Single-cell morphology analysis for disease profiling and drug discovery

A system comprising a processing device and a data storage coupled to the processing device, wherein the data storage stores a set of machine-readable instructions that, when executed by the processing device, cause the processing device to:

• receive at least one microscopic image of a test sample of biological cells and at least one microscopic image of a control test sample of biological cells;
• determine parameter values of a set of relevant parameters for each of a plurality of cells in the test sample and for each of a plurality of cells in the control test sample from the at least one microscopic image of the respective sample;
• determine feature values of a set of meta-features (f1, f2, fL) from the parameter values of the set of relevant parameters for the test sample;
• and
• determine feature values for the set of meta-features (f1, f2, fL) from the parameter values of the set of relevant parameters for the control test sample, wherein the system further comprises the test sample and the control test sample, wherein the test sample is to be exposed to a test substance,
• wherein each cluster of parameters used for calculating the feature values includes at least one parameter from each of two or more distinct measurement categories;
• and
• wherein a correlation structure of the clusters is validated as consistent across both the control test sample and a reference sample, such that only parameters showing robust correlation in both contexts are used for calculating the feature values.

A non-transitory computer-readable storage medium comprising instructions stored thereon that, when executed by a computing device cause the computing device to perform operations of studying an effect of a test substance on a test sample of biological cells using a set of relevant parameters and a set of meta-features (f1, f2, fL), the operations comprising:

• exposing the test sample to the test substance;
• determining parameter values of the set of relevant parameters for each of a plurality of cells in the test sample;
• and
• determining feature values of the set of meta-features-for the test sample (f1, f2, fL), wherein each of the feature values is calculated from the parameter values of a cluster (C1, C2, CL) of correlated parameters from the set of relevant parameters that is associated with the respective meta-feature (f1, f2, fL), wherein a reference sample for determining the set of meta-features (f1, f2, fL) was exposed to a stimulus substance,
• wherein each cluster (C1, C2, CL) used for calculating the feature values includes at least one parameter from each of two or more distinct measurement categories;
• and
• wherein a correlation structure of the clusters (C1, C2, CL) is validated as consistent across both a control sample and the reference sample, such that only parameters showing robust correlation in both contexts are used for calculating the feature values.