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The tears, covering the
epithelium of the exterior of the eye, are an intricate extracellular fluid
which is easily and noninvasively available for examination of proteins for
monitoring both ophthalmic and systemic disease conditions. The tiny (3–40 ?m) coating
of tears are arranged into an external lipid layer (15–160 nm, non-polar and
polar lipid layer with intercalated proteins), middle aqueous phase (small
molecule metabolites, proteins, electrolytes, gel-forming mucins, etc.) and internal
glycocalyx film (contains transmembrane glycoproteins and mucins) which joins
the tear film to the corneal surface epithelial cells
(Green-Church et al., 2011). Several means of
collection of tear samples are available for variable circumstances. The two
most common collection approaches are fire polished glass capillary tube and
the Schirmer’s strip (Green-Church et al., 2008),
which is mainly suitable as it is a typical clinical technique (Zhou et
al., 2009, Tong et al., 2011, Wong et al., 2011). A minute
volume of tears has been found to contain several types of molecules, i.e.
proteins, lipids, glycoproteins, small molecule metabolites, and electrolytes (Zhou
et al.,2006). De Souza et al. recognized 491 proteins
in human tear sample using in-gel/in-solution digestion, LC/MS-LTQ-FT and
LC/MS-LTQ-Orbitrap method. The work routinely detected around 100 tear proteins
using online 2D-LC/MS/MS on a QSTAR-XL mass spectrometer (Zhou et
al.2009., Wong et al.,2011). Other early studies on human tears only detected fewer
than a hundred proteins (Li et al., 2005).
Zhou et al. identified 60 small molecule metabolites in tears of healthy human
using LC-MS/MS on a Triple-ToF 5600 mass spectrometer (Chen et al.,2011).
Combined with earlier reports using targeted approaches,
about 100 different kinds of small molecule metabolites have been recognized in
human tears thus far (Chen et al.,2011). Tear
lipids generated from the oil manufacturing glands present in the eyelids, have
been examined and approximately 150 lipid species from 6 different lipid
classes were identified in healthy human tears (Rantamäki et al., 2011).
Tear constituents both quantitatively and
qualitatively shows the health of the ocular tissues and are pivotal in assessment
in pathophysiology and some systemic conditions. Thus, tears a much-appreciated
source for the analysis of abnormal fluctuations due to ocular complications
like corneal wound (Zhou et al.,2007, Zhou et al., 2003). This makes tears as a valuable source for discovering new
biomarkers of diseases such as, blepharitis (Koo et al., 2005), dry eye (Grus et al.,2005, Versura et al.,2010), keratoconus (Lema et al.,2010, Pannebaker et
al.,2010), autoimmune thyroid eye disease (Okrojek
et al.,2009), Sjögren’s Syndrome (Tomosugi et al., 2005), and patients under
anti-glaucoma medications (Wong et al.,2011). The
constituents of human tear fluid are comparatively less complex than plasma and
urine but how many proteins in tear fluid may appear during any pathogenic
conditions remains unclear.

Proteomic classification of human serum
for documentation of disease-specific biomarkers promises to be a commanding
prognostic means for detection of the onset, development and progression of
human diseases (Anderson et al.,2002) Serum offers a rich sample for diagnosis
since it has because of the condition specific expression and release of
proteomic biomarkers into the bloodstream in response to specific physiological
circumstances. The significant portion of serum is constituted by albumin
approximately 10 or more. So, before going for any proteomic analysis the crude
serum sample must be separated from the scarcest of proteins. In addition, numerous
serum proteins are alike in molecular weight and isoelectric point, making
protein separation problematic. Therefore, condition specific biomarkers for any
disease, that may be present at very minute concentrations in serum, could be
masked by more copious proteins having identical biophysical characteristics
(Hanash et al., 2003). Albumin constitutes approximately 55% to 75% of the
total protein in serum content. Even after albumin removal, serum remains a
complex protein mixture of five other high-abundant proteins IgG, IgA, haptoglobin,
transferrin and antitrypsin. Together, these six most abundant proteins
constitute over 85% of the total human serum proteome (Zhang et al.,2003).
Generally, Cibracon Blue (Gianazza et al.,1982) and protein A/G chromatography
techniques (Hage et al.,1999) have been used for depletion of albumin and the immunoglobulins.
However, an increasing number of procedures for the elimination of other
high-abundant serum proteins are becoming commercially available, making serum
proteomic analysis a more routine laboratory procedure (Zhang et al.,2003).
Albumin is a carrier/transport protein that binds other important entities in
blood (Burtis et al., 2001); and consequently, the removal of albumin from a serum
sample could also remove other physiologically important species. Following
major fraction of protein depletion, Coomassie blue stained 2-dimensional electrophoresis
(2-DE) gels reveal less number of protein spots compared to crude serum. Silver
staining of the protein-depleted sample is generally performed to reveal more
spots since it is sensitive to even very low concentration of protein. Chan and
coworkers (Wang et al., 2003) employed an affinity spin tube filter technique
to eliminate albumin and IgG to enrich the visible low-abundant cancer
biomarkers in serum. Steel and co-workers (Steel et al.,2003) employed an immuno-affinity
resin to eliminate albumin and IgG from healthy human serum samples to get a
better resolution for proteomic analysis. These examples relied on traditional
2-DE, and while 2-DE is limited in the ability to detect low abundant proteins
and it remains a significant method for the separating a complex mixture of
proteins (Pieper et al.,2003). Notably, the introduction of fluorescent dyes
for 2-DE analysis (Patton et al.,2000, Patton et al.,2001) has offered many
improvements over conventional silver staining and Coomassie blue staining
techniques. For example, fluorescent stains like SYPRO ruby, Deep purple, and
Lightning Fast (Mackintosh et al.,2003) which are particularly used for post-electrophoretic
staining. Which bind non-covalently and deliver a broader dynamic range of protein
detection. 2-D differential gel electrophoresis (DIGE) has been developed for
multiplex proteomic analysis based on the spectrally resolvable fluorescent dyes
Cy2, Cy3, and Cy5.15 The Cy dyes covalently attach to proteins via lysine
residues before electrophoresis. Three proteomic samples can be electrophoresed
together on the same gel lessening the complications of gel-to-gel evaluations.
This multiplex capability improves the consistency of comparative tests by
increasing the statistical importance of variable expression. In addition, the
Cy2 dye can be used to label a pooled, internal standard permitting more reliable
and accurate gel-to-gel evaluations in larger sample sets.15 There have been several
examples of the application of 2-D DIGE method to distinguish proteomic
differences (Kernec et al.,2001, Zhou et al.,2002, Yan et al., 2002, Ruepp et
al., 2002, Lee et al., 2003, Hu et al., 2003, Van den Bergh et al., 2003, Alaban
et al., 2003). In the study presented here, 2-D gel electrophoresis was
utilized to characterize human serum after removal of two abundant serum
proteins. The serum, before and after depletion of high-abundant proteins, was
analyzed by SDS-PAGE as a preliminary screen for protein removal efficacy.
Finally, 2-D gel electrophoresis was used to examine serum, after successful
removal of high-abundant proteins, defining over 255 protein spots. This result
represents a significant improvement in proteomic characterization of human

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In this study, we also reported
the human tear and serum proteome of normal (NOM) healthy, diabetic (DM) and
diabetic retinopathy (DR) affected individuals for differential proteomic
analysis. This was achieved using 2D gel electrophoresis followed by ESI-MS coupled
to a high-speed Triplet ToF 5000 mass spectrometer.

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