Extraction of level density and gamma strength function from primary gamma spectra

We present a new iterative procedure to extract the level density and the gamma strength function from primary gamma spectra for energies close up to the neutron binding energy. The procedure is tested on simulated spectra and on data from the Yb-173(He-3,alpha)Yb-172 reaction.Comment: 23 pages including 1 table and 7 figure

Raman Spectroscopic Imaging for Quantification of Depth-Dependent and Local Heterogeneities in Native and Engineered Cartilage

Articular cartilage possesses a remarkable, mechanically-robust extracellular matrix (ECM) that is organized and distributed throughout the tissue to resist physiologic strains and provide low friction during articulation. The ability to characterize the make-up and distribution of the cartilage ECM is critical to both understand the process by which articular cartilage undergoes disease-related degeneration and to develop novel tissue repair strategies to restore tissue functionality. However, the ability to quantitatively measure the spatial distribution of cartilage ECM constituents throughout the tissue has remained a major challenge. In this experimental investigation, we assessed the analytical ability of Raman micro-spectroscopic imaging to semi-quantitatively measure the distribution of the major ECM constituents in cartilage tissues. Raman spectroscopic images were acquired of two distinct cartilage tissue types that possess large spatial ECM gradients throughout their depth: native articular cartilage explants and large engineered cartilage tissue constructs. Spectral acquisitions were processed via multivariate curve resolution to decompose the “fingerprint” range spectra (800–1800 cm−1) to the component spectra of GAG, collagen, and water, giving rise to the depth dependent concentration profile of each constituent throughout the tissues. These Raman spectroscopic acquired-profiles exhibited strong agreement with profiles independently acquired via direct biochemical assaying of spatial tissue sections. Further, we harness this spectroscopic technique to evaluate local heterogeneities through the depth of cartilage. This work represents a powerful analytical validation of the accuracy of Raman spectroscopic imaging measurements of the spatial distribution of biochemical components in a biological tissue and shows that it can be used as a valuable tool for quantitatively measuring the distribution and organization of ECM constituents in native and engineered cartilage tissue specimens

Raman spectroscopy reveals new insights into the zonal organization of native and tissue-engineered articular cartilage

Tissue architecture is intimately linked with its functions, and loss of tissue organization is often associated with pathologies. The intricate depth-dependent extracellular matrix (ECM) arrangement in articular cartilage is critical to its biomechanical functions. In this study, we developed a Raman spectroscopic imaging approach to gain new insight into the depth-dependent arrangement of native and tissue-engineered articular cartilage using bovine tissues and cells. Our results revealed previously unreported tissue complexity into at least six zones above the tidemark based on a principal component analysis and k-means clustering analysis of the distribution and orientation of the main ECM components. Correlation of nanoindentation and Raman spectroscopic data suggested that the biomechanics across the tissue depth are influenced by ECM microstructure rather than composition. Further, Raman spectroscopy together with multivariate analysis revealed changes in the collagen, glycosaminoglycan and water distributions in tissue-engineered constructs over time. These changes were assessed using simple metrics that promise to instruct efforts towards the regeneration of a broad range of tissues with native zonal complexity and functional performance

Molecular imaging of extracellular vesicles in vitro via Raman metabolic labelling

Extracellular vesicles (EVs) are biologically-derived nanovectors important for intercellular communication and trafficking. As such, EVs show great promise as disease biomarkers and therapeutic drug delivery vehicles. However, despite the rapidly growing interest in EVs, understanding of the biological mechanisms that govern their biogenesis, secretion, and uptake remains poor. Advances in this field have been hampered by both the complex biological origins of EVs, which make them difficult to isolate and identify, and a lack of suitable imaging techniques to properly study their diverse biological roles. Here, we present a new strategy for simultaneous quantitative in vitro imaging and molecular characterisation of EVs in 2D and 3D based on Raman spectroscopy and metabolic labelling. Deuterium, in the form of deuterium oxide (D2O), deuterated choline chloride (d-Chol), or deuterated D-glucose (d-Gluc), is metabolically incorporated into EVs through the growth of parent cells on medium containing one of these compounds. Isolated EVs are thus labelled with deuterium, which acts as a bio-orthogonal Raman-active tag for direct Raman identification of EVs when introduced to unlabelled cell cultures. Metabolic deuterium incorporation demonstrates no apparent adverse effects on EV secretion, marker expression, morphology, or global composition, indicating its capacity for minimally obstructive EV labelling. As such, our metabolic labelling strategy could provide integral insights into EV biocomposition and trafficking. This approach has the potential to enable a deeper understanding of many of the biological mechanisms underpinning EVs, with profound implications for the design of EVs as therapeutic delivery vectors and applications as disease biomarkers

Quantitative volumetric Raman imaging of three dimensional cell cultures

The ability to simultaneously image multiple biomolecules in biologically relevant three-dimensional (3D) cell culture environments would contribute greatly to the understanding of complex cellular mechanisms and cell–material interactions. Here, we present a computational framework for label-free quantitative volumetric Raman imaging (qVRI). We apply qVRI to a selection of biological systems: human pluripotent stem cells with their cardiac derivatives, monocytes and monocyte-derived macrophages in conventional cell culture systems and mesenchymal stem cells inside biomimetic hydrogels that supplied a 3D cell culture environment. We demonstrate visualization and quantification of fine details in cell shape, cytoplasm, nucleus, lipid bodies and cytoskeletal structures in 3D with unprecedented biomolecular specificity for vibrational microspectroscopy

High-throughput molecular imaging via deep-learning-enabled Raman spectroscopy.

Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2-4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40-90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine

Bioenergetic-active materials enhance tissue regeneration by modulating cellular metabolic state

Cellular bioenergetics (CBE) plays a critical role in tissue regeneration. Physiologically, an enhanced metabolic state facilitates anabolic biosynthesis and mitosis to accelerate regeneration. However, the development of approaches to reprogram CBE, towards the treatment of substantial tissue injuries, hasbeen limited thus far. Here, we show that induced repair in a rabbit model of weight-bearing bone defects is greatly enhanced using a bioenergetic-active material (BAM) scaffold, compared to commercialized poly (lactic acid) and calcium phosphate ceramic scaffolds. This material was composed of energy-active units that can be released in a sustained degradation-mediated fashion once implanted. By establishing an intramitochondrial metabolic bypass, the internalized energy-active units significantly elevatemitochondria membrane potential (ΔΨm) to supply increased bioenergetic levels and accelerate bone formation. The ready-to-use material developed here represents a highly efficient and easy-to-implement therapeutic approach toward tissue regeneration, withpromise for bench-to-bedside translation

Observation of Thermodynamical Properties in the 162^{162}Dy, 166^{166}Er and 172^{172}Yb Nuclei

The density of accessible levels in the ($^3$He,$\alpha \gamma$) reaction has been extracted for the $^{162}$Dy, $^{166}$Er and $^{172}$Yb nuclei. The nuclear temperature is measured as a function of excitation energy in the region of 0 -- 6 MeV. The temperature curves reveal structures indicating new degrees of freedom. The heat capacity of the nuclear system is discussed within the framework of a canonical ensemble.Comment: 12 pages, 4 figures include

Surface enhanced raman scattering artificial nose for high dimensionality fingerprinting

Label-free surface-enhanced Raman spectroscopy (SERS) can interrogate systems by directly fingerprinting its components’ unique physicochemical properties. In complex biological systems however, this can yield highly overlapping spectra that hinder sample identification. Here, we present an artificial-nose inspired SERS fingerprinting approach where spectral data is obtained as a function of sensor surface chemical functionality. Supported by molecular dynamics modelling, we show that mildly selective self-assembled monolayers can influence the strength and configuration in which analytes interact with plasmonic surfaces, diversifying the resulting SERS fingerprints. Since each sensor generates a modulated signature, the implicit value of increasing the dimensionality of datasets is shown using cell lysates for all possible combinations of up to 9 fingerprints. Reliable improvements in mean discriminatory accuracy towards 100% is achieved with each additional surface functionality. This arrayed label-free platform illustrates the wide-ranging potential of high dimensionality artificial-nose based sensing systems for more reliable assessment of complex biological matrices