Dataset Information

Complete microviscosity maps of living plant cells and tissues with a toolbox of targeting mechanoprobes.

ABSTRACT: Mechanical patterns control a variety of biological processes in plants. The microviscosity of cellular structures effects the diffusion rate of molecules and organelles, thereby affecting processes such as metabolism and signaling. Spatial variations in local viscosity are also generated during fundamental events in the cell life cycle. While crucial to a complete understanding of plant mechanobiology, resolving subcellular microviscosity patterns in plants has remained an unsolved challenge. We present an imaging microviscosimetry toolbox of molecular rotors that yield complete microviscosity maps of cells and tissues, specifically targeting the cytosol, vacuole, plasma membrane, and wall of plant cells. These boron-dipyrromethene (BODIPY)-based molecular rotors are rigidochromic by means of coupling the rate of an intramolecular rotation, which depends on the mechanics of their direct surroundings, with their fluorescence lifetime. This enables the optical mapping of fluidity and porosity patterns in targeted cellular compartments. We show how apparent viscosity relates to cell function in the root, how the growth of cellular protrusions induces local tension, and how the cell wall is adapted to perform actuation surrounding leaf pores. These results pave the way to the noninvasive micromechanical mapping of complex tissues.

SUBMITTER: Michels L

PROVIDER: S-EPMC7395454 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Complete microviscosity maps of living plant cells and tissues with a toolbox of targeting mechanoprobes.

Michels Lucile L Gorelova Vera V Harnvanichvech Yosapol Y Borst Jan Willem JW Albada Bauke B Weijers Dolf D Sprakel Joris J

Proceedings of the National Academy of Sciences of the United States of America 20200715 30

Mechanical patterns control a variety of biological processes in plants. The microviscosity of cellular structures effects the diffusion rate of molecules and organelles, thereby affecting processes such as metabolism and signaling. Spatial variations in local viscosity are also generated during fundamental events in the cell life cycle. While crucial to a complete understanding of plant mechanobiology, resolving subcellular microviscosity patterns in plants has remained an unsolved challenge. W ...[more]

PMID: 32669427

Similar Datasets

Project description:Down syndrome (DS) is due to the presence of an extra full or partial chromosome 21 (Hsa21). The identification of genes contributing to DS pathogenesis could be the key to any rational therapy of the associated intellectual disability. We aim at generating quantitative transcriptome maps in DS integrating all gene expression profile datasets available for any cell type or tissue, to obtain a complete model of the transcriptome in terms of both expression values for each gene and segmental trend of gene expression along each chromosome. We used the TRAM (Transcriptome Mapper) software for this meta-analysis, comparing transcript expression levels and profiles between DS and normal brain, lymphoblastoid cell lines, blood cells, fibroblasts, thymus and induced pluripotent stem cells, respectively. TRAM combined, normalized, and integrated datasets from different sources and across diverse experimental platforms. The main output was a linear expression value that may be used as a reference for each of up to 37,181 mapped transcripts analyzed, related to both known genes and expression sequence tag (EST) clusters. An independent example in vitro validation of fibroblast transcriptome map data was performed through "Real-Time" reverse transcription polymerase chain reaction showing an excellent correlation coefficient (r = 0.93, p < 0.0001) with data obtained in silico. The availability of linear expression values for each gene allowed the testing of the gene dosage hypothesis of the expected 3:2 DS/normal ratio for Hsa21 as well as other human genes in DS, in addition to listing genes differentially expressed with statistical significance. Although a fraction of Hsa21 genes escapes dosage effects, Hsa21 genes are selectively over-expressed in DS samples compared to genes from other chromosomes, reflecting a decisive role in the pathogenesis of the syndrome. Finally, the analysis of chromosomal segments reveals a high prevalence of Hsa21 over-expressed segments over the other genomic regions, suggesting, in particular, a specific region on Hsa21 that appears to be frequently over-expressed (21q22). Our complete datasets are released as a new framework to investigate transcription in DS for individual genes as well as chromosomal segments in different cell types and tissues.

Project description:Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.

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'.

Dataset Information

Complete microviscosity maps of living plant cells and tissues with a toolbox of targeting mechanoprobes.

Publications

Complete microviscosity maps of living plant cells and tissues with a toolbox of targeting mechanoprobes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets