US20080300796A1

US20080300796A1 - Biological analysis methods, biological analysis devices, and articles of manufacture

Info

Publication number: US20080300796A1
Application number: US11/932,029
Authority: US
Inventors: Gordon D. Lassahn; Gregory D. Lancaster; William A. Apel; Vicki S. Thompson
Original assignee: Battelle Energy Alliance LLC
Current assignee: Battelle Energy Alliance LLC
Priority date: 2007-05-31
Filing date: 2007-10-31
Publication date: 2008-12-04
Also published as: US20080298667A1; WO2009014809A2; US8351674B2; WO2009014809A3; WO2009017879A2; WO2009017879A3

Abstract

Biological analysis methods, biological analysis devices, and articles of manufacture are described. Biological analysis methods access data regarding one or more images of a plurality of different combinations of biological receptors which individually have reacted with one or more biological indicators of a biological sample, analyze the data, and based on the analysis, create a profile comprising values representative of the biological indicators. Other biological analysis methods first access a plurality of first values individually corresponding to a plurality of biological indicators of a first subject, second access a plurality of second values individually corresponding to a plurality of biological indicators of a second subject, analyze the first and second values with respect to one another, and provide information regarding similarity of the first subject and the second subject using the analysis.

Description

RELATED APPLICATION DATA

This application claims the benefit of U.S. Provisional Patent Application Ser. No. 60/941,025 which was filed May 31, 2007, and which is incorporated by reference herein. This application is related to previously filed U.S. patent application Ser. No. 11/931,787 entitled “Image Portion Identification Methods, Image Parsing Methods, Image Parsing Systems, and Articles of Manufacture” and naming Gordon Dennis Lassahn, Gregory Dean Lancaster, William A. Apel, and Vicki S. Thompson as inventors.

CONTRACTUAL ORIGIN OF THE INVENTION

The United States Government has certain rights in this invention pursuant to Contract No. DE-AC07-05ID14517 between the United States Department of Energy and Battelle Energy Alliance, LLC.

TECHNICAL FIELD

The present disclosure relates to biological analysis methods, biological analysis devices, and articles of manufacture.

BACKGROUND OF THE DISCLOSURE

Various methods of identification of biological entities such as people are known. For example, fingerprints and DNA may be used to identify people. Antibodies may also be used to uniquely identify a person. At least some aspects of the disclosure are directed towards processing of biological samples of an individual, for example, to identify the individual.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the disclosure are described below with reference to the following accompanying drawings.

FIG. 1A is an illustrative representation of a blank biological substrate according to one embodiment.

FIG. 1B is an illustrative representation of a biological sample according to one embodiment.

FIG. 1C is an illustrative representation of a marked biological substrate according to one embodiment.

FIG. 1D is an illustrative representation of an image of a marked biological substrate according to one embodiment.

FIG. 2 is an illustrative representation of a portion of an image of a marked biological substrate according to one embodiment.

FIG. 3 is a block diagram of a biological analysis device according to one embodiment.

FIG. 4 is an illustrative representation of an image of a marked biological substrate according to one embodiment.

FIG. 5 is an illustrative representation of color component images of an image of a marked biological substrate according to one embodiment.

FIG. 6A is a chart illustrating a profile of an image of a biological substrate according to one embodiment.

FIG. 6B is a chart illustrating a profile of an image of another biological substrate according to one embodiment.

FIG. 6C is a chart illustrating a profile of an image of an unmarked biological substrate according to one embodiment.

DETAILED DESCRIPTION

This disclosure of the invention is submitted in furtherance of the constitutional purposes of the U.S. Patent Laws “to promote the progress of science and useful arts.” (Article 1, Section 8).
According to some embodiments of the disclosure, systems, apparatus, and methods for processing a biological sample (e.g., blood, urine, etc.) taken from a biological subject (e.g., a human) are described. The biological sample may comprise biological indicators (e.g., antibodies). A biological substrate comprising a plurality of biological receptors (e.g., antigens) may be exposed to the biological sample. As a result, some or all of the biological receptors of the biological substrate may react with some or all of the biological indicators of the biological sample to create marks on the biological substrate. The marks may be indicative of the presence and/or concentration of the biological indicators in the biological sample. For at least some of the receptors, no marks or different marks appear if certain indicators are absent from the sample.
Other details regarding processing of a biological sample taken from a subject are described in U.S. Pat. No. 6,989,276 and a U.S. patent application Ser. No. 11/931,787 entitled “Image Portion Identification Methods, Image Parsing Methods, Image Parsing Systems, and Articles of Manufacture” and naming Gordon Dennis Lassahn, Gregory Dean Lancaster, William A. Apel, and Vicki S. Thompson as inventors, assigned to the assignee hereof, the teachings of which are incorporated herein by reference.
In some embodiments, systems, apparatus, and methods for creating a set of values describing marks on a biological substrate are described. The set of values may be referred to as a profile of the biological substrate. Since the marks on the biological substrate may be indicative of the presence and/or concentration of biological indicators within a biological sample used to create the marks, the profile may be indicative of the presence and/or concentration of biological indicators within the biological sample. In other embodiments, systems, apparatus, and methods for calculating a quantitative measure of the similarity of profiles of biological substrates are described. Additional aspects of the disclosure are described in the illustrative embodiments below.
According to one embodiment, a biological analysis method comprises accessing data regarding one or more images of a plurality of different combinations of biological receptors which individually have reacted with one or more biological indicators of a biological sample, analyzing the data, and, based on the analysis, creating a profile comprising values representative of the biological indicators.
According to another embodiment, an article of manufacture comprises media comprising programming configured to cause processing circuitry to perform processing. The processing comprises analyzing data regarding an image of a biological substrate. The substrate comprises a plurality of locations and each location of the plurality comprises different biological receptors. The substrate has been exposed to a biological sample comprising biological indicators with which at least some of the biological receptors have reacted. The processing also comprises creating a profile of the image based on the analysis. The profile comprises values representative of the biological indicators. Individual values of the profile are derived respectively from different portions of the image and each image portion intersects a plurality of the locations.
According to yet another embodiment, a biological analysis device comprises processing circuitry configured to access image data regarding a plurality of separate biological receptors which have reacted with antibodies of a biological sample and the processing circuitry is configured to analyze the data and to generate information with respect to identification of a biological subject which provided the biological sample.
According to another embodiment, a biological analysis method comprises first accessing a plurality of first values individually corresponding to a plurality of biological indicators of a first subject, second accessing a plurality of second values individually corresponding to a plurality of biological indicators of a second subject, analyzing the first and second values with respect to one another, and providing information regarding similarity of the first subject and the second subject using the analysis.
According to still another embodiment, an article of manufacture comprises media comprising programming configured to cause processing circuitry to perform processing. The processing comprises first accessing a first profile of a first biological substrate. Individual values of the first profile are derived respectively from markings in different portions of a first image of the first biological substrate and each portion of the first image intersects a plurality of locations of the first substrate. Each location of the first substrate is formed by a different deposit of biological receptors.
The processing also includes second accessing a second profile of a second biological substrate. Individual values of the second profile are derived respectively from markings in different portions of a second image of the second biological substrate. Each portion of the second image intersects a plurality of locations of the second substrate and each location of the second substrate is formed by a different deposit of biological receptors. The processing also includes analyzing the first profile and the second profile with respect to one another and providing information regarding similarity of the two biological substrates using the analysis.
Referring to FIG. 1A, a blank biological substrate 100 according to one embodiment is illustrated. Substrate 100 may include a label 102 used to distinguish substrate 100 from other biological substrates. For example, label 102 may include an identifier that uniquely identifies substrate 100 such as a number, bar code, or combination of alphanumeric characters. Substrate 100 may also include guides 104 at specific positions on substrate 100. Guides 104 may be used to align substrate 100 with another substrate in order to compare substrate 100 with the other substrate. Use of guides 104 in aligning substrates is described in detail below. Although only three guides are illustrated in FIG. 1A, substrate 100 may comprise more or less than three guides.
Substrate 100 may include a plurality of biological receptors attached in various locations on a surface of substrate 100. The biological receptors may be deposited in locations on substrate 100 in a specific arrangement. For example, the biological receptors may be deposited in rows and columns. In one embodiment, the biological receptors may be antigens deposited in order by molecular weight. For example, a subset of the antigens deposited on substrate 100 having the lowest molecular weight may be deposited in locations at one end of substrate 100 and a subset of the antigens deposited on substrate 100 having the highest molecular weight may be deposited in locations at the other end of substrate 100.
As illustrated in FIG. 1A, substrate 100 may be rectangular in one embodiment. Of course, other substrate shapes may be used and biological receptors (other than antigens) configured to react with a biological sample could be deposited on substrate 100. For example, nucleic acid probes or gene probes may be deposited on substrate 100. Substrate 100 may be referred to as a blank biological substrate because substrate 100 has not yet been exposed to a biological sample with which the biological receptors of substrate 100 may react.
Referring to FIG. 1B, a biological sample 130 according to one embodiment is illustrated. Sample 130 may be a biological sample taken from a human and may contain biological indicators such as antibodies. For example, biological sample 130 may include blood, serum, saliva, urine, semen, perspiration, tears, body tissues, or other biological material containing antibodies.
FIG. 1C illustrates a marked biological substrate 150, according to one embodiment, representing substrate 100 after substrate 100 has been exposed to sample 130. Substrate 150 is similar to substrate 100 in that it includes label 102 and guides 104. Substrate 150 also includes a plurality of markings 152. Markings 152 may be created by reactions between biological indicators present in sample 130 and the biological receptors deposited on substrate 150. In an embodiment, markings 152 may be indicative of immune complexes (i.e., antigen/antibody combinations) present on substrate 150 and thus may be indicative of antibodies present in sample 130. Portions of substrate 150 that are not marked may indicate antigens for which a matching antibody was not present in sample 130.
The combination of biological indicators within an individual may be unique for the individual. Accordingly, samples taken from different individuals may result in substrates having different markings. The markings resulting from exposure to a sample from a particular person may be uniquely associated with the particular person.
FIG. 1D illustrates an image 170 of marked biological substrate 150. Image 170 may be captured using an image capture device such as a camera or scanner. Although one image 170 is shown for substrate 150, more than one image may be generated for a given single substrate 150 (e.g., markings of a single substrate may be captured in a plurality of images) or an image may be generated for plural substrates 150.
FIG. 2 illustrates one embodiment of a section 200 of image 170. Section 200 includes one of guides 104. Portion 200 also includes a plurality of deposits of antigens. The deposits are arranged in rows and columns and are represented as circles in FIG. 2. Of course, the deposits may take other shapes and may be located closer to each other than depicted in FIG. 2.
In one embodiment, deposits in locations of column 204 have a common characteristic (e.g., contain antigens having a same epitope). Similarly, deposits in locations of column 206 may have a common characteristic (e.g., contain antigens having a same epitope). However, the characteristic of column 204 may be different from the characteristic of column 206. Similarly, each column of deposits may have a different characteristic in one embodiment (e.g., a different epitope may be present in the deposits of each column). Accordingly, characteristics of each of the deposits in the locations of row 202 may be different (e.g., each of the deposits in the locations of row 202 may contain different epitopes).
Section 200 represents a magnified view of a section of image 170 used to describe marked substrate 150 that may be different from a naked eye view of section 200.
Each deposit of section 200 may contain one or more antigens. For example, the deposit in row 202 and column 204 may contain a combination of antigens. In one embodiment, individual antigens of the deposit have a same epitope.
Upon exposure to sample 130, some of the antigens may react with antibodies within sample 130 to form immune complexes. After forming, the immune complexes may change color so that the color of the immune complexes contrasts with a background color of substrate 150. The color changes may create markings 152 on substrate 150.
Markings 152 may indicate concentrations of antibodies within sample 130. For example, column 206 of section 200 illustrates deposits that have not reacted with sample 130 and are thus generally free from immune complexes since no markings are present in the deposits of column 206, in one example. The deposits of column 210, on the other hand, have dark shading symbolically representing a large number of colored immune complexes, in the example.
In some embodiments, substantially all of the antigens in the deposits of column 210 may react with sample 130 creating substantial markings. The substantial markings may indicate that sample 130 contains a high concentration of an antibody matching the epitope of the antigens in the deposits of column 210.
Column 208 of section 200 comprises deposits shaded with medium lines symbolic of a medium amount of markings. The medium markings may indicate that some of the antigens in column 208 reacted with sample 130 to create immune complexes, but many did not. The medium markings may further indicate that sample 130 contains a medium concentration of an antibody matching the epitope of the antigens in the deposits of column 208.
Column 212 of section 200 comprises deposits shaded with fine dots symbolic of a small amount of markings. The light markings may indicate that a few of the antigens in column 212 reacted with sample 130 to create immune complexes, but most did not. The light markings may further indicate that sample 130 contains a light concentration of an antibody matching the epitope of the antigens in the deposits of column 212.
The shading of deposits in FIG. 2 is symbolic. In actuality, the markings may appear in a random or semi-random pattern. In one embodiment, the greater the number of immune complexes in a deposit, the darker the deposit will appear since each immune complex may be a different color than un-reacted antigens and the background color of substrate 150.
Since section 200 is a section of image 170, section 200 may comprise a set of pixels arranged in rows and columns. In one embodiment, the pixels may be smaller than the deposits so that one pixel may represent a portion of one deposit. The pixels in a single pixel column (which is different from a column of deposits, such as column 204) of section 200 may be referred to as an image portion. Image portions 214, 216, or 218 are illustrated in FIG. 2. In other embodiments, individual image portions 214, 216, and 218 may correspond to a plurality of images and/or substrates.
Image portions 214, 216, and 218 are not necessarily illustrated to scale in FIG. 2. In reality, image portions 214, 216, and 218 may be smaller than illustrated. In one embodiment, a particular column of deposits may comprise many image portions. For example, portion 214 is illustrated as intersecting the deposits of column 210. However, other image portions (not illustrated) may also intersect the deposits of column 210 without intersecting each other and without intersecting portion 214. Accordingly, a plurality of image portions for each column are possible. Analysis of image portions 214, 216, and 218 may be performed by a biological analysis device described below.
Referring to FIG. 3, an embodiment of a biological analysis device 300 including a processing circuitry 302, storage circuitry 304, and a user interface 306 is illustrated. Processing circuitry 302 may comprise circuitry configured to implement desired programming provided by appropriate media in at least one embodiment. For example, processing circuitry 302 may be implemented as one or more of a processor and/or other structure configured to execute executable instructions including, for example, software and/or firmware instructions, and/or hardware circuitry. Exemplary embodiments of processing circuitry 302 include hardware logic, PGA, FPGA, ASIC, state machines, and/or other structures alone or in combination with a processor. These examples of processing circuitry 302 are for illustration and other configurations are possible.
Processing circuitry 302 may be configured to access data regarding image(s) of a plurality of different combinations of biological receptors that individually have reacted with one or more biological indicators, for example, image 170. Processing circuitry 302 may be further configured to analyze the data and based on the analysis, create a profile of the image comprising values representative of biological indicators.
The data regarding the image may be stored by storage circuitry 304. For example storage circuitry 304 may store image 170. Processing circuitry 302 may access the data by retrieving the data from storage circuitry 304. In one embodiment, storage circuitry 304 may store a profile of the image created by processing circuitry 302. User interface 306 may present the profile to a user and may alternatively or additionally present the image to the user.
Storage circuitry 304 may be embodied in a number of different ways using electronic, magnetic, optical, electromagnetic, or other techniques for storing information. Some specific examples of storage circuitry include, but are not limited to, a portable magnetic computer diskette, such as a floppy diskette, zip disk, hard drive, random access memory, read only memory, flash memory, cache memory, and/or other configurations capable of storing programming, data, or other digital information.
At least some embodiments or aspects described herein may be implemented using programming stored within appropriate processor-usable media and/or communicated via a network or other transmission media and configured to control appropriate processing circuitry. For example, programming may be provided via appropriate media including, for example, embodied within articles of manufacture, embodied within a data signal (e.g., modulated carrier wave, data packets, digital representations, etc.) communicated via an appropriate transmission medium, such as a communication network (e.g., the Internet and/or a private network), wired electrical connection, optical connection and/or electromagnetic energy, for example, via a communications interface, or provided using other appropriate communication structure or medium. Exemplary programming including processor-usable code may be communicated as a data signal embodied in a carrier wave in but one example.
In analyzing an image, processing circuitry 302 may ensure that the image is oriented in a specific manner by determining the current orientation of the image and re-orienting the image, if necessary, so that the image is oriented in the specific manner. For example, processing circuitry 302 may expect images to be rectangular with the longer of the two dimensions of the rectangle oriented horizontally and the shorter of the two dimensions oriented vertically. In one embodiment, upon accessing an image, processing circuitry 302 may determine whether the longer of the two dimensions of the image is oriented horizontally. If the longer of the two dimensions is oriented vertically, processing circuitry 302 may rotate the image ninety degrees so that the longer of the two dimensions is oriented horizontally.
In some cases, the longer of the two dimensions of the image may be oriented horizontally, but the image may need to be rotated 180 degrees to be oriented in the specific manner preferred by processing circuitry 302. Referring to FIG. 4, an image 400 of a biological substrate is illustrated. Image 400 includes label 102. Note that label 102 is located on the right hand side of image 400. If the preferred orientation for an image is one in which label 102 is located on the left hand side of the image, processing circuitry 302 may detect that label 102 of image 400 is on the right hand side and that image 400 is incorrectly oriented.
Processing circuitry 302 may detect label 102 of image 400 by counting a number of abrupt light-to-dark and dark-to-light transitions within a portion of image 400 (e.g., a column of pixels of image 400). Since a portion of image 400 comprising label 102 may have abrupt brightness changes due to lines and/or characters, rather than smoothly-varying brightness changes that may be characteristic of markings resulting from reactions between biological receptors and biological indicators, abrupt brightness changes may be indicative of label 102.
In one embodiment, processing circuitry 302 may calculate an absolute value of a derivative of pixel value versus row number and count the number of rows for which the derivative magnitude is greater than a constant threshold value. Consequently processing circuitry 302 may determine a data set comprising a number of light-dark transitions versus position (column number), for image 400. In some embodiments, processing circuitry 302 may determine the data set from a number of light-dark transitions versus position for each of a plurality of color component images associated with image 400. In one embodiment, processing circuitry 302 may combine the light-dark transition data from the color component images by simple addition to produce the data set.
In some configurations, the data set may be smoothed. A peak may occur in the smoothed data set values. If the peak is closer to the right-hand end (the high column number end) of image 400 than to the left-hand end, processing circuitry 302 may conclude that label 102 is located on the right hand side of image 400 and therefore image 400 is incorrectly oriented. Consequently, processing circuitry 302 may rotate image 400 180 degrees.
As was mentioned above, processing circuitry 302 may use brightness values in analyzing images. In some embodiments, processing circuitry 302 may determine brightness values for an image from color component images associated with the image.
Referring to FIG. 5, three color component images 502, 504, and 506 associated with image 170 are illustrated. Component image 502 may represent the red content of image 170, component image 504 may represent the green content of image 170, and component image 506 may represent the blue content of image 170.
Processing circuitry 302 may determine component brightness values for the pixels of component image 502 by finding the absolute difference between the individual pixel values of component image 502 and a background color of component image 502. Processing circuitry 302 may create a component brightness image associated with component image 502 comprised by the component brightness values.
The background color may be determined by creating a histogram of pixel values and selecting the most common pixel value as the background color. In some embodiments, the color component image may be smoothed in the horizontal direction prior to determining the background color. Processing circuitry 302 may similarly determine component brightness values and component brightness images for each of the pixels of component images 504 and 506.
In one embodiment, the component brightness images may be used to create a profile for image 170. The profile may comprise a plurality of values that describe the darkness of markings 152 of substrate 150. Individual values of the profile may be representative of the darkness markings 152 within a respective portion of image 170 such as image portions 214, 216, and 218. In one embodiment, the portions may be columns of pixels of image 170. The profile may be representative of antibody concentration as a function of position along the length of image 170.
In one embodiment, processing module 302 may use the method described below to derive individual values of the profile. First, processing module 302 may select a specific column of image 170 for which processing module will derive the individual value of the profile. Next, processing module may determine a column of the component brightness image associated with component image 502 that corresponds with the selected column of image 170. Processing module 302 may then determine a mean and standard deviation of the brightness values in the determined column of the component brightness image.
Processing module 302 may then apply a weight function to the brightness values of the determined column of the component brightness image. In one embodiment, the weight function may specify a weight to be applied to each of the brightness values. The peak of the weight function may be at the mean and the function decrease linearly in both directions from the peak, reaching a weight of zero at a distance from the peak equal to the standard deviation multiplied by a constant with a value near 0.7. Of course, other weighting functions may alternatively be used by processing module 302.
The brightness values of the determined column may be multiplied by the weight function and summed. Processing module 302 may then divide the sum of the weighted brightness values by the sum of the weights of the weighting function (i.e., processing module 302 may calculate the weighted average pixel value for the determined column).
Processing module 302 may repeat this method for individual columns of the component brightness image associated with brightness image 502 thereby determining individual weighted average pixel values respectively for the columns of the component brightness image associated with brightness image 502.
Processing module 302 may then similarly determine individual weighted average pixel values respectively for the columns of the component brightness images associated with brightness images 504 and 506. Weighted average pixel values from corresponding columns of the three component brightness images may then be averaged resulting in individual weighted average pixel values corresponding respectively with the columns of image 170.
Although the method of determining weighted average pixel values described above was based on using columns of the component images, image portions other than columns could be used. For example, individual image portions could comprise a plurality of columns rather than a single column.
In one embodiment, processing module 302 may determine the profile values for image 170 from the weighted average pixel values corresponding with the columns of image 170 by subtracting the weighted average pixel values from the constant value 255. The resulting profile values may be referred to as antibody concentration values, although the values may not precisely represent antibody concentrations. In one embodiment, column numbers associated with the profile values may be related to the molecular weight of the antibodies that have reacted with the antigens in the column, although there might not be a precise relationship between molecular weight and column number.
In one embodiment, some of the weighted average pixel values for image 170 may be undesirable. For example, some of the weighted average pixel values may be from columns that do not overlap biological receptors of substrate 150 and therefore do not depict any of markings 152. For example, substrate 150 might not have biological receptors deposited near the ends of substrate 150.
In one embodiment, processing circuitry 302 may find the ends of substrate 150 in image 170 in order to identify columns of image 170 that need not be analyzed and/or for which processing circuitry 302 need not determine a profile value. To find the ends, processing circuitry 302 may performs a second smoothing operation on the once-smoothed light-dark transition count versus column number data described above in relation to FIG. 4 and subtract the once-smoothed curve from the twice-smoothed curve. Processing circuitry 302 may then start at the previously-mentioned peak in the once-smoothed curve and proceed to the right (increasing column number) until processing circuitry 302 finds a positive peak in the difference curve. This peak position is taken to be the left-hand (small column number) limit of the valid antibody profile data and portions of the profile data to the left of this position are characterized as invalid.
In one embodiment, processing circuitry may additionally start at the right-hand edge of the image (the largest column number) and progresses leftward, until it finds a column for which the unsmoothed light-dark transition count is significantly above zero and the brightness, averaged over all three color component images, is significantly different from the background level. This column is taken to be the right-hand limit of the valid antibody profile data and portions of the profile data to the right of this position are characterized as invalid. In one embodiment, those data points that are characterized as invalid are set to a negative value.
Of course, processing circuitry 302 could use the method described above to find the right-hand and left-hand limits prior to determining the profile. According to this approach, processing circuitry 302 may invalidate columns of image 170 to the right of the right-hand limit and columns of image 170 to the left of the left-hand limit prior to determining the component brightness images and therefore prior to determining the profile for image 170.
Referring to FIG. 6A, a chart 600 is illustrated that depicts a profile 601 of image 170. Axis 610 of chart 600 may represent the column number of image 170 and axis 608 of chart 600 may represent a concentration of biological indicators (such as antibodies). Accordingly, chart 600 illustrates a biological indicator concentration value for columns of image 170.
As was described above in relation to FIG. 1C, substrate 150 may include guides 104. Guides 104 may be dark lines of a particular color. For example, guides 104 may be solid red marks that contrast with the background color of substrate 150, which in some embodiments may be white. Accordingly, guides 104 may show up prominently in the profile associated with image 170 as is illustrated in chart 600 at 602, 604, and 606 because guides 104 are relatively dark as compared with the background color of substrate 150. Note that locations 602, 604, and 606 correspond spatially with the positions of guides 104 in substrate 150.
Other peaks in the profile illustrated in chart 600 may be indicative of high concentrations of particular biological indicators within the biological sample to which substrate 150 was exposed. Likewise, valleys in the profile may be indicative of low concentrations of particular biological indicators.
Once a profile of a particular image of a biological substrate has been created, the profile may be compared to a profile of a different biological substrate to determine the similarity of the two profiles. Such comparison may be useful in a number of situations. For example, a biological sample recovered from a crime scene may be used to create a biological substrate. The source of the biological sample may be unknown. A profile of an image of the biological substrate created from the recovered sample may be compared with profiles of images of other biological substrates created from biological samples taken from known sources. If the profile of the recovered sample closely matches a profile from a known source, it may be determined that the recovered sample is from the known source.
In comparing a first profile to a second profile, processing circuitry 302 may access a plurality of first values of the first profile individually corresponding to a plurality of biological indicators of a first subject and a plurality of second values of the second profile individually corresponding to a plurality of biological indicators of a second subject. Processing circuitry 302 may then analyze the first and second values with respect to one another, and provide information regarding similarity of the first subject and the second subject using the analysis.
Referring to FIG. 6B, a profile 631 of an image (different from image 170) of a biological substrate is illustrated in chart 630. Like chart 600, chart 630 has an axis 640 representing a column number of the image and an axis 638 representing a concentration of biological indicators and peaks 632, 634, and 636 resulting from guides substantially similar to guides 104. By way of example, processing circuitry 302 may analyze profile 601 depicted in chart 600 with respect to profile 631 and provide information regarding the similarity of profile 601 and profile 631.
In one embodiment, processing circuitry 302 may provide a quantitative measure of the similarity of profiles 601 and 631. According to this approach, processing circuitry 302 may use the values of profiles 601 and 631 to calculate a quantitative measure such as a correlation, cross-correlation, or normalized covariance of the two profiles.
A number of different techniques may be used to increase the accuracy of the quantitative measure of the similarity of two profiles. These techniques may be employed prior to calculating the quantitative measure. Embodiments of some of these techniques are described below. One way of increasing the accuracy of the quantitative measure involves subtracting a blank profile from one or both of the profiles prior to calculating the quantitative measure.
Referring to FIG. 6C, a profile 661 of an image of blank biological substrate 100 is illustrated in chart 660. Like charts 600 and 630, chart 660 has an axis 670 representing a column number of the image of blank biological substrate 100 and an axis 668 representing a concentration of biological indicators. As was discussed above, blank biological substrate 100 may be a biological substrate that comprises biological receptors but which has not yet been exposed to a biological sample. Accordingly, blank biological substrate 100 might not comprise markings resulting from reactions between the biological receptors of blank biological substrate 100 and biological indicators.
However, a blank biological substrate may have some visible structure, such as label 102 or guides 104, which may be reflected in profile 661. For example, blank biological substrate 100 includes guides 104. Since guides 104 have a different color than a background color of blank biological substrate 100, guides 104 will influence profile 661 as is evident from peaks 662, 664, and 666 of profile 661, which are due to guides 104.
Since this visible structure may appear on non-blank biological substrates, such as substrate 150, it may be useful to subtract profile 661 from a profile of a non-blank biological substrate effectively removing the contributions of label 102 and guides 104 from the profile of the non-blank biological substrate.
Profile 661 may be subtracted from a profile of a non-blank biological substrate (e.g., profile 601 and/or profile 631) in a number of different ways. For example, a full magnitude of profile 661 may be subtracted from a profile of a non-blank biological substrate. Alternatively, the covariance of the profile of the non-blank biological substrate and profile 661 may calculated, and enough of profile 661 may be subtracted from the profile of the non-blank biological substrate to make the covariance zero. Further alternatively, an amount of profile 661 to be subtracted from the profile of the non-blank biological substrate may be chosen so as to maximize the correlation of profiles of two non-blank biological substrates after the subtraction of profile 661.
Another technique for increasing the accuracy of the quantitative measure of the similarity of two profiles involves removing peaks from the two profiles prior to calculating the quantitative measure. In one embodiment, processing circuitry 302 may remove peaks from the two profiles that have a height less than a specific height and a width less than a specific width.
Yet another technique for increasing the accuracy of the quantitative measure of the similarity of two profiles involves removing low frequency components from the two profiles prior to calculating the quantitative measure. Removing the low frequency component may include calculating magnitudes of low-frequency Fourier components by a least squares fit rather than by an orthogonality property of the Fourier components. In some embodiments, an operator of processing circuitry 402 may specify how many of the low-frequency components may be removed from the profiles.
Yet another technique for increasing the accuracy of the quantitative measure of the similarity of two profiles involves removing data points between the left end of the substrate and the left-most guide and data points between the right end of the strip and the right-most guide. In one embodiment, removing the data points may involve setting the data points to a negative value thereby preventing use of the data points when calculating the quantitative measure of similarity.
Yet another technique for increasing the accuracy of the quantitative measure of the similarity of two profiles involves changing data points of the profiles that are below a floor value up to the floor value while leaving data points above the floor value unchanged. In some embodiments, the floor value is user-selectable. In other embodiments, processing circuitry 302 may determine the floor value for an individual profile by calculating the floor value so that a specific percentage of the data points of the individual profile will be replaced by the floor value. The specific percentage may be user-selectable.
Yet another technique for increasing the accuracy of the quantitative measure of the similarity of two profiles involves removing trends from the profiles. For an individual profile, the trend may be removed by calculating a smoothed version of the profile smooth enough so that individual antibody peaks are substantially not visible in the smoothed version of the profile. The smoothed version of the profile may then be subtracted from the original profile, thereby removing general trends of the profile while preserving individual antibody peaks. In one embodiment, trend removal may be performed separately for subsets of the profile values.
Yet another technique for increasing the accuracy of the quantitative measure of the similarity of two profiles involves aligning the profiles prior to calculating the quantitative measure of similarity. Alignment may be useful since a particular biological receptor may appear at one point of one of the two profiles and a different point in the other of the two profiles. The two profiles might not be aligned for one or more of a number of reasons. For example, the two profiles might not be aligned due to differences in how the biological substrates associated with the profiles were scanned or photographed or due to differences in how images of the biological substrates associated with the profiles were cropped.
Alignment may involve changing the scale of one of the profiles so that it matches the scale of the other profile. According to one alignment technique, processing module 302 may align the two profiles using guides 104 by using least squares fitting to adjust coefficients of a linear remapping to make the guides of one profile match the guides of the other profile. According to another technique, the left-most guide 104 and right-most guide 104 of one of the profiles are lined up with the left-most guide 104 and right-most guide 104 of the other profile using a two linear equations with two unknowns rather than a least squares calculation.
According to another technique, processing module 302 may align the two profiles using data peaks of the profiles by using least squares fitting to adjust coefficients of a linear remapping to make the data peaks of one profile match the data peaks of the other profile. Alternatively, a quadratic remapping may be used that allows for non-uniform stretching of the profile. Determining which peaks of the profiles to use in performing the remapping may involve the method described below.
First, the profiles may be smoothed so that they have one, or a few peaks. These peaks that are still present subsequent to smoothing may be used to align the two raw profiles (not the smoothed versions of the profiles) using the data peak alignment technique described above.
Next, the raw profiles are smoothed again, this time with less smoothing than in the previous iteration so that there are more peaks present in the smoothed profiles than in the first iteration. Using the peaks present in the smoothed profiles, the raw profiles are aligned using the data peak alignment technique described above. This process of iteratively reducing the amount of smoothing and using the resulting peaks to align the profiles may be repeated until the smoothing width is small, for example until the smoothing width is two data points.
According to another alignment technique, the two profiles may be aligned by using a first set of remapping coefficients for a first subset of one of the profiles and a second set of remapping coefficients for a second subset of the one profile. The first subset may be bounded by a pair of guides 104 and the second subset may be bounded by a different pair of guides 104. This technique may be described as piecewise linear since a different linear remapping may be used for each subset.
The methods and techniques described above may be used to produce accurate quantitative measurements of the similarity of two profiles. Having a quantitative measure of similarity may enable processing circuitry 302 to determine a closest match of a specific profile derived from a biological sample from an unknown source with a collection of profiles derived from known sources. Accordingly, processing circuitry 302 may be configured to compare the specific profile with all or a subset of the collection of profiles derived from known sources that are available to processing circuitry 302 and identify one or more of the collection of profiles that are most similar to the specific profile.
In compliance with the statute, the invention has been described in language more or less specific as to structural and methodical features. It is to be understood, however, that the invention is not limited to the specific features shown and described, since the means herein disclosed comprise preferred forms of putting the invention into effect. The invention is, therefore, claimed in any of its forms or modifications within the proper scope of the appended claims appropriately interpreted in accordance with the doctrine of equivalents.
Further, aspects herein have been presented for guidance in construction and/or operation of illustrative embodiments of the disclosure. Applicant(s) hereof consider these described illustrative embodiments to also include, disclose and describe further inventive aspects in addition to those explicitly disclosed. For example, the additional inventive aspects may include less, more and/or alternative features than those described in the illustrative embodiments. In more specific examples, Applicants consider the disclosure to include, disclose and describe methods which include less, more and/or alternative steps than those methods explicitly disclosed as well as apparatus which includes less, more and/or alternative structure than the explicitly disclosed structure.

Claims

1. A biological analysis method comprising:

accessing data regarding one or more images of a plurality of different combinations of biological receptors which individually have reacted with one or more biological indicators of a biological sample;

analyzing the data; and

based on the analysis, creating a profile comprising values representative of the biological indicators.

2. The method of claim 1 further comprising identifying a subject which provided the sample using the profile.

3. The method of claim 1 wherein individual values of the profile are derived from a plurality of the combinations.

4. The method of claim 1 wherein:

individual values of the profile are derived from respective portions of the one or more images, each image portion intersecting a plurality of locations; and

the plurality of locations intersected by any one of the image portions comprise antigens having a same epitope.

5. The method of claim 4 wherein the locations are arranged in different directions on the substrate and the antigens of locations in a first of the directions have a same epitope and the antigens of locations in a second of the directions have different epitopes.

6. The method of claim 1 wherein the analyzing comprises:

locating a feature within the one or more images;

determining that at least one of the one or more images is improperly oriented based on the locating; and

properly orienting the at least one image in response to the determining.

7. The method of claim 1 wherein the substrate comprises markings resulting from reactions between the biological receptors and the biological indicators.

8. The method of claim 7 wherein individual values of the profile are derived from respective portions of the one or more images, each image portion intersecting a plurality of the locations.

9. The method of claim 8 wherein individual values of the profile are derived from brightness values associated with pixels comprised by the respective different portions of the one or more images.

10. An article of manufacture comprising:

media comprising programming configured to cause processing circuitry to perform processing comprising:

analyzing data regarding an image of a biological substrate, the substrate comprising a plurality of locations, each location of the plurality comprising different biological receptors, the substrate having been exposed to a biological sample comprising biological indicators with which at least some of the biological receptors have reacted; and

based on the analysis, creating a profile of the image comprising values representative of the biological indicators, individual values of the profile being derived respectively from different portions of the image, each image portion intersecting a plurality of the locations.

11. The article of manufacture of claim 10 wherein the programming is further configured to cause the processing circuitry to process brightness values associated with different pixels corresponding to markings resulting from reactions between the biological receptors and the sample.

12. A biological analysis device, comprising processing circuitry configured to access image data regarding a plurality of separate biological receptors which have reacted with antibodies of a biological sample, wherein the processing circuitry is configured to analyze the data and to generate information with respect to identification of a biological subject which provided the biological sample.

13. The device of claim 12 wherein the information indicates whether the biological subject is the source of the biological sample.

14. A biological analysis method comprising:

first accessing a plurality of first values individually corresponding to a plurality of biological indicators of a first subject;

second accessing a plurality of second values individually corresponding to a plurality of biological indicators of a second subject;

analyzing the first and second values with respect to one another; and

providing information regarding similarity of the first subject and the second subject using the analysis.

15. The method of claim 14 wherein, at the time of the analyzing, an identity of the first subject is known and an identity of the second subject is unknown.

16. The method of claim 14 wherein the information regarding the similarity of the first subject and the second subject comprises information indicating whether the first subject and the second subject are the same.

17. The method of claim 14 wherein:

the first values are derived respectively from markings in different portions of a first image of a first biological substrate, each portion of the first image intersecting a plurality of locations of the first substrate and each location of the first substrate being formed by a different deposit of biological receptors; and

the second values are derived respectively from markings in different portions of a second image of a second biological substrate, each portion of the second image intersecting a plurality of locations of the second substrate and each location of the second substrate being formed by a different deposit of biological receptors.

18. The method of claim 14 wherein the first values comprise concentration values representative of concentrations of the biological indicators of the first subject and the second values comprise concentration values representative of concentrations of the biological indicators of the second subject and the analyzing comprises analyzing using the concentration values.

19. The method of claim 14 wherein the analyzing comprises:

aligning the first values with the second values by changing a scale of the first values to match a scale of the second values; and

after the aligning, comparing the first values to the second values.

20. The method of claim 14 wherein one or more of the first values comprise first reference marks, one or more of the second values comprise second reference marks, and the analyzing comprises aligning the first and second values using the reference marks and after the aligning comparing the first values to the second values.

21. The method of claim 14 wherein the analyzing comprises:

aligning the first values with the second values using data peaks of the values; and

after the aligning, comparing the first values to the second values.

22. The method of claim 14 wherein the information regarding similarity of the two subjects comprises a normalized covariance of the first values and the second values.

23. An article of manufacture comprising:

first accessing a first profile of a first biological substrate, individual values of the first profile being derived respectively from markings in different portions of a first image of the first biological substrate, each portion of the first image intersecting a plurality of locations of the first substrate and each location of the first substrate being formed by a different deposit of biological receptors;

second accessing a second profile of a second biological substrate, individual values of the second profile being derived respectively from markings in different portions of a second image of the second biological substrate, each portion of the second image intersecting a plurality of locations of the second substrate and each location of the second substrate being formed by a different deposit of biological receptors;

analyzing the first profile and the second profile with respect to one another; and

providing information regarding similarity of the two biological substrates using the analysis.

24. The article of manufacture of claim 23 wherein the information regarding the similarity of the two biological substrates comprises information indicating that the two biological substrates were both derived from a single subject.

25. The article of manufacture of claim 23 wherein the markings of the first image result from the first substrate being exposed to a first biological sample comprising first biological indicators and the markings of the second image result from the second substrate being exposed to a second biological sample comprising second biological indicators.