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Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.

Untargeted data acquisition on high-resolution mass spectrometers (HRMSs) has been used in clinical toxicology for screening and identifying unknown compounds in patient samples. A common modality for untargeted HRMS data acquisition is information-dependent acquisition (IDA), which analyzes the most abundant small molecules within an acquisition cycle. This process can potentially lead to false negatives of clinically relevant compounds at low concentrations. Sequential window acquisition of all theoretical fragment ion spectra (SWATH) has emerged as a method of unbiased, untargeted HRMS data acquisition in which no spectral data are lost. SWATH has yet to be optimized and assessed for use in clinical toxicology. We developed a variable-window SWATH method (vSWATH) and compared it to IDA by limit of detection studies in drug-supplemented urine (81 compounds) and against a retrospective cohort of 50 clinical urine samples characterized by LC-MS/MS. vSWATH had a lower limit of detection than IDA for 33 (41%) drugs and metabolites added into urine samples. Both IDA and vSWATH were equivalent in discovering compounds from clinical urine samples and confirmed 26 additional compounds not previously discovered by targeted LC-MS/MS. Lastly, the unbiased acquisition of spectra in vSWATH allowed for identification of 5 low-abundance compounds missed by IDA. This vSWATH method for clinical toxicology demonstrated equivalent analytical sensitivity and specificity for untargeted drug screening and identification in urine samples. vSWATH provided the additional benefit of collecting all tandem mass spectrometry spectra in a sample, which could be useful in discovering low-abundance compounds not discovered by IDA.

Inborn errors of purine and pyrimidine metabolism are a diverse group of disorders with possible serious or life-threatening symptoms. They may be associated with neurological symptoms, renal stone disease or immunodeficiency. However, the clinical presentation can be nonspecific and mild so that a number of cases may be missed. Previously published assays lacked detection of certain diagnostically important biomarkers, including SAICAr, AICAr, beta-ureidoisobutyric acid, 2,8-dihydroxyadenine and orotidine, necessitating the use of separate assays for their detection. Moreover, the limited sensitivity for some analytes in earlier assays may have hampered the reliable detection of mild cases. Therefore, we aimed to develop a liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay that allows the simultaneous and sensitive detection of an extended range of purine and pyrimidine biomarkers in urine. The assay was developed and validated using LC-MS/MS and clinically tested by analyzing ERNDIM Diagnostic Proficiency Testing (DPT) samples and further specimens from patients with various purine and pyrimidine disorders. Reliable determination of 27 analytes including SAICAr, AICAr, beta-ureidoisobutyric acid, 2,8-dihydroxyadenine and orotidine was achieved in urine following a simple sample preparation. The method clearly distinguished pathological and normal samples and differentiated between purine and pyrimidine defects in all clinical specimens. A LC-MS/MS assay allowing the simultaneous, sensitive and reliable diagnosis of an extended range of purine and pyrimidine disorders has been developed. The validated method has successfully been tested using ERNDIM Diagnostic Proficiency Testing (DPT) samples and further clinical specimens from patients with various purine and pyrimidine disorders. Sample preparation is simple and assay duration is short, facilitating an easier inclusion of the assay into the diagnostic procedures.

We developed and evaluated a candidate reference measurement procedure (RMP) to standardize testosterone measurements, provide highly accurate and precise value assignments for the CDC Hormone Standardization Program, and ensure accurate and comparable results across testing systems and laboratories. After 2 liquid/liquid extractions of serum with a combination of ethyl acetate and hexane, we quantified testosterone by isotope-dilution liquid chromatography-tandem mass spectrometry with electrospray ionization in the positive ion mode monitoring 289→97 m/z (testosterone) and 292→112 m/z ((3)C(13) testosterone). We used calibrator bracketing and gravimetric measurements to give higher specificity and accuracy to serum value assignments. The candidate RMP was evaluated for accuracy by use of NIST-certified reference material SRM971 and validated by split-sample comparison to established RMPs. We evaluated intraassay and interassay imprecision, measurement uncertainty, potential interferences, and matrix effects. A weighted Deming regression comparison of the candidate RMP to established RMPs showed agreement with no statistical difference (slope 0.99, 95% CI 0.98-1.00, intercept 0.54, 95% CI -1.24 to 2.32) and a bias of ≤0.3% for NIST SRM971. The candidate RMP gave maximum intraassay, interassay, and total percent CVs of 1.5%, 1.4%, and 1.7% across the concentrations of testosterone typically found in healthy men and women. We tested structural analogs of testosterone and 125 serum samples and found no interferences with the measurement. This RMP for testosterone can serve as a higher-order standard for measurement traceability and can be used to provide an accuracy base to which routine methods can be compared in the CDC Hormone Standardization Program.

Identification of unknown chemical entities is a major challenge in metabolomics. To address this challenge, we developed a comprehensive targeted profiling strategy, combining 3 complementary liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) techniques and in-house accurate mass retention time (AMRT) databases established from commercial standards. This strategy was used to evaluate the effect of nitisinone on the urinary metabolome of patients and mice with alkaptonuria (AKU). Because hypertyrosinemia is a known consequence of nitisinone therapy, we investigated the wider metabolic consequences beyond hypertyrosinemia. A total of 619 standards (molecular weight, 45-1354 Da) covering a range of primary metabolic pathways were analyzed using 3 liquid chromatography methods-2 reversed phase and 1 normal phase-coupled to QTOF-MS. Separate AMRT databases were generated for the 3 methods, comprising chemical name, formula, theoretical accurate mass, and measured retention time. Databases were used to identify chemical entities acquired from nontargeted analysis of AKU urine: match window theoretical accurate mass ±10 ppm and retention time ±0.3 min. Application of the AMRT databases to data acquired from analysis of urine from 25 patients with AKU (pretreatment and after 3, 12, and 24 months on nitisinone) and 18 mice (pretreatment and after 1 week on nitisinone) revealed 31 previously unreported statistically significant changes in metabolite patterns and abundance, indicating alterations to tyrosine, tryptophan, and purine metabolism after nitisinone administration. The comprehensive targeted profiling strategy described here has the potential of enabling discovery of novel pathways associated with pathogenesis and management of AKU.

Background Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is widely used for quantitative proteomic investigations. The typical output of such studies is a list of identified and quantified peptides. The biological and clinical interest is, however, usually focused on quantitative conclusions at the protein level. Furthermore, many investigations ask complex biological questions by studying multiple interrelated experimental conditions. Therefore, there is a need in the field for generic statistical models to quantify protein levels even in complex study designs. Results We propose a general statistical modeling approach for protein quantification in arbitrary complex experimental designs, such as time course studies, or those involving multiple experimental factors. The approach summarizes the quantitative experimental information from all the features and all the conditions that pertain to a protein. It enables both protein significance analysis between conditions, and protein quantification in individual samples or conditions. We implement the approach in an open-source R-based software package MSstats suitable for researchers with a limited statistics and programming background. Conclusions We demonstrate, using as examples two experimental investigations with complex designs, that a simultaneous statistical modeling of all the relevant features and conditions yields a higher sensitivity of protein significance analysis and a higher accuracy of protein quantification as compared to commonly employed alternatives. The software is available at http://www.stat.purdue.edu/~ovitek/Software.html .

Extensive research has been conducted on post-mortem brain tissue in schizophrenia (SCZ), particularly the dorsolateral prefrontal cortex (DLPFC). However, to what extent the reported changes are due to the disorder itself, and which are the cumulative effects of lifetime medication remains to be determined. In this study, we employed label-free liquid chromatography–mass spectrometry-based proteomic and proton nuclear magnetic resonance-based metabonomic profiling approaches to investigate DLPFC tissue from two cohorts of SCZ patients grouped according to their lifetime antipsychotic dose, together with tissue from bipolar disorder (BPD) subjects, and normal controls ( n =10 per group). Both techniques showed profound changes in tissue from low-cumulative-medication SCZ subjects, but few changes in tissue from medium-cumulative-medication subjects. Protein expression changes were validated by Western blot and investigated further in a third group of subjects who were subjected to high-cumulative-medication over the course of their lifetime. Furthermore, key protein expression and metabolite level changes correlated significantly with lifetime antipsychotic dose. This suggests that the detected changes are present before antipsychotic therapy and, moreover, may be normalized with treatment. Overall, our analyses revealed novel protein and metabolite changes in low-cumulative-medication subjects associated with synaptogenesis, neuritic dynamics, presynaptic vesicle cycling, amino acid and glutamine metabolism, and energy buffering systems. Most of these markers were altered specifically in SCZ as determined by analysis of the same brain region from BPD patients.

Bacteria cells are protected from osmotic and environmental stresses by an exoskeleton-like polymeric structure called peptidoglycan (PG) or murein sacculus. This structure is fundamental for bacteria's viability and thus, the mechanisms underlying cell wall assembly and how it is modulated serve as targets for many of our most successful antibiotics. Therefore, it is now more important than ever to understand the genetics and structural chemistry of the bacterial cell walls in order to find new and effective methods of blocking it for the treatment of disease. In the last decades, liquid chromatography and mass spectrometry have been demonstrated to provide the required resolution and sensitivity to characterize the fine chemical structure of PG. However, the large volume of data sets that can be produced by these instruments today are difficult to handle without a proper data analysis workflow. Here, we present PG-metrics, a chemometric based pipeline that allows fast and easy classification of bacteria according to their muropeptide chromatographic profiles and identification of the subjacent PG chemical variability between e.g. bacterial species, growth conditions and, mutant libraries. The pipeline is successfully validated here using PG samples from different bacterial species and mutants in cell wall proteins. The obtained results clearly demonstrated that PG-metrics pipeline is a valuable bioanalytical tool that can lead us to cell wall classification and biomarker discovery.

The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving laboratories have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate software solutions significantly impedes the development of self-driving laboratories. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated laboratories. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the software package.

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.