Particle size analysis of perfluorocarbon emulsions in a complex whole blood matrix by sedimentation field-flow fractionation

Mapping kidney tubule diameter ex vivo by diffusion MRI

Tubular pathologies are a common feature of kidney disease. Current metrics to assess kidney health, in vivo or in transplant, are generally based on urinary or serum biomarkers and pathological findings from kidney biopsies. Biopsies, usually taken from the kidney cortex, are invasive and prone to sampling error. Tools to directly and noninvasively measure tubular pathology could provide a new approach to assess kidney health. This study used diffusion magnetic resonance imaging (dMRI) as a noninvasive tool to measure the size of the tubular lumen in ex vivo, perfused kidneys. We first used Monte Carlo simulations to demonstrate that dMRI is sensitive to restricted tissue water diffusion at the scale of the kidney tubule. We applied dMRI and biophysical modeling to examine the distribution of tubular diameters in ex vivo, fixed kidneys from mice, rats, and a human donor. The biophysical model to fit the dMRI signal was based on a superposition of freely diffusing water and water diffusing inside infinitely long cylinders of different diameters. Tubular diameters measured by dMRI were within 10% of those measured by histology within the same tissue. Finally, we applied dMRI to investigate kidney pathology in a mouse model of folic-acid-induced acute kidney injury. dMRI detected heterogeneity in the distribution of tubules within the kidney cortex of mice with acute kidney injury compared with control mice. We conclude that dMRI can be used to measure the distribution of tubule diameters in the kidney cortex ex vivo and that dMRI may provide a new noninvasive biomarker of tubular pathology.NEW & NOTEWORTHY Tubular pathologies are a common feature of kidney disease. Current metrics to assess kidney health, in vivo or in transplant, are generally based on urinary or serum biomarkers and pathological findings from kidney biopsies. Diffusion MRI can be used to measure the distribution of tubule diameters in the kidney cortex ex vivo and may provide a new noninvasive biomarker of tubular pathology.

Separation science: Principles and applications for the analysis of bionanoparticles by asymmetrical flow field-flow fractionation (AF4)

Field-flow fractionation is an analytical technique that allows the separation of particles over a size range, from a few nanometers to several microns in diameter. The separation takes place under mild conditions and is suited for the analysis of neutral or charged particles. A single measurement yields the size and concentration of each component of a mixture. However, developing a suitable fractionation method can be tedious and time-consuming. In this chapter, we present asymmetrical flow field-flow fractionation (AF4) conditions that have proven their reliability for the analysis of quantum dots and other nanoparticles in the 5-50 nm size range. Common pitfalls are emphasized together with strategies to overcome them.

Megakaryocyte cell sorting from diosgenin-differentiated human erythroleukemia cells by sedimentation field-flow fractionation

Anticancer differentiation therapy could be one strategy to stop cancer cell proliferation. Human erythroleukemia (HEL) cell line, incubated with 10 microM diosgenin, underwent megakaryocytic differentiation. Thus, the association diosgenin/HEL could be used as a model of chemically induced cellular differentiation and anticancer treatment. The goal of this work was to determine the capacity of sedimentation field-flow fractionation (SdFFF) to sort megakaryocytic differentiated cells. SdFFF cell sorting was associated with cellular characterization methods to calibrate specific elution profiles. As demonstrated by cell size measurement methods, cellular morphology, ploidy, and phenotype, we obtained an enriched, sterile, viable, and functional fraction of megakaryocytic cells. Thus, SdFFF is proposed as a routine method to prepare differentiated cells that will be further used to better understand the megakaryocytic differentiation process.