructures to control the positions of ephrin-A5 ligands at the nanoscale and investigated EphA2 activation and transcriptional responses following ligand binding. Using RNA-seq, we determined the transcriptional profiles of human glioblastoma cells treated with DNA nanocalipers presenting a single ephrin-A5 dimer or two dimers spaced 14, 40 or 100 nm apart. These cells displayed divergent transcriptional responses to the differing ephrin-A5 nano-organization. Specifically, ephrin-A5 dimers spaced 40 or 100 nm apart showed the highest levels of differential expressed genes compared to treatment with nanocalipers that do not present ephrin-A5. These findings show that the nanoscale organization of ephrin-A5 modulates transcriptional responses to EphA2 activation.
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Project description:Extracellular matrix (ECM) undergoes dynamic inflation that dynamically changes ligand nanospacing but has not been explored. Here we utilize ECM-mimicking photocontrolled supramolecular ligand-tunable Azo+ self-assembly composed of azobenzene derivatives (Azo+) stacked via cation-π interactions and stabilized with RGD ligand-bearing poly(acrylic acid). Near-infrared-upconverted-ultraviolet light induces cis-Azo+-mediated inflation that suppresses cation-π interactions, thereby inflating liganded self-assembly. This inflation increases nanospacing of "closely nanospaced" ligands from 1.8 nm to 2.6 nm and the surface area of liganded self-assembly that facilitate stem cell adhesion, mechanosensing, and differentiation both in vitro and in vivo, including the release of loaded molecules by destabilizing water bridges and hydrogen bonds between the Azo+ molecules and loaded molecules. Conversely, visible light induces trans-Azo+ formation that facilitates cation-π interactions, thereby deflating self-assembly with "closely nanospaced" ligands that inhibits stem cell adhesion, mechanosensing, and differentiation. In stark contrast, when ligand nanospacing increases from 8.7 nm to 12.2 nm via the inflation of self-assembly, the surface area of "distantly nanospaced" ligands increases, thereby suppressing stem cell adhesion, mechanosensing, and differentiation. Long-term in vivo stability of self-assembly via real-time tracking and upconversion are verified. This tuning of ligand nanospacing can unravel dynamic ligand-cell interactions for stem cell-regulated tissue regeneration.
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Project description:Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems. We generated single-cell RNA-seq profiles from dissociated cells from developing zebrafish embryos (late blastula stage - 50% epiboly)
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