This work was supported by NIH grant CA201402 and the Cornell Center for Vertebrate Genomics (CVG) Distinguished Scholar Award. The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org). Study data were provided through the Accelerating Medicine Partnership for AD (U01AG046161 and U01AG061357) based on samples provided by the Rush Alzheimer&#8217;s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152, the Illinois Department of Public Health, and the Translational Genomics Research Institute. The metabolomics dataset was generated at Metabolon and preprocessed by the ADMC.

NOTE: The data used here were ROSMAP data downloaded from the AD Knowledge portal. Informed consent is not needed to download and reuse the data. The protocol presented herein utilizes deep learning to analyze multi-omics data and identify signaling modules that distinguish specific patient or sample groups based, for example, on their diagnosis. The protocol also delivers a small set of extracted features that summarize the original large-scale data and can be used for further analysis such as training a predictive model using machine learning algorithms (Figure 1). Refer to Supplemental File 1 and the Table of Materials for information regarding accessing the code and setting up the computational environment prior to performing the protocol. The methods should be performed following the order specified below.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65910/65910fig01.jpg" /> 
Figure 1: Schematic of the DeepOmicsAE workflow. Schematic representation of the workflow for analyzing multi-omics data using the workflow. In the autoencoder depiction, rectangles represent layers of the neural network and circles represent neurons within layers. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/65910/65910fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
1. Data preprocessing
NOTE: The goal of this section is to preprocess the data, including handling missing data; normalizing and scaling proteomic, metabolomic expression, and clinical data; and removing outliers. The protocol is designed for a dataset that includes proteomics data expressed as log2(ratio); metabolomics data expressed as fold change; and clinical features including continuous and categorical features. The patients or samples should be grouped based on diagnosis or other similar parameters. Samples or patients should be across the rows and features across the columns.
<ol>
	<li>To start a new instance of Jupyter Notebook in the browser, open a new terminal window, type the following and press Enter. 
	jupyter notebook</li>
	<li>In the Jupyter home page on the browser, click on the notebook M01 - expression data pre-processing.ipynb to open it in a new tab (Supplemental File 2, Step 1.1).</li>
	<li>In the second cell of the notebook, type the name of the dataset file in place of your_dataset_name.csv.</li>
	<li>In the last cell of the notebook, type the desired name of the output data file in place of M01_output_data.csv.</li>
	<li>In the fifth cell of the notebook, specify the position of the columns for each data type as follows: proteomics data (cols_prot), metabolomics data (cols_met), continuous clinical data (e.g., age) (cols_clin_con), binary clinical data (e.g., sex) (cols_clin_bin). Enter the first column index for each data type in place of col_start and the last columns index in place of col_end; for example: cols_prot = slice(0, 8817). Ensure that the values specified in the slice objects correspond to the first and last columns indexes corresponding to each data type. Use the command in the fourth cell of the same notebook (df.iloc[:, :]) to determine the start and end position for each data type (Supplemental File 2, Step 1.2).</li>
	<li>Select Cell | Run all from the menu bar in Jupyter to create the output data file in the specified folder (Supplemental File 2, Step 1.3). 
	&#8203;NOTE: These data will be used as input for the protocols described in sections 2, 3, or 4.</li>
</ol>
2. Custom optimization of the workflow (optional)
NOTE: Section 2 is optional because it is computer-intensive. Users should skip directly to section 4 if they decide to not perform section 2. This protocol will guide the user through optimizing the workflow in an automated manner. Specifically, the method identifies the parameters that deliver the best performance of the autoencoder in terms of generating extracted features that separate the sample groups well. The optimized parameters generated as an output include the number of features to use for feature selection (k_prot and k_met) and the number of neurons in the autoencoder latent layer (latent). These parameters can then be used in the protocol described in section 3 to generate the model.
<ol>
	<li>On the Jupyter home page on the browser, click on the notebook M02 - DeepOmicsAE model optimization.ipynb to open it in a new tab (Supplemental File 2, Step 2.1).</li>
	<li>In the second cell of the notebook, type the name of the input file in place of M01_output_data.csv. The input to this function is the output data from section 1.</li>
	<li>In the fifth cell of the notebook, specify the position of the columns for each data type as follows: proteomics data (cols_X_prot), metabolomics data (cols_X_met), clinical data (cols_clin; includes all the clinical data), all molecular expression data, including proteomics and metabolomics data (cols_X_expr). Enter the first column index for each data type in place of col_start and the last columns index in place of col_end; for example, cols_prot = slice(0, 8817). Ensure that the values specified in the slice objects correspond to the first and last columns index corresponding to each data type, and use the commands in the third and fourth cells of the notebook to explore the data and determine the start and end positions for each data type. Specify the name of the column containing the target variable in place of y_column_name as y_label (Supplemental File 2, Step 2.2). 
	NOTE: The values of the indexes specified in cols_X_prot, cols_X_met, cols_clin, and cols_X_expr will be different from those used in section 1 due to the reshaping of the dataframe occurring during data preprocessing.</li>
	<li>In the sixth cell of the notebook, specify how many optimization rounds to perform by assigning a value to n_comb. Times for processing are approximately 4-5 min for 10 rounds; 20 min for 50 rounds, and 40 min for 100 rounds (Supplemental File 2, Step 2.3).</li>
	<li>Select Cell | Run all from the menu bar in Jupyter. 
	&#8203;NOTE: The output variables kprot, kmet, and latent will be stored and can be accessed from the other notebooks, which will be used to continue the analytical workflow. The plot AE_optimization_plot.pdf will be generated and saved in the local folder (Figure 2).</li>
</ol>
3. Workflow implementation with custom-optimized parameters
NOTE: Perform this protocol only following method optimization (section 2). If users choose to not perform method optimization, skip directly to section 4. This protocol will guide the user through generating a model using the custom-optimized parameters derived from section 2. The autoencoder will 1) generate a set of extracted features that recapitulate the original data and 2) identify the important features driving each neuron in the latent layer, effectively representing unique signaling modules. The signaling modules will be interpreted using the protocol provided in section 5.
<ol>
	<li>On the Jupyter home page on the browser, click on the notebook M03a - DeepOmicsAE implementation with custom-optimized parameters.ipynb to open it in a new tab (Supplemental File 2, Step 3.1).</li>
	<li>In the second cell of the notebook, type the name of the input file in place of M01_output_data.csv. The input to this function is the output data from section 1.</li>
	<li>In the fifth cell of the notebook, specify the position of the columns for each data type as follows: proteomics data (cols_prot), metabolomics data (cols_met), clinical data (cols_clin; includes all of the clinical data). Enter the first column index for each data type in place of col_start and the last columns index in place of col_end; for example: cols_prot = slice(0, 8817). Ensure that the values specified in the slice objects correspond to the first and last columns indexes corresponding to each data type, and use the commands in the third and fourth cells of the notebook to explore the data and determine the start and end positions for each data type. Specify the name of the column containing the target variable (e.g., 0 or 1, corresponding to healthy or diseased) in place of y_column_name as y_label. 
	NOTE: The value of the indexes specified in cols_X_prot, cols_X_met, cols_clin, and cols_X_expr will be different from those used in section 1 due to the reshaping of the dataframe occurring during data preprocessing.</li>
	<li>Select Cell | Run all from the menu bar in Jupyter to generate and save the plots PCA_initial_data.pdf, PCA_extracted_features.pdf, and distribution_important_feature_scores.pdf in the local folder (Figure 3 and Supplemental Figure S1). Additionally, lists of important features for each identified signaling module will be stored in text files in the local folder, named module_n.txt, where n will be substituted by the module number.</li>
</ol>
4. Workflow implementation with preset parameters
<ol>
	<li>Refer to section 3 for detailed instructions on how to run this method (Supplemental File 2, Step 4.1). The only difference between these two protocols is that the parameters kprot, kmet, and latent (in the seventh cell of the notebook) are mathematically derived based on the results of the optimization performed as shown in Figure 2. 
	&#8203;NOTE: If section 4 delivers a poor separation of the sample groups, indicating suboptimal model performance, it is recommended to execute model optimization (section 2) using at least 15 iterations, and if possible, up to 50.</li>
</ol>
5. Biological interpretation using MetaboAnalyst
<ol>
	<li>Open the browser and navigate to the link below to access the Joint Pathway Analysis functionality on the MetaboAnalyst website:&#160;https://www.metaboanalyst.ca/MetaboAnalyst/upload/JointUploadView.xhtml.</li>
	<li>Access the folder where the output files from Method 3 or Method 4 were saved and open the text files module_n.txt for each signaling module n generated by Method 3 or by Method 4.</li>
	<li>Locate the proteins in the text files and copy them.</li>
	<li>Paste the list of proteins into the window Genes/proteins with optional fold changes in the MetaboAnalyst web page.</li>
	<li>Repeat the above step for metabolites and paste them into the window Compound list with optional fold changes on the same web page.</li>
	<li>Select the appropriate organism and ID type, then click Submit at the bottom of the page (Supplemental File 2, Step 5.1). 
	NOTE: Ensure that the identifiers are recognized by MetaboAnalyst. Recognized identifiers include Entrez ID, official gene symbols, and Uniprot ID for proteins; compound name, HMDB ID, and KEGG ID for metabolites. If the identifiers are other than these types, appropriate conversion is necessary prior to the analysis.</li>
	<li>On the following page, check the ID mapping before clicking Proceed to verify that the identifiers are being recognized.</li>
	<li>In the Parameter Setting page, select Metabolic pathways (integrated) or All pathways (integrated) to visualize respectively the contribution of the input to metabolic pathways only or to all signaling pathways (Supplemental File 2, Step 5.2). In the Algorithm selection panel, choose Enrichment analysis: Hypergeometric test, Topology measure: Degree centrality, and Integration method: Combine p values (pathway-level). Click on Submit at the bottom of the page.</li>
	<li>The last page is the Result View, which presents the results of the enrichment analysis. Enriched pathways are plotted based on their impact and significance, and the list of pathways is also provided in tabular format.</li>
</ol>

Analyzing Signaling Modules in Alzheimer's Disease with Deep Learning

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The author declares that they have no conflicts of interest.

The structure of the dataset is critical to the success of the protocol and should be carefully checked. The data should be formatted as indicated in protocol section 1. The correct assignment of column positions is also critical to the success of the method. Proteomics and metabolomics data are preprocessed differently and feature selection is conducted separately due to the different nature of the data. Therefore, it is critical to assign column positions correctly in protocol steps 1.5, 2.3, and 3.3.
<p class="jov...

An increasingly large proportion of the population is aging and the burden of age-related diseases, such as neurodegeneration, is expected to sharply increase in the coming decades1. Alzheimer&#39;s disease is the most common type of neurodegenerative disease2. Progress in finding a treatment has been slow given our poor understanding of the fundamental&#160;molecular mechanisms driving the onset and progress of the disease. The majority of information on Alzheimer&#39;s disease is gained postmortem from the examination of brain tissue, which has made distinguishing causes and consequences a difficult task3. The Religious Orders Study/Memory and Aging Project (ROSMAP) is an ambitious effort to gain a broader understanding of neurodegeneration, which involves the study of thousands of individuals who have committed to undergo medical and psychological examinations yearly and to contribute their brains for research after their demise4. The study focuses on the transition from the normal functioning of the brain to Alzheimer&#39;s disease2. Within the project, postmortem brain samples&#160;were analyzed with a plethora of omics approaches, including genomics, epigenomics, transcriptomics, proteomics5, and metabolomics.
Omics technologies that offer functional readouts of cellular states (i.e., proteomics and metabolomics)6,7 are key to interpreting disease8,9,10,11,12, due to the direct relationship between protein and metabolite abundance and cellular activities. Proteins are the primary executors of cellular processes, while metabolites are the substrates and products for biochemical reactions. Multi-omics data analysis offers the possibility to understand the complex relationships between proteomics and metabolomics data instead of appreciating them in isolation. Multi-omics is a discipline that studies multiple layers of high-dimensional biological data, including molecular data (genome sequence and mutations, transcriptome, proteome, metabolome), clinical imaging data, and clinical features. Particularly, multi-omics data analysis aims to integrate such layers of biological data, understand their reciprocal regulation and interaction dynamics, and deliver a holistic understanding of disease onset and progression. However, methods to integrate multi-omics data remain in the early stages of development13.
Autoencoders, a type of unsupervised neural network14, are a powerful tool for multi-omics data integration. Unlike supervised neural networks, autoencoders do not map samples to specific target values (such as healthy or diseased), nor are they used to predict outcomes. One of their primary applications&#160;lies in dimensionality reduction. However, autoencoders offer several advantages over simpler dimensionality reduction methods such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), or uniform manifold approximation and projection (UMAP). Unlike PCA, autoencoders can capture non-linear relationships within the data. Unlike tSNE and UMAP, they can detect hierarchical and multi-modal relationships within the data since they rely on multiple layers of computational units each containing non linear activation functions. Therefore, they represent attractive models to capture the complexity of multi-omics data. Finally, while the primary application of PCA, tSNE, and UMAP is that of clustering the data, autoencoders compress the input data into extracted features that are well-suited for downstream predictive tasks15,16.
Briefly, neural networks comprise several layers, each containing multiple computational units or &#34;neurons.&#34; The first and last layers are referred to as the input and output layers, respectively. Autoencoders are neural networks with an hourglass structure, consisting of an input layer, followed by one to three hidden layers and a small &#34;latent&#34; layer typically containing between two and six neurons. This structure&#39;s first half is known as the encoder and is combined with a decoder mirroring the encoder. The decoder ends with an output layer containing the same number of neurons as the input layer. Autoencoders take the input through the bottleneck and reconstruct it in the output layer, with the goal of generating an output that mirrors the original information as closely as possible. This is achieved by mathematically minimizing a parameter termed &#34;reconstruction loss.&#34; The input consists of a set of features, which in the application showcased herein will be protein and metabolite abundances, and clinical characteristics (i.e., sex, education, and age at death). The latent layer contains a compressed and information-rich representation of the input, which can be used for subsequent applications such as predictive models17,18.
This protocol presents a workflow, DeepOmicsAE, which involves: 1) preprocessing of proteomics, metabolomics, and clinical data (i.e., normalization, scaling, outlier removal) to obtain data with a consistent scale for machine learning analysis; 2) selecting appropriate autoencoder input features, since feature overload may obscure relevant disease patterns; 3) optimizing and training the autoencoder, including determining the optimal number of proteins and metabolites to select, and of neurons for the latent layer; 4) extracting features from the latent layer; and 5) utilizing the extracted features for biological interpretation by identifying molecular signaling modules and their relationship with clinical features.
This protocol aims to be simple and applicable by&#160;biologists with limited computational experience who have a basic understanding of programming with Python. The protocol focuses on analyzing multi-omics data, including proteomics, metabolomics, and clinical features, but its use can be extended to other types of molecular expression data, including transcriptomics. One important novel application introduced by this protocol is mapping the importance scores of original features onto individual neurons in the latent layer. As a result, each neuron in the latent layer represents a signaling module, detailing the interactions between specific molecular alterations and the patients&#39; clinical characteristics. Biological interpretation of the molecular signaling modules is obtained by using MetaboAnalyst, a publicly available tool that integrates gene/protein and metabolite data to derive enriched metabolic and cell signaling pathways17.

<table><tbody><tr><td>Computer</td><td>Apple</td><td>Mac Studio</td><td>Apple M1 Ultra with 20-core CPU, 48-core GPU, 32-core Neural Engine; 64 GB unified memory</td></tr><tr><td>Conda v23.3.1</td><td>Anaconda, Inc.</td><td>N/A</td><td>package management system and environment manager</td></tr><tr><td>conda environment&lt;br/&gt; DeepOmicsAE</td><td>N/A</td><td>DeepOmicsAE_env.yml</td><td>contains packages necessary to run the worflow</td></tr><tr><td>github repository DeepOmicsAE</td><td>Microsoft</td><td>https://github.com/elepan84/DeepOmicsAE/</td><td>provides scripts, Jupyter notebooks, and the conda environment file</td></tr><tr><td>Jupyter notebook v6.5.4</td><td>Project Jupyter</td><td>N/A</td><td>a platform for interactive data science and scientific computing</td></tr><tr><td>DT01-metabolomics data</td><td>N/A</td><td>ROSMAP_Metabolon_HD4_Brain&lt;br/&gt; 514_assay_data.csv</td><td>This data was used to generate the Results reported in the article. Specifically, DT01-DT04 were merged by matching them based on the individualID. The column final consensus diagnosis (cogdx) was filtered to keep only patients classified as healthy or AD. Climnical features were filtered to keep the following: age at death, sex and education. Finally, age reported as 90+ was set to 91, then the age column was transformed to float64.&lt;br/&gt; The data is available at https://adknowledgeportal.synapse.org</td></tr><tr><td>DT02-TMT proteomics data</td><td>N/A</td><td>C2.median_polish_corrected_log2&lt;br/&gt; (abundanceRatioCenteredOn&lt;br/&gt; MedianOfBatchMediansPer&lt;br/&gt; Protein)-8817x400.csv</td><td></td></tr><tr><td>DT03-clinical data</td><td>N/A</td><td>ROSMAP_clinical.csv</td><td></td></tr><tr><td>DT04-biospecimen metadata</td><td>N/A</td><td>ROSMAP_biospecimen_metadata&lt;br/&gt; .csv</td><td></td></tr><tr><td>Python 3.11.3&amp;nbsp;</td><td>Python Software Foundation</td><td>N/A</td><td>programming language</td></tr></tbody></table>

deepomicsae: representing signaling modules in alzheimer's disease with deep learning analysis of proteomics, metabolomics, and clinical data

Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the analysis of multi-omics datasets, including&#160;proteomics, metabolomics, and clinical data. This workflow employs a type of neural network called autoencoder, to extract a concise set of features from the high-dimensional multi-omics input data. Furthermore, the workflow provides a method to optimize the key parameters needed to implement the autoencoder. To showcase this workflow, clinical data were analyzed from a cohort of 142 individuals who were either healthy or diagnosed with Alzheimer&#39;s disease, along with the proteome and metabolome of their postmortem brain samples. The features extracted from the latent layer of the autoencoder retain the biological information that separates healthy and diseased patients. In addition, the individual extracted features represent distinct molecular signaling modules, each of which interacts uniquely with the individuals&#39; clinical features,&#160;providing for a mean to integrate&#160;the proteomics, metabolomics, and clinical data.

To showcase the protocol, we analyzed&#160;a dataset comprising the proteome, metabolome, and clinical information derived from postmortem brains of 142 individuals who were either healthy or diagnosed with Alzheimer&#39;s disease.
After performing the protocol section 1 to preprocess the data, the dataset included 6,497 proteins, 443 metabolites, and three clinical features (sex, age at death, and education). The target feature is clinical consensus diagnosis of cognitive status at ti...

Watch this Scientific Journal Video about DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data at JoVE.com

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches

DeepOmicsAE is a workflow centered on the application of a deep learning method (i.e., an autoencoder) to reduce the dimensionality of multi-omics data, providing a foundation for predictive models and signaling modules representing multiple layers of omics data.

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the ...

deepomicsae-representing-signaling-modules-alzheimer-s-disease-with

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

897 Views.  Cornell University. DeepOmicsAE is a workflow centered on the application of a deep learning method (i.e., an autoencoder) to reduce the dimensionality of multi-omics data, providing a foundation for predictive models and signaling modules representing multiple layers of omics data.

Video: Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Detection of Neuritic Plaques in Alzheimer's Disease Mouse Model

Alzheimer's disease (AD) is the most common neurodegenerative disorder leading to dementia. Neuritic plaque formation is one of the pathological hallmarks of Alzheimer's disease. The central component of neuritic plaques is a small filamentous protein called amyloid &beta; protein (A&beta;)1, which is derived from sequential proteolytic cleavage of the beta-amyloid precursor protein (APP) by &beta;-secretase and &gamma;-secretase. The amyloid hypothesis entails that A&gamma;-containing plaques as the underlying toxic mechanism in AD pathology2. The postmortem analysis of the presence of neuritic plaque confirms the diagnosis of AD. To further our understanding of A&gamma; neurobiology in AD pathogenesis, various mouse strains expressing AD-related mutations in the human APP genes were generated. Depending on the severity of the disease, these mice will develop neuritic plaques at different ages. These mice serve as invaluable tools for studying the pathogenesis and drug development that could affect the APP processing pathway and neuritic plaque formation. In this protocol, we employ an immunohistochemical method for specific detection of neuritic plaques in AD model mice. We will specifically discuss the preparation from extracting the half brain, paraformaldehyde fixation, cryosectioning, and two methods to detect neurotic plaques in AD transgenic mice: immunohistochemical detection using the ABC and DAB method and fluorescent detection using thiofalvin S staining method.

One of the pathological characteristics of AD is the formation of Amyloid &#x03B2; protein positive neuritic plaques. In this protocol we describe two methods to detect neuritic plaques in transgenic AD model mice: immunohistochemical detection using the ABC and DAB method and fluorescent detection using thioflavin S staining method.

Alzheimer's disease (AD) is the most common neurodegenerative disorder leading to dementia. Neuritic plaque formation is one of the pathological hallmarks ...

Neuroscience

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. ...

Biochemistry

Using Enzyme-based Biosensors to Measure Tonic and Phasic Glutamate in Alzheimer's Mouse Models

Neurotransmitter disruption is often a key component of diseases of the central nervous system (CNS), playing a role in the pathology underlying Alzheimer's disease, Parkinson's disease, depression, and anxiety. Traditionally, microdialysis has been the most common (lauded) technique to examine neurotransmitter changes that occur in these disorders. But because microdialysis has the ability to measure slow 1-20 minute changes across large areas of tissue, it has the disadvantage of invasiveness, potentially destroying intrinsic connections within the brain and a slow sampling capability. A relatively newer technique, the microelectrode array (MEA), has numerous advantages for measuring specific neurotransmitter changes within discrete brain regions as they occur, making for a spatially and temporally precise approach. In addition, using MEAs is minimally invasive, allowing for measurement of neurotransmitter alterations in vivo. In our laboratory, we have been specifically interested in changes in the neurotransmitter, glutamate, related to Alzheimer's disease pathology. As such, the method described here has been used to assess potential hippocampal disruptions in glutamate in a transgenic mouse model of Alzheimer's disease. Briefly, the method used involves coating a multi-site microelectrode with an enzyme very selective for the neurotransmitter of interest and using self-referencing sites to subtract out background noise and interferents. After plating and calibration, the MEA can be constructed with a micropipette and lowered into the brain region of interest using a stereotaxic device. Here, the method described involves anesthetizing rTg(TauP301L)4510 mice and using a stereotaxic device to precisely target sub-regions (DG, CA1, and CA3) of the hippocampus.

Here, we describe the setup, software navigation, and data analysis for a spatially and temporally precise method of measuring tonic and phasic extracellular glutamate changes in vivo using enzyme-linked microelectrode arrays (MEA).

Neurotransmitter disruption is often a key component of diseases of the central nervous system (CNS), playing a role in the pathology underlying ...

A High Throughput, Multiplexed and Targeted Proteomic CSF Assay to Quantify Neurodegenerative Biomarkers and Apolipoprotein E Isoforms Status

Many neurodegenerative diseases are&#160;still lacking effective treatments. Reliable biomarkers for identifying and classifying these diseases will be important in the development of future novel therapies. Often&#160;potential new biomarkers do not make it&#160;into the clinic&#160;due to limitations&#160;in their development and&#160;high costs.&#160;However,&#160;targeted proteomics using Multiple Reaction Monitoring Liquid Chromatography-tandem/Mass Spectrometry (MRM LC-MS/MS), specifically using triple quadrupole mass spectrometers, is one method that can be used to rapidly evaluate and validate biomarkers&#160;for clinical translation into diagnostic laboratories. Traditionally, this platform has been used extensively for measurement of small molecules&#160;in clinical laboratories, but it is the potential to analyze proteins, that makes it an attractive alternative to ELISA (Enzyme-Linked Immunosorbent Assay)-based methods. We describe here how targeted proteomics can be used to measure multiplexed markers of dementia, including the detection and quantitation of the known risk factor apolipoprotein E isoform 4 (ApoE4).
In order to make the assay suitable for translation, it is designed to be rapid, simple, highly specific and cost effective. To achieve this, every step in the development of the assay must be optimized for the individual proteins and tissues they are analyzed in. This method describes a typical workflow including various tips and tricks to developing a targeted proteomics MRM LC-MS/MS for translation.
The method development is optimized using custom synthesized versions of tryptic quantotypic peptides, which calibrate&#160;the MS for detection and then spiked into CSF to determine correct identification of the endogenous peptide in the chromatographic separation prior to analysis in the MS. To achieve absolute quantitation, stable isotope-labeled internal standard versions of the peptides with short amino acid sequence tags and containing a trypsin cleavage site, are included in the assay.

We describe a high-throughput, multiplex, and targeted proteomic cerebrospinal fluid (CSF) assay developed with potential for clinical translation. The test can quantitate potential markers and risk factors for neurodegeneration, such as the apolipoprotein E variants (E2, E3 and E4), and measure their allelic expression.

Many neurodegenerative diseases are&#160;still lacking effective treatments. Reliable biomarkers for identifying and classifying these diseases will be ...

Medicine

Quantitative 3D In Silico Modeling (q3DISM) of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease

Neuroinflammation is now recognized as a major etiological factor in neurodegenerative disease. Mononuclear phagocytes are innate immune cells responsible for phagocytosis and clearance of debris and detritus. These cells include CNS-resident macrophages known as microglia, and mononuclear phagocytes infiltrating from the periphery. Light microscopy has generally been used to visualize phagocytosis in rodent or human brain specimens. However, qualitative methods have not provided definitive evidence of in vivo phagocytosis. Here, we describe quantitative 3D in silico modeling (q3DISM), a robust method allowing for true 3D quantitation of amyloid-&#946; (A&#946;) phagocytosis by mononuclear phagocytes in rodent Alzheimer&#39;s Disease (AD) models. The method involves fluorescently visualizing A&#946; encapsulated within phagolysosomes in rodent brain sections. Large z-dimensional confocal datasets are then 3D reconstructed for quantitation of A&#946; spatially colocalized within the phagolysosome. We demonstrate the successful application of q3DISM to mouse and rat brains, but this methodology can be extended to virtually any phagocytic event in any tissue.

Quantitative 3D In Silico Modeling q3DISM of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease

We developed a methodology for quantitative 3D in silico modeling (q3DISM) of cerebral amyloid-&#946; (A&#946;) phagocytosis by mononuclear phagocytes in rodent models of Alzheimer&#39;s disease. This method can be generalized for the quantitation of virtually any phagocytic event in vivo.

Neuroinflammation is now recognized as a major etiological factor in neurodegenerative disease. Mononuclear phagocytes are innate immune cells responsible ...

Detecting Amyloid-&#946; Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease that contributes to 60-70% dementia around the world. One of the hallmarks of AD undoubtedly lies on accumulation of amyloid-&#946; (A&#946;) in the brain. A&#946; is produced from the proteolytic cleavage of the beta-amyloid precursor protein (APP) by &#946;-secretase and &#947;-secretase. In pathological circumstances, the increased &#946;-cleavage of APP leads to overproduction of A&#946;, which aggregates into A&#946; plaques. Since A&#946; plaques are a characteristic of AD pathology, detecting the amount of A&#946; is very important in AD research. In this protocol, we introduce the immunofluorescent staining method to visualize A&#946; deposition. The mouse model used in our experiments is 5&#215;FAD, which carries five mutations found in human familial AD. The neuropathological and behavioral deficits of 5xFAD mice are well-documented, which makes it a good animal model to study A&#946; pathology. We will introduce the procedure including transcardial perfusion, cryosectioning, immunofluorescent staining and quantification to detect A&#946; accumulation in 5&#215;FAD mice. With this protocol, researchers can investigate A&#946; pathology in an AD mouse model.

In the neuropathology of Alzheimer's disease, one of the most crucial characteristics is the deposition of amyloid-&#946;. In this protocol, we describe the method of immunofluorescent staining in 5&#215;FAD transgenic mouse to detect amyloid-&#946; accumulation in plaques. The process of perfusion, cryosectioning, staining and quantification will be described in detail.

Alzheimer's disease (AD) is a neurodegenerative disease that contributes to 60-70% dementia around the world. One of the hallmarks of AD undoubtedly lies ...

Biochemical Purification and Proteomic Characterization of Amyloid Fibril Cores from the Brain

Proteinaceous fibrillar inclusions are key pathological hallmarks of multiple neurodegenerative diseases. In the early stages of Alzheimer&#39;s disease (AD), amyloid-beta peptides form protofibrils in the extracellular space, which act as seeds that gradually grow and mature into large amyloid plaques. Despite this basic understanding, current knowledge of the amyloid fibril structure, composition, and deposition patterns in the brain is limited. One major barrier has been the inability to isolate highly purified amyloid fibrils from brain extracts. Affinity purification and laser capture microdissection-based approaches have been previously used to isolate amyloids but are limited by the small quantity of material that can be recovered. This novel, robust protocol describes the biochemical purification of amyloid plaque cores using sodium dodecyl sulfate (SDS) solubilization with sucrose density gradient ultracentrifugation and ultrasonication and yields highly pure fibrils from AD patients and AD model brain tissues. Mass spectrometry (MS)-based bottom-up proteomic analysis of the purified material represents a robust strategy to identify nearly all the primary protein components of amyloid fibrils. Previous proteomic studies of proteins in the amyloid coronae have revealed an unexpectedly large and functionally diverse collection of proteins. Notably, after refining the purification strategy, the number of co-purifying proteins was reduced by more than 10-fold, indicating the high purity of the isolated SDS insoluble material. Negative staining and immuno-gold electron microscopy allowed confirmation of the purity of these preparations. Further studies are required to understand the spatial and biological attributes that contribute to the deposition of these proteins into amyloid inclusions. Taken together, this analytical strategy is well-positioned to increase the understanding of amyloid biology.

This biochemical purification method with mass spectrometry-based proteomic analysis facilitates the robust characterization of amyloid fibril cores, which may accelerate the identification of targets for preventing Alzheimer's disease.

Proteinaceous fibrillar inclusions are key pathological hallmarks of multiple neurodegenerative diseases. In the early stages of Alzheimer&#39;s disease ...

Sex-Specific Glial Biomarkers in Alzheimer&#39;s Disease: A Transcriptome Analysis

Biomarker Identification for Gender Specificity of Alzheimer's Disease Based on the Glial Transcriptome Profiles

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study analyzed 220,095 single-nuclei transcriptomes from the frontal cortex of thirty-three AD individuals in the GEO database. Sex-specific Differentially Expressed Genes (DEGs) were identified in glial cells, including 243 in astrocytes, 1,154 in microglia, and 572 in oligodendrocytes. Gene Ontology (GO) functional annotation analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed functional concentration in synaptic, neural, and hormone-related pathways. Protein-protein interaction network (PPI) identified MT3, CALM2, DLG2, KCND2, PAKACB, CAMK2D, and NLGN4Y in astrocytes, TREM2, FOS, APOE, APP, and NLGN4Y in microglia, and GRIN2A, ITPR2, GNAS, and NLGN4Y in oligodendrocytes as key genes. NLGN4Y was the only gene shared by the three glia and was identified as the biomarker for the gender specificity of AD. Gene-transcription factor (TF)-miRNA coregulatory network identified key regulators for NLGN4Y and its target TCMs. Ecklonia kurome Okam (Kunbu) and Herba Ephedrae (Mahuang) were identified, and the effects of the active ingredients on AD were displayed. Finally, enrichment analysis of Kunbu and Mahuang suggested that they might act as therapeutic candidates for gender specificity of AD.

Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer&#39;s Disease

This study analyzed single-nuclei transcriptomes of thirty-three individuals with Alzheimer's disease (AD), revealing sex-specific DEGs in glial cells. Functional enrichment analysis highlighted synaptic, neural, and hormone-related pathways. Key genes, namely NLGN4Y and its regulators, were identified, and potential therapeutic candidates for gender-specific AD were proposed.

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study ...

Fabrication of Amyloid-&#946;-Secreting Alginate Microbeads for Use in Modelling Alzheimer's Disease

According to the amyloid cascade hypothesis, the earliest trigger in the development of Alzheimer&#8217;s disease (AD) is the accumulation of toxic amyloid-&#946; (A&#946;) fragments, eventually leading to the classical features of the disease: amyloid plaques, neurofibrillary tangles and synaptic and neuronal loss. The lack of relevant non-transgenic preclinical models reflective of disease progression is one of the main factors hindering the discovery of effective drug treatments. To this end, we have developed a protocol for the fabrication of alginate microbeads containing amyloid-secreting cells useful for the study of the effects of chronic A&#946; production.
Chinese hamster ovary cells previously transfected with a human APP gene, secreting A&#946; (i.e., 7PA2 cells), were used in this study. A three-dimensional (3D) in vitro model for the sustained release of A&#946; was fabricated by encapsulation of 7PA2 cells in alginate. The process was optimized to target a bead diameter of 500-600 &#956;m for further in vivo studies. Optimization of 7PA2 cell encapsulation in alginate was performed altering fabrication parameters, e.g., alginate concentration, gel flow rate, electrostatic potential, head vibration frequency, gelling solution. Levels of secreted A&#946; were analyzed over time and compared between alginate beads and standard cell culture methods (up to 96 h).
A concentration of 1.5 x 106 7PA2 cells/mL and an alginate concentration of 2% (w/v) buffered with HEPES and subsequent gelation in 0.5 M calcium chloride for 5 min were found to fabricate the most stable microbeads. Fabricated microbeads were 1) of uniform size, 2) with an average diameter of 550 &#956;m, 3) containing about 100-150 cells per microbead and 4) able to secrete A&#946;.
In conclusion, our optimized method for the production of stable alginate microbeads containing amyloid-producing 7PA2 cells might enable the modeling of important aspects of AD both in vitro and in vivo.

This protocol illustrates a cell encapsulation method by rapid physical gelation of alginate to immobilize cells. Obtained microbeads allow controlled and sustained secretion of amyloid-&#946; over time and can be used to study the effects of secreted amyloid-&#946; in in vitro and in vivo models.

According to the amyloid cascade hypothesis, the earliest trigger in the development of Alzheimer&#8217;s disease (AD) is the accumulation of toxic ...

Stabilization of MAMs in a 3D Neural Model of Alzheimer

Quantitative Analysis of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Stabilization in a Neural Model of Alzheimer's Disease (AD)

A method to quantitate the stabilization of Mitochondria-Associated endoplasmic reticulum Membranes (MAMs) in a 3-dimensional (3D) neural model of Alzheimer&#39;s disease (AD) is presented here. To begin, fresh human neuro progenitor ReN cells expressing &#946;-amyloid precursor protein (APP) containing familial Alzheimer&#39;s disease (FAD) or na&#239;ve ReN cells are grown in thin (1:100) Matrigel-coated tissue culture plates. After the cells reach confluency, these are electroporated with expression plasmids encoding red fluorescence protein (RFP)-conjugated mitochondria-binding sequence of AKAP1(34-63) (Mito-RFP) that detects mitochondria or constitutive MAM stabilizers MAM 1X or MAM 9X that stabilize tight (6 nm &#177; 1 nm gap width) or loose (24 nm &#177; 3 nm gap width) MAMs, respectively. After 16-24 h, the cells are harvested and enriched by a fluorescence-activated cell sorter (FACS). An equal number of FACS-enriched cells are seeded in the 3-dimensional matrix (1:1 Matrigel) and allowed to differentiate into mature neurons for 10 days. Live cell images of the 10-day differentiated cells expressing the RFP-conjugated MAM stabilizers are captured under a fluorescent microscope equipped with a live-cell imaging culture chamber maintaining the CO2 (5%), temperature (37 &#176;C), and humidity (~90%). Toward this end, we performed live-cell imaging and kymographic analyses to measure the motility of free mitochondria labeled with Mito-RFP or ER-bound mitochondria of tight or loose gap widths stabilized by MAM 1X or MAM 9X, respectively, in the most extended neuronal process of each ReN GA neuron which is at least 500 nm long, considering these as axons.

Here, we describe measuring the axonal transport rate of constitutive stabilizers of mitochondria-associated endoplasmic reticulum (ER) membranes (MAMs) by increasing or maintaining&#160;neurotoxic &#946;-amyloid (A&#946;) generation from Alzheimer&#39;s disease (AD) neurons in real-time to serve as a direct and quantitative metric to measure MAM stabilization and aid the development of AD therapeutics.

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

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