{"doi":"10.1002/rcm.9638","title":"Evaluating the optimal tissue thickness for mass spectrometry imaging using infrared matrix‐assisted laser desorption electrospray ionization","abstract":"RATIONALE: Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) utilizes a 2970 nm mid-IR laser to desorb samples with depth resolutions (Z) on the order of micrometers. Conventionally, 5-20 μm thick tissue sections are used to characterize different applications of the IR-MALDESI source, but an optimal thickness has not been systematically investigated. METHODS: Mouse liver was sectioned to various thicknesses and analyzed using IR-MALDESI mass spectrometry imaging (MSI). Height profiles of tissue sections of various cryosectioned thicknesses were acquired to affirm tissue thickness. Tissue sections of each thickness were measured using a Keyence microscope. Paraffin wax was cryosectioned, mounted on microscope slides, and measured using a chromatic confocal sensor system to determine the cryostat sectioning accuracy. RESULTS: Analyzing sectioned tissues at higher thickness (>10 μm) leads to lower ion abundance, a decrease in signal over long analysis times, and more frequent instrument cleaning. Additionally, increasing tissue thickness above the optimum (7 μm) does not result in a significant increase in lipid annotations. CONCLUSIONS: This work defines an optimal sample thickness for IR-MALDESI-MSI and demonstrates the utility of optimizing tissue thickness for MSI platforms of comparable Z resolution.","journal":"Rapid Communications in Mass Spectrometry","year":2023,"id":344992,"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":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9535,"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":864689,"name":"Kevan T. Knizner","orcid":"0000-0002-8846-3135","position":1,"is_corresponding":false},{"id":743306,"name":"Ying Xi","orcid":"0000-0003-0289-9921","position":2,"is_corresponding":false},{"id":448808,"name":"David C. Muddiman","orcid":"0000-0003-2216-499X","position":3,"is_corresponding":false},{"id":913280,"name":"Alena N. Joignant","orcid":"0000-0003-0890-5480","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T01:11:35.133818Z","pmid":"37817341","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":[]}