Direct In‐Bone Protein Digestion With Subsequent LC Separation and Trap Ion Mobility MS Detection of Released Peptides as an Effective Tool for the Proteomic Characterization of Bone Tissues

ABSTRACT

Common pathological changes in bone tissues like osteomas or exostoses remain not fully understood at the molecular level due to the difficulties in analyzing bone tissues in which they occur. Therefore, new rapid and powerful techniques are needed that could become routine tools for such analysis. The primary aim of this study was to evaluate whether direct in‐bone tryptic protein digestion followed by LC separation and trap ion mobility MS detection and identification of released peptides is able to identify sufficient numbers of proteins in above mentioned bone tissues. The second aim was to verify whether the mathematical analysis of the obtained MS data would have a potential to distinguish pathological and control healthy bone tissues. It turned out that this approach made possible to identify altogether 4810 proteins in samples of control healthy skull bone tissues, 6284 proteins in pathological skull bone tissues, and 3000 proteins in mandibular bone tissues. Mathematical analysis of obtained MS data enabled to discriminate control healthy and pathological skull bone tissues samples with accuracy of 87%. Thus, the reported approach seems to have a high potential for routine and effective characterization of bone tissues, in which pathological changes like exostoses or osteomas may occur. Data are available via ProteomeXchange with identifier PXD065656.

Abbreviations

DIA

data independent analysis

EAC

external auditory canal

PLS‐DA

partial least squares‐discriminant analysis

TIMS

trap ion mobility mass spectrometry

1. Introduction

Advanced proteomic techniques offer valuable insights into various physiological and pathological processes at the molecular level. However, they are still rarely utilized for routine bone tissue characterization in medicine. It is mainly due to their insoluble nature, which complicates their analysis. The study of pathological phenomena occurring in bone tissues, such as exostoses or osteomas, is thus lagging behind.

Exostoses of the external auditory canal (EAC) and the mandibular region are benign bony outgrowths that can have significant clinical implications. These bone proliferations are generally associated with prolonged mechanical or environmental stimuli [1]. EAC exostoses, often called “surfer's ear,” are hypothesized to be linked to prolonged exposure to cold water and wind, which may trigger bone remodeling at the tympanic ring within the canal. This trouble primarily affects people involved in aquatic sports, with its frequency varying among different groups based on environmental exposure [2]. On the other hand, mandibular exostoses are less frequently researched and are thought to originate from a combination of genetic factors and ongoing mechanical pressure [3].

Osteomas, benign bone tumors, can also arise in the EAC and mandibular region, clinically mimicking aforementioned pathologies, yet differing from exostoses in several fundamental ways. Unlike exostoses, which tend to be multiple and bilateral, osteomas are typically solitary and unilateral. While exostoses are thought to result from prolonged environmental exposure, osteomas are believed to arise due to genetic predisposition or underlying inflammatory processes [4, 5].

Clinically, both these tumors may exhibit overlapping symptoms, especially in the EAC, where they can contribute to obstruction and conductive hearing loss. However, osteomas are more often asymptomatic and typically do not require intervention unless they grow large enough to impair hearing or cause discomfort [5]. On the other hand, the impact of exostoses largely depends on their size and location. In the EAC, excessive bone growth can progressively narrow the passage, leading to water retention, recurrent infections, and conductive hearing loss. The narrowing of the canal also predisposes individuals to frequent earwax impaction, further exacerbating hearing difficulties and discomfort [6]. A schematic representation of this process is shown in Figure 1.

FIGURE 1.

Schematic illustration of exostoses of the external auditory canal. The bony outgrowths (highlighted) originate from the tympanic ring and can progressively narrow the lumen of the canal.

In contrast, mandibular exostoses are often symptomless but can disrupt oral functions, the fitting of dental appliances, and sometimes cause pain or irritation. Large mandibular exostoses may lead to mucosal ulceration, especially in denture wearers, causing localized inflammation and discomfort [7]. Recognizing these differences is essential for accurate diagnosis and appropriate clinical management.

The exact pathophysiology underlying the formation of exostoses remains incompletely understood, but recent molecular biology and proteomic research have provided valuable insights. Proteomic analysis is increasingly used to detect differentially expressed proteins in pathological bone growth, offering clues about abnormal osteogenic processes [8]. Studies using liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) have demonstrated changes in the levels of extracellular matrix proteins and inflammatory mediators in exostotic tissues compared to normal bone [9]. These findings suggest that both genetic predisposition and environmental influences may contribute to the formation and progression of exostoses. It is plausible that continual mechanical strain and exposure to cold water affect osteoblast activity, leading to localized bone changes and abnormal growth [9, 10].

An emerging area of research focuses on comparing proteomic profiles of exostoses across different anatomical locations. Since EAC exostoses are believed to result mainly from environmental factors, while mandibular exostoses may be influenced by a mix of mechanical and genetic components, comparative proteomic analysis may help to clarify whether distinct molecular mechanisms are involved in these processes. Preliminary observations have indicated altered expression of structural and signalling proteins in exostotic bone, suggesting that environmental and biomechanical stimuli might interact with cellular remodelling pathways. The role of local tissue pressure and micro‐environmental changes on gene expression in affected bone also warrants further investigation. A better understanding of these site‐specific processes could provide insight into broader mechanisms of bone remodelling [8, 9, 10].

Proteomic techniques have also been successfully applied in other skeletal disorders, such as osteoarthritis and bone mineralization defects, using in‐sample tryptic digestion and mass spectrometry to identify key proteins involved in bone formation and resorption [11]. By adopting similar strategies, this study aimed to compare the proteomic composition of pathological bone tissues (exostoses and osteomas from both the EAC and the mandible) with that of healthy bone tissue. Both anatomical sites were included to investigate site‐specific molecular features and to distinguish pathological bone changes from normal bone remodelling processes. Comparative analyses may reveal unique molecular signatures and potential biomarkers, which could lead to more targeted clinical strategies. Integrating proteomic data with clinical and histological findings is expected to enhance our understanding of exostosis development and guide future diagnostic or therapeutic approaches.

Despite advances in the understanding of their histological and molecular characteristics, current treatment of exostoses remains primarily surgical and is reserved for symptomatic cases. The tendency of EAC exostoses to recur postoperatively highlights the importance of identifying preventive strategies [12]. A more detailed understanding of the underlying molecular mechanisms may eventually open new therapeutic avenues for controlling aberrant bone growth.

Routine proteomic analysis of bone tissues, which can be affected by exostoses or osteomas, could provide a view at the molecular level which can shed light on mechanism of these pathological processes. However, the proteomic characterization of bone tissues still represents analytical challenge since proteins are firmly captured in these insoluble tissues. Recently, it was shown that this problem can be overcome using a technique of proteolytic digestion directly in the tissue samples. Using this technique, specific peptide fragments are released and separated from insoluble tissue and subsequently can be analyzed LC or CE methods coupled with MS detection [13, 14]. This “in‐tissue” digestion procedure was already used for the proteomic characterization of swine alveolar bones, as well as human jaw bones, in relation to the field of oral surgery [15, 16, 17]. The mentioned technique was also succesfully applied to study various induced pathological states in human in vitro bone models [18], identification of different animal species according to their hair proteins [19], characterization of eye corner tissues as well as human in vitro eye corner substitute [20] and characterization of vestibular schwannoma tissues [21]. In‐tissue specific digestion with trypsin represents a fast tool for studying of insoluble tissue samples, since it does not require complicated and time demanding isolation of intact proteins. The major goal of this study was to verify (i) whether direct in‐bone specific protein digestion could be used for the characterization of bone tissues, in which exostoses and osteomas occur and (ii) if pathological bone tissues could be distinguished from control healthy bone tissues based on LC‐MS separation and detection of released peptides and statistical evaluation of MS data.

2. Materials and Methods

2.1. Chemicals

All chemicals used were at least of analytical reagent grade. Propan‐2‐ol (hyper grade for LC‐MS) and trifluoroacetic acid (TFA) (for ZipTip purification) were purchased from Merck KGaA (Darmstadt, Germany). Acetonitrile (LC‐MS grade) and ammonium hydrogen carbonate (NH₄HCO₃) were supplied by Sigma (St. Louis, MO, USA) and Porcine Pierce Trypsin Protease, MS Grade (TPCK treated) was supplied by Thermo Scientific (Waltham, MA, USA). The reverse phase C₁₈ ZipTip pipette tips were obtained from Millipore Corporation (Bedford, MA, USA).

2.2. Human Bone Tissue Samples

Human bone tissues were collected as surgical specimens from patients of the Department of Otorhinolaryngology and Head and Neck Surgery and the Department of Oral and Maxillofacial Surgery, Motol University Hospital. All procedures were performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants prior to surgery. The study was approved by the local ethics committee; the approval is attached.

In the ear, nose, and throat surgery part, samples of EAC bone tissues were obtained during surgical procedures indicated for canal recanalization, performed via an endaural approach. A total of 14 specimens of pathological tissues (osteomas and exostoses), group PATH, were collected. In the case of osteomas, histopathological confirmation was obtained. All samples were collected directly in the operating room and immediately stored at −80°C prior to further processing. As control samples, group CTRL, we used nine residual bone tissues obtained as surgical leftovers from unrelated otologic procedures, where small fragments of the EAC bone were removed for access or exposure purposes.

In the oral surgery part, human mandibular bone samples, group MAND, were collected as residual tissues during third molar extractions. All samples originated from the alveolar septum of the mandible. Following surgical excision, bone fragments were mechanically cleaned of blood using tweezers, scalpel, and subsequently stored at −80°C. In total, six samples of healthy mandibular bone tissue were collected and processed.

2.3. Specific Cleavage of Proteins in Bone Samples

Approximately 4 mg of each bone sample were placed into a 1.5 mL Eppendorf tube preincubated 30 min in 100 µL of propan‐2‐ol at temperature of 50°C under shaking at 500 rpm in Thermomixer compact Eppendorf (Hamburg, Germany). The supernatant was then removed by the pipette. Subsequently, all samples were vortexed 1 min with 100 µL of 50 mM ammonium hydrogen carbonate (NH₄HCO₃) solution at laboratory temperature (22°C). After the supernatant was again removed, specific digestion was carried out in 15 µL (samples were submerged) of 20 µg/mL sequencing grade trypsin in 50 mM NH₄HCO₃ at 37°C for 4 h. This specific digestion was performed without reduction and alkylation of the disulfide bonds. The solution containing the released peptides was purified on ZipTip packed with reversed phase (C₁₈) resin. Samples were eluted to 0.2 mL Eppendorf PCR tubes and dried in Thermomixer without shaking at 50°C about 2 h.

2.4. LC Separation and MS Detection of Released Peptides

Mass spectra were acquired using nanoElute 2 (Bruker Daltonics, Germany) connected to a timsTOF HT mass spectrometer (Bruker Daltonics, Bremen, Germany). The dry samples were resuspended with 3% v/v acetonitrile and 0.1% v/v formic acid. The samples were loaded on a PepMap Neo‐Trap column (300 µm × 5 mm, particle size 5 µm, Thermo Scientific) at a pressure of 85 bar, 100% of phase A for 2.5 min. Then the flow was directed to PepSep C18 (75 µm × 100 mm, particle size 1.9 µm, Bruker Daltonics). Mobile phase A was 0.1% v/v formic acid in water and mobile phase B was 0.1% v/v formic acid in acetonitrile. Peptides were eluted with a linear gradient of 3%–35% B in 60 min followed by column wash (95% B). Eluted peptides were introduced directly to Captive spray two electrospray ionization (ESI) source (Bruker Daltonics, Bremen, Germany). Spray voltage was set to 1600 V, temperature 150°C, and flow of dry nitrogen to 3 L/min. Data collection was performed in Data Independent Analysis—Parallel Accumulation Serial Fragmentation (DIA‐PASEF) mode. The mass range of the method was set to 100–1700 m/z. The ion mobility scan, expressed as the inverse of the reduced ion mobility, was performed in the range of 0.6–1.6 V·s/cm². The scan duration was 100 ms, and precursors for fragmentation were selected in the range of 400–1201 m/z in windows of 26 Da width, which completely covered the selected mass range (a total of 32 windows, two steps per ion mobility scan). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [22] partner repository with the dataset identifier PXD065656.

2.5. MS Raw Data Processing

Peptides in the raw spectra were identified and quantified by MaxQuant label‐free quantification software (version 2.6.4 for Windows) [23] using Homo sapiens NCBI protein database (assembly GRCh38.p14) and UniProt Homo sapiens proteome UP000005640. Reverse sequences were selected for target‐decoy database strategy [24] and a 1% false discovery rate (FDR) was applied to both peptide spectrum match and protein group levels. Trypsin was set as the proteolytic enzyme and two missed cleavages were allowed; no fixed modification was selected. Methionine oxidation and broken cysteine disulfide bonds (Cys–Cys) were searched as variable protein modifications. The trap ion mobility mass spectrometry (TIMS)‐MaxDIA search for Bruker TIMS instrument was used with default tolerances, match between runs, and unbiased search for dependent peptides. The minimum required peptide length was set to seven amino acids. The DIA library was also predicted by MaxQuant and the maximum number of missed cleavages for DIA was kept at one. Only unique peptides were used for quantification. Protein groups identified through target‐decoy database strategy (“Reverse” column in the MaxQuant proteinGroups file) and proteins not identified by MS/MS spectrum (“Only identified by site” column in the MaxQuant proteinGroups file) were removed and are not included in subsequent analyses. The overall protein intensity was estimated as a maximum of MaxQuant label‐free quantification values (LFQ) for unique detected peptides. The initial data summary set from MaxQuant is available in Table S1.

2.6. Mathematical Analysis

Partial least squares‐discriminant analysis (PLS‐DA) [25] for estimation of accuracy of classification was run in the R software (version 4.3.1 for Windows) [26] using the “caret” (6.0.94) package and the “pls” (2.8.5) package. For PLS‐DA, only proteins identified in at least 50% of samples in any group of PATHS, MAND, or CTRL, and identified with at least two unique peptides were used. The missing values were imputed using “nipals” (Nonlinear Iterative Partial Least Squares) algorithm for 40 components implemented in the “pcaMethods” (1.0.94) package. List of all proteins used for PLS‐DA was exported to Excel and is available in Table S2 including protein intensities. The PLS‐DA model produces latent variables—linear combinations of the centered and scaled protein‐abundance vectors that maximize the covariance between the protein matrix (X) and the dummy‐coded class affiliation vector (Y). The optimal number of latent variables for PLS‐DA was determined using one‐SE (standard error) rule and leave‐one‐out cross‐validation. The first latent variable (PLS 1) captures the largest share of that X–Y covariance; the second (PLS 2) captures the next largest share while remaining orthogonal to PLS 1 and in plots the projection of samples (i.e. scores) is always depicted to these two latent variables regardless the (optimal) number of latent variables used for discrimination.

3. Results and Discussion

3.1. Direct In‐Bone Protein Digestion With Subsequent LC‐TIMS Separation and Identification of Released Specific Peptide Fragments

The method of direct in‐bone protein digestion by trypsin and separation and identification of released specific peptide fragments by liquid chromatography online coupled with trapped ion mobility mass spectrometry (LC‐TIMS) was applied to bone tissue samples obtained as described above (Section 2.2).

This technique (for detailed description see Sections 2.3–2.5) is focused on characterization of proteins trapped in the insoluble bone matrix. Preincubation step with propan‐2‐ol separates molecules soluble in this organic solvent. After subsequent washing, in‐bone protein digestion with trypsin is employed, which leads to direct release and separation of specific peptide fragments from bone samples, which can easily diffuse into solution. The released peptide fragments are purified on ZipTip packed with reversed‐phase (C₁₈) resin and analyzed by LC‐TIMS analysis. These mass spectrometry data are evaluated using mathematical analysis.

The numbers of the proteins identified in the samples of control skull bone tissue, skull bone tissue with pathological changes, and mandibular bones are summarized in Table 1. It is crucial to mention that this study is focused on fraction of proteins, which remain entrapped in the insoluble matrix after preincubation step with propan‐2‐ol and subsequent washing with 50 mM NH₄HCO₃ solution. Total numbers of proteins identified by at least one unique peptide fragment in the three different groups (first column of Table 1) were 4810 in control skull bone tissue, 6284 in pathological skull bone tissue, and 3000 in mandibular bone tissue. The numbers of proteins identified in bone tissues are significantly higher than in previously published studies [9, 17, 27, 28, 29, 30, 31, 32].

TABLE 1.

Numbers of proteins identified in different bone tissues.

Bone tissue origin	Number of identified proteins a
	1	2	3
Control skull bone tissue	4810/4052	723/683	1421/1268
Pathological skull bone tissue	6284/4989	1168/1097	1888/1658
Mandibular bone tissue	3000/2682	924/854	1114/1015

^a Total number of proteins identified with at least one unique peptide fragment/two unique peptide fragments in: (1) at least in one sample (column no. 1), (2) at least in 50% of samples in that group (column no. 2), and (3) average number of proteins identified per sample (column no. 3).

Overlaps between proteins of above mentioned three groups of bone samples are depicted on Venn diagrams in Figure 2. The total number of identified unique peptide fragments in mandibular bones was 5841, in control group 11 521, and in pathological samples 18 607. In all three groups, totally 20 849 unique peptide fragments were identified.

FIGURE 2.

Number of proteins identified in the CTRL (skull base), PATH (skull base), and MAND (alveolar bones) groups. (A) Identified proteins with at least one unique peptide detected. (B) Identified proteins with at least two unique peptides and in at least 50% of samples of any group (CTRL—skull base; PATH—skull base, MAND—alveolar bones). (C) Identified proteins with at least two unique peptide fragments and detected in at least 50% of the samples of given group.

3.2. Mathematical Analysis of the Obtained Data

Partial least squares‐discrimination analysis (PLS‐DA) was applied to verify if the direct in‐bone protein digestion with trypsin followed by LC‐ TIMS‐TOF analysis is able to discriminate between different groups of obtained bone tissue samples.

The discrimination between mandibular bone samples and skull bone tissue control samples is shown in Figure 3. Mandibular bone samples can be distinguished from control skull bone tissues (Figure 3A) with accuracy 93% (one latent variable) as well as from pathological skull bone tissues (Figure 3B) with accuracy 100% (two latent variables).

FIGURE 3.

PLS‐DA scores plot: projection of MAND and CTRL samples (A), and MAND and PATH samples (B) onto the first two latent variables (PLS 1 and PLS 2).

Although both MAND and CTRL groups' samples originated from healthy individuals, they can be distinguished with a high accuracy. The reason for this high degree of discrimination is that they originate from different anatomical parts. The MAND group samples were collected as residual tissues during the third molar extractions while the CTRL group samples were obtained as surgical leftovers from unrelated otologic procedures, where small fragments of the EAC bone were removed for access or exposure purposes. When we considered that in our previous study [17], we were able to observe some differences even between mandibular and maxillary bones, it is not surprising that it was possible also with these two types of samples. In addition, in our previous study [17], we were able to identify totally 1151 proteins in mandibular bones while in the presented study using the new approach we succeeded to identify 3000 proteins in mandibular bone tissues. Thus, we can expect that identification of higher number of proteins would lead to higher discrimination between bone tissues from different anatomical sites.

The discrimination between control and pathological skull bone tissues reached accuracy 87% (three latent variables, see also Figure 4A).

FIGURE 4.

PLS‐DA scores plot: projection of CTRL and PATH samples (A), and MAND, CTRL, and PATH samples (B) onto the first two latent variables (PLS 1 and PLS 2).

This is important finding because it indicates that similarly as it was possible to distinguish between control healthy and pathological tissues of jaw bones in the field of oral surgery [16, 17], it also makes possible to discriminate between healthy and pathological bone tissues in the field of surgery performed on skull base.

In addition, all three groups of bone tissue samples can be distinguished from each other using classification directly to three groups with accuracy 86% (five latent variables; Figure 4B).

3.3. Biological and Clinical Roles of the Selected Identified Proteins

The most important proteins for discrimination between pathological and control skull bone tissue samples are presented in Table 2. Their biological and clinical roles are briefly discussed below.

TABLE 2.

Five most important proteins for discrimination of PATH and CTRL groups.

*UniProt protein ID	Protein name	log2 fold change	Up/Down regulation in PATH	p value	q value
P36955	PEDF_HUMAN Pigment epithelium‐derived factor	1.98	↑	1.20E‐05	1.77E‐02
Q13838	DX39B_HUMAN Spliceosome RNA helicase	−0.95	↓	1.53E‐03	2.77E‐01
P02743	SAMP_HUMAN Serum amyloid P‐component	−1.67	↓	6.17E‐03	3.77E‐01
Q96CX2	KCD12_HUMAN BTB/POZ domain‐containing protein	1.73	↑	1.29E‐04	9.47E‐02
E7EN24	E7EN24_HUMAN Cadherin 17	1.08	↑	1.10E‐02	4.05E‐01

Pigment epithelium‐derived factor (PEDF) is a multifunctional, secreted glycoprotein of approximately 46–50 kDa molecular mass that belongs to the serpin superfamily. It was originally identified in retinal pigment epithelium cells but is now known to be widely expressed throughout the human body, including the eyes, brain, liver, heart, lungs, skin, and blood plasma [33, 34, 35]. This glycoprotein plays key roles in inhibiting angiogenesis, supporting neuronal health, regulating stem cell behavior, and modulating tissue repair and tumor suppression [36]. In bones, PEDF plays a role in the regulation of proteins and genes involved in osteogenesis. Thus, it may play a role in osteoblastic differentiation [37]. In addition, PEDF increases bone mass and improves bone plasticity [38].

The spliceosome RNA helicase DDX39B (also known as UAP56 or BAT1) regulates the splicing of key immune‐related genes such as FOXP3, the master transcription factor for CD4+/CD25+ regulatory T cells (Tregs), which are crucial for immune tolerance and suppression of autoimmunity [39]. While specific studies on DDX39B in bone tissues are not available, its immunoregulatory functions suggest it could influence bone health indirectly by modulating immune responses within the bone microenvironment [40]. The p value = p value of the Welch's two‐sample t‐test (two‐sided) for protein intensities of selected proteins; q value = t‐test p value after the multiple comparison test correction by the Benjamini–Hochberg procedure [41].

Serum amyloid P component (SAMP_HUMAN or SAP), encoded by the APCS gene, is a calcium‐dependent pentraxin family protein primarily synthesized by hepatocytes and well‐known for its role in amyloidosis, where it binds and stabilizes amyloid fibrils in various tissues [42, 43].

The BTB/POZ domain‐containing protein KCTD12 (also known as KCD12_HUMAN) is primarily characterized as an auxiliary subunit of GABA‐B receptors, influencing receptor pharmacology and kinetics by increasing agonist potency [44, 45]. The BTB/POZ domain itself is a conserved protein–protein interaction module found in various transcription factors, where it often mediates oligomerization and transcriptional repression by recruiting corepressors and histone deacetylases, thus affecting gene expression and cell cycle regulation [46].

E7EN24_HUMAN Cadherin 17 is primarily recognized as a member of the cadherin superfamily, which are calcium‐dependent cell adhesion molecules. Some cadherins are known to play biological roles in bone tissues. Most research on cadherins in bone focuses on N‐cadherin (CDH2), E‐cadherin, and cadherin‐11, which are directly implicated in osteoblast differentiation, bone formation, and the regulation of Wnt/β‐catenin signaling [47, 48].

4. Conclusions

Direct in‐bone protein digestion by trypsin followed by separation and identification of released specific peptide fragments by LC‐TIMS method is able to quickly identify high numbers of proteins in bone tissues in which pathological changes like osteomas or exostoses occur, namely in skull bone tissues and mandibular bone tissues.

In addition, mathematical evaluation of obtained MS data has potential to distinguish between healthy and pathological bone tissues. The advantage of this approach is that even without the time‐consuming demineralization of bone tissues, it represents a powerful tool for their characterization at the molecular level. Another advantage of this procedure is the simplicity of the sample processing. It can be performed routinely by the laboratory assistant or medical technician. On the other hand, the weakness of the developed procedure is that potentially, it can provide lower number of identified proteins than the classical time‐consuming technique of demineralization of bone tissues. The advantage of the demineralization technique could be that when it will be also combined with LC separation and TIMS detection of released peptides, it could provide higher number of identified proteins and possibly more precise discrimination of healthy and pathological tissues. In future, we plan further optimization of the process developed in this study. It should result in more detailed characterization of bone tissues including posttranslational modifications, especially glycosylation and phosphorylation. Then, it could contribute to a deeper understanding of the mechanism of pathological processes in bone tissues.

With its simplicity and efficiency, this approach holds strong potential for widespread adoption in skull base surgery, oral surgery, or other fields of medicine dealing with bone tissues. Moreover, it is possible to assume that it could be advantageously used in paleoproteomics, where the study of bone tissues is obviously necessary too [49, 50]

Author Contributions

Lenka Peterková: processing of auricular bone tissue samples prior in‐bone trypsin digestion, coordination of sample workflow, manuscript preparation. Michaela Tesařová: ear surgical procedures, initial processing of auricular bone tissue samples. Adéla Soukupová: in‐bone digestion with trypsin of auricular bone tissue samples and further sample processing prior MS analysis, formal analysis. Iva Michalus: oral surgical procedures, processing of mandibular bone tissue samples including in‐bone digestion with trypsin and further sample processing prior MS analysis. Pavel Cejnar: mathematical analysis, formal analysis. Zdeněk Fík: ear surgical procedures, supervision of surgical indications, main responsibility at the ENT department. Jiří Šantrůček: mass spectrometry analysis, data processing. Václav Kašička: conceptualization, writing – review and editing, formal analysis, funding acquisition. Radovan Hynek: conceptualization, writing – review and editing, interpretation of obtained results, formal analysis.

Ethics Statement

The authors declare that the local ethical committees' approvals have been received and that the informed consent of all participating subjects was obtained. The permissions of the local authorities (ethical committees) are attached.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supplementary file 1: jssc70277‐sup‐0001‐TableS1.zip

Supplementary file 2: jssc70277‐sup‐0002‐TableS2.xlsx

Peterková L., Tesařová M., Sukupová A., et al. “Direct In‐Bone Protein Digestion With Subsequent LC Separation and Trap Ion Mobility MS Detection of Released Peptides as an Effective Tool for the Proteomic Characterization of Bone Tissues.” Journal of Separation Science 48, no. 9 (2025): e70277. 10.1002/jssc.70277

Data Availability Statement

The mass spectrometry proteomics data have been deposited to the ProteomeXchange [51] Consortium via the PRIDE [22] partner repository with the dataset identifier PXD065656.