Simultaneous measurement of kidney function by dynamic contrast enhanced MRI and FITC-sinistrin clearance in rats at 3 tesla: initial results.

Glomerular filtration rate (GFR) is an essential parameter of kidney function which can be measured by dynamic contrast enhanced magnetic resonance imaging (MRI-GFR) and transcutaneous approaches based on fluorescent tracer molecules (optical-GFR). In an initial study comparing both techniques in separate measurements on the same animal, the correlation of the obtained GFR was poor. The goal of this study was to investigate if a simultaneous measurement was feasible and if thereby, the discrepancies in MRI-GFR and optical-GFR could be reduced. For the experiments healthy and unilateral nephrectomised (UNX) Sprague Dawley (SD) rats were used. The miniaturized fluorescent sensor was fixed on the depilated back of an anesthetized rat. A bolus of 5 mg/100 g b.w. of FITC-sinistrin was intravenously injected. For dynamic contrast enhanced perfusion imaging (DCE-MRI) a 3D time-resolved angiography with stochastic trajectories (TWIST) sequence was used. By means of a one compartment model the excretion half-life (t1/2) of FITC-sinistrin was calculated and converted into GFR. GFR from DCE-MRI was calculated by fitting pixel-wise a two compartment renal filtration model. Mean cortical GFR and GFR by FITC-sinistrin were compared by Bland-Altman plots and pair-wise t-test. Results show that a simultaneous GFR measurement using both techniques is feasible. Mean optical-GFR was 4.34 ± 2.22 ml/min (healthy SD rats) and 2.34 ± 0.90 ml/min (UNX rats) whereas MRI-GFR was 2.10 ± 0.64 ml/min (SD rats) and 1.17 ± 0.38 ml/min (UNX rats). Differences between healthy and UNX rats were significant (p<0.05) and almost equal percentage difference (46.1% and 44.3%) in mean GFR were assessed with both techniques. Overall mean optical-GFR values were approximately twice as high compared to MRI-GFR values. However, compared to a previous study, our results showed a higher agreement. In conclusion, the possibility to use the transcutaneous method in MRI may have a huge impact in improving and validating MRI methods for GFR assessment in animal models

Transdermal Measurement of Glomerular Filtration Rate in Mice.

Transdermal analysis of glomerular filtration rate (GFR) is an established technique that is used to assess renal function in mouse and rat models of acute kidney injury and chronic kidney disease. The measurement system consists of a miniaturized fluorescence detector that is directly attached to the skin on the back of conscious, freely moving animals, and measures the excretion kinetics of the exogenous GFR tracer, fluorescein-isothiocyanate (FITC) conjugated sinistrin (an inulin analog). This system has been described in detail in rats. However, because of their smaller size, measurement of transcutaneous GFR in mice presents additional technical challenges. In this paper we therefore provide the first detailed practical guide to the use of transdermal GFR monitors in mice based on the combined experience of three different investigators who have been performing this assay in mice over a number of years

Single Bacteria Movement Tracking by Online Microscopy – A Proof of Concept Study

<div><p>In this technical report we demonstrate a low-cost online unit allowing movement tracking of flagellated bacteria on a single-cell level during fermentation processes. The system’s ability to distinguish different metabolic states (viability) of bacteria by movement velocity was investigated. A flow-through cuvette with automatically adjustable layer thickness was developed. The cuvette can be used with most commercially available laboratory microscopes equipped with 40× amplification and a digital camera. In addition, an automated sample preparation unit and a software module was developed measuring size, moved distance, and speed of bacteria. In a proof of principle study the movement velocities of <i>Bacillus amyloliquefaciens</i> FZB42 during three batch fermentation processes were investigated. In this process the bacteria went through different metabolic states, vegetative growth, diauxic shift, vegetative growth after diauxic shift, and sporulation. It was shown that the movement velocities during the different metabolic states significantly differ from each other. Therefore, the described setup has the potential to be used as a bacteria viability monitoring tool. In contrast to some other techniques, such as electro-optical techniques, this method can even be used in turbid production media.</p></div

Simplified process flow diagram of the sample preparation and image acquisition.

<p>The sample mixed with demineralized water is pumped through the flow cell. After evacuation the image acquisition can start. Before the next sampling aeration and disposal is necessary.</p

Comparison of current viability measurement techniques.

<p>Comparison of current viability measurement techniques.</p

Cultivation of <i>Bacillus amyloliquefaciens</i> FZB42.

<p>A) Monitored aeritation and agitation data. B) pH and mean bacteria velocity, below the bars are the numbers n of determined velocity vectors per sample. C) optical density (OD 600) and mean number of motile bacteria observed in the field of view of the flow cell, corrected by dilution factor of the sample.</p

Simplified software structure of the image analysis.

<p>First, the series of images (30 frames at 30 fps represents 1 s) are loaded, before the indexed serial subtraction and the image processing starts. Now particle characteristics (center of mass x, center of mass y, perimeter, area, orientation, length, width) are determined. In the next step the bacteria are associated with the characteristics. After post-processing the results are provided.</p

Validation of automatic image analysis.

<p>The validation of automatic image analysis by manual analysis of the image series on the four significant sampling points of interest (before diauxic shift, diauxic shift, after diauxic shift, start of sporulation) during fermentation 1. The error bars represent the Standard Error of the Mean of the bacterial velocity. The number of determined velocity vectors per sample are 232: n_man = 354, n_aut = 210; 292: n_man = 1150, n_aut = 673; 352: n_352man = 3818, n_aut = 2820; 382: n_man = 2600, n_aut = 1624.</p

Simplified flow chart of the sample preparation, image acquisition, vacuum and waste treatment.

<p>The measurement of OD at 600 nm (QIRC 2.02) regulates the dilution of the sample (stream 1). The degree of sample dilution is registered using weight control (WIR 2.03). In the flow cell, the image acquisition (AIR 2.01) can start after evacuating the system.</p