In vivo imaging of injured cortical axons reveals a rapid onset form of Wallerian degeneration.

BACKGROUND: Despite the widespread occurrence of axon and synaptic loss in the injured and diseased nervous system, the cellular and molecular mechanisms of these key degenerative processes remain incompletely understood. Wallerian degeneration (WD) is a tightly regulated form of axon loss after injury, which has been intensively studied in large myelinated fibre tracts of the spinal cord, optic nerve and peripheral nervous system (PNS). Fewer studies, however, have focused on WD in the complex neuronal circuits of the mammalian brain, and these were mainly based on conventional endpoint histological methods. Post-mortem analysis, however, cannot capture the exact sequence of events nor can it evaluate the influence of elaborated arborisation and synaptic architecture on the degeneration process, due to the non-synchronous and variable nature of WD across individual axons. RESULTS: To gain a comprehensive picture of the spatiotemporal dynamics and synaptic mechanisms of WD in the nervous system, we identify the factors that regulate WD within the mouse cerebral cortex. We combined single-axon-resolution multiphoton imaging with laser microsurgery through a cranial window and a fluorescent membrane reporter. Longitudinal imaging of > 150 individually injured excitatory cortical axons revealed a threshold length below which injured axons consistently underwent a rapid-onset form of WD (roWD). roWD started on average 20 times earlier and was executed 3 times slower than WD described in other regions of the nervous system. Cortical axon WD and roWD were dependent on synaptic density, but independent of axon complexity. Finally, pharmacological and genetic manipulations showed that a nicotinamide adenine dinucleotide (NAD+)-dependent pathway could delay cortical roWD independent of transcription in the damaged neurons, demonstrating further conservation of the molecular mechanisms controlling WD in different areas of the mammalian nervous system. CONCLUSIONS: Our data illustrate how in vivo time-lapse imaging can provide new insights into the spatiotemporal dynamics and synaptic mechanisms of axon loss and assess therapeutic interventions in the injured mammalian brain.

A systematic pan-cancer study on deep learning-based prediction of multi-omic biomarkers from routine pathology images.

BACKGROUND: The objective of this comprehensive pan-cancer study is to evaluate the potential of deep learning (DL) for molecular profiling of multi-omic biomarkers directly from hematoxylin and eosin (H&E)-stained whole slide images. METHODS: A total of 12,093 DL models predicting 4031 multi-omic biomarkers across 32 cancer types were trained and validated. The study included a broad range of genetic, transcriptomic, and proteomic biomarkers, as well as established prognostic markers, molecular subtypes, and clinical outcomes. RESULTS: Here we show that 50% of the models achieve an area under the curve (AUC) of 0.644 or higher. The observed AUC for 25% of the models is at least 0.719 and exceeds 0.834 for the top 5%. Molecular profiling with image-based histomorphological features is generally considered feasible for most of the investigated biomarkers and across different cancer types. The performance appears to be independent of tumor purity, sample size, and class ratio (prevalence), suggesting a degree of inherent predictability in histomorphology. CONCLUSIONS: The results demonstrate that DL holds promise to predict a wide range of biomarkers across the omics spectrum using only H&E-stained histological slides of solid tumors. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients.

Molecular profiling tests are used to check cancers for changes in certain genes, proteins, or other molecules. Results of such tests can be used to identify the most effective treatment for cancer patients. Faster and more accessible alternatives to standard tests are needed to improve cancer care. This study investigates whether deep learning (DL), a series of advanced computer techniques, can perform molecular profiling directly from routinely-collected images of tumor specimens used for diagnostic purposes. Over 12,000 DL models were utilized to evaluate thousands of biomarkers using statistical approaches. The results indicate that DL can effectively detect molecular changes in a tumor from these images, for many biomarkers and tumor types. The study shows that DL-based molecular profiling from images is possible. Introducing this type of approach into routine clinical workflows could potentially accelerate treatment decisions and improve outcomes.

ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans.

An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM.

Detection of axonal synapses in 3D two-photon images.

Studies of structural plasticity in the brain often require the detection and analysis of axonal synapses (boutons). To date, bouton detection has been largely manual or semi-automated, relying on a step that traces the axons before detection the boutons. If tracing the axon fails, the accuracy of bouton detection is compromised. In this paper, we propose a new algorithm that does not require tracing the axon to detect axonal boutons in 3D two-photon images taken from the mouse cortex. To find the most appropriate techniques for this task, we compared several well-known algorithms for interest point detection and feature descriptor generation. The final algorithm proposed has the following main steps: (1) a Laplacian of Gaussian (LoG) based feature enhancement module to accentuate the appearance of boutons; (2) a Speeded Up Robust Features (SURF) interest point detector to find candidate locations for feature extraction; (3) non-maximum suppression to eliminate candidates that were detected more than once in the same local region; (4) generation of feature descriptors based on Gabor filters; (5) a Support Vector Machine (SVM) classifier, trained on features from labelled data, and was used to distinguish between bouton and non-bouton candidates. We found that our method achieved a Recall of 95%, Precision of 76%, and F1 score of 84% within a new dataset that we make available for accessing bouton detection. On average, Recall and F1 score were significantly better than the current state-of-the-art method, while Precision was not significantly different. In conclusion, in this article we demonstrate that our approach, which is independent of axon tracing, can detect boutons to a high level of accuracy, and improves on the detection performance of existing approaches. The data and code (with an easy to use GUI) used in this article are available from open source repositories.