Project Roadmap

Author

Gage Rowden

Published

August 5, 2026

Overview

This roadmap outlines planned experiments to characterize the experimental parameters that contribute to the coefficients calculated by the (model)[model.qmd]; specifically, whether the post-growth dampening observed in normalized fluorescence traces is a genuine kinetic phenomenon or an artifact of the assay. Experiments are organized in ascending order of required labor, from simple protocol variations on existing infrastructure to broad cross-strain and cross-species comparisons.

Planned Experiments

flowchart LR
  subgraph T1["Tier 1 — Assay Parameter Exploration"]
    direction TB
    E1(["Seed Concentration"])
    E2(["Fragmentation Dynamics"])
    E7(["Substrate Concentration"])
    E1-->E7
    E7-->E2
  end

  subgraph T2["Tier 2 — Measurement Validation"]
    direction TB
    E3(["Absorbance & Fluorescence"])
    E4(["Fibril/Soluble Protein Quantification"])
    E3-->E4
  end

  subgraph T3["Tier 3 — Generalizability"]
    direction TB
    E5(["Prion Strain Effects"])
    E6(["Substrate Species Effects"])
    E5-->E6
  end

  T1 --> T2
  T2 --> T3

Tier 1 — Assay Parameter Exploration

Experiments in this tier require no additional materials beyond existing RT-QuIC infrastructure. They ask whether the secondary phase can be modulated by adjusting standard protocol parameters.

Experiment 1: Effect of Seed Concentration on the Secondary Phase

Question: Does seed concentration modulate the amplitude, timing, or inflection of the secondary phase?

Rationale: Serial dilution of seed is an established approach for probing reaction sensitivity and kinetics. If the secondary phase is seeding-dependent, its timing (\(b_2\)) or amplitude (\(S_2\)) should vary systematically with seed concentration. This experiment is partially underway — see kinetics.R and this image.

Approach:

  1. Prepare a serial dilution series of seed material (e.g., \(10^{-1}\) to \(10^{-8}\)).
  2. Run RT-QuIC under standard conditions with each dilution in replicate.
  3. Fit the double sigmoidal model to each reaction using the pipeline in kinetics.R.
  4. Compare fitted parameters (\(S_1\), \(a_1\), \(b_1\), \(S_2\), \(a_2\), \(b_2\)) and derived values (\(K_{app}\), \(t_{lag}\)) across dilutions.

Measurements: Normalized fluorescence over time; all six model parameters; \(K_{app}\) and \(t_{lag}\).

Expected Outcome: If the secondary phase is seeding-dependent, \(b_2\) and/or \(S_2\) should correlate with seed dilution factor. If the secondary phase parameters are independent of dilution while \(b_1\) shifts as expected, this implicates a seed-independent mechanism (e.g., substrate depletion or fibril maturation dynamics).

Experiment 2: Effect of Substrate Concentration on Model Coefficients

Question: Does substrate concentration modulate the amplitude, timing, or inflection of the secondary phase?

Rationale: In classical enzyme kinetics, the v\(_{\text{max}}\) is dependent on the substrate concentration. As the substrate increases, v\(_{\text{max}}\) increases until it reaches a saturation point. At that point, v\(_{\text{max}}\) can no longer increase because there is more substrate than the enzyme can process at its current concentration. We should be able to determine v\(_{\text{max}}\) by running RT-QuIC with varying substrate concentrations.

Approach:

  1. Run RT-QuIC with a fixed seed concentration and varying substrate concentrations.

Expected Outcome: If the substrate concentration determines v\(_{\text{max}}\), we should see a shift in the \(b_1\) parameter up to a certain point. I expect this shift to be consistent with the classical enzyme kinetics model.

Experiment 3: Effect of Fragmentation on the Model Coefficients

Question: Does mechanical fragmentation drive or modulate the secondary phase?

Rationale: RT-QuIC uses cyclic shaking to fragment growing fibrils, exposing new ends that act as seeds. Altering shaking intensity or temperature modifies fragmentation rate. If fragmentation is the proximal driver of the secondary phase, these perturbations should shift its kinetic parameters.

Approach:

  1. Run RT-QuIC with a fixed seed concentration and substrate across a matrix of:
    • Shaking speeds (e.g., 400, 700, 1000 rpm)
    • Temperatures (e.g., 37°C, 42°C, 55°C)
  2. Fit the double sigmoidal model to each condition.
  3. Compare secondary phase parameters (\(b_2\), \(a_2\), \(S_2\)) across the matrix.

Measurements: Normalized fluorescence over time; fitted model parameters per condition.

Expected Outcome: If fragmentation drives the secondary phase, harsher shaking should accelerate \(b_2\) or increase \(|S_2|\). A null result would indicate that the secondary phase arises independently of fragmentation rate — pointing instead toward a substrate-level mechanism.

Tier 2 — Measurement Validation

These experiments introduce orthogonal measurement modalities to determine whether the fluorescence-derived secondary phase corresponds to real changes in protein state or is a dye-specific artifact.

Experiment 4: Concurrent Absorbance and Fluorescence Measurement

Question: Do absorbance and fluorescence agree over the course of the reaction? Does fluorescence intensity correlate with substrate concentration?

Rationale: ThT (or equivalent) fluorescence is the standard readout for RT-QuIC, but it reflects dye-fibril interactions, not protein concentration directly. Turbidity (A600) provides an independent, dye-free proxy for fibril mass. Agreement between the two would validate the fluorescence-based kinetic model; disagreement would suggest the secondary phase is a spectroscopic artifact.

Approach:

  1. Run RT-QuIC reactions with simultaneous or parallel absorbance measurement at 600 nm.
  2. Collect fluorescence and absorbance traces on the same samples at matched time points.
  3. Cross-correlate the two time-course profiles.
  4. Fit the double sigmoidal model to both signals independently and compare parameters.

Measurements: Fluorescence intensity; A600 turbidity; cross-correlation of time-course profiles; model parameters from both signals.

Expected Outcome: Concordant biphasic profiles in both fluorescence and absorbance would confirm the secondary phase is a real change in fibril state. Fluorescence biphasic behavior with a monotonic absorbance signal would indicate the dye interaction changes (e.g., ThT displacement) rather than fibril mass are driving the secondary fluorescence phase.

Experiment 5: Fibril and Soluble Protein Quantification

Question: Is the secondary phase associated with a measurable change in the fibril-to-soluble protein ratio, or is it a fluorescence artifact?

Rationale: SyproOrange (SyO) fluorescence is sensitive to hydrophobic surface exposure, which differs between soluble and fibrillar forms of prion protein. A280 measures total protein content, and Bradford quantifies soluble protein. Together, these orthogonal methods can independently track fibril and soluble protein concentrations across the reaction time course and be compared against the model output.

Approach:

  1. At regular intervals spanning the full RT-QuIC time course, collect aliquots from ongoing reactions.
  2. Separate fibril and soluble fractions by centrifugation.
  3. Quantify each fraction using:
    • A280 — total protein concentration
    • A600 — turbidity as a proxy for fibril content
    • SyproOrange fluorescence — hydrophobic surface exposure (sensitive to fibril conformation)
    • Bradford assay — soluble protein concentration (a standard curve may be unnecessary if only relative changes between time points are needed)
  4. Map the orthogonal protein measurements onto the modeled \(g_1(t)\) and \(g_2(t)\) components.

Measurements: A280, A600, SyproOrange fluorescence, Bradford absorbance; fibril and soluble fractions at each time point.

Expected Outcome: A true secondary phase should correspond to a detectable shift in fibril/soluble protein ratio at the time coinciding with \(b_2\). A fluorescence-only artifact would show no corresponding signal in dye-independent measurements, which would motivate reconsidering the model’s physical interpretation.

Tier 3 — Generalizability

These experiments extend the kinetic model to different biological contexts. They require sourcing additional biological materials and replicating the full experimental workflow, making them the most labor-intensive.

Experiment 6: Effects of Prion Strain

Question: Is the biphasic kinetic profile consistent across prion strains, and do strain-specific differences map to the secondary phase parameters?

Rationale: Prion strains differ in fibril conformation, seeding efficiency, and incubation period. If the secondary phase is a conserved feature of RT-QuIC amplification, it should appear across strains — but its parameters may vary in strain-specific ways. Identifying strain-linked differences in \(b_2\) or \(S_2\) could link fibril structure to post-growth dynamics and potentially aid strain discrimination.

Approach:

  1. Identify a panel of well-characterized prion strains with distinct known kinetic profiles.
  2. Replicate a targeted subset of Tier 1 and Tier 2 experiments for each strain using matched conditions.
  3. Fit the double sigmoidal model for each strain and compare all six parameters and derived quantities (\(K_{app}\), \(t_{lag}\), \(t_{lag}'\)).
  4. Assess whether strain-specific clustering is more pronounced in primary-phase or secondary-phase parameters.

Measurements: All fluorescence-based measurements from Tier 1–2; model parameters stratified by strain.

Expected Outcome: Strain-specific differences in \(b_1\) and \(t_{lag}\) are expected based on known seeding kinetics. The key novel question is whether \(b_2\), \(a_2\), and \(S_2\) also cluster by strain — which would implicate fibril conformation in determining post-growth behavior and suggest the secondary phase parameters as potential strain biomarkers.

Experiment 7: Effects of Substrate Species

Question: Does the biphasic kinetic model generalize across recombinant substrate species, and does substrate species affect the secondary phase independently of the primary growth phase?

Rationale: RT-QuIC can be run with recombinant PrP (rPrP) from multiple species (hamster, bank vole, deer, etc.), each with different seeding compatibility and reaction efficiency. Understanding whether the secondary phase is species-dependent informs both the generalizability of the kinetic model and the mechanism underlying post-growth dynamics.

Approach:

  1. Produce or obtain rPrP substrates from a panel of species (at minimum: hamster, bank vole, deer).
  2. Run RT-QuIC with a standardized seed preparation and varying substrate species under matched conditions.
  3. Fit the double sigmoidal model to each species’ kinetics.
  4. Compare primary- and secondary-phase parameters across substrate species.

Measurements: Normalized fluorescence over time; model parameters stratified by substrate species.

Expected Outcome: Species-specific differences in primary-phase parameters (\(b_1\), \(a_1\)) are expected from the literature. The experiment will determine whether \(S_2\), \(a_2\), and \(b_2\) also vary with substrate species — and whether any species-specific trends in \(t_{lag}\) and \(K_{app}\) are consistent with known seeding compatibility biology.