Tb^(3+)-nucleic acid probe-based label-free and rapid detection of mercury pollution in food

Mercury is a threatening pollutant in food,herein,we developed a Tb^(3+)-nucleic acid probe-based label-free assay for mix-and-read,rapid detection of mercury pollution.The assay utilized the feature of light-up fluorescence of terbium ions(Tb^(3+))via binding with single-strand DNA.Mercury ion,Hg^(2+)induced thymine(T)-rich DNA strand to form a double-strand structure(T-Hg^(2+)-T),thus leading to fluorescence reduction.Based on the principle,Hg^(2+)can be quantified based on the fluorescence of Tb^(3+),the limit of detection was 0.0689μmol/L and the linear range was 0.1-6.0μmol/L.Due to the specificity of T-Hg^(2+)-T artificial base pair,the assay could distinguish Hg^(2+)from other metal ions.The recovery rate was ranged in 98.71%-101.34%for detecting mercury pollution in three food samples.The assay is low-cost,separation-free and mix-to-read,thus was a competitive tool for detection of mercury pollution to ensure food safety.

Ionically Imprinting-Based Copper(Ⅱ)Label-Free Detection for Preventing Hearing Loss

Copper is a microelement with important physiological functions in the body.However,the excess copper ion(Cu^(2+))may cause severe health problems,such as hair cell apoptosis and the resultant hearing loss.Therefore,the assay of Cu^(2+)is important.We integrate ionic imprinting technology(IIT)and structurally colored hydrogel beads to prepare chitosan-based ionically imprinted hydrogel beads(IIHBs)as a low-cost and high-specificity platform for Cu^(2+)detection.The IIHBs have a macroporous microstructure,uniform size,vivid structural color,and magnetic responsiveness.When incubated in solution,IIHBs recognize Cu^(2+)and exhibit a reflective peak change,thereby achieving label-free detection.In addition,benefiting from the IIT,the IIHBs display good specificity and selectivity and have an imprinting factor of 19.14 at 100μmol·L^(-1).These features indicated that the developed IIHBs are promising candidates for Cu^(2+)detection,particularly for the prevention of hearing loss.

Proteomic study of vitreous in proliferative diabetic retinopathy patients after treatment with aflibercept:a quantitative analysis based on 4D label-free technique

AIM:To identify different metabolites,proteins and related pathways to elucidate the causes of proliferative diabetic retinopathy(PDR)and resistance to anti-vascular endothelial growth factor(VEGF)drugs,and to provide biomarkers for the diagnosis and treatment of PDR.METHODS:Vitreous specimens from patients with diabetic retinopathy were collected and analyzed by Liquid Chromatography-Mass Spectrometry(LC-MS/MS)analyses based on 4D label-free technology.Statistically differentially expressed proteins(DEPs),Gene Ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway representation and protein interactions were analyzed.RESULTS:A total of 12 samples were analyzed.The proteomics results showed that a total of 58 proteins were identified as DEPs,of which 47 proteins were up-regulated and 11 proteins were down-regulated.We found that C1q and tumor necrosis factor related protein 5(C1QTNF5),Clusterin(CLU),tissue inhibitor of metal protease 1(TIMP1)and signal regulatory protein alpha(SIRPα)can all be specifically regulated after aflibercept treatment.GO functional analysis showed that some DEPs are related to changes in inflammatory regulatory pathways caused by PDR.In addition,protein-protein interaction(PPI)network evaluation revealed that TIMP1 plays a central role in neural regulation.In addition,CD47/SIRPαmay become a key target to resolve anti-VEGF drug resistance in PDR.CONCLUSION:Proteomic analysis is an approach of choice to explore the molecular mechanisms of PDR.Our data show that multiple proteins are differentially changed in PDR patients after intravitreal injection of aflibercept,among which C1QTNF5,CLU,TIMP1 and SIRPαmay become targets for future treatment of PDR and resolution of anti-VEGF resistance.

Multi-Compartment SCFA Quantification in Human

Short-chain fatty acids (SCFA) play an important role in human biochemistry. They originate primarily from the digestive system through carbohydrates microbial fermentation. Most SCFA produced in the colon are absorbed by the intestinal wall and enter the bloodstream to be distributed throughout the body for multiple purposes. At the intestinal level, SCFA play a role in controlling fat storage and fatty acid metabolism. The effects of these beneficial compounds therefore concern overall health. They facilitate energy expenditure and are valuable allies in the fight against obesity and diabetes. SCFA are also involved in the regulation of the levels of several neurotransmitters such as GABA (γ-aminobutyric acid), glutamate, serotonin, dopamine, and norepinephrine. Their role is also highlighted in many inflammatory and neurodegenerative diseases such as Alzheimer’s disease (AD) or Parkinson’s disease (PD). To have a realistic picture of the distribution of SCFA in different biological compartments of the human body, we propose to study SCFA simultaneously in five human biological samples: feces, saliva, serum, cerebrospinal fluid (CSF), and urine, as well as in Dried Blood Spot (DBS). To evaluate their concentration and repeatability, we used 10 aliquots from pooled samples, analyzed by 3-nitrophenylhydrazine (3-NPH) derivation and liquid chromatography coupled with high sensitivity mass spectrometry (LC-QqQ-MS). We also evaluated the SCFA assay on Dried Blood Spot (DBS). In this work, we adapted the pre-analytical parts for each sample to be able to use a common calibration curve, thus facilitating multi-assay quantification studies and so being less time-consuming. Moreover, we proposed new daughter ions from the same neutral loss (43 Da) to quantify SCFAs, thus improving the sensitivity. In conclusion, our methodology, based on a unique calibration curve for all samples for each SCFA, is well-suited to quantified them in a clinical context.

Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.

High-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer based on probability density evolution method

This paper proposed an efficient research method for high-dimensional uncertainty quantification of projectile motion in the barrel of a truck-mounted howitzer.Firstly,the dynamic model of projectile motion is established considering the flexible deformation of the barrel and the interaction between the projectile and the barrel.Subsequently,the accuracy of the dynamic model is verified based on the external ballistic projectile attitude test platform.Furthermore,the probability density evolution method(PDEM)is developed to high-dimensional uncertainty quantification of projectile motion.The engineering example highlights the results of the proposed method are consistent with the results obtained by the Monte Carlo Simulation(MCS).Finally,the influence of parameter uncertainty on the projectile disturbance at muzzle under different working conditions is analyzed.The results show that the disturbance of the pitch angular,pitch angular velocity and pitch angular of velocity decreases with the increase of launching angle,and the random parameter ranges of both the projectile and coupling model have similar influence on the disturbance of projectile angular motion at muzzle.

Uncertainty quantification of mechanism motion based on coupled mechanism—motor dynamic model for ammunition delivery system

In this paper,a dynamic modeling method of motor driven electromechanical system is presented,and the uncertainty quantification of mechanism motion is investigated based on this method.The main contribution is to propose a novel mechanism-motor coupling dynamic modeling method,in which the relationship between mechanism motion and motor rotation is established according to the geometric coordination of the system.The advantages of this include establishing intuitive coupling between the mechanism and motor,facilitating the discussion for the influence of both mechanical and electrical parameters on the mechanism,and enabling dynamic simulation with controller to take the randomness of the electric load into account.Dynamic simulation considering feedback control of ammunition delivery system is carried out,and the feasibility of the model is verified experimentally.Based on probability density evolution theory,we comprehensively discuss the effects of system parameters on mechanism motion from the perspective of uncertainty quantization.Our work can not only provide guidance for engineering design of ammunition delivery mechanism,but also provide theoretical support for modeling and uncertainty quantification research of mechatronics system.

Quantification of the concrete freeze–thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms

The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.

Revolutionizing disease diagnosis and management:Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification

Artificial intelligence(AI),particularly machine learning(ML)and deep learning(DL)techniques,such as convolutional neural networks(CNNs),have emerged as transformative technologies with vast potential in healthcare.Body iron load is usually assessed using slightly invasive blood tests(serum ferritin,serum iron,and serum transferrin).Serum ferritin is widely used to assess body iron and drive medical management;however,it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients.Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron.The ML and DL algorithms can be used to enhance the detection of minor changes.However,a lack of open-access datasets may delay the advancement of medical research in this field.In this letter,we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging.Despite the current limitations,embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.

基于label-free定量蛋白质组学方法筛选沉默CHAF1B基因后心肌细胞差异表达蛋白及调控网络分析

目的分析沉默染色质装配因子1亚基B(chromatin assembly factor 1 subunit B,CHAF1B)基因后心肌细胞中差异表达蛋白,预测CHAF1B基因调控网络,为寻找促进心肌细胞修复的潜在治疗靶点提供参考。方法采用转染和蛋白质印迹法筛选沉默CHAF1B基因的有效小干扰RNA(small interfering RNA,siRNA)。应用有效siRNA沉默人源心肌AC16细胞CHAF1B基因后,采用细胞活力检测方法检测细胞活力;提取总蛋白质进行定量、还原、烷基化和胰蛋白酶裂解成肽段,利用高效液相串联质谱法鉴定肽段;搜索UniProt蛋白库筛选差异表达的蛋白质进行基因本体(Gene Ontology,GO)富集分析、京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)通路富集和蛋白质互作网络(protein-protein interaction networks,PPI)分析。结果siRNA有效沉默CHAF1B基因后,心肌细胞存活明显受到抑制;label-free定量蛋白质组学方法鉴定结果显示,共有69个差异表达蛋白质,其中50个表达显著上调(差异倍数≥2,P<0.05),19个表达显著下调(差异倍数≤0.5,P<0.05)。GO分析显示,差异表达蛋白质主要参与大分子复合亚基体、细胞组分生物合成和组装等生物学过程,分布在细胞质和囊泡等区域,发挥蛋白质结合等分子功能。KEGG通路富集和PPI分析显示,差异表达蛋白质参与的信号通路包括蛋白酶体、氨酰tRNA生物合成、胞吞、嘧啶代谢和氨基酸生物合成等10条信号途径;表达显著上调的蛋白质如蛋白酶体亚单位A2和B7、26 S蛋白酶体调节亚单位6B和10B参与蛋白酶体途径,丝氨酸、甘氨酸、谷氨酰胺和赖氨酸tRNA合成酶介导氨酰tRNA生物合成;表达显著下调的蛋白质包括骨架相关蛋白2/3复合体亚单位3和热休克70蛋白1样参与胞吞作用,核糖核苷-二磷酸还原酶大亚基介导嘧啶代谢等通路。实时荧光定量聚合酶链式反应结果证实,转染CHAF1B siRNA后心肌细胞中合成骨架相关蛋白2/3复合体亚单位3的基因ARPC3和氨酰tRNA生物合成关键基因QARS1的mRNA水平均显著降低。结论CHAF1B为心肌细胞存活的关键蛋白质,参与调控心肌细胞的胞吞和氨基酸生物合成等多种生物学过程,参考其调控网络可帮助寻找促进心肌细胞修复的干预环节。

A strategy with label-free quantification of the targeted peptides for quantitative peptidome analysis of human serum

Peptidomics draws more and more attention in discovering useful biomarkers for early diagnosis of disease. However, there is lack of efficient quantification strategy in peptidome analysis. In this study, a strategy with label-free quantification of the targeted endogenous peptides based on peak intensity using μUPLC-Q-TOF-MS/MS was developed for quantitative peptidome analysis of human serum. Different amounts of standard BSA tryptic digesting peptides were added into the same serum extracts for evaluation of the developed strategy, and it was observed that the average relative error of the targeted peptides was 6.42%, which was superior to the result obtained directly by commercially available software PLGS. It was also demonstrated that this quantification strategy could obviously increase the detection sensitivity of the peptide by DDA analysis. Then, this strategy was applied to comparatively analyze the peptides extracted from the serum of HCC or breast cancer patients and healthy individuals, respectively. Peptides with charge states up to 5 and molecular weight over 4000 can be reliably identified and quantified. This quantitative analysis method based on μUPLC-Q-TOF-MS/MS exhibited superior sensitivity than that by MALDI-TOF-MS commonly used in peptidome analysis. Finally, some interesting endogenous peptides related to corresponding diseases were successfully obtained.

Label-free quantification of peptides in solution by disposable patterned hydrophilic chip based MALDI imaging

Quantification of a mixture of peptides in solution was achieved by disposable patterned hydrophilic chip based matrix-assisted laser desorption/ionization mass spectrometric imaging（MALDI MSI）.Compared with other quantitative methods for peptides in solution, this method is label-free and does not require separation of the multiple components of the solution before analysis. Uniform hydrophilic spots and high mass accuracy measurements provided confident identification and quantitative analysis of imaged compounds. The linear correlation between concentration and grayscale of image in the range of 5 fmol/μ L to 1 pmol/μ L was obtained for all four peptides. Good sensitivity and excellent reproducibility were also achieved. The method expands the application of MALDI MSI from tissues to solutions.

Label-free quantification of differentially expressed proteins in mouse liver cancer cells with high and low metastasis rates by a SWATH acquisition method

Label-free quantification is a valuable tool for the analysis of differentially expressed proteins identified by mass spectrometry methods.Herein,we used a new strategy:data-dependent acquisition mode identification combined with label-free quantification by SWATH acquisition mode,to study the differentially expressed proteins in mouse liver cancer metastasis cells.A total of 1528 protein groups were identified,among which 1159 protein groups were quantified and 249 protein groups were observed as differentially expressed proteins(86 proteins up-regulated and 163 down-regulated).This method provides a commendable solution for the identification and quantification of differentially expressed proteins in biological samples.

4D label-free proteomic analysis of vitreous from patients with rhegmatogenous retinal detachment

AIM:To identify metabolites,proteins,and related pathways involved in the etiology of rhegmatogenous retinal detachment(RRD)for use as biomarkers in diagnosing and treating RRD.METHODS:Vitreous specimens were collected and liquid chromatography-tandem mass spectrometry analysis was per formed using the four-dimensional label-free technique.Statistically significant differentially expressed proteins,gene ontology(GO)terms,Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway representations,and protein interactions were analyzed.RESULTS:Nine specimens were subjected to proteomic analysis.In total,161 proteins were identified as differentially expressed proteins(DEPs),including 53 upregulated proteins and 108 downregulated proteins.GO functional analysis revealed that some DEPs were enriched in neuron-related terms and membrane protein terms.Moreover,KEGG analysis indicated that the cell adhesion molecule metabolic pathway was associated with the greatest number of DEPs.Finally,the evaluation of protein-protein interaction network revealed that DEPs were clustered in neuronal adhesion,apoptosis,inflammation and immune responses,correct protein folding,and glycolysis.CONCLUSION:Proteomic profiling is useful for the exploration of molecular mechanisms that underlie RRD.This study reveals increased expression levels of proteins related to heat shock protein content,glycolysis,and inflammatory responses in RRD.Knowledge regarding biomarkers of RRD pathogenesis may help to prevent the occurrence of RRD in the future.

Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods

Complex systems exist widely,including medicines from natural products,functional foods,and biological samples.The biological activity of complex systems is often the result of the synergistic effect of multiple components.In the quality evaluation of complex samples,multicomponent quantitative analysis(MCQA)is usually needed.To overcome the difficulty in obtaining standard products,scholars have proposed achieving MCQA through the“single standard to determine multiple components(SSDMC)”approach.This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia.Depending on a convenient(ultra)high-performance liquid chromatography method,how can the repeatability and robustness of the MCQA method be improved?How can the chromatography conditions be optimized to improve the number of quantitative components?How can computer software technology be introduced to improve the efficiency of multicomponent analysis(MCA)?These are the key problems that remain to be solved in practical MCQA.First,this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years,as well as the method robustness and accuracy evaluation.Second,it also summarizes methods to improve peak capacity and quantitative accuracy in MCA,including column selection and twodimensional chromatographic analysis technology.Finally,computer software technologies for predicting chromatographic conditions and analytical parameters are introduced,which provides an idea for intelligent method development in MCA.This paper aims to provide methodological ideas for the improvement of complex system analysis,especially MCQA.

Characterization of natural peptides in Pheretima by integrating proteogenomics and label-free peptidomics

Pheretima,also called“earthworms”,is a well-known animal-derived traditional Chinese medicine that is extensively used in over 50 Chinese patent medicines(CPMs)in Chinese Pharmacopoeia(2020 edition).However,its zoological origin is unclear,both in the herbal market and CPMs.In this study,a strategy for integrating in-house annotated protein databases constructed from close evolutionary relationship-sourced RNA sequencing data from public archival resources and various sequencing algorithms(restricted search,open search,and de novo)was developed to characterize the phenotype of natural peptides of three major commercial species of Pheretima,including Pheretima aspergillum(PA),Pheretima vulgaris(PV),and Metaphire magna(MM).We identified 10,477 natural peptides in the PA,7,451 in PV,and 5,896 in MM samples.Five specific signature peptides were screened and then validated using synthetic peptides;these demonstrated robust specificity for the authentication of PA,PV,and MM.Finally,all marker peptides were successfully applied to identify the zoological origins of Brain Heart capsules and Xiaohuoluo pills,revealing the inconsistent Pheretima species used in these CPMs.In conclusion,our integrated strategy could be used for the in-depth characterization of natural peptides of other animal-derived traditional Chinese medicines,especially non-model species with poorly annotated protein databases.

基于Label-free技术的高州油茶铝胁迫蛋白质组学研究

【目的】南方地区拥有大面积的酸性红壤,导致土壤中铝元素多以离子的形式溶出,严重抑制油茶的生长及高产。研究采用Label-free技术探究高州油茶在铝胁迫下的蛋白质组学响应,为揭示高州油茶应答铝胁迫的分子响应机制奠定了理论基础。【方法】以高州油茶两年生实生苗为试验材料,测定不同浓度铝处理条件下高州油茶叶片部分代谢指标及抗氧化生理相关指标,同时比较对照组(CK)和4 mmol/L铝处理组(GZ4)差异蛋白质的表达。【结果】生理试验结果表明,不同浓度铝胁迫下油茶幼苗都会发生氧化胁迫导致细胞活性氧(ROS)积累,2 mmol/L铝浓度下油茶叶片通过增加渗透调节物质含量和增强多种抗氧化酶活性以清除细胞活性氧,使细胞免受氧化胁迫的损害;而4 mmol/L铝浓度下叶片丙二醛(MDA)含量和脯氨酸(PRO)含量明显升高,积累的活性氧超出了自身所能清除的范围,叶片可溶性糖含量和可溶性蛋白含量显著减少,抗氧化酶活性均有不同程度减弱,油茶正常的生理功能受到抑制。蛋白质组学数据表明,CK组和GZ4组共鉴定到4282个蛋白质,与CK组相比,GZ4处理组有207个蛋白质的表达丰度显著增加,129个蛋白质的表达丰度显著降低。差异表达蛋白质的通路富集和蛋白质互作网络分析结果表明,4 mmol/L铝胁迫下高州油茶主要通过提高谷胱甘肽代谢,增强细胞清除活性氧和自由基负离子的能力缓解铝胁迫造成的氧化损伤;4 mmol/L铝浓度下高州油茶蛋白质合成和光合作用受到明显抑制,抗氧化酶活性减弱可能与细胞供能能力不足有关。【结论】铝胁迫下高州油茶主要通过能量及碳水化合物代谢、氨基酸合成及代谢、生物碱合成、蛋白质加工、卟啉和叶绿素代谢等代谢途径抵抗铝毒。

Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering

Because the breast cancer is an important factor that threatens women's lives and health,early diagnosis is helpful for disease screening and a good prognosis.Exosomes are nanovesicles,secreted from cells and other body fluids,which can reflect the genetic and phenotypic status of parental cells.Compared with other methods for early diagnosis of cancer(such as circulating tumor cells(CTCs)and circulating tumor DNA),exosomes have a richer number and stronger biological stability,and have great potential in early diagnosis.Thus,it has been proposed as promising biomarkers for diagnosis of early-stage cancer.However,distinguishing different exosomes remain is a major biomedical challenge.In this paper,we used predictive Convolutional Neural model to detect and analyze exosomes of normal and cancer cells with surface-enhanced Raman scattering(SERS).As a result,it can be seen from the SERS spectra that the exosomes of MCF-7,MDA-MB-231 and MCF-10A cells have similar peaks(939,1145 and 1380 cm^(-1)).Based on this dataset,the predictive model can achieve 95%accuracy.Compared with principal component analysis(PCA),the trained CNN can classify exosomes from different breast cancer cells with a superior performance.The results indicate that using the sensitivity of Raman detection and exosomes stable presence in the incubation period of cancer cells,SERS detection combined with CNN screening may be used for the early diagnosis of breast cancer in the future.

Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks

High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.

Host cell protein quantification workflow using optimized standards combined with data-independent acquisition mass spectrometry

Monitoring of host cell proteins(HCPs)during the manufacturing of monoclonal antibodies(mAb)has become a critical requirement to provide effective and safe drug products.Enzyme-linked immunosorbent assays are still the gold standard methods for the quantification of protein impurities.However,this technique has several limitations and does,among others,not enable the precise identification of proteins.In this context,mass spectrometry(MS)became an alternative and orthogonal method that delivers qualitative and quantitative information on all identified HCPs.However,in order to be routinely implemented in biopharmaceutical companies,liquid chromatography-MS based methods still need to be standardized to provide highest sensitivity and robust and accurate quantification.Here,we present a promising MS-based analytical workflow coupling the use of an innovative quantification standard,the HCP Profiler solution,with a spectral library-based data-independent acquisition(DIA)method and strict data validation criteria.The performances of the HCP Profiler solution were compared to more conventional standard protein spikes and the DIA approach was benchmarked against a classical datadependent acquisition on a series of samples produced at various stages of the manufacturing process.While we also explored spectral library-free DIA interpretation,the spectral library-based approach still showed highest accuracy and reproducibility(coefficients of variation<10%)with a sensitivity down to the sub-ng/mg mAb level.Thus,this workflow is today mature to be used as a robust and straightforward method to support mAb manufacturing process developments and drug products quality control.