contribute to diabetic retinopathy, limitations of this candidate protein approach are that it is often directed by preexisting theories and the relatively small number of molecules that have been examined.

PROTEOMIC APPROACHES

Proteomics is the large-scale analysis of proteins, which often includes a combination of characterization of amino acid sequence, quantification, modifications, and interactions. Advances in proteomics over that past decade have created opportunities to use rapid de novo protein discovery methods to further characterize the composition of vitreous and identify protein changes associated with diabetes and diabetic retinopathy. Most of this work has utilized mass spectrometry as the centerpiece for protein identification; however, markedly different proteomic methods have been utilized, which limit the comparison of studies and findings across studies. As such, the present status of vitreous proteomic data in diabetes is a collection of somewhat unique studies. The following describes the proteomic approaches that have been used for the analysis of vitreous and discusses findings that are beginning to emerge from this work.

Proteomics is a multistep process with variety of options available at each step. Although the utilization of diverse methods yields important information and differences in perspectives, the lack of uniformity limits the assimilation of data among different studies. Further understanding of the differences among experimental design and data output is critical for interpreting the current body of vitreous proteomic information. The workflow of vitreous proteomics can be separated into a series of steps, including (1) vitreous acquisition, (2) sample fractionation, (3) mass spectrometry, (4) spectral analysis, and (5) data analysis (Fig. 1). The following section describes options and parameters that have been applied to vitreous proteomics within each of these steps.

Vitreous Acquisition

Study design has an overarching influence on the information generated from vitreous proteomics. Vitreous fluid is usually obtained during pars plana vitrectomy for treatment of specific retinal and vitreoretinal disorders, including, but not limited to, epiretinal membrane (ERM), macular hole (MH), vitreoretinal traction, and non-clearing vitreous hemorrhage. The potential influences of these surgical indications, apart from the influences of diabetes and diabetic retinopathy, on the vitreous proteome are unknown.

Additional factors that could influence the vitreous proteome at a given stage of diabetic retinopathy include patient demographics, rate of disease progression, disease duration, and treatment history, including, for example, laser photocoagulation and pharmacotherapy. A growing number of studies have shown that levels of specific proteins can differ markedly within a selected group of patients, for example, VEGF levels can differ markedly among individual PDR vitreous samples [20, 26]. Since obtaining multiple vitreous samples for longitudinal studies is generally not feasible, large numbers of samples from well-characterized patients will be needed to examine protein correlations with retinopathy stage.

Increases in total protein concentration in the vitreous in diabetic retinopathy are well documented (Table 1). Most studies have reported that vitreous protein levels are about

Fig. 1. Vitreous proteomics. An example of a work flow for 1-DE-based proteomics is shown. The major steps include vitreous acquisition, which involves both study design and clinical characteristics. Sample pre-fractionation in performed by separation by 1-D SDS-PAGE followed by fractionation into gel slices. Mass spectrometry involves analysis of m/z of peptides and fragmentation ions. Spectral analysis involves matching spectra with amino acid sequences using search algorithms such as Sequest, Mascot, and X!Tandem, and quantitative analysis based on spectral parameters. Data analysis enables proteome comparisons, analysis of pathways and functions, and posttranslational modifications (PTMs) and proteolysis.

Table 1. Total protein concentration in control and PDR vitreous

fourfold higher in PDR compared with vitreous obtained from NDM subjects with MH. A primary cause for increased total protein in diabetic retinopathy is due to elevated RVP, which occurs early in diabetic retinopathy and increases further during disease progression [33, 34]. These additional increases in protein content in advanced diabetic

Fig. 2. Biological processes that contribute to the vitreous proteome. A variety of biological processes contribute to the release of proteins into the vitreous, including secretion, plasma extravagation due to pathological retinal vascular permeability (RVP) and edema, hemorrhages, release of microparticles (MP) and cell lysis, and release due to retinal ischemia and clearance of blood cells. Vitreous proteins can be retained in the vitreous by binding to extracellular matrix (ECM) or removed by active or passive transport mechanisms. Vitreous proteins also undergo proteolysis, which may modify their activities and facilitate clearance.

retinopathy are likely the results of a combination of factors including increased RVP, vitreous and intraretinal hemorrhage, tissue damage associated with retinal ischemia, and neovascularization (Fig. 2).

Sample Pre-Fractionation

Sample fractionation provides opportunities to further characterize the vitreous proteins based on physiochemical properties and improves detection sensitivity. One of the goals of most pre-fractionation methods is to separate high-abundance proteins, such as serum albumin, from lower-abundance proteins to improve their detection. Most proteomic analyses of vitreous have utilized protein fractionation based on either 1-dimensional (1-D) or 2-D gel electrophoresis. 1-D SDS-PAGE provides a preparative method of fractionation that enables downstream mass spectrometry of the entire sample separated according to molecular weight (mw, mobility in SDS-PAGE). In 1-DE gel protein staining is typically performed using Coomassie Brilliant Blue stain, and quantitative comparison of proteins among samples utilizes mass spectrometry data. In 2-DE, samples are fractionated by isoelectric focusing (IEF) followed by SDS-PAGE and protein staining. This results in a 2-D display of vitreous proteins, and relative

protein staining can be used as a semiquantitative measure of abundance. 2-DE provides both isoelectric point and mw information, and selected proteins and protein isoforms, resolved by IEF, can be isolated for analysis. Quantifying proteins from 2-DE gel staining is complicated by protein isoform separation into multiple spots along an IEF gradient and the possibility that a single spots can contain multiple proteins. Albumin and IgG affinity chromatography has been used in a limited number of studies, prior to separation by gel electrophoresis, to increase detection sensitivity for low-abundance proteins [30, 35]. In solution digestion of protein methods for vitreous proteomics could provide opportunities for increasing throughput; however, this approach does not provide protein mw data, and high levels of glycated macromolecules could potentially interfere with digestion and downstream separation methods.

Mass Spectrometry

A variety of mass spectrometry platforms have been used for vitreous proteomics, which can be separated into two groups based on ionization source, including electrospray ionization (ESI) liquid chromatography–tandem mass spectrometry (LC MS/MS) [35–40] and matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) [29–31, 35, 38, 41]. In addition, the parameters for a given mass spectrometry platform can have a major impact on instrument sensitivity and performance with complex samples. Kim et al. performed side-by-side analyses of vitreous proteins using LC-MALDI- MS/MS and LC-ESI-MS/MS systems [35]. This study reports that MALDI and ESI systems identified 83 and 518 proteins, respectively, which resulted in 531 proteins in the merged datasets. While these findings demonstrate that different mass spectrometry platforms can provide complementary protein datasets, these results also show the limitations in comparing results from different systems. Since there are a number of inherent differences among mass spectrometry platform [42], in addition to user defined parameters, the assimilation of data across studies requires downstream solutions directed at spectral and data analysis.

Spectral Analysis

Spectra generated by mass spectrometry is matched to amino acid sequences using a variety of algorithms, including Sequest, Mascot, and X!Tandem. Gao et al. compared Sequest and X!Tandem analyses of LC-MS/MS data from a set of human vitreous samples [37]. This study generated 231 and 213 proteins using X!Tandem and Sequest, respectively, with 192 proteins identified by both algorithms and a total of 252 proteins in the merged dataset. As described above, these data show that different platforms provide complimentary data that increase the number of proteins identified. However, some low-abundance protein matches were limited to single search algorithms. The criteria used to identify a match are user-defined and instrument-dependent. Parameters and thresholds used to identify proteins are a balance between optimizing detection sensitivity and minimizing the false positive rate (FDR), which is determined by searches against a reversed or randomized protein database [43]. The criteria used to identify a protein vary among vitreous proteomic studies. For example, in two studies using a similar LC-ESI-MS/MS platform, Gao et al. used two unique peptides identified from the same or adjacent gel slice in at least two independent vitreous samples to generate