Ultrasonic Aspirator for Tissue Contact Detection: An Online Classification on Time-Series.

The goal of neurosurgical tumor surgery is to remove the tumor completely without damaging healthy brain structures and thereby impairing the patient's neurological functions. This requires careful planning and execution of the operation by experienced neurosurgeons using the latest intraoperative technologies to achieve safe and rapid tumor reduction without harming the patient. To achieve this goal, a standard ultrasonic aspirator designed for tissue removal is equipped with additional intraoperative tissue detection using machine learning methods.Since decision-making in a clinical context must be fast, online contact detection is critical. Data are generated on three types of artificial tissue models in a CNC machine-controlled environment with four different ultrasonic aspirator settings. Contact classification on artificial tissue models is evaluated on four classification algorithms: change point detection (CPD), random forest (RF), recurrent neural network (RNN) and temporal convolutional network (TCN). Data preprocessing steps are applied, and their impacts are investigated. All methods are evaluated on five-fold cross-validation and provide generally good results with a performance of up to 0.977±0.007 in mean F1-score. Preprocessing the data has a positive effect on the classification processes for all methods and consistently improves the metrics. Thus, this work indicates in a first step that contact classification is feasible in an online context for an ultrasonic aspirator. Further research is necessary on different tissue types, as well as hand-held use to more closely resemble the intraoperative clinical conditions.

Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation.

PURPOSE: During brain tumor surgery, care must be taken to accurately differentiate between tumorous and healthy tissue, as inadvertent resection of functional brain areas can cause severe consequences. Since visual assessment can be difficult during tissue resection, neurosurgeons have to rely on the mechanical perception of tissue, which in itself is inherently challenging. A commonly used instrument for tumor resection is the ultrasonic aspirator, whose system behavior is already dependent on tissue properties. Using data recorded during tissue fragmentation, machine learning-based tissue differentiation is investigated for the first time utilizing ultrasonic aspirators. METHODS: Artificial tissue model with two different mechanical properties is synthesized to represent healthy and tumorous tissue. 40,000 temporal measurement points of electrical data are recorded in a laboratory environment using a CNC machine. Three different machine learning approaches are applied: a random forest (RF), a fully connected neural network (NN) and a 1D convolutional neural network (CNN). Additionally, different preprocessing steps are investigated. RESULTS: Fivefold cross-validation is conducted over the data and evaluated with the metrics F1, accuracy, positive predictive value, true positive rate and area under the receiver operating characteristic. Results show a generally good performance with a mean F1 of up to 0.900 ± 0.096 using a NN approach. Temporal information indicates low impact on classification performance, while a low-pass filter preprocessing step leads to superior results. CONCLUSION: This work demonstrates the first steps to successfully differentiate healthy brain and tumor tissue using an ultrasonic aspirator during tissue fragmentation. Evaluation shows that both neural network-based classifiers outperform the RF. In addition, the effects of temporal dependencies are found to be reduced when adequate data preprocessing is performed. To ensure subsequent implementation in the clinic, handheld ultrasonic aspirator use needs to be investigated in the future as well as the addition of data to reflect tissue diversity during neurosurgical operations.

Dual NADPH oxidases DUOX1 and DUOX2 synthesize NAADP and are necessary for Ca2+ signaling during T cell activation.

The formation of Ca2+ microdomains during T cell activation is initiated by the production of nicotinic acid adenine dinucleotide phosphate (NAADP) from its reduced form NAADPH. The reverse reaction­NAADP to NAADPH­is catalyzed by glucose 6-phosphate dehydrogenase (G6PD). Here, we identified NADPH oxidases NOX and DUOX as NAADP-forming enzymes that convert NAADPH to NAADP under physiological conditions in vitro. T cells express NOX1, NOX2, and, to a minor extent, DUOX1 and DUOX2. Local and global Ca2+ signaling were decreased in mouse T cells with double knockout of Duoxa1 and Duoxa2 but not with knockout of Nox1 or Nox2. Ca2+ microdomains in the first 15 s upon T cell activation were significantly decreased in Duox2−/− but not in Duox1−/− T cells, whereas both DUOX1 and DUOX2 were required for global Ca2+ signaling between 4 and 12 min after stimulation. Our findings suggest that a DUOX2- and G6PD-catalyzed redox cycle rapidly produces and degrades NAADP through NAADPH as an inactive intermediate.

Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell [Formula: see text] fluorescence microscopy.

Advances in high-resolution live-cell [Formula: see text] imaging enabled subcellular localization of early [Formula: see text] signaling events in T-cells and paved the way to investigate the interplay between receptors and potential target channels in [Formula: see text] release events. The huge amount of acquired data requires efficient, ideally automated image processing pipelines, with cell localization/segmentation as central tasks. Automated segmentation in live-cell cytosolic [Formula: see text] imaging data is, however, challenging due to temporal image intensity fluctuations, low signal-to-noise ratio, and photo-bleaching. Here, we propose a reservoir computing (RC) framework for efficient and temporally consistent segmentation. Experiments were conducted with Jurkat T-cells and anti-CD3 coated beads used for T-cell activation. We compared the RC performance with a standard U-Net and a convolutional long short-term memory (LSTM) model. The RC-based models (1) perform on par in terms of segmentation accuracy with the deep learning models for cell-only segmentation, but show improved temporal segmentation consistency compared to the U-Net; (2) outperform the U-Net for two-emission wavelengths image segmentation and differentiation of T-cells and beads; and (3) perform on par with the convolutional LSTM for single-emission wavelength T-cell/bead segmentation and differentiation. In turn, RC models contain only a fraction of the parameters of the baseline models and reduce the training time considerably.

HN1L/JPT2: A signaling protein that connects NAADP generation to Ca2+ microdomain formation.

NAADP-evoked Ca2+ release through type 1 ryanodine receptors (RYR1) is a major mechanism underlying the earliest signals in T cell activation, which are the formation of Ca2+ microdomains. In our characterization of the molecular machinery underlying NAADP action, we identified an NAADP-binding protein, called hematological and neurological expressed 1-like protein (HN1L) [also known as Jupiter microtubule-associated homolog 2 (JPT2)]. Gene deletion of Hn1l/Jpt2 in human Jurkat and primary rat T cells resulted in decreased numbers of initial Ca2+ microdomains and delayed the onset and decreased the amplitude of global Ca2+ signaling. Photoaffinity labeling demonstrated direct binding of NAADP to recombinant HN1L/JPT2. T cell receptor/CD3-dependent coprecipitation of HN1L/JPT2 with RYRs and colocalization of these proteins suggest that HN1L/JPT2 connects NAADP formation with the activation of RYR channels within the first seconds of T cell activation. Thus, HN1L/JPT2 enables NAADP to activate Ca2+ release from the endoplasmic reticulum through RYR.

High-Resolution Calcium Imaging Method for Local Calcium Signaling.

All eukaryotic cells respond to extracellular signals in a physiologically meaningful way. For multicellular organisms, physiologically relevant cooperation is only possible, if cell-cell communication works properly. Here, the extracellular signals must be translated into intracellular signals that ultimately result in cellular responses. This process is termed signal transduction or signaling. Ca2+ signaling has been developed in almost all eukaryotic cells. The cellular components used for this highly versatile signaling system are often termed "Ca2+ toolbox". Besides Ca2+ pumps and Ca2+-binding proteins, the Ca2+ channels that are located in the plasma membrane and intracellular membranes and the Ca2+-mobilizing second messengers are major players in shaping the four-dimensional nature of Ca2+ signals.Here, we report on methodological developments to acquire and analyze cellular Ca2+ signals with high temporal and spatial resolution with specific focus on (1) photobleaching of Ca2+ indicators at high acquisition rate, (2) determination of system noise and spatiotemporal detection limits, and (3) image processing.

ORAI1, STIM1/2, and RYR1 shape subsecond Ca2+ microdomains upon T cell activation.

The earliest intracellular signals that occur after T cell activation are local, subsecond Ca2+ microdomains. Here, we identified a Ca2+ entry component involved in Ca2+ microdomain formation in both unstimulated and stimulated T cells. In unstimulated T cells, spontaneously generated small Ca2+ microdomains required ORAI1, STIM1, and STIM2. Super-resolution microscopy of unstimulated T cells identified a circular subplasmalemmal region with a diameter of about 300 nm with preformed patches of colocalized ORAI1, ryanodine receptors (RYRs), and STIM1. Preformed complexes of STIM1 and ORAI1 in unstimulated cells were confirmed by coimmunoprecipitation and Förster resonance energy transfer studies. Furthermore, within the first second after T cell receptor (TCR) stimulation, the number of Ca2+ microdomains increased in the subplasmalemmal space, an effect that required ORAI1, STIM2, RYR1, and the Ca2+ mobilizing second messenger NAADP (nicotinic acid adenine dinucleotide phosphate). These results indicate that preformed clusters of STIM and ORAI1 enable local Ca2+ entry events in unstimulated cells. Upon TCR activation, NAADP-evoked Ca2+ release through RYR1, in coordination with Ca2+ entry through ORAI1 and STIM, rapidly increases the number of Ca2+ microdomains, thereby initiating spread of Ca2+ signals deeper into the cytoplasm to promote full T cell activation.

A new cerebral vessel benchmark dataset (CAPUT) for validation of image-based aneurysm deformation estimation algorithms.

Hemodynamic properties and deformation of vessel structures are assumed to be correlated to the initiation, development, and rupture of cerebral aneurysms. Therefore, precise quantification of wall motion is essential. However, using standard-of-care imaging data, approaches for patient-specific estimation of pulsatile deformation are prone to uncertainties due to, e.g., contrast agent inflow-related intensity changes and small deformation compared to the image resolution. A ground truth dataset that allows evaluating and finetuning algorithms for deformation estimation is lacking. We designed a flow phantom with deformable structures that resemble cerebral vessels and exhibit physiologically plausible deformation. The deformation was simultaneously recorded using a flat panel CT and a video camera, yielding video data with higher resolution and SNR, which was used to compute 'ground truth' structure deformation measures. The dataset was further applied to evaluate registration-based deformation estimation. The results illustrate that registration approaches can be used to estimate deformation with adequate precision. Yet, the accuracy depended on the registration parameters, illustrating the need to evaluate and finetune deformation estimation approaches by ground truth data. To fill the existing gap, the acquired benchmark dataset is provided freely available as the CAPUT (Cerebral Aneurysm PUlsation Testing) dataset, accessible at https://www.github.com/IPMI-ICNS-UKE/CAPUT .

Analysis of the influence of imaging-related uncertainties on cerebral aneurysm deformation quantification using a no-deformation physical flow phantom.

Cardiac-cycle related pulsatile aneurysm motion and deformation is assumed to provide valuable information for assessing cerebral aneurysm rupture risk. Accordingly, numerous studies addressed quantification of cerebral aneurysm wall motion and deformation. Most of them utilized in vivo imaging data, but image-based aneurysm deformation quantification is subject to pronounced uncertainties: unknown ground-truth deformation; image resolution in the order of the expected deformation; direct interplay between contrast agent inflow and image intensity. To analyze the impact of the uncertainties on deformation quantification, a multi-imaging modality ground-truth phantom study is performed. A physical flow phantom was designed that allowed simulating pulsatile flow through a variety of modeled cerebral vascular structures. The phantom was imaged using different modalities [MRI, CT, 3D-RA] and mimicking physiologically realistic flow conditions. Resulting image data was analyzed by an established registration-based approach for automated wall motion quantification. The data reveals severe dependency between contrast media inflow-related image intensity changes and the extent of estimated wall deformation. The study illustrates that imaging-related uncertainties affect the accuracy of cerebral aneurysm deformation quantification, suggesting that in vivo imaging studies have to be accompanied by ground-truth phantom experiments to foster data interpretation and to prove plausibility of the applied image analysis algorithms.

Frontrunners of T cell activation: Initial, localized Ca2+ signals mediated by NAADP and the type 1 ryanodine receptor.

The activation of T cells is the fundamental on switch for the adaptive immune system. Ca(2+) signaling is essential for T cell activation and starts as initial, short-lived, localized Ca(2+) signals. The second messenger nicotinic acid adenine dinucleotide phosphate (NAADP) forms rapidly upon T cell activation and stimulates early Ca(2+) signaling. We developed a high-resolution imaging technique using multiple fluorescent Ca(2+) indicator dyes to characterize these early signaling events and investigate the channels involved in NAADP-dependent Ca(2+) signals. In the first seconds of activation of either primary murine T cells or human Jurkat cells with beads coated with an antibody against CD3, we detected Ca(2+) signals with diameters close to the limit of detection and that were close to the activation site at the plasma membrane. In Jurkat cells in which the ryanodine receptor (RyR) was knocked down or in primary T cells from RyR1(-/-) mice, either these early Ca(2+) signals were not detected or the number of signals was markedly reduced. Local Ca(2+) signals observed within 20 ms upon microinjection of Jurkat cells with NAADP were also sensitive to RyR knockdown. In contrast, TRPM2 (transient receptor potential channel, subtype melastatin 2), a potential NAADP target channel, was not required for the formation of initial Ca(2+) signals in primary T cells. Thus, through our high-resolution imaging method, we characterized early Ca(2+) release events in T cells and obtained evidence for the involvement of RyR and NAADP in such signals.