Detection and Quantification of Hydrogen Peroxide in Aqueous Solutions Using Chemical Exchange Saturation Transfer

The development of new analytical methods to accurately quantify hydrogen peroxide is of great interest. In the current study, we developed a new magnetic resonance (MR) method for noninvasively quantifying hydrogen peroxide (H₂O₂) in aqueous solutions based on chemical exchange saturation transfer (CEST), an emerging MRI contrast mechanism. Our method can detect H₂O₂ by its specific CEST signal at ∼6.2 ppm downfield from water resonance, with more than 1000 times signal amplification compared to the direct NMR detection. To improve the accuracy of quantification, we comprehensively investigated the effects of sample properties on CEST detection, including pH, temperature, and relaxation times. To accelerate the NMR measurement, we implemented an ultrafast <i>Z</i>-spectroscopic (UFZ) CEST method to boost the acquisition speed to 2 s per CEST spectrum. To accurately quantify H₂O₂ in unknown samples, we also implemented a standard addition method, which eliminated the need for predetermined calibration curves. Our results clearly demonstrate that the presented CEST-based technique is a simple, noninvasive, quick, and accurate method for quantifying H₂O₂ in aqueous solutions

¹⁵N Heteronuclear Chemical Exchange Saturation Transfer MRI

A two-step heteronuclear enhancement approach was combined with chemical exchange saturation transfer (CEST) to magnify ¹⁵N MRI signal of molecules through indirect detection via water protons. Previous CEST studies have been limited to radiofrequency (rf) saturation transfer or excitation transfer employing protons. Here, the signal of ¹⁵N is detected indirectly through the water signal by first inverting selectively protons that are scalar-coupled to ¹⁵N in the urea molecule, followed by chemical exchange of the amide proton to bulk water. In addition to providing a small sensitivity enhancement, this approach can be used to monitor the exchange rates and thus the pH sensitivity of the participating ¹⁵N-bound protons

Metal Ion Sensing Using Ion Chemical Exchange Saturation Transfer ¹⁹F Magnetic Resonance Imaging

Although metal ions are involved in a myriad of biological processes, noninvasive detection of free metal ions in deep tissue remains a formidable challenge. We present an approach for specific sensing of the presence of Ca²⁺ in which the amplification strategy of chemical exchange saturation transfer (CEST) is combined with the broad range of chemical shifts found in ¹⁹F NMR spectroscopy to obtain magnetic resonance images of Ca²⁺. We exploited the chemical shift change (Δω) of ¹⁹F upon binding of Ca²⁺ to the 5,5′-difluoro derivative of 1,2-bis­(<i>o</i>-amino­phenoxy)­ethane-<i>N</i>,<i>N</i>,<i>N</i>′,<i>N</i>′-tetra­acetic acid (5F-BAPTA) by radiofrequency labeling at the Ca²⁺-bound ¹⁹F frequency and detection of the label transfer to the Ca²⁺-free ¹⁹F frequency. Through the substrate binding kinetics we were able to amplify the signal of Ca²⁺ onto free 5F-BAPTA and thus indirectly detect low Ca²⁺ concentrations with high sensitivity

RSNs with significant linear trends in RSN outcome measures.

<p>Intercept and slope of the estimated linear trend, as well as the slope’s F statistic and p-value in three RSN outcome measures, namely the (a) spatial similarity (eta-squared, η²), (b) temporal fluctuation magnitude, and (c) BNC, for each RSNs with significant linear trends are listed.</p><p>RSNs with significant linear trends in RSN outcome measures.</p

Aggregate spatial maps of the resting state networks (RSNs).

<p>Group independent component analysis (GICA) was used to estimate the RSNs and obtain the aggregate spatial maps. The spatial maps of each RSN are shown as subfigures, with representative sagittal, coronal, and axial views (left-to-right) overlaid on structural images within the Montreal Neurological Institute (MNI) template space; coordinates (in mm) for each view are indicated below each subfigure. (Aud: auditory, Smot: seonsorimotor, Vis: visual, DMN: default mode network, Attn: attention, Exec: executive, Sal: salience, Cb: cerebellar, ven: ventral, dor: dorsal, R: right, L: left).</p

RSNs with significant temporal structures.

<p>TOP Existence of significant (after correction for multiple comparisons) linear trends in three RSN outcome measures, namely the (a) spatial similarity (eta-squared, η²), (b) temporal signal fluctuation magnitude, and (c) BNC, are visualized using matrices. Red blocks indicate significant positive linear trend, blue blocks negative trend, and black boxes no significant trend. MIDDLE Existence of significant (after correction for multiple comparisons) annual periodicity in three RSN outcome measures. Red blocks indicate significant annual periodicity and black boxes no annual periodicity. BOTTOM AR orders of the estimated ARMA models for RSNs and RSN pairs are visualized for each outcome measures, where black box indicates no autocorrelation, red box AR order of 1, yellow box AR order of 2, and white box AR order of 3. Refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140134#pone.0140134.s006" target="_blank">S3 Table</a> for information on full ARMA model parameters.</p

Reproducibility of between-network connectivity (BNC) measurements.

<p>The combined BNC matrices show the degree of temporal synchrony between RSN pairs. Mean (a) and standard deviation (SD) (b) BNC values of the single- (below the main diagonal) and multi-participant (above the main diagonal) are shown. The diagonal elements were zeroed for display purposes. (c) Absolute value of the difference between the single- and the multi-participant BNC values. (d) Ten RSN pairs with the smallest (top) and the biggest (bottom) differences between single- and multi-participant mean BNC values. Mean BNC values from the single-subject dataset are overlaid as magenta circles on boxplots reporting on multi-participant data.</p

Weekly BNC measures of RSN pairs with the two largest and smallest variations in BNC measurements.

<p>Weekly BNC measures are plotted against the corresponding image acquisition weeks for the RSN pairs with the two largest (top) and two smallest (bottom) variations in BNC measurements, as measured by SD.</p

Reproducibility of RSN spatial maps.

<p>Spatial similarity of each session’s RSN spatial map to the corresponding group mean map, measured using eta-squared (η²), for single-subject (blue) and multi-participant (yellow) datasets, is visualized using violin plots. The first, second, and third quartiles of the data are represented within the violin plots as dotted lines.</p

RSN spatial maps for representative weekly sessions.

<p>RSN mean spatial maps (leftmost column), representative backreconstructed weekly single-session spatial maps (middle eight columns), and overlap maps (rightmost column) for the 14 RSNs. The degree of spatial similarity of each session’s spatial map to the corresponding mean map, as measured using eta-squared (η²), is indicated below the single-session maps.</p