scholarly journals Gene expression atlas of a developing tissue by single cell expression correlation analysis

2018 ◽  
Author(s):  
Josephine Bageritz ◽  
Philipp Willnow ◽  
Erica Valentini ◽  
Svenja Leible ◽  
Michael Boutros ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Wing Disc ◽  
Proof Of Concept ◽  
Gene Expression Atlas ◽  
Expression Atlas ◽  
Novel Method ◽  
Functional Relevance ◽  
Cell Expression

ABSTRACTThe Drosophila wing disc has been a fundamental model system for the discovery of key signaling pathways and for our understanding of developmental processes. However, a complete map of gene expression in this tissue is lacking. To obtain a complete gene expression atlas in the wing disc, we employed single-cell sequencing (scRNA-seq) and developed a new method for analyzing scRNA-seq data based on gene expression correlations rather than cell mappings. This enables us to discover 824 genes with spatially restricted expression patterns, and to compute expression maps for all genes in the wing disc. This approach identifies both known and new clusters of genes with similar expression patterns and functional relevance. As proof of concept, we characterize the previously unstudied gene CG5151 and show it regulates Wnt signaling. This novel method will enable the leveraging of scRNA-seq data for generating expression atlases of undifferentiated tissues during development.

scholarly journals A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data

2021 ◽  
Author(s):  
Manuel Neumann ◽  
Xiaocai Xu ◽  
Cezary Smaczniak ◽  
Julia Schumacher ◽  
Wenhao Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Floral Meristem ◽  
Cellular Heterogeneity ◽  
Sequencing Data ◽  
Gene Expression Atlas ◽  
Spatial Reconstruction ◽  
Expression Atlas ◽  
3D Gene

Identity and functions of plant cells are influenced by their precise cellular location within the plant body. Cellular heterogeneity in growth and differentiation trajectories results in organ patterning. Therefore, assessing this heterogeneity at molecular scale is a major question in developmental biology. Single-cell transcriptomics (scRNA-seq) allows to characterize and quantify gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during the scRNA-seq procedure. To recover the original location of cells is essential to link gene activity with cellular function and morphology. Here, we reconstruct genome-wide gene expression patterns of individual cells in a floral meristem by combining single-nuclei RNA-seq with 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the data are used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com

scholarly journals Gene expression atlas of a developing tissue by single cell expression correlation analysis

2019 ◽  
Vol 16 (8) ◽  
pp. 750-756 ◽  
Author(s):  
Josephine Bageritz ◽  
Philipp Willnow ◽  
Erica Valentini ◽  
Svenja Leible ◽  
Michael Boutros ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Single Cell ◽  
Expression Correlation ◽  
Gene Expression Atlas ◽  
Expression Atlas ◽  
Cell Expression

scholarly journals A comprehensive RNA-Seq-based gene expression atlas of the summer squash (Cucurbita pepo) provides insights into fruit morphology and ripening mechanisms

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Aliki Xanthopoulou ◽  
Javier Montero-Pau ◽  
Belén Picó ◽  
Panagiotis Boumpas ◽  
Eleni Tsaliki ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cucurbita Pepo ◽  
Developmental Stages ◽  
Lateral Roots ◽  
Expression Patterns ◽  
Male Flower ◽  
Differentially Expressed ◽  
Gene Expression Atlas ◽  
Summer Squash ◽  
Expression Atlas

Abstract Background Summer squash (Cucurbita pepo: Cucurbitaceae) are a popular horticultural crop for which there is insufficient genomic and transcriptomic information. Gene expression atlases are crucial for the identification of genes expressed in different tissues at various plant developmental stages. Here, we present the first comprehensive gene expression atlas for a summer squash cultivar, including transcripts obtained from seeds, shoots, leaf stem, young and developed leaves, male and female flowers, fruits of seven developmental stages, as well as primary and lateral roots. Results In total, 27,868 genes and 2352 novel transcripts were annotated from these 16 tissues, with over 18,000 genes common to all tissue groups. Of these, 3812 were identified as housekeeping genes, half of which assigned to known gene ontologies. Flowers, seeds, and young fruits had the largest number of specific genes, whilst intermediate-age fruits the fewest. There also were genes that were differentially expressed in the various tissues, the male flower being the tissue with the most differentially expressed genes in pair-wise comparisons with the remaining tissues, and the leaf stem the least. The largest expression change during fruit development was early on, from female flower to fruit two days after pollination. A weighted correlation network analysis performed on the global gene expression dataset assigned 25,413 genes to 24 coexpression groups, and some of these groups exhibited strong tissue specificity. Conclusions These findings enrich our understanding about the transcriptomic events associated with summer squash development and ripening. This comprehensive gene expression atlas is expected not only to provide a global view of gene expression patterns in all major tissues in C. pepo but to also serve as a valuable resource for functional genomics and gene discovery in Cucurbitaceae.

scholarly journals A chelicerate Wnt gene expression atlas: novel insights into the complexity of arthropod Wnt-patterning

EvoDevo ◽  
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ralf Janssen ◽  
Matthias Pechmann ◽  
Natascha Turetzek
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Large Family ◽  
Animal Evolution ◽  
Gene Expression Atlas ◽  
Outgroup Species ◽  
Expression Atlas ◽  
Duplication Events ◽  
Complete Set ◽  
High Degree

AbstractThe Wnt genes represent a large family of secreted glycoprotein ligands that date back to early animal evolution. Multiple duplication events generated a set of 13 Wnt families of which 12 are preserved in protostomes. Embryonic Wnt expression patterns (Wnt-patterning) are complex, representing the plentitude of functions these genes play during development. Here, we comprehensively investigated the embryonic expression patterns of Wnt genes from three species of spiders covering both main groups of true spiders, Haplogynae and Entelegynae, a mygalomorph species (tarantula), as well as a distantly related chelicerate outgroup species, the harvestman Phalangium opilio. All spiders possess the same ten classes of Wnt genes, but retained partially different sets of duplicated Wnt genes after whole genome duplication, some of which representing impressive examples of sub- and neo-functionalization. The harvestman, however, possesses a more complete set of 11 Wnt genes but with no duplicates. Our comprehensive data-analysis suggests a high degree of complexity and evolutionary flexibility of Wnt-patterning likely providing a firm network of mutational protection. We discuss the new data on Wnt gene expression in terms of their potential function in segmentation, posterior elongation, and appendage development and critically review previous research on these topics. We conclude that earlier research may have suffered from the absence of comprehensive gene expression data leading to partial misconceptions about the roles of Wnt genes in development and evolution.

scholarly journals ScLRTC: Imputation for Single-Cell RNA-Seq Data Via Low-Rank Tensor Completion

2021 ◽  
Author(s):  
Zhong Li ◽  
Xiutao Pan ◽  
Shengwei Qin ◽  
Minzhe Yu ◽  
Hang Hu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
State Of The Art ◽  
Single Cells ◽  
Pearson Correlation ◽  
Expression Patterns ◽  
Low Rank ◽  
Tensor Completion ◽  
Rank Tensor ◽  
Cell Expression

Abstract Background: With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect downstream analyses. Accordingly, the true gene expression level should be recovered before the downstream analysis is carried out. Results: In this paper, a novel low-rank tensor completion-based method, termed as scLRTC, is proposed to impute the dropout entries of a given scRNA-seq expression. It initially exploits the similarity of single cells to build a third-order low-rank tensor and employs the tensor decomposition to denoise the data. Subsequently, it reconstructs the cell expression by adopting the low-rank tensor completion algorithm, which can restore the gene-to-gene and cell-to-cell correlations. ScLRTC is compared with other state-of-the-art methods on simulated datasets and real scRNA-seq datasets with different data sizes. Specific to simulated datasets, scLRTC outperforms other methods in imputing the dropouts closest to the original expression values, which is assessed by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). In terms of real datasets, scLRTC achieves the most accurate cell classification results in spite of the choice of different clustering methods (e.g., SC3 or t-SNE followed by K-means), which is evaluated by using adjusted rand index (ARI) and normalized mutual information (NMI). Lastly, scLRTC is demonstrated to be also effective in cell visualization and in inferring cell lineage trajectories.Conclusions: a novel low-rank tensor completion-based method scLRTC gave imputation results better than the state-of-the-art tools. Source code of scLRTC can be accessed at https://github.com/jianghuaijie/scLRTC.

scholarly journals ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiutao Pan ◽  
Zhong Li ◽  
Shengwei Qin ◽  
Minzhe Yu ◽  
Hang Hu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
State Of The Art ◽  
Single Cells ◽  
Pearson Correlation ◽  
Expression Patterns ◽  
Low Rank ◽  
Tensor Completion ◽  
Rank Tensor ◽  
Cell Expression

Abstract Background With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect downstream analyses. Accordingly, the true gene expression level should be recovered before the downstream analysis is carried out. Results In this paper, a novel low-rank tensor completion-based method, termed as scLRTC, is proposed to impute the dropout entries of a given scRNA-seq expression. It initially exploits the similarity of single cells to build a third-order low-rank tensor and employs the tensor decomposition to denoise the data. Subsequently, it reconstructs the cell expression by adopting the low-rank tensor completion algorithm, which can restore the gene-to-gene and cell-to-cell correlations. ScLRTC is compared with other state-of-the-art methods on simulated datasets and real scRNA-seq datasets with different data sizes. Specific to simulated datasets, scLRTC outperforms other methods in imputing the dropouts closest to the original expression values, which is assessed by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). In terms of real datasets, scLRTC achieves the most accurate cell classification results in spite of the choice of different clustering methods (e.g., SC3 or t-SNE followed by K-means), which is evaluated by using adjusted rand index (ARI) and normalized mutual information (NMI). Lastly, scLRTC is demonstrated to be also effective in cell visualization and in inferring cell lineage trajectories. Conclusions a novel low-rank tensor completion-based method scLRTC gave imputation results better than the state-of-the-art tools. Source code of scLRTC can be accessed at https://github.com/jianghuaijie/scLRTC.

scholarly journals A gene expression atlas of abicoid-depleted Drosophila embryo reveals early canalization of cell fate

2014 ◽  
Author(s):  
Max V Staller ◽  
Charless C Fowlkes ◽  
Meghan D.J. Bragdon ◽  
Zeba B. Wunderlich ◽  
Angela DePace
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Early Stage ◽  
Expression Patterns ◽  
Drosophila Embryo ◽  
Wild Type ◽  
Cell Fates ◽  
Gene Expression Atlas ◽  
Expression Atlas ◽  
Gene Regulatory

In developing embryos, gene regulatory networks canalize cells towards discrete terminal fates. We studied the behavior of the anterior-posterior segmentation network in Drosophila melanogaster embryos depleted of a key maternal input, bicoid (bcd), by building a cellular- resolution gene expression atlas containing measurements of 12 core patterning genes over 6 time points in early development. With this atlas, we determine the precise perturbation each cell experiences, relative to wild type, and observe how these cells assume cell fates in the perturbed embryo. The first zygotic layer of the network, consisting of the gap and terminal genes, is highly robust to perturbation: all combinations of transcription factor expression found in bcd depleted embryos were also found in wild type embryos, suggesting that no new cell fates were created even at this very early stage. All of the gap gene expression patterns in the trunk expand by different amounts, a feature that we were unable to explain using two simple models of the effect of bcd depletion. In the second layer of the network, depletion of bcd led to an excess of cells expressing both even skipped and fushi tarazu early in the blastoderm stage, but by gastrulation this overlap resolved into mutually exclusive stripes. Thus, following depletion of bcd, individual cells rapidly canalize towards normal cell fates in both layers of this gene regulatory network. Our gene expression atlas provides a high resolution picture of a classic perturbation and will enable further modeling of canalization in this transcriptional network.

An Organ-Wide Gene Expression Atlas of the Developing Human Heart

2018 ◽  
Author(s):  
Michaela Asp ◽  
Stefania Giacomello ◽  
Daniel Fürth ◽  
Johan Reimegård ◽  
Eva Wärdell ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Heart ◽  
Gene Expression Atlas ◽  
Expression Atlas
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