Digital electron microscopic examination of human sural nerve biopsies

Preparation of Samples for Large-Scale Automated Electron Microscopy of Tissue and Cell Ultrastructure

Manual selection of targets in experimental or diagnostic samples by transmission electron microscopy (TEM), based on single overview and detail micrographs, has been time-consuming and susceptible to bias. Substantial information and throughput gain may now be achieved by the automated acquisition of virtually all structures in a given EM section. Resulting datasets allow the convenient pan-and-zoom examination of tissue ultrastructure with preserved microanatomical orientation. The technique is, however, critically sensitive to artifacts in sample preparation. We, therefore, established a methodology to prepare large-scale digitization samples (LDS) designed to acquire entire sections free of obscuring flaws. For evaluation, we highlight the supreme performance of scanning EM in transmission mode compared with other EM technology. The use of LDS will substantially facilitate access to EM data for a broad range of applications.

Ultra-high-frequency ultrasound imaging of sural nerve: A comparative study with nerve biopsy in progressive neuropathies

INTRODUCTION

Nerve ultrasound has been used increasingly in clinical practice as a complementary test for diagnostic assessment of neuropathies, but nerve biopsy remains invaluable in certain cases. The aim of this study was to compare ultra-high-frequency ultrasound (UHF-US) to histologic findings in progressive polyneuropathies.

METHODS

Ten patients with severe, progressive neuropathies underwent ultrasound evaluation of the sural nerve before nerve biopsy. Ultrasound data were compared with histologic results in a retrospective manner.

RESULTS

Sural nerves were easily identified on UHF-US. Nerve hyperechogenicity correlated with inflammatory infiltrates on biopsy. Nerve fascicles could be identified and measured on ultrasound in the majority of patients.

DISCUSSION

Hyperechogenicity on UHF-US may be a marker of nerve inflammation in neuropathies. Furthermore, the UHF-US probe allows for evaluation of sensory nerves in spite of their small size, providing valuable information on their size and on their internal structure.

Determining Direction of Axonal Flow in the Equine Ramus Communicans by Ultrastructural Examination of the Plantar Nerves 2 Months after Transecting the Ramus

The ramus communicans, neural connection between medial and lateral plantar nerves of the horse, was transected to determine the degree to which medial and lateral plantar nerves contribute to the plantar ramus. After 2 months, sections of plantar nerves immediately proximal and distal to the communicating branch were collected and processed for electron microscopy. All examined nerves had undergone Wallerian degeneration and contained regenerating and mature fibers. Layers of the myelin sheath were separated by spaces and vacuoles, indicating demyelination of medial and lateral plantar nerves. Shrunken axons varied in diameter and were surrounded by an irregular axolemma. Shrunken axoplasm of both myelinated and non-myelinated fibers contained ruptured mitochondria and cristae, disintegrating cytoskeleton, and vacuoles of various sizes. The cytoplasm of neurolemmocytes contained various-sized vesicles, ruptured mitochondria within a fragile basal lamina and myelin whorls of multilayered structures indicative of Wallerian degeneration. These ultrastructural changes, found proximal and distal to the ramus in medial and lateral plantar nerves, suggest that axonal flow is bi-directional through the ramus communicans of the pelvic limbs of horses, a previously unreported finding. As well, maturity of nerves proximal and distal to the ramus indicates that all nerve fibers do not pass through the ramus.

Identification of factors associated with sural nerve regeneration and degeneration in diabetic neuropathy

OBJECTIVE

Patients with diabetic neuropathy (DN) demonstrate variable degrees of nerve regeneration and degeneration. Our aim was to identify risk factors associated with sural nerve degeneration in patients with DN.

RESEARCH DESIGN AND METHODS

Demographic, anthropometric, biochemical, and anatomical data of subjects with DN from a 52-week trial of acetyl-L-carnitine were retrospectively examined. Based on the change in sural nerve myelinated fiber density (ΔMFD%), subjects were divided into three groups: regenerator (top 16 percentiles, n = 67), degenerator (bottom 16 percentiles, n = 67), and intermediate (n = 290), with dramatically increased, decreased, and steady ΔMFD%, respectively. ANOVA, Fisher exact test, and multifactorial logistic regression were used to evaluate statistical significance.

RESULTS

ΔMFD%s were 35.6 ± 17.4 (regenerator), -4.8 ± 12.1 (intermediate), and -39.8 ± 11.0 (degenerator). HbA1c at baseline was the only factor significantly different across the three groups (P = 0.01). In multifactorial logistic regression, HbA1c at baseline was also the only risk factor significantly different between regenerator (8.3 ± 1.6%) and degenerator (9.2 ± 1.8%) (odds ratio 0.68 [95% CI 0.54-0.85]; P < 0.01). Support Vector Machine classifier using HbA1c demonstrated 62.4% accuracy of classifying subjects into regenerator or degenerator. A preliminary microarray experiment revealed that upregulated genes in the regenerator group are enriched with cell cycle and myelin sheath functions, while downregulated genes are enriched in immune/inflammatory responses.

CONCLUSIONS

These data, based on the largest cohort with ΔMFD% information, suggest that HbA1c levels predict myelinated nerve fiber regeneration and degeneration in patients with DN. Therefore, maintaining optimal blood glucose control is likely essential in patients with DN to prevent continued nerve injury.

Decreased glycolytic and tricarboxylic acid cycle intermediates coincide with peripheral nervous system oxidative stress in a murine model of type 2 diabetes

Diabetic neuropathy (DN) is the most common complication of diabetes and is characterized by distal-to-proximal loss of peripheral nerve axons. The idea of tissue-specific pathological alterations in energy metabolism in diabetic complications-prone tissues is emerging. Altered nerve metabolism in type 1 diabetes models is observed; however, therapeutic strategies based on these models offer limited efficacy to type 2 diabetic patients with DN. Therefore, understanding how peripheral nerves metabolically adapt to the unique type 2 diabetic environment is critical to develop disease-modifying treatments. In the current study, we utilized targeted liquid chromatography-tandem mass spectrometry (LC/MS/MS) to characterize the glycolytic and tricarboxylic acid (TCA) cycle metabolomes in sural nerve, sciatic nerve, and dorsal root ganglia (DRG) from male type 2 diabetic mice (BKS.Cg-m+/+Lepr(db); db/db) and controls (db/+). We report depletion of glycolytic intermediates in diabetic sural nerve and sciatic nerve (glucose-6-phosphate, fructose-6-phosphate, fructose-1,6-bisphosphate (sural nerve only), 3-phosphoglycerate, 2-phosphoglycerate, phosphoenolpyruvate, and lactate), with no significant changes in DRG. Citrate and isocitrate TCA cycle intermediates were decreased in sural nerve, sciatic nerve, and DRG from diabetic mice. Utilizing LC/electrospray ionization/MS/MS and HPLC methods, we also observed increased protein and lipid oxidation (nitrotyrosine; hydroxyoctadecadienoic acids) in db/db tissue, with a proximal-to-distal increase in oxidative stress, with associated decreased aconitase enzyme activity. We propose a preliminary model, whereby the greater change in metabolomic profile, increase in oxidative stress, and decrease in TCA cycle enzyme activity may cause distal peripheral nerves to rely on truncated TCA cycle metabolism in the type 2 diabetes environment.

The identification of gene expression profiles associated with progression of human diabetic neuropathy

Diabetic neuropathy is a common complication of diabetes. While multiple pathways are implicated in the pathophysiology of diabetic neuropathy, there are no specific treatments and no means to predict diabetic neuropathy onset or progression. Here, we identify gene expression signatures related to diabetic neuropathy and develop computational classification models of diabetic neuropathy progression. Microarray experiments were performed on 50 samples of human sural nerves collected during a 52-week clinical trial. A series of bioinformatics analyses identified differentially expressed genes and their networks and biological pathways potentially responsible for the progression of diabetic neuropathy. We identified 532 differentially expressed genes between patient samples with progressing or non-progressing diabetic neuropathy, and found these were functionally enriched in pathways involving inflammatory responses and lipid metabolism. A literature-derived co-citation network of the differentially expressed genes revealed gene subnetworks centred on apolipoprotein E, jun, leptin, serpin peptidase inhibitor E type 1 and peroxisome proliferator-activated receptor gamma. The differentially expressed genes were used to classify a test set of patients with regard to diabetic neuropathy progression. Ridge regression models containing 14 differentially expressed genes correctly classified the progression status of 92% of patients (P < 0.001). To our knowledge, this is the first study to identify transcriptional changes associated with diabetic neuropathy progression in human sural nerve biopsies and describe their potential utility in classifying diabetic neuropathy. Our results identifying the unique gene signature of patients with progressive diabetic neuropathy will facilitate the development of new mechanism-based diagnostics and therapies.

A semi-automated method for identifying and measuring myelinated nerve fibers in scanning electron microscope images

Diagnosing illnesses, developing and comparing treatment methods, and conducting research on the organization of the peripheral nervous system often require the analysis of peripheral nerve images to quantify the number, myelination, and size of axons in a nerve. Current methods that require manually labeling each axon can be extremely time-consuming as a single nerve can contain thousands of axons. To improve efficiency, we developed a computer-assisted axon identification and analysis method that is capable of analyzing and measuring sub-images covering the nerve cross-section, acquired using a scanning electron microscope. This algorithm performs three main procedures - it first uses cross-correlation to combine the acquired sub-images into a large image showing the entire nerve cross-section, then identifies and individually labels axons using a series of image intensity and shape criteria, and finally identifies and labels the myelin sheath of each axon using a region growing algorithm with the geometric centers of axons as seeds. To ensure accurate analysis of the image, we incorporated manual supervision to remove mislabeled axons and add missed axons. The typical user-assisted processing time for a two-megapixel image containing over 2000 axons was less than 1h. This speed was almost eight times faster than the time required to manually process the same image. Our method has proven to be well suited for identifying axons and their characteristics, and represents a significant time savings over traditional manual methods.

Criteria for creating and assessing mouse models of diabetic neuropathy

Diabetic neuropathy (DN) is a serious and debilitating complication of both type 1 and type 2 diabetes. Despite intense research efforts into multiple aspects of this complication, including both vascular and neuronal metabolic derangements, the only treatment remains maintenance of euglycemia. Basic research into the mechanisms responsible for DN relies on using the most appropriate animal model. The advent of genetic manipulation has moved mouse models of human disease to the forefront. The ability to insert or delete genes affected in human patients offers unique insight into disease processes; however, mice are still not humans and difficulties remain in interpreting data derived from these animals. A number of studies have investigated and described DN in mice but it is difficult to compare these studies with each other or with human DN due to experimental differences including background strain, type of diabetes, method of induction and duration of diabetes, animal age and gender. This review describes currently used DN animal models. We followed a standardized diabetes induction protocol and designed and implemented a set of phenotyping parameters to classify the development and severity of DN. By applying standard protocols, we hope to facilitate the comparison and characterization of DN across different background strains in the hope of discovering the most human like model in which to test potential therapies.