Flow or collective movement is a frequently observed phenomenon for many cellular components including the cytoskeletal proteins actin and myosin. To study protein flow in living cells, we and others have previously used spatiotemporal image correlation spectroscopy (STICS) analysis on fluorescence microscopy image time series. Yet, in cells, multiple protein flows often occur simultaneously on different scales resulting in superimposed fluorescence intensity fluctuations that are challenging to separate using STICS. Here, we exploited the characteristic that distinct protein flows often occur at different spatial scales present in the image series to disentangle superimposed protein flow dynamics. We employed a newly developed and an established spatial filtering algorithm to alternatively accentuate or attenuate local image intensity heterogeneity across different spatial scales. Subsequently, we analysed the spatially filtered time series with STICS, allowing the quantification of two distinct superimposed flows within the image time series. As a proof of principle of our analysis approach, we used simulated fluorescence intensity fluctuations as well as time series of nonmuscle myosin II in endothelial cells and actin-based podosomes in dendritic cells and revealed simultaneously occurring contiguous and noncontiguous flow dynamics in each of these systems. Altogether, this work extends the application of STICS for the quantification of multiple protein flow dynamics in complex biological systems including the actomyosin cytoskeleton.

Solid-state NMR spectroscopy (ssNMR) can provide details about the structure, host-guest/guest-guest interactions and dynamic behavior of materials at atomic length scales. A crucial use of ssNMR is for the characterization of zeolite catalysts that are extensively employed in industrial catalytic processes. This review aims to spotlight the recent advancements in ssNMR spectroscopy and its application to zeolite chemistry. We first review the current ssNMR methods and techniques that are relevant to characterize zeolite catalysts, including advanced multinuclear and multidimensional experiments, in situ NMR techniques and hyperpolarization methods. Of these, the methodology development on half-integer quadrupolar nuclei is emphasized, which represent about two-thirds of stable NMR-active nuclei and are widely present in catalytic materials. Subsequently, we introduce the recent progress in understanding zeolite chemistry with the aid of these ssNMR methods and techniques, with a specific focus on the investigation of zeolite framework structures, zeolite crystallization mechanisms, surface active/acidic sites, host-guest/guest-guest interactions, and catalytic reaction mechanisms.

Two-dimensional (2D) J-resolved spectroscopy provides valuable information on J-coupling constants for molecular structure analysis by resolving one-dimensional (1D) spectra. However, it is challenging to decipher the J-coupling connectivity in 2D J-resolved spectra because the J-coupling connectivity cannot be directly provided. In addition, 2D homonuclear correlation spectroscopy (COSY) can directly elucidate molecular structures by tracking the J-coupling connectivity between protons. However, this method is limited by the problem of spectral peak crowding and is only suitable for simple sample systems. To fully understand the intuitive coupling relationship and coupling constant information, we propose a three-dimensional (3D) COSY method called CTCOSY-JRES (Constant-Time COrrelation SpectroscopY and J-REsolved Spectroscopy) in this paper. By combining the J-resolved spectrum with the constant-time COSY technique, a doubly decoupled COSY spectrum can be provided while preserving the J-coupling constant along an additional dimension, ensuring high-resolution analysis of J-coupling connectivity and J-coupling information. Moreover, compression sensing and fold-over correction techniques are introduced to accelerate experimental acquisition. The CTCOSY-JRES method has been successfully validated in a variety of sample systems, including industrial, agricultural, and biopharmaceutical samples, revealing complex coupling interactions and providing deeper insights into the resolution of molecular structures.

Clinical antibodies are an important class of drugs for the treatment of both chronic and acute diseases. Their manufacturability is subject to evaluation to ensure product quality and efficacy. One critical quality attribute is deamidation, a non-enzymatic process that is observed to occur during thermal stress, at low or high pH, or a combination thereof. Deamidation may induce antibody instability and lead to aggregation, which may pose immunogenicity concerns. The introduction of a negative charge via deamidation may impact the desired therapeutic function (i) within the complementarity-determining region, potentially causing loss of efficacy; or (ii) within the fragment crystallizable region, limiting the effector function involving antibody-dependent cellular cytotoxicity. Here we describe a transformative solution that allows for a comparative assessment of deamidation and its impact on stability and aggregation. The innovative streamlined method evaluates the intact protein in its formulation conditions. This breakthrough platform technology is comprised of a quantum cascade laser microscope, a slide cell array that allows for flexibility in the design of experiments, and dedicated software. The enhanced spectral resolution is achieved using two-dimensional correlation, co-distribution, and two-trace two-dimensional correlation spectroscopies that reveal the molecular impact of deamidation. Eight re-engineered immunoglobulin G4 scaffold clinical antibodies under control and forced degradation conditions were evaluated for deamidation and aggregation. We determined the site of deamidation, the overall extent of deamidation, and where applicable, whether the deamidation event led to self-association or aggregation of the clinical antibody and the molecular events that led to the instability. The results were confirmed using orthogonal techniques for four of the samples.

Here we provide demonstration that fast fluorescence fluctuation spectroscopy is a fast and robust approach to extract information on the dynamics of molecules enclosed within subcellular nanostructures (e.g., organelles or vesicles) which are also moving in the complex cellular environment. In more detail, Raster Image Correlation Spectroscopy (RICS) performed at fast timescales (i.e., microseconds) reveals the fast motion of fluorescently labeled molecules within two exemplary dynamic subcellular nanostructures of biomedical interest, the lysosome and the insulin secretory granule (ISG). The measurement of molecular diffusion is then used to extract information on the average properties of subcellular nanostructures, such as macromolecular crowding or molecular aggregation. Concerning the lysosome, fast RICS on a fluorescent tracer allowed us to quantitatively assess the increase in organelle viscosity in the pathological condition of Krabbe disease. In the case of ISGs, fast RICS on two ISG-specific secreting peptides unveiled their differential aggregation propensity depending on intragranular concentration. Finally, a combination of fast RICS and feedback-based 3D orbital tracking was used to subtract the slow movement of subcellular nanostructures from the fast diffusion of molecules contained within them and independently validate the results. Results presented here not only demonstrate the acquired ability to address the dynamic behavior of molecules in moving, nanoscopic reference systems, but prove the relevance of this approach to advance our knowledge on cell function at the subcellular scale.

Coefficients for translational and rotational diffusion characterize the Brownian motion of particles. Emerging X-ray photon correlation spectroscopy (XPCS) experiments probe a broad range of length scales and time scales and are well-suited for investigation of Brownian motion. While methods for estimating the translational diffusion coefficients from XPCS are well-developed, there are no algorithms for measuring the rotational diffusion coefficients based on XPCS, even though the required raw data are accessible from such experiments. In this paper, we propose angular-temporal cross-correlation analysis of XPCS data and show that this information can be used to design a numerical algorithm (Multi-Tiered Estimation for Correlation Spectroscopy [MTECS]) for predicting the rotational diffusion coefficient utilizing the cross-correlation: This approach is applicable to other wavelengths beyond this regime. We verify the accuracy of this algorithmic approach across a range of simulated data.

Here, we present an augmented form of two-dimensional correlation spectroscopy, that integrates in a single format data from spectroscopic and multiple non-spectroscopic sources for analysis. The integration is affected by augmenting every spectrum in a hyperspectral data set with relevant non-spectroscopic data to permit two-dimensional correlation analysis(2D-COS) of the ensemble of augmented spectra. A k-means clustering is then applied to the results of the perturbation domain decomposition to determine which Raman peaks cluster with any of the non-spectroscopic data. We introduce and explain the method with the aid of synthetic spectra and synthetic non-spectroscopic data. We then demonstrate this approach with data using Raman spectra from human embryonic stem cell aggregates undergoing directed differentiation toward pancreatic endocrine cells and parallel bioassays of hormone mRNA expression and C-peptide levels in spent medium. These pancreatic endocrine cells generally contain insulin or glucagon. Insulin has disulfide bonds that produce Raman scattering near 513 cm-1, but no tryptophan. For insulin-positive cells, we found that the application of multisource correlation analysis revealed a high correlation between insulin mRNA and Raman scattering in the disulfide region. In contrast, glucagon has no disulfide bonds but does contain tryptophan. For glucagon-positive cells, we also observed a high correlation between glucagon mRNA and tryptophan Raman scattering (∼757 cm-1). We conclude with a discussion of methods to enhance spectral resolution and its effects on the performance of multisource correlation analysis.

Many interesting solid-state targets for biological research do not form crystalline structures; these materials include intrinsically disordered proteins, plant biopolymer composites, cell-wall polysaccharides, and soil organic matter. The absence of aligned repeating structural elements and atomic-level rigidity presents hurdles to achieving structural elucidation and obtaining functional insights. We describe strategies for adapting several solid-state NMR methods to determine the molecular structures and compositions of these amorphous biosolids. The main spectroscopic problems in studying amorphous structures by NMR are over/under-sampling of the spin signals and spectral complexity. These problems arise in part because amorphous biosolids typically contain a mix of rigid and mobile domains, making it difficult to select a single experiment or set of acquisition conditions that fairly represents all nuclear spins in a carbon-based organic sample. These issues can be addressed by running hybrid experiments, such as using direct excitation alongside cross polarization-based methods, to develop a more holistic picture of the macromolecular system. In situations of spectral crowding or overlap, the structural elucidation strategy can be further assisted by coupling 13C spins to nuclei such as 15N, filtering out portions of the spectrum, highlighting individual moieties of interest, and adding a second or third spectral dimension to an NMR experiment in order to spread out the resonances and link them pairwise through space or through bonds. We discuss practical aspects and illustrations from the recent literature for 1D experiments that use cross or direct polarization and both homo- and heteronuclear 2D and 3D solid-state NMR experiments.

We present single- and multiple-quantum correlation J-spectroscopy detected in zero (<1μG) magnetic field using a 87Rb vapor-cell magnetometer. At zero field the spectrum of ethanol appears as a mixture of 13C isotopomers, and correlation spectroscopy is useful in separating the two composite spectra. We also identify and observe the zero-field equivalent of a double-quantum transition in 13C2-acetic acid, and show that such transitions are of use in spectral assignment. Two-dimensional spectroscopy further improves the high resolution attained in zero-field NMR since selection rules on the coherence-transfer pathways allow for the separation of otherwise overlapping resonances into distinct cross-peaks.

A two-dimensional pulse sequence is introduced for correlating nuclear magnetic resonance anisotropic chemical shifts to a relaxation time (e.g., T1) in solids under static conditions. The sequence begins with a preparatory stage for measuring relaxation times, and is followed by a multiple pulse sequence for homonuclear dipolar decoupling. Data analysis involves the use of Fourier transform, followed by a one-dimensional inverse Laplace transform for each frequency index. Experimental results acquired on solid samples demonstrate the general approach, and additional variations involving heteronuclear decoupling and magic angle spinning are discussed.