Systems and methods for analyzing cutaneous biopsy for synucleinopathy

Abstract

The present disclosure in the various aspects and embodiments provides methods for detecting and / or distinguishing synucleinopathies using tissue sections form cutaneous biopsy. The disclosure further provides methods for treating subjects determined to have synucleinopathy, as well as Al-trained diagnostic systems. In the various aspects and embodiments, the disclosure improves accuracy and / or efficiency in diagnosing synucleinopathies, and in aspects provides diagnostic features and surrogate biomarkers for disease staging, selecting and / or guiding treatment, and evaluating effectiveness of candidate therapies in clinical trials.

Description

[0001]CND-002PC 128316-5003 SYSTEMS AND METHODS FOR ANALYZING CUTANEOUS BIOPSY FOR SYNUCLEINOPATHY PRIORITY This Application claims priority to, and the benefit of, U.S. Provisional Patent Application no.63 / 560,357, filed March 1, 2024, which is hereby incorporated by reference in its entirety. BACKGROUND Synucleinopathies, such as Parkinson disease (PD), Dementia with Lewy bodies (DLB), multiple system atrophy (MSA) and pure autonomic failure (PAF), are characterized by the deposition of phosphorylated α-synuclein (PSYN) as fibrillary aggregates in the central and / or peripheral nervous systems resulting in progressive neurological degeneration. The prevalence and incidence of these disorders increase with age. The fibrillary aggregates of phosphorylated alpha-synuclein can occur in the cytoplasm of neurons, and the fibrillary aggregates may interfere with the axonal transport of synaptic proteins and promote mitochondrial deficit and oxidative stress. There are over 2.5 million people in the United States who have a diagnosis of a synucleinopathy, and an approximately 180,000 are diagnosed every year. These synucleinopathies are progressive disorders with increasing disability, and with the exception of PAF, these synucleinopathies are uniformly fatal. Even PAF has a >10% risk of conversion to another synucleinopathy each year and therefore also carries a high mortality rate. Individuals with synucleinopathy are frequently undetected or misdiagnosed. Conventionally, a clinical evaluation by a movement disorder specialist (e.g., for PD and MSA), a movement or cognitive disorder specialist (e.g., for DLB) or an autonomic expert (e.g., for PAF and MSA) is the basis for diagnoses. However, the number of patients with synucleinopathies far exceeds the capacity of these specialists. Further, since diagnosis of a synucleinopathy is often based on clinical criteria, overall clinical diagnostic accuracy may be only 80 to 90%, and in early disease diagnostic accuracy may be only around 30%. Further, particularly in early disease, it is difficult to differentiate patients with a synucleinopathy from autoimmune, metabolic or toxic autonomic neuropathies. A correct DB1 / 155488515.1 1 CND-002PC 128316-5003 diagnosis is critical to select the appropriate intervention. Further, an early stage diagnosis can also be critical, since pharmaceutical interventions can be more effective if initiated in early stages of disease. Accordingly, diagnostic or disease monitoring biomarkers for synucleinopathies are needed, including to identify patients early in the clinical course of the disease, improve diagnostic accuracy, and provide a surrogate endpoint for neuroprotective and disease modifying therapies. In the various aspects and embodiments, the present disclosure meets these objectives. SUMMARY OF THE DISCLOSURE The present disclosure in the various aspects and embodiments provides methods for detecting and / or distinguishing synucleinopathies using immunostained tissue sections from cutaneous biopsy. The disclosure further provides methods for treating subjects determined to have synucleinopathy, as well as AI-trained diagnostic systems. In the various aspects and embodiments, the disclosure improves accuracy and / or efficiency in diagnosing synucleinopathies based on cutaneous biopsy with the use of automated outputs to characterize nerve structures and PSYN deposition within nerve structures. In aspects, the present disclosure provides diagnostic features and surrogate biomarkers for disease staging, selecting and guiding patient treatment, and evaluating effectiveness of candidate therapies in clinical trials. In an aspect, the disclosure provides a method for detecting synucleinopathy in cutaneous tissue sections. The method comprises obtaining tissue sections from one or more cutaneous biopsies of the subject and staining the tissue sections to discriminate nerve structures and to identify and / or quantify PSYN deposition in the nerve structures. In embodiments, the method further comprises quantifying sensory and autonomic nerve fiber number and / or densities. In embodiments, the method comprises imaging the tissue sections with a system comprising a confocal microscope, high resolution camera, and a computer programmed to operate an AI-trained object recognition algorithm, to determine at least the following features in the tissue sections: (1) amount of sweat gland innervation that contains phosphorylated-synuclein (PSYN) deposition; (2) amount of pilomotor muscle innervation that contains PSYN deposition; (3) amount of blood vessel innervation that contains PSYN DB1 / 155488515.1 2 CND-002PC 128316-5003 deposition; (4) amount of subepidermal plexus (SEP) nerves that contain PSYN deposition; (5) amount of nerve bundle nerves that contain PSYN deposition; (6) amount of free nerve that contains PSYN deposition; and (7) amount of hair follicle innervation that contains PSYN deposition. In various embodiments, PSYN deposition is identified / detected by immunostaining tissue sections for PSYN. In embodiments, the tissue sections are also immunostained for Protein Gene Product 9.5 (PGP9.5) to differentiate nerve structures, including different types of autonomic nerve structures. In embodiments, the area of PSYN deposition in nerves is determined by identifying areas of colocalization of PGP9.5 stain and PSYN stain using fluorescent confocal imaging. In embodiments, the system comprises a laser scanning confocal microscope with high resolution camera for creating Z-stack images. In embodiments, the method involves providing or obtaining one or more skin biopsy samples from the subject, and preparing tissue sections from the biopsy for immunostaining. In embodiments, the biopsy is a punch skin biopsy, such as a 3 mm diameter punch skin biopsy that is 3-4 mm in depth. In embodiments, a number of skin biopsies from different regions are obtained from a subject for analysis. In embodiments, punch skin biopsies are obtained from at least the distal leg, proximal and / or distal thigh, and posterior cervical region. Frozen tissue sections are cut, for example, to obtain from about 4 to about 8 sections per biopsy (e.g., six sections per biopsy). In embodiments, the tissue sections are cut to a thickness in the range of 10 to 100 µm, such as a thickness of about 50 µm. In embodiments, sections immunostained with cyanine dyes, for example cyanine dyes selected from Cy2, Cy3, and Cy5. Stained tissue can be viewed by laser scanning confocal microscope to quantify the areas of PSYN deposition that co-localizes with PGP9.5. For example, stained sections are initially examined under a fluorescent microscope, and images are analyzed to identify nerve structures and identify areas of fluorescent co-localization. Tissue sections are imaged using confocal Z-stack imaging. For example, a series of images of optical sections can be acquired at about 2 to 5 µm intervals (e.g., about 3 µm intervals) throughout the depth of the 50 µm section as a Z-stack (with co-localization). Various nerve structures including sweat gland nerves, pilomotor muscle nerves, DB1 / 155488515.1 3 CND-002PC 128316-5003 blood vessel nerves, SEP nerves, nerve bundle nerves, free nerves, and hair follicle nerves can be distinguished with AI-trained object recognition software. With dual immunofluorescent imaging, deposition of PSYN is determined (e.g., by the object recognition software) to be intra-neural or extra-neural. Locations of protein deposition are identified: if intra-neural, specific nerve subtypes are documented for data output. If extra- neural, locations can be documented for data output. In embodiments, quantitative analysis of nerve fiber number or densities are further determined by the trained object recognition software, including for the sensory and autonomic nerve fibers, such as one or more (or all) of: intra-epidermal nerve fibers, sweat gland nerve fibers, pilomotor nerve fibers, blood vessel nerve fibers, vasomotor nerve fibers, free nerve fibers, and nerve bundles. In embodiments, the method comprises automated analysis and recording for each subject or tissue section: the total number of sweat glands detected (NSG), total sweat gland area detected (TSGA) (e.g., in µm2), total area of nerve fibers within detected sweat glands (SGNA) (e.g., in µm2), percent nerve area within detected sweat glands (PMNFD), area of PSYN in detected sweat glands (SGP) (e.g., in µm2), and percent sweat gland nerve area containing PSYN (SG%P). In embodiments, the method comprises automated analysis and recording for each subject or tissue section: the total number of pilomotor muscles detected (NPM), total pilomotor muscle area detected (TPMA) (e.g., in µm2), total area of nerve fibers within detected pilomotor muscles (PMNA) (e.g., in µm2), nerve fiber density within detected pilomotor muscles (PMNFD) (e.g., in nerves / mm); area of PSYN in pilomotor muscles (PMP) (e.g., in µm2); percent of pilomotor muscle nerve fiber area containing PSYN (PM%P); and percent pilomotor muscle area containing detected nerves (PM%N). In embodiments, the method comprises automated analysis and recording for each subject or tissue section: the total number of blood vessels detected (NBV), total blood vessel area detected (TBVA) (e.g., in µm2), total area of nerve fibers within detected blood vessels (BVNA) (e.g., in µm2), percent blood vessel area containing nerves (BVNFD), area of PSYN in detected blood vessels (BVP) (e.g., in µm2), and percent blood vessel nerve area containing PSYN (BV%P). DB1 / 155488515.1 4 CND-002PC 128316-5003 In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: the total area of nerve fibers detected as SEP nerve fibers (SEPNA) (e.g., in µm2), area of PSYN in SEP fibers (SEPP) (e.g., in µm2), and percent SEP nerves containing PSYN (SEP%P). In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: the total number of nerve bundles detected (NNB), total nerve bundle area detected (TNBA) (e.g., in µm2), area of PSYN in detected nerve bundles (NBP) (e.g., in µm2), and percent nerve bundle nerves containing PSYN (NB%P). In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: the total number of free nerves detected (NFN), total free area detected (TFNA) (e.g., in µm2), area of PSYN in detected free nerves (FNP) (e.g., in µm2), and percent free nerves containing PSYN (FN%P). In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: the total number of hair follicles detected (NHF), total hair follicle area detected (THFA) (e.g., in µm2), area of PSYN in detected hair follicles (HFNA) (e.g., in µm2), and percent hair follicle nerves containing PSYN (HF%P). In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: total number of sebaceous glands detected (NSebG) and total sebaceous gland area detected (TSebGA) (e.g., in µm2). In embodiments, the method further comprises automated analysis and recording for each subject: a total tissue area detected (TTA) (e.g., in mm2or µm2), number of tissue sections (NS), total nerve area (TNA) (e.g., in µm2), and total PYSN area (TPA) (e.g., in µm2). In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: epidermis length (EL) (e.g., in mm), number of total nerve fibers crossing basement membrane (NFC), and intraepidermal nerve fiber density (IENFD) (e.g., in nerves / mm). In embodiments, the automated output and / or the regions of interest are reviewed by a pathologist, in which the PSYN deposition is reviewed and scored for distribution and DB1 / 155488515.1 5 CND-002PC 128316-5003 intensity. A combination score that factors both the distribution and intensity can be created to score samples, which can be based on the automated outputs and / or pathologist review. In embodiments, locations of PSYN deposition can be further used to characterize the subject’s condition. In embodiments, further algorithmic learning based on the feature output optionally with clinical symptoms can determine the likelihood of the result being a true positive based on the location within associated dermal structures of interest (optionally along with other features), and / or determine the likelihood of the subject having a particular synucleinopathy. In embodiments, detected features in tissue sections described herein (e.g., in the automated output) are used to train a supervised, semi-supervised, or unsupervised machine learning algorithm (e.g., a disease classifier) to classify biopsies. In embodiments, the classifier is further trained with demographic information (e.g., age and sex) and clinical symptoms of the training cohort. For example, such features are determined in a training cohort, and used for training the classifier. In embodiments, the cohort includes at least patients clinically diagnosed with PD, DLB, MSA, and PAF (and optionally REM sleep behavior disorder), as well as non-disease controls and disease controls having other neuropathologies (e.g., one or more of AD, ALS, MS, etc.). In embodiments, if a synucleinopathy is detected, the subject is treated for the synucleinopathy. In embodiments, the treatment is a pharmaceutical intervention, neurostimulation intervention (e.g., transcranial neurostimulation), and / or lifestyle intervention, and in embodiments is a candidate treatment or approved therapy. Exemplary, non-limiting pharmaceutical interventions are described herein. In some embodiments, the subject (or a cohort) is undergoing a treatment or candidate treatment for a synucleinopathy. In embodiments, the subject or cohort is evaluated according to this disclosure at least at the start of a trial and at the end of the trial. In embodiments, the subject or cohort is evaluated one or more times during the trial (e.g., a treatment course delivered as part of a clinical trial to a cohort). In aspects, the present disclosure provides a method for treating a patient or subject determined to have a synucleinopathy according to this disclosure. The method comprises administering a pharmacological intervention for treating the synucleinopathy, including an DB1 / 155488515.1 6 CND-002PC 128316-5003 approved or experimental therapy, to the subject. The treatment can be a treatment described herein or other pharmaceutical intervention or other intervention (e.g., neurostimulation). In other aspects, the disclosure provides a system comprising a confocal microscope, high resolution camera, and object recognition software that is trained according to this disclosure. In embodiments, the system further comprises a disease classifier, to classify biopsies for their probability of representing one or more of PD, DLB, MSA, and PAF. Thus, in embodiments, the disclosure provides the use of the system for evaluating tissue sections of cutaneous biopsies, according to the disclosure. Other aspects and embodiments of the disclosure will be apparent from the following detailed description and working examples. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 illustrates an exemplary system topology including an imaging system, a computer system, and one or more client devices, in accordance with an embodiment of the present disclosure; Figure 2 illustrates various modules and / or components of an imaging system, in accordance with an embodiment of the present disclosure; Figure 3 illustrates various modules and / or components of a computer system, in accordance with an embodiment of the present disclosure; Figure 4 illustrates various modules and / or components of a client device, in accordance with an embodiment of the present disclosure; and Figure 5 illustrates various logic functions that are implemented by a computer system in embodiments of the present disclosure. DESCRIPTION OF EMBODIMENTS The present disclosure in the various aspects and embodiments provides methods for detecting and / or distinguishing synucleinopathies using tissue sections from cutaneous biopsy. The disclosure further provides methods for treating subjects determined to have synucleinopathy, as well as providing AI-trained diagnostic systems. In the various aspects DB1 / 155488515.1 7 CND-002PC 128316-5003 and embodiments, the disclosure improves accuracy and / or efficiency in diagnosing synucleinopathies, and in aspects provides surrogate biomarkers for disease staging, treatment selection or monitoring, and evaluating effectiveness of candidate therapies in clinical trials. The present disclosure provides biomarkers (e.g., features measured from tissue sections of cutaneous biopsy, such as a PSYN deposition profile) that contribute to neurocutaneous characterization of subject pathology. These features ae amenable to standardized detection / identification and measurement / quantification using confocal microscopy and trained object recognition software, and in embodiments relate to key pathologic biomarkers involving tissue structures and substructures. In embodiments, the methods and systems described herein improve standardization and efficiency of pathologist review, while simultaneously providing a multitude of valuable automated outputs that would otherwise be manual, labor intensive, time consuming or otherwise unachievable with pathologist slide review only. In other aspects and embodiments, these outputs can support clinical trials by providing biomarker quantification and stratification in conjunction with neurological assessments, which can yield valuable data about enrollment homogeneity, treatment efficacy, and, potentially, drug safety. In embodiments, the outputs can establish “signatures” of subject populations, especially in the context of clinical disease features. Signatures can be used in the context of a disease classifier trained using machine learning platforms for discriminating subjects having the various synucleinopathies including from healthy subjects and subjects with other pathologies. In an aspect, the disclosure provides a method for detecting synucleinopathy in a sample. The method comprises obtaining tissue sections from one or more cutaneous biopsies (e.g., punch skin biopsies) of the subject and staining the tissue sections to discriminate nerve structures (including autonomic nerves) and to identify and / or quantify respective phosphorylated alpha-synuclein deposition within the nerve structures. As used herein, the term “subject” refers to a human subject. The terms “subject” and “patient” may be used interchangeably. In embodiments, the method further comprises quantifying sensory and autonomic nerve fiber number and / or densities. In embodiments, the method comprises imaging the DB1 / 155488515.1 8 CND-002PC 128316-5003 tissue sections with a system comprising a confocal microscope, high resolution camera, and trained object recognition software, to determine at least the following features in the tissue sections: (1) amount of sweat gland innervation that contains phosphorylated-synuclein (PSYN) deposition; (2) amount of pilomotor muscle innervation that contains PSYN deposition; (3) amount of blood vessel innervation that contains PSYN deposition; (4) amount of subepidermal plexus (SEP) nerves that contain PSYN deposition; (5) amount of nerve bundle nerves that contain PSYN deposition; (6) amount of free nerve that contains PSYN deposition; and (7) amount of hair follicle innervation that contains PSYN deposition. These features can be identified using the trained object recognition software, according to the dual-staining system described herein. Synucleins are small, soluble proteins expressed primarily in neural tissue. The family includes three known proteins: α-synuclein, β-synuclein, and γ-synuclein. The α- and β-synuclein proteins are found primarily in brain tissue, where they are seen mainly in presynaptic terminals. Mutations in α-synuclein are associated with rare familial cases of early-onset Parkinson's disease, and the protein accumulates abnormally in Parkinson's disease, Alzheimer's disease, and several other neurodegenerative illnesses. α-synuclein is a protein of 140 amino acids, and in its native form exists as a random coil. Aberrant accumulation of α-synuclein (e.g., in fibrils) may mediate cytotoxicity. In this disclosure, the terms “α-synuclein”, “alpha-synuclein”, “aSyn”, are used interchangeably to refer to all types and forms of α-synuclein protein, including wild-type and mutated forms and fragments thereof. An amino acid sequence for human α-synuclein is provided as SEQ ID NO: 1. The amino acid sequence of α-synuclein can be retrieved from the literature and pertinent databases. The term PSYN refers to the phosphorylated form of alpha-synuclein. In various embodiments, PSYN deposition is identified / detected by staining tissue sections with a molecule having specific binding affinity for PSYN. In embodiments, the molecule having specific binding affinity for PSYN is an antibody or antibody fragment or mimetic. Antibodies may be monoclonal or polyclonal. For example, in some embodiments, polyclonal antibodies against PSYN can be employed. In some embodiments, polyclonal antibodies recognize multiple binding sites included within amino acids 111–131 of α- DB1 / 155488515.1 9 CND-002PC 128316-5003 synuclein. In other embodiments, the molecule having specific binding affinity for PSYN is a monoclonal antibody (or fragment thereof, or antibody mimetic) that binds specifically PSYN, such as α-synuclein phosphorylated at serine 129. In some embodiments, the antibody can distinguish among human monomeric and aggregated forms of PSYN. An exemplary monoclonal antibody for use in certain embodiments of the invention include pSyn#64, which is commercially available (FUJIFILM Wako Pure Chemical Corp.). In still other embodiments, the disclosure can further employ a monoclonal antibody that specifically binds to non-phosphorylated α-synuclein. Various antibodies against α- synuclein are known or commercially available. Antibodies can be of any type, and preferably IgG, IgM, or IgA. The immunoglobulin subclasses (isotypes) e.g., IgG1, IgG2, IgG3, IgG4, IgA1, etc. are well characterized and can be employed in various embodiments. In addition to polyclonal and monoclonal antibodies, various other binding molecules may be employed for staining tissue sections. The various formats include a single-domain antibody, a recombinant heavy-chain-only antibody (VHH), a single-chain antibody (scFv), a shark heavy-chain-only antibody (VNAR), a microprotein (cysteine knot protein, knottin), a DARPin, a Tetranectin, an Affibody; a Transbody, an Anticalin, an AdNectin, an Affilin, a Microbody, a phylomer, a stradobody, a maxibody, an evibody, a fynomer, an armadillo repeat protein, a Kunitz domain, an avimer, an atrimer, a probody, an immunobody, a triomab, a troybody, a pepbody, a vaccibody, a UniBody, a DuoBody, a Fv, a Fab, a Fab′, or a F(ab′)2. See, US Patent Nos. or Patent Publication Nos. US 7,417,130, US 2004 / 132094, US 5,831,012, US 2004 / 023334, US 7,250,297, US 6,818,418, US 2004 / 209243, US 7,838,629, US 7,186,524, US 6,004,746, US 5,475,096, US 2004 / 146938, US 2004 / 157209, US 6,994,982, US 6,794,144, US 2010 / 239633, US 7,803,907, US 2010 / 119446, and / or US 7,166,697, the contents of which are hereby incorporated by reference in their entireties. In various embodiments, the molecule having specific binding affinity for PSYN is used in an immunostaining protocol, for example, using a detectable label that is conjugated to the PSYN binding molecule, or in some embodiments to a secondary antibody or binding member as is well known in the art. In some embodiments, the detectable label emits or can DB1 / 155488515.1 10 CND-002PC 128316-5003 be induced to emit an optical signal. In some embodiments, the detectable label comprises a dye, or a fluorescent or luminescent moiety. Examples of detectable labels include various enzymes that produce visual signals when contacted with a substrate, fluorescent materials (fluorophores), luminescent materials, among others. In embodiments, detectable labels are conjugated to secondary antibodies that recognize the PSYN binding molecule (e.g., the antibody constant region). Examples of suitable enzymes that can be used as detectable labels include horseradish peroxidase, alkaline phosphatase, β-galactosidase, or acetylcholinesterase; examples of suitable fluorescent materials include cyanine dyes, umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin; an example of a luminescent material includes luminol; and examples of bioluminescent materials include luciferase, luciferin, and aequorin. In embodiments, the tissue sections are immunostained for Protein Gene Product 9.5 (PGP9.5) to differentiate nerve structures (including different types of autonomic nerve structures). PGP9.5 is a soluble cytoplasmic protein with a molecular weight of approximately 25 kD. It is present in neurons and in cells of the neuroendocrine system. Because of its abundance in nerves, PGP9.5 is a marker for peripheral nerve fibers. In embodiments, the area of PSYN deposition in nerves is determined by identifying areas of colocalization (e.g., including but not limited to 3D co-localization) of PGP9.5 stain and PSYN stain using fluorescent confocal imaging. In embodiments, the system comprises a laser scanning confocal microscope with high resolution camera for creating Z-stack images, as described in further detailed herein. In embodiments, the method involves providing or obtaining one or more skin biopsy samples from the subject, and preparing tissue sections from the biopsy for immunostaining (e.g., immunofluorescent staining) as described herein. In embodiments, the biopsy is a punch skin biopsy (e.g., a 3 mm diameter punch skin biopsy, 3-4 mm in depth). In embodiments, a number of skin biopsies from different regions are obtained from a subject for analysis. In embodiments, from two to five biopsies from the subject are obtained from different regions, and in some embodiments three skin biopsies are obtained from different regions. In embodiments, punch skin biopsies are obtained from at least the distal leg, proximal and / or distal thigh, and posterior cervical region. For example, the skin biopsies DB1 / 155488515.1 11 CND-002PC 128316-5003 can be fixed in Zamboni solution and stored in cryoprotectant. Frozen tissue sections are cut, for example, to obtain from about 4 to about 8 sections per biopsy (e.g., six sections per biopsy). Tissue sections can be dual immunostained for PGP9.5 and PSYN. In embodiments, the tissue sections are cut to a thickness in the range of 10 to 100 µm, such as a thickness of from about 40 to about 60 µm, or about 50 µm. In embodiments, sections are washed and immunostained with secondary antibodies labeled with cyanine dyes, for example cyanine dyes selected from Cy2, Cy3, and Cy5. Stained tissue can be viewed by laser scanning confocal microscope to quantify the areas of PSYN deposition that co-localizes with PGP9.5. For example, stained sections are initially examined under a fluorescent microscope, and imaged using confocal Z-stack imaging with co-localization. For example, a series of images of optical sections can be acquired at about 2 to 5 µm intervals (e.g., about 3 µm intervals) throughout the depth of the 50 µm section as a Z-stack. The confocal scan conditions can be set to obtain an optimal ratio of signal intensity to noise. Images are analyzed to identify nerve structures and for quantification of fluorescent co-localization within the nerve structures. In embodiments, nerve fibers are stained by PGP9.5 and appear as a single monochromatic output in the light spectrum of 500-560 nanometers (e.g., using Cy2 label). The secondary imaging for PSYN will appear in the 635- 700 nanometer light spectrum (e.g., when using Cy5 label). Regions of overlap between the two sections create a new wavelength of visible light in the 560-630 light spectrum and serve to identify the regions of interest. Various nerve structures including sweat gland nerves, pilomotor muscle nerves, blood vessel nerves, SEP nerves, nerve bundle nerves, free nerves, and hair follicle nerves can be distinguished with the trained object recognition software from fluorescent images stained for at least PGP9.5 (e.g., in skin punch biopsies stained according to the present disclosure). In embodiments, the object recognition software is created by labeling the various nerve structures in a library of images (e.g., using the PGP9.5 immunostaining technique described herein), and the labeled images are used to train a supervised or semi- supervised machine learning algorithm to discriminate the various nerve structures. For example, the various labeled nerve structures can include one or more (or all) of intra- DB1 / 155488515.1 12 CND-002PC 128316-5003 epidermal nerve fibers, sweat gland nerve fibers, pilomotor nerve fibers, blood vessel nerve fibers, vasomotor nerve fibers, free nerve fibers, and nerve bundles. The various cell structures of pilomotor muscles, blood vessels, and sweat glands, among others, are also labeled, to allow for their identification and quantification. This process can successfully train a machine learning algorithm using supervised, unsupervised, semi-supervised, self- supervised, or reinforcement learning techniques, including but not limited to convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), and other deep learning architectures to automatically and accurately discriminate between the various types of cellular structures and specific nerve structures. In some embodiments, the machine learning comprises one or more of Nearest Neighbor, Naive Bayes, Decision Trees, Linear Regression, Support Vector Machines (SVM), and Neural Networks, including as further described herein. For example, in some embodiments, the machine learning comprises Convolutional Neural Network (CNN). These machine learning algorithms are further described elsewhere herein. In embodiments, the object recognition software recognizes the areas of PSYN deposition as well as co-localization of the immunostains (e.g., via the emission spectrums), to identify intraneural PSYN deposition. With dual immunofluorescent imaging, deposition of PSYN is determined to be intra-neural (within nerve fibers) or extra-neural (outside nerve fibers). Locations of protein deposition are identified: if intra-neural, specific nerve subtypes are documented for data output. If extra-neural, locations can be documented for data output (hair follicles, sweat glands, pilomotor muscles, blood vessels, epidermis, dermis). In embodiments, quantitative analysis of nerve fiber number or densities are further determined by the trained object recognition software, including for the sensory and autonomic nerve fibers, such as one or more (or all) of: intra-epidermal nerve fibers, sweat gland nerve fibers, pilomotor nerve fibers, blood vessel nerve fibers, vasomotor nerve fibers, free nerve fibers, and nerve bundles. These features can be useful for understanding or discriminating the subject’s pathology. Analysis for intra-epidermal nerve fiber density is a standardized method to calculate the number of sensory nerves in the epidermal layer (the most superficial layer of the skin). This feature is specific to small nerve fibers and does not include any larger nerve fibers that DB1 / 155488515.1 13 CND-002PC 128316-5003 are coated with myelin and does not include autonomic nerve fibers. This feature can be useful since some of the synucleinopathies may selectively damage the small nerve fibers and may provide the ability to distinguish between the disorders (for example, Parkinson disease is a length dependent small fiber neuropathy). In embodiments, the density of nerve fibers that surround sweat glands is determined. The nerve fibers that surround sweat glands are autonomic nerve fibers (specifically sympathetic cholinergic nerve fibers) and is a common nerve type where PSYN is deposited. Calculating the sweat gland nerve fiber density in some cases can distinguish between different disorders. This feature can be paired with intra-epidermal nerve fiber density. In addition, this feature can quantify the density of nerve fibers that surround sweat glands and quantify the deposition of PSYN within these nerve fibers. See, Gibbons CH, et al., Quantification of sweat gland innervation: a clinical-pathologic correlation. Neurology 2009; 72; 72:1479-1486. In embodiments, the number of nerve fibers within pilomotor muscles (the muscles in the skin that cause goosebumps) are quantified. These nerve fibers are autonomic nerve fibers (specifically they are sympathetic adrenergic nerve fibers). This is another area where phosphorylated alpha-synuclein is detected. This feature can quantify the density of nerve fibers with pilomotor muscles and quantify the deposition of PSYN within these nerve fibers. In embodiments, the number of nerve fibers that surround blood vessels is determined. These nerve fibers are autonomic nerve fibers (specifically they are sympathetic vasomotor nerve fibers). This is another area where PSYN can be detected. This feature can quantify the density of nerve fibers around blood vessels and quantify the deposition of PSYN within these nerve fibers. See, Gibbons CH, et al., Capsaicin induces degeneration of cutaneous autonomic nerve fibers. Annals of Neurology 2010; 68:888-898. In embodiments, the density of nerve fibers in the region just below the epidermal layer is determined. These nerve fibers are sensory nerve fibers. This is another area where PSYN is occasionally detected. In embodiments, PSYN deposits in the nerve subtypes are quantified. The intraneural deposits can be counted as discrete samples normalized to the density of the nerve fiber subtype. For example, the percent of each nerve subtype exhibiting PSYN deposition can be DB1 / 155488515.1 14 CND-002PC 128316-5003 quantified for data output. In embodiments, the area of PSYN deposition within the nerve subtypes is also determined. For extra-neural deposition, deposits can be counted as discrete samples normalized to the area / volume analyzed. Such features in stained tissue sections can be identified and analyzed using imaging and software systems described in the art, which can be employed for discriminating tissue structures and substructures according to this disclosure. See US Patent No.9,697,582 and US Patent No. 11,276,165, which are hereby incorporated by reference in their entireties. Such systems provide for co-registration and / or alignment of the images, to allow for automatic image analysis, e.g., segmentation, classification, detection, recognition, and object-based analysis. In embodiments, the object recognition software identifies sweat glands and nerve fibers within sweat glands, pilomotor muscles and nerve fibers within pilomotor muscles, blood vessels and nerve fibers within blood vessels, hair follicles including hair follicles containing nerve fibers, SEP nerves, nerve bundles, free nerves, and sebaceous glands. In embodiments, the method comprises generating a quantitative assessment of sensory and / or autonomic nerve fiber density of at least one of an intra-epidermal nerve, a sweat gland nerve, or a pilomotor nerve, a sub-epidermal nerve, and a dermal nerve. In embodiments, the method comprises automated analysis and recording for each subject or tissue section: the total number of sweat glands detected (NSG), total sweat gland area detected (TSGA) (e.g., in µm2), total area of nerve fibers within detected sweat glands (SGNA) (e.g., in µm2), percent nerve area within detected sweat glands (PMNFD), area of PSYN in detected sweat glands (SGP) (e.g., in µm2), and percent sweat gland nerve area containing PSYN (SG%P). In embodiments, the method comprises automated analysis and recording for each subject or tissue section: the total number of pilomotor muscles detected (NPM), total pilomotor muscle area detected (TPMA) (e.g., in µm2), total area of nerve fibers within detected pilomotor muscles (PMNA) (e.g., in µm2), nerve fiber density within detected pilomotor muscles (PMNFD) (e.g., in nerves / mm); area of PSYN in pilomotor muscles (PMP) (e.g., in µm2); percent of pilomotor muscle nerve fiber area containing PSYN (PM%P); and percent pilomotor muscle area containing detected nerves (PM%N). DB1 / 155488515.1 15 CND-002PC 128316-5003 In embodiments, the method comprises automated analysis and recording for each subject or tissue section: the total number of blood vessels detected (NBV), total blood vessel area detected (TBVA) (e.g., in µm2), total area of nerve fibers within detected blood vessels (BVNA) (e.g., in µm2), percent blood vessel area containing nerves (BVNFD), area of PSYN in detected blood vessels (BVP) (e.g., in µm2), and percent blood vessel nerve area containing PSYN (BV%P). In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: the total area of nerve fibers detected as SEP nerve fibers (SEPNA) (e.g., in µm2), area of PSYN in SEP fibers (SEPP) (e.g., in µm2), and percent SEP nerves containing PSYN (SEP%P). In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: the total number of nerve bundles detected (NNB), total nerve bundle area detected (TNBA) (e.g., in µm2), area of PSYN in detected nerve bundles (NBP) (e.g., in µm2), and percent nerve bundle nerves containing PSYN (NB%P). In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: the total number of free nerves detected (NFN), total free area detected (TFNA) (e.g., in µm2), area of PSYN in detected free nerves (FNP) (e.g., in µm2), and percent free nerves containing PSYN (FN%P). In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: the total number of hair follicles detected (NHF), total hair follicle area detected (THFA) (e.g., in µm2), area of PSYN in detected hair follicles (HFNA) (e.g., in µm2), and percent hair follicle nerves containing PSYN (HF%P). In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: total number of sebaceous glands detected (NSebG) and total sebaceous gland area detected (TSebGA) (e.g., in µm2). In embodiments, the method further comprises automated analysis and recording for each subject: a total tissue area detected (TTA) (e.g., in mm2or µm2), number of tissue sections (NS), total nerve area (TNA) (e.g., in µm2), and total PYSN area (TPA) (e.g., in µm2). DB1 / 155488515.1 16 CND-002PC 128316-5003 In embodiments, the method further comprises automated analysis and recording for each subject or tissue section: epidermis length (EL) (e.g., in mm), number of total nerve fibers crossing basement membrane (NFC), and intraepidermal nerve fiber density (IENFD) (e.g., in nerves / mm). An exemplary output is shown in Table 1. In embodiments, the automated output and / or the regions of interest are reviewed by a pathologist, in which the PSYN deposition is reviewed and scored for distribution and intensity. For example, a scoring system can use a scale system to characterize the PSYN distribution. For example, a distribution score of 0 can indicate that there is no PSYN deposition in nerve fibers. A distribution score of 1 can indicate that a single nerve fiber contains PSYN deposition. A distribution score of 2 can indicate that two or more nerve fibers contain PSYN deposition, but not in a majority of tissue sections. A distribution score of 3 can indicate that the PSYN deposition is detected in 4 to 6 tissue sections. A distribution score of 4 can indicate that PSYN deposition in nerve fibers is detected in all tissue sections (e.g., 6 of 6). Variations of this scoring system for PSYN distribution will be apparent to one of ordinary skill in the art. In embodiments, the automated system provided herein may characterize PSYN distribution and provide a distribution score. The PSYN deposition is further scored by the intensity of the staining. In embodiments, the score reports the maximally intensively stained fiber (or nerve fiber subtype) detected in the tissue sections. This can be based on the quantitative output and / or by visual observation by the pathologist. For example, an intensity score of 0 can indicate that no PSYN deposition is observed in nerve fibers. An intensity score of 1 can indicate that PSYN deposition in nerve fibers is detected, but is faint enough that it cannot be seen under a dual immunofluorescent filter (showing both PSYN deposition and protein gene product 9.5 simultaneously). An intensity score of 2 can indicate that PSYN deposition in nerve fibers can be seen under dual immunofluorescent filter, but is not immediately apparent. An intensity score of 3 can indicate that PSYN deposition in nerve fibers is immediately apparent with dual immunofluorescent filter. Alternatively, PSYN staining intensity can be determined by a quantitative scoring based on the automated output, optionally as confirmed by the pathologist with review of the images. DB1 / 155488515.1 17 CND-002PC 128316-5003 A combination score that factors both the distribution and intensity can be created to score samples (e.g., biopsies or subjects), which can be based on the automated outputs and / or pathologist review. For example, in embodiments, the distribution and intensity scores are multiplied, for example, providing a score range of 0 to 12. The sum of the scores for each biopsy can provide a total score of 0-36. Similar or equivalent combination scoring systems will be apparent to one of ordinary skill in the art. In embodiments, locations of PSYN deposition can be further used to characterize the subject’s condition. In embodiments, further algorithmic learning based on the feature output optionally with clinical symptoms can determine the likelihood of the result being a true positive based on the location within associated dermal structures of interest (optionally along with other features), and / or determine the likelihood of the subject having a particular synucleinopathy. In embodiments, the detected features (or a subset thereof) in tissue sections described herein (e.g., in the automated output) are used to train a supervised, semi- supervised, or unsupervised machine learning algorithm (e.g., a disease classifier) to classify biopsies. In embodiments, the classifier is further trained with demographic information (e.g., age and sex) and clinical symptoms of the training cohort, as described further below. For example, such features are determined in a training cohort, and used for training the classifier. In embodiments, the cohort includes at least patients clinically diagnosed with PD, DLB, MSA, and PAF (and optionally REM sleep behavior disorder), as well as non-disease controls and disease controls having other neuropathologies (e.g., one or more of AD, ALS, MS, etc.). In some embodiments, such a classifier is based on a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a logistic regression algorithm, a mixture model, a hidden Markov model, or a neural network algorithm. For example, the classifier is trained using one or more of supervised, unsupervised, semi-supervised machine learning models such as, for example: parametric / non-parametric distance measures, logistic regression, support vector machines, decision trees, random forests, neural networks, probit regression, Fisher's linear discriminant, Naive Bayes classifier, perceptron, quadratic classifiers, kernel estimation, k- nearest neighbor, learning vector quantization, and PCA. Such machine learning models are further described elsewhere herein. DB1 / 155488515.1 18 CND-002PC 128316-5003 In embodiments, the methods and systems described herein quantify innervation of autonomic substructures in the skin biopsy sample, and quantify the number and percent of each nerve subtype that exhibits PSYN deposition, and measures (e.g., area of) PSYN within these autonomic substructures in the tissue sections and / or skin biopsy sample. Together with factors from clinical evaluation of the subject, a score can be calculated that represents the probability that the subject has one or more synucleinopathies. In embodiments, the probability of the subject having one or more synucleinopathies is determined by the trained disease classifier. In embodiments, at least the following clinical information is collected for the subject: age, sex, ataxia, parkinsonism, orthostatic hypotension, dream enactment, and confusion / dementia. The presence of clinical symptoms can weight the diagnosis and / or be used as a feature by a disease classifier. In embodiments, the method can further determine a stage or severity of a synucleinopathy. Quantitative staging can be conducted by comparing protein deposition and / or nerve fiber degeneration to normative values based on age and gender, and / or for the same subject over time. In embodiments, where no PSYN deposition in neurons is detected (e.g., the PSYN Score is 0), the probability of synucleinopathy is less than 5%, and alternative diagnoses for any clinical symptoms should be considered. In embodiments, where PSYN deposition in neurons is detected (e.g., a PSYN score greater than 0), a diagnosis of synucleinopathy is made. Where there is low PSYN deposition in neurons (e.g., a low PSYN score of 1-5), a diagnosis can be PD, MSA, RBD or PAF (with probability of phenoconverting to MSA>80%). With low PSYN deposition / scores, presence of DLB is unlikely, unless extremely early in the disease and asymptomatic. Further, with reduced IENFD in distal leg biopsy, or distal leg+distal thigh, the diagnosis is likely PD (about 85%). However, if there is evidence of PSYN deposition within subepidermal plexus then likelihood of PD is reduced to <30%, and likelihood of MSA is >70%. Without reduced IENFD at any site, and with age<65, and with PSYN deposition within subepidermal plexus, condition is likely MSA DB1 / 155488515.1 19 CND-002PC 128316-5003 (>90%). With normal IENFD at all sites, and history of dream enactment without hallucinations or tremors, then the likelihood of RBD is >90%. For medium PSYN deposition in nerves (e.g., PSYN medium scores, e.g., 6-15), there is a possible diagnosis of PD, MSA, RBD, PAF, or DLB. For age >70 without ataxia or parkinsonism, likelihood of MSA is <10%. For age<70 without reduced IENFD, and PSYN deposition present in sub-epidermal plexus, the likelihood of MSA is >90%. With reduced IENFD at distal leg, or distal leg+distal thigh, the condition is likely PD or DLB (>90%). With normal IENFD at all sites, without parkinsonism or ataxia, and history of dream enactment without hallucinations or tremors, then condition is likely RBD (>90%). If confusion or dementia are present, then DLB is likely (>90%). If there is presence of orthostatic hypotension without parkinsonism, ataxia, or confusion / dementia, then the condition is likely PAF (>95%). For high PSYN deposition in nerves (e.g., high PSYN scores, e.g., 16-25), the likelihood of PD, DLB or PAF is >90%. With presence of reduced IENFD at distal leg or distal thigh the likelihood of PD or DLB is >95%. With normal IENFD, without ataxia, parkinsonism or confusion / dementia, and with orthostatic hypotension, condition is likely PAF (>95%). For very high PSYN deposition in nerves (e.g., PSYN scores very high, e.g., >25), the likelihood of DLB or PAF is >90%. With history of orthostatic hypotension without parkinsonism or confusion / dementia, the condition is likely PAF (>95%). With history of confusion / dementia or parkinsonism, condition is likely DLB (>95%). Additional variables of PSYN deposition within the various types of autonomic nerve fibers, along with other features described herein, may further refine the subtype of neuropathy that is likely to be present or provide information concerning disease progression, stage, or efficacy of therapeutic interventions. In embodiments, the subject presenting for analysis is exhibiting one or more clinical symptoms consistent with the presence of a synucleinopathy, or is otherwise at risk to develop a synucleinopathy. In embodiments, the synucleinopathy is selected from Parkinson’s disease (PD), Dementia with Lewy Bodies (DLB), multiple system atrophy (MSA), pure autonomic failure (PAF), and REM sleep behavior disorder. In embodiments, DB1 / 155488515.1 20 CND-002PC 128316-5003 the PSYN deposition in neurons (or select types of neurons) or level of such deposition corresponds to the presence of a synucleinopathy, such as PD, DLB, MSA, PAF, or REM sleep behavior disorder, or stage thereof (as described above). For example, in embodiments, the subject is suspected of having, or is diagnosed as having, an alpha-synucleinopathy. In exemplary embodiments, the subject may be suspected of having an alpha-synucleinopathy selected from PD, DLB, MSA, or PAF. The subject may be suspected of having such a disorder based on clinical signs, family history, and / or genetic evaluation. In embodiments, the method as described herein detects one or more of PD, DLB, MSA, or PAF, with an accuracy of at least 90%, or at least 95%. For example, in embodiments, an alpha-synucleinopathy is detected or discriminated with a positive predictive value of at least 90% and / or a negative predictive value of at least 90%. In embodiments, the method distinguishes between subjects having PD and MSA, e.g., with an accuracy of at least 90% or at least 95%. In some embodiments, the subject is suspected of having PD, or is considered at risk for developing PD. PD is an age-dependent neurodegenerative disease. It is believed that sporadic PD results from a combination of genetic vulnerability and environmental insults. It is further believed that PD, while triggered by disparate mechanisms, follows a shared pathophysiologic pathway involving α-synuclein. In certain embodiments, the subject is exhibiting symptoms of the alpha- synucleinopathy or is diagnosed as having the alpha-synucleinopathy (e.g., PD). In such embodiments, the subject is evaluated on a periodic basic (e.g., from 1 to 12 times per year) to monitor a therapeutic intervention or disease progression. In related embodiments, the invention is useful for clinical trials, as a biomarker for candidate drug efficacy. In embodiments, the method is performed a plurality of times at different time points, for example spaced by at least six months or at least one year. In embodiments, if a synucleinopathy is detected, the subject is treated for the synucleinopathy. In embodiments, the treatment is a pharmaceutical intervention, neurostimulation intervention (e.g., transcranial neurostimulation), and / or lifestyle intervention, and in embodiments is a candidate treatment or approved therapy. Early detection can be important for therapeutic intervention to treat or slow the disease DB1 / 155488515.1 21 CND-002PC 128316-5003 progression. It is believed that the pathological process in synucleinopathies starts years before the onset of clinical manifestations. For example, in PD, it is described that motor signs first appear when >50% of substantia nigra dopamine neurons are lost (Noyce AJ et al. Neurol. Neurosurg. Psychiatry 2016;87: 871-878). Features relating to alpha-synuclein deposition can be detected in the early stage of disease in peripheral tissues according to this disclosure. Exemplary pharmaceutical interventions for PD, if diagnosed, include carbidopa and / or levodopa, dopamine agonist (e.g., pramipexole, rotigotine, or apomorphine), monoamine oxidase B (MAO B) inhibitors (e.g., selegiline, rasagiline, or safinamide), catechol O-methyltransferase (COMT) inhibitors (e.g., entacapone, opicapone, or Tolcapone), anticholinergic (e.g., benztropine or trihexyphenidyl), amantadine, adenosine receptor antagonist (e.g., istradefylline), nuplazid, or a combination thereof. Other potential interventions include Bezisterim (NE-3107), BIIB122, KO706, and Dipraglurant. Exemplary pharmaceutical interventions for DLB, if diagnosed, include cholinesterase inhibitors (e.g., rivastigmine, donepezil, and galantamine), N-methyl-d- aspartate (NMDA) receptor antagonist (e.g., memantine), and any of the PD medications. Other interventions for DLB include CT812, nilotinib, ambroxol, neflamapimod, and fosgonimeton. Exemplary pharmaceutical interventions for MSA or PAF, if diagnosed, include medications to raise blood pressure (e.g., pyridostigmine, midodrine, droxidopa), as well as medications to reduce Parkinson's disease-like symptoms (described above). Other interventions for MSA include ONO-2808, TAK-341, and ampreloxetine. In some embodiments, the subject (or a cohort) is undergoing a treatment or candidate treatment for a synucleinopathy. In embodiments, the subject or cohort is evaluated according to this disclosure at least at the start of a trial and at the end of the trial. In embodiments, the subject or cohort is evaluated one or more times during the trial (e.g., a treatment course delivered as part of a clinical trial to a cohort). In aspects, the present disclosure provides a method for treating a patient or subject determined to have a synucleinopathy according to this disclosure. The method comprises administering a pharmacological intervention for treating the synucleinopathy, including an DB1 / 155488515.1 22 CND-002PC 128316-5003 approved or experimental therapy, to the subject. The treatment can be a treatment described herein or other pharmaceutical intervention or other intervention (e.g., neurostimulation). In other aspects, the disclosure provides a system comprising a confocal microscope, high resolution camera, and a computer operating object recognition software that is trained according to this disclosure, and which produces automated outputs according to this disclosure (e.g., as already described). Such automated outputs include at least: (1) quantified innervation of autonomic substructures in the tissue sections and skin biopsy samples, (2) number and / or density of each nerve subtype, (3) percent of each nerve subtype that exhibits PSYN deposition, and (4) measures (e.g., area of) PSYN deposition within each nerve subtype in the tissue sections and / or skin biopsy sample. Exemplary set of automated outputs is shown in Table 1. In embodiments, the system further comprises a disease classifier (e.g., as described herein), to computationally classify biopsies for their probability of representing one or more of PD, DLB, MSA, and PAF at least in part using the automated outputs. Thus, in embodiments, the disclosure provides the use of the system for evaluating tissue sections of cutaneous biopsies, according to the disclosure. In aspects and embodiments, this disclosure employs machine learning models. As used interchangeably herein, the term “classifier” or “model” refers to a machine learning model or algorithm, including unsupervised learning algorithms (e.g., cluster analysis), supervised learning algorithms, and semi-supervised learning algorithms. In some embodiments, a model is a supervised machine learning algorithm. Nonlimiting examples of supervised learning algorithms include, but are not limited to, logistic regression, neural networks, support vector machines, Naive Bayes algorithms, nearest neighbor algorithms, random forest algorithms, decision tree algorithms, boosted trees algorithms, multinomial logistic regression algorithms, linear models, linear regression, GradientBoosting, mixture models, hidden Markov models, Gaussian NB algorithms, linear discriminant analysis, or any combinations thereof. In some embodiments, a model is a multinomial classifier algorithm. In some embodiments, a model is a 2-stage stochastic gradient descent (SGD) model. In some embodiments, a model is a deep neural network (e.g., a deep-and-wide sample-level classifier). DB1 / 155488515.1 23 CND-002PC 128316-5003 In some embodiments, the model is a neural network (e.g., a convolutional neural network and / or a residual neural network). Neural network algorithms, also known as artificial neural networks (ANNs), include convolutional and / or residual neural network algorithms (deep learning algorithms). Neural networks can be machine learning algorithms that may be trained to map an input data set to an output data set, where the neural network comprises an interconnected group of nodes organized into multiple layers of nodes. For example, the neural network architecture may comprise at least an input layer, one or more hidden layers, and an output layer. The neural network may comprise any total number of layers, and any number of hidden layers, where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to an output value or set of output values. As used herein, a deep learning algorithm can be a neural network comprising a plurality of hidden layers, e.g., two or more hidden layers. Each layer of the neural network can comprise a number of nodes (or “neurons”). A node can receive input that comes either directly from the input data or the output of nodes in previous layers, and perform a specific operation, e.g., a summation operation. In some embodiments, a connection from an input to a node is associated with a parameter (e.g., a weight and / or weighting factor). In some embodiments, the node may sum up the products of all pairs of inputs, xi, and their associated parameters. In some embodiments, the weighted sum is offset with a bias, b. In some embodiments, the output of a node or neuron may be gated using a threshold or activation function, f, which may be a linear or non-linear function. The activation function may be, for example, a rectified linear unit (ReLU) activation function, a Leaky ReLU activation function, or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parametric rectified linear unit, exponential linear unit, softPlus, bent identity, softExponential, Sinusoid, Sine, Gaussian, or sigmoid function, or any combination thereof. The weighting factors, bias values, and threshold values, or other computational parameters of the neural network, may be “taught” or “learned” in a training phase using one or more sets of training data (e.g., as already described). For example, the parameters may be trained using the input data from a training data set and a gradient descent or backward propagation method so that the output value(s) that the ANN computes are consistent with DB1 / 155488515.1 24 CND-002PC 128316-5003 the examples included in the training data set. The parameters may be obtained from a back propagation neural network training process. Any of a variety of neural networks may be suitable for use in accordance with this disclosure. Examples can include, but are not limited to, feedforward neural networks, radial basis function networks, recurrent neural networks, residual neural networks, convolutional neural networks, residual convolutional neural networks, and the like, or any combination thereof. In some embodiments, the machine learning makes use of a pre-trained and / or transfer-learned ANN or deep learning architecture. Convolutional and / or residual neural networks can be used in accordance with the present disclosure. For instance, a deep neural network model comprises an input layer, a plurality of individually parameterized (e.g., weighted) convolutional layers, and an output scorer. The parameters (e.g., weights) of each of the convolutional layers as well as the input layer contribute to the plurality of parameters (e.g., weights) associated with the deep neural network model. In some embodiments, at least 100 parameters, at least 1000 parameters, at least 2000 parameters or at least 5000 parameters are associated with the deep neural network model. In some embodiments, the model is a support vector machine (SVM). When used for classification, SVMs separate a given set of binary labeled data with a hyper-plane that is maximally distant from the labeled data. For cases in which no linear separation is possible, SVMs can work in combination with the technique of̀ kernels`, which automatically realizesa non-linear mapping to a feature space. The hyper-plane found by the SVM in feature space can correspond to a non-linear decision boundary in the input space. In some embodiments, the plurality of parameters (e.g., weights) associated with the SVM define the hyper-plane. In some embodiments, the hyper-plane is defined by at least 10, at least 20, at least 50, or at least 100 parameters and the SVM model requires a computer to calculate because it cannot be mentally solved. In some embodiments, the model is a Naive Bayes algorithm. A Naive Bayes model is any model in a family of “probabilistic models” based on applying Bayes’ theorem with strong (naïve) independence assumptions between the features. In some embodiments, they are coupled with Kernel density estimation. See, for example, Hastie et al., 2001, The DB1 / 155488515.1 25 CND-002PC 128316-5003 elements of statistical learning: data mining, inference, and prediction, eds. Tibshirani and Friedman, Springer, New York, which is hereby incorporated by reference. In some embodiments, a model is a nearest neighbor algorithm. Nearest neighbor models can be memory-based and include no model to be fit. For nearest neighbors, given a query point x0 (a test subject), the k training points x(r), r, ..., k (here the training subjects) closest in distance to x0are identified and then the point x0is classified using the k nearest neighbors. Here, the distance to these neighbors is a function of the abundance values of the discriminating gene set. In some embodiments, Euclidean distance in feature space is used to determine distance as ^^(^) = ‖^^(^) − ^^(ை)‖. Typically, when the nearest neighboralgorithm is used, the the linear discriminant is standardized to have mean zero and neighbor rule can be refined to address issues of unequal class priors, differential misclassification costs, and feature selection. Many of these refinements involve some form of weighted voting for the neighbors. A k-nearest neighbor model is a non-parametric machine learning method in which the input consists of the k closest training examples in feature space. The output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. See, Duda et al., 2001, Pattern Classification, Second Edition, John Wiley & Sons, which is hereby incorporated by reference. In some embodiments, the number of distance calculations needed to solve the k-nearest neighbor model is such that a computer is used to solve the model for a given input because it cannot be mentally performed. In some embodiments, the model is a decision tree. Decision trees suitable for use as models are described generally by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 395-396, which is hereby incorporated by reference. Tree-based methods partition the feature space into a set of rectangles, and then fit a model (like a constant) in each one. In some embodiments, the decision tree is random forest regression. One specific algorithm that can be used is a classification and regression tree (CART). Other specific decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and DB1 / 155488515.1 26 CND-002PC 128316-5003 Random Forests. In some embodiments, the decision tree model includes at least 10, at least 20, at least 50, or at least 100 parameters (e.g., weights and / or decisions). In some embodiments, the model uses a regression algorithm. A regression algorithm can be any type of regression. For example, in some embodiments, the regression algorithm is logistic regression. In some embodiments, the regression algorithm is logistic regression with lasso, L2 or elastic net regularization. In some embodiments, those extracted features that have a corresponding regression coefficient that fails to satisfy a threshold value are pruned (removed from) consideration. In some embodiments, a generalization of the logistic regression model that handles multicategory responses is used as the model. In some embodiments, the logistic regression model includes at least 10, at least 20, at least 50, at least 100, or at least 1000 parameters (e.g., weights). Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis can be a generalization of Fisher’s linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination can be used as the model (linear model) in some embodiments of the present disclosure. In some embodiments, the model is a mixture model, such as that described in McLachlan et al., Bioinformatics 18(3):413-422, 2002. In some embodiments, in particular, those embodiments including a temporal component, the model is a hidden Markov model such as described by Schliep et al., 2003, Bioinformatics 19(1): i255-i263. In some embodiments, the model is an unsupervised clustering model. In some embodiments, the model is a supervised clustering model. The clustering problem can be described as one of finding natural groupings in a dataset. To identify natural groupings, two issues can be addressed. First, a way to measure similarity (or dissimilarity) between two samples can be determined. This metric (e.g., similarity measure) can be used to ensure that the samples in one cluster are more like one another than they are to samples in other clusters. Second, a mechanism for partitioning the data into clusters using the similarity measure can be determined. One way to begin a clustering investigation can be to define a distance function and to compute the matrix of distances between all pairs of samples in the training DB1 / 155488515.1 27 CND-002PC 128316-5003 set. If distance is a good measure of similarity, then the distance between reference entities in the same cluster can be significantly less than the distance between the reference entities in different clusters. However, clustering may not use a distance metric. For example, a nonmetric similarity function s(x, x') can be used to compare two vectors x and x'. s(x, x') can be a symmetric function whose value is large when x and x' are somehow “similar.” Once a method for measuring “similarity” or “dissimilarity” between points in a dataset has been selected, clustering can use a criterion function that measures the clustering quality of any partition of the data. Partitions of the data set that extremize the criterion function can be used to cluster the data. Particular exemplary clustering techniques that can be used in the present disclosure can include, but are not limited to, hierarchical clustering (agglomerative clustering using a nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering. In some embodiments, the clustering comprises unsupervised clustering (e.g., with no preconceived number of clusters and / or no predetermination of cluster assignments). In some embodiments, a model is a reinforcement learning model. In some embodiments, the reinforcement learning system comprises four main elements – an agent, a policy, a reward signal, and a value function, where the behavior of the agent is defined in terms of the policy. In some embodiments, the reinforcement learning system comprises a learning algorithm. In some implementations, the learning algorithm is an on-policy learning algorithm or an off-policy learning algorithm. On-Policy learning algorithms evaluate and improve the same policy which is being used to select the agent’s actions. Off-Policy learning algorithms evaluate and improve policies that are different from the policy being used for action selection. In some embodiments, an ensemble (two or more) of models is used. In some embodiments, a boosting technique such as AdaBoost is used in conjunction with many other types of learning algorithms to improve the performance of the model. In this approach, the output of any of the models disclosed herein, or their equivalents, is combined into a weighted sum that represents the final output of the boosted model. In some embodiments, the plurality of outputs from the models is combined using any measure of central tendency known in the art, including but not limited to a mean, median, mode, a weighted mean, DB1 / 155488515.1 28 CND-002PC 128316-5003 weighted median, weighted mode, etc. In some embodiments, the plurality of outputs is combined using a voting method. In some embodiments, a respective model in the ensemble of models is weighted or unweighted. In some embodiments, the model is a reinforcement learning model. In some embodiments, the reinforcement learning system comprises four main elements – an agent, a policy, a reward signal, and a value function, where the behavior of the agent is defined in terms of the policy. In some embodiments, the reinforcement learning system comprises a learning algorithm. In some implementations, the learning algorithm is an on-policy learning algorithm or an off-policy learning algorithm. On-Policy learning algorithms evaluate and improve the same policy which is being used to select the agent’s actions. Off-Policy learning algorithms evaluate and improve policies that are different from the policy being used for action selection. Reinforcement learning is further described, for example, in Sutton RS, Barto AG, “Reinforcement learning: an introduction,” IEEE Transactions on Neural Networks. 1998;9(5):1054-1054, which is hereby incorporated herein by reference in its entirety. In some embodiments, the reinforcement learning model includes at least 10, at least 100, at least 1000, at least 10,000, at least 100,000, at least 1 x 106, at least 1 x 107, or more parameters. In some embodiments, the reinforcement learning model includes no more than 1 x 108, no more than 1 x 107, no more than 1 x 106, no more than 100,000, no more than 10,000, no more than 1000, or no more than 100 parameters. In some embodiments, the reinforcement learning model consists of from 10 to 1000, from 100 to 100,000, from 10,000 to 1 x 107, or from 1 x 106to 1 x 108parameters. In some embodiments, the plurality of parameters for the reinforcement learning model falls within another range starting no lower than 10 parameters and ending no higher than 1 x 108parameters. The term “classification” can refer to any number(s) or other characters(s) that are associated with a particular property. For example, a “+” symbol (or the word “positive”) can signify a particular classification. In some embodiments, the classification is binary (e.g., positive or negative) or has more levels or types of classification. In some embodiments, the terms “cutoff” and “threshold” refer to predetermined numbers used in an operation. In one example, a cutoff value refers to a value above which results are excluded. In some embodiments, a threshold value is a value above or below which a particular classification applies. Either of these terms can be used in either of these contexts. DB1 / 155488515.1 29 CND-002PC 128316-5003 Moreover, as used herein, the term “parameter” refers to any coefficient or, similarly, any value of an internal or external element (e.g., a weight and / or a hyperparameter) in an algorithm, model, regressor, and / or classifier that can affect (e.g., modify, tailor, and / or adjust) one or more inputs, outputs, and / or functions in the algorithm, model, regressor and / or classifier. For example, in some embodiments, a parameter refers to any coefficient, weight, and / or hyperparameter that can be used to control, modify, tailor, and / or adjust the behavior, learning, and / or performance of an algorithm, model, regressor, and / or classifier. In some instances, a parameter is used to increase or decrease the influence of an input (e.g., a feature) to an algorithm, model, regressor, and / or classifier. As a nonlimiting example, in some embodiments, a parameter is used to increase or decrease the influence of a node (e.g., of a neural network), where the node includes one or more activation functions. Assignment of parameters to specific inputs, outputs, and / or functions is not limited to any one paradigm for a given algorithm, model, regressor, and / or classifier but can be used in any suitable algorithm, model, regressor, and / or classifier architecture for a desired performance. In some embodiments, a parameter has a fixed value. In some embodiments, a value of a parameter is manually and / or automatically adjustable. In some embodiments, a value of a parameter is modified by a validation and / or training process for an algorithm, model, regressor, and / or classifier (e.g., by error minimization and / or backpropagation methods). In some embodiments, an algorithm, model, regressor, and / or classifier of the present disclosure includes a plurality of parameters. In some embodiments, the plurality of parameters is n parameters, where: n ≥ 2; n ≥ 5; n ≥ 10; n ≥ 25; n ≥ 40; n ≥ 50; n ≥ 75; n ≥ 100; n ≥ 125; n ≥ 150; n ≥ 200; n ≥ 225; n ≥ 250; n ≥ 350; n ≥ 500; n ≥ 600; n ≥ 750; n ≥ 1,000; n ≥ 2,000; n ≥ 4,000; n ≥ 5,000; n ≥ 7,500; n ≥ 10,000; n ≥ 20,000; n ≥ 40,000; n ≥ 75,000; n ≥ 100,000; n ≥ 200,000; n ≥ 500,000, n ≥ 1 x 106, n ≥ 5 x 106, or n ≥ 1 x 107. In some embodiments, n is between 10,000 and 1 x 107, between 100,000 and 5 x 106, or between 500,000 and 1 x 106. In some embodiments, the algorithms, models, regressors, and / or classifier of the present disclosure operate in a k-dimensional space, where k is a positive integer of 5 or greater (e.g., 5, 6, 7, 8, 9, 10, etc.). In aspects and embodiments, the present application provides systems for performing the methods described herein. In the present disclosure, unless expressly stated otherwise, descriptions of devices and systems will include implementations of one or more computers, DB1 / 155488515.1 30 CND-002PC 128316-5003 such as one or more processing units (e.g., one or more central processing units, one or more graphics processing units, one or more tensor processing units, one or more neural processing units, etc.). For instance, and for purposes of illustration in Figure 1, a computer system 300, hereinafter “computer system,” can be represented as single device that includes all the functionality of the computer system 300. For instance, in some embodiments, the functionality of the computer system 300 is spread across any number of networked computers and / or resides on each of several networked computers and / or by hosted on one or more virtual machines and / or containers at a remote location accessible across a communication network (e.g., communication network 196 of Figure 1, communication network 196 of Figure 2, communication network 196 of Figure 3, communication network 196 of Figure 4, etc.). One of skill in the art will appreciate that a wide array of different computer topologies is possible for the computer system 300, and other devices and systems of the preset disclosure, and that all such topologies are within the scope of the present disclosure. Moreover, rather than relying on a physical communication network 196, the illustrated devices and systems may wirelessly transmit information between each other. In some embodiments, the system 100 includes the computer system 300 and an imaging system (e.g., imaging system 200 of Figure 1, imaging system 200 of Figure 2, etc.). In some such embodiments, the system 100 further includes a population of client devices (e.g., first client device 400-1, second client device 400-2, ..., client device 400-V of Figure 1, client device 400 of Figure 4, etc.) and the computer system 300. In some embodiments, the imaging system 200 and the computer system 300 are communicably connected to each other by one or more communication networks 196. In some embodiments, one or more client devices 400 and / or the computer system 300 are communicably connected to each other by one or more communication networks 196. Moreover, in some embodiments, in some embodiments, one or more client devices 400 and / or the computer system 300 are communicably connected to each other by one or more communication networks 196. In some embodiments, the computer system 300 is not proximate to the imaging system 200 and / or does not have wireless capabilities or such wireless capabilities are not DB1 / 155488515.1 31 CND-002PC 128316-5003 used for the purpose of detecting a subject for synucleinopathy. In such embodiments, the communication network 196 is utilized to communicate with the imaging system 200. Figure 1 illustrates an exemplary topography of an integrated system 100 for detecting PSYN deposition in nerve fibers and / or discriminating subjects having the various synucleinopathies (including from healthy subjects and subjects with other pathologies), in accordance with this disclosure. The integrated system 100 can include an imaging system 200 that captures images of tissue sections (as described herein), a computer device for obtaining the images from the image system 200 and processing the images to identify and quantify nerve structures and PSYN deposition (according to this disclosure), and evaluating some or all of the processed images and / or outputs using one or more client devices 400 (e.g., computing devices) that provide and / or receive communications to and / or from the computer system 300, including a report associated with the evaluation of the some or all of the images. Figure 2 depicts a block diagram of an imaging system (e.g., imaging system 200 of Figure 1, etc.) according to some embodiments of the present disclosure. The imaging system 200 at least facilitates imaging tissue sections obtained from a subject and / or communicating the images to the computer system 300. In various embodiments, the imaging system 200 includes one or more processing units (e.g., one or more central processing units (CPUs), one or more graphics processing units (GPUs), one or more tensor processing units, one or more neutral processing units, or a combination thereof 2902, a network or other communications interface 2904, and memory 2912. In some embodiments, the imaging system 200 includes one or more magnetic disk storage and or persistent devices 2920 optionally accessed by one or more controllers 2922, and one or more communication busses 2918 for interconnecting the aforementioned components. In some embodiments, the imaging system 200 includes a camera 2916 (e.g., a two- dimensional pixelated detector in communication with an objective lens of a confocal microscope, etc.) configured to capture data elements in accordance with one or more parameters (e.g., one or more instructions communicated by the controller 2920 of the imaging system 200 and / or by the computer system 300). In some embodiments, the camera DB1 / 155488515.1 32 CND-002PC 128316-5003 2916 receives light from the environment, communicates with one or more lens, such as the objective lens, and converts the light to data representing an image (e.g., a digital image). In some embodiments, the camera 2916 is incorporated into the imaging system 200 or is a component of the imaging system 200. In some embodiments, the camera 2916 is in communication with the imaging system 200. For example, in some embodiments, data captured by the camera 2916 is sent to and / or aggregated in the imaging system 200. In some embodiments, the camera 2916 is in communication with a remote system, e.g., connected to the computer system 300 and / or one or more client devices 400 via the communication network 106, such that data captured by the camera 2916 can be sent to the computer system 300, which collects, stores, and / or processes the captured data. In some embodiments, the imaging system 200 includes a confocal microscope 2940, which in configured to illuminate a field of view and / or eliminate some light emissions from the field of view. For instance, in some embodiments, the confocal lens is configured to illuminate a field of view with the objective lens with substantially uniform optical characteristics across the field of view. For instance, in some embodiments, the field of view is substantially uniform optical characteristics across the field of view by removing out-of- focus emissions using a pinhole disposed interposing between, at least in part, the objective lens and the field of view. Accordingly, the pinhole forms an aperture that allows the objective lens to have a center line view of the field of view of the objective lens narrowed by the aperture. However, the present disclosure is not limited thereto. As a non-limiting example, in some embodiments, the field of view is illuminated using a laser light source of the imaging system 200. As illustrated in Figure 2, the imaging system 200 preferably includes an operating system 2924 that includes procedures for handling various basic system services. The operating system 2924 includes various software components and / or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components. An electronic address 2926 is associated with the imaging system 200, which is utilized to at least uniquely identify the imaging system 200 from other devices and DB1 / 155488515.1 33 CND-002PC 128316-5003 components of the system 100. In some embodiments, the imaging system 200 includes a serial number, and optionally, a model number or manufacturer information that further identifies the imaging system 200. In some embodiments, the electronic address 2926 associated with the imaging system 200 is used to provide a source of a communication (e.g., data element) received from and / or transmitted to the imaging system 200. Figure 3 depicts a block diagram of a computer system (e.g., computer system 300) according to some embodiments of the present disclosure. In various embodiments, the computer system 300 includes one or more processing units (e.g., one or more central processing units (CPUs), one or more graphics processing units (GPUs), one or more tensor processing units, one or more neutral processing units, or a combination thereof 3902, a network or other communications interface 3904, and memory 3912. In some embodiments, the computer system 300 includes one or more magnetic disk storage and or persistent devices 3920 optionally accessed by one or more controllers 3922, and one or more communication busses 3918 for interconnecting the aforementioned components. In some embodiments, the computer system 300 includes a user interface 3906. The user interface 3906 typically includes a display 3908 for presenting media (e.g., one or more images of tissue sections, a report associated with the tissue sections, an input and / or output of the present disclosure, etc.). In some embodiments, the display 3908 is integrated within the computer system 300 (e.g., housed in the same chassis as the CPU 3902 and memory 3912). In some embodiments, the computer system 300 includes one or more input device(s) 3910, which allow a subject to interact with the computer system 300. In some embodiments, input devices 3910 include a keyboard, a mouse, and / or other input mechanisms. Alternatively, or in addition, in some embodiments, the display 3908 includes a touch- sensitive surface (e.g., where display 3908 is a touch-sensitive display or computer system 300 includes a touch pad). In some embodiments, the computer system 300 presents media to a user through the display 3908. Examples of media presented by the display 3908 include one or more images of stained tissue sections. In embodiments, the one or more images (e.g.., images of tissue sections) is presented by the display 3908 through a client application (e.g., client application 3932). DB1 / 155488515.1 34 CND-002PC 128316-5003 In some embodiments, the computer system 300 preferably includes an operating system 3924 that includes procedures for handling various basic system services. The operating system 3924 includes various software components and / or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components. An electronic address 3926 is associated with the computer system 300, which is utilized to at least uniquely identify the computer system 300 from other devices and components of the system 100. In some embodiments, the computer system 300 includes a serial number, and optionally, a model number or manufacturer information that further identifies the computer system 300. In some embodiments, the electronic address 3926 associated with the computer system 300 is used to provide a source of a communication (e.g., data element) received from and / or transmitted to the computer system 300. In some embodiments, the computer system 300 includes a model library 3928 that stores a plurality of models 3930 (e.g., classifiers, regressors, clustering, etc.). In some embodiments, the model library 3928 stores two more models 3930 (e.g., a first model 3930- 1 and a second model 3930-2), three or more models (e.g., the first model 3930-1, a second model 3930-2, a third model 3930-3), four or more models 3930, ten or more models 3930, 50 or more models 3930, or 100 or more models 3930. In some embodiments, the systems and methods of the present disclosure utilize more than one model 3930 according to this disclosure. For instance, in some embodiments, each respective model 3930 arrives at a corresponding output of data according to this disclosure when provided a respective data set associated with a request for receiving and / or obtaining images of tissue sections of a subject. Accordingly, in some embodiments, each respective model 3930 independently arrives at a result and then the result of each respective model 3930 is collectively verified through a comparison or amalgamation of the models 3930. From this, a cumulative result is provided by the models 3930, e.g., by virtue of the models arranged as an ensemble model 3930. In some embodiments, a respective model 3930 is tasked with performing a corresponding activity, such as a step or process of a method of the present disclosure. As a DB1 / 155488515.1 35 CND-002PC 128316-5003 non-limiting example, in some embodiments, the task performed by the respective model 3930 includes, but is not limited to obtaining images of tissue sections (according to the disclosure), and determining an amount of sweat gland innervation that contains PSYN deposition in the tissue sections, determining an amount of pilomotor muscle innervation that contains PSYN deposition in the tissue sections, determining an amount of blood vessel innervation that contains PSYN deposition in the tissue sections, determining an amount of subepidermal plexus (SEP) nerves that contain PSYN deposition in the tissue sections, determining amount of nerve bundle nerves that contain PSYN deposition in the tissue sections, determining an amount of free nerve that contains PSYN deposition in the tissue sections, and determining amount of hair follicle innervation that contains PSYN deposition in the tissue sections. In some embodiments, each respective model 3930 of the present disclosure makes use of 10 or more parameters, 100 or more parameters, 1,000 or more parameters, 10,000 or more parameters, or 100,000 or more parameters. In some embodiments, each parameter is a conditional logic (e.g., logic function of Figure 5) of the respective model 3930. In some embodiments, each model 3930 includes a plurality of heuristic instructions that describe various processes for the model 3930 to follow when evaluating an input in order to arrive at an output, such as an output described herein. For instance, in some embodiments, a pattern matching model 3930 includes a plurality of heuristic instructions that dictate how to evaluate the images of the tissue sections into one or more portions in accordance with a pattern matching analysis. In some embodiments, this analysis is provided by the plurality of heuristic instructions, for instance by identifying a type of content within an image and / or across a plurality of images. In some embodiments, one or more models 3930 share one or more instructions in a plurality of heuristic instructions. In some embodiments, the client application 3932 is a group of instructions that, when executed by a processor 3902, generates content for presentation to the user, such as a visualization of an image of a tissue section, a visualization of a request for an evaluation of the image, a visualization of a report associated with the evaluation of the image, and / or the like. In some embodiments, the client application 3932 generates content in response to inputs received from the user through the computer system 300, such as the inputs 3910 of DB1 / 155488515.1 36 CND-002PC 128316-5003 the computer system 300. In some embodiments, the client application 3932 is utilized to configure one or more parameters associated with a model 3930. In some embodiments, the object recognition module 3934 is a software module that allows a client application 3932 and / or model 3930 to operate in conjunction with the computer system 300. In some embodiments, the object recognition module 3934 is configured to evaluate a general structure of one or more images to define and identify patterns and classes, and provide determinations on the underlying content (e.g., types and numbers of nerve fibers, and detect and quantify PSYN deposition in nerve fibers according to this disclosure). Each of the above identified modules and applications correspond to a set of executable instructions for performing one or more functions described above and the methods described in the present disclosure (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules are, optionally, combined or otherwise re-arranged in various embodiments of the present disclosure. In some embodiments, the memory 3912 optionally stores a subset of the modules and data structures identified above. Furthermore, in some embodiments, the memory 3912 stores additional modules and data structures not described above. Referring to Figure 4 depicts a block diagram of a client device (e.g., first client device 300-1 of Figure 1, … second client device 400-2 of Figure 1, client device V 400-4 of Figure 1, client device 400 of Figure 4, etc.) according to some embodiments of the present disclosure. The client device 400 at least facilitates receiving and / or transmitting data elements, such as a report associated with a determination of evaluating images of tissue sections (e.g., identifying and quantifying nerve structures and identifying and quantifying PSYN deposition). In various embodiments, the client device 400 includes one or more processing units (e.g., one or more central processing units (CPUs), one or more graphics processing units (GPUs), one or more tensor processing units, one or more neutral processing units, or a combination thereof 4902, a network or other communications interface 4904, and memory DB1 / 155488515.1 37 CND-002PC 128316-5003 3912. In some embodiments, the client device 400 includes one or more magnetic disk storage and or persistent devices 4920 optionally accessed by one or more controllers 4922, and one or more communication busses 4918 for interconnecting the aforementioned components. In some embodiments, the client device 400 includes a user interface 4906. The user interface 4906 typically includes a display 4908 for presenting media, such as one or more images of stained tissue sections or a report associated with a determination in the images of the stained tissue sections. In some embodiments, the display 4908 is integrated within the client device 400 (e.g., housed in the same chassis as the CPU 4902 and memory 4912). In some embodiments, client device 400 includes one or more input device(s) 4910, which allow a subject to interact with the client device 400. Alternatively, or in addition, in some embodiments, the display 4908 includes a touch-sensitive surface. In some embodiments, the client device 400 presents media to a user through the display 4908. Examples of media presented by the display 4908 include one or more images (e.g., stained tissue sections). In some embodiments, the client application 4932 is a group of instructions that, when executed by a processor 4902, generates content for presentation to the user, such as a visualization of an image of a tissue section, a visualization of a request for an evaluation of the image, a visualization of a report associated with the evaluation of the image, and / or the like. In some embodiments, the client application 4932 generates content in response to inputs received from the user through the client device, such as the inputs 4910 of the client device 400. In some embodiments, the client application 4932 is utilized to configure one or more parameters associated with a model 3930 of the computer system 300. In some embodiments, the client application 4932 is accessible within any browser (e.g., laptop / desktop) displayed at the client device 400. In some embodiments, the client application 3932 is executed (e.g., runs) on native device frameworks, such as being available for download onto the client device 400 running an operating system 4924. In some embodiments, the client application 3932 is a group of instructions that, when executed by a processor 3902, generates content for presentation to the user, such as a visualization of an image of a stained tissue section, a visualization of a request for an evaluation of the image, DB1 / 155488515.1 38 CND-002PC 128316-5003 a visualization of a report associated with the evaluation of the image, and / or the like. In some embodiments, the client application 3932 generates content in response to inputs received from the user through the computer system 300, such as the inputs 3910 of the computer system 300. In some embodiments, the client application 3932 is utilized to configure one or more parameters associated with a model 3930. The term “and / or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. Unless the context requires otherwise, the terms “includes,” “comprising,” or any variations thereof, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Furthermore, to the extent that the terms “including,” “includes,” “having,” “has,” “with,” or variants thereof are used in either the detailed description and / or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.” The term “about”, unless the context requires otherwise, means ±10% of an associated value. The various aspects and embodiments of this disclosure are further illustrated below with reference to the following non-limiting examples. EXAMPLES Example 1: Sample Processing, Staining, Imaging Skin punch biopsies (3 mm in diameter) are fixed in Zamboni solution and stored in cryoprotectant. Biopsies are taken from distal leg, proximal thigh, and posterior cervical region. Frozen tissue sections are cut (e.g., about 50 µm thick) and six sections per biopsy are dual stained for protein gene product 9.5 (PGP9.5) and PSYN. Sections are washed and immunostained with secondary antibodies using Cy2 and Cy5 dyes (Jackson ImmunoResearch) and viewed by laser scanning confocal microscope. Biopsies are reviewed for positive areas of PSYN deposition that co-localize with PGP9.5. Slides are imaged using confocal Z-stack imaging of the tissue sections with 3-dimensional co- DB1 / 155488515.1 39 CND-002PC 128316-5003 localization. Specifically, tissue sections are washed in tris buffer at room temperature. The tissue sections are blocked with blocking solution at room temperature. After blocking, the tissue sections are incubated with mouse anti-PSYN and rabbit anti-PGP9.5 in TBS solution at room temperature. After incubation is complete, tissue sections are washed with Tris buffer at room temperature. To attach the first probe, the tissue sections are incubated in anti-mouse biotin, and anti-rabbit Cy2 in TBS solution at room temperature. After incubation is complete, tissue sections are washed with tris buffer at room temperature. To attach the second probe, the tissue sections are incubated in Cy5 in TBS solution at room temperature. After incubation is complete, tissue sections are washed with tris buffer at room temperature. As a result of this protocol, the tissue sections will be stained, and the target proteins will be tagged with Cy2 and Cy5 fluorescent dyes. The stained tissue samples are mounted on a slide and cover-slipped. Digital image acquisition is obtained through use of a confocal microscope with high resolution camera. Various nerve structures including sweat gland nerves, pilomotor muscle nerves, blood vessel nerves, SEP nerves, nerve bundle nerves, free nerves, and hair follicle nerves can be distinguished with a trained object recognition software. An object recognition software can be created by labeling the various nerve structures in a library of images (e.g., using the immunostaining technique), and the labeled images can be used to train a train a machine learning algorithm, e.g., using supervised, unsupervised, semi-supervised, self- supervised, or reinforcement learning techniques, including but not limited to convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), and other deep learning architectures to automatically and accurately discriminate between the various types of cellular structures and specific nerve structures, such as intra-epidermal nerve fibers, sweat gland nerve fibers, pilomotor nerve fibers, blood vessel nerve fibers, vasomotor nerve fibers, free nerve fibers, and nerve bundles. The various cell structures of pilomotor muscles, blood vessels, and sweat glands, among others, are also labeled, to allow for their identification and quantification. This DB1 / 155488515.1 40 CND-002PC 128316-5003 process can successfully train a machine learning algorithm to automatically and accurately discriminate the various types of cellular structures and specific nerve structures. The supervised or semi-supervised machine learning can employ a neutral network machine learning, such as Convolutional Neural Network (CNN). The object recognition software can further identify areas of co-staining which reflect intraneural PSYN deposition, and quantify volumes of PSYN intraneural deposition. Example 2: Determining PSYN Deposition Profile The deposition profile of PSYN in the tissue is determined using the trained object recognition software. For intraneural deposition, nerve subtypes are documented and quantified and amount of deposition quantified (e.g., for one or more of intra-epidermal nerves, sweat gland nerves, pilomotor muscle nerves, blood vessel nerves, subepidermal plexus (SEP) nerves, nerve bundle nerves, free nerves, and hair follicle nerves). For example, the following features may be quantified: (1) amount of sweat gland innervation that contains PSYN deposition; (2) amount of pilomotor muscle innervation that contains PSYN deposition; (3) amount of blood vessel innervation that contains PSYN deposition; (4) amount of subepidermal plexus (SEP) nerves that contain PSYN deposition; (5) amount of nerve bundle nerves that contain PSYN deposition; (6) amount of free nerve that contains PSYN deposition; and (7) amount of hair follicle innervation that contains PSYN deposition. Samples can be further characterized for number and / or density of the various nerve subtypes. Samples can be further characterized for certain quality control metrics, such as the presence and / or amount of air bubbles, dust fibers, and artifacts such as starbursts. Exemplary outputs for an MSA-positive sample, DLB-positive sample, and PSYN- negative sample are shown in Table 1 below. Table 1: Automated Output from Tissue Section Imaging Multiple system Atrophy (MSA) Dementia With Lewy Bodies (DLB) PSYN Negative Example eg DB1 / 155488515.1 41 CND-002PC 128316-5003 Number of Tissue 8 DB1 / 155488515.1 42 CND-002PC 128316-5003 (%) Blood 8 6 DB1 / 155488515.1 43 CND-002PC 128316-5003 Sweat Gland % 4 5 38 57 14 34 25 DB1 / 155488515.1 44 CND-002PC 128316-5003 Total Tissue Area (TTA) 27887939. 46295387. 31928392. 47691228. 53458401. 50925196. 23280053. 46590965. 49734151. of images, allowing the pathologist to focus on critical features. Table 1 provides an output for a subject having multiple system atrophy. There are three skin biopsies provided: the posterior cervical, the distal thigh, and the distal leg. Multiple components of the output provide clarity regarding the clinical diagnosis. The initial data points provide number of dermal structures that are present in the skin (including pilomotor muscles, blood vessels, sweat glands, nerve bundles and subepidermal plexus). The output provides nerve fiber densities for each of these substructures. Finally, the report provides the deposition of phosphorylated alpha-synuclein (PSYN)within each of the substructures. The multiple system atrophy example reveals adequate number of substructures for analysis, with a proximal to distal gradient of nerve fiber density across all dermal substructures (highest at the posterior cervical, lowest at the distal leg). There is DB1 / 155488515.1 45 CND-002PC 128316-5003 PSYN present within all three biopsies in a distal to proximal gradient (more at the distal leg site) that is characteristic of multiple system atrophy. In addition, the PSYN is present within the subepidermal plexus, a feature that strongly suggests multiple system atrophy. Finally, there is very low deposition of PSYN within autonomic nerve fibers around sweat glands, blood vessels and pilomotor muscles. In contrast, in a patient with dementia with Lewy bodies, there are similar outputs noted in Table 1 with very different results. The nerve fiber densities are lower compared to the multiple system atrophy example (reduced intra-epidermal nerve fibers, reduced sweat gland nerve fiber density). There is significantly greater PSYN deposition in total compared to multiple system atrophy, with a proximal to distal gradient of PSYN deposition. In addition, the PSYN deposition is not within the subepidermal plexus, it is within autonomic nerve fibers such pilomotor nerve fibers and blood vessels. The combination of high PSYN in nerve fibers surrounding blood vessels, with a large total PSYN deposition in a proximal to distal gradient and a superimposed neuropathy strongly indicates dementia with Lewy bodies as the diagnosis. Finally, the PSYN negative (control) case has no PSYN within any of the biopsies. Further, nerve fiber densities are normal. These patterns of PSYN deposition and nerve fiber densities provide confidence in differentiating the various disorders. DB1 / 155488515.1 46 CND-002PC 128316-5003 SEQUENCE Human alpha-synuclein (SEQ ID NO: 1) MDVFMKGLSKAKEGVVAAAEKTKQGVAEAAGKTKEGVLYVGSKTKEGVVHGVATVAEKTK EQVTNVGGAVVTGVTAVAQKTVEGAGSIAAATGFVKKDQLGKNEEGAPQEGILEDMPVDP DNEAYEMPSEEGYQDYEPEA DB1 / 155488515.1 47

Claims

CND-002PC 128316-5003 CLAIMS:

1. A method for detecting phosphorylated alpha-synuclein (PSYN) deposition in nerve fibers of a subject, the method comprising: obtaining tissue sections from one or more cutaneous biopsies of the subject, the tissue sections stained to discriminate nerves and stained for phosphorylated alpha-synuclein (PSYN); imaging the tissue sections using a system comprising a confocal microscope, high resolution camera, and a computer system operating an object recognition software; and determining in the tissue sections: amount of sweat gland innervation that contains PSYN deposition; amount of pilomotor muscle innervation that contains PSYN deposition; amount of blood vessel innervation that contains PSYN deposition; amount of subepidermal plexus (SEP) nerves that contain PSYN deposition; amount of nerve bundle nerves that contain PSYN deposition; amount of free nerve that contains PSYN deposition; and amount of hair follicle innervation that contains PSYN deposition.

2. The method of claim 1, wherein the object recognition software identifies sweat glands and nerve fibers within sweat glands, pilomotor muscles and nerve fibers within pilomotor muscles, blood vessels and nerve fibers within blood vessels, hair follicles including hair follicles containing nerve fibers, SEP nerves, nerve bundles, free nerves, and sebaceous glands.

3. The method of claim 1 or 2, wherein the object recognition software is created by labeling nerve structures in a library of images, and the labeled images used to train a machine learning algorithm to discriminate nerve structures.

4. The method of claim 3, wherein the labeled nerve structures comprise intra- epidermal nerve fibers, sweat gland nerve fibers, pilomotor nerve fibers, blood vessel nerve fibers, vasomotor nerve fibers, free nerve fibers, and nerve bundles.

5. The method of claim 3 or 4, wherein pilomotor muscles, blood vessels, and sweat glands are labeled in the library of images. DB1 / 155488515.1 48CND-002PC 128316-5003 6. The method of claim 5, wherein the machine learning comprises one or more of Nearest Neighbor, Naive Bayes, Decision Trees, Linear Regression, Support Vector Machines (SVM), and Neural Networks.

7. The method of claim 6, wherein the machine learning comprises Convolutional Neural Network (CNN).

8. The method of any one of claims 1 to 7, wherein the computer system records for each subject or tissue section: the total number of sweat glands detected (NSG), total sweat gland area detected (TSGA), total area of nerve fibers within detected sweat glands (SGNA), percent nerve area within detected sweat glands (PMNFD), area of PSYN in detected sweat glands (SGP), and percent sweat gland nerve area containing PSYN (SG%P).

9. The method of any one of claims 1 to 8, wherein the computer system records for each subject or tissue section: the total number of pilomotor muscles detected (NPM), total pilomotor muscle area detected (TPMA), total area of nerve fibers within detected pilomotor muscles (PMNA), nerve fiber density within detected pilomotor muscles (PMNFD); area of PSYN in pilomotor muscles (PMP); percent of pilomotor muscle nerve fiber area containing PSYN (PM%P); and percent pilomotor muscle area containing detected nerves (PM%N).

10. The method of any one of claims 1 to 9, wherein the computer system records for each subject or tissue section: the total number of blood vessels detected (NBV), total blood vessel area detected (TBVA), total area of nerve fibers within detected blood vessels (BVNA), percent blood vessel area containing nerves (BVNFD), area of PSYN in detected blood vessels (BVP), and percent blood vessel nerve area containing PSYN (BV%P).

11. The method of any one of claims 1 to 10, wherein the computer system records for each subject or tissue section: the total area of nerve fibers detected as SEP nerve fibers, area of PSYN in SEP fibers (BVP), and percent SEP nerves containing PSYN (BV%P).

12. The method of any one of claims 1 to 11, wherein the computer system records for each subject or tissue section: the total number of nerve bundles detected (NNB), total nerve bundle area detected (TNBA), area of PSYN in detected nerve bundles (NBP), and percent nerve bundle nerves containing PSYN (NB%P). DB1 / 155488515.1 49CND-002PC 128316-5003 13. The method of any one of claims 1 to 12, wherein the computer system records for each subject or tissue section: the total number of free nerves detected (NFN), total free area detected (TFNA), area of PSYN in detected free nerves (FNP), and percent free nerves containing PSYN (FN%P).

14. The method of any one of claims 1 to 13, wherein the computer system records for each subject or tissue section: the total number of hair follicles detected (NHF), total hair follicle area detected (THFA), area of PSYN in detected hair follicles (HFP), and percent hair follicle nerves containing PSYN (HF%P).

15. The method of any one of claims 1 to 14, wherein the computer system records for each subject or tissue section: total number of sebaceous glands detected (NSebG) and total sebaceous gland area detected (TSebGA).

16. The method of any one of claims 1 to 15, wherein the computer system records for each subject: a total tissue area detected (TTA), number of tissue sections (NS), total nerve area (TNA), and total PYSN area (TPA).

17. The method of any one of claims 1 to 16, wherein the computer system records for each subject or tissue section: epidermis length (EL), number of total nerve fibers crossing basement membrane (NFC), and intraepidermal nerve fiber density (IENFD).

18. The method of any one of claims 1 to 17, wherein the tissue sections are immunostained for Gene Product 9.5 to differentiate nerve structures.

19. The method of claim 18, wherein area of PSYN deposition in nerves is determined by 3-dimensional colocalization of Gene Product 9.5 stain and PSYN stain.

20. The method of any one of claims 1 to 19, wherein the system comprises a laser scanning confocal microscope creating Z-stack images.

21. The method of any one of claims 1 to 20, wherein the subject is exhibiting one or more symptoms, or is otherwise at risk to develop, a synucleinopathy selected from Parkinson’s disease (PD), Dementia with Lewy Bodies (DLB), multiple system atrophy (MSA), pure autonomic failure (PAF), or REM sleep behavior disorder. DB1 / 155488515.1 50CND-002PC 128316-5003 22. The method of claim 21, wherein the PSYN deposition in neurons corresponds to the presence of a synucleinopathy selected from Parkinson’s disease (PD), Dementia with Lewy Bodies (DLB), multiple system atrophy (MSA), pure autonomic failure (PAF), and REM sleep behavior disorder.

23. The method of claim 22, wherein the method is performed a plurality of times at different time points, optionally spaced by at least six months or at least one year.

24. The method of any one of claims 1 to 23, wherein detected features are evaluated using a disease classifier trained using a supervised, semi-supervised, or unsupervised machine learning algorithm.

25. The method of claim 24, wherein the classifier is trained to detect the presence of or type of synucleinopathy, or stage thereof.

26. The method of any one of claims 1 to 25, wherein, if a synucleinopathy is detected, the subject is treated for the synucleinopathy.

27. The method of any one of claims 1 to 25, wherein the subject is undergoing a treatment or candidate treatment for a synucleinopathy.

28. A method for treating a patient determined to have a synucleinopathy according to the method of any one of claims 1 to 25, comprising: administering a pharmacological intervention for treating the synucleinopathy to the patient.

29. A system comprising a confocal microscope, a high resolution camera, and a computer operating an object recognition software and which is suitable for performing the method of any one of claims 1 to 28.

30. The system of claim 29, wherein the computer comprises one or more processors, and a memory coupled to the one or more processors, the memory comprising one or more programs configured to be executed by the one or more processors, thereby causing the computer system to perform a method comprising: receiving, from the confocal microscope and a high resolution camera, images of tissue sections from one or more cutaneous biopsies of the subject, the tissue sections stained to discriminate nerves and stained for PSYN; and DB1 / 155488515.1 51CND-002PC 128316-5003 inputting the images of the tissue sections into an object recognition software of the computer system to obtain as output from the object recognition software a determination in the tissue sections comprising: amount of sweat gland innervation that contains PSYN deposition; amount of pilomotor muscle innervation that contains PSYN deposition; amount of blood vessel innervation that contains PSYN deposition; amount of subepidermal plexus (SEP) nerves that contain PSYN deposition; amount of nerve bundle nerves that contain PSYN deposition; amount of free nerve that contains PSYN deposition; and amount of hair follicle innervation that contains PSYN deposition.

31. The system of claim 30, wherein the method further comprises, prior to the receiving the images of the tissue sections, imaging the tissue sections with the confocal microscope and high resolution camera.

32. The system of claim 31, wherein the images comprise Z-stack images.

33. The system of any one of claims 30 to 32, wherein the object recognition software identifies sweat glands and nerve fibers within sweat glands, pilomotor muscles and nerve fibers within pilomotor muscles, blood vessels and nerve fibers within blood vessels, hair follicles including hair follicles containing nerve fibers, SEP nerves, nerve bundles, free nerves, and sebaceous glands.

34. The system of claim 33, wherein the object recognition software is created by labeling nerve structures in a library of images, and the labeled images used to train a supervised or semi-supervised machine learning algorithm to discriminate nerve structures.

35. The system of claim 34, wherein the labeled nerve structures comprise intra- epidermal nerve fibers, sweat gland nerve fibers, pilomotor nerve fibers, blood vessel nerve fibers, vasomotor nerve fibers, free nerve fibers, and nerve bundles.

36. The system of claim 34 or 35, wherein pilomotor muscles, blood vessels, and sweat glands are labeled in the library of images. DB1 / 155488515.1 52CND-002PC 128316-5003 37. The system of any one of claims 34 to 36, wherein the supervised or semi-supervised machine learning comprises one or more of Nearest Neighbor, Naive Bayes, Decision Trees, Linear Regression, Support Vector Machines (SVM), and Neural Networks.

38. The system of claim 37, wherein the machine learning comprises Convolutional Neural Network (CNN).

39. The system of any one of claims 30 to 38, wherein the method further comprises: generating, in electronic form, a report for a pathologist. DB1 / 155488515.1 53

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