Analyze time-series fluorescence data from PURE (Protein synthesis Using Recombinant Elements) experiments.

Setup¶

!pip install nucleus-cdk==0.5.0rc2 | tail -n2

Requirement already satisfied: asttokens in /opt/homebrew/anaconda3/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets==8.*->jupyter-bokeh<5.0.0,>=4.0.5->nucleus-cdk==0.5.0rc2) (2.0.5)
Requirement already satisfied: pure-eval in /opt/homebrew/anaconda3/lib/python3.12/site-packages (from stack-data->ipython>=6.1.0->ipywidgets==8.*->jupyter-bokeh<5.0.0,>=4.0.5->nucleus-cdk==0.5.0rc2) (0.2.2)

%load_ext autoreload
%autoreload 2
    
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Import the cdk platereader module
from cdk.analysis.cytosol import platereader as pr

# Set up plotting
pr.plot_setup()

Load Data¶

# Specify file paths
data_file = "20260120-cytation3-pure-timecourse-gfp-OnePotPURE-Opt-biotek-cdk.txt"
platemap_file = "20260120-OnePotPURE-Opt1-Platemap.csv"

# Load data
data, platemap = pr.load_platereader_data(
    data_file=data_file,
    platemap_file=platemap_file,
    platereader="biotek-cdk"  # Options: "cytation", "envision", "biotek-cdk"
)

# Checkout first few rows
data.head()

Plot Raw Curves¶

g = pr.plot_curves(data=data)

Normalize Data¶

# No HPTS/fluorescein normalization control present in this platemap's Name column - skipping normalize_data_to_controls, using raw (unnormalized) data

Now replot your curves to see them normalized

g = pr.plot_curves(data=data)

Kinetic Analysis¶

Metrics extracted:

• Vmax (Velocity Max): Maximum rate of fluorescence increase (slope at inflection point)

• Lag time: Time to reach the exponential phase

• Steady-state: Final fluorescence level and time to reach 95% of asymptote

• Drift: Rate of signal decay or increase after steady-state

• R²: Goodness of fit

# Perform kinetic analysis using sigmoid_drift model
kinetics = pr.kinetic_analysis(
    data=data,
    group_by=['Name'],  # Group by experimental condition
)

kinetics.head()

PROVIDING AVERAGED KINETICS

Visualize Fits on Individual Wells¶

# Plot kinetic fits 
g = pr.plot_kinetics(data, kinetics=kinetics, group_by=["Name"])

Summary Plots¶

summary_plot = pr.plot_summary(data)

Key Metrics Explained¶

1. Steady-State Level (Steady State, Data)¶

• The final fluorescence value reached by the reaction

• Represents the total amount of protein produced

• Higher values indicate greater expression yield

2. Maximum Velocity (Velocity, Max)¶

• The steepest slope of the fluorescence curve (at the inflection point)

• Units: RFU per second

• Reflects the peak rate of protein synthesis

• Sensitive to enzyme activity, substrate availability, and reaction conditions

3. Lag Time (Lag, Time)¶

• Time before exponential fluorescence increase begins

• May reflect time for ribosome assembly or initial translation steps

• Shorter lag times suggest faster reaction initiation

4. Drift (Fit, drift)¶

• Rate of fluorescence change after reaching steady-state

• Positive drift: continued synthesis or aggregation

• Negative drift: photobleaching, protein degradation, or quenching

• Units: RFU per second

5. R² Value (Fit, R^2)¶

• Goodness of fit (0 to 1, higher is better)

• R² > 0.98 indicates excellent fit

• Poor fits may indicate noisy data, overflow errors, or non-sigmoid kinetics

Tips and Troubleshooting¶

• Overflow errors: Wells with OVRFLW or NaN values are automatically excluded from fitting

• Poor fits (low R²): Inspect raw curves for anomalies (bubbles, evaporation, pipetting errors)

• Drift: Sometimes seen in kinetics curves; use sigmoid_drift model

• Multiple replicates: Always include technical replicates and report error bars

• Comparing conditions: Normalize or blank data consistently across all samples

Next Steps¶

• Export kinetics results: pr.export_kinetics(kinetics, 'results.csv')

• Statistical analysis: Use scipy.stats or statsmodels for ANOVA/t-tests

• Parameter optimization: Vary Mg²⁺, K⁺, or other conditions to maximize Vmax or steady-state

• Mechanistic modeling: Fit ODE models to extract biological rate constants