Nucleus Developer Notes

20251208 Analysis (Group 1)

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

20251208 Analysis (Group 1)

Setup

%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 = "./platereader/20251208-134617-cytation5-pure-timecourse-gfp--biotek-cdk.txt"
platemap_file = "platemap-G1.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()
Loading...

Plot Raw Curves

g = pr.plot_curves(data=data)
<Figure size 698.25x500 with 1 Axes>

Normalize Data

data = pr.normalize_data_to_controls(data, ctrl_name = '1 uM Fluorescein')
Data Normalized to 1 uM Fluorescein in col data_normalized. The active column for subsequent operations is: data_normalized

Now replot your curves to see them normalized

g = pr.plot_curves(data=data)
<Figure size 698.25x500 with 1 Axes>
replace_dict = {'ΔPPK-SMixΔCP':'-PPK Negative Control',
                'ΔpOpen-deGFP':'-DNA Negative Control',
                'PPK/PolyP-SMixΔCP_1': 'PolyP/PPK_1',
                'PPK/PolyP-SMixΔCP_2': 'PolyP/PPK_2',
                'PPK/PolyP-SMixΔCP_3': 'PolyP/PPK_3'
               }

custom_order = ['CP/CK_1',
                'CP/CK_2',
                'CP/CK_3',
                'PolyP/PPK_1', 
                'PolyP/PPK_2', 
                'PolyP/PPK_3', 
                '-PPK Negative Control', 
                '-DNA Negative Control']

color_dict = {
    'CP/CK_1': '#1f77b4',      # Dark blue
    'CP/CK_2': '#4fa3d1',      # Medium blue
    'CP/CK_3': '#87ceeb',      # Light blue
    'PolyP/PPK_1': '#d62728',  # Dark red
    'PolyP/PPK_2': '#ff6b6b',  # Medium red
    'PolyP/PPK_3': '#ff9999',  # Light red
    '-PPK Negative Control': '#9467bd',  # Purple
    '-DNA Negative Control': '#000000'   # Black
}

data_rename = data
data_rename['Name'] = data['Name'].replace(replace_dict)
p = pr.plot_curves(data_rename[data_rename["Sample Type"] != "Standard"], 
                   hue_order=custom_order, 
                   palette=color_dict)
p.set(ylim=(0, 1.1))
plt.savefig("kinetics-group1.png")
<Figure size 707.125x500 with 1 Axes>

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
Loading...

Visualize Fits on Individual Wells

# Plot kinetic fits 
g, kinetics = pr.plot_kinetics(data, kinetics=kinetics, group_by=["Name"])
plt.savefig("kinetic-fits-group1.png")
<Figure size 1800x1200 with 9 Axes>
pr.kinetic_analysis_summary(data)
PROVIDING AVERAGED KINETICS
Loading...

Summary Plots

pr.plot_summary(data[data["Sample Type"] == "Sample"], show_plot=False)
plt.savefig("kinetics-summary-1.png")
<Figure size 1500x500 with 4 Axes>

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

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20251208 Analysis (Group 2)