Patent Application: US-201414527297-A

Abstract:
a method is provided to translate from tissue dynamics spectroscopy data formats into high - content analysis data formats . the method utilizes tds feature vectors and hca feature vectors obtained from a shared set of compounds and cell lines to generate a translation matrix . the translator is applied to the unique data format of tds that carries information from deep inside 3d tissue to convert the data into a standard data 2d hca data format that fits into the standard workflow of potential customers .

Description:
a phenotypic profile includes a vector array of quantitative measurements of a number of properties or features of cells or tissues responding to an applied stimulus where | v i & gt ; is the i - th component of the feature vector with the index varying from 1 to p for p features . the symbolic notation in the expression combines bracket notation with contravariant vector notation . the index i that appears as a superscript denotes a column ( contravariant ) vector with contravariant row index i . the transpose of the foregoing equation is a row ( covariant ) vector denoted as & lt ; v i | with covariant column index i . the bracket notation is borrowed from quantum mechanical theory because it provides a transparent way of constructing projection operators that will be used to project from one phenotypic space to another . the contravariant - covariant notation is borrowed from tensor analysis because it makes it easy to denote inner and outer products of vectors to construct the projection operator . in high - content drug screening , the properties i = 1 : p can relate to morphological properties of cellular constituents , to densities , to functional properties such as mitochondrial membrane polarization , and to dynamic behavior , among others . the dimension of the phenotypic vector space is equal to the number of properties p . a phenotypic panel includes a set of feature vectors obtained across a panel of compounds or conditions for the index m varying from 1 to m compounds or conditions . the values of | v m i & gt ; are usually arranged as m column vectors of length p , e . g ., as a p by m matrix . the feature vector space has dimension p , but the coordinate axes are not orthogonal . in other words , the features are not in general independent and have correlations among them . for instance , the covariance matrix for a phenotypic panel | v m i & gt ; is given by where the μ i are the mean values of the i - th feature averaged across the conditions indexed by m . the off - diagonal terms of the covariance matrix are in general non - zero . the covariance matrix participates in multivariate statistical analyses such as principal component analysis and canonical correlation analysis . it is central to the process of dimensionality reduction by helping to identify the smallest number of properties , or combinations of properties , that capture most of the independent behaviors of the system . features measured by high - content image analysis of 2d culture are substantially different than the features obtained from tissue dynamics spectroscopy ( tds ). furthermore , the experimental conditions are very different . in the case of high - content analysis ( hca ), individual cells on plates are imaged using fluorophores specific to the nucleus and mitochondria . in the case of tds , on the other hand , the cells are within tissues , and intracellular motions constitute the label - free imaging contrast . both techniques measure physiological properties of the cells and tissues responding to the applied compounds . therefore , it is important to explore whether hca and tds features relate to the same physiological processes . the feature vectors for hca are expressed as | v m a & gt ; and for tds as | v m b & gt ; where the index m spans across m compounds . the dimension of the hca space is a , and the dimension of the tds space is b . the feature vectors are used to construct a projection operator , also known as a density matrix , which projects from one space to another and back . the projection operator that projects from the tds space to the hca space is { { circumflex over ( p )} b a } m =| v m a & gt ;& lt ; v b m | for the compound m . this a - by - b dimensional matrix is the outer product of the hca and the tds feature vector for the m th compound . the goal is to find a mapping that is consistent across a broad selection of compounds , and ideally across several cell lines . to construct an average mapping , a reduced projection operator is generated as the partial trace over the compounds the reduced density matrix { circumflex over ( p )} b a is the desired translation ( or projection ) matrix between the tds and the hca spaces . for a given compound m that has a tds feature vector | v m b & gt ; the back - projected hca feature vector | v m a & gt ; for that compound is projected to be | v m a & gt ;={ circumflex over ( p )} b a | v m b & gt ; this can be applied to each compound m to reconstruct the a - by - m matrix of hca feature vectors . in this last equation , the reduced projection operator is the general mapping that translates the tds feature vector into a corresponding hca feature vector . in general , back - projection maps a higher - dimensional space to a lower - dimensional space such that b & gt ; a when the dimension of the tds space is larger than the dimension of the hca space . the projection operator approach is strictly accurate only if there is a unique projection from one space to the other . however , in biological phenotypic assays , there may be features in one assay that are missed by the other , and there are often strong correlations among features within an assay . therefore , the projection operator approach must be incomplete when translating between two assay formats , as between tds and hca . the question then arises how faithfully the translation matrix captures the relationships between the two assays . the theoretical basis of the method disclosed herein for translating from biodynamic imaging spectrograms to conventional high - content data formats is depicted schematically in fig1 . the method seeks to project from physiological space to measurement space . two independent measurement techniques are used to probe the physiological space : a 2d imaging approach that measures the microscopic physiological properties of the sample ( hca ), and a 3d dynamic approach that measures the intracellular dynamics ( bdi ). it is assumed that the physiology subspace and the dynamics subspaces have a correspondence or correlation among a number n d features . furthermore , because dynamics and morphology are not identical , there are additional subspaces for dynamics with n di features and for morphology with n mi that are independent of each other . the correspondence between the dependent subspaces is denoted by a generalized rotation matrix r that “ scrambles ” the dependent feature sets . the specific experimental details of bdi and hca convert the features of physiological space into measurement features . this is represented by a generalized rotation matrix b for bdi and a for hca . these transformations take , as input , features from both the dependent and independent spaces . depending on what is measured , not all features from the physiological space are sampled . the resultant of these transformations is a measured set of dynamic features and a measured set of morphological or molecular features . because of the correspondence of some of the features in physiological space through the transformation r , there is a correspondence among the measured feature vectors represented by the translation matrix t . the goal of the method disclosed herein is to determine the translation matrix t that allows translation from the bdi feature vectors to hca feature vectors that fit into the work - flow and decision - making of lead selection in drug discovery . to test the fidelity of the back - projection , consider two subspaces of eigenvectors for each of | v m a & gt ; and | v m b & gt ;. one subspace is the dependent subspace in which as | v m a ′ & gt ; and | v m b ′ & gt ; are related through the real - valued unitary rotation operator r b ′ a ′ the second subspace is independent , denoted by double - primed indices , and have no correlations between | v m a ″ & gt ; and | v m b ″ & gt ;, such that where the average is over many compounds or conditions denoted by the index m . the first term is the rotation matrix . the other three terms have residual non - zero values that are of order ( 1 /√{ square root over ( m )}) which reflects the random and independent character of the subspaces . ( note : all vectors must be zero - averaged and normalized for this square root dependence ). the observables for | v m a & gt ; and | v m b & gt ; are denoted by superscripts p and q , respectively . the observables are linear combinations of the observables in the dependent and independent subspaces through for j ={ a ′, a ″} and k ={ b ′, b ″} where j spans both the dependent as | v m a ′ & gt ; subspace and the independent as | v m a ″ & gt ; subspace , and k spans the dependent | v m b ′ & gt ; subspace and the independent | v m b ′ & gt ; subspace . the transformations a j p and b k q are not complete and generally non - invertible . this non - completeness means that not all eigenvectors that span a space are included in the observables ( certain responses are missed ). furthermore , the non - invertibility means that some observables carry information that is partially redundant with other observables . for a compound or condition m , the inner product between feature vectors of the two spaces is & lt ; b q m | a m p & gt ;= a a ′ p b q b ′ & lt ; v b ′ m | v m a ′ & gt ;+ a a ′ p b q b ′ & lt ; v b ″ m | v m a ′ & gt ;+ a a ″ p b q b ′ & lt ; v b ′ m | v m a ″ & gt ;+ a a ″ p b q b ″ & lt ; v b ″ m | v m a ″ & gt ; by using the relations in equations 1 above after summing over a large number m of compounds and conditions , this generates the translation matrix where the inner products among the non - correlated subspaces vanish to lowest order , and the translation matrix is equal to the reduced density operator . the residuals fall under two types . random residual correlations are of order o ( 1 /√{ square root over ( m )})). these can be made arbitrarily small by averaging over a large set of compounds and conditions . the other residual is not random and is related to the size of the independent components a ″ and b ″ relative to the total sizes a and b of the feature vectors . this residual cannot be reduced by increasing the number m , but depends on the relative contributions of the independent subspaces to the defined feature vector elements . the translation matrix t q p is not in general unitary , even though r b ′ a ′ is unitary , because a a ′ p and b q b ′ are not complete and generally may be non - invertible and non - square matrices . if they were complete and invertible , then the translation would have perfect fidelity in the limit that the number of compounds tested m goes to infinity . however , in the experimental situation between tds and hca the fidelity will be less than unity because of the finite number of compounds . the question is : how much less ? the fidelity of the translation matrix is defined experimentally as where a p and b q are the experimentally measured feature vectors , and t q p is obtained from the experimental correlations . because the transformations are not complete and generally may be non - invertible , the transformation must be from the larger assay space to the smaller assay space . since the tds is larger , the translation matrix is constructed to translate a tds feature vector into an hca feature vector . the theoretical model depicted in fig1 was tested using monte - carlo computer simulations . an example of the simulation results is shown in fig2 . the size of the dependent subspace is n = 10 , and the sizes of the independent spaces were set equal to each other nia = nib . the observable feature size is na = 12 for bdi and nb = 7 for hca , which matches the hca / tds experimental conditions described below . the compound set size is m = 16 , which is also the same as the experimental conditions . the completeness fraction is the fractional subsampling of the full physiological space by the a and b transformations and is given by fraction = 0 . 5 for this simulation . fig2 shows the correlation coefficient between the hca and the bdi feature vectors as a function of the coherent fraction which is defined by n /( n + nia ). there are three correlation curves on the figure . the curve designated by r at the top of the graph is the “ true ” correlation when only the dependent a and b subspaces are included . the correlation is less than unity because only m = 14 compounds are used in the monte carlo simulation . the curve designated by r complete ( the second line from the top right of the graph ) uses a complete mixture for the transformations a and b and includes the non - orthogonal expansion to na and nb observables . the correlation coefficient of r complete is less than r because of the non - orthogonal features in the measurement space . the curve designated by r incomplete uses an incomplete mixture for the a and b transformations . in this figure , the incomplete fraction is 50 % which reduces it further from r complete . also shown in fig2 are the standard deviations on the mean values . the mean values only exceed the standard deviation for coherent fractions above 50 %. therefore , in a practical implementation of this method , correlation coefficients larger than 50 % can be considered significant . the dependence of the correlations and standard deviations on the number of test compounds m in the training set is shown in fig3 for a coherent fraction of 70 % showing the sqrt ( m ) dependences . again , a correlation coefficient above 50 % is significant . ht - 29 and dld - 1 cells are products of american type culture collection . dld1 cells were grown in rpmi - 1640 medium with 10 % fetal bovine serum . ht - 29 cells were grown in mccoy &# 39 ; s 5a modified medium . both cells were grown at 37 ° c . in a humidified 5 % co 2 atmosphere . to form tumor spheroids [ 61 - 63 ], cells were first grown in cell flasks , then moved to a rotating drum incubator where the cells were suspended in a pure growth medium environment . the medium was refreshed every other day . the cells form optimal experimental size ( 300 μm - 800 μm in diameter ) spheroids in the incubator in about 1 week for the dld - 1 cell line and about 4 weeks for the ht - 29 cell line . to perform biodynamic imaging experiments , the tumor spheroids were loaded into s - well lab - tek chamber slides ( lab - tek ® ii chamber slide system ). low - temperature porous agarose was applied to immobilize the tumor spheroids when planted in the chamber slide . the agarose powder from sigma - aldrich inc . was mixed with stock growth medium without serum . the solution concentration was 1 % by weight . the agarose solution was first warmed and then cooled to 37 ° c . the tumor spheroids were moved from the incubator to the chamber slide wells covered with growth medium , then the agarose solution was added to the chamber slide wells and fully mixed . as soon as the agarose gelled , serum and growth medium were added to the wells . each well contained about 5 to 20 spheroids . the prepared chamber slide was placed on a temperature - stabilized plate on the biodynamic imaging system , and the experiments were performed at 37 ° c . mitochondrial dysfunction is a central concern in the development of new drug entities because mitochondrial toxicity is one of the main off - target effects that leads to drug failures in clinical trials [ 64 ]. there are many known mitochondrial toxins that work through different mechanisms . valinomycin is a potassium ionophore that suppresses the mitochondrial membrane potential ( mmp ) without adversely affecting the viability , while accompanied by an increase in mitochondrial motility [ 65 ]. fccp is a well - studied mitochondrial uncoupler that permeabilizes the mitochondrial membrane to proton transport that also has a minor effect on cellular viability . nicardipine is a calcium - channel blocking agent that increases the mmp , and decreases intracellular calcium that is accompanied by increased mitochondrial motility [ 66 ]. ionomycin is a potent calcium ionophore that disables mmp accompanied by extinguished mitochondrial motility in astrocytes [ 67 , 68 ] and cell death . each of these drugs affects the mitochondria and cellular viability by different mechanisms that are expected to lead to different tds drug - response signatures . contrasted to mitochondrial toxins , signaling - pathway inhibitors have more subtle effects on cellular physiology , and many perform as cyotostatic drugs rather than as cytotoxic drugs . somatic point mutations in braf occur in approximately 8 % of human tumors [ 70 , 71 ], and in colon cancer it is as high as 12 % [ 72 ]. a single glutamic acid for valine substitution at codon 600 ( v600e ) is present in approximately 90 % of braf mutations and are associated with poor survival . these patients may be expected to respond to raf inhibitors [ 72 ]. sorafenib ( nexavar ) was the first raf inhibitor approved by the fda . however , it is not highly selective to raf and may operate primarily through anti - angiogenic pathways [ 73 , 74 ]. plx4032 / rg7204 ( plexikon / roche ) is active against three raf isoforms at nanomolar concentrations in serum [ 75 - 77 ]. in cells with v600e braf mutation , this drug induces cell cycle arrest and sometimes cell death [ 76 ]. plx4032 has minimal toxicity and can be tolerated at serum levels up to 50 μm [ 78 ]. plx4032 has a paradoxical effect on wild - type braf , including lines with ras mutation , in which the raf inhibitor actually causes activation of erk in the map kinase pathway [ 79 , 80 ] with similar effects for plx - 4720 [ 81 ] and gdc [ 82 ]. this would normally require genetic testing of patients to prevent the selection of plx therapy for cancers that have wild - type braf [ 78 ], but the development of phenotypic screening methods may provide a fast and inexpensive alternative to genetic testing . biodynamic imaging measures intracellular motions by performing digital holography with low - coherence light and capturing the fluctuating intensities of dynamic speckle . it uses a continuous - wave low - coherence light source ( superlum ) with a 20 mw output intensity at a wavelength of 840 nm and a bandwidth of 50 nm with a coherence length of approximately 15 microns . the light path is divided into a signal and reference arm in an ultrastable mach - zender interferometer by a polarizing beam splitter with variable polarizers to adjust the relative intensities in the signal and reference arms . the light scattering is performed in a back - scatter geometry because the intensity fluctuation rates depend on the momentum transfer vector that selects longitudinal motion along the backscatter direction . the light scattered by the living biological sample is collected by a long focal - length lens and transformed to a fourier plane where the ccd pixel array is placed . the reference wave is incident on the ccd array at a small angle of 3 degrees relative to the signal axis , creating an off - axis digital hologram that is acquired on a fourier plane of the optical imaging system . this fourier - domain hologram is transformed using an fft algorithm into the image domain . the transformation performs two functions : demodulating the spatial carrier frequency represented by the holographic interference fringes , and coherence - gating the low - coherence light to a specified depth inside the tissue sample . the coherence - gating role of digital holography creates a full - frame optical coherence tomography ( oct ) section of the tumor spheroid at a fixed depth . successive frames are acquired at a fixed coherence - gated depth at a frame rate of 25 fps . the reconstructed images includes speckle intensities that are modulated by the dynamic intracellular motion of the target , causing intensity fluctuations on each pixel . the fluctuating intensities of the speckle images have characteristic time scales that relate to the specific types of intracellular motion inside the living tissue samples . when there are many processes and many different characteristic times , the frequency domain is best suited to analyze the influence of applied drugs on dynamic light scattering . the time - traces of the fluctuating intensities are transformed into the frequency domain as a spectral power density denoted by s ( ω ). the measured power spectrum is affected by the frame rate of the acquisition and by the exposure time of the shutter . when a drug is applied to the sample , or the environmental conditions change , the relative power density at different frequencies is altered . this change is captured through the differential relative spectral power density . an example of a differential relative spectrogram is shown in fig4 a for the raf kinase inhibitor sorafenib applied to a dld1 tumor spheroid . this spectrogram is a time - frequency representation of the relative changes in the spectral power after a drug is applied at time t = 0 . the frequency axis is logarithmic and extends from 0 . 005 hz to 12 . 5 hz . the time axis in this figure extends for 9 hours after the application of the dose at time t 0 . the color map shows the relative increase / decrease of spectral power in response to the drug . the two - dimensional time - frequency data format for each drug is decomposed into feature values that become the coordinates of a high - dimensional feature vector . the composition is not unique , but one quasi - orthogonal choice of twelve feature masks is shown in fig4 b . these are multiplied by the spectrogram in fig4 a and integrated to yield a single value for each mask . the twelve feature values are the components of the twelve - dimensional feature vector shown in fig4 c for the spectrogram of fig4 a . this procedure is carried out for each drug . the spectrograms are averaged in triplicate for each drug to reduce variability . tissue dynamic spectroscopy was performed on 140 different drugs , doses and conditions . time - frequency tissue - response spectrograms were generated in each case and decomposed into feature vectors using the feature masks of fig4 b . a similarity matrix among the different feature vectors was generated through correlation , and was input into an unsupervised hierarchical clustering algorithm . the clustered similarity matrix and feature vectors are shown in fig5 . subgroups of spectrograms share common features that are different than other subgroups , which is reflected in the quasi - block - diagonal character of the similarity matrix . further details of the procedure utilized in these experiments and in the methods disclosed herein are disclosed in co - pending application ser . no . 13 / 760 , 827 , entitled “ system and method for determining modified states of health of living tissue ”, which was filed on feb . 26 , 2013 , and was published on jun . 6 , 2013 as pub . no . 2013 / 0144151 , the entire disclosure of which is incorporated herein by reference . hca of mitochondrial toxicity was performed [ 83 ] using live dld - 1 and ht - 29 cell cultures stained with three fluorescent dyes : tmrm , hoechst33342 , and to - pro - 3 ( invitrogen , carlsbad , calif .). the lipophilic cationic dye tmrm was used to monitor mitochondrial membrane potential ( mmp ). the cell - permeable nuclear marker hoechst33342 was used to identify cell events and to monitor nuclear morphology . the membrane - impermeable nuclear marker to - pro - 3 was used to characterize cell viability based on plasma membrane integrity . detailed mitochondrial toxicity hca with data collection and analysis protocols were recently described [ 80 ] and are briefly summarized here . following a four hour incubation of cells with the tool compounds , a cocktail of the three fluorescent dyes was added , and cultures were incubated for an additional 45 min at 37 ° and 5 % co 2 before analysis . the final concentrations of dyes in each of 96 wells were 125 nm tmrm , 133 nm to - pro - 3 , and 1 . 5 μg / ml hoechst33342 . along with the dyes , 20 μm verapamil was added to the cocktail to maintain consistent tmrm cell loading through multi - drug inhibition . liquid handling was performed using a biomek fx laboratory automation workstation ( beckman coulter ). data were collected using an imaging cytometer icys ( compucyte ) configured with three excitation lasers ( 405 nm , 488 nm , 633 nm ) and four emission detector pmts . tmrm emission was recorded using a pmt with a 580 / 30 band - pass filter , hoechst33342 with a 463 / 39 filter , and to - pro - 3 with a 650 long - pass ( lp ) filter . six fields of view ( 500 × 368 . 6 μm each ) were arbitrarily collected per well using a 20 × objective at 0 . 5 μm resolution . image segmentation was performed based on hoechst33342 intensity using the icyscytometric analysis software ( compucyte corp ., westwood , mass .) to identify cell events . primary contours were defined on the basis of adjacent hoechst33342 pixels above the preset intensity threshold value ( 3500 a . u . for dld1 cell line and 7900 a . u . for ht29 ) and expanded by four pixels for the analysis . a low pass 5 × 5 smoothing filter and watershed algorithm were applied to separate closely - spaced nuclei . additionally , an area filter was applied to eliminate clumps with areas larger than 250 μm 2 and cell debris with areas less than 20 μm 2 . peripheral contours were defined as a 14 pixel - width ring outside the expanded primary contour . integrated tmrm intensity within the peripheral contour ( tmrm pi ) and maximum tmrm pixel intensity within the peripheral contour ( tmrm max ) were selected as tmrm - based parameters . nuclear area , nuclear circularity , nuclear average and integral intensities were used as hoechst33342 - based parameters . to - pro - 3 average intensity was used as a viability measure . after image feature extraction , statistical analysis of the population distribution was performed using matlab 7 . 12 . 0 ( the mathworks ). kolmogorov - smirnov ( ks ) values were used as statistical measures for tmrm - and hoechst - based parameters . ks values were computed for each compound , dilution , and parameter of interest as where cdf is the cumulative distribution function of compound - treated and dmso - treated ( untreated ) negative control samples respectively . the sign of ks reflects the direction of the shift of the distribution relative to the negative control ( dmso ). percentage of live cells ( viability factor ) was evaluated on to - pro - 3 - derived parameters . the viability factor was rescaled from − 1 ( all dead ) to + 1 ( all live ) to match the range for ks values . statistical measures of seven parameters ( tmrm pi , tmrm max pix , nuclear area , circularity , average and total intensity , and cell viability ) measured at three different concentrations ( 100 μm , 33 . 3 μm , 11 . 1 μm ) for both cell lines were concatenated to create 21 - parameter numerical vectors for phenotypic cell response characterization . each experiment was performed in duplicates or quadruplicates and the obtained vectors were analyzed individually to evaluate statistical variability . as an example of the hca dataset and analysis , dld1 cells were incubated for four hours with 100 μm of different compounds including vehicle negative control dmso ( no treatment ). cells were stained with a cell marker cocktail including three fluorescent dyes : hoechst33342 , tmrm and topro3 and analyzed with icys imaging cytometer . arbitrary images ( 500 × 368 μm ) demonstrate a light - scatter channel , individual fluorescence channels for each cell marker and a merged channels with combination of all three fluorescence channels ( color image ). examples of dld1 responses to all 8 tested compounds and non - treated control ( dmso ) are demonstrated in fig6 . the high - content ( hca ) values for cellular morphology and fluorescent intensities were collected into feature vectors for three concentrations ( 11 . 1 μm , 33 . 3 μm , and 100 μm ). the non - parametric kolmogorov - smirnoff test was used as the measure of hca vector similarity . the first step in the method is the construction of a joint feature vector data format in which the tds and the hca feature vectors are concatenated for each of the tested compounds and cell lines . the joint concatenated feature vectors are shown in fig6 for 16 compound / cell - line combinations . the hca data includes the top three concentrations , while the tds feature vectors are at a single concentration . as discussed above , the density operator { circumflex over ( p )} b a is the outer product of the hca and tds feature vectors of a given compound m . the reduced density operator is the partial trace over a large set of m compounds , which is equivalent to calculating the correlation coefficients between each of the seven hca and twelve tds features in the present example . the translation matrix according to the present disclosure that is obtained from the joint feature vectors of fig6 , is shown in fig7 . the translation matrix is averaged over both cell lines ( dld - 1 and ht - 29 ) and averaged over all eight compounds ( four mitochondrial toxins and four raf inhibitors ). the values of the matrix represent the correlations that exist among the tds and hca features . in this example , the vertical axis corresponds to the seven hca features while the horizontal axis corresponds to the twelve tds features of their respective feature vectors . both strong positive correlation and strong negative correlation represent nearly one - to - one correspondence between the tds and hca features . for instance , the strongest positive correlations are between the mid - high tds frequencies ( tds features 1 , 2 , 4 , 5 , 7 and 8 ) and mitochondrial polarization ( tmrm signals ) ( hca features 1 and 2 ). the strongest negative correlations are between the tds high frequencies ( tds features 10 , 11 and 12 ) and nuclear morphology and permeability ( hca features 3 , 4 and 5 ). from previous studies , it is known that the high - frequency band in the tds spectrograms relates to the activated motion of mitochondria . strong mitochondrial polarization is seen here to correlate with strong mid - high frequencies in the spectrograms associated with organelles . similarly , degraded nuclear morphology and nuclear membrane permeability known to be related to cell death correlates here with diminished high - frequencies in the spectrograms . these trends are also consistent with the strong negative correlation between viability and organelle transport observed in fig6 . the weakest correlations are seen for the hca nuclear average and for the tds f2t2 band . the projection from tds back to hca is tested by calculating the correlation between the original hca vector and the back - projected hca vector that is obtained by applying t q p to the tds feature vectors . this procedure is performed in a “ leave - one - out ” process in which the translation matrix is constructed of 15 out of the 16 drug / cell line combinations ( i . e ., 8 drugs compounds for two cell lines ). the condition that is left out is then the test tds vector that is translated into an equivalent hca vector . the actual hca vector is compared to the translated hca vector and the correlation coefficient is calculated . the results of the leave - one - out correlation analysis are shown in fig8 . correlation coefficients larger than 0 . 5 are significant for this set m = 16 of conditions . there are ten conditions that have significant correlations ( ht - fccp , ht - nicardipine , ht - valinomycin , ht - sorafenib , ht - plx4032 , dld ionomycin , dld - valinomycin , dld - sorafenib , dld - plx4032 , dld - gdc ) there are six conditions that do not have strong correlations ( ht - ionomycin , ht - plx4720 , htgdc , dld - fccp , dld - nicardipine , dld - plx47 - 20 ). the agreement demonstrated in fig8 between mitochondrial signatures in tds ( high frequency bands ) and mitochondrial membrane polarization is an important step in the characterization and calibration of tds as a new type of phenotypic profiling that can operate in three - dimensional tissue . the importance of 3d tissues is growing in early drug screening , but there has been a lack of observational tools that can penetrate and obtain high - content information from inside living tissue without the need for fluorescent labeling . tissue dynamics spectroscopy may be able to fill that need as the methods disclosed herein are applied to more extensive sets of drug classes and cell lines . each row of the translation matrix t q p contains a linear superposition of all tds features . this makes it possible to define a tds feature mask that contains the time - frequency features that correlate most strongly with each hca property . this is expressed as where m q ( ω , t ) is the q th tds feature mask . examples of the linear superpositions are shown in fig9 for four of the hca properties . note that these masks are not orthogonal . for instance , the tmrm peripheral max is strongly anti - correlated with nuclear circularity . on the other hand , nuclear area and nuclear average are extremely similar , indicating that they measure essentially the same features from tds . the resulting masks m p ( ω , t ) can subsequently be used as their own feature masks to generate pseudo - hca feature vectors based on tds datasets . the p - th hca feature is obtained from a time - frequency tds spectrogram s ( ω , t ) by where the pseudo - hca feature value is normalized by the modulus of the mask m p ( ω , t ). this aspect of the disclosed method takes an experimental tds spectrogram as an input , matches that spectrogram with patterns that have an hca context , provides a mechanistic means to interpret tds datasets , and generates data in a format that can be used in established workflows that operate on hca data . because tds data are obtained from 3d tissues that have greater biological relevance , this method can provide valuable and more reliable information to researchers in many areas in the life sciences . those skilled in the art will recognize that numerous modifications can be made to the specific implementations described above . the implementations should not be limited to the particular limitations described and described in the claims provided below . other implementations may be possible . k . a . giuliano , j . r . haskins , and d . l . taylor , “ advances in high content screening for drug discovery ,” assay and drug development technologies , vol . 1 , pp . 565 - 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