Automated classification and analysis of the calcium response of single T lymphocytes using a neural network approach
Journal
IEEE Transactions on Neural Networks
Journal Volume
16
Journal Issue
4
Pages
949-958
Date Issued
2005
Author(s)
Abstract
The gene activities in T lymphocytes that regulate immune responses, are influenced by Ca2+ ([Ca2+]i). The intracellular calcium signals are highly heterogeneous and vitally important in determining the immune outcome. The signals in individual cells can be measured using fluorescence microscopy but to group the cells into classes with similar signal kinetics is currently laborious. Here, we demonstrate a method for the automated classification of the responses into four categories formerly identified by an expert's inspection. This method comprises characterising the response by a second-order model, performing frequency analysis, and using derived features as inputs to two multilayer perceptron neural networks (NNs). We compare the algorithm's performance on an example data set against the human classification: it was found to classify identically more than 70% of the data, despite small sample sizes in two categories and significant overlap between the other two classes. The group characterized by an oscillating signal showed the presence of a number of frequencies, which may be important in determining gene activation. A classification threshold enables the automatic identification of patterns with a low-classification certainty. Future refinement of the algorithm may allow the identification of more classes, which may be important in different immune responses associated with disease. ? 2005 IEEE.
Subjects
Antigen-antibody reactions
Calcium
Cells
Feature extraction
Immunology
Mathematical models
Multilayer neural networks
Time series analysis
Calcium response
Immune responses
Lymphocyte
Pattern recognition
calcium
phytohemagglutinin
phytohemagglutinin P
phytohemagglutinin-P
algorithm
article
artificial intelligence
automated pattern recognition
biological model
biological rhythm
calcium signaling
cell culture
comparative study
drug effect
evaluation
human
lymphocyte activation
metabolism
methodology
physiology
T lymphocyte
Algorithms
Artificial Intelligence
Biological Clocks
Calcium Signaling
Cells, Cultured
Humans
Lymphocyte Activation
Models, Biological
Pattern Recognition, Automated
Phytohemagglutinins
T-Lymphocytes
SDGs
Type
journal article