scholarly journals Spatially-Resolved Proteomic Analysis of the Lens Extracellular Diffusion Barrier

2021 ◽  
Author(s):  
Zhen Wang ◽  
Kevin L Schey
Keyword(s):  
Diffusion Barrier ◽  
Proteomic Analysis ◽  
Extracellular Space ◽  
Molecular Diffusion ◽  
Interaction Network ◽  
Lens Fiber ◽  
Fiber Cells ◽  
Protein Protein Interaction ◽  
Dye Penetration ◽  
Spatially Resolved

AbstractPurposeThe presence of a physical barrier to molecular diffusion through lenticular extracellular space has been repeatedly detected in multiple species. This extracellular diffusion barrier has been proposed to restrict the movement of solutes into the lens and to direct nutrients into the lens core via the sutures at both poles. The purpose of this study is to characterize the molecular components that could contribute to the formation of this barrier.MethodsThree distinct regions in the bovine lens cortex were captured by laser capture microdissection guided by dye penetration. Proteins were digested by endoproteinase Lys C and trypsin. Mass spectrometry-based quantitative proteomic analysis followed by gene ontology (GO) and protein-protein interaction network analysis was performed.ResultsDye penetration showed that lens fiber cells first shrink the extracellular spaces of the broad sides of fiber cells followed by closure of the extracellular space between narrow sides at normalized lens distance (r/a) of 0.9. Accompanying the closure of extracellular space of the broad sides, dramatic proteomic changes were detected including up-regulation of several cell junctional proteins. AQP0 and its interacting partners ERM proteins were among a few proteins that were upregulated accompanying the closure of extracellular space of the narrow sides suggesting a particularly important role for the major lens membrane protein AQP0 in controlling the narrowing of the extracellular spaces between lens fiber cells. The results also provided important information related to biological processes that occur during fiber cell differentiation such as organelle degradation, cytoskeletal remodeling and GSH synthesis.ConclusionsThe formation of lens extracellular diffusion barrier is accompanied by significant membrane and cytoskeletal protein remodeling.

scholarly journals Comparative Proteomic Analysis of Molecular Differences between Leaves of Wild-Type Upland Cotton and Its Fuzzless-Lintless Mutant

Molecules ◽  
2019 ◽  
Vol 24 (20) ◽  
pp. 3769
Author(s):  
Liping Zhu ◽  
Bowen Zheng ◽  
Wangyang Song ◽  
Chengcheng Tao ◽  
Xiang Jin ◽  
...  
Keyword(s):  
Proteomic Analysis ◽  
Upland Cotton ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gossypol Content ◽  
Pathway Enrichment Analysis ◽  
Protein Accumulation ◽  
Wild Type ◽  
Protein Protein Interaction ◽  
Comparative Proteomic Analysis

Fuzzless-lintless mutant (fl) ovules of upland cotton have been used to investigate cotton fiber development for decades. However, the molecular differences of green tissues between fl and wild-type (WT) cotton were barely reported. Here, we found that gossypol content, the most important secondary metabolite of cotton leaves, was higher in Gossypium hirsutum L. cv Xuzhou-142 (Xu142) WT than in fl. Then, we performed comparative proteomic analysis of the leaves from Xu142 WT and its fl. A total of 4506 proteins were identified, of which 103 and 164 appeared to be WT- and fl-specific, respectively. In the 4239 common-expressed proteins, 80 and 74 were preferentially accumulated in WT and fl, respectively. Pathway enrichment analysis and protein–protein interaction network analysis of both variety-specific and differential abundant proteins showed that secondary metabolism and chloroplast-related pathways were significantly enriched. Quantitative real-time PCR confirmed that the expression levels of 12 out of 16 selected genes from representative pathways were consistent with their protein accumulation patterns. Further analyses showed that the content of chlorophyll a in WT, but not chlorophyll b, was significantly increased compared to fl. This work provides the leaf proteome profiles of Xu142 and its fl mutant, indicating the necessity of further investigation of molecular differences between WT and fl leaves.

scholarly journals Comparative Proteomic Analysis of Sweet Orange Petiole Provides Insights Into the Development of Huanglongbing Symptoms

2021 ◽  
Vol 12 ◽  
Author(s):  
Bo Li ◽  
Yi Zhang ◽  
Dewen Qiu ◽  
Frédéric Francis ◽  
Shuangchao Wang
Keyword(s):  
Proteomic Analysis ◽  
Sweet Orange ◽  
Interaction Network ◽  
Resistance Breeding ◽  
Defense Responses ◽  
Protein Accumulation ◽  
Leaf Petiole ◽  
Protein Protein Interaction ◽  
Comparative Proteomic Analysis ◽  
Associated Proteins

Huanglongbing (HLB) is the most destructive citrus disease worldwide. This is associated with the phloem-limited bacterium Candidatus Liberibacter, and the typical symptom is leaf blotchy mottle. To better understand the biological processes involved in the establishment of HLB disease symptoms, the comparative proteomic analysis was performed to reveal the global protein accumulation profiles in leaf petiole, where there are massive HLB pathogens of Ca. L. asiaticus-infected Newhall sweet orange (Citrus sinensis) plants at the asymptomatic and symptomatic stages compared to their healthy counterpart. Photosynthesis, especially the pathway involved in the photosystem I and II light reactions, was shown to be suppressed throughout the whole Ca. L. asiaticus infection cycle. Also, starch biosynthesis was induced after the symptom-free prodromal period. Many defense-associated proteins were more extensively regulated in the petiole with the symptoms than the ones from healthy plants. The change of salicylic and jasmonic acid levels in different disease stages had a positive correlation with the abundance of phytohormone biosynthesis-related proteins. Moreover, the protein–protein interaction network analysis indicated that an F-type ATPase and an alpha-1,4 glucan phosphorylase were the core nodes in the interactions of differentially accumulated proteins. Our study indicated that the infected citrus plants probably activated the non-unified and lagging enhancement of defense responses against Ca. L. asiaticus at the expense of photosynthesis and contribute to find out the key Ca. L. asiaticus-responsive genes for tolerance and resistance breeding.

scholarly journals Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion

2020 ◽  
Author(s):  
Zhuliu Li ◽  
Tianci Song ◽  
Jeongsik Yong ◽  
Rui Kuang
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Tensor Completion ◽  
Gene Expressions ◽  
Protein Protein Interaction ◽  
Spatially Resolved ◽  
Spatial Graph ◽  
Protein Protein Interaction Network

AbstractHigh-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. One major limitation of in-situ capturing is the high dropout rate of mRNAs that fail the capture or the amplification, which leads to incomplete profiling of the gene expressions. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed several best performing single-cell RNAseq data imputation methods. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney. The source code and data are available at https://github.com/kuanglab/FIST.Author summaryBiological tissues are composed of different types of structurally organized cell units playing distinct functional roles. The exciting new spatial gene expression profiling methods have enabled the analysis of spatially resolved transcriptomes to understand the spatial and functional characteristics of these cells in the context of eco-environment of tissue. Similar to single-cell RNA sequencing data, spatial transcriptomics data also suffers from a high dropout rate of mRNAs in in-situ capture. Our method, FIST (Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion), focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x, y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Our comprehensive evaluation of FIST on ten 10x Genomics Visium spatial genomics datasets and comparison with the methods for single-cell RNA sequencing data imputation demonstrate that FIST is a better method more suitable for spatial gene expression imputation. Overall, we found FIST a useful new method for analyzing spatially resolved gene expressions based on novel modeling of spatial and functional information.

scholarly journals Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion

2021 ◽  
Vol 17 (4) ◽  
pp. e1008218
Author(s):  
Zhuliu Li ◽  
Tianci Song ◽  
Jeongsik Yong ◽  
Rui Kuang
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Tensor Completion ◽  
Gene Expressions ◽  
Protein Protein Interaction ◽  
Spatially Resolved ◽  
Spatial Graph ◽  
Protein Protein Interaction Network

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.

Calmodulin-mediated gating of lens gap junction channels in vesicles

1984 ◽  
Vol 42 ◽  
pp. 134-137
Author(s):  
Camillo Peracchia ◽  
Stephen J. Girsch
Keyword(s):  
Gap Junctions ◽  
Freeze Fracture ◽  
Orthogonal Arrays ◽  
Eye Lens ◽  
Lens Fiber ◽  
Cell Coupling ◽  
Particle Spacing ◽  
Fiber Cells ◽  
Gap Junction Channels ◽  
Communicating Junctions

The fiber cells of eye lens communicate directly with each other by exchanging ions, dyes and metabolites. In most tissues this type of communication (cell coupling) is mediated by gap junctions. In the lens, the fiber cells are extensively interconnected by junctions. However, lens junctions, although morphologically similar to gap junctions, differ from them in a number of structural, biochemical and immunological features. Like gap junctions, lens junctions are regions of close cell-to-cell apposition. Unlike gap junctions, however, the extracellular gap is apparently absent in lens junctions, such that their thickness is approximately 2 nm smaller than that of typical gap junctions (Fig. 1,c). In freeze-fracture replicas, the particles of control lens junctions are more loosely packed than those of typical gap junctions (Fig. 1,a) and crystallize, when exposed to uncoupling agents such as Ca++, or H+, into pseudo-hexagonal, rhombic (Fig. 1,b) and orthogonal arrays with a particle-to-particle spacing of 6.5 nm. Because of these differences, questions have been raised about the interpretation of the lens junctions as communicating junctions, in spite of the fact that they are the only junctions interlinking lens fiber cells.

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