The Relationship Between Wild-Type Transthyretin Amyloid Load and Ligamentum Flavum Thickness in Lumbar Stenosis Patients.

BACKGROUND: One key contributor to lumbar stenosis is thickening of the ligamentum flavum (LF), a process still poorly understood. Wild-type transthyretin amyloid (ATTRwt) has been found in the LF of patients undergoing decompression surgery, suggesting that amyloid may play a role. However, it is unclear whether within patients harboring ATTRwt, the amount of amyloid is associated with LF thickness. METHODS: From an initial cohort of 324 consecutive lumbar stenosis patients whose LF specimens from decompression surgery were sent for analysis (2018-2019), 33 patients met the following criteria: 1) Congo red-positive amyloid in the LF, 2) ATTRwt by mass spectrometry-based proteomics, and 3) an available preoperative magnetic resonance imaging. Histological specimens were digitized, and amyloid load was quantified through Trainable Weka Segmentation machine learning. LF thicknesses were manually measured on axial T2-weighted preoperative magnetic resonance imaging scans at each lumbar level, L1-S1. The sum of thicknesses at every lumbar LF level (L1-S1) equals "lumbar LF burden". RESULTS: Patients had a mean age of 72.7 years (range = 59-87), were mostly male (61%) and white (82%), and predominantly had surgery at L4-L5 levels (73%). Amyloid load was positively correlated with LF thickness (R = 0.345, P = 0.0492) at the levels of surgical decompression. Furthermore, amyloid load was positively correlated with lumbar LF burden (R = 0.383, P = 0.0279). CONCLUSIONS: Amyloid load is positively correlated with LF thickness and lumbar LF burden across all lumbar levels, in a dose-dependent manner. Further studies are needed to validate these findings, uncover the underlying pathophysiology, and pave the way toward using therapies that slow LF thickening.

Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum.

Wild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis before the development of systemic and cardiac amyloidosis. In order to study this phenomenon and its possible relationship with LF thickening and systemic amyloidosis, a precise method of quantifying amyloid deposits in histological slides of LF is critical. However, such a method is currently unavailable. Here, we present a machine learning quantification method with Trainable Weka Segmentation (TWS) to assess amyloid deposition in histological slides of LF. Images of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user-directed annotations, and then applied] to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS. We develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23). TWS machine learning closely correlates with the gold-standard comparator of manual segmentation and outperforms the color thresholding method. This novel machine learning method to quantify amyloid deposition in histological slides of ligamentum flavum is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications.

The life and legacy of William Beecher Scoville.

Dr. William Beecher Scoville (1906-1984) is a giant figure in the history of neurosurgery, well known by the public for his operation on Patient H.M. He developed dozens of neurosurgical instruments and techniques, with many tools named after him that are still widely used today. He founded numerous neurosurgical societies around the world. He led the movement in psychosurgery, developing the technique of selective orbital undercutting and performing hundreds of lobotomies throughout his career. However, his many contributions to the advancement of neurosurgery have not been well described in the medical literature. To bridge the knowledge gap, this article seeks to detail the life and career of William Beecher Scoville and bring to attention the enduring impact of his work.

Metal allergy hypersensitivity after posterior thoracic spinal fusion: A case report and review of the literature.

Background: Spine surgeons rarely consider metal allergies when placing hardware, as implants are thought to be inert. Case Description: A 32-year-old male presented with a skin rash attributed to the trace metal in his spinal fusion instrumentation. Patch testing revealed sensitivities to cobalt, manganese, and chromium. He underwent hardware removal and replacement with constructs of commercially pure titanium. His skin findings resolved at 2 weeks after surgery and were stable at 6 weeks. Conclusion: Hypersensitivity to metal (i.e., metal allergy) should be considered before performing instrumented spinal fusions.

Free circular introns with an unusual branchpoint in neuronal projections.

The polarized structure of axons and dendrites in neuronal cells depends in part on RNA localization. Previous studies have looked at which polyadenylated RNAs are enriched in neuronal projections or at synapses, but less is known about the distribution of non-adenylated RNAs. By physically dissecting projections from cell bodies of primary rat hippocampal neurons and sequencing total RNA, we found an unexpected set of free circular introns with a non-canonical branchpoint enriched in neuronal projections. These introns appear to be tailless lariats that escape debranching. They lack ribosome occupancy, sequence conservation, and known localization signals, and their function, if any, is not known. Nonetheless, their enrichment in projections has important implications for our understanding of the mechanisms by which RNAs reach distal compartments of asymmetric cells.

Cell surface profiling using high-throughput flow cytometry: a platform for biomarker discovery and analysis of cellular heterogeneity.

Cell surface proteins have a wide range of biological functions, and are often used as lineage-specific markers. Antibodies that recognize cell surface antigens are widely used as research tools, diagnostic markers, and even therapeutic agents. The ability to obtain broad cell surface protein profiles would thus be of great value in a wide range of fields. There are however currently few available methods for high-throughput analysis of large numbers of cell surface proteins. We describe here a high-throughput flow cytometry (HT-FC) platform for rapid analysis of 363 cell surface antigens. Here we demonstrate that HT-FC provides reproducible results, and use the platform to identify cell surface antigens that are influenced by common cell preparation methods. We show that multiple populations within complex samples such as primary tumors can be simultaneously analyzed by co-staining of cells with lineage-specific antibodies, allowing unprecedented depth of analysis of heterogeneous cell populations. Furthermore, standard informatics methods can be used to visualize, cluster and downsample HT-FC data to reveal novel signatures and biomarkers. We show that the cell surface profile provides sufficient molecular information to classify samples from different cancers and tissue types into biologically relevant clusters using unsupervised hierarchical clustering. Finally, we describe the identification of a candidate lineage marker and its subsequent validation. In summary, HT-FC combines the advantages of a high-throughput screen with a detection method that is sensitive, quantitative, highly reproducible, and allows in-depth analysis of heterogeneous samples. The use of commercially available antibodies means that high quality reagents are immediately available for follow-up studies. HT-FC has a wide range of applications, including biomarker discovery, molecular classification of cancers, or identification of novel lineage specific or stem cell markers.

Passively transferred human NMO-IgG exacerbates demyelination in mouse experimental autoimmune encephalomyelitis.

BACKGROUND: Neuromyelitis optica (NMO) is a devastating inflammatory disorder of the optic nerves and spinal cord characterized by frequently recurring exacerbations of humoral inflammation. NMO is associated with the highly specific NMO-IgG biomarker, an antibody that binds the aquaporin-4 water channel. Aquaporin-4 is present on glial endfeet in the central nervous system (CNS). In humans, the NMO-IgG portends more frequent exacerbations and a worse long-term clinical outcome. METHODS: We tested the longer-term outcome of mice with EAE injected with NMO-IgG and followed them for 60 days. Clinical exams and pathology of the spinal cord and optic nerves were compared to mice that received control human IgG. RESULTS: Passively transferred human NMO-IgG leads to more severe neurology disability over two months after onset of disease. Clinical worsening is associated with an increased concentration of large demyelinating lesions primarily to subpial AQP4-rich regions of the spinal cord. CONCLUSIONS: NMO-IgG is pathogenic in the context of EAE in mice.

Differential expression of aquaporin-4 isoforms localizes with neuromyelitis optica disease activity.

Neuromyelitis optica (NMO) is a devastating neuroinflammatory disorder that specifically targets the spinal cord and optic nerves. Aquaporin-4 (AQP4) is the target of the NMO-IgG biomarker. AQP4 is expressed as two isoforms: M1 and M23, which have different functions in the central nervous system (CNS). We characterized the expression pattern of these AQP4 isoform mRNAs in humans and found a pattern of AQP4 expression that correlates with NMO disease localization. The ratio of M1:M23 mRNA is highest in the optic nerve and spinal cord, followed by brainstem, then the cerebral and cerebellar cortices.