Abstract: Disclosed are methods and systems for: (i) sequentially illuminating a specimen with different spatial distributions of light, wherein each illumination causes an object embedded in the specimen to emit radiation in response to the light; (ii) for each different spatial distribution of illumination light, imaging the radiation emitted from the specimen from each of multiple sides of the specimen; and (iii) determining information about the object in the specimen based on the imaged radiation from each of the multiple sides for each of the different spatial distributions of illumination light.

Abstract: A method including: 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. Related apparatus are also disclosed.

Abstract: The disclosure features methods that include: contacting a biological sample having a first target analyte with a first agent, where the first agent includes a first binding species that specifically binds to the first target analyte, and a first oligonucleotide conjugated to the binding species; contacting the biological sample with a second agent, where the second agent includes a first reactive species and a second oligonucleotide conjugated to the first reactive species, to hybridize at least a portion of the second oligonucleotide to at least a portion of the first oligonucleotide; and contacting the biological sample with a first labeling species, where the first labeling species reacts with the first reactive species to deposit the first labeling species or a derivative thereof in the biological sample.

Abstract: A method including: 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. Related apparatus are also disclosed.

Abstract: A method including: 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. Related apparatus are also disclosed.

Abstract: The disclosure features methods and systems that include a detector configured to obtain multiple images of a sample stained with first and second stains, where the first and second stains have similar spectral absorption and emission profiles, and an electronic processor configured to decompose the multiple images into an unmixed image set, where the unmixed image set includes a first unmixed image corresponding to the first stain and a second unmixed image corresponding to the second stain, and identify nuclear regions in the sample based on the first unmixed image and identify cytoplasm regions in the sample based on the second unmixed image.

Abstract: Methods and systems for imaging a biological sample are disclosed, and include: (a) staining the sample with a first stain that includes eosin and at least one additional stain; (b) directing excitation light to the sample to cause each of the stains to emit fluorescence; and (c) recording images of the fluorescence emitted from the stains in the sample, where the amount of eosin in the sample is sufficiently dilute to cause the sample to have an average optical density of less than 0.10 at green wavelengths.

Abstract: Disclosed herein are methods and apparatus for obtaining at least one non-birefringence image and at least one birefringence image of a stained sample, and classifying regions of the stained sample into a plurality of classes based on the at least one non-birefringence image and the at least one birefringence image.

Abstract: Methods and systems are disclosed that include: applying an immunohistochemical stain, eosin, and a counterstain to a sample; obtaining a plurality of images of the sample, each of the plurality of images corresponding to radiation from the sample in a different wavelength band; decomposing the plurality of images of the sample to obtain component images corresponding to the immunohistochemical stain, eosin, and the counterstain; and generating a sample image based on the component images, where the sample image includes contributions from the counterstain and from one of the immunohistochemical stain and eosin, and substantially not from the other of the immunohistochemical stain and eosin.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: The disclosure features methods and systems that include a detector configured to obtain multiple images of a sample stained with first and second stains, where the first and second stains have similar spectral absorption and emission profiles, and an electronic processor configured to decompose the multiple images into an unmixed image set, where the unmixed image set includes a first unmixed image corresponding to the first stain and a second unmixed image corresponding to the second stain, and identify nuclear regions in the sample based on the first unmixed image and identify cytoplasm regions in the sample based on the second unmixed image.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images.

Abstract: The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

Abstract: Methods are disclosed that include: (a) applying a first stain to a first sample having a plurality of regions, where the first stain selectively binds to only a first subset of the regions of the first sample; (b) applying a second stain to the first sample, where the second stain binds to a second set of regions of the first sample; (c) obtaining an image of the first sample, and analyzing the image to obtain a first component image corresponding substantially only to spectral contributions from the first stain, and a second component image corresponding substantially only to spectral contributions from the second stain; and (d) training a classifier to identify regions of a second sample based on information derived from the first and second component images, the identified regions corresponding to the first subset of regions of the first sample.

Abstract: Methods are disclosed that include: (a) applying a first stain to a first sample having a plurality of regions, where the first stain selectively binds to only a first subset of the regions of the first sample; (b) applying a second stain to the first sample, where the second stain binds to a second set of regions of the first sample; (c) obtaining an image of the first sample, and analyzing the image to obtain a first component image corresponding substantially only to spectral contributions from the first stain, and a second component image corresponding substantially only to spectral contributions from the second stain; and (d) training a classifier to identify regions of a second sample based on information derived from the first and second component images, the identified regions corresponding to the first subset of regions of the first sample.