C.L. is supported by the Helmholtz Association under the joint research school &#34;Munich School for Data Science - MUDS&#34;.

Unsupervised Analysis of Multi-Omics Data in Coronary Syndromes Using MOFA

<ol>
	<li>L&#228;hnemann, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eleven+grand+challenges+in+single-cell+data+science.">Eleven grand challenges in single-cell data science.</a> Genome Biol. 21 (1), 31 (2020).</li><li>Colom&#233;-Tatch&#233;, M., Theis, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Statistical+single+cell+multi-omics+integration.">Statistical single cell multi-omics integration.</a> Curr Opin Syst Biol. 7, 54-59 (2018).</li><li>Hawe, J., Theis, F., Heinig, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inferring+interaction+networks+from+multi-omics+data.">Inferring interaction networks from multi-omics data.</a> Front Genet. 10, 535 (2019).</li><li>Hawe, J. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Network+reconstruction+for+trans+acting+genetic+loci+using+multi-omics+data+and+prior+information.">Network reconstruction for trans acting genetic loci using multi-omics data and prior information.</a> Genome Med. 14 (1), 125 (2022).</li><li>Koh, H. W. L., Fermin, D., Vogel, C., Choi, K. P., Ewing, R. M., Choi, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=iOmicsPASS:+network-based+integration+of+multiomics+data+for+predictive+subnetwork+discovery.">iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery.</a> NPJ Syst Biol Appl. 5, 22 (2019).</li><li>Ogris, C., Hu, Y., Arloth, J., M&#252;ller, N. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Versatile+knowledge+guided+network+inference+method+for+prioritizing+key+regulatory+factors+in+multi-omics+data.">Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data.</a> Sci Rep. 11, 6806 (2021).</li><li>Lee, C., vander Schaar, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+variational+information+bottleneck+approach+to+multi-omics+data+integration.">A variational information bottleneck approach to multi-omics data integration.</a> Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. 130, 1513-1521 (2021).</li><li>Singh, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DIABLO:+an+integrative+approach+for+identifying+key+molecular+drivers+from+multi-omics+assays.">DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays.</a> Bioinformatics. 35 (17), 3055-3062 (2019).</li><li>Argelaguet, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-omics+factor+analysis-a+framework+for+unsupervised+integration+of+multi-omics+data+sets.">Multi-omics factor analysis-a framework for unsupervised integration of multi-omics data sets.</a> Mol Syst Biol. 14 (6), e8124 (2018).</li><li>Cantini, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Benchmarking+joint+multi-omics+dimensionality+reduction+approaches+for+the+study+of+cancer.">Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer.</a> Nature Commun. 12 (1), 124 (2021).</li><li>Pekayvaz, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiomic+analyses+uncover+immunological+signatures+in+acute+and+chronic+coronary+syndromes.">Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes.</a> Nature Medicine. 30 (6), 1696-1710 (2024).</li><li>Swirski, F. K., Nahrendorf, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cardioimmunology:+the+immune+system+in+cardiac+homeostasis+and+disease.">Cardioimmunology: the immune system in cardiac homeostasis and disease.</a> Nat Rev Immunol. 18 (12), 733-744 (2018).</li><li>Janjic, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prime-seq,+efficient+and+powerful+bulk+RNA+sequencing.">Prime-seq, efficient and powerful bulk RNA sequencing.</a> Genome Biol. 23 (1), 88 (2022).</li><li>Wolf, F. A., Angerer, P., Theis, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SCANPY:+large-scale+single-cell+gene+expression+data+analysis.">SCANPY: large-scale single-cell gene expression data analysis.</a> Genome Biol. 19 (1), 15 (2018).</li><li>Cao, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrated+analysis+of+multimodal+single-cell+data+with+structural+similarity.">Integrated analysis of multimodal single-cell data with structural similarity.</a> Nucleic Acids Res. 50 (21), e121 (2022).</li><li>. Get Started - JupyterLab 4.1.0a4 documentation Available from: <a target="_blank" href="https://jupyterlab.readthedocs.io/en/latest/getting_started/overview.html">https://jupyterlab.readthedocs.io/en/latest/getting_started/overview.html</a> (2024)</li><li>. MOFA2: training a model in R Available from: <a target="_blank" href="https://raw.githack.com/bioFAM/MOFA2_tutorials/master/R_tutorials/getting_started_R.html">https://raw.githack.com/bioFAM/MOFA2_tutorials/master/R_tutorials/getting_started_R.html</a> (2020)</li><li>De Silva, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robust+T+cell+activation+requires+an+eIF3-driven+burst+in+T+cell+receptor+translation.">Robust T cell activation requires an eIF3-driven burst in T cell receptor translation.</a> eLife. 10, e74272 (2021).</li><li>Li, G., Liang, X., Lotze, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HMGB1:+The+central+cytokine+for+all+lymphoid+cells.">HMGB1: The central cytokine for all lymphoid cells.</a> Front Immunol. 4, 68 (2013).</li><li>Jassal, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+reactome+pathway+knowledgebase.">The reactome pathway knowledgebase.</a> Nucleic Acids Res. 48 (D1), D498-D503 (2020).</li><li>Argelaguet, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MOFA+:+a+statistical+framework+for+comprehensive+integration+of+multimodal+single-cell+data.">MOFA+: a statistical framework for comprehensive integration of multimodal single-cell data.</a> Genome Biol. 21 (1), 111 (2020).</li><li>Velten, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identifying+temporal+and+spatial+patterns+of+variation+from+multimodal+data+using+MEFISTO.">Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO.</a> Nat Methods. 19 (2), 179-186 (2022).</li><li>Qoku, A., Buettner, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Encoding+domain+knowledge+in+multi-view+latent+variable+models:+A+Bayesian+approach+with+structured+sparsity.">Encoding domain knowledge in multi-view latent variable models: A Bayesian approach with structured sparsity.</a> Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. 206, 11545-11562 (2022).</li><li>Multi-Omics Factor Analysis. MOFA Available from: <a target="_blank" href="https://biofam.github.io/MOFA2/">https://biofam.github.io/MOFA2/</a> (2024)</li><li>Mitchel, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tensor+decomposition+reveals+coordinated+multicellular+patterns+of+transcriptional+variation+that+distinguish+and+stratify+disease+individuals.">Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals.</a> bioRxiv. , (2023).</li></ol>

The authors declare no conflict of interest.

With the outlined protocol, a modular and extendible Jupyter-notebook-based workflow that can be used to quickly explore a complex multi-omics dataset is presented. The major parts of the workflow consist of the pre-processing and data harmonization part (offering different standard steps for filtering and normalization of the data), estimation of the MOFA9 model and some exemplary downstream analysis. One of the main critical steps is to pre-process and integrate, and harmonize the different omic...

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Deciphering the complex molecular mechanisms that lead to specific diseases or govern the evolution of a disease is a task with high medical relevance as it might reveal new insights for understanding and treating diseases.
Recent technological advances enable to measure those processes on a higher resolution (e.g., on the single-cell level) and at various biological layers (e.g., DNA, mRNA, chromatin accessibility, DNA methylation, proteomics) at the same time. This leads to the increasing generation of large multidimensional biological datasets, which can be jointly analyzed to generate more insights into the underlying processes. At the same time, combining and analyzing the different data sources in a biologically meaningful way remains a challenging task1.
Different technological limits, noises, and ranges of variability between different omics pose one challenge. For example, single-cell RNA-sequencing (scRNA-seq) data is very sparse and often influenced by large technical or batch effects. Additionally, the feature space is often very large, ranging across several thousand measured genes or proteins, while sample sizes are limited. This is further complicated by complex designs, which might include several disease conditions, confounding factors, time points, and resolutions. For example, in the presented use case, different data types were available either on the single-cell or sample (bulk) level. Besides that, the data may be incomplete, and not all measurements might be available for all analyzed subjects.
Due to these challenges, different omics and included features are still often analyzed only separately2 even though performing an integrated analysis cannot only provide a complete picture of the process but biological and technical noises from one omic might also be compensated by other omics3,4. Several different methods have been proposed to perform an integrated analysis of multi-omics data, including Bayesian methods, network-based methods5,6, multimodal deep learning7, and dimensionality reduction methods via matrix factorization8,9. For the latter, the results of a large benchmarking study10 have shown the MOFA9 (multi-omic factor analysis) method to be one of the better-suited tools when data should be linked to clinical annotations.
Especially in complex settings, unsupervised matrix factorization methods are a useful approach to reduce complexity and extract shared and complementary signals from different data sources and features. By decomposing the complex space into lower-rank latent representations, the major sources of variance within the data can be quickly explored and linked to known covariates. In case the same pattern of variation is shared across multiple features (e.g., genes or proteins), this may be aggregated to a few factors while noise is reduced. Regularization can be used to increase the sparsity of model coefficients, which makes the approach well-suited in settings where the feature space is large while the number of samples is limited9.
This protocol presents a flexible analysis workflow that uses the MOFA model to showcase how to quickly explore a complex multi-omics dataset and distill the main patterns of variation that characterize this dataset. The workflow consists of three main steps. In the first step, Data pre-processing and harmonization, different strategies for data preprocessing based on different input data types (scRNA-seq, proteomics, cytokine, clinical data) are presented. The protocol elaborates on how to process the features of the different input datasets, filter out low-quality features, and normalize them to harmonize their distributions. We also show how those pre-processing decisions might affect downstream results. In the second step, the MOFA model is applied to the data, and the resulting variance decomposition can be used to evaluate the integration of the different datasets. The third step shows how to link the captured factors to covariates and uncover the molecular programs that define those factors. With the presented workflow, we were able to extract several latent factors linked to clinical covariates in a dataset of patients suffering from coronary syndromes and identify potential underlying multicellular immune programs from a previous project11. We will use this dataset here, but the protocol can easily be applied to other contexts, including other omics.
The dataset consists of samples from patients with stable chronic coronary syndromes (CCS), acute coronary syndromes (ACS), and a control group with healthy coronaries (non-CCS) (Figure 1). ACS is caused by plaque rupture in preexisting CCS, leading to an acute disruption of blood flow to the myocardium and a subsequent ischaemic injury of the heart. This injury causes an inflammatory response by the immune system followed by a reparative phase, which lasts until several days after the acute event12. To be able to characterize this immune response for ACS patients, blood samples were taken at four different timepoints: acute (TP1); after recanalization (14 [&#177; 8] h) (TP2); 60 [&#177; 12] h later (TP3); before discharge (6.5 [&#177;1.5] days) (TP4) (Figure 1A). For CCS and patients with healthy coronaries, only one time point was available- (TP0). For all patients and time points different assays based on the blood samples were measured: clinical markers of inflammation (Creatine-Kinase (CK), CK-MB, Troponin, C-reactive protein (CRP)), scRNA-seq of peripheral blood mononuclear cells (PBMCs), cytokine analysis, plasma proteomics and prime-seq13 data of neutrophils.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66659/66659fig01v2.jpg" /> 
Figure 1: Myocardial infarction multi-omic input dataset. Input dataset: The analyzed data includes blood samples from patients (n = 62) with acute coronary syndrome (ACS), chronic coronary syndromes (CCS), and&#160;patients with healthy coronaries (non-CCS). For ACS patients blood samples were included at four&#160;different time points (TP1-4), for CCS and non-CCS patients at a single time point (TP0). Each patient and time point combination is treated as a separate sample in the analysis. Different omic assays were measured on the samples: clinical blood tests (n = 125), scRNA-seq (n = 121), plasma-proteomics (n = 119), cytokine assay (n = 127) and neutrophil prime-seq (n = 121). Subsequently, the described protocol was applied to integrate the data across all omics and explore it using the MOFA model and further downstream analysis (factor analysis, pathway enrichment). <a href="https://www.jove.com/files/ftp_upload/66659/66659fig01largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
As input for the workflow as presented here, we take raw counts from scRNA-seq data after processing with cellranger and quality control (QC) as, for example, outlined in the scanpy14 preprocessing tutorial. For cell-type annotation, we used the automated Azimuth15 pipeline. The counts are then aggregated on the sample level for each cell-type by taking the mean across all cells for each sample and cell-type (pseudobulk aggregation). Plasma-proteomics is included as normalized and median-centered intensities, and for neutrophils, we take the umi unique molecular identifier (UMI) exon counts from the prime-seq. On cytokine and clinical values, no previous preprocessing has been applied. Further details on the (experimental) data generation are outlined in the corresponding manuscript11. As the results presented here are based on using the automated Azimuth annotation for cell-types in the scRNA-seq data compared to the marker based strategy that was used in the referenced publication the results presented here are similar but not exactly the same as presented in the publication. In the manuscript it could be shown that the cell-type annotation strategy does not change the main patterns and biological interpretations of the analysis but small changes in the exact values resulting from the model might vary. Overall the input data was a complex multi-dimensional dataset including different time points and measurement levels (single-cells vs. bulk) of more than 10,000 different features (genes, proteins, clinical values). A strict pre-processing and data harmonization strategy followed by MOFA analysis has been shown to be a useful and quick tool for exploring the data and extracting relevant immune program. Each time point and patient combination is treated as an independent sample in the MOFA analysis.&#160;Each data type and cell-type is considered a separate view in the MOFA analysis.
This protocol provides instructions for preparing the input data for the workflow, executing the different workflow steps, customizing configurations, interpreting the resulting figures, and iteratively adjusting the configurations based on the interpretations. An overview of the different steps of the protocol, the required input datasets at each step, and resulting figures and datasets is given by the technical workflow overview (Figure 2).
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66659/66659fig02v2.jpg" /> 
Figure 2: Technical workflow overview. Outline of the workflow for the analysis of the multi-omics dataset. The different elements are highlighted by different colors and symbols. Jupyter Notebooks belonging to the Data Preprocessing and Harmonization (1) step are colored in blue. Jupyter Notebooks belonging to the &#39;MOFA Model&#39; (2) step are colored in orange. Jupyter Notebooks belonging to the &#39;Downstream Analysis&#39; (3) step are colored in green. One Jupyter Notebook to be used for comparison of the results is colored in yellow. Configuration files where parameters for the execution of the workflow can be modified are highlighted in purple. Input datasets that are required to run the workflow are indicated by the dataset symbol and highlighted in grey. All figure outputs that are generated during the workflow execution are indicated by the magnifying glass symbol. Datasets generated during workflow execution are indicated as tables. In general, the workflow is executed sequentially: (1) Data Preprocessing and Harmonization consists of two steps: first generation of a pseudobulk table based on the scRNA-seq input data (01_Prepare_Pseudobulk) and subsequent integration and normalization of this data together with all the other sample-level (bulk) inputs (02_Integrate_and_Normalize_Data). Within this step via the configuration files, it is possible to configure for each dataset separately which of the indicated pre-processing and normalization steps (e.g., Sample Filter) should be applied. (2) &#39;MOFA Model&#39;: runs the MOFA model on the generated input of the first step with the configurations specified in the configuration file (03_MOFA_configs.csv) (3) &#39;Downstream Analysis&#39;: consists of three different notebooks which may be run independently of each other to generate insights into the generated MOFA results and associate them with sample meta-data (covariates) provided as input via the &#39;Sample Meta Data.csv&#39; file. (4) &#39;Model Comparison&#39;: is a small separate step that may be used to compare different models generated in step 2. <a href="https://www.jove.com/files/ftp_upload/66659/66659fig02largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The workflow consists of several Jupyter Notebooks written in R and Python (knowledge of the R and Python language is not required to run the workflow but might be helpful in case errors appear). In various steps of the protocol, parameters are changed via configuration files (&#39;.csv&#39; files containing the postfix &#39;_Configs&#39; in the name). Within the protocol, we only outline the parameters that need to be changed starting from the default configuration.
Several other parameters may also be changed, for example to customize the pre-processing. A documentation of these parameters and explanations is given in the file &#39;Documentation_Config_Parameter&#39;, which is included in the downloaded repository.

<table><tbody><tr><td>Apptainer</td><td>NA</td><td>NA</td><td>https://apptainer.org/docs/admin/main/installation.html</td></tr><tr><td>Compute server or workstation or cloud&amp;nbsp; (Linux, Mac or Windows environment).&lt;br/&gt; Depending on the size of the different input datasets we recommend running the workflow on a suitable machine (in our setting we use: 16 CPU, 64GB Memory)</td><td>Any manufacturer</td><td>16 CPU, 64GB Memory</td><td>Large Memory is only required for the processing of the raw single cell data. After preprocessing the later analysis steps can also be performed on regular desktop or laptop computers</td></tr><tr><td>git</td><td>NA</td><td>NA</td><td>https://git-scm.com/book/en/v2/Getting-Started-Installing-Git</td></tr><tr><td>GitHub</td><td>GitHub</td><td>NA</td><td>https://github.com/heiniglab/mofa_workflow</td></tr></tbody></table>

application of unsupervised multi-omic factor analysis to uncover patterns of variation and molecular processes linked to cardiovascular disease

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Complex, multidimensional datasets are a valuable resource to generate more insights into those processes, but the analysis of such datasets can be challenging due to the high dimensionality resulting, for example, from different disease conditions, timepoints, and omics capturing the process at different resolutions.
Here, we showcase an approach to analyze and explore such a complex multiomics dataset in an unsupervised way by applying multi-omics factor analysis (MOFA) to a dataset generated from blood samples that capture the immune response in acute and chronic coronary syndromes. The dataset consists of several assays at differing resolutions, including sample-level cytokine data, plasma-proteomics and neutrophil prime-seq, and single-cell RNA-seq (scRNA-seq)&#160;data. Further complexity is added by having several different time points measured per patient and several patient subgroups.
The analysis workflow outlines how to integrate and analyze the data in several steps: (1) Data pre-processing and harmonization, (2) Estimation of the MOFA model, (3) Downstream analysis. Step 1 outlines how to process the features of the different data types, filter out low-quality features, and normalize them to harmonize their distributions for further analysis. Step 2 shows how to apply the MOFA model and explore the major sources of variance within the dataset across all omics and features. Step 3 presents several strategies for the downstream analysis of the captured patterns, linking them to the disease conditions and potential molecular processes governing those conditions.
Overall, we present a workflow for unsupervised data exploration of complex multi-omics datasets to enable the identification of major axes of variation composed of differing molecular features that can also be applied to other contexts and multi-omics datasets (including other assays as presented in the exemplary use case).

Following the successful execution of the workflow, several tables and figures are generated as indicated by Figure 2. Figures are placed in the /figures folder (Figure 6, Figure 7, Figure 8, Supplementary Figure 1, Supplementary Figure 2, Supplementary Figure 3, Supplementary Figure 4), and tables will be placed in the...

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

We present a flexible, extendible Jupyter-lab-based workflow for the unsupervised analysis of complex multi-omics datasets that combines different pre-processing steps, estimation of the multi-omics factor analysis model, and several downstream analyses.

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Complex, multidimensional datasets are a ...

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JoVE Journal

Genetics

1.1K Views.  Helmholtz Zentrum München. Onze workflow maakt het mogelijk om eenvoudig complexe multi-omics datasets met verschillende resoluties te analyseren. De aanpak extraheert een groot aantal patronen van varianten die uniek zijn of worden gedeeld met de specifieke gegevenstypen en voegt deze samen. De resulterende zogenaamde factoren kunnen vervolgens worden gekoppeld aan moleculaire processen en klinische of technische covariaten.Onze analyse was een van de eersten die het MOFA-model...

Video: Auteur in de schijnwerpers: geïntegreerde multi-omics-analyse voor het onthullen van meercellige immuunsignaturen in klinische hartaanvalcohorten

Analysis of Cap-binding Proteins in Human Cells Exposed to Physiological Oxygen Conditions

Translational control is a focal point of gene regulation, especially during periods of cellular stress. Cap-dependent translation via the eIF4F complex is by far the most common pathway to initiate protein synthesis in eukaryotic cells, but stress-specific variations of this complex are now emerging. Purifying cap-binding proteins with an affinity resin composed of Agarose-linked m7GTP (a 5' mRNA cap analog) is a useful tool to identify factors involved in the regulation of translation initiation. Hypoxia (low oxygen) is a cellular stress encountered during fetal development and tumor progression, and is highly dependent on translation regulation. Furthermore, it was recently reported that human adult organs have a lower oxygen content (physioxia 1-9% oxygen) that is closer to hypoxia than the ambient air where cells are routinely cultured. With the ongoing characterization of a hypoxic eIF4F complex (eIF4FH), there is increasing interest in understanding oxygen-dependent translation initiation through the 5' mRNA cap. We have recently developed a human cell culture method to analyze cap-binding proteins that are regulated by oxygen availability. This protocol emphasizes that cell culture and lysis be performed in a hypoxia workstation to eliminate exposure to oxygen. Cells must be incubated for at least 24 hr for the liquid media to equilibrate with the atmosphere within the workstation. To avoid this limitation, pre-conditioned media (de-oxygenated) can be added to cells if shorter time points are required. Certain cap-binding proteins require interactions with a second base or can hydrolyze the m7GTP, therefore some cap interactors may be missed in the purification process. Agarose-linked to enzymatically resistant cap analogs may be substituted in this protocol. This method allows the user to identify novel oxygen-regulated translation factors involved in cap-dependent translation.

Here, we present human cell culture protocols to analyze translation initiation factors that bind the 5' cap of mRNA during physiological oxygen conditions. This method utilizes an Agarose-linked m7GTP cap analog and is suitable to investigate cap-binding factors and their interacting partners.

Translational control is a focal point of gene regulation, especially during periods of cellular stress. Cap-dependent translation via the eIF4F complex ...

Chromatin Spread Preparations for the Analysis of Mouse Oocyte Progression from Prophase to Metaphase II

Chromatin spread techniques have been widely used to assess the dynamic localization of various proteins during gametogenesis, particularly for spermatogenesis. These techniques allow for visualization of protein and DNA localization patterns during meiotic events such as homologous chromosome pairing, synapsis and DNA repair. While a few protocols have been described in the literature, general chromatin spread techniques using mammalian prophase oocytes are limited and difficult due to the timing of meiosis initiation in fetal ovaries. In comparison, prophase spermatocytes can be collected from juvenile male mice with higher yields without the need for microdissection. However, it is difficult to obtain a pure synchronized population of cells at specific stages due to the heterogeneity of meiotic and post-meiotic germ cell populations in the juvenile and adult testis. For later stages of meiosis, it is advantageous to assess oocytes undergoing meiosis I (MI) or meiosis II (MII), because groups of mature oocytes can be collected from adult female mice and stimulated to resume meiosis in culture. Here, methods for meiotic chromatin spread preparations using oocytes dissected from fetal, neonatal and adult ovaries are described with accompanying video demonstrations. Chromosome missegregation events in mammalian oocytes are frequent, particularly during MI. These techniques can be used to assess and characterize the effects of different mutations or environmental exposures during various stages of oogenesis. As there are distinct differences between oogenesis and spermatogenesis, the techniques described within are invaluable to increase our understanding of mammalian oogenesis and the sexually dimorphic features of chromosome and protein dynamics during meiosis.

Oogenesis in mammals is known to be error-prone, particularly due to chromosome missegregation. This manuscript describes chromatin spread preparation methods for mouse prophase, metaphase I and II-staged oocytes. These fundamental techniques allow for the study of chromatin-bound proteins and chromosome morphology throughout mammalian oogenesis.

Chromatin spread techniques have been widely used to assess the dynamic localization of various proteins during gametogenesis, particularly for ...

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for example, during cellular differentiation, morphogenesis, and functional stimulation (such as activation of immune cells), or after exposure to drugs and other factors from the local environment. Depending on the stimulus and cell type, these changes occur rapidly and at any possible level of gene regulation. Displaying all molecular processes of a responding cell to a certain type of stimulus/drug is one of the hardest tasks in molecular biology. Here, we describe a protocol that enables the simultaneous analysis of multiple layers of gene regulation. We compare, in particular, transcription factor binding (Chromatin-immunoprecipitation-sequencing (ChIP-seq)), de novo transcription (4-thiouridine-sequencing (4sU-seq)), mRNA processing, and turnover as well as translation (ribosome profiling). By combining these methods, it is possible to display a detailed and genome-wide course of action.
Sequencing newly transcribed RNA is especially recommended when analyzing rapidly adapting or changing systems, since this depicts the transcriptional activity of all genes during the time of 4sU exposure (irrespective of whether they are up- or downregulated). The combinatorial use of total RNA-seq and ribosome profiling additionally allows the calculation of RNA turnover and translation rates. Bioinformatic analysis of high-throughput sequencing results allows for many means for analysis and interpretation of the data. The generated data also enables tracking co-transcriptional and alternative splicing, just to mention a few possible outcomes.
The combined approach described here can be applied for different model organisms or cell types, including primary cells. Furthermore, we provide detailed protocols for each method used, including quality controls, and discuss potential problems and pitfalls.

This protocol describes the combinatorial use of ChIP-seq, 4sU-seq, total RNA-seq, and ribosome profiling for cell lines and primary cells. It enables tracking changes in transcription-factor binding, de novo transcription, RNA processing, turnover and translation over time, and displaying the overall course of events in activated and/or rapidly changing cells.

Upon activation, cells rapidly change their functional programs and, thereby, their gene expression profile. Massive changes in gene expression occur, for ...

Production and Purification of Baculovirus for Gene Therapy Application

Baculovirus has traditionally been used for the production of recombinant protein and vaccine. However, more recently, baculovirus is emerging as a promising vector for gene therapy application. Here, baculovirus is produced by transient transfection of the baculovirus plasmid DNA (bacmid) in an adherent culture of Sf9 cells. Baculovirus is subsequently expanded in Sf9 cells in a serum-free suspension culture until the desired volume is obtained. It is then purified from the culture supernatant using heparin affinity chromatography. Virus supernatant is loaded onto the heparin column which binds baculovirus particles in the supernatant due to the affinity of heparin for baculovirus envelop glycoprotein. The column is washed with a buffer to remove contaminants and baculovirus is eluted from the column with a high-salt buffer. The eluate is diluted to an isotonic salt concentration and baculovirus particles are further concentrated using ultracentrifugation. Using this method, baculovirus can be concentrated up to 500-fold with a 25% recovery of infectious particles. Although the protocol described here demonstrates the production and purification of the baculovirus from cultures up to 1 L, the method can be scaled-up in a closed-system suspension culture to produce a clinical-grade vector for gene therapy application.

In this protocol, baculovirus is produced by transient transfection of baculovirus plasmid into Sf9 cells and amplified in a serum-free suspension culture. The supernatant is purified by heparin affinity chromatography and further concentrated by ultracentrifugation. This protocol is useful for the production and purification of baculovirus for gene therapy application.

Baculovirus has traditionally been used for the production of recombinant protein and vaccine. However, more recently, baculovirus is emerging as a ...

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling pathway is one of such essential molecular machinery for cell-to-cell communications, which plays key roles in normal development of embryos. This pathway involves a cell-to-cell transfer of oscillatory information with ultradian rhythms, but despite the progress in molecular biology techniques, it has been challenging to elucidate the impact of multicellular interactions on oscillatory gene dynamics. Here, we present a protocol that permits optogenetic control and live monitoring of gene expression patterns in a precise temporal manner. This method successfully revealed that intracellular and intercellular periodic inputs of Notch signaling entrain intrinsic oscillations by frequency tuning and phase shifting at the single-cell resolution. This approach is applicable to the analysis of the dynamic features of various signaling pathways, providing a unique platform to test a functional significance of dynamic gene expression programs in multicellular systems.

Here, we present a protocol to analyze cell-to-cell transfer of oscillatory information by optogenetic control and live monitoring of gene expression. This approach provides a unique platform to test a functional significance of dynamic gene expression programs in multicellular systems.

Cells should respond properly to temporally changing environments, which are influenced by various factors from surrounding cells. The Notch signaling ...

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

4D Ultrasound and MALDI Imaging for Cardiac and Vascular Analysis in Mice

Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging

Cardiovascular disease (CVD) is the leading cause of death in the United States. Damage in&#160;the cardiovascular system can be due to environmental exposure, trauma, drug toxicity, or numerous other factors.&#160;As a result, cardiac tissue and vasculature undergo structural changes and display diminished function. The damage and the resulting remodeling can be detected and quantified with ultrasound (US) imaging at the organ level and mass spectrometry imaging (MSI) at the molecular level. This manuscript describes an innovative methodology for studying murine cardiac pathophysiology, coupling in vivo four-dimensional (4D) ultrasound imaging and analysis with ex vivo matrix-assisted laser desorption/ionization (MADLI) MSI of the heart. 4D ultrasound can provide dynamic volumetric measurements, including radial displacement, surface area strain, and longitudinal strain throughout an entire cardiac cycle. In the vasculature, MSI and ultrasound are used to assess vessel wall compositions, hemodynamics, and vessel wall dynamics. The methodology can be tailored to study a myriad of CV diseases by adjusting functional metrics of interest and/or varying MALDI MSI protocol to target specific molecules. MALDI MSI can be used to study lipids, small metabolites, peptides, and glycans. This protocol outlines the use of MALDI MSI for untargeted lipidomic analysis and the use of ultrasound imaging for cardiovascular hemodynamics and biomechanics.

Here, we describe a protocol to use in vivo four-dimensional ultrasound imaging and ex vivo mass spectrometry imaging to assess biomechanical and biomolecular alterations in the murine cardiovascular system. This technique is applied to analyze cardiac remodeling in surgically induced myocardial infarction and vascular changes in aging animals.

Cardiovascular disease (CVD) is the leading cause of death in the United States. Damage in&#160;the cardiovascular system can be due to environmental ...

Bioengineering

Investigating Novel Mechanisms of Microvascular Dysfunction in Humans

Intradermal Microdialysis: An Approach to Investigating Novel Mechanisms of Microvascular Dysfunction in Humans

The cutaneous vasculature is an accessible tissue that can be used to assess microvascular function in humans. Intradermal microdialysis is a minimally invasive technique used to investigate mechanisms of vascular smooth muscle and endothelial function in the cutaneous circulation. This technique allows for the pharmacological dissection of the pathophysiology of microvascular endothelial dysfunction as indexed by decreased nitric oxide-mediated vasodilation, an indicator of cardiovascular disease development risk. In this technique, a microdialysis probe is placed in the dermal layer of the skin, and a local heating unit with a laser Doppler flowmetry probe is placed over the probe to measure the red blood cell flux. The local skin temperature is clamped or stimulated with direct heat application, and pharmacological agents are perfused through the probe to stimulate or inhibit intracellular signaling pathways in order to induce vasodilation or vasoconstriction or to interrogate mechanisms of interest (co-factors, antioxidants, etc.). The cutaneous vascular conductance is quantified, and mechanisms of endothelial dysfunction in disease states can be delineated.

Author Spotlight: A Pharmacodissection Approach to Uncover Mechanisms in Cardiovascular Disease Risk Populations 

Intradermal microdialysis is a minimally invasive technique used to investigate microvascular function in health and disease. Both dose-response and local heating protocols can be utilized for this technique to explore mechanisms of vasodilation and vasoconstriction in the cutaneous circulation.

The cutaneous vasculature is an accessible tissue that can be used to assess microvascular function in humans. Intradermal microdialysis is a minimally ...

Medicine

Analyzing Signaling Modules in Alzheimer's Disease with Deep Learning

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.

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.

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

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

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