Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
  • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
  • G01N33/54366—Apparatus specially adapted for solid-phase testing
  • G01N33/54373—Apparatus specially adapted for solid-phase testing involving physiochemical end-point determination, e.g. wave-guides, FETS, gratings
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/55—Specular reflectivity
  • G01N21/552—Attenuated total reflection
  • G01N21/553—Attenuated total reflection and using surface plasmons
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
  • G16B40/20—Supervised data analysis

Definitions

• the present invention relates to a method of analysing molecular binding interactions at a sensing surface, and more particularly to the quality control of the resulting data describing the molecular interactions.
• the invention also relates to an analytical system including such a quality control as well as to a computer program for performing the method and a computer readable medium containing the program.
• Analytical sensor systems that can monitor interactions between molecules, such as biomolecules, in real time are gaining increasing interest. These systems are often based on optical biosensors and usually referred to as interaction analysis sensors or biospecific interaction analysis sensors.
• a representative such biosensor system is the Biacore® instrumentation sold by Biacore AB (Uppsala, Sweden) which uses surface plasmon resonance (SPR) for detecting interactions between molecules in a sample and molecular structures immobilized on a sensing surface.
• SPR surface plasmon resonance
• Biacore® systems it is possible to determine in real time without the use of labeling, and often without purification of the substances involved, not only the presence and concentration of a particular molecule in a sample, but also additional interaction parameters such as, for instance, the association rate and dissociation rate constants for the molecular interaction.
• the Biacore® system is currently used in life science research as well as in the drug discovery industry and in food analysis.
• a typical output from the Biacore® and similar biosensor systems is a graph or curve describing the progress of the molecular interaction with time. This curve, which is usually displayed on a computer screen, is often referred to as a "sensorgram”. ⁇
• the present invention provides a method of analysis, wherein molecular, particularly biomolecular, interactions at one or more sensing surface areas are detected and respective response curves representing the progress of each interaction with time are produced.
• a resulting set of response curves is subjected to a quality assessment procedure comprising the steps of: a) selecting at least one quality-related parameter for the response curves, and for each different parameter defining at least one quality descriptor, b) computing for each response curve in the set thereof, values for the different quality descriptors, c) based on the values for the different quality descriptors, computing for each response curve a quality classification indicative of the quality of the response curve in relation to all response curves of the set, d) selecting response curves having deviating quality classifications, and e) subjecting the selected response curves to a validation procedure to determine whether a response curve or curves are to be rejected or not.
• the present invention provides an analytical system for studying molecular interactions, which comprises data processing means for classifying the response curves with regard to quality.
• the present invention provides a computer program product comprising program code means for performing the method.
• the present invention provides a computer program product comprising program code means stored on a computer readable medium for performing the method.
• BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a sensorgram showing the interaction between a sample and a target molecule.
• Figure 2 shows two acceptable (left) and two unacceptable (right) sensorgrams.
• Figure 3 is a flow chart showing the steps in an exemplary embodiment of the present invention.
• Figure 4 is an overlay plot of five good and four bad sensorgrams with disturbances indicated at A, B, C and D.
• Figure 5 is an illustration of a sensorgram where long term changes have been eliminated by a filter, whereas short term fluctuations at B and C are retained.
• the present invention relates to analytical sensor methods, particularly biosensor based, where molecular interactions are studied and the results are presented in real time, as the interactions progress, in the form of detection curves, often called sensorgrams.
• Biosensors may be based on a variety of detection methods. Typically such methods include, but are not limited to, mass detection methods, such as piezoelectric, optical, thermo-optical and surface acoustic wave (SAW) device methods, and electrochemical methods, such as potentiometric, conductometric, amperometric and capacitance methods.
• mass detection methods such as piezoelectric, optical, thermo-optical and surface acoustic wave (SAW) device methods
• electrochemical methods such as potentiometric, conductometric, amperometric and capacitance methods.
• representative methods include those that detect mass surface concentration, such as reflection-optical methods, including both internal and external reflection methods, angle, wavelength or phase resolved, for example ellipsometry and evanescent wave spectroscopy (EWS), the latter including surface plasmon resonance (SPR) spectroscopy, Brewster angle refractometry, critical angle refractometry, frustrated total reflection (FTR), evanescent wave ellipsometry, scattered total internal reflection (STIR), optical wave guide sensors, evanescent wave-based imaging such as critical angle resolved imaging, Brewster angle resolved imaging, SPR angle resolved imaging, and the like.
• SPR surface plasmon resonance
• FTR frustrated total reflection
• evanescent wave ellipsometry evanescent wave ellipsometry
• scattered total internal reflection (STIR) scattered total internal reflection
• optical wave guide sensors evanescent wave-based imaging such as critical angle resolved imaging, Brewster angle resolved imaging, SPR angle resolved imaging, and the like.
• photometric methods based on
• the presently most commonly used detection principle is surface plasmon resonance (SPR) spectroscopy.
• SPR surface plasmon resonance
• An exemplary type of SPR-based biosensors is sold by Biacore AB (Uppsala, Sweden) under the trade name BIACORE® (hereinafter referred to as "the BIACORE instrument”).
• These biosensors utilize a SPR based mass-sensing technique to provide a "real-time" binding interaction analysis between a surface bound ligand and an analyte of interest.
• the BIACORE instrument includes a light emitting diode (LED), a sensor chip including a glass plate covered with a thin gold film, an integrated fluid cartridge providing a liquid flow over the sensor chip, and a photo detector array.
• LED light emitting diode
• sensor chip including a glass plate covered with a thin gold film
• an integrated fluid cartridge providing a liquid flow over the sensor chip
• a photo detector array a photo detector array
• Incoming light from the LED is totally internally reflected at the glass/gold interface and detected by the photo detector array.
• a surface plasmon wave is set up in the gold layer which is detected as an intensity loss "or dip” in the reflected light.
• the phenomenon of SPR associated with the BIACORE instrument is dependent on the resonant coupling of monochromatic p-polarized light, incident on a thin metal film via a prism and a glass plate, to oscillations of the conducting electrons, called plasmons, at the metal film on the other side of the glass plate.
• the SPR angle depends on the refractive index of the medium close to the gold layer.
• dextran is typically coupled to the gold surface, with the analyte-binding ligand being bound to the surface of the dextran layer.
• the analyte of interest is injected in solution form onto the sensor surface through the fluid cartridge. Because the refractive index in the proximity of the gold film depends on (i) the refractive index of the solution (which is constant), and (ii) the amount of material bound to the surface, the binding interaction between the bound ligand and analyte can be monitored as a function of the change in SPR angle.
• a typical output from the BIACORE instrument is a "sensorgram", which is a plot of response (measured in “resonance units” or “RU") as a function of time.
• An increase of 1,000 RU corresponds to an increase of mass on the sensor surface of about 1 ng/mm ⁇ .
• association As sample containing an analyte contacts the sensor surface, the ligand bound to the sensor surface interacts with the analyte in a step referred to as "association.” This step is indicated on the sensorgram by an increase in RU as the sample is initially brought into contact with the sensor surface.
• dissociation normally occurs when sample flow is replaced by, for example, a buffer flow. This step is indicted on the sensorgram by a drop in RU over time as analyte dissociates from the surface-bound ligand.
• FIG. 1 A representative sensorgram for the BIACORE instrument is presented in Figure 1, which depicts a sensing surface having an immobilized ligand (e.g. an antibody) interacting with analyte in a sample.
• the y-axis indicates the response (here in resonance units (RU)) and the x-axis indicates the time (here in seconds).
• buffer is passed over the sensing surface giving the "baseline response" in the sensorgram.
• an increase in signal is observed due to binding of the analyte (i.e., association) to a steady state condition where the resonance signal plateaus.
• the sample is replaced with a continuous flow of buffer and a decrease in signal reflects the dissociation, or release, of analyte from the surface.
• the slope of the association dissociation curves provides valuable information regarding the interaction kinetics, and the height of the resonance signal represents surface concentration (i.e., the response resulting from an interaction is related to the change in mass concentration on the surface).
• the detection curves, or sensorgrams, produced by biosensor systems based on other detection principles will have a similar appearance.
• Figure 2 shows examples of two acceptable and two unacceptable sensorgrams.
• the two curves to the left are both acceptable.
• the top-right curve on the other hand, is too unstable, and the bottom-right curve is deformed due to air-peaks (air bubbles in the fluid flow).
• a control of the quality of sensorgrams is normally done by the user making an overlay plot of the curves to be analyzed and visually searching for oddities in the curves.
• this problem is overcome by providing for data processing of the sensorgrams to at least substantially assist the user in assessing their quality.
• An algorithm has been devised, which is applicable in situations where a large set of sensorgrams is studied and classified with regard to quality, for example to identify curves with an odd quality, i.e. which differs from the quality of most of the sensorgrams in the set.
• the quality of a sensorgram being odd does, however, not necessarily mean that the quality is bad, and the "odd" sensorgrams are therefore subjected to a validation procedure where it is decided if the sensorgram is to be accepted or discarded.
• the validation procedure includes the use of at least one decision support.
• One such decision support is ocular (visual) inspection of the sensorgrams.
• Another decision support includes information on the reason why a sensorgram has been classified as odd. Still another decision support includes information on "time clusters" of odd sensorgrams, i.e. many sensorgrams associated with a specific time period or periods when the sensorgrams were produced. Using one or more of these decision supports, the operator (user) manually removes unaccepted sensorgrams.
• the validation procedure may also comprise an automated decision support in the form of a "decision algorithm" replacing any manual operation. The procedure of data processing of remaining sensorgrams and inspection of identified odd sensorgrams by the user is then repeated in an iterative manner until no more unacceptable sensorgrams are identified.
• FIG. 3 A flow chart of an embodiment of the algorithm is shown in Figure 3.
• This algorithm is designed to remove curves with a quality different from most of a large set of sensorgrams, so-called "outliers", and basically comprises the steps of (i) representing the sensorgrams with a number of quality descriptors, (ii) applying a quality classification method to the descriptors to find outliers, and (iii) removing the outliers.
• a semi-supervised iterative approach is used. The process is started with a large set of sensorgrams, usually more than about 100, for example in the range of from about 1000 to about 4000, obtained by running a number of test cycles on a biosensor system, such as, e.g., the BIACORE instrument.
• the first step is to select the sensorgram features (curve parameters) used to dete ⁇ riine the quality of the sensorgrams.
• Such features are baseline slope, air spikes, and carry-over between measurements, just to mention a few. While it may be possible to use only a small number of features, such as e.g. three to five different features, it is usually preferred to use at least ten or fifteen different features.
• Each selected feature of a sensorgram is given a value, herein referred to as a "quality descriptor", which, for example, may be a numerical value or a vector.
• each sensorgram has been reduced to a set of descriptor values representing the different quality parameters.
• each sensorgram has been reduced to a set of descriptor values representing the different quality parameters.
• a quality metric usually an equation, is then applied to the descriptor matrix to estimate the difference in quality between each sensorgram and the rest of the sensorgrams in the set. This translates the descriptor matrix to a difference vector (containing differences) and validation matrix (containing estimates of the contribution to the difference of each descriptor).
• the difference vector is then sorted with regard to difference magnitude to obtain a sorted difference vector and validation matrix.
• a predetermined number of the largest difference values are extracted, e.g. the 50 or 100 largest values, to obtain a truncated difference vector and validation matrix, which is displayed to the user. It is understood that sensorgrams with large differences may be outliers with respect to the quality descriptors.
• the user inspects the corresponding sensorgrams (or only a fraction thereof as desired), to decide which sensorgrams have insufficient quality, and removes them (manually) as outliers.
• the user may also utilize other types of decision supports.
• the removed outliers are collected in a log of removed curves, and the remaining sensorgrams (i.e. all sensorgrams minus removed outliers) are represented in a new descriptor matrix (replacing the original descriptor matrix).
• the search for outliers is repeated by again applying the quality metric equation and proceeding as described above to display the, e.g., 50 new sensorgrams that represent the largest differences.
• the reason for applying the quality metric equation again is that the metric may use the entire set of sensorgrams as a reference, and the set has changed. The process is repeated until the user cannot find any unacceptable, or bad, sensorgrams among those presented to him, the end result being a large set of sensorgrams without outliers.
• a basic characteristic of the present invention is the selection of curve quality features and their descriptors.
• Generally applicable quality features, or parameters are odd curve shapes, such as baseline slope, spikes (e.g. an air spike during sample injection), oscillations and jumps.
• Other exemplary quality parameters include carryover between measurements, binding to a reference surface area, and dissociation to a negative value (below zero). Suitable quality parameters for each particular situation may readily be selected by the skilled person.
• Each quality parameter corresponds to one or more descriptors, a descriptor being a formula or algorithm that with one or more sensorgrams as input produces, for example, a numerical value as output. If, for instance, one of the descriptors is oscillations of the baseline, a sensorgram for which the baseline descriptor has the value 10 has a more oscillating baseline than a sensorgram where the descriptor has the value 5.
• a descriptor measuring the carry-over between measurements in the sensorgram is in its simplest form only a relative response (the response at the end of a buffer injection relative to the baseline level).
• An example of a descriptor table (matrix) is given in Table 1 below.
• Figure 4 shows an overlay plot of five acceptable (good) and four unacceptable (bad) sensorgrams.
• a and B are affected by disturbances during dissociation
• C has a discontinuity in the association phase
• D has a dissociation level less than zero.
• Figure 5 illustrates the sensorgrams in Figure 4 after applying to each sensorgram a filter that eliminates longer term fluctuations while retaining short-term fluctuations. As seen in Figure 5, the maximum deviation from zero for the resulting curves are clearly largest for B and C. Utilizing this value as a primitive descriptor, B and C can be detected as different from the rest of the set.
• Another basic characteristic of the present invention is the classification of the sensorgrams with regard to their quality by applying a quality classification method.
• Each sensorgram is represented by a descriptor vector, and the descriptor vectors are collected in a descriptor matrix.
• the quality classification method may, for example, comprise the use of a quality metric, usually an equation, as described with regard to Fig. 3 above.
• Alternative classification methods include the use of a cluster algorithm, e.g. a KNN cluster algorithm, which classifies the sensorgrams in groups having a similar quality; a neural network or an expert system. All these quality classification methods are er se well-known to a person skilled in the art.
• each vector may be seen as a point in space, and the similarity between sensorgrams may then be represented by the distances between the respective points.
• a statistical method may be used which measures the distance from each respective vector to all the other vectors seen as a group. Thereby each vector is reduced to a single value that describes how similar the descriptor vector is to all the other vectors. Sensorgrams having approximately the same value are then about equal qualitywise regarding the descriptors and the statistical method.
• Statistical methods that may be used include methods that are per se well known to the skilled person. Some specific exemplary methods are briefly described below.
• Mahalanobis distance is a generalisation of the Euclidian distance between two points. Areas with a constant distance are ellipsoids centered around the mean value. When the descriptors are uncorrelated and the variances are equal to one in all directions, the areas are spheres and the Mahalanobis distance is equivalent to the Euclidian distance. The measure as such comprises a normalization of the descriptors by means of the inverse of the covariance matrix. "Manhattan distance” sums up the descriptor vector.
• Principal component 1 vs 2 returns the score vectors 1 and 2 for the descriptor matrix. In contrast to the other methods mentioned above, this method does not provide any ranking.
• the quality classification may include rescaling, or "normalizing", the descriptor values to make them comparable.
• An exemplary normalization method is the “mean centre” method, which sets the mean value of the descriptor values to zero.
• Other examples of normalization procedures are "mean centre and unit variance” (sets the mean value of the descriptors to zero and variance to one), and “unit variance” (sets variances to one).
• the user makes use of at least one "decision support" when validating the sensorgrams classified as odd.
• the user obtains a visual plot of the classification result, and based thereon displays sensorgrams to be validated for possible removal.
• the user may, however, alternatively, or additionally, obtain information on which specific descriptor or descriptors that caused a particular classification of a sensorgram. He may also alternatively, or additionally, obtain information on time periods during the production of the sensorgrams to which many odd sensorgrams in a set may be related ("time clusters"). Alternatively, however, the whole validation procedure may be carried out by a decision algorithm without assistance by the user.
• the above described quality assessment procedure is readily reduced to practice in the form of a computer system running software which implements the steps of the procedure.
• the invention also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the quality assessment procedure of the invention into practice.
• the carrier may be any entity or device capable of carrying the program.
• %make descriptor matrix descr_matrix [negd2(:) assjmpmedian(r) dissjmpmedian(:)];
• %RU Sensorgram matrix, one sensorgram per row.
• %d2 time defining the d2 reportpoint
• %jmpmedian desc mpmedian(t,RU,start,stop) Descriptor for jumpy sensorgrams %t : time vector %RU : Sensorgram matrix, one sensorgram per row.
• Vostart time defining start of interval where jumps should be identified
• %stop time defining stop of interval where jumps should be identified
• RU(7, ) [ 0.03 0.17 0.15 0.05 0.15 0.04 0.11 0.00 0.21 0.06 -4.46 1.71.

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