A cell profiling framework for modeling drug responses from HCS imaging.
The authors present an unsupervised, scalable, and interpretable cell profiling framework that is compatible with data gathered from high-content screening. They demonstrate the effectiveness of their framework by modeling drug differential effects of IC-21 macrophages treated with microtubule and actin disrupting drugs. They identify significant features of cell phenotypes for unsupervised learning based on maximum relevancy and minimum redundancy criteria. A 2-stage clustering approach annotates, clusters cells, and then merges them together to form super-clusters. An interpretable cell profile consisting of super-cluster proportions profiled at each drug treatment, concentration, or duration is obtained. Differential changes in super-cluster profiles are the basis for understanding the drug's differential effect and biology. The authors' method is validated by significant chi-squared statistics obtained from similar drug-treated super-cluster profiles from a 5-fold cross-validation. In addition, drug profiles of 2 microtubule drugs with equivalent mechanisms of action are statistically similar. Several distinct trends are identified for the 5 cytoskeletal drugs profiled under different conditions.
Singapore MIT Alliance, Singapore. alvinng@pmail.ntu.edu.sg
Alvin Y J
AY
Ng
Jagath C
JC
Rajapakse
Roy E
RE
Welsch
Paul T
PT
Matsudaira
Victor
V
Horodincu
James G
JG
Evans
D016009:Chi-Square Distribution; D016000:Cluster Analysis; D057166:High-Throughput Screening Assays / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008264:Macrophages / Q000166:cytology / Q000187:drug effects* / Q000378:metabolism*; D008954:Models, Biological; D015203:Reproducibility of Results