The spatial organization of epidermal structures: hairy establishes the geometrical pattern of Drosophila leg bristles by delimiting the domains of achaete expression

Development ◽  
1993 ◽  
Vol 118 (1) ◽  
pp. 9-20 ◽  
Author(s):  
T.V. Orenic ◽  
L.I. Held ◽  
S.W. Paddock ◽  
S.B. Carroll
Keyword(s):  
Drosophila Melanogaster ◽  
Spatial Organization ◽  
Expression Patterns ◽  
Cell Types ◽  
Precursor Cells ◽  
Geometrical Pattern ◽  
Leg Development ◽  
Puparium Formation ◽  
Late Expression ◽  
Bristle Pattern

The spatial organization of Drosophila melanogaster epidermal structures in embryos and adults constitutes a classic model system for understanding how the two dimensional arrangement of particular cell types is generated. For example, the legs of the Drosophila melanogaster adult are covered with bristles, which in most segments are arranged in longitudinal rows. Here we elucidate the key roles of two regulatory genes, hairy and achaete, in setting up this periodic bristle pattern. We show that achaete is expressed during pupal leg development in a dynamic pattern which changes, by approximately 6 hours after puparium formation, into narrow longitudinal stripes of 3–4 cells in width, each of which represents a field of cells (proneural field) from which bristle precursor cells are selected. This pattern of gene expression foreshadows the adult bristle pattern and is established in part through the function of the hairy gene, which also functions in patterning other adult sense organs. In pupal legs, hairy is expressed in four longitudinal stripes, located between every other pair of achaete stripes. We show that in the absence of hairy function achaete expression expands into the interstripe regions that normally express hairy, fusing the two achaete stripes and resulting in extra-wide stripes of achaete expression. This misexpression of achaete, in turn, alters the fields of potential bristle precursor cells which leads to the misalignment of bristle rows in the adult. This function of hairy in patterning achaete expression is distinct from that in the wing in which hairy suppresses late expression of achaete but has no effect on the initial patterning of achaete expression. Thus, the leg bristle pattern is apparently regulated at two levels: a global regulation of the hairy and achaete expression patterns which partitions the leg epidermis into striped zones (this study) and a local regulation (inferred from other studies on the selection of neural precursor cells) that involves refinement steps which may control the alignment and spacing of bristle cells within these zones.

scholarly journals A spatial atlas of inhibitory cell types in mouse hippocampus

2018 ◽  
Author(s):  
Xiaoyan Qian ◽  
Kenneth D. Harris ◽  
Thomas Hauling ◽  
Dimitris Nicoloutsopoulos ◽  
Ana B. Muñoz-Manchado ◽  
...  
Keyword(s):  
Spatial Organization ◽  
Memory Function ◽  
Expression Patterns ◽  
Ground Truth ◽  
Spatial Arrangement ◽  
Cell Types ◽  
Cell System ◽  
Inhibitory Neurons ◽  
Hippocampal Area

Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related cell types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages prior scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method to map the inhibitory neurons of hippocampal area CA1, a cell system critical for memory function, for which ground truth is available from extensive prior work identifying the laminar organization of subtly differing cell types. Our method confidently identified 16 interneuron classes, in a spatial arrangement closely matching ground truth. This method will allow identifying the spatial organization of fine cell types across the brain and other tissues.

scholarly journals Three-dimensional organization of Drosophila melanogaster interphase nuclei. II. Chromosome spatial organization and gene regulation.

1987 ◽  
Vol 104 (6) ◽  
pp. 1471-1483 ◽  
Author(s):  
M Hochstrasser ◽  
J W Sedat
Keyword(s):  
Gene Regulation ◽  
Drosophila Melanogaster ◽  
Nuclear Envelope ◽  
Spatial Organization ◽  
Polytene Chromosomes ◽  
Three Dimensional ◽  
Nucleolar Organizer ◽  
Cell Types ◽  
Functional Changes ◽  
Specific Subset

In the preceding article we compared the general organization of polytene chromosomes in four different Drosophila melanogaster cell types. Here we describe experiments aimed at testing for a potential role of three-dimensional chromosome folding and positioning in modulating gene expression and examining specific chromosome interactions with different nuclear structures. By charting the configurations of salivary gland chromosomes as the cells undergo functional changes, it is shown that loci are not repositioned within the nucleus when the pattern of transcription changes. Heterologous loci show no evidence of specific physical interactions with one another in any of the cell types. However, a specific subset of chromosomal loci is attached to the nuclear envelope, and this subset is extremely similar in at least two tissues. In contrast, no specific interactions between any locus and the nucleolus are found, but the base of the X chromosome, containing the nucleolar organizer, is closely linked to this organelle. These results are used to evaluate models of gene regulation that involve the specific intranuclear positioning of gene sequences. Finally, data are presented on an unusual class of nuclear envelope structures, filled with large, electron-dense particles, that are usually associated with chromosomes.

scholarly journals Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution

2019 ◽  
Author(s):  
Samuel G. Rodriques ◽  
Robert R. Stickels ◽  
Aleksandrina Goeva ◽  
Carly A. Martin ◽  
Evan Murray ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Resolution ◽  
Large Scale ◽  
Spatial Organization ◽  
Expression Patterns ◽  
Cell Types ◽  
Genome Wide ◽  
Biological Discovery ◽  
Spatial Locations ◽  
Genome Wide Expression

AbstractThe spatial organization of cells in tissue has a profound influence on their function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. Here, we introduce Slide-seq, a highly scalable method that enables facile generation of large volumes of unbiased spatial transcriptomes with 10 µm spatial resolution, comparable to the size of individual cells. In Slide-seq, RNA is transferred from freshly frozen tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the spatial locations of the RNA to be inferred by sequencing. To demonstrate Slide-seq’s utility, we localized cell types identified by large-scale scRNA-seq datasets within the cerebellum and hippocampus. We next systematically characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, identifying new axes of variation across Purkinje cell compartments. Finally, we used Slide-seq to define the temporal evolution of cell-type-specific responses in a mouse model of traumatic brain injury. Slide-seq will accelerate biological discovery by enabling routine, high-resolution spatial mapping of gene expression.One Sentence SummarySlide-seq measures genome-wide expression in complex tissues at 10-micron resolution.

scholarly journals High-density spatial transcriptomics arrays for in situ tissue profiling

2019 ◽  
Author(s):  
Sanja Vickovic ◽  
Gökcen Eraslan ◽  
Johanna Klughammer ◽  
Linnea Stenbeck ◽  
Fredrik Salmén ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Expression Patterns ◽  
Cell Types ◽  
High Density ◽  
Rna Seq ◽  
Spatial Profiling ◽  
Tissue Profiling ◽  
Molecular Profiles

AbstractTissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays either lack global transcriptome information or are not at the single-cell level. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are then randomly deposited in individual wells on a slide. The barcoded beads are decoded and coupled to a specific spatial address. We then capture and spatially in situ label RNA from the same histological tissue sections placed on the bead array slide. HDST recovers hundreds of thousands of transcript-coupled barcodes per experiment at 2 μm resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to 2D spatial analysis of tissues at high resolution.

scholarly journals CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yang Xu ◽  
Rachel Patton McCord
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spatial Patterns ◽  
Spatial Organization ◽  
Expression Patterns ◽  
Quantitative Measure ◽  
Cell Types ◽  
Neural Network Learning ◽  
Pattern Similarity ◽  
Transcriptomics Data

Abstract Background The rise of spatial transcriptomics technologies is leading to new insights about how gene regulation happens in a spatial context. Determining which genes are expressed in similar spatial patterns can reveal gene regulatory relationships across cell types in a tissue. However, many current analysis methods do not take full advantage of the spatial organization of the data, instead treating pixels as independent features. Here, we present CoSTA: a novel approach to learn spatial similarities between gene expression matrices via convolutional neural network (ConvNet) clustering. Results By analyzing simulated and previously published spatial transcriptomics data, we demonstrate that CoSTA learns spatial relationships between genes in a way that emphasizes broader spatial patterns rather than pixel-level correlation. CoSTA provides a quantitative measure of expression pattern similarity between each pair of genes rather than only classifying genes into categories. We find that CoSTA identifies narrower, but biologically relevant, sets of significantly related genes as compared to other approaches. Conclusions The deep learning CoSTA approach provides a different angle to spatial transcriptomics analysis by focusing on the shape of expression patterns, using more information about the positions of neighboring pixels than would an overlap or pixel correlation approach. CoSTA can be applied to any spatial transcriptomics data represented in matrix form and may have future applications to datasets such as histology in which images of different genes are from similar but not identical biological sections.

scholarly journals An unsupervised learning method for reconstructing cell spatial organization with application to the DREAM Single Cell Transcriptomics Challenge

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 124
Author(s):  
Yang Chen ◽  
Disheng Mao ◽  
Yuping Zhang ◽  
Zhengqing Ouyang
Keyword(s):  
Gene Expression ◽  
Unsupervised Learning ◽  
Single Cell ◽  
Spatial Organization ◽  
Single Cells ◽  
Expression Patterns ◽  
Cell Types ◽  
Learning Method ◽  
Driver Genes ◽  
Clustering Approach

Single cell RNA sequencing (scRNA-seq) data analysis is important for building a global transcription landscape of all cell types in tissues, tracing cell lineages, and reconstructing cell spatial organizations. In this article, we propose an unsupervised learning method to predict spatial positions and gene expression of individual cells in Drosophila embryos using a small number of driver genes. Specifically, we develop a two-stage clustering approach, and compute a probability matrix of the spatial positions of single cells. This method is applied to dataset in the DREAM Single Cell Transcriptomics Challenge. The comparison with the “gold standard” suggests that our method is effective in reconstructing the cell positions and gene expression patterns in spatial tissues.

scholarly journals Chromosome Unipolar Division and Low Expression of Tws May Cause Parthenogenesis of Rice Water Weevil (Lissorhoptrus oryzophilus Kuschel)

Insects ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 278
Author(s):  
Pengcheng Wang ◽  
Fangyuan Yang ◽  
Zhuo Ma ◽  
Runzhi Zhang
Keyword(s):  
Drosophila Melanogaster ◽  
Quantitative Pcr ◽  
Ovarian Tissue ◽  
Expression Patterns ◽  
Meiotic Spindle ◽  
Lissorhoptrus Oryzophilus ◽  
Rice Water Weevil ◽  
Rice Water ◽  
Beijing China ◽  
Low Expression

Rice water weevil (RWW) is divided into two types of population, triploid parthenogenesis and diploid bisexual reproduction. In this study, we explored the meiosis of triploid parthenogenesis RWW (Shangzhuang Town, Haidian District, Beijing, China) by marking the chromosomes and microtubules of parthenogenetic RWW oocytes via immunostaining. The immunostaining results show that there is a canonical meiotic spindle formed in the triploid parthenogenetic RWW oocytes, but chromosomes segregate at only one pole, which means that there is a chromosomal unipolar division during the oogenesis of the parthenogenetic RWW. Furthermore, we cloned the conserved sequences of parthenogenetic RWW REC8 and Tws, and designed primers based on the parthenogenetic RWW sequence to detect expression patterns by quantitative PCR (Q-PCR). Q-PCR results indicate that the expression of REC8 and Tws in ovarian tissue of bisexual Drosophila melanogaster is 0.98 and 10,000.00 times parthenogenetic RWW, respectively (p < 0.01). The results show that Tws had low expression in parthenogenetic RWW ovarian tissue, and REC8 was expressed normally. Our study suggests that the chromosomal unipolar division and deletion of Tws may cause parthenogenesis in RWW.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

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