Patent Application
20080195322
The recent increased availability of high-precision robotic liquid handling machinery, automated imaging techniques, and high-performance computing has enabled advances in the development of high-throughput image-based biological assays. These assays enable the quantitative observation of cellular phenotypes, including morphological changes, protein expression, localization, and post-translational modifications, from biological samples, such as single cells. Automated image processing algorithms for cell segmentation and feature extraction offer the ability to extract objective measurements of these multidimensional phenotypes, and are particularly useful for the analysis of image data sets that are too large, or of phenotypes that are too subtle, for reliable human scoring. Comparisons of these measurements obtained from biological samples in different experimental conditions may be used to derive profiles that summarize phenotypic changes in response to different pharmacological or physiological perturbations, and presumably reveal important biological effects. Several recent studies have developed high-throughput image-based assays approaches to build profiles to characterize drug effects, screen for small molecules, classify sub cellular localizations, and characterize whole-genome phenotypes by using RNA interference or gene-deletion libraries.
In addition, quantitative measurement of a drug effect on biological samples is an important step toward discovering new drug candidates. To accomplish this, quantitative measurements of phenotypes, also referred to as features, are made on biological samples treated with a drug of interest. A profile, which characterizes the phenotypic changes between the treated and untreated biological samples, is then derived from features collected from these biological samples. Ideally, drugs with similar targets should have similar profiles; while drugs with dissimilar targets should have dissimilar profiles.
Profiling methods based on genomic, proteomic, or metabonomic assays have been used to study drug effects. However, these methods usually work on DNA or protein collected from cell lysate, and therefore fail to capture changes at the single cell level. When profiling at the individual cell level is required, flow cytometry may be used to identify subpopulations of cells with similar profiles. One of the disadvantages of flow cytometry is that features containing morphology and spatial information, such as sub cellular localization of a protein, co-localization of proteins and shape of a sub cellular organelle, are not measured.
Fluorescence microscopy, which is capable of extracting a richer set of features than flow cytometry, provides an alternative for building drug profiles at the single cell level. In fluorescence microscopy, proteins or organelles of interest inside a cell are labeled with fluorescence markers, which emit light when excited. Then, a variety of morphology- and intensity-based features, such as the total intensity, the area, and the eccentricity of each measured fluorescent region, may be extracted from such a fluorescence microscopy image.
However, several bottlenecks in data analysis have limited the full potential of high-throughput image-based assays. First, one of the challenges has been to effectively transform distributions of multivariate, phenotypic measurements from single cells into multivariate profiles that are both machine and human interpretable. Common univariate profiling approaches miss feature correlations at the single-cell level. Second, beyond the standard challenges of image preprocessing, cell segmentation, and feature extraction, which are partially solved by available automated image analysis software, it is in fact not apparent which or how many features should be measured. An unbiased approach allowing for the discovery of unexpected phenotypes calls for the inclusion of many objective measurements. However, the inclusion of irrelevant features not only increases the overhead of computation and storage, but also reduces the sensitivity of the data analysis. A final challenge has been to determine the effective dosage ranges and quantify possible dose-dependent multiphasic response of a compound. Traditional dose-response curves based on viable cell counts fail to distinguish between different responses of a compound within effective concentrations. This step is essential for discovering novel mechanisms of known compounds.
Thus, although these prior profiling methods attempted to build multidimensional profiles of cells by extracting a large number of features from microscopy images, the profiling methods proposed by them suffer from one or more of the following shortcomings:
Univariate—Each extracted feature was treated independently and profiles were not built from all features simultaneously. It should be noted that profiles built from multivariate features, such as the ratio of two features or the projections of multiple features into principal components, are not fully multivariate if the profiles are computed by only considering proper subset of the features.
Non-automated—Profiles were not built and compared automatically. Manual visual grouping of data points was used.
Poorly scalable—Each drug profile was built by using information extracted from the feature values of all the drugs considered. Thus, the addition of a new drug requires the recalculation of all profiles. As the number of drugs becomes large (>10,000), these methods may become computationally prohibitive. Examples for these methods include principal component projection and supervised classification. It would be preferable to extract a drug profile independent of other drug profiles.
This listing failings of prior approaches is not considered to be exhaustive, and other failings will also be apparent to one of ordinary skill in this field.
Presented is a compound profiling method that is multivariate, automated and scalable. The method takes into consideration all features simultaneously. Thus, it can produce profiles that give better separation of compounds, such as drugs, with different targets and association of compounds with similar targets than existing univariate approaches. The multivariate profiling approach of the present disclosure considers dependencies among features, and improves the ability to characterize, compare, and predict cellular changes in response to external perturbations.
One aspect of the invention is a method of profiling the effects of perturbations on biological samples, including, imaging control biological samples and perturbed biological samples to produce respective biological sample feature distributions in a multidimensional feature space, separating the control biological sample feature distribution and perturbed biological sample feature distributions using multivariate classification, and profiling the biological cell perturbations based on the separations.
Imaging may be, for example, by fluorescence microscopy, brightfield microscopy, differential interference contrast microscopy, phase contrast microscopy, confocal microscopy, flow cytometry, or any other acceptable imaging method. The biological samples may include, for example, cells, tissues, biopsies or serum samples. The perturbations may be, for example, pharmacological (for instance, drugs, chemical compounds, toxins, and/or synthetic or natural products), physiological (for instance, insulin, hormones, steroids, and/or peptides), environmental (for instance, temperature, radiation and/or pressure), or genetic perturbations (for instance, microRNA, siRNA, mutation, mutagenesis (chemical, transposition, radiation) and/or genetic insertions and/or deletions). Usable multivariate classification algorithms used may be, for example, a support vector machine that produces separating hyperplanes and classification accuracies, neural networks or classification and regression tree (CART) algorithms, among others.
An optional aspect of the invention includes reducing the feature set by selectively removing features from the feature distributions, reapplying multivariate classification after the selected features have been removed, and repeating the selective removal and reapplying steps until a classification accuracy is below a predetermined minimum.
Yet another aspect of the invention is a compound screening method, including, treating biological samples with a plurality of compounds, for example drugs, each at a plurality of concentrations, to produce treated biological samples, imaging an untreated biological sample and the treated biological sample to produce untreated and treated biological sample feature distributions in a multidimensional feature space. Then, multivariate classification is applied to the untreated and treated biological sample feature distributions using, for example a support vector machine algorithm to determine separating hyperplanes. Finally, the compounds are screened based on multivariate profiles derived from the separating hyperplanes.
Another aspect is titration clustering which may be performed on the multivariate profiles derived from the multivariate classification algorithm based on the plurality of concentrations of the compounds. Titration clustering may be used to determine biologically effective compound dosages and separating compound dosages with different biological effects.
The method may be used to screen compounds to determine efficacy for treating a target condition, or to determine common effects of different compounds.
The terms “a” and “an” are defined as one or more unless this disclosure explicitly requires otherwise.
The terms “substantially,” “about,” and “approximately,” their variations are defined as being largely but not necessarily wholly what is specified as understood by one of ordinary skill in the art, and in one non-limiting embodiment, the substantially refers to ranges within 10%, preferably within 5%, more preferably within 1%, and most preferably within 0.5% of what is specified.
The terms “comprise” (and any form of comprise, such as “comprises” and “comprising”), “have” (and any form of have, such as “has” and “having”), “include” (and any form of include, such as “includes” and “including”) and “contain” (and any form of contain, such as “contains” and “containing”) are open-ended linking verbs. As a result, a method or device that “comprises,” “has,” “includes” or “contains” one or more steps or elements possesses those one or more steps or elements, but is not limited to possessing only those one or more elements. Likewise, a step of a method or an element of a device that “comprises,” “has,” “includes” or “contains” one or more features possesses those one or more features, but is not limited to possessing only those one or more features. Furthermore, a device or structure that is configured in a certain way is configured in at least that way, but may also be configured in ways that are not listed.
Other features and associated advantages will become apparent with reference to the following detailed description of specific embodiments in connection with the accompanying drawings.
The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.
The invention and the various features and advantageous details are explained more fully with reference to the nonlimiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well known starting materials, processing techniques, components, and equipment are omitted so as not to unnecessarily obscure the invention in detail. It should be understood, however, that the detailed description and the specific examples, while indicating embodiments of the invention, are given by way of illustration only and not by way of limitation. Various substitutions, modifications, additions, and/or rearrangements within the spirit and/or scope of the underlying inventive concept will become apparent to those skilled in the art from this disclosure.
Referring to
The biological samples may be, for example, individual cell populations, tissues, biopsies or serum samples, and the treatment or perturbation of the biological samples may take many forms including, for example, pharmacological (for instance, drugs, chemical compounds, toxins, and/or synthetic or natural products), physiological (for instance, insulin, hormones, steroids, and/or peptides), environmental (for instance, temperature, radiation and/or pressure), or genetic perturbations (for instance, microRNA, siRNA, mutation, mutagenesis (chemical, transposition, radiation) and/or genetic insertions and/or deletions). The images may be obtained using various known techniques, including, for example, fluorescence microscopy, brightfield microscopy, differential interference contrast microscopy, phase contrast microscopy, confocal microscopy, flow cytometry, or any other acceptable imaging method.
The phenotype of each cell is represented by a vector of measured values in the multidimensional feature space. The phenotypes of the populations of treated and control cells are thereby represented as two distributions of points within the multidimensional feature space. These two distributions may be highly overlapping at low compound dosages, while easily separable at high compound dosages. For imagining, biological samples may be exposed to a serial compound titration and to a control condition, and may be fixed, stained with fluorescent markers if appropriate for the imaging technique employed, and imaged. If appropriate for the particular application, automated cell segmentation software identifies the DNA and cell boundaries. Image processing tools may quantify properties (such as intensities, textures, and morphologies) of the fluorescent markers, and may represent each cell in the biological sample as points in a high-dimensional feature space.
In step 102, for each dosage, a multivariate classification algorithm is applied to classify imaged biological samples into treated and untreated classes for each compound concentration. The multivariate classification algorithm, may be, for example, a support vector machine that produces separating hyperplanes and classification accuracies, neural networks or classification and regression tree (CART) algorithms, among others. When a separating hyperplane is used to classify the imaged biological samples into treated and untreated classes, the hyperplane may be determined, for example, using a support vector machine (SVM) algorithm which produces a separating hyperplane, a normal vector and a classification accuracy. The unit normal vector to the hyperplane is a multivariate measurement indicating the direction of maximum separation of the two distributions, and the coefficients of the unit normal vector indicate the relative importance of each feature in deciding whether a cell belongs to the treated or control class, as explained in more detail with reference to
In step 103, a dose-dependent profile is determined from the multivariate classification determined in step 102. Since a single compound at different dosages, and different compounds with different targets, may induce different phenotypic changes, when hyperplanes are used, the normal vector of the separating hyperplane may be used as a multivariate compound-dosage profile. The classification accuracy of the hyperplane may be estimated using standard k-fold cross-validation. The classification accuracy of perfectly separated distributions is 100%, while the accuracy of a random classification is 50%. By classifying different sets of control biological samples from each other, an empirical null distribution of classification accuracy may be estimated, and a classification significance threshold of p=0.05 may be set. At each compound concentration index i, the weight vector Wi of the hyperplane defines a profile of the compound at that concentration. The performance of Wi is given by the classification accuracy of the hyperplane. A threshold for classification accuracy may be determined above which classifications are deemed significant. More details regarding profile determination are discussed below with reference to
In optional step 104, for each extracted profile, redundant and non-informative features may be removed, using, for example, recursive feature removal with reclassification using the multivariate classification algorithm after feature removal. When employed with separating hyperplanes, this is an iterative process that removes feature dimensions corresponding to the coefficients of smallest absolute value in the profile vector, and then recomputes the separating hyperplane. The process of dimension reduction continues until the classification accuracy of the hyperplanes decreased significantly. The dimensionally reduced profiles may then be mapped back to the original feature space by padding with zeros in order to allow comparisons of profiles in the same dimension.
In step 105, a clustering algorithm is used to partition the titration series for each compound into ranges with maximum profile similarity, and a representative dosage range profile (d-profile) is determined from each of the determined titration ranges. Before the clustering, a reproducibility score indicating the similarity of dosage profiles across technical replicates is calculated and replicate profiles combined using vector averaging. The clustering may be performed on the combined profiles and the number of clusters may be determined automatically. For example, for each partition, a representative dosage range profile (d-profile) may be obtained by averaging the partition's constituent profiles that are both statistically significant and reproducible (as determined, for example, by a replicate reproducibility score threshold). Step 104 allows compounds to have multiple d-profiles across titrations, representing possible multiphasic responses. Clusters with no d-profiles may be discarded from further analysis, allowing the automated removal of low dosage ranges with no measured phenotypic effects and dosage ranges with poor replicate reproducibility. A compound may have more than one average d-profile, representing different effects at different concentrations. More details regarding dosage range profile determination are discussed below with reference to
In step 106, multivariate profiles extracted from a library of compounds may be used in typical applications of high-throughput image-based assays, such as drug screening, phenotypic change detection, and category prediction. For drug screening, compounds with d-profiles most similar to that of a reference d-profile may be selected to be lead candidates. For phenotypic change detection informative features may be selected and compared for a subset of the profiles that gave the best drug screening performance. For category prediction, the category of an “uncharacterized” compound may be inferred from previously categorized compounds with similar d-profiles. In other words, profiles obtained from a library of compounds may be used for drug screening, phenotypic change discovery, and category prediction.
While drug screening is one example of a practical application of the present invention, other possible applications include: pathological applications such as tumor biopsies where reactions of non-transformed and transformed cells are compared to determine viability, drug resistance, and the like; molecular drug target/mechanism identification; and molecular pathway elucidation. Other applications are also contemplated.
If a support vector machine is used for multivariate classification (steps 101 and 102,
Cik=[xi,1kxi,2k . . . xi,mk]. Eq. 1
Cik is a realization of a random vector Ck, which has a certain distribution in the m-dimensional feature space. For different Dk, the distribution of Ck will also be different. By performing the experiment, we obtained nk realizations of Ck, which may be combined into a data matrix
Given Xk and X0, where k≠0, the objective is to determine the profile of Dk under the experimental conditions. A profile is a row vector
W
k=[w1k w2k . . . wm′k], Eq. 3
which characterizes the difference between the distributions of Ck and C0. Note that m′, the dimension of Wk, may not be the same as m, the number of features.
If the measured features of the treated cells are similar to the untreated cells, i.e., no observable perturbation effect, the means of the distributions of Ck and C0 will be close to each other. If the perturbation induces observable feature changes on the cells, then the means of the distributions of Ck and C0 may be different from each other. This shift of distributions in the feature space may be characterized by a decision hyperplane that is optimally placed between the two distributions under a chosen criterion, which separates the two distributions.
For example, if there are two classes of cells: a negative class for the control (untreated) cells, and a positive class for the treated cells. The class label of a cell, Cik, is denoted by yik, where:
If Ci represents a cell whose treatment is not known a priori. A decision function, fk (Ci), for Dk is a function that associates the cell, Ci, with its class label by the following rule:
f
k(Ci)≧0yi=+1 Eq. 5
f
k(Ci)<0yi=−1
In this example, a linear decision function is used based on a hyperplane,
f
k(Ci)=Wk,C
+bk, Eq. 6
where <, > is the dot product operator in the Euclidean space Pm. This decision hyperplane is illustrated in
Several possible methods of separation hyperplane determination may be used. For example, a support vector machine (SVM) algorithm may be used to select hyperplanes separating treated and untreated populations in the multidimensional feature space. Hyperplanes determined by this method provide both a unit normal vector, and a measure of classification accuracy. Alternatively, the hyperplanes may be chosen that give the minimum Bayes decision error or that maximize the distance between two classes while minimizing average distance within each class, or that maximizes its margin with respect to the two distributions, defined to be:
Other methods of selecting the appropriate hyperplane may also be acceptable.
In the context of this example, the margin of a hyperplane will be positive if the control and treated cells are separable (i.e., no misclassification). If the control and treated cells are not linearly separable, a soft margin, which tolerates misclassifications, may be used. In this example, the soft margin approach was used to find the maximal margin hyperplane due to its robustness to noisy data and outliers, although methods would also be acceptable. The maximal margin hyperplane may be determined from a support vector machine algorithm in a known manner.
Since Wk specifies the orientation of the maximal margin hyperplane, this normal vector will point in the direction in which the distribution of Ck is shifting away from the distribution of C0,
One of the advantages of using Wk as a drug profile is that Wk is fully multivariate because the profiling method uses all features concurrently. Another advantage is that the building of Wk only requires Xk and X0, thus the complexity of the profiling algorithm is independent of nD. This kind of profiling method is well-suited for building profiles for huge number of drugs.
Turning now to the details of the dosage range profile (d-profile) determination (step 105,
In step 302, given a maximum limit of the number of clusters, H, a clustering algorithm is used to cluster {Wtk} into h clusters, for each h=1,2, . . . ,H. For example, a combinatorial clustering algorithm, which searches through all the possible partitions of {Wtk} into h clusters for the optimum partition that minimizes a loss function, may be used. For example, the following within cluster point scatter can be used as a loss function.
where G(t) is the cluster membership assignment to the profile Wtk, and d(Wtk,Wtk) is the similarity between two profiles, Wtk and Wt′k. The combinatorial clustering algorithm may be speeded up by putting certain constraints on the clustering. For example, the constraint that all profiles within a cluster must come from consecutive titrations can be used. Other suboptimal clustering algorithms can also be used in step 302.
In step 303, for each clustering result, the performance of the clustering is determined. For example, a consistency value for the clustering result after many trials of random disturbance can be used. When a dataset has a small number of profiles (e.g. 10-20), such as in the case of clustering of profiles obtained at different titrations, previous approaches based on resampling produces disturbances with low diversity. To overcome this difficulty, disturbance based on randomly generated, normally distributed noise can be used. The mean and the standard deviation of the noise were set to be zero and the standard deviation of the feature respectively. The algorithm is described below:
Given the number of cluster, h, and a set of profiles:
In step 304, the optimum number of partitions was determined manually or automatically by choosing the clustering result with the minimum average normalized consistency ratio.
In step 305, a representative d-profile is derived from each partition of profiles. For example, a d-profile may be obtained by averaging the partition's constituent profiles that are both statistically significant and reproducible (as determined, for example, by a replicate reproducibility score threshold).
To illustrate that Wk may be used as a drug profile, Wk were clustered from 23 compounds with different known targets. Since Wk may characterize drug effects, Wk's from compounds with similar targets will form a cluster, while Wk's from compounds with different targets will form separate clusters.
The list of compounds used and their known major target is listed in Table I. The data that was used were obtained from HeLa (human cancer) cells. Only groups of compounds that have more than four members were chosen. Multiple replicates of some compounds (Nacodazole, Scriptaid, and Emetine) were provided from the original dataset. Ideally, profiles from the replicates of a drug are expected to be the closest to the profile of another replicate of the same drug. The concentrations of the compounds used are the effective concentrations that have been determined previously. Plates with DNA, anillin, and SC35 markers were used in this example. A segmentation algorithm was used to segment cells from the obtained images, and values for 29 features were measured for each cell. Feature values for around 2500-5000 cells per compound were obtained.
For each compound, all the treated cells were split into 5 equal partitions. For every combination of four partitions, an equal number of cells were randomly selected from all the control cells, and a support vector machine (SVM) algorithm was used to determine the maximal margin hyperplane between the control and treated cells. The same process was repeated five times with different random splitting of partitions. The final decision hyperplane was an average of all the obtained hyperplanes.
Besides building the hyperplanes, an additional profile was built for each compound by using a prior art univariate method. This prior art method was based on z-scores derived from the Kolmogorov-Smimov (KS) statistics between the control and treated distributions of each feature. The clustering result obtained from the multivariate method was then compared with the result obtained from this prior art univariate method.
The profiles for all compounds were clustered by using a correlation-based hierarchical clustering algorithm, implemented in Matlab v14 SP3. The dendrogram obtained from the hierarchical clustering of the profiles obtained from the univariate profiling method is shown in
In the dendrogram of profiles obtained from univariate profiling,
To illustrate the performance of the present multivariate approach, the disclosed methods were applied to a compendium of fluorescence microscopy images in which HeLa cells were treated with 100 compounds, dissolved in dimethyl sulfoxide (DMSO), over 13 threefold titrations as shown in
In order to gather a comprehensive collection of phenotypic measurements, for each marker set and each cell, the values of 296 image features were computed from the DNA and non-DNA regions as shown in
For most of the compounds, the recursive feature removal step (optional step 104,
The importance of all feature categories were compared across different compounds on the same marker set. Despite the consistency in the number of retained features, the types of retained features were highly diverse. For example, on the DNA-SC35-anillin marker set, texture features were more important for Cholesterol inhibitors, but less important for compounds such as actin and DNA replication inhibitors. Overall, profile coefficients corresponding to texture and intensity features had the highest absolute values, while Zernike and moment features had comparatively lower absolute values.
Next, the importance of all feature categories were compared across different marker sets on the same compound. In general, texture features were more important than intensity features on the DNA-SC35-anillin and DNA-MT-actin marker sets; while the reverse was true on the DNA-cFos-p53 and DNA-p38-pERK marker sets. The results suggested that spatial pattern information was most relevant on the markers measuring cytoskeleton (DNA-MT-actin) or proteins with cell-cycle-dependent localization (DNA-SC35-anillin), while intensity information was most relevant on the markers measuring transcription factors (DNA-cFos-p53) or cell signaling proteins (DNA-p38-pERK).
In this example, compound effects were considered significant only when the ability to separate treated from control cells was significantly greater than the ability to separate control cells from different wells. Due to biological and experimental variability, the significance thresholds of classification accuracy at p=0.05 estimated on every plate were much higher than 50% (
The classification accuracy curves of most compounds showed classical sigmoidal dose-responses, with classification accuracies below the significance threshold at the lowest dosage ranges, and well above the significance threshold at the highest dosage ranges (
The titration clustering algorithm (
Across different marker sets, 73% of the compounds gave the same number of d-profiles on three or four marker set (p<0.01, permutation test), indicating significant consistency in the number of d-profiles extracted. For example, taxol consistently gave 2 d-profiles (
To simulate a drug screen for compounds of similar target to a known compound, a d-profile was selected to be the reference profile, while all other d-profiles from the compendium were used as blinded test profiles. Similarity scores between the reference profile and all other test profiles were computed and ranked. The test profiles that were most similar to the reference profile were selected as “drug candidates.”
For each reference profile, the performance in identifying test profiles was estimated with similar a target on each marker set by using prior target annotations as the “gold standard.” The receiver operating characteristic curve (AUC) was used as the performance evaluation criterion (Methods). “On-target” effects were defined as d-profiles whose AUC values were significant (p<0.05), and all other d-profiles were defined as “off-target.” 73%, 40%, 67%, and 56% of the compounds with more than one d-profile and at least one on-target d-profile had at least one off-target d-profile on the DNA-SC35-anillin, DNA-p53-cFos, DNA-p38-pERK and DNA-MT-actin marker sets respectively. For example, Camptothecin was found to have one on-target effect and one off-target effect. Thus, the present method can identify dose-dependent secondary or tertiary responses that were very different from the primary responses.
To summarize screening performance results, the AUC values of the compounds that had been annotated with the same target category were averaged for each marker set (
The performance of a compound category across different marker sets were evaluated. Some compound categories induced phenotypic changes that were highly specific for the marker set used. For example, the effects of energy metabolism, PKC, protein degradation, and RNA inhibitors could only be detected by the DNA-anillin-SC35 marker set, while the effects of MAPK/ERK pathway inhibitors could only be detected by the DNA-p38-pERK marker set (
Another use of the method is to identify a small number of features that most discriminated compound categories. For each marker set and compound category, three representative on-target d-profiles were selected with maximum average AUC. The exclusion of off-target effects enabled the selection of on-target d-profiles from five compound categories not found significant in the drug screening process discussed above. Further, a hierarchical bi-clustering was performed on the 10-15 selected features from these d-profiles with the highest average absolute values on each marker set. A leaf-ordering algorithm was used to reorder the resulting dendrogram for the best visualization as shown in
Since the most discriminative features from each compound category were used, near-perfect clustering of compounds by category was obtained. Some compounds were grouped together by obvious or easily interpretable phenotypic features, such as the area of DNA region and the ratio of p38 average intensity in DNA region over non-DNA region for compounds affecting DNA replication, while others were grouped together by non-obvious or novel phenotypic features, such as the DNA gray level co-occurrence matrix (GLCM) mean correlation and the p38 GLCM mean sum average for compounds annotated as neurotransmitter inhibitors. Some of these common phenotypic changes reflected cell cycle information, such as mitotic arrest, while some were independent of cell cycles, indicating that the present method provides more than cell cycle detection.
Further, the categories themselves formed natural “super-clusters” based on common blocks of features, which enabled the identification of common phenotypic changes among these categories. For instance, all the three categories of kinase inhibitors (CDK, PI3K and MAPK/ERK) formed a super-cluster sharing negative coefficients for the ratio of the pERK average intensity over the DNA average intensity in the DNA region, zero coefficient for the ratio of pERK total intensity in DNA region over the non-DNA region, and positive coefficient for the p38 average intensity in DNA region over the DNA average intensity in the DNA region.
The compound category of a novel d-profile may be inferred by comparison to a collection of previously categorized reference d-profiles. For instance, comparison of d-profiles indicated that oxamflatin is most similar to trichostatin, scriptaid, and apicidin on the DNA-p38-pERK marker set (
Category prediction for compounds with multiple d-profiles was typically accurate for at least one of their d-profiles. For camptothecin, its first d-profile was closest to another topoisomerase inhibitor, etoposide, while its second d-profile was closest to a CDK inhibitor, alsterpullone (
From the above-described Example 2, it may be seen that the disclosed method of profiling compound-dosage responses reduces approximately 300 unbiased single-cell phenotypic features to approximately 20 maximally informative features for each marker set. The large reduction in dimensionality comes with greatly enhanced human interpretability of the drug response profiles and improved detection of novel cellular phenotypic changes, yet at little loss of classification accuracy. Analysis of these selected features demonstrated maximally informative marker and feature set combinations for detecting and discriminating among categories of compound classes, and will be applicable enable streamlining future drug screens.
According to the present disclosure, d-profiles effectively summarize high-throughput, single cell phenotypic responses to compounds. Separating compound dosage effects into multiple d-profiles results in more sensitive screening and raises the possibility of identifying novel dosage-dependent mechanisms, even for previously characterized compounds. The method of the present disclosure for building compounds is computationally and experimentally scalable; compound profiles are created independently of each other and allow for incremental growth of a compound compendium.
When applied to drug screening, the present method provides accurate quantification of complex phenotypic changes that are complementary to other high-throughput approaches, such as transcript profiling, and offers the potential to bring the use of model biological systems earlier into the drug discovery process. The method is also broadly applicable for characterizing single-cell phenotypic changes due to other external perturbations (such as, for example, cytokines, stress factors and RNA interference), and internal cellular states (such as, for example, diseased versus normal cells). It provides the basis for more sophisticated analysis, such as the characterization of synergistic or antagonistic behavior of combination of perturbations, identification of sub-populations of cells beyond commonly known states such as cell cycle, and reconstruction of biological pathways based on monitoring multi-dimensional phenotypic readouts.
All of the methods disclosed and claimed herein may be executed without undue experimentation in light of the present disclosure. While the methods of this disclosure may have been described in terms of preferred embodiments, it will be apparent to those of ordinary skill in the art that variations may be applied to the methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the disclosure. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope, and concept of the disclosure as defined by the appended claims.
