Calculating Sample Size Requirements for Temporal Dynamics in Single-Cell Proteomics.

Area of Science:

• Proteomics
• Cell Biology
• Biostatistics

Background:

• Single-cell measurements reveal cell-to-cell heterogeneity, crucial for understanding diverse cell types and functions.
• Characterizing proteome dynamics during biological transitions (e.g., differentiation, disease) is a key application of single-cell proteomics.
• Time-course experiments are essential for tracking dynamic trajectories but face challenges due to cell heterogeneity and measurement variability.

Purpose of the Study:

• To analyze key variables impacting statistical confidence in detecting single-cell proteome dynamics.
• To determine the minimum number of data points required for robust statistical analysis in time-course single-cell proteomics.
• To guide experimental design for accurate proteome dynamics detection.

Main Methods:

• Analysis of variables including fold change, measurement variability, and cell number.
• Statistical assessment of data point requirements for time-course experiments.
• Evaluation of accuracy and false-positive rates based on the number of measurements.

Main Results:

• Identified fold change, measurement variability, and number of cells as critical factors for statistical confidence.
• Demonstrated that datasets with fewer than 16 measurements across the time domain yield low accuracy.
• Showed that insufficient measurements lead to a high false-positive rate in detecting proteome dynamics.

Conclusions:

• Robust statistical analysis of single-cell proteome dynamics necessitates careful consideration of experimental design parameters.
• A minimum of 16 measurements across the time domain is recommended to ensure accuracy and minimize false positives.
• Balancing experimental demands is crucial for achieving reliable results in time-course single-cell proteomics studies.