Ice formation within protein crystals is a major obstacle to the cryocrystallographic study of protein structure, and has limited studies of how the structural ensemble of a protein evolves with temperature in the biophysically interesting range from ∼260 K to the protein-solvent glass transition near 200 K. Using protein crystals with solvent cavities as large as ∼70 Å, time-resolved X-ray diffraction was used to study the response of protein and internal solvent during rapid cooling. Solvent nanoconfinement suppresses freezing temperatures and ice-nucleation rates so that ice-free, low-mosaicity diffraction data can be reliably collected down to 200 K without the use of cryoprotectants. Hexagonal ice (I_h) forms in external solvent, but internal crystal solvent forms stacking-disordered ice (I_sd) with a near-random stacking of cubic and hexagonal planes. Analysis of powder diffraction from internal ice and single-crystal diffraction from the host protein structure shows that the maximum crystallizable solvent fraction decreases with decreasing crystal solvent-cavity size, and that an ∼6 Å thick layer of solvent adjacent to the protein surface cannot crystallize. These results establish protein crystals as excellent model systems for the study of nanoconfined solvent. By combining fast cooling, intense X-ray beams and fast X-ray detectors, complete structural data sets for high-value targets, including membrane proteins and large complexes, may be collected at ∼220-240 K that have much lower mosaicities and comparable B factors, and that may allow more confident identification of ligand binding than in current cryocrystallographic practice.

Diffraction data acquired from cryocooled protein crystals often include diffraction from ice. Analysis of ice diffraction from crystals of three proteins shows that the ice formed within solvent cavities during rapid cooling is comprised of a stacking-disordered mixture of hexagonal and cubic planes, with the cubic plane fraction increasing with increasing cryoprotectant concentration and increasing cooling rate. Building on the work of Thorn and coworkers [Thorn et al. (2017), Acta Cryst. D73, 729-727], a revised metric is defined for detecting ice from deposited protein structure-factor data, and this metric is validated using full-frame diffraction data from the Integrated Resource for Reproducibility in Macromolecular Crystallography. Using this revised metric and improved algorithms, an analysis of structure-factor data from a random sample of 89 827 PDB entries collected at cryogenic temperatures indicates that roughly 16% show evidence of ice contamination, and that this fraction increases with increasing solvent content and maximum solvent-cavity size. By examining the ice diffraction-peak positions at which structure-factor perturbations are observed, it is found that roughly 25% of crystals exhibit ice with primarily hexagonal character, indicating that inadequate cooling rates and/or cryoprotectant concentrations were used, while the remaining 75% show ice with a stacking-disordered or cubic character.

The local Fourier-space relation between diffracted intensity I, diffraction wavevector q and dose D, , is key to probing and understanding radiation damage by X-rays and energetic particles in both diffraction and imaging experiments. The models used in protein crystallography for the last 50 years provide good fits to experimental I(q) versus nominal dose data, but have unclear physical significance. More recently, a fit to diffraction and imaging experiments suggested that the maximum tolerable dose varies as q -1 or linearly with resolution. Here, it is shown that crystallographic data have been strongly perturbed by the effects of spatially nonuniform crystal irradiation and diffraction during data collection. Reanalysis shows that these data are consistent with a purely exponential local dose dependence, = I 0(q)exp[-D/D e(q)], where D e(q) ∝ q α with α ≃ 1.7. A physics-based model for radiation damage, in which damage events occurring at random locations within a sample each cause energy deposition and blurring of the electron density within a small volume, predicts this exponential variation with dose for all q values and a decay exponent α ≃ 2 in two and three dimensions, roughly consistent with both diffraction and imaging experiments over more than two orders of magnitude in resolution. The B-factor model used to account for radiation damage in crystallographic scaling programs is consistent with α = 2, but may not accurately capture the dose dependencies of structure factors under typical nonuniform illumination conditions. The strong q dependence of radiation-induced diffraction decays implies that the previously proposed 20-30 MGy dose limit for protein crystallography should be replaced by a resolution-dependent dose limit that, for atomic resolution data sets, will be much smaller. The results suggest that the physics underlying basic experimental trends in radiation damage at T ≃ 100 K is straightforward and universal. Deviations of the local I(q, D) from strictly exponential behavior may provide mechanistic insights, especially into the radiation-damage processes responsible for the greatly increased radiation sensitivity observed at T ≃ 300 K.

When protein crystals are abruptly cooled, the unit-cell, protein and solvent-cavity volumes all contract, but the volume of bulk-like internal solvent may expand. Outflow of this solvent from the unit cell and its accumulation in defective interior crystal regions has been suggested as one cause of the large increase in crystal mosaicity on cooling. It is shown that when apoferritin crystals are abruptly cooled to temperatures between 220 and 260 K, the unit cell contracts, solvent is pushed out and the mosaicity grows. On temperature-dependent timescales of 10 to 200 s, the unit-cell and solvent-cavity volume then expand, solvent flows back in, and the mosaicity and B factor both drop. Expansion and reordering at fixed low temperature are associated with small-amplitude but large-scale changes in the conformation and packing of apoferritin. These results demonstrate that increases in mosaicity on cooling arise due to solvent flows out of or into the unit cell and to incomplete, arrested relaxation of protein conformation. They indicate a critical role for time in variable-temperature crystallographic studies, and the feasibility of probing interactions and cooperative conformational changes that underlie cold denaturation in the presence of liquid solvent at temperatures down to ∼200 K.

Time-resolved crystallography of biomolecules in action has advanced rapidly as methods for serial crystallography have improved, but the large number of crystals and the complex experimental infrastructure that are required remain serious obstacles to its widespread application. Here, millisecond mix-and-quench crystallography (MMQX) has been developed, which yields millisecond time-resolved data using far fewer crystals and routine remote synchrotron data collection. To demonstrate the capabilities of MMQX, the conversion of oxaloacetic acid to phosphoenolpyruvate by phosphoenolpyruvate carboxy-kinase (PEPCK) is observed with a time resolution of 40 ms. By lowering the entry barrier to time-resolved crystallography, MMQX should enable a broad expansion in structural studies of protein dynamics.

The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases - including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits - we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

Background

Genetic mutations underlying familial Alzheimer's disease (AD) were identified decades ago, but the field is still in search of transformative therapies for patients. While mouse models based on overexpression of mutated transgenes have yielded key insights in mechanisms of disease, those models are subject to artifacts, including random genetic integration of the transgene, ectopic expression and non-physiological protein levels. The genetic engineering of novel mouse models using knock-in approaches addresses some of those limitations. With mounting evidence of the role played by microglia in AD, high-dimensional approaches to phenotype microglia in those models are critical to refine our understanding of the immune response in the brain.

Methods

We engineered a novel App knock-in mouse model (App^SAA) using homologous recombination to introduce three disease-causing coding mutations (Swedish, Arctic and Austrian) to the mouse App gene. Amyloid-β pathology, neurodegeneration, glial responses, brain metabolism and behavioral phenotypes were characterized in heterozygous and homozygous App^SAA mice at different ages in brain and/ or biofluids. Wild type littermate mice were used as experimental controls. We used in situ imaging technologies to define the whole-brain distribution of amyloid plaques and compare it to other AD mouse models and human brain pathology. To further explore the microglial response to AD relevant pathology, we isolated microglia with fibrillar Aβ content from the brain and performed transcriptomics and metabolomics analyses and in vivo brain imaging to measure energy metabolism and microglial response. Finally, we also characterized the mice in various behavioral assays.

Results

Leveraging multi-omics approaches, we discovered profound alteration of diverse lipids and metabolites as well as an exacerbated disease-associated transcriptomic response in microglia with high intracellular Aβ content. The App^SAA knock-in mouse model recapitulates key pathological features of AD such as a progressive accumulation of parenchymal amyloid plaques and vascular amyloid deposits, altered astroglial and microglial responses and elevation of CSF markers of neurodegeneration. Those observations were associated with increased TSPO and FDG-PET brain signals and a hyperactivity phenotype as the animals aged.

Discussion

Our findings demonstrate that fibrillar Aβ in microglia is associated with lipid dyshomeostasis consistent with lysosomal dysfunction and foam cell phenotypes as well as profound immuno-metabolic perturbations, opening new avenues to further investigate metabolic pathways at play in microglia responding to AD-relevant pathogenesis. The in-depth characterization of pathological hallmarks of AD in this novel and open-access mouse model should serve as a resource for the scientific community to investigate disease-relevant biology.