Adipose triglyceride lipase regulates eicosanoid production in activated human mast cells

Human mast cells (MCs) contain TG-rich cytoplasmic lipid droplets (LDs) with high arachidonic acid (AA) content. Here, we investigated the functional role of adipose TG lipase (ATGL) in TG hydrolysis and the ensuing release of AA as substrate for eicosanoid generation by activated human primary MCs in culture. Silencing of ATGL in MCs by siRNAs induced the accumulation of neutral lipids in LDs. IgE-dependent activation of MCs triggered the secretion of the two major eicosanoids, prostaglandin D2 (PGD2) and leukotriene C4 (LTC4). The immediate release of PGD2 from the activated MCs was solely dependent on cyclooxygenase (COX) 1, while during the delayed phase of lipid mediator production, the inducible COX-2 also contributed to its release. Importantly, when ATGL-silenced MCs were activated, the secretion of both PGD2 and LTC4 was significantly reduced. Interestingly, the inhibitory effect on the release of LTC4 was even more pronounced in ATGL-silenced MCs than in cytosolic phospholipase A₂-silenced MCs. These data show that ATGL hydrolyzes AA-containing TGs present in human MC LDs and define ATGL as a novel regulator of the substrate availability of AA for eicosanoid generation upon MC activation.     

Analyzing the dynamics of cell cycle processes from fixed samples through ergodic principles

Tools to analyze cyclical cellular processes, particularly the cell cycle, are of broad value for cell biology. Cell cycle synchronization and live-cell time-lapse observation are widely used to analyze these processes but are not available for many systems. Simple mathematical methods built on the ergodic principle are a well-established, widely applicable, and powerful alternative analysis approach, although they are less widely used. These methods extract data about the dynamics of a cyclical process from a single time-point “snapshot” of a population of cells progressing through the cycle asynchronously. Here, I demonstrate application of these simple mathematical methods to analysis of basic cyclical processes—cycles including a division event, cell populations undergoing unicellular aging, and cell cycles with multiple fission (schizogony)—as well as recent advances that allow detailed mapping of the cell cycle from continuously changing properties of the cell such as size and DNA content. This includes examples using existing data from mammalian, yeast, and unicellular eukaryotic parasite cell biology. Through the ongoing advances in high-throughput cell analysis by light microscopy, electron microscopy, and flow cytometry, these mathematical methods are becoming ever more important and are a powerful complementary method to traditional synchronization and time-lapse cell cycle analysis methods.     

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Physicians considering stereotactic ablative body radiation therapy (SBRT) for the treatment of extracranial cancer targets must be aware of the sizeable risks for normal tissue injury and the hazards of physical tumor miss. A first-of-its-kind SBRT platform achieves high-precision ablative radiation treatment through a combination of versatile real-time imaging solutions and sophisticated tumor tracking capabilities. It uses dual-diagnostic kV x-ray units for stereoscopic open-loop feedback of cancer target intrafraction movement occurring as a consequence of respiratory motions and heartbeat. Image-guided feedback drives a gimbaled radiation accelerator (maximum 15 x 15 cm field size) capable of real-time ±4 cm pan-and-tilt action. Robot-driven ±60° pivots of an integrated ±185° rotational gantry allow for coplanar and non-coplanar accelerator beam set-up angles, ultimately permitting unique treatment degrees of freedom. State-of-the-art software aids real-time six dimensional positioning, ensuring irradiation of cancer targets with sub-millimeter accuracy (0.4 mm at isocenter). Use of these features enables treating physicians to steer radiation dose to cancer tumor targets while simultaneously reducing radiation dose to normal tissues. By adding respiration correlated computed tomography (CT) and 2-[¹⁸F] fluoro-2-deoxy-ᴅ-glucose (¹⁸F-FDG) positron emission tomography (PET) images into the planning system for enhanced tumor target contouring, the likelihood of physical tumor miss becomes substantially less¹. In this article, we describe new radiation plans for the treatment of moving lung tumors.     

Reliably Detecting Clinically Important Variants Requires Both Combined Variant Calls and Optimized Filtering Strategies

A diversity of tools is available for identification of variants from genome sequence data. Given the current complexity of incorporating external software into a genome analysis infrastructure, a tendency exists to rely on the results from a single tool alone. The quality of the output variant calls is highly variable however, depending on factors such as sequence library quality as well as the choice of short-read aligner, variant caller, and variant caller filtering strategy. Here we present a two-part study first using the high quality ‘genome in a bottle’ reference set to demonstrate the significant impact the choice of aligner, variant caller, and variant caller filtering strategy has on overall variant call quality and further how certain variant callers outperform others with increased sample contamination, an important consideration when analyzing sequenced cancer samples. This analysis confirms previous work showing that combining variant calls of multiple tools results in the best quality resultant variant set, for either specificity or sensitivity, depending on whether the intersection or union, of all variant calls is used respectively. Second, we analyze a melanoma cell line derived from a control lymphocyte sample to determine whether software choices affect the detection of clinically important melanoma risk-factor variants finding that only one of the three such variants is unanimously detected under all conditions. Finally, we describe a cogent strategy for implementing a clinical variant detection pipeline; a strategy that requires careful software selection, variant caller filtering optimizing, and combined variant calls in order to effectively minimize false negative variants. While implementing such features represents an increase in complexity and computation the results offer indisputable improvements in data quality.     

A Neural Model of Auditory Space Compatible with Human Perception under Simulated Echoic Conditions

In a typical auditory scene, sounds from different sources and reflective surfaces summate in the ears, causing spatial cues to fluctuate. Prevailing hypotheses of how spatial locations may be encoded and represented across auditory neurons generally disregard these fluctuations and must therefore invoke additional mechanisms for detecting and representing them. Here, we consider a different hypothesis in which spatial perception corresponds to an intermediate or sub-maximal firing probability across spatially selective neurons within each hemisphere. The precedence or Haas effect presents an ideal opportunity for examining this hypothesis, since the temporal superposition of an acoustical reflection with sounds arriving directly from a source can cause otherwise stable cues to fluctuate. Our findings suggest that subjects’ experiences may simply reflect the spatial cues that momentarily arise under various acoustical conditions and how these cues are represented. We further suggest that auditory objects may acquire “edges” under conditions when interaural time differences are broadly distributed.