Eomes is sufficient to regulate IL-10 expression and cytotoxic effector molecules in murine CD4+ T cells.

The T-box transcription factors T-bet and Eomesodermin regulate type 1 immune responses in innate and adaptive lymphocytes. T-bet is widely expressed in the immune system but was initially identified as the lineage-specifying transcription factor of Th1 CD4+ T cells, where it governs expression of the signature cytokine IFN- γ and represses alternative cell fates like Th2 and Th17. T-bet's paralog Eomes is less abundantly expressed and Eomes+ CD4+ T cells are mostly found in the context of persistent antigen exposure, like bone marrow transplantation, chronic infection or inflammation as well as malignant disorders. However, it has remained unresolved whether Eomes executes similar transcriptional activities as T-bet in CD4+ T cells. Here we use a novel genetic approach to show that Eomes expression in CD4+ T cells drives a distinct transcriptional program that shows only partial overlap with T-bet. We found that Eomes is sufficient to induce the expression of the immunoregulatory cytokine IL-10 and, together with T-bet, promotes a cytotoxic effector profile, including Prf1, Gzmb, Gzmk, Nkg7 and Ccl5, while repressing alternative cell fates. Our results demonstrate that Eomes+ CD4+ T cells, which are often found in the context of chronic antigen stimulation, are likely to be a unique CD4+ T cell subset that limits inflammation and immunopathology as well as eliminates antigen-presenting and malignant cells.

CANCOL, a Computer-Assisted Annotation Tool to Facilitate Colocalization and Tracking of Immune Cells in Intravital Microscopy.

Two-photon intravital microscopy (2P-IVM) has become a widely used technique to study cell-to-cell interactions in living organisms. Four-dimensional imaging data obtained via 2P-IVM are classically analyzed by performing automated cell tracking, a procedure that computes the trajectories followed by each cell. However, technical artifacts, such as brightness shifts, the presence of autofluorescent objects, and channel crosstalking, affect the specificity of imaging channels for the cells of interest, thus hampering cell detection. Recently, machine learning has been applied to overcome a variety of obstacles in biomedical imaging. However, existing methods are not tailored for the specific problems of intravital imaging of immune cells. Moreover, results are highly dependent on the quality of the annotations provided by the user. In this study, we developed CANCOL, a tool that facilitates the application of machine learning for automated tracking of immune cells in 2P-IVM. CANCOL guides the user during the annotation of specific objects that are problematic for cell tracking when not properly annotated. Then, it computes a virtual colocalization channel that is specific for the cells of interest. We validated the use of CANCOL on challenging 2P-IVM videos from murine organs, obtaining a significant improvement in the accuracy of automated tracking while reducing the time required for manual track curation.

Evaluation of an intensity-based algorithm for 2D/3D registration of natural knee videofluoroscopy data.

The accurate quantification of in-vivo tibio-femoral kinematics is essential for understanding joint functionality, but determination of the 3D pose of bones from 2D single-plane fluoroscopic images remains challenging. We aimed to evaluate the accuracy, reliability and repeatability of an intensity-based 2D/3D registration algorithm. The accuracy was evaluated using fluoroscopic images of 2 radiopaque bones in 18 different poses, compared against a gold-standard fiducial calibration device. In addition, 3 natural femora and 3 natural tibiae were used to examine registration reliability and repeatability. Both manual fitting and intensity-based registration exhibited a mean absolute error of <1 mm in-plane. Overall, intensity-based registration of the femoral bone model revealed significantly higher translational and rotational errors than manual fitting, while no statistical differences (except for y-axis translation) were found for the tibial bone model. The repeatability of 108 intensity-based registrations showed mean in-plane standard deviations of 0.23-0.56 mm, but out-of-plane position repeatability was lower (mean SD: femur 7.98 mm, tibia 6.96 mm). SDs for rotations averaged 0.77-2.52°. While the algorithm registered some images extremely well, other images clearly required manual intervention. When the algorithm registered the bones repeatably, it was also accurate, suggesting an approach that includes manual intervention could become practical for efficient and accurate registration.

3X-Knee: A Novel Technology for 3D Preoperative Planning and Postoperative Evaluation of TKA Based on 2D X-Rays.

This chapter introduces a solution called "3X-knee" that can robustly derive 3D models of the lower extremity from 2D long leg standing X-ray radiographs for preoperative planning and postoperative treatment evaluation of total knee arthroplasty (TKA). There are three core components in 3X-knee technology: (1) a knee joint immobilization apparatus, (2) an X-ray image calibration phantom, and (3) a statistical shape model-based 2D-3D reconstruction algorithm. These three components are integrated in a systematic way in 3X-knee to derive 3D models of the complete lower extremity from 2D long leg standing X-ray radiographs acquired in weight-bearing position. More specifically, the knee joint immobilization apparatus will be used to rigidly fix the X-ray calibration phantom with respect to the underlying anatomy during the image acquisition. The calibration phantom then serves two purposes. For one side, the phantom will allow one to calibrate the projection parameters of any acquired X-ray image. For the other side, the phantom also allowsone to track positions of multiple X-ray images of the underlying anatomy without using any additional positional tracker, which is a prerequisite condition for the third component to compute patient-specific 3D models from 2D X-ray images and the associated statistical shape models. Validation studies conducted on both simulated X-ray images and on patients' X-ray data demonstrate the efficacy of the present solution.

A novel technology for 3D knee prosthesis planning and treatment evaluation using 2D X-ray radiographs: a clinical evaluation.

PURPOSE:  To present a clinical validation of a novel technology called "3X" which allows for 3D prosthesis planning and treatment evaluation in total knee arthroplasty (TKA) using only 2D X-ray radiographs. MATERIALS AND METHODS:  After local institution review board approvals, 3X was evaluated on 43 cases (23 for preoperative planning and 20 for postoperative treatment evaluation). All the patients underwent CT scans according to a standard protocol. The results measured on the CT data were regarded as the ground truth. Additionally, two X-ray images were acquired for each affected leg and were used by 3X technology to derive patient-specific measurements of the leg. In total, we compared seven parameters for planning TKA and five parameters for postoperative prosthesis alignment. RESULTS:  Our experimental results demonstrated that the mean distances between the surface models reconstructed from 2D X-rays and the associated surface models obtained from 3D CT data were smaller than 1.5 mm. The average differences for all angular parameters were smaller than [Formula: see text]. In over 78% cases 3X technology derived the same femoral component size as the CT-based ground truth and this value went down to 70% when 3X technology was used to predict the size of tibial component. CONCLUSION:  3X is a technology that allows for true 3D preoperative planning and postoperative treatment evaluation based on 2D X-ray radiographs.

X-ray image calibration and its application to clinical orthopedics.

X-ray imaging is one of the most commonly used medical imaging modality. Albeit X-ray radiographs provide important clinical information for diagnosis, planning and post-operative follow-up, the challenging interpretation due to its 2D projection characteristics and the unknown magnification factor constrain the full benefit of X-ray imaging. In order to overcome these drawbacks, we proposed here an easy-to-use X-ray calibration object and developed an optimization method to robustly find correspondences between the 3D fiducials of the calibration object and their 2D projections. In this work we present all the details of this outlined concept. Moreover, we demonstrate the potential of using such a method to precisely extract information from calibrated X-ray radiographs for two different orthopedic applications: post-operative acetabular cup implant orientation measurement and 3D vertebral body displacement measurement during preoperative traction tests. In the first application, we have achieved a clinically acceptable accuracy of below 1° for both anteversion and inclination angles, where in the second application an average displacement of 8.06±3.71 mm was measured. The results of both applications indicate the importance of using X-ray calibration in the clinical routine.