Size exclusion chromatography of biopharmaceutical products: From current practices for proteins to emerging trends for viral vectors, nucleic acids and lipid nanoparticles.

The 21st century has been particularly productive for the biopharmaceutical industry, with the introduction of several classes of innovative therapeutics, such as monoclonal antibodies and related compounds, gene therapy products, and RNA-based modalities. All these new molecules are susceptible to aggregation and fragmentation, which necessitates a size variant analysis for their comprehensive characterization. Size exclusion chromatography (SEC) is one of the reference techniques that can be applied. The analytical techniques for mAbs are now well established and some of them are now emerging for the newer modalities. In this context, the objective of this review article is: i) to provide a short historical background on SEC, ii) to suggest some clear guidelines on the selection of packing material and mobile phase for successful method development in modern SEC; and iii) to highlight recent advances in SEC, such as the use of narrow-bore and micro-bore columns, ultra-wide pore columns, and low-adsorption column hardware. Some important innovations, such as recycling SEC, the coupling of SEC with mass spectrometry, and the use of alternative detectors such as charge detection mass spectrometry and mass photometry are also described. In addition, this review discusses the use of SEC in multidimensional setups and shows some of the most recent advances at the preparative scale. In the third part of the article, the possibility of SEC for the characterization of new modalities is also reviewed. The final objective of this review is to provide a clear summary of opportunities and limitations of SEC for the analysis of different biopharmaceutical products.

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.

Development of Optical Differential Sensing Based on Nanomaterials for Biological Analysis.

The discrimination and recognition of biological targets, such as proteins, cells, and bacteria, are of utmost importance in various fields of biological research and production. These include areas like biological medicine, clinical diagnosis, and microbiology analysis. In order to efficiently and cost-effectively identify a specific target from a wide range of possibilities, researchers have developed a technique called differential sensing. Unlike traditional "lock-and-key" sensors that rely on specific interactions between receptors and analytes, differential sensing makes use of cross-reactive receptors. These sensors offer less specificity but can cross-react with a wide range of analytes to produce a large amount of data. Many pattern recognition strategies have been developed and have shown promising results in identifying complex analytes. To create advanced sensor arrays for higher analysis efficiency and larger recognizing range, various nanomaterials have been utilized as sensing probes. These nanomaterials possess distinct molecular affinities, optical/electrical properties, and biological compatibility, and are conveniently functionalized. In this review, our focus is on recently reported optical sensor arrays that utilize nanomaterials to discriminate bioanalytes, including proteins, cells, and bacteria.

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.

Investigating cationic and zwitterionic successive multiple ionic-polymer layer coatings for protein separation by capillary electrophoresis.

Successive multiple ionic-polymer layers (SMILs) have long since proved their worth in capillary electrophoresis as they ensure stable electroosmotic flow (EOF) and relatively high separation efficiency. Recently, we demonstrated that plotting the plate height (H) against the solute migration velocity (u) enabled a reliable quantitative evaluation of the coating performances in terms of separation efficiency. In this work, various physicochemical and chemical parameters of the SMIL coating were studied and optimized in order to decrease the slope of the ascending part of the H vs u curve, which is known to be controlled by the homogeneity in charge of the coating surface and by the possible residual solute adsorption onto the coating surface. SMILs based on poly(diallyldimethylammonium chloride) (PDADMAC) and poly(sodium styrene sulfonate) (PSS) were formed and the effect of each polyelectrolyte molar mass and of the number of polyelectrolyte layers (up to 21 layers) was studied. The use of polyethylene imine as an anchoring first layer was considered. More polyelectrolyte couples based on PDADMAC, polybrene, PSS, poly(vinyl sulfate), and poly(acrylic acid) were tested. Finally, zwitterionic polymers based on the poly(α-l-lysine) scaffold were synthesized and used as the last layer of SMILs, illustrating their ability to finetune the EOF, while maintaining good separation efficiency.

Effect of Sample Preprocessing and Size-Based Extraction Methods on the Physical and Molecular Profiles of Extracellular Vesicles.

Extracellular vesicles (EVs) are nanometric lipid vesicles that shuttle cargo between cells. Their analysis could shed light on health and disease conditions, but EVs must first be preserved, extracted, and often preconcentrated. Here we first compare plasma preservation agents, and second, using both plasma and cell supernatant, four EV extraction methods, including (i) ultracentrifugation (UC), (ii) size-exclusion chromatography (SEC), (iii) centrifugal filtration (LoDF), and (iv) accousto-sorting (AcS). We benchmarked them by characterizing the integrity, size distribution, concentration, purity, and expression profiles for nine proteins of EVs, as well as the overall throughput, time-to-result, and cost. We found that the difference between ethylenediaminetetraacetic acid (EDTA) and citrate anticoagulants varies with the extraction method. In our hands, ultracentrifugation produced a high yield of EVs with low contamination; SEC is low-cost, fast, and easy to implement, but the purity of EVs is lower; LoDF and AcS are both compatible with process automation, small volume requirement, and rapid processing times. When using plasma, LoDF was susceptible to clogging and sample contamination, while AcS featured high purity but a lower yield of extraction. Analysis of protein profiles suggests that the extraction methods extract different subpopulations of EVs. Our study highlights the strengths and weaknesses of sample preprocessing methods, and the variability in concentration, purity, and EV expression profiles of the extracted EVs. Preanalytical parameters such as collection or preprocessing protocols must be considered as part of the entire process in order to address EV diversity and their use as clinically actionable indicators.

Two-Dimensional SEC-SEC-UV-MALS-dRI Workflow for Streamlined Analysis and Characterization of Biopharmaceuticals.

The emergence of complex biological modalities in the biopharmaceutical industry entails a significant expansion of the current analytical toolbox to address the need to deploy meaningful and reliable assays at an unprecedented pace. Size exclusion chromatography (SEC) is an industry standard technique for protein separation and analysis. Some constraints of traditional SEC stem from its restricted ability to resolve complex mixtures and notoriously long run times while also requiring multiple offline separation conditions on different pore size columns to cover a wider molecular size distribution. Two-dimensional liquid chromatography (2D-LC) is becoming an important tool not only to increase peak capacity but also to tune selectivity in a single online method. Herein, an online 2D-LC framework in which both dimensions utilize SEC columns with different pore sizes is introduced with a goal to increase throughput for biomolecule separation and characterization. In addition to improving the separation of closely related species, this online 2D SEC-SEC approach also facilitated the rapid analysis of protein-based mixtures of a wide molecular size range in a single online experimental run bypassing time-consuming deployment of different offline SEC methods. By coupling the second dimension with multiangle light scattering (MALS) and differential refractive index (dRI) detectors, absolute molecular weights of the separated species were obtained without the use of calibration curves. As illustrated in this report for protein mixtures and vaccine processes, this workflow can be used in scenarios where rapid development and deployment of SEC assays are warranted, enabling bioprocess monitoring, purity assessment, and characterization.

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.

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.

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.

Green and fast prediction of crude protein contents in bee pollen based on digital images combined with Random Forest algorithm.

Bee pollen is considered an excellent dietary supplement with functional characteristics, and it has been employed in food and cosmetics formulations and in biomedical applications. Therefore, understanding its chemical composition, particularly crude protein contents, is essential to ensure its quality and industrial application. For the quantification of crude protein in bee pollen, this study explored the potential of combining digital image analysis and Random Forest algorithm for the development of a rapid, cost-effective, and environmentally friendly analytical methodology. Digital images of bee pollen samples (n = 244) were captured using a smartphone camera with controlled lighting. RGB channels intensities and color histograms were extracted using open source softwares. Crude protein contents were determined using the Kjeldahl method (reference) and in combination with RGB channels and color histograms data from digital images, they were used to generate a predictive model through the application of the Random Forest algorithm. The developed model exhibited good performance and predictive capability for crude protein analysis in bee pollen (R2 = 80.93 %; RMSE = 1.49 %; MAE = 1.26 %). Thus, the developed analytical methodology can be considered environmentally friendly according to the AGREE metric, making it an excellent alternative to conventional analysis methods. It avoids the use of toxic reagents and solvents, demonstrates energy efficiency, utilizes low-cost instrumentation, and it is robust and precise. These characteristics indicate its potential for easy implementation in routine analysis of crude protein in bee pollen samples in quality control laboratories.

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.

CURTAIN-A unique web-based tool for exploration and sharing of MS-based proteomics data.

To facilitate analysis and sharing of mass spectrometry (MS)-based proteomics data, we created online tools called CURTAIN (https://curtain.proteo.info) and CURTAIN-PTM (https://curtainptm.proteo.info) with an accompanying series of video tutorials (https://www.youtube.com/@CURTAIN-me6hl). These are designed to enable non-MS experts to interactively peruse volcano plots and deconvolute primary experimental data so that replicates can be visualized in bar charts or violin plots and exported in publication-ready format. They also allow assessment of overall experimental quality by correlation matrix and profile plot analysis. After making a selection of protein "hits", the user can analyze known domain structure, AlphaFold predicted structure, reported interactors, relative expression as well as disease links. CURTAIN-PTM permits analysis of all identified PTM sites on protein(s) of interest with selected databases. CURTAIN-PTM also links with the Kinase Library to predict upstream kinases that may phosphorylate sites of interest. We provide examples of the utility of CURTAIN and CURTAIN-PTM in analyzing how targeted degradation of the PPM1H Rab phosphatase that counteracts the Parkinson's LRRK2 kinase impacts cellular protein levels and phosphorylation sites. We also reanalyzed a ubiquitylation dataset, characterizing the PINK1-Parkin pathway activation in primary neurons, revealing data of interest not highlighted previously. CURTAIN and CURTAIN-PTM are free to use and open source, enabling researchers to share and maximize the impact of their proteomics data. We advocate that MS data published in volcano plot format be reported containing a shareable CURTAIN weblink, thereby allowing readers to better analyze and exploit the data.

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.

High-Performance Gel-Free and Label-Free Size Fractionation of Extracellular Vesicles with Two-Dimensional Electrophoresis in a Microfluidic Artificial Sieve.

Extracellular vesicles (EVs) are cell-derived particles that exhibit diverse sizes, molecular contents, and clinical implications for various diseases depending on their specific subpopulations. However, fractionation of EV subpopulations with high resolution, efficiency, purity, and yield remains an elusive goal due to their diminutive sizes. In this study, we introduce a novel strategy that effectively separates EV subpopulations in a gel-free and label-free manner, using two-dimensional (2D) electrophoresis in a microfluidic artificial sieve. The microfabricated artificial sieve consists of periodically arranged micro-slit-well structures in a 2D array and generates an anisotropic electric field pattern to size fractionate EVs into discrete streams and steer the subpopulations into designated outlets for collection within a minute. Along with fractionating EV subpopulations, contaminants such as free proteins and short nucleic acids can be simultaneously directed to waste outlets, thus accomplishing both size fractionation and purification of EVs with high performance. Our platform offers a simple, rapid, and versatile solution for EV subpopulation isolation, which can potentially facilitate the discovery of biomarkers for specific EV subtypes and the development of EV-based therapeutics.

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.

LC-MS bioanalysis of protein biomarkers and protein therapeutics in formalin-fixed paraffin-embedded tissue specimens.

Formalin-fixed paraffin-embedded (FFPE) is a form of preservation and preparation for biopsy specimens. FFPE tissue specimens are readily available as part of oncology studies because they are often collected for disease diagnosis or confirmation. FFPE tissue specimens could be extremely useful for retrospective studies on protein biomarkers because the samples preserved in FFPE blocks could be stable for decades. However, LC-MS bioanalysis of FFPE tissues poses significant challenges. In this Perspective, we review the benefits and recent developments in LC-MS approach for targeted protein biomarker and protein therapeutic analysis using FFPE tissues and their clinical and translational applications. We believe that LC-MS bioanalysis of protein biomarkers in FFPE tissue specimens represents a great potential for its clinical applications.

Direct Injection Electrospray Ionization Mass Spectrometry (ESI-MS) Analysis of Proteins with High Matrix Content:Utilizing Taylor-Aris Dispersion.

This is the first work demonstrating the utility of the Taylor-Aris (TA) dispersion in avoiding serious interference issues commonly occurring in the electrospray ionization-mass spectrometric (ESI-MS) determination of therapeutic protein pharmaceuticals undergoing no pre-separation or sample purification. It was also pointed out that the TA dispersion conditions and its analytical utilization for proteomics can be easily accomplished in a commercial CE-MS instrument. In the proposed Taylor-Aris dispersion-assisted mass spectrometry (TADA-MS) analysis 0.5 µL sample is injected into a 65 cm long 50 µm i.d. capillary and pumped with 1 bar toward the MS. The procedure is efficient for the direct injection analysis of components having low diffusion coefficients (proteins) that are present in complex matrices of small organic and inorganic compounds.

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.