Lessons learned from spatial transcriptomic analyses in clear-cell renal cell carcinoma
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A first-in-human study of quantitative ultrasound to assess transplant kidney fibrosis
Kidney transplantation is the optimal treatment for renal failure. In the United States, a biopsy at the time of organ procurement is often used to assess kidney quality to decide whether it should be used for transplant. This assessment is focused on renal fibrotic burden, because fibrosis is an important measure of irreversible kidney injury. Unfortunately, biopsy at the time of transplant is plagued by problems, including bleeding risk, inaccuracies introduced by sampling bias and rapid sample preparation, and the need for round-the-clock pathology expertise. We developed a quantitative algorithm, called renal H-scan, that can be added to standard ultrasound workflows to quickly and noninvasively measure renal fibrotic burden in preclinical animal models and human transplant kidneys. Furthermore, we provide evidence that biopsy-based fibrosis estimates, because of their highly localized nature, are inaccurate measures of whole-kidney fibrotic burden and do not associate with kidney function post-transplant. In contrast, we show that whole-kidney H-scan fibrosis estimates associate closely with post-transplant renal function. Taken together, our data suggest that the addition of H-scan to standard ultrasound workflows could provide a safe, rapid and easy-to-perform method for accurate quantification of transplant kidney fibrotic burden, and thus better prediction of post-transplant renal outcomes.
User-specified inverse kinematics taught in virtual reality reduce time and effort to hand-guide redundant surgical robots
Medical robots should not collide with close by obstacles during medical procedures, such as lamps, screens, or medical personnel. Redundant robots have more degrees of freedom than needed for moving endoscopic tools during surgery and can be reshaped to avoid obstacles by moving purely in the space of these additional degrees of freedom (null space). Although state-of-the-art robots allow surgeons to hand-guide endoscopic tools, reshaping the robot in null space is not intuitive for surgeons. Here we propose a learned task space control that allows surgeons to intuitively teach preferred robot configurations (shapes) that avoid obstacles using a VR-based planner in simulation. Later during surgery, surgeons control both the endoscopic tool and robot configuration (shape) with one hand. In a user study, we found that learned task space control outperformed state-of-the-art naive task space control in all the measured performance metrics (time, effort, and user-perceived effort). Our solution allowed users to intuitively interact with robots in VR and reshape robots while moving tools in medical and industrial applications.
Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility. Here we demonstrate NePSTA (neuropathology spatial transcriptomic analysis) for comprehensive morphological and molecular neuropathological diagnostics from single 5-µm tissue sections. NePSTA uses spatial transcriptomics with graph neural networks for automated histological and molecular evaluations. Trained and evaluated across 130 participants with CNS malignancies and healthy donors across four medical centers, NePSTA predicts tissue histology and methylation-based subclasses with high accuracy. We demonstrate the ability to reconstruct immunohistochemistry and genotype profiling on tissue with minimal requirements, inadequate for conventional molecular diagnostics, demonstrating the potential to enhance tumor subtype identification with implications for fast and precise diagnostic workup.
Neddylation of RhoA impairs its protein degradation and promotes renal interstitial fibrosis progression in diabetic nephropathy
Diabetic nephropathy (DN) is a common and serious complication of diabetes, characterized by chronic fibro-inflammatory processes with an unclear pathogenesis. Renal fibrosis plays a significant role in the development and progression of DN. While recent research suggests that the neddylation pathway may influence fibrotic processes, its specific dysregulation in DN and the underlying mechanisms remain largely unexplored. This study identified the neddylation of RhoA as a novel post-translational modification that regulates its expression and promotes renal fibrosis in DN. We here demonstrated that two key components of the neddylation pathway—NEDD8-activating enzyme E1 subunit 1 (NAE1) and NEDD8—are significantly upregulated in human chronic kidney disease (CKD) specimens compared to healthy kidneys, implicating neddylation in CKD-associated fibrosis. Our findings further revealed that both pharmacological inhibition of neddylation using MLN4924 and genetic knockdown of NAE1 mitigate renal fibrosis in mouse models of streptozotocin-induced diabetes and unilateral ureteral obstruction (UUO). Immunoprecipitation-mass spectrometry (IP-MS) and subsequent function assays demonstrated a direct interaction between RhoA and NEDD8. Importantly, neddylation inhibition reduced RhoA protein expression, highlighting a potential therapeutic target. Additionally, a positive correlation was noted between elevated NEDD8 mRNA levels and RhoA mRNA expression in human CKD specimens. RhoA overexpression counteracted the antifibrotic effects of neddylation inhibition, underscoring its critical role in fibrosis progression. Mechanistically, we unveiled that neddylation enhances RhoA protein stability by inhibiting its ubiquitination-mediated degradation, which subsequently activates the ERK1/2 pathway. Collectively, this study provides novel insights into NAE1-dependent RhoA neddylation as a key contributor to renal fibrosis in DN.
New mouse models for exploring renal tumor extension into the inferior vena cava
Renal tumors with inferior vena cava tumor thrombus (IVCTT) remain a challenge in urology. However, in vivo models remain unavailable, which hampers the elucidation of its pathogenesis, identification of therapeutic targets, and screening for effective drugs. In this study, we initially develop two IVCTT models in BALB/c and BALB/c-nu/nu mice using the mouse Renca cell line. The pathological features and immune microenvironment of IVCTT in immunocompetent mice closely resembles those observed in humans. Single-cell transcriptome sequencing, immunohistochemistry and multiplex immunohistochemistry reveal a predominance of monocytes, macrophages, and neutrophils within IVCTT, mirroring the cellular composition of the human IVCTT; however, fewer lymphocytes are observed. The IVCTT in immunodeficient mice progresses much faster than in immunocompetent mice. More importantly, we successfully use the human tumor cell line on the BALB/c nu/nu mice to create an IVCTT model. The proposed in vivo models mimic the progression of renal tumors with IVCTT, clarify that the immune system can inhibit tumor thrombus progression, and provide tools for subsequent mechanistic research and translational preclinical studies.
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