Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, specular reflections, floating objects and pixel saturation impede the visual interpretation and the automated analysis of endoscopy videos. Given the widespread use of endoscopy in different clinical applications, robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem. Existing state-of-the-art methods only deal with the detection and restoration of selected artifacts. However, typically endoscopy videos contain numerous artifacts which motivates to establish a comprehensive solution. In this paper, a fully automatic framework is proposed that can: 1) detect and classify six different artifacts, 2) segment artifact instances that have indefinable shapes, 3) provide a quality score for each frame, and 4) restore partially corrupted frames. To detect and classify different artifacts, the proposed framework exploits fast, multi-scale and single stage convolution neural network detector. In addition, we use an encoder-decoder model for pixel-wise segmentation of irregular shaped artifacts. A quality score is introduced to assess video frame quality and to predict image restoration success. Generative adversarial networks with carefully chosen regularization and training strategies for discriminator-generator networks are finally used to restore corrupted frames. The detector yields the highest mean average precision (mAP) of 45.7 and 34.7, respectively for 25% and 50% IoU thresholds, and the lowest computational time of 88 ms allowing for near real-time processing. The restoration models for blind deblurring, saturation correction and inpainting demonstrate significant improvements over previous methods. On a set of 10 test videos, an average of 68.7% of video frames successfully passed the quality score (≥0.9) after applying the proposed restoration framework thereby retaining 25% more frames compared to the raw videos. The importance of artifacts detection and their restoration on improved robustness of image analysis methods is also demonstrated in this work.

ZBP1 is an interferon‐induced cytosolic nucleic acid sensor that facilitates antiviral responses via RIPK3. Although ZBP1‐mediated programmed cell death is widely described, whether and how it promotes inflammatory signaling is unclear. Here, we report a ZBP1‐induced inflammatory signaling pathway mediated by K63‐ and M1‐linked ubiquitin chains, which depends on RIPK1 and RIPK3 as scaffolds independently of cell death. In human HT29 cells, ZBP1 associated with RIPK1 and RIPK3 as well as ubiquitin ligases cIAP1 and LUBAC. ZBP1‐induced K63‐ and M1‐linked ubiquitination of RIPK1 and ZBP1 to promote TAK1‐ and IKK‐mediated inflammatory signaling and cytokine production. Inhibition of caspase activity suppressed ZBP1‐induced cell death but enhanced cytokine production in a RIPK1‐ and RIPK3 kinase activity‐dependent manner. Lastly, we provide evidence that ZBP1 signaling contributes to SARS‐CoV‐2‐induced cytokine production. Taken together, we describe a ZBP1‐RIPK3‐RIPK1‐mediated inflammatory signaling pathway relayed by the scaffolding role of RIPKs and regulated by caspases, which may induce inflammation when ZBP1 is activated below the threshold needed to trigger a cell death response.

Diseases such as cancer involve alterations in cell proportions, states and interactions, as well as complex changes in tissue morphology and architecture. Histopathological diagnosis of disease and most multiplexed spatial profiling relies on inspecting thin (4–5 µm) specimens. Here we describe a high-plex cyclic immunofluorescence method for three-dimensional tissue imaging and use it to show that few, if any, cells are intact in conventional thin tissue sections, reducing the accuracy of cell phenotyping and interaction analysis. However, three-dimensional cyclic immunofluorescence of sections eightfold to tenfold thicker enables accurate morphological assessment of diverse protein markers in intact tumor, immune and stromal cells. Moreover, the high resolution of this confocal approach generates images of cells in a preserved tissue environment at a level of detail previously limited to cell culture. Precise imaging of cell membranes also makes it possible to detect and map cell–cell contacts and juxtracrine signaling complexes in immune cell niches.

Mitotic centrosomes are formed when centrioles start to recruit large amounts of pericentriolar material (PCM) around themselves in preparation for mitosis. This centrosome “maturation” requires the centrioles and also Polo/PLK1 protein kinase. The PCM comprises several hundred proteins and, in Drosophila, Polo cooperates with the conserved centrosome proteins Spd‐2/CEP192 and Cnn/CDK5RAP2 to assemble a PCM scaffold around the mother centriole that then recruits other PCM client proteins. We show here that in Drosophila syncytial blastoderm embryos, centrosomal Polo levels rise and fall during the assembly process—peaking, and then starting to decline, even as levels of the PCM scaffold continue to rise and plateau. Experiments and mathematical modelling indicate that a centriolar pulse of Polo activity, potentially generated by the interaction between Polo and its centriole receptor Ana1 (CEP295 in humans), could explain these unexpected scaffold assembly dynamics. We propose that centrioles generate a local pulse of Polo activity prior to mitotic entry to initiate centrosome maturation, explaining why centrioles and Polo/PLK1 are normally essential for this process.

Correct cell/cell interactions and motion dynamics are fundamental in tissue homeostasis, and defects in these cellular processes cause diseases. Therefore, there is strong interest in identifying factors, including drug candidates that affect cell/cell interactions and motion dynamics. However, existing quantitative tools for systematically interrogating complex motion phenotypes in timelapse datasets are limited. We present Motion Sensing Superpixels (MOSES), a computational framework that measures and characterises biological motion with a unique superpixel ‘mesh’ formulation. Using published datasets, MOSES demonstrates single-cell tracking capability and more advanced population quantification than Particle Image Velocimetry approaches. From > 190 co-culture videos, MOSES motion-mapped the interactions between human esophageal squamous epithelial and columnar cells mimicking the esophageal squamous-columnar junction, a site where Barrett’s esophagus and esophageal adenocarcinoma often arise clinically. MOSES is a powerful tool that will facilitate unbiased, systematic analysis of cellular dynamics from high-content time-lapse imaging screens with little prior knowledge and few assumptions.