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      Development of an in situ cell-type specific proteome analysis method using antibody-mediated biotinylation

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          Abstract

          Since proteins are essential molecules exerting cellular functions, decoding proteome changes is the key to understanding the normal physiology and pathogenesis mechanism of various diseases. However, conventional proteomic studies are often conducted on tissue lumps, in which multiple cell types are entangled, presenting challenges in interpreting the biological dynamics among diverse cell types. While recent cell-specific proteome analysis techniques, like BONCAT, TurboID, and APEX, have emerged, their necessity for genetic modifications limits their usage. The alternative, laser capture microdissection (LCM), although it does not require genetic alterations, is labor-intensive, time-consuming, and requires specialized expertise, making it less suitable for large-scale studies. In this study, we develop the method for in situ cell-type specific proteome analysis using antibody-mediated biotinylation (iCAB), in which we combined immunohistochemistry (IHC) with the biotin-tyramide signal amplification approach. Poly-horseradish peroxidase (HRP) conjugated to the secondary antibody will be localized at a target cell type via a primary antibody specific to the target cell type and biotin-tyramide activated by HRP will biotinylate the nearby proteins. Therefore, the iCAB method can be applied to any tissues that can be used for IHC. As a proof-of-concept, we employed iCAB for mouse brain tissue enriching proteins for neuronal cell bodies, astrocytes, and microglia, followed by identifying the enriched proteins using 16-plex TMT-based proteomics. In total, we identified ~8,400 and ~6,200 proteins from enriched and non-enriched samples. Most proteins from the enriched samples showed differential expressions when we compared different cell type data, while there were no differentially expressed proteins from non-enriched samples. The cell type enrichment analysis with the increased proteins in respective cell types using Azimuth showed that neuronal cell bodies, astrocytes, and microglia data exhibited Glutamatergic Neuron, Astrocyte and Microglia/Perivascular Macrophage as the representative cell types, respectively. The proteome data of the enriched proteins showed similar subcellular distribution as non-enriched proteins, indicating that the iCAB-proteome is not biased toward any subcellular compartment. To our best knowledge, this study represents the first implementation of a cell-type-specific proteome analysis method using an antibody-mediated biotinylation approach. This development paves the way for the routine and widespread use of cell-type-specific proteome analysis. Ultimately, this could accelerate our understanding of biological and pathological phenomena.

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                14 June 2023
                : 2023.06.13.544682
                Affiliations
                [1 ]Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
                [2 ]Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
                Author notes
                [*]

                These authors contributed equally to this work.

                Author contributions

                C.H.N. conceived the iCAB method. C.H.N, T.R., S.Y.K., and T.T. designed the experiments. T.R., S.Y.K., T.T, and Y.J. performed the experiments and analyzed the data. T.R., S.Y.K., and C.H.N wrote the manuscript. C.H.N. supervised the research.

                []For correspondence: Chan Hyun Na, chanhyun@ 123456jhmi.edu
                Article
                10.1101/2023.06.13.544682
                10312661
                37398286
                8130fe57-cc4d-4dc7-822a-e8c9de207068

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

                History
                Funding
                This study was supported by the National Institutes of Health grant (R01NS123456 to C.H.N.). We acknowledge the National Institutes of Health shared instrumentation grant (S10OD021844).
                Categories
                Article

                cell-type-specific proteome analysis,mass spectrometry,brain,immunohistochemistry,biotin-tyramide,neuron,astrocytes,microglia

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