WO2016138120A1

WO2016138120A1 - Sample analysis methods with spectral information

Info

Publication number: WO2016138120A1
Application number: PCT/US2016/019340
Authority: WO
Inventors: Kenneth Edward Salsman
Original assignee: Nanoscopia (Cayman), Inc.; Nanoscopia, Inc.
Priority date: 2015-02-24
Filing date: 2016-02-24
Publication date: 2016-09-01

Abstract

The present disclosure provides a method of analyzing a biological sample to detect target structures. The method can comprise the step of obtaining a microscopic image of the sample. The method can comprise extracting from the image a spatial distribution of image elements of a first color band. The method can further comprise displaying shape information derived from the extracting step. In some embodiments, the method can further comprise analyzing shape information to detect a spectral signature of a target structure.

Description

SAMPLE ANALYSIS METHODS WITH SPECTRAL INFORMATION

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of US Application No. 62/120,362, filed February 24, 2015, which is incorporated herein by reference.

INCORPORATION BY REFERENCE

[0002] All publications and patent applications mentioned in this specification are incorporated herein by reference in their entirety to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.

BACKGROUND

[0003] Conventionally, the analysis and diagnosis of certain diseases include human inspection of a sample from the patient and/or human inspection of a test strip onto which a sample from a patient has been introduced. The human inspection of the sample or of the test strip often includes the use of a microscope. For example, microscopy has been used to analyze the target structures associated with malaria, tuberculosis (TB), African trypanosomiasis, and others.

Oftentimes, ambient lighting may be used for detection of the color stripes of the test strip, concurrent with the control and positive response reactions on a Rapid Diagnostic Test (RDT) strip.

[0004] The microscopic detection of the target structures in a sample of blood or other biological sample is time consuming and requires a trained expert (a microscopist) to make a valid detection. In many locations, especially in the third world, there are few trained microscopists that can provide reliable diagnosis, and possibly even fewer high power microscopes capable of magnifying a sample large enough to positively identify target structures such as parasites and other bacteria. Further, the examination of a sample that includes a low-level infection (i.e., a low concentration of the target structure in the sample) can require significant time, whereas typically, microscopists are limited to 10 minutes of examination per sample so as to handle a large number of samples in a typical clinical lab or in a field clinic in third world countries. In addition, RDTs have reliability issues that are both environmental and human in cause. Further adding to the challenges of conventional methods, the rate of flow of the sample and reagent in the lateral flow fiber varies with temperature, humidity and air pressure. This can result in samples being run in the tropic zones of the Earth requiring a different amount of time to react for an accurate diagnosis than in a temperate zone such as the US or EU. Relatedly, many of these test devices suffer from instructions that are based on the location where they were developed, rather than where they are to be used. Further, low-level infections cause only a limited reaction in the positive test stripe and, as a result, can be difficult for humans using ambient lighting or light from non-electrical sources or weak electrical sources to see.

[0005] Because of the limitations of the conventional methods, there is a need to develop novel methods for performing biological samples analysis in real time automatically with high reliability in the field, especially in the third world countries.

SUMMARY OF THE DISCLOSURE

[0006] The present disclosure provides a method of analyzing a biological sample to detect target structures. The method can be used to perform detection of target structures associated with cells, bacteria, viruses, drug resistant strains and asymptomatic carrier detection, etc. In general, the biological sample may be analyzed to identify target structures associated with certain diseases, for example, malaria, tuberculosis (TB), African trypanosomiasis, and others.

[0007] The method can comprise the step of obtaining a microscopic image of the sample. The microscopic image is broadly defined herein to include any image which is a magnified view of an object too small to be seen distinctly and in detail by a naked eye. The microscopic image can be obtained from all types of microscopes including, but not being limited to, optical microscope, electro-optical microscope, electron microscope, transmission microscope, lens-less microscope, etc. The method disclosed herein can utilize self-calibrating light sources and solid- state imaging sensors that have the ability to detect even extremely slight shifts in the spectral response of the sample, thus overcoming the problems of the conventional methods discussed above.

[0008] The method can comprise extracting from the image a spatial distribution of image elements of a first color band. The method can further comprise displaying shape information derived from the extracting step. In some embodiments, the method can further comprise analyzing shape information to detect a spectral signature of a target structure.

[0009] In some embodiments, the sample has been treated with one or more dyes to help to highlight the presence of the target structures of interest. The biological sample is stained with a stain within the first color band before the step of obtaining a microscopic image and the stain having an affinity for the target structure. In some embodiments, the method can further comprise extracting from the image a spatial distribution of a second color band. In some embodiments, the method can further comprise displaying a two-dimensional representation of the spatial distribution of the first color band. In some embodiments, the method can further comprise extracting from the image a spatial distribution of a second color band. In some embodiments, the image sensor comprises a first color channel of the image sensor. The method can further comprise obtaining the first color band by using the first color channel of an image sensor.

[0010] Various embodiments disclosed herein comprise a non-transitory, computer-readable storage medium storing a set of instructions capable of being executed by a processor within an analysis apparatus to analyze a biological sample to detect a target structure, and that when executed by the processor, causes the processor to receive a microscopic image of the sample, extract from the image a spatial distribution of image elements of a first color band, and further display on a display shape information derived from the extracting step.

[0011] In some embodiments of the non-transitory, computer-readable storage medium, the set of instructions, when executed by the processor, further causes the processor to extract from the image a spatial distribution of a second color band, display a two-dimensional representation of the spatial distribution of the first color band, and analyze shape information to detect a spectral signature of the target structure. In some embodiments of the non-transitory, computer-readable storage medium, the set of instructions, when executed by the processor, further causes the processor to receive a microscopic image of the sample through an image sensor and obtain the first color band by using the first color channel of an image sensor.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The novel features of the invention are set forth with particularity in the claims that follow. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative

embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

[0013] FIG. 1 is a block diagram of a method of analyzing a biological sample to detect target structures according to one embodiment of this disclosure.

[0014] FIG. 2 is an illustration of a red blood malaria sample.

[0015] FIG. 3 is an illustration of a rapid diagnostic test strip.

DETAILED DESCRIPTION

[0016] The present disclosure provides method of analyzing a biological sample to detect target structures. The method can be used to perform detection of target structures associated with cells, bacteria, viruses, drug resistant strains and asymptomatic carrier detection, etc. In general, the biological sample may be analyzed to identify target structures associated with certain diseases, for example, malaria, tuberculosis (TB), African trypanosomiasis, and others. The sample can include a variety of samples such as blood, urine, saliva, mucous, feces, semen, tissue, cells, food, liquids, solids, gases, etc. The sample often has been treated with one or more dyes to help to highlight the presence of the target structures of interest.

[0017] FIG. 1 is a block diagram of the method 100 of analyzing a biological sample to detect a target structure. The method can comprise the step of obtaining a microscopic image of the sample 1 10. The microscopic image is broadly defined herein to include any image which is a magnified view of an object too small to be seen distinctly and in detail by a naked eye. The microscopic image can be obtained from all types of microscopes including, but not being limited to, optical microscope, electro-optical microscope, electron microscope, transmission microscope, lens-less microscope, etc. The method disclosed herein can utilize self-calibrating light sources and solid-state imaging sensors that have the ability to detect even extremely slight shifts in the spectral response of the sample, thus overcoming the problems of the conventional methods discussed above.

[0018] In general, microscopic samples can be examined via a bright field microscope after the sample has been subjected to treatment, for example, treatment by a dye to enhance the visibility of the target structures. For example, a light source can be disposed to illuminate a sample, by reflection or transmission of the light. The sample can be disposed on a slide or a cartridge or a cassette. A microscope can be used to obtain a magnified image of the sample. In some embodiments, the microscope can include an image sensor and a display. The color channels of the microscope can be used to perform spectral analysis of the microscopic image of the sample.

[0019] The method can comprise extracting from the image a spatial distribution of image elements of one or more color bands 120. For example, in some embodiments, the method of analyzing a biological sample can comprise obtaining the microscopic image through an image sensor. The image sensor can be a CMOS image sensor, a CCD image sensor, etc. The image sensor can comprise one or more color channels for the pixels of the image sensor. The color channels of the image sensor can be used to perform spectral analysis for one or more color bands. In some embodiments, the method can further comprise displaying a two-dimensional representation of the spatial distribution of the first color band. In some embodiments, the method can further comprise extracting from the image a spatial distribution of a second color band 130.

[0020] In one embodiment, a standard white LED, producing a given white light at a common color temperature (a D65 LED for example) can be used as a light source of the microscope. The solid-state imaging sensor can be a typical color filter patterned sensor such that it contains an array of pixels that are combined to form groups across the array that are composed of individual photo sensors, each with either a red, green, or blue color filter such that the combination within the group provides a full color image. Other color filter schemes, such as cyan, yellow, and magenta can also be used, however it is best to match the sensing filter scheme with the spectrum of the lighting source. Each color filter covered pixel is limited to the spectra that it is sensitive to, and therefore provides a limited but effective spectral differentiating analyzer. The imaging sensor can then be used to look at the lighting source without the sample so that it can capture the relative ratio of the colors and generate a luminance plot which can be used to verify the color temperature of the light source.

[0021] When the sample is introduced between the light source and the imaging sensor, the microscope optics will produce an image of the cells within the sample and a target structure, for example, a parasite, either within the cells or in the fluid outside the cells. Chemical color dyes can be used in various processing techniques. For example, an external-to-cell parasite may absorb the dye. Alternatively, a dye may penetrate the cell membrane and be absorbed by the parasite and in subsequent processing steps be washed out of the host cells. In this manner, the parasite may be left with the dye while the host cells remain clear. By scanning the sample and analyzing the image from each color channel, it is possible to determine the color content of the sample at each point within the sample that corresponds to each pixel group. In this manner, the intensity of each color that is allowed to pass through the sample (in a typical bright field microscope) can be measured and compared to the reference intensity of the light source at that color. The ratio of this result for each color is used to determine the exact color of the object within the sample that corresponds to the location by the pixel group.

[0022] A similar method, used for determining the number of colors that can be produced by displays, shows that an 8-bit depth color data per channel can provide up to 65 million different colors. Most CMOS imaging sensors used today produce between 10 to 12 bits color data. As a result of this sensitivity to the color of the sample, the current solid-state imaging sensors available today are capable of detecting concentrations of the color dyes used based on the color response measured in the sample.

[0023] The method can further comprise displaying shape information 140 derived from the extracting step 120 and 130. In some embodiments, the method can further comprise analyzing shape information to detect a spectral signature of a target structure 150.

[0024] The sample may include a dye to increase visibility. Various types of dyes may be used. For example, Toluidine Blue, Giesma stain, etc. Fluorescent dyes can also be used for a fluorescence imaging microscope. In some embodiments, the method can comprise staining the biological sample with a stain within the first color band before the step of obtaining a microscopic image. [0025] FIG. 2 illustrates a sample containing red blood cells infected with the falciparum malaria parasite. The sample has been dyed with the Giesma stain, and the cells have been treated to open the cell membrane to allow the dye into the cells. The cells were then washed to remove the excess dye leaving the parasites full of the dye and the cells clear. To test this sample for the presence of the malaria parasite, an image of the stained sample is acquired, and a spectral analysis of the color channels is applied to the output of the image sensor. The malaria parasite shows a distinct spectral signature with the blue channel at high transmission and the green channel at low transmission, and the red channel closely matching the luminance of the parasite. Examination of samples with this treatment show the parasite as the only item within the sample to have this spectral signature, since the cell membrane and the inner and outer fluids of the cells do not show this spectral signature. The method can comprise displaying the shape information, which is a spectral signature of the target structure.

[0026] In some samples, especially for parasites external to the cells, the cells and the parasites both show a common dye spectral response, however, the parasite (i.e. African trypanosomiasis) concentrate the dye within their internal structures and show significantly higher spectral differentiations than the cells. The method can further comprise analyzing the shape information extracted from the image and using a pattern recognition method to detect a target structure, for example, a malaria parasite as shown in FIG. 2.

[0027] In another embodiment, the image sensor can be a monochrome sensor with no color filter pattern or pixel groupings. In this format, the system can utilize spectrally selective lighting (such as narrow band LEDs) to illuminate the sample. Calibration of these various LED sources can be provided by a quick sequence of the LEDs prior to the sample being introduced into the optical pathway. Once the sample is in the optical pathway, the narrow band lighting can be sequenced through select color bands to determine the exact spectral response of the contents of the sample. This approach is much more specific than the approach described in the previous embodiment. It is possible to utilize LEDs that have an emission that is only 10 nm to 20 nm in bandwidth, allowing the system, without any filters, to provide a detailed analysis of narrow spectral responses of samples. In all examples, use of this method enables the identification of target structures that have not been magnified sufficiently to be seen by the naked eye by determining the distribution of spectral patterns associated with the shape of the target structures.

[0028] FIG. 3 illustrates a sample in a Rapid Diagnostic Test strip. In another embodiment, the system can be used with lateral flow test strips, where the illumination can be arranged to light the sample for reflection and the imaging sensor can analyze the surface of the lateral flow fiber for variations in its spectral reflectivity. As in the previously described embodiments, the illumination source can be white while the sensor is equipped with a color-filter-patterned pixel groupings. Alternatively, the illumination source may be a sequence of narrow band light sources and the image sensor may be a monochrome broad band sensor. As the sample and any reagents used in the test are applied, they will start migrating across the fiber. This can be observed as a shift in the reflectance spectral response of the fiber. At locations where the fiber has been treated with an enzyme, nano-particles, or other indicator, the fiber reflectivity will show a very sharp shift in the spectral response. This allows the system to provide the data required to indicate to the user that the test has passed through the correct amount of time to generate a valid positive or negative response, while also using light sources of a known and calibrated nature.

[0029] In various embodiments, a processor can be used to automatically analyze the image and extract from the image a spatial distribution of image elements of one or more color bands and analyze the shape information. In some embodiments, the processor can be configured to detect the presence or concentration of a specific target structure. Further, the method can further comprise detecting drug resistance and other factors associated with a detected target structure associated with a certain disease. Computational processing of the data can be done to highlight the objects within the image of the sample with specific characteristics, thereby allowing a trained professional to evaluate these objects without having to make a detailed visual inspection of the entire sample. This would reduce the amount of time to process a sample and reduce the amount of training required for the operator to effectively make a determination from the image. It also reduces the potential drift in performance of the operator as they examine a large number of samples in a single shift, where currently the risk of wrong results increases during each shift. A further level of computational programming can be performed to incorporate learning and object recognition to fully automate the diagnostic analysis from the system based on the spectral data and the image of the sample structure. The nature of the shape information of the target structure can be utilized along with the spectral response to determine the disease, sub-species, and drug resistance nature of the detected disease in a sample.

[0030] Various embodiments disclosed herein comprise a non-transitory, computer-readable storage medium storing a set of instructions capable of being executed by a processor to analyze a biological sample to detect a target structure, and that when executed by the processor, causes the processor to receive a microscopic image of the sample, extract from the image a spatial distribution of image elements of a first color band, and further display shape information derived from the extracting step.

[0031] In some embodiments of the non-transitory, computer-readable storage medium, the set of instructions, when executed by the processor, further causes the processor to extract from the image a spatial distribution of a second color band, display a two-dimensional representation of the spatial distribution of the first color band, and analyze shape information to detect a spectral signature of the target structure. In some embodiments of the non-transitory, computer-readable storage medium, the set of instructions, when executed by the processor, further causes the processor to receive a microscopic image of the sample through an image sensor and obtain the first color band by using the first color channel of an image sensor.

[0032] While the present disclosure has been disclosed in example embodiments, those of ordinary skill in the art will recognize and appreciate that many additions, deletions and modifications to the disclosed embodiments and their variations may be implemented without departing from the scope of the disclosure.

[0033] A wide range of variations to those implementations and embodiments described herein are possible. Components and/or features may be added, removed, rearranged, or combinations thereof. Similarly, method steps may be added, removed, and/or reordered.

[0034] Likewise various modifications to the implementations described in this disclosure may be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other implementations without departing from the spirit or scope of this disclosure. Thus, the claims are not intended to be limited to the implementations shown herein, but are to be accorded the widest scope consistent with this disclosure, the principles and the novel features disclosed herein.

[0035] Accordingly, reference herein to a singular item includes the possibility that there are a plurality of the same items present. More specifically, as used herein and in the appended claims, the singular forms "a," "an," "said," and "the" include plural referents unless specifically stated otherwise. In other words, use of the articles allow for "at least one" of the subject item in the description above as well as the claims below.

[0036] Additionally as used herein, a phrase referring to "at least one of a list of items refers to any combination of those items, including single members. As an example, "at least one of: a, b, or c" is intended to cover: a, b, c, a-b, a-c, b-c, and a-b-c.

[0037] Certain features that are described in this specification in the context of separate embodiments also can be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment also can be

implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

[0038] Similarly, while operations may be described as occurring in a particular order, this should not be understood as requiring that such operations be performed in the particular order described or in sequential order, or that all described operations be performed, to achieve desirable results. Further, other operations that are not disclosed can be incorporated in the processes that are described herein. For example, one or more additional operations can be performed before, after, simultaneously, or between any of the disclosed operations. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single product or packaged into multiple products. Additionally, other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results.

Claims

CLAIMS What is claimed is:

1. A method of analyzing a biological sample to detect a target structure, the method comprising:

obtaining a microscopic image of the sample;

extracting from the image a spatial distribution of image elements of a first color band; and

displaying shape information derived from the extracting step.

2. The method of claim 1 wherein the biological sample is stained with a stain within the first color band before the step of obtaining a microscopic image, the stain having an affinity for the target structure.

3. The method of claim 1 further comprising extracting from the image a spatial distribution of a second color band.

4. The method of claim 1 wherein the step of displaying shape information comprises displaying a two-dimensional representation of the spatial distribution of the first color band.

5. The method of claim 1 further comprising analyzing shape information to detect a spectral signature of the target structure.

6. The method of claim 1 wherein the step of obtaining a microscopic image of the sample comprises obtaining a microscopic image of the sample through an image sensor.

7. The method of claim 6 wherein the image sensor comprises a first color channel of pixels.

8. The method of claim 7 further comprising obtaining the first color band by using the first color channel of the image sensor.

9. A non-transitory, computer-readable storage medium storing a set of instructions capable of being executed by a processor to analyze a biological sample to detect a target structure, and that when executed by the processor, causes the processor to: receive a microscopic image of the sample;

extract from the image a spatial distribution of image elements of a first color band; and display on a display shape information derived from the extracting step.

10. The non-transitory, computer-readable storage medium of claim 9, wherein the set of instructions, when executed by the processor, further causes the processor to extract from the image a spatial distribution of a second color band.

1 1. The non-transitory, computer-readable storage medium of claim 9, wherein the set of instructions, when executed by the processor, further causes the processor to display a two- dimensional representation of the spatial distribution of the first color band.

12. The non-transitory, computer-readable storage medium of claim 9, wherein the set of instructions, when executed by the processor, further causes the processor to analyze shape information to detect a spectral signature of the target structure.

13. The non-transitory, computer-readable storage medium of claim 9, wherein the set of instructions, when executed by the processor, further causes the processor to receive a microscopic image of the sample through an image sensor.

14. The non-transitory, computer-readable storage medium of claim 9, wherein the set of instructions, when executed by the processor, further causes the processor to obtain the first color band by using the first color channel of an image sensor.