Analysis of vibration fluorescence from biological cell

Abstract

To provide an analysis of vibration fluorescence from a biological cell.SOLUTION: A method and a system are for analyzing vibration fluorescence showing vibration ion flux associated with one or more biological cells. An exemplary method (50) may include a step (54) of detecting fluorescence (91) from one or more biological cells (72) to generate a series of data points (120) describing a vibration pattern (110). A series of gradients may be calculated (60) with respect to the vibration pattern (110). For example, sliding window (122) may be used to define a subset (126) of a series of data points (120) from which the series of gradients are calculated. The peaks (118, 150a, 150b) of the vibration pattern (110) may be identified using a series of gradients (62).SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] (Related Applications) This application claims priority to U.S. Provisional Application No. 62 / 866,524, filed June 25, 2019, the contents of which are incorporated herein by reference in their entirety.

[0002] The present disclosure relates to methods and systems for analyzing oscillatory fluorescence from biological cells. More specifically, disclosed embodiments relate to methods and systems for analyzing oscillatory fluorescence representative of oscillatory ion flux, such as calcium oscillations, in biological cells and for testing the effect of cell treatments on the oscillatory fluorescence. [Background technology]

[0003] Introduction Most of the drugs fail in clinical studies due to cardiotoxicity or neurotoxicity.To reduce the occurrence of these failures, highly sensitive in vitro assays are required to reliably evaluate the adverse effects of compounds on heart and nerve cells before clinical studies are started.These in vitro assays can facilitate and streamline drug development.

[0004] Culture systems for cardiomyocytes and neurons have been developed, and these cells exhibit spontaneous synchronized ion fluxes, such as calcium oscillations, which can be detected by labeling the cells with fluorescent calcium indicators.

[0005] Imaging systems including analysis software have also been developed to record and analyze oscillatory fluorescence representative of calcium oscillations in cells. Imaging systems have been utilized to measure the effects of pharmacological compounds on calcium oscillations in cultured cardiomyocytes and neurons. Significantly, these calcium oscillations are perturbed in vitro by pharmacological compounds with known cardiotoxic or neurotoxic effects. Thus, the methodology shows promise for predicting efficacy and dosage of drug candidates prior to safety testing and clinical studies of drugs and other chemicals. The oscillatory patterns produced when calcium oscillations are perturbed by drugs and other compounds can be highly complex. Better methods and systems are needed to detect the biologically relevant features of these complex oscillatory patterns and extract values ​​for the most informative readouts. Summary of the Invention [Means for solving the problem]

[0006] The present disclosure provides methods and systems for recording and analyzing oscillatory fluorescence representative of oscillatory ion flux associated with one or more biological cells. An illustrative method of analysis may include detecting fluorescence from one or more biological cells to generate a series of data points describing an oscillatory pattern. A series of gradients may be calculated for the oscillatory pattern. A sliding window may be used to define a subset of the series of data points from which a series of gradients is calculated. A peak of the oscillatory pattern may be identified using the series of gradients. Another illustrative method of analysis may include detecting fluorescence from one or more biological cells to generate a series of data points describing an oscillatory pattern. A primary peak and a secondary peak in the oscillatory pattern, if present, may be identified. A side of the secondary peak may be determined. The present invention provides, for example, the following: (Item 1) A method of analysis (50), comprising: detecting (54) fluorescence (91) representative of oscillatory ion flux associated with one or more biological cells (72) to generate a series of data points (120) describing an oscillatory pattern (110); calculating (60) a series of gradients for said vibration pattern (110); using said series of gradients to identify (62) peaks (118, 150a, 150b) in said vibration pattern (110); A method comprising: (Item 2) 2. The method of claim 1, wherein calculating (60) uses a sliding window (122) to define a subset (126) of the set of data points (120) from which the set of gradients is calculated. (Item 3) 3. The method of claim 2, further comprising selecting a size of the sliding window (122) from a plurality of allowed sizes, the size of the sliding window (122) corresponding to a number of data points (126) from the set of data points (120) that are encompassed by the sliding window (122). (Item 4) 4. The method of claim 3, wherein the size of the sliding window (122) is automatically assigned by a processor (96) based on the level of noise in the vibration pattern (110) and / or a sampling interval for the series of data points (120), the processor (96) also calculating the slope of the series and identifying the peaks (118, 150a, 150b). (Item 5) 4. The method of claim 3, wherein selecting the size of the sliding window (122) is performed by a user and communicated to a processor (96) that also calculates the series of gradients and identifies the peaks (118, 150a, 150b). (Item 6) 2. The method of claim 1, wherein the set of data points (120) is not filtered to reduce noise prior to calculating (60) the set of gradients. (Item 7) 2. The method of claim 1, wherein the peaks include a series of primary peaks (118), the vibration pattern (110) comprises a series of events (138) each including only one of the primary peaks (118), and the method further comprises determining at least one side of secondary peaks (150a, 150b) of the identified peaks, each secondary peak following one of the primary peaks (118) within an event (138). (Item 8) 8. The method of claim 7, wherein at least one aspect of the secondary peaks (150a, 150b) is related to a number, a frequency, or a periodicity of the secondary peaks (150a, 150b) in the vibration pattern (110). (Item 9) 8. The method of claim 7, wherein the vibration pattern (110) crosses a predetermined trigger level (144) twice per event (138), the trigger level (144) being set relative to a baseline (116) of the vibration pattern (110). (Item 10) 2. The method of claim 1, wherein identifying (62) a peak (118, 150a, 150b) comprises searching for a positive-to-negative or negative-to-positive transition in slope within the series of slopes. (Item 11) Item 11. The method of item 10, wherein identifying (62) the peaks comprises filtering peaks associated with the transition to obtain a set of peaks that are considered valid. (Item 12) 12. The method of claim 11, further comprising determining values ​​of peak-related parameters for the set of peaks considered to be valid. (Item 13) 2. The method of claim 1, further comprising labeling (52) the one or more biological cells (72) with a calcium indicator, wherein the fluorescence (91) is emitted by the calcium indicator. (Item 14) 2. The method of claim 1, wherein the one or more biological cells (72) comprise one or more cardiomyocytes or neurons. (Item 15) 2. The method of claim 1, wherein the vibration pattern (110) includes a series of events (138) each including a single primary peak (118), and the vibration pattern (110) includes one or more secondary peaks (150a, 150b) each included in an event of the series of events (138), and the method further includes determining (64) at least one value for one or more parameters associated with the one or more secondary peaks (150a, 150b). (Item 16) 1. A method of analysis, the method comprising: detecting (54) fluorescence (91) representative of oscillatory ion flux associated with one or more biological cells (72) to generate a series of data points (120) describing an oscillatory pattern (110); identifying (62) a primary peak (118) and secondary peaks (150a, 150b) in the vibration pattern (110); determining the profile of said secondary peak (150a, 150b); A method comprising: (Item 17) 17. The method of claim 16, wherein determining the side of the secondary peaks (150a, 150b) comprises determining a number, a frequency, or a period of the secondary peaks (150a, 150b). (Item 18) 17. The method of claim 16, further comprising determining a spacing regularity / irregularity of the primary peaks (118) and / or an amplitude regularity / irregularity of the primary peaks (118). (Item 19) 17. The method of claim 16, further comprising comparing the amplitude (194) of each primary peak (118) to a predetermined threshold to enumerate smaller peaks of said primary peak, if any. (Item 20) A system (70), an optical sensor (88) configured to detect fluorescence (91) representative of oscillatory ion flux associated with one or more biological cells (72) to generate a series of data points (120) describing an oscillation pattern (110); a processor (96) configured to (1) calculate a set of gradients for the vibration pattern (110) using a sliding window (122) to define a subset (126) of the set of data points (120) from which the set of gradients is calculated; and (2) identify peaks (118, 150a, 150b) in the vibration pattern (110) using the set of gradients; The system (70) comprises: [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a flow chart of steps that may be performed in an illustrative method for analyzing oscillatory fluorescence patterns from biological cells.

[0008] [Diagram 2] FIG. 2 is a schematic diagram of an illustrative system for detecting and analyzing oscillatory fluorescence patterns from biological cells.

[0009] [Diagram 3] FIG. 3 is a graph of a relatively simple undisturbed oscillatory pattern produced by a fluorescent signal detected from a biological cell undergoing an oscillatory ion flux.

[0010] [Figure 4] FIG. 4 is a graph of a series of data points for a region of the fluorescent signal of FIG. 3 encompassing a single oscillation, the region generally indicated at "4" in FIG. 3, and the sliding window shown diagrammatically at three different time locations to illustrate how a subset of data points may be selected using a sliding window for calculation of a series of slopes.

[0011] [Diagram 5] FIG. 5 is a graph of a single oscillation ("event") of a more complex disturbed oscillation pattern detected as in FIG. 3, illustrating how the gradient calculated as in FIG. 4 can be used to detect biologically relevant peaks and troughs, while ignoring smaller "spurious" fluctuations in the fluorescence signal, which are assumed to be noise.

[0012] [Figure 6] FIG. 6 is a graph of only a portion of a single oscillation of FIG. 5 taken around the secondary peak and illustrates how the primary / secondary peaks can be filtered using predetermined amplitude and / or duration criteria to reject small and / or transient peaks as invalid.

[0013] [Figure 7] FIG. 7 is a graph of a vibration pattern detected as in FIG. 3 and illustrates aspects of an algorithm for automatically setting a baseline for the vibration pattern, i.e., setting a temporary baseline and a threshold line for the baseline.

[0014] [Figure 8] FIG. 8 is a fragmentary view of the lower portion of the vibration pattern of FIG. 7, showing a negative peak (relative to the baseline of FIG. 7) that is located below the threshold line and is identified based on the slope.

[0015] [Figure 9] FIG. 9 is a graph of three vibrations of a complex vibration pattern detected generally as in FIG. 3, from which illustrative parameters are identified.

[0016] [Figure 10]10 and 11 are graphs of the same complex vibration pattern analyzed using different sizes for the sliding window (11 points and 5 points, respectively), with primary peaks marked by circles and secondary peaks marked by diamonds. [Figure 11] 10 and 11 are graphs of the same complex vibration pattern analyzed using different sizes for the sliding window (11 points and 5 points, respectively), with primary peaks marked by circles and secondary peaks marked by diamonds.

[0017] [Figure 12A] 12A and 12B are fragmentary views of an exemplary dialog box of a graphical user interface generated by the analysis software, with the "Options" tab of the dialog box selected. [Figure 12B] 12A and 12B are fragmentary views of an exemplary dialog box of a graphical user interface generated by the analysis software, with the "Options" tab of the dialog box selected.

[0018] [Figure 13A] 13A and 13B are fragmentary views of the dialog box of FIGS. 12A and 12B, except that the "Measurements" tab of the dialog box has been selected. [Figure 13B] 13A and 13B are fragmentary views of the dialog box of FIGS. 12A and 12B, except that the "Measurements" tab of the dialog box has been selected.

[0019] [Figure 14] Figures 14-21 are graphs of representative fluorescence oscillation patterns detected from cardiomyocytes treated as a control (Figure 14) and with various known cardiotoxic compounds (Figures 15-21). The primary ("main") and secondary peaks are marked by the software with circles and diamonds, respectively. [Figure 15] Figures 14-21 are graphs of representative fluorescence oscillation patterns detected from cardiomyocytes treated as a control (Figure 14) and with various known cardiotoxic compounds (Figures 15-21). The primary ("main") and secondary peaks are marked by the software with circles and diamonds, respectively. [Figure 16] Figures 14-21 are graphs of representative fluorescence oscillation patterns detected from cardiomyocytes treated as a control (Figure 14) and with various known cardiotoxic compounds (Figures 15-21). The primary ("main") and secondary peaks are marked by the software with circles and diamonds, respectively. [Figure 17] Figures 14-21 are graphs of representative fluorescence oscillation patterns detected from cardiomyocytes treated as a control (Figure 14) and with various known cardiotoxic compounds (Figures 15-21). The primary ("main") and secondary peaks are marked by the software with circles and diamonds, respectively. [Figure 18] Figures 14-21 are graphs of representative fluorescence oscillation patterns detected from cardiomyocytes treated as a control (Figure 14) and with various known cardiotoxic compounds (Figures 15-21). The primary ("main") and secondary peaks are marked by the software with circles and diamonds, respectively. [Figure 19] Figures 14-21 are graphs of representative fluorescence oscillation patterns detected from cardiomyocytes treated as a control (Figure 14) and with various known cardiotoxic compounds (Figures 15-21). The primary ("main") and secondary peaks are marked by the software with circles and diamonds, respectively. [Figure 20] Figures 14-21 are graphs of representative fluorescence oscillation patterns detected from cardiomyocytes treated as a control (Figure 14) and with various known cardiotoxic compounds (Figures 15-21). The primary ("main") and secondary peaks are marked by the software with circles and diamonds, respectively. [Figure 21] Figures 14-21 are graphs of representative fluorescence oscillation patterns detected from cardiomyocytes treated as a control (Figure 14) and with various known cardiotoxic compounds (Figures 15-21). The primary ("main") and secondary peaks are marked by the software with circles and diamonds, respectively.

[0020] [Figure 22] FIG. 22 is a graph plotting various peak-related readouts obtained using software as disclosed herein by analyzing the fluorescence oscillation patterns detected from cardiomyocytes treated with the indicated high-risk, medium-risk, and low-risk TdP (Torsades de Pointes) compounds at the indicated concentrations relative to the maximum clinical level in blood for each compound.

[0021] [Diagram 23] Figures 23-30 are graphs of representative fluorescence oscillation patterns detected from neurons treated as a control (Figure 23) and treated with various known neurotoxic compounds (Figures 24-30). Primary and secondary peaks are marked by the software with circles and diamonds, respectively. Modifications in the oscillation patterns reflect the effects of the compounds and can be characterized through multiple measurements provided by the software analysis. [Figure 24] Figures 23-30 are graphs of representative fluorescence oscillation patterns detected from neurons treated as a control (Figure 23) and treated with various known neurotoxic compounds (Figures 24-30). Primary and secondary peaks are marked by the software with circles and diamonds, respectively. Modifications in the oscillation patterns reflect the effects of the compounds and can be characterized through multiple measurements provided by the software analysis. [Diagram 25]Figures 23-30 are graphs of representative fluorescence oscillation patterns detected from neurons treated as a control (Figure 23) and treated with various known neurotoxic compounds (Figures 24-30). Primary and secondary peaks are marked by the software with circles and diamonds, respectively. Modifications in the oscillation patterns reflect the effects of the compounds and can be characterized through multiple measurements provided by the software analysis. [Figure 26] Figures 23-30 are graphs of representative fluorescence oscillation patterns detected from neurons treated as a control (Figure 23) and treated with various known neurotoxic compounds (Figures 24-30). Primary and secondary peaks are marked by the software with circles and diamonds, respectively. Modifications in the oscillation patterns reflect the effects of the compounds and can be characterized through multiple measurements provided by the software analysis. [Figure 27] Figures 23-30 are graphs of representative fluorescence oscillation patterns detected from neurons treated as a control (Figure 23) and treated with various known neurotoxic compounds (Figures 24-30). Primary and secondary peaks are marked by the software with circles and diamonds, respectively. Modifications in the oscillation patterns reflect the effects of the compounds and can be characterized through multiple measurements provided by the software analysis. [Figure 28] Figures 23-30 are graphs of representative fluorescence oscillation patterns detected from neurons treated as a control (Figure 23) and treated with various known neurotoxic compounds (Figures 24-30). Primary and secondary peaks are marked by the software with circles and diamonds, respectively. Modifications in the oscillation patterns reflect the effects of the compounds and can be characterized through multiple measurements provided by the software analysis. [Figure 29]Figures 23-30 are graphs of representative fluorescence oscillation patterns detected from neurons treated as a control (Figure 23) and treated with various known neurotoxic compounds (Figures 24-30). Primary and secondary peaks are marked by the software with circles and diamonds, respectively. Modifications in the oscillation patterns reflect the effects of the compounds and can be characterized through multiple measurements provided by the software analysis. [Diagram 30] Figures 23-30 are graphs of representative fluorescence oscillation patterns detected from neurons treated as a control (Figure 23) and treated with various known neurotoxic compounds (Figures 24-30). Primary and secondary peaks are marked by the software with circles and diamonds, respectively. Modifications in the oscillation patterns reflect the effects of the compounds and can be characterized through multiple measurements provided by the software analysis. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] Detailed Description The present disclosure provides methods and systems for recording and analyzing oscillatory fluorescence representing oscillatory ion flux associated with one or more biological cells. An illustrative method of analysis may include detecting fluorescence from one or more biological cells to generate a series of data points describing an oscillatory pattern (interchangeably referred to as an oscillatory trace or a fluorescent trace). A series of gradients may be calculated for the oscillatory pattern. For example, a sliding window may be used to define a subset of the series of data points from which a series of gradients are calculated. A peak of the oscillatory pattern may be identified using the series of gradients. Another illustrative method of analysis may include detecting fluorescence from one or more biological cells to generate a series of data points describing an oscillatory pattern. A primary peak and a secondary peak in the oscillatory pattern, if present, may be identified. A side of the secondary peak may be determined.

[0023] The disclosed methods and systems may utilize Complex Event Analysis (CEA) algorithms that are designed to analyze vibrational fluorescence having several components of interest. These components may include multiple shapes of peaks, clustering of peaks, secondary peaks, regular or irregular anomalous events, irregular amplitudes or frequencies, moving threshold levels, etc. The CEA algorithm has the ability to report multiple measurements for both primary and secondary peaks, either averaged or individual values, including many additional readouts.

[0024] The goal of the CEA algorithm is to digitally "visualize" the general shape of a biological event in order to detect and measure various parameters such as peak amplitude, rise and decay times, event duration, frequency, etc. In order to visualize a biological event, spurious non-biological transitions (noise) need to be rejected.

[0025] Many "point-by-point" detection methods lose accuracy due to pre-filtering that compromises data integrity or due to an inability to distinguish biologically relevant transitions from noise. By the nature of its design, the CEA algorithm significantly reduces the effects of noise without pre-filtering by calculating gradients from a subset of data points that describe the oscillatory pattern. The gradients allow for the detection of peaks and troughs while ignoring lower amplitude and / or rapid transitions (i.e., invalid peaks) generated by noise.

[0026] Further aspects of the present disclosure are described in the following sections: (I) Definitions, (II) Method and System Overview, and (III) Examples. I. Definition

[0027] Technical terms used in this disclosure have meanings commonly recognized by those of ordinary skill in the art. However, the following terms may be further defined as follows:

[0028] Event - a single oscillation of an oscillation pattern. An event may, among other things, begin and end at a predefined amplitude from the baseline and / or within a predefined amplitude range from the baseline.

[0029] Light - ultraviolet, visible, and / or infrared radiation.

[0030] Maximum - a point of an actual / conceptual graph (and / or of a set of points) that has a larger value and / or is farther from a reference line (e.g., lower or upper baseline) than the points around it. A plural of "maximum" is a "maxima."

[0031] Minimum - a point of an actual / conceptual graph (and / or of a set of points) that has a smaller value and / or is closer to a reference line (e.g., lower or upper baseline) than the points surrounding it. A plural "minimum" is a "minima."

[0032] Peak - a sequence of points (and / or a series of points) on an actual / conceptual graph, including a maximum and points around the maximum, and optionally bounded by a pair of minima. A peak may be characterized according to the time location and amplitude of the peak's maximum, which may collectively define the "peak location," while the amplitude of the maximum defines the "peak value" or "peak amplitude." When a maximum is defined relative to a reference line (e.g., a lower or upper baseline), a maximum above the reference line forms a positive peak, while a maximum below the reference line forms a negative peak.

[0033] Primary Peak - The preceding / only peak (or valid peak) of an event.

[0034] Secondary Peak - Any peak (or valid peak) that follows the primary peak within an event.

[0035] Trough - a sequence of points (and / or a series of points) on a real / conceptual graph, including a minimum and the points around the minimum. A trough may be characterized according to the location in time and the amplitude of the trough's minimum, which may collectively define the "trough location," while the amplitude of the minimum defines the "trough value" or "trough amplitude." When a minimum is defined relative to a reference line (such as a baseline), a minimum above the reference line forms a positive trough, while a minimum below the reference line forms a negative trough.

[0036] Valid Peak--Any peak that meets predefined criteria for validity.

[0037] Window - an algorithm that selects a given number of data points from a dataset for processing. For example, a window with a width of 5 selects 5 points, such as 5 consecutive points of the dataset for processing. A "sliding window" moves across the dataset, point by point, etc., to select a subset of data points for processing. For example, a sliding window with a width of 5 points might process points 1-5, then move point by point and process points 2-6, 3-7, etc. II. Method and System Overview

[0038] This section provides an overview of exemplary methods and systems of the present disclosure (see FIGS. 1 and 2).

[0039] 1 shows a flowchart 50 of steps that may be performed in an illustrative method of analyzing vibrational fluorescence from one or more biological cells (also referred to as "cells"). The steps may be performed in any suitable order and combination.

[0040] The cells may include any cell type that exhibits oscillatory ion flux in culture. Thus, the cells may include muscle cells (i.e., cardiac muscle cells (cardiomyocytes), skeletal muscle cells, or smooth muscle cells) or nerve cells (neurons). The oscillatory ion flux may occur spontaneously, at will, in a synchronized manner, for a set of cells that are cultured in close association with one another so that the cells can communicate. The set of cells may be associated with one another in a three-dimensional aggregate, such as a cell spheroid, among others, or may be arranged as a substantial monolayer. As further described below, a single set of cells may be analyzed in a vessel, or isolated replicate sets of cells may be exposed to different treatments in separate vessels before / during analysis. Exemplary vessels include petri dishes, wells of a microplate, flasks, or the like.

[0041] The cells may be obtained from any suitable source by any suitable procedure. The cells may be differentiated in vitro from stem cells. The stem cells may be embryonic stem cells, adult stem cells, or induced pluripotent stem cells (iPSCs), among others. In other examples, the cells may be primary cells, such as primary cardiomyocytes or primary neurons, obtained from an animal.

[0042] The oscillatory ion flux may be an ion-specific flux or a collective ion flux. An exemplary ion-specific flux is an oscillatory calcium flux that generates calcium oscillations. The flux may represent the movement of ions across the plasma membrane (i.e., into and / or out of the cell) and / or within the cell (e.g., across the membrane of the sarcoplasmic reticulum (SR) (i.e., from the SR into or out of the cytoplasm)), or the like.

[0043] Each set of cells may be labeled and / or treated as shown at 52. Labeling may be performed with a fluorescent indicator having a fluorescence that is sensitive to oscillatory ion flux occurring to the cells. The fluorescent indicator may be, for example, a fluorescent calcium indicator that is sensitive to intracellular calcium concentration and emits more light (or less light) as the intracellular calcium concentration increases. Exemplary fluorescent calcium indicators are chemical indicators such as FLIPR® Calcium 6, FLIPR® Calcium 6-QF, Calcium Green 1, Fluo-3, Fluo-4, Fura-2, Indo-1, Oregon Green 488, Bapta-1, Fura-4F, Fura-5F, Calcium Crimson, X-rhod-1, and the like. Cells may be labeled with the chemical indicator by contact through the medium in which the cells are placed. Other exemplary fluorescent calcium indicators are genetically encoded and expressed in cells following introduction of the coding sequence into the cells (such as via transfection, infection, or the like). Suitable genetically encoded calcium indicators may include cameleon, pericam, GCaMP, TN-L15, TN-humTnC, TN-XL, TN-XXL, Twitches, or the like. In other examples, the fluorescent indicator may be a membrane potential indicator (e.g., FluoVolt® membrane potential dyes, Di-3-ANEPPDHQ, Di-4-ANEPPDHQ, etc.), a potassium indicator, a sodium indicator, a magnesium indicator, a zinc indicator, a pH indicator, or the like.

[0044] Each set of cells may be treated with at least one substance of interest at a selected concentration and the substance / concentration, if present, tested for its effect on the oscillating fluorescence of the cells detected from the fluorescent indicator. The substance may be a compound such as a small molecule (e.g., a drug or drug candidate), a protein, an RNA or DNA molecule, having a molecular weight below 10, 5, 2, or 1 kilodaltons, among others. Different compounds and / or different concentrations of the same compound may be tested on separate sets of cells to screen compounds and / or determine a dose-response curve for a given compound. Each set of cells may be treated with the substance for any suitable length of time, such as at least 1, 2, 5, 10, 30, or 60 minutes, or 2, 4, 6, 8, 10, 12, 18, or 24 hours, or 24-72 hours, among others. Treatment and labeling may be performed in parallel, sequentially, or at overlapping times with a time offset.

[0045] Fluorescence may be detected from at least one cell of each set of biological cells, as shown at 54. The fluorescence detection samples a fluorescence signal from at least one cell, such as the intensity of the signal versus time, to generate a series of data points (also called time points) that describe an oscillation pattern for the fluorescence signal. The oscillation pattern may thus be graphed as the detected fluorescence intensity as a function of time. The fluorescence signal may be sampled at any suitable rate, typically at a constant rate greater than 1 Hz, such as a rate between 1 and 100 Hz, among others, and for any suitable sampling duration, such as at least 10, 30, or 60 seconds, or at least 2, 5, or 10 minutes. The fluorescence detection may be performed using an image sensor, an optical point sensor, or the like.

[0046] A baseline for the vibration pattern may be established, as shown at 56. The baseline may be set automatically using software, for example, as described below in Example 2, and / or may be set by the user via a graphical user interface. The baseline may be located below or above the vibration pattern, either by default or in response to input from the user (see, for example, Examples 2 and 5). In some embodiments, a temporary baseline may be set above (or below) the vibration pattern, opposite the likely location of the baseline to be established. Peaks relative to the baseline may be identified, and a linear regression of the peak or trough locations may find a line that serves as a baseline during subsequent identification of the peaks and troughs.

[0047] The noise level of the vibration pattern may be estimated, as shown at 58. The noise level may be estimated based on signal variations near the upper or lower sides of the vibration pattern (see Examples 2 and 5). For example, an average of consecutive differences between peak values ​​(i.e., maxima) (or trough values ​​(i.e., minima)) near the baseline of the vibration pattern may be used to determine an estimate of the noise level.

[0048] A series of slopes may be calculated from a series of data points of the vibration pattern, as shown at 60. For example, a sliding window that slides along the time axis of the vibration pattern may be utilized to select successive subsets of data points for calculation of a series of slopes by fitting a line to each subset by linear regression (see, e.g., Example 1). In other words, the sliding window may slide along the time axis of the vibration pattern by one point (or two or more points) for each successive slope calculation. The sliding window has a constant size that encompasses a fixed number of three or more points during which a series of slopes are calculated for a given data set (e.g., from a given well). The size of the sliding window may be automatically selected by the software (e.g., based on the noise level of the data set and / or the sampling rate of the data points) or may be selected by the user. The size of the sliding window may be changed between different data sets (e.g., each data set is collected from a different well), allowing the size to be optimized for each data set.

[0049] The peaks (and troughs) of each vibration pattern may be identified using the gradients, as shown at 62. That is, a series of maxima and minima may be identified based on the change in sign of the gradient along the series of gradients (i.e., with respect to time). Each maximum above the baseline may be closely approximated by a change in the series of gradients from positive to negative (or from negative to positive with respect to the baseline at the upper side of the vibration pattern). Similarly, each minimum above the baseline may be closely approximated by a change in the series of gradients from negative to positive (or from positive to negative with respect to the baseline at the upper side of the vibration pattern). The maxima and minima identified using the series of gradients may be refined, if necessary, by, among other things, local analysis and / or interpolation of the points. The maxima and / or minima (and corresponding peaks and troughs) may also be filtered (i.e., limited) according to one or more other predetermined criteria to exclude maxima (and peaks) and / or minima (and troughs) that do not meet the predetermined criteria. The predetermined criteria may relate to at least one of the amplitude and / or duration of each corresponding peak / trough and / or of each event containing at least one of the peaks (see, e.g., Examples 1 and 5). The predetermined criteria may be assigned automatically based on the noise level of the fluorescent signal and / or may be assigned subjectively by a user.

[0050] Values ​​of one or more peak-related parameters of each vibration pattern may be determined, as shown at 64. The parameters and their values ​​may be interchangeably referred to as readouts or descriptors. Values ​​may be determined using the peaks identified at 62. Values ​​may be average values ​​for the vibration pattern measurements (or at least for a number of events thereof) or values ​​for individual events / peaks. Exemplary values ​​are for the primary peak only, the secondary peak only, or for the primary and secondary peaks collectively. Values ​​may include individual / average primary / secondary peak amplitudes, average primary / secondary peak frequency, average intra-event frequency of the secondary peaks, event area, primary / secondary peak spacing, rise slope, decay slope, rise time, decay time, etc. Further examples of peak-related parameters are described in Examples 1, 3, and 5.

[0051] The effectiveness of each treatment, if any, may be assessed based on at least one of the values ​​determined from the corresponding vibration pattern, as shown at 66. For example, the effectiveness may be assessed based on aspects of the secondary peaks in the vibration pattern, such as the number, frequency, and / or period of the secondary peaks.

[0052] Any combination of the steps of Figure 1 or other methods disclosed herein may be embodied as a computer method, computer system, or computer program product. Thus, aspects of the analysis method may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, and the like), or an embodiment combining software and hardware aspects, all of which may be generally referred to herein as a "circuit," "module," or "system." Additionally, aspects of the analysis method may take the form of a computer program product embodied in a computer readable medium (or media) having computer readable program code / instructions embodied thereon.

[0053] Any combination of computer readable media may be utilized. The computer readable medium may be a computer readable signal medium and / or a computer readable storage medium. The computer readable storage medium may include an electronic, magnetic, optical, electromagnetic, infrared, and / or semiconductor system, apparatus, or device, or any suitable combination thereof. More specific examples of computer readable storage media may include the following: portable computer diskettes, hard disks, random access memories (RAMs), read only memories (ROMs), erasable programmable read only memories (EPROMs or flash memories), portable compact disk read only memories (CD-ROMs), optical storage devices, magnetic storage devices, any suitable combinations thereof, and / or the like. In the context of this disclosure, a computer readable storage medium may include any suitable non-transitory tangible medium that may contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0054] A computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electromagnetic, optical, and / or any suitable combination thereof. A computer-readable signal medium is not a computer-readable storage medium, but may include any computer-readable medium capable of communicating, propagating, or transporting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0055] The program code embodied on the computer readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wired, fiber optic cable, and / or any suitable combination thereof, among others.

[0056] Computer program code for performing operations related to aspects of the methods disclosed herein may be written in one or any combination of programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and / or equivalents, and traditional procedural programming languages ​​such as C. Mobile apps may be developed using any suitable language, including those mentioned above and Objective-C, Swift, C#, HTML5, and equivalents. The program code may run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), and / or the connection may be made to an external computer (e.g., through the Internet using an Internet Service Provider).

[0057] Aspects of the methods disclosed herein are described below with reference to flowchart illustrations and / or block diagrams of methods, apparatus, systems, and / or computer program products. Each block and / or combination of blocks in the flowcharts and / or block diagrams may be implemented by computer program instructions. The computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions are executed via a processor of the computer or other programmable data processing apparatus to implement the functions / acts specified in the blocks of the flowcharts and / or block diagrams. In some embodiments, the machine-readable instructions may be programmed onto a programmable logic device, such as a field programmable gate array (FPGA).

[0058] Computer program instructions can also be stored in a computer-readable medium, which can instruct a computer to function in a particular manner to generate an article of manufacture, including instructions that implement the functions / acts specified in the flowchart and / or block diagram blocks, where the instructions stored in the computer-readable medium.

[0059] Computer program instructions can also be loaded onto a computer to cause a series of operational steps to be performed on a device to generate a computer-implemented process, such that the instructions, which execute on the computer, provide a process for implementing the functions / acts defined in the flowchart and / or block diagram blocks.

[0060] Any flowcharts and / or block diagrams in the drawings are intended to illustrate the architecture, functionality, and / or operation of possible implementations of systems, methods, and computer program products according to aspects of the methods disclosed herein. In this regard, each block may represent a module, segment, or portion of code constituting one or more executable instructions for implementing the specified logical function(s). In some implementations, the functions described in the blocks may occur out of the order described in the drawings. For example, two blocks shown in succession may in fact be executed substantially in parallel, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. Each block and / or combination of blocks may be implemented by a dedicated hardware-based system (or a combination of dedicated hardware and computer instructions) that performs the specified functions or acts.

[0061] 2 shows an illustrative system 70 for detecting and analyzing vibrational fluorescence from biological cells 72 held by a sample holder 74. The sample holder 74 is depicted here as a microplate 76 having a plurality of wells 78, each containing a replicate set of cells 72. The system 70 may include a stage 80 for supporting the sample holder, and a drive mechanism 82 for moving the stage 80 and the sample holder 74 relative to one another to position each well 78 on an optical axis of the system 70.

[0062] System 70 may also include an objective lens 84, a light source 86, and an optical sensor 88 (e.g., an image sensor). Light source 86 may generate light 87 to illuminate each set of cells 72 and induce fluorescence from the cells. The light for cell illumination may propagate to the cells via a beam splitter 90 and objective lens 84 in an epi-illumination configuration shown in FIG. 2. Light-induced fluorescence 91 may be collected by objective lens 84 for propagation through objective lens 84, beam splitter 90, and optional tube lens 92 for detection by optical sensor 88, creating a time-dependent fluorescence signal.

[0063] A computer 94 communicates with the optical sensor 88 and processes the fluorescent signals received from the optical sensor 88. The computer 94 may include a processor 96 for processing instructions, a memory 98 for storing instructions, and a user interface 100 for communication between the computer 94 and a user. The user interface may include a user input device, such as a keyboard, mouse, or touch screen, and a display device, such as a monitor. EXAMPLES

[0064] III. Examples This section describes further examples and aspects of the analytical methods and systems of the present disclosure. These examples and aspects are intended to be illustrative only and should not limit the overall scope of the invention. Example 1: Calculation of gradient

[0065] This example describes an exemplary calculation of a gradient for an oscillation pattern using a sliding window and the use of the gradient to identify peaks and troughs in the oscillation pattern of the gradient (see FIGS. 3-6).

[0066] 3 shows a graph of an oscillatory pattern 110 produced by a fluorescent signal 112 detected from a biological cell undergoing a simple oscillatory flux of calcium. Fluorescence is detected from a fluorescent calcium indicator that labels the cell. The fluorescence intensity is detected at discrete times to produce a series of data points that are plotted as a function of time using a point-connecting line trace.

[0067] The oscillation pattern 110 consists of a series of oscillations 114 (also called events) of increased fluorescence from a baseline 116. Each oscillation 114 may have only one peak 118, as shown here, or may have a primary peak and one or more secondary peaks, as described below. In other embodiments, the baseline 116 may be located above the oscillation pattern 110, and each event may be characterized by a fluorescence signal 112 that progresses below the baseline.

[0068] FIG. 4 shows a graph of a sub-series of data points 120 from the oscillation pattern 110 of FIG. 3 for a single oscillation 114 of the fluorescence signal. The dot-connecting line traces of FIG. 3 are omitted. A sliding window 122 may then be used to select a subset of the data points 120 from which, optionally, a series of best-fit lines and a corresponding series of slopes, centered on each data point, may be calculated. The size of the sliding window 122 represents its width measured parallel to the time axis. The sliding window 122 is shown diagrammatically on the left in solid outline and in dashed outline at two other illustrative positions after conceptually sliding along the time axis as indicated by the movement arrow at 124. The sliding window 122 "stops" at multiple incremental positions along the time axis to select a subset 126 of the data points 120 centered at each position from which a slope is then calculated. Here, the sliding window 122 has a width that selects five data points 120 at each incremental position, although any suitable number of data points 120 or more may be selected. The size (i.e., duration) of the sliding window may remain constant while a series of gradients are calculated for a given vibration pattern 110 (i.e., a data set of data points) to select the same number of data points 120 for each gradient calculation. The gradients may be calculated point-by-point. The size of the sliding window may be changed to recalculate a new series of gradients for the same vibration pattern or to calculate a series of gradients for a different vibration pattern. The series of gradients calculated for a vibration pattern may represent a corresponding series of incrementally offset positions of the sliding window 122. In other words, the sliding window 122 may be incrementally successively offset by a fixed integer number of data points, such as one or two data points, and / or by fixed time increments, among others, to select a subset 126 of the data points 120. Subsets 126 of data points 120 may overlap one another if the size of the sliding window 122 exceeds the incremental offset of the sliding window for successive subsets.

[0069] In some embodiments, the gradient is calculated at each data point, and the "sliding window" over which the gradient is calculated moves one point at a time. In these embodiments, the subsets of data points selected by the window always overlap each other. The "direction" of the gradient at each point is evaluated to assess the overall curvature of the event as implied by the change in gradient direction. The gradient calculated at each point is both forward and backward, since one or more points ahead of the point in time and one or more points behind the point in time contribute to the gradient.

[0070] The slope for each subset 126 may be calculated by fitting a line to the points 120 of the subset 126 by linear regression and then taking the slope of the line. FIG. 4 shows three vectors 128 that are parallel to respective lines that are fitted to three illustrative subsets 126. The orientation of each vector 128 matches the slope of the corresponding fitted line. Each vector 128 may be centered on a midpoint 130 of the corresponding subset 126 and may have a time component that matches the size (i.e., width) of the sliding window 122. Thus, a series of vectors 128 can be generated using the sliding window 122 to relate the size of the sliding window to a series of slopes with a corresponding series of data points 130 or positions (time and amplitude).

[0071] The successive gradients of the vectors 128 allow the shape of the data, and in particular the direction of the data deviations, to be examined. With respect to the baseline located below the vibration pattern, a positive gradient reflects a rise phase, while a negative gradient is associated with a decay phase. Because each gradient is calculated from a subset 126 having three or more points 120, noise in the subset 126 does not significantly affect the accuracy of the gradient if the noise level is low relative to the size of the sliding window. In this way, noise is essentially rejected. The change in direction of successive vectors 128 reflects the corresponding change in direction of the transition in the vibration pattern. Thus, when the gradient of successive vectors 128 changes from positive to negative (with respect to the baseline located below the vibration pattern), this is an indication that the maximum of the peak has been crossed. Similarly, for the same baseline, when the gradient changes from negative to positive, this is an indication that the minimum of the trough has been crossed. Further examination of data points within each transition region may then be performed to more precisely determine the location (time and amplitude) of each maximum and minimum in the vibration pattern.

[0072] The size of the sliding window 122 can be optimized for a particular data set according to the noise level. Because the sliding window spans a range of data points, the window acts as a natural "damper" that reduces the contribution of random noise. Also, the gradient does not affect the integrity of the associated data points and therefore does not alter the detection and measurement of amplitude values ​​or valid secondary peaks. Thus, the use of gradients calculated from a subset of data points selected with the sliding window allows accurate detection and measurement of various parameters of the data without the data destruction that occurs with conventional filtering.

[0073] Figure 5 shows a graph of a single vibration 114 ("event" 138) of a more complex vibration pattern 140 detected as in Figure 3. The baseline 116 is located below the vibration pattern 140. This figure illustrates how a vector 128 with a slope calculated as in Figure 4 can be used to search for larger peaks and troughs while ignoring smaller "spurious" fluctuations in the fluorescent signal that may result from noise instead of biologically relevant activity. (Vector 128 is shown here with a constant overall length rather than a constant time component for ease of illustration.)

[0074] One or more predefined criteria may be used to identify a valid event 138 in the vibration pattern 140. Each of the criteria is met by a valid event. The criteria may include at least one amplitude threshold 142 and / or at least one duration threshold to distinguish a valid event from other deviations in the fluorescent signal. For example, the amplitude threshold 142 may be a single point of a trigger level 144 (line) crossed by the fluorescent signal, with an event being characterized by the fluorescent signal crossing the trigger level in both directions. The trigger level 144 may be set at a percentage of the total amplitude span of the vibration pattern, such as 10%, 15%, 20% of the span from the baseline 116. The trigger level may be set automatically and / or by a user, and may be constant or variable along the time axis (e.g., along a trigger line that defines the trigger level). In some cases, a second amplitude threshold may be set to exclude each event and / or each peak therein having a maximum value less than a predefined amplitude offset from the baseline (or trigger level 144). The second amplitude threshold may be set, inter alia, at a percentage of the total amplitude span (eg, 25%, 50%, 60%, 70%, 80%, etc.) or as an absolute value in relative fluorescence units.

[0075] During a search along the oscillation pattern 140, the beginning of an event 138 may be detected when the fluorescent signal crosses the trigger level 144 in a direction away from the baseline 116. This crossing may initiate peak detection. The event 138 may be considered to end when the trigger level 144 is crossed in a direction towards the baseline 116. However, left and right troughs around the event 138 may be marked below the trigger level 144, as explained below.

[0076] The slope of the vector 128 at each point 130 may be evaluated during the search to determine the direction of the transition. (Example points 130a-130h are shown here.) A positive slope at point 130a is associated with the rising phase 146 of the fluorescent signal, and a negative slope at point 130b is associated with the decay phase 148 of the fluorescent signal. The vicinity of the maximum value per peak is detected when the slope of successive vectors 128 changes from positive to negative (with the baseline 116 below). For example, points 130c, 130d, and 130e of the vector 128 are at least very close to the respective maximum values ​​for the primary peak 118 and secondary peaks 150a, 150b of the same event 138. The vicinity of the minimum value per trough is detected when the slope of successive vectors 128 changes from negative to positive (with the baseline 116 below). For example, points 130f, 130g of vector 128 are at least very close to the respective minima for left trough 152a and right trough 152b that bound event 138. (The troughs between peak 118 and 150a and 150b are not explicitly identified here for ease of illustration.)

[0077] The size of the sliding window used to create vector 128 determines the magnitude of low amplitude transitions that will be bypassed by the search. For example, transitions 154 around point 130h are not identified as peaks or troughs in the search because the gradients in this region do not undergo a sign change (i.e., they remain negative). The larger the size of the window (and thus the length of vector 128), the larger the transition amplitudes that will be bypassed. Thus, vectors 128 collectively act as a filter that does not destroy the fluorescent signal.

[0078] Noise analysis within a narrow region around each transition point can optionally be applied to further improve the detection accuracy of each peak / trough location by rejecting very short or "low amplitude" events that may be in close proximity to better peak locations several points away.

[0079] 6 shows a graph of only a portion of the single vibration 114 (and event 138) of FIG. 5 taken around the secondary peak 150a. The peaks may be filtered for validity according to one or more criteria, which may relate to the peak maximum 156 and at least one adjacent left or right trough minimum 158a, 158b. For example, the predetermined criteria may relate to exceeding a threshold value for the local amplitude 160 of each peak (measured between the maximum 156 and the left minimum 158a and / or right minimum 158b) and / or exceeding a threshold value for the duration 162 of the peak measured at a predetermined amplitude (e.g., at the left minimum 158a). The thresholds for the local amplitude 160 and / or duration 162 may be user-defined or automatically generated from the noise level of the vibration pattern and / or the sampling interval (i.e., the inverse of the sampling rate) of the vibration pattern. Example 2: Baseline setting and noise estimation

[0080] This example describes an exemplary approach to automatically setting a baseline for a vibration pattern and estimating the noise level of the vibration pattern from negative peaks identified while setting the baseline (see FIGS. 7 and 8).

[0081] The algorithm disclosed herein can automatically estimate a baseline for each data set by measuring the amplitude of variation that falls within 10% of the data amplitude span, removing outliers using interquartile range analysis, and finally, fitting a line to the resulting amplitudes using linear regression. The resulting regression line may be proposed or used as the baseline.

[0082] 7 and 8 illustrate an exemplary approach to establishing a baseline 116 for a data set. A temporary reference line 170 (e.g., an upper baseline) may be set. For example, the reference line 170 may be defined by a pair of points 172, 174 that represent the maximum amplitude for a leading segment 176 and a trailing segment 178 of the data set, respectively. Each segment 176, 178 may represent any suitable percentage of the data set, such as 10, 20, 30, 40, or 50%, among others. Points near the leading and trailing ends of the vibration pattern may be excluded, since the ends often contain artifacts. The position of the reference line 170 may be changed at one or both ends by allowing the user to move handles 180a, 180b via a graphical user interface. Also, or alternatively, the user may adjust the time range of the data set that is considered valid for analysis at one or both ends by moving respective handles 182a, 182b along the time axis via a graphical user interface.

[0083] The amplitude span of the data set may be calculated. A global amplitude maximum (Ymax) and a global amplitude minimum (Ymin) may be found using a search. The amplitude span is defined as follows: Ymax-Ymin.

[0084] A threshold line 183 may be set toward the downside of the amplitude span opposite the reference line 170, such as at 70, 80, 90, or 95% of the amplitude span from the reference line 170. For example, the position for the threshold line 183 at 90% of the amplitude span from the reference line 170 may be calculated as follows: Ymin+0.1×(amplitude span). A user may adjust the threshold line 183 at one or both ends by moving the respective handles 184a, 184b along the amplitude axis via a graphical user interface.

[0085] A search for a peak relative to the baseline 170 may be performed (see Figures 7 and 8). In this example, the search finds negative peaks by identifying local maximum deviations away from (below) the baseline 170, which are also located below the threshold line 183. (In other words, this peak search is the inverse of that described above with respect to Figure 5.) The search is performed using a series of gradients as described above with respect to Figures 4 and 5, where the gradients may be obtained using a relatively small size for the sliding window (e.g., a width of 3, 4, or 5 data points). A small sliding window provides high sensitivity for peak detection with little noise rejection. Hence, the noise level for the data set may also be estimated from the amplitude values ​​of the peaks found in the search.

[0086] A primary peak 118 and a secondary peak 150 may be identified. Here, and in the following examples, a circle marks the maximum of the primary peak 118 and a diamond marks the maximum of the secondary peak 150 (see FIG. 8). These two types of peaks 118, 150 may also be marked distinguishably when the vibration pattern is graphed and displayed to the user (e.g., via a graphical user interface). However, for purposes of establishing a baseline, all detected peaks may be recorded and treated equally, i.e., without any distinction between primary and secondary peaks.

[0087] Using linear regression, a straight line may be fitted through the detected peak amplitudes. This line may be taken as an initial approximation of the baseline 116. A trigger level 144 may be initially set at 10% of the data span above the baseline. These automatically assigned levels can be subsequently adjusted by the user via a graphical interface or by explicitly setting values.

[0088] Prior to linear regression, data points at the maximum of the peaks 118, 150 may or may not be filtered. For example, before selecting the baseline, outliers may be removed, such as using an interquartile range analysis. Points above 1.5 interquartile ranges below the first quartile or above the third quartile may be rejected.

[0089] A noise level may be estimated from the peaks 118, 150 (see FIG. 8). For example, the noise estimate may be calculated as the average of successive differences between the amplitudes of the peaks 118, 150 (considered as a group). The noise estimate may be used directly as the noise level or may be used to calculate the noise level. This noise level, alone or in conjunction with the sampling interval, may enable automatic selection of a suitable size for a sliding window (see Example 2) for a given data set. An exemplary algorithm for this size selection is a heuristic: [0.3 / sampling interval (in seconds)] plus a "noise factor" of [noise level / 100]. The algorithm may select the window size from a range of allowed sizes. The range may include only odd data points (e.g., 3, 5, 7, etc.), only even data points (e.g., 4, 6, 8, etc.), or odd and even numbers (e.g., 3, 4, 5, 6, etc.). The noise level may also be used to calculate an automatically generated value for a suitable threshold for local amplitude 160 to reject smaller peaks as invalid (see also Examples 1 and 5). The data sampling rate may be used to automatically generate a suitable threshold for duration 162 to reject very short-lived peaks as invalid. (Example 3) Focused parameters

[0090] This example describes exemplary parameters of interest that can be measured from vibration patterns using the algorithms described herein (see FIG. 9).

[0091] Three events 138 of the oscillation pattern are shown. A start point 190 and an end point 192 are marked for each event 138, where the fluorescent signal crosses the trigger level 144 in opposite directions.

[0092] Any of the parameters described herein may be reported for individual peaks / events or may be averaged over a series of peaks / events of a given vibration pattern. Each primary peak 118 of an event 138 has a primary peak amplitude 194. The primary peak amplitude may be measured as the amplitude difference between the maximum of the primary peak 118 and the baseline 116. A linear decay slope 196 may be defined by a straight line extending between the maximum of the primary peak 118 and the end point 192 for the event 138.

[0093] A primary peak interval 198 (also called a primary peak period) is defined between the maxima of adjacent primary peaks 118. A series of events 138 defines successive primary peak intervals 198, which may be converted to a primary peak rate for the series of events, expressed as primary peaks per unit time, such as peaks per minute (PpM).

[0094] A secondary peak period 200 is defined between the maxima of adjacent secondary peaks 150 within or between events. The secondary peak period 200 for a vibration pattern may be converted to a secondary peak rate, expressed as secondary peaks per unit time, such as peaks per minute (PpM). Each secondary peak 150 has a secondary peak amplitude 202, which may be measured between the secondary peak maximum and the baseline 116.

[0095] A rise slope 204 and a decay slope 206 may be calculated for each event 138. Each of these slopes may be defined as a percentage of the event's maximum value, such as 10-80% or 30-70% of the maximum value.

[0096] The duration 208 of each event 138 may be calculated from the maximum amplitude at a defined percentage from the maximum amplitude of the event, such as at 10, 20, 30, 40, 50, 60, 70, 80, or 90%, among others. Alternatively, or in addition, the duration from the peak 210 may be measured for each event starting at the maximum value of the primary peak 118 of the event. The duration from the peak may be measured from the maximum value at a defined percentage from the maximum value of the event, such as at 10, 20, 30, 40, 50, 60, 70, 80, or 90%, among others. Example 4: Effect of window size on peak detection

[0097] This example illustrates the effect of varying the window size on the sensitivity of peak detection (see Figures 10 and 11).

[0098] The performance of the slope-based peak-finding algorithm calculated as in Example 1 was tested on fluorescence intensity data collected from beating cardiomyocytes. The cardiomyocytes were labeled in vitro with a fluorescent calcium indicator and treated with a cardiotoxic compound to disrupt the regular calcium oscillations present in control cardiomyocytes (see, e.g., Example 1). The same data are graphed in Figures 10 and 11, producing a complex oscillation pattern 220. The pattern consists of a series of oscillations 114, each of which exhibits a longer irregular decay phase relative to the oscillations of the control cells. The decay phase is characterized by an early afterdepolarization (EAD)-like peak (i.e., a secondary peak).

[0099] Peak searches were performed on the data using an automatically determined sliding window size of 11 points (FIG. 10) or a user-selected window size of 3 points (FIG. 11). The primary peak 118 of each oscillation is marked with a circle and the secondary peaks 150 are marked with a diamond. In FIG. 10, with larger window sizes, low amplitude transitions are ignored. In FIG. 11, with smaller window sizes, low amplitude transitions are detected as peaks. Thus, the sensitivity of peak detection can be controlled by judicious selection of window size, optionally in conjunction with noise rejection to consider detected peaks that are too small / transient as invalid (see, e.g., Examples 1 and 5). Example 5: Dialog Box for Analysis Software

[0100] This example describes exemplary dialog boxes (see FIGS. 12A, 12B, 13A, and 13B) that are displayed by the graphical user interface to facilitate user input into the analysis software (see also FIG. 9).

[0101] 12A, 12B, 13A, and 13B show screenshots of a dialog box that controls the list of readouts (i.e., descriptors) for the analysis optimization and peak analysis. The dialog box contains two tabs. The "Options" tab provides access to analysis settings and data ranges. The "Measurements" tab contains the nature of events that can be selected for display in the output sheet. Exemplary aspects and features of these tabs are described below. Options tab

[0102] Event Polarity. Two buttons, Positive and Negative, are available to select event polarity (see FIG. 12A). A positive event is accompanied by an increase in fluorescence above the lower baseline and a negative event is accompanied by a decrease in fluorescence below the upper baseline.

[0103] Selecting the Vector Length: The search vector length (i.e., the size of the sliding window) may be the most important variable for successful peak detection (see FIG. 12A, also see FIGS. 4 and 5). The search vector is generated from a series of subsets of the data points of the data set, where the subsets are selected by incrementally moving the sliding window along the time axis. A search vector is generated at each point. A vector is calculated using both forward and backward looking data points, centered on the window width and spanning from -((window width-1) / 2 to +((window width)-1) / 2. The gradients near the opposite ends of the data set are calculated using an appropriately decreasing number of data points. A linear regression is performed on each subset of data points to generate a set of search vectors. In other words, a sliding window slides along the data set, checking the gradient at each data point and determining the presence of transitions (maxima / peaks and minima / troughs) by noting the change in the direction of the gradient (i.e., the sign change of the gradient from positive to negative or negative to positive). The length of the search vector relative to the sampling rate determines the sensitivity of detecting transitions. A longer vector length will automatically filter out noise (i.e., events / peaks of lower amplitude and / or shorter duration), while a shorter vector length will detect transitions of lower amplitude and shorter duration.

[0104] Three buttons are available to select how vector lengths are assigned: "Automatically assigned length", "Same length for all wells", and "Per well" (see FIG. 12A). The software uses an estimate of the sampling rate and noise amplitude to automatically calculate the vector length when "Automatically assigned length" is selected. The automatically generated vector length is generally a good starting point for data analysis. The user can select a different vector length per well (i.e., for each set of data points describing each different vibration pattern) when the "Per well" button is selected.

[0105] Dynamic Threshold. This option sets a lower limit for valid peaks (see FIG. 12A). All detected peaks with peak amplitudes below this threshold will be considered invalid and ignored. The value of this limit is given as relative fluorescence units (RFU) above the baseline in this example. Two boxes are available for typing in different threshold amplitude values ​​above the baseline for the left and right ends of the line that defines the dynamic threshold. In other examples, the dynamic threshold may be set as a percentage of the amplitude span of the data set. For example, if the data points for a given well span a range of 10,000 relative fluorescence units (RFU), a setting of 80% would define a cutoff limit of 8,000 RFU.

[0106] Trigger level. This level is defined by a trigger line extending along the time axis and determines when an event is considered to start and end (see FIG. 12A). An "event" is defined by a fluorescent signal crossing the trigger line in both directions. However, the amplitude of the event is measured relative to the baseline. Detection of the left and right troughs of the event is not limited by the trigger level, and these measurements can extend to the baseline. Event duration can be measured, among others, as the temporal distance between the two lateral trough positions or by the temporal distance between the two crossing points of the event on the trigger line. The trigger level is set by default (for the data range) 10% above the baseline. This level can be set and changed individually for each data set (well data).

[0107] Baseline Level. The baseline level defines the lower end of the detection range and the location of the baseline. All amplitudes are measured relative to the baseline level, not the trough values.

[0108] The level names (dynamic threshold, trigger level, and baseline level) are color coded in the dialog box to match the color of the corresponding line in the graph window. Each of these levels can be defined by a separate line with a left and right end. The ends can be moved separately by grabbing the handle at the end with the left mouse button and moving it up or down. The entire level line can be moved up or down by grabbing the line anywhere away from the handle with the left mouse button and moving it up or down. The level line position can also be set by changing the values ​​in the separate L (left) or R (right) edit boxes, either with the spinners or by typing in the value directly. The "lock" feature will move the line ends in unison in response to adjustments to either the right or left edge value. This feature applies only to the dialog box and does not affect positioning with the mouse.

[0109] Set Default Levels: Pressing this button will reset the baseline and trigger levels to their default positions (see Figure 12A).

[0110] Process all wells (see Figure 12B). If this item is checked, all selected well data will be processed. If not checked, only the currently selected well will be processed. Data set (see Figure 12B):

[0111] Wells. The analysis will be applied only to wells that are selected and appear in the "Details" graph window. Only selected wells will appear in the graph at any given time. Well data to display can be defined by selecting individual data series in the "Well" combo box. Event Exclusion Group (see Figure 12B):

[0112] Minimum Amplitude. This item defines the minimum amplitude, as measured from the baseline, that will be accepted as valid. Deviations forming primary / secondary peaks with a maximum value below the minimum amplitude value will be rejected. The minimum amplitude is substantially identical to the "Dynamic Threshold" (see FIG. 12A), except that the minimum amplitude is a single value, instead of being defined by values ​​along a line with left and right ends that are independently adjustable.

[0113] Minimum Duration. This item specifies the minimum duration of an event that will be accepted as valid (see FIG. 12B). Event duration may be measured between the left and right trough values, or between the start and end of the event, as defined by crossing a trigger level. Excursions with durations shorter than the minimum duration value will be rejected.

[0114] Apply to all wells. If this item is checked, the amplitude and duration values ​​that appear in the edit boxes will apply to data from all wells. If unchecked, the values ​​defined for each individual well will be used. Noise Cancellation Group (see Figure 12B):

[0115] Minimum amplitude. This item specifies the minimum amplitude threshold for the local amplitude of a valid peak. The local amplitude for a peak (valid or invalid) is measured as the amplitude difference between the amplitude value of the peak and the amplitude value of the nearest left (and / or right) trough. Peaks with a local amplitude below the minimum amplitude threshold will be rejected as invalid.

[0116] Minimum Duration. This item specifies the minimum duration threshold for a valid peak. The duration for a peak (valid or invalid) is measured as the absolute value of the time difference between (a) the time value for the lower of two adjacent troughs and (b) the time value for the equivalent amplitude on the opposite side of the peak. Peaks with durations shorter than the minimum duration threshold will be rejected as invalid.

[0117] Apply to all wells. If this item is checked, the values ​​for amplitude and duration thresholds that appear in the edit boxes will be applied to data from all wells (i.e., all of the data sets for the distinct vibration patterns). If not checked, the values ​​defined for each individual well will be used.

[0118] Automatic. The software measures the approximate noise level of each data set. First, the peak finding algorithm locates all peaks ("noise peaks") within a percentage range (e.g., 0-10%) of the amplitude span of the data set from the baseline using a short search vector length (e.g., 3). The noise peaks may have a maximum value relative to a reference line opposite the baseline. (For example, at a lower baseline, the noise peak may be a negative peak with a maximum value relative to a reference line located on the upper side of the vibration pattern (see Example 2).) A noise estimate is then calculated as the average of the absolute difference between successive maxima of the noise peaks that fall within the interquartile range. The noise level is taken as 30% of the noise estimate. The noise estimate and noise level are only valid if the data are relatively clean and most of the noise is located near the baseline. Measurements tab (see Figures 13A and 13B):

[0119] This tab is used to define those measurements that should be reported on the statistics page. This page displays the results per well (group), the average of the events detected in the well, and, if applicable, the individual values ​​per event.

[0120] Select All: This button selects all of the events for display (see Figure 13A).

[0121] Select neither. This button clears the event display selection.

[0122] Average peak amplitude. Average peak amplitude is the representative amplitude of the effective primary peak maximum of the events detected for a given well, expressed in relative fluorescence units (RFU). Each peak amplitude for calculating the representative value is measured between the peak maximum and the baseline.

[0123] Number of Peaks. This descriptor is the total number of valid primary peaks or the total number of valid primary peaks and valid secondary peaks combined for a given well.

[0124] Average Peak Rate. This descriptor is the number of valid primary peaks per minute (PpM). For cardiac data, this is equivalent to "beats per minute" (BPM).

[0125] Number of EAD-like peaks per event. This descriptor is the number of valid secondary peaks (EAD-like peaks) in an event or for a data set. This item reports the average number of secondary peaks per valid event, the standard deviation of the average, and the total number of valid secondary peaks for the data set.

[0126] Mean EAD-like Peak Rate (PpM). This descriptor is the representative rate of valid secondary peaks (EAD-like peaks) detected in a dataset (for a given well), expressed in peaks per minute (PpM).

[0127] SD of number of EAD-like peaks. Standard deviation of number of EAD-like peaks per event.

[0128] 10-90% CTD. These descriptors are the duration of the event in seconds (e.g., calcium transient duration) at a defined level from the maximum of the primary peak of the event to the baseline. For example, CTD10 is the duration of the event measured at an amplitude level that is 10% of the amplitude distance from the maximum to the baseline. CTDs from 10% on the upper side to 90% on the lower side are measured. Occasionally, the trough is some distance from the baseline, omitting the measurement of the CTD furthest from the peak.

[0129] 10-90% CTDP. These descriptors are the duration of the event from the time location of the primary peak maximum to a defined level from the maximum relative to the baseline. (CTDP is calcium transient duration from the peak.) For example, CTDP10 is the duration of the event from the peak to the baseline at a level that is 10% of the amplitude distance from the peak maximum. 10% to 90% of the CTD are measured. Occasionally, the trough is at some distance from the baseline, omitting the measurement of the CTDP furthest from the peak.

[0130] Area. The area between the vibration trace for a given event and the baseline, measured from the start to the end of the event, as defined by the left and right troughs for the event. The units for area are RFU·sec.

[0131] Peak Spacing. This measurement determines the regularity (in seconds) of the spacing of valid primary peaks by comparing the standard deviation and mean of the spacing. The measurement either reports even spacing of peaks (OK) if the standard deviation is below a threshold percentage (e.g., 50%) of the mean, or reports irregular spacing (IRREG) if the standard deviation is above a threshold percentage.

[0132] Slope. The percentage to and from the peak for the slope measurement is specified in the associated edit box. Typically slope is measured from 10-80% or 30-70% from the peak value. Example 6: Compound testing using cardiomyocytes

[0133] This example illustrates results obtained from testing cardiotoxic compounds on cardiomyocytes for their effect on various parameters measured by the software described in Example 5 (see Figures 14-22).

[0134] The development of biologically relevant predictive cell-based assays for compound screening and toxicity assessment is a major challenge in drug discovery. The focus of this study was to establish a high-throughput compatible cardiotoxicity assay using human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. To assess the utility of human iPSC-derived cardiomyocytes as an in vitro arrhythmia induction model, concentration dependence and response were evaluated for 28 drugs associated with low, intermediate, and high Torsade de Pointes (TdP) risk categories. (Compounds were assessed using the Comprehensive in Vitro Proarrhythmia Assay.) The effect of various compounds on the rate and pattern of spontaneous cardiomyocyte activity was measured using fast kinetic fluorescence with calcium-sensitive dyes to measure intracellular Ca. 2+ The changes in Ca 2+ A multiparameter characterization of the oscillation pattern was implemented. The assay allows for the characterization of parameters such as beating frequency, amplitude, peak width, and rise and decay times. The results demonstrate the utility of hiPSC-cardiomyocytes for detecting drug-induced proarrhythmic effects in vitro.

[0135] iPSC-derived cardiomyocytes generate spontaneous synchronized calcium oscillations. High-speed fluorescence imaging with the FLIPR® Penta® system allows for the detection of intracellular Ca using the EarlyTox® cardiotoxicity kit. 2+ In cardiomyocytes as monitored by changes in Ca levels 2+ It was used to measure the pattern and frequency of vibrations. A set of 28 known cardiotoxic compounds, plus several benchmark compounds and a negative control group, were tested in the assay.

[0136] iPSC-derived cardiomyocytes: Cryopreserved human iPSC-derived cells from Cellular Dynamics International (CDI) iCell® Cardiomyocytes 2 were used for the experiments. Cells were thawed and plated at 20,000 / well (96-well format) or 10,000 / well in 384-well format plates (Corning) and cultured for 7 days in maintenance medium. The presence of robust synchronous contractions in the 3D cultures was visually confirmed prior to performing the experiments. In addition, assay 384-well plates with cardiomyocytes 1 were obtained from Ncardia, Inc. Plates were shipped pre-plated and were allowed to recover for 2 days after arrival.

[0137] Cardiomyocytes were exposed to compounds for 15, 30, 60, or 90 minutes, or for 24 hours.

[0138] Intracellular Ca 2+ Oscillations were assessed using EarlyTox® calcium dye (Molecular Devices) according to standard protocols, and cells were loaded with the dye for 2 hours before measurements.

[0139] Measurement of calcium oscillations in iPSC-derived cardiomyocytes is a promising method for toxicity assessment. This work focuses on the evaluation of a set of 28 CiPA compounds categorized as high, medium, or low risk according to clinical data.

[0140] The FLIPR® Penta® system equipped with a new high-speed camera allows better resolution of calcium oscillation patterns in cardiomyocytes. ScreenWorks® Peak Pro® 2 software allows complex event analysis and detailed characterization of patterns using more than 20 available pattern descriptors. The assay can be used to test drugs in development and to screen chemicals for potential cardiotoxicity harms.

[0141] Figures 14-21 show representative traces of calcium oscillations for control and compound-treated cardiomyocytes recorded over 2 min, starting after 30 min of treatment with the compounds and concentrations indicated. Perturbations to calcium oscillation patterns are described for each compound.

[0142] A control trace (DMSO treatment of cardiomyocytes) is shown in Figure 14. Only the primary peaks 118 (marked with circles) are detectable. The primary peaks have uniform amplitudes and spacing from each other.

[0143] 15 shows a trace from cardiomyocytes treated with E-4031 at a concentration of 1 μM. There is a primary peak 118 and a secondary peak 150. The primary peak is elongated.

[0144] 16 shows a trace from cardiomyocytes treated with ibutilide at a concentration of 1 μM. There is a primary peak 118 and a secondary peak 150. The primary peak is elongated.

[0145] 17 shows a trace from cardiomyocytes treated with dofetilide at a concentration of 10 μM. Only primary peaks 118 are detected, but with irregular spacing from each other.

[0146] 18 shows a trace from cardiomyocytes treated with quinidine at a concentration of 10 μM. A primary peak 118 and secondary peaks 150 are detected. The primary peaks are elongated and irregularly spaced from any one another.

[0147] 19 shows a trace from cardiomyocytes treated with sotalol at a concentration of 10 μM. A primary peak 118 and secondary peaks 150 are detected. The primary peaks are elongated and irregularly spaced from any one another.

[0148] Figure 20 shows a trace from a cardiomyocyte treated with astemizole at a concentration of 1 M. No significant peaks were detected, therefore a "stop" oscillation condition is reported.

[0149] Figure 21 shows a trace from a cardiomyocyte treated with nifedipine at a concentration of 1 M. Only the primary peak 118 is detected, but at a significantly higher frequency than in the control group.

[0150] Figure 22 shows a compilation and comparison of different readouts for compounds over a range of normalized concentrations, where the compounds are grouped into three groups according to their known cardiotoxicity: high toxicity, high toxicity, and low toxicity. The readouts include the presence of secondary peaks, peak prolongation, irregularity in the spacing / amplitude of the primary peaks, increased frequency of the primary peaks relative to the control group, stopping conditions (no valid peaks), decreased frequency of the primary peaks relative to the control group, and amplitude changes of the primary peaks in the oscillation pattern. The graph demonstrates that the appearance of secondary peaks and peak prolongation at concentrations comparable to Cmax (maximum clinical concentration in blood) is clearly a strong indicator of (or correlates with) cardiotoxicity, while modifications of other readouts do not necessarily indicate a cardiotoxic effect. Example 7: Compound testing using neurons

[0151] This example illustrates results obtained from testing neurotoxic compounds with neurons in vitro for their effects on various parameters measured by the analysis software (see Figures 23-30).

[0152] There is an increasing need for more complex biologically relevant predictive cell-based assays for drug discovery and toxicology screening to accelerate the development of more effective and safer drugs. Human iPSC-derived neural 3D co-cultures (StemoniX® microBrain® 3D platform) have been developed as a high-throughput screening platform that more closely resembles the organization of native human cortical brain tissue. Neural spheroid 3D co-cultures are physiologically relevant co-cultures of iPSC-derived functionally active cortical glutamatergic and GABAergic neurons that are co-differentiated and matured with astrocytes from the same donor. 3D neural spheroids contain synapse-enriched neural networks that create highly functional neural circuits and exhibit spontaneous synchronized and easily detectable calcium oscillations.

[0153] A new method for the analysis of complex calcium oscillations is disclosed herein. The method allows for the detection and multi-parameter characterization of oscillatory peaks. Multi-parameter characterization may include oscillation rate (i.e., frequency of the primary peak), width and amplitude of the primary peak, descriptors of secondary peaks, waveform irregularity, and several other important readouts.

[0154] Neurons in microBrain® 3D spheroids generate spontaneous synchronized calcium oscillations. High-speed kinetic fluorescence imaging with the FLIPR® Penta® System captures intracellular Ca using the FLIPR® Calcium 6 Assay Kit. 2+ Neuronal spheroids as monitored by changes in Ca levels 2+The oscillation patterns and frequencies were measured. A set of known neuromodulators was tested, including NMDA, GABA, and AMPA receptor agonists and antagonists, kainic acid, and analgesics and antiepileptic drugs. Fluorescence from each well across the 384-well plate was recorded simultaneously at a frequency of 2 Hz (0.5 s sampling interval) by high-speed imaging using a FLIPR® Penta® system.

[0155] Advanced analytical methods implemented in the ScreenWorks® Peak Pro® 2 software module are used to analyze Ca 2+ It provides a multiparameter characterization of flux oscillation patterns. The phenotypic assay produces readouts such as oscillation frequency, amplitude, peak width, peak rise and decay times, and peak amplitude / interval irregularity. The effects of modulators of neuronal activity were assessed by measuring changes in several measurements.

[0156] A set of more than 20 compounds, including several known modulators of neuronal activity, was assayed at different time points and concentrations, and EC50 values ​​were calculated for compound effects. Time points included 0, 15, 30, 60, 90, and 120 minutes, and 24 hours. Changes were observed as peak frequency suppression or activation, or other measurements, corresponding to the expected effects of the corresponding neuromodulators.

[0157] Figures 23-30 show representative traces of calcium oscillations for control and compound-treated neural spheroids recorded over 10 minutes, starting after 30 minutes of treatment with the indicated compounds and concentrations.

[0158] The control trace (DMSO treatment of spheroids) is shown in Figure 23. Only the primary peaks 118 (marked with circles) are detectable. The primary peaks have somewhat uniform amplitudes and spacing from each other.

[0159] Figure 24 shows traces from spheroids treated with MK-801 at a concentration of 3 μM. The primary peak 118 has a variable amplitude (compared to Figure 23). Only one secondary peak 150 is detected.

[0160] Figure 25 shows a trace from a spheroid treated with GABA at a concentration of 10 μM. Only the primary peak 118 is detected, but at a significantly lower frequency than the control group.

[0161] Figure 26 shows a trace from a spheroid treated with baclofen at a concentration of 10 μM. Only the primary peak 118 is detected, but at a frequency significantly lower than the control group, and with irregular spacing and amplitude.

[0162] Figure 27 shows a trace from a spheroid treated with 4-aminopyridine at a concentration of 30 μM. A primary peak 118 and a secondary peak 150 are detected. The frequency of the primary peak 118 is increased relative to the control group.

[0163] Figure 28 shows traces from spheroids treated with valinomycin at a concentration of (0.3 μM). Primary peaks 118 and secondary peaks 150 are detected. The frequency of the primary peaks 118 is much lower than the control group, and the amplitude and spacing are much more variable.

[0164] 29 shows a trace from a spheroid treated with kainic acid at a concentration of 1 μM. A primary peak 118 and multiple secondary peaks 150 are detected. The frequency of the primary peaks 118 is increased relative to the control group.

[0165] Figure 30 shows a trace from a spheroid treated with tamoxifen at a concentration of 30 μM. Only the primary peak 118 is detected, but at a significantly lower frequency than the control group and with variable amplitude.

[0166] The assay can be used to test compound effects and screen neurotoxic chemicals. The appearance of secondary peaks and low amplitude primary peaks may indicate compounds with neurotoxic activity. Effects on peak rate and amplitude alone may not be sufficient to predict neurotoxicity. Example 8: Selected Examples

[0167] This example describes selected embodiments of the present disclosure as a series of indexed paragraphs.

[0168] Paragraph 1. A method of analysis comprising: (i) detecting fluorescence representative of oscillatory ion flux associated with one or more biological cells to generate a series of data points describing an oscillatory pattern; (ii) calculating a series of gradients related to the oscillatory pattern; and (iii) using the series of gradients to identify peaks in the oscillatory pattern.

[0169] Paragraph 2. The method of paragraph 1, wherein the calculating step uses a sliding window to define a subset of the set of data points from which a set of gradients is calculated.

[0170] Paragraph 3. The method of paragraph 2, further comprising the step of selecting a size of the sliding window from a plurality of allowed sizes, the size of the sliding window corresponding to a number of data points from the set of data points encompassed by the sliding window.

[0171] Paragraph 4. The method of paragraph 3, wherein the size of the sliding window is automatically assigned by the processor based on the level of noise in the vibration pattern and / or the sampling interval for the set of data points, and optionally, the processor also calculates a series of gradients and identifies peaks.

[0172] Paragraph 5. The method of paragraph 3 or 4, wherein the step of selecting the size of the sliding window is performed by a user and communicated to a processor, which also calculates a series of gradients and identifies peaks.

[0173] Paragraph 6. The method of any of paragraphs 1-5, wherein the set of data points is not filtered to reduce noise prior to the step of calculating a series of gradients.

[0174] Paragraph 7. A method according to any of paragraphs 1-6, wherein the peaks include a series of primary peaks and the vibration pattern comprises a series of events, each including only one of the primary peaks, and the method further comprises determining at least one aspect of secondary peaks of the identified peaks, each secondary peak following one of the primary peaks within an event.

[0175] Paragraph 8. The method of paragraph 7, wherein at least one aspect of the secondary peaks is related to a number, frequency, or period of the secondary peaks in the vibration pattern.

[0176] Paragraph 9. The method of paragraphs 7 or 8, wherein the vibration pattern crosses a predefined trigger level twice per event, the trigger level being set relative to a baseline of the vibration pattern.

[0177] Paragraph 10. The method of paragraph 9, wherein the trigger level is set at a percentage of the amplitude range spanned by the series of data points.

[0178] Paragraph 11. The method of paragraph 9, wherein the trigger level is a trigger level selected by a user.

[0179] Paragraph 12. The method of any of paragraphs 9-11, wherein the baseline and trigger levels are adjustable by a user via a graphical user interface.

[0180] Paragraph 13. The method of any of paragraphs 1-12, wherein identifying the peak includes searching for a positive-to-negative or negative-to-positive transition in slope within the series of slopes.

[0181] Paragraph 14. The method of paragraph 13, wherein identifying the peaks comprises filtering peaks associated with transitions to obtain a set of peaks that are considered valid.

[0182] Paragraph 15. The method of paragraph 14, wherein filtering the peaks includes filtering the peaks based on one or more predetermined amplitude and / or duration criteria.

[0183] Paragraph 16. The method of paragraphs 14 or 15, further comprising determining values ​​of peak-related parameters for the set of peaks considered to be valid.

[0184] Paragraph 17. A method according to any of paragraphs 14-16, wherein the step of identifying peaks includes the steps of determining at least one amplitude and / or duration of a peak associated with each transition of a plurality of transitions found by the search, comparing the at least one amplitude and / or duration to at least one threshold value, and rejecting the peak associated with the transition as invalid if the comparing step does not satisfy one or more predetermined criteria for the at least one amplitude and / or duration.

[0185] Paragraph 18. The method of paragraph 17, wherein the step of comparing at least one amplitude and / or duration includes a step of comparing an amplitude for a peak measured against a baseline for the vibration pattern.

[0186] Paragraph 19. The method of paragraphs 17 or 18, wherein the step of comparing at least one amplitude and / or duration comprises comparing a local amplitude for a peak measured against a local trough adjacent the peak.

[0187] Paragraph 20. The method of any of Paragraphs 17-19, wherein the step of comparing at least one amplitude and / or duration comprises a step of comparing a duration relating to the measured peak against at least one local trough.

[0188] Paragraph 21. A method according to any of paragraphs 17-20, wherein each threshold of the at least one threshold is adjustable by a user via a graphical user interface and / or at least one threshold is set automatically by a processor.

[0189] Paragraph 22. A method according to any of paragraphs 1-21, wherein the vibration pattern comprises a series of events, each event in the series crossing a predetermined trigger level twice, and the method further comprises filtering the series of events to reject each event, if present, having less than a predetermined duration, and each peak within the rejected event is considered to be invalid.

[0190] Paragraph 23. The method of any of Paragraphs 1-22, further comprising labeling one or more biological cells with a calcium indicator, wherein the fluorescence is emitted by the calcium indicator.

[0191] Paragraph 24. The method of any of Paragraphs 1-23, wherein the one or more biological cells comprise one or more cardiomyocytes or neurons.

[0192] Paragraph 25. The method of paragraph 24, wherein the one or more biological cells are primarily cardiomyocytes or neurons.

[0193] Paragraph 26. The method of paragraph 24 or 25, wherein the one or more biological cells comprise one or more cardiomyocytes or neurons differentiated in vitro from at least one stem cell.

[0194] Paragraph 27. The method of any of Paragraphs 1-26, wherein the one or more biological cells are contained by a vessel selected from a Petri dish, a flask, and a multi-well microplate.

[0195] Paragraph 28. The method of any of Paragraphs 1-27, wherein the series of data points represents a sampling rate greater than 1 Hz.

[0196] Paragraph 29. A method according to any of paragraphs 1-28, wherein the vibration pattern comprises a series of events, each comprising a single primary peak, and the vibration pattern comprises one or more secondary peaks, each comprised in an event of the series of events, and the method further comprises determining at least one value for one or more parameters associated with the one or more secondary peaks.

[0197] Paragraph 30. The method of paragraph 29, wherein at least one value corresponds to a number, frequency, or period of secondary peaks.

[0198] Paragraph 31. The method of any of paragraphs 1-30, wherein the steps of detecting fluorescence, calculating a series of gradients, and identifying peaks are performed for each distinct set of a plurality of distinct sets of one or more biological cells, each distinct set being exposed to a different compound or different concentrations of the same compound.

[0199] Paragraph 32. The method of paragraph 31, further comprising the step of determining the effect of each different compound or concentration, if present, on one or more parameters associated with at least a subset of the peaks.

[0200] Paragraph 33. The method of paragraph 32, wherein the one or more parameters correspond to a number, frequency, or period of secondary peaks in the vibration pattern.

[0201] Paragraph 34. The method of paragraph 32 or 33, further comprising predicting the degree of cardiotoxicity or neurotoxicity, if any, of each compound or concentration based on the effect on one or more parameters.

[0202] Paragraph 35. The method of any of Paragraphs 1-34, further comprising automatically establishing a baseline for the vibration pattern.

[0203] Paragraph 36. The method of Paragraph 35, wherein the step of establishing a baseline includes the steps of creating a reference line above the vibration pattern and a threshold line below the vibration pattern, finding maximum values ​​relative to the reference line, the maximum values ​​being below the threshold line, and performing a linear regression using at least a subset of the maximum values.

[0204] Paragraph 37. The method of Paragraph 36, further comprising the step of calculating a noise level of the vibration pattern using at least a subset of the maximum values.

[0205] Paragraph 38. A method according to any of Paragraph 37, wherein the step of calculating a series of gradients uses a sliding window to define a subset of the series of data points from which the series of gradients are calculated, the size of the sliding window being selected based on the noise level.

[0206] Paragraph 39. A method of analysis comprising: (i) detecting fluorescence representative of oscillatory ion flux associated with one or more biological cells to generate a series of data points describing an oscillatory pattern; (ii) identifying primary and secondary peaks in the oscillatory pattern, if present; and (iii) determining a side of the secondary peak.

[0207] Paragraph 40. The method of Paragraph 39, wherein determining the aspect of the secondary peaks comprises determining the number, frequency, or period of the secondary peaks.

[0208] Paragraph 41. The method of paragraph 39 or 40, further comprising exposing one or more biological cells to a compound and determining the effect of the compound on the profile of the secondary peak.

[0209] Paragraph 42. A method according to any of Paragraphs 39-41, wherein the vibration pattern comprises a series of events, each of which includes only one of the primary peaks, and each secondary peak occurs after the primary peak within an event.

[0210] Paragraph 43. The method of any of paragraphs 39-42, wherein the secondary peak is a secondary peak generated by cardiomyocytes or neurons.

[0211] Paragraph 44. The method of any of Paragraphs 39-43, further comprising labeling one or more biological cells with a calcium indicator, wherein fluorescence is detected from the calcium indicator.

[0212] Paragraph 45. The method of any of Paragraphs 39-44, further comprising the step of determining the spacing regularity / irregularity of the primary peak.

[0213] Paragraph 46. The method of paragraph 45, wherein the step of determining spacing regularity / irregularity comprises a step of comparing the standard deviation of the spacing of the primary peaks with the average spacing of the primary peaks.

[0214] Paragraph 47. The method of any of Paragraphs 39-46, further comprising a step of determining the amplitude regularity / irregularity of the primary peak.

[0215] Paragraph 48. The method of paragraph 47, wherein the step of determining the amplitude regularity / irregularity includes a step of comparing the standard deviation of the amplitudes of the primary peaks with the mean amplitude of the primary peaks.

[0216] Paragraph 49. The method of any of paragraphs 39-48, further comprising the step of comparing the amplitude of each primary peak with a predetermined threshold to enumerate smaller peaks of the primary peak, if any.

[0217] Paragraph 50. The method of any of Paragraphs 39-49, wherein the detecting, identifying, and determining steps are performed for each distinct set of a plurality of distinct sets of one or more biological cells, each distinct set being exposed to a different compound or a different concentration of the same compound.

[0218] Paragraph 51. The method of Paragraph 50, further comprising determining the effect of each different compound or concentration on the flank of the secondary peak, if any.

[0219] Paragraph 52. The method of paragraph 50 or 51, further comprising the step of determining the effect, if any, of each different compound or concentration on the spacing regularity / irregularity of the primary peak.

[0220] Paragraph 53. The method of any of paragraphs 50-52, further comprising determining the effect, if any, of each different compound or concentration on the amplitude regularity / irregularity of the primary peak.

[0221] Paragraph 54. The method of any of paragraphs 50-53, further comprising predicting the degree of cardiotoxicity or neurotoxicity for each compound or concentration based on the profile of the secondary peak and the spacing regularity / irregularity of the primary peak.

[0222] Paragraph 55. The method of any of paragraphs 50-54, further comprising predicting the degree of cardiotoxicity or neurotoxicity of each compound or concentration based on the profile of the secondary peak and the amplitude regularity / irregularity of the primary peak.

[0223] Paragraph 56. The method of any of paragraphs 50-55, further comprising predicting the degree of cardiotoxicity or neurotoxicity for each compound or concentration based on the profile of the secondary peaks and the number, frequency, or periodicity of primary peaks that are smaller peaks having amplitudes below a predetermined threshold.

[0224] Paragraph 57. A system comprising: (i) an optical sensor configured to detect fluorescence representative of oscillatory ion flux associated with one or more biological cells to generate a set of data points describing an oscillatory pattern; and (ii) a processor configured to (1) optionally calculate a set of gradients for the oscillatory pattern using a sliding window to define a subset of the set of data points from which the set of gradients is calculated, and (2) use the set of gradients to identify peaks in the oscillatory pattern.

[0225] Paragraph 58. A system according to Paragraph 57, configured to perform any combination of the steps of Paragraphs 1-56.

[0226] Paragraph 59. A system comprising: (i) an optical sensor configured to detect fluorescence representative of oscillatory ion flux associated with one or more biological cells to generate a series of data points describing an oscillatory pattern; and (ii) a processor configured to (1) identify primary and secondary peaks in the oscillatory pattern, if present, and (2) determine sides of the secondary peaks.

[0227] Paragraph 60. The system of Paragraph 59, configured to perform any combination of the steps of Paragraphs 1-56.

[0228] The term "exemplary" as used in this disclosure means "illustrative" or "serving as an example." Similarly, the term "exemplary" means "to illustrate by giving an example." Neither term implies desirability or superiority.

[0229] The disclosure set forth above may encompass multiple distinct inventions with independent utility. Each of these inventions has been disclosed in its preferred form, but the specific embodiments thereof as disclosed and illustrated herein are not to be considered in a limiting sense, since numerous variations are possible. The subject matter of the invention includes all novel and non-obvious combinations and subcombinations of the various elements, features, functions, and / or properties disclosed herein.

Claims

[Claim 1] The invention described in this specification.

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