Time-resolved role of P2X4 and P2X7 during CD8+ T cell activation.

CD8+ T cells are a crucial part of the adaptive immune system, responsible for combating intracellular pathogens and tumor cells. The initial activation of T cells involves the formation of highly dynamic Ca2+ microdomains. Recently, purinergic signaling was shown to be involved in the formation of the initial Ca2+ microdomains in CD4+ T cells. In this study, the role of purinergic cation channels, particularly P2X4 and P2X7, in CD8+ T cell signaling from initial events to downstream responses was investigated, focusing on various aspects of T cell activation, including Ca2+ microdomains, global Ca2+ responses, NFAT-1 translocation, cytokine expression, and proliferation. While Ca2+ microdomain formation was significantly reduced in the first milliseconds to seconds in CD8+ T cells lacking P2X4 and P2X7 channels, global Ca2+ responses over minutes were comparable between wild-type (WT) and knockout cells. However, the onset velocity was reduced in P2X4-deficient cells, and P2X4, as well as P2X7-deficient cells, exhibited a delayed response to reach a certain level of free cytosolic Ca2+ concentration ([Ca2+]i). NFAT-1 translocation, a crucial transcription factor in T cell activation, was also impaired in CD8+ T cells lacking P2X4 and P2X7. In addition, the expression of IFN-γ, a major pro-inflammatory cytokine produced by activated CD8+ T cells, and Nur77, a negative regulator of T cell activation, was significantly reduced 18h post-stimulation in the knockout cells. In line, the proliferation of T cells after 3 days was also impaired in the absence of P2X4 and P2X7 channels. In summary, the study demonstrates that purinergic signaling through P2X4 and P2X7 enhances initial Ca2+ events during CD8+ T cell activation and plays a crucial role in regulating downstream responses, including NFAT-1 translocation, cytokine expression, and proliferation on multiple timescales. These findings suggest that targeting purinergic signaling pathways may offer potential therapeutic interventions.

Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT.

BACKGROUND: Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respiratory intrafractional tumor motion. Commonly, an external breathing signal is continuously acquired that serves as a surrogate of the tumor motion and forms the basis of strategies like breathing-guided imaging and gated dose delivery. However, due to inherent system latencies, there exists a temporal lag between the acquired respiratory signal and the system response. Respiratory signal prediction models aim to compensate for the time delays and to improve imaging and dose delivery. PURPOSE: The present study explores and compares six state-of-the-art machine and deep learning-based prediction models, focusing on real-time and real-world applicability. All models and data are provided as open source and data to ensure reproducibility of the results and foster reuse. METHODS: The study was based on 2502 breathing signals ( t t o t a l ≈ 90 $t_{total} \approx 90$  h) acquired during clinical routine, split into independent training (50%), validation (20%), and test sets (30%). Input signal values were sampled from noisy signals, and the target signal values were selected from corresponding denoised signals. A standard linear prediction model (Linear), two state-of-the-art models in general univariate signal prediction (Dlinear, Xgboost), and three deep learning models (Lstm, Trans-Enc, Trans-TSF) were chosen. The prediction performance was evaluated for three different prediction horizons (480, 680, and 920 ms). Moreover, the robustness of the different models when applied to atypical, that is, out-of-distribution (OOD) signals, was analyzed. RESULTS: The Lstm model achieved the lowest normalized root mean square error for all prediction horizons. The prediction errors only slightly increased for longer horizons. However, a substantial spread of the error values across the test signals was observed. Compared to typical, that is, in-distribution test signals, the prediction accuracy of all models decreased when applied to OOD signals. The more complex deep learning models Lstm and Trans-Enc showed the least performance loss, while the performance of simpler models like Linear dropped the most. Except for Trans-Enc, inference times for the different models allowed for real-time application. CONCLUSION: The application of the Lstm model achieved the lowest prediction errors. Simpler prediction filters suffer from limited signal history access, resulting in a drop in performance for OOD signals.

Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches.

Background: Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values. Materials and methods: Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit). Results: The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration. Conclusions: For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.

Deep learning-based conditional inpainting for restoration of artifact-affected 4D CT images.

BACKGROUND: 4D CT imaging is an essential component of radiotherapy of thoracic and abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality and image information reliability. PURPOSE: In this work, deep learning (DL)-based conditional inpainting is proposed to restore anatomically correct image information of artifact-affected areas. METHODS: The restoration approach consists of a two-stage process: DL-based detection of common interpolation (INT) and double structure (DS) artifacts, followed by conditional inpainting applied to the artifact areas. In this context, conditional refers to a guidance of the inpainting process by patient-specific image data to ensure anatomically reliable results. The study is based on 65 in-house 4D CT images of lung cancer patients (48 with only slight artifacts, 17 with pronounced artifacts) and two publicly available 4D CT data sets that serve as independent external test sets. RESULTS: Automated artifact detection revealed a ROC-AUC of 0.99 for INT and of 0.97 for DS artifacts (in-house data). The proposed inpainting method decreased the average root mean squared error (RMSE) by 52 % (INT) and 59 % (DS) for the in-house data. For the external test data sets, the RMSE improvement is similar (50 % and 59 %, respectively). Applied to 4D CT data with pronounced artifacts (not part of the training set), 72 % of the detectable artifacts were removed. CONCLUSIONS: The results highlight the potential of DL-based inpainting for restoration of artifact-affected 4D CT data. Compared to recent 4D CT inpainting and restoration approaches, the proposed methodology illustrates the advantages of exploiting patient-specific prior image information.

Dose reduction in sequence scanning 4D CT imaging through respiratory signal-guided tube current modulation: A feasibility study.

BACKGROUND: Respiratory signal-guided 4D CT sequence scanning such as the recently introduced Intelligent 4D CT (i4DCT) approach reduces image artifacts compared to conventional 4D CT, especially for irregular breathing. i4DCT selects beam-on periods during scanning such that data sufficiency conditions are fulfilled for each couch position. However, covering entire breathing cycles during beam-on periods leads to redundant projection data and unnecessary dose to the patient during long exhalation phases. PURPOSE: We propose and evaluate the feasibility of respiratory signal-guided dose modulation (i.e., temporary reduction of the CT tube current) to reduce the i4DCT imaging dose while maintaining high projection data coverage for image reconstruction. METHODS: The study is designed as an in-silico feasibility study. Dose down- and up-regulation criteria were defined based on the patients' breathing signals and their representative breathing cycle learned before and during scanning. The evaluation (including an analysis of the impact of the dose modulation criteria parameters) was based on 510 clinical 4D CT breathing curves. Dose reduction was determined as the fraction of the downregulated dose delivery time to the overall beam-on time. Furthermore, under the assumption of a 10-phase 4D CT and amplitude-based reconstruction, beam-on periods were considered negatively affected by dose modulation if the downregulation period covered an entire phase-specific amplitude range for a specific breathing phase (i.e., no appropriate reconstruction of the phase image possible for this specific beam-on period). Corresponding phase-specific amplitude bins are subsequently denoted as compromised bins. RESULTS: Dose modulation resulted in a median dose reduction of 10.4% (lower quartile: 7.4%, upper quartile: 13.8%, maximum: 28.6%; all values corresponding to a default parameterization of the dose modulation criteria). Compromised bins were observed in 1.0% of the beam-on periods (72 / 7370 periods) and affected 10.6% of the curves (54/510 curves). The extent of possible dose modulation depends strongly on the individual breathing patterns and is weakly correlated with the median breathing cycle length (Spearman correlation coefficient 0.22, p < 0.001). Moreover, the fraction of beam-on periods with compromised bins is weakly anti-correlated with the patient's median breathing cycle length (Spearman correlation coefficient -0.24; p < 0.001). Among the curves with the 17% longest average breathing cycles, no negatively affected beam-on periods were observed. CONCLUSION: Respiratory signal-guided dose modulation for i4DCT imaging is feasible and promises to significantly reduce the imaging dose with little impact on projection data coverage. However, the impact on image quality remains to be investigated in a follow-up study.

Self-Supervision for Medical Image Classification: State-of-the-Art Performance with ~100 Labeled Training Samples per Class.

Is self-supervised deep learning (DL) for medical image analysis already a serious alternative to the de facto standard of end-to-end trained supervised DL? We tackle this question for medical image classification, with a particular focus on one of the currently most limiting factor of the field: the (non-)availability of labeled data. Based on three common medical imaging modalities (bone marrow microscopy, gastrointestinal endoscopy, dermoscopy) and publicly available data sets, we analyze the performance of self-supervised DL within the self-distillation with no labels (DINO) framework. After learning an image representation without use of image labels, conventional machine learning classifiers are applied. The classifiers are fit using a systematically varied number of labeled data (1-1000 samples per class). Exploiting the learned image representation, we achieve state-of-the-art classification performance for all three imaging modalities and data sets with only a fraction of between 1% and 10% of the available labeled data and about 100 labeled samples per class.

Discordant and Converting Receptor Expressions in Brain Metastases from Breast Cancer: MRI-Based Non-Invasive Receptor Status Tracking.

Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Therefore, personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted treatment options. Radiological in vivo techniques may allow receptor status tracking at high frequencies at low risk and cost. The present study aims to investigate the potential of receptor status prediction through machine-learning-based analysis of radiomic MR image features. The analysis is based on 412 brain metastases samples from 106 patients acquired between 09/2007 and 09/2021. Inclusion criteria were as follows: diagnosed cerebral metastases from breast cancer; histopathology reports on progesterone (PR), estrogen (ER), and human epidermal growth factor 2 (HER2) receptor status; and availability of MR imaging data. In total, 3367 quantitative features of T1 contrast-enhanced, T1 non-enhanced, and FLAIR images and corresponding patient age were evaluated utilizing random forest algorithms. Feature importance was assessed using Gini impurity measures. Predictive performance was tested using 10 permuted 5-fold cross-validation sets employing the 30 most important features of each training set. Receiver operating characteristic areas under the curves of the validation sets were 0.82 (95% confidence interval [0.78; 0.85]) for ER+, 0.73 [0.69; 0.77] for PR+, and 0.74 [0.70; 0.78] for HER2+. Observations indicate that MR image features employed in a machine learning classifier could provide high discriminatory accuracy in predicting the receptor status of brain metastases from breast cancer.

Adhesion to laminin-1 and collagen IV induces the formation of Ca2+ microdomains that sensitize mouse T cells for activation.

During an immune response, T cells migrate from blood vessel walls into inflamed tissues by migrating across the endothelium and through extracellular matrix (ECM). Integrins facilitate T cell binding to endothelial cells and ECM proteins. Here, we report that Ca2+ microdomains observed in the absence of T cell receptor (TCR)/CD3 stimulation are initial signaling events triggered by adhesion to ECM proteins that increase the sensitivity of primary murine T cells to activation. Adhesion to the ECM proteins collagen IV and laminin-1 increased the number of Ca2+ microdomains in a manner dependent on the kinase FAK, phospholipase C (PLC), and all three inositol 1,4,5-trisphosphate receptor (IP3R) subtypes and promoted the nuclear translocation of the transcription factor NFAT-1. Mathematical modeling predicted that the formation of adhesion-dependent Ca2+ microdomains required the concerted activity of two to six IP3Rs and ORAI1 channels to achieve the increase in the Ca2+ concentration in the ER-plasma membrane junction that was observed experimentally and that required SOCE. Further, adhesion-dependent Ca2+ microdomains were important for the magnitude of the TCR-induced activation of T cells on collagen IV as assessed by the global Ca2+ response and NFAT-1 nuclear translocation. Thus, adhesion to collagen IV and laminin-1 sensitizes T cells through a mechanism involving the formation of Ca2+ microdomains, and blocking this low-level sensitization decreases T cell activation upon TCR engagement.

Clinical application of breathing-adapted 4D CT: image quality comparison to conventional 4D CT.

PURPOSE: 4D CT imaging is an integral part of 4D radiotherapy workflows. However, 4D CT data often contain motion artifacts that mitigate treatment planning. Recently, breathing-adapted 4D CT (i4DCT) was introduced into clinical practice, promising artifact reduction in in-silico and phantom studies. Here, we present an image quality comparison study, pooling clinical patient data from two centers: a new i4DCT and a conventional spiral 4D CT patient cohort. METHODS: The i4DCT cohort comprises 129 and the conventional spiral 4D CT cohort 417 4D CT data sets of lung and liver tumor patients. All data were acquired for treatment planning. The study consists of three parts: illustration of image quality in selected patients of the two cohorts with similar breathing patterns; an image quality expert rater study; and automated analysis of the artifact frequency. RESULTS: Image data of the patients with similar breathing patterns underline artifact reduction by i4DCT compared to conventional spiral 4D CT. Based on a subgroup of 50 patients with irregular breathing patterns, the rater study reveals a fraction of almost artifact-free scans of 89% for i4DCT and only 25% for conventional 4D CT; the quantitative analysis indicated a reduction of artifact frequency by 31% for i4DCT. CONCLUSION: The results demonstrate 4D CT image quality improvement for patients with irregular breathing patterns by breathing-adapted 4D CT in this first corresponding clinical data image quality comparison study.

DARTS: an open-source Python pipeline for Ca2+ microdomain analysis in live cell imaging data.

Ca2+ microdomains play a key role in intracellular signaling processes. For instance, they mediate the activation of T cells and, thus, the initial adaptive immune system. They are, however, also of utmost importance for activation of other cells, and a detailed understanding of the dynamics of these spatially localized Ca2+ signals is crucial for a better understanding of the underlying signaling processes. A typical approach to analyze Ca2+ microdomain dynamics is live cell fluorescence microscopy imaging. Experiments usually involve imaging a larger number of cells of different groups (for instance, wild type and knockout cells), followed by a time consuming image and data analysis. With DARTS, we present a modular Python pipeline for efficient Ca2+ microdomain analysis in live cell imaging data. DARTS (Deconvolution, Analysis, Registration, Tracking, and Shape normalization) provides state-of-the-art image postprocessing options like deep learning-based cell detection and tracking, spatio-temporal image deconvolution, and bleaching correction. An integrated automated Ca2+ microdomain detection offers direct access to global statistics like the number of microdomains for cell groups, corresponding signal intensity levels, and the temporal evolution of the measures. With a focus on bead stimulation experiments, DARTS provides a so-called dartboard projection analysis and visualization approach. A dartboard projection covers spatio-temporal normalization of the bead contact areas and cell shape normalization onto a circular template that enables aggregation of the spatiotemporal information of the microdomain detection results for the individual cells of the cell groups of interest. The dartboard visualization allows intuitive interpretation of the spatio-temporal microdomain dynamics at the group level. The application of DARTS is illustrated by three use cases in the context of the formation of initial Ca2+ microdomains after cell stimulation. DARTS is provided as an open-source solution and will be continuously extended upon the feedback of the community. Code available at: 10.5281/zenodo.10459243.

Evaluation of magnetic resonance imaging-based radiomics characteristics for differentiation of benign and malignant peripheral nerve sheath tumors in neurofibromatosis type 1.

BACKGROUND: Patients with neurofibromatosis type 1 (NF1) develop benign (BPNST), premalignant atypical (ANF), and malignant (MPNST) peripheral nerve sheath tumors. Radiological differentiation of these entities is challenging. Therefore, we aimed to evaluate the value of a magnetic resonance imaging (MRI)-based radiomics machine-learning (ML) classifier for differentiation of these three entities of internal peripheral nerve sheath tumors in NF1 patients. METHODS: MRI was performed at 3T in 36 NF1 patients (20 male; age: 31 ± 11 years). Segmentation of 117 BPNSTs, 17 MPNSTs, and 8 ANFs was manually performed using T2w spectral attenuated inversion recovery sequences. One hundred seven features per lesion were extracted using PyRadiomics and applied for BPNST versus MPNST differentiation. A 5-feature radiomics signature was defined based on the most important features and tested for signature-based BPNST versus MPNST classification (random forest [RF] classification, leave-one-patient-out evaluation). In a second step, signature feature expressions for BPNSTs, ANFs, and MPNSTs were evaluated for radiomics-based classification for these three entities. RESULTS: The mean area under the receiver operator characteristic curve (AUC) for the radiomics-based BPNST versus MPNST differentiation was 0.94, corresponding to correct classification of on average 16/17 MPNSTs and 114/117 BPNSTs (sensitivity: 94%, specificity: 97%). Exploratory analysis with the eight ANFs revealed intermediate radiomic feature characteristics in-between BPNST and MPNST tumor feature expression. CONCLUSION: In this proof-of-principle study, ML using MRI-based radiomics characteristics allows sensitive and specific differentiation of BPNSTs and MPNSTs in NF1 patients. Feature expression of premalignant atypical tumors was distributed in-between benign and malignant tumor feature expressions, which illustrates biological plausibility of the considered radiomics characteristics.

P2X4 and P2X7 are essential players in basal T cell activity and Ca2+ signaling milliseconds after T cell activation.

Initial T cell activation is triggered by the formation of highly dynamic, spatiotemporally restricted Ca2+ microdomains. Purinergic signaling is known to be involved in Ca2+ influx in T cells at later stages compared to the initial microdomain formation. Using a high-resolution Ca2+ live-cell imaging system, we show that the two purinergic cation channels P2X4 and P2X7 not only are involved in the global Ca2+ signals but also promote initial Ca2+ microdomains tens of milliseconds after T cell stimulation. These Ca2+ microdomains were significantly decreased in T cells from P2rx4-/- and P2rx7-/- mice or by pharmacological inhibition or blocking. Furthermore, we show a pannexin-1-dependent activation of P2X4 in the absence of T cell receptor/CD3 stimulation. Subsequently, upon T cell receptor/CD3 stimulation, ATP release is increased and autocrine activation of both P2X4 and P2X7 then amplifies initial Ca2+ microdomains already in the first second of T cell activation.

First clinical evaluation of breathing controlled four-dimensional computed tomography imaging.

BACKGROUND AND PURPOSE: Four-dimensional computed tomography (4DCT) has become an essential part of radiotherapy planning but is often affected by artifacts. A new breathing controlled 4DCT (i4DCT) algorithm has been introduced. This study aims to present the first clinical data and to evaluate the achieved image quality, projection data coverage and beam-on time. MATERIAL & METHODS: The analysis included i4DCT data for 129 scans of patients with thoracic tumors. Projection data coverage and beam-on time were evaluated. Additionally, image quality was exemplarily discussed and rated by ten clinical experts with a 5-score-scale for 30 patients with large variations in their breathing pattern ('challenging subgroup'). Rated images were reconstructed amplitude- and phase-based. RESULTS: Expert scoring revealed that 78% (amplitude-based) and 63% (phase-based) of the challenging subgroup were artifact-free (rating ≥4). For the entire cohort, average beam-on time per couch position was 4.9 ± 1.6 s. For the challenging subgroup, time increased slightly but not significantly compared to the remaining patients (5.1 s vs. 4.9 s; p = 0.64). Median projection data coverage was 93% and 94% for inhalation and exhalation, respectively, for the entire cohort. The comparison for the subgroup and the remaining patients revealed a small but significant decrease of the median coverage values for the challenging cases (inhalation: 90% vs. 94%, p = 0.02; exhalation: 93% vs. 94%, p = 0.02). CONCLUSIONS: This first clinical evaluation of i4DCT shows very promising results in terms of image quality and projection data coverage. The results agree with and support the results of previous i4DCT phantom studies.

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.

Time-Dependent Image Restoration of Low-SNR Live-Cell Ca2 Fluorescence Microscopy Data.

Live-cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes on the subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.

Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study.

Background and Purpose: Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by visual inspection of X-ray digital subtraction angiography data. However, expert-based TICI scoring is highly observer-dependent. This represents a major obstacle for mechanical thrombectomy outcome comparison in, for instance, multicentric clinical studies. Focusing on occlusions of the M1 segment of the middle cerebral artery, the present study aimed to develop a deep learning (DL) solution to automated and, therefore, objective TICI scoring, to evaluate the agreement of DL- and expert-based scoring, and to compare corresponding numbers to published scoring variability of clinical experts. Methods: The study comprises 2 independent datasets. For DL system training and initial evaluation, an in-house dataset of 491 digital subtraction angiography series and modified TICI scores of 236 patients with M1 occlusions was collected. To test the model generalization capability, an independent external dataset with 95 digital subtraction angiography series was analyzed. Characteristics of the DL system were modeling TICI scoring as ordinal regression, explicit consideration of the temporal image information, integration of physiological knowledge, and modeling of inherent TICI scoring uncertainties. Results: For the in-house dataset, the DL system yields Cohen's kappa, overall accuracy, and specific agreement values of 0.61, 71%, and 63% to 84%, respectively, compared with the gold standard: the expert rating. Values slightly drop to 0.52/64%/43% to 87% when the model is, without changes, applied to the external dataset. After model updating, they increase to 0.65/74%/60% to 90%. Literature Cohen's kappa values for expert-based TICI scoring agreement are in the order of 0.6. Conclusions: The agreement of DL- and expert-based modified TICI scores in the range of published interobserver variability of clinical experts highlights the potential of the proposed DL solution to automated TICI scoring.

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.