Pre-scanning with low-magnification microscope objective lens and scanning with high-magnification microscope objective lens in x, y, and z directions for imaging objects such as cells using microscope

Abstract

SOLUTION: There is provided a method for imaging a three-dimensional object present in a matrix using a microscope, the method including pre-scanning the matrix using a first magnification objective lens to identify the x-y position of the three-dimensional object in the matrix and to identify the z-height of the three-dimensional object in the matrix; and scanning the identified three-dimensional object in a z-direction, using a second magnification objective lens, on the basis of the identified z-height of the identified three-dimensional object to provide a three-dimensional image of at least a part of the three-dimensional object present in the matrix, the second magnification objective lens having a magnification equal to or higher than the first magnification objective lens.EFFECT: The method and the system allow a three-dimensional object with a reduced data volume to be taken in a shorten time for imaging.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] Certain configurations relate to methods and systems that can be used to image three-dimensional objects. More specifically, certain methods and systems are described that can image three-dimensional objects, such as living cells, in an automated, high-throughput manner. [Background technology]

[0002] Imaging of three-dimensional objects is often performed to investigate the structure and function of the objects. Imaging of three-dimensional objects is time consuming and may not yield usable results within a desired timeframe, especially when the imaged objects include living cells. Summary of the Invention [Means for solving the problem]

[0003] In one aspect, a method for imaging three-dimensional objects present in a matrix using a microscope is described. In some embodiments, the method includes pre-scanning the matrix using a first magnification objective to identify an xy position of the three-dimensional object within the matrix and identify a z-height of the three-dimensional object within the matrix. In other examples, the method also includes using the identified z-height of the identified three-dimensional object to scan the identified three-dimensional object in the z-direction using a second magnification objective to provide a three-dimensional image of at least a portion of the three-dimensional object present in the matrix, wherein the second magnification objective is equal to or greater than the first magnification objective.

[0004] In certain examples, pre-scanning includes acquiring a plurality of discrete z-plane images using a first magnification objective lens of the microscope to provide a first image set of discrete z-plane images used to identify the z-height. In other examples, scanning includes starting at the identified z-height of the three-dimensional object and acquiring a plurality of discrete z-plane images of the identified three-dimensional object using a second magnification objective lens of the microscope to provide a second image set. In some embodiments, the plurality of discrete z-plane images of the second image set are used to provide an entire three-dimensional image of the three-dimensional object in the matrix. In certain examples, prior to scanning the identified three-dimensional object using the second magnification objective lens, image correction may be applied to the first image set to provide a corrected first image set. In other examples, a corrected x-y position of the three-dimensional object and a corrected z-height of the three-dimensional object are obtained from the corrected first image set and used in scanning the three-dimensional object using the second magnification objective lens to provide a second image set. In some embodiments, image correction may be applied to the second image set to provide a corrected second image set used to provide a three-dimensional image of the three-dimensional object.

[0005] In a particular example, the pre-scanning using the first magnification objective is performed using an air objective of the microscope, for example, a 5x or 10x air objective, and the scanning using the second magnification objective is performed using a liquid immersion objective of the microscope, for example, a water immersion objective or an oil immersion objective, which may be an objective with a magnification of 40x, 50x, 60x or more.

[0006] In some examples, both pre-scanning and scanning are performed using a laser confocal scanning microscope. In certain embodiments, the three-dimensional object is a biological organism, a biological organ, a biological tissue, a biological cell, or a component or organ thereof. In additional examples, the three-dimensional object is a biological organism, a biological The pre-scanning and scanning of the organ, biological tissue, biological cell, or component or organ thereof is performed in a matrix comprising a hydrogel or in a matrix comprising a three-dimensional scaffold.

[0007] In certain examples, the method includes pre-scanning the matrix using a first magnification objective of a microscope to identify the xy position of each of a plurality of individual three-dimensional objects in the matrix and to identify the z-height of each of the identified three-dimensional objects in the matrix. The method may also include scanning each identified three-dimensional object in the z-direction using a second magnification objective, using the identified z-height of each identified three-dimensional object, to provide a three-dimensional image of at least a portion of each of the three-dimensional objects present in the matrix. When multiple objects are imaged in the matrix, the pre-scanning using the first magnification objective may be performed using an air objective of the microscope, such as a 5x or 10x air objective, and the scanning using the second magnification objective may be performed using a liquid immersion objective of the microscope, such as a water immersion objective or an oil immersion objective, which may have a magnification of 40x, 50x, 60x, or more. When multiple objects are imaged within the matrix, each of the three-dimensional objects is independently a biological organism, biological organ, biological tissue, biological cell, or component or organ thereof, and the matrix comprises a hydrogel or three-dimensional scaffold. The objects need not be the same type of cell, tissue, organism, etc.

[0008] In certain embodiments, the method includes using a pre-scan to identify z-widths of identified three-dimensional objects in the matrix, and using the identified z-widths to select a scan time or a data volume obtained from the scan of the three-dimensional objects in the matrix.

[0009] In another aspect, a microscope system configured to image three-dimensional objects within a three-dimensional matrix is ​​described. In some examples, the system includes: a sample holder configured to receive a sample including three-dimensional objects within a three-dimensional semi-solid or three-dimensional solid matrix; a first light source optically coupled to the sample holder and configured to illuminate at least a portion of the three-dimensional object within the three-dimensional matrix received by the sample holder, wherein a wavelength provided by the first light source is selected to excite at least one species present within the three-dimensional object (or to excite the entire three-dimensional object); at least one objective lens optically coupled to the light source and configured to receive optical emissions from the at least one excited species present within the three-dimensional object within the three-dimensional matrix; a detector optically coupled to the at least one objective lens and configured to receive optical emissions from the objective lens; and a processor electrically coupled to the detector. In a particular configuration, the microscope system is configured to pre-scan a three-dimensional semi-solid or three-dimensional solid matrix using a first magnifying objective lens to identify an xy position of the three-dimensional object and identify a z-height of the three-dimensional object, and the microscope system is configured to scan the identified three-dimensional object in the z-direction using a second magnifying objective lens and the identified z-height of the identified three-dimensional object to provide a three-dimensional image of at least a portion of the three-dimensional object.

[0010] In certain embodiments, the system includes a first objective lens and a second objective lens, the first objective lens being used as a first magnification objective lens and the second objective lens being used as a second magnification objective lens, the second objective lens being different from the first objective lens and providing a magnification equal to or greater than the magnification of the first objective lens. In some examples, the first objective lens is an air objective lens and the second objective lens is a water immersion objective lens (or other liquid immersion objective lens).

[0011] In certain examples, the detector includes at least one camera and the first light source includes at least one laser. For example, the system can be configured to perform laser scanning confocal microscopy using the camera and laser during pre-scanning and scanning of the three-dimensional object.

[0012] In some embodiments, the processor is configured to execute instructions to construct a three-dimensional image of the three-dimensional object in a three-dimensional matrix from the set of images acquired from the scanning step, hi other embodiments, the processor is configured to execute instructions to apply image corrections to the set of images before constructing the image of the three-dimensional object.

[0013] In a particular example, the system applies image correction using a second image set acquired from one or more images of a two-dimensional graphical pattern using a detector at the same fixed position used to acquire the image set, the two-dimensional graphical pattern including dots in a lattice, the dots being at vertices that define one or more types of geometric shapes, the lattice of dots being non-periodic, and absolute positions of the imaged dots being determinable, and the correction is applied to the images of the image set using the one or more images of the two-dimensional graphical pattern to correct for geometric distortions in the images of the image set.

[0014] In some configurations, the system includes at least two separate light sources configured to provide light of different wavelengths to the sample on the sample holder.

[0015] In other configurations, the system further includes a confocal unit optically coupled to each of the at least two separate light sources and positioned between the at least two separate light sources and the sample holder.

[0016] In certain configurations, the system includes at least two separate detectors configured to receive different wavelengths of light emitted by the sample on the sample holder, while in other configurations, each of the at least two separate detectors includes a camera.

[0017] In some embodiments, one of the at least two separate detectors is configured to detect optical emissions during the pre-scanning step, and another of the at least two separate detectors is configured to detect optical emissions during the scanning step.

[0018] In another embodiment, the system is configured to use a first magnifying objective of the microscope to identify an xy position of each of a plurality of individual three-dimensional objects in the matrix and to identify a z-height of each of the identified plurality of three-dimensional objects in the matrix, and the system is configured to use the respective identified z-height of each identified three-dimensional object to scan the identified three-dimensional objects in the z-direction using a second magnifying objective to provide a three-dimensional image of at least a portion of each of the three-dimensional objects present in the matrix.

[0019] In a particular example, the system includes four independent light sources, each configured to provide a different wavelength of light to a sample on a sample holder, and may also include four independent cameras, each configured to detect light emissions from the sample.

[0020] In other examples, the system is further configured to identify z-widths of identified three-dimensional objects in the matrix and use the identified z-widths to select a scan time or to select a data volume obtained from scanning the three-dimensional objects in the matrix.

[0021] In another aspect, a non-transitory computer-readable medium having instructions stored thereon, the instructions is a non-transitory computer-readable medium that, when executed by a processor, causes the processor to pre-scan a matrix including three-dimensional objects using a first magnifying objective lens to identify x-y positions of the three-dimensional objects in the matrix, identify z-heights of the three-dimensional objects in the matrix, and use the identified z-heights of the identified three-dimensional objects to scan the identified three-dimensional objects in the z-direction using a second magnifying objective lens to provide three-dimensional images of at least some of the three-dimensional objects present in the matrix.

[0022] In some cases, the instructions cause the processor to select a magnification of the second magnification objective that is equal to or greater than the magnification of the first magnification objective.

[0023] In an additional aspect, a method of imaging three-dimensional objects present in a matrix using a laser confocal scanning microscope, the method further comprising: pre-scanning the matrix using a first magnifying objective to identify x-y positions of the three-dimensional objects in the matrix, identifying z-heights of the three-dimensional objects in the matrix, and recording the identified x-y positions and z-heights as a first image set; correcting imaging anomalies from the pre-scanning step to provide a corrected first image set; scanning the identified three-dimensional objects in the z direction using a second magnifying objective to provide a second image set using the corrected z-heights and corrected x-y positions from the corrected first image set; correcting imaging anomalies from the scanning step to provide a corrected second image set; and constructing an image of the three-dimensional objects in the matrix using the corrected second image set.

[0024] In some examples, correcting imaging anomalies from the pre-scan step further includes acquiring one or more images of a two-dimensional graphical pattern using a detector at the same fixed position used to acquire the first image set, the graphical pattern being a grid including dots at vertices defining one or more types of geometric shapes, the grid of dots being non-periodic and absolute positions of the imaged dots being determinable, and automatically adjusting, by a processor, the images of the first image set using the one or more images of the two-dimensional graphical pattern to correct for geometric distortions in the images of the first image set. In other examples, the method further includes automatically adjusting, by a processor, the images of a second image set using one or more images of the two-dimensional graphical pattern (which may be the same images used to correct the pre-scan values) to correct for geometric distortions in the images of the second image set.

[0025] In another example, correcting imaging anomalies from the scanning step further includes acquiring one or more images of a two-dimensional graphical pattern using a detector at the same fixed position used to acquire the second image set, the graphical pattern being a grid including dots at vertices defining one or more types of geometric shapes, the grid of dots being non-periodic and absolute positions of the imaged dots being determinable, and automatically adjusting, by a processor, the images of the second image set using the one or more images of the two-dimensional graphical pattern to correct for geometric distortions in the images of the second image set.

[0026] In some examples, pre-scanning includes acquiring a plurality of discrete z-plane images using a first magnifying objective of the microscope to provide a first image set. In other examples, scanning includes starting at a z-height of the corrected, identified three-dimensional object and acquiring a plurality of discrete z-plane images of the identified three-dimensional object using a second magnifying objective of the microscope to provide a second image set. In a further example, the plurality of discrete z-plane images of the second image set after image correction provide an entire three-dimensional image of the three-dimensional object in the matrix. In a specific embodiment, the pre-scanning using the first magnification objective is performed using a 10x air objective of the microscope, and the scanning using the second magnification objective is performed using a 40x water immersion objective of the microscope.

[0027] In certain examples, a laser confocal scanning microscope is used to perform both pre-scanning and scanning when imaging correction is also performed. In some embodiments, the three-dimensional object is a biological organism, a biological organ, a biological tissue, a biological cell, or a component or organ thereof. In other embodiments, the pre-scanning and scanning of the biological organism, the biological organ, the biological tissue, the biological cell, or a component or organ thereof is performed in a matrix comprising a hydrogel or in a matrix comprising a three-dimensional scaffold.

[0028] In some embodiments, the method includes using a pre-scan to identify z-widths of identified three-dimensional objects in the matrix, and using the identified z-widths to select a scan time or a data volume obtained from the scan of the three-dimensional objects in the matrix. If desired, image corrections can be applied to the identified z-widths to provide corrected z-widths.

[0029] In another aspect, a microscope system configured to image three-dimensional objects in a three-dimensional matrix includes: a sample holder configured to receive a sample including the three-dimensional objects in the three-dimensional matrix; a first light source optically coupled to the sample holder and configured to illuminate at least a portion of the three-dimensional objects in the three-dimensional matrix received by the sample holder, wherein a wavelength provided by the first light source is selected to excite at least one species present in the three-dimensional objects; at least one objective lens optically coupled to the light source and configured to receive optical emissions from the at least one excited species present in the three-dimensional objects in the three-dimensional matrix; a detector optically coupled to the at least one objective lens and configured to receive optical emissions from the objective lens; and a processor electrically coupled to the detector. In some configurations, the microscope system is configured to pre-scan a matrix using a first magnifying objective to identify x-y positions of three-dimensional objects in the matrix, identify z-heights of the three-dimensional objects in the matrix, record the identified x-y positions and z-heights as a first image set, correct for imaging anomalies from the pre-scan to provide a corrected first image set, scan the identified three-dimensional objects in the z direction using a second magnifying objective using the corrected z-heights and corrected x-y positions from the corrected first image set to provide a second image set, and correct for imaging anomalies from the scan to provide a corrected second image set.

[0030] In certain embodiments, the system includes a first objective lens and a second objective lens, wherein the first objective lens is used as a first magnification objective lens and the second objective lens is used as a second magnification objective lens, the second objective lens being different from the first objective lens and providing a magnification equal to or greater than that of the first objective lens. In some examples, the first objective lens is an air-immersion objective lens and the second objective lens is a water-immersion objective lens. In other examples, the detector includes at least one camera, the first light source includes at least one laser, and the system is configured to perform laser scanning confocal microscopy during pre-scanning and scanning of the three-dimensional object using the camera and laser.

[0031] In certain embodiments, the processor is configured to execute instructions to construct a three-dimensional image of the three-dimensional object in a three-dimensional matrix from the image set acquired from the scanning step. In other embodiments, the image correction is applied to the first image set using another image set acquired from one or more images of the two-dimensional graphical pattern using a detector at the same fixed position used to acquire the image set, thereby correcting the two-dimensional graphical pattern. The pattern includes dots in a lattice, the dots being at vertices that define one or more types of geometric shapes, the lattice of dots being non-periodic, absolute positions of the imaged dots being determinable, and a correction is applied to the images of the first image set using one or more images of the two-dimensional graphical pattern to correct for geometric distortions in the images of the first image set and provide a corrected image set.

[0032] In other embodiments, the system includes at least two separate light sources configured to provide light of different wavelengths to the sample on the sample holder. In certain examples, the microscope system further includes a confocal unit optically coupled to each of the at least two separate light sources and positioned between the at least two separate light sources and the sample holder. In some examples, the system includes at least two separate detectors configured to receive light of different wavelengths emitted by the sample on the sample holder. In other examples, each of the at least two separate detectors includes a camera. In certain configurations, one of the at least two separate detectors is configured to detect optical emissions during a pre-scanning step, and another of the at least two separate detectors is configured to detect optical emissions during a scanning step.

[0033] In a particular example, the system is configured to use a first magnifying objective of the microscope to identify an xy position of each of a plurality of individual three-dimensional objects in the matrix and to identify a z-height of each of the identified plurality of three-dimensional objects in the matrix, and the system is configured to use the respective identified z-height of each identified three-dimensional object to scan the identified three-dimensional objects in the z-direction using a second magnifying objective to provide a three-dimensional image of at least a portion of each of the three-dimensional objects present in the matrix.

[0034] In another configuration, the system includes four independent light sources, each configured to provide light of a different wavelength to a sample on a sample holder, and four independent cameras, each configured to detect light emissions from the sample.

[0035] In other embodiments, the system is further configured to identify z-widths of identified three-dimensional objects in the matrix and use the identified z-widths to select a scan time or to select a data volume obtained from scanning the three-dimensional objects in the matrix.

[0036] In another aspect, a non-transitory computer-readable medium having stored thereon instructions that, when executed by a processor, cause the processor to: pre-scan a matrix using a first magnification objective lens to identify x- and y-positions of three-dimensional objects in the matrix, identify z-heights of the three-dimensional objects in the matrix, record the identified x- and y-positions and z-heights as a first image set, correct imaging anomalies from the pre-scan to provide a corrected first image set, scan the identified three-dimensional objects in the z-direction using a second magnification objective lens to provide a second image set, correct imaging anomalies from the scan to provide a corrected second image set, and construct an image of the three-dimensional objects in the matrix using the corrected second image set. In some examples, the instructions cause the processor to select a magnification ratio of a second magnification objective lens that is equal to or greater than the magnification ratio of the first magnification objective lens.

[0037] Additional aspects, embodiments, examples, configurations, and features are described in more detail below. Particular examples of methods and systems are described with reference to the accompanying drawings. [Brief explanation of the drawings]

[0038] [Figure 1] FIG. 1 is an illustration of a three-dimensional object in a matrix, according to some examples. [Figure 2] FIG. 2 is a diagram illustrating scanning of a well containing a matrix and a three-dimensional object, according to certain embodiments. [Figure 3] FIG. 3 is a diagram illustrating a pre-scan of a well containing a matrix and a three-dimensional object, in accordance with a particular example. [Figure 4] FIG. 4 is a diagram illustrating scanning of a three-dimensional object identified in a pre-scan, according to some examples. [Figure 5]FIG. 5 is a diagram illustrating two three-dimensional objects of different sizes according to some embodiments. [Figure 6] FIG. 6 is a diagram illustrating a scan of each of the three-dimensional objects of FIG. 5 after a pre-scan, according to some examples. [Figure 7] FIG. 7 is a diagram of a system that may be used to image a three-dimensional object, according to some embodiments. [Figure 8] FIG. 8 is an illustration of another system that may be used to image three-dimensional objects, in accordance with certain embodiments. [Figure 9] FIG. 9 is an illustration of an additional system that may be used to image three-dimensional objects, according to certain embodiments. [Figure 10A] FIG. 10A is an illustration of three spherical objects present in a hydrogel matrix, according to some examples. [Figure 10B] FIG. 10B shows various image fields of the object shown in FIG. 10A according to some embodiments. [Figure 10C] FIG. 10C shows various image fields of the object shown in FIG. 10A according to some embodiments. [Figure 10D] FIG. 10D shows various image fields of the object shown in FIG. 10A according to some embodiments. [Figure 10E] FIG. 10E shows various image fields of the object shown in FIG. 10A according to some embodiments. [Figure 10F] FIG. 10F shows various image fields of the object shown in FIG. 10A according to some embodiments. [Figure 11A] FIG. 11A illustrates pre-scan measurements in various xy planes at different z-dimension values ​​to determine the x-, y-, and z-coordinates (or z-heights) of two objects, according to some examples. [Figure 11B] FIG. 11B shows a high-resolution scan using the pre-scan measurements of FIG. 11A, according to a particular example. [Figure 12A]FIG. 12A illustrates various samples and sample configurations that can be imaged using the methods and systems described herein. [Figure 12B] FIG. 12B illustrates various samples and sample configurations that can be imaged using the methods and systems described herein. [Figure 12C] FIG. 12C illustrates various samples and sample configurations that can be imaged using the methods and systems described herein. [Figure 12D] FIG. 12D illustrates various samples and sample configurations that can be imaged using the methods and systems described herein. [Figure 12E] FIG. 12E illustrates various samples and sample configurations that can be imaged using the methods and systems described herein. [Figure 12F] FIG. 12F illustrates various samples and sample configurations that can be imaged using the methods and systems described herein. [Figure 13] FIG. 13 is a flowchart illustrating a pre-scan and scan method for imaging a three-dimensional object, in accordance with a particular example. [Figure 14] FIG. 14 is a flowchart illustrating a pre-scan and scan method for imaging a three-dimensional object using image correction, in accordance with certain embodiments. [Figure 15] FIG. 15 is a diagram illustrating determining z-widths of objects in a matrix, in accordance with some examples. [Figure 16] FIG. 16 is a diagram illustrating the field of view of 225 when an entire 384 micro-well plate is scanned, according to some examples. [Figure 17] FIG. 17 illustrates 25 fields that can be scanned after pre-scanning a 384-microwell plate, according to some embodiments. [Figure 18A] FIG. 18A shows a z-stack for a pre-scan of a spheroid, according to some embodiments. [Figure 18B] FIG. 18B shows an image generated from scanning a spheroid using pre-scan information, according to some embodiments. [Figure 19A]FIG. 19A shows a 3D visualization of a pre-scan acquired with a 10x air objective, according to a particular example. [Figure 19B] FIG. 19B shows an xyz view from a scan of an MDCK cyst, according to a particular example. [Figure 19C] FIG. 19C shows a box plot according to a specific example. [Figure 20A] FIG. 20A shows a pre-scan of a 5x air objective, according to a specific example. [Figure 20B] FIG. 20B shows segmented tumor cells on a global image with coordinates of a higher magnification scan, according to a particular example. [Figure 20C] FIG. 20C shows a maximum intensity projection of a global image acquired with a 20x water immersion objective (280 planes, 1 micron step size), according to a particular example. [Figure 20D] FIG. 20D shows a 3D view of segmented tumor cells from a higher magnification scan, according to a particular example. [Figure 20E] FIG. 20E shows a 3D view of segmented tumor cells from a higher magnification scan, according to a particular example. [Figure 20F] FIG. 20F shows a 3D view of segmented tumor cells from a higher magnification scan, according to a particular example. DETAILED DESCRIPTION OF THE INVENTION

[0039] It will be appreciated by those skilled in the art that the objects shown in the figures are provided as examples to facilitate discussion of some aspects and features of the present technology, and no particular shape, size, dimension, location, or material is intended to be implied or required unless clear from the context in describing that particular illustration or example.

[0040] Certain exemplary configurations of methods and systems that can be used in an automated manner to image three-dimensional objects residing within a matrix are described to facilitate a better understanding of some of the configurations and examples that the present technology may employ. Additional, different configurations may also be fabricated and used by those skilled in the art given the benefit of this disclosure. The methods described herein are typically implemented in systems that include a microscope, such as a confocal scanning microscope, that allows for automated imaging of three-dimensional objects within a matrix. However, the present methods can be used in imaging systems that include components other than a microscope and provide similar attributes and advantages. The matrix may be a separate, external material, or in the case of cells imaged in vivo, the matrix may be another part of the organism. While the matrix can take many forms, it is desirable that the matrix not interfere with the imaging of selected cells, tissues, organisms, or other three-dimensional objects.

[0041] In certain embodiments, it may be desirable to rapidly image three-dimensional objects, such as living cells, to monitor the metabolic and / or physiological response of the cells to stimuli. For example, to better predict the effects of drug candidates during preclinical screening, more physiologically relevant three-dimensional model systems are being deployed in high-content screening assays. Furthermore, imaging of nanosystems, nanostructures, or other complex three-dimensional structures present in semi-solid or solid matrices can provide information about the overall properties and structure of these materials.

[0042] In certain instances, one drawback of imaging three-dimensional objects within a matrix, especially if the three-dimensional objects are randomly distributed within the sample container, is that the resulting large volume of data results in many empty images, with portions of the three-dimensional objects either missing or out of focus. This data volume presents several challenges, including the need to store all of the data, at least temporarily, and the need to process all of the data to construct an image of the three-dimensional objects within the matrix. This challenge is illustrated in a simplified manner in Figure 1, where a spherical object 110 is shown, for example, from a two-dimensional side view as residing in a flat-bottom well 105. While a flat-bottom well 105 is used in various figures, other well shapes, including, but not limited to, non-flat-bottom wells such as rounded wells, may also be used. Furthermore, the sidewalls of the well 105 need not be orthogonal to the flat bottom; instead, they may be rounded, tapered, or otherwise configured such that the longitudinal axis of the sidewall is not oriented at 90 degrees from the flat bottom. The object 110 resides within a matrix 115 within the well 105; as described herein, the object 110 may be adhered or suspended within the well 105, with the surrounding matrix being air or liquid, or with the matrix comprising a liquid layer over a solid or semi-solid layer. For example, in the case of biological cells, the matrix 115 may be solid or semi-solid, optionally with nutrients that support the survival of the cells within the matrix 115, or other materials that promote a biological response by the cells within the matrix 115. To image the object 110 within the well 105 using a microscope, for example, the well 105 must be scanned at many different locations throughout its three dimensions to provide an image map of the well 105 and the three-dimensional object 110 therein. For example, referring to FIG. 2, various scan lines (collectively 210) are shown, which represent scan points throughout the x-y dimensions of the well in different z-planes.For purposes of discussion herein, the z-dimension is the vertical (top / bottom) dimension of the figure, the x-dimension is the horizontal (left / right) dimension of the figure, and the y-dimension is the dimension into / out of the page of the figure. Different scan lines are different scans in the z-dimension. By scanning across the well 105 in the xy-dimensions and at various heights in the z-dimension, sufficient data can be acquired and used to construct a three-dimensional image of the object 110. However, this scanning step generates a vast amount of data, much of which is not relevant to constructing an image of the object 110. For example, most of the scan data represents the matrix 115, which does not contain any objects therein. For example, an image field 230 is shown that does not contain any data regarding any three-dimensional objects within the well 105. Scanning the image field 230 takes a lot of time, and scanning this field 230 generates a large amount of data that is not used to image the object 110. Scan lines 210 are shown for illustrative purposes, but the exact number and spacing of scans in the z-dimension may vary depending on the desired resolution. While FIG. 2 shows each data point as a dashed line for illustrative purposes, when imaging a three-dimensional object such as a biological cell, the z-distance between scan lines is often 1 micrometer or less, resulting in significantly more scan lines and data points than those shown in FIG. 2. These sequential scanning processes can generate vast amounts of data that must be stored, at least temporarily, and / or processed into an image of the object 110. Large data volumes can significantly slow imaging times and require additional data storage and processing power for imaging, potentially increasing costs. Long scan times also make real-time imaging difficult. .

[0043] In certain embodiments, to increase imaging speed while providing images with the desired resolution, the methods and systems described herein can use a pre-scan to determine the z-height and x-y coordinates of the object to be imaged. For example, rather than scanning the entire well to image any three-dimensional object in the matrix, a pre-scan may be performed to identify the x-y coordinates and z-height of the object in the matrix. A simplified diagram is shown in FIG. 3. The pre-scan step uses a scan with a lower resolution than the scanning step to quickly distinguish the object 310 in the matrix 315 from spaces / regions in the matrix 315 of wells 305 that do not contain the object to be imaged. For example, a pre-scan can be performed using a low-magnification objective, such as a 10x air objective, to provide rapid identification of the object in the well 305. The x-y coordinates of the object 310 can be identified in the pre-scan, along with the z-height 320. In this illustration, the z-height 320 is the vertical distance from the bottom of the well 305 to the bottom of the object 310. The bottom of the well 305 can be used as a reference point, although other areas or portions of the well can be used as reference points instead, if desired. For example, if the microscope objective is positioned above the well 305, the z-height may instead be the distance from the top of the well 305 to the top of the object 310. The z-height 320 need not be the distance from the bottom of the well 305 to the bottom of the object 310; it may be, for example, 10-20% lower or higher and still be used as a sufficiently accurate z-height measurement. Also, as described in more detail below, a z-width, e.g., an estimate of the z-height of the three-dimensional object 310, may be determined in a pre-scan and used to select a particular scanning resolution based on a desired scanning speed and / or desired data volume. Furthermore, depending on the position of the objective, an x-distance, e.g., the x-distance from the left side of the well 305 to the left side of the object 310, may be used instead, or a y-distance, e.g., the y-distance from the side of the well 305 to the side of the object 310, may be used instead of or in addition to the z-height.

[0044] In certain embodiments, once the x-y coordinates and z-height of the three-dimensional object 310 are determined using a pre-scan, the methods and systems described herein can then focus on the identified x-y coordinates of the three-dimensional object and then begin scanning from the identified z-height. For example, scans can be performed within the identified x-y plane in various z directions to provide image slices of the object 310. Other areas within the well can be ignored or otherwise not scanned during the high-resolution scan step, facilitating faster scan times and reduced data volume. For example, with reference to FIG. 4 , scans using a high-resolution objective, e.g., a 40x liquid immersion objective, can be performed across different x-y planes in the z direction to provide data representative of the object 310. In this simplified illustration, eight x-y scan planes (collectively 410) in different z dimensions are shown, with the first x-y scan plane beginning at the z-height 320 determined in the pre-scan, and scans of successive x-y planes being performed above the z-height in the z direction. For example, a scan of the xy plane above the z height can be performed with a z dimension distance separation length of 1 micron to provide multiple image slices of the object 410. If desired, the resulting data can be stored and used to construct an image of the object 310 that can be displayed.

[0045] 4 shows that the combination of a low-resolution pre-scan to identify the x-y coordinates of the object 310 and the z-height 320 of the object 310, followed by a high-resolution scan across the identified x-y coordinates in the z-direction starting from the z-height, can significantly reduce scan time and data volume. Compared to the data volume acquired using the scan shown in FIG. 2, the scan shown in FIG. 4 allows the system to This allows the scan to be focused on the area within the well 305 where the object 310 resides. The remainder of the matrix can be ignored (or otherwise not scanned) during the high resolution scanning step. The reduction in data volume can decrease the time required to image the object 310 within the well 305, and the reduced data volume can be used to enable real-time, or near real-time, imaging.

[0046] In certain instances, multiple objects are often present in any one well or sample container. These objects may be located at different z-heights and may have different overall dimensions / shapes. Referring to FIG. 5, a side view is shown in which well 505 contains a first object 510 and a second object 511 within a matrix 515. The objects 510, 511 have different sizes and shapes and reside in different regions of the matrix 515. A pre-scanning step can be performed at a low magnification to determine the x-y coordinates of each of the objects 510, 511 and to determine the z-heights of each of the objects 510, 511. Then, using the data obtained from the pre-scan, each of the objects 510, 511 can be scanned using its respective x-y coordinates and z-heights. Referring to FIG. 6, z-height 610 is shown for object 510 and z-height 611 is shown for object 511. The object 510 can be scanned at high resolution by starting at z-height 610 and scanning over specified x- and y-coordinates of the object 510. Additional scans in the z-direction (across specified x- and y-coordinates of the object 510) can be performed to provide image data of the object 510 that can be used to reconstruct a three-dimensional image of the object 510. Similarly, the object 511 can be scanned at high resolution by starting at z-height 611 and scanning over specified x- and y-coordinates of the object 511. Additional scans (across specified x- and y-coordinates of the object 511 in the z-direction) can be performed to provide image data of the object 511 that can be used to reconstruct a three-dimensional image of the object 511. The combination of a low-magnification pre-scan that identifies the x- and y-coordinates and z-heights of each of the objects 510, 511, followed by a high-resolution scan using the specified x- and y-coordinates and z-heights of each of the objects 510, 511 can enable more rapid imaging with a smaller data volume than would be achieved with a high-resolution scan of the entire well 505.

[0047] In certain examples, the methods described herein can be implemented using a system including an excitation source, optics, one or more objective lenses, and a detector. A general illustration of the system is shown in FIG. 7. System 700 includes an excitation source 710 optically coupled to optional system optics 720, which can be designed to pass, filter, pulse, or perform other optical manipulations on the excitation light. The excitation light from excitation source 710 is provided to a sample space 730, which can be designed to receive the sample in a container (e.g., a tube, a chamber, a microwell plate, or other device capable of holding a sample). The excitation light is incident on a specific region of the sample on sample region holder 730. For example, the excitation light can be used to excite lumiphores, such as fluorophores, present in or on a three-dimensional object. The excited fluorophores can then emit light as fluorescent emission 735. The fluorescent emission 735 may be collected by the objective lens 740 and provided to a detector 750 optically coupled to the objective lens 740. Although not shown, additional optics, such as a diffraction grating, mirrors, rotating disks, filters, dichroic sliders, monochromators, etc., may be present between the objective lens 740 and the detector 750 to select specific wavelengths of light for detection or otherwise direct the light emission to specific ports on the detector 750. The position of the sample within the sample space 730 may be moved, for example, in three dimensions, to change the specific spot or region of the sample on which the excitation light is incident. Alternatively, the excitation source 71 may be moved in a manner similar to that shown in FIG. 1B. The detector 750 can be moved to change the specific spot or region of the sample onto which the excitation light is incident. Scanning of the sample can be performed over specified x-y coordinates, starting at the z-height, to acquire multiple data values ​​over a specified dimension of the object. The detector 750 can be electrically coupled to a processor 760. The processor 760 can store the fluorescent emissions in the various x-, y-, and z-dimensions and use them (along with the fluorescent emissions from other scan measurements) to construct an image of the object in the sample. The image can be displayed on an optional display 770 or in other formats, for example, using a mobile device, tablet, or other electronic device coupled to the processor 760 in a wired or wireless manner. The excitation source 710 can be configured in many different forms, including a lamp, laser, arc, or other device capable of emitting light. In some cases, there can be multiple excitation sources that illuminate different wavelengths of light onto different lumiphores, as described below. For example, different fluorescent labels have different excitation wavelengths. Detector 750 can be many different types of detectors, including, but not limited to, photomultiplier tubes, cameras, complementary metal-oxide semiconductor (CMOS) cameras, charged-coupled device (CCD) cameras, scientific-grade CMOS (sCMOS) cameras, electron-multiplying CCDs (EMCCDs), photodiodes, avalanche photodiodes (APDs), microchannel plates (MCPs), and other detectors capable of detecting specific wavelengths of light emission from lumiphores. Detector 750 typically detects light emission that is red-shifted compared to the wavelength of excitation source 710 and can be designed to detect a very narrow wavelength range (e.g., 10-50 nm) or a wide wavelength range (e.g., 400-800 nm). Optical filters, such as bandpass filters, can be used to filter out undesired emission wavelengths to reduce background signal. Although the light source 710 is shown providing light at a direction approximately 90 degrees relative to the opening of the objective lens 940, the light can be provided at generally the same angle (e.g., parallel) or in a direction such that the light strikes the top or bottom surface of the sample space 730.

[0048] In some cases, the same system is typically used to perform the pre-scanning and scanning steps described herein, although separate detection systems can be used for the pre-scanning and scanning steps, if desired. For example, a low-resolution system can be used for the pre-scanning step, and a high-resolution system can be used for the scanning step. The detectors for the low-resolution pre-scanning system may be the same as or different from the detectors for the high-resolution scanning system. For example, a low-sensitivity detector may be present in the pre-scanning system to identify the x-y coordinates and z-height of the object. A more sensitive detector may be present in the high-resolution scanning system used to image the object. Furthermore, the low-resolution pre-scanning system may include a low-magnification air objective, while the high-resolution scanning system may include a high-magnification immersion objective. The two systems may share a light source, optics, etc., or may include separate light sources, optics, etc. However, by performing both the pre-scanning and scanning steps using a single system as described herein, significant data savings, reduced equipment complexity and footprint, and higher scanning speeds can be implemented using a single system.

[0049] In some configurations, the objectives used in the pre-scanning and scanning steps can be part of a confocal laser scanning microscope to improve scanning speed and provide high-throughput automated scanning. An example of a confocal laser scanning microscope system is shown in FIG. 8. The system 800 includes at least one laser 810 optically coupled to a mirror 815 or other optical element, such as a diffraction grating. The mirror 815 reflects light from the laser 810 as beam 811 onto a dichroic changer 820. The beam 811 typically impinges on an optional confocal rotating disk 825, which typically contains multiple pinholes or other apertures, and another mirror 830, and is provided through an objective lens 835 onto a sample residing in a sample holder 840. As described herein, different Magnifications and / or objective lenses can be used for the pre-scan and high-resolution scanning steps, as needed. Light emitted from the object being scanned is returned as beam 841 through objective lens 835 and incident on mirror 830, optional confocal rotating disk 825, dichroic changer 820, and mirrors 845, 850, and 855. The light can be filtered using filter 860 before being provided to detector 865, such as a camera, or other detector described with reference to detector 770 in FIG. 7. In some examples, laser 810 can include a single light-emitting diode or multiple light-emitting diodes, e.g., two, three, four, five, six, seven, eight, or more LEDs, each with a unique wavelength. If desired, any two of the light sources can have similar wavelengths but provide different magnifications and / or different pulse durations. The wavelength of light from laser 810 is typically selected so that any lumiphores (e.g., fluorophores or phosphors) present in the object can be excited and emit light by fluorescence or phosphorescence. In some cases, multiple wavelengths of light may be used in parallel with confocal rotating disk 825 to simultaneously monitor emissions at different wavelengths and / or collect multiple scan points simultaneously to further increase image processing. Additionally, the presence of confocal rotating disk 825 can reduce photobleaching and phototoxicity. Although not shown in Figure 8, a transmitted light source may be positioned above sample holder 840. Also, if desired, two or more detectors may be present in system 800. Detector 865 can be many different types of detectors, including, but not limited to, photomultiplier tubes, cameras, complementary metal-oxide semiconductor (CMOS) cameras, charged-coupled device (CCD) cameras, scientific-grade CMOS (sCMOS) cameras, electron-multiplying CCDs (EMCCDs), photodiodes, avalanche photodiodes (APDs), microchannel plates (MCPs), and other detectors capable of detecting specific wavelengths of light emission from lumiphores. Additionally, a first detector may be present and used for pre-scan measurements, and a second detector may be used for high-resolution scanning measurements.While not wishing to be bound by any particular theory, the detector's response often limits how fast a scan can be performed. By selecting a low-resolution detector with a fast response time for the pre-scan step, a faster pre-scan can be performed and the higher-resolution scan step can be initiated quickly. The faster initiation of the higher-resolution scan can enable visualization of the ongoing metabolism of cells, which would not be possible with an otherwise slower scan speed. System 800 can be used to acquire optical responses on thin optical sections passing through an object, and the acquired optical responses can then be used to construct an overall three-dimensional image of the object. Using less data and a high scan rate, images can be constructed or displayed in real time or near real time.

[0050] In some cases, the pre-scanning step performed using system 800 may use an air objective, while the scanning step may use a liquid (aqueous) immersion objective or other immersion objective, such as an oil immersion objective. As described herein, the low-magnification objective used during the pre-scanning step may typically be 5x, 10x, 15x, or 20x. Using a low-magnification air objective allows for the x-y coordinates and z-height (or z-coordinate) to be quickly determined during the pre-scanning step. Once these values ​​are determined, the objective can be switched or changed to a higher-magnification objective, such as an immersion objective. For example, a high-magnification objective of 30x, 40x, or more can be used during the scanning step. In some cases, system 800 includes three or more different objectives, providing the possibility of performing pre-scanning and / or scanning at different magnifications for different objects being measured. The system 800 or the user can select the particular objectives used for the pre-scan and scan steps depending on the desired scan rate, desired data volume, desired resolution, and / or desired image acquisition time. The system 800 can be used with different low magnification objectives to identify the x-y coordinates and z-height of each image present in each well or container of sample on the sample holder 840. These coordinates and values ​​can be stored and used. This allows each 3D object to be scanned at higher resolution (using a higher magnification objective) while simultaneously reducing the overall data volume.

[0051] In some examples, the systems described herein may include two or more separate detectors, allowing for simultaneous detection of different emission wavelengths by the different detectors. Referring to FIG. 9, a system 900 is shown that includes four light sources 902, 903, 904, and 905. Each of the light sources 902-905 is typically a laser, although non-laser light sources, choppers, etc., can be used instead of lasers if desired. Light from one or more of the light sources 902-905 is incident on a confocal unit 910, which includes two rotating disks. When light sources 904 and 905 are used, the light passes through the confocal unit 910 and strikes a mirror 912. The light from light sources 902-905 then strikes a dichroic slider 915 and then a mirror 920 before being provided to an objective lens 925. The light passes through the objective lens 925 and strikes the sample in a sample holder 930. Although not shown, a transmission light source may be present above the sample holder 930. Light from different light sources 902-905 may be incident on the same or different regions of the sample on sample holder 930. Specific light may be absorbed by lumiphores (e.g., fluorophores or phosphors) in the sample and emitted as fluorescent or phosphorescent emissions. The optical emissions return through objective lens 925 and are incident on mirror 920, dichroic slider 915, and confocal unit 910. Confocal unit 910 can select / direct specific light to mirrors 932 and 934, respectively. The optical emissions are then provided to dichroic slider 935. The optical emissions may then be provided to mirror 936 and mirror 938. For example, optical emissions reflected from mirror 932 may be provided to dichroic slider 935 and mirror 938. Optical emissions reflected from mirror 934 may be provided to dichroic slider 935 and mirror 936. Optical emissions reflected by mirror 936 may be provided to filters 941, 943 and to detectors 942, 944. Optical emissions reflected by mirror 938 may be provided to filters 945, 947 and to detectors 946, 948. The various filters and other optical elements in system 900 may be selected to minimize overlap of optical emissions from the sample, allowing for rapid and accurate simultaneous detection of different lumiphore emission signals from the sample.Detectors 942, 944, 946, and 948 can be the same or different. For example, each of detectors 942, 944, 946, and 948 can independently be a photomultiplier tube, a camera, a complementary metal oxide semiconductor (CMOS) camera, a charged coupled device (CCD) camera, a scientific grade CMOS (sCMOS) camera, an electron multiplying CCD (EMCCD), a photodiode, an avalanche photodiode (APD), a microchannel plate (MCP), and other detectors capable of detecting light emissions of specific wavelengths from lumiphores.

[0052] In some cases, the pre-scanning step performed using system 900 may use an air objective, while the scanning step may use a liquid (aqueous) immersion objective or other immersion objective, such as an oil immersion objective. As described herein, the low-magnification objective used during the pre-scanning step may typically be 5x, 10x, 15x, or 20x. Using a low-magnification air objective allows for the rapid identification of x-, y-, and z-height coordinates during the pre-scanning step. Once these values ​​are identified, the objective can be switched or changed to a higher-magnification immersion objective, such as a 30x, 40x, or higher immersion objective, for use during the scanning step. Because the majority of scanned samples may contain living biological cells or living biological organisms, a liquid immersion objective can be used to scan cells or organisms without disrupting or altering their ongoing metabolic and cellular activity. If the three-dimensional object being imaged using system 900 is a non-biological particle, colloid, nanostructure or nanosystem, or dead cell or organism, oil immersion can alternatively be used during the scanning step, although liquid immersion can also be used for these and other non-biological three-dimensional objects. In some cases, system 900 may use three or more different objective lenses. This includes a 3D objective lens (900) for scanning three-dimensional objects at different magnifications, providing the possibility to perform pre-scanning and / or scanning at different magnifications for different objects being measured. The system 900 or a user can select the particular objective lens used for the pre-scanning and scanning steps depending on the desired scan rate, desired data volume, desired resolution, and / or desired image acquisition time. As described herein, the system 900 can be used to identify the x-y coordinates and z-height of each image present in each well or container of sample on the sample holder 930. These coordinates and values ​​can be stored and used to scan each three-dimensional object at higher resolution while simultaneously reducing the overall data volume.

[0053] In certain embodiments, the methods and systems described herein can be used to provide three-dimensional images of objects residing in solid or semi-solid matrices, such as gels, hydrogels, three-dimensional scaffolds, or three-dimensional networks. Referring to FIG. 10A, three three-dimensional spheroids 1010, 1020, and 1030 are shown residing within a hydrogel matrix 1005. To cover all of the spheroids 1010, 1020, and 1030 within a well, extensive z-scans must be acquired in each field across the well. Many fields may not contain any three-dimensional spheroids, or may only partially contain the three-dimensional object. For example, five fields are shown in FIGS. 10B-10F. The field in FIG. 10B shows the spheroid 1010 being imaged. While the z-scan captures the entire object 1010, many scans above the object in the z-direction are also acquired, even though no object is present. This increases scan time and data volume. Referring to Figure 10C, the scanning of the field captures the spherical object 1020, but also scans a large amount of open space above the object 1020. The field of Figure 10D simply scans an area of ​​the hydrogel matrix that does not contain any objects at all. In the field of Figure 10E, only a portion of the object 1030 is scanned. In the field of Figure 10F, the object 1030 is outside the scanning field and is completely missed.

[0054] In a specific example, to reduce the amount of data and accelerate the generation of a three-dimensional image of an object, a pre-scan can be performed to identify the x, y, and z positions of the object of interest from a monochromatic, low-magnification pre-scan at a low z-sampling rate. This position information can then be used to acquire a high-magnification / high-resolution z-stack of the central object in a re-scan experiment. In a re-scan experiment, the minimum number of planes required to capture the object of interest can be determined. This methodology can reduce the number of planes, preventing the acquisition of empty images or partial images of the three-dimensional object and reducing the amount of data that needs to be acquired and analyzed. Furthermore, the identified object of interest is positioned in the center of the field of view to be re-scanned. Referring to Figure 11A, a pre-scan using a low-magnification objective can identify the x, y, and z positions of objects 1110 and 1120 present in a hydrogel matrix. In this figure, eight z-plane scans 1131-1138 are acquired and used to identify the x and y positions of each of the objects 1110 and 1120. As described herein, z-plane scanning can be used to determine the z-heights of each of the objects 1110, 1120. These z-heights can be used as the initial starting positions (in the z-direction) of the objects 1110, 1120 during the high-resolution scanning step. Referring to FIG. 11B, the z-height 1132 of the object 1110 can be used as the starting point for the scan. By performing scans in the x-y plane at incremental z-plane increments, the object 1110 can be imaged at high resolution. For example, the object 1110 can be imaged at high resolution by sequentially scanning images in the z-direction in each x-y plane using a liquid immersion objective. Combining the pre-scanning and scanning steps can reduce data volume by 20, 30, 40, or even 50 times or more, enabling fast image scanning and construction.

[0055] In certain embodiments, the methods and systems described herein can be used to image many different types of cells, tissues, organs, organisms, and the like. For example, FIG. 12A is a diagram of a spherical object residing in a hydrogel, FIG. 12B is a diagram of a hollow object (e.g., a cyst in a hydrogel), and FIG. 12C is a diagram of a spherical object on top of a hydrogel, e.g., in an aqueous layer containing media or other material present on top of the hydrogel. The methods and systems can also be used to monitor cellular activity and cell status. For example, imaging of cell invasion assays (FIG. 12D), cells seeded within a hydrogel (FIG. 12E), or cells seeded on top of a hydrogel (FIG. 12F) may be performed using the methods and systems described herein. When tissues, organs, or entire organisms are imaged, it is desirable that the matrix surrounding the object being imaged does not interfere with (or can be distinguished from) pre-scanning and scanning of the object of interest.

[0056] In certain embodiments, the methods described herein can be implemented in the form of a software package or executable code, which can be used in combination with an imaging system including a processor. Various processors and associated components are described in more detail below. In some examples, the software package can be downloaded from a remote website or otherwise installed from a non-transitory computer-readable medium having executable code (e.g., instructions stored thereon). Referring to FIG. 13 , the instructions, when executed by a processor, cause the processor to initiate a pre-scan in step 1302. The pre-scan identifies the x-y position and z-height of each object in a well (or other sample container) using a low-magnification scan in step 1304. The low-magnification imaging can be performed using a low-magnification objective, for example, a 10x air objective. The processor then stores the recorded x-y position and z-height of each identified object in step 1306. The processor can then initiate a high-magnification scan in step 1312. Once initiated, the high-magnification scan obtains the x, y position and z-height of each object recorded and identified from the pre-scan in step 1314, and then, using that information, each object is scanned using the high-magnification objective in step 1316. For example, using a 40x liquid immersion objective, the high-magnification scan can be initiated at a z-height on the x-y plane formed by the x, y coordinates. As each pixel is read at its z-height in that particular x-y plane, its value can be saved in step 1318. The processor can then repeat the high-magnification scan above the z-height in the z-direction, e.g., 1 micron above the z-height in the z-direction. The pixel values ​​read at this new z-dimensional x-y plane can also be saved. The processor can repeat this process until enough data has been acquired from different z-stacks to form an image of the entire three-dimensional object.These recorded pixel values ​​may be used by the processor to construct a three-dimensional image of the scanned object in step 1330, and the constructed image may be displayed in step 1332. If the image needs to be updated so that the real-time display of the image can be visualized, or if the image needs to be updated at a later time, the processor may resume the high-magnification scan in step 1316 and the process may be repeated. If only a single image of the object is needed, the processor may end the scan in step 1350. A single pre-scan may be used to identify the x-y location and z-height, even if multiple high-magnification scan / image construction iterations are performed, or pre-scan steps 1302-1306 may be repeated periodically, as needed, to ensure that cells or other objects have not altered their initial positions identified in the pre-scan. In other configurations, the pre-scan and scan steps may be combined, with a low-magnification pre-scan being performed before the high-magnification scan. In such a configuration, the imaging process returns from decision step 1334 to pre-scan step 1304 (instead of scan step 1316). and the whole process can be repeated.

[0057] In other embodiments, the methods described herein can be implemented with image correction to account for imaging aberrations and anomalies. For example, a software package including image correction functionality can be downloaded from a remote website or otherwise installed from executable code, e.g., a non-transitory computer-readable medium having instructions stored thereon. Referring to FIG. 14 , the instructions, when executed by a processor, cause the processor to initiate a pre-scan in step 1402. The pre-scan identifies the x-y position and z-height of each object in a well (or other sample container) using a low-magnification scan in step 1404. The low-magnification imaging can be performed using a low-magnification objective, e.g., a 10x air objective. The processor then stores the recorded x-y position and z-height of each identified object in step 1406. The processor can then apply corrections to the recorded x-y position and z-height to account for any imaging anomalies or exceptions in step 1408. The processor can then store the corrected x-, y-, and z-height of each identified object in step 1410. The processor can then initiate a high-magnification scan in step 1412. Once initiated, the high-magnification scan obtains the x, y position and z-height of each recorded, corrected, and identified object from the pre-scan in step 1414, and then uses that information to scan each object using the high-magnification objective in step 1416. For example, using a 40x liquid immersion objective, the high-magnification scan can be initiated at a z-height on the x-y plane formed by the x, y coordinates. Once each pixel has been read at the z-height in that particular x-y plane, its value can be saved in step 1418. The processor can then repeat the high-magnification scan above the z-height in the z-direction, e.g., 1 micron above the z-height in the z-direction. The pixel values ​​read at this new z-dimensional x-y plane can also be saved. The processor can repeat this process until enough data has been acquired from different z-stacks to form an image of the entire three-dimensional object.Corrections can be applied to each pixel as it is scanned. Alternatively, corrections can be applied to a dataset containing multiple pixels, e.g., an image, in step 1420. Corrections can be used to account for imaging anomalies, as discussed in more detail below. These corrected and recorded pixel values ​​(or corrected image set) can be used by a processor to construct a three-dimensional image of the scanned object in step 1430, and the corrected, constructed image can be displayed in step 1432. If the image needs to be updated so that a real-time display of the image can be visualized, or if the image needs to be updated at a later time, the processor can resume the high-magnification scan in step 1416 and the process can be repeated. If only a single image of the object is needed, the processor can terminate the scan in step 1450. A single pre-scan can be used to identify the x-y location and z-height, even if multiple high-magnification scan / image construction iterations are performed, or pre-scan steps 1402-1410 can be repeated periodically, as needed, to ensure that cells or other objects have not altered their initial positions identified in the pre-scan. In other configurations, the pre-scan and scan steps can be combined, with a low magnification pre-scan being performed before the high magnification scan. In such configurations, the imaging process can return from decision step 1434 to pre-scan step 1404 (instead of scan step 1416) and the entire process can be repeated.

[0058] In certain embodiments, the various optical emission signals acquired for the scanned three-dimensional objects can be used to construct a three-dimensional image of each scanned object in the sample. For example, the scan information can be used to register the images. The exact methodology used to construct the images from the optical signals can vary, but is not limited to the method described in U.S. Pat. No. 9,582,866. A method similar to that described in Issue 4 can be used for data acquired in all three dimensions. In some cases, it may be desirable to correct image values ​​to account for any distortions or anomalies. For example, geometric distortions often occur when optical signals are provided to the objective lens through a matrix. Using an air objective lens in the pre-scanning step can introduce distortions and chromatic aberrations as the emission light passes through the space between the sample and the objective lens. These distortions / aberrations can be compensated for by automatically correcting the z-values ​​of individual objective lenses. For example, z-correction can correct for the shift between the z-position of the top plate bottom determined with the infrared autofocus laser and the z-position of the top plate bottom determined with visible wavelengths. These values ​​can vary due to imperfect correction of chromatic aberrations between infrared and visible wavelengths. These values ​​also vary depending on the objective lens. Because measurements are performed in the visible range, this shift can be corrected to determine the correct object position relative to a common reference point (i.e., the top plate bottom as detected by the laser-based autofocus system). If the z-offsets of both objective lenses are corrected, the correct object z-height can be obtained before starting the high-resolution scanning step.

[0059] In certain embodiments, the penta pattern can be measured with a z-stack and used to correct for image anomalies. In one example, the penta pattern is measured with a z-stack (using the desired visible wavelength) to determine the sharpest plane of the pattern. This z-height information is used to z-correct the individual objective lens relative to the z-height detected at the autofocus laser wavelength. Differences in refractive index between the penta pattern measurement and the sample measurement are taken into account in the calculation. This z-correction can also correct for additional variables that contribute to offsets, such as autofocus laser collimation. Because these variables may change over time and / or be subject to adjustment, this correction can be implemented for each pre-scan measurement to ensure that the higher-resolution scan begins at the appropriate z-height.

[0060] In some examples, the method and system can adjust one or more images of the sample to correct for geometric distortions and / or properly align one or more images. This image correction can be performed on one or both of the pre-scanning and scanning steps to obtain corrected images that represent the actual three-dimensional object. In a particular example, the sample is a microtiter plate of wells containing three-dimensional biological cells, organelles, or components thereof. In one example, an artificial pattern of dots can be used to enable image adjustment and alignment at various magnifications. For example, one or more images of the pattern of dots can be acquired, and for each image, the expected, undistorted positions of the dots (in the x, y, and z directions) can be determined and compared to the positions in the images. In some embodiments, a geometric transformation is performed that moves (or otherwise repositions) the center positions of the imaged dots to their expected positions. These expected positions can then be saved and used, for example, in a high-resolution scanning step. The transformation values ​​can be saved and saved before the high-resolution scanning step. Next, for each three-dimensional object sample to be imaged, a set of images of the object is acquired using the same detector settings used to acquire the images of the pattern of dots. The stored geometric transformation, determined using the dot pattern, is applied to the image of the sample to correct / align it, and the corrected image is saved and / or displayed.

[0061] In certain embodiments, the exact dot pattern used in the image correction process may be different. For example, a set of one or more images of the quasi-periodic grid pattern may be acquired using a similar camera or set of cameras (or other detectors) used to acquire images of the three-dimensional object. The same camera adjustments and the same relative positions of the fields of view may be used when acquiring the images of the three-dimensional object as when acquiring the images of the grid pattern. When acquiring multiple images using the same camera (or other detector), the images acquired using the same camera (or other detector) shall be made under similar (or substantially the same) optical conditions and / or relative alignment of the different cameras, and the relative displacement of the image fields shall be kept constant.

[0062] In some embodiments, the true geometry of the acquired image can be reconstructed using known properties of the quasi-periodic grid. This step eliminates geometric distortions induced by the optical system. In some embodiments, the approach involves using the local uniqueness of the imaged pattern to identify dots within the pattern and determine their true positions from the known design of the grid pattern. In another approach, the geometric properties of the quasi-periodic grid can be used to derive the true geometry from known relative position constraints (e.g., quantized nearest neighbor positions and quantized angles) used to create the pattern. Knowing the true geometry allows for the derivation and application of a transformation that eliminates the geometric distortion.

[0063] In certain configurations where images are acquired from different cameras, the images from the different cameras can be correlated using the known, unique positions of the imaged dots identified within the pattern. The relative positions of the image fields are derived when the unique positions of the imaged dots are known. Alternatively, the relative positions of the image fields can be determined when the fields capture overlapping regions. In some embodiments, the geometric transformation derived in the steps described herein can be applied to images of the three-dimensional object acquired using the same detector (e.g., the same camera) and the same optical arrangement used to acquire the images of the quasi-periodic grid pattern. This image correction can be applied to only the pre-scan measurements, only the high-resolution scan measurements, or both. Applying the image correction to the pre-scan measurements can correct the determined x-y coordinates and the determined z-height for anomalies. This can be particularly desirable to ensure that the high-resolution scan begins at the correct z-height and in the correct x-y plane for each three-dimensional object.

[0064] While not wishing to be bound to any one particular geometric pattern or shape, the use of quasi-periodic gratings offers numerous advantages over the use of periodic gratings: for example, by using quasi-periodic gratings, alignment errors due to grid periods can be avoided, while preserving average grid density and allowing geometric properties such as nearest neighbor length and angle to be defined (e.g., quantized).

[0065] In some embodiments, the pattern used in image correction can be a grid with pentagonal symmetry. One way to achieve such a grid is to provide a Penrose tiling and associate the structural elements of the pattern (dots) with tile positions or vertices. In one approach, the pattern begins with a pentagon subdivided into two types of triangles, which are then hierarchically subdivided using the golden ratio division to create new triangles of the same shape. Reduced-size dots are associated with the newly created vertices. In some embodiments, a non-periodic grid (e.g., a grid that is not replicated by translating its points) can be used to avoid matching erroneous dots. For example, a procedure for adjusting one or more images of a sample to correct for geometric distortions and / or properly align one or more images desirably uses a unique position that can be determined for a given dot. In some examples, it may be desirable for the dots of the pattern to fill a field of interest uniformly so that the entire field can be corrected. The dot size hierarchy can be used simultaneously at different scales (e.g., different resolutions / magnifications). Particular configurations of quasi-periodic grid patterns can provide some or all of these desired properties, among others. One suitable pattern for a quasi-periodic lattice is similar to a Penrose tiling. For example, a pentagon can be triangulated with the golden ratio division to produce a pattern with desired properties. At each step of the grid refinement, the size of the dots located at the vertices can be reduced.

[0066] In certain embodiments, the pattern may be rotated slightly to avoid juxtaposition with the image boundary (and any resulting distortion). For example, the image may be rotated 1-30 degrees (e.g., 9 degrees). In some embodiments, the image may be rotated 1-5 degrees, 3-10 degrees, 5-15 degrees, 10-15 degrees, 10-20 degrees, 15-25 degrees, or 25-30 degrees. In some embodiments, the image may be rotated by any suitable degree. The exact dimensions of the image rotation can be varied as needed.

[0067] In some examples, image analysis functions can be used to determine the desired focal plane for the pre-scanning step, aligned with the three-dimensional object being measured. For example, image analysis functions can include segmenting the pre-scanned image and subsequently identifying and quantifying x-, y-, and z-height coordinates. These values ​​can be corrected as described herein before initiating the high-resolution scanning step. In some examples, image analysis functions can include determining features such as spatial texture, morphology, and biological activity required by biological applications. This can be achieved either by providing the user with a selection of predefined image analysis functions or by allowing the user to define or program their own custom image analysis functions.

[0068] In certain configurations, image correction can generally be performed by acquiring a set of images of the pattern. For each image, expected undistorted dot locations can be determined. A geometric transformation can then be calculated to change the dot center locations to the expected locations. This transformation can then be saved. For each three-dimensional object being scanned, a set of images can be acquired, and for each image, a geometric transformation can be applied. The resulting corrected images can be saved and used to construct corrected images for each three-dimensional object.

[0069] In other configurations, determining the expected undistorted dot locations can be achieved by geometric reconstruction. For example, for each image in the pattern image set, the image can be segmented to determine all dots, the dot center locations can be calculated, and any dot in the center of the image can be selected and its location accurately assumed. While not necessarily true in all cases, the nearest neighbor dot is typically located at an angle of 36 degrees and 1, φ, φ. 2The dots are considered to be at a distance having a ratio of φ = 1.618, where φ is the golden ratio. Based on this geometric relationship, each neighboring dot can be assigned a predicted location. For each neighboring dot identified in the previous step, a predicted location can be assigned until there are no more dots in the image. Dots whose location relative to the nearest neighbor point is beyond a predetermined tolerance from the predicted location can be discarded. In some examples, a common reference dot with the same predicted location across all pattern images is selected (e.g., arbitrarily), and an appropriate shift is applied to the predicted dot center location. Once the nearest neighbor point is located, an expected angle is selected (e.g., arbitrarily), and the predicted coordinates of the dot are rotated accordingly. Once the nearest neighbor point is located, an expected baseline length is selected (e.g., arbitrarily), and the predicted coordinates of the dot are scaled accordingly. After these steps, the predicted locations of all correctly identified dots will match. The values ​​of the actual dot location and the predicted dot location can then be saved. In some embodiments, as an alternative approach, the pattern image location can be identified in an expected dot location table used for pattern generation. The predicted dot locations can be converted to image coordinates by similar steps, as described herein.

[0070] In other embodiments, the geometric transformation is performed by taking the center of the dot at the expected location. In some embodiments, the geometric transformation can be performed for each table of expected and actual positions corresponding to the pattern image as follows: (1) calculate the shift required to move each dot to its expected position. The collection of shifts, along with the positions to which they are applied, is referred to herein as the shift field. (2) extend the shift field to the entire image by approximating the determined shift field with a continuous model (e.g., a second-order polynomial). This can be achieved by least-squares fitting or other suitable approximation. (3) save the polynomial coefficients for use in the sample images. Then, in some embodiments, the geometric transformation can be applied to the three-dimensional object image, for example, as follows: For each image in the sample set, (A) calculate the shift corresponding to each pixel in the image from the determined polynomial, and (B) apply the shift to the image raster data.

[0071] Given the benefit of this disclosure, those skilled in the art will recognize that other image correction processes and systems can also be used to provide image correction (or data correction) of various images acquired during pre-scan and higher magnification scan measurements. For example, different types of artificial objects or printed patterns can be used for image correction and / or alignment, such as a square pattern drawn by lines, a non-uniform line pattern, randomly collected dots, or a sparse sample of randomly placed beads or quantum dots. The approximate position of the microscope stage can be determined using an intrinsic positioning system. Further correction can then be achieved by identifying specific landmarks or objects in the calibration pattern or by correlating portions of different images from different cameras, filter settings, different objective lenses, and other imaging conditions. From such data parameters, other geometric image alignment models and correction processes can be derived and used. While not required, the penta pattern can serve as a robust and essentially simple method for achieving absolute and precise positioning of the complete image field and individual portions of the image.

[0072] In certain embodiments, the pre-scan measurements described herein can be used to determine the z-width of each three-dimensional object. The z-width is generally the z-dimensional distance occupied by a three-dimensional object, e.g., the z-distance from the top to the bottom of the object. The z-width need not be precise and can be estimated to select the scan speed, scan resolution, data volume, etc. that can be used or acquired in higher magnification scans. Referring to FIG. 15 , a simplified diagram is shown showing three z-stack pre-scans z1, z2, and z3. While well 1505 is shown to include a rounded bottom, flat bottoms or other shapes can also be used to determine the z-width of various objects. As described herein, the z-dimension distance from the bottom of well 1505 to z-plane z1 can be considered the z-height. The z-width of object 1510 in matrix 1515 is the z-dimension distance between z1 and z3. This distance can be determined or estimated and used to select the scan resolution and / or scan speed in higher magnification scans. For example, many high-magnification scans can use a z-distance separation length of 1 micron or less between different scans. While this scan resolution can provide high-resolution images, the overall time to perform a scan of the object 1510 may be too slow to capture certain metabolic events or cellular changes. To capture these events at a lower resolution, the system can use the z-width to select a specific z-dimension scan interval and / or overall scan time to view desired physiological or chemical changes undergone by the object 1510. For example, instead of using a z-distance interval of 1 micron for a higher-magnification scan, changing the z-distance interval to 2 or 3 microns can provide fewer images, but allow for faster overall scan times, enabling monitoring of cellular activity. Additionally, the z-width can be used to select an upper limit for the data volume. For example, the z-distance scan interval can be selected based on the z-width and desired data volume. It can be selected to provide less than the desired data volume acquired from a higher magnification pre-scan. The z-width may be determined in the same pre-scan used to determine the z-height, or it may be determined in a separate pre-scan.

[0073] In certain examples, as mentioned above, the systems and methods described herein may include or use a processor for controlling certain aspects of the system or process. In particular, the processor may receive and store the luminescence signals as an image, apply image correction as desired, and then display the corrected image on a display to allow an end user to view the three-dimensional object. The processor may be part of the system or instrument, or may reside in an associated device used with the instrument, such as a computer, laptop, mobile device, etc. For example, the processor may be used to control the sample position, detector parameters, image correction, and other operations performed by the system. Such processes may be performed automatically by the processor without user intervention, or the user may input parameters via a user interface. For example, the processor may use signal intensity and fragment peaks, along with one or more calibration curves, to determine the identity and extent of a particular lumiphore present within each individual cell. The processor may also color-code different labels present within the cells to more easily visualize different cellular components. In certain configurations, the processor may reside in one or more computer systems and / or general hardware circuits, including, for example, a microprocessor and / or suitable software for controlling the sample introduction device, ionization source, mass analyzer, detector, etc. for operating the system. In some examples, the detectors themselves may include their own respective processors, operating systems, and other features that enable detection of various emission signals. The processor may be integral to the system or may reside on one or more accessory boards, printed circuit boards, or computers electrically coupled to the components of the system. The processor is typically electrically coupled to one or more memory units to receive data from other components of the system and enable adjustment of various system parameters as needed or desired.The processor may be part of a general-purpose computer, such as one based on a Unix®, Intel PENTIUM®-type processor, Motorola PowerPC, Sun UltraSPARC, Hewlett-Packard PA-RISC processor, or any other type of processor. One or more of any type of computer system may be used in accordance with various embodiments of the present technology. Furthermore, the system may be connected to a single computer or distributed among multiple computers attached by a communications network. It should be understood that other functions, including network communications, may be performed, and the present technology is not limited to any particular functionality or set of functions. Various aspects may be implemented as dedicated software running on a general-purpose computer system. The computer system may include a processor connected to one or more memory devices, such as a disk drive, memory, or other device for storing data. Memory is typically used to store programs, calibration curves, chemical or physical properties measured by the cellular analyzer, and data values ​​during operation of the system. The components of a computer system may be coupled by interconnection devices, which may include one or more buses (e.g., between components integrated within the same machine) and / or networks (e.g., between components residing on separate, discrete machines). Interconnection devices provide communication (e.g., signals, data, instructions) exchanged between components of the system. The computer system can typically receive and / or issue commands within processing times of, for example, milliseconds, microseconds, or less, allowing rapid control of the system. For example, computer control can be implemented to control pre-scan speeds, scan speeds, objective lenses used for imaging, etc. The processor typically controls the power supply. The computer system may be electrically coupled to a power source, which may be, for example, a DC power source, an AC power source, a battery, a fuel cell, or other power source, or a combination of power sources. The power source may be shared with other components of the system. The system may also include one or more input devices, such as a keyboard, a mouse, a trackball, a microphone, a touchscreen, a manual switch (e.g., an override switch), and one or more output devices, such as a printing device, a display screen, and a speaker. Furthermore, the system may include one or more communication interfaces (in addition to or as an alternative to the interconnection device) that connect the computer system to a communications network. The system may include suitable circuitry for converting signals received from various electrical devices present in the system. Such circuitry may reside on the printed circuit board or may reside on a separate board or device that is electrically coupled to the printed circuit board via a suitable interface, such as a serial ATA interface, an ISA interface, a PCI interface, etc., or via one or more wireless interfaces, such as Bluetooth®, Wi-Fi, near-field communications, or other wireless protocols and / or interfaces.

[0074] In certain embodiments, the storage system used in the system described herein includes a computer-readable and writable non-volatile recording medium, which typically stores software code that can be used by a program executed by a processor, or information stored on or in a medium processed by the program. The medium may be, for example, a hard disk, a solid-state drive, or a flash memory. The program or instructions executed by the processor may be located locally or remotely and may be obtained by the processor via an interconnection mechanism, a communication network, or other means as needed. Typically, during operation, the processor reads data from the non-volatile recording medium to another memory that allows the processor faster access to information than the medium. This memory is typically a volatile random access memory such as dynamic random access memory (DRAM) or static random access memory (SRAM). This may be located within the storage system or memory system. The processor generally manipulates data in the integrated circuit memory and copies the data to the medium after processing is complete. Various mechanisms are known for managing data movement between the medium and the integrated circuit storage elements, and the present technology is not limited thereto. The present technology is also not limited to any particular memory or storage system. In particular embodiments, a system may include specially programmed, dedicated hardware, such as, for example, an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA). Aspects of the present technology may be implemented in software, hardware, or firmware, or any combination thereof. Furthermore, such methods, acts, systems, system elements, and components may be implemented as part of the above-described system or as separate components. While a particular system is described as an example of one type of system in which various aspects of the present technology may be practiced, it should be understood that aspects are not limited to implementation on the described system.Various aspects may be implemented on one or more systems with different architectures or components. The system may include a general-purpose computer system that is programmable using a high-level computer programming language. The system may also be implemented using specially programmed dedicated hardware. Various data values ​​corresponding to different x-y planes and z-heights or z-dimension values ​​from the pre-scan are stored in a storage system and can be acquired and used in the scan measurements of each scanned three-dimensional object.

[0075] In this system, the processor is typically a commercially available processor, such as the well-known Pentium class processor available from Intel Corporation. The processor typically runs an operating system, such as the Windows 95, Windows 98, Windows NT, Windows 2000 (Windows ME), Windows XP, Windows Vista, Windows 7, Windows 8, or Windows 10 operating systems available from Microsoft Corporation; MAC OS X, such as Snow Leopard, Lion, Mountain Lion, or other versions available from Apple; the Solaris operating system available from Sun Microsystems; or a UNIX or Linux operating system available from a variety of sources. Many other operating systems may be used, and in certain embodiments, a simple set of commands or instructions may function as the operating system. Additionally, the processor may be designed as a quantum processor, designed to perform one or more functions using one or more qubits.

[0076] In certain examples, the processor and operating system may together define a platform on which application programs in a high-level programming language may be written. It should be understood that the present technology is not limited to a particular system platform, processor, operating system, or network. It will also be apparent to those skilled in the art, given the benefit of this disclosure, that the present technology is not limited to a particular programming language or computer system. It should also be understood that other suitable programming languages ​​and other suitable systems may be used. In certain examples, the hardware or software may be configured to implement a cognitive architecture, a neural network, or other suitable implementation. Where desired, one or more portions of the computer system may be distributed across one or more computer systems coupled to a communications network. These computer systems may also be general-purpose computer systems. For example, various aspects may be distributed among one or more computer systems configured to provide services (e.g., servers) to one or more client computers or to perform overall tasks as part of a distributed system. Various aspects may be implemented on a client-server or multi-tier system with components distributed among one or more server systems that perform various functions according to various embodiments. These components may be executable, intermediate (e.g., IL) or interpreted (e.g., JAVA) code that communicates over a communications network (e.g., the Internet) using a communications protocol (e.g., TCP / IP). It should also be understood that the technology is not limited to running on any particular system or group of systems. It should also be understood that the technology is not limited to any particular distributed architecture, network, or communications protocol.

[0077] In some examples, various embodiments may be programmed using an object-oriented programming language such as, for example, SQL, SmallTalk, Basic, Java, Javascript, PHP, C++, Ada, Python, iOS / Swift, Ruby on Rails, or C# (C Sharp). Other object-oriented programming languages ​​may also be used. Alternatively, functional, scripting, and / or logic programming languages ​​may be used. Various configurations may be implemented in a non-programmed environment (e.g., a document created in HTML, XML, or other format that, when viewed in a browser program window, renders aspects of a graphical user interface (GUI) or performs other functions). Particular configurations may be implemented using programmed or non-programmed elements, or In some cases, the system may include a remote interface, such as one present on a mobile device, tablet, laptop computer, wearable device, or other portable device that may communicate via a wired or wireless interface, allowing operation of the system remotely as needed.

[0078] In certain examples, the processor may also include or have access to a database of information regarding fluorophores, biological cells, and other common information. For example, the database may store excitation and emission wavelengths for different fluorophores, and the processor may retrieve and use that information based on user selection of the specific fluorophores being used in the experiment. Instructions stored in the memory may execute software modules or control routines for the system, effectively providing a controllable model of the system. The processor may use information accessed from the database, along with one or more software modules executed within the processor, to determine control parameters or values ​​for different components of the system, such as various detector values, various scan rates, and general z-boundaries for specific cells. Using input interfaces to receive control instructions and output interfaces linked to various system components within the system, the processor may actively control the system. For example, the processor may control detectors, optical elements (confocal units, mirrors, dichroic sliders, etc.), objective lenses, sample holder position, and other components of the system.

[0079] In certain embodiments, the exact three-dimensional object that can be imaged using the methods and systems described herein can vary. The object can be a non-biological object or a biological object, or a component of a biological object, for example, an organelle such as a mitochondria. Cells, tissues, organs, and entire organisms can be imaged as needed.

[0080] In some embodiments, living cells are imaged using the methods and systems described herein. In some cases, the cells may be prokaryotic cells. For example, a drug discovery method using potential antibiotics may be implemented to determine the extent to which a particular antibiotic is taken up by individual bacterial cells, and the phenotype of, for example, living or dead cells may be determined to evaluate the effectiveness of a particular antibiotic candidate. When the cell is a bacterial cell, the bacterial cell may be selected from the group consisting of Acidobacteria, Actinomycetes, Acquifex, Armatimonads, Bacteroidetes, Caldiserica, Chlamydiae, Chlorobi, Chloroflexi, Chrysiogenes, Cyanobacteria, Deferribacteres, Deinococcus-Thermus, Dictyoglomi, Elusimicrobia, Fibrobacteria, Firmicutes, Fusobacteria The bacterial cells may be from one or more of the phyla: Acidobacteria, Blastocatechua, Holophaga, Rubrobacteria, Thermoleophila, Coriobacteria, Acidimicrobium, Nitriliptera, Actinomycetes, Acinetobacteria, and the like. Exemplary classes, orders, and / or families of bacterial cells that can be analyzed include: Acidobacteria, Blastocatechua, Holophaga, Rubrobacteria, Thermoleophila, Coriobacteria, Acidimicrobium, Nitriliptera, Actinomycetes, Acinetobacteria, and the like. Phylum Faecalis, Family Goldenrodaceae, Family Hydrogenothermidaceae, Family Desulfurobacteriales, Family Desulfurobacteriaceae, Family Thermosulfidibacter, Family Fimbriimonadia, Family Armatimonadia, Class Cthnomonadia, Class Rhodothermidae, Family Rhodothermidales, Family Balneolia, Family Balneolales, Class Sitophaga, Family Sitophagales, Class Sphingobacteria, Family Sphingobacterium Order, Chitinophagia, Chitinophagales, Bacteroidia, Bacteroidales, Flavobacteriales, Flavobacteriales, Caldisericaceae, Chlamydiales, Chlamydiaceae, Candidatus, Claviclamydiaceae, Parachlamydiales, Crybramidiaceae, Parachlamydiaceae, Simcaniaceae, Waddriaceae, CandidatusPiscichlamydia, Candidatus Actinochlamydiaceae, Candidatus Palichlamydiaceae, Candidatus Labochlamydiaceae, Ignavibacteria, Ignavibacteriales, Ignavibacteriaceae, Ignavibacterium, Melioribacter, Chlorobea, Chlorobium, Chlorobidae, Ancalochloris, Chlorobaculum, Chlorobium, Chloroherpeton, Clathrochloris, Pelodictyon, Prosthe cochloris, Thermoflexia, Dehalococcoidia, Anaeroliniaceae, Ardenticatenia, Caldilineae, Ktedonobacteria, Thermomicrobia, Chloroflexia, Phylum Chrysiogenes, Family Halichondidae, Family Chrysiogenes, Order Chroococci, Order Colococcidiopsida, Order Gloeobacterales, Order Nostocales, Order Oscillatoryes, Order Pleurocapsales, Order Spirulinales, Order Synechococcales, Order Incertaesedis, Deferribacterales, Deferribacteraceae, Deinococcales, Deinococcaceae, Toluepellaceae, Thermales, Thermaceae, Dictyoglomeridae, Dictyoglomeridae, Elusimicrobia, Endomicrobia, Blastocatellia, Chitinispirillia, Chitinivibrionia, Fibrobacteria, Bacillales, Bacillales, Lactobacillales, Clostridiales, Clostridiales, Haloanaerobium, Natranaerobiales, Thermoanaerobacteriales Order Rmoanaerobacterales, Erysipelotrichia, Order Erysipelotrichiales, Negativicutes, Order Selenomonadales, Thermolithobacteria, Fusobacteriia, Order Fusobacteriales, Family Leptotrichia, Sebaldella, Sneathia, Streptobacillus, Family Leptotrichia, Family Fusobacteriaceae, Cetobacterium, Fusobacterium, Ilyobacter, Propionigenium, Cycloriobacter Psychrilyobacter, Longimicrobia, Gemmatimonadetes, Oligosphaeria, Lentisphaeria, Nitrospiria, Order Nitrospirales, Phyllosphaeria, Class Phycisphaera, Class Planctomycetes, Alphaproteobacteria, Betaproteobacteria, Genus Hydrogenori, Gammaproteobacteria, Acidithiobacillia, Deltaproteobacteria ), Epsilonproteobacteria and Oligoflexidae, Spirochaetia, Brachyspirales, Brachyspiraceae, Brevinematales, Brevinematales, Leptospirales, Leptospiraceae, Spirochaetales, Borreliaceae, Spirochaetaceae, Salpullinaceae, Synergistia, Synergistidae, Synergistidae, Mollicutes, Thermodesulfobacteria, Thermodesulfobacteriale, Thermodesulfobacteriaceae, Thermotoga, Kosmotoga, Kosmotoga, Mesoaciditoga, Mesoaciditoga, Petrotogidae, Thermotoga, Thermotoga, Thermotoga, Ferbidobacteriaceae, Candidatus epixenosoma These include, but are not limited to, those from the classes Epixenosoma, Lentimonas, Methyloacida, Methylacidimicrobium, Methylacidiphilales, Spartobacteria, Opitatus, or Verrucomicrobium. Various genera and species within these classes, orders, and families can be selected for analysis using the methods and systems described herein.

[0081] In other embodiments, the cells may be eukaryotic cells, including both "normal" eukaryotic cells present in properly functioning tissues and "abnormal" eukaryotic cells present in cancerous or abnormal conditions. Furthermore, the eukaryotic cells may be derived from protozoa, fungi, animals, plants, algae, or other eukaryotic cells. The methods and systems may be particularly desirable for use in investigating the treatment of abnormal cellular metabolism, fungal infections, the effectiveness of cancer treatments, the effectiveness of pesticide treatments, tissue repair conditions, and other conditions that can be monitored based on the extent to which certain substances are present or transported into cells.

[0082] If the cell is a fungal cell, the fungal cell may be from one or more of the phyla: Bacillus, Chytridiomycota, Glomeromycota, Microsporidia, Neocallimastyx, Dicaria (including Deuteromycota), Ascomycota, Subphylum Chalcogenida, Subphylum Saccharomycetes, Subphylum Taphrina, Subphylum Basidiomycota, Subphylum Urobacterium, Subphylum Urobacterium, Subphylum Enterobacteriaceae, Subphylum Kickella, Subphylum Mucor, or Subphylum Amphitheater. Exemplary classes, orders, and / or families of fungal cells that can be analyzed include the following: Blepharopodidae, Blepharopodiales, Blepharopodaceae, Blepharopodidae, Blepharopodaceae, Physodermataceae, Solooxyidae, Chytridiomycota, Chytrioides, Cladochytriales, Rhizophydiales, Polychytriales, Spizelomycetales, Chytridiomycota, Lobulomycetales, Gromochytriales. , Mesochytriales, Synchytriales, Polyphagales, Monoblepharidomycetes, Monoblepharidales, Harpochytriales, Hyaloraphidiomycetes, Hyaloraphidiales, Glomerales, Archaeosporales, Diversi Sporales, Glomerales, Paraglomerales, Nematoda, Metchnikovellea, Metchnikovellida, Amphiacanthoideae, Metchnikovellidae, Microsporidae, Cougordellidae, Facilisporidae, Heterovesiculidae, Myosporidae, Nadelsporidae (Nadelsporidae), Neonosemoidiidae, Ordosporidae, Pseudonosematidae, Telomyxidae, Toxoglugeidae, Tubulinosematidae, Haplophasea, Chytridiopsida, Chytridiopsidae, Buk Buxtehudiidae, Enterocytozoonidae, Burkeidae, Hesseidae, Glugeida, Glugeidae, Gurleyidae, Encephalitozoonidae, Abelsporidae, Tuzetiidae, Microfilidae, Unicaryonidae Unikaryonidae, Dihaplophasea, Meiodihaplophasida, Thelohanioidea, Thelohaniidae, Duboscqiidae, Janacekiidae, Pereziidae, Striatosporidae, Cylindrosporidae,Burenelloidea, Burenellidae, Amblyosporoidea, Amblyosporidae, Dissociodihaplophasida, Nosematoidea, Nosematidae, Ichthyosporidiidae, Caudosporidae, Pseudopleistospho Pseudopleistophoridae, Mrazekiidae, Culicosporoidea, Culicosporidae, Culicosporellidae, Golbergidae, Spragueidae, Ovavesiculoidea, Ovavesiculidae, Tetramicridae, Ludimicrospora (Rudimicrospora), Minisporea, Minisporida, Metchnikovellea, Metchnikovellida, Polaroplasta, Pleistophoridea, Pleistophorida, Disporea, Unikaryotia, Diplokaryoti a), Neocallimastigomycetes, Neocallimastigales, Neocallimastigaceae, Subclass Acanthogonaceae, Pycnogonaceae, Coniocybomycetes, Class Acanthogonaceae, Eurotium, Geoglossomycetes, Laboulbeniomycetes, Lecanoromycetes,Leotiomycetes (Leotiomyc, etes, Lichinomycetes, Orbiliomycetes, Chauhantomycetes, Sordariomycetes, Xylonomycetes, Ramiales, Itchiclahmadion, Tribridiales, Saccharomycotina, Hemiascomycetes, Taphrinae, Archaeorhizomyces, Neolectomycetes Neolectomycetes, Pneumocystida, Schizosaccharomycetes, Taphrina, Pycnomycetes, Coniocybomycetes, Pycnomycetes, Eurotium, Pycnomycetes, Laboulbeniomycetes, Lecanoromycetes, Leotiomycetes, Lichinomycetes, Orbiliomycetes ycetes, Class Chauhantomycetes, Sordariomycetes, Xylonomycetes, Order Ramiales, Medeolariales, Tribridiales, Saccharomycetales, Family Ascoideae, Family Cephaloascaceae, Family Debalyomycetaceae, Family Dipodascaceae, Family Endomycetaceae, Family Metschnikowiaceae, Family Phaffomycetaceae, Family Pi Family: Saccharomycetes, Saccharomycodaceae, Saccharomycopsidaceae, Trichomonasscaceae, Archaeorhizomycetes, Neolectomycetes, Pneumocystids, Schizosaccharomycetes, Taphrina, Subphylum: Agaricida, Subphylum: Sabikinida, Subphylum: Urobocytida, Class: Wallemia, Class: Tremella,Class Auricularia, Eubasidiomycetes, Agaricostilbomycetes, Atractiellomycetes, Classiculomycetes, Cryptomycocolacomycetes, Cystobasidiomycetes, Microbial Fungi, Myxia, Class Ubiquitinae, Tritiomycetes rachiomycetes, Exobasidiomycetes, Ceraceosorales, Doassansiales, Entylomatales, Exobasidiales, Georgefischeriales, Microstromatales, Ti lletiales, Urocystales, Ustilaginales, Malasseziomycetes, Malassezioales, Moniliellomycetes, Moniliellales, Basidiomycetes, Neozygitomycetes The classes, orders, and families include, but are not limited to, the classes, orders, and families of fungi, including, but ...

[0083] If the cell is a plant cell, the plant cell may be from one or more of the following divisions and subdivisions: Nematoda, Chlorophyta, Palmophyllum, Prasinophytes, Nephroselmiphyceae, Pseudoscoulfieldiales, Pyramimonadophyceae, Mammillophyceae, Scoulfieldiales, Pedinophyceae, Chlorodendronphyceae, Trebouxiaceae, Ulvaphyceae, Chlorophyceae, Streptophyta, Chlorocybusphyta, Mesostigmatphyceae, Klebsormidiophyta, Charophyta, Chatosphaeridiales, Coleochaetaphyceae, Zygophyta, or land plants. Exemplary classes, orders, families, and genera of plant cells that can be analyzed include Nematothallus, Cosmochlaina, Nematoda, Nematoplexus, Nematasketum, Prototaxites, Ulvaphyceae, Trebouxiophyceae, Chlorophyceae, Chlorodendronychia, Mammillellaphyceae, Nephroselmiphyceae, Palmophyllales, Pedinophyceae, Prasinophyceae, Pseudoscoulfieldiales, Pyramimonadaceae, Scopolia, and the like. Order Wolfieldiales, Genus Palmocrassurus, Genus Palmophilum, Genus Verdigerus, Order Prasinococcales, Prasinophyceae Incertae cedis, Order Pseudoscowolfieldiales, Order Pyramimonadales, Order Nephoselmis, Family Pycnococcaceae, Family Scorefieldae, Order Pedinomonas, Order Resultor, Order Marsupiomonas, Order Chlorochtridion tubulatumtuberculatum, Chlorellales, Prasiolales, Trebouxiales, Brachyales, Cladophorales, Paracetales, Bacillales, Scotinosphaerales, Violales, Ectophytales, Ulvales, Chaetopeltis, Chaetophorales, Chlamydomonadales, Chlorococcales, Chlorocystis, Microsporales, Pyralidiales, Pyralidiales, Pyralidiales, Chlorocybioides, Mesostigmatoides, Entransia, Unbranched Filaments, Interfilum, Klebsormidium The classes, orders, families, and genera include, but are not limited to, the following: (a) Phytophytes, (b) Mesostigmatidae, (c) Klebsormidiophyceae, (d) Zygophytes, (e) Zygophytes, (f) Zygophytes, (g) Zygophytes, (g) Charales, (g) Chlorocybes, (g) Coleochaetales, (g) Polychaetophora, (h) Chaetosphaeridiaceae, (i) Coleochaetophyceae, (i) Zygophytes, (i) Zygophytes, (i) Bryophytes, (b) Liverworts, (c) Horneophytopsida, (d) Vascular plants, (e) Linear plants, (f) Zosterophyllophyta, (g) Microphytes, (i) Trilobites, (f) Pteridophytes, (i) Sperm plants, (i) Seed ferns, (i) Conifers, (i) Cycads, (i) Ginkgo, (i) Gnetophytes, or (i) Angiosperms. Various species within these classes, orders, families, and genera can be selected for analysis using the methods and systems described herein.

[0084] In some cases, one or more structures in plant organelles can be imaged using the methods and systems described herein.For example, plant organelles can include, but are not limited to, plant cell nuclei, nuclear membranes, endoplasmic reticulum, ribosomes, mitochondria, vacuoles, chloroplasts, cell membranes or cell walls.Plant organelles can be separated from other cellular materials, allowing the metabolism and / or function of isolated plant organelles to be monitored during imaging.

[0085] When the cell is an animal cell, the animal cell can be an embryonic stem cell, an adult stem cell, a tissue-specific stem cell, a mesenchymal stem cell, an induced pluripotent stem cell, an epithelial tissue cell, a connective tissue cell, a muscle tissue cell, or a neural tissue cell.The animal cell can be derived from the ectoderm, the endoderm, or the mesoderm.Cells derived from the ectoderm include, but are not limited to, skin cells, anterior pituitary cells, peripheral nervous system cells, neuroendocrine cells, teeth, eye cells, central nervous system cells, epithelial cells, and pineal gland cells.Cells derived from the endoderm include, but are not limited to, respiratory cells, stomach cells, intestinal cells, liver cells, Cells of mesodermal origin include, but are not limited to, osteochondrocytes, myofibroblasts, hemangioblasts, stromal cells, macular cells, stromal cells, telocytes, podocytes, Sertoli cells, Leydig cells, granulocytes, PEG cells, germ cells, hematopoietic stem cells, lymphoid cells, bone marrow cells, endothelial progenitor cells, endothelial colony-forming cells, endothelial stem cells, hemangioblasts / mesodermal cells, pericytes, and mural cells.

[0086] In some examples, the animal cells are typically mammalian cells, such as, for example, human cells, canine cells, equine cells, feline cells, bovine cells, and other animal cells, and are cells from one or more of the following mammalian subphyla: Artiodactyla, Carnivora, Cetacea, Chiroptera, Dermoptera, Xenarthra, Hyracoidea, Talentiformes, Lagomorpha, Moraspilia, Monotreme, Perissodactyla, Pholidata, Pinnipedia, Primates, Proboscidea, Rodentia, Sirenia, and Turbulidentata. In some examples, the mammalian cells may be derived from the Prosimianidae or Simianidae families. In other examples, the mammalian cells may be derived from one or more of the order Adapiformes, Lemuriformes, Omomyiformes, and Tarsiformes. In additional examples, the mammalian cells may be derived from one or more of the suborder Platyrhynchia or the infraorder Anthropoidea. In some examples, the mammalian cells may be derived from the family Hominidae or genus Homo, e.g., human cells.

[0087] In some cases, cancerous animal cells can also be imaged using the methods and systems described herein to assess the effectiveness of treatment with a particular drug or material. The exact drug may vary depending on the specific type of cancer being treated, but the drug desirably causes cancer cell death in some manner. Exemplary types of cancer whose cells can be imaged include acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), adrenocortical carcinoma, AIDS-related cancer Kaposi's sarcoma (soft tissue sarcoma), AIDS-related lymphoma (lymphoma), primary CNS lymphoma (lymphoma), anal cancer, appendix cancer, astrocytoma, atypical teratoid / rhabdoid tumor, basal cell carcinoma of the skin, bile duct cancer, bladder cancer, bone cancer, brain tumor, breast cancer, bronchial tumor, Burkitt's lymphoma, carcinoid tumor, carcinoma of unknown primary, cardiac (heart) tumor, atypical teratoid / rhabdoid tumor, and others. Idiopathic tumor, embryonal tumor, germ cell tumor, primary CNS lymphoma, cervical cancer, childhood cancer, bile duct cancer, chordoma, chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), chronic myeloproliferative neoplasm, colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, ductal carcinoma, endometrial cancer, uterine cancer, ependymoma, esophageal cancer, olfactory neuroblastoma, Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, eye cancer, intraocular melanoma, retinoblastoma, fallopian tube cancer, osteofibrous histiocytoma, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor , germ cell tumors, pediatric extracranial germ cell tumors, extragonadal germ cell tumors, ovarian germ cell tumors, testicular tumors, gestational trophoblastic disease, hairy cell leukemia, head and neck cancer, hepatocellular (liver) carcinoma, histiocytosis, Hodgkin's lymphoma, hypopharyngeal cancer, head and neck cancer, intraocular melanoma, pancreatic islet cell tumors, Kaposi's sarcoma, renal cell carcinoma, Langerhans cell histiocytosis, laryngeal cancer, leukemia, lip and oral cancer, liver cancer, lymphoma, male breast cancer, malignant fibrous histiocytoma of bone and osteosarcoma, melanoma, pediatric melanoma, Merkel cell carcinoma, mesothelioma, metastatic cancer, occult primary (head and neck) cancer Metastatic squamous cell carcinoma with NUT gene alterations, midline carcinoma with NUT gene alterations, oral cancer (head and neck cancer), multiple endocrine neoplasia syndrome, multiple myeloma / plasma cell neoplasm, mycosis fungoides (lymphoma), myelodysplastic syndrome, myelodysplastic / myeloproliferative neoplasm, myeloid leukemia, chronic myeloid leukemia (CML), myeloid leukemia, leukemia (AML), myeloproliferative neoplasm, nasal cavity and paranasal sinus cancer (head and neck cancer), nasopharyngeal carcinoma (head and neck cancer), neuroblastoma, non-Hodgkin's lymphoma, non-small cell lung cancer, oral cavity cancer, lip and oral cavity cancer,Osteosarcoma and malignant fibrous histiocytoma of bone, ovarian cancer, pancreatic cancer, pancreatic neuroendocrine tumors (islet cell tumors), papillomatosis, paraganglioma, sinus and nasal cancer, parathyroid cancer, penile cancer, pharyngeal cancer (head and neck cancer), pheochromocytoma, pituitary tumor, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary peritoneal cancer, prostate cancer, rectal cancer, recurrent cancer, renal cell carcinoma, retinal, These include, but are not limited to, bladder cancer, rhabdomyosarcoma, salivary gland cancer, childhood rhabdomyosarcoma, childhood vascular tumors, Ewing's sarcoma (bone cancer), Kaposi's sarcoma (soft tissue sarcoma), osteosarcoma (bone cancer), uterine sarcoma, Sezary syndrome (lymphoma), skin cancer, small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma of the skin, occult primary squamous cell carcinoma, metastatic (head and neck cancer), gastric (stomach) cancer, T-cell lymphoma, testicular tumors, nasopharyngeal cancer, oropharyngeal cancer, hypopharyngeal cancer, thymoma and thymic carcinoma, thyroid cancer, transitional cell carcinoma of the renal pelvis and ureter, urethral and renal pelvic cancer, urethral cancer, uterine cancer, uterine sarcoma, vaginal cancer, vascular tumors, vulvar cancer, Wilms' tumor, and other forms of cancer.

[0088] In some cases, organelles of animal cells can be isolated from other components of the animal cell, and then the phenotype of the organelle and the material content of the animal organelle can be determined using the imaging methods and systems described herein. If necessary, the organelle phenotype (or the biological response of the organelle) can then be correlated with specific materials or components of the organelle. For example, isolated organelles can include, but are not limited to, the nucleus, nuclear membrane, microtubules, microfilaments, endoplasmic reticulum, sarcoplasmic reticulum, ribosomes, mitochondria, vasculature, lysosomes, cell membranes, or other organelles present in animal cells.

[0089] In some examples, the methods and systems described herein can also be used to image viruses that may exist inside or outside cells, or both.For example, the effectiveness of antiviral agents that bind to viral protein coats can be evaluated using the imaging methods and systems described herein.When analyzing viruses, the viruses can be, for example, double-stranded DNA viruses, single-stranded DNA viruses, double-stranded RNA viruses, positive-sensing single-stranded RNA viruses, negative-sensing single-stranded RNA viruses, single-stranded RNA reverse transcription viruses (retroviruses), or double-stranded DNA reverse transcription viruses. Various specific viruses include Papovaviridae, Adenoviridae, Herpesviridae, Herpesviridae, Ascoviridae, Ampulaviridae, Asfarviridae, Baculoviridae, Fuselloviridae, Globuloviridae, Guttaviridae, Hidrosaviridae, Iridoviridae, Lipotrixviridae, White Spot Disease, Poxviridae, Tectoviridae, Corticoviridae, Sulfolobus, Caudoviridae, Corticoviridae, Tectoviridae, Ligamenviridae, Ampulaviridae, Bicaudaviridae, Clavaviridae, Fuseroviridae, Globuloviridae, Guttaviridae, Vorticellaviridae, Ascoviruses, Baculoviruses, Hidrosaviviridae, Iridoviridae, Polydnaviridae, Mimiviridae, Marseillevirus, Megavirus, Mabilisvirophage, Sputnikvirophage, Nimaviridae, Phycodnaviridae, Prelipovirus, Plasmaviridae, Pandoraviridae, Dinodenavirus, Rigidiovirus, Salterprovirus, Spherolipoviridae, Anelloviridae, Bidnaviridae, Circoviridae, Geminiviridae, Genomoviridae, Inoviridae, Microviridae, Nanoviridae, Parvoviridae, Spiralviridae, Amargaviridae, Birnaviridae, Chrysoviridae, Cystoviridae, Endornaviridae, Hypoviridae, Megavirnaviridae, Partizanviridae, Picovirnaviridae, Quadriviridae, Reoviridae, Tochiviridae, Nidovirales, Picornavirales, Tymovirales, Mononegavirales, Bornaviridae, FiloviridaeMymonaviridae, Nyamiviridae, Paramyxoviridae, Pneumoviridae, Rhabdoviridae, Sanviridae, Ampheviruses, Arliviruses, Centiviruses, Clusterviruses, Wostoviruses, Bunyaviridae, Feraviridae, Fimoviridae, Hantaviridae, Jonviridae, Nairobiviridae, Peripnyaviridae, Pharmaviridae, Fenuiviridae, Tospoviridae, Arenaviridae, Ophioviridae, Orthomyxoviridae, Deltaviruses, Taastrupviruses, Alpharetroviruses, Avian leukosis viruses, Rous sarcoma virus, Betaretroviruses, Mouse mammary tumor virus, Gammaretroviruses, Murine leukemia virus, Feline leukemia virus, These include, but are not limited to, viruses, bovine leukemia virus, human T-lymphotropic virus, epsilon retrovirus, Alaska pollock dermal sarcoma virus, lentivirus, human immunodeficiency virus 1, simian and feline immunodeficiency virus, spumavirus, simian foamy virus, Orthoretroviridae, Spumaretroviridae, Metaviridae, Pseudoviridae, Retroviridae, Hepadnaviridae, or Caulimoviridae. A variety of species within these classes, orders, families, and genera can be selected for analysis using the methods and systems described herein.

[0090] In certain configurations, imaging methods and systems can detect one or more labels present on antibodies that specifically bind to sites on individual cells. Antibodies are generally polypeptides containing multiple amino acids linked together via peptide bonds. Antibodies can include immunoglobulin chains or fragments thereof, which contain at least one immunoglobulin variable domain sequence. The term antibody includes, for example, monoclonal antibodies (including full-length antibodies with an immunoglobulin Fc region), polyclonal antibodies, and other polypeptides capable of specifically binding to one or more sites on a cell. In one embodiment, an antibody molecule comprises a full-length antibody or a full-length immunoglobulin chain. In another embodiment, an antibody molecule comprises an antigen-binding or functional fragment of a full-length antibody or a full-length immunoglobulin chain. The amino acids of an antibody can be natural or synthetic, contain both amino and acid functional groups, and can be included in polymers of naturally occurring amino acids. Exemplary amino acids include naturally occurring amino acids, their analogs, derivatives, and congeners, amino acid analogs with variant side chains, and stereoisomers of any of the foregoing. Both D- and L-enantiomers of amino acids and peptidomimetics can be used. The term antibody also includes intact molecules as well as functional fragments thereof. The constant region of an antibody can be mutated, for example, to modify the properties of the antibody (e.g., to increase or decrease one or more of Fc receptor binding, antibody glycosylation, the number of cysteine ​​residues, effector cell function, or complement function).

[0091] If desired, antibodies can be generated using conservative amino acid substitutions, in which an amino acid residue is replaced with an amino acid residue having a similar side chain. Families of amino acid residues having similar side chains can be selected by one of skill in the art with the benefit of this disclosure. These families include amino acids with basic side chains (e.g., lysine, arginine, histidine), acidic side chains (e.g., aspartic acid, glutamic acid), uncharged polar side chains (e.g., glycine, asparagine, glutamine, serine, threonine, tyrosine, cysteine), nonpolar side chains (e.g., alanine, valine, leucine, isoleucine, proline, phenylalanine, methionine, tryptophan), beta-branched side chains (e.g., threonine, valine, isoleucine), and aromatic side chains (e.g., tyrosine, phenylalanine, tryptophan, histidine). The exact length of the antibody can be varied as desired. In some cases, synthetic polypeptides can be linked to each other to form antibodies. Antibodies can be linear or branched, can contain modified amino acids, and can be interrupted by non-amino acids. Antibodies can be modified, for example, by disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation, such as labeling or conjugation with a labeling component. Polypeptides can be isolated from natural sources, produced by recombinant technology from eukaryotic or prokaryotic hosts, or can be the product of synthetic procedures.

[0092] Antibodies can be expressed in host systems by inserting a suitable nucleic acid sequence into the host organism. The terms "nucleic acid," "nucleic acid sequence," "nucleotide sequence," or "polynucleotide sequence," and "polynucleotide" are used interchangeably. These terms generally refer to a polymeric form of nucleotides of any length, either deoxyribonucleotides or ribonucleotides, or analogs thereof. Polynucleotides can be either single-stranded or double-stranded, and if single-stranded, can be either a coding strand or a non-coding (antisense) strand. A polynucleotide may be a sequence of nucleotides (or a combination thereof). A polynucleotide may comprise modified nucleotides, such as methylated nucleotides and nucleotide analogs. The sequence of nucleotides may be interrupted by non-nucleotide components. A polynucleotide may be further modified after polymerization, such as by conjugation with a labeling component. A nucleic acid may be a recombinant polynucleotide, or a polynucleotide of genomic, cDNA, semisynthetic, or synthetic origin that is not naturally occurring or is connected to another polynucleotide in a non-natural configuration.

[0093] An antibody can be isolated from a host or other expression or production organism. As used herein, the term "isolated" refers to material that has been removed from its original or natural environment (e.g., the natural environment in which it occurs in nature). For example, a naturally occurring polynucleotide or polypeptide present in a living animal is not isolated, but the same polynucleotide or polypeptide that has been separated from some or all of the coexisting materials in the natural system by human intervention is isolated. Such a polynucleotide can be part of a vector, and / or such a polynucleotide or polypeptide can be part of a composition and still be isolated in that it is not part of the environment in which such a vector or composition naturally occurs.

[0094] In one configuration, the antibody molecule binds to an animal cell, e.g., a mammalian cell. For example, the antibody molecule specifically binds to an epitope, e.g., a linear or conformational epitope (e.g., an epitope described herein), on a mammalian cell. In some embodiments, the antibody molecule binds to one or more extracellular Ig-like domains present on a mammalian cell, e.g., the first, second, third, or fourth extracellular Ig-like domain of a particular epitope.

[0095] In one embodiment, the antibody molecule is a monospecific antibody molecule that binds to a single epitope, e.g., a monospecific antibody molecule having multiple immunoglobulin variable domain sequences, each of which binds to the same epitope.

[0096] In another embodiment, the antibody molecule is a multispecific antibody molecule, e.g., comprises multiple immunoglobulin variable domain sequences, wherein a first immunoglobulin variable domain sequence of the plurality has binding specificity for a first epitope and a second immunoglobulin variable domain sequence of the plurality has binding specificity for a second epitope. In one embodiment, the first and second epitopes are present on the same antigen, e.g., the same protein (or subunit of a multimeric protein). In another example, the first and second epitopes overlap. In one embodiment, the first and second epitopes do not overlap. In another embodiment, the first and second epitopes are present on different antigens, e.g., different proteins (or different subunits of a multimeric protein). In a further embodiment, the multispecific antibody molecule comprises a third, fourth, or fifth immunoglobulin variable domain. In one embodiment, the multispecific antibody molecule is a bispecific antibody molecule, a trispecific antibody molecule, or a tetraspecific antibody molecule.

[0097] In some examples, a multispecific antibody molecule may be a bispecific antibody molecule. A bispecific antibody has specificity for no more than two antigens or two binding sites. A bispecific antibody molecule is characterized by a first immunoglobulin variable domain sequence having binding specificity for a first epitope and a second immunoglobulin variable domain sequence having binding specificity for a second epitope. In another embodiment, the first and second epitopes are present on the same antigen, e.g., the same protein (or subunit of a multimeric protein). In one embodiment, the first and second epitopes overlap. In another embodiment, the first and second epitopes do not overlap. In one embodiment, the first and second epitopes are present on different antigens, e.g., different proteins (or different subunits of a multimeric protein). In other embodiments, a bispecific antibody molecule is characterized by a first epitope. In some cases, the bispecific antibody molecule comprises a heavy chain variable domain sequence and a light chain variable domain sequence that have binding specificity for a first epitope, and a heavy chain variable domain sequence and a light chain variable domain sequence that have binding specificity for a second epitope. In other cases, the bispecific antibody molecule comprises a half antibody that has binding specificity for a first epitope and a half antibody that has binding specificity for a second epitope. In additional embodiments, the bispecific antibody molecule comprises a half antibody, or fragment thereof, that has binding specificity for a first epitope, and a half antibody, or fragment thereof, that has binding specificity for a second epitope. In one embodiment, the bispecific antibody molecule comprises a single-chain variable fragment (scFv), or fragment thereof, that has binding specificity for a first epitope, and an scFv, or fragment thereof, that has binding specificity for a second epitope.

[0098] In other configurations, antibody molecules include diabodies, single-chain molecules, and antigen-binding fragments of antibodies (e.g., Fab, F(ab')2, and Fv). For example, an antibody molecule can include a heavy (H) chain variable domain sequence (abbreviated herein as VH) and a light (L) chain variable domain sequence (abbreviated herein as VL). In one embodiment, an antibody molecule comprises or consists of a heavy chain and a light chain (referred to herein as a half antibody). In another example, an antibody molecule includes two heavy (H) chain variable domain sequences and two light (L) chain variable domain sequences, thereby forming two antigen-binding sites, e.g., Fab, Fab', F(ab')2, Fc, Fd, Fd', Fv, single-chain antibodies (e.g., scFv), single variable domain antibodies, diabodies (DAbs) (bivalent and bispecific), and chimeric (e.g., humanized) antibodies. These can be produced by modification of whole antibodies or de novo synthesized antibodies using recombinant DNA technology. While not wishing to be bound to any one configuration, these functional antibody fragments generally retain the ability to selectively bind with their respective antigens or receptors. Antibodies and antibody fragments can be derived from any class of antibody, including, but not limited to, IgG, IgA, IgM, IgD, and IgE, and any subclass (e.g., IgG1, IgG2, IgG3, and IgG4). Preparations of antibody molecules can be monoclonal or polyclonal. Antibody molecules can be human, humanized, CDR-grafted, or in vitro generated. Antibodies can have heavy chain constant regions selected from, for example, IgG1, IgG2, IgG3, or IgG4. Antibodies can also have light chains selected from, for example, kappa or lambda. The term "immunoglobulin" (Ig) is used interchangeably with the term "antibody" herein.

[0099] Examples of antigen-binding fragments of antibody molecules include (i) a Fab fragment, a monovalent fragment consisting of the VL, VH, CL, and CH1 domains; (ii) a F(ab')2 fragment, a bivalent fragment comprising two Fab fragments linked by a disulfide bridge at the hinge region; (iii) a Fd fragment consisting of the VH and CH1 domains; (iv) a Fv fragment consisting of the VL and VH domains of a single arm of an antibody; (v) a diabody (dAb) fragment consisting of a VH domain; (vi) a camelid or camelized variable domain; and (vii) a single-chain Fv (scFv) (e.g., Bird (See, e.g., Huston et al., (1988) Science, 242:423-426 and Huston et al., (1988) Proc. Natl. Acad. Sci. USA 85:5879-5883), and (viii) single domain antibodies. These antibody fragments are obtained using conventional techniques known to those of skill in the art, and the fragments are screened for utility in the same manner as are intact antibodies.

[0100] In some examples, antibodies that can be used in the imaging methods and systems described herein can also be single domain antibodies. Single domain antibodies can include antibodies whose complementary determining regions are part of a single domain polypeptide. Examples include, but are not limited to, heavy chain antibodies, antibodies that naturally lack light chains, single domain antibodies derived from traditional four-chain antibodies, engineered antibodies, and single domain scaffolds other than those derived from antibodies. Single domain antibodies can be any antibody that exists in the art or any future single domain antibody. The single domain antibody may be a main antibody. Single domain antibodies can be derived from any species, including, but not limited to, mouse, human, primate, camel, llama, fish, shark, goat, rabbit, and cow. Single domain antibodies are naturally occurring single domain antibodies known as heavy chain antibodies lacking light chains. Such single domain antibodies are described, for example, in WO94 / 04678. For clarity, this variable domain derived from a heavy chain antibody naturally lacking light chains is known herein as a VHH or nanobody to distinguish it from the conventional VH of four-chain immunoglobulins. Such VHH molecules can be derived from antibodies bred in Camelidae species, such as camel, llama, dromedary, alpaca, and guanaco. Species other than Camelidae may naturally produce heavy chain antibodies lacking light chains, and such VHHs are within the scope of the present invention.

[0101] In certain instances, the VH and VL regions can be subdivided into regions of hypervariability called "complementarity-determining regions" (CDRs), interspersed with more conserved regions called "framework regions" (FR or FW). The extent of framework regions and CDRs has been precisely defined by several methods (Kabat, EA, et al., (1991) "Sequences of Proteins of Immunological (See, for example, "Antibody Variable Domains: A Complementary Determining Region of Interest," Fifth Edition, USDapartment of Health and Human Services, NIH Publication No. 91-3242; Chothia, C. et al., (1987) J. Mol. Biol. 196:901-917; and the definition of AbM used in Oxford Molecular's AbM antibody modeling software. See generally, for example, "Protein Sequence and Structure Analysis of Antibody Variable Domains" in the Antibody Engineering Lab Manual (eds. Duebel, S. and Kontermann, R., Springer-Verlag, Heidelberg). As used herein, the terms "complementarity-determining region" and "CDR" refer to the sequence of amino acids in an antibody variable region that confers antigen specificity and binding affinity. Generally, there are three CDRs in each heavy chain variable region (HCDR1, HCDR2, HCDR3), and three CDRs in each light chain variable region (LCDR1, LCDR2, LCDR3). The precise amino acid sequence boundaries of a given CDR can be determined using any of several well-known schemes, including those described in Kabat et al., (1991), "Sequences of Proteins of Immunological Interest," 5th Ed. Public Health Service, National Institutes of Health, Bethesda, Md. (the "Kabat" numbering scheme) and AL-Lazikani et al., (1997) JMB273, 927-948 (the "Chothia" numbering scheme). As used herein, CDRs defined according to the "Chothia" numbering scheme may also be referred to as "hypervariable loops."

[0102] For example, under Kabat, the CDR amino acid residues in the heavy chain variable domain (VH) are numbered 31-35 (HCDR1), 50-65 (HCDR2), and 95-102 (HCDR3), and the CDR amino acid residues in the light chain variable domain (VL) are numbered 24-34 (LCDR1), 50-56 (LCDR2), and 89-97 (LCDR3). Under Chothia, the CDR amino acids in VH are numbered 26-32 (HCDR1), 52-56 (HCDR2), and 95-102 (HCDR3), and the amino acid residues in VL are numbered 26-32 (LCDR1), 50-52 (LCDR2), and 91-96 (LCDR3). Combining the CDR definitions of both Kabat and Chothia, the CDRs are amino acid residues 26-35 (HCDR1), 50-65 (HCDR2), and 95-102 (HCDR3) of human VH and HCDR1 of human VL. It consists of amino acid residues 24-34 (LCDR1), 50-56 (LCDR2), and 89-97 (LCDR3). Unless otherwise specified, an antibody molecule can contain any combination of one or more Kabat CDRs and / or Chothia hypervariable loops. Under all definitions, each VH and VL typically contains three CDRs and four FRs arranged from amino terminus to carboxy terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. An immunoglobulin variable domain sequence refers to an amino acid sequence capable of forming the structure of an immunoglobulin variable domain. For example, the sequence may include all or part of the amino acid sequence of a naturally occurring variable domain. For example, the sequence may include or not include one, two, or more N- or C-terminal amino acids, or may contain other modifications compatible with forming a protein structure.

[0103] In certain examples, antibodies generally comprise one or more antigen-binding sites or epitopes. The term antigen-binding site refers to the portion of an antibody molecule that contains determinants that form an interface that binds to a cellular site or its epitope. With respect to proteins (or protein mimetics), the antigen-binding site typically comprises one or more loops (of at least four amino acids or amino acid mimetics) that form an interface that binds to a cellular polypeptide. Typically, the antigen-binding site of an antibody molecule comprises at least one or two CDRs and / or hypervariable loops, or more typically at least three, four, five, or six CDRs and / or hypervariable loops.

[0104] In some instances, different antibodies may compete with each other to bind to a specific epitope or site on a cell. The terms "compete" or "cross-compete" are used interchangeably herein to refer to the ability of an antibody molecule to prevent the binding of another antibody molecule. Interference with binding can be direct or indirect (e.g., through allosteric regulation of the antibody molecule or target). The extent to which an antibody molecule can prevent the binding of another antibody molecule to a target, i.e., whether it can be said to compete, can be determined using a competitive binding assay, such as a FACS assay, an ELISA assay, or a BIACORE assay. In some embodiments, the competitive binding assay is a quantitative competitive assay. In some embodiments, a first antibody molecule is said to compete with a second antibody molecule for binding to a target when binding of the first antibody molecule to the target is reduced by 10% or more, e.g., 20% or more, 30% or more, 40% or more, 50% or more, 55% or more, 60% or more, 65% or more, 70% or more, 75% or more, 80% or more, 85% or more, 90% or more, 95% or more, 98% or more, or 99% or more, in a competitive binding assay (e.g., a competitive assay described herein). As used herein, the term "epitope" refers to a portion of an antigen that specifically interacts with an antibody molecule. Such portions are referred to herein as epitopic determinants and typically include or are part of elements such as amino acid side chains or sugar side chains. Epitope determinants can be defined by methods known in the art or by methods disclosed herein, for example, by crystallography or hydrogen-deuterium exchange. At least one or more portions of an antibody molecule that specifically interact with an epitope determinant are typically located in CDR. Typically, an epitope has specific three-dimensional structural characteristics. An epitope may also include specific charge characteristics. Some epitopes are linear epitopes, while other epitopes are conformational epitopes.

[0105] In some cases, the antibody that can be used in the imaging method described herein can be a monoclonal antibody. As used herein, the term "monoclonal antibody" or "monoclonal antibody composition" refers to a preparation of antibody molecules of a single molecular composition. A monoclonal antibody composition exhibits a single binding specificity and affinity for a particular epitope. Monoclonal antibodies can be produced by hybridoma technology or by methods that do not use hybridoma technology (e.g., recombinant methods).

[0106] In other examples, the antibody molecule may be a polyclonal antibody or a monoclonal antibody. In other embodiments, the antibody may be recombinantly produced (e.g., by phage display or by combinatorial methods). For example, phage display and combinatorial methods for generating antibodies are known in the art (e.g., U.S. Pat. No. 5,223,409 to Ladner et al., International Application No. WO 92 / 18619 to Kang et al., International Application No. WO 91 / 17271 to Dower et al., International Application No. WO 92 / 20791 to Winter et al., International Application No. WO 92 / 15679 to Markland et al., International Application No. WO 93 / 01288 to Breitling et al., International Application No. WO 92 / 01047 to McCafferty et al., International Application No. WO 92 / 09690 to Garrard et al., International Application No. WO 90 / 02809 to Ladner et al., Fuchs et al., al., (1991) Bio / Technology9:1370-1372, Hay et al., (1992) Hum Antibod Hybridomas3:81-85, Huse et al., (1989) Science 246:1275-1281, Griffiths et al., (1993) EMBO J 12:725-734, Hawkins et al. al., (1992) J Mol Biol 226:889-896, Clackson et al., (1991) Nature 352:624-628, Gram et al., (1992) PNAS89:3576-3580, Garrad et al., (1991) Bio / Technology 9:1373-1377, Hoogenboom et al. al., (1991) Nuc Acid Res 19:4133-4137, and Barbas et al., (1991) PNAS 88:7978-7982, the contents of all of which are incorporated herein by reference.

[0107] In one embodiment, the antibody is a fully human antibody (e.g., an antibody generated in a mouse genetically engineered to produce antibodies from human immunoglobulin sequences), or a non-human antibody, e.g., a rodent (mouse or rat), goat, primate (e.g., monkey), or camel antibody. Preferably, the non-human antibody is a rodent (mouse or rat antibody). Methods for producing rodent antibodies are known in the art. Human monoclonal antibodies can be generated using transgenic mice carrying human immunoglobulin genes rather than the mouse system. Splenocytes from these transgenic mice immunized with an antigen of interest are used to produce hybridomas secreting human monoclonal antibodies (mAbs) with specific affinity for epitopes derived from human proteins (see, e.g., Wood et al., International Application No. WO 91 / 00906; Kucherlapati et al., PCT Application No. WO 91 / 10741; Lonberg et al., International Application No. WO 92 / 03918; Kay et al., International Application No. 92 / 03917; Lonberg, N. et al., (1994) Nature 368:856-859; Green, L.L. et al., (1994) Nature Genet. 7:13-21; Morrison, S. et al., (1994) Proc. Natl. Acad. Sci. USA 81:6851-6855; Bruggeman et al. (See, e.g., et al., (1993) Year Immunol 7:33-40; Tuaillon et al., (1993) PNAS 90:3720-3724; Bruggeman et al., (1991) Eur J Immunol 21:1323-1326). The antibody may be one whose variable region, or a portion thereof, such as a CDR, is produced in a non-human organism, such as a rat or mouse. Chimeric, CDR-grafted, and humanized antibodies can be used. The antibody is produced in a non-human organism, such as a rat or mouse, and then modified, for example, in the variable framework or constant region to reduce antigenicity in humans.Chimeric antibodies can be produced by recombinant DNA techniques known in the art (Robinson et al., International Patent Application No. PCT / US86 / 02269; Akira, et al., European Patent Application No. 184,187; Ta. European Patent Application No. 171,496 to Niguchi, M., European Patent Application No. 173,494 to Morrison et al., International Application No. WO86 / 01533 to Neuberger et al., U.S. Patent No. 4,816,567 to Cabilly et al., European Patent Application No. 125,023 to Cabilly et al., Better et al., (1988), Science 240:1041-1043, Liu et al., (1987), PNAS84:3439-3443, Liu et al. (see, e.g., Sun et al., (1987), J. Immunol. 139:3521-3526; Sun et al., (1987), PNAS 84:214-218; Nishimura et al., (1987), Canc. Res. 47:999-1005; Wood et al., (1985), Nature 314:446-449; and Shaw et al., (1988), J. Natl Cancer Inst. 80:1553-1559).

[0108] In certain examples, a humanized antibody or CDR-grafted antibody has at least one or two, and generally all, of the three recipient CDRs (of the heavy and / or light chain) replaced with donor CDRs. The antibody may have at least a portion of a non-human CDR replaced, or only a portion of the CDRs replaced with non-human CDRs. It may be desirable to replace only the number of CDRs required for binding to a specific epitope of the humanized antibody. Preferably, the donor is a rodent antibody, e.g., a rat or mouse antibody, and the recipient is a human framework or human consensus framework. Typically, the immunoglobulin providing the CDRs is referred to as the "donor," and the immunoglobulin providing the framework is referred to as the "acceptor." In one embodiment, the donor immunoglobulin is non-human (e.g., rodent). The acceptor framework is a naturally occurring (e.g., human) framework or consensus framework, or a sequence that is about 85% or more, preferably 90%, 95%, or 99% or more identical thereto. As used herein, the term "consensus sequence" refers to a sequence formed from the amino acids (or nucleotides) that occur most frequently within a family of related sequences (see, e.g., Winnaker, "From Genes to Clones," Verlagsgesellschaft, Weinheim, Germany (1987)). In a protein family, each position in the consensus sequence is occupied by the amino acid that occurs most frequently at that position in the family. If two amino acids occur equally frequently, either can be included in the consensus sequence. "Consensus framework" refers to the framework region in a consensus immunoglobulin sequence.

[0109] In some cases, antibodies can be humanized by methods known in the art (see, e.g., Morrison, SL, (1985), Science 229:1202-1207; Oi et al., (1986), BioTechniques 4:214; and Queen et al., U.S. Patent Nos. 5,585,089, 5,693,761, and 5,693,762, all of which are incorporated herein by reference). Humanized or CDR-grafted antibodies can be produced by CDR-grafting or CDR-substitution, which can replace one, two, or all CDRs of an immunoglobulin chain. See, for example, U.S. Patent No. 5,225,539, Jones et al. (1986) Nature 321:552-525, Verhoeyan et al. (1988) Science 239:1534, Beidler et al. (1988) J. Immunol. 141:4053-4060, and Winter, U.S. Patent No. 5,225,539, the contents of all of which are expressly incorporated herein by reference. Winter describes a CDR-grafting method that can be used to prepare humanized antibodies (UK Patent Application No. GB2188638A filed March 26, 1987, Winter, U.S. Patent No. 5,225,539), the contents of which are expressly incorporated by reference. Humanized antibodies in which amino acids are substituted, deleted, or added can also be used. Criteria for selecting amino acids from donors are described, for example, in U.S. Patent No. 5,585,089, e.g., columns 12-16 of U.S. Patent No. 5,585,089, the contents of which are incorporated herein by reference. Other techniques for humanizing antibodies are described in Padlan et al., EP 519596 A1 (published December 23, 1992).

[0110] In some instances, the antibody molecule may be a single chain antibody. Single chain antibodies (scFv) may be engineered (see, e.g., Colcher, D. et al. (1999) Ann NY Acad Sci 880:263-80, and Reiter, Y. (1996) Clin (See Cancer Res 2:245-52). Single-chain antibodies can be dimerized or multimerized to generate multivalent antibodies with specificities for different epitopes of the same target protein. In yet other embodiments, the antibody molecule has a heavy chain constant region selected from, e.g., IgG1, IgG2, IgG3, IgG4, IgM, IgA1, IgA2, IgD, and IgE heavy chain constant regions, particularly, e.g., IgG1, IgG2, IgG3, and IgG4 (e.g., human) heavy chain constant regions. In another embodiment, the antibody molecule has a light chain constant region selected from, e.g., kappa or lambda (e.g., human) light chain constant regions. The constant region can be mutated, for example, to modify the properties of the antibody (e.g., to increase or decrease one or more of Fc receptor binding, antibody glycosylation, the number of cysteine ​​residues, effector cell function, and / or complement function). In one embodiment, the antibody has effector function and is capable of fixing complement. In other embodiments, the antibody does not recruit effector cells or fix complement. In another embodiment, the antibody has a reduced or no reduced ability to bind to Fc receptors. For example, it is an isotype or subtype, fragment, or other variant that does not support Fc receptor binding, for example, it has a mutated or deleted Fc receptor binding region.

[0111] The method for changing antibody constant region is known in the art.Antibodies with altered function, for example, altered affinity for effector ligands such as FcR on cells, or C1 component of complement, can be produced by replacing at least one amino acid residue in the constant part of antibody with a different residue (see, for example, EP388,151A1, U.S. Patent No. 5,624,821 and U.S. Patent No. 5,648,260, the contents of all of which are incorporated herein by reference).When applied to mice, or immunoglobulins of other species, similar types of modifications can be described to reduce or eliminate these functions.

[0112] In certain examples, antibodies can be derivatized or linked to another functional molecule (e.g., another peptide or protein) or conjugated to a label. As used herein, a "derivatized" antibody is a modified antibody. Methods of derivatization include, but are not limited to, the addition of a fluorophore, phosphorescent, light-scattering, or light-emitting moiety complexed with an affinity ligand such as a metal, a releasable nucleotide, a toxin, an enzyme, or biotin. Antibodies can include derivatized and otherwise modified forms of antibodies, including immunoadhesion molecules. For example, an antibody molecule can be functionally linked (by chemical coupling, genetic fusion, noncovalent bonding, or otherwise) to one or more other molecular entities, such as another antibody (e.g., a bispecific antibody or diabody), a detectable agent, one or more labels, a cytotoxic agent, a pharmaceutical agent, and / or a protein or peptide that can mediate association of an antibody or antibody portion with another molecule (such as a streptavidin core region or a polyhistidine tag). One type of derivatized antibody molecule is produced by crosslinking two or more antibodies (of the same type or of different types, e.g., to create bispecific antibodies). Suitable crosslinkers include those that are heterobifunctional, having two distinct reactive groups separated by an appropriate spacer (e.g., m-maleimide-benzyl esters of 2-methyl-2-propanol). Examples of linkers include those that are homobifunctional (e.g., disuccinimidyl suberate), or homobifunctional (e.g., disuccinimidyl suberate). Such linkers are available from Pierce Chemical Company, Rockford, Illinois.

[0113] In some instances, the antibody may contain a detectable label that is selected based on the particular excitation wavelength provided by the imaging system. Useful detectable agents with which antibody molecules can be derivatized (or labeled) include fluorescent compounds (described below), various enzymes, artificial groups, luminescent materials, bioluminescent materials, fluorescence-emitting metal atoms such as europium (Eu), phosphorescent labels and phosphorescent metal atoms, as well as transition metals, lanthanides, and radioactive materials.

[0114] In certain embodiments, cells or other objects imaged using the methods and systems described herein may contain fluorescent labels, for example, on their interior or exterior surfaces, or within cell membranes, cell walls, or organelles. Exemplary fluorescent labels include, but are not limited to, fluorescein, fluorescein isothiocyanate, rhodamine, 5-dimethylamine-1-naphthalenesulfonyl chloride, phycoerythrin, and the like. Antibodies may also be derivatized with detectable enzymes, such as alkaline phosphatase, horseradish peroxidase, β-galactosidase, acetylcholinesterase, glucose oxidase, and the like. When derivatized with a detectable enzyme, the antibody is detected by adding additional reagents that the enzyme uses to generate a detectable reaction product. For example, in the presence of the detectable agent horseradish peroxidase, the addition of hydrogen peroxide and diaminobenzidine results in a detectable colored reaction product. Antibody molecules may also be derivatized with artificial groups (e.g., streptavidin / biotin and avidin / biotin). For example, an antibody may be derivatized with biotin and detected through indirect measurement of avidin or streptavidin binding. Other examples of suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride, or phycoerythrin; examples of luminescent materials include luminol; and examples of bioluminescent materials include luciferase, luciferin, and aecolin.

[0115] Labeled antibody molecules can be used, for example, to characterize one or more phenotypes or biological responses of cells using a cell analyzer. Antibodies are typically used to assess protein abundance and expression patterns, detect the presence (or absence) of antigens, monitor protein levels in tissues as part of clinical testing procedures, for example, to determine the effectiveness of a given treatment regimen, or otherwise differentiate specific cells from each other within a cell population. The imaging methods and systems described herein can be used to monitor real-time changes in cells by monitoring the increase or decrease in signal from the label.

[0116] In certain embodiments, the antibody molecule may be conjugated to another molecular entity, typically a label or a therapeutic (e.g., cytotoxic or cytostatic) agent or moiety. Radioisotopes can be used for diagnostic or therapeutic applications and can also be measured using the imaging methods described herein. Radioisotopes that can be bound to antibodies include, but are not limited to, alpha, beta, or gamma ray emitters, or beta and gamma ray emitters. Such radioisotopes include iodine ( 131 I or 125 I), yttrium ( 90 Y), lutetium ( 177 Lu), actinium ( 225 Ac), plus odymium, astatine ( 211 At), rhenium ( 186 Re), Bismuth ( 212 Bi or 213 Bi), Indium ( 111 In), technetium ( 99m Tc), phosphorus ( 32 P), rhodium ( 188 Rh), sulfur ( 35 S), carbon ( 14 C), tritium ( 3 H), chromium ( 51 Cr), chlorine ( 36 Cl), cobalt ( 57 Co or 58 Co), iron ( 59 Fe), Selenium ( 75Se), or gallium ( 67 Ga) are mentioned. Radioisotopes useful as therapeutic agents include, but are not limited to, yttrium ( 90 Y), lutetium ( 177 Lu), actinium ( 225 Ac), plus odymium, astatine ( 211 At), rhenium ( 186 Re), Bismuth ( 212 Bi or 213 Bi), and rhodium ( 188 For example, radioisotopes useful as labels for diagnostic uses include, but are not limited to, iodine ( 131 I or 125 I), indium ( 111 In), technetium ( 99m Tc), phosphorus ( 32 P), carbon ( 14 C), tritium ( 3 H), or one or more of the therapeutic isotopes mentioned above. Radioactive species can be indirectly imaged by monitoring the increase in fluorescence emission of an acceptor molecule that can accept energy from the radioactive decay of the radioisotope. For example, imaging systems and methods can be used to detect whether these radioisotopes are taken up by specific cells and whether many of the radioisotopes are present in each individual cell.

[0117] In some embodiments, the antibody molecule may also be conjugated to a therapeutic agent. Therapeutically active radioisotopes have been mentioned above. Other therapeutic agents include taxol, cytochalasin B, gramicidin D, ethidium bromide, emetine, mitomycin, etoposide, tenoposide, vincristine, vinblastine, colchicine, doxorubicin, daunorubicin, dihydroxyanthracin dione, mitoxantrone, mithramycin, actinomycin D, 1-dehydrotestosterone, glucocorticoids, procaine, tetracaine, lidocaine, propranolol, puromycin, maytansinoids, such as maytansinol (see U.S. Pat. No. 5,208,020), CC-1065 (see U.S. Pat. Nos. 5,475,092, 5,585,499, and 5,846,545), and analogs or homologs thereof. Therapeutic agents include, but are not limited to, antimetabolites (e.g., methotrexate, 6-mercaptopurine, 6-thioguanine, cytarabine, 5-fluorouracil decarbazine), alkylating agents (e.g., mechlorethamine, thioepachlorambucil, CC-1065, melphalan, carmustine (BSNU) and lomustine (CCNU), cyclophosphamide, busulfan, dibromomannitol, streptozotocin, mitomycin C, and cis-dichlorodiamineplatinum(II) (DDP) cisplatin), anthracyclines (e.g., daunorubicin (formerly daunomycin) and doxorubicin), antibiotics (e.g., dactinomycin (formerly actinomycin), blenomycin, mithramycin, and anthramycin (AMC)), and antimitotic agents (e.g., vincristine, vinbastine, taxolin, and mysoloid). If desired, the antibody can be conjugated to both a therapeutic agent and a detectable label. The effectiveness of treatment using a therapeutic conjugated antibody can be assessed using the imaging systems and methods described herein.

[0118] In certain embodiments, the antibody molecule is a multispecific (e.g., bispecific or trispecific) antibody molecule. Protocols for generating bispecific or heterodimeric antibody molecules are known in the art, and include, for example, the "knob-in-the-hole" approach described in U.S. Pat. No. 5,731,168, electrostatic steering Fc pairing described, for example, in WO09 / 089004, WO06 / 106905, and WO2010 / 129304, strand-exchange engineered domain (seed) heterodimerization as described, for example, in WO07 / 110205, and the like, as described, for example, in WO08 / 119353, WO2011 / 131746, and WO2013 / 060867. Bispecific antibody conjugates by antibody cross-linking to generate bispecific structures using heterobifunctional reagents with amine- and sulfhydryl-reactive groups, as described, for example, in U.S. Pat. No. 4,433,059; bispecific antibody determinants generated by recombining half-antibodies (heavy chain pairs or Fabs) from different antibodies through cycles of reduction and oxidation of the disulfide bond between the two heavy chains, as described, for example, in U.S. Pat. No. 4,444,878; trispecific antibodies, as described, for example, in U.S. Pat. No. 5,273,743; Examples of suitable antibodies include three Fab' fragments cross-linked via sulfhydryl-reactive groups, as described in, for example, U.S. Pat. No. 5,534,254; biosynthetic binding proteins, e.g., pairs of scFvs cross-linked via their C-terminal tails, preferably via disulfide or amine-reactive chemical bridges, as described in, for example, U.S. Pat. No. 5,534,254; bifunctional antibodies, e.g., Fab fragments with different binding specificities dimerized via leucine zippers (e.g., c-fos and c-jun) replacing the constant domains, as described in, for example, U.S. Pat. No. 5,582,996; bispecific and oligospecific monovalent and oligovalent receptors, e.g., the VH-CH1 regions of two antibodies (two Fab fragments) linked by their normally associated light chains via a polypeptide spacer between the CH1 region of one antibody and the VH region of the other antibody; bispecific DNA-antibody conjugates, cross-linking antibodies or Fab fragments via a piece of double-stranded DNA, as described in, for example, U.S. Pat. No. 5,635,602; bispecific fusion proteins, e.g., For example, expression constructs comprising two scFvs with a hydrophilic helical peptide linker and a complete constant region between them, as described, for example, in U.S. Pat. No. 5,637,481; multivalent and multispecific binding proteins, e.g., dimers of polypeptides having a first domain with a binding region of an Ig heavy chain variable region and a second domain with a binding region of an Ig light chain variable region, commonly referred to as diabodies (e.g., as described, for example, in U.S. Pat. No. 5,837,242, to create bispecific, trispecific, or tetraspecific molecules);

[0010] These include, for example, minibody constructs with linked VL and VH chains further connected by peptide spacers at the antibody hinge and CH3 regions, which may dimerize to form bispecific / multivalent molecules, as described in U.S. Pat. No. 5,837,821; VH and VL domains may be linked by short peptide linkers (e.g., 5 or 10 amino acids) or may lack any linker in either orientation, thereby potentially dimerizing to form bispecific diabodies, as described in U.S. Pat. No. 5,844,821;No. 5,869,620, and single-chain binding polypeptides having both VH and VL domains linked via peptide linkages, such as, but not limited to, trimers and tetramers as described in U.S. Pat. No. 5,864,094, a series of VH domains (or VL domains of family members) connected by peptide bonds with C-terminal crosslinkable groups further associated with the VL domain to form a series of FVs (or scFvs), as described in U.S. Pat. No. 5,864,019, and single-chain binding polypeptides having both VH and VL domains linked via peptide linkers, as described in U.S. Pat. No. 5,869,620, have been linked in multivalent structures via non-covalent or chemical crosslinking, including, but not limited to, forming homobivalent, heterobivalent, trivalent, and tetravalent structures using both scFV or diabody-type formats. Additional exemplary multispecific and bispecific molecules and methods of making the same are described in, e.g., U.S. Pat. Nos. 5,910,573, 5,932,448, 5,959,083, 5,989,830, 6,005,079, 6,239,259, 6,294,353, 6,333,396, 6,477,777, and the like. 6,198, U.S. Patent No. 6,511,663, U.S. Patent No. 6,670,453, U.S. Patent No. 6,743,896, U.S. Patent No. 6,809,185, U.S. Patent No. 6,833,441, U.S. Patent No. 7,129,330, U.S. Patent No. 7,183,076, U.S. Patent No. 7,521,056, U.S. Patent No. 7,527,787, U.S. Patent No. 7,534,866, U.S. Patent No. 7,612,No. 181, US2002 / 004587A1, US2002 / 076406A1, US2002 / 103345A1, US2003 / 207346A1, US2003 / 211078A 1, US2004 / 219643A1, US2004 / 220388A1, US2004 / 242847A1, US2005 / 003403A1, US2005 / 004352A1, U S2005 / 069552A1, US2005 / 079170A1, US2005 / 100543A1, US2005 / 136049A1, US2005 / 136051A1, US20 05 / 163782A1, US2005 / 266425A1, US2006 / 083747A1, US2006 / 120960A1, US2006 / 204493A1, US2006, / 263367A1, US2007 / 004909A1, US2007 / 087381A1, US2007 / 128150A1, US2007 / 141049A1, US2007 / 154901A1, US2007 / 274985A1, US2008 / 050370A1, US2008 / 069820A1, US2008 / 152645A1, US2008 / 171855A1, US2008 / 241884A1, US2008 / 254512A1, US2008 / 260738A1, US2009 / 130106A1, US2009 / 148905A1, US2009 / 155275A1, US2009 / 162359A1, US2009 / 162360A1, US2009 / 175851A1, US2009 / 175867A1, US2009 / 232811A1, US2009 / 234105A1, US2009 / 263392A1US2009 / 274649A1, EP34608 7A2, WO0006605A2, WO02072635A2, WO04081051A1, WO06020258A2, WO2007 / 044887A2, WO2007 / 09 5338A2, WO2007 / 137760A2, WO2008 / 119353A1, WO2009 / 021754A2, WO2009 / 068630A1, WO9103493A1, WO9323537A1, WO9409131A1, WO9412625A2, WO9509917A1, WO9637621A2, WO9964460A1.

[0119] In other embodiments, an antibody molecule (e.g., a monospecific, bispecific, or multispecific antibody molecule) is covalently linked to another partner, e.g., as a fusion molecule, e.g., a fusion protein. In other embodiments, the fusion molecule comprises one or more proteins, e.g., one, two, or more cytokines. In one embodiment, the cytokine is an interleukin (IL) selected from one, two, three, or more of IL-1, IL-2, IL-12, IL-15, or IL-21. In one embodiment, a bispecific antibody molecule has a first binding specificity for a first target and a second binding specificity for a second target, and is optionally linked to an interleukin (e.g., IL-12) domain, e.g., full-length IL-12 or a portion thereof. "Fusion protein" and "fusion polypeptide" refer to a polypeptide having at least two covalently linked portions, each portion having a distinct property. The property may be a biological property, such as in vitro or in vivo activity. This property may also be a simple chemical or physical property, such as binding to a target molecule or catalysis of a reaction. The two moieties can be directly linked by a single peptide bond or via a peptide linker, but are in reading frame with each other. Optionally, the fusion protein can contain a detectable label that can be detected using the cell analysis device described herein.

[0120] While certain fluorophores are described above in connection with antibodies, other fluorophores or phosphors can be monitored using the imaging methods and systems described herein. For example, the excitation wavelength used in the imaging process is typically selected based on the specific fluorophores or phosphors present in the cell or object being imaged. For example, excitation wavelengths in the ultraviolet, visible, and infrared ranges can be used to excite fluorophores. Exemplary fluorophores include, but are not limited to, acridine dyes (e.g., acridine orange), tetramethylrhodamine, isothiocyanate derivatives (e.g., FITC, TRITC), phenanthridine derivatives (e.g., propidium iodide), 4',6-diamidino-2-phenylindole (DAPI), and dyes such as the bisbenzimide Hoechst dyes designated 33258, 33342, and 34580. Other examples include Alexafluor dyes such as sulfonated rhodamine derivatives and reactive intermediates containing maleimide, succinimidyl ester, and hydrazide groups. In some examples, cyanine dyes, such as Cy2, Cy3, Cy5, Cy7, and their derivatives, as well as dyes containing two or more fluorophores. Other molecules with a partially saturated indole nitrogen heterocyclic core containing aromatic units are connected via polyalkene bridges of various carbon numbers. In additional examples, fluorophores may sequester or otherwise bind metals such as calcium, exhibiting different emission wavelengths in the presence and absence of binding to the metal species. Certain xanthene derivatives, polynuclear imidazole and benzofuran heterocycles, Fura Red, pyranine, BCECF, SNARF-1, and other materials can be used as fluorescent probes to monitor changes in intracellular metal species concentrations or changes in cellular pH. BODIPY and other species can be used to monitor changes in specific organelles, such as mitochondria. Quantum dots, nanoparticles, nanostructures, or nanosystems capable of emitting light can also be used to monitor cellular activity during cellular imaging, if desired.

[0121] In certain embodiments, the methods and systems described herein can be used to provide three-dimensional imaging of cells or other objects in a sample, although the cells or other objects can reside on a two-dimensional surface. For example, in two-dimensional (2D) cell culture, cells are often cultured on flat dishes made of plastic or other materials. Cells are attached to the surface via one or more coatings or other materials. Coatings on flat surfaces can be intentionally selected to have different heights so that the cultured cells are not necessarily flat. The pre-scanning steps described herein can be used to identify different heights, allowing for more rapid imaging of cells in different z-dimensions in 2D cell culture. In some examples, cells in 2D cell cultures using a Matrigel layer of material can be imaged using the methods and systems described herein. In other examples, 2D cultures can be performed in transwell plates, where the 2D cell layer resides at any height above the plate bottom. For example, transwell inserts can suspend cells above the bottom of the plate or well. Pre-scanning methods can be used to determine the xy position and z-height of each cell in the insert, and high-resolution scanning can be used to image the cells. Cells grown in a variety of different ways can be imaged using the methods and systems described herein, as long as the matrix is ​​generally transparent to imaging at the particular wavelength being used.

[0122] Particular examples are described to further illustrate some of the novel and inventive aspects of the technology.

[0123] Example 1 This example is intended to illustrate the data volume obtained from a typical high-magnification scan of a well containing a three-dimensional object using a laser scanning confocal microscope. Referring to Figure 16, an example is shown in which each well of a plate is scanned using high-magnification scanning. For example, the wells of a 384-microwell plate, containing a hydrogel matrix approximately 500 microns high and an object approximately 30-50 microns z-width, can be scanned at high magnification (63x) using a liquid immersion objective. For a 225-field-of-view (FOV), approximately 1000 planar scans are required to cover each well. This scanning generates approximately 1 terabyte of data per well, or 384 terabytes of data per microwell plate. This amount of data increases processing time and requires significant storage capacity.

[0124] Example 2 Compared to Example 1, the amount of acquired data can be reduced by using a pre-scan to identify the x, y coordinates and z height of the object, followed by a higher magnification scan. Referring to Figure 17, using a laser scanning confocal microscope, a pre-scan can be performed using a 10x air objective, and the system reduces the scan to 25 fields of view. By scanning only the objects identified from the pre-scan with a higher magnification scan (e.g., using a 63x liquid immersion objective), the data volume can be reduced to approximately 18 gigabytes per well, or approximately 7 terabytes for the entire 384-microwell plate. The combination of the pre-scan and higher magnification scans can reduce the data volume by 54 times compared to the data volume obtained in Example 1.

[0125] Example 3 This example is intended to illustrate the scanning time for scanning an entire 384-microwell plate using a laser scanning confocal microscope. For the purposes of this illustration, the objects are assumed to be spheroids randomly distributed within a gel matrix. The height of the matrix is ​​500 microns, and the z-width of the spheroids is 40-60 microns. The total scanning time for scanning a single well of a 384-microwell plate is estimated to be approximately 105 minutes, with 481 planes scanned at 1 micron plane spacing. 81 fields of view can be scanned in a 105-minute scanning time.

[0126] Example 4 Compared to Example 3, the time required to scan a spherical object in a well of a 384-microwell plate using a laser scanning confocal microscope can be significantly reduced by using a pre-scan to identify the object's x-y coordinates and z-height, followed by a higher magnification scan. A 10x air objective was used to perform a pre-scanning step with 24 planes spanning 350 microns in the z-dimension, using the four fields of view used in the pre-scanning step. The results of the pre-scanning can then be used to scan the identified image at higher magnification using a 40x liquid immersion objective. For example, the higher magnification scan can use 81 planes spanning a height of 80 microns (scan spacing of approximately 1 micron in the z-direction). The total scan time (pre-scan + higher magnification scan) to image the object in the well is estimated to be less than 6 minutes. Compared to Example 3, the pre-scanning and scanning steps allow for imaging the object in the well approximately 19 times faster.

[0127] Example 5 HeLa cell spheroids were grown in a Geltrex matrix and imaged using a combination of pre-scanning (10x air objective) and higher magnification scanning (40x water immersion objective). Spheroids were randomly distributed throughout the matrix. For pre-scanning, a z-stack spanning 450 microns in the z-dimension with 15 micron spacing was used (see Figure 18A). For higher magnification scanning, a z-stack spanning 80 microns with 1 micron spacing in the z-dimension was used (see Figure 18B). The combination of the pre-scanning and scanning steps reduces the data volume by approximately 35x and the scan time by approximately 23x.

[0128] Example 6 MDCK cells were cultured in a 3D Life hydrogel matrix (Cellendes, Germany) and imaged using a combination of lower magnification pre-scanning and higher magnification scanning. The combination of pre-scanning and scanning reduced data by 54-fold and scan time by 36-fold. The gels were 500 μm high, and MDCK cells formed 3D aggregates throughout the gel. Cells were imaged with a 63× water-immersion objective for higher magnification scanning and a 10× air objective for lower magnification scanning. Figure 19A shows a 3D visualization of a pre-scan acquired with a 10× objective (24 planes, 15 μm step size). Figure 19B shows an XYZ view of MDCK cysts grown in the presence of RGD peptide (which promotes cyst formation) or a scrambled control peptide (scrambled peptide). Images were acquired using a 63x water immersion objective (160 planes, 0.5 μm step size).

[0129] After acquisition, MDCK cyst morphology was analyzed using the Harmony 4.93D analysis tool commercially available from PerkinElmer (Waltham, MA). The lumen of MDCK cysts was segmented and the volume was calculated. Single-cell results are shown as box plots in Figure 19C. The left box plot represents the results without added peptide, the middle box plot represents the results with added scrambled control peptide, and the right box plot represents the results with added RGD-peptide.

[0130] Example 7 The methods and systems described herein can also be used to identify cells within larger structures, such as in vivo. Tumor cells were imaged within zebrafish larvae. RFP-positive tumor cells were segmented in a pre-scan using a 5x air objective and targeted for higher magnification scanning using a 20x water immersion objective. Identified tumor cells varied in z-height from 150 microns to 550 microns.

[0131] Nine fields were acquired in a 25-plane z-stack with 30-micron steps to cover the entire well of a 96-well CellCarrier Ultra plate. Figure 20A shows a 5x pre-scan, maximum intensity projection of the global image and individual images. Figure 20B shows segmented tumor cells on the global image with the coordinates of the higher magnification scan. Figure 20C shows a maximum intensity projection of the global image acquired with a 20x water-immersion objective (280 planes, 1-micron step size). Figures 20D, 20E, and 20F each show a 3D view from a higher magnification scan of one of the three identified clusters of segmented tumor cells.

[0132] When introducing elements of the embodiments disclosed herein, the articles "a," "an," "the," and "said" are intended to mean that there are one or more elements. The terms "comprising," "including," and "having" are intended to be open-ended, meaning that there may be additional elements other than the listed elements. Those skilled in the art will, given the benefit of this disclosure, recognize that various components of the embodiments may be interchanged or substituted for various components in other embodiments.

[0133] While certain aspects, examples, and embodiments have been described above, it will be appreciated by those skilled in the art, given the benefit of this disclosure, that additions, substitutions, modifications, and variations of the disclosed exemplary aspects, examples, and embodiments are possible.

Claims

1. 1. A method for imaging a three-dimensional object present in a matrix using a microscope, said method comprising: pre-scanning the matrix using a first magnifying objective to identify xy positions of the three-dimensional objects within the matrix and to identify z-heights of the three-dimensional objects within the matrix; using the identified z-height of the identified three-dimensional object, scanning the identified three-dimensional object in the z-direction using a second magnification objective lens to provide a three-dimensional image of at least a portion of the three-dimensional object present in the matrix, wherein the second magnification objective lens is equal to or greater than the first magnification objective lens.

2. 2. The method of claim 1, wherein the pre-scanning includes acquiring a plurality of discrete z-plane images using the first magnification objective of the microscope to provide a first image set of discrete z-plane images used to identify the z-height.

3. 3. The method of claim 2, wherein the scanning includes acquiring a plurality of discrete z-plane images of the identified three-dimensional object using the second magnification objective of the microscope starting at the identified z-height of the three-dimensional object to provide a second image set.

4. The method of claim 3 , wherein the plurality of discrete z-plane images of the second image set are used to provide an entire three-dimensional image of the three-dimensional object in the matrix.

5. 5. The method of claim 4, wherein image correction is applied to the first image set prior to scanning the identified three-dimensional object using the second magnification objective lens to provide a corrected first image set.

6. 6. The method of claim 5, wherein a corrected x-y position of the three-dimensional object and a corrected z-height of the three-dimensional object are obtained from the corrected first image set and used in the scanning of the three-dimensional object using the second magnifying objective lens to provide the second image set.

7. 7. The method of claim 6, wherein image correction is applied to the second set of images to provide a corrected second set of images used to provide the three-dimensional image of the three-dimensional object.

8. 2. The method of claim 1, wherein the pre-scanning using the first magnification objective lens is performed using a 10x air objective lens of the microscope, and the scanning using the second magnification objective lens is performed using a 40x water immersion objective lens of the microscope.

9. The method of claim 1 , wherein both the pre-scanning and the scanning are performed using a laser confocal scanning microscope.

10. The method of claim 9 , wherein the three-dimensional object is a biological organism, a biological organ, a biological tissue, a biological cell, or a component or organ thereof.

11. the biological organism, the biological organ, the biological tissue, the biological cell, or the component thereof 10. The method of claim 9, wherein the pre-scanning and scanning of the element or organ is performed in a matrix comprising a hydrogel or in a matrix comprising a three-dimensional scaffold.

12. 10. The method of claim 9, further comprising: pre-scanning the matrix using the first magnification objective of the microscope to identify an x-y location of each of a plurality of individual three-dimensional objects in the matrix and to identify a z-height of each of the identified three-dimensional objects in the matrix; and scanning each identified three-dimensional object in the z-direction using the second magnification objective using the identified z-height of each identified three-dimensional object to provide a three-dimensional image of at least a portion of each of the three-dimensional objects present in the matrix.

13. 14. The method of claim 13, wherein the pre-scanning using the first magnification objective lens is performed using a 10x air objective lens of the microscope, and the scanning using the second magnification objective lens is performed using a 40x water immersion objective lens of the microscope.

14. 14. The method of claim 13, wherein each of the three-dimensional objects is independently a biological organism, a biological organ, a biological tissue, a biological cell, or a component or organ thereof, and the matrix comprises a hydrogel or a three-dimensional scaffold.

15. 10. The method of claim 1, further comprising: using the pre-scan to identify a z-width of the identified three-dimensional object in the matrix; and using the identified z-width to select a scan time or a data volume resulting from the scan of the three-dimensional object in the matrix.

16. 1. A microscope system configured to image a three-dimensional object in a three-dimensional matrix, the system comprising: a sample holder configured to receive a sample including the three-dimensional objects in the three-dimensional matrix; a first light source optically coupled to the sample holder and configured to illuminate at least a portion of the three-dimensional object in the three-dimensional matrix received by the sample holder, wherein a wavelength provided by the first light source is selected to excite at least one species present in the three-dimensional object; at least one objective lens optically coupled to the light source and configured to receive optical emissions from the at least one excited species present in the three-dimensional object within the three-dimensional matrix; a detector optically coupled to the at least one objective lens and configured to receive the optical emissions from the objective lens; a processor electrically coupled to the detector; the microscope system is configured to pre-scan the three-dimensional matrix using a first magnifying objective lens to identify an x-y position of the three-dimensional object and identify a z-height of the three-dimensional object, and the microscope system is configured to scan the identified three-dimensional object in the z-direction using a second magnifying objective lens and the identified z-height of the identified three-dimensional object to provide a three-dimensional image of at least a portion of the three-dimensional object.

17. 17. The microscope system of claim 16, wherein the system includes a first objective lens and a second objective lens, the first objective lens being used as the first magnification objective lens and the second objective lens being used as the second magnification objective lens, the second objective lens being different from the first objective lens and providing a magnification greater than or equal to the magnification of the first objective lens.

18. 18. The microscope system of claim 17, wherein the first objective lens is an air objective lens and the second objective lens is a water immersion objective lens.

19. 20. The microscope system of claim 18, wherein the detector includes at least one camera, the first light source includes at least one laser, and the system is configured to perform laser scanning confocal microscopy using the camera and the laser during the pre-scanning and scanning of the three-dimensional object.

20. 20. The microscope system of claim 19, wherein the processor is configured to execute instructions to construct a three-dimensional image of the three-dimensional object in the three-dimensional matrix from a set of images acquired from the scanning step.

21. 20. The microscope system of claim 19, wherein the processor is configured to execute instructions to apply image correction to the set of images before constructing the image of the three-dimensional object.

22. 22. The microscope system of claim 21, wherein the image correction is applied using a second image set acquired from one or more images of a two-dimensional graphical pattern using the detector at the same fixed position used to acquire the image set, the two-dimensional graphical pattern including dots in a grid, the dots being at vertices that define one or more types of geometric shapes, the grid of dots being non-periodic, and absolute positions of the imaged dots being determinable, and the correction is applied to images of the image set using the one or more images of the two-dimensional graphical pattern to correct for geometric distortions in the images of the image set.

23. 22. The microscope system of claim 21, wherein the system includes at least two separate light sources configured to provide light of different wavelengths to the sample on the sample holder.

24. 24. The microscope system of claim 23, further comprising a confocal unit optically coupled to each of the at least two separate light sources and positioned between the at least two separate light sources and the sample holder.

25. 25. The microscope system of claim 24, wherein the system includes at least two separate detectors configured to receive light of different wavelengths emitted by the sample on the sample holder.

26. 26. The microscope system of claim 25, wherein each of the at least two separate detectors includes a camera.

27. 26. The microscope system of claim 25, wherein one of the at least two separate detectors is configured to detect optical emissions during the pre-scanning step, and another of the at least two separate detectors is configured to detect optical emissions during the scanning step.

28. The system is configured to identify an xy position of each of a plurality of individual three-dimensional objects in the matrix using the first magnification objective of the microscope and identify a z-height of each of the identified plurality of three-dimensional objects in the matrix, and the system is configured to use the second magnification objective to image each identified three-dimensional object in the z-direction using the identified z-height of each identified three-dimensional object.

26. The microscope system of claim 25, configured to scan a matrix of three-dimensional objects at a frequency of 1000 kHz to provide a three-dimensional image of at least a portion of each of the three-dimensional objects present in the matrix.

29. 25. The microscope system of claim 24, wherein the system includes four independent light sources each configured to provide light of a different wavelength to the sample on the sample holder, and four independent cameras each configured to detect light emissions from the sample.

30. 17. The microscope system of claim 16, wherein the system is further configured to identify a z-width of the identified three-dimensional object in the matrix and use the identified z-width to select a scan time or a data volume obtained from the scanning of the three-dimensional object in the matrix.

31. 1. A non-transitory computer-readable medium having stored thereon instructions that, when executed by a processor, cause the processor to pre-scan a matrix including three-dimensional objects using a first magnifying objective lens to identify x-y positions of the three-dimensional objects in the matrix, identify z-heights of the three-dimensional objects in the matrix, and scan the identified three-dimensional objects in the z-direction using a second magnifying objective lens using the identified z-heights of the identified three-dimensional objects to provide three-dimensional images of at least some of the three-dimensional objects present in the matrix.

32. 32. A non-transitory computer-readable medium having stored thereon instructions according to claim 31, wherein the instructions cause the processor to select a magnification of the second magnification objective lens that is equal to or greater than a magnification of the first magnification objective lens.

33. 1. A method for imaging a three-dimensional object present in a matrix using a laser confocal scanning microscope, said method comprising: pre-scanning the matrix using a first magnifying objective to identify xy positions of the three-dimensional objects within the matrix, identify z-heights of the three-dimensional objects within the matrix, and record the identified xy positions and z-heights as a first set of images; correcting imaging anomalies from the pre-scanning step to provide a corrected first set of images; using the corrected z-height and the corrected x-y position from the corrected first image set, scanning the identified three-dimensional object in the z-direction using a second magnifying objective to provide a second image set; correcting imaging anomalies from the scanning step to provide a corrected second set of images; and constructing an image of the three-dimensional object in the matrix using the corrected second set of images.

34. correcting the imaging anomalies from the pre-scanning step; acquiring one or more images of a two-dimensional graphical pattern using a detector at the same fixed position used to acquire the first set of images, the graphical pattern being a grid including dots at vertices that define one or more types of geometric shapes, the grid of dots being non-periodic such that absolute positions of the imaged dots are determinable; and automatically adjusting, by a processor, images of the first image set using the one or more images of the two-dimensional graphical pattern to correct for geometric distortions in the images of the first image set.

35. 35. The method of claim 34, further comprising automatically adjusting, by a processor, images of the second image set using the one or more images of the two-dimensional graphical pattern to correct for geometric distortions in the images of the second image set.

36. correcting the imaging anomalies from the scanning step; acquiring one or more images of a two-dimensional graphical pattern using a detector at the same fixed position used to acquire the second set of images, the graphical pattern being a grid including dots at vertices that define one or more types of geometric shapes, the grid of dots being non-periodic such that absolute positions of the imaged dots are determinable; and automatically adjusting, by a processor, images of the second image set using the one or more images of the two-dimensional graphical pattern to correct for geometric distortions in the images of the second image set.

37. 34. The method of claim 33, wherein the pre-scanning comprises acquiring a plurality of individual z-plane images using the first magnification objective of the microscope to provide the first image set.

38. 38. The method of claim 37, wherein the scanning includes acquiring a plurality of discrete z-plane images of the identified three-dimensional object using the second magnification objective of the microscope starting at a corrected, identified z-height of the three-dimensional object to provide the second image set.

39. 39. The method of claim 38, wherein the plurality of discrete z-plane images of the second image set after image correction are used to provide an entire three-dimensional image of the three-dimensional object in the matrix.

40. 34. The method of claim 33, wherein the pre-scanning using the first magnification objective lens is performed using a 10x air objective lens of the microscope, and the scanning using the second magnification objective lens is performed using a 40x water immersion objective lens of the microscope.

41. 34. The method of claim 33, wherein both the pre-scanning and the scanning are performed using a laser confocal scanning microscope.

42. 42. The method of claim 41, wherein the three-dimensional object is a biological organism, a biological organ, a biological tissue, a biological cell, or a component or organ thereof.

43. 43. The method of claim 42, wherein the pre-scanning and scanning of the biological organism, the biological organ, the biological tissue, the biological cell, or the component or organ thereof is performed in a matrix comprising a hydrogel or in a matrix comprising a three-dimensional scaffold.

44. 34. The method of claim 33, further comprising: using the pre-scan to identify a z-width of the identified three-dimensional object in the matrix; and using the identified z-width to select a scan time or a data volume resulting from the scan of the three-dimensional object in the matrix.

45. 1. A microscope system configured to image a three-dimensional object in a three-dimensional matrix, the system comprising: configured to receive a sample containing the three-dimensional object within the three-dimensional matrix. a sample holder; a first light source optically coupled to the sample holder and configured to illuminate at least a portion of the three-dimensional object in the three-dimensional matrix received by the sample holder, wherein a wavelength provided by the first light source is selected to excite at least one species present in the three-dimensional object; at least one objective lens optically coupled to the light source and configured to receive optical emissions from the at least one excited species present in the three-dimensional object within the three-dimensional matrix; a detector optically coupled to the at least one objective lens and configured to receive the optical emissions from the objective lens; and a processor electrically coupled to the detector, wherein the microscope system is configured to: pre-scan the matrix using a first magnifying objective lens to identify x-y positions of the three-dimensional objects in the matrix, identify z-heights of the three-dimensional objects in the matrix, record the identified x-y positions and z-heights as a first image set; correct imaging anomalies from the pre-scan to provide a corrected first image set; scan the identified three-dimensional objects in the z direction using a second magnifying objective lens using the corrected z-heights and corrected x-y positions from the corrected first image set to provide a second image set; and correct imaging anomalies from the scan to provide a corrected second image set.

46. 46. ​​The microscope system of claim 45, wherein the system includes a first objective lens and a second objective lens, the first objective lens being used as the first magnification objective lens and the second objective lens being used as the second magnification objective lens, the second objective lens being different from the first objective lens and providing a magnification greater than or equal to the magnification of the first objective lens.

47. 47. The microscope system of claim 46, wherein the first objective lens is an air objective lens and the second objective lens is a water immersion objective lens.

48. 48. The microscope system of claim 47, wherein the detector includes at least one camera, the first light source includes at least one laser, and the system is configured to perform laser scanning confocal microscopy using the camera and the laser during the pre-scanning and scanning of the three-dimensional object.

49. 49. The microscope system of claim 48, wherein the processor is configured to execute instructions to construct a three-dimensional image of the three-dimensional object in the three-dimensional matrix from a set of images acquired from the scanning step.

50. 50. The microscope system of claim 49, wherein the image correction is applied to the first image set using another image set acquired from one or more images of a two-dimensional graphical pattern using the detector at the same fixed position used to acquire the image set, the two-dimensional graphical pattern including dots in a lattice, the dots being at vertices that define one or more types of geometric shapes, the lattice of dots being non-periodic, and absolute positions of the imaged dots being determinable, and the correction is applied to images of the first image set using the one or more images of the two-dimensional graphical pattern to correct for geometric distortions in the images of the first image set and provide the corrected image set.

51. the system is configured to provide light of different wavelengths to the sample on the sample holder; 51. The microscope system of claim 50, comprising at least two separate light sources.

52. 52. The microscope system of claim 51, further comprising a confocal unit optically coupled to each of the at least two separate light sources and positioned between the at least two separate light sources and the sample holder.

53. 53. The microscope system of claim 52, wherein the system includes at least two separate detectors configured to receive light of different wavelengths emitted by the sample on the sample holder.

54. 54. The microscope system of claim 53, wherein each of the at least two separate detectors includes a camera.

55. 54. The microscope system of claim 53, wherein one of the at least two separate detectors is configured to detect optical emissions during the pre-scanning step, and another of the at least two separate detectors is configured to detect optical emissions during the scanning step.

56. 54. The microscope system of claim 53, wherein the system is configured to use the first magnifying objective of the microscope to identify an x-y position of each of a plurality of individual three-dimensional objects in the matrix and to identify a z-height of each of the identified three-dimensional objects in the matrix, and the system is configured to use the second magnifying objective to scan each identified three-dimensional object in the z-direction using the identified z-height of each identified three-dimensional object to provide a three-dimensional image of at least a portion of each of the three-dimensional objects present in the matrix.

57. 53. The microscope system of claim 52, wherein the system includes four independent light sources each configured to provide light of a different wavelength to the sample on the sample holder, and four independent cameras each configured to detect light emissions from the sample.

58. 46. ​​The microscope system of claim 45, wherein the system is further configured to identify a z-width of the identified three-dimensional object in the matrix and use the identified z-width to select a scan time or a data volume obtained from the scanning of the three-dimensional object in the matrix.

59. A non-transitory computer-readable medium having instructions stored thereon, the instructions, when executed by a processor, causing the processor to: pre-scanning a matrix using a first magnifying objective to identify xy locations of three-dimensional objects within the matrix, identify z-heights of the three-dimensional objects within the matrix, and record the identified xy locations and z-heights as a first set of images; correcting imaging anomalies from the pre-scan to provide a corrected first image set; using the corrected z-height and the corrected x-y position from the corrected first image set, scanning the identified three-dimensional object in the z-direction using a second magnifying objective to provide a second image set; correcting imaging anomalies from the scan to provide a corrected second set of images; and constructing an image of the three-dimensional object in the matrix using the corrected second set of images.

60. 60. A non-transitory computer-readable medium having stored thereon instructions according to claim 59, wherein the instructions cause the processor to select a magnification of the second magnification objective lens that is equal to or greater than a magnification of the first magnification objective lens.

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