Detection of Cellular Sialic Acid Content Using Nitrobenzoxadiazole Carbonyl-Reactive Chromophores

The selective ligation of hydrazine and amino-oxy compounds with carbonyls has gained popularity as a detection strategy with the recognition of aniline catalysis as a way to accelerate the labeling reaction in water. Aldehydes are a convenient functional group choice since there are few native aldehydes found at the cell surface. Aldehydes can be selectively introduced into sialic acid containing glycoproteins by treatment with dilute sodium periodate. Thus, the combination of periodate oxidation with aniline-catalyzed ligation (PAL) has become a viable method for detection of glycoconjugates on live cells. Herein we examine two fluorescent nitrobenzoxadiazole dyes for labeling of glycoproteins and cell surface glycoconjugates. We introduce a novel 4-aminooxy-7-nitro-benz-[2,1,3-<i>d</i>]-oxadiazole (NBDAO) (<b>5</b>) fluorophore, and offer a comparison to commercial dyes including the known 4-hydrazino-7-nitro-benz-[2,1,3-<i>d</i>]-oxadiazole (NBDH) (<b>2</b>) and Bodipy FL hydrazide. We confirm specificity for sialic acid moieties and that both dyes are suitable for in vitro and in vivo labeling studies using PAL and fluorescence spectroscopy. The dyes examined here are attractive labeling agents for microscopy, as they can be excited by a 488 nm laser line and can be made in a few synthetic steps. These carbonyl-reactive chromophores provide a one step alternative to avidin–biotin labeling strategies and simplify the detection of sialic acid in cells and glycoproteins

Observed variation in the diffusion coefficient of LFA-1 in single particle tracking trajectories, with proposed mechanisms.

<p>Observed variation in the diffusion coefficient of LFA-1 in single particle tracking trajectories, with proposed mechanisms.</p

Model selection and proportion of time spent in the immobile state.

<p>Diffusion coefficient units are nm² s⁻¹ with standard error based on the number of trajectories.</p><p>¹ Model selection between approximate one-state and two-state diffusion models with measurement noise, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140759#sec002" target="_blank">Methods</a>, with trajectories with no strong model preference (−3 < log_<i>e</i><i>B</i>_{1<i>D</i>, 2<i>D</i>} < 3) removed.</p><p>² Fast switching trajectories (<math><mrow><msub><mi>p</mi><mo>^</mo><mn>01</mn></msub><mo>></mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math> or <math><mrow><msub><mi>p</mi><mo>^</mo><mn>10</mn></msub><mo>></mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math>) also removed.</p><p>³ Defined as log_<i>e</i><i>D</i> < 8 or log_<i>e</i><i>D</i>₁ < 8, for mean posterior parameters <i>D</i>, <i>D</i>₁ nm² s⁻¹ from one-state and two-state diffusion models with measurement noise.</p><p>⁴ Over all trajectories with either one-state or two-state model preference, with fast switching trajectories removed (i.e. Table notes 1 and 2 apply). For one-state preference, the proportion in the immobile state is 0 if log_<i>e</i><i>D</i> > 8, 1 if log_<i>e</i><i>D</i> > 8. For two-state preference, the proportion is 0 if log_<i>e</i><i>D</i>₁ > 8, and if log_<i>e</i><i>D</i>₁ < 8 the proportion of time that was spent in the <i>z</i> = 1 state (diffusion with <i>D</i> = <i>D</i>₁), i.e. <math><mrow><mn>1</mn><mo>/</mo><mi>N</mi><msubsup><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>N</mi></msubsup><mi>π</mi><mrow><mo>(</mo><msub><mi>z</mi><mi>i</mi></msub><mo>|</mo><mi>X</mi><mo>)</mo></mrow></mrow></math>.</p><p>⁵ Over all posterior samples, for trajectories with two-state model preference, with fast switching trajectories removed.</p><p>⁶ Over all posterior samples, for trajectories with one-state model preference, restricted to log_<i>e</i><i>D</i> > 8.</p><p>⁷ Over all posterior samples, for trajectories with one-state model preference, restricted to log_<i>e</i><i>D</i> < 8.</p><p>Model selection and proportion of time spent in the immobile state.</p

Linear drifts in LFA-1 trajectories categorised as immobile (log_<i>e</i><i>D</i> < 8 in the one-state model).

<p>(A) Vertical displacements for two example trajectories. Blue line: DMSO treatment, <math><mrow><msub><mi>v</mi><mo>¯</mo><mi>y</mi></msub><mo>=</mo><mn>117</mn>nms<mrow><mo>−</mo><mn>1</mn></mrow></mrow></math>; red line: PMA treatment, <math><mrow><msub><mi>v</mi><mo>¯</mo><mi>y</mi></msub><mo>=</mo><mn>110</mn>nms<mrow><mo>−</mo><mn>1</mn></mrow></mrow></math>. (B-C) Displacements for a trajectory (PMA treatment) with a switch in drift direction. Estimated velocities: <math><mrow><msub><mi>v</mi><mo>¯</mo><mi>x</mi></msub><mo>=</mo><mn>64</mn>nms<mrow><mo>−</mo><mn>1</mn></mrow></mrow></math>, <math><mrow><msub><mi>v</mi><mo>¯</mo><mi>y</mi></msub><mo>=</mo><mn>80</mn>nms<mrow><mo>−</mo><mn>1</mn></mrow></mrow></math>, (average between 0 s and 2.25 s), <math><mrow><msub><mi>v</mi><mo>¯</mo><mi>y</mi></msub><mo>=</mo><mo>-</mo><mn>79</mn>nms<mrow><mo>−</mo><mn>1</mn></mrow></mrow></math> (average between 2.25 s and 3.75 s), giving an average speed of 102 nm s⁻¹.</p

Fit of a two-state diffusion model without measurement noise to three stationary latex bead trajectories.

<p>MCMC output from chains of 20000 MCMC steps with a 10000 step burn-in. (A-C) Inference of the hidden state <b>z</b> shown as the probability of being in the low diffusion state. (D-F) Posterior distributions for the two diffusion coefficients: <i>D</i>₀ (red) and <i>D</i>₁ (blue). See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140759#sec002" target="_blank">Methods</a> for priors and initial conditions.</p

Dependences of parameter estimates from two-state diffusion model.

<p>(A-D) Scatter plots of posterior means for the two-state model with measurement noise, for trajectories where the approximate two-state diffusion model was preferred (fast switching, <math><mrow><msub><mi>p</mi><mo>^</mo><mn>01</mn></msub><mo>></mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math> or <math><mrow><msub><mi>p</mi><mo>^</mo><mn>10</mn></msub><mo>></mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math>, trajectories removed). Treatments: DMSO, blue asterisks; Cyto D, red crosses; PMA, black circles; PMA+Cal-I, green triangles. In panel (A) the black solid line is a linear fit with two outlier trajectories removed, <i>D</i>₁ = <i>aD</i>₀ + <i>b</i>, <i>a</i> = 0.68, <i>b</i> = −1.5 × 10⁴ nm² s⁻¹; black dashed line is the double iterate, <i>D</i>₁ = <i>a</i>(<i>aD</i>₀ + <i>b</i>) + <i>b</i>.</p

Posterior estimates of diffusion coefficients for single LFA-1 trajectories.

<p>(A-D) Pooled posterior samples of log_<i>e</i><i>D</i>₀ and log_<i>e</i><i>D</i>₁ for trajectories preferring the two-state diffusion model (fast switching, <math><mrow><msub><mi>p</mi><mo>^</mo><mn>01</mn></msub><mo>></mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math> or <math><mrow><msub><mi>p</mi><mo>^</mo><mn>10</mn></msub><mo>></mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math>, trajectories removed). The posterior means for log_<i>e</i><i>D</i>₀ (red squares) and log_<i>e</i><i>D</i>₁ (green triangles), are also shown. Black line indicates value of <i>σ</i>²/2Δ<i>t</i>. Dashed line indicates threshold used to categorise immobile and mobile diffusion states. Treatments: (A) DMSO, two-state model preferred for 13 trajectories; (B) Cyto D, 3 trajectories; (C) PMA, 8 trajectories; (D) PMA+Cal-I, 6 trajectories. (E) Pooled log_<i>e</i><i>D</i> estimates and posterior means (blue circles) over all treatments, for trajectories where one-state diffusion model was preferred (132 trajectories).</p

Model selection for one-state and two-state diffusion models on simulated stationary beads and stationary latex bead trajectories.

<p>Blue bars: Bayes factors from model selection on simulated stationary beads (<i>n</i> = 240) with added Gaussian noise (<i>σ</i>² = 41.09nm²). Single data points on axis: Bayes factors from model selection on stationary latex bead trajectories, both without (red asterisks) and with (green circles, <i>σ</i>² = 41.09nm²) measurement noise incorporated into the inference algorithm. Priors, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140759#sec002" target="_blank">Methods</a>.</p

Comparison of parameter estimates for exact and approximate two-state diffusion models with measurement noise.

<p>(A-D) Scatter plots of two-state parameter estimates for exact model against approximate model, for 30 trajectories preferring the approximate two-state model (fast-switching, <math><mrow><msub><mi>p</mi><mo>^</mo><mn>01</mn></msub><mo>></mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math> or <math><mrow><msub><mi>p</mi><mo>^</mo><mn>10</mn></msub><mo>></mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math> in the exact model, trajectories removed). Line of equality is shown as dashed. Treatments: DMSO (blue asterisks), Cyto D (red squares), PMA (black circles), PMA+Cal-I (green triangles).</p

Pooled posterior distribution of diffusion coefficients for single LFA-1 trajectories.

<p>(A) Pooled posterior samples of <i>D</i> for trajectories where one-state diffusion model was preferred, restricted to log_<i>e</i><i>D</i> > 8 (99 trajectories). The posterior distribution from a single trajectory (black line, DMSO treatment) is also plotted, normalised to equal height. (B) Pooled posterior samples of <i>D</i>₀ and <i>D</i>₁ for trajectories where two-state diffusion model was preferred, restricted to log_<i>e</i><i>D</i>₁ > 8 (29 trajectories), with fast switching (<math><mrow><msub><mi>p</mi><mo>^</mo><mn>01</mn></msub><mo>></mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math> or <math><mrow><msub><mi>p</mi><mo>^</mo><mn>10</mn></msub><mo>></mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math>) trajectories removed. One data point with <i>D</i>₀ > 2 × 10⁵ nm² s⁻¹ not shown.</p