Advances in Microscopy for Visualizing Neurologic Disease
Date: October 18, 2026
Time: 1:30 pm to 3:00 pm
Room: Pacific Jewel Ballroom
Track: Plenary - Presidential Symposium
Session Description
Understanding neurologic disease requires tools that can resolve the brain across molecular, cellular, and systems‑level scales. This session highlights how super‑resolution imaging, organelle‑level analysis, and AI‑driven single‑cell profiling together provide a multidimensional view of disease mechanisms in neuronal model systems.
Super‑resolution microscopy breaks the diffraction limit to visualize nanoscale structures in living neurons. Techniques such as STED, PALM, and STORM reveal early pathogenic changes in synaptic nanodomains, receptor clustering, cytoskeletal organization, and axonal transport. These methods capture dynamic molecular events—such as protein aggregation or trafficking defects—that precede overt degeneration, offering insight into how subtle disruptions propagate through neural circuits.
Advanced electron microscopy complements this by exposing ultrastructural abnormalities in organelles central to neuronal health. Serial block‑face EM, cryo‑EM, and focused ion beam tomography reconstruct neurons in 3D, revealing mitochondrial swelling, cristae loss, lysosomal dysfunction, ER–mitochondria contact disruption, and impaired autophagosome maturation. These organelle‑level defects illuminate how metabolic stress, proteostasis failure, and calcium imbalance contribute to disease vulnerability.
AI‑driven analysis integrates these imaging datasets with single‑cell transcriptomics and spatial profiling. Machine‑learning models classify neuronal and glial subtypes, detect subtle phenotypes invisible to human observers, quantify organelle dynamics, and map disease trajectories across thousands of cells. By linking structural abnormalities to gene‑expression states and spatial context, AI identifies vulnerable cell populations and mechanistic pathways that drive pathology.
Together, these approaches form a unified framework.
Learning Objectives
At the conclusion of this session, attendees will be able to:
- Describe AI-based single-cell analytics and the integration of multimodal datasets.
- Explain the principles of super-resolution imaging and how these techniques overcome diffraction limits.
- Identify early nanoscale disease signatures and their relevance for clinical translation.
Speakers
- (Chair) Dimitri Krainc, MD, PhD, FANA
- (Co-Chair, Speaker) Craig Blackstone, MD, PhD, FANA
- (Speaker) Erika Holzbaur, PhD
- (Speaker) Steve Finkbeiner, MD, PhD, FANA
NeuroZoom: Super-Resolution for Neurologic Disease Discovery
Description
In this presentation Dr. Blackstone will discuss how advances in super‑resolution light microscopy and electron microscopy are increasingly employed to reveal dynamic nanoscale changes in neurons and glia that drive neurologic disease. These tools can be applied to cellular and in vivo models to map synaptic architecture, track protein movement and aggregation, and visualize changes in organelle morphology, movement and function, enabling precise pathologic insights and accelerating therapeutic discovery.
Organelle Quality Control in Neuronal Homeostasis, Neurodevelopment, and Neurodegeneration
Description
The cellular mechanisms that lead to the onset of many neurodevelopmental and neurodegenerative diseases remain unclear, despite decades of research. Fortunately, the development of powerful new imaging technologies now allows researchers to obtain novel insights into the pathogenic mechanisms driving disease at the cellular level. Over the past decade there has been explosive growth in imaging approaches including super-resolution microscopy and cryo-electron tomography that are revealing new aspects of cellular biology. Importantly, this progress provides new insights into how neurons function, and how cellular dysfunction can lead to neurological disease.
This presentation will discuss how state-of-the-art imaging approaches are allowing us to decipher molecular mechanisms driving neurodevelopmental and/or neurodegenerative diseases including Fragile X Syndrome (FXS) and KIF1A Associated Neurological Disorder (KAND). Our group uses live-imaging of organelle dynamics in human iPSC-derived neurons in conjunction with super-resolution and cryo-ET microscopy to study mechanisms that maintain cellular homeostasis. We are focusing on mitochondrial dynamics and autophagy, and how defects in these pathways contribute to pathogenesis in diseases including FXS, KAND, ALS, and Parkinson’s disease.
Attendees will gain insights into the powerful imaging technologies now available to interrogate disease mechanisms at the cellular level, and how these approaches can lead to unexpected findings as well as an improved understanding of the mechanisms leading to neuronal dysfunction or degeneration.
State-of-the-Art Technologies to Generate and Analyze Large Amounts of Imaging and Genetic Data, Including Robotic Microscopy and AI
Description
Progress in understanding and treating neurological disease is often limited not by a shortage of ideas, but by the ability to observe disease biology directly, at the scale and resolution needed to find patterns that hold up across many cells, patients, or postmortem samples. Automated (robotic) microscopy paired with artificial intelligence (AI) is changing this by making it possible to image living cells and human tissue at scale and extract objective, quantitative measures of disease that were previously invisible or impractical to capture.
This presentation will describe how robotic microscopy and AI can be combined to generate and analyze large amounts of imaging and genetic data across neurological disease models. Dr. Finkbeiner will discuss automated live-cell imaging platforms that track individual human stem cell-derived neurons over time, and how machine learning applied to these images can decode disease-specific cellular signatures — illustrated by work distinguishing and stratifying subtypes of amyotrophic lateral sclerosis (ALS) directly from live-cell imaging data. The talk will also cover how related AI-based image analysis can be applied to digital pathology, using automated, high-accuracy classification of Alzheimer disease pathology in postmortem brain tissue to link cellular changes to clinical severity at a scale far beyond conventional manual assessment.
Attendees will leave with a clearer understanding of how robotic microscopy and AI can be used to model neurological disease, uncover pathogenic mechanisms, and identify candidate therapeutic targets and patient subgroups, along with practical insight into how these imaging and computational technologies may inform future approaches to diagnosis, stratification, and treatment development across neurodegenerative disease.
