Spectral bio-imaging methods for biological research, medical diagnostics and therapy

• US 5,784,162 A
• Filed: 12/12/1995
• Issued: 07/21/1998
• Est. Priority Date: 08/18/1993
• Status: Expired due to Term

• Current Assignee: Applied Spectral Imaging Ltd.
• Original Assignee: Applied Spectral Imaging Ltd.
• Inventors: Garini, Yuval, Cabib, Dario, Katzir, Nir, Malik, Zvi, Buckwald, Robert A., Soeknsen, Dirk G.
• Primary Examiner(s): Turner, Samuel A.
• Application Number:
  US08/571,047
• Time in Patent Office: 952 Days
• Field of Search: 356/346, 356/300, 356/326, 250/339.02, 250/458.1, 250/459.1, 250/461.1, 250/461.2, 128/665, 382/128, 382/129, 382/133, 435/6
• US Class Current: 356/456
• CPC Class Codes: C12Q 1/6841 In situ hybridisation
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• CPC Class Codes: C12Q 2563/107 fluorescence
• CPC Class Codes: C12Q 2563/131 the label being a member of a cognate binding pair, i.e. extends to antibodies, haptens, avidin
• CPC Class Codes: C12Q 2563/173 staining/intercalating agent, e.g. ethidium bromide
• CPC Class Codes: G01J 2003/2866 Markers; Calibrating of scan
• CPC Class Codes: G01J 3/02 Details
• CPC Class Codes: G01J 3/12 Generating the spectrum; Monochromators
• CPC Class Codes: G01J 3/1256 using acousto-optic tunable filter; acousto-optic elements or systems G02F1/11, G02F1/33
• CPC Class Codes: G01J 3/26 using multiple reflection, e.g. Fabry-Perot interferometer, variable interference filters
• CPC Class Codes: G01J 3/2823 Imaging spectrometer
• CPC Class Codes: G01J 3/4406 Fluorescence spectrometry
• CPC Class Codes: G01J 3/453 by correlation of the amplitudes
• CPC Class Codes: G01N 2021/6417 Spectrofluorimetric devices
• CPC Class Codes: G01N 2021/6423 Spectral mapping, video display
• CPC Class Codes: G01N 21/6428 Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" in vivo A61B5/00; immunoassay G01N33/53
• CPC Class Codes: G01N 21/6458 Fluorescence microscopy fluorescence microscopes per se G02B21/0076 and G02B21/16
• CPC Class Codes: G01N 21/6486 Measuring fluorescence of biological material, e.g. DNA, RNA, cells G01N21/6428 takes precedence
• CPC Class Codes: G01N 33/5005 involving human or animal cells immunoassay G01N33/56966; immunoassays of protozoa G01N33/56905; protozoa in screening assays C12Q1/025
• CPC Class Codes: G01N 33/56966 Animal cells
• CPC Class Codes: G01N 33/582 : with fluorescent label
• CPC Class Codes: G06V 10/88 : Image or video recognition using optical means, e.g. reference filters, holographic masks, frequency domain filters or spatial domain filters
• CPC Class Codes: G06V 20/69 : Microscopic objects, e.g. biological cells or cellular parts

Abstract

According to the present invention there are provided spectral imaging methods for biological research, medical diagnostics and therapy comprising the steps of (a) preparing a sample to be spectrally imaged; (b) viewing the sample through an optical device, the optical device being optically connected to an imaging spectrometer, the optical device and the imaging spectrometer obtaining a spectrum of each pixel of the sample by: (i) collecting incident light simultaneously from all pixels of the sample using collimating optics; (ii) passing the incident collimated light through an interferometer system having a number of elements, to form an exiting light beam; (iii) passing the exiting light beam through a focusing optical system which focuses the exiting light beam on a detector having a two-dimensional array of detector elements, so that at each instant each of the detector elements is the image of one pixel of the sample, so that the real image of the sample is stationary on the plane of the detector array, and so that each of the detector elements produces a signal which is a particular linear combination of light intensity emitted by the pixel at different wavelengths, wherein the linear combination is a function of the instantaneous optical path difference; (iv) rotating one or more of the elements of the interferometer system, so that the optical path difference between the two coherent beams generated by the interferometer system is scanned simultaneously for all the pixels of the sample; and (v) recording signals of each of the detector elements as function of time using a recording device to form a first spectral cube of data; and (c) interpreting the first spectral cube of data using a mathematical algorithm.

Claims

1. A spectral bio-imaging method characterized by high spatial and high spectral resolutions, the method comprising the steps of:

(a) preparing a sample to be spectrally imaged;

(b) viewing said sample through an optical device, said optical device being optically connected to an imaging spectrometer, said optical device and said imaging spectrometer being for obtaining a spectrum of each pixel of said sample by;

(i) collecting incident light simultaneously from all pixels of said sample using collimating optics;

(ii) passing said incident collimated light through an interferometer system having a number of elements, so that said light is first split into two coherent beams which travel in different directions inside said interferometer and then said two coherent beams recombine to interfere with each other to form an exiting light beam;

(iii) passing said exiting light beam through a focusing optical system which focuses said exiting light beam on a detector having a two-dimensional an-ay of detector elements, so that at each instant each of said detector elements is the image of one and always the same pixel of said sample for the entire duration of the measurement, so that the real image of the sample is stationary on the plane of the detector array and at any time during the measurement the image is still visible and recognizable, and so that each of said detector elements produces a signal which is a particular linear combination of light intensity emitted by said pixel at different wavelengths, wherein said linear combination is a function of the instantaneous optical path difference;

(iv) rotating one or more of said elements of said interferometer system, so that said optical path difference between said two coherent beams generated by said interferometer system is scanned simultaneously for all said pixels of said sample; and

(v) recording signals of each of said detector elements as function of time using a recording device to form a first spectral cube of data; and

(c) interpreting said first spectral cube of data using a mathematical algorithm.

2. A method as in claim 1, further comprising the step of:

(d) displaying a map of said interpreted spectral cube of data.

3. A method as in claim 1, wherein said optical device is selected from the group consisting of a microscope, a camera lens, an endoscope, a fundus camera and a funduscope.

4. A method as in claim 3, wherein said microscope is selected from the group consisting of a reflection microscope, a transmission microscope, a fluorescence microscope, an upright microscope, an inverted microscope, a dark field microscope, a confocal microscope, a standing wave confocal microscope and a reflection contrast microscope.

5. A method as in claim 1, wherein said collimated light is selected from the group consisting of light transmitted through said sample, light reflected from said sample, light scattered from said sample and light emitted from said sample.

6. A method as in claim 5, wherein said light emitted from said sample is selected from the group consisting of administered probe fluorescence, administered probe induced fluorescence and auto-fluorescence.

7. A method as in claim 1, wherein said light originates from a source selected from the group consisting of laser, white light, filtered light, ultraviolet light and a light having a small wavelength range.

8. A method as in claim 1, wherein said light originates from a multiplicity of light sources, said sources operate simultaneously.

9. A method as in claim 1, wherein said light originates from a multiplicity of light sources, said sources operate successively.

10. A method as in claim 1, wherein said two-dimensional array is selected from the group consisting of a video rate CCD, a cooled high dynamic range CCD, an intensified CCD and a time gated intensified CCD.

11. A method as in claim 1, wherein said sample is selected from the group consisting of a cell, a tissue and an organism.

12. A method as in claim 11, wherein said cell and said tissue are from a human.

13. A method as in claim 11, wherein said cell is selected from the group consisting of a cell collected by a Pap smear, a blood cell, a fetal cell, a cell suspected of being malignant, a cell during interphase, a cell during mitosis and a cell during meiosis.

14. A method as in claim 11, wherein said tissue is selected from the group consisting of eye retina, a retinal blood vessel, a tumor, skin, cornea, hair, lungs, stomach, intestines, bladder, colon, prostate, cervix, arteries, veins and heart.

15. A method as in claim 1, wherein said sample is the eye retina, the method is for detecting oxygenated and deoxygenated hemoglobin in the retinal blood vessels.

16. A method as in claim 1, wherein said sample is the eye retina, the method is for detecting melanin pigmentation level in the retina.

17. A method as in claim 1, wherein said sample is selected from the group consisting of a cell, a tissue section and an organism;

said light is induced by a probe, said probe binds to a specific cellular constituent, the method is for detecting the presence or the level of said cellular constituent.

18. A method as in claim 17, wherein said probe includes a conjugated fluorescent moiety and said induction is a fluorescence light emission of said fluorescent moiety.

19. A method as in claim 18, wherein said probe further includes a nucleic acid molecule, the method is for detecting the presence or the level of a cellular nucleic acid hybridizing with said nucleic acid molecule.

20. A method as in claim 19, wherein said cellular nucleic acid is selected from the group consisting of deoxyribonucleic acid and ribonucleic acid.

21. A method as in claim 17, wherein said probe includes an antibody, the method is for detecting the presence or the level of a cellular protein recognized by said antibody.

22. A method as in claim 18, wherein said fluorescent moiety is selected from the group consisting of Aqua, Texas-Red, FITC, rhodamine, rhodamie derivative, fluorescein, fluorescein derivative, cascade blue and any combination thereof.

23. A method as in claim 1, wherein said mathematical algorithm is a point operation analysis of said spectrum of each of said pixels in said sample.

24. A method as in claim 23, wherein said point operation analysis includes mapping said spectrum of each of said pixels in said sample into a scalar according to a transformation function.

25. A method as in claim 23, wherein said point operation analysis includes mapping said spectrum of each of said pixels of said sample into another spectrum according to a transformation function.

26. A method as in claim 1, wherein said mathematical algorithm is a morphological analysis.

27. A method as in claim 1, wherein said mathematical algorithm is a similarity mapping analysis for computing for each of said pixels in said sample a spectral difference from a reference spectrum.

28. A method as in claim 27, wherein said similarity mapping analysis results in generating a gray level or a pseudocolor image, in which bright pixels correspond to a small spectral difference and dark pixels correspond to a large spectral difference.

29. A method as in claim 27, wherein said similarity mapping analysis results in generating a gray level or a pseudocolor image, in which bright pixels correspond to a large spectral difference and dark pixels correspond to a small spectral difference.

30. A method as in claim 27, wherein said spectral difference is a scalar defined as the integral over a predefined wavelength range of the absolute value of the difference between said spectrum of each of said pixels and said reference spectrum.

31. A method as in claim 1 wherein said mathematical algorithm is a classification mapping analysis computing for said spectrum of each of said pixels a spectral difference from several reference spectra.

32. A method as in claim 31, wherein said classification mapping analysis results in generating a pseudocolors image, in which groups of pixels having a predetermined maximal spectral differences from one of said several reference spectra are colored with a predetermined pseudocolor.

33. A method as in claim 31, wherein said spectral difference is a scalar defined as the integral over a predefined wavelength range of the absolute value of the difference between said spectrum of each of said pixels and one of said several reference spectra.

34. A method as in claim 1, wherein said mathematical algorithm is a principal component analysis.

35. A method as in claim 34, wherein said principal component analysis includes:

(a) building a covariant matrix for all of said pixels and said wavelengths of said measurement, including wavelengths of exciting sources when multiple wavelengths are used;

(b) diagonalizing said covariant matrix and finding all independent orthogonal spectral base elements;

(c) finding which of said base elements tag certain features in said sample.

36. A method as in claim 1, wherein said mathematical algorithm is a linear combination analysis.

37. A method as in claim 36, wherein said linear combination analysis includes applying an arithmetical function between corresponding wavelengths of corresponding pairs of pixels belonging to said first spectral cube of data and to a second spectral cube of data, to obtain a resulting third spectral cube of data.

38. A method as in claim 36, wherein said linear combination analysis is for a purpose selected from the group consisting of averaging two spectral cubes of data, time changes follow-up and spectral normalization.

39. A method as in claim 36, wherein said linear combination analysis includes applying a given scalar to every wavelength of said spectra of each of said pixels by an arithmetical function, said function is selected from the group consisting of addition, subtraction, multiplication, division and combinations thereof.

40. A method as in claim 36, wherein said linear combination analysis is for background subtraction in which a spectrum of a pixel located in a background region of said sample is subtracted from said spectra of said pixels of said sample.

41. A method as in claim 36, wherein said linear combination analysis is for a calibration procedure in which a spectrum measured prior to said viewing said sample is for dividing said spectra of said pixels of said sample.

42. A method as in claim 1, wherein said mathematical algorithm is an optical density analysis.

43. A method as in claim 42, wherein said optical density analysis is for obtaining an interpreted image which is an optical density map.

44. A method as in claim 1, wherein said mathematical algorithm computes a Red-Green-Blue color image using predefined wavelength ranges.

45. A method as in claim 1, wherein said mathematical algorithm computes a ratio between intensities at two different wavelengths for each of said spectra of said pixels.

46. A method as in claim 1, wherein said mathematical algorithm computes a ratio between intensities at two different wavelengths for each of said spectra of said pixels and paints each of said pixels in a lighter or darker artificial color, according to said computed ratio.

47. A method as in claim 1, wherein the method is for spectral identification of multiple fluorophores administered to said sample.

48. A method as in claim 1, wherein the method is for detecting micro-environmental changes in said sample.

49. A method as in claim 48, wherein said micro-environmental changes are selected from the group consisting of local electrical potential, pH level and intracellular ions concentration.

50. A method as in claim 49, wherein said ions are selected from the group consisting of hydrogen ions, sodium ions, magnesium ions, zinc ions and calcium ions.

51. A method as in claim 1, wherein the method is for measuring auto-fluorescence from a natural constituent in said sample.

52. A method as in claim 1, wherein said natural constituent is selected from the group consisting of chlorophyll, porphyrins and cytoplasmic proteins.

53. A method as in claim 51, wherein said sample is selected from the group consisting of eye retina, a retinal blood vessel, a tumor, skin, cornea, hair, lungs, stomach, intestines, bladder, colon, prostate, cervix, arteries, veins, heart and cells obtained by smears.

54. A method as in claim 1, wherein the method is for an application selected from the group of applications consisting of biology research, drug development industry, cell and tissue classification in pathology, hematology, urine analysis, gene identification and mapping in chromosomes, genetic disease diagnosis, cell organelles anatomy and physiology, chromatin distribution and condensation in a cell nuclei, cytoplasm organelles and constituents mapping, cell membrane mapping, nuclear membrane mapping mapping of skin cancers, differentiating between melanoma and nevi, port wine stains mapping and, skin imaging before, during, and after a photodynamic therapy treatment.

55. A method as in claim 54, wherein said cytoplasm constituents are selected from the group consisting of NAD⁺, NADH, flavin and cytochromes.

56. A method as in claim 1, wherein the method is for measuring fluorescence resonance energy transfer to determine spatial separation between at least two fluorophores in said sample.

57. A method as in claim 56, wherein at least one of said fluorophores is externally administered to said sample.

58. A method as in claim 1, wherein said sample is selected from the group consisting of a cell, a tissue and an organism, the method is for identifying and mapping cellular and subcellular details in said sample.

59. A method as in claim 58, wherein said sample is stained using a method selected from the group consisting of Romanowsky-Giemsa staining, Haematoxylin-Eosin staining and May-Grunwald-Giemsa staining.

60. A method as in claim 59, wherein said subcellular details are types of chromatin organization in the nucleus, said types are selected from the group consisting of heterochromatin and euchromatin.

61. A method as in claim 1, wherein said sample is selected from the group consisting of a cell, tissues and organisms, the method is for monitoring life processes in said sample as function of time.

62. A fluorescent in situ hybridization method comprising the steps of:

(a) labeling with at least one fluorescent dye at least one nucleic acid molecule to obtain at least one fluorescently tagged nucleic acid probe;

(b) hybridizing said probe in situ with cellular nucleic acids of a biological sample;

(c) viewing said biological sample through a fluorescence microscope, said fluorescence microscope being optically connected to an imaging spectrometer, said fluorescence microscope and said imaging spectrometer being for obtaining a spectrum of each pixel of said biological sample by;

(i) collecting incident light simultaneously from all pixels of said biological sample using collimating optics;

(ii) passing said incident collimated light through an interferometer system having a number of elements, so that said light is first split into two coherent beams which travel in different directions inside said interferometer and then said two coherent beams recombine to interfere with each other to form an exiting light beam;

(iii) passing said exiting light beam through a focusing optical system which focuses said exiting light beam on a detector having a two-dimensional allay of detector elements, so that at each instant each of said detector elements is the image of one and always the same pixel of said biological sample for the entire duration of the measurement, so that the real image of the biological sample is stationary on the plane of the detector array and at any time during the measurement the image is still visible and recognizable, and so that each of said detector elements produces a signal which is a particular linear combination of light intensity emitted by said pixel at different wavelengths, wherein said linear combination is a function of the instantaneous optical path difference;

(iv) rotating one or more of said elements of said interferometer system, so that said optical path difference between said two coherent beams generated by said interferometer system is scanned simultaneously for all said pixels of said biological sample; and

(v) recording signals of each of said detector elements as function of time using a recording device to form a first spectral cube of data; and

(d) interpreting said first spectral cube of data using a mathematical algorithm.

64. A method as in claim 62, wherein said mathematical algorithm is a classification mapping analysis, said analysis computing for said spectrum of each of said pixels a spectral difference from at least one reference spectrum.

65. A method as in claim 62, wherein said mathematical algorithm is a linear combination analysis, said analysis is for a background subtraction.

66. A method as in claim 65, further comprising the step of using an additional mathematical algorithm being a classification mapping analysis, said additional mathematical algorithm computing for said spectrum of each of said pixels a spectral difference from at least one reference spectrum.

67. A method as in claim 64, wherein said classification mapping analysis includes computing for said spectrum of each of said pixels a spectral difference from at least one reference spectrum.

68. A method as in claim 66, wherein said classification mapping analysis includes computing for said spectrum of each of said pixels a spectral difference from at least one reference spectrum.

63. A fluorescent in situ hybridization method comprising the steps of:

(a) hybridizing at least one nucleic acid probe in situ with cellular nucleic acids of a biological sample;

(b) labeling each of said at least one probe with at least one fluorescent dye;

(c) viewing said biological sample tough a fluorescence microscope, said fluorescence microscope being optically connected to an imaging spectrometer, said fluorescence microscope and said imaging spectrometer being for obtaining a spectrum of each pixel of said biological sample by;

(i) collecting incident light simultaneously from all pixels of said biological sample using collimating optics;

(ii) passing said incident collimated light through an interferometer system having a number of elements, so that said light is first split into two coherent beams which travel in different directions inside said interferometer and then said two coherent beams recombine to interfere with each other to form an exiting light beam;

(iii) passing said exiting light beam through a focusing optical system which focuses said exiting light beam on a detector having a two-dimensional array of detector elements, so that at each instant each of said detector elements is the image of one and always the same pixel of said biological sample for the entire duration of the measurement, so that the real image of the biological sample is stationary on the plane of the detector array and at any time during the measurement the image is still visible and recognizable, and so that each of said detector elements produces a signal which is a particular linear combination of light intensity emitted by said pixel at different wavelengths, wherein said linear combination is a function of the instantaneous optical path difference;

(iv) rotating one or more of said elements of said interferometer system, so that said optical path difference between said two coherent beams generated by said interferometer system is scanned simultaneously for all said pixels of said biological sample; and

(v) recording signals of each of said detector elements as function of time using a recording device to form a first spectral cube of data; and

(d) interpreting said first spectral cube of data using a mathematical algorithm.

69. A cell classification method comprising the steps of:

(a) preparing a smear of cells for analysis;

(b) viewing said smear of cells through a transmission microscope, said transmission microscope being optically connected to an imaging spectrometer, transmission microscope and said imaging spectrometer being for obtaining a spectrum of each pixel of said smear of cells by;

(i) collecting incident light simultaneously from all pixels of said smear of cells using collimating optics;

(ii) passing said incident collimated light through an interferometer system having a number of elements, so that said light is first split into two coherent beams which travel in different directions inside said interferometer and then said two coherent beams recombine to interfere with each other to form an exiting light beam;

(iii) passing said exiting light beam through a focusing optical system which focuses said exiting light beam on a detector having a two-dimensional array of detector elements, so that at each instant each of said detector elements is the image of one and always the same pixel of said smear of cells for the entire duration of the measurement, so that the real image of the smear of cells is stationary on the plane of the detector array and at any time during the measurement the image is still visible and recognizable, and so that each of said detector elements produces a signal which is a particular linear combination of light intensity emitted by said pixel at different wavelengths, wherein said linear combination is a function of the instantaneous optical path difference;

(iv) rotating one or more of said elements of said interferometer system, so that said optical path difference between said two coherent beams generated by said interferometer system is scanned simultaneously for all said pixels of said smear of cells; and

(v) recording signals of each of said detector elements as function of time using a recording device to form a first spectral cube of data; and

(c) interpreting said first spectral cube of data using a principal component algorithm.