Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
  • G01N15/10—Investigating individual particles
  • G01N15/14—Optical investigation techniques, e.g. flow cytometry
  • G01N15/1468—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle
  • G01N15/147—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle the analysis being performed on a sample stream
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
  • G01N15/10—Investigating individual particles
  • G01N15/14—Optical investigation techniques, e.g. flow cytometry
  • G01N15/1429—Signal processing
  • G01N15/1433—Signal processing using image recognition
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/60—Type of objects
  • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
  • G01N15/10—Investigating individual particles
  • G01N15/14—Optical investigation techniques, e.g. flow cytometry
  • G01N2015/1497—Particle shape

Definitions

• Cellular hematopathologies have been traditionally identified and studied by a variety of slide based techniques that include morphological analysis of May- Grunwald/Giemsa or Wright/Giemsa stained blood films and cytoenzymology. Additionally, other techniques, such as cell population analysis by flow cytometry, and molecular methods, such as polymerase chain reaction (PCR) or in situ hybridization to determine gene expression, gene mutations, chromosomal translocations and duplications, have added to the understanding of these pathologies.
• PCR polymerase chain reaction
• the conventional hematology clinical laboratory includes technologies to rapidly and automatically analyze large numbers of samples of peripheral blood, with minimal human intervention.
• Companies such as Abbott Laboratories (Abbott Park, Illinois), Beckman Coulter Inc. (Fullerton, CA), and TOA Corporation (Kobe, Japan) continue to advance these technologies with regard to throughput levels, the degree of accuracy of the analysis, as well as moderately increasing the information content gathered in each sample run.
• traditional slide based methodologies are largely used to determine the probable cause of the abnormality.
• Diagnostic criteria in hematology are based on the morphological identification of abnormalities in cell numbers, size, shape and staining patterns. Although these have been supplemented over the past decades with cell population analysis, by staining with monoclonal antibodies to various cell surface determinants and acquiring data via flow cytometry, the most important element in the diagnostic evaluation is the visual inspection of the peripheral blood film, bone marrow and lymph node biopsy using a microscope, which enables a subjective categorization of putative abnormalities. The manual evaluation of tissue and blood films from patients is tedious, time consuming, and subject to significant intra-laboratory and intra-observer variability.
• Chronic lymphocytic leukemia is a type of cancer in which the bone marrow, produces an excess of lymphocytes (a type of white blood cell) due to a malignant transformation event (e.g., chromosomal translocation).
• CLL is the most frequent type of leukemia in the Western world.
• stem cells Immature cells
• the numbers and types of these blood cells are tightly regulated.
• lymphocytes there is a chronic pathological overproduction of a type of white blood cell called lymphocytes.
• lymphocytes There are three types of lymphocytes: (1) B lymphocytes that make antibodies to help fight infection; (2) T lymphocytes that help B lymphocytes make antibodies to fight infection; and, (3) killer cells that attack cancer cells and viruses.
• CLL is a disease involving an increase in B lymphocyte cell numbers in the peripheral blood, usually reflective of a clonal expansion of a malignantly transformed CD5+ B lymphocyte cell.
• ZAP7O expression a tyrosine kinase required for T lymphocyte cell signaling
• CD38 expression increased CD38 expression
• Ig Vh genes un-mutated Ig Vh genes
• chromosomal abnormalities a tyrosine kinase required for T lymphocyte cell signaling
• routine assessment of these factors has not evolved to a standard clinical practice, due to technical challenges with data standardization and interpretation.
• Morphological evaluation remains the "gold standard" in the assessment of hematopathologies, and patients with CLL present with morphological heterogeneity. Attempts to correlate a particular morphological profile with clinical prognosis have been made, but to date, no association has been widely accepted, and the morphologic sub-classification of CLL and its correlation with clinical prognosis remains to be explored.
• aspects of the concepts disclosed herein relate to the collection of multispectral images from a population of cells, and the analysis of the collected images to measure at least one characteristic of the population, using photometric and/or morphometric markers identifiable in the collection of images, where the marker is associated with a disease condition.
• the term marker is intended to refer to an optical or spatial characteristic of a cell (or a group of cells) that is determined using one or more images of that cell (or that group of cells), hi an exemplary application, the cells are obtained from bodily fluids and cellular compartments, and in a particularly preferred implementation, from blood, most preferably where the cellular compartments are bone marrow and lymph nodes, hi a further particularly preferred implementation, both photometric and morphometric markers are used in the analysis.
• the plurality of images for each individual object are collected simultaneously.
• Exemplary steps that can be used to analyze biological cells in accord with an aspect of the concepts disclosed herein includes collecting image data from a population of cells, and identifying one or more subpopulations of cells from the image data, hi one implementation, a subpopulation corresponding to cells exhibiting abnormalities associated with a disease condition is identified. Such subpopulations can be identified based on empirical evidence indicating that one or more photometric and/or morphometric features are typically associated with the cellular abnormality associated with disease condition. For example, photometric and/or morphometric data from the collected images are analyzed. Such data can relate to one or more features of the cells.
• the term feature is intended to refer to a particular structure, region, characteristic, property, or portion of cell that can be readily discerned from one or more images of the cell.
• the photometric and/or morphometric data from the collected images are analyzed to enable at least one characteristic of a selected feature to be measured. Characteristics that have been empirically associated with the cellular abnormalities present during a particular disease condition can be detected in the data to determine whether a particular disease condition is present in the population of cells originally imaged. hi yet another implementation, a disease condition may be detected even when the cells themselves do not exhibit any abnormalities that can be identified by photometric and/or morphometric parameters.
• a sample will include a plurality of different subpopulations, each of which is identified by its normal characteristic morphometric and photometric markers.
• the ratio of the subpopulations relative to one another will vary within a determinable range across different patients.
• the disease condition can alter the ratio of subpopulations, such that a change in the ratio beyond a normal range can indicate the presence of a disease condition.
• CLL the disease condition discussed in the Background above
• the ratio of lymphocytes to other types of blood cells can be determined by analyzing image data of the entire population of blood cells to classify the images according to blood cell type.
• the ratio of lymphocytes to other types of blood cells will be significantly different than the ratio identified in a patient not afflicted with CLL.
• a disease condition can be detected by analyzing a population of cells to identify subpopulations present in the population, and by determining changes in the ratios of the subpopulations that suggest the presence of a disease condition.
• a disease condition may be detected by the presence of an uncharacteristic cell type.
• a sample will include a plurality of different subpopulations, each of which is identified by its characteristic morphometric and photometric markers. Where a disease condition is not present, only the expected subpopulations will be evident within the sample, though they vary within a determinable range across different patients. Where a disease condition is present, an entirely atypical cell type may be evident in the sample. For example, metastatic cancer of the breast may be evidenced by the presence of distinctive epithelial cells at some level in the blood.
• a disease condition can be detected by analyzing a population of cells to identify subpopulations present in the population, and determining the prevalence of atypical subpopulations that suggest the presence of a disease condition.
• the disease condition may be further refined by analyzing the morphometric and photometric markers of the atypical cell population to determine if it includes characteristic subpopulations. For example, the presence of a large fraction of rapidly dividing cells, as evidenced by a marker defining a high nuclear to cellular size ratio, may characterize a cancer as aggressive.
• a disease condition may be detected by the analysis not only of the cell subpopulations and their relative abundance, but also by an analysis of free (not cell-associated) bio-molecules within the cell sample,
• a reagent may be added to the cell sample, the reagent comprising reactive substrates, each of which indicates the abundance of a particular bio- molecule.
• Each reactive substrate e.g. a microsphere
• Each reactive substrate includes a unique optical signature that both identifies the species of bio-molecule to which it preferentially binds, as well as indicating the abundance of that bio-molecule in the sample.
• Image data for the population and subpopulation(s) can be manipulated using several different techniques.
• An exemplary technique is referred to as gating, a manipulation of data relating to photometric or morphometric imaging.
• a further exemplary technique is backgating, which involves further defining a subset of the gated data. While not strictly required, signal processing is preferably performed on the collected image data to reduce crosstalk and enhance spatial resolution, particularly for image data collected using simultaneous multi-channel imaging.
• image data from a population of cells exhibiting a disease condition are collected.
• One or more photometric or morphometric markers associated with the disease condition are identified.
• a marker may be indicative of a measurable difference of some parameter between a healthy cell and a diseased cell.
• Such photometric or morphometric markers used to distinguish healthy cells from diseased cells are generally associated with specific features.
• the identified marker can represent data present in image data collected from diseased cells, but not likely to be present in image data collected from healthy cells.
• the identified marker can also represent data present in image data collected from cells exhibiting the disease condition, and also likely to be present in image data collected from healthy cells, yet present to a different degree that is quantifiable and identifiable.
• the marker can represent a measurable change in subpopulations associated with a disease condition, as opposed to subpopulations associated with the absence of the disease condition.
• an increase in the number of lymphocytes in blood relative to other blood cell types is indicative of the CLL disease condition.
• a population of cells can be imaged and analyzed to determine whether the identifying marker(s) is/are present in the sample population, and to determine whether the disease condition is present.
• the disease condition is chronic lymphocytic leukemia
• the marker relates to an increase in the size or shape of the lymphocytic cellular subpopulation.
• At least one aspect of the concepts disclosed herein is directed to labeling either diseased cells or healthy cells, and imaging a mixed population of healthy and diseased cells together, such that the identifying markers are determined from a mixed population of cells.
• the labels enable a subpopulation of labeled cells to be extracted from the imaged data collected from the mixed population sample.
• the labeling thus facilitates separating the aggregate image data into images corresponding to diseased cells and images corresponding to healthy cells, which enables the photometric and/or morphometric markers corresponding to the disease condition to be more readily identified.
• Yet another aspect of the techniques disclosed herein relates to monitoring the treatment of a patient exhibiting a disease condition. Baseline data are collected by imaging a population of cells from the patient before treatment.
• the population of cells is obtained from a bodily fluid, such as blood.
• additional data are obtained by imaging additional populations of cells collected from the patient during and after various stages of the treatment process.
• Such data will provide a quantitative indication of the improved condition of the patient suffering from the disease condition, as indicated by either the amount of cells expressing the disease condition versus normal cells, or by a change in a ratio of the subpopulations present in the population.
• quantification is not feasible with standard microscopy and/or conventional flow cytometry.
• the imagery collected from a population of biological cells includes collection of multimodal images. That is, the images collected will include at least two of the following types of images: one or more images corresponding to light emitted from the cell, one or more images corresponding to light transmitted by the cell, and one or more images corresponding to light scattered by the cell.
• Such multimode imaging can encompass any of the following types of images or combinations: (1) one or more fluorescent images and at least one bright field image; (2) one or more fluorescent images and at least one dark field image; (3) one or more fluorescent images, a bright field image, and a dark field image; and (4) a bright field image.
• Simultaneous collection of a plurality of different fluorescent images can also be beneficial, as well as simultaneous collection of a plurality of different bright field images (using transmitted light with two different spectral filters).
• the multimode images are collected simultaneously.
• FIGURE 1 is a schematic diagram of an exemplary flow imaging system that can be used to simultaneously collect a plurality of images from an object in flow;
• FIGURE 2 is a pictorial representation of an image recorded by the flow imaging system of FIGURE 1;
• FIGURE 3 is a flow chart of the overall method steps implemented in one aspect of the concepts disclosed herein;
• FIGURE 4 is an exemplary graphical user interface used to implement the method steps of FIGURE 3;
• FIGURE 5 is an exemplary graphical user interface used to implement the method steps of FIGURE 3 as applied to the analysis of human peripheral blood;
• FIGURE 6 includes images of normal (i.e., healthy) mammary epithelial cells
• FIGURE 7 includes images of mammary carcinoma (i.e., diseased) cells, illustrating how quantification of data in a fluorescent channel serves as a marker for the disease condition;
• FIGURE 8 is an exemplary graphical user interface used to implement the method steps of FIGURE 3, illustrating a plurality of different photometric and morphometric descriptors that can be used to automatically distinguish images of healthy mammary epithelial cells from images of mammary carcinoma cells;
• FIGURE 9 graphically illustrates the separation of cells in human peripheral blood into a variety of subpopulations based on photometric properties;
• FIGURE 1OA graphically illustrates a distribution of normal peripheral blood mononuclear cells (PBMC) based on image data collected from a population of cells that do not include mammary carcinoma cells
• FIGURE 1OB graphically illustrates a distribution of normal PBMC and mammary carcinoma cells based on image data collected from a population of cells that includes both cell types, illustrating how the distribution of mammary carcinoma cells is distinguishable from the distribution of the normal PBMC cells;
• PBMC peripheral blood mononuclear cells
• FIGURE IlA graphically illustrates a distribution of normal PBMC and mammary carcinoma cells based on measured cytoplasmic area derived from image data collected from a population of cells that includes both cell types, illustrating how the distribution of cytoplasmic area of the mammary carcinoma cells is distinguishable from the distribution of cytoplasmic area of the normal PBMC cells;
• FIGURE IlB graphically illustrates a distribution of normal PBMC and mammary carcinoma cells based on measured scatter frequency derived from image data collected from a population of cells that includes both cell types, illustrating how the distribution of the scatter frequency of the mammary carcinoma cells is distinguishable from the distribution of the scatter frequency of the normal PBMC cells;
• FIGURE 12 is composite images of cells generated by combining bright field and fluorescent images of mammary carcinoma cells;
• FIGURE 13 are representative images of five different PBMC populations that can be defined by scatter data derived from image data of a population of cells; and FIGURE 14 schematically illustrates an exemplary computing system used to implement the method steps of FIGURE 3.
• the present disclosure encompasses a method of using flow imaging systems that can combine the speed, sample handling, and cell sorting capabilities of flow cytometry with the imagery, sensitivity, and resolution of multiple forms of microscopy and full visible/near infrared spectral analysis to collect and analyze data relating to disease conditions in blood, particularly detecting and monitoring chronic lymphocytic leukemia.
• An aspect of the concepts disclosed herein relates to a system and method for imaging and analyzing biological cells entrained in a flow of fluid.
• a plurality of images of biological cells are collected simultaneously; the plurality of images including at least two of the following types of images: a bright field image, a dark field image, and a fluorescent image.
• Images are collected for a population of biological cells. Once the imagery has been collected, the images can be processed to identify a subpopulation of images, where the subpopulation shares photometric and/or morphometry characteristics empirically determined to be associated with a disease condition.
• population of cells refers to a group of cells including a plurality of objects. Thus, a population of cells must include more than one cell.
• ImageStream TM platform represents a particularly preferred imaging instrument used to acquire the image data that will be processed in accord with the concepts disclosed herein, it should be understood that the concepts disclosed herein are not limited only to the use of that specific instrument.
• an aspect of the concepts disclosed herein involves processing the image data collected to measure at least one characteristic associated with a disease condition in the imaged population.
• a preferred image analysis software package is IDEASTM (Amnis Corporation, Seattle WA).
• FIGURE 1 is a schematic diagram of a preferred flow imaging system 510
• System 510 includes a velocity detecting subsystem that is used to synchronize a TDI imaging detector 508 with the flow of fluid through the system.
• imaging system 510 is capable of simultaneously collecting a plurality of images of an object.
• imaging system 510 is configured for multi-spectral imaging and can operate with six spectral channels: DAPI fluorescence (400-460 nm), Dark field (460-500 nm), FITC fluorescence (500-560 nm), PE fluorescence (560-595 nm), Bright field (595- 650 nm), and Deep Red (650-700 nm).
• the TDI detector can provide 10 bit digital resolution per pixel.
• the numeric aperture of the preferred imaging system is typically 0.75, with a pixel size of approximately 0.5 microns. However, those skilled in the art will recognize that this flow imaging system is neither limited to six spectral channels, nor limited to either the stated aperture size or pixel size and resolution.
• Moving objects 502 are illuminated using a light source 506.
• the light source may be a laser, a light emitting diode, a filament lamp, a gas discharge arc lamp, or other suitable light emitting source, and the system may include optical conditioning elements such as lenses, apertures, and filters that are employed to deliver broadband or one or more desired wavelengths or wavebands of light to the object with an intensity required for detection of the velocity and one or more other characteristics of the object.
• Light from the object is split into two light paths by a beam splitter 503. Light traveling along one of the light paths is directed to the velocity detector subsystem, and light traveling along the other light path is directed to TDI imaging detector 508.
• a plurality of lenses 507 are used to direct light along the paths in a desired direction, and to focus the light.
• a filter or a set of filters can be included to deliver to the velocity detection subsystem and/or TDI imaging detector 508, only a narrow band of wavelengths of the light corresponding to, for example, the wavelengths emitted by fluorescent or phosphorescent molecules in/on the object, or light having the wavelength(s) provided by the light source 506, so that light from undesired sources is substantially eliminated.
• the velocity detector subsystem includes an optical grating 505a that amplitude modulates light from the object, a light sensitive detector 505b (such as a photomultiplier tube or a solid-state photodetector), a signal conditioning unit 505c, a velocity computation unit 505d, and a timing control unit 505e, which assures that TDI imaging detector 508 is synchronized to the flow of fluid 504 through the system.
• the optical grating preferably comprises a plurality of alternating transparent and opaque bars that modulate the light received from the object, producing modulated light having a frequency of modulation that corresponds to the velocity of the object from which the light was received.
• the optical magnification and the ruling pitch of the optical grating are chosen such that the widths of the bars are approximately the size of the objects being illuminated.
• the light collected from cells or other objects is alternately blocked and transmitted through the ruling of the optical grating as the object traverses the interrogation region, i.e., the field of view.
• the modulated light is directed toward a light sensitive detector, producing a signal that can be analyzed by a processor to determine the velocity of the object.
• the velocity measurement subsystem is used to provide timing signals to TDI imaging detector 508.
• signal conditioning unit 505c comprises a programmable computing device, although an ASIC chip or a digital oscilloscope can also be used for this purpose.
• the frequency of the photodetector signal is measured, and the velocity of the object is computed as a function of that frequency.
• the velocity dependent signal is periodically delivered to a TDI detector timing control 505e to adjust the clock rate of TDI imaging detector 508.
• TDI detector clock rate is adjusted to match the velocity of the image of the object over the TDI detector to within a small tolerance selected to ensure that longitudinal image smearing in the output signal of the TDI detector is within acceptable limits.
• Beam splitter 503 has been employed to divert a portion of light from an object 502 to light sensitive detector 505b, and a portion of light from object 502a to TDI imaging detector 508.
• TDI imaging detector 508 In the light path directed toward TDI imaging detector 508, there is a plurality of stacked dichroic filters 509, which separate light from object 502a into a plurality of wavelengths.
• One of lenses 507 is used to form an image of object 502a on TDI imaging detector 508.
• the theory of operation of a TDI detector like that employed in system 510 is as follows. As objects travel through a flow tube 511 (FIGURE 1) and pass through the volume imaged by the TDI detector, light from the objects forms images of the objects, and these images travel across the face of the TDI detector.
• the TDI detector preferably comprises a charge coupled device (CCD) array, which is specially designed to allow charge to be transferred on each clock cycle, in a row-by-row format, so that a given line of charge remains locked to, or synchronized with, a line in the image. The row of charge is clocked out of the array and into a memory when it reaches the bottom of the array.
• CCD charge coupled device
• the intensity of each line of the signal produced by the TDI detector corresponding to an image of an object is integrated over time as the image and corresponding resulting signal propagate over the CCD array.
• This technique greatly improves the signal-to-noise ratio of the TDI detector compared to non-integrating type detectors — a feature of great benefit in a detector intended to respond to images from low-level fluorescence emission of an object.
• Proper operation of the TDI detector requires that the charge signal be clocked across the CCD array in synchronization with the rate at which the image of the object moves across the CCD array.
• An accurate clock signal to facilitate this synchronization can be provided by determining the velocity of the object, and the concepts disclosed herein use an accurate estimate of the object's velocity, and thus, of the velocity of the image as it moves over the CCD array of the TDI detector.
• a flow imaging system of this type is disclosed in commonly assigned U.S. Patent No. 6,249,341, the complete disclosure, specification, and drawings of which are hereby specifically incorporated herein by reference.
• FIGURE 2 is a pictorial representation of images produced by the flow imaging system of FIGURE 1.
• a column 520 labeled “BF,” includes images created by the absorption of light from light source 506 by spherical objects 502 entrained in fluid flow 504.
• the "BF” label refers to "bright field,” a term derived from a method for creating contrast in an image whereby light is passed through a region and the absorption of light by objects in the region produces dark areas in the image. The background field is thus bright, while the objects are dark in this image.
• column 520 is the "bright field channel.” It should be understood that the inclusion of a bright field image is exemplary, rather than limiting on the scope of the concepts disclosed herein.
• the concepts disclosed herein utilize a combination of bright field images and fluorescent images, or of dark field images and fluorescent images.
• the remaining three columns 522, 524, and 526 shown in FIGURE 2 are respectively labeled “ ⁇ l,” “ ⁇ 2,” and “ ⁇ 3.” These columns include images produced using light that has been emitted by an object entrained in the fluid flow. Preferably, such light is emitted through the process of fluorescence (as opposed to images produced using transmitted light).
• fluorescence is the emission of light (or other electromagnetic radiation) by a substance that has been stimulated by the absorption of incident radiation. Generally, fluorescence persists only for as long as the stimulating radiation persists. Many substances (particularly fluorescent dyes) can be identified based on the spectrum of the light that is produced when they fluoresce. Columns 522, 524, and 526 are thus referred to as "fluorescence channels.”
• each object can be spectrally decomposed to discriminate object features by absorption, scatter, reflection, or emissions, using a common TDI detector for the analysis.
• Other systems include a plurality of detectors, each dedicated to a single spectral channel. These imaging systems can be employed to determine morphological, photometric, and spectral characteristics of cells and other objects by measuring optical signals including light scatter, reflection, absorption, fluorescence, phosphorescence, luminescence, etc.
• Morphological parameters include area, perimeter, texture or spatial frequency content, centroid position, shape (i.e., round, elliptical, barbell-shaped, etc.), volume, and ratios of selected pairs (or subsets) of these parameters.
• Photometric measurements with the preferred imaging system enable the determination of nuclear optical density, cytoplasm optical density, background optical density, and ratios of selected pairs of these values.
• An object being imaged with the concepts disclosed herein can either be stimulated into fluorescence or phosphorescence to emit light, or may be luminescent, producing light without stimulation.
• the light from the object is imaged on the TDI detector to use the concepts disclosed herein to determine the presence and amplitude of the emitted light, the number of discrete positions in a cell or other object from which the light signal(s) originate(s), the relative placement of the signal sources, and the color (wavelength or waveband) of the light emitted at each position in the object.
• an Imaging System to Analyze Bodily Fluid for a Disease Condition aspects of the concepts disclosed herein involve both the collection of multispectral images from a population of biological cells, and the analysis of the collected images to identify at least one photometric or morphological feature that has been empirically determined to be associated with a disease condition.
• an aspect of the present disclosure relates to the use of both photometric and morphometry features derived from multi-mode imagery of objects (e.g., cells) in flow to discriminate cell features in populations of cells, to facilitate the detection of the presence of a disease condition.
• objects e.g., cells
• methods for analyzing cells in suspension or flow which may be combined with comprehensive multispectral imaging to provide morphometric and photometric data to enable, for example, the quantization of characteristics exhibited by both normal cells and diseased cells, to facilitate the detection of diseased or abnormal cells indicative of a disease condition.
• such methods have not been feasible with standard microscopy and/or flow cytometry.
• a preferred flow imaging system e.g., the hnageStream TM platform
• the ImageStream TM platform is a commercial embodiment based on the imaging systems described in detail above.
• cells are hydrodynamically focused into a core stream and orthogonally illuminated for both dark field and fluorescence imaging.
• the cells are simultaneously trans-illuminated via a spectrally- limited source (e.g., filtered white light or a light emitting diode) for bright field imaging.
• Light is collected from the cells with an imaging objective lens and is projected on a CCD array.
• the optical system has a numeric aperture of 0.75 and the CCD pixel size in object space is 0.5 ⁇ 2 , enabling high resolution imaging at event rates of approximately 100 cells per second.
• Each pixel is digitized with 10 bits of intensity resolution in this example, providing a minimuiri dynamic range of three decades per pixel.
• the spread of signals over multiple pixels results in an effective dynamic range that typically exceeds four decades per image.
• the sensitivity of the CCD can be independently controlled for each multispectral image, resulting in a total of approximately six decades of dynamic range across all the images associated with an object.
• ImageStream TM platform represents a particularly preferred flow imaging system for acquiring image data in accord with the concepts disclosed herein
• ImageStream TM platform is intended to represent an exemplary imaging system, rather than limiting the concepts disclosed. Any imaging instrument capable of collecting images of a population of biological cells sufficient to enable the image analysis described in greater detail below to be achieved can be implemented in accord with the concepts presented herein.
• the ImageStream TM platform prior to projection on the CCD, the light is passed through a spectral decomposition optical system that directs different spectral bands to different lateral positions across the detector (such spectral decomposition is discussed in detail above in connection with the description of the various preferred embodiments of imaging systems).
• a spectral decomposition optical system that directs different spectral bands to different lateral positions across the detector (such spectral decomposition is discussed in detail above in connection with the description of the various preferred embodiments of imaging systems).
• an image is optically decomposed into a set of a plurality of sub-images (preferably 6 sub-images, including: bright field, dark field, and four different fluorescent images), each sub-image corresponding to a different spectral (i.e., color) component and spatially isolated from the remaining sub-images.
• Spectral decomposition also enables multimode imaging, i.e., the simultaneous detection of bright field, dark field, and multiple colors of fluorescence.
• the process of spectral decomposition occurs during the image formation process, rather than via digital image processing of a conventional composite image.
• the CCD may be operated using TDI to preserve sensitivity and image quality even with fast relative movement between the detector and the objects being imaged.
• image photons are converted to photo charges in an array of pixels.
• TDI operation the photo charges are continuously shifted from pixel to pixel down the detector, parallel to the axis of flow. If the photo charge shift rate is synchronized with the velocity of the image of the cell, the effect is similar to physically panning a camera. Image streaking is avoided despite signal integration times that are orders of magnitude longer than in conventional flow cytometry.
• an instrument may operate at a continuous data rate of approximately 30 mega pixels per second and integrate signals from each object for 10 milliseconds, enabling the detection of even faint fluorescent probes within cell images to be acquired at relatively high speed.
• Careful attention to pump and fluidic system design to achieve highly laminar, non-pulsatile flow eliminates any cell rotation or lateral translation on the time scale of the imaging process (see, e.g., U.S. Patent No. 6,532,061).
• a real-time algorithm analyzes every pixel read from the CCD to detect the presence of object images and calculate a number of basic morphometry and photometric features, which can be used as criteria for data storage.
• Data files encompassing 10,000-20,000 cells are typically about 100 MB in size and, therefore, can be stored and analyzed using standard personal computers.
• the TDI readout process operates continuously without any "dead time," which means every cell can be imaged and the coincidental imaging of two or more cells at a time either in contact or not, presents no barrier to data acquisition.
• morphological parameters may be basic (e.g., nuclear shape) or may be complex (e.g., identifying cytoplasm size as the difference between cell size and nuclear size).
• morphological parameters may include nuclear area, perimeter, texture or spatial frequency content, centroid position, shape (i.e., round, elliptical, barbell-shaped, etc.), volume, and ratios of selected pairs of these parameters.
• Morphological parameters of cells may also include cytoplasm size, texture or spatial frequency content, volume, and the like.
• photometric measurements with the aforementioned imaging system can enable the determination of nuclear optical density, cytoplasm optical density, background optical density, and the ratios of selected pairs of these values.
• An object being imaged can be stimulated into fluorescence or phosphorescence to emit light, or may be luminescent, wherein light is produced by the object without stimulation, m each case, the light from the object may be imaged on a TDI detector of the imaging system to determine the presence and amplitude of the emitted light, the number of discrete positions in a cell or other object from which the light signal(s) originate(s), the relative placement of the signal sources, and the color (wavelength or waveband) of the light emitted at each position in the object.
• the present disclosure provides methods of using both photometric and morphometric features derived from multi-mode imagery of objects in flow. Such methods can be employed as a cell analyzer to determine if a marker corresponding to a disease condition is present in the population of cells imaged.
• the marker can be indicative of the cellular abnormality associated with a disease condition, or the marker can be indicative of a change in a ratio of subpopulations present in the population of the cells imaged, where the change in ratio is indicative of a disease condition.
• the population of cells is imaged while entrained in a fluid flowing through an imaging system.
• gating refers to a subset of data relating to photometric or morphometric imaging.
• a gate may be a numerical or graphical boundary of a subset of data that can be used to define the characteristics of particles to be further analyzed.
• gates have been defined, for example, as a plot boundary that encompasses "in focus" cells, or sperm cells with tails, or sperm cells without tails, or cells other than sperm cells, or sperm cell aggregates, or cell debris.
• backgating may be a subset of the subset data. For example, a forward scatter versus a side scatter plot in combination with a histogram from an additional marker may be used to backgate a subset of cells within the initial subset of cells.
• a light source can also be used to stimulate emission of light from the object.
• a cell having been contacted with probe conjugated to a fluorochrome e.g., such as FITC, PE, APC, Cy3, Cy5, or Cy5.5
• a fluorochrome e.g., such as FITC, PE, APC, Cy3, Cy5, or Cy5.5
• Light sources may alternatively be used for causing the excitation of fluorochrome probes on an object, enabling a TDI detector to image fluorescent spots produced by the probes on the TDI detector at different locations as a result of the spectral dispersion of the light from the object that is provided by a prism.
• the disposition of these fluorescent spots on the TDI detector surface will depend upon their emission spectra and their location in the object.
• Each light source may produce light that can either be coherent, non-coherent, broadband, or narrowband light, depending upon the application of the imaging system desired.
• a tungsten filament light source can be used for applications in which a narrowband light source is not required.
• narrowband laser light is preferred, since it also enables a spectrally decomposed, non-distorted image of the object to be produced from light scattered by the object. This scattered light image will be separately resolved from the fluorescent spots produced on a TDI detector, so long as the emission spectra of any of the spots are at different wavelengths than the wavelength of the laser light.
• the light source can be either of the continuous wave (CW) or pulsed type, such as a pulsed laser. If a pulsed type illumination source is employed, the extended integration period associated with TDI detection can enable the integration of signals from multiple pulses. Furthermore, it is not necessary for the light to be pulsed in synchronization with the TDI detector.
• FIGURE 3 is a flow chart 400 schematically illustrating exemplary steps that can be used to analyze a population of cells based on images of the cell population, in order to identify a disease condition.
• the cell population is obtained from a bodily fluid, such as blood.
• an imaging system such as the exemplary imaging system described above in detail, is used to collect image data from a first population of biological cells where a disease condition is known to be present.
• a photometric or morphometric marker associated with the disease condition is identified.
• two distinctly different types of markers were developed. One type of marker relates to identifying a photometric and/or morphometric difference between healthy cells and diseased cells.
• One technique in identifying such a marker is to label carcinoma cells with a fluorescent label, and compare images of fluorescently labeled carcinoma cells with images of healthy cells, to identify a plurality of photometric and morphometric markers associated with the carcinoma cells.
• markers include differences in the average nucleus size between healthy cells and carcinoma cells, and differences in fluorescent images of healthy cells and carcinoma cells. These differences can be quantified based on processing the image data for the population of cells, to identify images that are more likely to be images of carcinoma cells, and to identify images that are more likely to be images of healthy cells.
• Another type of marker relates to identifying some difference between subpopulations present in a cellular population absent the disease condition, and subpopulations present in a cellular population during the disease condition.
• CLL is a disease condition where the number of lymphocytes in blood increases relative to the numbers of other blood cell types.
• a change in the ratio of lymphocytes to other blood cell types can be indicative of a disease condition.
• image data are collected from a second population of cells, in which it is not known whether the disease condition exists or not.
• image data are collected for the second population of cells, and then the image data are analyzed for the presence of the previously identified marker, to determine whether the disease condition is present in the second population of cells.
• the image data can be collected quite rapidly, hi general, the analysis (i.e., analyzing the collected image data to either initially identify a marker or to determine the presence of a previously identified marker in a population of cells) will be performed off-line, i.e., after the collection of the image data.
• Current implementations of imaging processing software are capable of analyzing a relatively large population of cells (i.e., tens of thousands of cells) within tens of minutes using readily available personal computers.
• off-line processing of the image data is intended to be exemplary, rather than limiting, and it is contemplated that realtime processing of the image data is an alternative.
• the marker relates to some photometric and/or morphometric difference between a healthy cell and a diseased cell
• the first population of cells the population known to be associated with the disease condition
• This approach facilitates separating the collected image data into images corresponding to diseased cells and images corresponding to healthy cells, to facilitate identification of photometric and/or mo ⁇ hometric markers that can be used to distinguish the two.
• the first population could include only diseased cells, and that if the image data of the first population is compared with image data of a cell population known to include only healthy cells, the photometric and/or mo ⁇ hometric markers that can be used to distinguish the diseased cells from the healthy cells can readily be identified.
• the marker relates to some photometric and/or morphometric difference between subpopulations present in a cellular population absent the disease condition, and subpopulations present in a cellular population associated with disease condition
• image data corresponding to the subpopulations present in a healthy cellular population must be provided before the image data corresponding to the first population of cells (the population known to be associated with the disease condition) can be analyzed to identify some photometric and/or morphometric difference between the subpopulations present in the healthy cellular population, and the subpopulations present in the cellular population having the disease condition.
• the multi-spectral imaging flow cytometer described above employs UV excitation capabilities and algorithms to quantitate DNA content and nuclear morphology, for the purpose of detecting and monitoring disease conditions, such as chronic lymphocytic leukemia.
• an imaging processing system is employed to process the image data.
• a personal computer executing image processing software represents an exemplary imaging processing system.
• the imaging processing software incorporates algorithms enabling photometric and/or morphometric properties of cells to be determined based on images of the cells.
• Exemplary algorithms include masking algorithms, algorithms that define nuclear morphology, algorithms for the quantization of cell cycle histograms, algorithms for analyzing DNA content, algorithms for analyzing heterochromaticity, algorithms for analyzing N/C ratio, algorithms for analyzing granularity, algorithms for analyzing CD45 expression, and algorithms for analyzing other parameters.
• the imaging processing software incorporates an algorithm referred to as a classifier, a software based analysis tool that is configured to evaluate a sample population of cells to determine if any disease condition markers are present. For determining the presence of cancer cells, the classifier will analyze the images of the sample population for images having photometric and/or morphometric properties corresponding to previously identified photometric and/or morphometric properties associated with cancer cells.
• the classifier will analyze the images of the sample population to separate the images into different cellular subpopulations (based on different types of blood cells), and determine if the ratios of the subpopulations indicates the presence of CLL (for example, because of a higher than normal amount of lymphocytes).
• the classifier configured to detect CLL will separate blood cells into the following subpopulations: lymphocytes, monocytes, basophils, neutrophils, and eosinophils.
• the classifier configured to detect CLL will be based on empirical data from healthy patients and from patients with CLL. Classifier profiles for CLL can be improved by collecting and comparing classifier data for a variety of patients with the same diagnosis. Preferably, large (10,000 to 20,000-cell) data sets from each patient will be collected to assess the existence and diagnostic significance of CLL cell subpopulations for classifier optimization. Such an optimized classifier can then be used to monitor patient treatment response and assess residual disease after treatment.
• the quantitative cell classifiers eliminate the subjectivity of human evaluation, giving comparisons between patients a degree of confidence previously unattainable. Longitudinal studies will also benefit greatly by the quantitative analysis, and the ability to digitally store and retrieve large numbers of cellular image files, particularly as compared to prior art techniques for the retrieval of microscope slides and /or digital photographs of relatively small numbers of cells .
• a technology employed in detection of cancer cells in a bodily fluid based on image data of a population of cells from the bodily fluid was the development of preliminary absorbance and fluorescence staining protocols for simultaneous morphological analysis of bright field and fluorescence imagery.
• the primary fluorescence-based alternatives to chromogenic stains useful in conjunction with the optical systems discussed above are fluorescent DNA binding dyes.
• a wide variety of such dyes are excitable at 488 nm, including several SYTO dyes (Molecular Probes), DRAQ5 (BioStatus), 7-AAD, Propidium Iodide (PI), and others.
• These dyes are alternatives to chromogenic nuclear stains such as Toluidine Blue, Methyl Green, Crystal Violet, Nuclear Fast Red, Carmalum, Celestine Blue, and Hematoxylin.
• a fluorescent DNA binding dye is generally included in assay protocols developed for use with the optical systems described above, for the purposes of defining the shape and boundaries of the nucleus, its area, its texture (analogous to heterochromaticity), as well as to provide DNA content information.
• IDEASTM the software image analysis program discussed above, enables evaluation of combinations of features from different images of the same cell, in order to expand the utility of the fluorescence nuclear image.
• the nuclear image mask can be subtracted from the bright field image mask (which covers the entire cell) as a means for generating a mask that includes only the cytoplasmic region.
• the cytoplasmic mask can be used to calculate the cytoplasmic area, the N/C ratio, the relative fluorescence intensity of probes in the cytoplasm and nucleus, etc., via an intuitive "Feature Manager.”
• An example of a Feature Manager session for the definition of the N/C ratio is shown in FIGURE 4.
• Basic features associated with any cell image are selected from a list and combined algebraically using a simple expression builder. Measurement of Photometric and Morphometric Parameters
• ImageStream TM data analysis and cell classification are performed post-acquisition using the IDEASTM software package.
• An annotated IDEASTM software screen capture of an analysis of human peripheral blood is shown in FIGURE 5.
• the IDEASTM software enables the visualization and photometric/morphometric analysis of data files containing imagery from tens of thousands of cells, thereby combining quantitative image analysis with the statistical power of flow cytometry.
• the exemplary screen shot of FIGURE 5 includes images and quantitative data from 20,000 human peripheral blood mononuclear cells.
• Whole blood was treated with an erythrocyte lysing agent, and the cells were labeled with an anti- CD45-PerCP mAb (red) and a DNA binding dye (green).
• Each cell was imaged in fluorescence using the FLl and FL4 spectral bands, as well as dark field and bright field. Images of a plurality of cells in a dark field channel 51a, a green fluorescent channel 51b, a bright field channel 51c, and a red fluorescent channel 5 Id can readily be identified in this Figure.
• Such a thumbnail image gallery (in the upper left of the interface) enables the "list mode" inspection of any population of cells.
• Cell imagery can be pseudo-colored and superimposed for visualization in the image gallery or enlarged, as shown at the bottom of the interface, for four different cell types (eosinophils 53a, NK cells 53b, monocytes 53c, and neutrophils 53d
• the software also enables one- and two-dimensional plotting of features calculated from the imagery. Dots 55 that represent cells in the two-dimensional plots can be "clicked" to view the associated imagery in the gallery. The reverse is true as well. Cell imagery can be selected to highlight the corresponding dot in every plot in which that cell appears. In addition, gates 57 can be drawn on the plots to define subpopulations, which can then be inspected in the gallery using a "virtual cell sort" functionality. Any feature calculated from the imagery or defined by the user (i.e., selected from a list of basic and automatically combined algebraically using a simple expression builder) can be plotted.
• a dot plot 59a (displayed at the center left of FIGURE 5) shows the clustering resulting from an analysis of CD45 expression (x-axis) versus a dark field granularity metric (y-axis), which is similar to side-scatter intensity measured in conventional flow cytometry.
• Plot 59a reveals lymphocytes (green in a full color image), monocytes (red in a full color image), neutrophils (turquoise in a full color image), and eosinophils (orange in a full color image).
• a dot plot 59b substitutes a nuclear texture parameter, "nuclear frequency" for CD45 expression on the x-axis, revealing a putative NEC cell population (purple in a full color image).
• the frequency parameter is one member of the morphologic and photometric feature set that was developed and incorporated into the IDEASTM software package. Table 1 below provides an exemplary listing of photometric and morphometric definitions that can be identified for every image (or subpopulation, as appropriate). It should be recognized that FIGURE 5 has been modified to facilitate its reproduction. As a full- color image, the background of each frame including a cell is black, and the background for each dot plot is black, to facilitate visualization of the cells and data.
• bladder epithelial cells would be used to investigate morphometric differences between normal and epithelial carcinoma cells.
• the initial samples of bladder washings that were analyzed revealed that the cell number per sample was highly variable, and generally too low to be practical for use in tiie LnageStream TM instrument.
• Mammary epithelial cells were therefore used in place of bladder cells.
• Mammary cells were chosen because normal, primary cells of this kind are commercially available (Clonetics/IhVitrogen) and will expand as adherent cells in short-term tissue culture with specialized growth media.
• mammary epithelial carcinoma cells derived from breast cancer metastases are available from the American Type Tissue Culture Collection (ATCC).
• ATCC American Type Tissue Culture Collection
• HCC-I 500 HCC-I 569
• HCC-1428 mammary epithelial carcinoma cell lines
• Normal mammary epithelial cells were stained with a fluorescein-conjugated monoclonal antibody to the HLA Class I MHC cell surface protein by incubating the cells with the appropriate, predetermined dilution of the mAb for 30 minutes at 4 degrees C.
• mammary carcinomas are known to down-regulate Class I MHC expression, as a precaution, the normal cells were fixed in 1% paraformaldehyde to limit passive transfer to the carcinoma cells.
• the combined mammary carcinoma cells lines were also fixed in 1% paraformaldehyde and added to the normal mammary cell population.
• DRAQ5 BioStatus, Ltd, Leicestershire, UK
• a DNA binding dye that can be excited with a 488 nm laser and emits in the red waveband was added to the sample prior to running on the ImageStreamTM instrument.
• the labeling of normal mammary epithelial cells with anti-Class I MHC mAb enabled the normal cells to be identified in mixes of normal and carcinoma cells, thereby providing an objective "truth" to facilitate the identification of image features distinguishing normal epithelial cell from epithelial carcinoma cells.
• Normal peripheral blood was obtained from AlICells (San Diego, CA).
• Image files containing image data of the cell mixes described above (normal mammary epithelial cells mixed with mammary carcinoma cells, and normal peripheral blood cells mixed with mammary carcinoma cells) were analyzed using the IDEASTM software package with the results described below.
• each horizontal row includes four simultaneously acquired images of a single cell. Images in columns 61a and 71a correspond to blue laser side scatter images (i.e., dark field images), images in columns 61b and 71b correspond to green HLA-FITC fluorescence images, images in columns 61c and 71c correspond to bright field images, and images in columns 61d and 71d correspond to red nuclear fluorescence.
• images in columns 61a and 71a correspond to blue laser side scatter images (i.e., dark field images)
• images in columns 61b and 71b correspond to green HLA-FITC fluorescence images
• images in columns 61c and 71c correspond to bright field images
• images in columns 61d and 71d correspond to red nuclear fluorescence.
• the preferred imaging system is capable of simultaneously collecting six different types of images of a single cell (a dark field image, a bright field image, and four fluorescence images); in FIGURES 6 and 7, two of the fluorescence channels have not been utilized. It should be recognized that FIGURES 6 and 7 have been modified to facilitate their reproduction. As full-color images, the backgrounds of FIGURES 6 and 7 are black, images in columns 61a and 11a axe blue, images in columns 61b and 71b are green, images in columns 61c and 71c are grayscale images on a gray background, and images in columns 61d and 7 Id are red.
• FIGURE 8 The analysis shown in FIGURE 8 proceeded from a dot plot 81 in the upper left of the Figure.
• Single cells were first identified, based on dot plot 81, which was defined as bright field area versus aspect ratio.
• a gate (not separately shown) was drawn around the population containing putative single cells based on the criteria of the area being sufficiently large to exclude debris, and the aspect ratio being greater than - 0.5, which eliminates doublets and clusters of cells.
• the veracity of the gating was tested by examining random cells both within and outside of the gate using the click-on-a-dot visualization functionality.
• FIGURE 8 has been modified to facilitate its reproduction.
• the background of each frame including a cell is black, and the background for each dot plot and histogram is black, to facilitate visualization of the cells and data.
• This modification resulted in the even distribution of dots 81a, even though such an even distribution was not present in the full color image.
• histograms 85b-85k are differential histograms of the normal cells 87a (shown as green in a full-color image) and carcinoma cells 87b (shown as red in a full-color image), with each histogram representing a different quantitative feature.
• the ten discriminating features fell into five distinct classes: scatter intensity, scatter texture, morphology, nuclear intensity, and nuclear texture.
• Differential histograms 85b, 85c, and 85d demonstrate the difference between the two populations using three different, but correlated, scatter intensity features: "scatter mean intensity” (total intensity divided by cell area), “scatter intensity” (total intensity minus background), and “scatter spot small total” (total intensity of local maxima). Although all three scatter intensity features provided good discrimination, “scatter mean intensity” (histogram 85b) was the most selective.
• Differential histograms 85e and 85f quantitated scatter texture using either an intensity profile gradient metric ("scatter gradient RMS"; histogram 85e) or the variance of pixel intensities ("scatter frequency”; histogram 85f), which proved more selective.
• Differential histograms 85g, 85h and 85i plotted the cellular area (bright field area, histogram 85g), nuclear area (from the DNA fluorescence imagery, histogram 85h), and cytoplasmic area (cellular/nuclear area, histogram 85i).
• the carcinoma cell lines were generally smaller in bright field area, confirming the qualitative observations from cell imagery. While the nuclear area of the carcinoma cell lines was proportionately smaller than that of the normal cells (e.g. the Nuclear/Cellular area ratio was not discriminatory), the cytoplasmic area was significantly lower in the carcinoma cells.
• differential histograms 85j and 85k plotted the nuclear mean intensity
• the multispectral/multimodal imagery collected by the ImageStream TM instrument and analyzed using the IDEASTM software package in this engineered experiment revealed a number of significant differences in dark field scatter, morphology, and nuclear staining between normal epithelial and epithelial carcinoma cells. While it is well-recognized that cells adapted to tissue culture have undergone a selection process that may have altered their cellular characteristics, these data demonstrate that it is feasible to build an automated classifier that uses the morphometric and photometric features identified and described above to separate normal from transformed epithelial cells, and possibly other cell types.
• FIGURE 5 A further experimental investigation analyzed image data collected from a mixture of normal peripheral blood cells and mammary carcinoma cells.
• cell classification of human peripheral blood can be achieved using a flow imaging system configured to simultaneously obtain a plurality of images of each cell, and using an automatic image analysis program (with the ImageStreamTM instrument representing an exemplary imaging system, and the IDEASTM software package representing an exemplary image analysis program).
• an automatic image analysis program with the ImageStreamTM instrument representing an exemplary imaging system, and the IDEASTM software package representing an exemplary image analysis program.
• CD45 expression combined with an analysis of dark field light scatter properties, cells can be separated into five distinct populations based on the image data collected by the flow imaging system: lymphocytes, monocytes, neutrophils, eosinophils and basophils.
• FIGURE 9 This separation of human peripheral blood into distinct subpopulations is shown in greater detail in FIGURE 9, which includes exemplary relative abundance data for the different subpopulations.
• the veracity of the classifications was determined by using population-specific monoclonal antibody markers and backgating marker-positive cells on the scatter vs. CD45 plot, as well as morphological analysis of the associated imagery.
• the x-axis of the graph in FIGURE 9 corresponds to anti-CD45-FITC Intensity, while the y-axis corresponds to dark field scatter intensity.
• FIGURE 1OA graphically illustrates a distribution of normal peripheral blood mononuclear cells (PBMC) based on image data collected from a population of cells that does not include mammary carcinoma cells.
• FIGURE 1OB graphically illustrates a distribution of normal PBMC and mammary carcinoma cells based on image data collected from a population of cells that includes both cell types, illustrating how the distribution of the mammary carcinoma cells is distinguishable from the distribution of the normal PBMC cells.
• carcinoma cells 101a fall well outside of a normally defined PBMC population 101b, as confirmed by visual inspection of the outlier population.
• carcinoma cells Ilia can also be discriminated from normal PBMC 111b using some of the morphometric and photometric features identified in FIGURE 8 (e.g., nuclear area, cytoplasmic area, scatter intensity, and scatter frequency).
• FIGURE IlA graphically illustrates a distribution of normal PBMC and mammary carcinoma cells based on measured cytoplasmic area derived from image data collected from a population of cells that includes both cell types, illustrating how the distribution of cytoplasmic area of mammary carcinoma cells is distinguishable from the distribution of cytoplasmic area of the normal PBMC cells.
• FIGURE 1 IB graphically illustrates a distribution of normal PBMC and mammary carcinoma cells based on measured scatter frequency derived from image data collected from a population of cells that includes both cell types, illustrating how the distribution of the scatter frequency of the mammary carcinoma cells is distinguishable from the distribution of the scatter frequency of the normal PBMC cells.
• FIGURES 1OA, 1OB, HA 3 . and HB have been modified to facilitate their reproduction.
• the background of each frame including a dot plot is black, to facilitate visualization of the cells and/or data, and dots representing PBMC cells and carcinoma cells are different colors.
• FIGURE 12 includes representative images from the carcinoma cell population, obtained using an overlay composite of bright field and DRAQ5 DNA fluorescence (red, with the image processing being performed by the image analysis software).
• FIGURE 13 includes images of the five peripheral blood mononuclear cell populations defined using dark field scatter, CD45 (green), and DRAQ5 (red) for nuclear morphology. Note that the two Figures are at different size scales. It should be recognized that FIGURE 12 has been modified to facilitate its reproduction.
• the background of FIGURE 12 is black, the background of each frame including a cell is brown/grey, and the nucleus of each is cell is red.
• FIGURE 13 has been similarly modified to facilitate its reproduction. As a full-color image, the background of FIGURE 13 is black, the periphery of each cell is green, and the nucleus of each is cell is red.
• the above studies demonstrate the feasibility of optically discrirninating a subpopulation of normal epithelial cells from a s ⁇ bpopulation of transformed cells by analyzing multi-spectral/multimodal image data from a mixed population of such cells, where the image data are simultaneously collected.
• the above studies also demonstrate the feasibility of detecting epithelial carcinoma cells in blood by analyzing multi-spectral/multimodal image data from a mixed population of such cells, where the image data are simultaneously collected.
• a 360 nm UV laser will be incorporated into the simultaneous multispectral/multimodal imaging system, and the optics of the imaging system will be optimized for diffraction-limited imaging performance in the 400- 460 nm (DAPI emission) spectral band.
• the exemplary imaging system used in the empirical studies detailed above i.e., the ImageStream TM instrument
• the beam is configured to have a narrow width, which improves overall sensitivity in exchange for increased measurement variation from cell to cell. Feasibility studies employing propidium iodide as a DNA stain indicate that the imaging system employing the 488 nm laser can generate cell cycle histograms having G0/G1 peak coefficients of variation of ⁇ 5%.
• the DAPI optimized 360 nm UV laser will instead be used.
• the beam will be configured to have a relatively wide illumination cross-section ( ⁇ 100 microns), so that under typical operating conditions, DAPI excitation consistency will be within 1% from cell to cell. Overall, cell cycle histogram CV is expected to be about 2-3%.
• the optics in the exemplary instrument used in the empirical studies discussed above are diffraction-limited from 460 — 750 nm, which does not cover the DAPI spectral emission band.
• such optics will be replaced with optics that are configured to achieve diffraction-limited imaging performance in the 400-460 nm spectral band, in order to measure detailed nuclear characteristics of diagnostic value, such as notched morphology and heterochromaticity.
• the morphometric feature set available in the exemplary image processing software discussed above does not include boundary contour features that quantitate nuclear lobicity, number of invaginations, and similar parameters. Because such features capture many of the qualitative observations of nuclear morphology traditionally used by hematopathologists, they would be of extremely high utility in the analysis of leukocytes. Incorporation of such algorithms and features would enable improved automated classification of normal cells, precursors, and transformed cells.
• the boundary contour masking algorithm and associated features employed in the empirical studies discussed above improve cell classification between eosinophils, neutrophils, monocytes, basophils, and lymphocytes in about 1/3 of cells of each type, as a function of their orientation with respect to the imaging plane.
• the boundary contour algorithm and features can be extended to consistently classify normal leukocytes, independent of their rotational orientation, which will lead to a first-pass classifier between normal and transformed cells, by increasing the statistical resolution between the expected locations of normal cell distributions, thereby improving the ability to flag abnormal cells that fall outside the expected positions.
• a classifier will also enable the features to be characterized for the morphologic differences observed between normal and transformed lymphocytes, to further improve discrimination, using the techniques generally discussed above.
• DNA content associated with CLL an automated classifier will be incorporated into the software package.
• the automated classifier will incorporate at least one or more of the following photometric and/or morphometric parameters: DNA content, nuclear morphology, heterochromaticity, N/C ratio, granularity, CD45 expression, and other parameters.
• the classifier will be configured to analyze image data corresponding to a population of blood cells, to classify the population into the following subpopulations: lymphocytes, monocytes, basophils, neutrophils, and eosinophils.
• PBMC will be stained with FITC conjugated anti-CD45 and the DNA binding dye, DAPI.
• Peripheral blood leukocytes will be classified in a five-part differential analysis into lymphocytes, monocytes, basophils, neutrophils, and eosinophils, generally as indicated in FIGURES 5, 9, and 13.
• peripheral blood leukocytes from CLL patients will be acquired and analyzed, as discussed above.
• the classification scheme developed for normal peripheral blood leukocytes will be applied to these data sets, and the identification of CLL cells will be determined by comparison with normal profiles.
• Various classifiers will be evaluated to determine which segments CLL cells best exemplify, generally as described above with respect to the histograms of FIGURE 8. Among these will be: cell size, nuclear size, nuclear to cytoplasmic ratio, nuclear contour, nuclear texture, and cytoplasmic granules. Results will be compared with standard blood films from CLL patient samples to determine the veracity of the technique. Ih addition to the normal staining protocol utilizing anti-CD45 as a marker, peripheral blood leukocytes will be stained with monoclonal antibodies to CD5 and
• CD2O plus DAPI, before image data are collected.
• This approach will enable the identification of the CLL cells according to accepted flow cytometric criteria. In this way, morphologic criteria can be correlated with the immunophenotype.
• Analyzing large (10,000 to 20,000 white blood cell) data sets from multiple CLL patients will facilitate the optimization and selection of photometric and morphometric markers that can be used classify blood cells by subpopulation (i.e., lymphocytes, monocytes, basophils, neutrophils, and eosinophils).
• subpopulation i.e., lymphocytes, monocytes, basophils, neutrophils, and eosinophils.
• Morphological heterogeneity has been observed in CLL cells; however, an accurate objective appreciation of the degree of this has not been achieved due to the technical difficulty of preparing and assessing peripheral blood films from patients consistently. Acquisition of large data sets from CLL patients using the multimodal imaging systems discussed above will enable the objective analysis of the degree of morphological heterogeneity by the imaging processing software package.
• the classifier(s) developed above will be applied to these data sets, and morphological heterogeneity assessed by analyzing the degree to which the particular classifier (e.g., nuclear size, N/C ratio, etc.) applies across the large populations of CLL cells. Based on this analysis, the classifier that most accurately identifies the greatest percentage of CLL cells will be optimized, so that the entire population is included by the classifier.
• the particular classifier e.g., nuclear size, N/C ratio, etc.
• CLL chronic myelolism
• image data collected from a population of blood cells will be used to separate the population of blood cells into subpopulations based on blood cell type (i.e., lymphocytes, monocytes, basophils, neutrophils, and eosinophils).
• blood cell type i.e., lymphocytes, monocytes, basophils, neutrophils, and eosinophils.
• CLL is associated with an increase in the amount of lymphocytes present in the blood cell population (i.e., an increase in the lymphocytes subpopulation)
• detecting an increase in lymphocytes provides an indication of the existence of the disease condition (i.e., CLL).
• a CLL detection technique could be implemented simply by separating the blood cell population into a lymphocyte subpopulation and a non-lymphocyte subpopulation. Using empirical data representing average lymphocyte subpopulations in healthy patients, detection of a higher-than-average lymphocyte subpopulation provides an indication of a CLL disease condition.
• an aspect of the present invention involves image analysis of a plurality of images simultaneously collected from members of the population of cells. Reference has been made to an exemplary image analysis software package.
• FIGURE 14 and the following related discussion are intended to provide a brief, general description of a suitable computing environment for practicing the present invention, where the image processing required is implemented using a computing device generally like that shown in FIGURE 14.
• An exemplary computing system 150 suitable for implementing the image processing required in the present invention includes a processing unit 154 that is functionally coupled to an input device 152, and an output device 162, e.g., a display.
• Processing unit 154 include a central processing unit (CPU 158) that executes machine instructions comprising an image processing/image analysis program for implementing the functions of the present invention (analyzing a plurality of images simultaneously collected for members of a population of objects to enable at least one characteristic exhibited by members of the population to be measured).
• the machine instructions implement functions generally consistent with those described above, with reference to the flowchart of FIGURE 3, as well as the exemplary screenshots.
• processors or central processing units (CPUs) suitable for this purpose are available from Intel Corporation, AMD Corporation, Motorola Corporation, and from other sources.
• RAM random access memory
• non- volatile memory 160 typically includes read only memory (ROM) and some form of memory storage, such as a hard drive, optical drive, etc.
• ROM read only memory
• CPU 158 Such storage devices are well known in the art.
• Machine instructions and data are temporarily loaded into RAM 156 from non-volatile memory 160.
• operating system software and ancillary software While not separately shown, it should be understood that a power supply is required to provide the electrical power needed to energize computing system 150.
• Input device 152 can be any device or mechanism that facilitates input into the operating environment, including, but not limited to, a mouse, a keyboard, a microphone, a modem, a pointing device, or other input devices. While not specifically shown in FIGURE 14, it should be understood that computing system 150 is logically coupled to an imaging system such as that schematically illustrated in FIGURE 1, so that the image data collected are available to computing system 150 to achieve the desired image processing. Of course, rather than logically coupling the computing system directly to the imaging system, data collected by the imaging system can simply be transferred to the computing system by means of many different data transfer devices, such as portable memory media, or via a network (wired or wireless). Output device 162 will most typically comprise a monitor or computer display designed for human visual perception of an output image.

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