Quantification of the Effects of Perturbations on Biological Samples

• US 20080195322A1
• Filed: 02/12/2007
• Published: 08/14/2008
• Est. Priority Date: 02/12/2007
• Status: Abandoned Application

• Current Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Original Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Inventors: Loo, Lit-Hsin, Wu, Lani F., Altschuler, Steven J.
• Application Number:
  US11/673,910
• Publication Number:
  US 20080195322A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 20/50 Mutagenesis
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis

Abstract

A multivariate, automated and scalable method for extracting profiles from images to quantify the effects of perturbations on biological samples. Morphological features are determined from images of treated (perturbed) and control (unperturbed) biological samples, and multivariate classification, for example, using a separating decision hyperplane, is used to separate the distribution of measured feature data into control and treated groups. This classification may be used to determine a magnitude of the effect of the particular perturbation under study. A practical application is high-throughput image-based drug screening, wherein the effects of many different compounds, each applied at different doses and for different exposure times, may be profiled to, for example, characterize compound activities and to identify dose-dependent multiphasic drug responses, or to determine and classify the biological effects of new compounds.

Claims

1. A method of profiling the effect of a perturbation relative to the effect of another perturbation on biological samples, comprising:

subjecting each of at least first and second biological samples to a perturbation;

extracting multiple numerical features from the at least first and second biological samples after perturbation;

classifying the multiple numerical features extracted from each perturbed biological sample using a multivariate classification algorithm;

determining a multivariate profile of the effect a perturbation relative to the effect of another perturbation from the multivariate classification.

2. The method of claim 1, the perturbation including treatment with a compound at a concentration.

3. The method of claim 1, the perturbation including treatment with a mixture of compounds each at a concentration.

4. The method of claim 1, the perturbation including silencing the expression of a gene by RNA interference.

5. The method of claim 1, the perturbation including knocking out a gene.

6. The method of claim 1, the perturbation including treatment with a cytokine.

7. The method of claim 1, the perturbed cells including treatment with a free fatty acid.

8. The method of claim 1, the step of extracting multiple numerical features comprising:

labeling the at least first and second biological samples after perturbation using fluorescent probes to produce labeled cells;

illuminating the labeled biological samples using a light source;

imaging the labeled biological samples using fluorescence microscopy to produce biological sample images;

segmenting cell regions from the biological sample images; and

computing multiple numerical features from the segmented cell regions.

9. The method of claim 1, the step of extracting multiple numerical features comprising:

imaging the at least first and second biological samples after perturbation using brightfield microscopy to produce biological sample images;

segmenting cell regions from the biological sample images; and

computing multiple numerical features from the segmented cell regions.

10. The method of claim 1, the step of extracting multiple numerical features comprising:

imaging the at least first and second biological samples after perturbation using differential interference contrast microscopy to produce biological sample images;

segmenting cell regions from the biological sample images; and

computing multiple numerical features from the segmented cell regions.

11. The method of claim 1, the step of extracting multiple numerical features comprising:

imaging the at least first and second biological samples after perturbation using phase contrast microscopy to produce biological sample images;

segmenting cell regions from the biological sample images; and

computing multiple numerical features from the segmented cell regions.

12. The method of claim 1, the step of extracting multiple numerical features comprising:

labeling the at least first and second biological samples after perturbation using fluorescent probes to produce labeled cells;

passing the labeled cells through a flow cytometer;

illuminating the labeled cells using a light source;

detecting scattered and emitted light from labeled cells; and

computing multiple numerical features from the detected light.

13. The method of claim 1, the classifying and determining steps step comprising:

determining a separating hyperplane between the multiple numerical features extracted from each of the perturbed biological samples;

determining a normal vector and a classification accuracy score for each hyperplane; and

determining a multivariate profile of the effect of a perturbation relative to the effect of another perturbation from the normal vector.

14. The method of claim 13, the step of determining the separating hyperplane comprising, subjecting the features from the at least first and second biological samples after perturbation to a multivariate classification algorithm to determine the separating hyperplanes.

15. The method of claim 14, the classification algorithm comprising, a support vector machine algorithm.

16. The method of claim 13, the step of determining a multivariate profile from the normal vector comprising, dividing the normal vector with the sum of the absolute values of the elements of the normal vector.

17. The method of claim 1, further comprising, after the classifying step:

selectively removing features from the extracted multiple numerical features;

reclassifying multiple numerical features the after the selected features have been removed using a multivariate classification algorithm; and

repeating the selective removal and reclassifying steps until a classification accuracy classifying step is below a predetermined minimum to produce a reduced biological sample feature set.

18. The method of claim 13, further comprising:

after determining the classification accuracy score, comparing the classification accuracy score to a predetermined significance threshold; and

characterizing the perturbation as a function of the comparison.

19. A method of profiling the effect of a perturbation at a plurality of levels relative to the effect of a reference perturbation on biological samples, comprising:

subjecting a plurality of biological samples to a plurality of levels of a perturbation to produce a plurality of perturbed biological samples;

subjecting a biological sample to a reference perturbation to produce a reference perturbed biological sample;

extracting multiple numerical features from each of the perturbed biological samples;

classifying the multiple numerical features extracted from each perturbed biological sample using a multivariate classification algorithm; and

determining a plurality of multivariate profiles from the multivariate classification.

20. The method of claim 19, the step of subjecting to a plurality of levels of a perturbation comprising, treatment with a compound at a plurality of concentrations.

21. The method of claim 19, the step of subjecting to a plurality of levels of a perturbation comprising, treatment with a mixture of compounds at a plurality of mixing concentration ratios.

22. The method of claim 19, the step of subjecting to a plurality of levels of a perturbation comprising, treatment with a cytokine at a plurality of concentrations.

23. The method of claim 19, the step of subjecting to a plurality of levels of a perturbation comprising, treatment with a free fatty acid at a plurality of concentrations.

24. The method of claim 19, the step of extracting multiple numerical features, comprising:

labeling the perturbed biological samples using fluorescent probes to produce labeled biological samples;

illuminating the labeled biological samples using a light source;

imaging the labeled biological samples using fluorescence microscopy to produce biological sample images;

segmenting cell regions from the biological sample images; and

computing multiple numerical features from the segmented cell regions.

25. The method of claim 19, the step of extracting multiple numerical features, comprising:

imaging the perturbed biological samples using brightfield microscopy to produce biological sample images;

segmenting cell regions from the biological sample images; and

computing multiple numerical features from the segmented cell regions.

26. The method of claim 19, the step of extracting multiple numerical features, comprising:

imaging the perturbed biological samples using differential interference contrast microscopy to produce biological sample images;

segmenting cell regions from the biological sample images; and

computing multiple numerical features from the segmented cell regions.

27. The method of claim 19, the step of extracting multiple numerical features, comprising:

imaging the biological samples using phase contrast microscopy to produce biological sample images;

segmenting cell regions from the biological sample images; and

computing multiple numerical features from the segmented cell regions.

28. The method of claim 19, the step of extracting multiple numerical features, comprising:

labeling the perturbed biological samples using fluorescent probes to produce labeled biological samples;

passing the labeled biological samples through a flow cytometer;

illuminating the labeled biological samples using a light source;

detecting scattered and emitted lights from the illuminated labeled biological samples; and

computing multiple numerical features from the detected light.

29. The method of claim 19, the classifying and determining steps comprising:

determining a plurality of separating hyperplanes using the multivariate classification algorithm, each separating hyperplane being between the features extracted from a respective perturbed biological sample and features extracted from the reference perturbed biological sample;

determining a normal vector and classification accuracy score for each hyperplane; and

determining the plurality of multivariate profiles from the normal vectors.

30. The method of claim 29, the multivariate classification algorithm comprising, a support vector machine algorithm.

31. The method of claim 29, the step of determining the plurality of multivariate profiles from the normal vectors comprising, dividing the normal vectors with the sum of the absolute values of the elements of the normal vector.

32. The method of claim 19, further comprising, after the classifying step:

selectively removing features from the extracted multiple numerical features;

reclassifying the multiple numerical features using the multivariate classification algorithm after the selected features have been removed; and

repeating the selective removal and reclassifying steps until a classification accuracy of the classifying step is below a predetermined minimum to produce a reduced biological sample feature set.

33. The method of claim 29, further comprising:

after determining classification accuracy scores, comparing each classification accuracy score to a predetermined significance threshold; and

characterizing the respective perturbation as a function of the comparison.

34. The method of claim 19, further comprising:

after determination of the a plurality of multivariate profiles, performing titration clustering on the profiles.

35. The method of claim 34, further comprising:

after titration clustering, determining a representative profile from each cluster.

36. The method of claim 35, the step of determining a representative profile from each cluster, comprising:

determining profiles in a cluster that are not reproducible;

removing profiles in a cluster that are not reproducible; and

averaging the remaining profiles.

37. A method of profiling an effect on cells of a plurality of perturbations each at a plurality of levels relative to the effect of a reference perturbation, comprising:

subjecting a plurality of populations of cells to a plurality of perturbations each at a plurality of levels to produce a plurality of perturbed cell populations;

subjecting a population of cells to a reference perturbation to produce a reference perturbed cell population;

extracting multiple numerical features from each of the perturbed cell populations;

determining a plurality of separating hyperplanes, each being between the features extracted from a respective perturbed cell population and features extracted from the reference perturbed cell population;

determining a normal vector and classification accuracy score for each hyperplane; and

determining a plurality of multivariate profiles from the normal vectors.

38. The method of claim 37, the step of subjecting a plurality of populations of cells to a plurality of perturbations each at a plurality of levels comprising, treatment with a plurality of compounds each at a plurality of concentrations.

39. The method of claim 37, the step of subjecting a plurality of populations of cells to a plurality of perturbations each at a plurality of levels comprising, treatment with a plurality of compound mixtures each at a plurality of mixing concentration ratios.

40. The method of claim 37, the step of subjecting a plurality of populations of cells to a plurality of perturbations each at a plurality of levels comprising, silencing the expression of a plurality of genes by RNA interference.

41. The method of claim 37, the step of subjecting a plurality of populations of cells to a plurality of perturbations each at a plurality of levels comprising, knocking out a plurality of genes.

42. The method of claim 37, the step of subjecting a plurality of populations of cells to a plurality of perturbations each at a plurality of levels comprising, treatment with a cytokine at a plurality of concentrations.

43. The method of claim 37, the step of subjecting a plurality of populations of cells to a plurality of perturbations each at a plurality of levels comprising, treatment with a free fatty acid at a plurality of concentrations.

44. The method of claim 37, the step of determining the separating hyperplanes comprising, subjecting the features extracted from the respective perturbed cell populations and features extracted from the reference perturbed cell population to a multivariate classification algorithm to determine the separating hyperplanes.

45. The method of claim 44, the classification algorithm comprising, a support vector machine algorithm.

46. The method of claim 37, the step of determining a profile from the normal vector comprising, dividing the normal vector with the sum of the absolute values of the elements of the normal vector.

47. The method of claim 37, further comprising, after the step of determining a plurality of separating hyperplanes:

selectively removing features from the extracted features;

redetermining the separating hyperplanes after the selected features have been removed; and

repeating the selective removal and redetermining steps until a classification accuracy of the separating hyperplane is below a predetermined minimum to produce a reduced cell feature set.

48. The method of claim 37, further comprising:

after determining classification accuracy scores, comparing each classification accuracy score to a predetermined significance threshold; and

characterizing the respective perturbation as a function of the comparison.

49. The method of claim 37, further comprising:

after determination of the a plurality of profiles, performing titration clustering on the profiles.

50. The method of claim 49, further comprising:

after titration clustering, determining a representative profile from each cluster.

51. The method of claim 50, the step of determining a representative profile from each cluster, comprising:

determining profiles in a cluster that are not reproducible;

removing profiles in a cluster that are not reproducible; and

averaging the remaining profiles.

52. The method of claim 50, further comprising:

after the step of determining the representative profile, screening the perturbations to determine perturbations with representative profiles most similar to a target perturbation.

53. The method of claim 50, further comprising:

after the step of determining the representative profile, comparing the representative profiles of perturbations to determine common effects of different perturbations.

54. The method of claim 50, further comprising:

after the step of determining the representative profile, predicting effects of a perturbation from other perturbations with known effects with the most similar representative profiles.