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Bacterial biomass was collected and suspended in 1× phosphate-buffered saline (PBS). Bacterial cell suspension optical densities (ODs) were measured at a wavelength of 600 nm and adjusted in 1 mL of PBS to an OD600 of 0.8 (109 colony-forming units/mL). The bacterial biomass was washed with PBS three times by centrifuging the sample for 5 min at 12000g, discarding the supernatant and resuspending the pellet in 1.0 mL of PBS. Cells were resuspended in 150 μL of PBS and transferred to small vials (200 μL) containing glass beads (G-8772, Sigma-Aldrich, St. Louis, MO), in preparation for cell lysis, by bead-beating, using a PowerLyzer 24 Homogenizer (Qiagen, Hilden, Germany; settings of 4000 rpm for 45 s). Finally, the bacterial biomass was prepared for MS with an iST Sample Preparation Kit (Preomics, Martinsried, Germany). Dried peptides were labeled with the TMT reagent (Thermo Scientific, San Jose, CA) per the manufacturer’s instructions. The unreacted reagent was separated by purification of the peptides on a C18 desalting spin column (Harvard Apparatus, Holliston, MA)."],"repository":["Pride"],"modification":[""],"quantification_method":["Not available"],"data_protocol":["LC-MS/MS data files were analyzed using MiCId’s workflow (version 11.08.2023). The peptide-centric microorganismal database, DB-1, used for analysis of the in-house samples is composed of (i) the union set of high-quality proteomes from reference and representative strains (with priority given to reference over representative strains) of 1586 bacteria taken from a previous publication,20 (ii) the proteomes from the reference/representative strains of 340 bacteria that have been associated with urinary tract infections,72 and (iii) the reference proteome of Homo sapiens. The proteomes from the 340 bacteria constitute our target database, and it was specially built on the basis of the prior knowledge that the in-house samples are designed to mimic samples collected from patients having urinary tract infections."],"omics_type":["Proteomics"],"labhead":["Gelio Alves"],"instrument_platform":[""],"submission_type":["PARTIAL"],"labhead_affiliation":["Division of Intramural Research, NIH"],"species":["Escherichia Coli"],"publication":["38740383 Alves G, Ogurtsov AY, Porterfield H, Maity T, Jenkins LM, Sacks DB, Yu YK. Multiplexing the Identification of Microorganisms via Tandem Mass Tag Labeling Augmented by Interference Removal through a Novel Modification of the Expectation Maximization Algorithm. J Am Soc Mass Spectrom. 2024 35(6):1138-1155 10.1021/jasms.3c00445"],"submitter_mail":["alves@ncbi.nlm.nih.gov"],"submitter_affiliation":["CBB"],"submitter_country":["United States"],"pubmed_abstract":["Having fast, accurate, and broad spectrum methods for the identification of microorganisms is of paramount importance to public health, research, and safety. Bottom-up mass spectrometer-based proteomics has emerged as an effective tool for the accurate identification of microorganisms from microbial isolates. However, one major hurdle that limits the deployment of this tool for routine clinical diagnosis, and other areas of research such as culturomics, is the instrument time required for the mass spectrometer to analyze a single sample, which can take ∼1 h per sample, when using mass spectrometers that are presently used in most institutes. To address this issue, in this study, we employed, for the first time, tandem mass tags (TMTs) in multiplex identifications of microorganisms from multiple TMT-labeled samples in one MS/MS experiment. A difficulty encountered when using TMT labeling is the presence of interference in the measured intensities of TMT reporter ions. To correct for interference, we employed in the proposed method a modified version of the expectation maximization (EM) algorithm that redistributes the signal from ion interference back to the correct TMT-labeled samples. We have evaluated the sensitivity and specificity of the proposed method using 94 MS/MS experiments (covering a broad range of protein concentration ratios across TMT-labeled channels and experimental parameters), containing a total of 1931 true positive TMT-labeled channels and 317 true negative TMT-labeled channels. The results of the evaluation show that the proposed method has an identification sensitivity of 93-97% and a specificity of 100% at the species level. Furthermore, as a proof of concept, using an in-house-generated data set composed of some of the most common urinary tract pathogens, we demonstrated that by using the proposed method the mass spectrometer time required per sample, using a 1 h LC-MS/MS run, can be reduced to 10 and 6 min when samples are labeled with TMT-6 and TMT-10, respectively. The proposed method can also be used along with Orbitrap mass spectrometers that have faster MS/MS acquisition rates, like the recently released Orbitrap Astral mass spectrometer, to further reduce the mass spectrometer time required per sample."],"pubmed_title":["Multiplexing the Identification of Microorganisms via Tandem Mass Tag Labeling Augmented by Interference Removal through a Novel Modification of the Expectation Maximization Algorithm."],"pubmed_authors":["Alves Gelio G, Ogurtsov Aleksey Y AY, Porterfield Harry H, Maity Tapan T, Jenkins Lisa M LM, Sacks David B DB, Yu Yi-Kuo YK"],"additional_accession":[]},"is_claimable":false,"name":"Multilexing the Identificaiton of Microorganisms","description":"Having fast, accurate, and broad spectrum methods for the identification of microorganisms is of paramount importance to public health, research, and safety. Bottom-up mass spectrometer-based proteomics has emerged as an effective tool for the accurate identification of microorganisms from microbial isolates. However, one major hurdle that limits the deployment of this tool for routine clinical diagnosis, and other areas of research such as culturomics, is the instrument time required for the mass spectrometer to analyze a single sample, which can take ∼1 h per sample, when using mass spectrometers that are presently used in most institutes. To address this issue, in this study, we employed, for the first time, tandem mass tags (TMTs) in multiplex identifications of microorganisms from multiple TMT-labeled samples in one MS/MS experiment. A difficulty encountered when using TMT labeling is the presence of interference in the measured intensities of TMT reporter ions. To correct for interference, we employed in the proposed method a modified version of the expectation maximization (EM) algorithm that redistributes the signal from ion interference back to the correct TMT-labeled samples. We have evaluated the sensitivity and specificity of the proposed method using 94 MS/MS experiments (covering a broad range of protein concentration ratios across TMT-labeled channels and experimental parameters), containing a total of 1931 true positive TMT-labeled channels and 317 true negative TMT-labeled channels. The results of the evaluation show that the proposed method has an identification sensitivity of 93-97% and a specificity of 100% at the species level. Furthermore, as a proof of concept, using an in-house-generated data set composed of some of the most common urinary tract pathogens, we demonstrated that by using the proposed method the mass spectrometer time required per sample, using a 1 h LC-MS/MS run, can be reduced to 10 and 6 min when samples are labeled with TMT-6 and TMT-10, 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