{"doi":"10.1016/j.mcpro.2023.100546","title":"Cellular Proteomic Profiling Using Proximity Labeling by TurboID-NES in Microglial and Neuronal Cell Lines","abstract":"•Cytosolic TurboID biotinylates >50% of the proteome in microglia and neuronal cells.•TurboID-NES has minimal impacts on cellular proteomic composition and function.•About 1340 proteins labeled by TurboID differentiate microglia from neurons in vitro.•TurboID proteomic profiling captures microglial activation by lipopolysaccharide. Different brain cell types play distinct roles in brain development and disease. Molecular characterization of cell-specific mechanisms using cell type–specific approaches at the protein (proteomic) level can provide biological and therapeutic insights. To overcome the barriers of conventional isolation-based methods for cell type–specific proteomics, in vivo proteomic labeling with proximity-dependent biotinylation of cytosolic proteins using biotin ligase TurboID, coupled with mass spectrometry (MS) of labeled proteins, emerged as a powerful strategy for cell type–specific proteomics in the native state of cells without the need for cellular isolation. To complement in vivo proximity labeling approaches, in vitro studies are needed to ensure that cellular proteomes using the TurboID approach are representative of the whole-cell proteome and capture cellular responses to stimuli without disruption of cellular processes. To address this, we generated murine neuroblastoma (N2A) and microglial (BV2) lines stably expressing cytosolic TurboID to biotinylate the cellular proteome for downstream purification and analysis using MS. TurboID-mediated biotinylation captured 59% of BV2 and 65% of N2A proteomes under homeostatic conditions. TurboID labeled endolysosome, translation, vesicle, and signaling proteins in BV2 microglia and synaptic, neuron projection, and microtubule proteins in N2A neurons. TurboID expression and biotinylation minimally impacted homeostatic cellular proteomes of BV2 and N2A cells and did not affect lipopolysaccharide-mediated cytokine production or resting cellular respiration in BV2 cells. MS analysis of the microglial biotin-labeled proteins captured the impact of lipopolysaccharide treatment (>500 differentially abundant proteins) including increased canonical proinflammatory proteins (Il1a, Irg1, and Oasl1) and decreased anti-inflammatory proteins (Arg1 and Mgl2). Different brain cell types play distinct roles in brain development and disease. Molecular characterization of cell-specific mechanisms using cell type–specific approaches at the protein (proteomic) level can provide biological and therapeutic insights. To overcome the barriers of conventional isolation-based methods for cell type–specific proteomics, in vivo proteomic labeling with proximity-dependent biotinylation of cytosolic proteins using biotin ligase TurboID, coupled with mass spectrometry (MS) of labeled proteins, emerged as a powerful strategy for cell type–specific proteomics in the native state of cells without the need for cellular isolation. To complement in vivo proximity labeling approaches, in vitro studies are needed to ensure that cellular proteomes using the TurboID approach are representative of the whole-cell proteome and capture cellular responses to stimuli without disruption of cellular processes. To address this, we generated murine neuroblastoma (N2A) and microglial (BV2) lines stably expressing cytosolic TurboID to biotinylate the cellular proteome for downstream purification and analysis using MS. TurboID-mediated biotinylation captured 59% of BV2 and 65% of N2A proteomes under homeostatic conditions. TurboID labeled endolysosome, translation, vesicle, and signaling proteins in BV2 microglia and synaptic, neuron projection, and microtubule proteins in N2A neurons. TurboID expression and biotinylation minimally impacted homeostatic cellular proteomes of BV2 and N2A cells and did not affect lipopolysaccharide-mediated cytokine production or resting cellular respiration in BV2 cells. MS analysis of the microglial biotin-labeled proteins captured the impact of lipopolysaccharide treatment (>500","journal":"Molecular & Cellular Proteomics","year":2023,"id":326864,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":27,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9594,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":493945,"name":"Christine A Bowen","orcid":"0000-0002-2981-8629","position":1,"is_corresponding":false},{"id":997803,"name":"Hollis Zeng","orcid":null,"position":2,"is_corresponding":false},{"id":289793,"name":"Sruti Rayaprolu","orcid":"0000-0002-5212-3745","position":3,"is_corresponding":false},{"id":847988,"name":"Prateek Kumar","orcid":"0000-0001-5875-4065","position":4,"is_corresponding":false},{"id":741066,"name":"Pritha Bagchi","orcid":"0000-0001-7229-9476","position":5,"is_corresponding":false},{"id":107078,"name":"Eric B. Dammer","orcid":"0000-0003-2947-7606","position":6,"is_corresponding":false},{"id":1046451,"name":"Qi Guo","orcid":"0000-0001-6762-2929","position":7,"is_corresponding":false},{"id":288609,"name":"Duc M. Duong","orcid":"0000-0002-3325-9233","position":8,"is_corresponding":false},{"id":817310,"name":"Sara Bitarafan","orcid":"0000-0002-8571-2490","position":9,"is_corresponding":false},{"id":997262,"name":"Aditya Natu","orcid":"0000-0002-3399-5513","position":10,"is_corresponding":false},{"id":284965,"name":"Levi B. Wood","orcid":"0000-0002-4248-0888","position":11,"is_corresponding":false},{"id":21539,"name":"Nicholas T. Seyfried","orcid":"0000-0002-4507-624X","position":12,"is_corresponding":false},{"id":107093,"name":"Srikant Rangaraju","orcid":"0000-0003-2765-1500","position":13,"is_corresponding":false},{"id":817311,"name":"Sydney Sunna","orcid":"0000-0003-1882-6970","position":0,"is_corresponding":true}],"reference_count":86,"raw_metadata":null,"created_at":"2026-07-19T01:08:37.840107Z","pmid":"37061046","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}