Methods for diagnosing and treating eye diseases

Abstract

Provided herein is a method of measuring spontaneous slow voltage fluctuation (SSVF). The method includes recording a glial activity profiling (GAP) baseline signal from a subject; converting the GAP baseline signal from the time domain to the frequency domain; calculating a magnitude spectrum of the GAP baseline signal; and defining a peak value of magnitude in a low-frequency range as GAP Value.

Description

[0001] METHODS FOR DIAGNOSING AND TREATING EYE DISEASES

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] The present application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63 / 559,712, filed February 29, 2024, which application is incorporated herein by reference in its entirety.

[0004] BACKGROUND

[0005] Retinal glial cells play an important role in the maintenance of normal retinal biology. In the mammalian retina, there are three types of glial cells: microglia, astrocytes, and Muller cells. Microglia are the resident immune cells of the retina and are distributed in the outer plexiform layer, inner plexiform layer, retinal ganglion cell layer, and nerve fiber layer; astrocytes are mostly located in the nerve fiber layer, and they accompany the blood vessels in the inner nuclear layer; Muller cells are the principal glial cells of the vertebrate retina and span the entire depth of the retina from the outer limiting membrane to the inner limiting membrane. In healthy retinas, retinal glial cells are responsible for maintaining the homeostasis of the extracellular milieu (ions, water, and pH), as well as removing excess glutamate from the extracellular space to avoid excitotoxicity to retinal neurons. Glial cells are also important for the metabolic support and nutrition of retinal neurons and release antioxidant and neuroactive substances. Moreover, spontaneous Muller cell Ca2+waves have been observed in vivo in healthy rat retinas and could play a role in modulating blood vessel diameter.

[0006] Dysfunction of glial cells is related to many eye diseases and neurologic disorders. For example, it has been reported that retinal glial cells are likely to be involved in glaucoma pathogenesis. Glaucoma is a chronic, progressive eye disease characterized by cupping of the optic disc with corresponding visual field defects due to retinal ganglion cell (RGC) losses. Elevated intraocular pressure (IOP) is a key risk factor for glaucoma, and IOP reduction is currently the only available treatment. In an inherited glaucoma mouse model (DBA / 2J), the location and activation status of microglia change in the early stages of the disease, preceding RGC degeneration. In several models of glaucoma, astrocytes show increased GFAP immunoreactivity and extracellular matrix remodeling at the optic disc. Astrocytes are also involved in metabolic redistribution in a mouse glaucoma model. In glaucomatous human retinas, astrocytes and Muller cells exhibited an abnormal morphology and increased immunostaining for GFAP. In addition, glial cell activity is related to many neurologic disorders, such as Alzheimer’s (AD) and Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS), and Parkinson’s disease (PD). However, there is currently no method to clinically record human glial cell activity. Currently, the most widely used electrophysiological method for diagnosing retinal diseases is electroretinogram (ERG), which measures the neuronal activity of a retina in response to light flashes and does not account for glial activity.

[0007] Accordingly, there is a need in the art for articles and methods that improve on existing articles and methods by providing measurement of glial activity. The present invention addresses this need.

[0008] SUMMARY

[0009] In one aspect, a method of measuring spontaneous slow voltage fluctuation (SSVF) includes recording a glial activity profile (GAP) baseline signal from a subject; converting the GAP baseline signal from the time domain to the frequency domain; calculating a magnitude spectrum of the GAP baseline signal; and defining a peak value of magnitude in a low-frequency range as GAP Value.

[0010] In some embodiments, at least one of the GAP baseline signal and a full-field light stimulation signal is recorded using an electroretinogram (ERG) recording system including recording electrodes, an amplifier, a digitizer, and a computer. In some embodiments, the ERG recording system is portable, handheld, a smartphone version, integrated with other ophthalmic equipment, or a combination thereof.

[0011] In some embodiments, the GAP baseline signal is converted from the time domain to the frequency domain using Fast Fourier Transform (FFT) according to the equation: wherein N is the length of data (the total number of elements in the recorded signal); x[n] is the / / -th element of the recorded electrical signal; y[n] is the / / -th element of the result after applying FFT, representing the amplitude and phase of the frequency component at frequency k'N; k is the current frequency index in the result array, ranging from 0 to N-I; e is Euler’s number; and j is the imaginary unit.

[0012] In some embodiments, the magnitude spectrum is calculated according to the equation: wherein Y is a sequence representing the magnitude spectrum; N is the length of data; and j / 77 / is the 77-th element of the frequency component.

[0013] In some embodiments, the method further includes prior to recording the GAP baseline signal, positioning an electrode on each lower eyelid of the subject. In some embodiments, a medial border of each electrode rests underneath a midpoint of each lower eyelid, and a lateral border of each electrode rests on the subject’s temple. In some embodiments, the method further includes positioning a grounding electrode on a forearm of the subject.

[0014] In some embodiments, the GAP baseline signal is recorded for at least 10 minutes without light stimulation. In some embodiments, the GAP baseline signal is recorded while the subject’s eyes are closed. In some embodiments, the recording step comprises a data acquisition rate of 100Hz - 5 kHz. In some embodiments, the low-frequency range comprises 0-2 Hz.

[0015] In some embodiments, the method further includes recording a full-field light stimulation signal from the subject. In some embodiments, the method further includes using the full-field light stimulation signal as a reference for analysis of the GAP recording. In some embodiments, the method further includes a light stimulation pattern of flashes with several repeats and a light intensity of less than 1600 nits, wherein the light comprises the visible light spectrum.

[0016] In some embodiments, the method further includes determining whether the subject has abnormal SSVF and diagnosing the subject with glial dysfunction when abnormal SSVF is determined. In some embodiments, determining whether the subject has abnormal SSVF comprises detecting differences in GAP average value, waveform characteristics, peak frequency, gradient value, cumulative value, or a combination thereof. In some embodiments, the glial dysfunction comprises eye disease. In some embodiments, the glial dysfunction comprises at least one of glaucoma, diabetic retinopathy, macular degeneration, choroidal diseases, retinal diseases, optic nerve diseases, Alzheimer’s, Parkinson’s, other dementias, other movement disorders, vascular diseases, inflammatory diseases, nutritional diseases, or cancers. In some embodiments, the method further includes administering medications to reduce eye pressure to the subject. In some embodiments, the glial dysfunction comprises neurologic disease.

[0017] In another aspect, a device for measuring and calculating glial activity profile (GAP) includes a graphical user interface (GUI); a computer module; a data acquisition module; and a power source. In some embodiments, the GUI comprises a touchscreen display. In some embodiments, the computer module comprises a circuit board configured for signal processing and computation. In some embodiments, the computer module comprises a Raspberry Pi platform. In some embodiments, the data acquisition module comprises a PiEEG16 chip. In some embodiments, the data acquisition module is integrated with the computer module. In some embodiments, the power source comprises a power bank.

[0018] In some embodiments, the device further includes electrodes coupled to the device, the coupled electrodes being designed to capture electrophysiological signals with high resolution and reduced noise.

[0019] In some embodiments, the device further includes software for measuring and calculating GAP. In some embodiments, the software is configured to display real-time electrophysiological traces, frequency spectrum data, and the calculated GAP value on the GUI. In some embodiments, the software includes integrated diagnostic algorithms configured to analyze the patient’s GAP value against expected parameters and generate automated alerts when significant deviations occur. In some embodiments, the software interface supports longitudinal tracking of GAP values.

[0020] In some embodiments, the device further includes support for wireless data transfer.

[0021] BRIEF DESCRIPTION OF THE DRAWINGS

[0022] For a fuller understanding of the nature and desired objects of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawing figures wherein like reference characters denote corresponding parts throughout the several views.

[0023] FIG. 1 shows a diagram of the glial activity profile (GAP) recording setup. FIGS. 2A-C show slow spontaneous voltage fluctuations (SSVFs) in a wild-type retina recorded with an HD-MEA chip attached to the RGC layer. (A) Photos of an HD-MEA chip (4225 electrodes, 65 x 65 grid, 1 mm2area) with a retina attached. The sensor area is indicated by the blue square in the bottom image. (B) Pseudocolor heatmaps show 5 spontaneous events at different phases (indicated by red and black arrows). Each pixel in the heatmap represents a readout from one electrode. The red arrow and gray box indicate an event from start to finish. (C) Original data of the gray box (10 x 10 electrodes) shows the distribution and kinetics of the event. Vertical red lines indicate the time when voltage minimum occurs, which defines time 0 sec in (B).

[0024] FIGS. 3A-B show that Muller cell-specific inhibitor (DL-AAA) nearly eliminates SSVFs. (A) Representative snapshots of HD-MEA recordings before and after bath application of 10 mM DL-AAA in the same area (1mm2). (B) Comparison of SSVF density (n=3 for 10 mins recording in each group; p < 0.05, t-test)

[0025] FIGS. 4A-B show expression patterns of halorhodopsin in genetically engineered mice and light-induced impact on SSVFs. (A) Expression patterns of and Gfap-Cre+;Ai39+~ and retinas show cell-appropriate expression of Ai39 in RGC layer and retina cross section. ONL, outer nuclear layer; INL, inner nuclear layer; RGL, retinal ganglion cell layer. Scale bar: 20 pm. (B) SSVF density is reduced by light in Glast-CreER+;Ai39(Het) retinas but not in Gfap-Cre+;Ai39(Het) or wild-type retinas. SSVF amplitude is not affected in either genotype. Data were normalized to values from the same tissue under dark condition (gray bars). *** p < 0.001; n = 3 (retinas) in each group.

[0026] FIGS. 5A-B show that SSVF density in glaucomatous retinas is increased 2-3 weeks after microbead injection. (A) SSVF density in control and microbead -induced glaucomatous retinas at three time points. Four regions with the highest activities in each group were analyzed. N = 3 (retinas); * indicates p < 0.05. (B) The averaged kinetics of the SSVF center are not changed in glaucomatous retinas, suggesting that the features of individual SSVFs are unchanged. Data were normalized according to minimum and maximum values.

[0027] FIG. 6 shows that the diameter of SSVF is significantly increased in a diabetic retinopathy (DR) mouse model. Left, a diagram showing the change of SSVF diameter. Right, statistic results. WT, n = 507; DR, n = 530; *, p<0.001. FIGS. 7A-C show that spectrum analysis of ERG data, averaged HD-MEA data, and the event template exhibit a similar low-frequency spectrum profile. (A) Representative ERG data and spectrum profile. Difference of simultaneous recordings on both eyes of a wild-type mouse (left) and frequency spectrum obtained via Fast Fourier Transform (right). (B) Representative HD-MEA data averaged over all electrodes (left) and its frequency spectrum profile (right). (C) Individual event template obtained by averaging over 1077 events recorded with HD-MEA (left) and its frequency spectrum profile (right). The amplitude scale (not shown) is irrelevant to the kinetics. Red arrowheads indicate the similar spectrum peaks.

[0028] FIGS. 8A-B show that the amplitude of ERG low-frequency fluctuation is sensitive to the Muller-specific inhibitor (DL-AAA) and optogenetic inhibition of Muller cells. (A) Left, representative spectra of ERG baseline fluctuations with intravitreally injected DL-AAA (red) or PBS (gray). Right, statistical results. PBS, n=4; DL-AAA, n=4. (B) Low-frequency ERG fluctuation is sensitive to halorhodopsin-mediated inhibition of Muller cells. WT, n=4; Glast- CreER;Ai39+ / ~, n=5.

[0029] FIGS. 9A-B show glial activity profiling (GAP) data from healthy eyes of human subjects. (A) Fast Fourier transformation plot of all healthy control human subjects depicted from 0 - 2 Hz. The bold red line depicts the mean trace. (B) Blue area depicts the area under the curve of the mean trace. Black bars represent the standard deviation. All subjects were recorded for 10 minutes in the dark without light stimulation. N = 38 eyes.

[0030] FIGS. 10A-B show design and prototype of a handheld device for measuring glial activity profile (GAP). (A) A schematic of the device including a touchscreen interface for data display and interaction, the Raspberry Pi platform for signal processing and computation, PiEEG16 for data acquisition, and a power bank for portability and extended use. (B) A photograph of the assembled prototype device, including the Raspberry Pi 5 platform and integrated PiEEG16 chip. The connected electrodes are shown in the foreground, designed to capture electrophysiological signals with high resolution and minimal noise. The touchscreen displays real-time signal traces and analysis results, demonstrating the functionality of the device for clinical and research applications.

[0031] FIGS. 11A-C show images of a graphical user interface (GUI) for GAP Device. The GUI presents patient-specific information and displays the measured GAP value, which is calculated from the baseline voltage fluctuations in retinal glial cells. (A) The primary screen allows the user to input or retrieve patient information and offers buttons for viewing the recorded signal trace or the frequency spectrum (FFT). The patient details, including name, date of birth, gender, and contact information, are displayed, alongside technician information. (B) The “Raw Recording Trace” view shows the time-series data of spontaneous slow voltage fluctuations (SSVFs) recorded from the patient’s retina. The vertical axis represents the signal amplitude, and the horizontal axis represents time in seconds. (C) The “FFT Spectrum” view presents the frequency domain analysis of the recorded signal, showing the GAP value. The dominant frequency component, corresponding to glial activity, is visualized, with the GAP value displayed at the top of the screen in red. The x-axis represents the frequency (0-2 Hz), and the y- axis shows the signal amplitude. The data displayed are pseudo data used for testing purposes.

[0032] DEFINITIONS

[0033] The instant invention is most clearly understood with reference to the following definitions:

[0034] As used herein, the singular form “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.

[0035] Unless specifically stated or obvious from context, as used herein, the term “about” is understood as within a range of normal tolerance in the art, for example within 2 standard deviations of the mean. “About” can be understood as within 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.05%, or 0.01% of the stated value. Unless otherwise clear from context, all numerical values provided herein are modified by the term about.

[0036] As used in the specification and claims, the terms “comprises,” “comprising,” “containing,” “having,” and the like can have the meaning ascribed to them in U.S. patent law and can mean “includes,” “including,” and the like.

[0037] Unless specifically stated or obvious from context, the term “or,” as used herein, is understood to be inclusive.

[0038] Ranges provided herein are understood to be shorthand for all of the values within the range. For example, a range of 1 to 50 is understood to include any number, combination of numbers, or sub-range from the group consisting 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,

[0039] 42, 43, 44, 45, 46, 47, 48, 49, or 50 (as well as fractions thereof unless the context clearly dictates otherwise).

[0040] DETAILED DESCRIPTION OF THE INVENTION

[0041] Provided herein are articles and methods of measuring glial cell activity. The method analyzes an electrical spectrum of baseline fluctuation in the spontaneous activity of a retina to measure retinal glial cell activity. In some embodiments, the method includes measuring a newly discovered electrophysiological property, referred to herein as spontaneous slow voltage fluctuation (SSVF). In some embodiments, the method includes recording a glial activity profiling (GAP) baseline signal from a subject; converting the GAP baseline signal from the time domain to the frequency domain; calculating a magnitude spectrum of the GAP baseline signal; and defining a peak value of magnitude in a low-frequency range as GAP Value. In some embodiments, the method further includes recording a full-field light stimulation signal from the subject. When recorded, the full-field light stimulation signal provides a normalization parameter to increase the accuracy of the measurement and / or is used as a reference for analysis of the GAP recording.

[0042] The GAP baseline signal may be recorded using any suitable parameters. For example, in some embodiments, the GAP baseline signal is recorded for at least 3 minutes, at least 5 minutes, at least 7 minutes, at least 10 minutes without light stimulation, or any suitable combination, subcombination, range, or sub-range thereof. In some embodiments, the GAP baseline signal is recorded while the subject’s eyes are closed. In some embodiments, the recording step comprises a data acquisition rate of 100Hz - 5 kHz. In some embodiments, the low-frequency range comprises 0-2 Hz. Similarly, the full-field light stimulation signal may be recorded using any suitable parameters. For example, in some embodiments, the full-field light simulation signal is recorded with a light stimulation pattern of 30 flashes and a light intensity of less than 1600 nits. In some embodiments, the light comprises the visible light spectrum with no significant ultraviolet or infrared component.

[0043] In some embodiment, the GAP baseline signal and / or the full-field light stimulation signal are recorded using an electroretinogram (ERG) recording system. In some embodiments, the ERG recording system includes recording electrodes, an amplifier, a digitizer, and a computer. In some embodiments, the ERG recording system does not utilize light stimulations. Additionally or alternatively, the ERG recording system may be portable, handheld, a smartphone version, integrated with other ophthalmic equipment, or include any other suitable modification or variation.

[0044] In some embodiments, the GAP baseline signal is converted from the time domain to the frequency domain using Fast Fourier Transform (FFT). For example, in some embodiments, the GAP baseline signal is converted using FFT according to the equation: where N is the length of data (the total number of elements in the recorded signal); x[n] is the n- th element of the recorded electrical signal; y[n] is the / / -th element of the result after applying FFT, representing the amplitude and phase of the frequency component at frequency k / N; k is the current frequency index in the result array, ranging from 0 to N-l; e is Euler’s number; and / is the imaginary unit. The magnitude spectrum is calculated according to the equation: where Fis a sequence representing the magnitude spectrum; N is the length of data; m y[n] is the / z-th element of the frequency component.

[0045] In some embodiments, the method further includes, prior to recording the GAP baseline signal, positioning one or more electrodes on the subject. The electrode(s) may be positioned in any suitable location for measuring the SSVF. For example, in some embodiments, the electrodes are positioned on each lower eyelid of the subject. In some embodiments, a medial border of each electrode rests underneath a midpoint of each lower eyelid, and a lateral border of each electrode rests on the subject’s temple. In some embodiments, a grounding electrode is positioned on a forearm of the subject.

[0046] Also provided herein are methods of detecting glial dysfunction using the SSVF measured according to any of the embodiments disclosed herein. In some embodiments, the method of detecting glial dysfunction includes determining whether the subject has abnormal SSVF, and diagnosing the subject with glial dysfunction when abnormal SSVF is determined. In some embodiments, determining whether the subject has abnormal SSVF includes measuring GAP average value, waveform characteristics, peak frequency, gradient value, cumulative value, or a combination thereof. Additionally or alternatively, the method may include calculating GAP average value and other parameters from the difference of the time-domain data simultaneously recorded from the two eyes of the same subject. The difference of the time-domain data is recorded as a new number sequence, and FFT is performed on the difference sequence to calculate GAP value, which further reduces noise in the measurement and provides a more accurate GAP value.

[0047] Further provided herein are devices for measuring and calculating GAP. For example, in some embodiments, as illustrated in FIG. 10A, a handheld device 100 for measuring and calculating GAP includes a graphical user interface (GUI) 110, a computer module 120, a data acquisition module 130, and a power source 140. The GUI 110 includes any suitable interface for data display and interaction, such as, but not limited to, a touchscreen. The computer module 120 includes any suitable circuit board or other computer module for signal processing and computation, such as, but not limited to, a Raspberry Pi platform. The data acquisition module 130 includes any suitable chip or other module for data acquisition, such as, but not limited to, a PiEEG16 chip. In some embodiments, the data acquisition module 130 is integrated with the computer module 120. The power source 140 includes any suitable portable power source, such as, but not limited to, a power bank. In some embodiments, electrodes 150 can be detachably coupled to the device, the coupled electrodes being designed to capture electrophysiological signals with high resolution and reduced noise.

[0048] The device includes any suitable software for measuring and calculating GAP. In some embodiments, the software is configured to display real-time electrophysiological traces, frequency spectrum data, and the calculated GAP value on the GUI. In some embodiments, the software includes integrated diagnostic algorithms configured to analyze the patient’s GAP value against expected parameters and generate automated alerts when significant deviations occur. These automated alerts facilitate the early detection of retinal or neurological disorders. Additionally, in some embodiments, the interface supports longitudinal tracking of GAP values, which is valuable for monitoring disease progression or therapeutic responses. Furthermore, in some embodiments, the device supports wireless data transfer. In some such embodiments, the wireless data transfer enables seamless integration with hospital information systems for remote analysis and telemedicine applications. Accordingly, using the device disclosed herein, patient data can be accessed in real time by authorized clinicians across diverse clinical settings.

[0049] As will be appreciated by those skilled in the art, glial dysfunction is present in various eye diseases as well as may diseases of the central nervous system. Accordingly, in some embodiments, the method further includes detecting any suitable eye disease and / or central nervous system disease involving glial dysfunction. Suitable diseases include, but are not limited to, glaucoma, diabetic retinopathy, macular degeneration, choroidal diseases, retinal diseases, optic nerve diseases, Alzheimer’s, Parkinson’s, other dementias, other movement disorders, vascular diseases, inflammatory diseases, nutritional diseases, and / or cancers. In some embodiments, the method further includes treating the disease detected according to any of the embodiments disclosed herein. For example, in some embodiments, following detection of glaucoma, the method further includes administering medications to reduce eye pressure to the subject.

[0050] The following examples further illustrate aspects of the present invention. However, they are in no way a limitation of the teachings or disclosure of the present invention as set forth herein.

[0051] EXAMPLES

[0052] EXAMPLE 1 - Glial Activity Profile (GAP) - a Novel Method to Diagnose Eye Diseases Using an Electroretinogram Recording Platform

[0053] Introduction

[0054] Retinal glial cells play an important role in the maintenance of normal retinal biology. In the mammalian retina, there are three types of glial cells: microglia, astrocytes, and Muller cells. Microglia are the resident immune cells of the retina and are distributed in the outer plexiform layer, inner plexiform layer, retinal ganglion cell layer, and nerve fiber layer; astrocytes are mostly located in the nerve fiber layer, and they accompany the blood vessels in the inner nuclear layer; Muller cells are the principal glial cells of the vertebrate retina and span the entire depth of the retina from the outer limiting membrane to the inner limiting membrane. In healthy retinas, retinal glial cells are responsible for maintaining the homeostasis of the extracellular milieu (ions, water, and pH), as well as removing excess glutamate from the extracellular space to avoid excitotoxicity to retinal neurons. Glial cells are also important for the metabolic support

[0055] -l i and nutrition of retinal neurons and release antioxidant and neuroactive substances. Moreover, spontaneous Muller cell Ca2+waves have been observed in vivo in healthy rat retinas and could play a role in modulating blood vessel diameter.

[0056] Dysfunction of glial cells is related to many eye diseases and neurologic disorders. For example, it has been reported that retinal glial cells are likely to be involved in glaucoma pathogenesis. Glaucoma is a chronic, progressive eye disease characterized by cupping of the optic disc with corresponding visual field defects due to retinal ganglion cell (RGC) losses. Elevated intraocular pressure (IOP) is a key risk factor for glaucoma, and IOP reduction is currently the only available treatment. In an inherited glaucoma mouse model (DBA / 2J), the location and activation status of microglia change in the early stages of the disease, preceding RGC degeneration. In several models of glaucoma, astrocytes show increased GFAP immunoreactivity and extracellular matrix remodeling at the optic disc. Astrocytes are also involved in metabolic redistribution in a mouse glaucoma model. In glaucomatous human retinas, astrocytes and Muller cells exhibited an abnormal morphology and increased immunostaining for GFAP. In addition, glial cell activity is related to many neurologic disorders, such as Alzheimer’s (AD) and Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS), and Parkinson’s disease (PD). However, there is currently no method to clinically record human glial cell activity. Currently, the most widely used electrophysiological method for diagnosing retinal diseases is electroretinogram (ERG), which measures the neuronal activity of a retina in response to light flashes and does not account for glial activity.

[0057] Recently, we discovered a new phenomenon in explanted mouse retinas using a high- density multi el ectrode (HD-MEA) system with 4225 electrodes. With this approach, we found a new electrophysiological property of normal adult mouse retinas, which we call spontaneous slow voltage fluctuation (SSVF). We further demonstrated that SSVFs are sensitive to a Mullerspecific inhibitor (DL-AAA) and a Muller-specific optogenetic manipulation, suggesting that the source of SSVFs is likely to be Muller cells (a subtype of retinal glial cells). More importantly, we also found that the overall SSVF activity can be quantified by analyzing the baseline fluctuation of ERG recordings independent of light flashes.

[0058] Based on this groundbreaking discovery, we have developed a revolutionary method to diagnose eye diseases using a novel modification to an electroretinogram (ERG) recording platform. The method analyzes the electrical spectrum of the retina’s baseline, light-independent fluctuation to measure retinal glial cell activity. This technology has the potential to diagnose various eye diseases related to glial cells. Since abnormal glial function is also a component of many diseases of the central nervous system, this technology may have extremely broad applications. The method can potentially be developed into a portable device for at-home testing, making it an exciting prospect for improving healthcare accessibility, convenience, and speed of diagnosis for patients with eye diseases and other related neurological disorders.

[0059] Methods

[0060] Glial activity profiling (GAP) is conducted on a seated subject resting in an examination chair. The patient is made comfortable to avoid movement during the testing period. Prior to testing, the patient’s skin is prepared. This entails washing the skin with water, scrubbing the skin with an exfoliating skin rub, washing off the skin rub with water and then patting the skin dry with paper towels. These steps are essential for removing dead skin and additional lotions, etc. that may increase impedance to the recording electrode (LKC RETeval). Following the skin preparation, 2-3 grams (roughly fills up one fingertip) of electrode-compatible skin preparation gel (NuPrep) is applied to the skin on and underneath the lower eyelids bilaterally. The gel covers the entirety of the lower eyelids and is spread to the temples bilaterally. The gel is thin enough as to not interfere with the connection between the electrode and the skin but thick enough to be in between the electrode and the skin throughout the area of the electrode. After application of the skin preparation gel, the electrodes are placed on the lower eyelids. The medial border of the electrode will rest underneath the midpoint of the lower eyelid, and the lateral border of the electrode will rest on the temple (FIG. 1). Medical tape is then used to firmly adhere the electrodes to the skin to ensure close contact between the electrode components and the skin. These electrodes contain the recording, reference, and ground electrodes within the same adhesive patch. Each electrode is discarded after each use.

[0061] An additional large grounding electrode (Electrode Store) is attached to the forearm of the subject (FIG. 1). The skin on the anterior forearm two inches distal to the elbow will be prepared as described above (washed, scrubbed, washed, and dried) prior to application of skin preparation gel and the large grounding electrode. This electrode is also reinforced with medical tape. Following application of the electrodes, the LKC electrode-compatible recording cables are attached to the skin electrodes on the face and the DTL-compatible recording cable is attached to the skin electrode on the arm.

[0062] The subject is asked to remain seated without excessive movement for 10 minutes without light stimulation. The room is dimly lit but does not need to be entirely dark. The subject is asked to keep their eyes closed, as to avoid distraction and excessive movement. The GAP baseline signal is recorded bilaterally during this period. The recorded data are analyzed after the recording period is complete.

[0063] After this period of baseline recording, a full-field light stimulation recording is performed using a computer monitor as the light stimulus. The light stimulation pattern is 30 flashes (100-millisecond duration, 30-second interval). The light contains the visible light spectrum and has no significant ultraviolet or infrared component. The light intensity is less than 1600 nits, which is roughly the lighting level for office space. The results from the light responsive electroretinogram (ERG) recordings are used as a reference for analysis of the GAP recording data.

[0064] To test whether a fiber electrode can provide a better signal-to-noise ratio than skin electrodes, in some experiments, a conductive fiber electrode (DTL-PLUS electrode; Unimed Electrode Supplies Limited) is placed in the lower conjunctival fornix. For experiments with fiber electrodes, new single-use electrodes are used for each experiment. Proparacaine is used to anesthetize the eye prior to DTL electrode placement. Tropicamide and Phenylephrine are used to dilate the eye to decrease electrical impedance.

[0065] Data acquisition rate is set as 5 kHz for all recordings. To convert the signal from the time domain to the frequency domain, Fast Fourier Transform (FFT, Equation 7) is performed using scipy.fft function from Python Scipy package (version 1.11.3). Given the discrete data of a raw recording of baseline fluctuation x(n) and the length of data N, the magnitude spectrum Y is calculated by Equation 2. Equation 2

[0066] Finally, the peak value of magnitude in the low-frequency range (0 - 2 Hz) is defined as “GAP Value”. All statistical analyses leverage t-test or ANOVA. The analysis process is optimized to fit the needs of the experiments and automated by a program developed by the inventors. Furthermore, the features of the low-frequency spectrum, such as average value, waveform, peak frequency, etc., can be used as parameters to determine abnormal activities.

[0067] Results

[0068] Slow spontaneous voltage fluctuation (SSVF) occurs in mouse retinas

[0069] Using a high-density MEA (HD-MEA) recording system, we discovered Slow Spontaneous Voltage Fluctuations (SSVFs) in the RGC layer. FIGS. 2A-C show recording data from a wild-type adult mouse retina that was freshly dissociated and flattened on an HD-MEA chip with the RGC layer facing the chip. An SSVF from start to finish is highlighted by the gray boxes in FIG. 2B. SSVFs occurred with a density of 107 ± 18 events per minute per mm2in randomly selected peripheral retinal tissues (the optic nerve head region is avoided as it is difficult to flatten it onto an HD-MEA sensor). Each SSVF starts with a large drop of the extracellular potential (depolarization of cells), followed by an increase of the potential (hyperpolarization of cells) with a smaller amplitude.

[0070] SSVFs are mediated by retinal glial cells

[0071] To further prove that Muller cells are critical to SSVFs, we applied (in bath solution) a Muller-specific inhibitor, DL-a-aminoadipic acid (DL-AAA), which specifically inhibits the metabolic pathway in Muller cells. We found that SSVFs were essentially eliminated (FIGS. 3A-B) during the 15-minute perfusion and did not recover after a 60-minute washout period (likely due to the difficulty of washing out the inhibitor from a wholemount retina tissue), suggesting that the primary source of SSVF source is the Muller cell. We also further confirmed the involvement of Muller cells in SSVFs with an optogenetic approach, which is commonly used in neuroscience research to manipulate activities of neurons and glial cells with light. We used the Cre-lox system to specifically express halorhodopsin (an inhibitory light-gated ion pump) in either Muller cells or astrocytes, and used red light to electrically inhibit the specific type of glial cell. We imported Glast-CreER and Gfap-Cre BAC transgenic mouse lines from the Jackson Laboratory (JAX, strain #012586 and #024098), which express CRE specifically in Muller cells (Glasf) or astrocytes (Gfap . These two lines were bred to Ai39, which expresses the halorhodopsin / EYFP fusion protein under the control of CRE. We examined the specificity of halorhodopsin expression in Glast-CreER+;Ai39+ / ~ and Gfap-Cre+;Ai39+ / ~ mice by immunostaining. FIG. 4A top panel shows that Gfap-Cre+;Ai39+'~ retinas expressed halorhodopsin / EYFP in the membrane of astrocytes that are labeled by GFAP antibody (because halorhodopsin / EYFP is a membrane protein and GFAP is a cytosolic one, a partial overlap is observed). FIG. 4A bottom panel shows the expression pattern of halorhodopsin / EYFP in a Glast-CreER+;Ai39+ / ~ retinas. Finally, we tested SSVF properties with the HD-MEA in the absence or presence of red light (to suppress glial activity). When halorhodopsin was activated, we observed a significant reduction of SSVF density in Glast-CreER+;Ai39+'~ mice but not in Gfap-Cre+;Ai39+ / ~(FLG. 4B left). These data rule out the involvement of astrocytes. Thus, the change in Glast-CreER ' ;Ai39 '~ represents an effect on Muller cells. Taken together, we conclude that Muller cells are involved in SSVF. Interestingly, the amplitude of SSVFs was not significantly reduced (FIG. 4B right), suggesting that the hyperpolarization of Muller cells affects the initiation but not the quality of SSVFs.

[0072] The density of SSVFs is significantly reduced in an early-stage glaucoma mouse model

[0073] To further examine the Muller cell activities in a disease model, we tested retinas at the stages prior to RGC loss in a glaucoma model induced by microbead injection (1, 2, and 3 weeks after injection). Our data show that the density of SSVFs increased dramatically after 2 and 3 weeks (FIG. 5A), suggesting that Muller cells are hyperactive in these early stages of glaucoma. We also analyzed the SSVF kinetics in glaucoma and sham -injection eyes but did not find a difference (FIG. 5B), implying that the quality of SSVFs remains unchanged. Importantly, this change in SSVF density occurred well before the typical neuronal damage seen in this glaucoma model. Since neuronal damage is the only way to determine glaucoma in humans, this suggests that SSVF changes (Muller glial changes) may occur prior to any currently available human tests for glaucoma.

[0074] The diameter of SSVFs is significantly increased in a diabetic retinopathy (DR) mouse model We further explored the change of SSVFs in a diabetic retinopathy (DR) mouse model37.

[0075] Mice of the C57BL / 6 strain, aged 6-7 weeks, received an intraperitoneal injection of streptozotocin (STZ) after fasting for 4 hours at a dosage of 50 pg / g of body weight for five days in a row. Blood sugar levels were checked every two weeks. Mice that displayed blood glucose levels of 350 mg / dL or higher were used after a period of 4 months. Next, we measured the SSVFs with HD-MEA recording and analyzed the diameter of each individual SSVF by two- dimensional Gaussian fit. The results show that the diameter of SSVFs is significantly increased in the DR mice (FIG. 6), suggesting that the Muller cell activity is significantly altered in the retina of DR mice.

[0076] Mouse SSVFs can be quantified by ERG recordings

[0077] Next, we asked whether SSVFs occur in vivo by analyzing the baseline fluctuation of ERG recordings. We conducted the experiment on a custom system, which was previously validated. The ERG recordings were performed simultaneously on both eyes of wild-type mice in complete darkness for 30 minutes. To further reduce system noise from ERG recordings, we mathematically obtained the difference between the two baselines (FIG. 7A, left). Then we performed Fast Fourier Transform to analyze the frequency spectrum profile (FIG. 7A, right) and compared it to the spectrum of the averaged HD-MEA electrode data over all electrodes (FIG. 7B) and the event template obtained by averaging 1077 individual HD-MEA events (FIG. 7C). We found that in the low-frequency range (0 to 0.5 Hz) the spectrum profile of the ERG has a peak around 0.2 Hz, which represents underlying low-frequency activities. This peak is close to the peaks in the averaged HD-MEA recordings and the event template, suggesting that SSVFs are likely to be the source of the low-frequency fluctuation in the ERG baseline. Furthermore, we found that the low-frequency fluctuation in the ERG baseline is sensitive to intravitreally injected Muller-specific inhibitor (FIG. 8A) DL-AAA, confirming that SSVFs occur in vivo.

[0078] To further assess if low-frequency activity in ERGs contains SSVF signals, we measured the amplitude of the fluctuation in wildtype and Glast-CreER;Ai39^'~ animals and compared the amplitudes of low-frequency signals in dark or constant red light (the first 30 seconds after light onset or offset were not included to avoid light-induced ERG changes). We found that light did not alter the low-frequency fluctuation in wild-type animals. As expected, when Muller cells were inhibited by light in the genetically engineered mice, the amplitude of low-frequency signals was significantly reduced (FIG. 8B). Similar to the MEA results in FIG. 4B, we did not observe a complete elimination of the low-frequency activities. This was likely due to the insufficient inhibition via halorhodopsin and the limited light intensity projecting through the pupil. Nevertheless, these ERG results provide additional evidence supporting that SSVFs occur in vivo. More importantly, it also opens a new avenue for non-invasively recording glial activities via ERG.

[0079] Glial activity profile can be measured in human subjects in laboratory and clinic settings

[0080] To further prove the feasibility of measuring the glial activity profile in human subjects (see Method), we recorded the baseline function in ERG of 19 human subjects (38 eyes) and performed Fast Fourier Transform (FFT) to obtain their spectrum profiles (FIGS. 9A-B). We found that a magnitude peak occurs in the low-frequency range (0 - 1 Hz). The similarity between the human and mouse profile suggests that this peak represents glial activities in the retina and it potentially can be used as a diagnostic measure. Furthermore, our recent studies have demonstrated that the glial activity profile (GAP) can be measured reliably in clinical settings using ERG recordings. This measurement has been obtained in clinical patients without eye diseases and provides a parameter reflecting retinal glial cell function. The ability to quantify the GAP value in patients without overt pathology underscores its potential as a predictive and diagnostic tool in routine clinical evaluations.

[0081] Applications

[0082] Since Muller glial dysfunction in the eye and glial dysfunction in the brain both precede neuronal phenotypes in a number of diseases, GAP in humans has the potential to diagnose diseases earlier than current standards. For example: 1) Glaucoma. The current standard is anatomic change of the optic nerve head. In patients with a normal optic nerve head but other risk factors, an abnormal GAP could predict future glaucoma. 2) Diabetic retinopathy. The current standard is the presence of abnormal blood or lipid within the layers of the retina in patients with known diabetes. An abnormal GAP in a patient with known diabetes but a normal retinal appearance could predict future diabetic retinopathy. 3) Age-related macular degeneration (ARMD). The current standard for dry ARMD is the presence of intermediate or confluent drusen. The current standard for wet ARMD is retinal fluid / exudate. In patients with small drusen and no retinal fluid, an abnormal GAP could predict future conversion to dry or wet ARMD. 4) The same logic could apply to any retinal disease and potentially any neurologic disease with a glial component. Variations

[0083] The first iteration of the GAP is a stand-alone computer-controlled ERG system. This could be modified in many ways. For example: 1) A handheld, portable version. 2) A smartphone version. 3) A version that integrates with other common ophthalmic equipment (OCT, perimeter, photography, intraocular lens calculator, etc.) 4 Any other conceived adaptation.

[0084] Conclusions

[0085] This invention presents a novel method for assessing retinal glial cell activity through the detection of spontaneous slow voltage fluctuations using a modified electroretinogram (ERG). By isolating the electrophysiological signature of Muller cells, it enables early, non-invasive diagnosis of ocular conditions such as glaucoma and diabetic retinopathy — long before irreversible neuronal damage occurs.

[0086] The adaptable design supports various formats, from stand-alone systems to portable, smartphone-integrated devices, making it suitable for clinical use and at-home monitoring. Ultimately, this technology transforms early diagnosis and personalized care by providing a sensitive, quantifiable measure of glial function.

[0087] EXAMPLE 2 - Glial Activity Profile (GAP) Device

[0088] To enable both clinical and at-home applications for measuring the glial activity profile (GAP), a dedicated handheld device prototype has been developed. This innovative device integrates the Raspberry Pi 5 microprocessor with PiEEG16 chip, leveraging advanced computing power and multichannel capabilities to optimize performance and user convenience.

[0089] Design

[0090] Multi-Channel Architecture:

[0091] The device employs 16 recording channels to capture electrophysiological data from several electrode sites simultaneously (FIGS. 10A-B). This multi-channel design significantly improves the signal-to-noise ratio (SNR) through spatial averaging and robust signal processing, ensuring that the subtle low-frequency signals associated with retinal glial activity are accurately detected. Compact and Ergonomic Form Factor:

[0092] Designed for portability, the handheld device is lightweight and ergonomically contoured, making it ideal for use in various clinical settings — including outpatient clinics and bedside examinations. Its compact size also facilitates at-home monitoring by patients.

[0093] Sampling Rate:

[0094] High-resolution signal acquisition is achieved with a sampling rate of up to 5 kHz per channel, ensuring the detailed characteristics of the fluctuations linked to glial activity are captured with precision.

[0095] Data Processing and FFT Analysis:

[0096] At the core of the device, the Raspberry Pi 5 microprocessor is capable of performing real-time Fast Fourier Transform (FFT) analysis. This processing unit converts time-domain ERG fluctuation signals into the frequency domain, effectively isolating and quantifying the GAP value with high precision.

[0097] Battery Life:

[0098] Engineered for extended use, the device operates on a rechargeable battery that provides up to 6 hours of continuous operation on a single charge. This capability supports prolonged screening sessions and multiple patient assessments without the need for frequent recharging.

[0099] Software and Graphical User Interface (GUI)

[0100] User-Friendly Interface:

[0101] The device features customized software with an intuitive graphical user interface (GUI) that displays real-time electrophysiological traces, frequency spectrum data, and the calculated GAP value (FIGS. 11A-C). This allows clinicians to view and interpret data immediately during or after a recording session.

[0102] Diagnostic Tools:

[0103] Integrated diagnostic algorithms within the software analyze the patient’s GAP value against expected parameters and generate automated alerts when significant deviations occur, facilitating the early detection of retinal or neurological disorders. Additionally, the interface supports longitudinal tracking of GAP values, which is valuable for monitoring disease progression or therapeutic responses. Connectivity and Data Management:

[0104] The device supports wireless data transfer, enabling seamless integration with hospital information systems for remote analysis and telemedicine applications. Leveraging the advanced networking capabilities of the Raspberry Pi 5, patient data can be accessed in real time by authorized clinicians across diverse clinical settings.

[0105] EQUIVALENTS

[0106] Although preferred embodiments of the invention have been described using specific terms, such description is for illustrative purposes only, and it is to be understood that changes and variations may be made without departing from the spirit or scope of the following claims.

[0107] INCORPORATION BY REFERENCE

[0108] The entire contents of all patents, published patent applications, and other references cited herein are hereby expressly incorporated herein in their entireties by reference.

Claims

CLAIMS1. A method of measuring spontaneous slow voltage fluctuation (SSVF), the method comprising: recording a glial activity profile (GAP) baseline signal from a subject; converting the GAP baseline signal from the time domain to the frequency domain; calculating a magnitude spectrum of the GAP baseline signal; and defining a peak value of magnitude in a low-frequency range as GAP Value.

2. The method of claim 1, wherein at least one of the GAP baseline signal and a full-field light stimulation signal is recorded using an electroretinogram (ERG) recording system including recording electrodes, an amplifier, a digitizer, and a computer.

3. The method of claim 2, wherein the ERG recording system is portable, handheld, a smartphone version, integrated with other ophthalmic equipment, or a combination thereof.

4. The method of claim 1, wherein the GAP baseline signal is converted from the time domain to the frequency domain using Fast Fourier Transform (FFT) according to the equation:wherein:A is the length of data (the total number of elements in the recorded signal); x[n] is the zz-th element of the recorded electrical signal; y[n] is the zz-th element of the result after applying FFT, representing the amplitude and phase of the frequency component at frequency k N; k is the current frequency index in the result array, ranging from 0 to N-l,' e is Euler’s number; andJ is the imaginary unit.

5. The method of claim 1, wherein the magnitude spectrum is calculated according to the equation:wherein:F is a sequence representing the magnitude spectrum;.V is the length of data; and y[n] is the / / -th element of the frequency component.

6. The method of claim 1, further comprising, prior to recording the GAP baseline signal, positioning an electrode on each lower eyelid of the subject.

7. The method of claim 6, wherein a medial border of each electrode rests underneath a midpoint of each lower eyelid, and a lateral border of each electrode rests on the subject’s temple.

8. The method of claim 7, further comprising positioning a grounding electrode on a forearm of the subject.

9. The method of claim 1, wherein the GAP baseline signal is recorded for at least 10 minutes without light stimulation.

10. The method of claim 1, wherein the GAP baseline signal is recorded while the subject’s eyes are closed.

11. The method of claim 1, wherein the recording step comprises a data acquisition rate of 100Hz - 5 kHz.

12. The method of claim 1, wherein the low-frequency range comprises 0-2 Hz.

13. The method of claim 1, further comprising recording a full-field light stimulation signal from the subject.

14. The method of claim 13, further comprising using the full-field light stimulation signal as a reference for analysis of the GAP recording.

15. The method of claim 13, further comprising: a light stimulation pattern of flashes with several repeats; and a light intensity of less than 1600 nits; wherein the light comprises the visible light spectrum.

16. The method of claim 1, further comprising: determining whether the subject has abnormal SSVF; and diagnosing the subject with glial dysfunction when abnormal SSVF is determined.

17. The method of claim 16, wherein determining whether the subject has abnormal SSVF comprises detecting differences in GAP average value, waveform characteristics, peak frequency, gradient value, cumulative value, or a combination thereof.

18. The method of claim 16, wherein the glial dysfunction comprises eye disease.

19. The method of claim 16, wherein the glial dysfunction comprises at least one of glaucoma, diabetic retinopathy, macular degeneration, choroidal diseases, retinal diseases, optic nerve diseases, Alzheimers, Parkinsons, other dementias, other movement disorders, vascular diseases, inflammatory diseases, nutritional diseases, or cancers.

20. The method of claim 19, further comprising administering medications to reduce eye pressure to the subject.

21. The method of claim 16, wherein the glial dysfunction comprises neurologic disease.

22. A device for measuring and calculating glial activity profile (GAP) comprising: a graphical user interface (GUI); a computer module; a data acquisition module; anda power source.

23. The device of claim 22, wherein the GUI comprises a touchscreen display.

24. The device of claim 22, wherein the computer module comprises a circuit board configured for signal processing and computation.

25. The device of claim 24, wherein the computer module comprises a Raspberry Pi platform.

26. The device of claim 22, wherein the data acquisition module comprises a PiEEG16 chip.

27. The device of claim 22, wherein the data acquisition module is integrated with the computer module.

28. The device of claim 22, wherein the power source comprises a power bank.

29. The device of claim 22, further comprising electrodes coupled to the device, the coupled electrodes being designed to capture electrophysiological signals with high resolution and reduced noise.

30. The device of claim 22, further comprising software for measuring and calculating GAP.

31. The device of claim 30, wherein the software is configured to display real-time electrophysiological traces, frequency spectrum data, and the calculated GAP value on the GUI.

32. The device of claim 30, wherein the software includes integrated diagnostic algorithms configured to analyze the patient’s GAP value against expected parameters and generate automated alerts when significant deviations occur.

33. The device of claim 30, wherein the software interface supports longitudinal tracking of GAP values.

34. The device of claim 30, further comprising support for wireless data transfer.

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