S-Nitrosoglutathione Alters Fibrin Architecture domain towards the E domain [8,48,49]. Thrombin catalyzes the conversion of fibrinogen to fibrin by cleaving fibrinopeptides A,B from the central E domain. Fibrin monomers self assemble, via complementary E and D domain interactions, forming double stranded half-staggered protofibrils, that branch, elongate, and laterally associate, via released C-terminal a-domains, to form fibrin fibers that constitute the fibrin network [48,49]. S- nitrosglutathione (GSNO), a low molecular weight endogenous s-nitrosothiol present in blood, targets both fibrinogen altering its secondary structure [29] and the exposed thiol group (R-SH) on factor XIIIa [31] which stabilizes fibrin networks by cross-linking fibrin, panel B. GSNO has no effect on thrombin activity [29]. (solid arrows indicate thrombin targets, while dashed lines indicate GSNO targets.) Clotting conditions control fibrin clot architecture [8] between extreme forms of a fine (thin) fiber and dense network or a thick fiber and coarse (sparse) network, panel B [14]. Panel C shows multiphoton images of fibrin network structural elements, including fibrin fibers (i), fiber branch junctions (ii), crossing fibers (iii), fibrin clusters (iv) and fibrin agglomerates (v). Fibrinogen schematic adapted from Undas et al. [50] Scale bars are 700 nm. doi:10.1371/journal.pone.0043660.g001 this location, see Figure S1 for description of fiber measurements. Having established fiber location, calculated fiber density (fibers/ 100 um) and determined fiber diameter (nm), the user then interactively assessed each identified fiber in the image for any associated trifunctional branch junctions [6,7] (xbranching) or crossing fibers (xfibers), using contour maps and surface plots at local regions of interest. Note, crossing fibers (xfibers) are distinguished from trifunctional junctions (xbranches) by local regions of high fluorescence intensity indicating that two or more fibers are crossing or in contact at a single point in space, as shown in Figure 1, panels Cii and Ciii. Figure 1 summarizes fibrin fiber formation, known GSNO coagulation targets (fibrinogen and FXIIIa) and the fibrin network structural elements evaluated in this study. At the end of image analysis, the computer software generated a clot ‘‘read out’’ image listing key fibrin parameters. including nfibers, fiber density (fibers/100 um), fiber intensity (au), fiber diameter (nm), clot void area (%), fiber branching (xbranching/fiber), crossing fibers (xfibers/fiber), fibrin cluster (FbgCluster/mm2) and fibrin agglom- erate (AG/mm2) densities. The program also assessed the distribution of fiber diameters and plotted fiber diameter against fiber intensity, Figure S2, to test the theoretical assumption that fiber diameter was directly related to fiber intensity [9]. Statistics P values ,0.05 were considered to be statistically significant. Linear regression was used to evaluate the correlation between fibrin density and fiber diameter. One-way ANOVA was used to test for GSNO dependent differences in fibrin network structural parameters and Dunn’s test was used to test for multiple comparisons within a group. (Sigma Stat 3.5, Systat Software, Inc. Richmond, Ca). Normality plot and maximum likelihood estimation (MLE) MATLAB functions (MathWorks, Natick, MA) were used to statistically analyze and fit diameter data, respec- tively. Results GSNO Effects Fiber Density and Fiber Diameter The first step in computer analysis of fibrin clot networks was the identification of fibrin fibers. Computer analysis was found to have a 98.9% positive predictive value (PPV), where the PPV (%) = true positive fiber/(true positive fiber + false positive fiber) *100. Fibers within the fibrin network show a random orientation and we exploited this geometry by horizontally sampling the clot at regular intervals using LN test lines. The corresponding intensity profiles, shown in Figure S1 were then used to identify and number individual fibers. Their respective positions were then mapped back into the original clot image and displayed in fibrin clot ‘‘read out’’ images, as shown in Figure 2, panels A,B,C,G. When human platelet-poor plasma was incubated with increasing concentrations of GSNO, we found fiber density remained relatively unchanged (between 26.462.5 and 29.962.5 fibers/ 100 mm) over an order of magnitude (0.01–0.1 mM GSNO), followed by a sharp 53.2% decrease in fiber density as GSNO increased to 2.5 mM, and subsequently to zero, as fibrin formation was completely inhibited, Figure 2D. The next step in clot analysis was to measure the diameter of all fibers previously identified in the density analysis. Diameter data was then automatically plotted as a histogram, subjected to a normality statistical test and fit to either a normal or bimodal distribution, as shown in Figure S2. We found that as GSNO concentration increased, mean fiber diameter also increased from 677.4631.6 nm to a maximum of 82268.6 nm at 1 mM GSNO, followed by a gradual decrease to 754.3629.8 nm at 2.5 mM GSNO before fibrin fiber formation was completely inhibited, Figure 2E. Visual inspection of the latter clot images revealed the decrease in mean diameter was associated with the presence of abnormal fibrin cluster structures which had a number of thin fibers protruding from their dense core, Figure 2C. Given the inverse relationship between fiber density and fiber diameter, that is as fiber density decreases fiber diameter increases, plotting fiber density against fiber diameter should yield a straight line. However, fibers formed at 2.5 mM GSNO did not fit on a best fit line between the density-diameter values at lower GSNO concentrations, Figure 2F. Thus the expected fiber density- diameter relationship had been altered by high dose GSNO. We also calculated the fiber thin-thick ratio (TTR) based on the finding that fiber diameter was not normally distributed in control clots (Figure S2). The diameter data was fit to a bimodal distribution using the maximum likelihood estimation (MLE) method and the local minimum found to be 761.9 nm. This value was considered to be the threshold between thin and thick fibers. This was consistent with the report that plasma fibrin networks had a bimodal distribution of fiber thicknesses [35]. We found that fiber TTR decreased 74% (from 2.760.9 at 0.01 mM GSNO to 0.760.1) indicating fibers became thicker as GSNO concentration increased to 1 mM, before gradually becoming thinner again, TTR increased 85% (from 0.760.1 1.360.2) as GSNO increased to 2.5 mM. Taken together, as GSNO concentration increased, human fibrin fiber density decreased while fiber diameter increased to a maximum thickness before gradually decreasing as fibrin polymerization was inhibited. The change in fiber diameter was reflected by changes in the thin-to-thick fiber ratio and the appearance of abnormal fibrinogen cluster structural elements. These GSNO mediated changes in fiber diameter may be significant in vivo, as fiber diameter is a determinant of fibrinolysis, [36] suggesting that GSNO may function at an optimal concentration. Clot Void Volume is Relatively Resistant to Low Concentrations of GSNO Fibrin network images contain spatial information that can be used to calculate the clot void area in a 2D image, and clot void volume over a stack of 2D images. Figure 3, panel A shows a series of control and GSNO binarized clot images, taken at 40,50,60 micron depths, and their respective projected images, for control (Figure 3C) and 2.5 mM GSNO (Figure 3B), respectively. Visual PLOS ONE | www.plosone.org 4 August 2012 | Volume 7 | Issue 8 | e43660