To the bones: mapping the skeletal LEPR + pool to component cell types

Abstract Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell-type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell-type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell-type-specific markers are ideally suited to deconvolute both the expression and co-expression patterns of an individual cell type. We provide a Shiny application for users to interactively explore the effect of cell-type composition on correlation-based co-expression estimation for any cell types of interest.

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Functional units in rainbow trout (Salmo gairdneri, Richardson) liver: III. Morphometric analysis of parenchyma, stroma, and component cell types

American Journal of Anatomy ◽

10.1002/aja.1001850107 ◽

1989 ◽

Vol 185 (1) ◽

pp. 58-73 ◽

Cited By ~ 67

Author(s):

James A. Hampton ◽

R. Clark Lantz ◽

David E. Hinton

Keyword(s):

Rainbow Trout ◽

Morphometric Analysis ◽

Cell Types ◽

Salmo Gairdneri ◽

Component Cell ◽

Functional Units

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Whole Animal Multiplexed Single-Cell RNA-Seq Reveals Plasticity of Clytia Medusa Cell Types

10.1101/2021.01.22.427844 ◽

2021 ◽

Author(s):

Tara Chari ◽

Brandon Weissbourd ◽

Jase Gehring ◽

Anna Ferraioli ◽

Lucas Leclère ◽

...

Keyword(s):

High Resolution ◽

Single Cell ◽

Cell Types ◽

Model Organisms ◽

Traditional Model ◽

Rna Seq ◽

Batch Effects ◽

Future Studies ◽

Component Cell ◽

Jellyfish Species

AbstractWe present an organism-wide, transcriptomic cell atlas of the hydrozoan medusa Clytia hemisphaerica, and determine how its component cell types respond to starvation. Utilizing multiplexed scRNA-seq, in which individual animals were indexed and pooled from control and perturbation conditions into a single sequencing run, we avoid artifacts from batch effects and are able to discern shifts in cell state in response to organismal perturbations. This work serves as a foundation for future studies of development, function, and plasticity in a genetically tractable jellyfish species. Moreover, we introduce a powerful workflow for high-resolution, whole animal, multiplexed single-cell genomics (WHAM-seq) that is readily adaptable to other traditional or non-traditional model organisms.

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A Stereological Approach for Estimation of Cellular Immunogold Labeling and Its Spatial Distribution in Oriented Sections Using the Rotator

Journal of Histochemistry & Cytochemistry ◽

10.1369/jhc.2008.952671 ◽

2009 ◽

Vol 57 (8) ◽

pp. 709-719 ◽

Cited By ~ 9

Author(s):

John Milton Lucocq ◽

Christian Gawden-Bone

Keyword(s):

Cell Biology ◽

Quantitative Methods ◽

Protein Translocation ◽

Cell Types ◽

Cellular Structures ◽

Spatial Mapping ◽

Cell Axis ◽

Thin Sections ◽

Component Cell ◽

Gold Label

Particulate gold labeling applied to ultrathin sections is a powerful approach for locating cellular proteins and lipids on thin sections of cellular structures and compartments. Effective quantitative methods now allow estimation of both density and distribution of gold labeling across aggregate organelles or compartment profiles. However, current methods generally use random sections of cells and tissues, and these do not readily present the information needed for spatial mapping of cellular quantities of gold label. Yet spatial mapping of gold particle labeling becomes important when cells are polarized or show internal organization or spatial shifts in protein/lipid localization. Here we have applied a stereological approach called the rotator to estimate cellular gold label and proportions of labeling over cellular compartments at specific locations related to a chosen cell axis or chosen cellular structures. This method could be used in cell biology for mapping cell components in studies of protein translocation, cell polarity, cell cycle stages, or component cell types in tissues.

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The effect of tissue composition on gene co-expression

10.1101/492223 ◽

2018 ◽

Author(s):

Yun Zhang ◽

Jonavelle Cuerdo ◽

Marc K Halushka ◽

Matthew N McCall

Keyword(s):

Expression Patterns ◽

Real Data ◽

Cell Types ◽

Tissue Level ◽

Tissue Composition ◽

Cell Type ◽

Tissue Samples ◽

Cell Type Composition ◽

Type Composition ◽

Component Cell

Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell type specific markers are ideally suited to deconvolute both the expression and co expression patterns of an individual cell type. Finally, we provide a Shiny application for users to interactively explore the effect of cell type composition on correlation-based co-expression estimation for any cell types of interest.

Download Full-text