Name: divergence of root microbiota in different habitats based on weighted correlation networks
Uploaded: 2021-09-25T13:30:00
Description: Watch this Scientific Journal Video about Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks at JoVE.com

The development of this manuscript was supported by funds from National Natural Science Foundation of China-Guizhou Provincial People&#39;s Government Karst Science Research Center Project (U1812401), Doctoral Research Project of Guizhou Normal University (GZNUD[2017]1), Science and Technology Support Project of Guizhou Province (QKHZC[2021]YB459) and the Science and Technology Project of the Guiyang([2019]2-8).
The authors would like to thank Edwards J.A et al for providing rice microbiome data in public databases and support from TopEdit (www.topeditsci.com) for its linguistic assistance during the preparation of this manuscript.

1. Data Download
<ol>
	<li>Download the data of the accession PRJNA386367 form the NCBI database. From the data of the accession PRJNA386367, select the rhizosphere, rhizoplane, and endosphere microbiome data from rice plants grown for 14 weeks in a submerged rice field in Arbuckle, California in 2014. 
	&#8203;NOTE: The rhizosphere, rhizoplane, and endosphere microbiome data were presented by the OTUs table in accession PRJNA386367.</li>
</ol>
2. Optimal power value determination
<p clas...

<ol>
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Correlation networks have been increasingly used in bioinformatics applications. WGCNA is a systems biology method for descriptive analysis of the relationships between various elements of a biological system12. R software package was used in earlier work on WGCNA13,14,15. The package includes functions for network construction, module detection, calculations of topological properties, data simulation, vi...

Microbiome research has important implications for understanding and manipulating ecosystem processes1,2. Microbial populations are interconnected by interacting ecological networks, whose characteristics can affect the response of microorganisms to environmental changes3,4. Furthermore, the properties of these networks affect the stability of microbial communities, and are closely associated with soil function5. Weighted gene correlation network analysis has now been widely applied for research on the relationship between genes and microbial communities6. Previous studies have focused mainly on the associations between networks of different genes or populations and the outside world7. However, the differences in correlation networks formed by microbial populations under different environmental conditions have been scarcely investigated. The purpose of the research presented in this paper is to provide insights and details on the rapid implementation of the WGCNA algorithm to construct a co-occurrence network of microbiome samples collected under different environmental conditions. Based on the analysis results, we assessed the composition and differences of the population and further discussed the relationship between different microbial populations. The following basic flow of weighted correlation network algorithm8 was applied. First, a similarity matrix needed to be constructed by calculating the Pearson correlation coefficient between the Operational Taxonomic Units (OTU) expression profiles. Then, the parameters of the adjacency functions (the power or the sigmoid adjacency functions) were adopted with a scale-free topology criterion, the similarity matrix was transformed into an adjacency matrix, and each co-occurrence network corresponded to an adjacency matrix. We used average linkage hierarchical clustering coupled with the TOM-based dissimilarity to group OTUs with coherent expression profiles into modules. Further, we calculated the relationship between conservative statistics and the related parameter analysis modules, finally identifying the hub OTU in the module. These methods are particularly suitable for analysis of the differences in network structures among various microbial populations under divergent environmental conditions. In this manuscript, we have described in detail the method of co-expression network development, the analysis of the dissimilarities between the modules, and have provided a brief overview of the steps in the procedure applied to obtain the core species in different module networks.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>R</td>
			<td>The University of Auckland</td>
			<td>version 4.0.2</td>
			<td>R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.</td>
		</tr>
		<tr>
			<td>RStdio</td>
			<td>JJ Allaire</td>
			<td>version 1.4.1103</td>
			<td>The RStudio IDE is a set of integrated tools designed to help you be more productive with R and Python.</td>
		</tr>
		<tr>
			<td>Cytoscape</td>
			<td></td>
			<td>version 3.7.1</td>
			<td>Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data.</td>
		</tr>
		<tr>
			<td>NCBI database</td>
			<td></td>
			<td></td>
			<td>The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information.</td>
		</tr>
	</tbody>
</table>

divergence of root microbiota in different habitats based on weighted correlation networks

The root microbiome plays an important role in plant growth and environmental adaptation. Network analysis is an important tool for studying communities, which can effectively explore the interaction relationship or co-occurrence model of different microbial species in different environments. The purpose of this manuscript is to provide details on how to use the weighted correlation network algorithm to analyze different co-occurrence networks that may occur in microbial communities due to different ecological environments. All analysis of the experiment is performed in the WGCNA package. WGCNA is an R package for weighted correlation network analysis. The experimental data used to demonstrate these methods were microbial community data from the NCBI (National Center for Biotechnology Information) database for three niches of the rice (Oryza sativa) root system. We used the weighted correlation network algorithm to construct co-abundance networks of microbial community in each of the three niches. Then, differential co-abundance networks among endosphere, rhizoplane and rhizosphere soil were identified. In addition, the core genera in network were obtained by the "WGCNA" package, which plays an important regulated role in network functions. These methods enable researchers to analyze the response of microbial network to environmental disturbance and verify different microbial ecological response theories. The results of these methods show that the significant differential microbial networks identified in the endosphere, rhizoplane and rhizosphere soil of rice.

The representative results in this article were downloaded from the 2014 California Abaker rice root microbiome data in the NCBI database (PRJNA386367)9. The data includes the rhizosphere, rhizoplane, and endosphere microbiome samples from rice plants grown for 14 weeks in a submerged rice field. We used the WGCNA algorithm to select the power value that satisfied the three networks that were close to the scale-free network (Figure 1) and developed three co-expression...

Watch this Scientific Journal Video about Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks at JoVE.com

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Network analysis was applied to evaluate the association of various ecological microbial communities, such as soil, water and rhizosphere. Presented here is a protocol on how to use the WGCNA algorithm to analyze different co-occurrence networks that may occur in the microbial communities due to different ecological environments.

The root microbiome plays an important role in plant growth and environmental adaptation. Network analysis is an important tool for studying communities, ...

This method can effectively explore the interactive relationship or co-occurrence network of different microbial species in different environments. It provides detail on how to use the WGCNA algorithm to construct a co-occurrence network of microbiota. In addition, based on the results, this method assesses the differentiation of microbial relationships and composition between microbial consortia.Here is the basic flow of the method. The composition and abundance data of microbiota are downloaded from the NCBI database or sequence data for your samples. First, open the RStudio software and install the WGCNA package.Then, load the data and use the good samples genes function to check the correctness of the data. Check for outliers and store samples that meet our requirements. When the check result is true, continue to the next step.Save the result. Use the picks off threshold function to calculate the scale free index, R squared, of the data under different power values. Visualize the results.When the scale free index is closer to one, the network structure is closer to the scale free network. When the scale free index, R squared, is greater than 0.9, select the power value. Finally, use the same method to analyze the rest of the microbiome data.First, use the adjacency function to construct a symbolic co-occurrence network. In addition, use the TOM similarity function to develop a topological, overlapping network. Second, use the Hclust function to perform hierarchical clustering and draw the resulting cluster tree.Third, use the cutree dynamic function to perform dynamic branch cutting and use the min cluster size parameter to set the smallest module size. The smallest module size is usually set over 30. Fourth, calculate the microbial characteristic of each module.Hierarchical clustering is performed according to the correlation coefficient, and modules with a height of less than 0.25 were merged to acquire the distribution of each module. Fifth, use the plot dendro and colors function to visualize the results. The assignment display diagram of the co-occurrence network module is obtained.Then, rename the module colors and construct digital labels corresponding to the colors. Save them for use in subsequent parts. Finally, repeat the above process for other sets of data.In this part, compare and analyze the two sets of data and conduct the preservation test. First, load the parameters and results of the two datasets saved in the previous steps. Then, set the module results of one data set as the reference group, another as the test group and perform the module comparison.Next, calculate the values of the conservativeness, statistical parameters, Z summary and median rank to quantify the conservativeness between modules. Finally, visualize the results. Determine the network module that satisfies both the Z summary value, less than two, and the median rank value at the top.This module is the most non-preserved module in the two microbiota data. Perform correlation analysis of the module membership. Set the module assignment results of two networks as the reference and the test group respectively.The settings need to be the same as the preservation test. First, the KME value of each OTU was calculated in several candidate modules based on the results of the preservation test. Take the yellow module as an example.Calculate the correlation coefficient of the KME value in the two yellow modules, then draw the correlation analysis diagram of the KME value in the module. Finally, according to the correlation coefficient in the figure, judge the conservativeness of the module in the two data set. Select the module with the smallest correlation coefficient.First, use the export network to Cytoscape function to export the network to an edge and node list file that Cytoscape can read. Then, import the file into Cytoscape. Set the threshold to 0.5 and adjust other parameters as needed.Finally, obtain a co-occurrence network of different microorganisms. In this article, the WGCNA algorithm is used to analyze the differences in three niches of the rice root system. Select the power value that satisfied the three networks that were close to the scale free network.In the endosphere, rhizoplane, and rhizosphere microbial occurrence network, identify 23, 22 and 21 models respectively. Use the preservation test and correlation analysis to find the extremely non-conservative modules between each two niches of the rice root system. Construct a co-occurrence network for these three modules using different colors to represent different microbial phylum.Proteobacteria, Actinobacteria, Bacteroidetes, Firmicutes and Verrucomicrobia dominated the three different microbial networks. Moreover, 17 core genera mainly regulate these networks. After watching this video, you should have a good understanding of how to perform a series of steps to use the WGCNA algorithm to analyze different co-occurrence networks that may occur in the microbial communities due to different ecological environments.

divergence-root-microbiota-different-habitats-based-on-weighted

Research

JoVE Journal

Biology

How to Culture, Record and Stimulate Neuronal Networks on Micro-electrode Arrays (MEAs)

For the last century, many neuroscientists around the world have dedicated their lives to understanding how neuronal networks work and why they stop working in various diseases. Studies have included neuropathological observation, fluorescent microscopy with genetic labeling, and intracellular recording in both dissociated neurons and slice preparations. This protocol discusses another technology, which involves growing dissociated neuronal cultures on micro-electrode arrays (also called multi-electrode arrays, MEAs).
There are multiple advantages to using this system over other technologies. Dissociated neuronal cultures on MEAs provide a simplified model in which network activity can be manipulated with electrical stimulation sequences through the array's multiple electrodes. Because the network is small, the impact of stimulation is limited to observable areas, which is not the case in intact preparations. The cells grow in a monolayer making changes in morphology easy to monitor with various imaging techniques. Finally, cultures on MEAs can survive for over a year in vitro which removes any clear time limitations inherent with other culturing techniques.1
Our lab and others around the globe are utilizing this technology to ask important questions about neuronal networks. The purpose of this protocol is to provide the necessary information for setting up, caring for, recording from and electrically stimulating cultures on MEAs. In vitro networks provide a means for asking physiologically relevant questions at the network and cellular levels leading to a better understanding of brain function and dysfunction.

How to Culture, Record and Stimulate Neuronal Networks on Micro-electrode Arrays MEAs

This protocol provides the necessary information for setting up, caring for, recording from and electrically stimulating cultures on MEAs. In vitro networks provide a means for asking physiologically relevant questions at the network and cellular levels leading to a better understanding of brain function and dysfunction.

For the last century, many neuroscientists around the world have dedicated their lives to understanding how neuronal networks work and why they stop ...

Bioengineering Human Microvascular Networks in Immunodeficient Mice

The future of tissue engineering and cell-based therapies for tissue regeneration will likely rely on our ability to generate functional vascular networks in vivo. In this regard, the search for experimental models to build blood vessel networks in vivo is of utmost importance 1. The feasibility of bioengineering microvascular networks in vivo was first shown using human tissue-derived mature endothelial cells (ECs) 2-4; however, such autologous endothelial cells present problems for wide clinical use, because they are difficult to obtain in sufficient quantities and require harvesting from existing vasculature. These limitations have instigated the search for other sources of ECs. The identification of endothelial colony-forming cells (ECFCs) in blood presented an opportunity to non-invasively obtain ECs 5-7. We and other authors have shown that adult and cord blood-derived ECFCs have the capacity to form functional vascular networks in vivo 7-11. Importantly, these studies have also shown that to obtain stable and durable vascular networks, ECFCs require co-implantation with perivascular cells. The assay we describe here illustrates this concept: we show how human cord blood-derived ECFCs can be combined with bone marrow-derived mesenchymal stem cells (MSCs) as a single cell suspension in a collagen/fibronectin/fibrinogen gel to form a functional human vascular network within 7 days after implantation into an immunodeficient mouse. The presence of human ECFC-lined lumens containing host erythrocytes can be seen throughout the implants indicating not only the formation (de novo) of a vascular network, but also the development of functional anastomoses with the host circulatory system. This murine model of bioengineered human vascular network is ideally suited for studies on the cellular and molecular mechanisms of human vascular network formation and for the development of strategies to vascularize engineered tissues.

Here, we describe a methodology to deliver human cord blood-derived endothelial colony-forming cells (ECFCs) and bone marrow-derived mesenchymal stem cells (MSCs), embedded in a collagen/fibronectin gel, subcutaneously into immunodeficient mice. This cell/gel combination generates a human vascular network that connects with the mouse vasculature.

The future of tissue engineering and cell-based therapies for tissue regeneration will likely rely on our ability to generate functional vascular networks ...

Lateral Root Inducible System in Arabidopsis and Maize

Lateral root development contributes significantly to the root system, and hence is crucial for plant growth. The study of lateral root initiation is however tedious, because it occurs only in a few cells inside the root and in an unpredictable manner. To circumvent this problem, a Lateral Root Inducible System (LRIS) has been developed. By treating seedlings consecutively with an auxin transport inhibitor and a synthetic auxin, highly controlled lateral root initiation occurs synchronously in the primary root, allowing abundant sampling of a desired developmental stage. The LRIS has first been developed for Arabidopsis thaliana, but can be applied to other plants as well. Accordingly, it has been adapted for use in maize (Zea mays). A detailed overview of the different steps of the LRIS in both plants is given. The combination of this system with comparative transcriptomics made it possible to identify functional homologs of Arabidopsis lateral root initiation genes in other species as illustrated here for the CYCLIN B1;1 (CYCB1;1) cell cycle gene in maize. Finally, the principles that need to be taken into account when an LRIS is developed for other plant species are discussed.

Lateral Root Inducible System in Arabidopsis and Maize

The Lateral Root Inducible System (LRIS) allows for synchronous induction of lateral roots and is presented for Arabidopsis thaliana and maize.

Lateral root development contributes significantly to the root system, and hence is crucial for plant growth. The study of lateral root initiation is ...

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and product development. Stress-strain relationships are typically obtained from mechanical testing data to enable comparative assessment among samples and in some cases identification of constitutive mechanical properties. However, errors may be introduced through the use of average strain measures, as significant heterogeneity in the strain field may result from geometrical non-uniformity of the sample and stress concentrations induced by mounting/gripping of soft tissues within the test system. When strain field heterogeneity is significant, accurate assessment of the sample mechanical response requires measurement of local strains. This study demonstrates a novel biomechanical testing protocol for calculating local surface strains using a mechanical testing device coupled with a high resolution camera and a digital image correlation technique. A series of sample surface images are acquired and then analyzed to quantify the local surface strain of a vascular tissue specimen subjected to ramped uniaxial loading. This approach can improve accuracy in experimental vascular biomechanics and has potential for broader use among other native soft tissues, engineered soft tissues, and soft hydrogel/polymeric materials. In the video, we demonstrate how to set up the system components and perform a complete experiment on native vascular tissue.

We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and ...

A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice

Very intriguing questions arise with our advancing knowledge on gut microbiota composition and the relationship with health, particularly relating to the factors that contribute to maintaining the population balance. However, there are limited available methodologies to evaluate these factors. Bacteriocins are antimicrobial peptides produced by many bacteria that may confer a competitive advantage for food acquisition and/or niche establishment. Many probiotic lactic acid bacteria (LAB) strains have great potential to promote human and animal health by preventing the growth of pathogens. They can also be used for immuno-modulation, as they produce bacteriocins. However, the antagonistic activity of bacteriocins is normally determined by laboratory bioassays under well-defined but over-simplified conditions compared to the complex gut environment in humans and animals, where bacteria face multifactorial influences from the host and hundreds of microbial species sharing the same niche. This work describes a complete and efficient procedure to assess the effect of a variety of bacteriocins with different target specificities in a murine system. Changes in the microbiota composition during the bacteriocin treatment are monitored using compositional 16S rDNA sequencing. Our approach uses both the bacteriocin producers and their isogenic non-bacteriocin-producing mutants, the latter giving the ability to distinguish bacteriocin-related from non-bacteriocin-related modifications of the microbiota. The fecal DNA extraction and 16S rDNA sequencing methods are consistent and, together with the bioinformatics, constitute a powerful procedure to find faint changes in the bacterial profiles and to establish correlations, in terms of cholesterol and triglyceride concentration, between bacterial populations and health markers. Our protocol is generic and can thus be used to study other compounds or nutrients with the potential to alter the host microbiota composition, either when studying toxicity or beneficial effects.

Bacteriocins are believed to play a key role in defining microbial diversity in different ecological niches. Here, we describe an efficient procedure to assess how bacteriocins affect gut microbiota composition in an animal model.

Very intriguing questions arise with our advancing knowledge on gut microbiota composition and the relationship with health, particularly relating to the ...

Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities

Plant biologists often need to observe the growth behavior of their chosen species. To this end, the plants need constant environmental and stable light conditions, which are preferably variable in quantity and quality so that studies under different setups can be conducted. These requirements are met by climatic chambers featuring light emitting diodes (LED) lights, which can &#8211; in contrast to fluorescent lights &#8211; be set to different wavelengths. LEDs are energy conserving and emit virtually no heat even at light intensities, which often constitutes a problem with other light sources. The presented protocol provides a step-by-step guidance of how to program a climatic chamber equipped with variable LED lights as well as describing several approaches for in depth analysis of growth phenotypes. Depending on the experimental set-up various characteristics of the growing plants can be observed and analyzed. Here we describe how to determine fresh weight, leaf area, photosynthetic activity, and stomatal density. We demonstrate that in order to obtain reliable data and draw valid conclusions it is mandatory to use a sufficient number of individuals for statistical evaluation. Taking too few plants for this kind of analysis results in high statistical errors and consequently in less clear interpretations of the data.

Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities

Here, we present a protocol for studying plant growth behavior and especially phenotypes in a reproducible manner. We show how to provide variable and at the same time stable light conditions. Proper analyses depend on sufficient sample numbers and valid statistical evaluations.

Plant biologists often need to observe the growth behavior of their chosen species. To this end, the plants need constant environmental and stable light ...

SA-&#946;-Galactosidase-Based Screening Assay for the Identification of Senotherapeutic Drugs

Cell senescence is one of the hallmarks of aging known to negatively influence a healthy lifespan. Drugs able to kill senescent cells specifically in cell culture, termed senolytics, can reduce the senescent cell burden in vivo and extend healthspan. Multiple classes of senolytics have been identified to date including HSP90 inhibitors, Bcl-2 family inhibitors, piperlongumine, a FOXO4 inhibitory peptide and the combination of Dasatinib/Quercetin. Detection of SA-&#946;-Gal at an increased lysosomal pH is one of the best characterized markers for the detection of senescent cells. Live cell measurements of senescence-associated &#946;-galactosidase (SA-&#946;-Gal) activity using the fluorescent substrate C12FDG in combination with the determination of the total cell number using a DNA intercalating Hoechst dye opens the possibility to screen for senotherapeutic drugs that either reduce overall SA-&#946;-Gal activity by killing of senescent cells (senolytics) or by suppressing SA-&#946;-Gal and other phenotypes of senescent cells (senomorphics). Use of a high content fluorescent image acquisition and analysis platform allows for the rapid, high throughput screening of drug libraries for effects on SA-&#946;-Gal, cell morphology and cell number.

SA-&amp;#946;-Galactosidase-Based Screening Assay for the Identification of Senotherapeutic Drugs

Cellular senescence is the key factor in the development of chronic age-related pathologies. Identification of therapeutics that target senescent cells show promise for extending healthy aging. Here, we present a novel assay to screen for the identification of senotherapeutics based on measurement of senescence associated &#946;-Galactosidase activity in single cells.

Cell senescence is one of the hallmarks of aging known to negatively influence a healthy lifespan. Drugs able to kill senescent cells specifically in cell ...

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

It is necessary to accurately quantify the anatomical structures of maize materials based on high-throughput image analysis techniques. Here, we provide a 'sample preparation protocol' for maize materials (i.e., stem, leaf, and root) suitable for ordinary microcomputed tomography (micro-CT) scanning. Based on high-resolution CT images of maize stem, leaf, and root, we describe two protocols for the phenotypic analysis of vascular bundles: (1) based on the CT image of maize stem and leaf, we developed a specific image analysis pipeline to automatically extract 31 and 33 phenotypic traits of vascular bundles; (2) based on the CT image series of maize root, we set up an image processing scheme for the three-dimensional (3-D) segmentation of metaxylem vessels, and extracted two-dimensional (2-D) and 3-D phenotypic traits, such as volume, surface area of metaxylem vessels, etc. Compared with traditional manual measurement of vascular bundles of maize materials, the proposed protocols significantly improve the efficiency and accuracy of micron-scale phenotypic quantification.

We provide a novel method to improve the X-ray absorption contrast of maize tissue suitable for ordinary microcomputed tomography scanning. Based on CT images, we introduce a set of image-processing workflows for different maize materials to effectively extract microscopic phenotypes of vascular bundles of maize.

It is necessary to accurately quantify the anatomical structures of maize materials based on high-throughput image analysis techniques. Here, we provide a ...

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome&#8217;s role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Described here is a simplified standard operating procedure for microbiome profiling using 16S rRNA metagenomic sequencing and analysis using freely available tools. This protocol will help researchers who are new to the microbiome field as well as those requiring updates on methods to achieve bacterial profiling at a higher resolution.

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the ...

Quantification of Skeletal Muscle Fatty Infiltration via Decellularization

Decellularization-Based Quantification of Skeletal Muscle Fatty Infiltration

Fatty infiltration is the accumulation of adipocytes between myofibers in skeletal muscle and is a prominent feature of many myopathies, metabolic disorders, and dystrophies. Clinically in human populations, fatty infiltration is assessed using noninvasive methods, including computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US). Although some studies have used CT or MRI to quantify fatty infiltration in mouse muscle, costs and insufficient spatial resolution remain challenging. Other small animal methods utilize histology to visualize individual adipocytes; however, this methodology suffers from sampling bias in heterogeneous pathology. This protocol describes the methodology to qualitatively view and quantitatively measure fatty infiltration comprehensively throughout intact mouse muscle and at the level of individual adipocytes using decellularization. The protocol is not limited to specific muscles or specific species and can be extended to human biopsy. Additionally, gross qualitative and quantitative assessments can be made with standard laboratory equipment for little cost, making this procedure more accessible across research laboratories.

Author Spotlight: Decellularization-Based Quantification of Skeletal Muscle Fatty Infiltration  

The present study describes decellularization-based methodologies for visualizing and quantifying intramuscular adipose tissue (IMAT) deposition through intact muscle volume, as well as quantifying metrics of individual adipocytes that comprise IMAT.