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Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but standard analysis algorithms require sparse emitters, which limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE (deep context dependent), a computational tool that can localize single emitters at high density in three dimensions with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 datasets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to acquire fast dynamic live-cell SMLM data with reduced light exposure and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many laboratories to reduce imaging times and increase localization density in SMLM.DECODE uses deep learning for localizing single emitters in high-density two-dimensional and three-dimensional single-molecule localization microscopy data. DECODE outperforms available methods and enables fast live-cell SMLM of dynamic processes.

The point spread function (PSF) of a microscope describes the image of a point emitter. Knowing the accurate PSF model is essential for various imaging tasks, including single-molecule localization, aberration correction and deconvolution. Here we present universal inverse modeling of point spread functions (uiPSF), a toolbox to infer accurate PSF models from microscopy data, using either image stacks of fluorescent beads or directly images of blinking fluorophores, the raw data in single-molecule localization microscopy (SMLM). Our modular framework is applicable to a variety of microscope modalities and the PSF model incorporates system- or sample-specific characteristics, for example, the bead size, field- and depth- dependent aberrations, and transformations among channels. We demonstrate its application in single or multiple channels or large field-of-view SMLM systems, 4Pi-SMLM, and lattice light-sheet microscopes using either bead data or single-molecule blinking data. uiPSF (universal inverse modeling of point spread functions) offers a versatile solution for inferring accurate PSF models from images of beads and blinking fluorophores, enabling improved imaging and image processing in localization microscopy.

Single molecule localization microscopy (SMLM) allows deciphering the nanoscale organization and dynamics of biomolecules in their native environment with unprecedented resolution. While SMLM was quickly adopted by the scientific community for its performance and simple instrumentation, it still remains limited in its in-depth capability, precluding many biological processes to be investigated. We here present a solution to perform in-depth volumetric SMLM, called soSMARt. It relies on innovative microfabricated devices allowing both single-objective light-sheet microscopy, aberrations correction via adaptive optics, and real-time feedback-loop registration with nanometric precision. We illustrated the performances of soSMARt to assess the 3D nanoscale organization of several protein of interest in isolated cells, and explore optimizations and proof-of-concepts for the investigation of more complex tissues such as 3D cell cultures. We believe our method addresses key limitations of single molecule microscopy, paving the way for novel biological applications. Galland et al. present soSMARt, a method for in-depth single molecule localisation microscopy using microfabricated devices, which enables single-objective light-sheet microscopy, adaptive optics correction, real-time registration, and axially extended volume reconstruction with nanometer precision.

PurposeAs human failure has been shown to be one primary cause for post-operative death, surgical training is of the utmost socioeconomic importance. In this context, the concept of surgical telestration has been introduced to enable experienced surgeons to efficiently and effectively mentor trainees in an intuitive way. While previous approaches to telestration have concentrated on overlaying drawings on surgical videos, we explore the augmented reality (AR) visualization of surgical hands to imitate the direct interaction with the situs.MethodsWe present a real-time hand tracking pipeline specifically designed for the application of surgical telestration. It comprises three modules, dedicated to (1) the coarse localization of the expert’s hand and the subsequent (2) segmentation of the hand for AR visualization in the field of view of the trainee and (3) regression of keypoints making up the hand’s skeleton. The semantic representation is obtained to offer the ability for structured reporting of the motions performed as part of the teaching.ResultsAccording to a comprehensive validation based on a large data set comprising more than 14,000 annotated images with varying application-relevant conditions, our algorithm enables real-time hand tracking and is sufficiently accurate for the task of surgical telestration. In a retrospective validation study, a mean detection accuracy of 98%, a mean keypoint regression accuracy of 10.0 px and a mean Dice Similarity Coefficient of 0.95 were achieved. In a prospective validation study, it showed uncompromised performance when the sensor, operator or gesture varied.ConclusionDue to its high accuracy and fast inference time, our neural network-based approach to hand tracking is well suited for an AR approach to surgical telestration. Future work should be directed to evaluating the clinical value of the approach.

Objective: To optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis. Summary Background Data: Esophagectomy is the mainstay of esophageal cancer treatment but anastomotic insufficiency related morbidity and mortality remain challenging for patient outcome. Methods: A live porcine model (n=50) for MIE was used with gastric conduit formation and linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic site was evaluated using HSI and was validated with histopathology. Results: The tissue oxygenation (ΔStO2) after the anastomosis remained constant only for the short stapler in caudal position (-0.4±4.4%, n.s.) while it dropped markedly in the other groups (short-cranial: -15.6±11.5%, p=0.0002; long-cranial: -20.4±7.6%, p=0.0126; long-caudal: -16.1±9.4%, p<0.0001) Tissue samples from deoxygenated stomach as measured by HSI showed correspondent eosinophilic pre-necrotic changes in 35.7±9.7% of the surface area. Conclusions: Tissue oxygenation at the anastomotic site of the gastric conduit during MIE is influenced by stapling technique. Optimal oxygenation was achieved with a short stapler (3 cm) and sufficient distance of the anastomosis to the cranial end of the gastric conduit. HSI tissue deoxygenation corresponded to histopathologic necrotic tissue changes. These findings allow for optimization of gastric conduit perfusion and anastomotic technique in MIE. Competing Interest Statement The authors have declared no competing interest.