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Updated: Mar 27, 2026
Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry
Published on: April 19, 2017
Stochastic Measurement Models for Quantifying Lymphocyte Responses Using Flow Cytometry
Andrey Kan1,2, Damian Pavlyshyn1,2, John F Markham1,2
1Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Insights
Researchers developed a new model to accurately analyze flow cytometry data, addressing variability in lymphocyte response measurements. This improves mathematical modeling for immune system studies.
Area of Science:
- Immunology
- Computational Biology
- Biophysics
Background:
- Adaptive immune responses involve B and T cell division and differentiation, crucial for host defense.
- Flow cytometry is a key technique for tracking lymphocyte responses, providing data for mathematical modeling.
- Variability in flow cytometry measurements, due to experimental noise and cell differences, challenges accurate mathematical modeling.
Purpose of the Study:
- To investigate the nature of measurement errors in flow cytometry data from various experiments.
- To evaluate the validity of assumptions made by current model-fitting methods.
- To propose and validate a new measurement model for flow cytometry data.
Main Methods:
- Analysis of flow cytometry measurement errors across diverse experimental datasets.
- Characterization of the relationship between mean and variance of measurement noise.
- Development and theoretical justification (maximum entropy) of a novel measurement model.
- Empirical validation of the new model using collected flow cytometry data.
Main Results:
- A power-law relationship (exponent 1.3-1.8) was found between the mean and variance of flow cytometry noise.
- This relationship violates assumptions of common model-fitting methods like least squares and log-transformation.
- The proposed new measurement model accurately describes the observed data and error characteristics.
Conclusions:
- Current model-fitting methods for flow cytometry data may be unreliable due to violated assumptions.
- The novel measurement model provides a more accurate and robust approach for analyzing flow cytometry data.
- This work enhances quantitative studies of lymphocyte responses by improving data modeling.
Abstract:
Adaptive immune responses are complex dynamic processes whereby B and T cells undergo division and differentiation triggered by pathogenic stimuli. Deregulation of the response can lead to severe consequences for the host organism ranging from immune deficiencies to autoimmunity. Tracking cell division and differentiation by flow cytometry using fluorescent probes is a major method for measuring progression of lymphocyte responses, both in vitro and in vivo. In turn, mathematical modeling of cell numbers derived from such measurements has led to significant biological discoveries, and plays an increasingly important role in lymphocyte research. Fitting an appropriate parameterized model to such data is the goal of these studies but significant challenges are presented by the variability in measurements. This variation results from the sum of experimental noise and intrinsic probabilistic differences in cells and is difficult to characterize analytically. Current model fitting methods adopt different simplifying assumptions to describe the distribution of such measurements and these assumptions have not been tested directly. To help inform the choice and application of appropriate methods of model fitting to such data we studied the errors associated with flow cytometry measurements from a wide variety of experiments. We found that the mean and variance of the noise were related by a power law with an exponent between 1.3 and 1.8 for different datasets. This violated the assumptions inherent to commonly used least squares, linear variance scaling and log-transformation based methods. As a result of these findings we propose a new measurement model that we justify both theoretically, from the maximum entropy standpoint, and empirically using collected data. Our evaluation suggests that the new model can be reliably used for model fitting across a variety of conditions. Our work provides a foundation for modeling measurements in flow cytometry experiments thus facilitating progress in quantitative studies of lymphocyte responses.
