Multi-spectral imaging including at least one common stain

1. A method comprising:

providing a sample with M components to be labeled, where M>2;

labeling the components with N stains, where N<M so that at least two components are labeled with a common stain;

obtaining a set of spectral images of the sample;

classifying different parts of the sample into respective classes that distinguish the commonly stained components based on the set of spectral images; and

determining relative amounts of multiple ones of the M components in different regions of the sample,

wherein the at least two components that are labeled with a common stain comprise nuclear and non-nuclear components of the sample, and wherein the sample is a biological sample.

2. The method of claim 1 , further comprising applying at least one histological stain to the sample prior to obtaining the set of spectral images.

3. The method of claim 2 , wherein the classifying comprises distinguishing the commonly stained components from one another in the different parts of the sample based on one or more of signal strength, shape, spectral features, and textural features in one or more of the spectral images that include contributions from the at least one histological stain.

4. The method of claim 2 , wherein the classifying comprises decomposing the set of spectral images into an unmixed image set, wherein each member of the unmixed image set corresponds to a spectral contribution from a respective one of N stains for labeling the M components or the histological stain; and

determining the relative amounts of the multiple ones of the M components based on the unmixed image set.

5. The method of claim 3 , wherein the classifying comprises decomposing the set of spectral images into an unmixed image set, wherein each member of the unmixed image set corresponds to a spectral contribution from a respective one of the N stains for labeling the M components and the histological stain; and

classifying the different parts of the sample into the respective classes based on an image stack comprising one or more of the unmixed images.

6. The method of claim 1 , wherein M>2 and N>1.

7. The method of claim 1 , wherein the N stains are labeled to the M components as immunohistochemical probes, immunohistological probes, immunofluorescent probes or a combination thereof.

8. The method of claim 1 , wherein the stains comprise quantum dots that are labeled to the components by conjugation with antibodies through streptavidin-biotin binding.

9. The method of claim 1 , further comprising applying a counter-stain for nuclei to the sample prior to obtaining the set of spectral images, and wherein the classifying comprises distinguishing between nuclear components and non-nuclear components in the different parts of the sample based on one or more of signal strength, shape, spectral features, and textural features in the spectral images resulting from at least the counter-stain.

10. The method of claim 2 , wherein the histological stain comprises DAPI, a Hoechst stain, eosin, or hematoxylin.

11. The method of claim 1 , wherein the determination is based on the set of spectral images and the classification.

12. A method comprising:

providing a set of spectral images of a sample, wherein the sample has M>2 components labeled with N<M stains so that at least two components are labeled with a common stain and the sample is further stained with at least one histological stain;

classifying different parts of the sample into respective classes that distinguish the commonly stained components based on the set of spectral images; and

determining relative amounts of multiple ones of the M components in different regions of the sample,

wherein the at least two components that are labeled with a common stain comprise nuclear and non-nuclear components of the sample, and wherein the sample is a biological sample.

13. The method of claim 12 , wherein the commonly stained components are distinguished based on one or more of signal strength, shape, spectral features, and textural features in one or more of the spectral images that include contributions from the at least one histological stain.

14. The method of claim 12 , wherein the classifying comprises decomposing the set of spectral images into an unmixed image set, wherein each member of the unmixed image set corresponds to a spectral contribution from a respective one of the stains; and

classifying different parts of the sample into the respective classes based on an image stack comprising one or more of the unmixed images.

15. The method of claim 12 , wherein the determination is based on the set of spectral images and the classification.