Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses.

Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.

Turn-off enzyme activity of histidine-rich peptides for the detection of lysozyme.

An assay that integrates histidine-rich peptides (HisRPs) with high-affinity aptamers was developed enabling the specific and sensitive determination of the target lysozyme. The enzyme-like activity of HisRP is inhibited by its interaction with a target recognized by an aptamer. In the presence of the target, lysozyme molecules progressively assemble on the surface of HisRP in a concentration-dependent manner, resulting in the gradual suppression of enzyme-like activity. This inhibition of HisRP's enzyme-like activity can be visually observed through color changes in the reaction product or quantified using UV-visible absorption spectroscopy. Under optimal conditions, the proposed colorimetric assay for lysozyme had a detection limit as low as 1 nM and exhibited excellent selectivity against other nonspecific interferents. Furthermore, subsequent research validated the practical applicability of the developed colorimetric approach to saliva samples, indicating that the assay holds significant potential for the detection of lysozymes in samples derived from humans.

Biochemical changes in lipid and protein metabolism caused by mannose-Raman spectroscopy studies.

Biochemical analysis of human normal bronchial cells (BEpiC) and human cancer lung cells (A549) has been performed by using Raman spectroscopy and Raman imaging. Our approach provides a biochemical compositional mapping of the main cell components: nucleus, mitochondria, lipid droplets, endoplasmic reticulum, cytoplasm and cell membrane. We proved that Raman spectroscopy and Raman imaging can distinguish successfully BEpiC and A549 cells. In this study, we have focused on the role of mannose in cancer development. It has been shown that changes in the concentration of mannose can regulate some metabolic processes in cells. Presented results suggest lipids and proteins can be considered as Raman biomarkers during lung cancer progression. Analysis obtained for bands 1444 cm-1, and 2854 cm-1 characteristic for lipids and derivatives proved that the addition of mannose reduced levels of these compounds. Results obtained for protein compounds based on bands 858 cm-1, 1004 cm-1 and 1584 cm-1 proved that the addition of mannose increases the values of protein in BEpiC cells and blocks protein glycolisation in A549 cells. Noticing Raman spectral changes in BEpiC and A549 cells supplemented with mannose can help to understand the mechanism of sugar metabolism during cancer development and could play in the future an important role in clinical treatment.

Human epididymal protein 4 and its combined detection show good diagnostic value in lung cancer: A retrospective study.

OBJECTIVES: This study aimed to assess the diagnostic value of human epididymal protein 4 (HE4), a potential novel biomarker for lung cancer, and its combined detection with five other conventional biomarkers in lung cancer diagnosis and subtyping. METHODS: In this retrospective study, 115 lung cancer patients, 50 patients with benign pulmonary disease, and 50 healthy controls were included. Serum HE4, progastrin-releasing peptide (ProGRP), squamous cell carcinoma (SCC) antigen, cytokeratin-19 fragment (CYFRA21-1), neuron-specific enolase (NSE), and carcinoembryonic antigen (CEA) were analyzed using the electrochemiluminescence immunoassay and chemiluminescence immunoassay. The receiver operating characteristic curve was performed to analyze the diagnostic efficacy of individual biomarkers in identifying both lung cancer and its histologic subtypes. RESULTS: All six biomarkers showed significantly elevated levels in the lung cancer group compared to both benign pulmonary disease and control groups (P < 0.05). Among the biomarkers evaluated, HE4 exhibited the highest diagnostic performance for lung cancer, lung adenocarcinoma, and lung squamous cell carcinoma with area under the curve (AUC) values of 0.921, 0.891, and 0.937, respectively. ProGRP was the optimal biomarker for small cell lung cancer with an AUC of 0.973. The combination of all six biomarkers yielded the largest AUCs in the diagnosis of lung cancer subtypes (0.937 for lung adenocarcinoma, 0.998 for lung squamous cell carcinoma, and 0.985 for small cell lung cancer). Furthermore, specific combinations, such as HE4 + CEA, HE4 + SCC, and ProGRP + HE4 + NSE, showed strong diagnostic performance in lung cancer. CONCLUSIONS: HE4 and its combined detection held substantial clinical significance in the diagnosis of lung cancer and its histologic subtyping, especially for lung adenocarcinoma and lung squamous cell carcinoma.

Improved assay development of pharmaceutical modalities using feedback-controlled liquid chromatography optimization.

Development of meaningful and reliable analytical assays in the (bio)pharmaceutical industry can often be challenging, involving tedious trial and error experimentation. In this work, an automated analytical workflow using an AI-based algorithm for streamlined method development and optimization is presented. Chromatographic methods are developed and optimized from start to finish by a feedback-controlled modeling approach using readily available LC instrumentation and software technologies, bypassing manual user intervention. With the use of such tools, the time requirement of the analyst is drastically minimized in the development of a method. Herein key insights on chromatography system control, automatic optimization of mobile phase conditions, and final separation landscape for challenging multicomponent mixtures are presented (e.g., small molecules drug, peptides, proteins, and vaccine products) showcased by a detailed comparison of a chiral method development process. The work presented here illustrates the power of modern chromatography instrumentation and AI-based software to accelerate the development and deployment of new separation assays across (bio)pharmaceutical modalities while yielding substantial cost-savings, method robustness, and fast analytical turnaround.

A Review for Artificial Intelligence Based Protein Subcellular Localization.

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

A Water-Soluble Wavy Coordination Polymer of Cu(II) as a Turn-On Luminescent Probe for Histidine and Histidine-Rich Proteins/Peptides.

Histidine plays an essential role in most biological systems. Changes in the homeostasis of histidine and histidine-rich proteins are connected to several diseases. Herein, we report a water-soluble Cu(II) coordination polymer, labeled CuCP, for the fluorimetric detection of histidine and histidine-rich proteins and peptides. Single-crystal structure determination of CuCP revealed a two-dimensional wavy network structure in which a carboxylate group connects the individual Cu(II) dimer unit in a syn-anti conformation. The weakly luminescent and water-soluble CuCP shows turn-on blue emission in the presence of histidine and histidine-rich peptides and proteins. The polymer can also stain histidine-rich proteins via gel electrophoresis. The limits of quantifications for histidine, glycine-histidine, serine-histidine, human serum albumin (HSA), bovine serum albumin, pepsin, trypsin, and lysozyme were found to be 300, 160, 600, 300, 600, 800, 120, and 290 nM, respectively. Utilizing the fluorescence turn-on property of CuCP, we measured HSA quantitatively in the urine samples. We also validated the present urinary HSA measurement assay with existing analytical techniques. Job's plot, 1H NMR, high-resolution mass spectrometry (HRMS), electron paramagnetic resonance (EPR), fluorescence, and UV-vis studies confirmed the ligand displacement from CuCP in the presence of histidine.

Perspective: The complex relationship between charge, mobility, and gas-phase protein structure.

Ion mobility spectrometry coupled to mass spectrometry (IMS/MS) is a widely used tool for biomolecular separations and structural elucidation. The application of IMS/MS has resulted in exciting developments in structural proteomics and genomics. This perspective gives a brief background of the field, addresses some of the important issues in making structural measurements, and introduces complementary techniques.

Similar construction of spicules and shell plates: Implications for the origin of chiton biomineralization.

The hard shells of mollusks are products of biomineralization, a distinctive feature of the Cambrian explosion. Despite our understanding of shell structure and mechanical properties, their origin remains mysterious. In addition to their shell plates, most chitons have calcium deposits on their girdles. However, the similarity of these two mineralized structures still needs to be determined, limiting our comprehension of their origins. In our study, we analyzed the matrix proteins in the spicules of chiton (Acanthopleura loochooana) and compared them with the matrix proteins in the shells of the same species. Proteomics identified 96 unique matrix proteins in spicules. Comparison of biomineralization-related matrix proteins in shell plates and spicules revealed shared proteins, including carbonic anhydrases, tyrosinase-hemocyanin, von Willebrand factor type A, cadherin, and glycine-rich unknown proteins. Based on similarities in key matrix proteins, we propose that spicules and shell plates originated from a common mineralization system in their ancestral lineage, suggesting the existence of a common core or toolkit of matrix proteins among calcifying organisms. SIGNIFICANCE: In this study, we try to understand the types and diversity of matrix proteins in the biomineralization of chiton shell plates and spicules. Through a comparative analysis, we seek insights into the core biomineralization toolkit of ancestral mollusks. To achieve this, we conducted LC-MS/MS and RT-qPCR analyses to identify the types and relative expression levels of matrix proteins in both shell plates and spicules. The analysis revealed 96 matrix proteins in the spicules. A comparison of biomineralization-related matrix proteins in shell plates and spicules from the same species revealed shared proteins including many unknown proteins unique to chitons. Blast searching reveals a universal conservation of these proteins among other chitons. Hence, we propose that spicules and shell plates originated from a common mineralization system in their ancestral lineage. Our work provides a molecular basis for studying biomineralization in polyplacophoran mollusks and understanding biomineralization evolution. In addition, it identifies potential matrix proteins that could be applied to control crystal growth.

Increase in plasma resveratrol levels and in acid-resistant proteins in the acquired enamel pellicle after use of resveratrol-containing orodispersible tablets.

OBJECTIVE: This study evaluated the effect of administration of trans-resveratrol-containing orodispersible tablets on the protein composition of the AEP and on blood plasma trans-resveratrol concentrations. METHODS: Ten volunteers participated in two crossover double-blind phases. In each phase, after dental prophylaxis, they received a trans-resveratrol (15 mg) orodispersible tablet, or a placebo tablet (without actives). The AEP formed after 120 min was collected with electrode filter papers soaked in 3 % citric acid. Blood samples were collected 30, 45, 60 and 120 min after the use of the tablet. After protein extraction, AEP samples were analyzed by shotgun labelfree quantitative proteomics and plasma samples were analyzed by high-performance liquid chromatography (HPLC). RESULTS: Eight hundred and two proteins were identified in the AEP. Among them, 336 and 213 were unique to the trans-resveratrol and control groups, respectively, while 253 were common to both groups. Proteins with important functions in the AEP had increased expression in the trans-resveratroltreated group, such as neutrophil defensins, S100 protein isoforms, lysozyme C, cystatin-D, mucin-7, alphaamylase, albumin, haptoglobin and statherin. Trans-resveratrol was detected in the plasma at all the times evaluated, with the peak at 30 min. CONCLUSIONS: The administration of trans-resveratrol in sublingual orodispersible tablets was effective both to increase the bioavailability of the polyphenol and the expression of antibacterial and acid-resistant proteins in the AEP, which might benefit oral and general health.

Preoperative serum level of CA153 and a new model to predict the sub-optimal primary debulking surgery in patients with advanced epithelial ovarian cancer.

OBJECTIVE: The aim of this study was to establish a preoperative model to predict the outcome of primary debulking surgery (PDS) for advanced ovarian cancer (AOC) patients by combing Suidan predictive model with HE4, CA125, CA153 and ROMA index. METHODS: 76 AOC Patients in revised 2014 International Federation of Gynecology and Obstetrics (FIGO) stage III-IV who underwent PDS between 2017 and 2019 from Yunnan Cancer Hospital were included. Clinical data including the levels of preoperative serum HE4, CA125, CA153 and mid-lower abdominal CT-enhanced scan results were collected. The logistics regression analysis was performed to find factors associated with sub-optimal debulking surgery (SDS). The receiver operating characteristic curve was used to evaluate the predictive performances of selected variables in the outcome of primary debulking surgery. The predictive index value (PIV) model was constructed to predict the outcome of SDS. RESULTS: Optimal surgical cytoreduction was achieved in 61.84% (47/76) patients. The value for CA125, HE4, CA153, ROMA index and Suidan score was lower in optimal debulking surgery (ODS) group than SDS group. Based on the Youden index, which is widely used for evaluating the performance of predictive models, the best cutoff point for the preoperative serum HE4, CA125, CA153, ROMA index and Suidan score to distinguish SDS were 431.55 pmol/l, 2277 KU/L, 57.19 KU/L, 97.525% and 2.5, respectively. Patients with PIV≥5 may not be able to achieve optimal surgical cytoreduction. The diagnostic accuracy, NPV, PPV and specificity for diagnosing SDS were 73.7%, 82.9%, 62.9% and 72.3%, respectively. In the constructed model, the AUC of the SDS prediction was 0.770 (95% confidence interval: 0.654-0.887), P<0.001. CONCLUSION: Preoperative serum CA153 level is an important non-invasive predictor of primary SDS in advanced AOC, which has not been reported before. The constructed PIV model based on Suidan's predictive model plus HE4, CA125, CA153 and ROMA index can noninvasively predict SDS in AOC patients, the accuracy of this prediction model still needs to be validated in future studies.

Target-decoy false discovery rate estimation using Crema.

Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.

A simple and sensitive differential digestion method to analyze adeno-associated virus residual host cell proteins by LC-MS.

Many methods using liquid chromatography-mass spectrometry (LC-MS) have been established for identifying residual host cell proteins (HCPs) to aid in the process development and quality control of therapeutic proteins. However, the use of MS-based techniques for adeno-associated virus (AAV) is still in its infancy, with few methods reported and minimal information available on potentially problematic HCPs. In this study, we developed a highly sensitive and effective differential digestion method to profile residual HCPs in AAV. Unlike direct digestion, which completely digests both AAV and HCPs, our differential digestion method takes advantage of AAV's unique characteristics to maintain the integrity of AAV while preferentially digesting HCPs under denaturing and reducing conditions. This differential digestion method requires only several micrograms of sample and significantly enhances the identification of HCPs. Furthermore, this method can be applied to all five different AAV serotypes for comprehensive HCP profiling. Our work fills a gap in AAV HCP analysis by providing a sensitive and robust strategy for detecting, monitoring, and measuring HCPs.

Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping.

Proteogenomics studies generate hypotheses on protein function and provide genetic evidence for drug target prioritization. Most previous work has been conducted using affinity-based proteomics approaches. These technologies face challenges, such as uncertainty regarding target identity, non-specific binding, and handling of variants that affect epitope affinity binding. Mass spectrometry-based proteomics can overcome some of these challenges. Here we report a pQTL study using the Proteograph™ Product Suite workflow (Seer, Inc.) where we quantify over 18,000 unique peptides from nearly 3000 proteins in more than 320 blood samples from a multi-ethnic cohort in a bottom-up, peptide-centric, mass spectrometry-based proteomics approach. We identify 184 protein-altering variants in 137 genes that are significantly associated with their corresponding variant peptides, confirming target specificity of co-associated affinity binders, identifying putatively causal cis-encoded proteins and providing experimental evidence for their presence in blood, including proteins that may be inaccessible to affinity-based proteomics.

Effects of microbubble pretreatment on physiochemical and microbial properties of excess activated sludge.

Fast increased amount of excess activated sludge (EAS) from wastewater treatment plants has aroused universal concerns on its environmental risks and demands for appropriate treatments, while effective treatment is dependent upon proper pretreatment. In this study, air-supplied microbubbles (air-MBs) with generated size of 25.18 to 28.25 µm were used for EAS pretreatment. Different durations (30, 60, 90, and 120 s) yielded sludge with varied physiochemical conditions, and 60 s decreased sludge oxidation status and significantly increased adenosine triphosphate (ATP) content. Soluble, loosely-bound, and tightly-bound extracellular polymeric substances (SEPS, LB-EPS, and TB-EPS) were extracted from the sludge through a stepwise approach and examined through three-dimensional excitation-emission matrix (3D-EEM) and quantitative analysis. The results showed that 60- and 120-s treatments generated stronger fluorescence intensities on dissolved organic matters (DOMs) of protein-like and fulvic acid in LB-EPS and TB-EPS, which indicated the decrease of counterparts in EAS, and therefore facilitated sludge dewaterability and reduction. The dominant microbial communities in EAS, including Proteobacteria, Bacteroidota, Chloroflexi, and Actinobacteriota, were not significantly affected by MB pretreatment. The results collectively revealed the effects of MB pretreatment on EAS and indicated that MBs could be an effective pretreatment technique for EAS treatment process.

Utilizing Precursor Ion Connectivity of Different Charge States to Improve Peptide and Protein Identification in MS/MS Analysis.

Tandem mass spectrometry (MS/MS) has become a key method for the structural analysis of biomolecules such as peptides and proteins. A pervasive problem in MS/MS analyses, especially for top-down proteomics, is the occurrence of chimeric spectra, when two or more precursor ions are co-isolated and fragmented, thus leading to complex MS/MS spectra that are populated with fragment ions originating from different precursor ions. This type of convoluted data typically results in low sequence database search scores due to the vast number of mixed-source fragment ions, of which only a fraction originates from a specific precursor ion. Herein, we present a novel workflow that deconvolutes the data of chimeric MS/MS spectra, improving the protein search scores and sequence coverages in database searching and thus providing a more confident peptide and protein identification. Previously misidentified proteins or proteins with insignificant search scores can be correctly and significantly identified following the presented data acquisition and analysis workflow with search scores increasing by a factor of 3-4 for smaller precursor ions (peptides) and >6 for larger precursor ions such as intact ubiquitin and cytochrome C.

Development of an Online Isotope Dilution CE/ICP-MS Method for the Quantification of Sulfur in Biological Compounds.

We report an analytical methodology for the quantification of sulfur in biological molecules via a species-unspecific postcolumn isotope dilution (online ID) approach using capillary electrophoresis (CE) coupled online with inductively coupled plasma-mass spectrometry (online ID CE/ICP-MS). The method was optimized using a mixture of standard compounds including sulfate, methionine, cysteine, cystine, and albumin, yielding compound recoveries between 98 and 105%. The quantity of sulfur is further converted to the quantity of the compounds owing to the prior knowledge of the sulfur content in the molecules. The limit of detection and limit of quantification of sulfur in the compounds were 1.3-2.6 and 4.1-8.4 mg L-1, respectively, with a correlation coefficient of 0.99 within the concentration range of sulfur of 5-100 mg L-1. The capability of the method was extended to quantify albumin in its native matrix (i.e., in serum) using experimentally prepared serum spiked with a pure albumin standard for validation. The relative expanded uncertainty of the method for the quantification of albumin was 6.7% (k = 2). Finally, we tested the applicability of the method on real samples by the analysis of albumin in bovine and human sera. For automated data assessment, a software application (IsoCor)─which was developed by us in a previous work─was developed further for handling of online ID data. The method has several improvements compared to previously published setups: (i) reduced adsorption of proteins onto the capillary wall owing to a special capillary-coating procedure, (ii) baseline separation of the compounds in less than 30 min via CE, (iii) quantification of several sulfur species within one run by means of the online setup, (iv) SI traceability of the quantification results through online ID, and (v) facilitated data processing of the transient signals using the IsoCor application. Our method can be used as an accurate approach for quantification of proteins and other biological molecules via sulfur analysis in complex matrices for various fields, such as environmental, biological, and pharmaceutical studies as well as clinical diagnosis.

Exosomal biomarkers in the differential diagnosis of ovarian tumors: the emerging roles of CA125, HE4, and C5a.

OBJECTIVE: Investigating the utility of serum exosomal markers CA125, HE4, and C5a, both individually and in combination, for distinguishing between benign and malignant ovarian tumors. METHODS: In this study, we selected a total of 234 patients diagnosed with ovarian tumors, including 34 with malignant tumors, 10 with borderline ovarian tumors, and 190 with benign tumors. This study conducted comparisons of exosomal levels of CA125, HE4, and C5a among distinct groups, as well as making comparisons between serum and exosomal levels of CA125 and HE4. Furthermore, the diagnostic performance was assessed through Receiver Operating Characteristic (ROC) curve analysis. The Area Under the Curve (AUC) was computed, and a comparative evaluation of sensitivity and specificity was conducted to ascertain their effectiveness in determining the nature of ovarian tumors across different markers. RESULTS: Serum CA125 and HE4 levels, the ROMA index, exosomal CA125, HE4, C5a levels, and their combined applied value (OCS value) were notably elevated in the ovarian non-benign tumor group compared to the benign tumor group, with statistical significance (P < 0.05). Exosomal and serum levels of CA125 and HE4 exhibited a positive correlation, with concentrations of these markers in serum surpassing those in exosomes. The combined OCS (AUC = 0.871) for CA125, HE4, and C5a in exosomes demonstrated superior sensitivity (0.773) and specificity (0.932) compared to serum tumor markers (CA125, HE4) and the ROMA index. The tumor stage represents an autonomous risk factor influencing the prognosis of individuals with ovarian malignancies. CONCLUSION: The stage of ovarian malignancy is an independent risk factor for its prognosis. The combination of exosomal CA125, HE4 and C5a has a higher clinical value for the identification of the nature of ovarian tumours.

High-Abundance Protein-Guided Hybrid Spectral Library for Data-Independent Acquisition Metaproteomics.

Metaproteomics offers a direct avenue to identify microbial proteins in microbiota, enabling the compositional and functional characterization of microbiota. Due to the complexity and heterogeneity of microbial communities, in-depth and accurate metaproteomics faces tremendous limitations. One challenge in metaproteomics is the construction of a suitable protein sequence database to interpret the highly complex metaproteomic data, especially in the absence of metagenomic sequencing data. Herein, we present a high-abundance protein-guided hybrid spectral library strategy for in-depth data independent acquisition (DIA) metaproteomic analysis (HAPs-hyblibDIA). A dedicated high-abundance protein database of gut microbial species is constructed and used to mine the taxonomic information on microbiota samples. Then, a sample-specific protein sequence database is built based on the taxonomic information using Uniprot protein sequence for subsequent analysis of the DIA data using hybrid spectral library-based DIA analysis. We evaluated the accuracy and sensitivity of the method using synthetic microbial community samples and human gut microbiome samples. It was demonstrated that the strategy can successfully identify taxonomic compositions of microbiota samples and that the peptides identified by HAPs-hyblibDIA overlapped greatly with the peptides identified using a metagenomic sequencing-derived database. At the peptide and species level, our results can serve as a complement to the results obtained using a metagenomic sequencing-derived database. Furthermore, we validated the applicability of the HAPs-hyblibDIA strategy in a cohort of human gut microbiota samples of colorectal cancer patients and controls, highlighting its usability in biomedical research.

Quantitative mass spectrometry imaging: therapeutics & biomolecules.

Mass spectrometry imaging (MSI) has become increasingly utilized in the analysis of biological molecules. MSI grants the ability to spatially map thousands of molecules within one experimental run in a label-free manner. While MSI is considered by most to be a qualitative method, recent advancements in instrumentation, sample preparation, and development of standards has made quantitative MSI (qMSI) more common. In this feature article, we present a tailored review of recent advancements in qMSI of therapeutics and biomolecules such as lipids and peptides/proteins. We also provide detailed experimental considerations for conducting qMSI studies on biological samples, aiming to advance the methodology.