Integrated proteomics sample preparation and fractionation: Method development and applications

Salivary Proteome Insights: Evaluation of Saliva Preparation Methods in Mucopolysaccharidoses Research

Background: This research aimed to compare the traditional in-solution digestion (inSol) and solid-phase-enhanced sample preparation (SP3) methods for salivary proteomics, with a focus on identifying mucopolysaccharidosis (MPS)-relevant proteins. Methods: Saliva samples were processed under multiple analytical conditions, including two precipitation methods (methanol or incubation with trichloroacetic acid), paired with either Rapigest or 8M urea/2M thiourea (UT) solubilization buffers. Additionally, the SP3 method was directly applied to raw saliva without pre-processing. Proteome coverage, reproducibility, digestion efficiency, and gene function were assessed. Results: The inSol method consistently provided superior proteome coverage, with trichloroacetic acid precipitation and Rapigest buffer yielding 74 MPS-relevant proteins, compared to 40 with SP3 MeOH UT. Both methods showed high digestion efficiency, particularly with Rapigest buffer, achieving over 80% full cleavage across conditions. Functional analysis revealed broad similarities, with protocol-specific impacts on protein classes and cellular components. Conclusions: This study is the first to compare SP3 and in-solution digestion for salivary proteomics, emphasizing the importance of method selection to address matrix-specific challenges. The results highlight the robustness of inSol for comprehensive proteome profiling and SP3's potential for streamlined clinical workflows, offering valuable insights into optimizing salivary proteomics for biomarker discovery in MPS and other diseases.

A Fast, High-Sensitivity 96-Well Plate-Based MICROFASP Method for Processing Low Microgram Proteomics Sample within 1.5 h

A rapid, sensitive, and high-throughput sample preparation method is of paramount significance for proteomics analysis. Here, we report a fast, high-sensitivity MICROFASP method that is capable of completing sample preparation within 1.5 h, enhancing the throughput by over 13 times compared to the previous reports. Protein digestion time was significantly cut from 17 h to 20 min in a limited volume. Simultaneous reduction and alkylation occurred within 30 min. The label-free quantitation intensities of proteins from the fast and conventional MICROFASP methods were highly correlated (r = 0.91), validating the reliability of the fast-MICROFASP method. When starting with 1 μg of K562 cell lysate, the fast-MICROFASP method identified over 6 times more protein groups and 19 times more peptides than did the iST method. A 96-well plate-based version was developed to process 8 brain tissue samples from APP/PS1 transgenic mice in parallel. Averagely, with only 1 μg of protein lysate, 2826 protein groups (n = 8, RSD = 0.7%) and 12,972 peptides (n = 8, RSD = 1.5%) were identified from each sample. Amyloid-beta protein was successfully identified as a highly expressed protein, which shows its potential for detecting diagnostic markers and proteome profiling with low-microgram samples. We anticipate the high-sensitivity 96-well plate-based fast-MICROFASP method will have wide application in high-throughput and rapid preparation of large cohorts of low-microgram samples (e.g., clinical biopsy) for comprehensive proteome profiling. Data are available via ProteomeXchange with the identifier PXD053720.

Multimodal single cell-resolved spatial proteomics reveal pancreatic tumor heterogeneity

Despite the advances in antibody-guided cell typing and mass spectrometry-based proteomics, their integration is hindered by challenges for processing rare cells in the heterogeneous tissue context. Here, we introduce Spatial and Cell-type Proteomics (SCPro), which combines multiplexed imaging and flow cytometry with ion exchange-based protein aggregation capture technology to characterize spatial proteome heterogeneity with single-cell resolution. The SCPro is employed to explore the pancreatic tumor microenvironment and reveals the spatial alternations of over 5000 proteins by automatically dissecting up to 100 single cells guided by multi-color imaging of centimeter-scale formalin-fixed, paraffin-embedded tissue slide. To enhance cell-type resolution, we characterize the proteome of 14 different cell types by sorting up to 1000 cells from the same tumor, which allows us to deconvolute the spatial distribution of immune cell subtypes and leads to the discovery of subtypes of regulatory T cells. Together, the SCPro provides a multimodal spatial proteomics approach for profiling tissue proteome heterogeneity.

Rapid Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry

Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

Fit for Purpose Approach To Evaluate Detection of Amino Acid Substitutions in Shotgun Proteomics

Amino acid substitutions (AASs) alter proteins from their genome-expected sequences. Accumulation of substitutions in proteins underlies numerous diseases and antibiotic mechanisms. Accurate global detection of AASs and their frequencies is crucial for understanding these mechanisms. Shotgun proteomics provides an untargeted method for measuring AASs but introduces biases when extrapolating from the genome to identify AASs. To characterize these biases, we created a "ground-truth" approach using the similarities betweenEscherichia coli and Salmonella typhimurium to model the complexity of AAS detection. Shotgun proteomics on mixed lysates generated libraries representing ∼100,000 peptide-spectra and 4161 peptide sequences with a single AAS and defined stoichiometry. Identifying S. typhimurium peptide-spectra with only the E. coli genome resulted in 64.1% correctly identified library peptides. Specific AASs exhibit variable identification efficiencies. There was no inherent bias from the stoichiometry of the substitutions. Short peptides and AASs localized near peptide termini had poor identification efficiency. We identify a new class of "scissor substitutions" that gain or lose protease cleavage sites. Scissor substitutions also had poor identification efficiency. This ground-truth AAS library reveals various sources of bias, which will guide the application of shotgun proteomics to validate AAS hypotheses.

High-throughput drug target discovery using a fully automated proteomics sample preparation platform

Drug development is plagued by inefficiency and high costs due to issues such as inadequate drug efficacy and unexpected toxicity. Mass spectrometry (MS)-based proteomics, particularly isobaric quantitative proteomics, offers a solution to unveil resistance mechanisms and unforeseen side effects related to off-targeting pathways. Thermal proteome profiling (TPP) has gained popularity for drug target identification at the proteome scale. However, it involves experiments with multiple temperature points, resulting in numerous samples and considerable variability in large-scale TPP analysis. We propose a high-throughput drug target discovery workflow that integrates single-temperature TPP, a fully automated proteomics sample preparation platform (autoSISPROT), and data independent acquisition (DIA) quantification. The autoSISPROT platform enables the simultaneous processing of 96 samples in less than 2.5 hours, achieving protein digestion, desalting, and optional TMT labeling (requires an additional 1 hour) with 96-channel all-in-tip operations. The results demonstrated excellent sample preparation performance with >94% digestion efficiency, >98% TMT labeling efficiency, and >0.9 intra- and inter-batch Pearson correlation coefficients. By automatically processing 87 samples, we identified both known targets and potential off-targets of 20 kinase inhibitors, affording over a 10-fold improvement in throughput compared to classical TPP. This fully automated workflow offers a high-throughput solution for proteomics sample preparation and drug target/off-target identification.

1.4 min Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry

Non-invasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method, which integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein coronas enrichment for high throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 minutes collection time, which enables a potential throughput of approximately 1,000 samples daily. The identified proteins are involved in valuable pathways and 44 of the proteins are FDA approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation at 17%. Moreover, different protein corona profiles were observed among various nanoparticles based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichments. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

Scaled-Down Thermal Profiling and Coaggregation Analysis of the Proteome for Drug Target and Protein Interaction Analysis

Thermal proteome profiling (TPP), an experimental technique combining the cellular thermal shift assay (CETSA) with quantitative protein mass spectrometry (MS), identifies interactions of drugs and chemicals with endogenous proteins. Thermal proximity coaggregation (TPCA) profiling extended TPP to study the intracellular dynamics of protein complexes. In TPP and TPCA, samples are subjected to multiple denaturing temperatures, each requiring over 100 μg of proteins, which restricts their applications for rare cells and precious clinical samples. We developed a workflow termed STASIS (scaled-down thermal profiling and coaggregation analysis with SISPROT) that scales down the required protein to as low as 1 μg per temperature. This is achieved by heating and centrifugation using the same PCR tube, processing samples with the SISPROT technology (simple and integrated spintip-based proteomics technology), and tip-based manual fractionation of TMT-labeled peptides. We evaluate the STASIS workflow with starting protein quantities of 10, 5, and 1 μg per temperature prior to heating, identifying between 4000 and 5000 proteins with 6 h of acquisition time. Importantly, we observed a high correlation in the Tm of proteins with minimal difference in TPCA performance for predicting protein complexes. Moreover, STASIS could identify the targets of methotrexate and panobinostat with high precision with 1 μg of proteins per temperature. In conclusion, STASIS is a robust cost-effective technique for target deconvolution and extended TPCA to rare primary cells and precious clinical samples for the analysis of protein complexes.

Bottom-Up Proteomics: Advancements in Sample Preparation

Liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomics is a powerful technique for profiling proteomes of cells, tissues, and body fluids. Typical bottom-up proteomic workflows consist of the following three major steps: sample preparation, LC–MS/MS analysis, and data analysis. LC–MS/MS and data analysis techniques have been intensively developed, whereas sample preparation, a laborious process, remains a difficult task and the main challenge in different applications. Sample preparation is a crucial stage that affects the overall efficiency of a proteomic study; however, it is prone to errors and has low reproducibility and throughput. In-solution digestion and filter-aided sample preparation are the typical and widely used methods. In the past decade, novel methods to improve and facilitate the entire sample preparation process or integrate sample preparation and fractionation have been reported to reduce time, increase throughput, and improve reproducibility. In this review, we have outlined the current methods used for sample preparation in proteomics, including on-membrane digestion, bead-based digestion, immobilized enzymatic digestion, and suspension trapping. Additionally, we have summarized and discussed current devices and methods for integrating different steps of sample preparation and peptide fractionation.

Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes

The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.

Influence of different sample preparation strategies on hypothesis-driven shotgun proteomic analysis of human saliva

This research aimed to find an efficient and repeatable bottom-up proteolytic strategy to process the unstimulated human saliva. The focus is on monitoring immune system activation via the cytokine and interleukin signaling pathways. Carbohydrate metabolism is also being studied as a possible trigger of inflammation and joint damage in the context of the diagnostic procedure of temporomandibular joint disorder. The preparation of clean peptide mixtures for liquid chromatography–mass spectrometry analysis was performed considering different aspects of sample preparation: the filter-aided sample preparation (FASP) with different loadings of salivary proteins, the unfractionated saliva, amylase-depleted, and amylase-enriched salivary fractions. To optimize the efficiency of the FASP method, the protocols with the digestion in the presence of 80% acetonitrile and one-step digestion in the presence of 80% acetonitrile were used, omitting protein reduction and alkylation. The digestion procedures were repeated in the standard in-solution mode. Alternatively, the temperature of 24 and 37°C was examined during the trypsin digestion. DyNet analysis of the hierarchical networks of Gene Ontology terms corresponding to each sample preparation method for the bottom-up assay revealed the wide variability in protein properties. The method can easily be tailored to the specific samples and groups of proteins to be examined.

Seamlessly Integrated Miniaturized Filter-Aided Sample Preparation Method to Fractionation Techniques for Fast, Loss-Less, and In-Depth Proteomics Analysis of 1 μg of Cell Lysates at Low Cost

We report an integrated platform that enabled a seamlessly coupling miniaturized filter-aided sample preparation (MICROFASP) method to high-pH reversed phase (RP) or strong cation exchange (SCX) microreactors for low-loss sample preparation and fractionation of 1 μg of cell lysates prior to LC-ESI-MS/MS analysis. Due to the reduced size of the microreactor, only 5 μL of buffer volume is required to generate each fraction, which speeds both elution and lyophilization. The fraction was directly eluted into an autosampler insert vial for LC-MS analysis to reduce sample transfer steps and minimize sample loss as well as contamination. The flow-through sample generated during the loading step was also collected and analyzed. The integrated platform generated 48,890 unique peptides and 4723 protein groups from 1 μg of a K562 cell lysate using MICROFASP and C18 microreactor-based high-pH RP fractionation methods, which are comparable with the state-of-the-art result using in-StageTip sample preparation and nanoflow RPLC-based fractionation methods but with a significant reduction in cost and time. Both pH gradient elution and salt gradient elution approaches provide high reproducibility for the SCX microreactor-based fractionation method. This integrated platform has significant potential in deep proteomics analysis of mass-limited samples with reduced time and equipment requirements.

A review of suspension trapping digestion method in bottom-up proteomics

The standard bottom-up proteomic workflow is comprised of sample preparation, data acquisition, and data analysis. While the latter two parts have made considerable advances in the last decade, sample preparation has remained an important challenge within the workflow due to the multi-step nature of complex biological samples, and still requires much development. Several sample preparation methods have been developed and used in the last two decades, including in-gel, in-solution, on-bead, filter-aided sample preparation, and suspension trapping, to improve reproducibility, efficiency, scalability, and reduce handling time of this process. One of the most recent methods developed and applied in proteomics studies in recent years is suspension trapping, which combines rapid detergent removal, reactor-type protein digestion, and peptide clean-up in a tip or spin column. Suspension trapping is a simple, rapid, and reproducible digestion method that can effectively handle proteins in low microgram or sub-microgram amounts. This review discusses the benefits of the suspension trapping digestion method in relation to its development and application in bottom-up proteomics studies. We also discuss recent applications of suspension trapping digestion to different sample types and the features of the suspension trapping digestion method compared with other sample preparation methods. This article is protected by copyright. All rights reserved.

Coupling suspension trapping-based sample preparation and data-independent acquisition mass spectrometry for sensitive exosomal proteomic analysis

It has been a challenge to analyze minute amounts of proteomic samples in a facile and robust manner. Herein, we developed a quantitative proteomics workflow by integrating suspension trapping (S-Trap)-based sample preparation and label-free data-independent acquisition (DIA) mass spectrometry and then applied it for the analysis of microgram and even nanogram amounts of exosome samples. S-Trap-based sample preparation outperformed the traditional in-solution digestion-based approach and the commonly used filter-aided sample preparation (FASP)-based approach with regard to the number of proteins and peptides identified. Moreover, S-Trap-based sample preparation coupled with DIA mass spectrometry also showed the highest reproducibility for protein quantification. In addition, this approach allowed for identification and quantification of exosome proteins with low starting amounts (down to 50 ~ 200 ng). Finally, the proposed method was successfully applied to label-free quantification of exosomal proteins extracted from MDA-MB-231 breast cancer cells and MCF-10A non-tumorigenic epithelial breast cells. Prospectively, we envision the integrated S-Trap sample preparation coupled with DIA quantification strategy as a promising alternative for highly efficient and sensitive analysis of trace amounts of proteomic samples (e.g., exosomal samples).

Ultrasensitive Trace Sample Proteomics Unraveled the Protein Remodeling during Mesenchymal-Amoeboid Transition

Deep mining the proteome of trace biological samples is critical for biomedical applications. However, it remains a challenge due to the loss of analytes caused by current sample preparation procedures. To address this, we recently developed a single-pot and miniaturized in-solution digestion (SMID) method for minute sample handling with three streamlined steps and completed within 3 h. The SMID approach outperformed the traditional workflow in substantially saving time, reducing sample loss, and exhibiting extensive applicability for 10-100 000 cell analysis. This user-friendly and high-sensitivity strategy enables ∼5300 proteins and 53 000 peptides to be confidently identified within 1 h of mass spectrometry (MS) time from a small amount of 1000 HeLa cells. In addition, we accurately and robustly detected proteomes in 10 mouse oocytes with excellent reproducibility. We further adopted SMID for the proteome analysis in cell migration under confinement, which induced cells to undergo a mesenchymal-amoeboid transition (MAT). During the MAT, a systematic quantitative proteome map of 1000 HeLa cells was constructed with seven expression profile clusters, which illustrated the application of SMID and provided a fundamental resource to investigate the mechanism of MAT.

Technique development of high-throughput and high-sensitivity sample preparation and separation for proteomics

Sample preparation and separation methods determine the sensitivity and the quantification accuracy of the proteomics analysis. This article covers a comprehensive review of the recent technique development of high-throughput and high-sensitivity sample preparation and separation methods in proteomics research.

Sample Preparation and Diagnostic Methods for a Variety of Settings: A Comprehensive Review

Sample preparation is an essential step for nearly every type of biochemical analysis in use today. Among the most important of these analyses is the diagnosis of diseases, since their treatment may rely greatly on time and, in the case of infectious diseases, containing their spread within a population to prevent outbreaks. To address this, many different methods have been developed for use in the wide variety of settings for which they are needed. In this work, we have reviewed the literature and report on a broad range of methods that have been developed in recent years and their applications to point-of-care (POC), high-throughput screening, and low-resource and traditional clinical settings for diagnosis, including some of those that were developed in response to the coronavirus disease 2019 (COVID-19) pandemic. In addition to covering alternative approaches and improvements to traditional sample preparation techniques such as extractions and separations, techniques that have been developed with focuses on integration with smart devices, laboratory automation, and biosensors are also discussed.

Spatial proteomics for understanding the tissue microenvironment

The human body comprises rich populations of cells, which are arranged into tissues and organs with diverse functionalities. These cells exhibit a broad spectrum of phenotypes and are often organized as a heterogeneous but sophisticatedly regulated ecosystem - tissue microenvironment, inside which every cell interacts with and is reciprocally influenced by its surroundings through its life span. Therefore, it is critical to comprehensively explore the cellular machinery and biological processes in the tissue microenvironment, which is best exemplified by the tumor microenvironment (TME). The past decade has seen increasing advances in the field of spatial proteomics, the main purpose of which is to characterize the abundance and spatial distribution of proteins and their post-translational modifications in the microenvironment of diseased tissues. Herein, we outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted. In detail, we focus on high-quality resources built by scalpel macrodissection-based region-resolved proteomics, method development of sensitive sample preparation for laser microdissection-based spatial proteomics, and antibody recognition-based multiplexed tissue imaging. In the end, critical issues and potential future directions for spatial proteomics are also discussed.

Identification of 7 000-9 000 Proteins from Cell Lines and Tissues by Single-Shot Microflow LC-MS/MS

A current trend in proteomics is to acquire data in a "single-shot" by LC-MS/MS because it simplifies workflows and promises better throughput and quantitative accuracy than schemes that involve extensive sample fractionation. However, single-shot approaches can suffer from limited proteome coverage when performed by data dependent acquisition (ssDDA) on nanoflow LC systems. For applications where sample quantities are not scarce, this study shows that high proteome coverage can be obtained using a microflow LC-MS/MS system operating a 1 mm i.d. × 150 mm column, at a flow-rate of 50 μL/min and coupled to an Orbitrap HF-X mass spectrometer. The results demonstrate the identification of ∼9 000 proteins from 50 μg of protein digest from Arabidopsis roots, 7 500 from mouse thymus, and 7 300 from human breast cancer cells in 3 h of analysis time in a single run. The dynamic range of protein quantification measured by the iBAQ approach spanned 5 orders of magnitude and replicate analysis showed that the median coefficient of variation was below 20%. Together, this study shows that ssDDA by μLC-MS/MS is a robust method for comprehensive and large-scale proteome analysis and which may be further extended to more rapid chromatography and data independent acquisition approaches in the future.̀.

Combinatory strategy using nanoscale proteomics and machine learning for T cell subtyping in peripheral blood of single multiple myeloma patients

T cells play crucial roles in our immunity against hematological tumors by inducing sustained immune responses. Flow cytometry-based detection of a limited number of specific protein markers has been routinely applied for basic research and clinical investigation in this area. In this study, we combined flow cytometry with the simple integrated spintip-based proteomics technology (SISPROT) to characterize the proteome of primary T cell subtypes in the peripheral blood (PB) from single multiple myeloma (MM) patients. Taking advantage of the integrated high pH reversed-phase fractionation in the SISPROT device, the global proteomes of CD3⁺, CD4⁺ and CD8⁺ T cells were firstly profiled with a depth of >7 000 protein groups for each cell type. The sensitivity of single-shot proteomic analysis was dramatically improved by optimizing the SISPROT and data-dependent acquisition parameters for nanogram-level samples. Eight subtypes of T cells were sorted from about 4 mL PB of single MM patients, and the individual subtype-specific proteomes with coverage among 1 702 and 3 699 protein groups were obtained from as low as 70 ng and up to 500 ng of cell lysates. In addition, we developed a two-step machine learning-based subtyping strategy for proof-of-concept classifying eight T cell subtypes, independent of their cell numbers and individual differences. Our strategy demonstrates an easy-to-use proteomic analysis on immune cells with the potential to discover novel subtype-specific protein biomarkers from limited clinical samples in future large scale clinical studies.

Proteins from Rhinella jimi parotoid gland secretion: A comprehensive analytical approach

Toad skin secretions are sources of complex mixtures of bioactive compounds, such as proteins and peptides. Rhinella jimi species is a common toad in the Brazilian northeast, considered by only a few known studies. The experimental design was applied to optimize the protein extraction method from R. jimi parotoid gland secretions. The optimum condition was using 100 mmol L^-1 Tris-HCl buffer pH 7.2 under vortexing for 5 min. The FTIR analysis combined with PCA revealed high-protein purity of the extracts, confirming the success of the proposed extraction method. The total protein concentration by the Bradford method was 102.4 and 66.5 mg g^-1 on toad poisons from Teresina and Picos, respectively. The comparative proteomic analysis using HPLC-SEC-DAD and 1D SDS-PAGE revealed significant differences in protein abundance. HMW biomolecules showed greater abundance in toads from Teresina, while LMW protein species were more abundant in toads from Picos. The significant difference in amphibian proteome can be attributed to the edaphoclimatic conditions of their habitat. The cytotoxicity of the protein extract from Teresina was higher on the tumor cell lines 4T1 and CT26.WT. These new findings are fundamental for future studies the on identity and biological activity of biomolecules from this noble sample.

A Critical Review of Bottom-Up Proteomics: The Good, the Bad, and the Future of this Field

Proteomics is the field of study that includes the analysis of proteins, from either a basic science prospective or a clinical one. Proteins can be investigated for their abundance, variety of proteoforms due to post-translational modifications (PTMs), and their stable or transient protein–protein interactions. This can be especially beneficial in the clinical setting when studying proteins involved in different diseases and conditions. Here, we aim to describe a bottom-up proteomics workflow from sample preparation to data analysis, including all of its benefits and pitfalls. We also describe potential improvements in this type of proteomics workflow for the future.