Highly Efficient Gene Editing of Cystic Fibrosis Patient-Derived Airway Basal Cells Results in Functional CFTR Correction

There is a strong rationale to consider future cell therapeutic approaches for cystic fibrosis (CF) in which autologous proximal airway basal stem cells, corrected for CFTR mutations, are transplanted into the patient's lungs. We assessed the possibility of editing the CFTR locus in these cells using zinc-finger nucleases and have pursued two approaches. The first, mutation-specific correction, is a footprint-free method replacing the CFTR mutation with corrected sequences. We have applied this approach for correction of ΔF508, demonstrating restoration of mature CFTR protein and function in air-liquid interface cultures established from bulk edited basal cells. The second is targeting integration of a partial CFTR cDNA within an intron of the endogenous CFTR gene, providing correction for all CFTR mutations downstream of the integration and exploiting the native CFTR promoter and chromatin architecture for physiologically relevant expression. Without selection, we observed highly efficient, site-specific targeted integration in basal cells carrying various CFTR mutations and demonstrated restored CFTR function at therapeutically relevant levels. Significantly, Omni-ATAC-seq analysis revealed minimal impact on the positions of open chromatin within the native CFTR locus. These results demonstrate efficient functional correction of CFTR and provide a platform for further ex vivo and in vivo editing. © 2020 The American Society of Gene and Cell TherapySuzuki et al. report correction of the CFTR defect in cystic fibrosis airway basal stem cells. They utilized gene-editing strategies either specific for the ΔF508 CFTR mutation or applicable to most CFTR mutations. Both approaches yielded highly efficient correction without selection, restoring CFTR function to therapeutically relevant levels

Markerless 3D Human Pose Tracking in the Wild with fusion of Multiple Depth Cameras: Comparative Experimental Study with Kinect 2 and 3

International audienceHuman-robot interaction requires a robust estimate of human motion in real-time. This work presents a fusion algorithm for joint center positions tracking from multiple depth cameras to improve human motion analysis accuracy. The main contribution is the proposed algorithm based on body tracking measurements fusion with an extended Kalman filter and anthropomorphic constraints, independent of sensors. As an illustration of the use of this algorithm, this paper presents the direct comparison of joint center positions estimated with a reference stereophotogrammetric system and the ones estimated with the new Kinect 3 (Azure Kinect) sensor and its older version the Kinect 2 (Kinect for Windows). The experiment was made in two parts, one for each model of Kinect, by comparing raw and merging body tracking data of two sided Kinect with the proposed algorithm. The proposed approach improves body tracker data for Kinect 3 which has not the same characteristics as Kinect 2. This study shows also the importance of defining good heuristics to merge data depending on how the body tracking works. Thus, with proper heuristics, the joint center position estimates are improved by at least 14.6 %. Finally, we propose an additional comparison between Kinect 2 and Kinect 3 exhibiting the pros and cons of the two sensors