WO2000006774A1 - Procede d'analyse in situ de cellules - Google Patents

Procede d'analyse in situ de cellules Download PDF

Info

Publication number
WO2000006774A1
WO2000006774A1 PCT/US1999/016629 US9916629W WO0006774A1 WO 2000006774 A1 WO2000006774 A1 WO 2000006774A1 US 9916629 W US9916629 W US 9916629W WO 0006774 A1 WO0006774 A1 WO 0006774A1
Authority
WO
WIPO (PCT)
Prior art keywords
antibody
stain
stains
kda
red
Prior art date
Application number
PCT/US1999/016629
Other languages
English (en)
Inventor
George Mcnamara
Dirk Soenksen
Dario Cabib
Robert Buckwald
Original Assignee
Applied Spectral Imaging Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US09/122,704 external-priority patent/US6007996A/en
Application filed by Applied Spectral Imaging Ltd filed Critical Applied Spectral Imaging Ltd
Priority to IL14085199A priority Critical patent/IL140851A0/xx
Priority to AU51238/99A priority patent/AU5123899A/en
Priority to EP99935853A priority patent/EP1100966A4/fr
Priority to JP2000562556A priority patent/JP2002521682A/ja
Publication of WO2000006774A1 publication Critical patent/WO2000006774A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J3/457—Correlation spectrometry, e.g. of the intensity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J3/2823—Imaging spectrometer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56966—Animal cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J2003/2866—Markers; Calibrating of scan

Definitions

  • the present invention relates to in situ analysis of cells and, more particularly, to simultaneous in situ analysis of a plurality of immunohistochemical stains, histological stains and/or DNA ploidy stains using spectral imaging techniques, which enable high spatial resolution as well as spectral resolution.
  • immunohistochemistry which is also known as immunocytochemistry (ICC) when applied to cells
  • ICC immunocytochemistry
  • Panels of monoclonal antibodies can be used in the differential diagnosis of undifferentiated neoplasms (e.g., to distinguish lymphomas, carcinomas, and sarcomas); to reveal markers specific for certain tumor types; to diagnose and phenotype malignant lymphomas; and to demonstrate the presence of viral antigens, oncoproteins, hormone receptors, and proliferation-associated nuclear proteins.
  • QIHC quantitative IHC
  • IHC is in theory nonlinear for two reasons: (i) the enzyme reaction forming the precipitation reaction product is nonlinear, and (ii) a variety of amplification strategies are typically employed, using either bridging or complexing reagents, the goal being to increase the staining sensitivity.
  • the result is that the amount of antigen cannot be directly calculated from the intensity of the immunohistochemical staining.
  • there may be substantial day-to-day variation in the quality of immunostaining although automation of the staining procedure may provide more consistency than is usually obtained using manual methods.
  • tomurs may exhibit striking heterogeneity with regard to expression of these molecules, and measurements that reflect this heterogeneity may provide information that is more biologically relevant.
  • two tumors may exhibit identical biochemical levels of a given molecule and yet have very different proportions of tumor cells that actually express any level at all. This problem is perhaps best illustrated with regard to estrogen receptor » measurement in breast cancer.
  • the ability to measure immunohistochemical reactions in tissue sections or cytological preparations is highly dependent on the choice of staining and imaging methods.
  • the levels of immunostaining (signal) must be detectable above the background (noise), and should be distinguishable from other cellular or tissue features. In IHC, this is standardly addressed by carefully titering the antibodies (primary and secondary) to maximize the specific staining and minimize the background staining.
  • the tissues are visualized using histological stains (also known as counterstains) having a different color than the chromogen, so that the staining reaction can be easily visualized.
  • immunoperoxidase reactions which yield a brown reaction product, are typically combined with methyl green or toluidine blue histological stains (counterstains) to provide visual contrast.
  • methyl green or toluidine blue histological stains counterstains
  • the CAS 200 System (Cell Analysis Systems, Inc., Lombard, 111.), employs two video cameras, one coupled with a 500 nm bandpass filter and the other with a 650 nm filter. Tissues are immunostained using the immunoperoxidase method with diaminobenzidine (DAB) as the chromogen (brown reaction product) and methyl green as the tissue (nuclear) counterstain. Images of the nuclei of all of the cells in the field are captured by one camera using the 650 nm filter, while the other camera captures images of the brown reaction product at 500 nm, a wavelength at which DAB is maximally transmitted and methyl green is absorbed.
  • DAB diaminobenzidine
  • the optical density of the reaction product can be determined by converting the • light transmission (gray level) to optical density using a calibrated look-up table, and the relative area of the staining reaction (expressed as a percent of the area stained by methyl green) can be calculated.
  • Simple thresholding methods are used to establish the gray levels for capturing the cell nuclei and for setting the lower limit for detection of DAB, based on a slide stained with control antibody.
  • Combinations other than methyl green and DAB may be used, restricted only by the spectral overlap of the reagents and the availability of suitable filter combinations. This system, however, has an inherent limitation. It can only detect two spectrally non-overlapping spectral components.
  • lymphoma Braylan RC, Diamond LW, Powell ML, Harty-Golder B. Percentage of cells in the S phase of the cell cycle in human lymphoma determined by flow cytometry: Correlation with labeling index and patient survival. Cytometry 1980; 1:171-174; and Bauer KD, Merkel DE, Winter JN, et al. Prognostic implications of ploidy and proliferative activity in diffuse large cell lymphomas. Cancer Res 1986; 46:3173-3178], breast * cancer [Clark GM, Dressier LG, Owens MA, Pounds G, Oldaker T, McGuire WL.
  • Mitotic counts are generally regarded as a poor and unreliable measure of proliferation; yet they require no special preparative methods. Uptake of radiolabeled thymidine, or "thymidine labeling index"
  • TLI tumor cell kinetics
  • FCM Flow cytometry
  • the measurement of DNA content by either flow or image cytometry is based on the assumptions that the amount of stain represents the amount of DNA and that this amount of stain is correctly measured by the instrument. These assumptions would imply that (i) the DNA labelling procedure (fluorescent dye, chromogenic reaction or staining) is specific (all DNA is labelled and only DNA), stoichiometric (staining intensity changes proportionally to DNA content) and stable (staining intensity does not change with time or repeated measurements); (ii) the instrument used to measure either the light emitted by the fluorescent dye or absorbed by the stain is accurate (giving a result close to the true amount of stain) and reproducible (giving very similar measurements when repeated on the same nucleus), even though not close to the true amount of stain, and linear (giving a result that is perfectly proportional to the amount of stain).
  • very specific fluorescent dyes bind to either G-C (e.g., mithramycin, chromycin A3) or A-T (e.g., Hoechst, DAPI) DNA base pairs and thus detect the DNA only partially and generate measurements that depend on base pair sequences;
  • the chromogenic reactions like Feulgen, involve an acid hydrolysis that removes some DNA fragments as rapidly as they are released from the decondensed chromatin. These reactions thus detect the DNA only partially and provide measurements that depend on the euchromatin versus heterochromatin balance;
  • the fixative medium may impair or facilitate further hydrolysis, depending on the way they interact with histones.
  • monochromators are not used in routine image cytometry, but filter are used whose centre on the maximum abso ⁇ tion is +10 nm, which usually varies from one system to another.
  • the preparation and the prior art instruments employed are optical compromises and are thus responsible for reflection, refraction and diffraction due to the glass slide, mounting medium, lenses and prisms.
  • the light not following the expected geometrical pathways contributes to glare, also called Schwarzchild - Villiger effect, which distorts the ratio between the light beam intensity incident to the nucleus and that emerging from the specimen. Therefore, all the pixel OD s calculated are slightly erroneous. This error increases as the optical field size increases.
  • the use of a high-quality microscope is thus mandatory to decrease this systematic error of densitometric measurements, which is the most important factor contributing to variations of image cytometry measurements.
  • CCD charge-coupled device
  • the quantitation of DNA-specific stains can be inte ⁇ reted in terms of overall proliferative activity and gross cytogenetic aberrations, thus giving a clear indication as to how to proceed further in investigating those tumour characteristics that are of interest for differential diagnosis and prognosis.
  • the Feulgen reaction is a complicated cytohistochemical method which consists of various preparatory steps. Principally, the reaction starts with a procedure called acid hydrolysis: slides with the fixed cytological material are immersed in hydrochloric acid (HCl) which splits off the purine bases adenine and guanine from the DNA molecule, thereby generating aldehyde groups in the purine- free DNA molecule, which is then called apurinic acid (APA). In a second step, the slides are immersed in Schiff s reagent containing a dye which binds covalently to the aldehyde groups. After removal of su ⁇ lus dye the slides are dehydrated and mounted as usual.
  • HCl hydrochloric acid
  • APA apurinic acid
  • HCl is used for acid hydrolysis, but in principle any acid is suitable.
  • the acid has two effects on the DNA molecules: (i) removal of purine bases, which generates aldehyde groups in the DNA molecules; and (ii) depolymerization of the large APA molecules into smaller fragments. These fragments are partly removed from the cell nuclei by diffusion into the acid solution.
  • Generation of aldehyde groups leads to an increase in nuclear staining intensity, whereas the loss of APA fragments leads to a loss » of stainable material from the cell nucleus and thus to a decrease of staining intensity.
  • the resulting reaction curve is called the hydrolysis profile or the hydrolysis curve.
  • This hydrolysis profile can be subdivided into four phases: (i) increase of staining intensity; (ii) peak phase with maximum staining intensity; (iii) plateau phase with constant staining intensity; and (iv) decrease in staining intensity.
  • Phase (i) is characterized by continuous generation of aldehyde groups.
  • the loss of APA fragments is minimal in the beginning, and the amount of stainable material in the cell nucleus increases constantly until phase (ii) is reached.
  • the peak can be so short that it is sometimes hardly visible in hydrolysis profiles.
  • the plateau phase, (iii) one finds a balance between the continuous generation of aldehyde groups and the loss of APA fragments, and consequently, the staining intensity remains constant over a certain period of time.
  • the loss of APA fragments outruns the generation of new aldehyde groups, and the staining intensity decreases. After prolonged hydrolysis we find the generation of the maximum number of aldehyde groups, but all APA fragments have been removed from the cell nucleus, and staining intensity is zero.
  • the shape of the hydrolysis curve is influenced by several factors, only some of which can be standardized.
  • phase (i) and (iv) are less steep, and the plateau phase is retarded.
  • DNA compactness as a biological characteristic of cells, varies between different cell types and in the same cell during the cell cycle. Cell preparation techniques such as specimen sampling and especially fixation of the material may artificially influence DNA compactness and thus acid sensitivity. A high temperature of the acid bath shortens all four phases. Phase
  • (iii) may be as short as only a few seconds, and a prominent plateau may not be detectable. If hydrolysis is topped in the very steep phases (i) or (iv), even minimal variations of processing time lead to considerable variations in staining. Short hydrolysis profiles with an extremely short peak phase are usually found with so-called hot hydrolysis techniques using an acid bath at * 60 °C, and frequently it is impossible to stop hydrolysis at the right moment, namely in phase (iii). More frequently, cold hydrolysis with 4-5 mol/1 HCl is performed at about 22 °C where under routine conditions the plateau phase has a length of several minutes. Acid hydrolysis is then stopped by a short rinse of the slide in tap water.
  • Schiff s reagent is a colourless aqueous reagent which contains a dye mixture called basic fuchsin.
  • Basic fuchsin is normally composed of four cationic triarylmehine dyes pararosanilin and its methylated homologues rosanilin, magenta II and new fuchsin.
  • Basic fuchsin of high quality contains a high proportion of pararosanilin.
  • Schiff s reagent is colourless because the relevant dyes are present in their leucoform with sulphite bound to the dye molecules. Coloration of Schiff s reagent (based on basic fuchsin) proves loss of sulphite and deterioration of the solution, which should then be discarded.
  • thiazine dye thionin Various substitutes for basic fuchsin have been recommended, among them the thiazine dye thionin.
  • the advantage of thionin is that it stains cell nuclei blue, a color the cystologists and pathologists are used to when they want to check the slide visually), and cytopllasmic counterstaining with eosin Y or Congo red is easily feasible.
  • Schiff s reagent based on thionin is usually not completely colourless.
  • the material After staining with the Schiff s reagent the material is rinsed in dye- free sulphite water.
  • the sulphite removes su ⁇ lus dye from the cell nuclei and cytoplasm, and only the covalently bound dye molecules stay fixed to the APA molecules within the cell nucleus.
  • the background of the slide should be completely unstained when the Feulgen reaction has been performed correctly.
  • Acid hydrolysis is the most critical step of the Feulgen reaction. A correctly performed hydrolysis should be stopped in the plateau phase. It is important to have HCl of suitable molar concentration at the right temperature (e.g., 5 mol/1 HCl at 22 °C or 4.0 mol/1 HCl at 27.5 °C). Acid of suitable molar strength is commercially available or can easily be prepared from concentrated HCl. Frequently, HCl stored in the refrigerator at 4 °C is used immediately without waiting for the acid to warm up; this leads to retardation of the hydrolic reaction, which is often stopped before the plateau phase is reached. The use of temperature-controlled water baths is » recommended. Where this is not feasible, scrupulous measurement of the temperature of the acid solution helps to avoid erroneous photometric results.
  • the staining procedure itself is uncritical. Staining should be carried out for at least 45 minutes to give the reaction sufficient time to be completed. Schiff s reagent of high and consistent quality is commercially available.
  • Gallocyanin cromalum is a cationic oxadine dye which forms complexes with metals. GCA stains DNA and RNA quantitatively. Thus, it is not specific for DNA. Photometric determination of DNA requires either photometric correction for stained RNA or enzymatic or hydrolytic removal of RNA.
  • the Einarson GCA staining protocol prescribes staining times of up to 48 hours at elevated temperature which makes it impossible to use for routine cytology.
  • a modified GCA was proposed by Husain and Watts with staining times of about 15 minutes.
  • GCA after Husain and Watts has the following advantages: (i) no acid bath; (ii) staining time only 15 min; and (iii) gray-blue cell nuclei.
  • the disadvantages are: (i) not specific for DNA; (ii) background staining (due to RNA); and (iii) short shelf-life of the staining solution (about 6 weeks). Specificity of staining can be improved by mild hydrolysis (1 mol/1 HCl at 22 °C for 10 minutes) which removes RNA but not DNA. Anionic counterstaining is possible without loss of GCA from the stained DNA.
  • GCA stains both wet and dry fixed material. Dry fixed slides are significantly less intensely stained. Ethanol 99 % (v/v) for 10 minutes on wet fixed material or neutral buffered formaldehyde 3.7 % (v/v) for dry fixed material are suitable fixatives. If commercial spray fixatives containing polyethylene glycol (PEG) are used, the PEG film on the slide has to be removed prior to staining by washing the slide for 5 minutes in ethanol 99 % (v/v). »
  • PEG polyethylene glycol
  • GCA is all in all less critical than the Feulgen reaction and easier to perform. Nevertheless, the authors prefer the Feulgen reaction due to its substrate specificity and the stability of the staining solution. A careful standardization of the protocol, however, is a prerequisite for consistent staining quality.
  • Ki-67 which stains a proliferation-related nuclear antigen in human cells of all lineages
  • Ki-67 Immunobiochemical and molecular biologic characterization of the cell proliferation-associated nuclear antigen that is defined by monoclonal antibody Ki-67. Am J Pathol 1991;138:867-873]. This antibody has the useful property of staining nuclei of cells in Gi , S, G , and M phases of the cell cycle, but not the nuclei of resting (G phase) cells. Several studies have demonstrated that the proportion of tumor cell nuclei stained by Ki-67 antibody correlates with tumor grade and other prognostic features for a variety of tumor types, including breast carcinoma, lymphoma, meningioma, glial and astrocytic brain tumors, malignant melanoma, and sarcoma.
  • Ki-67 staining is associated with working classification grade, and increasing proliferative fraction as measured by Ki-67 staining is associated with a worse prognosis.
  • Ki-67 staining has been correlated with nuclear grade and lymph node status and several studies have shown the prognostic significance of » Ki-67 staining in this type of cancer.
  • the clinical usefulness of Ki-67 antibody had been somewhat limited by the fact that the antigen is preserved in frozen tissue and is lost on standard fixation.
  • MIB 1 monoclonal antibody
  • MIB 1 monoclonal antibody
  • Ki-Sl appears to have similar staining characteristics as Ki-67 and can be used on paraffin sections.
  • the relative fraction of tumor cell nuclei positive for Ki-Sl staining appears to have prognostic significance in breast cancer.
  • PCNA/cyclin is a 36 kDa nuclear protein present in proliferating cells, and is an accessory protein to DNA polymerase-delta.
  • Flow cytometric studies have demonstrated that two fractions of PCNA/cyclin exist in cell nuclei, the fraction that is insoluble in nonionic detergent being more restricted to the S phase of the cell cycle. Immunohistochemical staining for this protein has been demonstrated in frozen and paraffin-embedded tissues fixed in alcohol or formalin, thus permitting study of archival tissues.
  • Antibodies to alpha DNA polymerase have been applied to tissue sections of normal and malignant tissue, and may find clinical utility.
  • PI 05 is a nuclear antigen expressed starting at the Go/Gj phase transition, with increasing expression through M phase.
  • attempts to use this antibody in tissue sections have met with variable success, and the antibody may prove to be a more useful tool in flow cytometric studies than in tissue section.
  • There are changes in the level expression of a number of oncoproteins during the cell cycle but use of these markers in analysing cell * cycle activity in solid tumors is doubtful as overexpression is not entirely related. Additional markers are listed hereinunder. In most immunohistochemical studies using antibodies to proliferation- associated nuclear antigens, staining is measured by estimation or by tedious manual counting.
  • Quantitation of staining for nuclear antigens by image analysis provides the means of attaining the speed, accuracy, and reproducibility of such measurements.
  • the CAS 200 System mentioned hereinabove makes use of two video cameras to view the microscopic image separately through optical filters that capture either all of the ethyl green stained nuclei within the field (650 nm), or the brown reaction product of the immunoperoxidase reaction (500 nm) in the same field. By superimposing maps of the green and brown staining regions, a rapid calculation is made of the percent nuclear area occupied by the immunohistochemical reaction. The threshold for positive staining is established using a slide stained with control antibody.
  • this measurement is not identical to "percent positive nuclei", which would require that individual nuclei be counted and correlated with the staining reaction. Such measurements are more easily made on cytological preparations than on tissue sections, where distinguishing individual nuclei, which are frequently touching or overlapping, may be difficult or impossible. Nuclear area measurements are performed rapidly, and the result is expressed as the cumulative average of all the microscopic fields measured. The intensity of the staining reaction is not taken into account in this measurement, although the threshold for positive staining can be adjusted, for example, to measure only the most intensely stained nuclei rather than all positive nuclei. This system is however highly limited in spectral resolution and therefore has limited applications.
  • biochemical assays for estrogen receptor and progesterone receptor are predominantly used in breast cancer, particularly the dextran-coated charcoal (DCC) method.
  • DCC dextran-coated charcoal
  • a number of technical problems with the biochemical assay limit its usefulness.
  • the widespread use of mammography has resulted in an increasing proportion of resected tumors too small for biochemical analysis, which requires 300 to 500 mg of tissue, and fine-needle aspiration samples similarly cannot be evaluated by conventional methods.
  • approximately half of the resected breast tumors are too small for DCC analysis.
  • the lack of mo ⁇ hologic correlation can cause considerable error in the biochemical assay, as intermixed benign breast structures may lead to false-positive results, and excessive tumor dilution by normal and inflammatory cells can lead to false-negative results. Sampling error can be difficult to detect unless frozen sections are performed on the tissue prior to processing, a step not usually performed in most laboratories.
  • the DCC method is very labor-intensive. Therefore, there has been great interest in developing assay methods that circumvent these problems and improve the prognostic and therapeutic usefulness of tumor hormone receptor determination.
  • the HSCORE used by McCarty and co-workers [McCarty KS, Szabo E, Flowers JL, et al. Use of a monoclonal anti- estrogen receptor antibody in the immunohistochemical evaluation of human tumors. Cancer Res 1986; 46:(Suppl.):4244s-4248s] is derived from a weighted average of the intensity of staining and the percent positive tumor nuclei, and requires subjective evaluation of staining intensity. Using such a method, the sensitivity and specificity of the antibody method compared to the biochemical assay were 88 % and 94 %, respectively (85). Other studies have shown similar results.
  • a serious technical limitation in the use of immunohistochemical ER termination has been the difficulty of objectively analyzing and quantitating the staining reaction.
  • Semiquantitative methods, such as the HSCORE do not provide the reproducibility required among laboratories, and standards for measuring and reporting the staining reaction have not yet been established.
  • the use of image analysis does offer an opportunity to provide such a standard of practice for quantitation of hormone receptors in breast cancer, and the nuclear staining pattern is easily adapted for QIHC, as described above for Ki-67 antigen, however, since it is highly limited in spectral resolution it is not applicable for a multiple markers/counter stains study.
  • the main features of interest to be measured are the percent tumor cell nuclei stained for ER/PR, as well as the intensity of the staining.
  • Nuclear oncoproteins may be measured using image analysis in a manner similar to that described for hormone receptors and Ki- 67 antigen, and expressed either as percent positive nuclear area or as a value that combines staining intensity with positivity. This approach was used by Figge et al. [Figge J, Bakst G, Weisheit K, Solis O, Ross JS.
  • QIHC may require the use of internal controls and calibration standard Bacus et al. [Bacus SS, Ruby SG, Weinberg DS, Chin D, Oriz R, Bacus JW. HER-2/neu one expression and proliferation in breast cancers. Am J Pathol 1990;137:103-111] used a combination of Feulgen stain (to measure DN content) and immunohistochemical staining for HER-2/neu in order to normalize the oncoprotein measurement to DNA content. Tumor cell lines with known levels of expression of the oncogene were used for calibration, to allow for daily variation in staining intensity.
  • the average concentration oncoprotein could thus be computed on a per cell basis and was expressed as a percent of the level measured in a cell line known to exhibit high levels of overexpression.
  • a threshold for overexpression of HER-2/neu was established on the basis of the low level of expression present in normal tissue. Using this approach, the authors were able to correlate oncogene expression with proliferative fraction growth factor expression and DNA content ("ploidy") in breast cancer Bacus SS, Chin D, Stern RK, Ortiz R, Ruby SG, Weinberg DS.
  • HER-2/neu one expression, DNA ploidy and proliferation index in breast cancers. Anal Quant Histol 1992;14:433-445. »
  • a cellular protein that may have therapeutic importance is the product of the multiple drug resistance (mdr) gene, P- glycoprotein, a cell surface-associated ATPase that appears to be responsible for one mechanism of non-specific chemotherapy resistance.
  • mdr multiple drug resistance
  • Grogan et al. [Grogan T, Dalton W, Rybski J, et al. Optimization of immunohistochemical P-glycoprotein assessment in multidrug-resistant plasma cell myeloma using three antibodies. Lab Invest 1991; 63:815-824] have shown a correlation between expression of P-glycoprotein and relapse in multiple myeloma, and have demonstrated the use of image analysis for measuring this protein as detected by IHC. They found that QIHC was more sensitive than Western blot for detecting P-glycoprotein expression. Other markers of therapy resistance, such as glutathione transferase and topoisomerase II, might be similarly studied.
  • tumor markers having prognostic and therapeutic significance is becoming an important part of diagnostic surgical pathology and cytology. Given the small size of many of the tumor samples (biopsies and needle aspirates), it will be necessary to use in situ methods, such as IHC, to detect these tumor markers, combined with measurement analysis if quantitation is needed. As for all laboratory tests, standards of practice and performance must be developed for image analysis to ensure the accuracy and reproducibility of these measurements.
  • histological stains are routinely used in histopathology has proven useful in cancer evaluation.
  • Histopathology which forms the foundation of our knowledge about diseases, is a non- quantitative mo ⁇ hological evaluation using stained histological samples in which the formation of chromatin-stain complexes enhances the shape and structure of cells and subcellular components.
  • the nuclear structure may, in isolation, be viewed as a dynamic reflection of the metabolic state of the nucleus and as a physical correlate of its total content of biochemical constituents.
  • the major nuclear components are DNA, RNA, histone and non-histone proteins, inorganic materials and water. These do change during malignant transformation along with cytoplasmic and functional de-differentiation and increase in the nucleo-cytoplasmic ratio. Studies have proven that alterations in nuclear structure, chromatin pattern and nucleolar size and number are mo ⁇ hologic hallmarks of cancer diagnosis.
  • CCM computerized nuclear mo ⁇ hometry
  • Histopathological classification uses different staining protocols in order to emphasize cellular structures of tissues and cells.
  • the most common staining procedure used is hematoxylin and eosin (H&E); other procedures are the PAS stain for the demarcation of hydrocarbon moieties, Masson's tri chrome stain for extracellular staining of collagen and the Romanowsky-Giemsa stain in hematopathology.
  • H&E staining technique In the classical H&E stain, hematoxylin staining of the nuclei is followed by counter-staining of the cytoplasm and various extracellular materials by eosin; in this process of nuclear staining, hematoxylin is oxidized to the pu ⁇ le dye hematein and is provided with a net positive charge by this metallic salt.
  • the H&E staining technique has remained mostly unchanged for over half a century except for automation of some of the steps. This may be due to the fact that the technique is relatively quick, inexpensive, suitable for most situations, comparatively easy to master and, most important, enables accurate microscopic diagnosis of most of the specimens.
  • PAS staining substances containing vicinal glycol groups or their amino or alkylamino derivatives are oxidized by periodic acid to form dialdehydes, which combine with Schiff s reagent to form an insoluble magenta compound.
  • Masson's trichrome staining uses phosphotungstic or phosphomolybdic acid in combination with several anionic dyes such as basic fuchsin, light green and hematoxylin.
  • Romanowsky-Giemsa staining is used routinely in hematological practice to demonstrate the various hemopoietic cells, both in the normal and in the diseased state.
  • ductal and lobular carcinomas may present similar histological appearances [Azzopardi JG, Chepick OF, Hartmann WH, Jafarey NA, Lombart-Bosch A, Ozello L (1982). The World Health Organization histological typing of breast tumors. 2nd ed. Am J Clin Pathol 78:806-816].
  • Some quantitative histopathological variables have been identified by mo ⁇ hological methods as an aid to the differentiation between ductal and lobular carcinomas [Ladekarl M and Sorensen FB (1993). Quantitative histopathological variables in in situ and invasive ductal carcinoma of the breast. AMPIS 101(12):895-903].
  • lobular carcinoma is more multifocal and bilateral than ductal carcinoma
  • the World Health Organization histological typing of breast tumors. 2nd ed. Am J Clin Pathol 78:806-816]
  • patient survival expectancy is usually better
  • Prognosis in infiltrating lobular carcinoma an analysis of "classical" and variant tumors.
  • Infiltrating ductal carcinoma cells have more prominent nucleoli [Azzopardi JG, Chepick OF, Hartmann » WH, Jafarey NA, Lombart-Bosch A, Ozello L (1982). The World Health Organization histological typing of breast tumors. 2nd ed. Am J Clin Pathol 78:806-816].
  • Lobular carcinomas are more often bilateral and multifocal [Ladekarl M, Sorensen FB: Prognostic, quantitative histopathologic variables in lobular carcinoma of the breast. Cancer 72:2602, 1993] and the pattern of metastasis from the tumors was found to be different.
  • histological classification of breast carcinomas is subjected to low reproducibility and attempts to classify mo ⁇ hological subtypes of lobular carcinomas with different prognoses, therefore seem futile [Ladekarl M, Sorensen FB: Prognostic, quantitative histopathologic variables in lobular carcinoma of the breast. Cancer 72:2602, 1993]. Both lobular and ductal types are now thought to arise from the terminal duct - lobular unit.
  • Characterization of nuclear features by different techniques is used for determination of diagnosis, treatment and prognosis. Quantitative estimation of various histopathological parameters such as two dimensional estimates of nuclear profile area, nuclear profile densities and mitotic profile numbers have been shown to correlate with differentiation and prognosis. Alterations in nuclear structure are the mo ⁇ hologic hallmark of cancer diagnosis. Nuclear size, shape, chromatin pattern have all been reported to change in breast cancer [Pienta KJ, Coffey DS: Correlation of * nuclear mo ⁇ hometry with progression of breast cancer. Nuclear Mo ⁇ hometry of breast cancer 2012, 1991]. However, heterogeneity in mo ⁇ hology and biology of tumors belonging to the same classification group has been found to be the most prominent feature of breast cancer [ Komitowski DD and Janson CP (1990). Quantitative features of chromatin structure in the prognosis of breast cancer. Cancer 65:2725-2730].
  • Ladekarl and Sorensen found that the main three-dimensional nuclear size, the main nuclear profile area and the mitotic index were all significantly larger in ductal than in lobular carcinomas, whereas the main nuclear density index was smaller in ductal carcinoma [Ladekarl M, Sorensen FB: Prognostic, quantitative histopathologic variables in lobular carcinoma of the breast. Cancer 72:2602, 1993]. Yu et al. also identified some distinct nuclear features useful in the differentiation of infiltrating ductal and lobular carcinoma [Yu GH, Sneige N, Kidd LD, Johnston and Katz RL (1995). Image analysis derived mo ⁇ hometric differences in fine needle aspirated of ductal and lobular breast carcinoma. Anal Quant Cytol Histol 17(2):88-92].
  • CLL chronic lymphocytic leukemia
  • CLL The majority of normal lymphocytes and the dominant cellular population of CLL both consist of small cells with dense, clumped nuclear chromatin, which makes the distinction between these lymphocytic populations difficult by conventional light microscopy. Some cases differ in # mo ⁇ hological feature from the typical mature, small cell B-CLL: (i) a mixture of small lymphocytes and prolymphocytes (>10 % and ⁇ 55 %) designated as CLL/PL, or (ii) CLL mixed with large lymphocytes.
  • the French-American-British group has proposed criteria based on cytochemical and immunological methods in order to establish a clear diagnosis.
  • Immunophenotypic analysis reveals large amounts of surface immunoglobulin (slg) in normal B cells which is only weakly expressed, or undetectable, on B-CLL cells.
  • the CLL cell expresses the pan-B antigens CD 19 and CD20 and the activation antigens CD5 and CD23, however it does not express the terminal B-cell differentiation antigens exhibited by plasma cells.
  • B-CLL cells express either kappa or lambda-light chains and the monoclonality is essential to establish the diagnosis.
  • Receptors for mouse red blood cell rosettes MRBC-R
  • MRBC-R mouse red blood cell rosettes
  • U.S. Pat. No. 5,086,476, to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches an image processing method and apparatus for determining a proliferation index of a cell sample by staining the cells with a chromogen for a proliferation substance and a counterstain for the cell nuclei.
  • the chromogen is activated by an antibody- enzyme conjugate which binds to the proliferation substance to produce a stained cell sample.
  • the stained cell sample is examined with an optical microscope, forming a portion of the apparatus, which produces a magnified cell sample image.
  • the apparatus optically filters the cell sample image and produces a pair of optically enhanced proliferation substance and cell nuclei images.
  • the enhanced images are electronically analyzed to determine the amounts of cell nuclei and proliferation substance appearing in the images, respectively. The amounts are then compared to yield a proliferation index for the portion of the cell sample appearing in the cell sample image.
  • U.S. Pat. No. 5,109,429 to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches a kit for the quantitation of components in cell nuclei, wherein the kit includes a stain and microscopic slides. Each slide has reference cell objects and a specimen cell area for receipt of specimen cells which are stained simultaneously with the reference cell objects.
  • U.S. Pat. No. 5,202,931 to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches an image analysis system for » the quantitation of nuclear proteins in cell populations. Particularly, the hormonal receptor content of fine needle aspirates of human breast carcinomas are evaluated.
  • Estrogen or progesterone receptors are amplified and visualized in the specimen by a staining technique of the immunoperoxidase type. Monoclonal antibodies specific against the receptor are attached to the receptor sites and are then amplified by a bridging antibody which attaches to the monoclonal antibody and a peroxidase-antiperoxidase complex. A chromogen, diaminobenzidine is combined with the complex and treated with hydrogen peroxide to react with the peroxidase forming an insoluble brown precipitate which marks the receptor sites for optical identification. The specimen is then counterstained with another chromogen, methyl green which is specific to the nucleus of each cell.
  • Two monochromatic filterings optically separate the areas stained by the receptor site optical enhancer and the nuclear area optical enhancer. Measurements of the optical density values of the stained receptor areas yield an intensity value directly related to the quantity of hormonal receptor in the specimen. A comparison of the nuclear area containing hormonal receptor with the total nuclear area yields a percentage value which indicates the distribution of cells throughout of the specimen which contain receptor. These two values for intensity and distribution are then combined to yield a predictive score for an assay. The measured score when compared to an empirically derived reference score is predictive of the prognosis of endocrine therapy.
  • U.S. Pat. No. 5,281,517 to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches a method and apparatus for selecting and analyzing a subpopulation of cells or cell objects for a certain parameter such as DNA using image analysis means.
  • the cells are first stained with an alkaline phosphatase technique including a monoclonal antibody specific to a protein in at least one of the cell's cytoplasm or on a cell membrane, thereby marking any cells including the protein as to type.
  • a second staining of the DNA in the nucleus is accomplished by a Feulgen technique that destroys the cell cytoplasm.
  • the cells may then be gated using the image analysis means on the visual parameter such as colored DNA or colored antigen into a subpopulation that is to be measured.
  • the selected cells may then be examined by digital image processing and measured for a parameter such as a true actual measurement of DNA in picograms. A quantitation of the measured parameter may be generated and provided.
  • U.S. Pat. No. 5,428,690 to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches an apparatus and method for automated assay of biological specimens positioned on microscope slides.
  • the apparatus comprises an interactive optical subsystem for viewing the biological specimen on the slide and for producing an interactive video signal corresponding to the viewed image.
  • An automated optical subsystem includes a single high power microscope objective for scanning a rack of slides, portions of which having been previously identified for assay in the interactive optical means.
  • the system also includes a processor for processing the interactive and automatic video signals for the two optical subsystems.
  • the processor receives the automatic video signal and performs biological assay functions upon it.
  • a method and apparatus are also disclosed for marking points for later analysis on the microscope slides and for associating an analysis function with each marked point.
  • U.S. Pat. No. 5,252,487 to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches an apparatus and method for determining an amount of oncogene protein product copies in a cell includes an optical conversion module for measuring an amount of optically enhanced DNA in a cell sample.
  • a subsystem for measuring an amount of an optically enhanced oncogene protein product protein product is coupled to the DNA measuring means.
  • a subsystem for comparing the measured DNA amount and measured oncogene protein product amount produces an oncogene protein product copy measurement which is fed to an output device for producing an output indicative of the amounts of the oncogene protein product in the cells of the cell sample.
  • U.S. Pat. No. 5,288,477 to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches a method for prognosticating the effectiveness of a chemotherapy using monoclonal antibodies and ligand molecules.
  • the putative anti-cancer agent has binding specificity for a oncogenic receptor molecule on the membrane of a cancer cell, such as HER-2/neu. When the putative agent binds to the oncogenic receptor, the receptor translocates from the membrane to the cytoplasm or perinucleus of the cancer cell, accompanied by a transient increase in the total cellular content of the receptor, and results in terminal cell differentiation.
  • the efficacy of the agent in vivo can be determined in vitro by treatment of biopsied cancer cells with the agent and subsequent examination of the cells for evidence of terminal cell differentiation. Such evidence includes mo ⁇ hological change, reduction in cell growth, or production of chemicals » associated with the mature phenotype. Additionally, treated cells may be examined with immunohistochemicals specific for the oncogenic receptor, to determine translocation of the receptor from the membrane to the cytoplasm or perinucleus. Quantification of receptor levels in treated cells by measuring optical densities after staining can be used to determine translocation, as well as a transient increase in total cellular content of the receptor.
  • 5,134,662 to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches a method and apparatus for use in performing automated classification of cells and other microscopic specimens.
  • the apparatus provides a compact, adjustable assembly that is operable to provide: an operator-apparatus interactive classification system for the cell analysis; alternative techniques for different cells, cytoplasms and cell populations; and enhanced image or color separation and analysis.
  • U.S. Pat. No. 5,473,706 to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches an apparatus and method for automated assay of biological specimens positioned on microscope slides.
  • the apparatus comprises an interactive optical subsystem for viewing the biological specimen on the slide and for producing an interactive video signal corresponding to the viewed image.
  • An automated optical subsystem includes a single high power microscope objective for scanning a rack of slides, portions of which having been previously identified for assay in the interactive optical subsystem.
  • the system also includes a processor for processing the interactive and automatic video signals from the two optical subsystems. The processor receives the automatic video signal and performs biological assay functions upon it.
  • U.S. Pat. No. 5,526,258 to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches an apparatus and method for analyzing the cell objects of a cell sample for the diagnosis and treatment of actual or suspected cancer is disclosed.
  • An image of the cell sample is first digitized and mo ⁇ hological attributes, including area and DNA mass of the cell objects are automatically measured from the digitized image. The measured attributes are compared to ranges of attribute values which are preestablished to select particular cell objects having value in cancer analysis. After the selection of cell objects, the image is displayed to an operator and indicia of selection is displayed with each selected cell object.
  • each selected cell object is assigned to one of six classes and the indicia of selection consists of indicia of the class into which the associated cell object has been placed.
  • the measured DNA mass of identified cell object fragments in tissue section samples may also be increased to represent the DNA mass of the whole cell object from which the fragment was sectioned.
  • each selected cell object is assigned to one of three classes corresponding to diploid, tetraploid and octoploid cell mo ⁇ hology and the measured DNA mass of the identified cell object fragments in the rat liver tissue section sample may be corrected.
  • the selected cell objects of the measurement material e.g., DNA Mass
  • U.S. Pat. No. 4,998,284 to Bacus et al which is inco ⁇ orated by reference as if fully set forth herein, teaches a method and apparatus for use in performing automated classification of cells and other microscopic specimens.
  • the apparatus provides a compact, adjustable assembly that is operable to provide: an operator- apparatus interactive classification system for the cell analysis; alternative techniques for different cells, cytoplasms and cell populations; and enhanced image or color separation and analysis.
  • U.S. Pat. No. 4,741,043 to Bacus et al. which is inco ⁇ orated by reference as if fully set forth herein, teaches a user interactive system for dynamically testing and evaluating various cells, antigens, or other materials taken from the human body.
  • the DNA in specimen cells is analyzed and quantified by image analysis using pattern recognition techniques.
  • the user is provided with a unique slide or support on which there are specimen and reference materials or objects which are simultaneously stained or otherwise image enhanced at the time of analysis.
  • histological stains unique immunohistochemical markers and DNA ploidy stains, both conventional and immunostains, are all useful for that pu ⁇ ose.
  • a spectrometer is an apparatus designed to accept light, to separate (disperse) it into its component wavelengths, and measure the lights spectrum, that is the intensity of the light as a function of its wavelength.
  • An imaging spectrometer is a spectrometer which collects incident light from a scene and measures the spectra of each pixel (i.e., picture element) thereof.
  • Spectroscopy is a well known analytical tool which has been used for decades in science and industry to characterize materials and processes * based on the spectral signatures of chemical constituents therein. The physical basis of spectroscopy is the interaction of light with matter. Traditionally, spectroscopy is the measurement of the light intensity emitted, scattered or reflected from or transmitted through a sample, as a function of wavelength, at high spectral resolution, but without any spatial information.
  • Spectral imaging is a combination of high resolution spectroscopy and high resolution imaging (i.e., spatial information).
  • high resolution spectroscopy i.e., spatial information
  • Most of the works so far described concern either obtaining high spatial resolution information from a biological sample, yet providing only limited spectral information, for example, when high spatial resolution imaging is performed with one or several discrete band-pass filters [See, Andersson-Engels et al. (1990) Proceedings of SPIE - Bioimaging and Two-Dimensional Spectroscopy, 1205, pp. 179-189], or alternatively, obtaining high spectral resolution (e.g., a full spectrum), yet limited in spatial resolution to a small number of points of the sample or averaged over the whole sample [See for example, U.S. Pat. No. 4,930,516, to Alfano et al.].
  • a spectral imaging system consists of (i) a measurement system, and (ii) an analysis software.
  • the measurement system includes all of the optics, electronics and the manner in which the sample is illuminated (e.g., light source selection), the mode of measurement (e.g., fluorescence or transmission), as well as the calibration best suited for extracting the desired results from the measurement.
  • the analysis software includes all of the software and mathematical algorithms necessary to analyze and display important results in a meaningful way.
  • Spectral imaging has been used for decades in the area of remote sensing to provide important insights in the study of Earth and other planets by identifying characteristic spectral abso ⁇ tion features originating therefrom.
  • the high cost, size and configuration of remote sensing spectral imaging systems e.g., Landsat, AVIRIS
  • has limited their use to air and satellite-born applications See, Maymon and Neeck (1988) Proceedings of SPIE - Recent Advances in Sensors, Radiometry and Data Processing for Remote Sensing, 924, pp. 10-22; Dozier (1988) Proceedings of SPIE - Recent Advances in Sensors, Radiometry and Data Processing for Remote Sensing, 924, pp. 23-30].
  • spectral dispersion methods There are three basic types of spectral dispersion methods that might be considered for a spectral bio-imaging system: (i) spectral grating or t prism, (ii) spectral filters and (iii) interferometric spectroscopy. As will be described below, the latter is best suited to implement the method of the present invention, yet as will be appreciated by one ordinarily skilled in the art, grating, prism and filters based spectral bio-imaging systems may also be found useful in some applications.
  • a grating or prism (i.e., monochromator) based systems also known as slit-type imaging spectrometers, such as for example the DILOR system: [see, Valisa et al. (Sep. 1995) presentation at the SPIE Conference European Medical Optics Week, BiOS Europe 1995, Barcelona, Spain], only one axis of a CCD (charge coupled device) array detector (the spatial axis) provides real imagery data, while a second (spectral) axis is used for sampling the intensity of the light which is dispersed by the grating or prism as function of wavelength.
  • the system also has a slit in a first focal plane, limiting the field of view at any given time to a line of pixels.
  • a full image can only be obtained after scanning the grating (or prism) or the incoming beam in a direction parallel to the spectral axis of the CCD in a method known in the literature as line scanning.
  • the inability to visualize the two-dimensional image before the whole measurement is completed makes it impossible to choose, prior to making the measurement, a desired region of interest from within the field of view and/or to optimize the system focus, exposure time, etc.
  • Grating and prism based spectral imagers are in use for remote sensing applications, because an ai ⁇ lane (or satellite) flying over the surface of the Earth provides the system with a natural line scanning mechanism.
  • slit-type imaging spectrometers have a major disadvantage since most of the pixels of one frame are not measured at any given time, even though the fore-optics of the instrument actually collects incident light from all of them simultaneously. The result is that either a relatively large measurement time is required to obtain the necessary information with a given signal-to-noise ratio, or the signal-to-noise ratio (sensitivity) is substantially reduced for a given measurement time. Furthermore, slit-type spectral imagers require line scanning to collect the necessary information for the whole scene, which may introduce inaccuracies to the results thus obtained.
  • Filter based spectral dispersion methods can be further categorized into discrete filters and tunable filters.
  • the spectral image is built by filtering the radiation for all the pixels of the scene simultaneously at a different wavelength at a time by » inserting in succession narrow band filters in the optical path, or by electronically scanning the bands using acousto-optic tunable filters (AOTF) or liquid-crystal tunable filter (LCTF), see below.
  • AOTF acousto-optic tunable filters
  • LCTF liquid-crystal tunable filter
  • Tunable filters such as AOTFs and LCTFs have no moving parts and can be tuned to any particular wavelength in the spectral range of the device in which they are implemented.
  • One advantage of using tunable filters as a dispersion method for spectral imaging is their random wavelength access; i.e., the ability to measure the intensity of an image at a number of wavelengths, in any desired sequence without the use of filter wheels.
  • OPD optical path difference
  • This method may be practiced by utilizing various types of interferometers wherein the OPD is varied to build the interferograms by moving the entire interferometer, an element within the interferometer, or the angle of incidence of the incoming radiation. In all of these cases, when the scanner completes one scan of the interferometer, the interferograms for all pixels of the scene are completed.
  • Apparatuses in accordance with the above features differ from the conventional slit- and filter type imaging spectrometers by utilizing an interferometer as described above, therefore not limiting the collected energy with an aperture or slit or limiting the incoming wavelength with narrow band interference or tunable filters, thereby substantially increasing the total throughput of the system.
  • interferometer based apparatuses better utilize all the information available from the incident light of the scene to be analyzed, thereby substantially decreasing the measurement time and/or substantially increasing the signal-to-noise ratio (i.e., sensitivity).
  • n be the number of detectors in the linear array
  • m x m the number of pixels in a frame
  • T the frame time.
  • the total time spent on each pixel in one frame summed over all the detectors of the array is nTlm .
  • the energy seen by every detector at any time is of the order of In of the total, because the wavelength » resolution is I In of the range
  • the modulating function is an oscillating function (e.g., sinusoidal (Michelson) or similar periodic function such as low finesse Airy function with Fabry-Perot) whose average over a large OPD range is 50%.
  • Spectral bio-imaging systems are potentially useful in all applications in which subtle spectral differences exist between chemical constituents whose spatial distribution and organization within an image are of interest.
  • the measurement can be carried out using virtually any optical system attached to the system described in U.S. Pat. No. 5,539,517, for example, an upright or inverted microscope, a fluorescence microscope, a macro lens, an endoscope and a fundus camera.
  • any standard experimental method can be used, including light transmission (bright field and dark field), auto-fluorescence and fluorescence of administered probes, etc.
  • Fluorescence measurements can be made with any standard filter cube (consisting of a barrier filter, excitation filter and a dichroic mirror), or any customized filter cube for special applications, provided that the emission spectra fall within the spectral range of the system sensitivity.
  • Spectral bio-imaging can also be used in conjunction with any standard spatial filtering method such as dark field and phase contrast, and even with polarized light microscopy. The effects on spectral information when using such methods must, of course, be understood to correctly inte ⁇ ret the measured spectral images.
  • a sample including at least a portion of at least one cell is prepared to be spectrally imaged;
  • the sample is viewed through an optical device optically connected to an imaging spectrometer for obtaining a spectrum of each pixel of the sample;
  • each of the pixels is classified into classification groups according to the pixels spectra; and
  • by analyzing the classification groups of pixels the cells of the sample are classified into cell classes.
  • a method of in situ analysis of a biological sample comprising the steps of (a) staining the biological sample with N stains of which a first stain is selected from the group consisting of a first immunohistochemical stain, a first histological stain and a first DNA ploidy stain, and a second stain is selected from the group consisting of a second immunohistochemical stain, a second histological stain and a second DNA ploidy stain, with provisions that N is an integer greater than three and further that (i) if the first stain is the first immunohistochemical stain then the second stain is either the second histological stain or the second DNA ploidy stain; (ii) if the first stain is the first histological stain then the second stain is either the second immunohistochemical stain or the second DNA ploidy stain; whereas (iii) if the first stain is the first DNA ploidy stain then the second stain
  • a method of in situ analysis of a biological sample comprising the steps of (a) staining the biological sample with a plurality of stains of which a first stain is selected from the group consisting of a first immunohistochemical stain, a first histological stain and a first DNA ploidy stain, and a second stain is selected from the group consisting of a second immunohistochemical stain, a second histological stain and a second DNA ploidy stain, with a provision that (i) if the first stain is the first immunohistochemical stain then the second stain is * either the second histological stain or the second DNA ploidy stain; (ii) if the first stain is the first histological stain then the second stain is either the second immunohistochemical stain or the second DNA ploidy stain; whereas (iii) if the first stain is the first DNA ploidy stain
  • a method of in situ analysis of a biological sample comprising the steps of (a) staining the biological sample with at least four different immunohistochemical stains; and (b) using a spectral data collection device for collecting spectral data from the biological sample, the spectral data collection device and the at least four immunohistochemical stains are selected such that a spectral component associated with each of the at least four immunohistochemical stains is collectable.
  • a method of in situ analysis of a biological sample comprising the steps of (a) staining the biological sample with at least three stains of which at least one stain is an immunohistochemical stain and at least one additional stain is a histological stain or a DNA ploidy stain; and (b) using a spectral data collection device for collecting spectral data from the biological sample, the spectral data collection device and the at least three stains are selected such that a spectral component associated with each of the at least three stains is collectable.
  • a method of in situ analysis of a biological sample comprising the steps of (a) staining the biological sample with at least three stains of which a first stain is an immunohistochemical stain, a second stain is a histological stain and a third stain is a DNA ploidy stain; and (b) using a spectral data collection device for collecting spectral data from the biological sample, the spectral data collection device and the at least three stains are selected such that a spectral component specifically associated with each of the at least three stains is collectable.
  • an immunohistochemical composition # comprising at least four different immunohistochemical stains, each being for staining a respective cytological marker and each being individually detectable in a presence of all others using a spectral data collection device.
  • the first and second immunohistochemical stains each independently includes a primary antibody and a signal amplification mechanism.
  • the signal amplification mechanism is selected from the group consisting of a secondary antibody capable of binding a constant region of the primary antibody, avidin or strepavidin capable of binding biotin conjugated to the primary antibody and biotin capable of binding avidin or strepavidin conjugated to the primary antibody.
  • the secondary antibody, avidin, strepavidin and biotin are each independently labeled with a detectable moiety.
  • the detectable moiety is a fluorescent dye.
  • the fluorescent dye is selected from the group consisting of Fluorescein, Rhodamine, Texas Red, Cy2, Cy3, Cy5, CyO, Cy0.5, Cyl, Cyl.5, Cy3.5, Cy7, VECTOR Red, ELFTM (Enzyme-Labeled Fluorescence), FluorX, Calcein, Calcein-AM, CRYPTOFLUORTM'S, Orange (42 kDa), Tangerine (35 kDa), Gold (31 kDa), Red (42 kDa), Crimson (40 kDa), BHMP, BHDMAP, Br-Oregon, Lucifer Yellow, Alexa dye family, N-[6-(7- nitrobenz-2-oxa-l, 3-diazol-4-yl)amino]caproyl] (NBD), BODIPYTM, boron dipyrromethene difluoride, Oregon Green, MITOTRACKERTM Red, DiOC7(3), DilCis,
  • the non-fluorescent dye is selected from the group consisting of alkaline phosphatase, horseradish peroxidase, glucose oxidase and beta- galactosidase substrates.
  • the detectable moiety is an enzyme catalyzing a colorimetric reaction of a substrate having a substantially non-soluble color reaction product.
  • the enzyme is selected from the group consisting of alkaline phosphatase, horseradish peroxidase, ⁇ -galactosidase, and glucose oxidase.
  • the substrate is selected from the group consisting of alkaline phosphatase, horseradish peroxidase, ⁇ -galactosidase, and glucose oxidase substrates.
  • the detectable moiety is an enzyme catalyzing a luminescence reaction of a substrate having a substantially non-soluble reaction product capable of luminescencing or of directing a second reaction of a second substrate having a luminescencing product.
  • the enzyme is selected from the group consisting of luciferase and aequorin.
  • the first and second substrates are each independently selected from the group consisting of luciferine, ATP, Ca ++ and coelenterazine.
  • the first and second histological stains are each independently selected from the group consisting of 4',6-diamidino-2-phenylindole, Eosin, Fluorescein isothiocyanate, Hoechst 33258, Hoechst 33342, Propidium Iodide, Quinacrine, Fluorescein-phalloidin, Resorufin, hematoxylin, Orange G, Light Green SF, Romanowsky-Giemsa, May-Grunwald, Blue counterstain, ethyl green, Feulgen-naphthol yellow S, Giemsa, Methylene Blue, Methyl Green, pyronin, Naphthol-yellow, Neutral Red, Papanicolaou stain, Red Counter
  • the first and second DNA ploidy stains are each independently selected from the group consisting of Chromomycin A 3,
  • the spectral data collection device is selected from the group consisting of an interferometer-based spectral data collection device, filters- based spectral data collection device and a dispersion element-based spectral data collection device.
  • each of the first and second immunohistochemical stains independently includes a primary antibody.
  • the primary antibody is selected from the group consisting of anti-estrogen receptor antibody, anti-progesterone receptor antibody, anti- p53 antibody, anti-Her-2/neu antibody, anti-EGFR antibody, anti-cathepsin D antibody, anti-Bcl-2 antibody, anti-E-cadherin antibody, anti-CA125 antibody, anti-CA15-3 antibody, anti-CA19-9 antibody, anti-c-erbB-2 antibody, anti-P-glycoprotein antibody, anti-CEA antibody, anti- retinoblastoma protein antibody, anti-ras oncoprotein antibody, anti-Lewis X antibody, anti-Ki-67 antibody, anti-PCNA antibody, anti-CD3 antibody, anti-CD4 antibody, anti-CD5 antibody, anti-CD7 antibody, anti-CD8 antibody, anti-CD9/ ⁇ 24 antibody, anti-CD 10 antibody, anti-CDl lc antibody, anti-CD 13 antibody, anti-CD 14 antibody, anti-CD 15 antibody, anti-CD
  • the present invention successfully addresses the shortcomings of the presently known configurations by providing a method of simultaneous in situ analysis of a plurality of immunohistochemical stains, histological stains and DNA ploidy stains, using spectral imaging techniques of high # spatial and spectral resolutions.
  • FIG. 1 is a block diagram illustrating the main components of an imaging spectrometer constructed in accordance with U.S. Pat application No. 08/392,019 (prior art).
  • FIG. 2 illustrates a non-moving type interferometer, namely, a Sagnac interferometer, as used in an imaging spectrometer in accordance with U.S. Pat application No. 08/392,019 (prior art).
  • FIGs. 3a-c show inte ⁇ hase FISH performed with two different probes attached to Texas-Red and Rhodamine wherein (a) is an original image, the way it looks thorough a microscope; (b) is the same sample, after being measured and processed by the SPECTRACUBETM system; and (c) are the fluorescence spectra of the Texas-Red and Rhodamine fluorophores.
  • FIGs. 4a-c show inte ⁇ hase FISH performed using the SPECTRACUBETM system with six different probes each labeled with a different fluorophore wherein (a) is an original image, the way it looks thorough a microscope, cells were counter stained with DAPI; (b) is the same sample, after being measured and processed by the SPECTRACUBE
  • TM system and (c) are the fluorescence spectra of the six fluorophores which were employed for classification.
  • FIGs. 5a-c present hybridization results of 24 chromosomal paints with a normal male chromosome spread using the SPECTRACUBETM system, wherein Figure 5 a is an RGB image obtained using an RGB algorithm, Figures 5b and 5 c are classification images obtained using a classification algorithm, whereas Figures 5a and 5b present the original spread and Figure 5 c presents the chromosome spread arranged as a karyotype.
  • FIG. 6 shows a definition of pseudo-RGB (Red, Green and Blue) colors for emphasizing chosen spectral ranges.
  • the intensity for each pseudo-color is calculated by integrating the area under the curve, after multiplying it by one of the curves.
  • FIG. 7 presents non-normalized spectra of two histological stains (hematoxylin and eosin) and of four immunohistochemical stains (DAB, Fast Red, AEC and BCIP/NBT) measured using the SPECTRACUBETM system from six single stain stained breast cancer samples. Peak * wavelengths are indicated on the right.
  • FIGs. 8a-e show images of a breast cancer sample which was previously determined to be ER(+)/PR(+) co-stained with the histological stain hematoxylin and with the immunohistochemical stain anti-ER-DAB, wherein Figure 8a presents an RGB image of the sample, Figures 8b-d present binarized images of hematoxylin, DAB and AEC spectral components, respectively, whereas Figure 8e presents a classification overlay image, wherein the above spectral components are highlighted in red, green and blue, respectively, all as measured using the SPECTRACUBETM system.
  • FIGs. 9a-e show images of a breast cancer sample which was previously determined to be ER(+)/PR(+) co-stained with the histological stain hematoxylin and with the immunohistochemical stain anti-PR- AEC, wherein Figure 9a presents an RGB image of the sample, Figures 9b-d present binarized images of hematoxylin, DAB and AEC spectral components, respectively, whereas Figure 9e presents a classification overlay image, wherein the above spectral components are highlighted in red, green and blue, respectively, all as measured using the SPECTRACUBETM system.
  • FIGs. lOa-e show images of a breast cancer sample which was previously determined to be ER(+)/PR(+) co-stained with the histological stain hematoxylin and with the immunohistochemical stain anti-PR-Fast Red, wherein Figure 10a presents an RGB image of the sample, Figures lOb-d present binarized images of hematoxylin, DAB and Fast Red spectral components, respectively, whereas Figure lOe presents a classification overlay image, wherein the above spectral components are highlighted in red, green and blue, respectively, all as measured using the SPECTRACUBETM system.
  • FIGs. 11 a-e show images of a breast cancer sample which was previously determined to be ER(+)/PR(+) co-stained with the histological stain hematoxylin and with the immunohistochemical stains anti-ER-DAB and anti-PR-Fast Red
  • Figure 11a presents an RGB image of the sample
  • Figures l lb-d present binarized images of hematoxylin, DAB and Fast Red spectral components, respectively
  • Figure l ie presents a classification overlay image, wherein the above spectral components are highlighted in red, green and blue, respectively, regions co-stained with anti-ER-DAB and anti-PR-Fast Red are shown in yellow, all as measured using the SPECTRACUBETM system. »
  • FIGs. 12a-f show images of a breast cancer sample which was previously determined to be ER(+)/PR(+) co-stained with the histological stains hematoxylin and eosin and with the immunohistochemical stains anti- ER-DAB and anti-PR-Fast Red, wherein Figure 12a presents an RGB image of the sample, Figures 12b-e present binarized images of hematoxylin, eosin, DAB and Fast Red spectral components, respectively, whereas Figure 12f presents a classification overlay image, wherein the above spectral components are highlighted in blue, pu ⁇ le green and red, respectively, all as measured using the SPECTRACUBETM system.
  • FIG. 13 presents non-normalized spectra of five histological stains (Harris hematoxylin, eosin, orange G, light green SF and Bismark brown Y) measured using the SPECTRACUBETM system from five single stain stained cervix cancer samples. Peak wavelengths are indicated on the right.
  • FIGs. 14a-g show images of a cervix cancer sample co-stained with the histological stains Harris hematoxylin, eosin, orange G, light green SF and Bismark brown Y, which collectively form what is known in the art as
  • Figure 14a presents an RGB image of the sample
  • Figures 14b-f present binarized images of Harris hematoxylin, eosin, orange G, light green SF and Bismark brown Y spectral components, respectively
  • Figure 14e presents a classification overlay image, wherein the above spectral components are highlighted in blue, pink, orange, green and gray, respectively, to form by combinations thereof the colorful classification overlay image, all as measured using the SPECTRACUBETM system.
  • the present invention is of a method of simultaneous in situ analysis of a plurality of immunohistochemical stains, histological stains and/or
  • DNA ploidy stains which can be used in pathological examination of cells.
  • the present invention can be used to provide a pathologist with cumulative information regarding an examined biological sample and assist in decision making.
  • the principles and operation of a method according to the present invention may be better understood with reference to the drawings and accompanying descriptions.
  • a method of in situ analysis of a biological sample is effected by executing the following method steps, in which, in a first step, the biological sample is stained with N (i.e., a plurality of) stains of which a first stain is a first immunohistochemical stain, a first histological stain or a first DNA ploidy stain, and a second stain is a second immunohistochemical stain, a second histological stain or a second DNA ploidy stain. Provisions are taken such that N is an integer greater than three (e.g.,
  • a spectral data collection device is used for collecting spectral data from the * biological sample, the spectral data collection device and the N stains are selected such that a spectral component associated with each of the N stains is collectable.
  • a method of in situ analysis of a biological sample is effected by executing the following method steps, in which, in a first step, the biological sample is stained with at least four different immunohistochemical stains.
  • a spectral data collection device is used for collecting spectral data from the biological sample, the spectral data collection device and the at least four immunohistochemical stains are selected such that a spectral component associated with each of the at least four immunohistochemical stains is collectable.
  • yet another method of in situ analysis of a biological sample is effected by executing the following method steps, in which, in a first step, the biological sample is stained with at least three stains of which at least one stain is an immunohistochemical stain and at least one additional stain is a histological stain or a DNA ploidy stain.
  • a spectral data collection device is used for collecting spectral data from the biological sample, the spectral data collection device and the at least three stains are selected such that a spectral component associated with each of the at least three stains is collectable.
  • Still another method of in situ analysis of a biological sample is effected by executing the following method steps, in which, in a first step, the biological sample is stained with at least three stains of which a first stain is an immunohistochemical stain, a second stain is a histological stain and a third stain is a DNA ploidy stain.
  • a spectral data collection device is used for collecting spectral data from the biological sample, the spectral data collection device and the at least three stains are selected such that a spectral component specifically associated with each of the at least three stains is collectable.
  • a immunohistochemical composition » which includes in a mixture at least four different immunohistochemical stains, each being for staining a respective cytological marker in the biological sample and each being individually detectable in a presence of all others using a spectral data collection device.
  • in situ or “in situ analysis” refers to an analysis of cellular or tissue components situated and preferably fixated in their natural place or position within the cell or tissue.
  • biological sample refers to a sample retrieved from an animal, mammals and human beings in particular.
  • the sample may be of a healthy tissue, disease tissue or tissue suspected of being disease tissue.
  • the sample may be a biopsy taken, for example, during a surgical procedure.
  • the sample may be collected via means of fine needle aspiration, scraping or washing a cavity to collects cells or tissue therefrom.
  • the sample may be of a tumor both solid and hematopoietic tumors, as well as of neighboring healthy tissue.
  • the sample may be a smear of individual cells or a tissue section.
  • the term “stained” or “staining” refers to a process in which coloration is produced by foreign matter having penetrated into and/or interacted with the biological sample.
  • stain or “stains” refers to colorants, either fluorescent, luminescent and/or non-fluorescent and further to reagents or matter used for effecting coloration.
  • immunohistochemical stain refers to colorants, reactions and associated reagents in which a primary antibody which binds a cytological marker is used to directly or indirectly (via “sandwich” reagents and/or an enzymatic reaction) stain the biological sample examined.
  • Immunohistochemical stains are in many cases referred to in the scientific literature as immunostains, immunocytostains, immunohistopathological stains, etc.
  • antibody refers to any monoclonal or polyclonal immunoglobulin, or a fragment of an immunoglobin such as sFv (single chain antigen binding protein), Fabl or Fab2.
  • the term “histological stain” refers to any colorant, reaction and/or associated reagents used to stain cells and tissues in association with cell components such as types of proteins (acidic, basic), DNA, RNA, lipids, cytoplasm components, nuclear components, membrane components, etc. Histological stains are in many cases referred to as counterstains, cytological stains, histopathological stains, etc.
  • the term “DNA ploidy stain” refers to stains which stoichiometrically bind to chromosome components, such as, but not limited to, DNA or histones.
  • spectral data collection device refers to any device capable of detecting light intensity associated with a plurality, typically four or more, of distinct spectral bands in each spatial element (pixel) of the examined sample.
  • the SPECTRACUBETM system optically connected to a microscope preferably serves as the spectral data collection device according to the present invention.
  • any spectral imager i.e., an instrument that measures and stores in memory for later retrieval and analysis the spectrum of light emitted by every point of an object which is placed in its field of view, including filters (e.g., conventional, acousto- optic tunable filter (AOTF) or liquid-crystal tunable filter (LCTF)) and dispersive element (e.g., grating or prism) based spectral imagers, or other spectral data or multi-band light collection devices (e.g., a device in accordance with the disclosure in Speicher R. M., Ballard S. G. and Ward C. D. (1996) Karyotyping human chromosomes by combinatorial multi- fluor FISH.
  • filters e.g., conventional, acousto- optic tunable filter (AOTF) or liquid-crystal tunable filter (LCTF)
  • dispersive element e.g., grating or prism
  • the spectral data collection device can be an interferometer- based spectral data collection device, filter(s)-based spectral data collection » device and a dispersion element-based spectral data collection device.
  • spectral component refers to a part of a spectrum which is unique to a specific substance and therefore may be used as a spectral signature of that substance, to differentiate that substance from other substances.
  • the immunohistochemical stains each independently includes a primary antibody and a signal amplification mechanism.
  • the signal amplification mechanism can employ, for example, a secondary antibody capable of binding a constant region of the primary antibody, avidin or strepavidin capable of binding biotin conjugated to the primary antibody, and biotin capable of binding avidin or strepavidin conjugated to the primary antibody.
  • the secondary antibody, avidin, strepavidin or biotin are each independently labeled with a detectable moiety, which can be an enzyme directing a colorimetric reaction of a substrate having a substantially non-soluble color reaction product, a fluorescent dye (stain), a luminescent dye or a non- fluorescent dye. Examples concerning each of these options are listed hereinbelow.
  • the enzyme employed can be, for example, alkaline phosphatase, horseradish peroxidase, ⁇ -galactosidase and/or glucose oxidase; and the substrate can respectively be an alkaline phosphatase, horseradish peroxidase, ⁇ -galactosidase or glucose oxidase substrate.
  • the enzyme can also be directed at catalyzing a luminescence reaction of a substrate, such as, but not limited to, luciferase and aequorin, having a substantially non-soluble reaction product capable of luminescencing or of directing a second reaction of a second substrate, such as but not limited to, luciferine and ATP or coelenterazine and Ca ++ , having a luminescencing product.
  • a substrate such as, but not limited to, luciferase and aequorin
  • a substantially non-soluble reaction product capable of luminescencing capable of luminescencing
  • a second reaction of a second substrate such as but not limited to, luciferine and ATP or coelenterazine and Ca ++
  • each of the immunohistochemical stains employed includes a primary antibody.
  • external calibration is employed to account for day-to-day » variations experienced when staining is attempted.
  • at least one calibration is preferably attempted for every staining batch.
  • calibration material is employed, wherein the biological sample and the calibration material are stained at the same time with the same staining solutions.
  • the spectral data collection device is first used to analyze the stained calibration material such that adjustment thereof or extraction of calibrating data for algorithmic post measurement calibration becomes feasible.
  • the calibration material can be an optical density reference material.
  • the calibration material can include control cells which are simultaneously co-stained' together with the examined biological sample.
  • Immunohistochemical stains for use in transmittance microscopy In principle, any enzyme that (i) can be conjugated to or bind indirectly to (e.g., via conjugated avidin, strepavidin, biotin, secondary antibody) a primary antibody, and (ii) uses a soluble substrate to provide an insoluble product (precipitate) could be used.
  • Such enzymes include, for example, HRP, AP, LacZ and glucose oxidase.
  • Alkaline phosphatase (AP) substrates include, but are not limited to, AP-Blue substrate (blue precipitate, Zymed catalog p. 61); AP-Orange substrate (orange, precipitate, Zymed), AP-Red substrate (red, red precipitate, Zymed), 5-bromo, 4-chloro, 3-indolyphosphate (BCIP substrate, turquoise precipitate), 5-bromo, 4-chloro, 3-indolyl phosphate/nitroblue tetrazolium/ iodonitrotetrazolium (BCIP/INT substrate, yellow-brown precipitate, Biomeda), 5-bromo, 4-chloro, 3-indolyphosphate/nitroblue tetrazolium (BCIP/NBT substrate, blue/pu ⁇ le), 5-bromo, 4-chloro, 3- * indolyl phosphate/nitroblue tetrazolium/iodonitrotetrazolium
  • BCIP/NBT/INT brown precipitate, DAKO, Fast Red (Red), Magenta-phos (magenta), Naphthol AS-BI-phosphate (NABP)/Fast Red TR (Red), Naphthol AS-BI-phosphate (NABP)/New Fuchsin (Red), Naphthol AS- MX-phosphate (NAMP)/New Fuchsin (Red), New Fuchsin AP substrate (red), p-Nitrophenyl phosphate (PNPP, Yellow, water soluble), VECTORTM Black (black), VECTORTM Blue (blue), VECTORTM Red (red), Vega Red (raspberry red color),
  • Horseradish Peroxidase (HRP, sometimes abbreviated PO) substrates include, but are not limited to, 2,2' Azino-di-3-ethylbenz-thiazoline sulfonate (ABTS, green, water soluble), aminoethyl carbazole, 3-amino, 9- ethylcarbazole AEC (3A9EC, red).
  • ABTS 2,2' Azino-di-3-ethylbenz-thiazoline sulfonate
  • aminoethyl carbazole aminoethyl carbazole
  • 3-amino 9- ethylcarbazole AEC (3A9EC, red).
  • Alpha-naphthol pyronin (red), 4-chloro- 1 -naphthol (4C1N, blue, blue-black), 3,3 '-diaminobenzidine tetrahydrochloride (DAB, brown), ortho-dianisidine (green), o-phenylene diamine (OPD, brown, water soluble), TACS Blue (blue), TACS Red (red), 3,3',5,5' Tetramethylbenzidine (TMB, green or green/blue), TRUE BLUETM (blue), VECTORTM VIP (pu ⁇ le), VECTORTM SG (smoky blue-gray), and Zymed Blue HRP substrate (vivid blue).
  • Glucose Oxidase (GO) substrates include, but are not limited to, nitroblue tetrazolium (NBT, pu ⁇ le precipitate), tetranitroblue tetrazolium (TNBT, black precipitate), 2-(4-iodo ⁇ henyl)-5-(4-nit ⁇ henyl)-3- phenyl tetrazolium chloride (INT, red or orange precipitate), Tetrazolium blue (blue), Nitrotetrazolium violet (violet), and 3-(4,5-dimethylthiazol-2- yl)-2,5-diphenyltetrazolium bromide (MTT, pu ⁇ le).
  • Beta-Galactosidase substrates include, but are not limited to, 5- bromo-4-chloro-3-indoyl beta-D-galactopyranoside (X-gal, blue precipitate).
  • Antibody which links heavy metals can be used for immunostaining using reflection contrast, bright-field or dark-field imaging, or electron microscopy. Such heavy metals include, but are not limited to, gold and silver, typically in a colloidal form.
  • the following references, which are inco ⁇ orated herein provide additional examples. J.M Elias (1990) Immunohistopathology: A practical * approach to diagnosis. ASCP Press (American Society of Clinical Pathologists), Chicago; J.F. McGinty, F.E. Bloom (1983) Double immunostaining reveals distinctions among opioid peptidergic neurons in the medial basal hypothalamus. Brain Res.
  • Histological stains for use in transmittance microscopy The following lists some histological stains used in transmitted light microscopy: eosin, hematoxylin, Orange G, Light Green SF, Romanowsky- Giemsa, May-Grunwald, Blue counterstain (Trevigen), ethyl green (CAS), Feulgen-naphthol yellow S, Giemsa, Methylene Blue, Methyl Green, pyronin, Naphthol-yellow, Neutral Red, Papanicolaou stain (which typically includes a mixture of Hematoxylin, Eosin Y, Light Green SF, Orange G and Bismarck Brown, Red Counterstain B (Trevigen), Red Counterstain
  • DNA ploidy stains for use in transmittance microscopy The following lists some DNA ploidy stains used in transmitted light microscopy.
  • Feulgen reagent pararosanilin
  • Gallocyanin chrom-alum Gallocyanin chrom-alum and naphthol yellow S
  • Methyl green-pyronin Y Methyl green-pyronin Y
  • Immunohistochemical stains for use in fluorescence microscopy Fluorescein, Rhodamine, Texas Red, Cy2, Cy3, Cy5, VECTOR Red, ELFTM (Enzyme-Labeled Fluorescence), CyO, Cy0.5, Cyl, Cyl .5, Cy3, Cy3.5, Cy5, Cy7, FluorX, Calcein, Calcein-AM, CRYPTOFLUORTM'S, Orange (42 kDa), Tangerine (35 kDa), Gold (31 kDa), Red (42 kDa), Crimson (40 kDa), BHMP, BHDMAP, Br-Oregon, Lucifer Yellow, Alexa dye family, N- [6-(7-nitrobenz-2-oxa-l, 3-diazol-4-yl)amino]caproyl] (NBD), BODIPYTM, boron dipyrromethene difluoride, Oregon Green, MITOTRACKERTM Red, DiOC7(3), DilCig, D
  • Histological stains for use in fluorescence microscopy 4',6- diamidino-2-phenylindole (DAPI), Eosin, Fluorescein isothiocyanate (FITC), Hoechst 33258 and Hoechst 33342 (two bisbenzimides), Propidium Iodide, Quinacrine, Fluorescein-phalloidin and Resorufin, *
  • DNA ploidy stains for use in fluorescence microscopy DNA ploidy stains for use in fluorescence microscopy:
  • Chromomycin A 3 DAPI, Acriflavine-Feulgen reaction, Auramine O- Feulgen reaction, Ethidium Bromide, Propidium iodide, high affinity DNA fluorophores such as POPO, BOBO, YOYO and TOTO and others, Green Fluorescent Protein fused to DNA binding protein, such as histones, ACMA, Quinacrine and Acridine Orange.
  • Endogenous pigments Hemoglobin, myoglobin, po ⁇ hyrin, hemosiderin and other ferrous pigments, lipoftiscin, melanin, neuromelanin, ceroid a fluorescent oxidation product of lipid/protein, carotenoids, pyridine, flavin nucleotides.
  • Anti-estrogen receptor antibody (breast cancer), anti-progesterone receptor antibody (breast cancer), anti-p53 antibody (multiple cancers), anti-Her-2/neu antibody (multiple cancers), anti-EGFR antibody (epidermal growth factor, multiple cancers), anti-cathepsin D antibody (breast and other cancers), anti-Bcl-2 antibody (apoptotic cells), anti- E-cadherin antibody, anti-CA125 antibody (ovarian and other cancers), anti-CA15-3 antibody (breast cancer), anti- CA19-9 antibody (colon cancer), anti-c-erbB-2 antibody, anti-P- glycoprotein antibody (MDR, multi-drug resistance), anti-CEA antibody (carcinoembryonic antigen), anti-retinoblastoma protein (Rb) antibody,
  • a breast cancer panel therefore can include the anti-ER, anti-PR, anti-Her2/neu, anti-p53, anti-Ki-67 and anti-CD31 immunohistochemical marker stains, a DNA ploidy stain and a H&E counterstain.
  • Ovarian and/or endometrial cancer panel can therefore include the anti-Her2/neu, anti-p53, anti-Ki-67 and anti-CD31 immunohistochemical * stains, a DNA ploidy stain and a H&E counterstain.
  • Prostrate and/or bladder cancer panel can therefore include the anti- Ki-67, anti-p53, anti-CD31 and anti-retinoblastoma protein (Rb) marker stains, a DNA ploidy stain and a H&E counterstain.
  • Rb retinoblastoma protein
  • a colorectal cancer panel can therefore include the anti-Ki- 67, anti-p53, anti-CD31 and anti-p21 (ras oncoprotein) marker stains, a DNA ploidy stain and a H&E counterstain.
  • the present invention as herein described has several advantages over prior art methods in which high resolution imaging was combined with very low and inefficient spectral resolution (typically two discrete spectral bands) for co-detection of two stains, because using a spectra collection device as herein described, characterized by high spatial and spectral resolutions enables to co-detect any desired numbers of stains even when the stains employed are very similar spectrally.
  • a clinician can simultaneously detect multiple cytological markers with prognostic/therapeutic significance (e.g.; ER, PR, p53, her-2/neu, Ki-67 and CD31), significant changes in DNA ploidy and in sample staining with conventional histological stains.
  • the clinician can therefore manage and monitor patient therapy with improved accuracy (e.g., select appropriate therapeutic regimen), reduce patient management costs through more accurate diagnosis, and reduce hospital costs through more efficient sampling.
  • Scanning fields of the stained biological sample according to the present invention can be effected manually, semi-automatically or automatically, as well known in the art, using for example slide loading and scanning devices.
  • Pattern recognition and spectrally resolved pattern recognition approaches can be used to enhance analysis and diagnosis.
  • Figure 1 is a block diagram illustrating the main components of a prior art imaging spectrometer disclosed in U.S. Pat application No. 08/392,019 to Cabib et al, filed Feb. 21st, 1995, now U.S. Pat. No.
  • This imaging spectrometer is constructed highly suitable to implement the method of the present invention as it has high spectral (Ca. 4- 14 nm depending on wavelength) and spatial (Ca. 30/M ⁇ m where M is the effective microscope or fore optics magnification) resolutions.
  • the prior art imaging spectrometer of Figure 1 includes: a collection optical system, generally designated 20; a one-dimensional scanner, as indicated by block 22; an optical path difference (OPD) generator or interferometer, as indicated by block 24; a one-dimensional or two-dimensional detector array, as indicated by block 26; and a signal processor and display, as indicated by block 28.
  • a collection optical system generally designated 20
  • a one-dimensional scanner as indicated by block 22
  • OPD optical path difference
  • interferometer as indicated by block 24
  • a one-dimensional or two-dimensional detector array as indicated by block 26
  • signal processor and display as indicated by block 28.
  • a critical element in system 20 is the OPD generator or interferometer 24, which outputs modulated light corresponding to a predetermined set of linear combinations of the spectral intensity of the light emitted from each pixel of the scene to be analyzed.
  • the output of the interferometer is focused onto the detector array 26.
  • all the required optical phase differences are scanned simultaneously for all the pixels of the field of view, in order to obtain all the information required to reconstruct the spectrum.
  • the spectra of all the pixels in the scene are thus collected simultaneously with the imaging information, thereby permitting analysis of the image in a real-time manner.
  • the apparatus according to U.S. Pat. No. 5,539,517 may be practiced in a large variety of configurations.
  • interferometer used * may be combined with other mirrors as described in the relevant Figures of U.S. Pat. No. 5,539,517.
  • alternative types of interferometers may be employed.
  • a moving type interferometer in which the OPD is varied to modulate the light namely, a Fabry-Perot interferometer with scanned thickness
  • a Michelson type interferometer which includes a beamsplitter receiving the beam from an optical collection system and a scanner, and splitting the beam into two paths
  • a Sagnac interferometer optionally combined with other optical means in which interferometer the OPD varies with the angle of incidence of the incoming radiation, such as the four-mirror plus beamsplitter interferometer as further described in the cited U.S. patent (see Figure 14 there).
  • Figure 2 illustrates an imaging spectrometer constructed in accordance with U.S. Pat. No. 5,539,517, utilizing an interferometer in which the OPD varies with the angle of incidence of the incoming radiation.
  • a beam entering the interferometer at a small angle to the optical axis undergoes an OPD which varies substantially linearly with this angle.
  • every pixel has been measured through all the OPD'S, and therefore the spectrum of each pixel of the scene can be reconstructed by Fourier transformation.
  • a beam parallel to the optical axis is compensated, and a beam at an angle (6) to the optical axis undergoes an OPD which is a function of the thickness of the beamsplitter 33, its index of refraction, and the angle 6.
  • the OPD is proportional to 6 for small angles.
  • Equation 2 It follows from Equation 2 that by scanning both positive and negative angles with respect to the central position, one gets a double-sided interferogram for every pixel, which helps eliminate phase errors giving more accurate results in the Fourier transform calculation.
  • the scanning amplitude determines the maximum OPD reached, which is related to the spectral resolution of the measurement.
  • the size of the angular steps determines the OPD step which is, in turn, dictated by the shortest wavelength to which the system is sensitive. In fact, according to the sampling theorem [see, Chamberlain (1979) The principles of interferometric spectroscopy, John Wiley and Sons, pp. 53-55], this OPD step must be smaller than half the shortest wavelength to which the system is sensitive.
  • Another parameter which should be taken into account is the finite size of a detector element in the matrix.
  • the element Through the focusing optics, the element subtends a finite OPD in the interferometer which has the effect of convolving the interferogram with a rectangular function. This brings about, as a consequence, a reduction of system sensitivity at short wavelengths, which drops to zero for wavelengths equal to or below the OPD subtended by the element. For this reason, one must ensure that the modulation transfer function (MTF) condition is satisfied, i.e., that the OPD subtended by a detector element in the interferometer must be smaller than the shortest wavelength at which the instrument is sensitive.
  • MTF modulation transfer function
  • imaging spectrometers constructed in accordance with the invention disclosed in U.S. Pat. No. 5,539,517 do not merely measure the intensity of light coming from every pixel in the field of view, but also measure the spectrum of each pixel in a predefined wavelength range. They also better utilize all the radiation emitted by each pixel in the field of view at any given time, and therefore permit, as explained above, a significant decrease in the frame time and/or a significant increase in the sensitivity of the spectrometer.
  • imaging spectrometers may include various types of interferometers and optical collection and focusing systems, and may therefore be used in a wide variety of applications, including medical * diagnostic and therapy and biological research applications, as well as remote sensing for geological and agricultural investigations, and the like.
  • an imaging spectrometer in accordance with the invention disclosed in U.S. Pat. No. 5,539,517 was developed by Applied Spectral Imaging Ltd., Industrial Park, Migdal Haemek, Israel and is referred herein as SPECTRACUBETM.
  • the SPECTRACUBETM system has the following or better characteristics, listed hereinbelow in Table 3:
  • Sensitivity 20 milliLux (for 100 msec integration time, increases for longer integration times linearly with r )
  • any spectral imager i.e., an instrument that measures and stores in memory for later retrieval and analysis the spectrum of light emitted by every point of an object which is placed in its field of view, including filter (e.g., acousto-optic tunable filters (AOTF) or liquid- crystal tunable filter (LCTF)) and dispersive element (e.g., grating or prism) based spectral imagers, or other spectral data or multi-band light collection devices (e.g., a device in accordance with the disclosure in Speicher R. M., Ballard S. G.
  • filter e.g., acousto-optic tunable filters (AOTF) or liquid- crystal tunable filter (LCTF)
  • dispersive element e.g., grating or prism
  • Figures 3 a- c include an example of an inte ⁇ hase FISH measurement performed with chromosome 1 and chromosome 17 specific DNA probes tagged with the fluorophores Texas-Red and Rhodamine, respectively, whose fluorescence spectra are very similar.
  • the chromosome 1 probe was a midsatellite probe for the subtelomeric region of the chromosome and was tagged with Texas-Red linked to the DNA probe via biotin post hybridization.
  • the chromosome 17 probe was an a satellite probe for the centromeric region of the chromosome and was tagged with Rhodamine, linked to the second DNA probe via digoxigenin post hybridization.
  • Figure 3 a shows the original image, the way it looks to the eye through the microscope.
  • Figure 3b shows the same sample, after being measured and processed by the SPECTRACUBETM system.
  • Figure 3 c shows the fluorescence spectra of the Texas-Red (marked as T) and Rhodamine (marked as R) fluorophores.
  • the confidence level of recognizing the correct number of dots (marked 1-4) and of probe types appearing in the image is not particularly high.
  • the SPECTRACUBETM system taking advantage of the spectrum measured for each pixel, is able both to verify the existence of the dots, to count them exactly, and to discriminate between the different pairs with a high level of confidence, due to the small spectral difference between them.
  • By artificial coloring of Texas-Red and Rhodamine fluorescence as shown in Figure 3c the location of probe specific fluorescence could be determined with high accuracy wherein dots 1 and 2 are of Texas-Red and dots 3 and 4 are of Rhodamine.
  • Figures 4a-b provide an example of FISH measurement after hybridization of nuclear inte ⁇ hase DNA with six different probes.
  • Figure 4a shows the original image as perceived through a microscope.
  • Figure 4b shows the same image following a SPECTRACUBETM system measurement, spectral processing and artificial color display of all the detected pairs.
  • Figure 4c shows the spectra of the six fluorophores after hybridization (marked according to the chromosomes each of which labels: 1, 8, 10, 11, 17 and X), as detected through a triple dichroic filter using the SPECTRACUBETM system.
  • Table 3 Table 3 below and to Chroma Co ⁇ . Cat. No. 61502).
  • Figure 4a shows the original RGB image of the inte ⁇ hasic cell nucleus, that it is difficult to distinguish the colors from one another by eye or even by using a simple RGB color measurement. An experienced observer may, in the best case, detect three different colors of the six.
  • Figure 4b shows the same sample shown in Figure 4a, after processing the spectral data with a classification algorithms, and the resulting dots have been highlighted with artificial colors: orange, cyan, blue, yellow, green, and red, while the background was given a black, artificial color. As observed, it is possible to see all the six pairs of fluorophores and to easily differentiate among the pairs.
  • one pair the one highlighted in blue, can hardly be noticed by eye, or by using a color camera, however, it is detected after applying a background subtraction algorithm on the spectral cube (compare Figures 4a and 4b).
  • the probes used were five a satellite probes for the centromeric regions of chromosomes 8, 10, 11, 17 and X, and a midsatellite probe for the subtelomeric region of chromosome 1.
  • the fluorophores used to label each of the above chromosomes and the DAPI counter stain (background), their emission peak and artificial displayed color classification are summarized in Table 4, below.
  • Figures 5a-c present hybridization results of normal male chromosomes using a combinatorial hybridization approach in accordance with the scheme disclosed in U.S. Pat. application No. 09/025,131, filed February 17, 1998, which is inco ⁇ orated by reference as if fully set forth herein.
  • Figure 5a is an RGB image of the chromosome spread obtained using an RGB algorithm.
  • the RGB algorithm integrates the optical signal over the spectral range (e.g., 400 nm to 760 nm) of the CCD array to provide an RGB image of the chromosomes, in which each pixel is attributed a combination of red, green and blue intensities according to three weighting functions, ⁇ w r ( ⁇ ), w e ( ⁇ ), w 0 ( ⁇ ) ⁇ , which correspond to the tristimulus response functions for red (R), green (G) and blue (B).
  • the simple weighting functions w r , w Q are integrated to generate an RGB image of the chromosomes.
  • Figures 5b is a classification image of the chromosome spread of Figure 5 a.
  • Figure 5 c is the karyotype derived from the chromosome spread of Figure 5b.
  • the classification image is calculated by a classification algorithm wherein each pixel is classified according its spectrum.
  • One of the most important analysis algorithms is the spectral-based classification algorithm that enables multiple different spectra in the image to be identified and highlighted in classification-colors. This allows assignment of a specific classification-color to all human chromosomes based on their spectra.
  • This algorithm assumes that a reference spectrum of each chromosome has been measured and stored in a reference library in the computer.
  • a distinguishing classification-color is assigned to each pixel in the image according to the classification-color assigned to the reference spectrum that is most similar to the spectrum at that given pixel, as defined, for example, by a minimal square error algorithm.
  • a spectral image is a three dimensional array of data, I(x,y, ⁇ ), that combines spectral information with spatial organization of the image.
  • a spectral image is a set of data called a spectral cube, due to its dimensionality, which enables the extraction of features and the evaluation of quantities that are difficult, and in some cases even impossible, to obtain otherwise.
  • One possible type of analysis of a spectral cube is to use spectral and spatial data separately, i.e., to apply spectral algorithms to the spectral data and two-dimensional image processing algorithms to the spatial data.
  • a spectral algorithm consider an algorithm computing the similarity between a reference spectrum and the spectra of all pixels (i.e., similarity mapping) resulting in a gray (or other color) scale image (i.e., a similarity map) in which the intensity at each pixel is proportional to the degree of 'similarity'.
  • This gray scale image can then be further analyzed using image processing and computer vision techniques (e.g., image enhancement, pattern recognition, etc.) to extract the desired features and parameters.
  • similarity mapping involves computing the integral of the absolute value of the difference between the spectrum of each pixel of the spectral image with respect to a reference spectrum (either previously memorized in a library, or belonging to a pixel of the same or other spectral image), and displaying a gray level or pseudocolor (black and white or color) image, in which the bright pixels correspond to a small spectral difference, and dark pixels correspond to a large spectral difference, or vice versa.
  • classification mapping perform the same calculation as described for similarity mapping, yet takes several spectra as reference spectra, and paints each pixel of the displayed image with a different predetermined pseudocolor, according to its classification as being most similar to one of the several reference spectra.
  • spectral image algorithms based on non- separable operations; i.e., algorithms that include both local spectral information and spatial correlation between adjacent pixels (one of these algorithms is, as will be seen below, a principal component analysis).
  • a spectral image is a sequence of images representing the intensity of the same two- dimensional plane (i.e., the sample) at different wavelengths.
  • the two most intuitive ways to view a spectral cube of data is to either view the image plane (spatial data) or the intensity of one pixel or a set of pixels as function of wavelength in a three-dimensional mountain- valley display.
  • the image plane can be used for displaying either the intensity measured at any single wavelength or the gray scale image that results after applying a spectral analysis algorithm, over a desired spectral region, at every image pixel.
  • the spectral axis can, in general, be used to present the resultant spectrum of some spatial operation performed in the vicinity of any desired pixel (e.g., averaging the spectrum). *
  • the spectral image can be displayed as a gray scale image, similar to the image that might be obtained from a simple monochrome camera, or as a multicolor image utilizing one or several artificial colors to highlight and map important features. Since such a camera simply integrates the optical signal over the spectral range (e.g., 400 nm to 760 nm) of the CCD array, the 'equivalent' monochrome CCD camera image can be computed from the 3D spectral image data base by integrating along the spectral axis, as follows: gray_scale(x,y) (2)
  • w( ⁇ ) is a general weighting response function that provides maximum flexibility in computing a variety of gray scale images, all based on the integration of an appropriately weighted spectral image over some spectral range.
  • equation 2 by evaluating equation 2 with three different weighting functions, ⁇ w r ( ⁇ ), w p ( ⁇ ), w 0 ( ⁇ ) ⁇ , corresponding to the tristimulus response functions for red (R), green (G) and blue (B), respectively, it is possible to display a conventional RGB color image. It is also possible to display meaningful non-conventional (pseudo) color images.
  • Figure 6 presents an example of the power of this simple algorithm.
  • Point operations are defined as those that are performed on single pixels, (i.e., do not involve more than one pixel at a time).
  • a point operation can be one that maps the intensity of each pixel (intensity function) into another intensity according to a predetermined transformation function.
  • a particular case of this type of transformation is the multiplication of the intensity of each pixel by a constant. Additional examples include similarity and classification mapping as described hereinabove.
  • each pixel has its own intensity function (spectrum), i.e., an n- dimensional vector V ⁇ ( ⁇ ); Ae[ ⁇ , A n ].
  • a point operation applied to a spectral image can be defined as one that maps the spectrum of each pixel into a scalar (i.e., an intensity value) according to a transformation function: * Building a gray scale image according to Equation 3 is an example of this type of point operation.
  • a point operation maps the spectrum (vector) of each pixel into another vector according to a transformation function:
  • V2 l g(V ⁇ ( ⁇ )); /e[l, N], ⁇ e[ ⁇ ⁇ n ] (4), where N ⁇ n.
  • N n
  • optical density analysis Optical density is employed to highlight and graphically represent regions of an object being studied spectroscopically with higher dynamic range than the transmission spectrum.
  • the optical density is related to transmission by a logarithmic operation and is therefore always a positive function.
  • Equation 5 is calculated for every pixel for every wavelength where I 0 ( ⁇ ) is selected from (1) a pixel in the same spectral cube for which OD is calculated; (2) a corresponding pixel in a second cube; and (3) a spectrum from a library.
  • optical density does not depend on either the spectral response of the measuring system or the non-uniformity of the CCD detector. This algorithm is useful to map the relative concentration, and in some cases the absolute concentration of absorbers in a sample, when their abso ⁇ tion coefficients and the sample thickness are known.
  • Additional examples include various linear combination analyses, such as, but not limited to, (i) applying a given spectrum to the spectrum of each of the pixels in a spectral image by an arithmetical function such as addition, subtraction, multiplication division and combinations thereof to yield a new spectral cube, in which the resulting spectrum of each pixel is the sum, difference, product ratio or combination between each spectrum of the first cube and the selected spectrum; and (ii) applying a given scalar to the spectra of each of the pixels of the spectral image by an arithmetical function as described above.
  • Such linear combinations may be used, for example, for background subtraction in which a spectrum of a pixel located in the background region is subtracted from the spectrum of each of the pixels; and for a calibration procedure in which a spectrum measured prior to sample analysis is used to divide the spectrum of each of the pixels in the spectral image.
  • Another example includes a ratio image computation and display as a gray level image.
  • This algorithm computes the ratio between the intensities at two different wavelengths for every pixel of the spectral image and paints each of the pixels in a lighter or darker artificial color accordingly. For example, it paints the pixel bright for high ratio, and dark for low ratio (or the opposite), to display distributions of spectrally sensitive materials.
  • Spatial-spectral combined operations In all of the spectral image analysis methods mentioned above, algorithms are applied to the spectral data. The importance of displaying the spectrally processed data as an image is mostly qualitative, providing the user with a useful image. It is also possible, however, depending on the application, to use the available imaging data in even more meaningful ways by applying algorithms that utilize the spatial-spectral correlation that is inherent in a spectral image.
  • a sample contains k cell types stained with k different stains (the term 'cell' here is used both for a biological cell, and also as 'a region in the field of view of the instrument').
  • Each stain has a distinct spectrum and binds to only one of the k cell types. It is important to find the average intensity per cell for each one of the k cell types.
  • the following procedure can be used: (i) classify each pixel in the image as belonging to one of k+ ⁇ classes (k cell types plus a background) according to its spectrum; (2) segment the image into the various cell types and count the number of cells from each type; and (3) sum the spectral energy contributed by each class, and divide it by the total number of cells from the corresponding class.
  • the relevant spectral data takes the form of characteristic cell spectra (i.e., spectral "signatures"), while the spatial data consists of data about various types of cells (i.e., cell blobs) many of which appear similar to the eye.
  • cells can be differentiated by their characteristic spectral signature.
  • a suitable point operation will be performed to » generate a synthetic image in which each pixel is assigned one of k+I values.
  • Steps 2 and 3 above image segmentation and calculation of average intensity are now straight-forward using standard computer vision operations on the synthetic image created in accordance with the algorithm described in equations 6 and 7.
  • Another approach is to express the measured spectrum s x v ( ⁇ ) at each pixel as a linear combination of the k known fluorescence spectra sf( ⁇
  • Arithmetic operations may similarly be applied to two or more spectral cubes and/or spectra of given pixels or from a library. For example consider applying an arithmetic operations between corresponding wavelengths of corresponding pairs of pixels belonging to a first spectral cube of data and a second spectral cube of data to obtain a resulting third spectral cube of data for the pu ⁇ ose of, for example, averaging two spectral cubes of data, time changes follow-up, spectral normalization, etc.
  • objects e.g., cells
  • objects present in a spectral image differ from one another in chemical constituents and/or structure to some degree, especially when stained.
  • Decorrelation statistical analysis is directed at extracting decorrelated data out of a greater amount of data, and average over the correlated portions thereof.
  • PCA principal component analysis
  • canonical variable analysis and singular value decomposition, etc. of these methods PCA is perhaps the more common one, and is used according to the present invention for decorrelation of spectral data, as this term is defined above.
  • PCA principal component analysis
  • Staining protocols Staining was performed following standard Vantana or DAKO automated immunostaining protocols. Measurement: A microscope (Nikon Eclipse E-800) connected to a
  • SPECTRACUBETM system was adjusted for Koehler illumination with transmitted light lamp power to maximum voltage (12 V) for most stable illumination.
  • Neutral density and color filters (FG3 and anti reflection filters) were introduced in the optical path to adjust intensity and spectral color balance.
  • SPECTRACUBE acquisition parameters were 300 frames, 512 virtual frames, wavelength range 440 to 760 nm, 176 ms/frame.
  • pure dye i.e., hematoxylin, DAB, AEC and Fast Red (each stain alone) spectral cubes were acquired and representative spectra therefrom were used to form pure dyes spectral library. Then spectral cubes of each of the samples were acquired. And the
  • Staining protocols Staining was essentially as described in G. Papanicolaou (1942) A new procedure for staining vaginal smears. Science 95: 438-439, which is inco ⁇ orated by reference as if fully set forth herein. Measurement: A microscope (Nikon Eclipse E-800) connected to a SPECTRACUBETM system was adjusted for Koehler illumination with transmitted light lamp power to maximum voltage (12 V) for most stable illumination. Neutral density and color filters (FG3 and anti reflection filters) were introduced in the optical path to adjust intensity and spectral color balance. SPECTRACUBE acquisition parameters were 300 frames, 512 virtual frames, wavelength range 440 to 760 nm, 176 ms/frame.
  • pure dye i.e., Harris hematoxylin, eosin, orange G, light green SF and Bismark brown Y (each stain alone) spectral cubes were acquired and representative spectra therefrom were used to form pure dyes spectral library.
  • spectral cubes of the cervix cancer sample were acquired.
  • SPECTRACUBETM algorithms Spy View, as described in the SPECTRACUBETM manual were employed to obtain RGB images, gray- level images of each spectral component, threshold binarized images and composite classification images thereof.
  • each of the stains has a characterizing spectrum, which, as further exemplified hereinbelow, enables simultaneous co-detection of spectral components associated with each of which.
  • Figures 8-11 show such experiments. All images were measured using the SPECTRACUBETM system and its various measurement and analysis algorithms, as described hereinabove.
  • Figures 8a-e show images of a breast cancer sample which was previously and independently determined to be ER(+)/PR(+). The sample was co-stained with the histological stain hematoxylin and with the immunohistochemical stain anti-ER-DAB.
  • Figure 8a presents an RGB image of the sample, using the RGB algorithm described hereinabove with respect to Figure 6.
  • Figures 8b-d present binarized images of hematoxylin, DAB and AEC spectral components, respectively. These binarized images were obtained by thresholding gray-scale images showing the intensity of each component in each pixel.
  • Figure 8d serves as a simulation for a PR(-) tumor.
  • Figure 8e presents a classification overlay image, wherein the above # spectral components are highlighted in red, green and blue, respectively. Please note that as expected no AEC spectral components are detectable, yet, spectral components of both hematoxylin and DAB are readily detectable.
  • the classification image assists the pathologist in evaluating the presence/absence/aggression level/diagnosis and/or prognosis of cancer cells or tissue examined.
  • Figures 9a-e show images of a breast cancer sample which was previously and independently determined to be ER(+)/PR(+). This sample was also co-stained with the histological stain hematoxylin and with the immunohistochemical stain anti-PR-AEC.
  • Figure 9a presents an RGB image of the sample.
  • Figures 9b-d present binarized images of hematoxylin, DAB and AEC spectral components, respectively, whereas Figure 9e presents a classification overlay image, wherein the above spectral components are highlighted in red, green and blue, respectively. Please note that as expected no DAB spectral components are detectable, yet, spectral components of both hematoxylin and AEC are readily detectable.
  • Figures 1 Oa-e show images of a breast cancer sample which, like the former sample, was previously and independently determined to be ER(+)/PR(+). However, in this case the sample was co-stained with the histological stain hematoxylin and with the immunohistochemical stain anti- PR-Fast Red, which replaced the AEC previously used.
  • Figure 10a presents an RGB image of the sample.
  • Figures lOb-d present binarized images of hematoxylin, DAB and Fast Red spectral components, respectively.
  • Figure lOe presents a classification overlay image, wherein the above spectral components are highlighted in red, green and blue, respectively. Please note that as expected no DAB spectral components, except for the artifact crystals formed especially in the lower part of the field, are detectable in this sample, yet, spectral components of both hematoxylin and Fast Red are readily detectable.
  • Figures 11 a-e show images of a breast cancer sample which was previously and independently determined to be ER(+)/PR(+). The sample was co-stained with the histological stain hematoxylin and with the immunohistochemical stains anti-ER-DAB and anti-PR-Fast Red.
  • Figure 11a presents an RGB image of the sample.
  • Figures 1 lb-d present binarized images of hematoxylin, DAB and Fast Red spectral components, respectively.
  • Figure l ie presents a classification overlay image, wherein the above spectral components are highlighted in red, green and blue, respectively. Regions co-stained with anti-ER-DAB and anti-PR-Fast Red are shown in yellow. Please note that as expected hematoxylin, DAB, as well as Fast Red spectral components are readily detectable.
  • Figures 12a-f show images of a breast cancer sample which was previously and independently determined to be ER(+)/PR(+). The sample was co-stained with the histological stains hematoxylin and eosin and with the immunohistochemical stains anti-ER-DAB and anti-PR-Fast Red.
  • Figure 12a presents an RGB image of the sample.
  • Figures 12b-e present binarized images of hematoxylin, eosin, DAB and Fast Red spectral components, respectively.
  • Figure 12f presents a classification overlay image, wherein the above spectral components are highlighted in blue, pu ⁇ le green and red, respectively, showing the ability of the SPECTRACUBETM system to resolve four different spectral components.
  • this example shows a stained breast cancer tissue section simultaneously showing staining to nuclei (hematoxylin), cytoplasm (eosin), estrogen receptor (ER, detected with anti-ER/HRP/DAB), and progesterone receptor (PR, detected with anti-PR/HRP/DAB).
  • nuclei hematoxylin
  • cytoplasm eosin
  • ER estrogen receptor
  • PR progesterone receptor
  • Figure 13 presents non-normalized spectra of five histological stains (Harris hematoxylin, eosin, orange G, light green SF and Bismark brown Y) measured using the SPECTRACUBETM system from five single stain stained cervix cancer samples. Peak wavelengths are indicated on the right.
  • Figures 14a-g show images of a cervix cancer sample co-stained with the histological stains Harris hematoxylin, eosin, orange G, light green SF and Bismark brown Y, which collectively form what is known in the art as Papanicolaou (Pap) stain.
  • Figure 14a presents an RGB image of the sample.
  • Figures 14b-f present binarized images of Harris hematoxylin, eosin, orange G, light green SF and Bismark brown Y spectral components, respectively.
  • Figure 14e presents a classification overlay image, wherein the above spectral components are highlighted in blue, pink, orange, green and gray, respectively, to form by combinations thereof the colorful classification overlay image shown.
  • each of the stains employed has a unique staining pattern which could be resolved only due to the high spectral and spatial resolutions of the SPECTRACUBETM system employed.
  • the data presented herein demonstrates the usefulness of a device having high spectral and spatial resolutions in the analysis of biological samples co-stained with multiple stains.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Immunology (AREA)
  • Engineering & Computer Science (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Hematology (AREA)
  • Chemical & Material Sciences (AREA)
  • Urology & Nephrology (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Cell Biology (AREA)
  • Biotechnology (AREA)
  • Pathology (AREA)
  • Microbiology (AREA)
  • Tropical Medicine & Parasitology (AREA)
  • General Health & Medical Sciences (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Zoology (AREA)
  • Virology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Investigating Or Analysing Materials By The Use Of Chemical Reactions (AREA)

Abstract

Ce procédé d'analyse in situ d'un échantillon biologique comprend les étapes consistant: (a) à colorer l'échantillon biologique à l'aide de N colorants dont un premier est choisi dans le groupe constitué par un premier colorant immunohistochimique, un premier colorant histologique et un premier colorant de la ploïdie de l'ADN, et un second est choisi dans le groupe constitué par un second colorant immunohistochimique, un second colorant histologique et un second colorant de la ploïdie de l'ADN, à condition que N représente un nombre entier supérieur à trois et qu'en outre: (i) si le premier colorant est le premier colorant immunohistochimique, alors le second colorant est soit le second colorant histologique, soit le second colorant de la ploïdie de l'ADN; (ii) si le premier colorant est le premier colorant histologique, alors le second colorant est soit le second colorant immunohistochimique, soit le second colorant de la ploïdie de l'ADN, tandis que (iii) si le premier colorant est le premier colorant de la ploïdie de l'ADN, alors le second colorant est soit le second colorant immunohistochimique, soit le second colorant histologique; et (b) à utiliser un dispositif de collecte de données spectrales aux fins de collecte de telles données à partir de l'échantillon biologique, ce dispositif et les N colorants étant choisis de façon qu'un composant spectral associé à chacun des N colorants puisse être collecté.
PCT/US1999/016629 1998-07-27 1999-07-23 Procede d'analyse in situ de cellules WO2000006774A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
IL14085199A IL140851A0 (en) 1998-07-27 1999-07-23 In situ method of analyzing cells
AU51238/99A AU5123899A (en) 1998-07-27 1999-07-23 (in situ) method of analyzing cells
EP99935853A EP1100966A4 (fr) 1998-07-27 1999-07-23 Procede d'analyse in situ de cellules
JP2000562556A JP2002521682A (ja) 1998-07-27 1999-07-23 細胞のインサイチュ分析方法

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US09/122,704 1998-07-27
US09/122,704 US6007996A (en) 1995-12-12 1998-07-27 In situ method of analyzing cells

Publications (1)

Publication Number Publication Date
WO2000006774A1 true WO2000006774A1 (fr) 2000-02-10

Family

ID=22404245

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US1999/016629 WO2000006774A1 (fr) 1998-07-27 1999-07-23 Procede d'analyse in situ de cellules

Country Status (5)

Country Link
EP (1) EP1100966A4 (fr)
JP (1) JP2002521682A (fr)
AU (1) AU5123899A (fr)
IL (1) IL140851A0 (fr)
WO (1) WO2000006774A1 (fr)

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6615141B1 (en) 1999-05-14 2003-09-02 Cytokinetics, Inc. Database system for predictive cellular bioinformatics
US6651008B1 (en) 1999-05-14 2003-11-18 Cytokinetics, Inc. Database system including computer code for predictive cellular bioinformatics
US6956961B2 (en) 2001-02-20 2005-10-18 Cytokinetics, Inc. Extracting shape information contained in cell images
EP1586897A1 (fr) * 2002-11-07 2005-10-19 Fujitsu Limited Procede d'assistance a l'analyse d'images, programme d'assistance a l'analyse d'images et dispositif d'assistance a l'analyse d'images
US7151847B2 (en) 2001-02-20 2006-12-19 Cytokinetics, Inc. Image analysis of the golgi complex
US7218764B2 (en) 2000-12-04 2007-05-15 Cytokinetics, Inc. Ploidy classification method
US7235353B2 (en) 2003-07-18 2007-06-26 Cytokinetics, Inc. Predicting hepatotoxicity using cell based assays
US7246012B2 (en) 2003-07-18 2007-07-17 Cytokinetics, Inc. Characterizing biological stimuli by response curves
US7323318B2 (en) 2004-07-15 2008-01-29 Cytokinetics, Inc. Assay for distinguishing live and dead cells
US7657076B2 (en) 2001-02-20 2010-02-02 Cytokinetics, Inc. Characterizing biological stimuli by response curves
US7817840B2 (en) 2003-07-18 2010-10-19 Cytokinetics, Inc. Predicting hepatotoxicity using cell based assays
CN103384825A (zh) * 2010-12-23 2013-11-06 霍夫曼-拉罗奇有限公司 通过二价结合剂检测经翻译后修饰的多肽
US9418414B2 (en) 2012-05-30 2016-08-16 Panasonic Intellectual Property Management Co., Ltd. Image measurement apparatus, image measurement method and image measurement system
US9442065B2 (en) 2014-09-29 2016-09-13 Zyomed Corp. Systems and methods for synthesis of zyotons for use in collision computing for noninvasive blood glucose and other measurements
US9554738B1 (en) 2016-03-30 2017-01-31 Zyomed Corp. Spectroscopic tomography systems and methods for noninvasive detection and measurement of analytes using collision computing
EP3907497A1 (fr) * 2020-05-08 2021-11-10 Leica Microsystems CMS GmbH Appareil et procédé d'affichage et/ou d'impression d'images d'un spécimen comprenant un fluorophore
WO2022252103A1 (fr) * 2021-06-01 2022-12-08 张慧敏 Utilisation de double coloration à la fluorescéine sodique et au bleu de méthylène dans l'imagerie de cellules vivantes

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005331394A (ja) * 2004-05-20 2005-12-02 Olympus Corp 画像処理装置
JP5490568B2 (ja) 2010-02-26 2014-05-14 オリンパス株式会社 顕微鏡システム、標本観察方法およびプログラム
JP5451552B2 (ja) 2010-08-09 2014-03-26 オリンパス株式会社 顕微鏡システム、標本観察方法およびプログラム

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4888278A (en) * 1985-10-22 1989-12-19 University Of Massachusetts Medical Center In-situ hybridization to detect nucleic acid sequences in morphologically intact cells

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4998284A (en) * 1987-11-17 1991-03-05 Cell Analysis Systems, Inc. Dual color camera microscope and methodology for cell staining and analysis
US5719024A (en) * 1993-08-18 1998-02-17 Applied Spectral Imaging Ltd. Method for chromosome classification by decorrelation statistical analysis and hardware therefore

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4888278A (en) * 1985-10-22 1989-12-19 University Of Massachusetts Medical Center In-situ hybridization to detect nucleic acid sequences in morphologically intact cells

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
EBERWINE J. ET AL: "Complementary DNA synthesis in situ: Methods and applications", METHODS IN ENZYMOLOGY, vol. 216, 1992, pages 80 - 100, XP002922050 *
NEDERLOF P.M. ET AL: "Three-color fluorescence in situ hybridization for the simultaneous detection of multiple nucleic acid sequences", CYTOMETRY, vol. 10, 1989, pages 20 - 27, XP002922051 *
RYE H.S. ET AL: "Fluorometric assay using dimeric dyes for double- and single-stranded DNA and RNA picogram sensitivity", ANALYTICAL BIOCHEMISTRY, vol. 208, 1993, pages 144 - 150, XP002922052 *
See also references of EP1100966A4 *
TASK B.J.: "Fluorescence in situ hybridization", TRENDS IN GENETICS, vol. 7, no. 5, 1991, pages 149 - 154, XP002922047 *
VAN DEN BERG H. ET AL: "Detection of Y chromosome by in situ hybridization in combination with membrane antigens b two-color immunufluorescence", LABORATORY INVESTIGATIONS, vol. 64, no. 5, 1991, pages 623 - 628, XP002922014 *
WEIER H-U. G. ET AL: "Two-color hybridization with high complexity chromosome-specific probes and a degenerate alpha satellite probe DNA allows unambiguous discrimination between symmetrical and asymmetrical translocations", CHROMOSOMA, vol. 100, 1991, pages 371 - 376, XP002922048 *

Cited By (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6631331B1 (en) 1999-05-14 2003-10-07 Cytokinetics, Inc. Database system for predictive cellular bioinformatics
US6651008B1 (en) 1999-05-14 2003-11-18 Cytokinetics, Inc. Database system including computer code for predictive cellular bioinformatics
US6738716B1 (en) 1999-05-14 2004-05-18 Cytokinetics, Inc. Database system for predictive cellular bioinformatics
US6743576B1 (en) 1999-05-14 2004-06-01 Cytokinetics, Inc. Database system for predictive cellular bioinformatics
US6615141B1 (en) 1999-05-14 2003-09-02 Cytokinetics, Inc. Database system for predictive cellular bioinformatics
US7218764B2 (en) 2000-12-04 2007-05-15 Cytokinetics, Inc. Ploidy classification method
US7657076B2 (en) 2001-02-20 2010-02-02 Cytokinetics, Inc. Characterizing biological stimuli by response curves
US7269278B2 (en) 2001-02-20 2007-09-11 Cytokinetics, Inc. Extracting shape information contained in cell images
US6956961B2 (en) 2001-02-20 2005-10-18 Cytokinetics, Inc. Extracting shape information contained in cell images
US7151847B2 (en) 2001-02-20 2006-12-19 Cytokinetics, Inc. Image analysis of the golgi complex
EP1586897A1 (fr) * 2002-11-07 2005-10-19 Fujitsu Limited Procede d'assistance a l'analyse d'images, programme d'assistance a l'analyse d'images et dispositif d'assistance a l'analyse d'images
EP1586897A4 (fr) * 2002-11-07 2007-08-15 Fujitsu Ltd Procede d'assistance a l'analyse d'images, programme d'assistance a l'analyse d'images et dispositif d'assistance a l'analyse d'images
US7235353B2 (en) 2003-07-18 2007-06-26 Cytokinetics, Inc. Predicting hepatotoxicity using cell based assays
US7246012B2 (en) 2003-07-18 2007-07-17 Cytokinetics, Inc. Characterizing biological stimuli by response curves
US7817840B2 (en) 2003-07-18 2010-10-19 Cytokinetics, Inc. Predicting hepatotoxicity using cell based assays
US7323318B2 (en) 2004-07-15 2008-01-29 Cytokinetics, Inc. Assay for distinguishing live and dead cells
CN103384825B (zh) * 2010-12-23 2017-05-24 霍夫曼-拉罗奇有限公司 通过二价结合剂检测经翻译后修饰的多肽
CN103384825A (zh) * 2010-12-23 2013-11-06 霍夫曼-拉罗奇有限公司 通过二价结合剂检测经翻译后修饰的多肽
US9418414B2 (en) 2012-05-30 2016-08-16 Panasonic Intellectual Property Management Co., Ltd. Image measurement apparatus, image measurement method and image measurement system
US9448164B2 (en) 2014-09-29 2016-09-20 Zyomed Corp. Systems and methods for noninvasive blood glucose and other analyte detection and measurement using collision computing
US9448165B2 (en) 2014-09-29 2016-09-20 Zyomed Corp. Systems and methods for control of illumination or radiation collection for blood glucose and other analyte detection and measurement using collision computing
US9453794B2 (en) 2014-09-29 2016-09-27 Zyomed Corp. Systems and methods for blood glucose and other analyte detection and measurement using collision computing
US9459202B2 (en) 2014-09-29 2016-10-04 Zyomed Corp. Systems and methods for collision computing for detection and noninvasive measurement of blood glucose and other substances and events
US9459203B2 (en) 2014-09-29 2016-10-04 Zyomed, Corp. Systems and methods for generating and using projector curve sets for universal calibration for noninvasive blood glucose and other measurements
US9459201B2 (en) 2014-09-29 2016-10-04 Zyomed Corp. Systems and methods for noninvasive blood glucose and other analyte detection and measurement using collision computing
US9610018B2 (en) 2014-09-29 2017-04-04 Zyomed Corp. Systems and methods for measurement of heart rate and other heart-related characteristics from photoplethysmographic (PPG) signals using collision computing
US9442065B2 (en) 2014-09-29 2016-09-13 Zyomed Corp. Systems and methods for synthesis of zyotons for use in collision computing for noninvasive blood glucose and other measurements
US9554738B1 (en) 2016-03-30 2017-01-31 Zyomed Corp. Spectroscopic tomography systems and methods for noninvasive detection and measurement of analytes using collision computing
EP3907497A1 (fr) * 2020-05-08 2021-11-10 Leica Microsystems CMS GmbH Appareil et procédé d'affichage et/ou d'impression d'images d'un spécimen comprenant un fluorophore
WO2022252103A1 (fr) * 2021-06-01 2022-12-08 张慧敏 Utilisation de double coloration à la fluorescéine sodique et au bleu de méthylène dans l'imagerie de cellules vivantes

Also Published As

Publication number Publication date
JP2002521682A (ja) 2002-07-16
EP1100966A4 (fr) 2006-03-22
IL140851A0 (en) 2002-02-10
AU5123899A (en) 2000-02-21
EP1100966A1 (fr) 2001-05-23

Similar Documents

Publication Publication Date Title
US6007996A (en) In situ method of analyzing cells
US6165734A (en) In-situ method of analyzing cells
WO2000006774A1 (fr) Procede d'analyse in situ de cellules
US6690817B1 (en) Spectral bio-imaging data for cell classification using internal reference
EP0832417B1 (fr) Procede de detection simultanee de multiples fluorophores en vue de l'hybridation in situ et de la coloration de chromosomes
US5991028A (en) Spectral bio-imaging methods for cell classification
JP2002521682A5 (fr)
EP0896631B1 (fr) Procede de detection simultanee de fluophores multiples pour l'hybridation in situ
US6294331B1 (en) Methods for assessing genetic and phenotypic markers by simultaneous multicolor visualization of chromogenic dyes using brightfield microscopy and spectral imaging
US20020106685A1 (en) Method for specifically detecting tumor cells and their precursors in uterine cervical smears by simultaneously measuring at least 2 different molecular markers
US8135187B2 (en) Method and apparatus for removing tissue autofluorescence
EP3377865A1 (fr) Systèmes et procédés de dosage immunologique basés sur raman
CN116868054A (zh) 包括用一个或多个可检测部分标记的一种或多种生物标志物的染色生物学样本
Macville et al. Spectral imaging of multi‐color chromogenic dyes in pathological specimens
US20120264110A1 (en) Automated pap screening using a plurality of biomarkers and multi-spectral imaging
Katzilakis et al. Spectral characteristics of acute lymphoblastic leukemia in childhood
WO2013179279A2 (fr) Procédé et dispositif pour analyser un échantillon biologique
Masin et al. Cytophotometric data on fluorochromed cervical cells
EP2038651A2 (fr) Procédé de détection et/ou de diagnostic in vitro d'un cancer au moyen de la cytométrie d'image d'adn basée sur le rayonnement uv
JP2024516380A (ja) 形態学的マーカー染色
Rothmann et al. Spectral imaging of histological and cytological specimens

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AE AL AM AT AU AZ BA BB BG BR BY CA CH CN CU CZ DE DK EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MD MG MK MN MW MX NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT UA UG US UZ VN YU ZA ZW

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): GH GM KE LS MW SD SL SZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE BF BJ CF CG CI CM GA GN GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
DFPE Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101)
WWE Wipo information: entry into national phase

Ref document number: 140851

Country of ref document: IL

WWE Wipo information: entry into national phase

Ref document number: 1999935853

Country of ref document: EP

ENP Entry into the national phase

Ref country code: JP

Ref document number: 2000 562556

Kind code of ref document: A

Format of ref document f/p: F

WWP Wipo information: published in national office

Ref document number: 1999935853

Country of ref document: EP

REG Reference to national code

Ref country code: DE

Ref legal event code: 8642