scholarly journals Parallelized analysis of spatial gene expression patterns by database integration

2018 ◽  
Author(s):  
Daisuke Miyamoto ◽  
Hidetoshi Ikeno ◽  
Yuko Okamura-Oho ◽  
Akira Sato ◽  
Teiichi Furuichi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Coordinate Space ◽  
Database Integration ◽  
Computational Framework ◽  
Link Type ◽  
Best Fit ◽  
2D Images ◽  
Image Datasets

AbstractWe developed a computational framework for automated integration of a large number of two-dimensional (2D) images with three-dimensional (3D) image datasets located in the standard 3D coordinate. We applied the framework to 2,810 para-sagittal sectioned mouse brain 2D images of in situ hybridization (ISH), archived in the BrainTx database (http://www.cdtdb.neuroinf.jp). We registered the ISH images into the mouse standard coordinate space for MR images, Waxholm space (WHS, https://www.nitrc.org/projects/incfwhsmouse) by linearly transforming them into each of a series of para-sagittal MR image slices, and identifying the best-fit slice by calculating the similarity metric value (δ). Transformed 2D images were compared with 3D gene expression image datasets, which were made using a microtomy-based microarray assay system, Transcriptome Tomography, and archived in the ViBrism DB (http://vibrism.neuroinf.jp): the 3D images are located in the WHS.We first transformed ISH images of 10 regionally expressed genes and compared them to signals of corresponding 3D expression images in ViBrism DB for evaluating the integration schema: two types of data, produced with different modalities and originally located in different dimensions, were successfully compared after enhancing ISH signals against background noise. Then, for the massive transformation of BrainTx database images, we parallelized our framework, using the IPython cluster package, and implemented it on the PC cluster provided for the Brain Atlasing Hackathon activity hosted by Neuroinformatics Japan Center in Japan. We could identify the best-fit positions for all of the ISH images. All programs were made available through the GitHub repository, at the web site of neuroinformatics/bah2016_registration (https://github.com/neuroinformatics/bah2016_registration).

scholarly journals ClusterMap: multi-scale clustering analysis of spatial gene expression

2021 ◽  
Author(s):  
Yichun He ◽  
Xin Tang ◽  
Jiahao Huang ◽  
Haowen Zhou ◽  
Kevin Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dimensional Space ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Computational Framework ◽  
Spatially Resolved ◽  
Tissue Organization

AbstractQuantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell-cell interactions, and tissue organization principles from high-dimensional transcriptomic images.

scholarly journals ClusterMap: multi-scale clustering analysis of spatial gene expression

2021 ◽  
Author(s):  
Yichun He ◽  
Xin Tang ◽  
Jiahao Huang ◽  
Haowen Zhou ◽  
Kevin Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dimensional Space ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Computational Framework ◽  
Spatially Resolved ◽  
Tissue Organization

Abstract Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell-cell interactions, and tissue organization principles from high-dimensional transcriptomic images. 

scholarly journals Global Analysis of Cell Type-Specific Gene Expression

2003 ◽  
Vol 4 (2) ◽  
pp. 208-215 ◽  
Author(s):  
David W. Galbraith
Keyword(s):  
Gene Expression ◽  
Fluorescent Protein ◽  
Global Analysis ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Global Gene Expression ◽  
Cell Types ◽  
Reasonable Assumption ◽  
Specific Gene ◽  
Different Cell Types

The tissues and organs of multicellular eukaryotes are frequently observed to comprise complex three-dimensional interspersions of different cell types. It is a reasonable assumption that different global patterns of gene expression are found within these different cell types. This review outlines general experimental strategies designed to characterize these global gene expression patterns, based on a combination of methods of transgenic fluorescent protein (FP) expression and targeting, of flow cytometry and sorting and of high-throughput gene expression analysis.

scholarly journals Spatial organization of different sigma factor activities and c-di-GMP signalling within the three-dimensional landscape of a bacterial biofilm

Open Biology ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 180066 ◽  
Author(s):  
Gisela Klauck ◽  
Diego O. Serra ◽  
Alexandra Possling ◽  
Regine Hengge
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Stationary Phase ◽  
Sigma Factor ◽  
Spatial Organization ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Biomechanical Properties ◽  
Ribosomal Gene ◽  
Bacterial Biofilm

Bacterial biofilms are large aggregates of cells embedded in an extracellular matrix of self-produced polymers. In macrocolony biofilms of Escherichia coli , this matrix is generated in the upper biofilm layer only and shows a surprisingly complex supracellular architecture. Stratified matrix production follows the vertical nutrient gradient and requires the stationary phase σ S (RpoS) subunit of RNA polymerase and the second messenger c-di-GMP. By visualizing global gene expression patterns with a newly designed fingerprint set of Gfp reporter fusions, our study reveals the spatial order of differential sigma factor activities, stringent control of ribosomal gene expression and c-di-GMP signalling in vertically cryosectioned macrocolony biofilms. Long-range physiological stratification shows a duplication of the growth-to-stationary phase pattern that integrates nutrient and oxygen gradients. In addition, distinct short-range heterogeneity occurs within specific biofilm strata and correlates with visually different zones of the refined matrix architecture. These results introduce a new conceptual framework for the control of biofilm formation and demonstrate that the intriguing extracellular matrix architecture, which determines the emergent physiological and biomechanical properties of biofilms, results from the spatial interplay of global gene regulation and microenvironmental conditions. Overall, mature bacterial macrocolony biofilms thus resemble the highly organized tissues of multicellular organisms.

scholarly journals Cellular-resolution gene expression profiling in the neonatal marmoset brain reveals dynamic species- and region-specific differences

2021 ◽  
Vol 118 (18) ◽  
pp. e2020125118
Author(s):  
Yoshiaki Kita ◽  
Hirozumi Nishibe ◽  
Yan Wang ◽  
Tsutomu Hashikawa ◽  
Satomi S. Kikuchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Neural Circuit ◽  
Expression Patterns ◽  
Three Dimensional ◽  
Control Of Gene Expression ◽  
Cellular Resolution

Precise spatiotemporal control of gene expression in the developing brain is critical for neural circuit formation, and comprehensive expression mapping in the developing primate brain is crucial to understand brain function in health and disease. Here, we developed an unbiased, automated, large-scale, cellular-resolution in situ hybridization (ISH)–based gene expression profiling system (GePS) and companion analysis to reveal gene expression patterns in the neonatal New World marmoset cortex, thalamus, and striatum that are distinct from those in mice. Gene-ontology analysis of marmoset-specific genes revealed associations with catalytic activity in the visual cortex and neuropsychiatric disorders in the thalamus. Cortically expressed genes with clear area boundaries were used in a three-dimensional cortical surface mapping algorithm to delineate higher-order cortical areas not evident in two-dimensional ISH data. GePS provides a powerful platform to elucidate the molecular mechanisms underlying primate neurobiology and developmental psychiatric and neurological disorders.

scholarly journals Uncovering the genetic blueprint of the C. elegans nervous system

2020 ◽  
Vol 117 (52) ◽  
pp. 33570-33577
Author(s):  
István A. Kovács ◽  
Dániel L. Barabási ◽  
Albert-László Barabási
Keyword(s):  
Gene Expression ◽  
Expression Profiling ◽  
Expression Patterns ◽  
Computational Framework ◽  
Computational Tools ◽  
C Elegans ◽  
Junction Formation ◽  
Cell Gene Expression ◽  
Genetic Mechanisms ◽  
Cell Gene

Despite rapid advances in connectome mapping and neuronal genetics, we lack theoretical and computational tools to unveil, in an experimentally testable fashion, the genetic mechanisms that govern neuronal wiring. Here we introduce a computational framework to link the adjacency matrix of a connectome to the expression patterns of its neurons, helping us uncover a set of genetic rules that govern the interactions between neurons in contact. The method incorporates the biological realities of the system, accounting for noise from data collection limitations, as well as spatial restrictions. The resulting methodology allows us to infer a network of 19 innexin interactions that govern the formation of gap junctions in Caenorhabditis elegans, five of which are already supported by experimental data. As advances in single-cell gene expression profiling increase the accuracy and the coverage of the data, the developed framework will allow researchers to systematically infer experimentally testable connection rules, offering mechanistic predictions for synapse and gap junction formation.

scholarly journals Transcriptomic profiling of three-dimensional cholangiocyte spheroids long term exposed to repetitive Clonorchis sinensis excretory-secretory products

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jung-Woong Kim ◽  
Junyeong Yi ◽  
Jinhong Park ◽  
Ji Hoon Jeong ◽  
Jinho Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Fluke ◽  
Expression Patterns ◽  
Clonorchis Sinensis ◽  
Three Dimensional ◽  
Global Changes ◽  
Rna Seq ◽  
Extracellular Region ◽  
Secretory Products

Abstract Background Biliary tract infection with the carcinogenic human liver fluke, Clonorchis sinensis, provokes chronic inflammation, epithelial hyperplasia, periductal fibrosis, and even cholangiocarcinoma. Complications are proportional to the intensity and duration of the infection. In addition to mechanical irritation of the biliary epithelia from worms, their excretory-secretory products (ESPs) cause chemical irritation, which leads to inflammation, proliferation, and free radical generation. Methods A three-dimensional in vitro cholangiocyte spheroid culture model was established, followed by ESP treatment. This allowed us to examine the intrinsic pathological mechanisms of clonorchiasis via the imitation of prolonged and repetitive in vivo infection. Results Microarray and RNA-Seq analysis revealed that ESP-treated cholangiocyte H69 spheroids displayed global changes in gene expression compared to untreated spheroids. In ESP-treated H69 spheroids, 185 and 63 probes were found to be significantly upregulated and downregulated, respectively, corresponding to 209 genes (p < 0.01, fold change > 2). RNA-Seq was performed for the validation of the microarray results, and the gene expression patterns in both transcriptome platforms were well matched for 209 significant genes. Gene ontology analysis demonstrated that differentially expressed genes were mainly classified into immune system processes, the extracellular region, and the extracellular matrix. Among the upregulated genes, four genes (XAF1, TRIM22, CXCL10, and BST2) were selected for confirmation using quantitative RT-PCR, resulting in 100% similar expression patterns in microarray and RNA-Seq. Conclusions These findings broaden our understanding of the pathological pathways of liver fluke-associated hepatobiliary disorders and suggest a novel therapeutic strategy for this infectious cancer. Graphic abstract

Factorization-based Imputation of Expression in Single-cell Transciptomic Analysis (FIESTA) recovers Gene-Cell-State relationships

2021 ◽  
Author(s):  
Elnaz Mirzaei Mehrabad ◽  
Aditya Bhaskara ◽  
Benjamin T. Spike
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Missing Values ◽  
Expression Patterns ◽  
Cellular Systems ◽  
False Negatives ◽  
Data Set ◽  
Link Type ◽  
Single Cell Rna Sequencing

AbstractMotivationSingle cell RNA sequencing (scRNA-seq) is a powerful gene expression profiling technique that is presently revolutionizing the study of complex cellular systems in the biological sciences. Existing single-cell RNA-sequencing methods suffer from sub-optimal target recovery leading to inaccurate measurements including many false negatives. The resulting ‘zero-inflated’ data may confound data interpretation and visualization.ResultsSince cells have coherent phenotypes defined by conserved molecular circuitries (i.e. multiple gene products working together) and since similar cells utilize similar circuits, information about each each expression value or ‘node’ in a multi-cell, multi-gene scRNA-Seq data set is expected to also be predictable from other nodes in the data set. Based on this logic, several approaches have been proposed to impute missing values by extracting information from non-zero measurements in a data set. In this study, we applied non-negative matrix factorization approaches to a selection of published scRNASeq data sets to recommend new values where original measurements are likely to be inaccurate and where ‘zero’ measurements are predicted to be false negatives. The resulting imputed data model predicts novel cell type markers and expression patterns more closely matching gene expression values from orthogonal measurements and/or predicted literature than the values obtained from other previously published imputation [email protected] and implementationFIESTA is written in R and is available at https://github.com/elnazmirzaei/FIESTA and https://github.com/TheSpikeLab/FIESTA.

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