In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI.

Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency, ω $$ \omega $$ , in addition to the diffusion tensor, D $$ \mathbf{D} $$ , and relaxation, R 1 $$ {R}_1 $$ , R 2 $$ {R}_2 $$ , correlations. A D ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their D ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.

Nonparametric distributions of tensor-valued Lorentzian diffusion spectra for model-free data inversion in multidimensional diffusion MRI.

Magnetic resonance imaging (MRI) is the method of choice for noninvasive studies of micrometer-scale structures in biological tissues via their effects on the time- and frequency-dependent (restricted) and anisotropic self-diffusion of water. While new designs of time-dependent magnetic field gradient waveforms have enabled disambiguation between different aspects of translational motion that are convolved in traditional MRI methods relying on single pairs of field gradient pulses, data analysis for complex heterogeneous materials remains a challenge. Here, we propose and demonstrate nonparametric distributions of tensor-valued Lorentzian diffusion spectra, or "D(ω) distributions," as a general representation with sufficient flexibility to describe the MRI signal response from a wide range of model systems and biological tissues investigated with modulated gradient waveforms separating and correlating the effects of restricted and anisotropic diffusion.

Quantifying H&E staining results, grading and predicting IDH mutation status of gliomas using hybrid multi-dimensional MRI.

OBJECTIVE: To assess the performance of hybrid multi-dimensional magnetic resonance imaging (HM-MRI) in quantifying hematoxylin and eosin (H&E) staining results, grading and predicting isocitrate dehydrogenase (IDH) mutation status of gliomas. MATERIALS AND METHODS: Included were 71 glioma patients (mean age, 50.17 ± 13.38 years; 35 men). HM-MRI images were collected at five different echo times (80-200 ms) with seven b-values (0-3000 s/mm2). A modified three-compartment model with very-slow, slow and fast diffusion components was applied to calculate HM-MRI metrics, including fractions, diffusion coefficients and T2 values of each component. Pearson correlation analysis was performed between HM-MRI derived fractions and H&E staining derived percentages. HM-MRI metrics were compared between high-grade and low-grade gliomas, and between IDH-wild and IDH-mutant gliomas. Using receiver operational characteristic (ROC) analysis, the diagnostic performance of HM-MRI in grading and genotyping was compared with mono-exponential models. RESULTS: HM-MRI metrics FDvery-slow and FDslow demonstrated a significant correlation with the H&E staining results (p < .05). Besides, FDvery-slow showed the highest area under ROC curve (AUC = 0.854) for grading, while Dslow showed the highest AUC (0.845) for genotyping. Furthermore, a combination of HM-MRI metrics FDvery-slow and T2Dslow improved the diagnostic performance for grading (AUC = 0.876). DISCUSSION: HM-MRI can aid in non-invasive diagnosis of gliomas.

Cryogenic probe technology enables multidimensional solid-state NMR of the stratum corneum without isotope labeling.

Solid-state NMR has great potential for investigating molecular structure, dynamics, and organization of the stratum corneum, the outer 10-20 µm of the skin, but is hampered by the unfeasibility of isotope labelling as generally required to reach sufficient signal-to-noise ratio for the more informative multidimensional NMR techniques. In this preliminary study of pig stratum corneum at 35 °C and water-free conditions, we demonstrate that cryogenic probe technology offers sufficient signal boost to observe previously undetectable minor resonances that can be uniquely assigned to fluid cholesterol, ceramides, and triacylglycerols, as well as enables 1H-1H spin diffusion monitored by 2D 1H-13C HETCOR to estimate 1-100 nm distances between specific atomic sites on proteins and lipids. The new capabilities open up for future multidimensional solid-state NMR studies to answer long-standing questions about partitioning of additives, such as pharmaceutically active substances, between solid and liquid domains within the protein and lipid phases in the stratum corneum and the lipids of the sebum.

Solubility of Foreign Molecules in Stratum Corneum Brick and Mortar Structure.

The barrier function of the skin is mainly assured by its outermost layer, stratum corneum (SC). One key aspect in predicting dermal drug delivery and in safety assessment of skin exposure to chemicals is the need to determine the amount of chemical that is taken up into the SC. We here present a strategy that allows for direct measures of the amount of various solid chemicals that can be dissolved in the SC in any environmental relative humidity (RH). A main advantage of the presented method is that it distinguishes between molecules that are dissolved within the SC and molecules that are not dissolved but might be present at, for example, the skin surface. In addition, the method allows for studies of uptake of hydrophobic chemicals without the need to use organic solvents. The strategy relies on the differences in the molecular properties of the added molecules in the dissolved and the excess states, employing detection methods that act as a dynamic filter to spot only one of the fractions, either the dissolved molecules or the excess solid molecules. By measuring the solubility in SC and delipidized SC at the same RHs, the same method can be used to estimate the distribution of the added chemical between the extracellular lipids and corneocytes at different hydration conditions. The solubility in porcine SC is shown to vary with hydration, which has implications for the molecular uptake and transport across the skin. The findings highlight the importance of assessing the chemical uptake at hydration conditions relevant to the specific applications. The methodology presented in this study can also be generalized to study the solubility and partitioning of chemicals in other heterogeneous materials with complex composition and structure.

Molecular Mobility in Keratin-Rich Materials Monitored by Nuclear Magnetic Resonance: A Tool for the Evaluation of Structure-Giving Properties.

Keratins are structural proteins that are abundant in human skin, nails, and hair, where they provide mechanical strength. In the present study, we investigate the molecular mobilities and structures of three keratin-rich materials with clearly different mechanical properties: nails, stratum corneum (upper layer of epidermis), and keratinocytes (from lower layer of epidermis). We use solid-state NMR on natural-abundance 13C to characterize small changes in molecular dynamics in these biological materials with close to atomistic resolution. One strong advantage of this method is that it detects small fractions of mobile components in a molecularly complex material while it simultaneously gives information on the rigid components in the very same sample. The molecular mobility can be linked to mechanical material properties in different conditions, including hydration or exposure to osmolytes or organic solvents. Importantly, the study revealed that the response to both hydration and addition of urea is clearly different for the nail keratin compared to the stratum corneum keratin. The comparative examination of these materials may provide a better understanding of skin diseases originating from keratin malfunction and contributes to the design and development of new materials.

Molecular dynamics simulations and solid-state nuclear magnetic resonance spectroscopy measurements of C-H bond order parameters and effective correlation times in a POPC-GM3 bilayer.

Glycolipids such as gangliosides affect the properties of lipid membranes and in extension the interactions between membranes and other biomolecules like proteins. To better understand how the properties of individual lipid molecules can contribute to shape the functional aspects of a membrane, the spatial restriction and dynamics of C-H bond segments can be measured using nuclear magnetic resonance (NMR) spectroscopy. We combine solid-state NMR spectroscopy with all-atom molecular dynamics (MD) simulations to investigate how ganglioside GM3 affects the bilayer structure and dynamics of C-H bond segments. These two methods yield reorientational correlation functions, molecular profiles of C-H bond order parameters |SCH| and effective correlation times τe, which we compare for lipids in POPC bilayers with and without 30 mol% GM3. Our results revealed that all C-H segments of POPC reorient slower in the presence of GM3 and that the defining features of the GM3-POPC bilayer lie in the GM3 headgroup; it gives the bilayer an extended headgroup layer with high order (|SCH| up to 0.3-0.4) and slow dynamics (τe up to 100 ns), a character that may be mechanistically important in ganglioside interactions with other biomolecules.

In situ 13 C solid-state polarization transfer NMR to follow starch transformations in food.

Convenience food products tend to alter their quality and texture while stored. Texture-giving food components are often starch-rich ingredients, such as pasta or rice. Starch transforms depending on time, temperature and water content, which alters the properties of products. Monitoring these transformations, which are associated with a change in mobility of the starch chain segments, could optimize the quality of food products containing multiple ingredients. In order to do so, we applied a simple and efficient in situ 13 C solid-state magic angle spinning (MAS) NMR approach, based on two different polarization transfer schemes, cross polarization (CP) and insensitive nuclei enhanced by polarization transfer (INEPT). The efficiency of the CP and INEPT transfer depends strongly on the mobility of chain segments-the time scale of reorientation of the CH-bond and the order parameter. Rigid crystalline or amorphous starch chains give rise to CP peaks, whereas mobile gelatinized starch chains appear as INEPT peaks. Comparing 13 C solid-state MAS NMR experiments based on CP and INEPT allows insight into the progress of gelatinization, and other starch transformations, by reporting on both rigid and mobile starch chains simultaneously with atomic resolution by the 13 C chemical shift. In conjunction with 1 H solid-state MAS NMR, complementary information about other food components present at low concentration, such as lipids and protein, can be obtained. We demonstrate our approach on starch-based products and commercial pasta as a function of temperature and storage.

Nonparametric D-R1-R2 distribution MRI of the living human brain.

Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R1 and D-R2 correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R2 or R1 in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R1-R2 correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R1-R2 correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R1-R2 distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R1-, and R2-weightings based on the rich information contained in the D-R1-R2 distributions.

Computing and visualising intra-voxel orientation-specific relaxation-diffusion features in the human brain.

Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation-diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation-diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.

Toward nonparametric diffusion- T1 characterization of crossing fibers in the human brain.

PURPOSE: To estimate T1 for each distinct fiber population within voxels containing multiple brain tissue types. METHODS: A diffusion- T1 correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P(D,R1) of diffusion tensors and longitudinal relaxation rates R1=1/T1 . Orientation distribution functions (ODFs) of the highly anisotropic components of P(D,R1) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles. RESULTS: Parameter maps corresponding to P(D,R1) 's statistical descriptors were obtained, exhibiting the expected R1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R1 between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup. CONCLUSIONS: Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- T1 features, thereby showing potential for characterizing developmental or pathological changes in T1 within a given fiber bundle, and for investigating interbundle T1 differences.

Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging.

OBJECTIVE: To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas. MATERIALS AND METHODS: Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), total mean kurtosis (MKT), MKA/MKT, and microscopic fractional anisotropy (µFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG) and 17 low-grade gliomas (LGG). Tumor diffusion metrics were compared between HGG and LGG, among grades, and between wild and mutated IDH types using appropriate tests according to normality assessment results. Receiver operating characteristic and Spearman correlation analysis were also used for statistical evaluations. RESULTS: FA, MD, MKA, MKI, MKT, µFA, and MKA/MKT differed between HGG and LGG (FA: p = 0.047; MD: p = 0.037, others p < 0.001), and among glioma grade II, III, and IV (FA: p = 0.048; MD: p = 0.038, others p < 0.001). All diffusion metrics differed between wild-type and mutated IDH tumors (MKI: p = 0.003; others: p < 0.001). The metrics that best discriminated between HGG and LGGs and between wild-type and mutated IDH tumors were MKT and FA respectively (area under the curve 0.866 and 0.881). All diffusion metrics except FA showed significant correlation with Ki-67 LI, and MKI had the highest correlation coefficient (rs = 0.618). CONCLUSION: DIVIDE is a promising technique for glioma characterization and diagnosis. KEY POINTS: • DIVIDE metrics MKI is related to cell density heterogeneity while MKA and µFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MKI was larger than MKA which indicates predominant cell density heterogeneity in gliomas. • MKA and MKI increased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MKA in relation to the cell density heterogeneity metric MKI.

Solid and fluid segments within the same molecule of stratum corneum ceramide lipid.

The outer layer of the skin, stratum corneum (SC) is an efficient transport barrier and it tolerates mechanical deformation. At physiological conditions, the majority of SC lipids are solid, while the presence of a small amount of fluid lipids is considered crucial for SC barrier and material properties. Here we use solid-state and diffusion nuclear magnetic resonance to characterize the composition and molecular dynamics of the fluid lipid fraction in SC model lipids, focusing on the role of the essential SC lipid CER EOS, which is a ceramide esterified omega-hydroxy sphingosine linoleate with very long chain. We show that both rigid and mobile structures are present within the same CER EOS molecule, and that the linoleate segments undergo fast isotropic reorientation while exhibiting extraordinarily slow self-diffusion. The characterization of this unusual self-assembly in SC lipids provides deepened insight into the molecular arrangement in the SC extracellular lipid matrix and the role of CER EOS linoleate in the healthy and diseased skin.

Accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms.

In biological tissues, typical MRI voxels comprise multiple microscopic environments, the local organization of which can be captured by microscopic diffusion tensors. The measured diffusion MRI signal can, therefore, be written as the multidimensional Laplace transform of an intravoxel diffusion tensor distribution (DTD). Tensor-valued diffusion encoding schemes have been designed to probe specific features of the DTD, and several algorithms have been introduced to invert such data and estimate statistical descriptors of the DTD, such as the mean diffusivity, the variance of isotropic diffusivities, and the mean squared diffusion anisotropy. However, the accuracy and precision of these estimations have not been assessed systematically and compared across methods. In this article, we perform and compare such estimations in silico for a one-dimensional Gamma fit, a generalized two-term cumulant approach, and two-dimensional and four-dimensional Monte-Carlo-based inversion techniques, using a clinically feasible tensor-valued acquisition scheme. In particular, we compare their performance at different signal-to-noise ratios (SNRs) for voxel contents varying in terms of the aforementioned statistical descriptors, orientational order, and fractions of isotropic and anisotropic components. We find that all inversion techniques share similar precision (except for a lower precision of the two-dimensional Monte Carlo inversion) but differ in terms of accuracy. While the Gamma fit exhibits infinite-SNR biases when the signal deviates strongly from monoexponentiality and is unaffected by orientational order, the generalized cumulant approach shows infinite-SNR biases when this deviation originates from the variance in isotropic diffusivities or from the low orientational order of anisotropic diffusion components. The two-dimensional Monte Carlo inversion shows remarkable accuracy in all systems studied, given that the acquisition scheme possesses enough directions to yield a rotationally invariant powder average. The four-dimensional Monte Carlo inversion presents no infinite-SNR bias, but suffers significantly from noise in the data, while preserving good contrast in most systems investigated.

Diffusion tensor distribution imaging of an in vivo mouse brain at ultrahigh magnetic field by spatiotemporal encoding.

Diffusion tensor distribution (DTD) imaging builds on principles from diffusion, solid-state and low-field NMR spectroscopies, to quantify the contents of heterogeneous voxels as nonparametric distributions, with tensor "size", "shape" and orientation having direct relations to corresponding microstructural properties of biological tissues. The approach requires the acquisition of multiple images as a function of the magnitude, shape and direction of the diffusion-encoding gradients, leading to long acquisition times unless fast image read-out techniques like EPI are employed. While in previous in vivo human brain studies performed at 3 T this proved a viable option, porting these measurements to very high magnetic fields and/or to heterogeneous organs induces B0 - and B1 -inhomogeneity artifacts that challenge the limits of EPI. To overcome such challenges, we demonstrate here that high spatial resolution DTD of mouse brain can be carried out at 15.2 T with a surface-cryoprobe, by relying on SPatiotemporal ENcoding (SPEN) imaging sequences. These new acquisition and data-processing protocols are demonstrated with measurements on in vivo mouse brain, and validated with synthetic phantoms designed to mimic the diffusion properties of white matter, gray matter and cerebrospinal fluid. While still in need of full extensions to 3D mappings and of scanning additional animals to extract more general physiological conclusions, this work represents another step towards the model-free, noninvasive in vivo characterization of tissue microstructure and heterogeneity in animal models, at ≈0.1 mm resolutions.

Quantification of the amount of mobile components in intact stratum corneum with natural-abundance 13C solid-state NMR.

The outermost layer of the skin is the stratum corneum (SC), which is mainly comprised of solid proteins and lipids. Minor amounts of mobile proteins and lipids are crucial for the macroscopic properties of the SC, including softness, elasticity and barrier function. Still this minor number of mobile components are not well characterized in terms of structure or amount. Conventional quantitative direct polarization (Q-DP) 13C solid-state NMR gives signal amplitudes proportional to concentrations, but fails to quantify the SC mobile components because of spectral overlap with the overwhelming signals from the solids. Spectral editing with the INEPT scheme suppresses the signals from solids, but also modulates the amplitudes of the mobile components depending on their values of the transverse relaxation times T2, scalar couplings JCH, and number of covalently bound hydrogens nH. This study describes a quantitative INEPT (Q-INEPT) method relying on systematic variation of the INEPT timing variables to estimate T2, JCH, nH, and amplitude for each of the resolved resonances from the mobile components. Q-INEPT is validated with a series of model systems containing molecules with different hydrophobicity and dynamics. For selected systems where Q-DP is applicable, the results of Q-INEPT and Q-DP are similar with respect to the linearity and uncertainty of the obtained molar ratios. Utilizing a reference compound with known concentration, we quantify the concentrations of mobile lipids and proteins within the mainly solid SC. By melting all lipids at high temperature, we obtain the total lipid concentration. These Q-INEPT results are the first steps towards a quantitative understanding of the relations between mobile component concentrations and SC macroscopic properties.

Tracking solvents in the skin through atomically resolved measurements of molecular mobility in intact stratum corneum.

Solvents are commonly used in pharmaceutical and cosmetic formulations and sanitary products and cleansers. The uptake of solvent into the skin may change the molecular organization of skin lipids and proteins, which may in turn alter the protective skin barrier function. We herein examine the molecular effects of 10 different solvents on the outermost layer of skin, the stratum corneum (SC), using polarization transfer solid-state NMR on natural abundance 13C in intact SC. With this approach it is possible to characterize the molecular dynamics of solvent molecules when present inside intact SC and to simultaneously monitor the effects caused by the added solvent on SC lipids and protein components. All solvents investigated cause an increased fluidity of SC lipids, with the most prominent effects shown for the apolar hydrocarbon solvents and 2-propanol. However, no solvent other than water shows the ability to fluidize amino acids in the keratin filaments. The solvent molecules themselves show reduced molecular mobility when incorporated in the SC matrix. Changes in the molecular properties of the SC, and in particular alternation in the balance between solid and fluid SC components, may have significant influences on the macroscopic SC barrier properties as well as mechanical properties of the skin. Deepened understanding of molecular effects of foreign compounds in SC fluidity can therefore have strong impact on the development of skin products in pharmaceutical, cosmetic, and sanitary applications.

Diffusion tensor distribution imaging.

Conventional diffusion MRI yields voxel-averaged parameters that suffer from ambiguities for heterogeneous anisotropic materials such as brain tissue. Using principles from solid-state NMR spectroscopy, we have previously introduced the shape of the diffusion encoding tensor as a separate acquisition dimension that disentangles isotropic and anisotropic contributions to the observed diffusivities, thereby allowing for unconstrained data inversion into diffusion tensor distributions with "size," "shape," and orientation dimensions. Here we combine our recent non-parametric data inversion algorithm and data acquisition protocol with an imaging pulse sequence to demonstrate spatial mapping of diffusion tensor distributions using a previously developed composite phantom with multiple isotropic and anisotropic components. We propose a compact format for visualizing two-dimensional arrays of the distributions, new scalar parameters quantifying intra-voxel heterogeneity, and a binning procedure giving maps of all relevant parameters for each of the components resolved in the multidimensional distribution space.

Nanostructured Lipid-based Films for Substrate Mediated Applications in Biotechnology.

Amphiphilic in nature, lipids spontaneously self-assemble into a range of nanostructures in the presence of water. Among lipid self-assembled structures, liposomes and supported lipid bilayers have long held scientific interest for their main applications in drug delivery and plasma membrane models, respectively. In contrast, lipid-based multi-layered membranes on solid supports only recently begun drawing scientists' attention. New studies on lipid films show that the stacking of multiple bilayers on a solid support yields interestingly complex features to these systems. Namely, multiple layers exhibit cooperative structural and dynamic behavior. In addition, the materials enable compartmentalization, templating, and enhanced release of several molecules of interest. Importantly, supported lipid phases exhibit long-range periodic nano-scale order and orientation that is tunable in response to a changing environment. Herein, we summarize current and pertinent understanding of lipid-based film research focusing on how unique structural characteristics enable the emergence of new applications in biotechnology including label-free biosensors, macroscale drug delivery, and substrate-mediated gene delivery. Our very recent contributions to lipid-based films, focusing on the structural characterization at the meso, nano, and molecular-scale, using Small-Angle X-ray Scattering, Atomic Force Microscopy, Photothermal Induced Resonance, and Solid-State NMR will be also highlighted.

Effects of imaging gradients in sequences with varying longitudinal storage time-Case of diffusion exchange imaging.

PURPOSE: To illustrate the potential bias caused by imaging gradients in correlation MRI sequences using longitudinal magnetization storage (LS) and examine the case of filter exchange imaging (FEXI) yielding maps of the apparent exchange rate (AXR). METHODS: The effects of imaging gradients in FEXI were observed on yeast cells. To analyze the AXR bias, signal evolution was calculated by applying matrix exponential operators. RESULTS: A sharp threshold for the slice thickness was identified, below which the AXR is increasingly underestimated. The bias can be understood in terms of an extended low-pass diffusion filtering during the LS interval, which is more pronounced at lower exchange rates. For a total exchange rate constant larger than 1 s-1 , the AXR bias is expected to be negligible when slices thicker than 2.5 mm are used. CONCLUSION: In correlation experiments like FEXI, relying on LS with variable duration, imaging gradients may cause disrupting effects that cannot be easily mitigated and should be carefully considered for unbiased results. In typical clinical applications of FEXI, the imaging gradients are expected to cause a negligible AXR bias. However, the AXR bias may be significant in preclinical settings or whenever thin imaging slices are used. Magn Reson Med 79:2228-2235, 2018. © 2017 International Society for Magnetic Resonance in Medicine.