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A cell-level model to predict the spatiotemporal dynamics of neurodegenerative disease

Shih-Huan Huang, Matthew W. Cotton, Tuomas P. J. Knowles, David Klenerman, Georg Meisl

TL;DR

This model naturally explains the characteristic long, slow development of pathology followed by a rapid acceleration, a hallmark of many neurodegenerative diseases, and reveals the existence of a critical switch point at which the system's dynamics transition from being dominated by slow, spontaneous formation of diseased cells to being driven by fast propagation.

Abstract

A central challenge in modeling neurodegenerative diseases is connecting cellular-level mechanisms to tissue-level pathology, in particular to determine whether pathology is driven primarily by cell-autonomous triggers or by propagation from cells that are already in a pathological, runaway aggregation state. To bridge this gap, we here develop a bottom-up physical model that explicitly incorporates these two fundamental cell-level drivers of protein aggregation dynamics. We show that our model naturally explains the characteristic long, slow development of pathology followed by a rapid acceleration, a hallmark of many neurodegenerative diseases. Furthermore, the model reveals the existence of a critical switch point at which the system's dynamics transition from being dominated by slow, spontaneous formation of diseased cells to being driven by fast propagation. This framework provides a robust physical foundation for interpreting pathological data and offers a method to predict which class of therapeutic strategies is best matched to the underlying drivers of a specific disease.

A cell-level model to predict the spatiotemporal dynamics of neurodegenerative disease

TL;DR

This model naturally explains the characteristic long, slow development of pathology followed by a rapid acceleration, a hallmark of many neurodegenerative diseases, and reveals the existence of a critical switch point at which the system's dynamics transition from being dominated by slow, spontaneous formation of diseased cells to being driven by fast propagation.

Abstract

A central challenge in modeling neurodegenerative diseases is connecting cellular-level mechanisms to tissue-level pathology, in particular to determine whether pathology is driven primarily by cell-autonomous triggers or by propagation from cells that are already in a pathological, runaway aggregation state. To bridge this gap, we here develop a bottom-up physical model that explicitly incorporates these two fundamental cell-level drivers of protein aggregation dynamics. We show that our model naturally explains the characteristic long, slow development of pathology followed by a rapid acceleration, a hallmark of many neurodegenerative diseases. Furthermore, the model reveals the existence of a critical switch point at which the system's dynamics transition from being dominated by slow, spontaneous formation of diseased cells to being driven by fast propagation. This framework provides a robust physical foundation for interpreting pathological data and offers a method to predict which class of therapeutic strategies is best matched to the underlying drivers of a specific disease.

Paper Structure

This paper contains 13 sections, 22 equations, 9 figures.

Table of Contents

  1. Introduction
  2. A cell-level model for neurodegenerative diseases
  3. Results
  4. Spontaneous Aggregation ($k_s=0$) Results in Random Spatial Patterns
  5. Propagating-Only Aggregation ($k_a\rightarrow 0$) Leads to Growing, Spatially Correlated Clusters
  6. Short-Range Coupling Generates Dense Clusters
  7. Long-Range Coupling Gives Rise to Continuous Traveling Waves
  8. Intermediate regime: switch fraction
  9. Discussion
  10. Total cell fraction aggregated in weak spatial coupling case
  11. Derivation of the RDF in the spatially uncorrelated limit
  12. NND distribution in weak spatial coupling case
  13. Time Convergence Test

Figures (9)

  • Figure 1: Model description.
  • Figure 2: No spatial coupling case.
  • Figure 3: Short-range coupling case.
  • Figure 4: Long range spatial coupling.(A) Temporal evolution of total aggregated fraction. The red dots are simulated points. (B) Snapshots of simulations of the strong spatial coupling case at fraction aggregated = 2%, 5%, 20%. (C) Radial distribution function of (B). (D) Corresponding nearest neighbour distance distribution of (B). Scale bar on (B): 1000 µ m. Simulation conditions: total cell number = 90000, $k_a$ = 0/s, $k_s$ = 1.0/s, $\sigma$ = 400$\mu m$, coupling kernel: Gaussian, $\Delta t$ = 0.1 s, $\rho$ = 1341.76/$mm^2$, initial aggregated cell number = 1.
  • Figure 5: Wavefront development in long-range coupling cases.
  • ...and 4 more figures