Pub Date : 2025-01-07DOI: 10.1021/acs.analchem.4c03393
Maaria Malkamäki, Julie-Anne Gandier, Kristoffer Meinander, Markus B. Linder
Accurately quantifying specific proteins from complex mixtures like cell lysates, for example, during in vivo studies, is difficult, especially for aggregation-prone proteins. Herein, we describe the development of a specific protein quantification method that combines a solid-state dot blot approach with radiolabel detection via liquid scintillation counting. The specific detection with high sensitivity is achieved by using the Twin-Strep protein affinity tag and tritium-labeled 3HStrep-TactinXT probe. While the assay was developed with the recombinant silk protein CBM-AQ12-CBM as a target, the method can be adapted to other recombinant proteins. Variations of the protein tag and Strep-Tactin probe were tested, and it was found that only the combination of Strep-TactinXT and Twin-Strep-tag performed adequately: with this combination, a precision of 95% and an accuracy of 86% were achieved with a linear region from 19 to 400 ng and a limit of quantification at 0.4 pmol. To achieve this, critical optimization steps were preventing nonspecific adsorption and promoting surface adhesion of the target protein to the solid nitrocellulose membrane. The often-overlooked challenges of sample preparation and protein immobilization in quantification assays are discussed and insights into overcoming such issues are provided.
{"title":"Specific Protein Quantification by Radioimmuno-Dot-Blot Assay for Complex Mixture Samples Utilizing Strep-Tag and Tritium-Labeled Strep-Tactin","authors":"Maaria Malkamäki, Julie-Anne Gandier, Kristoffer Meinander, Markus B. Linder","doi":"10.1021/acs.analchem.4c03393","DOIUrl":"https://doi.org/10.1021/acs.analchem.4c03393","url":null,"abstract":"Accurately quantifying specific proteins from complex mixtures like cell lysates, for example, during in vivo studies, is difficult, especially for aggregation-prone proteins. Herein, we describe the development of a specific protein quantification method that combines a solid-state dot blot approach with radiolabel detection via liquid scintillation counting. The specific detection with high sensitivity is achieved by using the Twin-Strep protein affinity tag and tritium-labeled <sup>3H</sup>Strep-TactinXT probe. While the assay was developed with the recombinant silk protein CBM-AQ12-CBM as a target, the method can be adapted to other recombinant proteins. Variations of the protein tag and Strep-Tactin probe were tested, and it was found that only the combination of Strep-TactinXT and Twin-Strep-tag performed adequately: with this combination, a precision of 95% and an accuracy of 86% were achieved with a linear region from 19 to 400 ng and a limit of quantification at 0.4 pmol. To achieve this, critical optimization steps were preventing nonspecific adsorption and promoting surface adhesion of the target protein to the solid nitrocellulose membrane. The often-overlooked challenges of sample preparation and protein immobilization in quantification assays are discussed and insights into overcoming such issues are provided.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"8 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Polycyclic aromatic hydrocarbons (PAHs) are pervasive environmental pollutants with significant health risks due to their carcinogenic, mutagenic, and teratogenic properties. Traditional methods for PAH identification, primarily relying on gas chromatography–mass spectrometry (GC–MS), utilize spectral library searches together with other techniques, such as mass defect analysis. However, these methods are limited by incomplete spectral libraries and a high false positive rate. Here, we present PAH-Finder, a data-driven workflow that integrates machine learning with high-resolution mass spectrometry (HRMS). PAH-Finder introduces a novel approach to evaluate the fragment distribution of PAH backbones in MS spectra by normalizing fragment m/z values to a 0–100% range relative to the molecular ion peak. Seven machine learning features capture PAH fragmentation characteristics, and a random forest model trained on 98 PAH spectra and 1003 background spectra achieved an F1 score of ∼0.9 in 5-fold cross validation. Additionally, PAH-Finder leverages the presence of doubly charged fragments and molecular formula prediction to enhance the identification accuracy. In a case study, PAH-Finder identified 135 PAHs, including 7 types of previously unreported PAH formulas in particulate matter samples, demonstrating a 246% increase in annotation efficiency compared to the NIST20 library search. It also identified 32 heteroatom-doped PAHs not included in the training data set, showcasing its robustness of generalization. PAH-Finder’s high accuracy in detecting a broad spectrum of PAHs facilitates efficient data processing and interpretation for nontargeted analysis, enhancing our understanding of air pollution and public health protection. PAH-Finder is freely available at Github (https://github.com/FangLabNTU/PAH-Finder).
{"title":"PAH-Finder: A Pattern Recognition Workflow for Identification of PAHs and Their Derivatives","authors":"Zixuan Zhang, Xin Xu, Shipei Xing, Changzhi Shi, Zecang You, Xiaojun Deng, Ling Tan, Zhe Mo, Mingliang Fang","doi":"10.1021/acs.analchem.4c04249","DOIUrl":"https://doi.org/10.1021/acs.analchem.4c04249","url":null,"abstract":"Polycyclic aromatic hydrocarbons (PAHs) are pervasive environmental pollutants with significant health risks due to their carcinogenic, mutagenic, and teratogenic properties. Traditional methods for PAH identification, primarily relying on gas chromatography–mass spectrometry (GC–MS), utilize spectral library searches together with other techniques, such as mass defect analysis. However, these methods are limited by incomplete spectral libraries and a high false positive rate. Here, we present PAH-Finder, a data-driven workflow that integrates machine learning with high-resolution mass spectrometry (HRMS). PAH-Finder introduces a novel approach to evaluate the fragment distribution of PAH backbones in MS spectra by normalizing fragment <i>m</i>/<i>z</i> values to a 0–100% range relative to the molecular ion peak. Seven machine learning features capture PAH fragmentation characteristics, and a random forest model trained on 98 PAH spectra and 1003 background spectra achieved an F1 score of ∼0.9 in 5-fold cross validation. Additionally, PAH-Finder leverages the presence of doubly charged fragments and molecular formula prediction to enhance the identification accuracy. In a case study, PAH-Finder identified 135 PAHs, including 7 types of previously unreported PAH formulas in particulate matter samples, demonstrating a 246% increase in annotation efficiency compared to the NIST20 library search. It also identified 32 heteroatom-doped PAHs not included in the training data set, showcasing its robustness of generalization. PAH-Finder’s high accuracy in detecting a broad spectrum of PAHs facilitates efficient data processing and interpretation for nontargeted analysis, enhancing our understanding of air pollution and public health protection. PAH-Finder is freely available at Github (https://github.com/FangLabNTU/PAH-Finder).","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"30 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prior to mass spectrometry (MS) analysis, pretreatment of low-abundance glycopeptides is vital for identifying protein glycosylation. In this study, we fabricated an environmentally friendly citric-acid-modified cellulose monolith (CCM) characterized by a coral-like porous structure and high-density hydrophilic groups using a thermally induced phase separation (TIPS) method. The CCM production leverages biomass resources, specifically cellulose and citric acid, utilizing TIPS to synthesize continuous porous materials through a straightforward heating and cooling process of polymer solutions. We demonstrated the efficacy of CCM as a hydrophilic interaction liquid chromatography (HILIC) medium for the efficient enrichment of glycopeptides. It exhibited remarkable selectivity in enriching glycopeptides from trypsin-digested immunoglobulin G (IgG), serving as a model protein, even in the presence of a significant amount of non-glycopeptide contaminants from bovine serum albumin (BSA) at a ratio of BSA/IgG of 1000/1. Additionally, CCM showed a low detection limit (0.25 fmol μL–1) and commendable reusability in glycopeptide enrichment, successfully enriching 35 glycopeptides from IgG. Additionally, 641 unique N-glycosylation sites of 698 unique glycopeptides from 393 glycosylated proteins were identified from the triplicate analysis of 900 μg of human hepatocellular carcinoma tissue. Therefore, CCM holds significant promise as an eco-friendly stationary phase for hydrophilic interaction liquid chromatography aimed at glycopeptide enrichment.
{"title":"Development of Citric-Acid-Modified Cellulose Monolith for Enriching Glycopeptides","authors":"Guan Wang, Luwei Zhang, Akihide Sugawara, Yu-I Hsu, Taka-Aki Asoh, Hiroshi Uyama","doi":"10.1021/acs.analchem.4c03857","DOIUrl":"https://doi.org/10.1021/acs.analchem.4c03857","url":null,"abstract":"Prior to mass spectrometry (MS) analysis, pretreatment of low-abundance glycopeptides is vital for identifying protein glycosylation. In this study, we fabricated an environmentally friendly citric-acid-modified cellulose monolith (CCM) characterized by a coral-like porous structure and high-density hydrophilic groups using a thermally induced phase separation (TIPS) method. The CCM production leverages biomass resources, specifically cellulose and citric acid, utilizing TIPS to synthesize continuous porous materials through a straightforward heating and cooling process of polymer solutions. We demonstrated the efficacy of CCM as a hydrophilic interaction liquid chromatography (HILIC) medium for the efficient enrichment of glycopeptides. It exhibited remarkable selectivity in enriching glycopeptides from trypsin-digested immunoglobulin G (IgG), serving as a model protein, even in the presence of a significant amount of non-glycopeptide contaminants from bovine serum albumin (BSA) at a ratio of BSA/IgG of 1000/1. Additionally, CCM showed a low detection limit (0.25 fmol μL<sup>–1</sup>) and commendable reusability in glycopeptide enrichment, successfully enriching 35 glycopeptides from IgG. Additionally, 641 unique N-glycosylation sites of 698 unique glycopeptides from 393 glycosylated proteins were identified from the triplicate analysis of 900 μg of human hepatocellular carcinoma tissue. Therefore, CCM holds significant promise as an eco-friendly stationary phase for hydrophilic interaction liquid chromatography aimed at glycopeptide enrichment.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"10 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1021/acs.analchem.4c04288
Paul-Albert Schneide, Oskar Munk Kronik
The data processing workflows for comprehensive two-dimensional liquid chromatography (LC × LC) hyphenated to high-resolution mass spectrometry (HRMS) operated in data-independent acquisition (DIA) are limited compared to their one-dimensional counterparts. A two-step workflow is proposed to extract pure mass spectra from LC × LC-HRMS. First, a mass filtering (MF) algorithm groups ions belonging to the same compound based on their elution profile similarity in the first (1D) and second dimension (2D). Second, the filtered data are deconvoluted using multivariate curve resolution (MCR) to address potential coelution. The presented workflow is termed MF + MCR and was tested on pulsed elution-LC × LC-HRMS data from a wastewater effluent extract. The proposed workflow was benchmarked to the following three data processing strategies for mass spectra extraction: peak apex (PAM), using the MF approach alone, or using MCR without prior MF. The MF + MCR workflow identified 25 suspect compounds, compared to 23, 16, and 10 identified by MF, MCR, and PAM, respectively. The nine suspects that could not be identified using MCR compared to the MF + MCR all had low total signal contributions, i.e., low intensities compared to the TIC. This showed that adequate preprocessing prior to MCR is essential for trace level analysis. Additionally, it was shown that the MF + MCR workflow extracted statistically significantly purer mass spectra compared to PAM (p-value: 0.003) and MCR (p-value: 0.04) from a spiked blank sample. The results highlight that by utilizing the elution profiles in both chromatographic dimensions, clean mass spectra of analytes at trace levels measured in DIA can be extracted, allowing for more reliable compound identification compared to the workflows that were used for benchmarking.
{"title":"Signal Processing Workflow for Suspect Screening in LC × LC-HRMS: Efficient Extraction of Pure Mass Spectra for Identification of Suspects in Complex Samples Using a Mass Filtering Algorithm","authors":"Paul-Albert Schneide, Oskar Munk Kronik","doi":"10.1021/acs.analchem.4c04288","DOIUrl":"https://doi.org/10.1021/acs.analchem.4c04288","url":null,"abstract":"The data processing workflows for comprehensive two-dimensional liquid chromatography (LC × LC) hyphenated to high-resolution mass spectrometry (HRMS) operated in data-independent acquisition (DIA) are limited compared to their one-dimensional counterparts. A two-step workflow is proposed to extract pure mass spectra from LC × LC-HRMS. First, a mass filtering (MF) algorithm groups ions belonging to the same compound based on their elution profile similarity in the first (<sup>1</sup>D) and second dimension (<sup>2</sup>D). Second, the filtered data are deconvoluted using multivariate curve resolution (MCR) to address potential coelution. The presented workflow is termed MF + MCR and was tested on pulsed elution-LC × LC-HRMS data from a wastewater effluent extract. The proposed workflow was benchmarked to the following three data processing strategies for mass spectra extraction: peak apex (PAM), using the MF approach alone, or using MCR without prior MF. The MF + MCR workflow identified 25 suspect compounds, compared to 23, 16, and 10 identified by MF, MCR, and PAM, respectively. The nine suspects that could not be identified using MCR compared to the MF + MCR all had low total signal contributions, i.e., low intensities compared to the TIC. This showed that adequate preprocessing prior to MCR is essential for trace level analysis. Additionally, it was shown that the MF + MCR workflow extracted statistically significantly purer mass spectra compared to PAM (<i>p</i>-value: 0.003) and MCR (<i>p</i>-value: 0.04) from a spiked blank sample. The results highlight that by utilizing the elution profiles in both chromatographic dimensions, clean mass spectra of analytes at trace levels measured in DIA can be extracted, allowing for more reliable compound identification compared to the workflows that were used for benchmarking.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"20 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1021/acs.analchem.4c03423
Yasmin Liu, Rishi Pandey, Mary Jane McCarthy, Onyekachi Raymond
Electrochemical aptamer-based biosensors (E-aptasensors) are emerging platforms for point-of-care (POC) detection of complex biofluids. Human saliva particularly offers a noninvasive matrix and unprecedented convenience for detecting illicit drugs, such as cocaine. However, the sensitivity of cocaine E-aptasensors is significantly compromised in saliva. Herein, we investigated the influence of salivary components on the sensing performance of a methylene blue (MB)-labeled classic cocaine aptamer by square-wave voltammetry (SWV), and in parallel, we report the development and optimization of a disposable E-aptasensor for cocaine detection fabricated by laser ablation. Cyclic voltammetry (CV), scanning electron microscopy (SEM), and atomic force microscopy (AFM) were used to study the cleanliness and surface topography of the disposable electrode surface. To enhance the sensing performance of the disposable platform, we developed a co-immobilization strategy by introducing both the target and 6-mercapto-1-hexanol (MCH) into the aptamer immobilization solution, achieving optimal sensing performance at the aptamer-to-MCH ratio of 1:100. In a buffer solution, we revealed that the aptasensor performs best at low ionic strength, the absence of multivalent ions, and neutral pH conditions, while salivary components such as viscosity and mucin have minimal impact. However, upon transition to human saliva, the presence of salivary proteins exerted a profound effect on the sensing performance. To reduce this impact, we discovered that a high NaCl concentration could significantly enhance the sensing response in saliva. This approach circumvents centrifugation and extensive dilution and facilitates cocaine detection in human saliva through a straightforward “mix-and-detect” method. This disposable aptasensor achieved a limit of detection (LOD) of 3.7 μM in 90% saliva, demonstrating immense promise for the application of electrochemical aptasensors in detecting cocaine, especially when administered via smoking.
{"title":"Electrochemical Aptamer-Based Biosensors for Cocaine Detection in Human Saliva: Exploring Matrix Interference","authors":"Yasmin Liu, Rishi Pandey, Mary Jane McCarthy, Onyekachi Raymond","doi":"10.1021/acs.analchem.4c03423","DOIUrl":"https://doi.org/10.1021/acs.analchem.4c03423","url":null,"abstract":"Electrochemical aptamer-based biosensors (E-aptasensors) are emerging platforms for point-of-care (POC) detection of complex biofluids. Human saliva particularly offers a noninvasive matrix and unprecedented convenience for detecting illicit drugs, such as cocaine. However, the sensitivity of cocaine E-aptasensors is significantly compromised in saliva. Herein, we investigated the influence of salivary components on the sensing performance of a methylene blue (MB)-labeled classic cocaine aptamer by square-wave voltammetry (SWV), and in parallel, we report the development and optimization of a disposable E-aptasensor for cocaine detection fabricated by laser ablation. Cyclic voltammetry (CV), scanning electron microscopy (SEM), and atomic force microscopy (AFM) were used to study the cleanliness and surface topography of the disposable electrode surface. To enhance the sensing performance of the disposable platform, we developed a co-immobilization strategy by introducing both the target and 6-mercapto-1-hexanol (MCH) into the aptamer immobilization solution, achieving optimal sensing performance at the aptamer-to-MCH ratio of 1:100. In a buffer solution, we revealed that the aptasensor performs best at low ionic strength, the absence of multivalent ions, and neutral pH conditions, while salivary components such as viscosity and mucin have minimal impact. However, upon transition to human saliva, the presence of salivary proteins exerted a profound effect on the sensing performance. To reduce this impact, we discovered that a high NaCl concentration could significantly enhance the sensing response in saliva. This approach circumvents centrifugation and extensive dilution and facilitates cocaine detection in human saliva through a straightforward “mix-and-detect” method. This disposable aptasensor achieved a limit of detection (LOD) of 3.7 μM in 90% saliva, demonstrating immense promise for the application of electrochemical aptasensors in detecting cocaine, especially when administered via smoking.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"100 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1021/acs.analchem.4c05745
Wenyao Hu, Ling Xia, Yufei Hu, Gongke Li
Rapid and accurate analysis of trace targets in complex samples remains an enormous challenge. Herein, the calix[x]arene-based magnetic cross-linked polymer decorating AgNPs, abbreviated Fe3O4–CXA-DAB@AgNPs nanosponge, was developed for fast surface-enhanced Raman scattering (SERS) analysis in complex samples. The Fe3O4–CXA-DAB@AgNPs nanosponge surface was constructed by high-density CXA units with special cavity size and structure, which could selectively recognize and enrich targets to the sensing surface by the host–guest effect and molecule interactions. The Fe3O4–C4A-DAB@AgNPs showed significant SERS enhancement to choline chloride (ChCl) and succinylcholine chloride (SCC) with an enhancement factor (EF) of 2.9 × 107 and 6.3 × 106, respectively. The Fe3O4–C6A-DAB@AgNPs exhibited high SERS activity to thiabendazole with an EF of 7.6 × 106. Introducing recognition–enrichment–separation with SERS sensing, the nanosponge could achieve rapid enrichment sensing of targets within 6–8 min. Also, the Fe3O4–CXA-DAB@AgNPs nanosponge exhibited good stability for rapid detection with relative standard deviations less than 6.3% for intra-batch (n = 25) and 6.8% for inter-batch (n = 15). Benefiting from these merits, the Fe3O4–C4A-DAB@AgNPs was employed for fast SERS analysis of ChCl and SCC in real samples. The limits of detection were 0.62 μg/L for ChCl and 2.0 μg/L for SCC. ChCl was found in feed sample with recoveries of 85.3–108%, and SCC was found in serum samples with recoveries of 85.7–111%. The methods provided a significant reference for the selective analysis of targets by regulating the calix[x]arenes cavity size to satisfy different molecules and rapid quantification strategy by integrating sample pretreatment technology with sensing detection all-in-one.
{"title":"Calixarene-Based Magnetic Nanosponge Decorating AgNPs for Rapid and Selective Surface-Enhanced Raman Scattering Analysis in Complex Samples","authors":"Wenyao Hu, Ling Xia, Yufei Hu, Gongke Li","doi":"10.1021/acs.analchem.4c05745","DOIUrl":"https://doi.org/10.1021/acs.analchem.4c05745","url":null,"abstract":"Rapid and accurate analysis of trace targets in complex samples remains an enormous challenge. Herein, the calix[<i>x</i>]arene-based magnetic cross-linked polymer decorating AgNPs, abbreviated Fe<sub>3</sub>O<sub>4</sub>–CXA-DAB@AgNPs nanosponge, was developed for fast surface-enhanced Raman scattering (SERS) analysis in complex samples. The Fe<sub>3</sub>O<sub>4</sub>–CXA-DAB@AgNPs nanosponge surface was constructed by high-density CXA units with special cavity size and structure, which could selectively recognize and enrich targets to the sensing surface by the host–guest effect and molecule interactions. The Fe<sub>3</sub>O<sub>4</sub>–C4A-DAB@AgNPs showed significant SERS enhancement to choline chloride (ChCl) and succinylcholine chloride (SCC) with an enhancement factor (EF) of 2.9 × 10<sup>7</sup> and 6.3 × 10<sup>6</sup>, respectively. The Fe<sub>3</sub>O<sub>4</sub>–C6A-DAB@AgNPs exhibited high SERS activity to thiabendazole with an EF of 7.6 × 10<sup>6</sup>. Introducing recognition–enrichment–separation with SERS sensing, the nanosponge could achieve rapid enrichment sensing of targets within 6–8 min. Also, the Fe<sub>3</sub>O<sub>4</sub>–CXA-DAB@AgNPs nanosponge exhibited good stability for rapid detection with relative standard deviations less than 6.3% for intra-batch (<i>n</i> = 25) and 6.8% for inter-batch (<i>n</i> = 15). Benefiting from these merits, the Fe<sub>3</sub>O<sub>4</sub>–C4A-DAB@AgNPs was employed for fast SERS analysis of ChCl and SCC in real samples. The limits of detection were 0.62 μg/L for ChCl and 2.0 μg/L for SCC. ChCl was found in feed sample with recoveries of 85.3–108%, and SCC was found in serum samples with recoveries of 85.7–111%. The methods provided a significant reference for the selective analysis of targets by regulating the calix[<i>x</i>]arenes cavity size to satisfy different molecules and rapid quantification strategy by integrating sample pretreatment technology with sensing detection all-in-one.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"15 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1021/acs.analchem.4c03325
Lucas Pradeau-Phélut, Stacy Alvès, Léo Le Tareau, Cyann Larralde, Emma Bernard, Claire Schirmer, Josephine Lai-Kee-Him, Eléonore Lepvrier, Patrick Bron, Christian Delamarche, Cyrille Garnier
Amyloidosis is a group of proteinopathies characterized by the systemic or organ-specific deposition of proteins in the form of amyloid fibers. Nearly 40 proteins play a role in these pathologies, and the structures of the associated fibers are beginning to be determined by Cryo-EM. However, the molecular events underlying the process, such as fiber nucleation and elongation, are poorly understood, which impairs developing efficient therapies. In most cases, only a few dozen amino acids of the pathological protein are found in the final structure of the fibers, while amyloid peptides comprising five to 10 amino acids are involved in the fiber nucleation process. The identification and biochemical characterization of these peptides are therefore of major scientific and clinical importance. We demonstrated that in silico approaches are limited due to the peptides’ small size and long-distance intra- and intermolecular interactions that occur during nucleation. To address this problem, we developed a novel biochemical method for characterizing and classifying batches of related peptides. Initial work to optimize our approach is based on the reference peptide PHF6 (β1) from Microtubule-Associated Protein Tau (MAPT) as compared to 22 related peptides. Depending on their biochemical properties and using the Garnier–Delamarche plot we propose, we classified these peptides into three groups: aggregative, amyloid, and soluble (neither aggregative nor amyloid). We emphasize that our biochemical classification method is applicable to any family of peptides and could be scaled up for high-throughput analyses.
{"title":"Efficient Biochemical Method for Characterizing and Classifying Related Amyloidogenic Peptides","authors":"Lucas Pradeau-Phélut, Stacy Alvès, Léo Le Tareau, Cyann Larralde, Emma Bernard, Claire Schirmer, Josephine Lai-Kee-Him, Eléonore Lepvrier, Patrick Bron, Christian Delamarche, Cyrille Garnier","doi":"10.1021/acs.analchem.4c03325","DOIUrl":"https://doi.org/10.1021/acs.analchem.4c03325","url":null,"abstract":"Amyloidosis is a group of proteinopathies characterized by the systemic or organ-specific deposition of proteins in the form of amyloid fibers. Nearly 40 proteins play a role in these pathologies, and the structures of the associated fibers are beginning to be determined by Cryo-EM. However, the molecular events underlying the process, such as fiber nucleation and elongation, are poorly understood, which impairs developing efficient therapies. In most cases, only a few dozen amino acids of the pathological protein are found in the final structure of the fibers, while amyloid peptides comprising five to 10 amino acids are involved in the fiber nucleation process. The identification and biochemical characterization of these peptides are therefore of major scientific and clinical importance. We demonstrated that in silico approaches are limited due to the peptides’ small size and long-distance intra- and intermolecular interactions that occur during nucleation. To address this problem, we developed a novel biochemical method for characterizing and classifying batches of related peptides. Initial work to optimize our approach is based on the reference peptide PHF6 (β1) from Microtubule-Associated Protein Tau (MAPT) as compared to 22 related peptides. Depending on their biochemical properties and using the Garnier–Delamarche plot we propose, we classified these peptides into three groups: aggregative, amyloid, and soluble (neither aggregative nor amyloid). We emphasize that our biochemical classification method is applicable to any family of peptides and could be scaled up for high-throughput analyses.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"43 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1021/acs.analchem.4c04933
Yaguo Gong, Yangbo Dai, Qibiao Wu, Li Guo, Xiaojun Yao, Qingxia Yang
Single-cell proteomics (SCP) detected based on different technologies always involves batch-specific variations because of differences in sample processing and other potential biases. How to integrate SCP data effectively has become a great challenge. Integration of SCP data not only requires the conservation of true biological variances, but also realizes the removal of unwanted batch effects. In this study, benchmarking analysis of popular data integration methods was conducted to determine the most suitable method for SCP data. To comprehensively evaluate the performance of these integration methods, a novel evaluation system was proposed for integrating SCP data. This evaluation system consists of three objective measures from different perspectives: category (a), the efficacy of correcting batch effects; category (b), the power of conserving biological variances; and category (c), the ability to identify consistent markers. For this comprehensive evaluation, five benchmark data sets under different scenarios (containing substantial proteins, substantial cells, multiple batches, multiple cell types, and unbalanced data) were utilized for selecting the most suitable data integration method. As a result, three methods, ComBat, Scanorama, and Seurat version 3 CCA, were identified as the most recommended methods for integrating SCP data. Overall, this systematic evaluation might provide valuable guidance in choosing the appropriate method for data integration in the SCP.
{"title":"Benchmark of Data Integration in Single-Cell Proteomics","authors":"Yaguo Gong, Yangbo Dai, Qibiao Wu, Li Guo, Xiaojun Yao, Qingxia Yang","doi":"10.1021/acs.analchem.4c04933","DOIUrl":"https://doi.org/10.1021/acs.analchem.4c04933","url":null,"abstract":"Single-cell proteomics (SCP) detected based on different technologies always involves batch-specific variations because of differences in sample processing and other potential biases. How to integrate SCP data effectively has become a great challenge. Integration of SCP data not only requires the conservation of true biological variances, but also realizes the removal of unwanted batch effects. In this study, benchmarking analysis of popular data integration methods was conducted to determine the most suitable method for SCP data. To comprehensively evaluate the performance of these integration methods, a novel evaluation system was proposed for integrating SCP data. This evaluation system consists of three objective measures from different perspectives: category (<i><b>a</b></i>), the efficacy of correcting batch effects; category (<i><b>b</b></i>), the power of conserving biological variances; and category (<i><b>c</b></i>), the ability to identify consistent markers. For this comprehensive evaluation, five benchmark data sets under different scenarios (containing substantial proteins, substantial cells, multiple batches, multiple cell types, and unbalanced data) were utilized for selecting the most suitable data integration method. As a result, three methods, ComBat, Scanorama, and Seurat version 3 CCA, were identified as the most recommended methods for integrating SCP data. Overall, this systematic evaluation might provide valuable guidance in choosing the appropriate method for data integration in the SCP.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"62 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1021/acs.analchem.4c03124
Amy Hughes, Pablo Vangeenderhuysen, Marilyn De Graeve, Beata Pomian, Tim S. Nawrot, Jeroen Raes, Simon J. S. Cameron, Lynn Vanhaecke
Maximizing the extraction of true, high-quality, nonredundant features from biofluids analyzed via LC-MS systems is challenging. Here, the R packages IPO and AutoTuner were used to optimize XCMS parameter settings for the retrieval of metabolite or lipid features in both ionization modes from either faecal or urine samples from two cohorts (n = 621). The feature lists obtained were compared with those where the parameter values were selected manually. Three categories were used to compare feature lists: 1) feature quality through removing false positives, 2) tentative metabolite identification using the Human Metabolome Database (HMDB) and 3) feature utility such as analyzing the proportion of features within intensity threshold bins. Furthermore, a PCA-based approach to feature filtering using QC samples and variable loadings was also explored under this category. Overall, more features were observed after automated selection of parameter values for all data sets (1.3- to 3.7-fold), which propagated through comparative exercises. For example, a greater number of features (on average 51 vs 45%) had a coefficient of variation (CV) < 30%. Additionally, there was a significant increase (7.6–10.4%) in the number of faecal metabolites that could be tentatively annotated, and more features were present in higher intensity threshold bins. Considering the overlap across all three categories, a greater number of features were also retained. Automated approaches that guide selection of optimal parameter values for preprocessing are important to decrease the time invested for this step, while taking advantage of the wealth of data that LC-MS systems provide.
{"title":"Toward Automated Preprocessing of Untargeted LC-MS-Based Metabolomics Feature Lists from Human Biofluids","authors":"Amy Hughes, Pablo Vangeenderhuysen, Marilyn De Graeve, Beata Pomian, Tim S. Nawrot, Jeroen Raes, Simon J. S. Cameron, Lynn Vanhaecke","doi":"10.1021/acs.analchem.4c03124","DOIUrl":"https://doi.org/10.1021/acs.analchem.4c03124","url":null,"abstract":"Maximizing the extraction of true, high-quality, nonredundant features from biofluids analyzed via LC-MS systems is challenging. Here, the R packages IPO and AutoTuner were used to optimize XCMS parameter settings for the retrieval of metabolite or lipid features in both ionization modes from either faecal or urine samples from two cohorts (<i>n</i> = 621). The feature lists obtained were compared with those where the parameter values were selected manually. Three categories were used to compare feature lists: 1) feature quality through removing false positives, 2) tentative metabolite identification using the Human Metabolome Database (HMDB) and 3) feature utility such as analyzing the proportion of features within intensity threshold bins. Furthermore, a PCA-based approach to feature filtering using QC samples and variable loadings was also explored under this category. Overall, more features were observed after automated selection of parameter values for all data sets (1.3- to 3.7-fold), which propagated through comparative exercises. For example, a greater number of features (on average 51 vs 45%) had a coefficient of variation (CV) < 30%. Additionally, there was a significant increase (7.6–10.4%) in the number of faecal metabolites that could be tentatively annotated, and more features were present in higher intensity threshold bins. Considering the overlap across all three categories, a greater number of features were also retained. Automated approaches that guide selection of optimal parameter values for preprocessing are important to decrease the time invested for this step, while taking advantage of the wealth of data that LC-MS systems provide.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"37 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1021/acs.analchem.4c05287
Li Song, Fei Xue, Tingmiao Li, Qian Zhang, Xuesong Xu, Chengyan He, Bing Zhao, Xiao Xia Han, Linjun Cai
Bladder, kidney, and prostate cancers are prevalent urinary cancers, and developing efficient detection methods is of significance for the early diagnosis of them. However, noninvasive and sensitive detection of urinary cancers still challenges traditional techniques. In this study, we developed a SERS-based method to analyze serum samples from patients with urinary cancers. Rapid, label-free, and highly sensitive detection of human sera is achieved by cleaning and aggregating silver nanoparticles. Furthermore, a long short-term memory deep learning algorithm is used to distinguish serum spectra, and the performance of the model is evaluated by comparing the accuracy, sensitivity, specificity, and receiver operating characteristic curves. Taking advantage of SERS and machine learning in sensitivity and data processing, the three urinary cancers are clearly classified. This is the first attempt to exploit the SERS-machine learning strategy to discriminate multiple urinary cancers with clinical serum samples, and our results showed the potential application of this method in the early diagnosis and screening of cancers.
{"title":"Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning","authors":"Li Song, Fei Xue, Tingmiao Li, Qian Zhang, Xuesong Xu, Chengyan He, Bing Zhao, Xiao Xia Han, Linjun Cai","doi":"10.1021/acs.analchem.4c05287","DOIUrl":"https://doi.org/10.1021/acs.analchem.4c05287","url":null,"abstract":"Bladder, kidney, and prostate cancers are prevalent urinary cancers, and developing efficient detection methods is of significance for the early diagnosis of them. However, noninvasive and sensitive detection of urinary cancers still challenges traditional techniques. In this study, we developed a SERS-based method to analyze serum samples from patients with urinary cancers. Rapid, label-free, and highly sensitive detection of human sera is achieved by cleaning and aggregating silver nanoparticles. Furthermore, a long short-term memory deep learning algorithm is used to distinguish serum spectra, and the performance of the model is evaluated by comparing the accuracy, sensitivity, specificity, and receiver operating characteristic curves. Taking advantage of SERS and machine learning in sensitivity and data processing, the three urinary cancers are clearly classified. This is the first attempt to exploit the SERS-machine learning strategy to discriminate multiple urinary cancers with clinical serum samples, and our results showed the potential application of this method in the early diagnosis and screening of cancers.","PeriodicalId":27,"journal":{"name":"Analytical Chemistry","volume":"19 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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