A comparison of parametric and integrative approaches for X-ray fluorescence analysis applied to a Stroke model

A high-throughput big-data orchestration and processing system for the High Energy Photon Source

High-data-throughput and multimodal-acquisition experiments will prevail in next-generation synchrotron beamlines. Orchestrating dataflow pipelines connecting the data acquisition, processing, visualization and storage ends are becoming increasingly complex and essential for enhancing beamline performance. Mamba Data Worker (MDW) has been developed to address the data challenges for the forthcoming High Energy Photon Source (HEPS). It is an important component of the Mamba experimental control and data acquisition software ecosystem, which enables fast data acquisition and transmission, dynamic configuration of data processing pipelines, data multiplex in streaming, and customized data and metadata assembly. This paper presents the architecture and development plan of MDW, outlines the essential technologies involved, and illustrates its current application at the Beijing Synchrotron Radiation Facility (BSRF).

Sex differences in the mouse photothrombotic stroke model investigated with X-ray fluorescence microscopy and Fourier transform infrared spectroscopic imaging

Stroke is a leading cause of death and disability around the world. To date, the majority of pre-clinical research has been performed using male lab animals and results are commonly generalized to both sexes. In clinical stoke cases females have a higher incidence of ischemic stroke and poorer outcomes, compared to males. Best practices for improving translatability of findings for stroke, encourage the use of both sexes in studies. Since estrogen and progesterone have recognized neuroprotective effects, it is important to compare the size, severity and biochemical composition of the brain tissue following stroke in female and male animal models. In this study a photothrombotic focal stroke was induced in male and female mice. Vaginal secretions were collected twice daily to track the stage of estrous. Mice were euthanized at 24 h post-stroke. Histological staining, Fourier transform infrared imaging and X-ray fluorescence imaging were performed to better define the size and metabolic markers in the infarct core and surrounding penumbra. Our results show while the female mice had a significantly lower body mass than males, the cross-sectional area of the brain and the size of infarct and penumbra were not significantly different between the groups. In addition to the general expected sex-linked differences of altered NADH levels between males and females, estrus females had significantly elevated glycogen in the penumbra compared with males and total phosphorus levels were noted to be higher in the penumbra of estrus females. Elevated glycogen reserves in the tissue bordering the infarct core in females may present alternatives for improved functional recovery in females in the early post-stroke phase.

X-ray Fluorescence Microscopy Methods for Biological Tissues

Synchrotron-based X-ray fluorescence microscopy is a flexible tool for identifying the distribution of trace elements in biological specimens across a broad range of sample sizes. The technique is not particularly limited by sample type and can be performed on ancient fossils, fixed or fresh tissue specimens, and in some cases even live tissue and live cells can be studied. The technique can also be expanded to provide chemical specificity to elemental maps, either at individual points of interest in a map or across a large field of view. While virtually any sample type can be characterized with X-ray fluorescence microscopy, common biological sample preparation methods (often borrowed from other fields, such as histology) can lead to unforeseen pitfalls, resulting in altered element distributions and concentrations. A general overview of sample preparation and data acquisition methods for X-ray fluorescence microscopy is presented, along with outlining the general approach for applying this technique to a new field of investigation for prospective new-users. Considerations for improving data acquisition and quality are reviewed as well as the effects of sample preparation, with a particular focus on soft tissues. The effects of common sample pre-treatment steps as well as the underlying factors that govern which, and to what extent, specific elements are likely to be altered are reviewed along with common artifacts observed in X-ray fluorescence microscopy data.

Multimodal imaging of hemorrhagic transformation biomarkers in an ischemic stroke model

Hemorrhagic transformation of ischemic stroke has devastating consequences, with high mortality and poor functional outcomes. Animal models of ischemic stroke also demonstrate the potential for hemorrhagic transformation, which complicates biochemical characterization, treatment studies, and hinders poststroke functional outcomes in affected subjects. The incidence of hemorrhagic transformation of ischemic stroke in animal model research is not commonly reported. The postmortem brain of such cases presents a complex milieu of biomarkers due to the presence of healthy cells, regions of varying degrees of ischemia, dead and dying cells, dysregulated metabolites, and blood components (especially reactive Fe species released from lysed erythrocytes). To improve the characterization of hemorrhage biomarkers on an ischemic stroke background, we have employed a combination of histology, X-ray fluorescence imaging (XFI), and Fourier transform infrared (FTIR) spectroscopic imaging to assess 122 photothrombotic (ischemic) stroke brains. Rapid freezing preserves brain biomarkers in situ and minimizes metabolic artifacts due to postmortem ischemia. Analysis revealed that 25% of the photothrombotic models had clear signs of hemorrhagic transformation. The XFI and FTIR metabolites provided a quantitative method to differentiate key metabolic regions in these models. Across all hemorrhage cases, it was possible to consistently differentiate otherwise healthy tissue from other metabolically distinct regions, including the ischemic infarct, the ischemic penumbra, blood vessels, sites of hemorrhage, and a region surrounding the hemorrhage core that contained elevated lipid oxidation. Chemical speciation of deposited Fe demonstrates the presence of heme-Fe and accumulation of ferritin.

In-situ x-ray fluorescence imaging of the endogenous iodine distribution in murine thyroids

X-ray fluorescence imaging (XFI) is a non-invasive detection method of small quantities of elements, which can be excited to emit fluorescence x-ray photons upon irradiation with an incident x-ray beam. In particular, it can be used to measure nanoparticle uptake in cells and tissue, thus making it a versatile medical imaging modality. However, due to substantially increased multiple Compton scattering background in the measured x-ray spectra, its sensitivity severely decreases for thicker objects, so far limiting its applicability for tracking very small quantities under in-vivo conditions. Reducing the detection limit would enable the ability to track labeled cells, promising new insights into immune response and pharmacokinetics. We present a synchrotron-based approach for reducing the minimal detectable marker concentration by demonstrating the feasibility of XFI for measuring the yet inaccessible distribution of the endogenous iodine in murine thyroids under in-vivo conform conditions. This result can be used as a reference case for the design of future preclinical XFI applications as mentioned above.

Abridged spectral matrix inversion: parametric fitting of X-ray fluorescence spectra following integrative data reduction

Recent improvements in both X-ray detectors and readout speeds have led to a substantial increase in the volume of X-ray fluorescence data being produced at synchrotron facilities. This in turn results in increased challenges associated with processing and fitting such data, both temporally and computationally. Herein an abridging approach is described that both reduces and partially integrates X-ray fluorescence (XRF) data sets to obtain a fivefold total improvement in processing time with negligible decrease in quality of fitting. The approach is demonstrated using linear least-squares matrix inversion on XRF data with strongly overlapping fluorescent peaks. This approach is applicable to any type of linear algebra based fitting algorithm to fit spectra containing overlapping signals wherein the spectra also contain unimportant (non-characteristic) regions which add little (or no) weight to fitted values, e.g. energy regions in XRF spectra that contain little or no peak information.

Tracking elemental changes in an ischemic stroke model with X-ray fluorescence imaging

Stroke is a leading cause of long-term disability in adults and a leading cause of death in developed nations. The cascade of cellular events and signalling that occur after cerebral ischemia are complex, however, analyzing global element markers of metabolic state affords the means to monitor stroke severity, status of injury, and recovery. These markers provide a multi-parameter method for assessing changes through the post-stroke time course. We employ synchrotron-based elemental mapping to follow elemental changes in the brain at 1 h, 1-, 2-, and 3-days, and at 1-, 2-, 3-, and 4-weeks post-stroke in a photothrombotic stroke model in mice. Our analysis reveals a highly consistent metabolic penumbra that can be readily identified based on the level of dysregulated potassium and other key elements. Maps of elemental distributions are also useful to demarcate events in the cellular response to the inflammatory cascade, including ion dysregulation, recruitment of cells to the lesion, and glial scar formation.

M-BLANK: a program for the fitting of X-ray fluorescence spectra

The X-ray fluorescence data from X-ray microprobe and nanoprobe measurements must be fitted to obtain reliable elemental maps. The most common approach in many fitting programs is to initially remove a per-pixel baseline. Using X-ray fluorescence data of yeast and glial cells, it is shown that per-pixel baselines can result in significant, systematic errors in quantitation and that significantly improved data can be obtained by calculating an average blank spectrum and subtracting this from each pixel.