Pub Date : 2025-12-01DOI: 10.1016/j.jmsacl.2025.11.004
Per Bengtson , Magnus Förnvik Jonsson
Background
Detecting and monitoring monoclonal free light immunoglobulin chains in serum is important for managing patients with B-cell neoplasms. Established methods have relied on immunochemistry, with monoclonality determined through an abnormal ratio of free kappa and lambda chains and an increased concentration of the involved chain. This indirect approach has limitations. Mass spectrometric methods that directly demonstrate the monoclonal fraction have been described; however, all reported approaches so far are affinity-dependent. The aim of this study was to develop a non–affinity-dependent high-resolution mass spectrometry (HRMS) method.
Methods
Samples were prepared using ultrafiltration, then separated by reversed-phase liquid chromatography and analyzed by HRMS. The performance of the method was evaluated, including a comparison with a nephelometric immunoassay for free light immunoglobulin chains.
Results
Concordance between HRMS and the immunoassay in classifying a sample as containing a monoclonal free light chain or not was 84% (based on 100 unique patient samples). HRMS identified more samples containing a monoclonal free light chain than the immunoassay. Some monoclonal fractions were glycosylated and/or cysteinylated. Imprecision (CV) for the concentration measurements of monoclonal fractions ranged from 10 to 14%.
Conclusions
The HRMS method presented can detect, isotype, and semi-quantify monoclonal monomeric and dimeric light chains in serum, as well as demonstrate post-translational modifications. It is a selective, non–affinity-dependent method with a simple workflow that has the potential to become a valuable tool in the management of B-cell diseases.
{"title":"A non-affinity-dependent high-resolution mass spectrometry method for detecting and typing monoclonal free light immunoglobulin chains","authors":"Per Bengtson , Magnus Förnvik Jonsson","doi":"10.1016/j.jmsacl.2025.11.004","DOIUrl":"10.1016/j.jmsacl.2025.11.004","url":null,"abstract":"<div><h3>Background</h3><div>Detecting and monitoring monoclonal free light immunoglobulin chains in serum is important for managing patients with B-cell neoplasms. Established methods have relied on immunochemistry, with monoclonality determined through an abnormal ratio of free kappa and lambda chains and an increased concentration of the involved chain. This indirect approach has limitations. Mass spectrometric methods that directly demonstrate the monoclonal fraction have been described; however, all reported approaches so far are affinity-dependent. The aim of this study was to develop a non–affinity-dependent high-resolution mass spectrometry (HRMS) method.</div></div><div><h3>Methods</h3><div>Samples were prepared using ultrafiltration, then separated by reversed-phase liquid chromatography and analyzed by HRMS. The performance of the method was evaluated, including a comparison with a nephelometric immunoassay for free light immunoglobulin chains.</div></div><div><h3>Results</h3><div>Concordance between HRMS and the immunoassay in classifying a sample as containing a monoclonal free light chain or not was 84% (based on 100 unique patient samples). HRMS identified more samples containing a monoclonal free light chain than the immunoassay. Some monoclonal fractions were glycosylated and/or cysteinylated. Imprecision (CV) for the concentration measurements of monoclonal fractions ranged from 10 to 14%.</div></div><div><h3>Conclusions</h3><div>The HRMS method presented can detect, isotype, and semi-quantify monoclonal monomeric and dimeric light chains in serum, as well as demonstrate post-translational modifications. It is a selective, non–affinity-dependent method with a simple workflow that has the potential to become a valuable tool in the management of B-cell diseases.</div></div>","PeriodicalId":52406,"journal":{"name":"Journal of Mass Spectrometry and Advances in the Clinical Lab","volume":"38 ","pages":"Pages 88-96"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carbapenem-resistant Klebsiella pneumoniae (CRKP) poses a significant public health threat. Rapid detection of CRKP and its resistance mechanisms is essential for optimizing antibiotic therapy and infection control. However, clinical implementation faces several challenges.
Methods
Machine learning classifiers were applied using MALDI-TOF MS data to discriminate KPC-type, NDM-type CRKP, and carbapenem-susceptible strains (CSKP). Model performance was validated across platforms and strain collections. SHapley Additive exPlanations (SHAP) analysis and phylogenetic reconstruction were used to interpret feature contributions and genetic determinants.
Results
Significant spectral divergence was observed among K. pneumoniae phenotypes, particularly between KPC and non-KPC strains. Random forest (RF) classifiers demonstrated excellent performance, perfectly discriminating KPC from non-KPC strains (AUC = 1.00) and achieving robust classification between CRKP and CSKP isolates (AUC = 0.809). However, differentiation between NDM and CSKP isolates remained challenging, showing moderate diagnostic reliability (AUC = 0.67–0.87) and inconsistent performance across platforms. Optimization strategies did not yield significant improvements in NDM-CSKP classification, underscoring the minimal spectral differences. SHAP analysis identified the 4521.91 m/z peak as the key feature for KPC classification, whereas NDM strains lacked distinctive spectral features. Phylogenetic analysis revealed that KPC strains formed a distinct cluster, while NDM and CSKP strains were intermixed, emphasizing the difficulty of differentiating them based on MALDI-TOF MS profiles.
Conclusion
This study developed models to classify KPC, NDM, and CSKP strains using MALDI-TOF MS combined with machine learning. KPC strains were effectively classified across platforms, whereas NDM and CSKP strains showed limited differentiation due to their close evolutionary relationship. Effective classification requires consideration of regional strain variation and periodic model updates informed by local epidemiology.
{"title":"Interpretable machine learning of clinical MALDI-TOF spectra discriminates carbapenem-resistant Klebsiella pneumoniae while revealing phylogenetic heterogeneity that limits model generalizability","authors":"Chuangye Cai , Mengxue Zou , Mingxiao Chen , Peibo Yuan , Zhencheng Fang , Lanlan Zhong , Dingqiang Chen , Hongwei Zhou , Nianyi Zeng","doi":"10.1016/j.jmsacl.2025.11.003","DOIUrl":"10.1016/j.jmsacl.2025.11.003","url":null,"abstract":"<div><h3>Introduction</h3><div>Carbapenem-resistant <em>Klebsiella pneumoniae</em> (CRKP) poses a significant public health threat. Rapid detection of CRKP and its resistance mechanisms is essential for optimizing antibiotic therapy and infection control. However, clinical implementation faces several challenges.</div></div><div><h3>Methods</h3><div>Machine learning classifiers were applied using MALDI-TOF MS data to discriminate KPC-type, NDM-type CRKP, and carbapenem-susceptible strains (CSKP). Model performance was validated across platforms and strain collections. SHapley Additive exPlanations (SHAP) analysis and phylogenetic reconstruction were used to interpret feature contributions and genetic determinants.</div></div><div><h3>Results</h3><div>Significant spectral divergence was observed among <em>K. pneumoniae</em> phenotypes, particularly between KPC and non-KPC strains. Random forest (RF) classifiers demonstrated excellent performance, perfectly discriminating KPC from non-KPC strains (AUC = 1.00) and achieving robust classification between CRKP and CSKP isolates (AUC = 0.809). However, differentiation between NDM and CSKP isolates remained challenging, showing moderate diagnostic reliability (AUC = 0.67–0.87) and inconsistent performance across platforms. Optimization strategies did not yield significant improvements in NDM-CSKP classification, underscoring the minimal spectral differences. SHAP analysis identified the 4521.91 <em>m</em>/<em>z</em> peak as the key feature for KPC classification, whereas NDM strains lacked distinctive spectral features. Phylogenetic analysis revealed that KPC strains formed a distinct cluster, while NDM and CSKP strains were intermixed, emphasizing the difficulty of differentiating them based on MALDI-TOF MS profiles.</div></div><div><h3>Conclusion</h3><div>This study developed models to classify KPC, NDM, and CSKP strains using MALDI-TOF MS combined with machine learning. KPC strains were effectively classified across platforms, whereas NDM and CSKP strains showed limited differentiation due to their close evolutionary relationship. Effective classification requires consideration of regional strain variation and periodic model updates informed by local epidemiology.</div></div>","PeriodicalId":52406,"journal":{"name":"Journal of Mass Spectrometry and Advances in the Clinical Lab","volume":"38 ","pages":"Pages 71-80"},"PeriodicalIF":3.4,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-07DOI: 10.1016/j.jmsacl.2025.11.002
J. Brandsma , J.W. Thompson , K.L. Schully , J.G. Chenoweth , P. Genzor , S. Krishnan , D.A. Striegel , L.St. John-Williams , A. Moseley , G. Oduro , N. Adams , T. Vantha , E.L. Tsalik , C.W. Woods , D.V. Clark
Introduction
Severe infections and sepsis significantly impact military operational readiness and costs through loss of duty days, high treatment rates, and medical evacuations. Early diagnosis is critical for preventing sepsis progression and mortality, but it requires validated biomarkers to guide clinical decision-making. Study protocols for host biomarker discovery in infections usually require inactivation of high-risk pathogens prior to sample analysis, which limits the utility of metabolomics assays designed for untreated samples.
Methods
Matched blood plasma aliquots obtained from an international, observational sepsis cohort were analyzed to quantify metabolites following the commercial AbsoluteIDQ p180 protocol, with and without the addition of an organic solvent extraction method for metabolites, proteins, and lipids (MPLEx), previously validated for inactivating BSL-3/4 pathogens. We evaluated analyte detection rates and concentrations for each method, as well as differences in extraction efficiency.
Results
Levels of agreement between the unmodified AbsoluteIDQ p180 and combined MPLEx-p180 methods varied by metabolite class. Most targeted amino acids, glycerophospholipids, sphingolipids, and monosaccharides were reliably measured and correlated well between methods. However, the higher sample dilution in the MPLEx-p180 method significantly reduced detection rates for biogenic amines and acylcarnitines, and overall extraction efficiencies also differed.
Conclusions
This study extends the applicability of commercial metabolomics assays designed for untreated samples by improving their suitability for high-risk infectious disease studies. Differences in metabolite extraction efficiencies and detection rates, as well as data harmonization strategies, should be considered if results from both protocols are to be combined.
{"title":"Adapting a commercial sample extraction protocol for biosafety level 3/4 compatible plasma metabolomics analysis","authors":"J. Brandsma , J.W. Thompson , K.L. Schully , J.G. Chenoweth , P. Genzor , S. Krishnan , D.A. Striegel , L.St. John-Williams , A. Moseley , G. Oduro , N. Adams , T. Vantha , E.L. Tsalik , C.W. Woods , D.V. Clark","doi":"10.1016/j.jmsacl.2025.11.002","DOIUrl":"10.1016/j.jmsacl.2025.11.002","url":null,"abstract":"<div><h3>Introduction</h3><div>Severe infections and sepsis significantly impact military operational readiness and costs through loss of duty days, high treatment rates, and medical evacuations. Early diagnosis is critical for preventing sepsis progression and mortality, but it requires validated biomarkers to guide clinical decision-making. Study protocols for host biomarker discovery in infections usually require inactivation of high-risk pathogens prior to sample analysis, which limits the utility of metabolomics assays designed for untreated samples.</div></div><div><h3>Methods</h3><div>Matched blood plasma aliquots obtained from an international, observational sepsis cohort were analyzed to quantify metabolites following the commercial Absolute<em>IDQ</em> p180 protocol, with and without the addition of an organic solvent extraction method for metabolites, proteins, and lipids (MPLEx), previously validated for inactivating BSL-3/4 pathogens. We evaluated analyte detection rates and concentrations for each method, as well as differences in extraction efficiency.</div></div><div><h3>Results</h3><div>Levels of agreement between the unmodified Absolute<em>IDQ</em> p180 and combined MPLEx-p180 methods varied by metabolite class. Most targeted amino acids, glycerophospholipids, sphingolipids, and monosaccharides were reliably measured and correlated well between methods. However, the higher sample dilution in the MPLEx-p180 method significantly reduced detection rates for biogenic amines and acylcarnitines, and overall extraction efficiencies also differed.</div></div><div><h3>Conclusions</h3><div>This study extends the applicability of commercial metabolomics assays designed for untreated samples by improving their suitability for high-risk infectious disease studies. Differences in metabolite extraction efficiencies and detection rates, as well as data harmonization strategies, should be considered if results from both protocols are to be combined.</div></div>","PeriodicalId":52406,"journal":{"name":"Journal of Mass Spectrometry and Advances in the Clinical Lab","volume":"38 ","pages":"Pages 81-87"},"PeriodicalIF":3.4,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-04DOI: 10.1016/j.jmsacl.2025.11.001
Manuela R. Martinefski , Verónica Ambao , María Gabriela Ballerini , María Eugenia Rodriguez , Rodolfo A. Rey , María Eugenia Monge , María Gabriela Ropelato
Background and aims
There is growing interest in replacing steroid immunoassays with liquid chromatography–tandem mass spectrometry-based methods in many clinical and research applications. The aim of the present work is to develop and validate an ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS)-based method using atmospheric pressure chemical ionisation (APCI) to quantify cortisol, androstenedione, testosterone, pregnenolone, progesterone, 17-hydroxyprogesterone, 17-hydroxypregnenolone, and dehydroepiandrosterone in paediatric serum samples.
Methods
Sample preparation was performed by protein precipitation using 100 µL of sample. The analytical method was validated in accordance with FDA and EMA international guidelines. Commercial quality control materials and a reference material (NIST®SRM®1950) were analysed for external assessment. Clinical applicability was tested in 61 infants under three months of age (50 healthy infants, eight preterm neonates, and three patients with confirmed adrenal disorders).
Results
The LLOQs were lower than 8 nmol/L; within- and between-run CVs were <12 %. Accuracy, in terms of recovery, was 89–111 %. Serum cortisol levels varied widely in healthy infants, and only testosterone levels exhibited sexual dimorphism (p < 0.0001). Androstenedione and 17-hydroxyprogesterone levels were significantly higher in the preterm group compared with babies born at term. Patients with confirmed adrenal pathologies exhibited abnormal steroid profiles.
Conclusion
A novel, accurate, and sensitive UHPLC-APCI-MS/MS-based method for the simultaneous analysis of eight steroids in serum was developed, validated and successfully applied to infants younger than three months of age. The method enabled detection of abnormal values of precursors and steroid hormones in patients with adrenal disorders, with the potential for a large impact on paediatric patient care.
{"title":"Quantification of eight clinically relevant serum adrenal steroids in infants by UHPLC-APCI-MS/MS","authors":"Manuela R. Martinefski , Verónica Ambao , María Gabriela Ballerini , María Eugenia Rodriguez , Rodolfo A. Rey , María Eugenia Monge , María Gabriela Ropelato","doi":"10.1016/j.jmsacl.2025.11.001","DOIUrl":"10.1016/j.jmsacl.2025.11.001","url":null,"abstract":"<div><h3>Background and aims</h3><div>There is growing interest in replacing steroid immunoassays with liquid chromatography–tandem mass spectrometry-based methods in many clinical and research applications. The aim of the present work is to develop and validate an ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS)-based method using atmospheric pressure chemical ionisation (APCI) to quantify cortisol, androstenedione, testosterone, pregnenolone, progesterone, 17-hydroxyprogesterone, 17-hydroxypregnenolone, and dehydroepiandrosterone in paediatric serum samples.</div></div><div><h3>Methods</h3><div>Sample preparation was performed by protein precipitation using 100 µL of sample<strong>.</strong> The analytical method was validated in accordance with FDA and EMA international guidelines. Commercial quality control materials and a reference material (NIST®SRM®1950) were analysed for external assessment. Clinical applicability was tested in 61 infants under three months of age (50 healthy infants, eight preterm neonates, and three patients with confirmed adrenal disorders).</div></div><div><h3>Results</h3><div>The LLOQs were lower than 8 nmol/L; within- and between-run CVs were <12 %. Accuracy, in terms of recovery, was 89–111 %. Serum cortisol levels varied widely in healthy infants, and only testosterone levels exhibited sexual dimorphism (<em>p</em> < 0.0001). Androstenedione and 17-hydroxyprogesterone levels were significantly higher in the preterm group compared with babies born at term. Patients with confirmed adrenal pathologies exhibited abnormal steroid profiles.</div></div><div><h3>Conclusion</h3><div>A novel, accurate, and sensitive UHPLC-APCI-MS/MS-based method for the simultaneous analysis of eight steroids in serum was developed, validated and successfully applied to infants younger than three months of age. The method enabled detection of abnormal values of precursors and steroid hormones in patients with adrenal disorders, with the potential for a large impact on paediatric patient care.</div></div>","PeriodicalId":52406,"journal":{"name":"Journal of Mass Spectrometry and Advances in the Clinical Lab","volume":"38 ","pages":"Pages 63-70"},"PeriodicalIF":3.4,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1016/j.jmsacl.2025.10.006
Judi Abdelrazik , Naomi Iwai , Yonghua Liu , Nathaniel Taylor , Ruben Y. Luo
Background
Adalimumab (ADL), a monoclonal antibody targeting TNF-⍺, is widely used to treat autoimmune diseases, but its efficacy can diminish over time due to the development of antidrug antibodies (ADAs), particularly neutralizing or blocking ADAs. Immunoassays used for mAb drug TDM typically measure ADA concentrations rather than their functional impact. This article describes an attempt to establish a label-free blocking immunoassay (LF-BIA) to measure the ADA blocking activity in serum samples.
Methods
The LF-BIA was designed to measure the ADA blocking activity against the interaction between ADL and its therapeutic target, TNF-⍺. The complete time course of ADL-ADAs and ADL-TNF-⍺ immune complex formation on each sensing probe was recorded as a sensorgram, and an ADA blocking rate was calculated from the sensorgram.
Results
The LF-BIA detected ADA blocking activity in serum samples. A 1:25 dilution was selected to determine ADA blocking activity, and 42 patient samples were analyzed. The LF-BIA results showed partial concordance with ELISA results, likely reflecting methodological differences: LF-BIA measures blocking ADAs, whereas ELISA quantifies total ADAs.
Conclusions
The LF-BIA may provide an efficient approach to specifically measure blocking ADAs rather than total ADAs. Alongside with previously reported applications of label-free immunoassays in clinical testing, the LF-BIA highlights a promising area in which label-free technologies such as BLI can play an important role.
{"title":"A Label-Free blocking immunoassay to evaluate Anti-Adalimumab antibody activity in clinical samples","authors":"Judi Abdelrazik , Naomi Iwai , Yonghua Liu , Nathaniel Taylor , Ruben Y. Luo","doi":"10.1016/j.jmsacl.2025.10.006","DOIUrl":"10.1016/j.jmsacl.2025.10.006","url":null,"abstract":"<div><h3>Background</h3><div>Adalimumab (ADL), a monoclonal antibody targeting TNF-⍺, is widely used to treat autoimmune diseases, but its efficacy can diminish over time due to the development of antidrug antibodies (ADAs), particularly neutralizing or blocking ADAs. Immunoassays used for mAb drug TDM typically measure ADA concentrations rather than their functional impact. This article describes an attempt to establish a label-free blocking immunoassay (LF-BIA) to measure the ADA blocking activity in serum samples.</div></div><div><h3>Methods</h3><div>The LF-BIA was designed to measure the ADA blocking activity against the interaction between ADL and its therapeutic target, TNF-⍺. The complete time course of ADL-ADAs and ADL-TNF-⍺ immune complex formation on each sensing probe was recorded as a sensorgram, and an ADA blocking rate was calculated from the sensorgram.</div></div><div><h3>Results</h3><div>The LF-BIA detected ADA blocking activity in serum samples. A 1:25 dilution was selected to determine ADA blocking activity, and 42 patient samples were analyzed. The LF-BIA results showed partial concordance with ELISA results, likely reflecting methodological differences: LF-BIA measures blocking ADAs, whereas ELISA quantifies total ADAs.</div></div><div><h3>Conclusions</h3><div>The LF-BIA may provide an efficient approach to specifically measure blocking ADAs rather than total ADAs. Alongside with previously reported applications of label-free immunoassays in clinical testing, the LF-BIA highlights a promising area in which label-free technologies such as BLI can play an important role.</div></div>","PeriodicalId":52406,"journal":{"name":"Journal of Mass Spectrometry and Advances in the Clinical Lab","volume":"38 ","pages":"Pages 58-62"},"PeriodicalIF":3.4,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-26DOI: 10.1016/j.jmsacl.2025.10.005
Tsung-Ying Yang , Hung Su , Shu-Huei Jain , Hsien-Ho Lin , Chieh-Yin Wu , Chao-Ju Chen , Jentaie Shiea , Po-Liang Lu
Background
Typing Mycobacterium tuberculosis (MTB) isolates is important for identifying clusters and guiding infection control measures. Although whole-genome sequencing (WGS) and 24-loci mycobacterial interspersed repetitive units–variable-number tandem repeat (MIRU-VNTR) are widely used for molecular typing, they may not capture phenotypic or metabolic variation. Alternative approaches, such as mass spectrometric–based profiling, could provide complementary insights.
Methods
A total of 247 clinical MTB isolates were analyzed by thermal desorption–electrospray ionization mass spectrometry (TD-ESI/MS). The resulting mass spectral profiles were evaluated using principal component analysis (PCA) and hierarchical clustering analysis (HCA). Results were compared with lineage classifications based on WGS and MIRU-VNTR to assess concordance.
Results
While WGS and MIRU-VNTR were highly concordant for lineage classification (98.8%; 244/247), TD-ESI/MS revealed diverse spectral profiles that did not align consistently with genetic lineages. However, HCA showed isolate-level clustering, including among genetically similar strains, suggesting that TD-ESI/MS may detect metabolic or lipidomic differences not captured by genome-based methods.
Conclusion
Although TD-ESI/MS does not generate similar results from MTB molecular typing, it shows potential for identifying specific spectral signatures. The technique may be useful in future investigations into phenotypic diversity and other clinically relevant features not captured by genotyping alone.
{"title":"Evaluation of thermal desorption–electrospray ionization mass spectrometry for characterization of Mycobacterium tuberculosis: Comparison with MIRU-VNTR and whole-genome sequencing","authors":"Tsung-Ying Yang , Hung Su , Shu-Huei Jain , Hsien-Ho Lin , Chieh-Yin Wu , Chao-Ju Chen , Jentaie Shiea , Po-Liang Lu","doi":"10.1016/j.jmsacl.2025.10.005","DOIUrl":"10.1016/j.jmsacl.2025.10.005","url":null,"abstract":"<div><h3>Background</h3><div>Typing <em>Mycobacterium tuberculosis</em> (MTB) isolates is important for identifying clusters and guiding infection control measures. Although whole-genome sequencing (WGS) and 24-loci mycobacterial interspersed repetitive units–variable-number tandem repeat (MIRU-VNTR) are widely used for molecular typing, they may not capture phenotypic or metabolic variation. Alternative approaches, such as mass spectrometric–based profiling, could provide complementary insights.</div></div><div><h3>Methods</h3><div>A total of 247 clinical MTB isolates were analyzed by thermal desorption–electrospray ionization mass spectrometry (TD-ESI/MS). The resulting mass spectral profiles were evaluated using principal component analysis (PCA) and hierarchical clustering analysis (HCA). Results were compared with lineage classifications based on WGS and MIRU-VNTR to assess concordance.</div></div><div><h3>Results</h3><div>While WGS and MIRU-VNTR were highly concordant for lineage classification (98.8%; 244/247), TD-ESI/MS revealed diverse spectral profiles that did not align consistently with genetic lineages. However, HCA showed isolate-level clustering, including among genetically similar strains, suggesting that TD-ESI/MS may detect metabolic or lipidomic differences not captured by genome-based methods.</div></div><div><h3>Conclusion</h3><div>Although TD-ESI/MS does not generate similar results from MTB molecular typing, it shows potential for identifying specific spectral signatures. The technique may be useful in future investigations into phenotypic diversity and other clinically relevant features not captured by genotyping alone.</div></div>","PeriodicalId":52406,"journal":{"name":"Journal of Mass Spectrometry and Advances in the Clinical Lab","volume":"38 ","pages":"Pages 50-57"},"PeriodicalIF":3.4,"publicationDate":"2025-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-11DOI: 10.1016/j.jmsacl.2025.10.004
Maria M. Derkach , Anatoly A. Sorokin , Andrey A. Kuzin , Eugene N. Nikolaev , Igor A. Popov , Stanislav I. Pekov
Introduction
Image segmentation is an important challenge in mass spectrometry imaging data processing. Here, we report an unsupervised topological segmentation method adapted to the specific nature of mass spectrometry data. Unlike machine learning clustering algorithms, the proposed method retains the physical and chemical integrity of the mass spectrum, as no dimensionality reduction is required.
Methods
Using the cosine similarity measure, we discard outliers, detect spectrally homogeneous regions, and filter pixels with mixed cell origin on the border of different tissue subtypes. Then, we evaluate the actual data manifold dimensionality to determine spectrally homogeneous regions within samples. The method was implemented to discriminate regions related to sections of aggressive human glial tumours analysed by MALDI-TOF mass spectrometry.
Results
Analysis of parallel sections reveals correlated region allocation throughout the sample. The presence of tumour cells decreases progressively from the tumour core toward the sample edge. Filtering pixels with mixed cellular content is essential for investigating highly heterogeneous tumour tissues and their infiltration regions. Therefore, only homogeneous regions were selected using topological segmentation, as identifying metabolic alterations associated with tumour infiltration and metastasis in the native microenvironment is critical for cancer biology.
Conclusions
Topological segmentation helps filter pixels from transition zones where cells of different types contribute comparably to the resulting signal. Consequently, the regions identified by spectral similarity are homogeneous data clusters that represent the characteristic molecular composition of the analyzed cells while preserving their natural variability.
{"title":"Topological segmentation of mass spectrometry imaging data","authors":"Maria M. Derkach , Anatoly A. Sorokin , Andrey A. Kuzin , Eugene N. Nikolaev , Igor A. Popov , Stanislav I. Pekov","doi":"10.1016/j.jmsacl.2025.10.004","DOIUrl":"10.1016/j.jmsacl.2025.10.004","url":null,"abstract":"<div><h3>Introduction</h3><div>Image segmentation is an important challenge in mass spectrometry imaging data processing. Here, we report an unsupervised topological segmentation method adapted to the specific nature of mass spectrometry data. Unlike machine learning clustering algorithms, the proposed method retains the physical and chemical integrity of the mass spectrum, as no dimensionality reduction is required.</div></div><div><h3>Methods</h3><div>Using the cosine similarity measure, we discard outliers, detect spectrally homogeneous regions, and filter pixels with mixed cell origin on the border of different tissue subtypes. Then, we evaluate the actual data manifold dimensionality to determine spectrally homogeneous regions within samples. The method was implemented to discriminate regions related to sections of aggressive human glial tumours analysed by MALDI-TOF mass spectrometry.</div></div><div><h3>Results</h3><div>Analysis of parallel sections reveals correlated region allocation throughout the sample. The presence of tumour cells decreases progressively from the tumour core toward the sample edge. Filtering pixels with mixed cellular content is essential for investigating highly heterogeneous tumour tissues and their infiltration regions. Therefore, only homogeneous regions were selected using topological segmentation, as identifying metabolic alterations associated with tumour infiltration and metastasis in the native microenvironment is critical for cancer biology.</div></div><div><h3>Conclusions</h3><div>Topological segmentation helps filter pixels from transition zones where cells of different types contribute comparably to the resulting signal. Consequently, the regions identified by spectral similarity are homogeneous data clusters that represent the characteristic molecular composition of the analyzed cells while preserving their natural variability.</div></div>","PeriodicalId":52406,"journal":{"name":"Journal of Mass Spectrometry and Advances in the Clinical Lab","volume":"38 ","pages":"Pages 18-25"},"PeriodicalIF":3.4,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145332744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.jmsacl.2025.10.002
Mahdiyeh Shahi, R. Graham Cooks
<div><div>Cancer is the second leading cause of death in the United States, and with ongoing population growth and aging, its annual incidence continues to rise. Glioma is a malignant tumor of the brain and central nervous system (CNS). Neurosurgical tumor resection is a critical component of glioma treatment, significantly affecting patient prognosis. However, due to the diffuse nature of gliomas, achieving gross total resection is challenging, and residual tumor cells often lead to recurrence and disease progression following surgery. Intraoperative cancer diagnosis using rapid and sensitive techniques, such as ambient ionization mass spectrometry (AIMS), can provide crucial molecular insights to guide surgical decision-making and potentially improve patient outcomes. AIMS techniques, including desorption electrospray ionization-mass spectrometry (DESI-MS), require minimal or no sample pretreatment, making them particularly advantageous for intraoperative applications where time efficiency is essential. Several AIMS methods have been investigated in brain cancer studies, either intraoperatively or offline, to analyze molecular alterations in cancerous tissues. Among these, DESI-MS is the most extensively reported AIMS technique in brain cancer research. This review focuses on the developments and applications of DESI-MS in both offline and intraoperative brain cancer diagnosis. Additionally, other AIMS methods employed in brain cancer research are discussed. The potential impact of AIMS techniques on glioma diagnosis is also explored.</div><div>Abbreviations: 5-ALA, 5-Aminolevulinic Acid; 2HG, 2-Hydroxyglutaric Acid; AIMS, Ambient Ionization Mass Spectrometry; AI, Artificial Intelligence; Arg, Arginine; AUC, Area Under the Curve; BWH, Brigham and Women’s Hospital; CBS-MS, Coated Blade Spray Mass Spectrometry; CL, Cardiolipins; CNS, Central Nervous System; CT, Computed Tomography; CUSA, Cavitron Ultrasonic Surgical Aspirator; CUSA/SSI-MS, Cavitron Ultrasonic Surgical Aspiration/Sonic Spray Ionization Mass Spectrometry; DESI, Desorption Electrospray Ionization; DSC, Direct Sampling Cartridge; e.e.%, Enantiomeric Excess %;ESI, Electrospray Ionization; Extraction-nESI, Extraction-Nanoelectrospray Ionization; FA, Fatty Acid; FAIMS, High-Field Asymmetric Ion Mobility Spectrometry; GABA, Higher Gamma-Aminobutyric Acid; GalCer, Galactoceramides; GBM, Glioblastoma; Glu, Glutamate; PC, Glycerophosphocholines; PI, Glycerophosphoinositols; PG, Glycerophosphoglycerols; PS, Glycerophosphoserines; H&E, Hematoxylin and Eosin; HLB, Hydrophilic–Lipophilic Balance; HRMS, High Resolution Mass Spectrometry; HT, High-Throughput; ICE, Inline Cartridge Extraction; IC, Ion Counts; IDH, Isocitrate Dehydrogenase; IDH-mut, IDH-Mutant; IDH-wt, IDH-Wildtype; iKnife, Intelligent Knife; LASSO, Least Absolute Shrinkage and Selection Operator; LC, Liquid Chromatography; LDA, Linear Discriminant Analysis; LIT, Linear Ion Trap; LMJ-SSP, Liquid Micro-Junction Surface Sampling
{"title":"Ambient ionization mass spectrometry in brain cancer diagnosis","authors":"Mahdiyeh Shahi, R. Graham Cooks","doi":"10.1016/j.jmsacl.2025.10.002","DOIUrl":"10.1016/j.jmsacl.2025.10.002","url":null,"abstract":"<div><div>Cancer is the second leading cause of death in the United States, and with ongoing population growth and aging, its annual incidence continues to rise. Glioma is a malignant tumor of the brain and central nervous system (CNS). Neurosurgical tumor resection is a critical component of glioma treatment, significantly affecting patient prognosis. However, due to the diffuse nature of gliomas, achieving gross total resection is challenging, and residual tumor cells often lead to recurrence and disease progression following surgery. Intraoperative cancer diagnosis using rapid and sensitive techniques, such as ambient ionization mass spectrometry (AIMS), can provide crucial molecular insights to guide surgical decision-making and potentially improve patient outcomes. AIMS techniques, including desorption electrospray ionization-mass spectrometry (DESI-MS), require minimal or no sample pretreatment, making them particularly advantageous for intraoperative applications where time efficiency is essential. Several AIMS methods have been investigated in brain cancer studies, either intraoperatively or offline, to analyze molecular alterations in cancerous tissues. Among these, DESI-MS is the most extensively reported AIMS technique in brain cancer research. This review focuses on the developments and applications of DESI-MS in both offline and intraoperative brain cancer diagnosis. Additionally, other AIMS methods employed in brain cancer research are discussed. The potential impact of AIMS techniques on glioma diagnosis is also explored.</div><div>Abbreviations: 5-ALA, 5-Aminolevulinic Acid; 2HG, 2-Hydroxyglutaric Acid; AIMS, Ambient Ionization Mass Spectrometry; AI, Artificial Intelligence; Arg, Arginine; AUC, Area Under the Curve; BWH, Brigham and Women’s Hospital; CBS-MS, Coated Blade Spray Mass Spectrometry; CL, Cardiolipins; CNS, Central Nervous System; CT, Computed Tomography; CUSA, Cavitron Ultrasonic Surgical Aspirator; CUSA/SSI-MS, Cavitron Ultrasonic Surgical Aspiration/Sonic Spray Ionization Mass Spectrometry; DESI, Desorption Electrospray Ionization; DSC, Direct Sampling Cartridge; e.e.%, Enantiomeric Excess %;ESI, Electrospray Ionization; Extraction-nESI, Extraction-Nanoelectrospray Ionization; FA, Fatty Acid; FAIMS, High-Field Asymmetric Ion Mobility Spectrometry; GABA, Higher Gamma-Aminobutyric Acid; GalCer, Galactoceramides; GBM, Glioblastoma; Glu, Glutamate; PC, Glycerophosphocholines; PI, Glycerophosphoinositols; PG, Glycerophosphoglycerols; PS, Glycerophosphoserines; H&E, Hematoxylin and Eosin; HLB, Hydrophilic–Lipophilic Balance; HRMS, High Resolution Mass Spectrometry; HT, High-Throughput; ICE, Inline Cartridge Extraction; IC, Ion Counts; IDH, Isocitrate Dehydrogenase; IDH-mut, IDH-Mutant; IDH-wt, IDH-Wildtype; iKnife, Intelligent Knife; LASSO, Least Absolute Shrinkage and Selection Operator; LC, Liquid Chromatography; LDA, Linear Discriminant Analysis; LIT, Linear Ion Trap; LMJ-SSP, Liquid Micro-Junction Surface Sampling ","PeriodicalId":52406,"journal":{"name":"Journal of Mass Spectrometry and Advances in the Clinical Lab","volume":"38 ","pages":"Pages 37-49"},"PeriodicalIF":3.4,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.jmsacl.2025.10.001
Saleh Ahmed , Jeremy Altman , Garrett Jones , Drew Mayernik , Eliza Williams , Amr Mahmoud , Tae Jin Lee , Wenbo Zhi , Shruti Sharma , Ashok Sharma
Background
Mass spectrometry is a powerful technique for tear fluid proteomics, offering critical insights into its complex molecular composition. Traditional data-dependent acquisition (DDA) often favors high-abundance proteins because it selects only the most intense precursor ions within a given window during each scan cycle. A newer approach, data-independent acquisition (DIA), addresses this by fragmenting all precursor ions within defined mass windows, offering broader coverage and improved quantification. This study presents a systematic comparison of DDA and DIA workflows to assess their relative performance in detecting tear fluid proteins.
Methods
Tear fluid samples were collected from healthy individuals using Schirmer strips, processed using in-strip protein digestion, and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS). DDA and DIA workflows were compared for proteomic depth, reproducibility, and data completeness. Quantification accuracy was assessed using serial dilutions of tear fluid in a complex biological matrix.
Results
DIA identified 701 unique proteins and 2,444 peptides, outperforming DDA, which identified 396 unique proteins and 1,447 peptides. Across eight replicates, DIA exhibited greater data completeness (78.7% for proteins and 78.5% for peptides) compared with DDA (42% for proteins and 48% for peptides). Reproducibility was markedly improved with DIA, with a median coefficient of variation (CV) of 9.8% for proteins and 10.6% for peptides, compared to 17.3% and 22.3%, respectively, for DDA. Quantification accuracy was also enhanced, with superior consistency across the dilution series.
Conclusion
Overall, DIA provides deeper, more reproducible, and more accurate proteome profiling of tear fluid than DDA, making it well suited for biomarker discovery.
{"title":"Tear fluid proteomics: a comparative study of DIA and DDA mass spectrometry","authors":"Saleh Ahmed , Jeremy Altman , Garrett Jones , Drew Mayernik , Eliza Williams , Amr Mahmoud , Tae Jin Lee , Wenbo Zhi , Shruti Sharma , Ashok Sharma","doi":"10.1016/j.jmsacl.2025.10.001","DOIUrl":"10.1016/j.jmsacl.2025.10.001","url":null,"abstract":"<div><h3>Background</h3><div>Mass spectrometry is a powerful technique for tear fluid proteomics, offering critical insights into its complex molecular composition. Traditional data-dependent acquisition (DDA) often favors high-abundance proteins because it selects only the most intense precursor ions within a given window during each scan cycle. A newer approach, data-independent acquisition (DIA), addresses this by fragmenting all precursor ions within defined mass windows, offering broader coverage and improved quantification. This study presents a systematic comparison of DDA and DIA workflows to assess their relative performance in detecting tear fluid proteins.</div></div><div><h3>Methods</h3><div>Tear fluid samples were collected from healthy individuals using Schirmer strips, processed using in-strip protein digestion, and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS). DDA and DIA workflows were compared for proteomic depth, reproducibility, and data completeness. Quantification accuracy was assessed using serial dilutions of tear fluid in a complex biological matrix.</div></div><div><h3>Results</h3><div>DIA identified 701 unique proteins and 2,444 peptides, outperforming DDA, which identified 396 unique proteins and 1,447 peptides. Across eight replicates, DIA exhibited greater data completeness (78.7% for proteins and 78.5% for peptides) compared with DDA (42% for proteins and 48% for peptides). Reproducibility was markedly improved with DIA, with a median coefficient of variation (CV) of 9.8% for proteins and 10.6% for peptides, compared to 17.3% and 22.3%, respectively, for DDA. Quantification accuracy was also enhanced, with superior consistency across the dilution series.</div></div><div><h3>Conclusion</h3><div>Overall, DIA provides deeper, more reproducible, and more accurate proteome profiling of tear fluid than DDA, making it well suited for biomarker discovery.</div></div>","PeriodicalId":52406,"journal":{"name":"Journal of Mass Spectrometry and Advances in the Clinical Lab","volume":"38 ","pages":"Pages 26-36"},"PeriodicalIF":3.4,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.jmsacl.2025.10.003
Joon Hee Lee , Kyunghoon Lee , Sun-Hee Jun , Yun Jeong Lee , Choong Ho Shin , Young Ah Lee , Junghan Song
Background
Congenital adrenal hyperplasia (CAH) represents a group of inherited disorders affecting steroidogenesis. Early and accurate diagnosis is crucial for effective treatment, particularly for preventing adrenal insufficiency and minimizing androgen excess. This study aims to develop and validate an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the simultaneous quantification of 21 steroid hormones, including 11-oxygenated androgens, which are critical for diagnosing and monitoring various forms of CAH.
Methods
We utilized a microbore column UPLC combined with hydroxylamine derivatization, which enabled excellent chromatographic separation and enhanced sensitivity of all target compounds. The method was evaluated for precision, linearity, recovery, ion suppression, and carryover according to FDA and CLSI guidelines. Steroid profiles from healthy controls and CAH patients were compared using Mann-Whitney tests.
Results
The UPLC-MS/MS method demonstrated excellent precision (<20 % except for 11-ketoandrostenedione), linearity (R2 > 0.99), low limits of detection and quantification, and satisfactory recovery (57–86 % absolute, 99–111 % relative). Our method showed good correlation with proficiency testing group means, although significant negative biases were noted for androstenedione, progesterone, and 11-deoxycortisol. In a clinical setting, significant increases in pregnenolone, progesterone, 17-hydroxyprogesterone, dehydroepiandrosterone, and other key steroids were observed in patients with 21-hydroxylase deficiency, while distinct profiles were identified for patients with 17-hydroxylase deficiency, cytochrome P450 oxidoreductase deficiency, and lipoid CAH.
Conclusions
Our UPLC-MS/MS method provides a sensitive and specific tool for the comprehensive profiling of adrenal steroids, offering improved diagnostic accuracy for CAH. Its ability to differentiate between various CAH subtypes highlights its potential clinical utility in both diagnosis and monitoring.
{"title":"Measurement of twenty-one serum steroid profiles by UPLC-MS/MS for the diagnosis and monitoring of congenital adrenal hyperplasia","authors":"Joon Hee Lee , Kyunghoon Lee , Sun-Hee Jun , Yun Jeong Lee , Choong Ho Shin , Young Ah Lee , Junghan Song","doi":"10.1016/j.jmsacl.2025.10.003","DOIUrl":"10.1016/j.jmsacl.2025.10.003","url":null,"abstract":"<div><h3>Background</h3><div>Congenital adrenal hyperplasia (CAH) represents a group of inherited disorders affecting steroidogenesis. Early and accurate diagnosis is crucial for effective treatment, particularly for preventing adrenal insufficiency and minimizing androgen excess. This study aims to develop and validate an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the simultaneous quantification of 21 steroid hormones, including 11-oxygenated androgens, which are critical for diagnosing and monitoring various forms of CAH.</div></div><div><h3>Methods</h3><div>We utilized a microbore column UPLC combined with hydroxylamine derivatization, which enabled excellent chromatographic separation and enhanced sensitivity of all target compounds. The method was evaluated for precision, linearity, recovery, ion suppression, and carryover according to FDA and CLSI guidelines. Steroid profiles from healthy controls and CAH patients were compared using Mann-Whitney tests.</div></div><div><h3>Results</h3><div>The UPLC-MS/MS method demonstrated excellent precision (<20 % except for 11-ketoandrostenedione), linearity (<em>R</em><sup>2</sup> > 0.99), low limits of detection and quantification, and satisfactory recovery (57–86 % absolute, 99–111 % relative). Our method showed good correlation with proficiency testing group means, although significant negative biases were noted for androstenedione, progesterone, and 11-deoxycortisol. In a clinical setting, significant increases in pregnenolone, progesterone, 17-hydroxyprogesterone, dehydroepiandrosterone, and other key steroids were observed in patients with 21-hydroxylase deficiency, while distinct profiles were identified for patients with 17-hydroxylase deficiency, cytochrome P450 oxidoreductase deficiency, and lipoid CAH.</div></div><div><h3>Conclusions</h3><div>Our UPLC-MS/MS method provides a sensitive and specific tool for the comprehensive profiling of adrenal steroids, offering improved diagnostic accuracy for CAH. Its ability to differentiate between various CAH subtypes highlights its potential clinical utility in both diagnosis and monitoring.</div></div>","PeriodicalId":52406,"journal":{"name":"Journal of Mass Spectrometry and Advances in the Clinical Lab","volume":"38 ","pages":"Pages 10-17"},"PeriodicalIF":3.4,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145332743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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