Biocytosolic oscillatory fluorescence assay

Abstract

Methods and systems for analyzing oscillatory fluorescence indicative of oscillatory ion flux associated with one or more biological cells. An illustrative analysis method (50) can include detecting (54) fluorescence (91) from one or more biological cells (72) to produce a series of data points (120) describing an oscillatory pattern (110). A series of slopes of the oscillatory pattern (110) can be computed (60). For example, a sliding window (122) can be used to define a subset (126) of the series of data points (120) from which the series of slopes is computed. The series of slopes can be used to identify (62) peaks (118, 150a, 150b) of the oscillatory pattern (110). Primary (118) and secondary (150a, 150b) peaks in the oscillatory pattern (110) can be identified and characterized, if any, over multiple measurements. An aspect of the secondary peaks (150a, 150b) can be determined. The presence of secondary peaks (150a, 150b) can be indicative of a potential risk of cardiotoxicity or neurotoxicity.

Description

[0001] Related applications

[0002] This application claims priority to U.S. Provisional Application No. 62 / 866524, filed June 25, 2019, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] This disclosure relates to methods and systems for analyzing oscillating fluorescence from biological cells. More specifically, the disclosed examples relate to methods and systems for analyzing oscillating fluorescence representing oscillating ion currents (such as calcium oscillations) in biological cells, and for testing the effect of cell treatment on oscillating fluorescence. Background Technology

[0004] Most drugs fail in clinical trials due to cardiotoxicity or neurotoxicity.

[0005] To reduce the incidence of these failures, sensitive in vitro assays are needed to reliably assess the adverse effects of compounds on cardiac and nerve cells before the start of clinical studies. These in vitro assays can accelerate and streamline drug development.

[0006] Culture systems for cardiomyocytes and neurons have been developed, in which these cells exhibit spontaneous, synchronized ion flows, such as calcium oscillations. Calcium oscillations can be detected by labeling cells with a fluorescent calcium indicator.

[0007] An imaging system, including analytical software, was also developed to record and analyze oscillatory fluorescence representing calcium oscillations in cells. This imaging system has been used to measure the effects of pharmacological compounds on calcium oscillations in cultured cardiomyocytes and neurons. Notably, these calcium oscillations are interfered with in vitro by pharmacological compounds with known cardiotoxic or neurotoxic effects. Therefore, this method shows promise for safety testing of drugs and other chemicals prior to clinical studies, as well as for predicting the efficacy and dosage of drug candidates.

[0008] The oscillation patterns resulting from interference with calcium oscillations by drugs and other compounds can be highly complex. Better methods and systems are needed to detect the biologically relevant characteristics of these complex oscillation patterns and extract values ​​for the most informative readings. Summary of the Invention

[0009] This disclosure provides methods and systems for recording and analyzing oscillating fluorescence, which represents an oscillating ion flow associated with one or more biological cells. An illustrative analytical method may include detecting fluorescence from one or more biological cells to generate a series of data points describing an oscillating pattern. A series of slopes may be calculated for the oscillating pattern. A sliding window may be used to define a subset of the series of data points, and the series of slopes may be calculated based on the subset. A series of slopes may be used to identify peaks of the oscillating pattern. Another illustrative analytical method may include detecting fluorescence from one or more biological cells to generate a series of data points describing an oscillating pattern. A primary peak and secondary / subpeaks (if any) in the oscillating pattern may be identified. An aspect of the secondary peak may be determined. Attached Figure Description

[0010] Figure 1 This is a flowchart of the steps that can be performed in an illustrative method for analyzing oscillating fluorescence patterns from biological cells.

[0011] Figure 2 This is a schematic diagram of an illustrative system for detecting and analyzing oscillating fluorescence patterns from biological cells.

[0012] Figure 3 It is a graph of a relatively simple, undisturbed oscillation pattern generated by the fluorescence signal detected from biological cells subjected to an oscillating ion current.

[0013] Figure 4 yes Figure 3 A graph of a series of data points containing a region of single oscillation in the fluorescence signal, where the region is in Figure 3 The number "4" is generally used to indicate this, and a sliding window is schematically shown at three different time points to illustrate how to use the sliding window to select a subset of data points for calculating a series of slopes.

[0014] Figure 5 Is it like this? Figure 3 A graph of single oscillations (“events”) of more complex disturbance oscillation patterns detected in the data, and showing, as Figure 4 The slope calculated in the figure can be used to detect biologically relevant peaks and troughs while ignoring smaller “false” fluctuations in the fluorescence signal that are assumed to be noise.

[0015] Figure 6 yes Figure 5 The graph shows only a portion of the curve of a single oscillation around the secondary peak, and illustrates how to use predetermined amplitude and / or duration criteria to filter the primary / secondary peaks to exclude small and / or transient peaks as invalid.

[0016] Figure 7 Is it like this? Figure 3The graph shows the oscillation patterns detected in the graph, and illustrates aspects of the algorithm used to automatically set the baseline for the oscillation patterns (i.e., setting a temporary reference line and a threshold line relative to the reference line).

[0017] Figure 8 yes Figure 7 A partial view of the bottom of the oscillation pattern, showing the negative peak (relative to) located below the threshold line and identified based on the slope. Figure 7 (Reference line).

[0018] Figure 9 Is it like this? Figure 3 The graphs of three oscillations of a complex oscillation mode generally detected in the data are shown, with illustrative parameters identified.

[0019] Figure 10 and Figure 11 These are graphs of the same complex oscillation pattern analyzed using sliding windows of different sizes (eleven points and five points, respectively), where the main peak is marked by a circle and the secondary peak by a diamond.

[0020] Figure 12A and Figure 12B This is a partial view of an exemplary dialog box created by the analysis software, showing the "Options" tab of the dialog box selected.

[0021] Figure 13A and 13B yes Figure 12A and Figure 12B A partial view of the dialog box, except that the "Measurement" tab of the dialog box is selected.

[0022] Figures 14 to 21 From the perspective of ( Figure 14 ) and with various known cardiotoxic compounds ( Figures 15 to 21 A graph showing representative fluorescence oscillation patterns detected in cardiomyocytes treated with [a specific method / technology]. The main peak and secondary peaks are marked with circles and diamonds by the software, respectively.

[0023] Figure 22 It is a graph plotting of various peak-related readings obtained using the software disclosed herein, which analyzes the fluorescence oscillation patterns detected from cardiomyocytes treated with the indicated high-risk, intermediate-risk, and low-risk TdP (torsades de pointes) compounds at indicated concentrations relative to the maximum clinical levels of each compound in the blood.

[0024] Figures 23 to 30 From the perspective of ( Figure 23 ) and with various known neurotoxic compounds ( Figures 24 to 30A graph showing representative fluorescence oscillation patterns detected in neurons treated with [compound name missing]. The main peak and secondary peaks are marked with circles and diamonds by the software, respectively. Changes in oscillation patterns reflect the effects of the compound and can be characterized by multiple measurements provided by the software analysis. Detailed Implementation

[0025] This disclosure provides methods and systems for recording and analyzing oscillating fluorescence, which represents an oscillating ion flow associated with one or more biological cells. An illustrative analytical method may include detecting fluorescence from one or more biological cells to generate a series of data points (interchangeably referred to as an oscillation trajectory or fluorescence trajectory) describing an oscillation pattern. A series of slopes may be calculated for the oscillation pattern. For example, a sliding window may be used to define a subset of the series of data points from which a series of slopes are calculated. A series of slopes may be used to identify peaks in the oscillation pattern. Another illustrative analytical method may include detecting fluorescence from one or more biological cells to generate a series of data points describing an oscillation pattern. A primary peak and secondary peaks (if any) in the oscillation pattern may be identified. An aspect of the secondary peak may be determined.

[0026] The methods and systems disclosed herein can utilize the Complex Event Analysis (CEA) algorithm, which is designed to analyze oscillating fluorescence with several components of interest. These components may include various peak shapes, peak clusters, secondary peaks, regular or irregular anomalous events, irregular amplitudes or frequencies, migration threshold levels, etc. The CEA algorithm is capable of reporting multiple measurements, averages, or individual values ​​of the main and secondary peaks, including numerous additional readings.

[0027] The purpose of the CEA algorithm is to digitally “visualize” the general shape of biological events in order to detect and measure various parameters such as peak amplitude, rise and fall times, event duration, and frequency. To visualize biological events, it is necessary to reject false abiotic transitions (noise). Many “point-by-point” detection methods lose accuracy due to prior filtering that compromises data integrity or because they cannot distinguish between biologically relevant transitions and noise. By its very nature, the CEA algorithm significantly reduces the impact of noise without prior filtering by calculating the slope of a subset of data points describing the oscillation pattern. The slope allows for the detection of peaks and troughs while ignoring lower amplitudes and / or rapid transitions (i.e., invalid peaks) caused by noise.

[0028] Further aspects of this disclosure are described in the following sections: (I) Definitions, (II) Methods and System Overview, and (III) Examples.

[0029] I. Definition

[0030] The technical terms used in this disclosure have meanings generally recognized by those skilled in the art. However, the following terms may be further defined as follows.

[0031] event —A single oscillation of an oscillation mode. Events may begin and end at one or more predefined amplitudes from the baseline and / or within one or more predefined amplitude ranges from the baseline, etc.

[0032] Light —Ultraviolet, visible and / or infrared radiation.

[0033] Maximum value —A point (and / or a series of points) in an actual / conceptual map that has a value greater than the points around it, and / or is further away from a reference line (e.g., a bottom or top baseline) than the points around it. The plural of “maximum” is “maxima”.

[0034] Minimum value —A point (and / or a series of points) in an actual / conceptual map that has a smaller value than the points around it, and / or is closer to a reference line (e.g., a bottom or top baseline) than the points around it. The plural of “minimum” is “minima”.

[0035] Peak —A sequence (and / or a series of points) of the actual / conceptual map, including the maximum value and the points surrounding it, and optionally demarcated by a pair of minimum values. A peak can be characterized by its time position and the amplitude of its maximum value, which together define the "peak position," while the amplitude of the maximum value defines the "peak value" or "peak amplitude." When the maximum value is defined relative to a reference line (e.g., a bottom or top baseline), a maximum value above the reference line forms a positive peak, while a maximum value below the reference line forms a negative peak.

[0036] Primary peak —The preceding / unique peak (or valid peak) of the event.

[0037] Secondary peak —Any peak (or valid peak) following the main peak within the event.

[0038] Trough—This includes a sequence (and / or a series of points) of the actual / conceptual map of the minimum and the points surrounding it. A trough can be characterized by its time location and the amplitude of its minimum value; the time location and the amplitude of the minimum value together define the "trough location," while the amplitude of the minimum value defines the "trough value" or "trough amplitude." When the minimum is defined relative to a reference line (e.g., a baseline), a minimum above the reference line forms a positive trough, while a minimum below the reference line forms a negative trough.

[0039] Effective peak —Any peak that meets the predefined validity criteria.

[0040] window —An algorithm that selects a given number of data points from a dataset for processing. For example, a window with a width of five selects five points, such as five consecutive points in a dataset, for processing. A "sliding window," for example, moves point by point across the dataset to select a subset of data points for processing. For example, a sliding window with a width of five points can process points 1 to 5, and then move point by point to process points 2 to 6, points 3 to 7, and so on.

[0041] II. Methods and Systems Overview

[0042] This section provides an overview of exemplary methods and systems of this disclosure; see also Figure 1 and Figure 2 .

[0043] Figure 1 A flowchart 50 illustrates the steps that can be performed in an illustrative method for analyzing oscillating fluorescence from one or more biological cells (also referred to as “cells”). These steps can be performed in any suitable order and combination.

[0044] Cells can include any cell type that exhibits an oscillating ion flow in culture. Therefore, cells can include muscle cells (i.e., cardiomyocytes, skeletal muscle cells, or smooth muscle cells) or nerve cells (neurons). For a cell assembly cultured in close association with each other, the oscillating ion flow can occur synchronously, optionally spontaneously, allowing the cells to communicate. The cell assembly can associate with each other as three-dimensional aggregates (such as cell spheroids), or can be arranged in a basic monolayer, etc. Individual cell assemblies can be analyzed in a container, or isolated, duplicated cell assemblies can be exposed to different treatments in separate containers before / during analysis, as explained further below. Exemplary containers include Piebald plates, wells / wells of microplates(s), flasks, etc.

[0045] Cells can be obtained from any suitable source(s) by any appropriate method. Cells can differentiate from stem cells in vitro. Stem cells can be embryonic stem cells, adult stem cells, or induced pluripotent stem cells (iPSCs), etc. In other instances, cells can be primary cells, such as primary cardiomyocytes or primary neurons obtained from animals.

[0046] Oscillating ion fluxes can be either ion-specific fluxes or collective ion fluxes. An exemplary ion-specific flux is an oscillating calcium flux that generates calcium oscillations. A flux can 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).

[0047] Each cell assembly can be labeled and / or treated, as shown in Figure 52. Labeling can be performed using a fluorescent indicator that has fluorescence sensitive to oscillating ion currents with respect to the presence of cells. The fluorescent indicator could be, for example, a fluorescent calcium indicator that is sensitive to intracellular calcium concentration and emits more (or less) light as intracellular calcium concentration increases. An exemplary fluorescent calcium indicator is a chemical indicator, such as... Calcium 6 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, etc. Cells can be labeled with chemical indicators by contact with the medium in which they exist. Other exemplary fluorescent calcium indicators are genetically encoded and expressed in cells after the coding sequence is introduced into the cells (e.g., through transfection, infection, etc.). Suitable genetically encoded calcium indicators may include Cameleons, Pericams, GCaMP, TN-L15, TN-humTnC, TN-XL, TN-XXL, Twitches, etc. In other examples, the fluorescent indicator may be a membrane potential indicator (e.g., Membrane Potential Dye, Di-3-ANEPPDHQ, Di-4-ANEPPDFIQ, etc.), potassium indicators, sodium indicators, magnesium indicators, zinc indicators, pH indicators, etc.

[0048] Each cell set can be treated with at least one selected concentration of a target substance to test the effect of the substance / concentration on the oscillating fluorescence of the cells detected from a fluorescent indicator, if any. The substance can be a compound, such as a small molecule (e.g., a drug or drug candidate) with a molecular weight less than 10, 5, 2, or 1 kilodaltons, a protein, RNA, or DNA molecule, etc. Different compounds and / or different concentrations of the same compound can be tested on individual cell sets to screen compounds and / or determine the dose-response profile for a given compound. Each cell set can be treated with the substance for any suitable duration, 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 to 72 hours, etc. Treatment and labeling can be performed in parallel, serially, or at overlapping times with time offsets.

[0049] Fluorescence can be detected in at least one cell in each collection of biological cells, as shown in Figure 54. This fluorescence detection samples the fluorescence signal, such as signal intensity, of at least one cell relative to time to produce a series of data points (also called time points) that describe the oscillation pattern of the fluorescence signal. Therefore, the oscillation pattern can be plotted as the detected fluorescence intensity as a function of time. The fluorescence signal can be sampled at any suitable rate (typically a constant rate greater than 1 Hz, such as rates from 1 Hz to 100 Hz) and at any suitable sampling duration (e.g., at least 10, 30, or 60 seconds, or at least 2, 5, or 10 minutes). Fluorescence detection can be performed using image sensors, optical point sensors, etc.

[0050] A baseline for the oscillation pattern can be established, as shown in Figure 56. The baseline can be set automatically using software, for example, as described in Example 2 below, and / or by a user through a graphical user interface. The baseline can be positioned at the bottom or top of the oscillation pattern by default or in response to user input (see, for example, Examples 2 and 5). In some embodiments, a temporary reference line can be set at the top (or bottom) of the oscillation pattern, relative to the expected location of the baseline to be established. Peaks relative to the reference line can be identified, and linear regression of the peak or trough locations can find the line that acts as the baseline during subsequent peak and trough identification.

[0051] The noise level of an oscillation pattern can be estimated, as shown in Figure 58. The noise level can be estimated based on signal fluctuations near the top or bottom of the oscillation pattern (see Examples 2 and 5). For example, the average of consecutive differences between peaks (i.e., maximum values) (or troughs (i.e., minimum values)) near the baseline of the oscillation pattern can be used to determine an estimate of the noise level.

[0052] As shown in 60, a series of slopes can be calculated from a series of data points in an oscillating mode. For example, a sliding window that slides along the time axis of the oscillating mode can be used to select consecutive subsets of data points to calculate a series of slopes by fitting a line to each subset via linear regression (see, for example, Example 1). In other words, the sliding window can slide one point (or two or more points) along the time axis of the oscillating mode to calculate each consecutive slope. The sliding window has a constant size containing a fixed number of three or more points while calculating a series of slopes for a given dataset (e.g., from a given well). The size of the sliding window can be automatically selected by software (e.g., based on the noise level of the dataset and / or the sampling rate of the data points) or can be selected by the user. The size of the sliding window can be varied between different datasets (e.g., each dataset is collected from different wells), which allows for optimization of the size of each dataset.

[0053] The peaks (and troughs) of each oscillation mode can be identified using slopes, as shown at point 62. That is, a series of maximum and minimum values ​​can be identified based on the change in the sign of the slopes along a series of slopes (e.g., relative to time). Each maximum value above the baseline can be approximated by a change from positive to negative (or from negative to positive for the baseline at the top of the oscillation mode) within a series of slopes. Similarly, each minimum value above the baseline can be approximated by a change from negative to positive (or from positive to negative for the baseline at the top of the oscillation mode) within a series of slopes. If desired, the maximum and minimum values ​​identified using this series of slopes can be refined by local analysis of points and / or interpolation, etc. The maximum and / or minimum values ​​(and the corresponding peaks and troughs) can also be filtered (i.e., restricted) according to one or more other predetermined criteria to reject maximum values ​​(and peaks) and / or minimum values ​​(and troughs) that do not meet the predetermined criteria. The predetermined criteria can involve at least one amplitude and / or duration for each corresponding peak / trough and / or for each event containing at least one peak (e.g., see Examples 1 and 5). Predefined criteria can be automatically specified based on the noise level of the fluorescence signal, and / or can be subjectively specified by the user.

[0054] Values ​​of one or more peak-related parameters for each oscillation mode can be determined, as shown in 64. Parameters and their values ​​can be interchangeably referred to as readings or descriptors. These values ​​can be determined using the peaks identified at 62. These values ​​can be the average of oscillation mode measurements (or at least multiple events thereof), or the value of a single event / peak. Exemplary values ​​may be specific to the primary peak only, specific to the secondary peak only, or both the primary and secondary peaks. Values ​​may include single / average primary / secondary peak amplitude, average primary / secondary peak frequency, average intra-event frequency of the secondary peak, event area, primary / secondary peak spacing, rise slope, decay slope, rise time, decay time, etc. Other examples of peak-related parameters are described in Examples 1, 3, and 5.

[0055] If applicable, the effect of each treatment can be evaluated based on at least one of the values ​​determined from the corresponding oscillation mode, as indicated at 66. For example, the effect can be evaluated based on one aspect of the secondary peaks in the oscillation mode (such as the number, frequency, and / or period of the secondary peaks).

[0056] Figure 1 The steps or any combination of other methods disclosed herein may be embodied as a computer method, a computer system, or a computer program product. Therefore, aspects of the analytical method may take the form of a completely hardware example, a completely software example (including firmware, resident software, microcode, etc.), or an example combining software and hardware aspects, all of which may generally be referred to herein as a “circuit,” “module,” or “system.” Furthermore, aspects of the analytical method may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code / instructions embodied thereon.

[0057] Any combination of computer-readable media may be used. Computer-readable media can be computer-readable signal media and / or computer-readable storage media. Computer-readable storage media can include electronic, magnetic, optical, electromagnetic, infrared, and / or semiconductor systems, apparatuses, or devices, or any suitable combination thereof. More specific examples of computer-readable storage media may include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, any suitable combination thereof, etc. In the context of this disclosure, computer-readable storage media may include any suitable non-transitory tangible medium that may contain or store programs used by or in conjunction with an instruction execution system, apparatus, or device.

[0058] Computer-readable signal media may include, for example, data signals propagated in baseband or as part of a carrier wave, wherein the propagated data signals have computer-readable program code embodied therein. Such propagated signals may take any of a variety of forms, including, but not limited to, electromagnetic, optical, and / or any suitable combination thereof. Computer-readable signal media may include any computer-readable medium that is not a computer-readable storage medium and is capable of transmitting, propagating, or transferring programs for use by or in connection with an instruction execution system, apparatus, or device.

[0059] Program code contained on a 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.

[0060] Computer program code used to perform the operations of various aspects of the methods disclosed herein can be written in one or any combination of programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, C++, etc., and conventional procedural programming languages ​​such as C. Mobile applications can be developed using any suitable language, including the languages ​​mentioned above, as well as Objective-C, Swift, C#, HTML5, etc. The program code can execute 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 case, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)) and / or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0061] 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 flowchart illustrations and / or block diagrams can be implemented by computer program instructions. The computer program instructions can 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, which execute via the processor of the computer or other programmable data processing apparatus, implement the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. In some examples, machine-readable instructions can be programmed onto a programmable logic device, such as a field-programmable gate array (FPGA).

[0062] Computer program instructions may also be stored in a computer-readable medium that can direct a computer to operate in a particular manner, such that the instructions stored in the computer-readable medium produce an article of art including instructions that implement the functions / actions specified in the flowcharts and / or block diagrams.

[0063] Computer program instructions can also be loaded onto a computer to cause a series of operational steps to be performed on the device to produce a computer-implemented process, such that the instructions executed on the computer provide a process for implementing the functions / actions specified in the flowcharts and / or block diagrams.

[0064] Any flowcharts and / or block diagrams in the accompanying 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 comprising one or more executable instructions for implementing one or more specified logical functions. In some embodiments, the functions marked in the blocks may occur in a non-linear order as indicated in the drawings. For example, two blocks shown consecutively may actually execute substantially simultaneously, or these blocks may sometimes execute in reverse order, depending on the functions 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) performing the specified functions or actions.

[0065] Figure 2 An exemplary system 70 is shown for detecting and analyzing oscillating fluorescence from biological cells 72 fixed in a sample holder 74. The sample holder 74 is described herein as a microplate 76 having a plurality of wells 78, each well containing a repeating set of cells 72. The system 70 may include a platform 80 supporting a sample holder, and a drive mechanism 82 that moves the platform 80 and the sample holder 74 relative to each other to position each well 78 on the optical axis of the system 70.

[0066] System 70 may also include an objective lens 84, a light source 86, and an optical sensor 88 (e.g., an image sensor). The light source 86 can generate light 87 to illuminate each cell cluster 72, thereby inducing fluorescence from the cells. Figure 2 In the illustrated incident irradiation configuration, light used for cell irradiation can be propagated to the cells via beam splitter 90 and objective lens 84. Light-induced fluorescence 91 can be collected by objective lens 84 and propagated through objective lens 84, beam splitter 90 and optional tube lens 92, and detected by optical sensor 88 to generate a time-dependent fluorescence signal.

[0067] Computer 94 communicates with optical sensor 88 and processes fluorescence signals received from optical sensor 88. Computer 94 may include processor 96 for processing instructions, memory 98 for storing instructions, and user interface 100 for communication between computer 94 and user. User interface may include user input devices such as keyboard, mouse, or touchscreen, and display devices such as monitor.

[0068] III. Example

[0069] This section describes further examples and aspects of the analytical methods and systems disclosed herein. These examples and aspects are for illustrative purposes only and should not limit the full scope of the invention.

[0070] Example 1. Calculation of slope

[0071] This example describes an exemplary calculation of the slope of an oscillation mode using a sliding window, and how to use the slope to identify peaks and troughs in the oscillation mode; see also Figures 3 to 6 .

[0072] Figure 3 A graph showing the oscillation pattern 110 generated by the fluorescence signal 112 detected by biological cells undergoing a simple calcium oscillation flow is presented. Fluorescence is detected from a fluorescent calcium indicator of labeled cells. Fluorescence intensity is detected at discrete times to generate a series of data points, and the data points are plotted as a function of time using a point-connecting line trajectory.

[0073] Oscillation mode 110 consists of a series of oscillations 114 (also referred to as events) that increase fluorescence from baseline 116. Each oscillation 114 may have only one peak 118, as shown here, or it may have a main peak and one or more secondary peaks, as described below. In other examples, baseline 116 may be located at the top of oscillation mode 110, and each event may be characterized by a fluorescence signal 112 propagating below the baseline.

[0074] Figure 4 Showing from Figure 3 The curve of subsequence data points 120 of the oscillation mode 110 for the single oscillation 114 of the fluorescence signal. Figure 3The point connection line trajectory is omitted. A sliding window 122 can be used to select a subset of data points 120 from which a series of best-fit lines and corresponding slopes can be computed, optionally centered at each data point. The size of the sliding window 122 represents its width measured parallel to the time axis. After a conceptual slide along the time axis indicated by the motion arrow at 124, the sliding window 122 is schematically shown on the left in solid line and in dashed line at two other illustrative locations. The sliding window 122 “stops” at multiple increments along the time axis and selects a subset 126 of data points 120 centered at each location from which slopes are computed. Here, the sliding window 122 has a width that selects five data points 120 at each increment location, but any suitable number of three or more data points 120 can be selected. The size of the sliding window (i.e., its duration) can remain constant to select the same number of data points 120 for each slope calculation, while simultaneously computing a series of slopes for a given oscillation pattern 110 (i.e., the dataset of data points). A slope can be calculated for each point. The size of the sliding window can be changed to recalculate a new set of slopes for the same oscillation pattern, or to calculate a set of slopes for different oscillation patterns. The set of slopes calculated for an oscillation pattern can represent a corresponding set of incremental offset positions for the sliding window 122. In other words, the sliding window 122 can be incrementally offset by a fixed integer number of data points, such as one or two data points, etc., and / or offset by a fixed time increment, to select a subset 126 of data points 120. If the size of the sliding window 122 is greater than the incremental offset of the sliding window for consecutive subsets, the subsets 126 of data points 120 can overlap each other.

[0075] In some embodiments, a slope is calculated at each data point, and a “sliding window” for calculating the slope moves one point at a time. In these embodiments, the subset of data points selected by the window always overlaps with each other. The “direction” of the slope at each point is evaluated to assess the overall curvature of the event implied by the change in the slope direction. The slope calculated at each point is both forward and backward, because one or more points in time preceding and following that point contribute to the slope.

[0076] By fitting a straight line to point 120 of subset 126 using linear regression, and then taking the slope of that line, the slope of each subset 126 can be calculated. Figure 4Three vectors 128 are shown parallel to the corresponding lines fitted to three illustrative subsets 126. The direction of each vector 128 matches the slope of the corresponding fitted line. Each vector 128 may be centered at one or more midpoints 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 correlate the size of the sliding window with a series of slopes having a corresponding series of data points 130 or positions (time and amplitude).

[0077] A series of slopes in vector 128 allows for examination of the shape of the data, and in particular, the direction of data offset. For a baseline at the bottom of an oscillation mode, a positive slope reflects the rising phase, while a negative slope is associated with the decaying phase. Because each slope is calculated from a subset 126 with three or more points 120, noise within subset 126 will not significantly affect the accuracy of the slope if the noise level is low relative to the size of the sliding window. In this way, noise is inherently suppressed. The directional changes of the continuous vector 128 reflect the corresponding directional changes of transitions within the oscillation mode. Therefore, when the slope of the continuous vector 128 changes from positive to negative (for a baseline at the bottom of an oscillation mode), it indicates that the maximum value of a peak has been passed. Similarly, for the same baseline, when the slope changes from negative to positive, it indicates that the minimum value of a trough has been passed. Further examination of the data points within each transition region can then be performed to more accurately determine the location (time and amplitude) of each maximum and minimum value of the oscillation mode.

[0078] The size of the sliding window 122 can be optimized for a specific dataset based on the noise level. Since the sliding window spans a range of data points, it acts as a natural "damper" to reduce the impact of random noise. Furthermore, the slope does not affect the integrity of the relevant data points, thus not altering the detection and measurement of amplitude values ​​or effective secondary peaks. Therefore, using a slope calculated from a subset of data points selected using the sliding window enables the accurate detection and measurement of various parameters of the data without the data corruption that occurs under conventional filtering.

[0079] Figure 5 As shown Figure 3 The graph shows a single oscillation 114 (“Event” 138) of the more complex oscillation pattern 140 detected. Baseline 116 is located at the bottom of oscillation pattern 140. The graph shows a pattern with... Figure 4 The vector 128 of the slope calculated in the diagram can be used to search for larger peaks and troughs while ignoring smaller "spurious" fluctuations in the fluorescence signal that may be caused by noise rather than biologically relevant activity. (For simplicity, vector 128 is shown here as having a constant total length, rather than a constant time component.)

[0080] One or more predetermined criteria can be used to identify valid events 138 within oscillation mode 140. Valid events satisfy each criterion. Criteria may include at least one amplitude threshold 142 and / or at least one duration threshold to distinguish valid events from other offsets of the fluorescence signal. For example, amplitude threshold 142 may be a single point on trigger level 144 (line) crossed by the fluorescence signal, wherein the event is characterized by the fluorescence signal crossing the trigger level in both directions. Trigger level 144 may be set as a percentage of the full amplitude span of the oscillation mode, such as 10%, 15%, or 20% of the span from baseline 116. Trigger level may be set automatically and / or by the user, and may be constant or may vary along a time axis (e.g., along a trigger line defining the trigger level). In some cases, a second amplitude threshold may be set to reject each event and / or each peak having a maximum value with a predefined amplitude offset from the baseline (or trigger level 144). The second amplitude threshold can be set as a percentage of the full amplitude span (e.g., 25%, 50%, 60%, 70%, 80%, etc.) or as an absolute value relative to the fluorescence unit, etc.

[0081] During the search along oscillation mode 140, the start of event 138 can be detected when the fluorescence signal crosses trigger level 144 in a direction away from baseline 116. This crossing can initiate peak detection. Event 138 can be considered to have ended when trigger level 144 is crossed in a direction toward baseline 116. However, the left and right troughs around event 138 can be marked as being below trigger level 144, as described below.

[0082] During the search, the slope of vector 128 at each point 130 can be evaluated to determine the direction of transition. (Exemplary points 130a to 130h are shown here.) A positive slope at point 130a is associated with the rising phase 146 of the fluorescence signal, while a negative slope at point 130b is associated with the decay phase 148 of the fluorescence signal. When the slope of consecutive vectors 128 changes from positive to negative (baseline 116 at the bottom), the vicinity of the maximum value of each peak is detected. For example, points 130c, 130d, and 130e of vector 128 are at least very close to the corresponding maximum values ​​of the primary peak 118 and secondary peaks 150a and 150b of the same event 138. When the slope of consecutive vectors 128 changes from negative to positive (baseline 116 at the bottom), the vicinity of the minimum value of each trough is detected. For example, points 130f and 130g of vector 128 are at least very close to the corresponding minimum values ​​of the left trough 152a and right trough 152b that define event 138. (The troughs between peaks 118, 150a, and 150b are not clearly marked here for the sake of simplicity.)

[0083] The size of the sliding window used to create vector 128 determines the magnitude of the low-amplitude transitions that will be searched and bypassed. For example, transitions 154 around point 130h are not identified as peaks or troughs in the search because the slopes in this region do not undergo a change of sign (i.e., they remain negative). The larger the window size (and therefore the length of vector 128), the larger the amplitude of the transitions will be bypassed. Thus, vector 128 collectively acts as a filter that does not disrupt the fluorescence signal.

[0084] Noise analysis can be optionally applied within a narrow area around each transition point to further improve the detection accuracy of each crest / trough location by rejecting very brief events or “low-amplitude” events at a few points that are close to a good crest location.

[0085] Figure 6 The image shown is a cutoff near the secondary peak 150a. Figure 5 A graph of only a portion of a single oscillation 114 (and event 138). The peaks can be filtered for effectiveness based on one or more criteria, which may be related to the peak maximum value 156 and at least one adjacent left or right trough minimum value 158a, 158b. For example, predetermined criteria may involve thresholds for local amplitude 160 exceeding each peak (measured between the maximum value 156 and the left minimum value 158a and / or the right minimum value 158b) and / or thresholds for duration 162 exceeding the peak (measured at a predetermined amplitude, e.g., at the left minimum value 158a). The thresholds for local amplitude 160 and / or duration 162 can be user-specified or automatically generated based on the noise level of the oscillation mode and / or the sampling interval of the oscillation mode (i.e., the reciprocal of the sampling rate).

[0086] Example 2 Baseline setup and noise estimation

[0087] This example describes an automatic method for setting the baseline of an oscillation pattern and an exemplary method for estimating the noise level of the oscillation pattern based on negative peaks identified when setting the baseline; see also Figure 7 and Figure 8 .

[0088] The algorithm disclosed in this paper automatically estimates the baseline for each dataset by measuring the fluctuation amplitude falling within 10% of the data amplitude span, removing outliers using interquartile range analysis, and finally fitting a line to the obtained amplitude using linear regression. The resulting regression line can be suggested or used as the baseline.

[0089] Figure 7 and Figure 8An exemplary method for establishing a baseline 116 for a dataset is shown. A temporary reference line 170 (e.g., a top baseline) can be set. For example, reference line 170 can be defined by a pair of points 172, 174 representing the maximum amplitude of the front portion 176 and the back portion 178 of the dataset, respectively. Each portion 176, 178 can represent any suitable proportion of the dataset, such as 10%, 20%, 30%, 40%, or 50%, etc. Points near the front and back ends of the oscillation mode can be excluded, as these ends typically include artifacts. The position of reference line 170 can be changed at either end by allowing the user to move control keys 180a, 180b via a graphical user interface. Additionally or alternatively, the user can adjust the time range of the dataset considered valid for analysis at either end by moving the corresponding control keys 182a, 182b along the time axis via a graphical user interface.

[0090] The amplitude span of the dataset can be calculated. The global maximum amplitude (Ymax) and global minimum amplitude (Ymin) can be found through a search. The amplitude span is defined as follows: Ymax to Ymin.

[0091] The threshold line 183 can be set towards the bottom of the amplitude span opposite to 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 of the threshold line 183 at 90% of the amplitude span from the reference line 170 can be calculated as: Ymin + 0.1 × (amplitude span). The user can adjust the threshold line 183 at either end by moving the corresponding control keys 184a, 184b along the amplitude axis via the graphical user interface.

[0092] A peak search relative to reference line 170 can be performed (see...) Figure 7 and Figure 8 In this example, the search finds the negative peak by identifying a local maximum offset that is far from (below) the reference line 170 and also below the threshold line 183. (In other words, this peak search is similar to the one above for...) Figure 5 The described peak search is inverse. (This can be used as described above for...) Figure 4 and Figure 5 A series of slopes are described to perform the search, where a relatively small size of the sliding window (e.g., the width of 3, 4, or 5 data points) is used to obtain the slope. The small sliding window provides high sensitivity for peak detection while eliminating minimal noise. Therefore, the noise level of the dataset can also be estimated from the amplitude values ​​of the peaks found in the search.

[0093] The primary peak 118 and the secondary peak 150 can be identified. In this and subsequent embodiments, circles mark the maximum value of the primary peak 118, and diamonds mark the maximum value of the secondary peak 150 (see [link to documentation]). Figure 8 When the oscillation pattern is illustrated and displayed to the user (e.g., via a graphical user interface), the two types of peaks 118 and 150 can also be marked differently. However, in order to establish a baseline, all detected peaks can be recorded and treated equally, i.e., there is no distinction between the primary and secondary peaks.

[0094] Using linear regression, a straight line can be fitted through the detected peak amplitude. This line can be considered an initial approximation of baseline 116. The trigger level 144 can initially be set to 10% of the data span above the baseline. These automatically assigned levels can then be adjusted by the user via a graphical interface or by explicitly setting values.

[0095] Before linear regression, data points at the maximum values ​​of peaks 118 and 150 can be filtered, or not. For example, outliers can be removed before selecting a baseline, such as using interquartile range analysis. Points below the first quartile or more than 1.5 interquartile ranges above the third quartile can be rejected.

[0096] The noise level can be estimated from peaks 118 and 150 (see...) Figure 8 For example, a noise estimate can be calculated as the average of the continuous differences between the amplitudes of peaks 118 and 150 (considered as a group). The noise estimate can be used directly as the noise level, or it can be used to calculate the noise level. This noise level, alone or in combination with the sampling interval, can automatically select an appropriate size for a sliding window (see Example 2) for a given dataset. An exemplary algorithm for this size selection is a heuristic: [0.3 / sampling interval (seconds)], plus the "noise factor," i.e., [noise level / 100]. The algorithm can select the window size from an allowed range of sizes. This range can include only an odd number of data points (e.g., 3, 5, 7, etc.), only an even number of data points (e.g., 4, 6, 8, etc.), or both odd and even numbers (e.g., 3, 4, 5, 6, etc.). The noise level can also be used to calculate an automatically generated value for an appropriate threshold of local amplitude 160 to reject smaller peaks as invalid (see also Examples 1 and 5). The data sampling rate can be used to automatically generate an appropriate threshold of duration 162 to reject very short peaks as invalid.

[0097] Example 3 Parameters of interest

[0098] This example describes exemplary parameters of interest that can be measured from oscillation modes using the algorithms described in this article; see also Figure 9 .

[0099] Three events 138 of the oscillation mode are shown. For each event 138, a start point 190 and an end point 192 are marked, where the fluorescence signal crosses the trigger level 144 in opposite directions.

[0100] Any parameters described herein can be reported for an individual peak / event, or averaged over a series of peaks / events for a given oscillation pattern. Each major peak 118 of event 138 has a major peak amplitude 194. The major peak amplitude can be measured as the amplitude difference between the maximum value of major peak 118 and the baseline 116. The linear decay slope 196 can be defined by a straight line extending between the maximum value of major peak 118 of event 138 and the endpoint 192.

[0101] The peak interval 198 (also known as the peak period / time period) is limited to the maximum values ​​of adjacent peaks 118. A series of events 138 defines a continuous peak interval 198, which can be converted into the peak rate of the series of events, expressed as peaks per unit time, such as peaks per minute (PpM).

[0102] The secondary peak period 200 is defined between the maximum values ​​of adjacent secondary peaks 150 within or between events. The secondary peak period 200 for oscillation modes can be converted into a secondary peak rate, which is expressed as secondary peaks per unit time, such as peaks / minute (PpM). Each secondary peak 150 has a secondary peak amplitude 202, which can be measured between the maximum value of the secondary peak and the baseline 116.

[0103] The rising slope 204 and the decay slope 206 can be calculated for each event 138. Each of these slopes can be defined as a percentage of the maximum value of the event, such as between 10% and 80% or between 30% and 70% of the maximum value.

[0104] The duration 208 of each event 138 can be calculated as a specified percentage of the event's maximum amplitude, such as 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the maximum amplitude. Alternatively or additionally, the duration from the peak 210, which begins at the maximum value of the event's main peak 118, can be measured for each event. The duration from the peak can be measured as a specified percentage of the event's maximum value, such as 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the maximum value.

[0105] Example 4 The Influence of Window Size on Peak Detection

[0106] This example illustrates the effect of changing the window size on the sensitivity of peak detection; see [link to relevant documentation]. Figure 10 and 11 .

[0107] The performance of the peak-finding algorithm based on the slope 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 (e.g., see Example 1). The same data were... Figure 10 and Figure 11 The diagram shows a complex oscillation pattern 220. This pattern consists of a series of oscillations 114, each exhibiting a longer, unstable decay phase relative to the oscillations in control cells. The decay phase is characterized by an early post-depolarization (EAD) type peak (i.e., a secondary peak).

[0108] Using an automatically determined sliding window size of eleven points ( Figure 10 ) or the window size of the three points selected by the user ( Figure 11 Perform a peak search on the data. The primary peak (118) of each oscillation is marked with a circle, and the secondary peak (150) with a diamond. Figure 10 In the case of a larger window size, low-amplitude transitions are ignored. Figure 11 In this approach, using a smaller window size, low-amplitude transitions are detected as peaks. Therefore, the sensitivity of peak detection can be controlled by carefully selecting the window size, optionally combined with noise suppression, to treat detected peaks that are too small / transient as invalid (e.g., see Examples 1 and 5).

[0109] Example 5 Analysis software dialog box

[0110] This example describes a model dialog box displayed by a graphical user interface to facilitate user input into the analysis software; see also Figure 12A , Figure 12B , Figure 13A and Figure 13B (See also) Figure 9 ).

[0111] Figure 12A , Figure 12B , Figure 13A and Figure 13B A screenshot of the dialog box is shown, displaying a list of readings (i.e., descriptors) for controllable 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 attributes that can be selected for events to be displayed in the output table. Exemplary aspects and features of these tabs are described below.

[0112] Option tabs :

[0113] Event polarity Two buttons, positive and negative, can be used to select the event polarity (see...). Figure 12APositive events involve an increase in fluorescence above the bottom baseline, while negative events involve a decrease in fluorescence below the top baseline.

[0114] Select vector length The search vector length (i.e., the size of the sliding window) can be considered the most important variable for good peak detection (see [reference]). Figure 12A See also Figure 4 and Figure 5 Search vectors are generated from a series of subsets of data points in the dataset, where subsets are selected by incrementally moving a sliding window along the time axis. A search vector is generated at each point. Vectors are computed using both forward and backward data points, centered on the window width and ranging from -((window width)-1) / 2 to +((window width)-1) / 2. The slope near opposite ends of the dataset is computed using an appropriate number of data points. Linear regression is performed on each subset of data points to generate a series of search vectors. In other words, the sliding window slides along the dataset to examine the slope at each data point, and the presence of transitions (maximum / peak and minimum / trough) is determined by recording changes in the slope direction (i.e., changes in the sign of the slope from positive to negative or from negative to positive). The length of the search vector relative to the sampling rate determines the sensitivity for detecting transitions. Longer vector lengths will automatically filter out noise (i.e., events / peaks with lower amplitude and / or shorter duration), while shorter vector lengths will detect transitions with lower amplitude and shorter duration.

[0115] Three buttons are available to select how to allocate vector lengths: "Automatic Length Allocation", "Same Length for All Wells", and "Well-by-Well" (see [link]). Figure 12A When "Auto Assign Length" is selected, the software automatically calculates the vector length using estimates of the sampling rate and noise amplitude. The automatically generated vector length is often a good starting point for data analysis. If the "Well-by-Well" button is selected, the user can choose a different vector length for each well (i.e., for each set of data points describing each different oscillation mode).

[0116] Dynamic threshold. This option sets a lower limit for valid peaks (see [link]). Figure 12A All detected peaks with amplitudes below this threshold will be considered invalid and ignored. In this embodiment, the limit is given as relative fluorescence units (RFU) above the baseline. Two boxes can be used to enter different threshold amplitude values ​​above the baseline for the left and right ends of the line defining the dynamic threshold. In other examples, the dynamic threshold can be set as a percentage of the amplitude span of the dataset. For example, if the data points of a given well span a range of 10,000 relative fluorescence units (RFU), an 80% setting would specify a cutoff limit of 8,000 RFU.

[0117] Trigger levelThis level is defined by trigger lines extending along the time axis and determines when an event is considered to begin and end (see...). Figure 12A An “event” is defined by a fluorescent signal that crosses 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; these measurements can extend to the baseline. The duration of the event can be measured as the time distance between the two trough locations, or as the time distance between the two intersections of the event on the trigger line, etc. The trigger level is set by default to 10% above the baseline (relative to the data range). This level can be set and changed individually for each dataset (well data).

[0118] 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.

[0119] 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 graphics window. Each of these levels can be defined by a corresponding line with left and right ends. An end can be moved individually by left-clicking and holding the control key at one end and moving it up or down. The entire horizontal line can be moved up or down by left-clicking and holding the line anywhere away from the control key and moving it up or down. The position of the horizontal line can also be set by changing the values ​​in the individual L (left) or R (right) edit boxes, or by using the spinner or by directly entering values. The "Lock" feature causes the line end to move consistently when the right or left edge value is adjusted. This feature only applies to the dialog box and does not affect mouse positioning.

[0120] Set default level Pressing this button will reset the baseline and trigger level to their default positions (see...). Figure 12A ).

[0121] Treat all wells (see Figure 12B If the project has been checked, all selected well data will be processed. If it has not been checked, only the currently selected well will be processed.

[0122] Data group (see) Figure 12B ):

[0123] well The analysis will only apply to the selected wells that appear in the "Details" graphics window. At any given time, only the selected wells will appear in the graph. The well data to be displayed can be specified by selecting the corresponding data sequence in the "Wells" combo box.

[0124] Event denial group (see Figure 12B ):

[0125] minimum amplitude This specifies the minimum amplitude measured from the baseline that will be accepted as valid. Offsets forming major / minor peaks with maximum values ​​below the minimum amplitude will be rejected. Except that the minimum amplitude is a single value rather than defined by values ​​along a line with independently adjustable left and right ends, the minimum amplitude is related to a “dynamic threshold” (see [link to relevant documentation]). Figure 12A They are basically the same.

[0126] Minimum Duration This item specifies the minimum duration for an event to be accepted as valid (see [link]). Figure 12B The event duration can be measured between the left and right trough values, or between the start and end of the event, as defined by crossing the trigger level. Offsets with a duration shorter than the minimum duration value will be rejected.

[0127] Applicable to all wells If this option is checked, the amplitude and duration values ​​that appear in the edit box will be applied to the data from all wells. If not checked, the values ​​specified for each individual well will be used.

[0128] Noise rejection group (see Figure 12B ):

[0129] minimum amplitude This specifies the minimum amplitude threshold for the local amplitude of a valid peak. The local amplitude of a peak (valid or invalid) is measured as the difference between the peak's amplitude value and the amplitude value of the nearest left (and / or right) trough. Peaks with local amplitudes below the minimum amplitude threshold will be rejected as invalid.

[0130] Minimum duration This specifies the minimum duration threshold for a valid peak. The duration of a peak (valid or invalid) is measured as the absolute value of the time difference between (a) the time value of the lower of two adjacent troughs and (b) the time value of the equivalent amplitude on the opposite side of the peak. Peaks with durations shorter than the minimum duration threshold will be rejected as invalid.

[0131] Applicable to all wells If this option is checked, the amplitude and duration threshold values ​​that appear in the edit box will be applied to data from all wells (i.e., all datasets for individual oscillation patterns). If not checked, the values ​​specified for each individual well will be used.

[0132] automaticThe software measures an approximate noise level for each dataset. First, a peak-finding algorithm with a short search vector length (e.g., 3) locates all peaks (“noise peaks”) within a percentage range (e.g., 0 to 10%) of the dataset’s amplitude span from the baseline. Noise peaks can have a maximum value relative to a reference line opposite the baseline. (For example, for a bottom baseline, a noise peak could be a negative peak with a maximum value relative to a reference line at the top of the oscillation pattern (see Example 2).) The noise estimate is then calculated as the average of the absolute differences between consecutive maximum values ​​of noise peaks falling within the interquartile range. The noise level is considered to be 30% of the noise estimate. Noise estimates and noise levels are only reasonable if the data is relatively clean and most noise is located near the baseline.

[0133] Measurement Labels (see Figure 13A and Figure 13B ):

[0134] This tab is used to specify which measurements will be reported on the statistics page. This page displays the results for each well (set), the average of the events detected in the well, and, where applicable, the individual value for each event.

[0135] Select all This button selects all events to display (see...). Figure 13A ).

[0136] Select None The button clears the event display selection.

[0137] Mean peak amplitude The average peak amplitude is the average amplitude of the maximum values ​​of the valid peaks for a given well-detected event, expressed in relative fluorescence units (RFU). The amplitude of each peak used to calculate the average is measured between the peak maximum and the baseline.

[0138] Number of peaks This descriptor represents the total number of valid primary peaks or the total number of valid primary and secondary peaks for a given well combination.

[0139] Average peak rate This descriptor represents the number of effective peaks per minute (PpM). For cardiac data, this is equivalent to "beats per minute" (BPM).

[0140] Number of EAD-type peaks for each event This descriptor represents the number of valid secondary peaks (EAD-type peaks) in an event or dataset. This item reports the mean of valid secondary peaks for each event, the standard deviation of the mean, and the total number of valid secondary peaks in the dataset.

[0141] Average EAD-type peak velocity (PpM)This descriptor represents the average rate of valid secondary peaks (EAD-type peaks) detected in the dataset (for a given well), expressed in peaks per minute (PpM).

[0142] Number of EAD-type peaks SD The standard deviation of the number of EAD-type peaks for each event.

[0143] 10% to 90% of calcium transient duration CTD These descriptors are event durations (e.g., calcium transient duration) measured in seconds, ranging from the maximum value of the event's peak to a specified level from the baseline. For example, CTD10 is the duration of an event measured at an amplitude level of 10% of the amplitude distance from the maximum value to the baseline. Measurements are taken for the top 10% to the bottom 90% of CTDs. Sometimes, troughs are located at a certain distance from the baseline, avoiding the measurement of CTDs furthest from the peak.

[0144] 10% to 90% of the duration of the calcium transient from the peak to the peak CTDP These descriptors represent the duration of an event from the time position of the peak's maximum value to a specified level relative to the baseline. (CTDP is the duration of the calcium transient from the peak). For example, CTDP10 is the duration of an event at the peak at a level 10% of the amplitude distance from the peak's maximum value to the baseline. Measurements are taken for CTDs ranging from 10% to 90%. Sometimes, troughs are located at a distance from the baseline, avoiding the measurement of CTDPs furthest from the peak.

[0145] area The area between the oscillation trajectory of a given event and the baseline, measured from the beginning to the end of the event, is defined by the left and right troughs of the event. The unit of this area is relative fluorescence unit per second.

[0146] Crest Spacing This measurement determines the regularity (in seconds) of the spacing of the valid main peaks by comparing the standard deviation and the mean of the spacing. If the standard deviation is less than a threshold percentage of the mean (e.g., 50%), the measurement reports a uniform peak spacing (OK), or if the standard deviation is greater than the threshold percentage, the measurement reports an irregular spacing (IRREG).

[0147] slope Specify the percentage of the slope measurement peak and the percentage derived from the slope measurement peak in the relevant edit boxes. Typically, the slope is measured as between 10% and 80% or 30% and 70% of the peak value.

[0148] Example 6 Compound testing using cardiomyocytes

[0149] This example describes results obtained by testing the effects of cardiotoxic compounds on cardiomyocytes on various parameters measured by the software described in Example 5; see also Figures 14 to 22 .

[0150] Developing biologically relevant and predictive cell-based assays for compound screening and toxicity assessment is a major challenge in drug discovery. This study focuses on establishing a high-throughput compatible cardiotoxicity assay using human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. To evaluate the utility of human iPSC-derived cardiomyocytes as an in vitro arrhythmia model, the concentration dependence and response of 28 drugs associated with low, intermediate, and high tip torsades de pointes (TdP) risk categories were evaluated. (The compounds were proposed under the Comprehensive In Vitro Arrhythmogenic Assay (CiPA) Initiative.) Intracellular Ca2+ was measured by rapid kinetic fluorescence with a calcium-sensitive dye. 2+ The study monitored the effects of various compounds on the contractile rate and patterns of spontaneous cardiomyocyte activity by observing changes in oscillations. Advanced image analysis methods were implemented to provide Ca... 2+ Multi-parameter characterization of oscillation patterns. This assay allows for the characterization of parameters such as beat frequency, amplitude, peak width, and rise and decay times. The results demonstrate the utility of hiPSC cardiomyocytes for in vitro detection of drug-induced arrhythmias.

[0151] iPSC-derived cardiomyocytes produce spontaneous synchronous calcium oscillations. (Using...) The system's high-speed fluorescence imaging was used to measure Ca in cardiomyocytes. 2+ The oscillation mode and frequency, such as those using EarlyTox. TM Cardiotoxicity kit via intracellular Ca 2+ The changes in levels were monitored. In this trial, a panel of 28 known cardiotoxic compounds were tested, along with several baseline compounds and a negative control.

[0152] iPSC-derived cardiomyocytes: from Cell Dynamics International (CDI) Cryopreserved human iPSC-derived cells of Cardiomyocytes2 were used for experiments. Cells were thawed and plated at 20,000 / well (96-well size) or 10,000 / well into 384-well plates and cultured in maintenance medium for 7 days. Strong synchronous contraction in the 3D cultures was visually confirmed prior to experiments. Additionally, plates with Cardiomyocytes1 were obtained from Ncardia for assays using 384-well plates. Plates were transported before plating and allowed to recover 2 days after arrival.

[0153] Expose cardiomyocytes to the compound for 15, 30, 60 or 90 minutes or 24 hours.

[0154] According to standard protocols, use Calcium dyes (Molecular Devices) assess intracellular calcium 2+ Oscillate; load cells with dye for 2 hours before measurement.

[0155] Measuring calcium oscillations in iPSC-derived cardiomyocytes is a promising method for toxicity assessment. This work focuses on evaluating a group of 28 CiPA compounds classified as high, intermediate, or low risk based on clinical data.

[0156] Equipped with a new high-speed camera The system allows for better resolution of calcium oscillation patterns in cardiomyocytes. (Screen) Peak Pro TM The software allows for complex event analysis and detailed pattern characterization using over 20 available pattern descriptors. This analysis can be used for drug development testing and screening of chemicals with potential cardiotoxic hazards.

[0157] Figures 14 to 21 Representative trajectories of calcium oscillations in control and compound-treated cardiomyocytes were recorded for two minutes, starting thirty minutes after treatment with the indicated compounds and concentrations. The perturbation of the calcium oscillation pattern by each compound was described.

[0158] The control trajectory (DMSO treatment of cardiomyocytes) is shown in Figure 14 Only the main peak 118 (marked with a circle) was detected. The main peaks have uniform amplitudes and are spaced apart from each other.

[0159] Figure 15 The image shows the trajectory from cardiomyocytes treated with 1 μM E-4031. A primary peak at 118 and a secondary peak at 150 are present. The primary peak is prolonged.

[0160] Figure 16 The image shows the trajectory from cardiomyocytes treated with 1 μM ibutilide. A primary peak at 118 and a secondary peak at 150 are present. The primary peak is prolonged.

[0161] Figure 17 Trajectories from cardiomyocytes treated with 10 μM dofeliide are shown. Only the main peak 118 was detected, but it had irregular spacing with each other.

[0162] Figure 18 The image shows the trajectory from cardiomyocytes treated with 10 μM quinidine. A primary peak (118) and a secondary peak (150) were detected. The primary peak is elongated and has arbitrary, irregular spacing between it.

[0163] Figure 19The image shows the trajectory from cardiomyocytes treated with 10 μM sotalol. A primary peak (118) and a secondary peak (150) were detected. The primary peak is elongated and has arbitrary, irregular spacing between it.

[0164] Figure 20 The trajectories from cardiomyocytes treated with 1 μM astemizole (ASTEMIZOLE) are shown. No valid peaks were detected, therefore the oscillation condition was reported as "stopped".

[0165] Figure 21 Trajectories from cardiomyocytes treated with 1 μM nifedipine (NIFEDIPINE) are shown. Only peak 118 was detected, but its frequency was significantly higher than that of the control.

[0166] Figure 22 This figure presents a compilation and comparison of different readings for compounds within a range of standardized concentrations, where compounds are categorized into three groups based on known cardiotoxicity: high toxicity, moderate toxicity, and low toxicity. Readings include the presence of secondary peaks, peak prolongation, irregularity of the main peak spacing / amplitude, increased main peak frequency relative to the control, stopping conditions (no valid peaks), decreased main peak frequency relative to the control, and changes in the main peak amplitude within the oscillation mode. The figure demonstrates that secondary peaks and peak prolongation at concentrations equivalent to Cmax (maximum clinical concentration in blood) are clearly strong indicators of (or associated with) cardiotoxicity, while changes in other readings do not necessarily indicate cardiotoxic effects.

[0167] Example 7 Testing with compounds of neurons

[0168] This example describes results obtained by testing the effects of neurotoxic compounds on various parameters measured by analytical software using in vitro neuronal assays; see [link to relevant documentation]. Figures 23 to 30 .

[0169] To accelerate the development of more effective and safer drugs, there is an increasing need for more sophisticated, biologically relevant, and predictive cell-based assays for drug discovery and toxicology screening. Human iPSC-derived neural 3D co-cultures have been developed... The 3D platform, serving as a high-throughput screening platform, more closely resembles the structure of natural human cerebral cortex tissue. The neurosphere 3D co-culture is a physiologically related co-culture of functionally active cortical glutamatergic and gamma-aminobutyric acid (GABAergic) neurons derived from iPSCs, which co-differentiate and mature with astrocytes from the same donor. The 3D neurosphere contains synaptic-rich neural networks, generating highly functional neuronal circuits and exhibiting spontaneously synchronized, easily detectable calcium oscillations.

[0170] This paper discloses a novel method for analyzing complex calcium oscillations. This method allows for the detection and multi-parameter characterization of oscillation peaks. Multi-parameter characterization can include the oscillation rate (i.e., the frequency of the main peak), the width and amplitude of the main peak, the descriptor of the secondary peaks, waveform irregularities, and several other important readings.

[0171] exist Neurons within the 3D sphere generate spontaneous, synchronized calcium oscillations. (Using...) Rapid dynamic fluorescence imaging of the system to measure Ca in the neurosphere 2+ The oscillation mode and frequency, such as using The Calcium 6 assay kit detects intracellular calcium... 2+ The changes in levels were monitored. A panel of known neuromodulators were tested, including agonists and antagonists of NMDA, GABA, and AMPA receptors; phycocyanin; and analgesics and antiepileptic drugs. Through the use of... The system employs high-speed imaging, simultaneously recording fluorescence from each well across the entire plate containing 384 wells at a frequency of 2 Hz (0.5-second sampling intervals).

[0172] In Screen Peak Pro TM The advanced analytical methods implemented in the 2 software modules provide Ca 2+ Multiparameter characterization of flow oscillation patterns. This phenotypic measurement produces readings such as oscillation frequency, amplitude, peak width, peak rise and fall times, and peak amplitude / spacing irregularity. The effects of neuronal activity modulators are assessed by measuring changes in several measurements.

[0173] A group of over twenty compounds, including many known modulators of neuronal activity, were measured at different time points and concentrations, and the EC50 values ​​of the compounds were calculated. Time points included 0, 15, 30, 60, 90, and 120 minutes, as well as 24 hours. Changes in inhibition or activation, or other measures, as peak frequencies, were observed, corresponding to the expected effects of the respective neuromodulators.

[0174] Figures 23 to 30 The diagram shows representative trajectories of calcium oscillations in the control and compound-treated neurospheres, recorded for ten minutes starting thirty minutes after treatment with the indicated compounds and concentrations.

[0175] The comparison trajectory (DMSO treatment of the spherical body) is shown in Figure 23 In the middle range, only the main peak 118 (marked with a circle) was detected. The main peaks have fairly uniform amplitudes and spacing between each other.

[0176] Figure 24The trajectory of the spherical particles treated with MK-801 at a concentration of 3 μM is shown. The main peak 118 has a variable amplitude (with...). Figure 23 (Compared to). Only one secondary peak at 150 was detected.

[0177] Figure 25 The trajectory from spheroids treated with 10 μM GABA is shown. Only the main peak 118 was detected, but its frequency was significantly lower than that of the control.

[0178] Figure 26 The trajectory of the spheres treated with 10 μM baclofen is shown. Only the main peak 118 was detected, but its frequency was significantly lower than that of the control and it had irregular spacing and amplitude.

[0179] Figure 27 The trajectory from spheroids treated with 30 μM 4-aminopyridine is shown. A main peak (118) and a secondary peak (150) were detected. The frequency of the main peak (118) increased relative to the control.

[0180] Figure 28 The trajectory of the spheroids treated with 0.3 μM valinomycin is shown. A main peak 118 and a secondary peak 150 were detected. The frequency of the main peak 118 was much lower than that of the control, and the amplitude and spacing varied more significantly.

[0181] Figure 29 The trajectory from spheroids treated with 1 μM kainic acid is shown. A main peak 118 and several secondary peaks 150 were detected. The frequency of the main peak 118 was increased relative to the control.

[0182] Figure 30 The trajectory of the spheres treated with 30 μM tamoxifen is shown. Only the main peak 118 was detected, but its frequency was significantly lower than that of the control and it had variable amplitude.

[0183] This assay can be used to test the effects of compounds and screen neurotoxic chemicals. The presence of secondary peaks and low-amplitude main peaks indicates that the compound has neurotoxic activity. The effect of peak rate and amplitude alone may not be sufficient to predict neurotoxicity.

[0184] Example 8 Selected Example

[0185] This example describes the selected examples in this disclosure as a series of index paragraphs.

[0186] Paragraph 1. An analytical method comprising: (i) detecting fluorescence representing an oscillating ion current associated with one or more biological cells to generate a series of data points describing an oscillation pattern; (ii) calculating a series of slopes of the oscillation pattern; and (iii) using the series of slopes to identify a peak of the oscillation pattern.

[0187] Paragraph 2. The method as described in paragraph 1, wherein the calculation uses a sliding window to define a subset of the series of data points, and the series of slopes is calculated based on the subset.

[0188] Paragraph 3. The method as described in paragraph 2 further includes selecting the size of the sliding window from a plurality of allowed sizes, wherein the size of the sliding window corresponds to the number of data points from the set of data points contained in the sliding window.

[0189] Paragraph 4. The method as described in paragraph 3, wherein the size of the sliding window is automatically allocated by the processor based on the noise level in the oscillation mode and / or the sampling interval of the data point set, and wherein, optionally, the processor also calculates the series of slopes and identifies the peaks.

[0190] Paragraph 5. The method as described in paragraph 3 or 4, wherein the selection of the size of the sliding window is performed by the user and is transmitted to a processor that also calculates the series of slopes and identifies the peaks.

[0191] Paragraph 6. The method described in any of paragraphs 1 through 5, wherein the set of data points is not filtered or denoised before calculating a series of slopes.

[0192] Paragraph 7. The method as described in any of paragraphs 1 to 6, wherein the peaks comprise a series of primary peaks, wherein the oscillation pattern comprises a series of events, each event comprising only one of the primary peaks, the method further comprising determining at least one aspect of secondary peaks among the identified peaks, each secondary peak following one of the primary peaks within an event.

[0193] Paragraph 8. The method as described in paragraph 7, wherein at least one aspect of the secondary peak relates to the number, frequency, or period of the secondary peaks within the oscillation mode.

[0194] Paragraph 9. The method as described in paragraph 7 or 8, wherein, for each event, the oscillation pattern intersects twice with a predetermined trigger level, and wherein the trigger level is set relative to the baseline of the oscillation pattern.

[0195] Paragraph 10. The method as described in paragraph 9, wherein the trigger level is set as a percentage of the amplitude range spanned by the series of data points.

[0196] Paragraph 11. The method as described in paragraph 9, wherein the trigger level is a trigger level selected by the user.

[0197] Paragraph 12. The method as described in any of paragraphs 9 to 11, wherein the baseline and the trigger level can be adjusted by the user via a graphical user interface.

[0198] Paragraph 13. The method as described in any of paragraphs 1 to 12, wherein identifying peaks involves searching for transitions in slope from positive to negative or from negative to positive within the series of slopes.

[0199] Paragraph 14. The method of paragraph 13, wherein identifying peaks includes filtering peaks associated with the transition to obtain a set of peaks considered valid.

[0200] Paragraph 15. The method as described in paragraph 14, wherein the filtered peak comprises a filtered peak based on one or more predefined amplitude and / or duration criteria.

[0201] Paragraph 16. The method, as in paragraphs 14 or 15, also includes determining the values ​​of the crest correlation parameters for a set of crests considered valid.

[0202] Paragraph 17. The method as described in any of paragraphs 14 to 16, wherein identifying a peak includes determining at least one amplitude and / or duration of a peak associated with each of a plurality of transitions found by a search; comparing the at least one amplitude and / or the duration with at least one threshold; and rejecting the peak associated with the transition as invalid if the comparison does not meet one or more predefined criteria for the at least one amplitude and / or the duration.

[0203] Paragraph 18. The method as described in paragraph 17, wherein comparing the at least one amplitude and / or the duration comprises comparing the amplitude of the peak measured relative to a baseline of the oscillation mode.

[0204] Paragraph 19. The method as described in paragraphs 17 or 18, wherein comparing the at least one amplitude and / or duration includes comparing the local amplitude of the peak relative to a local trough adjacent to the peak.

[0205] Paragraph 20. The method as described in any of paragraphs 17 to 19, wherein comparing at least one amplitude and / or duration includes comparing the duration of a peak relative to at least one local trough.

[0206] Paragraph 21. The method as described in any of paragraphs 17 to 20, wherein each of the at least one threshold can be adjusted by a user via a graphical user interface, and / or at least one threshold is automatically set by a processor.

[0207] Paragraph 22. A method of any of paragraphs 1 to 21, wherein the oscillation pattern comprises a series of events, and for each of the events in the series, the oscillation pattern crosses a predetermined trigger level twice, the method further comprising filtering the series of events to reject each event having a duration less than a predetermined duration, wherein, if any, each peak within a rejected event is considered invalid.

[0208] Paragraph 23. The method as described in any of paragraphs 1 to 22 further includes labeling the one or more biological cells with a calcium indicator, wherein the fluorescence is emitted by the calcium indicator.

[0209] Paragraph 24. The method as described in any of paragraphs 1 through 23, wherein one or more biological cells include one or more cardiomyocytes or neurons.

[0210] Paragraph 25. The method as described in paragraph 24, wherein the one or more biological cells are primarily cardiomyocytes or neurons.

[0211] Paragraph 26. The method as described in paragraphs 24 or 25, wherein the one or more biological cells include one or more cardiomyocytes or neurons differentiated in vitro from at least one stem cell.

[0212] Paragraph 27. The method as described in any of paragraphs 1 to 26, wherein the one or more biological cells are contained in a vessel selected from petri dishes, flasks, and multiwell microplates.

[0213] Paragraph 28. As in any of paragraphs 1 to 27, where the series of data points represents a sampling rate greater than 1 Hz.

[0214] Paragraph 29. The method as described in any of paragraphs 1 to 28, wherein the oscillation mode comprises a series of events, each event comprising a single primary peak, and wherein the oscillation mode comprises one or more secondary peaks, each secondary peak being included in one of the events in the series of events, the method further comprising determining at least one value of one or more parameters associated with the one or more secondary peaks.

[0215] Paragraph 30. The method as described in paragraph 29, wherein the at least one value corresponds to the number, frequency, or period of the subpeaks.

[0216] Paragraph 31. The method as described in any of paragraphs 1 to 30, wherein fluorescence is detected, a series of slopes are calculated, and peaks are identified for each of a plurality of individual collections of one or more biological cells, and wherein each individual collection is exposed to a different compound or different concentrations of the same compound.

[0217] Paragraph 32. The method as described in paragraph 31 further includes, if present, determining the effect of each different compound or concentration on one or more parameters that are all associated with at least one subset of the peak.

[0218] Paragraph 33. The method as described in paragraph 32, wherein the one or more parameters correspond to the number, frequency, or period of subpeaks within the oscillation mode.

[0219] Paragraph 34. The method as described in paragraphs 32 or 33 further includes, if present, predicting the degree or concentration of cardiotoxicity or neurotoxicity of each compound based on its effect on one or more of the parameters.

[0220] Paragraph 35. The method, as described in any of paragraphs 1 through 34, also includes automatically establishing a baseline for the said oscillation mode.

[0221] Paragraph 36. The method as described in paragraph 35, wherein establishing a baseline includes creating a reference line at the top of the oscillation pattern and a threshold line toward the bottom of the oscillation pattern, finding a maximum value relative to the reference line, the maximum value being below the threshold line, and performing linear regression using at least a subset of the maximum values.

[0222] Paragraph 37. The method described in paragraph 36 further includes using at least a subset of the maximum values ​​to calculate the noise level of the oscillation mode.

[0223] Paragraph 38. The method as described in any of paragraphs 37, wherein a series of slopes is calculated using a sliding window to define a subset of the series of data points, from which the series of slopes is calculated, and wherein the size of the sliding window is selected based on the noise level.

[0224] Paragraph 39. An analytical method comprising: (i) detecting fluorescence representing an oscillating ion current associated with one or more biological cells to generate a series of data points describing an oscillation pattern; (ii) identifying, if applicable, a primary peak and a secondary peak in the oscillation pattern; and (iii) determining an aspect of the secondary peak.

[0225] Paragraph 40. As in paragraph 39, one aspect of determining secondary peaks includes determining the number, frequency, or period of secondary peaks.

[0226] Paragraph 41. The method, as described in paragraphs 39 or 40, further includes exposing one or more biological cells to the compound and determining the effect of the compound on said one aspect of the secondary peak.

[0227] Paragraph 42. The method as described in any of paragraphs 39 to 41, wherein the oscillation mode comprises a series of events, each event comprising only one primary peak, and wherein each secondary peak within an event occurs after the primary peak.

[0228] Paragraph 43. As in any of paragraphs 39 to 42, the secondary peak is generated by myocardial cells or neurons.

[0229] Paragraph 44. The method as described in any of paragraphs 39 to 43 further includes labeling the one or more biological cells with a calcium indicator, wherein fluorescence is detected from the calcium indicator.

[0230] Paragraph 45. The method, as in any of paragraphs 39 to 44, also includes determining the regularity / irregularity of the spacing between the main peaks.

[0231] Paragraph 46. As in paragraph 45, determining spacing regularity / irregularity involves comparing the standard deviation of the spacing of the main peaks with the average spacing of the main peaks.

[0232] Paragraph 47. The method, as in any of paragraphs 39 through 46, also includes determining the amplitude regularity / irregularity of the main peak.

[0233] Paragraph 48. As in paragraph 47, determining amplitude regularity / irregularity involves comparing the standard deviation of the amplitude of the main peak with the mean amplitude of the main peak.

[0234] Paragraph 49. The method, as in any of paragraphs 39 through 48, further includes comparing the amplitude of each main peak with a predetermined threshold to enumerate smaller peaks among the main peaks, if any.

[0235] Paragraph 50. The method as described in any of paragraphs 39 to 49, wherein each of a plurality of individual collections of one or more biological cells is detected, identified and determined, and wherein each individual collection is exposed to a different compound or to different concentrations of the same compound.

[0236] Paragraph 51. The method of paragraph 50 further includes determining the effect of each different compound or concentration on one aspect of the secondary peak, if any.

[0237] Paragraph 52. The method, as described in paragraphs 50 or 51, also includes determining the effect of each different compound or concentration on the regularity / irregularity of the main peak spacing, if any.

[0238] Paragraph 53. The method described in any of paragraphs 50 to 52 also includes determining the effect of each different compound or concentration on the amplitude regularity / irregularity of the main peak, if any.

[0239] Paragraph 54. The method, as described in any of paragraphs 50 through 53, also includes predicting the degree or concentration of cardiotoxicity or neurotoxicity for each compound based on an aspect of the secondary peak and the regularity / irregularity of the spacing between the primary peaks.

[0240] Paragraph 55. The method, as described in any of paragraphs 50 through 54, also includes predicting the degree or concentration of cardiotoxicity or neurotoxicity for each compound based on one aspect of the secondary peak and the regularity / irregularity of the amplitude of the primary peak.

[0241] Paragraph 56. The method as described in any of paragraphs 50 to 55 further includes predicting the degree or concentration of cardiotoxicity or neurotoxicity of each compound based on an aspect of the secondary peaks and the number, frequency, or period of the primary peak, said primary peak being a smaller peak with an amplitude below a predetermined threshold.

[0242] Paragraph 57. A system comprising: (i) an optical sensor configured to detect fluorescence representing an oscillating ion flow associated with one or more biological cells to generate a series of data points describing an oscillation pattern; and (ii) a processor configured to (1) calculate a series of slopes of the oscillation pattern, optionally, calculating the series of slopes of the oscillation pattern using a sliding window to define a subset of the series of data points, calculating the series of slopes from the subset, and (2) using the series of slopes to identify peaks of the oscillation pattern.

[0243] Paragraph 58. The system as described in paragraph 57 is configured to perform any combination of the steps in paragraphs 1 through 56.

[0244] Paragraph 59. A system comprising: (i) an optical sensor configured to detect fluorescence representing an oscillating ion flow associated with one or more biological cells to generate a series of data points describing an oscillation pattern; and (ii) a processor configured to (1) identify a primary peak and a secondary peak in the oscillation pattern, if any, and (2) determine an aspect of the secondary peak.

[0245] Paragraph 60. The system, as described in paragraph 59, is configured to perform any combination of the steps in paragraphs 1 through 56.

[0246] As used in this disclosure, the term "exemplary" means "illustrative" or "used as an example." Similarly, the term "illustrative" means "illustrated by way of example." The terms do not imply desirability or superiority.

[0247] The above disclosure may include several different inventions with independent utility. Although each of these inventions is disclosed in its preferred form(s), the specific embodiments disclosed and illustrated herein should not be considered limiting, as many variations are possible. The subject matter of this invention includes all novel and non-obvious combinations and sub-combinations of the various elements, features, functions, and / or characteristics disclosed herein.

Claims

1. An analytical method (50), the method comprising: Detection (54) represents fluorescence (91) of an oscillating ion flow associated with one or more biological cells (72) to generate a series of data points (120) describing an oscillation pattern (110), wherein the oscillation pattern (110) includes a series of events (138), wherein the oscillation pattern (110) intersects twice with a predetermined trigger level (144) for each event (138), and wherein the trigger level (144) is set relative to a baseline (116) of the oscillation pattern (110); Calculate a series of slopes for the oscillation mode (110) described in (60); The series of slopes is used to identify the peaks (118, 150a, 150b) of the oscillation mode (110) (62), wherein the primary peak (118) and secondary peaks (150a, 150b) in the oscillation mode (110) (62) are identified, wherein each event includes a single primary peak (118), and wherein each secondary peak (150a, 150b) is included in one event of the series of events (138), and wherein each secondary peak appears after the primary peak within the event; and Determine one aspect of the secondary peaks (150a, 150b) of the identified peak in the oscillation mode (110); In this process, each of a plurality of individual sets of one or more biological cells (72) is detected, identified and determined, and each individual set is exposed to different compounds or different concentrations of the same compound. The method further includes: for each different compound or concentration, determining the effect in the presence of an effect of that compound or concentration on one aspect of the secondary peaks (150a, 150b); and The degree or concentration of cardiotoxicity or neurotoxicity of each compound is predicted based on one aspect of the secondary peak and the regularity / irregularity of the spacing between the primary peaks, the regularity / irregularity of the amplitude, the number, frequency, or period of the primary peaks, wherein the primary peaks are wave peaks with amplitudes below a predetermined threshold.

2. The method according to claim 1, wherein, The calculation (60) uses a sliding window (122) to define a subset (126) of the series of data points (120) and calculates the series of slopes based on the subset.

3. The method of claim 2, further comprising selecting the size of the sliding window (122) from a plurality of allowed sizes, wherein, The size of the sliding window (122) corresponds to the number of data points contained in the sliding window (122) from the series of data points (120).

4. The method according to claim 3, wherein, The size of the sliding window (122) is automatically allocated by the processor (96) based on the noise level in the oscillation mode (110) and / or the sampling interval of the series of data points (120), wherein the processor (96) also calculates the series of slopes and identifies the peaks (118, 150a, 150b).

5. The method according to claim 3, wherein, The size of the sliding window (122) is selected by the user and transmitted to the processor (96), which also calculates the series of slopes and identifies the peaks (118, 150a, 150b).

6. The method according to claim 1, wherein, No filtering or noise reduction is performed on the series of data points (120) before calculating the series of slopes (60).

7. The method according to claim 1, wherein, At least one aspect of the secondary peaks (150a, 150b) is related to the number, frequency, or period of the secondary peaks (150a, 150b) within the oscillation mode (110).

8. The method according to claim 1, wherein, Identifying (62) peaks (118, 150a, 150b) involves searching for slope transitions from positive to negative or from negative to positive within the range of said slopes.

9. The method according to claim 8, wherein, Identifying (62) peaks involves filtering the peaks associated with the transition to obtain a set of peaks that are considered valid.

10. The method of claim 9, further comprising determining values ​​for peak correlation parameters for the deemed valid set of peaks.

11. 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.

12. The method according to claim 1, wherein, The one or more biological cells (72) include one or more cardiomyocytes or neurons.

13. The method according to claim 1, wherein, The oscillation mode (110) includes one or more sub-peaks (150a, 150b), and the method further includes determining (64) at least one value of one or more parameters associated with the one or more sub-peaks (150a, 150b).

14. An analytical method, the method comprising: Detection (54) represents fluorescence (91) of an oscillating ion flow associated with one or more biological cells (72) to generate a series of data points (120) describing an oscillation pattern (110), wherein the oscillation pattern (110) includes a series of events (138), wherein the oscillation pattern (110) intersects twice with a predetermined trigger level (144) for each event (138), and wherein the trigger level (144) is set relative to a baseline (116) of the oscillation pattern (110); Identify the primary peak (118) and secondary peaks (150a, 150b) in the oscillation mode (110) described in (62), wherein each event comprises a single primary peak (118), and wherein each secondary peak (150a, 150b) is included in one of the events in the series of events (138), and wherein each secondary peak appears after the primary peak within the event; and Determine one aspect of the secondary peaks (150a, 150b) in the oscillation mode (110); In this process, each of a plurality of individual sets of one or more biological cells (72) is detected, identified and determined, and each individual set is exposed to different compounds or different concentrations of the same compound. The method further includes determining the effect, for each different compound or concentration, in the presence of an effect of that compound or concentration on one aspect of the secondary peaks (150a, 150b); and The degree or concentration of cardiotoxicity or neurotoxicity of each compound is predicted based on one aspect of the secondary peak and the regularity / irregularity of the spacing between the primary peaks, the regularity / irregularity of the amplitude, the number, frequency, or period of the primary peaks, wherein the primary peaks are wave peaks with amplitudes below a predetermined threshold.

15. The method according to claim 14, wherein, One aspect of determining the secondary peaks (150a, 150b) includes determining the number, frequency, or period of the secondary peaks (150a, 150b).

16. The method of claim 14, further comprising comparing the amplitude (194) of each main peak (118) with a predetermined threshold to enumerate the peaks in the presence of peaks having amplitudes lower than the predetermined threshold.

17. An analysis system (70), comprising: An optical sensor (88) is configured to detect fluorescence (91) representing an oscillating ion flow associated with one or more biological cells (72) to generate a series of data points (120) describing an oscillation pattern (110); as well as The processor (96) is configured to (1) use a sliding window (122) to define a subset (126) of the series of data points (120) to calculate a series of slopes of the oscillation mode (110) calculated from the subset, and (2) use the series of slopes to identify the peaks (118, 150a, 150b) of the oscillation mode (110) and determine an aspect of the secondary peaks (150a, 150b) in the oscillation mode (110); The oscillation pattern (110) includes a series of events (138), wherein the oscillation pattern (110) intersects twice with a predetermined trigger level (144) for each event (138), and wherein the trigger level (144) is set relative to the baseline (116) of the oscillation pattern (110). Each event includes a single primary peak (118), and each secondary peak (150a, 150b) is included in one of the events in the series of events (138), wherein each secondary peak appears after the primary peak within the event; In this process, each individual set of one or more biological cells (72) is detected, identified, and determined, and each individual set is exposed to different compounds or different concentrations of the same compound; and The processor (96) is further configured to determine the influence of each different compound or concentration on the secondary peaks (150a, 150b) if such compound or concentration has an effect on the secondary peaks (150a, 150b); and to predict the degree or concentration of cardiotoxicity or neurotoxicity of each compound based on the regularity / irregularity of the spacing between the secondary peaks and the primary peaks, the regularity / irregularity of the amplitude, the number, frequency or period of the primary peaks, wherein the primary peaks are peaks with amplitudes below a predetermined threshold.

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