The privilege of a new core facility: Optimising technical repeatability and workflow efficiency in proteomics across diverse biological matrices

The privilege of a new core facility: Optimising technical repeatability and workflow efficiency in proteomics across diverse biological matrices | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL PROTEOMICS This is a preprint and has not been peer reviewed. Data may be preliminary. 20 March 2025 V1 Latest version Share on The privilege of a new core facility: Optimising technical repeatability and workflow efficiency in proteomics across diverse biological matrices Authors : Paraskevi Karousi 0000-0001-8435-4428 , Maria Voumvouraki , Panagiota Efstathia Nikolaou , Ioannis Kollias , Foteini Paradeisi , Elena Sampanai , Vasiliki Gkalea , … Show All … , Ioannis Morianos , Jerome Zoidakis 0000-0002-4557-3430 , Efstathios Kastritis , Nikolaos Thomaidis , Guillaume Médard 0000-0002-4782-4029 [email protected] , and Julie Courraud 0000-0002-6797-7129 Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.174249556.68892754/v1 458 views 355 downloads Contents Abstract Supplementary Material References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Bottom-up proteomics relies on efficient and repeatable sample preparation for accurate protein identification and precise quantification. This study evaluates the performance of adapted SPEED (Sample Preparation by Easy Extraction and Digestion) protocols, a simplified, detergent-free approach tailored for various biological matrices, including lysis-resistant samples. Protein extraction and denaturation steps were refined for 12 biological matrices enabling standardized, high-throughput, and scalable proteomics analysis on 96-well plates. For tissue samples requiring downstream applications like Western blotting, we used a low-detergent RIPA buffer, ensuring robust protein extraction and epitope integrity. Notably, the protocols demonstrate remarkable down-scalability, enabling robust proteomics measurements from as few as 3,000 cells per sample and even down to 300 cells per injection. Key advancements include a 30-minute nLC-MS/MS run, significantly enhancing throughput, and leveraging the power of DIA-PASEF using thoroughly optimized DIA-windows to enhance proteome coverage. These adaptations streamline workflows, enabling proteomics analyses in matrices with challenging physical and biochemical properties. This study underscores the importance of early-stage optimization and feasibility testing in proteomics pipelines to inform study design and sample selection. By showcasing robust, scalable adaptations of the SPEED protocol, we provide a foundation for reproducible, high-throughput proteomic studies across diverse biological contexts. Technical Brief The privilege of a new core facility: Optimising technical repeatability and workflow efficiency in proteomics across diverse biological matrices Paraskevi Karousi 1,2 , Maria Voumvouraki 2,3 , Panagiota Efstathia Nikolaou 4,5 , Ioannis Kollias 5 , Foteini Paradeisi 6 , Elena Sampanai 1 , Vasiliki Gkalea 7 , Ioannis Morianos 8 , Jerome Zoidakis 1,6 , Efstathios Kastritis 5 , Nikolaos Thomaidis 3 , Guillaume Médard 2,+ , Julie Courraud 2,5,+ 1 Section of Biochemistry and Molecular Biology, Department of Biology, School of Science, National and Kapodistrian University of Athens, Greece; 2 Proteomics Core Facility, School of Science, National and Kapodistrian University of Athens, Greece; 3 Laboratory of Analytical Chemistry, Department of Chemistry, School of Science, National & Kapodistrian University of Athens, Greece; 4 Laboratory of Pharmacology, Department of Pharmacy, School of Health Sciences, National & Kapodistrian University of Athens, Greece; 5 Section of Clinical Therapeutics, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Greece; 6 Proteomics Laboratory, Biomedical Research Foundation, Academy of Athens, Greece; 7 Hematology Department, Alexandra General Hospital, Athens, Greece; 8 Host Defense & Fungal Pathogenesis Lab, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Greece. + Corresponding authors: Dr. Guillaume Médard, Proteomics Core Facility, School of Science, National and Kapodistrian University of Athens, 15701 Athens, Greece. E-mail: [email protected] ; Dr. Julie Courraud, Proteomics Core Facility, School of Science, National and Kapodistrian University of Athens, 15701 Athens, Greece. E-mail: [email protected] . Abbreviations: BAL, bronchoalveolar lavage; CV, coefficient of variation; DIA, Data-Independent Acquisition; dia-PASEF, Data-Independent Acquisition-Parallel Accumulation Serial Fragmentation; ESI, electrospray ionization; FACS, Fluorescence-activated Cell Sorting; FDR, false discovery rate; LC-MS, liquid chromatography-mass spectrometry; MeCN, acetonitrile; RIPA, Radioimmunoprecipitation Assay; SPEED, Sample Preparation by Easy Extraction and Digestion; SP3, solid-phase-enhanced sample preparation. Keywords: Bottom-up proteomics; SPEED protocol; Protein quantification; Biological matrices; Technical repeatability Total number of words: 5,127(including legends and references). Abstract Bottom-up proteomics relies on efficient and repeatable sample preparation for accurate protein identification and precise quantification. This study evaluates the performance of adapted SPEED (Sample Preparation by Easy Extraction and Digestion) protocols, a simplified, detergent-free approach tailored for various biological matrices, including lysis-resistant samples. Protein extraction and denaturation steps were refined for 12 biological matrices enabling standardized, high-throughput, and scalable proteomics analysis on 96-well plates. For tissue samples requiring downstream applications like Western blotting, we used a low-detergent RIPA buffer, ensuring robust protein extraction and epitope integrity. Notably, the protocols demonstrate remarkable down-scalability, enabling robust proteomics measurements from as few as 3,000 cells per sample and even down to 300 cells per injection. Key advancements include a 30-minute nLC-MS/MS run, significantly enhancing throughput, and leveraging the power of DIA-PASEF using thoroughly optimized DIA-windows to enhance proteome coverage. These adaptations streamline workflows, enabling proteomics analyses in matrices with challenging physical and biochemical properties. This study underscores the importance of early-stage optimization and feasibility testing in proteomics pipelines to inform study design and sample selection. By showcasing robust, scalable adaptations of the SPEED protocol, we provide a foundation for reproducible, high-throughput proteomic studies across diverse biological contexts. Data are available via ProteomeXchange with several identifiers (keyword NKUAProt001). Bottom-up proteomics, where proteins are enzymatically digested into peptides for mass spectrometric analysis, is one of the most widely used techniques for protein identification and quantification. However, the success of this approach is heavily influenced by the efficiency, scalability, and repeatability of the sample preparation process. At the National and Kapodistrian University of Athens, we set up a new Proteomics Core Facility, facing the challenge (and opportunity) to implement sample preparation pipelines for over 50 projects within the last two years. Therefore, we chose a versatile approach to simplify method development across 12 different biological matrices, quickly providing cheap, easy, and fast sample preparation workflows, while ensuring down-scalability, without compromising on proteome coverage or robustness. One of the primary challenges in bottom-up proteomics is the effective extraction of proteins while avoiding detergents and chaotropic agents, which, though useful for extraction, interfere with enzymatic digestion and liquid chromatography-mass spectrometry (LC-MS) analysis (Dupree et al., 2020). The SPEED (Sample Preparation by Easy Extraction and Digestion) protocol offers a simplified, detergent-free alternative that has proven effective across a wide range of biological matrices (Abele et al., 2023, 2025; Doellinger et al., 2020). By employing a streamlined three-step process of acidification, neutralization, and digestion, SPEED enables rapid, efficient, and highly reproducible proteomic sample preparation. This protocol is especially suited for diverse and lysis-resistant samples, providing consistent results while eliminating the need for detergents. Protein extraction from tissues presents a significant challenge due to their heterogeneity and the dynamic range of the proteome. The extraction method is critical, often requiring specific considerations for compatibility with techniques like Western blotting. the RIPA Radioimmunoprecipitation Assay (RIPA) buffer is well-established for robust protein extraction while retaining epitope integrity, making it suitable for workflows requiring both proteomic analysis and immunodetection. In this study, we used a low-detergent RIPA method for tissues, alongside SPEED for other matrices, to ensure optimal extraction while addressing the specific needs of each sample type. The aim of this study was to assess and discuss these workflows applied across various biological matrices with differing protein compositions and lysis resistances. Additionally, we emphasize the importance of evaluating technical repeatability in proteomics to assess the feasibility and reliability of the chosen sample preparation methods and to guide study design from the outset. Tested biological matrices and protocol details are presented in Table 1. The sample types included human serum, plasma, platelets, dental plaque, and saliva. Additional matrices included CD138+ cells isolated from human bone marrow, either as directly analyzed or as protein phenol phase samples after RNA extraction [PPP; inspired by (Mundt et al., 2023)]. Human samples were collected after informed written consent from the donors. Murine heart tissues, murine bronchoalveolar lavage (BAL) samples, and plant-based samples such as Cistus creticus hard and soft seeds were also included. All animal procedures conformed to the Presidential Decree 56/2013 for the protection of the animals used for scientific purposes, in harmonization with the European Directive 2010/63/EU. The animal tissue collection was performed as part of the experimental protocols approved by the competent Veterinary Service of the Prefecture of Athens (Lisence protocol numbers 862879/12-09-2022 and 179489/13-02-2023). All samples were analyzed in three to five replicates. While all these technical replicates were performed in microfuge tubes, the presented protocols are routinely performed on 96-well plates from the digestion step onwards, minimizing the batch effect for studies encompassing more than 24 samples. For serum and EDTA-plasma samples, 1 μL of sample was mixed with 9 μL of trifluoroacetic acid (TFA) and neutralized with 90 μL of 2 M tris base after vortexing. Protein concentration was measured using the Lowry protein assay (Waterborg & Matthews, 1984), and 3 μg of protein were transferred to a new 0.5-mL microfuge tube. For CD138+ cell pellets (3,000, 30,000, and 300,000 cells) and platelet samples, 10-120 μL TFA was added for lysis and protein denaturation, followed by vortexing and neutralization with eight volumes of 2 M tris base. For CD138+ cell pellets, all or up to 30,000 lysed cells were transferred for digestion. For platelet samples, protein concentration was first measured using a Nanodrop device (Quawell), and a volume corresponding to 2 μg of protein was transferred to a new 0.5-mL microfuge tube. In all the above-mentioned samples, tris base 0.1 M was added to reach a final volume of approximately 50 μL. Protocol for CD138+ cell isolation is described is provided in the supplementary material. The platelet isolation protocol (modified from previously described protocols) is also detailed in the supplementary material. (Hechler et al., 2019; Trichler et al., 2013; Wrzyszcz et al., 2017). Additionally, two freezing methods of the platelet pellets were compared: snap freezing in liquid nitrogen (platelets LN2), or immediate freezing of the platelet pellet at -80°C, to examine whether the freezing method affects the ex vivo proteomic composition of platelets. Dental plaque was collected as described earlier (Overmyer et al., 2021). Cistus creticus seeds were collected from the forest of Kaisariani, Greece. After drying at room temperature for 10 days, the seeds were saturated with water at room temperature and categorized by water absorption ability (to address the research question only). Seeds were then frozen and stored at -80°C before being pulverized using mortar and pestle on dry ice. Only 5 mg of seed powder was necessary to run the analysis. For these two hard matrices (dental plaque and plant seeds), the same sample preparation steps were followed as for other matrices, but with two extra steps: the addition of a 10-min sonication step to ensure effective lysis of the samples while the sample is in contact with TFA. Also, after digestion, a 5-min centrifugation step at 4,000 rpm on a single-layer filter paper column (MN-QF10, Macherey-Nagel GmbH & Co., Duren, Germany) was used to remove particles. For bronchoalveolar lavage (BAL) fluid (Kalidhindi et al., 2021), four different protein precipitation methods were compared: ethanol, acetonitrile (MeCN), acetone, and the single-pot, solid-phase-enhanced sample preparation (SP3) method. First, protein concentration was measured using the Bradford assay. A volume corresponding to 5 μg of protein was transferred to a 1.5-mL tube, followed by the addition of six volumes of acetone, acetonitrile (MeCN), or ethanol. After brief vortexing, the mixture was incubated at 4°C for 4 hours to precipitate proteins, then centrifuged at 18,200 RCF for 10 min at 4°C. Saliva [200 μL, collected following Pappa et al. (Pappa et al., 2022)] and PPP [from CD138+ cells] samples were treated similarly with six or three volumes of acetone, respectively, for protein precipitation. After pelleting, the supernatant was discarded, and TFA was added to the protein pellet, followed by neutralisation with 2 M tris base. In the case of saliva, protein concentration was measured using the Lowry assay after neutralization, and an equivalent of 4 μg of protein was digested. In the case of PPP samples, an equivalent of 30,000 cells was transferred for digestion. The subsequent digestion steps were conducted as described above; details are shown in Table 1. Protein precipitation from BAL fluid was also performed using the SP3 method (Hughes et al., 2019). Sera-Mag A (GE45152105050250) and B (GE65152105050250) magnetic beads were removed from refrigeration, vortexed, and 20 μL each of A and B were combined with 160 μL of H₂O. The tube was placed on a magnetic rack for 2 min to settle the beads, and the supernatant was discarded. The beads were rinsed three times with 200 μL of water. For precipitation, 5 μg of protein was transferred to a new tube, and 2 μL of beads and six volumes of MeCN were added, followed by incubation at 4°C for 18 min with shaking at 800 rpm. After immobilization on a magnetic rack for 2 min, the supernatant was discarded. The beads were washed twice with 80% ethanol and once with MeCN. For lysis, a digestion buffer was prepared by mixing 10 μL TFA with 90 μL tris base (2 M). Then 10 μL of TCEP/CAA was added for reduction-alkylation of proteins at 37°C for 90 min. Finally, 360 μL H2O and 50 ng of trypsin were added to each sample, followed by overnight incubation at 37°C. The beads were immobilized on a magnetic rack for peptide elution and the eluate (~470 μL) was first removed from the beads and kept aside. The beads were then resuspended in 50 μL of water containing 1% TFA, immobilized on a magnetic rack, and this eluate was also removed. Both eluants were combined for downstream analysis. The pH was adjusted with 2% TFA. For murine heart tissue samples (NOD.Cg- Prkdc scid Il2rg tm1Wjl /SzJ male mice), frozen tissue was ground with a pestle and mortar on dry ice, and lysis was conducted with 8 μL RIPA buffer per mg powder (see composition in Table S11). Protein concentration was measured using the Lowry assay, and 4 μg of protein were transferred to a new tube, and diluted with tris base 0.1 M up to 50 μL. Reduction and alkylation were performed by adding TCEP/CAA solution and heating as detailed in Table 1. Trypsin was then added for overnight digestion. For all matrices, stage tips were prepared by punching five C18 disks [Empore SPE disks, CDS Analytical] and packing them into a 200-μL pipette tip (referred to as a ”microcolumn”)(Rappsilber et al., 2007). Each microcolumn was conditioned with 250 μL of LC-MS grade MeCN, centrifuged at 3500–4000 RCF for 5–10 min, and the flow-through was discarded. Conditioning was continued with 250 μL of solvent B (40% MeCN with 0.5% acetic acid), followed by 250 μL of solvent A (H₂O with 0.5% acetic acid). Peptides were loaded onto the microcolumn and centrifuged, and the flow-through was discarded. Peptides were then washed with 250 μL of solvent A and eluted with 40 μL of solvent B in a new tube. The eluted peptides were dried in a HyperVac (VC2124, Gyrozen) at 2,000 rpm at 30°C for at least 45 min. The peptides were reconstituted in 10 μL solvent A per 1 μg of digested protein or 15,000 cells, ensuring standardized preparation for nanoflow LC-MS/MS analysis. nLC-MS/MS analysis was performed by injecting 100-400 ng peptides (or an equivalent of 4500 cells) onto an analytical column (Pepsep #1893477, 25 cm × 75 µm, 1.9 µm beads, C18 ReproSil AQ, Bruker GmbH, Mannheim, Germany), followed by gradient elution in a nanoElute® 2 system (Bruker Daltonics GmbH), during a 30-min run. The gradient started with 2% mobile phase B [0.5% acetic acid (v/v) in 100% MeCN] and 98% mobile phase A [0.5% acetic acid (v/v) in milliQ water], followed by an increase to 30% B over 22 min, a second increase to 95% B over 3 min and a plateau at 95%B for 5 min (flowrate of 300 nL/min). Acetic acid was preferred over traditional formic acid due to its ability to enhance ionization efficiency during electrospray ionization (ESI), thereby improving peptide detection sensitivity in bottom-up proteomics workflows, as recently demonstrated (Battellino et al., 2023) and also confirmed in our own experiments using peptides from HeLa cells (Figure S1). Additionally, acetic acid was used in the solvents for stage tips (solvents A and B, mentioned above) to ensure consistency with the mobile phases. The separated peptides were ionized and sprayed into the timsTOF fleX mass spectrometer (Bruker Daltonics GmbH) through a 20-μm ZDV Sprayer (Bruker Daltonics GmbH) using the CaptiveSpray source. Mass spectra were acquired in library-free dia-PASEF (Data-Independent Acquisition-Parallel Accumulation Serial Fragmentation) mode in an optimised mass range of m/z 300 to 1300 and an optimised mobility range of 0.64 to 1.45 V.s/cm 2 , with a ramp time of 100 ms and duty cycle of 100%. Collision energy settings were optimized from 28 eV at 1/K0 of 0.7 V.s/cm 2 to 50.5 eV at 1/K0 of 1.45 V.s/cm 2 . The DIA window scheme followed a 3 × 8 pattern of 40 m/z widths covering the whole mass and mobility ranges and is shown in Table S12 and Figure S2. The raw data were analyzed using FragPipe v22.0 (Yu et al., 2023), using quantification with DIA-NN v1.9.0 (Demichev et al., 2020), in library-free mode. DIA-Tracer was used to enhance feature alignment and quantification consistency across runs (Li et al., 2025). The search included a tolerance for up to four missed cleavages, peptide length ranging from 5 to 30 amino acids, and peptide mass ranging from 300 to 5,000 Da. The false discovery rate (FDR) was set to 1%. For most cases, data were queried against the human or mouse reference proteome, as listed in Table 1. Exceptions included dental plaque and saliva proteomic data, which were queried against the human and the most commonly found oral bacterial proteomes (Valm, 2019). Up to two variable modifications were allowed per peptide. Variable modifications included oxidation of methionine residues, and N-terminal methionine excision, while carbamidomethylation of cysteine residues was set as a fixed modification. For BAL samples, analysis and visualization of quantitative proteomics data was done using FragPipe-Analyst (Hsiao et al., 2024). The lists of proteins detected in each matrix can be found in Tables S1-S10. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al., 2025) partner repository with the dataset identifiers listed in Tables S1-10. The coefficient of variation (CV) of each protein detected in all technical replicates was computed for all biological matrices to assess repeatability in protein quantification. The results demonstrated that most matrices exhibited low variability, with a significant proportion of protein groups achieving CV below 10% and 20% (Figure 1). For BAL samples, the different precipitation methods tested (ethanol, acetonitrile, acetone, and the SP3 method) performed differently. Acetone and acetonitrile precipitation emerged as the most effective methods (Figure S3), achieving the highest number of identified protein groups (average 1,265 and 1,221, respectively) and a precursor count of 12,408 and 11,572, respectively, with most protein groups displaying low variability. However, ethanol precipitation and the SP3 method were less effective and less robust, as they identified fewer protein groups, and exhibited broader CV distributions, suggesting reduced consistency in protein quantification. These results underscore the importance of precipitation method evaluation for specific matrices, as what could be considered similar approaches actually lead to very different results, significantly impacting protein recovery and repeatability. Based on these results, we chose acetone precipitation for the remaining sample types. While acetonitrile yielded slightly higher precursor identifications, acetone precipitation had the added advantage of producing pellets that were more easily soluble in TFA. Previous studies have employed various approaches for BAL sample proteomics, including methanol: chloroform precipitation to remove lipids and surfactants (Weise et al., 2023). However, this method was found to be inconsistent in contaminant removal, sometimes leading to interference in LC-MS/MS analysis and requiring additional handling steps, such as molecular weight cutoff filtration and protein trapping using S-Trap. In contrast, the adapted SPEED protocol used here provides a simpler, faster, and safer alternative. The critical advantage of this method is the early addition of TFA, which significantly minimizes biological hazards as the acidic environment inactivates potential pathogens, offering a streamlined approach without compromising analytical depth or repeatability. Plasma and serum showed the highest reproducibility, with the majority of protein groups concentrated in lower CV ranges. However, and as expected due to the high dynamic range of protein concentrations in blood, only 309 proteins could be identified in both serum and plasma, inferred from 5,306 and 5,646 precursors, respectively. No depletion method was applied in this study (Zhou et al., 2024), as the depletion methods tested so far have not provided satisfactory results (low protein count and low repeatability). Other matrices, such as CD138+ cells, exhibited strong repeatability and good coverage, with the identification of up to 4,376 proteins when injecting an equivalent of 4,500 cells. For this analysis, we specifically selected the 30,000-cell pellet, which provided the best results compared to the smaller and the larger ones (Figure S4). However, pellets of 3,000 cells also provided decent coverage with 3,839 proteins identified for injections of an equivalent of 1,800 cells. When challenging our analytical pipeline to inject as low as 300 cells, we obtained close to 3,300 proteins. This shows that our protocol enables the analysis of proteomes from specific cell fractions obtained from Fluorescence-activated Cell Sorting (FACS) for instance, where cell counts are often below 10,000. Additionally, 3,429 proteins were identified in a CD138+ sample as protein phenol phase samples after RNA extraction. This is a valuable finding, as it enables multiomics studies to be conducted from a single sample, maximizing data acquisition while preserving precious biological material. The analysis of platelet pellets, frozen with liquid nitrogen or at -80°C, also demonstrated good repeatability, identifying around 2,250 proteins despite the freezing method. Further evaluation revealed that samples frozen at -80°C showed better clustering in both the PCA and Pearson correlation matrix, indicating slightly higher consistency across replicates. Additionally, the median CV was marginally lower for these samples, further supporting their reproducibility. Overall, both freezing methods appear valid for platelet proteomics. However, to minimize technical bias, it is advisable to maintain consistency by using a single freezing approach within a given study. Additionally, freezing methods were tested within 3 months of platelet collection and the authors cannot conclude on the freezing method for long term storage. Cistus creticus hard and soft seed samples exhibited higher variability compared to other matrices, with the identification of 694 and 739 proteins, respectively, but still 75% of the proteins with CVs below 20% (hard shell). These results highlight the challenges inherent in seed proteomics, including the complex and resistant nature of seed matrices, which often impede protein extraction and digestion. Despite these difficulties, as noted in previous studies (Mergner et al., 2020), we achieved significant proteome coverage using our adapted protocols, demonstrating the feasibility of proteomics in this challenging sample type. The biggest challenge in plant proteomics remains to access annotated protein sequences, as many species have not been sequenced. In our case, we used the Gossypium barbadense proteome, which is the closest species in terms of phylogeny. This success underscores the potential for further refinement of seed proteomics workflows to enhance reproducibility and protein identifications, with applications in plant biology. But it also stresses the need to correctly plan such studies including proteomics only when protein sequences are available. Dental plaque and saliva samples showed variable proteome profiles, with the identification of 2,122 and 1,414 proteins, respectively, and a broader distribution of CVs, especially for dental plaque samples. As for seeds, dental plaque hard and heterogeneous matrix can explain the higher heterogeneity. Here we present the results from a sample collected from a male individual aged 44, with the plaque located on tooth number 36, which exhibited the best performance. Despite its liquid nature, saliva is also somewhat heterogeneous and viscous, which makes protein extraction more challenging. These results reflect the inherent challenges associated with these sample types, which, in addition, host diverse microbial communities (Overmyer et al., 2021) significantly influencing their biological variability. The nature of these samples requires particular attention to biosafety and consistency during processing. The early addition of TFA in our protocol played a critical role in addressing these challenges by rapidly acidifying the samples, minimizing microbiological growth and shearing potential viral DNA, and improving repeatability. This highlights the suitability of the workflow for microbiome-rich and complex biological matrices, demonstrating its potential for applications in oral microbiology and related fields. Finally, murine heart tissues also exhibited strong repeatability, with 3,217 proteins identified across four technical replicates of the same sample and 3,241 proteins identified among three biological replicates. In summary, despite using low-input material, the adapted SPEED protocol provided consistent and robust proteomic data, showcasing its potential for analyzing challenging or limited sample types. This simplicity and repeatability are especially valuable for clinical and translational research, where small-scale, reproducible workflows are critical. By incorporating TFA into the preparation process, we enhanced biosafety and streamlined workflows, simplifying sample handling without compromising analytical depth. The adoption of a 30-minute gradient represents a significant improvement in throughput, enabling scalable and efficient proteomics pipelines suitable for high-demand applications. Careful optimization of DIA fragmentation windows further enhanced the depth of our analyses, optimally exploiting the power of the PASEF technology. In conclusion, our study stresses the benefits of conducting a minimal assessment of new protocols to detect technical limitations inherent to each matrix and acquire critical knowledge about technical variability. Indeed, as proteomics studies attract more and more attention in research, it is essential to educate collaborators about the impact of technical variability on study design and statistical power. Only with proper knowledge about biological and technical variability, as well as an understanding of the proteome coverage, can one appropriately design a study to address specific biological questions. We strongly advocate for incorporating such preliminary assessments early in the planning stages of any proteomics-based study, as they provide a foundation for experimental success and pave the way for meaningful biological insights. Tables Table 1. Sample preparation workflows and adaptations for bottom-up proteomics across various biological matrices, detailing key steps from lysis to nLC-MS/MS analysis. Figures Figure 1. We detected numerous protein groups across the different sample types, demonstrating high depth of analysis compared to other published studies, with robust repeatability in all matrices. LN2, liquid N2. Supporting information • Tables S1-S10. Lists of proteins detected in each matrix. • Table S11. Composition of the low-detergent RIPA buffer. • Table S12. Mass spectrometry acquisition settings. • Figure S1. Comparison of protein sequence coverage between the usage of acetic or formic acid in the A and B mobile phases. Acetic acid provides higher sequence coverage and is therefore the preferable choice for improved proteomic analysis. • Figure S2. DDA-PASEF distribution of HeLa >2+ tryptic peptide overlayed on chosen windows. The x-axis represents m/z (mass-to-charge ratio), while the y-axis represents 1/K0, which corresponds to ion mobility. The peptide distribution aligns well within the selected windows, confirming the appropriateness of the window selection for efficient sampling. • Figure S3. BAL samples were precipitated with ethanol, MeCN, or acetone, as well as with the SP3 method. MeCN and acetone precipitation yielded the best results regarding repeatability (A) and protein number identification (B) . • Figure S4. Counts of detected precursors and proteins after injecting different numbers of CD138+ cells, derived from varying initial cell pellet counts. Good proteome coverage is achieved even with low cell numbers, demonstrating the downscalability of the method. • CD138+ isolation protocol. • Platelet isolation protocol. Conflicts of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We are grateful to Dr. Georgios Chamilos for providing BAL samples, Dr. Eftychia Pappa and Dr. Konstantinos Tzimas for collecting the dental plaque samples, and Dr. Manousos Makridakis for measuring protein concentrations in BAL samples. We are also grateful to Dr. Paraskevi Skourou and Mrs. Angeliki Tsoka for collecting and processing Cistus creticus seeds. We thank Christine Ivy-Liacos for her intellectual and material contributions, and for providing CD138+ cells from bone marrow. We thank Thomas Pappas for his contribution to designing the platelet isolation protocol and the performance of platelet isolation from peripheral blood. We would like to express our deepest gratitude to the late Professor Ioanna Andreadou, whose support and generous provision of resources contributed to this study. Her legacy of dedication and mentorship will continue to inspire our work. Part of this work was funded by the European Union (Project 101097094 — ELMUMY). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or HADEA. Neither the European Union nor the granting authority can be held responsible for them. The study was also funded by the Horizon Europe STEPUPIORS Project (HORIZON-WIDERA-2021-ACCESS-03, European Commission, Agreement No.101079217). Part of this work was supported by the General Secretariat for Research and Innovation of Greece, Grant PRO-sCAP (Project Code TAEDR-0541976) carried out within the framework of the National Recovery and Resilience Plan Greece 2.0 and funded by the European Union-Next Generation EU. PEN was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) “ElucidatioN of LIGHt chain amyloidosis induced cardioToxicity: EstablishMENT of in vitro and in vivo models” (ENLIGHTEnMENT) and by the International Myeloma Society Translational Research Award “Comprehensive exploration of molecular pathways and biomarkers in myeloma-induced bone disease: a multi-omics approach for personalized therapeutic management” Supplementary Material File (table 1.xlsx) Download 83.43 KB References 1. Abele, M., Doll, E., Bayer, F. P., Meng, C., Lomp, N., Neuhaus, K., Scherer, S., Kuster, B., & Ludwig, C. (2023). Unified Workflow for the Rapid and In-Depth Characterization of Bacterial Proteomes. 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Collection PROTEOMICS Keywords biological matrices bottom-up proteomics protein quantification speed protocol technical repeatability Authors Affiliations Paraskevi Karousi 0000-0001-8435-4428 National and Kapodistrian University of Athens School of Science View all articles by this author Maria Voumvouraki National and Kapodistrian University of Athens School of Science View all articles by this author Panagiota Efstathia Nikolaou National and Kapodistrian University of Athens School of Health Sciences View all articles by this author Ioannis Kollias National and Kapodistrian University of Athens School of Health Sciences View all articles by this author Foteini Paradeisi View all articles by this author Elena Sampanai National and Kapodistrian University of Athens School of Science View all articles by this author Vasiliki Gkalea General Hospital of Athens Alexandra Department of Oncology and Haematology View all articles by this author Ioannis Morianos Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, Greece. View all articles by this author Jerome Zoidakis 0000-0002-4557-3430 Academy of Athens View all articles by this author Efstathios Kastritis National and Kapodistrian University of Athens School of Health Sciences View all articles by this author Nikolaos Thomaidis National and Kapodistrian University of Athens School of Science View all articles by this author Guillaume Médard 0000-0002-4782-4029 [email protected] National and Kapodistrian University of Athens School of Science View all articles by this author Julie Courraud 0000-0002-6797-7129 National and Kapodistrian University of Athens School of Science View all articles by this author Metrics & Citations Metrics Article Usage 458 views 355 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Paraskevi Karousi, Maria Voumvouraki, Panagiota Efstathia Nikolaou, et al. 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