Skeletal stem/progenitor cells (SSPCs) in the bone marrow can differentiate into osteoblasts or adipocytes in response to microenvironmental signalling input, including hormonal signalling. Glucocorticoids (GC) are corticosteroid hormones that promote adipogenic differentiation and are endogenously increased in patients with Cushing´s syndrome (CS). Here, we investigate bone marrow adiposity changes in response to endogenous or exogenous GC increases. For that, we characterize bone biopsies from patients with CS and post-menopausal women with glucocorticoid-induced osteoporosis (GC-O), compared to age-matched controls, including postmenopausal osteoporotic patients (PM-O).Transiliac crest bone biopsies from CS patients and healthy controls, and from postmenopausal women with GC-O and matched controls were analysed; an additional cohort included biopsies from women with PM-O. Plastic-embedded biopsies were sectioned for histomorphometric characterization and quantification of adipocytes. The fraction of adipocyte area per tissue (Ad.Ar/T.Ar) and marrow area (Ad.Ar/Ma.Ar), mean adipocyte profile area (Ad.Pf.Ar) and adipocyte profile density (N.Ad.Pf/Ma.Ar) were determined and correlated to steroid levels. Furthermore, the spatial distribution of adipocytes in relation to trabecular bone was characterized and correlations between bone marrow adiposity and bone remodeling parameters investigated.Biopsies from patients with CS and GC-O presented increased Ad.Ar/Ma.Ar, along with adipocyte hypertrophy and hyperplasia. In patients with CS, both Ad.Ar/Ma.Ar and Ad.Pf.Ar significantly correlated with serum cortisol levels. Spatial distribution analyses revealed that, in CS, the increase in Ad.Ar/Ma.Ar near to trabecular bone (<100 µm) was mediated by both adipocyte hypertrophy and hyperplasia, while N.Ad.Pf/Ma.Ar further into the marrow (>100 µm) remained unchanged. In contrast, patients with GC-O only presented increased Ad.Ar/Ma.Ar and mean Ad.Pf.Ar>100 µm from trabecular bone surface, highlighting the differential effect of increased endogenous steroid accumulation. Finally, the Ad.Ar/Ma.Ar and Ad.Ar/T.Ar correlated with the canopy coverage above remodeling events.Increased cortisol production in patients with CS induces increased bone marrow adiposity, primarily mediated by adipocyte hypertrophy. This adiposity is particularly evident near trabecular bone surfaces, where hyperplasia also occurs. The differential pattern of adiposity in patients with CS and GC-O highlights that bone marrow adipocytes and their progenitors may respond differently in these two GC-mediated bone diseases. WhileIn physiological conditions, the microscopic basic multicellular units (BMUs), which consist of a tightly regulated sequence of events including an initial osteoclastic bone resorption, a reversal-resorption phase and an osteoblastic bone formation phase, maintain bone homeostasis . On trabCS is a rare disorder in which patients present abnormally high levels of endogenous cortisol. CS has an annual incidence of 1.8–3.2 cases/million population  and presHere, we investigate the effect of endogenous cortisol increases on BMAT and its effect on bone formation in patients with CS. Furthermore, we will compare it with the effect of exogenous glucocorticoids through a cohort of patients with GC-O.22.1The study was approved by the Danish National Committee on Biomedical Research Ethics (S-20070121) and performed in agreement with the Declaration of Helsinki. All biological tissue was collected by skilled medical professionals based on informed consent.A total of 62 bone biopsies were included in the study. All tissue originates from our two previous studies on premature loss of canopies and its association to deficient bone formation in patients with CS  and on GControl (C): Middle aged healthy volunteers; n = 9 , 31–33.Cushing´s syndrome (CS): Middle aged patients diagnosed with CS and presenting increased endogenous cortisol serum levels of 400–1000 nmol/l; n = 18 .Elderly control (eC): Elderly postmenopausal healthy volunteers; n = 8 , 20, 21.Postmenopausal osteoporosis (PM-O): Elderly PM-O patients, who had experienced ≥1 low energy fracture and had not received any osteoporosis treatment; n = 14 , 21.Glucocorticoid-induced osteoporosis (GC-O): Elderly postmenopausal patients diagnosed with GC-O. Patients had received GC therapy for 0.6-15 years (mean duration of therapy: 6.6 years), with daily doses of 5–60 mg (average prescribed daily dose: 15.9 mg), including prednisone (n = 12) or prednisolone (n = 1) treatment. The underlying disorders requiring GC treatment included polymyalgia (n = 2), collagenosis (n = 1), temporal arthritis (n = 1), arthritis (n = 3), dermatitis (n = 1) and asthma (n = 5). All patients had experienced at least one low energy trauma leading to vertebral fracture, and have not received any osteoporosis treatment; n = 13 .2.2Ethanol-fixed, 7-mm trephine iliac crest bone biopsies were embedded in methyl methacrylate (MMA) and cut into 7-µm-thick sections for histomorphometric analyses. One section per biopsy was Masson-Goldner trichrome stained; briefly, deplastification was performed through slide immersion in 2-methoxyethyl-acetat  for 45 min, followed by 5 min wash in 99% ethanol, 2 min wash in 96% ethanol and immersion in tap water. Slides were then stained with Weigert´s iron hematoxylin  for 1 h, washed with tap water for 10 minutes, immersed in Ponceau/Fuchsin solution  for 15 min and washed in 1% acetic acid twice. Slides were then stained with fast green  for 20 minutes and washed twice with 1% acetic acid, before dehydration in 96% ethanol and 99% ethanol. Subsequently, slides were mounted in Pertex mounting media  and scanned at 20x on a NanoZoomer S360 scanner .2.3Tissue slides were pseudo-anonymized to a random code and analyzed using NDP.view2 viewing software , as illustrated in 2) was estimated as the mean profile area of the investigated adipocyte profiles in each section. N.Ad.Pf/Ma.Ar (#/mm2) was calculated by dividing the number adipocyte profiles situated within the box-grid by the total area of the investigated boxes within the marrow cavity. As previously described, Ad.Pf.Ar and N.Ad.Pf/Ma.Ar were re-calculated according to whether they were <100 µm or >100µm of the bone surface (The mean adipocyte profile area (Ad.Pf.Ar) and the number of adipocyte profiles per marrow area (N.Ad.Pf/Ma.Ar) were estimated using a box-grid superimposed upon the digitalized slide at 5x , and significance was set at p<0.05.Statistical analyses and plots were performed using GraphPad Prism v5 . Data were tested for normality using the D’Agostino-Pearson omnibus normality test. Differences between control and CS biopsies were determined using Student´s 33.1Previous reports have shown that GCs promote the adipogenic differentiation of SSPCs, leading to increased BMAT fraction –30. To cpost-hoc Dunn´s test, Ad.Ar/Ma.Ar in eC vs GC-O vs PM-O: p = 0.041 by one-way ANOVA followed by Bonferroni´s multiple comparison correction]. Similarly, adiposity was increased in patients with PM-O [Ad.Ar/T.Ar in eC vs PM-O: p = 0.0073 by post-hoc Dunn´s test and Ad.Ar/Ma.Ar in eC vs PM-O: p = 0.013 by Bonferroni´s correction]  or accumulated GC-doses (GC-O) at the time of biopsy. The median accumulated GC dose in the GC-O group was 17.18 mg (2.25–186.60 mg) over a variable treatment period of 1.5 to 20 years (median treatment period: 6.25 years). In patients with CS, Ad.Ar/Ma.Ar was significantly correlated with serum cortisol levels Figure 3E3.2Because aging is a known risk factor for BMAT accumulation , 36, 37,Correlation analyses demonstrated that Ad.Ar/T.Ar and N.Ad.Pf/Ma.Ar are independent of age in all the included cohorts, except for an age-dependent increase in Ad.Ar/T.Ar in the eC group  proximal and distal to the bone surface, including the MixC group as a combination of all healthy individual biopsies analyzed (C and eC).The Ad.Ar/Ma.Ar within and above 100 µm of trabecular bone surfaces was significantly correlated in biopsies from patients with elevated glucocorticoids (i.e. CS and GC-O) and PM-O, but not in any of the control groups  and eroded surface (ES/BS), and the canopy coverage above these bone surfaces , illustrated in Having previously established a significant correlation between serum cortisol levels and histological adiposity parameters in patients with CS Figure 34CS is a disorder characterized by increased endogenous cortisol production that commonly induces the development of an osteoporotic-like condition , 32, 33.Our results suggest that both endogenous and exogenous glucocorticoids may induce BMAT accumulation, which is mediated solely by hypertrophy in GC-O, but both by hypertrophy and hyperplasia in CS. While increased BMAT in response to elevated glucocorticoid levels is well established , 38, 39,In this study, we evaluated cohorts that represent two differential age ranges: middle age (C and CS) and elderly  patients. Our analyses demonstrate that age is significantly correlated with Ad.Ar/Ma.Ar and Ad.Pf.Ar in healthy individuals, as expected , 46, butTo better understand the effect of altered BMAT on bone metabolism in patients with CS, we examined the correlation between BMAT parameters and our previous histomorphometric characterization of bone formation, resorption and canopy coverage in these samples , 20. OnlAs the field of histomorphometry continues to advance, automated systems that evaluate all cells and surfaces present in a bone biopsy are becoming more widely available , 52 and In conclusion, this study provides a comprehensive characterization of BMAT accumulation in patients with CS, PM-O and GC-O that demonstrates increased adiposity, hypertrophy and hyperplasia. Moreover, we show that adiposity and hypertrophy in CS patients are directly correlated with serum cortisol and that hyperplasia occurs proximal to trabecular bone surface, and a V-shaped correlation with changes in the canopy coverage. The disparity in the pattern of adiposity in the marrow of patients with increase endogenous or exogenous cortisol highlights the differential mechanisms of glucocorticoid-induced adiposity, which may pose differential therapeutic targets to improve bone disease in these patient populations.The data analyzed in this study is subject to the following licenses/restrictions: Human data; all data is available upon reasonable request. Requests to access these datasets should be directed to marta@forens.au.dk.The studies involving humans were approved by Danish National Committee on Biomedical Research Ethics. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because samples were collected from Danish pathological biobanks.Project conceptualization and funding acquisition: TLA, JMD, MK, AJ. Data obtention and analyses: NS, CA, PJ, EH, JB, MDC. Manuscript writing: NS, MDC. All authors contributed to manuscript revision and editing and approved the submitted version.


Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human–robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems , and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future. Over the past couple of decades, significant advancements in the performance of robotic platforms have been achieved by researchers in the academic community, with the deployment of such robots soaring amidst the COVID-19 pandemic. Studies show that the high probability of a resurgence in COVID-19 cases necessitates cost-effective and self-deploying telepresence robots to ensure pathogen control worldwide , the marThe application of such cutting-edge robotic technologies remains diverse in various sectors, from carrying out military or manufacturing tasks to airborne or underwater operations, due to their dexterity, ease of operation, high adaptability, and multi-functionality. The widespread demand for AR in surgery is the impetus for our work, with a core focus on the challenges encountered in their deployment in the existing literature as well as our proposed solutions in counteracting these issues, emphasizing the precision of end-effector placement and feedback from control systems. The field of surgery has seen a quantum leap in the evolution of procedural ergonomics and the level of autonomy in robots during an intervention. Since the first robotic-assisted surgery was successfully used to treat neurological tumors from a spin-out industrial robot called the PUMA 200 in 1985 , scientiThe collaboration between AR and RAS is a breakthrough in the world of minimally invasive robotic surgeries, with the earliest publications on this principle dating back to the 2000s, by Worn et al. . More reAlthough most advanced robots perform pre-programmed repetitive tasks with minimal artificial intelligence (AI) training, existing surgical robots with supplementary visual, olfactory, and haptic modalities prove to augment human–robot interaction and hence improve overall system performance. In this paper, we evaluate the types of surgical scenarios that involve AR technology, their corresponding navigation strategies, and the DL methods used in their operation. We also focus on identifying the loopholes in the existing literature, involving the levels of autonomy (LoA) in surgical RAS, accuracy in GPU performance, experiment genericity, and clinical evaluation, amongst others. In the conventional robot hierarchy adapted from works by Haidegger et al.  and Attahttps://www.iso.org/standard/68473.html) [accessed on 20 March 2023] as well as the IEC60601-1 [https://www.iso.org/standard/65529.html) [accessed on 20 March 2023] safety standard series [The ability to perform surgeries autonomously has been debated by several law courts against Intuitive Surgical, with up to 3000 cases in 2016 . This isC60601-1  . AR systems have proven to be an indispensable medium for human interaction with the external virtual world by bridging the gap between the required task and assisting tools through unobscured user display. According to Krevelen et al. , AR provOn the other hand, virtual reality (VR) creates a computer-generated back-end scene for complete immersion, such that the user can experience real-world scenes in a completely virtual environment. The supporting device system for the generation of a virtual world consists of joysticks, controllers, or trackpads for navigation; head-tracking devices for pose estimation; and microphones for voice recognition. VR headsets such as the Oculus Rift   and OculSince its introduction to the scientific world in the 2000s, AR in surgery has been developing at a soaring rate, although it has been criticized by many due to the heavy wearable devices, limited sensory input, and inefficient real-time object registration due to tissue deformation . The newDespite the prevalence of AR technologies in several sectors, there is a significant gap in their performance in handling cross-modalities during surgical manipulation, which may lead to targeting errors and inaccuracies such as false negatives. This paper aims to conduct a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human–robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. The plethora of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems , and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. Our paper presents a stepping stone for researchers to explore the possibilities of adapting AR to RAS for the navigation and control of surgical tools within a plethora of anatomical environments.The organization of this paper is as follows. We started our search with papers obtained from several peer-reviewed databases, such as IEEExplore, Google Scholar, SCOPUS, and PubMed, to perform a thorough initial literature search . We focuThe focus of our paper remains the increasing demand for visualization in surgical robotics, especially in preoperative scenarios, onto the real-world patient from the object detection process of landmarks of interest beyond the visible surface and by merging preoperative and real-world images together. A higher rate of deployment of AR devices in surgery is encouraged more than ever today, stemming from the expected increase in the market demand of at least 18.02% by the end of 2023. The most common areas of robotic surgery employing AR at present include neurosurgery, cardiothoracic, orthopedic, gastrointestinal, and ENT, amongst others . For exaWhat is the current state-of-the-art research in integrating AR technologies with surgical robotics?What are the various hardware and software components used in the development of AR-assisted surgical robots and how are they intertwined?What are some of the current application paradigms that have enhanced these robotic platforms? How can we solve the research gaps in previous literature reviews and promote faster performance and accuracy in image reconstruction and encourage high LoA surgical robots with computer vision methods?In this paper, we aim to contribute to future research by building a foundation on the current state of the art and proof of concept in AR for surgical robotics, whilst addressing the following research questions:To understand and elaborate on the methodologies used in AR-based robotic surgeries, we decided to classify the systems in terms of their hardware and software features as an initial literature search from our meta-analysis and based on the logic relationship in In surgical navigation systems, AR-based scenes require seamless interaction between the real world and the digital world, increasing the need for precise motion tracking via marker-based or location-based mechanisms . Often, Contrary to marker-based AR calibration systems, which use pre-defined tracking markers such as QR codes to leverage objects onto a real-world scene, markerless systems tend to enable user-friendly referencing cues to position an object in space. They operate by experimenting with different human skin textures, internal vessel structures, and geometrical features from medical scans of a patient . The use(i)Electromagnetic Tracking Systems (EMTs)There exist a multitude of sensors that are integrated into robotic platforms for the detection of precise locations in a surgical procedure, ranging from ultrasonic sensors , mechani(ii)Optical tracking systems (OTSs)Contrary to mechanical tracking, which depends on the positions of end-effectors to deliver fast update rates via rotary encoders or potentiometers, electromagnetic (EM) tracking uses a stationary source with orthogonal magnetic coils within an operating range of 1–3 m. Nowadays, the only AR technique that limits conventional occlusion limits is EM tracking, which operates based on field generation placed near the patient and connected to the latter by coil wires . The oriOptical tracking systems (OTSs) are extensively adopted in surgical navigation, the first proof of concept used during the Second World War, when optical sighting systems and gun detectors were a requirement for strategy planning. Zhou et al.  developeAlongside the multitude of advances in other areas, such as dexterity and accurate image acquisition, commercial surgical robots are currently equipped with AR technology for the manipulation of resection tools. Their ability to visualize the patient-specific anatomy during affected tissue extraction allows them to work within safe workspace boundaries. While the precise mapping of medical images is unlikely due to the constant deformation of tissue pre- and post-surgery, many research papers ,53,54,55(i)Marker-based ARIn marker-based AR technology, the main objective remains to drive a robotic system while performing coordinate estimation from the cameras relative to the markers. A plethora of marker sizes and types, with Vuforia and ArUco being the most popular, are utilized in a back-end working environment, enabling fluctuating marker information from the robot to be registered by the AR interface. This type of AR is useful in surgery that requires the triangulation of end-effectors to calculate their positions based on an added or moving fiducial marker with respect to the reference point (see (ii)Markerless ARIn the existing literature, Yavas et al.  used AR-https://www.accuvein.com/why-accuvein/ar/ (accessed on 12 February 2023)) [https://nextvein.com (accessed on 12 February 2023)) [(iii)HMD-Based AR for Surgery and RehabilitationIn its evolution from the detection of tangible markers in a surrounding environment, markerless AR registration requires no trigger for the detection of objects of interest in a workspace. Users can extract specific areas during visualization, estimate the corresponding transformation from the reference to image coordinate frame, and overlay the generated image above a real landmark. For instance, Liu et al.  recountey 2023))  and the y 2023))  are mercy 2023)) . Kastnery 2023))  applied y 2023))  recountsIn the NAVIO Surgical System , which is built onto the HoloLens HMD, the surgeon performs intraoperative customized bone preparation and confirmation of the correct cut guide sizes through the overlaying of augmented drawings produced by surgical resection tools . Moreove(i)Superimposition-based ARThere are a wide range of applications for AR devices in the field of surgical robotics, which operate based on superimposition. The benefit of augmenting the surgeon’s FoV with reconstructed medical images and computer vision interfaces is primarily the ability to superpose useful information over real-world scenarios. This will enable an increase in efficiency in surgical setup and clinical arrangements. Widely appreciated by the surgical community, AR-based technologies include video-based displays that augment the surgeon’s FoV through video streaming , see-thrhttps://www.murabproject.eu/about-murab/ (accessed on 22 March 2023)).Pessaux et al.  investigAR has also been used for the location of subcutaneous veins in preoperative surgery, as depicted in , where t(ii)Projection-based ARAnother paper by Ma et al.  presente(iii)HMD-based ARIn projection-based  AR, the areas of interest in the human body are displayed in a virtual world, without the use of HMDs and high-definition display screens. Using projection mapping, the augmented model of a landmark may be overlaid and dragged out of a screen using a tracking pen for realistic cardiac anatomy examination, as described in . The avaAccording to Burström et al. , augmentFrom the master–slave testbed to the operating theater, AR plays a pivotal role in the visualization of anatomical landmarks, particularly the ear, nose, and throat, as well as gastro-intestinal areas. AR-assisted robotic surgery has facilitated the surgeon’s task in reducing hand tremors and loss of triangulation during complicated procedures. Studies show that the transition from invasive open surgery to indirect, perception-driven surgery has resulted in a lower number of cases of tissue perforation, blood loss, and post-operative trauma . In contThe preliminary steps in diagnosing the area of concern in a patient include the use of computer guiding software to visualize inner anatomical landmarks. The loss of direct feel of the inner anatomy, reduced depth perception due to the monocularity of cameras, and distorted images have been addressed in novel techniques such as the segmentation of tissue in medical scans and 3D modeling for an augmented 360-degree field of view (FoV) . In seveUsually, the preferred choice of registration method depends on the type of robotic environment that the surgeon is navigating, where feature-based registration attracts the most attention within the academic community. These methods are less computationally heavy and can be used to effectively match fiducials between preoperative and intraoperative images, with primarily deformable methods of surface registration. Due to the sole use of 2D parameters, the possibility of obtaining highly accurate 3D information is low, hence driving the research community to establish novel sensing technologies for 3D marker tracking. Registration methods such as point-based registration, feature-based registration, segmentation-based registration, and fluoroscopy-based registration are widely used in the image processing of medical scans. The geometric transformations of deformable objects are computed using fiducial markers, which act as positioning cues and can be analyzed for fiducial localization errors (FLEs). In cases where images have varying gray levels, DL algorithms are able to segregate different features using parameters such as the sum of squared or absolute differences (SSD), correlation coefficients, and mean squared difference (MSD). For real-time X-ray image processing, a contrasting material, such as barium or iodine, is used to create more subtle contrast differences for clinicians to analyze. The process of 2D to 3D image registration involves the alignment of matching preoperative and intraoperative features, which can be reconstructed in AR and superposed over a live fluoroscopic image with respect to reference points in the image sequence .Automatic camera calibration and corresponding image alignment in intraoperative ultrasound is used to determine internal structural characteristics such as the focal length and surface anatomy of different organs. Analysis, visualization, and pre-planning using registered medical images enable the development of patient-specific models of the relevant anatomy. The researchers in  created The next steps in creating an AR model include image processing techniques such as direct volume rendering, which are used to remove outliers and delineators from raw DICOM data. A method proposed by Calhoun et al.  involvesSurface rendering techniques in  depict t2 inflation of the patient’s body and highlighting target locations with contrasting colors  facilitate the surgeon’s task, especially when performing cross-modality interventions with AR systems such as headsets. A study by Zhang et al. [In studies by El-Hariri et al.  and Hussg et al.  explaineg et al.  introducg et al.  proved tg et al. .With the groundbreaking development of artificial intelligence (AI) in assistive surgical robots, the healthcare sector today has seen a booming increase in the data collected and stored in databases, such as in the NHS. During the COVID-19 pandemic, technology has lessened the burden of healthcare workers behind the scenes, minimizing the need to sort, collect, and store data manually, as well as cutting down costs in decision-making tasks. Training datasets for early symptom recognition, estimating patient mortality rates, and abnormality detection in specific tissue images have enabled researchers to obviate error-prone concepts during robot training and prepare novices for unexpected fallacies . The surThe introduction of the DL-based optimization of surgical robot performance, according to Govers et al. , enablesRecently, papers by Garon et al.  and AlhaThe position and orientation of surgical robots are determined by the linkage arrangements and their relative degrees of freedom. In each workspace, the configuration of the manipulator is specified for each joint to allow inference of the position of any variable. According to Adhami et al. , the conOnce the topology of the robot is achieved, the orientation profile is checked for the collision-free volume (CFV) using a swept volume visualization process, as described in . A sequeMost malleable robots in surgical settings require accurate port placement and end-effector positioning, adapting to the desired user requirements within a specific workspace reconfiguration. Each revolute joint of the robot can be aligned and positioned using augmented visual cues, hence guiding the user towards the required robot topology. This method is gaining popularity in surgery due to the ease of motion tracking and calibration, with demonstrated accuracy of up to ±2 mm. In works by Ranne et al. , an AR-aThe applications of the daVinci research kit have been reported to be broad in the academic community, ranging from collaborative research in RAMIS to independent surgeon manipulation using a stereoscopic system . The devIn the existing literature, a multitude of haptic sensors have been proposed to enable high-speed performance in data pre-processing and rendering. The stereotaxy phenomena in surgical scenarios such as gamma-knife surgeries  create aIn a surgical setting, works by Okamura et al.  and WestRobotic surgery has improved the way in which surgeons gain access to the difficult internal anatomy, bringing significant advancements in transmission latency from the first transatlantic robotic-assisted laparoscopic cholecystectomy performed in the late 20th century . With thThe link between communication and patient safety has been highlighted in works such as ,12, wherIn the post-pandemic world, medical DT provide the integrated and virtual visualization of patient data and hence create a user-friendly software platform for surgeons to access complex information such as physical, physiological, and cognitive characteristics. DT can play a pivotal role in remote patient monitoring through advanced diagnostic tools, whilst incorporating AR for precision medicine, for a more patient-centric method of treatment. The core purpose of DT in AR-based robotic surgery is personalized medicine using patient-specific modeling from deep learning databases to accurately determine the cause and treatment of a disease. The table in  summarizMqttOmronMovePublisher package the joint angles and publish them on the /omron/command MQTT topic, which is then received by the /omron/move/command at the ROS side after conversion and JSON deserialization. This information is then received by MqttOmronMoveSubscriber, which attaches these joint angles to the corresponding robot arm and enables the DT controller to achieve the desired configuration. In this way, the OMRON robot can perform multiple movements whilst visualizing them on the ghost robot in the background. In another study, a similar virtual-to-real mapping technique was used to simulate an abdominal surgery, estimating the virtual-to-real 6-DoF robot’s alignment within an AR environment [Owing to its high success rates in industrial robot integration, the authors in  have estironment . The intNowadays, most DT technologies enable AR integration in order to support model adjustment based on user feedback, immersion, and intuitiveness. For example, the ARAILIS prototype in  providesDespite the prevalent breakthroughs in AR for robotic surgery, there are several pitfalls that have been reported by users of such technology, which need to be addressed. Several papers have been reviewed in the existing literature, addressing the core gaps in the field and potential improvements in the efficiency of robotic platforms. These papers were classified in terms of hardware, software, and DL applications, with a total of 170 papers. We realized that several papers lacked a focus on accuracy in feedback systems, alignment during interactions, registration techniques, and patient safety for robots of varying LoA. We also identified difficulties in sight diversion during hand–eye coordination for a surgeon, which means that rather than switching between real and virtual scenes, an integrated lectern or platform can be used for the experiments instead . EmployiThe integration of AR in surgery requires the precise calibration of the end-effectors to localize the coordinates of the objects of interest within the workspace. Fiducial markers are predominantly used in object localization on medical scans, especially in the pre-planning of convoluted navigation procedures. Techniques such as the rigid and non-rigid registration of such markers, using image deformation methods to move tissue, enable the accurate reconstruction of unstructured robotic environments, allowing for the generation of a historical trajectory map. In the image registration stage, the accurate transferring of matching features from preoperative to intraoperative images requires virtual potential fields to reference the locations of areas of interest, such as tumors or blood vessels. The use of manual registration techniques before a fully autonomous navigation procedure allows the elimination, to an extent, of any errors that may be caused by misleading situational awareness, as in the two-way DaVinci robot registration. The integration of DL methods at this stage of validation and testing helps the system to learn the correct position of the defect within an exhaustive database, hence ensuring higher repeatability.Despite the plethora of advantages that the development of CNN databases for AR navigation provides, our studies showed that the feasibility of many procedures is hindered by human errors, such as the movement of the patient, the rapid deformation of the tissue, such as the lung, and the instability of clinical equipment, as well as high levels of background noise in captured medical images. It is therefore of the utmost importance that researchers bridge the gap in alleviating these challenges by employing more efficient deep learning algorithms, such as image deformation methods, to parametrize the no-risk zones within an anatomical space. Registration algorithms such as the head and hat algorithm and the iterative closest point (ICP) algorithm facilitate the extraction and geometric transformation of specific 2D or 3D surfaces on deformable multimodal images, based on neural network architectures .The ability to detect collision-free zones for accurate end-effector placement requires complex calculations of workspace reconfigurations, whilst plotting the coordinates of the output curve within the safe margin of interest. The authors in ,129 haveThe legal and ethical aspects of AR in surgery have been debated in several courts, due to the skepticism that comes with performing operations from a distance or even cross-territory. The process of the clinical evaluation and validation of AR-based surgical robots remains in its early stages of development, and experiments performed on phantoms such as 3D-printed organs followed by cadavers and patients are claimed to have the highest rates of accuracy . HoweverThis paper provided a general overview of various surgical robotic platforms available on the market and in the existing literature, with an emphasis on their system architectures, software platforms, and learning methods. A total of 170 papers were selected and analyzed in a systematic manner for the purpose of identifying the relevant literature that described the types of AR technologies used in surgery. AR remains a promising tool in facilitating the surgeon’s task, from docking the station to port placement and end-effector guidance. To counteract the difficulties experienced by manual operation, AR visualization helps surgeons to perform interventions efficiently through HMDs, spatial projectors, and marker-based or markerless interfaces. This review focused mainly on the plethora of AR interfaces used in surgery, focusing on three main aspects: “hardware”, “software”, and “application”. The roadblocks towards achieving optimum AR integration were addressed and a wide range of solutions was presented to increase the efficiency of existing robots. The ability to eliminate visual clutter and occlusion within the surgeon’s FoV opens the door to novel augmented models with different layers and windows, which can be chosen according to the degree of importance. In areas such as thoracic surgery, gynecology, and plastic surgery, where the haptic feedback system provides an indication of the type of force required for an intervention, we found that the use of AR integration with force feedback sensors, as in the DaVinci master–slave console, increases the sensory stimulus of the surgeon, with a direct correlation between the fiducial cues and the real-time feedback from the object of interest.Owing to its popularity amongst surgeons, AR is widely commended by the research community for surgeries ranging from tumor detection to vein location through fiducial markers, despite its restrictions in terms of spatial awareness and occlusion. To reduce the risks of bleeding in conditions where the surgeon is required to coordinate hand–eye movements, we introduce the novel concept of reflective AR and DT technologies, which are in their pilot stages. The level of accuracy in areas such as suturing, knot tying, and pick-and-place operations has significantly increased as compared to manual operations, which inspires further research in this sector. At the time of this literature review, to the best of our knowledge, there exists a limited pool of specialized papers in the field of AR for surgical robots containing a detailed rundown of novel AR platforms with DL algorithmic approaches. Our paper aims to identify the research gaps in areas such as hardware components, software specifications, and the application of DL in various surgical stages. We believe that we have laid the foundation for the future of AR for surgery, which will not only be useful for researchers but also surgeons and novices who wish to learn about AR-assisted surgical interventions with accurate tool placement, without limiting the reader to previous conventional trends in the sector.


Ribosomes are complex macromolecules assembled from 4 rRNAs and 79 ribosomal proteins (RPs). Their assembly is organized in a highly hierarchical manner, which is thought to avoid dead-end pathways, thereby enabling efficient assembly of ribosomes in the large quantities needed for healthy cellular growth. Moreover, hierarchical assembly also can help ensure that each RP is included in the mature ribosome. Nonetheless, how this hierarchy is achieved remains unknown, beyond the examples that depend on direct RP-RP interactions, which account for only a fraction of the observed dependencies. Using assembly of the small subunit head and a disease-associated mutation in the assembly factor Ltv1 as a model system, we dissect here how the hierarchy in RP binding is constructed. A combination of data from yeast genetics, mass spectrometry, DMS probing and biochemical experiments demonstrate that the LIPHAK-disease-associated Ltv1 mutation leads to global defects in head assembly, which are explained by direct binding of Ltv1 to 5 out of 15 RPs, and indirect effects that affect 4 additional RPs. These indirect effects are mediated by conformational transitions in the nascent subunit that are regulated by Ltv1. Mechanistically, Ltv1 aids the recruitment of some RPs via direct protein-protein interactions, but surprisingly also delays the recruitment of other RPs. Delayed binding of key RPs also delays the acquisition of RNA structure that is stabilized by these proteins. Finally, our data also indicate direct roles for Ltv1 in chaperoning the folding of a key rRNA structural element, the three-helix junction j34-35-38. Thus, Ltv1 plays critical roles in organizing the order of both RP binding to rRNA and rRNA folding, thereby enabling efficient 40S subunit assembly. Ribosomes make proteins in all cells. During that process, they are responsible not just for the correct translation of the genetic code into amino acids, but also for selecting the correct mRNAs for translation. Finally, damage in mRNAs requires active translation. Changes in ribosome numbers or ribosome composition can perturb each of these functions and can lead to diseases. Thus, a better understanding of how cells ensure efficient and correct ribosome assembly is paramount. Using a mutation that is associated with a dermatological condition, LIPHAK syndrome, we describe here novel roles for the assembly factor Ltv1 in orchestrating the assembly of RPs into the head of the small ribosomal subunit. Our experiments, a combination of mass spectrometry, RNA structure probing, genetics and biochemical assays, demonstrate that Ltv1 helps recruit some RPs, while delaying the incorporation of others, thereby establishing the hierarchy of RP binding. In addition, our results also demonstrate roles for Ltv1 in modulating rRNA folding. Finally, the data establish the identity of a previously described quality control step that is bypassed by a cancer-associated mutation. In all forms of life, ribosomes are responsible not just for producing the right amount of the correct protein, but also the detection of damaged mRNAs. Both functions depend on the functional integrity of the ribosome pool, as well as efficient ribosome assembly, as reduced ribosome numbers can affect both mRNA selection ,2, as weIn eukaryotes, ribosomes are assembled from their constituent 4 ribosomal RNAs (rRNAs) and 79 ribosomal proteins (RPs) with the help of ~ 200 assembly factors (AFs), which are largely conserved from yeast to humans. These factors integrate rRNA processing with binding of RPs, chaperone rRNA folding, allow for regulation, prevent premature translation initiation, and facilitate quality control .The binding of ribosomal proteins (RPs) to ribosomal RNA (rRNA) is very hierarchical, as first characterized by Nomura and Nierhaus –9. A potin vivo [The small subunit head is constructed from the 18S rRNA 3’-domain and is the last of the subunit substructures to emerge. Because of its relatively late assembly, individual assembly steps can be identified using cryo-electron microscopy , as in vivo –24,26. Iin vivo , and thein vivo . While te.g., Utp15, a component of the UtpA complex, blocks premature recruitment of Rps25. However, other parts of the hierarchy are not explained in this manner: e.g., it appears that the recruitment of Rps31 might be delayed, as it occurs much later than the binding of its only binding partner, Rps12, and the folding of its binding site  can rescue the depletion of the Hrr25 kinase , and proThe observation that Ltv1_L216S cannot be phosphorylated by Hrr25, and that a phosphomimetic mutation cannot rescue the release or growth defects from the L216S mutation, suggest that Ltv1_L216S is positioned differently on pre-40S ribosomes, so that it is no longer accessible to the Hrr25 kinase, and such that S336, S339 and S342 no longer weaken binding when mutated to phosphomimetic aspartate residues. In contrast, the interface with Enp1 appears to remain largely unchanged: an Enp1 mutation, Enp1_WKK , rescues the non-phosphorylatable alanine mutations in Ltv1, Ltv1_S/A , becauseTogether, the data in this section demonstrate that Ltv1_L216S impairs ribosome assembly, because it cannot be phosphorylated and released, and strongly suggest that these defects arise because Ltv1 is mispositioned on pre-40S ribosomes. We also note that the small growth defects of the Ltv1_L216S mutation, as well as most of the other mutations within this work indicate that these mutations do not block the individual steps that they are associated with but rather make them less likely to occur, thereby impairing assembly.https://alphafold.ebi.ac.uk/), and then utilized the matchmaker function in Chimera [To better understand how the Ltv1_L216S mutation alters its function in the context of the genetic and biochemical data that indicate mispositioning of the mutant protein, we wanted to visualize the location of the mutation in the context of the pre-40S structure. Unfortunately, only a small portion of Ltv1 has been visualized in current cryo-EM structures ,15. We t Chimera  to placeOverall, this structure for Ltv1 and its placement in pre-40S does not lead to major steric clashes but is rather compatible with the pre-40S structure . The comTo further validate this structure and the placement of Ltv1, we next designed point mutants in ribosomal proteins, which are expected to disrupt Ltv1/RP interactions, and then tested their genetic interaction with Ltv1_L216S.The placement of the Ltv1 structure on the nascent 40S predicted an interaction between Ltv1 and residues K75 and R76 in Rps3 . We therIn addition to interactions with Rps3, Rps15 and Rps20, which have been previously validated ,32–34, tTogether, these mutational and genetic analyses, combined with the previous biochemical data validate the structure of Ltv1. Additional validation comes from the analysis of rRNA mutations in the next section.The structure predicts that L216 is part of a short alpha-helical segment adjacent to helix 34 h34, . H34 is However, the structure also Ltv1 contacts l31 . To valiTo test whether Ltv1 chaperones the folding of j34-35-38 directly, or rather affects its folding indirectly by preventing the premature folding of l31, or both, we carried out a series of genetic experiments. Deletion of snR35, a snoRNA which directs pseudouridylation of U1191 in l31, and prevents the premature folding of l31, results in misfolding of j34-35-38 . This isTo further confirm this conclusion, we next tested whether rRNA mutations in the helices that surround the junction and are adjacent to Ltv1 would be sensitive to the Ltv1_L216S mutation. We therefore mutated U1285, U1286 and A1422, which are part of a bulge at the top of helix 34, as well as G1288 and C1327, the base pair which closes h35 , and tesFor this purpose, we deleted the Ltv1 gene from a strain where RNA polymerase I has a mutation that renders it temperature sensitive, thereby shutting off all transcription from the endogenous rDNA genes above 32°C . When suGiven that the Ltv1_L216S mutant is only partially functional, we wanted to use its defects to gain additional insight into the functions of the wild type protein. Our previous work had shown that Asc1 and Rps10 were substoichiometric in ribosomes from cells lacking Ltv1 , suggestTo better understand the molecular basis for the loss of many head RPs and determine whether additional rRNA regions are misfolded in the ribosomes from the L216S mutant yeast, we utilized DMS-MaPseq to probe the structure of mature 40S subunits from yeast expressing wild type Ltv1 or L216S Ltv1. As previously described , 41, ribThe Ltv1_L216S mutation impairs movement of Tsr1 to the beak).Six of the 18S rRNA residues with altered DMS accessibility are located in the 40S head. In addition, there is an additional cluster of 5 residues with altered DMS accessibility on the subunit interface . This clFour of the residues that are more DMS accessible in ribosomes from Ltv1_L216S cells, A1189, A1196, C1197 and A1515 are directly adjacent to residues in Ltv1 , furthernot depleted from the head of the 40S subunit in ribosomes from Ltv1_L216S cells , and which express either wt Ltv1 or Ltv1_L216S. While reducing Rps29 concentration reduces growth in yeast with wt Ltv1, it has no effect on the growth of yeast expressing Ltv1_L216S . This suNext, we used the observation that depletion of Rps29 is epistatic with Ltv1_L216S, while depletion of Rps20 is synthetically sick with Ltv1_L216S, to test if the Rps20_EYER mutation behaves akin to the Rps20 depletion or the Rps29 depletion. Indeed, when we combine Rps20_EYER with Ltv1_L216S, we observe synthetically sick interactions , akin toStrongly supporting these genetic interactions, are direct measurements of Rps20 occupancy in ribosomes from wt yeast or yeast expressing Rps20_EYER , which sAs described above, a second cluster of RNA residues altered in its DMS accessibility in the L216S mutant is on the subunit interface Figs . IntriguWe have previously shown that the growth defect from a mutation in Tsr1, Tsr1_RK , which mutates an interaction between Tsr1 and rRNA in the head , or is an artifact from the GFP fusion, or the stalled assembly is not clear.As described above, in ribosomes from cells expressing the Ltv1_L216S mutant most RPs are depleted from the 40S subunit head . NotablyIntriguingly, if Tsr1 were still located in its stable position near the decoding site, then the N-terminal extension of Rps31 would have to slide under Tsr1 . MoreoveTo test this model, we investigated genetic interactions between Rps31 and Tsr1. If Tsr1_RK and Tsr1_Δhinge impair the movement of Tsr1 to the beak, and this movement is important for the incorporation of Rps31, then we expect that Tsr1_RK is rescued by deletion of the ubiquitin in Rps31_Δubi. Indeed, Tsr1_RK shows small epistatic interactions with Rps31_Δubi .In addition, previous work has indicated that removing an N-terminal tail in Rps31, which is eukaryote-specific and not conserved in archaea, which also do not conserve Tsr1, is required for assembly of Rps31 into ribosomes . This taTo further support the model that Ltv1 affects Rps31 incorporation via Tsr1, we combined the Ltv1_L216S mutation with deletion of the ubiquitin in Rps31, Rps31_Δubi. If Ltv1_L216S blocks assembly upstream of the movement of Tsr1 to the beak, then removing the ubiquitin-portion of the gene, which cannot be accommodated without Tsr1 movement, should partially rescue the defects from the L216S mutation. Indeed, while the ubiquitin deletion has no effect on growth in the background of wt Ltv1, it slightly but reproducibly rescues the growth defect of the Ltv1_L216S mutation .Together, these data provide strong evidence that indeed movement of Tsr1 towards the beak is required for the incorporation of Rps31, and that this movement is impaired by the Ltv1_L216S mutation, thereby explaining the reduced incorporation of Rps31.Taking advantage of a novel mutation in Ltv1, Ltv1_L216S, which is associated with a human dermatological condition known as LIPHAK disease , we havePrevious work has shown that Ltv1 directly binds Rps3/uS3, Rps15/uS19 and Rps20/uS10, and is required for the efficient recruitment of Asc1 and Rps10 ,26,32–34More importantly, the data herein demonstrate for the first time how Ltv1 carries out its roles. By directly binding Rps12 and Rps29, Ltv1 may help in their recruitment. Moreover, the direct interaction with Rps3, Rps15 and Rps20 enables proper positioning of these RPs, as shown previously for Rps3 and here for Rps15 and Rps20. More surprisingly, the data also demonstrate that by binding to Rps3 and Rps20 directly, Ltv1 prevents the premature formation of their contact, thereby explaining previous genetic and biochemical data . The datThe observation that Ltv1 blocks the formation of the contact between Rps3 and Rps20 also suggests that Ltv1 must move to enable the formation of this contact and allow for its phosphorylation by Hrr25 , indicatIn addition to using direct binding interactions to orchestrate the RPs assembly hierarchy of the head, Ltv1 also affects recruitment of two RPs indirectly via conformational transitions in the nascent 40S subunit. Our analysis of previous structures of assembling ribosomes indicates that Rps20 blocks access of Rps29 both directly, as it is located on top of Rps29, as well as directly, by promoting folding of h39 and h41, which further restricts access of Rps29 to its binding site . Thus, RIn addition, our data also demonstrate how Ltv1 orchestrates the recruitment of Rps31. Our genetic data strongly suggest that Rps31 binding is blocked by Tsr1, which has to rotate towards the beak to enable Rps31 binding and Ltv1 dissociation . This fiLtv1 dissociation is required for Enp1 binding , which iThese observations are summarized in in vivo between a Tsr1-TAP and a Nob1 pulldown [In addition, our data also indicate that Ltv1 plays direct roles in chaperoning the folding of j34-35-38 . We havepulldown , which eWe have previously shown that Rio2 plays similar roles in delaying the formation of a tertiary contact between the loop of h31 and j34-35-38 . TogetheIn addition to chaperoning the folding of j34-35-38, our data also strongly suggest roles for Ltv1 in folding of h39, and its tertiary contact with h41 . The strper se but its regulation via the Ltv1/Rps15/Tsr1 network. Given that Tsr1_Δloop rescues all the mutants that block the hinge movement, we speculate that the loop that is removed by the Tsr1_Δloop mutation holds Tsr1 near the beak, via interactions with Rps15, as shown in We have previously shown that the Tsr1_RK mutation can be bypassed by a mutation in Rps15 that is associated with chronic lymphocytic leukemia (CLL), Rps15_S136F, which leads to defects in the resulting ribosome population . HoweverSaccharomyces cerevisiae strains  were grown at 30°C for >16h in YPD to deplete Fap7, and harvested in the presence of cycloheximide as described ,36,47. C5,000–7,500 OD of clarified lysate (~ 200 μl) were loaded onto a 10–50% sucrose-gradient and centrifuged at 40,000 rpm for 2 hours using a SW41Ti rotor before fractionation into 700 μl fractions. Northern blot samples were prepared by phenol chloroform extraction on 200 μl of each fraction. For Western blotting, fractions were mixed with SDS loading buffer, and ran on an SDS-PAGE gel, transferred and probed with the appropriate antibodies, as explained in , 36, 47.Northern blots were prepared from ΔLtv1 strain supplemented with either Ltv1, or Ltv1_L216S. The cells were grown overnight in minimal media, diluted into fresh YPD and harvested. Total RNA was isolated through phenol/chloroform RNA extraction, denatured in formaldehyde, and separated on an agarose gel. The following probes were used: 20S: GCTCTCATGCTCTTGCC; 18S: CATGGCTTAATCTTTGAGAC; 25S: GCCCGTTCCCTTGGCTGTG; and U2: CAGATACTACACTTG.600 for 48 hours while the plate was shaking at 30°C.Cells were grown in glucose minimal media or YPD overnight, diluted into fresh minimal media or YPD for 3–6 hours, before diluting them into a 96-well plate at a starting OD of 0.04–0.1 and placed in Synergy.2 plate reader (BioTek) to record ODrRNA mutants were grown in minimal media or YPD at 30°C overnight and diluted into fresh YPD to incubate for 3h at 30°C before changing the temperature to 37°C for 3h. Cells were then inoculated into YPD in a 96-well plate, where the growth was recorded for 48 hours at 37°C.2) supplemented with 1 mg/ml heparin, 1 mM benzamidine, 2 mM DTT, and protease inhibitor cocktail (Sigma-Aldrich) and lysed under liquid N2. The cell lysates were thawed and clarified before layering 200 μl onto 500 μl sucrose cushion and centrifuging at 70k rpm for 65 min in a Ti110 rotor. The supernatant was discarded, and the pellet containing the ribosomes was resuspended in high salt buffer  and layered onto another 500 μl sucrose cushion. The tubes were centrifuged at 100k rpm for 75 min, and the pelleted ribosomes were resuspended in subunit separation buffer , incubated for 1h at 37°C, and loaded onto a 5–20% sucrose gradient  and centrifuged for 8 h at 30k rpm. To isolate the separated subunits the gradients were fractionated and 40S subunits collected, buffer exchanged into ribosome storage buffer (ribosome buffer with 250 mM sucrose and 2 mM DTT) and concentrated to be flash frozen and stored at -80°C for subsequent mass spectrometry or Western analysis.Cells were resuspended in ribosome lysis buffer Purified ribosomes were prepared as stated above and precipitated in acetone. Three biological replicates of WT Ltv1 and Ltv1_L216S were analyzed. Raw data were deposited to the PRIDE database under accession number PXD046239.2, and 0.2 μM RNaseP RNA at 30°C for 5 min. DMS reactions were stopped by the addition of 0.4 volumes stop buffer , and purified using phenol chloroform precipitation.Prior to flash freezing the purified ribosomes were treated with or without 1% DMS (Sigma-Aldrich) in the presence of 80 mM Hepes pH 7.4, 50 mM NaCl, 5 mM Mg(OAc)2 at 94°C for 10 min. Fragments were separated on a denaturing 15% gel, and fragments between 50–80 nt were cut out and RNA was eluted overnight in RNA gel extraction buffer , 0.25% vol/vol SDS (sodium dodecyl sulfate). Eluted RNA fragments were dephosphorylated using T4 PNK (NEB), ligated to an adaptor using T4 RNA ligase II truncated KQ(NEB), and then gel purified. TGIRT III (InGex) was used to reverse transcribe RNAs in 50 mM Tris HCl, pH = 8.3, 75 mM KCl, 3 mM MgCl2, 1 mM dNTPs, 5 mM DTT, and 10 U SUPERase·In (Invitrogen) for 1.5 h at 60°C. The RNA was later hydrolyzed by 250 mM NaOH and the cDNA was circularized using CircLigase II (Lucigen). The circularized cDNA was shipped to the Scripps La Jolla Genomics Core for final library preparation with Illumina sequencing adaptors and sequenced using single-end sequencing on the Illumina NextSeq 500 platform.RNA library preparation was adapted from  and perfhttps://github.com/borisz264/mod_seq). The adaptor sequence (GATCGGAAGAGCACACGTCTGAACTCCAGTCA) was trimmed from raw sequence via Skewer [DMS-MaPseq data processing followed a GitHub pipeline . The HA-antibody was from BioLegend.S1 Fig(A) Sequence alignment of a portion of Ltv1 highlighting N168 and L216 in human and yeast Ltv1, respectively. (B) Doubling times of wt yeast cells (BY4741), or ΔLtv1 cells either expressing Ltv1 from a TEF-promoter-driven plasmid (WT Ltv1), or containing and empty vectors (EV). (C) Normalized doubling time of yeast lacking endogenous Ltv1 and expressing either wt Ltv1, Ltv1_L216S or Ltv1_L217S from TEF-promoter-driven plasmids. Note that the data for WT Ltv1 and Ltv1_L216S are the same as in (EPS)Click here for additional data file.S2 Fig(A) Doubling time of yeast depleted for endogenous Enp1 and lacking endogenous Ltv1 and expressing either wt Enp1 or Enp1_WKK and wt Ltv1 or phosphorylation-deficient Ltv1_S/A from TEF-promoter-driven plasmids. Significance was tested using a two-way ANOVA. ****, P<0.0001. (B) Absorbance profile (top) and Western blot (bottom) of yeast lysates from cells expressing either wt Enp1 or Enp1_WKK. (C) Doubling time of yeast depleted for endogenous Enp1 and lacking endogenous Ltv1 and expressing either wt Enp1 or Enp1_WKK and wt Ltv1 or Ltv1_L216S from TEF-promoter-driven plasmids. Significance was tested using a two-way ANOVA. ****, P<0.0001.(EPS)Click here for additional data file.S3 FigDoubling times for yeast cells containing a temperature sensitive RNAPol I mutation (NOY504) and expressing plasmid encoded wt or mutant 18S rRNA and wt Ltv1. Significance was tested using a two-way ANOVA. **, P<0.01; ****, P<0.0001.(EPS)Click here for additional data file.S4 Fig(A) Mutational rate changes for A and C residues upon DMS addition. Note that G also does get modified by DMS. (B) High read depth over the entire molecule. (C) Mature 18S rRNA is captured in all samples.(EPS)Click here for additional data file.S5 Fig(A) Structural detail of the 40S head (from PDB ID 7WTT) before Rps20 binding. For clarity only Rps29 is shown. (B) Structural detail of the 40S head before  and after Rps20 binding . For clarity only Rps29 is shown. Access for Rps29 is compromised after the folding of h39 and the h41 bulge, induced by Rps20 binding. (C-D) Rps29 (red) is located under Rps20 (cyan). Structures from PDB ID 7WTT and 6FAI, respectively. (E) Schematic for effects on Rps20, or Rps29 recruitment expected from the EYER mutation, when Rps29 binds before Rps20 (top) or vice versa (bottom).(EPS)Click here for additional data file.S6 Fig(A) Structural detail of a composite structure of yeast pre-40S (PDB:6FAI) and human Ltv1 (PDB ID 6G18) highlighting elements in Tsr1, Rps15 and Ltv1. The residues mutated in Rps15_YRR and Rps15_RK are shown in blue and cyan spheres, respectively. The residues mutated in Tsr1_RK are shown in magenta spheres, and the first and last amino acids of the loop removed in Tsr1_Δloop are shown in magenta. The last amino acid in the Ltv1 truncation Ltv1_1–394 is shown in green space fill. (B) Doubling times for yeast cells expressing either wt Ltv1 or Ltv1_3–194, and Tsr1_RK_Δloop. Data for WT Ltv1 and Tsr1_RK_Δloop are in . (C) Dou(EPS)Click here for additional data file.S7 FigResidues with altered SHAPE reactivity between intermediates enriched with Tsr1 and Nob1  are mapp(EPS)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file. 19 Aug 2023Dear Dr. Karbstein,Thank you very much for submitting your Research Article entitled 'A disease associated mutant reveals how Ltv1 orchestrates RP assembly and rRNA folding of the small ribosomal subunit head' to PLOS Genetics.Your manuscript was fully evaluated at the editorial level and by three independent peer reviewers. I am delighted to inform you that the three reviewers agree that you have submitted a well-conducted study that advances the knowledge of the 40S ribosome subunit assembly and possibly leads to an understanding of a human disease . None of the reviewers requests additional data, but each provides thoughtful comments regarding alternative interpretation of some of the data and as well as suggestions for improving the clarity and presentation of your manuscript.Therefore, we ask you address these concerns/comments in a revised manuscript. Your revisions should address the specific points made by each reviewer.In addition we ask that you:1) Provide a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript.archive. If your image is from someone other than yourself, please ensure that the artist has read and agreed to the terms and conditions of the Creative Commons Attribution License. Note: we cannot publish copyrighted images.2) Upload a Striking Image with a corresponding caption to accompany your manuscript if one is available (either a new image or an existing one from within your manuscript). 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Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, you will need to go to the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.Please let us know if you have any questions while making these revisions.Yours sincerely,Anita K. HopperAcademic EditorPLOS GeneticsGregory P. CopenhaverEditor-in-ChiefPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: This manuscript by Blomqvist et al. focuses on the role(s) of the ribosome assembly factor Lvt1 in 40S subunit maturation. Using yeast as a model, the authors primarily use ΔLtn1 cells and express WT or disease mutant (L216S) to determine affects on 40S maturation. The major hypothesis is that the Lvt1 L216S mutant is mispositioned on the 40S ribosome which is tested by a multitude of genetic mutants that support this hypothesis. The authors overlay the pre-40S cryo-EM and Lvt1 Alphafold structures. The position was confirmed by mutating specific putative contacts and observing synergistic or epistatic genetic interactions with yeast doubling time as a readout. The L216S mutant was positioned on the Head substructure of the pre-40S, which is highly supported by the loss (mass spec of purified 40S subunits) of primarily head subdomain RPs, but not RPs of the Platform or Body. DMS MaPSeq also demonstrated 18S rRNA changes in the Head and elsewhere, with the ladder aiding to explain downstream maturing defects. In total, the manuscript is strong, novel, and the conclusions are supported by genetic and/or biochemical evidence.My only stronger/major comments are:1) It would be worth pointing out to readers whether or not expressing Lvt1 on plasmids in stains that lack endogenous Ltn1 fully or partially rescues doubling time. I don’t feel this is a big weakness as the authors nicely show the difference between WT and L216S. But it would be informative to the readers. The polysomes for the Lvt1 strain in Figure 2B (left) still has a small 40S peak suggesting there is still a 40S maturation defect.2) At times the nomenclature for the mutants goes back and forth of being very clear of the specific mutation  to being a bit confusing  For Figure 4F, the authors write “…demonstrate strong synthetic sick effects”. The legends notes for panel F, “Significance was tested using an unpaired t-test. ****, P<0.0001” but figure does not have markings (****) above the data to highlight this statistical test. Given that the standard deviation in these data sets must be pretty large, I am not convinced that a t test would demonstrate such a low p value or a difference that is statistically significant. Do these data accurately represent a strong effect?Other minor comments/suggestions/typos:4) At times, the data are presented differently, which makes it hard to follow at times. For example, Figure 2D, some data are presented in box plots and some data are presented as (what I think are) the median and range. I think this is due to some groups having 3 data points and some having >3 data points. If other reviewers also comment on this, it may more informative and more straightforward to represented the data with the individual points but include the mean as the bar/line and error bars showing SD or SEM.5) On page 7, second paragraph, the authors state they tested whether Ltv1 chaperons the folding of j34-35-38 or indirectly affects its folding by preventing the premature folding of 131, or both. However, the subsequent text states the indirect effect would be inferred by an epistatic result and the direct effect would be inferred by a synergistic result. What would one expect if it is both (as the authors suggested was tested)?6) On page 7, “The structure shows that Ltv1 also contacts 131 , and mutation of residues Y82, D83, and Y84, which are adjacent to 131 to VAV, to make Ltv1_YDY produces a small growth defect , therefore further validating the structure.” This sentence is very hard to follow in the current form.7) Figure 5C is first discussed after Figure 6A-G.8) Is the structure of Tsr1 also from Alphafold? Is its positioning on the pre-40S also made through molecular docking (as was done for Ltv1) or is it empirically determined?9) In Figure 7E, the mutant is written as “Ltv1_1-394” on the x axis but written as “Ltv1_C394” in the text.10) In Figure 7F and G, the text is on the x axis is squished.11) In Figure 7H, the mutant is written as “Rio2_Nloop” on the x axis but written as “Rio2_loop” in the text and legend.12) In Figure 8C-F, different sized asterisks/stars are used to denote statistical significance. I believe the legends is also missing “***, P<0.001.” for panel F.Reviewer #2: In this work, the authors examine the role of the ribosome assembly factor, Ltv1 using a yeast model system. Ltv1 is known to be involved in assembly of the pre-40S head domain. Previous work demonstrated that Ltv1 helps recruit r-proteins, is implicated in helping rRNA folding, and is involved in quality control during 40S maturation. Here, the authors use a disease-relevant mutation, Ltv1_L216S, which exhibits a modest growth phenotype, to characterize the functions of Ltv1. The authors begin by demonstrating that Ltv1_L216S mutation impairs 80S-like ribosome formation and inhibits Ltv1 phosphorylation, which suggests that Ltv1_L216S is mispositioned on 40S and is a useful marker for dissecting the genetic interaction networks involving Ltv1.The authors use structural modeling and targeted mutational analyses to model Ltv1 structure in the pre-40S head domain. Strikingly, the structure predicts that Ltv1 is extended along the majority of the head domain, interacting directly with Rps3, Rps15, Rps20, Rps29, and Rps12 and h34. They carry out mass spectrometry analysis on the Ltv1_L216S ribosomes and revealed that recruitment of many RPs is impaired indicating that Ltv1 positioning is critical for RP recruitment. The authors also performed DMS-MapSeq and uncovered multiple clustered sites of altered reactivity supporting global mispositioning of Ltv1_L216S on the ribosome. Guided by the structure probing, the authors use genetic analyses to uncover that premature contact between Rps20 and Rps3 may prevent binding of other RPs, specifically Rps29. They also showed that altered DMS reactivity near the subunit interface is likely due to disruption of an interaction network between Tsr1 and Rps15. Finally, the authors show that mutations in the hinge region of Tsr1 causes the same genetic interactions with other mutations that influence Ltv1 release and that Tsr1 hinge mutations also lead to impaired recruitment of Rps31. Taken together their data suggests a mechanism in which Tsr1 movement is required to release Ltv1 and recruit Rps31.The authors have done a lot of work and the data presented is scientifically sound. Overall, I think that the data supports their growing model for the master regulator role of Ltv1 in the late stages of 40S head assembly and will be of significant interest to the field. I have a few comments for the authors to address for clarity:Major Comments:1. For the impact of Ltv1_L216S on 80S-like ribosome formation, would the authors explain why, in Figure 2B, the amount of 18S rRNA in the 80S fractions increases relative to wt Ltv1? Is that expected?2. The authors compare doubling times for the mutant Ltv_S/A that abolishes phosphorylation to the Ltv1_L216S mutant, which also is not phosphorylated . The difference in doubling time for the Ltv1_L216S mutant is very small compared to the difference for the Ltv_S/A mutant. Would the authors explain why the L216S mutation has only a small effect on growth when it also cannot be phosphorylated? Is there some other effect of the Ltv_S/A mutant or is there some level of phosphorylation of Ltv_L216S?3. I think it is very interesting that the modeled Ltv1 structure is quite extended, spanning almost the entire head domain. I am wondering how to think about a single residue altering the network of interactions to globally effect the position of Ltv1, particularly since mutation of the neighboring residue has no growth defect (L217S). Can the authors comment on this? Have they modeled the L216S mutant protein to see if there are any predicted changes relative to wt? Also, based on their structure, would the authors comment on why L216S has an effect but L217S does not?4. Interestingly, Rps9 is the only depleted body domain RP in the mass spec analysis of Ltv1_L216S ribosomes. The DMS data also show a cluster of altered reactivity near the Rps9 binding site. Do the authors think that this is real and significant? If so, would they comment on how Ltv1 influences Rps9 recruitment?Minor comments:1. In the introduction – Rps5 is referred to as uS2, but I think it is uS7.2. Figure 2C – I think the Ltv1-L216C label for the right panel is missing.3. In Figure 4F, should there be a significance comparison as noted in the legend? The authors state in the text that when the rRNA mutations are combined with Ltv1_L216S, there is a strong synthetic sick effect, but I am not sure which ones they are referring to. For example, the mutant 18S_C1327A appears to get better when combined with the Ltv1_L216S mutation.4. Related to question 3, I am confused by the statement: “the rRNA mutations have little to no growth defect on their own (data not shown)”. Is this the data in Figure 4F as the blue bars with wt Ltv1? Can the authors also comment on the large distribution of doubling times for these mutants?5. I think Figure S5 and 7 have some inconsistent labels and references in the text. In Figure 7, Ltv1_1-394 is referred to as Ltv1-C394 in the text. Figure S5B is labeled as Rps15 /Rps15_1-394, should that also be Ltv1_C394? Also, in the legend for S5B, it says Ltv1_3-394 but should be Ltv1_C394. S5E also has Ltv1_1-394 in the figure, should this be Ltv1_C394?6. Figure 9A: what do the red and green lines mean? If it is the same as in B, I would suggest getting rid of the colors on part A because it is a little confusing when comparing direct interactions versus effects of Ltv1 on assembly of these proteins.Reviewer #3: Previous work in the Karbstein lab showed that the ribosome assembly factor Ltv1 facilitates correct folding of critical helix junctions in the 18S rRNA. This report takes the story one step further by investigating the effect of a mutation in Ltv1 that maps to a region of human Ltv1 associated with a rare disease. An extensive set of mutations plus DMS structure probing convincingly show that a mutation in this region of yeast Ltv1 causes Ltv1 to bind pre-40S complexes improperly, preventing Ltv1 from facilitating assembly as it normally would. The structure probing results also led to a nice connection with Tsr1 movement.I am not wholly convinced that the genetics experiments define the order of events as unambiguously as the authors propose (see comments below). However, the abundant data presented here clearly show that the Ltv1 mutation disrupts a very delicate set of RNA and protein interactions that are essential for normal 40S assembly. Overall, this study advances the current understanding of 40S ribosome assembly in yeast.The experiments are thorough and well done. I have some comments related to the interpretation and presentation:1. On pg. 4, human Ltv1 N168S is said to correspond directly to yeast Ltv1 L216S. However, this segment of Ltv1 is polar in mammals but hydrophobic in yeasts , and the sequence alignment seems tricky. Did the authors test other mutations in this region of Ltv1? It is clear from the results that the chosen mutation is consequential in yeast, but should one call it a “disease-associated mutation”?2. In the introduction (pg. 3) the authors state that the hierarchy of ribosomal protein binding is difficult to explain because it cannot be accounted for by direct protein-protein interactions; it must depend on conformational changes that are induced by protein binding. The concept of induced fit is well established for bacterial ribosome assembly!! The authors may want to cite papers by Noller & coworkers PMID: 2459390, 3373531 or 2658053. The order of protein addition in yeast follows the bacterial 30S assembly map, only with the insertion of eukaryotic domain RPs. Perhaps this section of the introduction could be rephrased to better convey this point.3. When interpreting the genetics results, the authors envision a defined sequence of molecular interactions that can be over-stabilized or blocked by mutations; as I understand it, these phenotypes are used to sequence the action of Ltv1. If any mutation were to totally block a particular step, however, pre-40S complexes would pile up and the strain would be dead. In reality, many of the mutations described here have mild effects on cell growth, suggesting there is some work-around. Can the authors comment on this?4. The alphafold model of Ltv1 was impressively useful for interpreting and designing the experiments. I appreciate the effort to validate the predicted interactions. Nevertheless, “Inspection of the structure for Ltv1 on the nascent 40S revealed…” and like phrases imply an experimental structure model, not a computational one. The text and figure legends often state that the modeled Ltv1 complex “demonstrates” or “reveals” an interaction, which of course, it cannot. It can only predict. Please rephrase such claims wherever they occur.5. On pg. 7, the rRNA mutations are said to have no strong effect on growth but a strong synthetic effect with Ltv1_L216S. In Fig. 4, some 18S mutations in fact cause a large spread in growth rates; can the authors comment? Is it possible that the strains are picking up some second site mutations? By contrast, the synthetic effect with L216S seems modest, and generally smaller for the 18S mutants than wt 18S, which I found surprising. Have I missed something here?Sometimes it is easier to evaluate rRNA mutations by expressing a marked copy of the 18S from the plasmid so one can avoid shutting down the chromosomal rDNA. Did the authors consider this approach?6. The authors conclude from the mass spec analysis that assembly of the head is generally perturbed, and I agree. In the next section, however, they interpret the relative abundance of Rps3, Asc1 and Rps20 as supporting the idea that Ltv1_L216S aberrantly stabilizes interactions between Rps3 and Rps20. I think the MS data are not precise enough to be interpreted with this level of detail because early assembly proteins  are depleted whereas other late assembly proteins (rpoS0) have normal stoichiometry. This goes against the idea of a specific block. This conclusion should be toned down a lot.7. The data in Fig. 6 showing that addition of Rps29 (uS14) is impaired by Ltv1_L216S is very convincing. However, I had trouble following the logic of why Rps29 depletion should have a smaller effect in the Ltv1_L216S background than the wt background. If Ltv1_L216S tends to mask the binding site for Rps29, then low levels of Rps29 should make this even worse (synthetic), because the probability of Rps29 recruitment is reduced even further. Conversely, by this logic, Rps29 over-expression should rescue Ltv1_L216S. Since they observe the opposite, I wonder if the model is right. Perhaps I missed something?Similarly, if Ltv1_L216S masks the Rps29 binding site by over-stabilizing Rps20-Rps3 interactions, then mutations that weaken the Rps20-Rps3 interface , or low RP levels , should rescue this effect rather than exacerbate it, which is not what the authors observed.I agree that the results show that mispositioning of mutant Ltv1 results in assembly defects in the head, including impaired recruitment of Rps29, but the evidence for the sequence of events cartooned in Fig. 8 is weak, in my view. It would be helpful if the authors can better explain the logic  or reconsider their model.One alternative is that Ltv1 increases the rate of productive Rps29 recruitment and thus the likelihood that it will happen before Rps20 binds. This would not require Ltv1 to act directly on Rps20 and Rps3. Another model is that Ltv1 embraces Rps29, preventing its dissociation but also requiring Ltv1 to move to permit Rps20 and Rps3 binding. If the mutant is less able to respond to Rps20 or Rps3, it would explain why the Ltv1 mutant is less sensitive to low levels of Rps29 (less dissociation of this protein) but more sensitive to Rps20 and Rp3 mutations.8. Please briefly indicate the methods used in the abstract (what type of data?).9. I appreciate that the authors mention the systematic ribosomal protein names in the text. It would be even better if they would use these names throughout. The reason is that this will make their work more accessible to readers working on human or bacterial ribosome biogenesis. The other reason is that prefixes “u” or “e” in Figure 1 or Figure 8 will instantly convey that the key ribosomal proteins in this study are universally conserved and define the (ancient) architecture of the 40S head.10. Pg 3, Rps5 is said to correspond to uS2; it should be uS7.11. Fig. 3 legend, “Ltv1 separates Rps20_DE from Rps3_KK” – do the authors mean that Ltv1 lies between these two proteins?12. Fig. 3 legend, “Ltv1 binds Rps3K75R76” – does this refer to association of Ltv1 and Rps3 proteins, or does it refer to a predicted molecular interaction between Ltv1 and these particular residues of Rps3? The latter is not usually referred to as binding, which typically implies a bimolecular association.13. Fig. 7 & 8 – consider colors for Tsr1 early and late with higher contrast.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). 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CopenhaverEditor-in-ChiefPLOS Geneticswww.plosgenetics.orgTwitter: @PLOSGenetics----------------------------------------------------Comments from the reviewers (if applicable):Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #3: The authors have responded well to the critiques of their manuscript, and the revised manuscript is much improved. In particular, they have addressed concerns about the phenotypes of the 18S rRNA mutations (raised by both reviewers). The structural context for the genetics experiments is also presented more carefully in the revised manuscript than in the original. Overall this is an interesting and extensive study that advances the current understanding of the role of Ltv1 in yeast 40S biogenesis.Although I am generally satisfied with the revisions, I gently point out to the authors that their model of the assembly pathway in Fig. 9B is structurally implausible. First, the topology of the contacts is cartooned incorrectly in Fig. 9B. 18S h39 does not lie between h41 and uS10 (Rps20) as diagrammed; uS10 binds at the interface of h39 and h41that are almost parallel with each other. uS14 (Rps29) has almost no contact with h39 or h41 – it binds h31 and the tip of h43. Second, uS10 (Rps20) truly does lie under the C-terminus of uS14 (Rps29), in the region of the Rps20_EYER mutations that define the interface between these two proteins. (The point of view is not relevant here.) Therefore, steric occlusion cannot be the reason why uS14 should bind first. However, because each protein mainly interacts with a separate domain of the 18S rRNA, not with each other, I agree that they could initially join the complex in either order. If uS14 binds first, its C-terminus presumably folds over the uS10 EYER residues after uS10 joins the complex.I am convinced that the results here identify 40S interactions that depend on the Ltv1 chaperone. If the authors were to modify their cartoon in Fig. 9B to match the actual structure of the 40S ribosome, and perhaps acknowledge some ambiguity in the temporal sequence of events, that would be wonderful.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #3: Yes**********what does this mean?). 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  24 Oct 2023PGENETICS-D-23-00751R1 A disease associated mutant reveals how Ltv1 orchestrates RP assembly and rRNA folding of the small ribosomal subunit head Dear Dr Karbstein, We are pleased to inform you that your manuscript entitled "A disease associated mutant reveals how Ltv1 orchestrates RP assembly and rRNA folding of the small ribosomal subunit head" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. 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