In vivo detection of secreted proteins from wounded skin using capillary ultrafiltration probes and mass spectrometric proteomics

The identification of in vivo secreted proteins is a major challenge in systems biology. Here we report a novel technique using capillary ultrafiltration (CUF) probes to identify the secreted proteins involved in wound healing. CUF probes, which use semipermeable membrane hollow fibers to continuously capture secreted proteins, were used to sample skin wound fluids. To identify low-abundance proteins, we digested the CUF probe-collected wound fluid with trypsin and then directly subjected it to MS without using 2-DE separation. Two protein fragments with masses of 1565.7 and 1694.8 Da were identified by MS as peptides of thymosin beta10 and beta4, respectively. This is the first identification of thymosin beta10 as an in vivo constituent of the skin wound fluid. The LKKTETQ peptide, a common actin-binding domain of thymosin beta4 and beta10, significantly enhanced skin wound healing in vitro and in vivo. Our data suggest that the enhancement of wound healing by LKKTETQ may be mediated by purinergic receptors. The technique of using CUF probes linked to mass spectrometric proteomics represents a powerful method to identify in vivo secreted proteins, and may be applicable for identification of proteins relevant in various human diseases.     

Recent advances in protein profiling of tissues and tissue fluids

Creating protein profiles of tissues and tissue fluids, which contain secreted proteins and peptides released from various cells, is critical for biomarker discovery as well as drug and vaccine target selection. It is extremely difficult to obtain pure samples from tissues or tissue fluids, however, and identification of complex protein mixtures is still a challenge for mass spectrometry analysis. Here, we summarize recent advances in techniques for extracting proteins from tissues for mass spectrometry profiling and imaging. We also introduce a novel technique using a capillary ultrafiltration (CUF) probe to enable in vivo collection of proteins from the tissue microenvironment. The CUF probe technique is compared with existing sampling techniques, including perfusion, saline wash, fine-needle aspiration and microdialysis. In this review, we also highlight quantitative mass spectrometric proteomic approaches with, and without, stable-isotope labels. Advances in quantitative proteomics will significantly improve protein profiling of tissue and tissue fluid samples collected by CUF probes.     

Breast tumor microenvironment: proteomics highlights the treatments targeting secretome

Tumor secreted substances (secretome), including extracellular matrix (ECM) components, act as mediators of tumor-host communication in the breast tumor microenvironment. Proteomic analysis has emphasized the value of the secretome as a source of prospective markers and drug targets for the treatment of breast cancers. Utilizing bioinformatics, our recent studies revealed global changes in protein expression after the activation of ECM-mediated signaling in breast cancer cells. A newly designed technique integrating a capillary ultrafiltration (CUF) probe with mass spectrometry was demonstrated to dynamically sample and identify in vivo and pure secretome from the tumor microenvironment. Such in vivo profiling of breast cancer secretomes may facilitate the development of novel drugs specifically targeting secretome.     

Quantitative proteomes and in vivo secretomes of progressive and regressive UV-induced fibrosarcoma tumor cells: mimicking tumor microenvironment using a dermis-based cell-trapped system linked to tissue chamber

The alterations of tumor proteome and/or in vivo secretome created by host-tumor cell interaction may be crucial factors for tumors to undergo progression or regression in a host system. Two UV-induced fibrosarcoma tumor cell lines (UV-2237 progressive cells and UV-2240 regressive cells) were used as models to address this issue. Hundreds of proteins including in vivo secretome have been identified and quantified via an isotope-coded protein label (ICPL) in conjunction with high-throughput NanoLC-LTQ MS analysis. A newly designed technology using a dermis-based cell-trapped system was employed to encapsulate and grow 3-D tumor cells. A tissue chamber inserted with a tumor cell-trapped dermis was implanted into mice to mimic the tumor microenvironment. The in vivo secretome created by host-tumor interaction was characterized from samples collected from tissue chamber fluids via ICPL labeling mass spectrometric analysis. Twenty-five proteins including 14-3-3 proteins, heat shock proteins, profilin-1, and a fragment of complement C3 with differential expression in proteomes of UV-2237 and UV-2240 cells were revealed. Three secreted proteins including myeloperoxidase, alpha-2-macroglobulin, and a vitamin D-binding protein have different abundances in the in vivo secretome in response to UV-2237 and UV-2240 cells. Differential tumor proteomes and in vivo secretome were thus accentuated as potential therapeutic targets to control tumor growth.     

Secreted proteins as a fundamental source for biomarker discovery

The proteins secreted by various cells (the secretomes) are a potential rich source of biomarkers as they reflect various states of the cells at real time and at given conditions. To have accessible, sufficient and reliable protein markers is desirable as they mark various stages of disease development and their presence/absence can be used for diagnosis, prognosis, risk stratification and therapeutic monitoring. As direct analysis of blood/plasma, a common and noninvasive patient screening method, can be difficult for candidate protein biomarker identification, the alternative/complementary approaches are required, one of them is the analysis of secretomes in cell conditioned media in vitro. As the proteins secreted by cells as a response to various stimuli are most likely secreted into blood/plasma, the identification and pre-selection of candidate protein biomarkers from cell secretomes with subsequent validation of their presence at higher levels in serum/plasma is a promising approach. In this review, we discuss the proteins secreted by three progenitor cell types (smooth muscle, endothelial and cardiac progenitor cells) and two adult cell types (neonatal rat ventrical myocytes and smooth muscle cells) which can be relevant to cardiovascular research and which have been recently published in the literature. We found, at least for secretome studies included in this review, that secretomes of progenitor and adult cells overlap by 48% but the secretomes are very distinct among progenitor cell themselves as well as between adult cells. In addition, we compared secreted proteins to protein identifications listed in the Human Plasma PeptideAtlas and in two reports with cardiovascular-related proteins and we performed the extensive literature search to find if any of these secreted proteins were identified in a biomarker study. As expected, many proteins have been identified as biomarkers in cancer but 18 proteins (out of 62) have been tested as biomarkers in cardiovascular diseases as well.     

Secretome analysis using a hollow fiber culture system for cancer biomarker discovery

Secreted proteins, collectively referred to as the secretome, were suggested as valuable biomarkers in disease diagnosis and prognosis. However, some secreted proteins from cell cultures are difficult to detect because of their intrinsically low abundance; they are frequently masked by the released proteins from lysed cells and the substantial amounts of serum proteins used in culture medium. The hollow fiber culture (HFC) system is a commercially available system composed of small fibers sealed in a cartridge shell; cells grow on the outside of the fiber. Recently, because this system can help cells grow at a high density, it has been developed and applied in a novel analytical platform for cell secretome collection in cancer biomarker discovery. This article focuses on the advantages of the HFC system, including the effectiveness of the system for collection of secretomes, and reviews the process of cell secretome collection by the HFC system and proteomic approaches to discover cancer biomarkers. The HFC system not only provides a high-density three-dimensional (3D) cell culture system to mimic tumor growth conditions in vivo but can also accommodate numerous cells in a small volume, allowing secreted proteins to be accumulated and concentrated. In addition, cell lysis rates can be greatly reduced, decreasing the amount of contamination by abundant cytosolic proteins from lysed cells. Therefore, the HFC system is useful for preparing a wide range of proteins from cell secretomes and provides an effective method for collecting higher amounts of secreted proteins from cancer cells. This article is part of a Special Issue entitled: An Updated Secretome.     

Sampling Human Indigenous Saliva Peptidome Using a Lollipop-Like Ultrafiltration Probe: Simplify and Enhance Peptide Detection for Clinical Mass Spectrometry

Although human saliva proteome and peptidome have been revealed ^1-2 they were majorly identified from tryptic digests of saliva proteins. Identification of indigenous peptidome of human saliva without prior digestion with exogenous enzymes becomes imperative, since native peptides in human saliva provide potential values for diagnosing disease, predicting disease progression, and monitoring therapeutic efficacy. Appropriate sampling is a critical step for enhancement of identification of human indigenous saliva peptidome. Traditional methods of sampling human saliva involving centrifugation to remove debris ^3-4 may be too time-consuming to be applicable for clinical use. Furthermore, debris removal by centrifugation may be unable to clean most of the infected pathogens and remove the high abundance proteins that often hinder the identification of low abundance peptidome.Conventional proteomic approaches that primarily utilize two-dimensional gel electrophoresis (2-DE) gels in conjugation with in-gel digestion are capable of identifying many saliva proteins ^5-6. However, this approach is generally not sufficiently sensitive to detect low abundance peptides/proteins. Liquid chromatography-Mass spectrometry (LC-MS) based proteomics is an alternative that can identify proteins without prior 2-DE separation. Although this approach provides higher sensitivity, it generally needs prior sample pre-fractionation ⁷ and pre-digestion with trypsin, which makes it difficult for clinical use. To circumvent the hindrance in mass spectrometry due to sample preparation, we have developed a technique called capillary ultrafiltration (CUF) probes ^8-11. Data from our laboratory demonstrated that the CUF probes are capable of capturing proteins in vivo from various microenvironments in animals in a dynamic and minimally invasive manner ^8-11. No centrifugation is needed since a negative pressure is created by simply syringe withdrawing during sample collection. The CUF probes combined with LC-MS have successfully identified tryptic-digested proteins ^8-11. In this study, we upgraded the ultrafiltration sampling technique by creating a lollipop-like ultrafiltration (LLUF) probe that can easily fit in the human oral cavity. The direct analysis by LC-MS without trypsin digestion showed that human saliva indigenously contains many peptide fragments derived from various proteins. Sampling saliva with LLUF probes avoided centrifugation but effectively removed many larger and high abundance proteins. Our mass spectrometric results illustrated that many low abundance peptides became detectable after filtering out larger proteins with LLUF probes. Detection of low abundance saliva peptides was independent of multiple-step sample separation with chromatography. For clinical application, the LLUF probes incorporated with LC-MS could potentially be used in the future to monitor disease progression from saliva.     

Secretome Prediction and Analysis in Sacred Lotus (Nelumbo nucifera Gaertn.)

Sacred lotus, Nelumbo nucifera (Gaertn.), is a basal eudicot with agricultural and medicinal importance. The secretome and proteins in some other subcellular locations including endoplasmic reticulum (ER), mitochondrion, chloroplast, and membrane of sacred lotus were predicted using a set of computational tools. The distribution of proteins in each subcellular location in sacred lotus was compared with proteins in five other plant species. Plant proteomes contained approximately 6–9 % of secreted proteins, 13–15 % membrane proteins, 12–20 % mitochondrial or chloroplast proteins, respectively. Plant secreted proteins consist of a large number of hydrolases and peroxidases which may contribute to cell wall formation, rhizome development and seed germination regulation. The information of secretome and other protein subcellular locations in sacred lotus and other species can be accessed at the PlantSecKB website (http://proteomics.ysu.edu/secretomes/plant.php).     

Mining the gastric cancer secretome: identification of GRN as a potential diagnostic marker for early gastric cancer

Gastric cancer is the second leading cause of cancer deaths worldwide, and currently, there are no clinically relevant biomarkers for gastric cancer diagnosis or prognosis. In this study, we applied a 2D-LC-MS/MS based approach, in combination with iTRAQ labeling, to study the secretomes of the gastric cancer cell lines AGS and MKN7. By performing a comparative analysis between the conditioned media and the whole cell lysates, our workflow allowed us to differentiate the bona fide secreted proteins from the intracellular contaminants within the conditioned media. Ninety proteins were found to have higher abundance in the conditioned media as compared to the whole cell lysates of AGS and MKN7 cells. Using a signal peptide and nonclassical secretion prediction tool and an online exosome database, we demonstrated that up to 92.2% of these 90 proteins can be exported out of the cells by classical or nonclassical secretory pathways. We then performed quantitative comparisons of the secretomes between AGS and MKN7, identifying 43 differentially expressed secreted proteins. Among them, GRN was found to be frequently expressed in gastric tumor tissues, but not in normal gastric epithelia by immunohistochemistry. Sandwich ELISA assay also showed elevation of serum GRN levels in gastric cancer patients, particularly those with early gastric cancer. Receiver operating characteristic (ROC) curves analysis confirmed that serum GRN can provide diagnostic discriminations for gastric cancer patients.     

Sixth Annual Meeting on Proteomic Sample Preparation,part of the second annual Getting Optimized Targets Summit

Atherosclerosis is a disease with higher levels of mortality in developed countries. Comprehension of the molecular mechanisms can yield very useful information in clinics for prevention, diagnosis and recovery monitoring. Proteomics represents an ideal methodology for this purpose, as proteins constitute the effectors of the different biological processes running during pathogenesis. To date, studies in atherosclerosis have been mainly focused on the search for plasma biomarkers. However, tissue proteomics allows going deeper into tissue secretomes, arterial layers or particular cells of interest, which, in turn, constitutes a more direct approximation to in vivo operating mechanisms. The aim of this review is to report latest advances in tissue proteomics in atherosclerosis and related diseases (e.g., aortic stenosis and ischemic injury).     

Recent advances in atherosclerosis-based proteomics: new biomarkers and a future perspective

Vascular proteomics is providing two main types of data: proteins that actively participate in vascular pathophysiological processes and novel protein candidates that can potentially serve as useful clinical biomarkers. Although both types of proteins can be identified by similar proteomic strategies and methods, it is important to clearly distinguish biomarkers from mediators of disease. A particular protein, or group of proteins, may participate in a pathogenic process but not serve as an effective biomarker. Alternatively, a useful biomarker may not mediate pathogenic pathways associated with disease (i.e., C-reactive protein). To date, there are no clear successful examples in which discovery proteomics has led to a novel useful clinical biomarker in cardiovascular diseases. Nevertheless, new sources of biomarkers are being explored (i.e., secretomes, circulating cells, exosomes and microparticles), an increasing number of novel proteins involved in atherogenesis are constantly described, and new technologies and analytical strategies (i.e., quantitative proteomics) are being developed to access low abundant proteins. Therefore, this presages a new era of discovery and a further step in the practical application to diagnosis, prognosis and early action by medical treatment of cardiovascular diseases.     

Identification of secreted proteins that reflect autophagy dynamics within tumor cells

Macroautophagy, a catabolic process of cellular self-digestion, is an important tumor cell survival mechanism and a potential target in antineoplastic therapies. Recent discoveries have implicated autophagy in the cellular secretory process, but potential roles of autophagy-mediated secretion in modifying the tumor microenvironment are poorly understood. Furthermore, efforts to inhibit autophagy in clinical trials have been hampered by suboptimal methods to quantitatively measure tumor autophagy levels. Here, we leveraged the autophagy-based involvement in cellular secretion to identify shed proteins associated with autophagy levels in melanoma. The secretome of low-autophagy WM793 melanoma cells was compared to its highly autophagic metastatic derivative, 1205Lu in physiological 3-dimensional cell culture using quantitative proteomics. These comparisons identified candidate autophagy biomarkers IL1B (interleukin 1, β), CXCL8 (chemokine (C-X-C motif) ligand 8), LIF (leukemia inhibitory factor), FAM3C (family with sequence similarity 3, member C), and DKK3 (dickkopf WNT signaling pathway inhibitor 3) with known roles in inflammation and tumorigenesis, and these proteins were subsequently shown to be elevated in supernatants of an independent panel of high-autophagy melanoma cell lines. Secretion levels of these proteins increased when low-autophagy melanoma cells were treated with the autophagy-inducing tat-BECN1 (Beclin 1) peptide and decreased when ATG7 (autophagy-related 7) was silenced in high-autophagy cells, thereby supporting a mechanistic link between these secreted proteins and autophagy. In addition, serum from metastatic melanoma patients with high tumor autophagy levels exhibited higher levels of these proteins than serum from patients with low-autophagy tumors. These results suggest that autophagy-related secretion affects the tumor microenvironment and measurement of autophagy-associated secreted proteins in plasma and possibly in tumors can serve as surrogates for intracellular autophagy dynamics in tumor cells.     

Identification of secreted proteins that reflect autophagy dynamics within tumor cells

Macroautophagy, a catabolic process of cellular self-digestion, is an important tumor cell survival mechanism and a potential target in antineoplastic therapies. Recent discoveries have implicated autophagy in the cellular secretory process, but potential roles of autophagy-mediated secretion in modifying the tumor microenvironment are poorly understood. Furthermore, efforts to inhibit autophagy in clinical trials have been hampered by suboptimal methods to quantitatively measure tumor autophagy levels. Here, we leveraged the autophagy-based involvement in cellular secretion to identify shed proteins associated with autophagy levels in melanoma. The secretome of low-autophagy WM793 melanoma cells was compared to its highly autophagic metastatic derivative, 1205Lu in physiological 3-dimensional cell culture using quantitative proteomics. These comparisons identified candidate autophagy biomarkers IL1B (interleukin 1, β), CXCL8 (chemokine (C-X-C motif) ligand 8), LIF (leukemia inhibitory factor), FAM3C (family with sequence similarity 3, member C), and DKK3 (dickkopf WNT signaling pathway inhibitor 3) with known roles in inflammation and tumorigenesis, and these proteins were subsequently shown to be elevated in supernatants of an independent panel of high-autophagy melanoma cell lines. Secretion levels of these proteins increased when low-autophagy melanoma cells were treated with the autophagy-inducing tat-BECN1 (Beclin 1) peptide and decreased when ATG7 (autophagy-related 7) was silenced in high-autophagy cells, thereby supporting a mechanistic link between these secreted proteins and autophagy. In addition, serum from metastatic melanoma patients with high tumor autophagy levels exhibited higher levels of these proteins than serum from patients with low-autophagy tumors. These results suggest that autophagy-related secretion affects the tumor microenvironment and measurement of autophagy-associated secreted proteins in plasma and possibly in tumors can serve as surrogates for intracellular autophagy dynamics in tumor cells.     

Use of Composite Protein Database including Search Result Sequences for Mass Spectrometric Analysis of Cell Secretome

Mass spectrometric (MS) data of human cell secretomes are usually run through the conventional human database for identification. However, the search may result in false identifications due to contamination of the secretome with fetal bovine serum (FBS) proteins. To overcome this challenge, here we provide a composite protein database including human as well as 199 FBS protein sequences for MS data search of human cell secretomes. Searching against the human-FBS database returned more reliable results with fewer false-positive and false-negative identifications compared to using either a human only database or a human-bovine database. Furthermore, the improved results validated our strategy without complex experiments like SILAC. We expect our strategy to improve the accuracy of human secreted protein identification and to also add value for general use.     

Glioblastoma cell secretome: analysis of three glioblastoma cell lines reveal 148 non-redundant proteins

Glioblastoma multiforme (GBM) is the most aggressive among human gliomas with poor prognosis. Study of tumor cell secretome - proteins secreted by cancer cell lines, is a powerful approach to discover potential diagnostic or prognostic biomarkers. Here we report, for the first time, proteins secreted by three GBM cell lines, HNGC2, LN229 and U87MG. Analysis of the conditioned media of these cell lines by LC-MS/MS using ESI-IT mass spectrometer (LTQ) resulted in the confident identification of 102, 119 and 64 proteins, respectively. Integration of the results from all the three cell lines lead to a dataset of 148 non-redundant proteins. Subcellular classification using Genome Ontology indicated that 42% of the proteins identified belonged to extracellular or membrane proteins, viz. Vinculin, Tenascin XB, SERPIN F1 and TIMP-1. 52 proteins matched with the secretomes of 11 major cancer types reported earlier whereas remaining 96 are unique to our study. 25 protein identifications from the dataset represent proteins related to GBM or other cancer tissues as per Human Protein Atlas; at least 22 are detectable in plasma, 11 of them being reported even in cerebrospinal fluid. Our study thus provides a valuable resource of GBM cell secretome with potential for further investigation as GBM biomarkers.     

A caspase-3 ‘death-switch' in colorectal cancer cells for induced and synchronous tumor apoptosis in vitro and in vivo facilitates the development of minimally invasive cell death biomarkers

Novel anticancer drugs targeting key apoptosis regulators have been developed and are undergoing clinical trials. Pharmacodynamic biomarkers to define the optimum dose of drug that provokes tumor apoptosis are in demand; acquisition of longitudinal tumor biopsies is a significant challenge and minimally invasive biomarkers are required. Considering this, we have developed and validated a preclinical ‘death-switch'' model for the discovery of secreted biomarkers of tumour apoptosis using in vitro proteomics and in vivo evaluation of the novel imaging probe [¹⁸F]ML-10 for non-invasive detection of apoptosis using positron emission tomography (PET). The ‘death-switch'' is a constitutively active mutant caspase-3 that is robustly induced by doxycycline to drive synchronous apoptosis in human colorectal cancer cells in vitro or grown as tumor xenografts. Death-switch induction caused caspase-dependent apoptosis between 3 and 24 hours in vitro and regression of ‘death-switched'' xenografts occurred within 24 h correlating with the percentage of apoptotic cells in tumor and levels of an established cell death biomarker (cleaved cytokeratin-18) in the blood. We sought to define secreted biomarkers of tumor apoptosis from cultured cells using Discovery Isobaric Tag proteomics, which may provide candidates to validate in blood. Early after caspase-3 activation, levels of normally secreted proteins were decreased (e.g. Gelsolin and Midkine) and proteins including CD44 and High Mobility Group protein B1 (HMGB1) that were released into cell culture media in vitro were also identified in the bloodstream of mice bearing death-switched tumors. We also exemplify the utility of the death-switch model for the validation of apoptotic imaging probes using [¹⁸F]ML-10, a PET tracer currently in clinical trials. Results showed increased tracer uptake of [¹⁸F]ML-10 in tumours undergoing apoptosis, compared with matched tumour controls imaged in the same animal. Overall, the death-switch model represents a robust and versatile tool for the discovery and validation of apoptosis biomarkers.     

Current advances in tumor proteomics and candidate biomarkers for hepatic cancer

Tumor proteomics apply proteomics techniques to tumor biological research, mainly by screening candidate biomarkers for early tumor diagnosis, prognosis and treatment. Hepatocellular carcinoma (HCC) is a type of malignant tumor with one of the highest death rates in the world. With the advent of the post-genomic age, tumor biological research developing the technology of proteomics has become a major focus of researchers. The discovery of novel candidate biomarkers is one of crucial problems for the early diagnosis of HCC. In general, there are three distinct types of candidate biomarkers for HCC based on different areas: biochemical biomarkers, antigenic biomarkers and epigenetic biomarkers. This review mainly discusses current advances in the problems and prospects of candidate biomarker for the early diagnosis of HCC, discovered by technologies of tumor proteomics.     

Secretome compartment is a valuable source of biomarkers for cancer-relevant pathways

In principle, targeted therapies have optimal activity against a specific subset of tumors that depend upon the targeted molecule or pathway for growth, survival, or metastasis. Consequently, it is important in drug development and clinical practice to have predictive biomarkers that can reliably identify patients who will benefit from a given therapy. We analyzed tumor cell-line secretomes (conditioned cell media) to look for predictive biomarkers; secretomes represent a potential source for potential biomarkers that are expressed in intracellular signaling and therefore may reflect changes induced by targeted therapy. Using Gene Ontology, we classified by function the secretome proteins of 12 tumor cell lines of different histotypes. Representations and hierarchical relationships among the functional groups differed among the cell lines. Using bioinformatics tools, we identified proteins involved in intracellular signaling pathways. For example, we found that secretome proteins related to TGF-beta signaling in thyroid cancer cells, such as vasorin, CD109, and βIG-H3 (TGFBI), were sensitive to RPI-1 and dasatinib treatments, which have been previously demonstrated to be effective in blocking cell proliferation. The secretome may be a valuable source of potential biomarkers for detecting cancer and measuring the effectiveness of cancer therapies.     

Integrated Proteomic Analysis of Human Cancer Cells and Plasma from Tumor Bearing Mice for Ovarian Cancer Biomarker Discovery

Background

The complexity of the human plasma proteome represents a substantial challenge for biomarker discovery. Proteomic analysis of genetically engineered mouse models of cancer and isolated cancer cells and cell lines provide alternative methods for identification of potential cancer markers that would be detectable in human blood using sensitive assays. The goal of this work is to evaluate the utility of an integrative strategy using these two approaches for biomarker discovery.

Methodology/Principal Findings

We investigated a strategy that combined quantitative plasma proteomics of an ovarian cancer mouse model with analysis of proteins secreted or shed by human ovarian cancer cells. Of 106 plasma proteins identified with increased levels in tumor bearing mice, 58 were also secreted or shed from ovarian cancer cells. The remainder consisted primarily of host-response proteins. Of 25 proteins identified in the study that were assayed, 8 mostly secreted proteins common to mouse plasma and human cancer cells were significantly upregulated in a set of plasmas from ovarian cancer patients. Five of the eight proteins were confirmed to be upregulated in a second independent set of ovarian cancer plasmas, including in early stage disease.

Conclusions/Significance

Integrated proteomic analysis of cancer mouse models and human cancer cell populations provides an effective approach to identify potential circulating protein biomarkers.     

Comparative secretome analysis of human follicular dermal papilla cells and fibroblasts using shotgun proteomics

The dermal papilla cells (DPCs) of hair follicles are known to secrete paracrine factors for follicular cells. Shotgun proteomic analysis was performed to compare the expression profiles of the secretomes of human DPCs and dermal fibroblasts (DFs). In this study, the proteins secreted by DPCs and matched DFs were analyzed by 1DE/LTQ FTICR MS/MS, semi-quantitatively determined using emPAI mole percent values and then characterized using protein interaction network analysis. Among the 1,271 and 1,188 proteins identified in DFs and DPCs, respectively, 1,529 were further analyzed using the Ingenuity Pathway Analysis tool. We identified 28 DPC-specific extracellular matrix proteins including transporters (ECM1, A2M), enzymes (LOX, PON2), and peptidases (C3, C1R). The biochemically- validated DPC-specific proteins included thrombospondin 1 (THBS1), an insulin-like growth factor binding protein3 (IGFBP3), and, of particular interest, an integrin beta1 subunit (ITGB1) as a key network core protein. Using the shotgun proteomic technique and network analysis, we selected ITGB1, IGFBP3, and THBS1 as being possible hair-growth modulating protein biomarkers.