MRI-based fat quantification of the liver: Is it time for commercially available products?

PURPOSE: (1) To assess the clinical applicability of commercially available solutions for MR-based quantification of the hepatic fat fraction (HFF) and (2) to compare their results with clinically established in-phase/oppose-phase (IP/OP) imaging as proposed by Dixon. METHODS: Twenty-eight patients underwent MRI examinations using multigradient-echo sequences including multi-peak modeling and T2∗ correction, IP/OP imaging and multi-echo spectroscopy with successive HFF evaluation. Histopathological examination yielded the fraction of adipose hepatocytes (fAH) and the presence of increased liver iron concentration (LIC). We correlated HFF with fAH, and assessed concordance correlations among the MR-based methods with the presence of increased LIC as a control parameter. We investigated the liver segmentation quality and overall workflow of the postprocessing solutions (Philips LiverHealth and Siemens LiverLab). RESULTS: IP/OP imaging yielded a very strong correlation (r=0.88) with fAH when excluding three cases with increased LIC. Multigradient echo imaging and multiecho spectroscopy quantifications yielded similar correlations (r=0.87…0.93) as IP/OP imaging but were insensitive to increased LIC. Visceral fat, kidney tissue and major vessels were included regularly in the segmentation. Spectroscopic fat quantification was sensitive to the inclusion of visceral fat. CONCLUSIONS: IP/OP imaging allows HFF quantification when ruling out hepatic siderosis, whereas dedicated multi-echo imaging sequences and spectroscopy show no bias for increased iron concentration. The segmentation quality and workflow of both postprocessing solutions need to be improved. Nevertheless, all solutions are able to bring MRI-based hepatic fat quantification into the clinical application. We therefore recommend commercial hepatic fat quantification tools for institutions specialised to abdominal imaging.

Light-Driven ATP Regeneration in Diblock/Grafted Hybrid Vesicles.

Light-driven ATP regeneration systems combining ATP synthase and bacteriorhodopsin have been proposed as an energy supply in the field of synthetic biology. Energy is required to power biochemical reactions within artificially created reaction compartments like protocells, which are typically based on either lipid or polymer membranes. The insertion of membrane proteins into different hybrid membranes is delicate, and studies comparing these systems with liposomes are needed. Here we present a detailed study of membrane protein functionality in different hybrid compartments made of graft polymer PDMS-g-PEO and diblock copolymer PBd-PEO. Activity of more than 90 % in lipid/polymer-based hybrid vesicles could prove an excellent biocompatibility. A significant enhancement of long-term stability (80 % remaining activity after 42 days) could be demonstrated in polymer/polymer-based hybrids.

Quantitative single cell analysis uncovers the life/death decision in CD95 network.

CD95/Fas/APO-1 is a member of the death receptor family that triggers apoptotic and anti-apoptotic responses in particular, NF-κB. These responses are characterized by a strong heterogeneity within a population of cells. To determine how the cell decides between life and death we developed a computational model supported by imaging flow cytometry analysis of CD95 signaling. Here we show that CD95 stimulation leads to the induction of caspase and NF-κB pathways simultaneously in one cell. The related life/death decision strictly depends on cell-to-cell variability in the formation of the death-inducing complex (DISC) on one side (extrinsic noise) vs. stochastic gene expression of the NF-κB pathway on the other side (intrinsic noise). Moreover, our analysis has uncovered that the stochasticity in apoptosis and NF-kB pathways leads not only to survival or death of a cell, but also causes a third type of response to CD95 stimulation that we termed ambivalent response. Cells in the ambivalent state can undergo cell death or survive which was subsequently validated by experiments. Taken together, we have uncovered how these two competing pathways control the fate of a cell, which in turn plays an important role for development of anti-cancer therapies.

A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data.

Imaging flow cytometry is a powerful experimental technique combining the strength of microscopy and flow cytometry to enable high-throughput characterization of cell populations on a detailed microscopic scale. This approach has an increasing importance for distinguishing between different cellular phenotypes such as proliferation, cell division and cell death. In the course of undergoing these different pathways, each cell is characterized by a high amount of properties. This makes it hard to filter the most relevant information for cell state discrimination. The traditional methods for cell state discrimination rely on dye based two-dimensional gating strategies ignoring information that is hidden in the high-dimensional property space. In order to make use of the information ignored by the traditional methods, we present a simple and efficient approach to distinguish biological states within a cell population based on machine learning techniques. We demonstrate the advantages and drawbacks of filter techniques combined with different classification schemes. These techniques are illustrated with two case studies of apoptosis detection in HeLa cells. Thereby we highlight (i) the aptitude of imaging flow cytometry regarding automated, label-free cell state discrimination and (ii) pitfalls that are frequently encountered. Additionally a MATLAB script is provided, which gives further insight regarding the computational work presented in this study.

Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems.

MOTIVATION: Biological cells operate in a noisy regime influenced by intrinsic, extrinsic and external noise, which leads to large differences of individual cell states. Stochastic effects must be taken into account to characterize biochemical kinetics accurately. Since the exact solution of the chemical master equation, which governs the underlying stochastic process, cannot be derived for most biochemical systems, approximate methods are used to obtain a solution. RESULTS: In this study, a method to efficiently simulate the various sources of noise simultaneously is proposed and benchmarked on several examples. The method relies on the combination of the sigma point approach to describe extrinsic and external variability and the τ -leaping algorithm to account for the stochasticity due to probabilistic reactions. The comparison of our method to extensive Monte Carlo calculations demonstrates an immense computational advantage while losing an acceptable amount of accuracy. Additionally, the application to parameter optimization problems in stochastic biochemical reaction networks is shown, which is rarely applied due to its huge computational burden. To give further insight, a MATLAB script is provided including the proposed method applied to a simple toy example of gene expression. AVAILABILITY AND IMPLEMENTATION: MATLAB code is available at Bioinformatics online. CONTACT: flassig@mpi-magdeburg.mpg.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.