Proteomics-based receptor-ligand matching for optimizing stem cell reprogramming

Abstract

The present invention provides a composition comprising a ligand for modulating the function of stem cell-derived cell culture. The present invention also provides a method for identifying a protein in a population of proteins in a tissue and / or organ and / or in vitro cell culture and / or purified subcellular fraction. The present invention further provides a method for quantifying a target protein in a tissue and / or organ and / or in vitro cell culture and / or purified subcellular fraction, further comprising identifying a protein that is expressed at a higher or lower level in iPSC-derived cell culture or ESC-derived cell culture than in iPSC culture or ESC culture, and identifying a ligand or transcription factor for a protein that is expressed at a higher or lower level in iPSC-derived cell culture or ESC-derived cell culture than in iPSC culture or ESC culture.

Description

[Technical field]

[0001] [Related Applications] This application claims the benefit of the filing date of Japanese Patent Application No. 2021-194719, filed November 30, 2021, the entire contents of which are incorporated herein by reference.

[0002] The present invention relates to a method for identifying hidden proteins, a method for identifying ligands for proteins, a method for quantifying target proteins, proteins or ligands identified by the method, and uses thereof.

[0003] The invention also relates to a method for identifying proteotypic peptides, a method for generating isotopically labeled proteotypic peptides, a peptide or isotopically labeled peptide generated by the method, and a composition comprising the peptide or isotopically labeled peptide. The invention also relates to a composition comprising a ligand for modulating the function of stem cell-derived cells, a composition comprising a ligand for optimizing stem cell differentiation in stem cell-derived cell cultures, the ligand being determined based on "super-resolution" (UD) proteomics.

[0004] The present invention also relates to methods of diagnosing diseases and / or disorders in a subject, methods of disease modeling, disease therapy, and immunotherapy, and methods of treating diseases and / or disorders associated with the nervous system in a subject, comprising administering an effective amount of the ligand. [Background technology]

[0005] In conventional non-targeted proteomics, protein samples are fractionated by liquid chromatography, and m / z spectra and peptide sequences are obtained by tandem mass spectrometry (MS / MS). However, this method has the problem that the number of proteins that can be detected is limited. In particular, in the case of complex samples such as biological samples, even if they are separated by liquid chromatography, each fraction contains many peptides with similar masses but different sequences. Next, from the spectra obtained from the various peptides observed in the MS in the first step, those with high spectral intensity are automatically selected as parent ions and fragmented, and the m / z spectrum of the generated fragment ions is obtained. Next, the sequence of the peptide from which the parent ion is derived is determined from the spectrum. As described above, this method preferentially analyzes proteins with high spectral intensity and preferentially analyzes high-abundance proteins, so that low-abundance proteins are not analyzed, or it takes a very long time to analyze low-abundance proteins. Furthermore, if there are two or more peptides with similar masses but different sequences, only the most abundant peptide is automatically analyzed.

[0006] On the other hand, targeted proteomics is a method to solve the above problems. In this method, parameters such as the retention time of liquid chromatography are set so that the analysis can focus on only specific peptides derived from the target protein. Furthermore, a peptide labeled with a stable isotope that has the same sequence as the target peptide (hereinafter referred to as a "stable isotope proteotype peptide") is mixed into a known amount of sample and analyzed, and the amount of the target protein present in the sample can be estimated by comparing the area ratio of the m / z spectrum (parent ion before fragmentation) between the natural peptide and the stable isotope proteotype peptide.

[0007] However, this method requires the selection of proteotype peptides from the sequence of the protein to be analyzed that is "visible" or detectable by mass spectrometry. For proteotype peptides, if a peptide sequence has been published in a paper or the like, such a peptide sequence can be used, but if there is no known peptide sequence, a peptide sequence selected by software must be used. However, the number of peptide sequences that can be proteotype peptides published in papers or the like is still small, and candidate sequences of proteotype peptides selected by software are often not detectable even when actually analyzed by mass spectrometry, so it is necessary to enrich the library of highly reliable proteotype peptides.

[0008] The hidden proteome cannot be identified and monitored by antibody-based techniques. The problems with antibodies to probe unknown proteins are: -Making an unknown protein is costly and time-consuming (e.g. expressing and purifying the target protein, immunizing animals for months, collecting serum, purifying and testing antibodies); - availability, the majority of proteomic antibodies are not yet commercially available; - detectability, low abundance protein ranges may not be detected; -Accessibility, may not bind to target within the complex; -Specificity, may fail to recognize closely related proteins within an isoform or family.

[0009] The functioning of eukaryotic cells, in all its complexity, relies on a highly specific compartmentalization into subcellular domains, including organelles. These compartments represent functional units characterized by specific supramolecular protein complexes. A major goal of modern biology is to establish a comprehensive and quantitative inventory of the protein constituents of each subcellular compartment. Such an inventory would be the starting point for many studies, not only for the functional understanding and reconstruction of biological systems, but also for the evolutionary diversification and derivation of general principles of biological regulation and homeostasis.

[0010] Chemical synapses, essential for information transmission within the nervous system, constitute highly specific compartments connected by axons to often distant neuronal cell bodies. Common to all chemical synapses is the protein apparatus that coordinates the exocytosis of neurotransmitter-filled synaptic vesicles (SVs) in response to presynaptic action potentials and the resulting activation of postsynaptic receptors. Moreover, synapses consist of structurally and functionally distinct subcompartments, e.g., free and docked SVs, endosomes, the active site (AZ) on the presynaptic side, and the receptor-containing membrane with associated scaffolding proteins on the postsynaptic side. It is therefore not surprising that the combination of mass spectrometry (MS)-based proteomics and subcellular fractionation results in a highly complex protein inventory. For example, more than 2,000 protein species were identified in synaptosomes (1), about 400 in SV fractions (2), about 1,500 in postsynaptic membrane density (3), and about 100 in active site (AZ)-enriched preparations (4).

[0011] Although these studies provide insights into the protein composition of synaptic structures, they are inherently limited for two reasons. First, synapses are functionally diverse with regard to neurotransmitter chemistry, properties of synaptic strength, dynamics, and plasticity (5). Thus, the analyzed subcellular fractions represent an “average” of the large diversity of synapses (6) or SVs (2). A second limitation is that proteins known to be present in specific subsets were not found in these studies, despite the unprecedented sensitivity of modern mass spectrometers. Indeed, many functionally important synaptic proteins remain undetected. For example, the Ca2+ receptor, a major regulator of SV exocytosis, is involved in the synthesis of synapses that are highly diverse. 2+The synaptotagmin (Syt) family of sensors consists of more than 15 members, of which only five had been identified in previous SV proteomics (2, 4, 7). Missing isoforms included Syt7, involved in asynchronous transmitter release (8), synaptic plasticity (9), and SV recycling (10). Similarly, vesicular transporters of monoamine (VMAT) and acetylcholine (VAChT) neurotransmitters were missing in these studies. Clearly, known components of the diversified synaptic proteome are missing, and it is not possible to predict how many more such proteins lie hidden.

[0012] Why does the inventory of synaptic proteins remain incomplete? Proteome identification and quantification are highly dependent on MS detectability of peptides generated by digesting extracted proteins with sequence-specific enzymes such as trypsin. However, in MS analysis of complex biological samples, peptide signals from a few abundant proteins often mask peptide signals from low-abundance proteins. Furthermore, the probability of obtaining peptides with similar masses but different amino acid sequences increases with increasing sample complexity (11, 12). To overcome these limitations, we constructed a workflow using sequential dual enzymatic protein digestion combined with extensive peptide separation prior to MS analysis. As a proof of concept, we used SV fractions purified from whole rat brains to benchmark quantitative organelle proteomics (2). As a result, we detected approximately 1,500 proteins in the SV fraction, three times more than previously reported. This new proteome not only encompasses known canonical SV proteins but also includes previously overlooked proteins, such as low-abundance Syts and SV transporters. Furthermore, peptide quantification allowed us to distinguish between "SV resident" and "SV visitor" proteins. Indeed, most of the "SV resident" proteins newly detected in our SV proteomics are low abundant with an average copy number of less than one per SV, suggesting a greater molecular and functional diversity of SVs than previously thought. Notably, over 200 proteins detected in the SV fraction are genetically associated with brain disorders, of which 76% were newly identified in the SV fraction.

[0013] Eukaryotic cells organize the intracellular space into multiple specialized membrane-bound and non-bound cytoplasmic compartments. These compartments contain a densely organized protein apparatus that collectively controls and executes almost all biological reactions. Subcellular compartmentalization is a fundamental life strategy that has enabled cells to optimize the activity and interactions of their proteins and create many new biological processes. In multicellular organisms, specialized subcellular compartments exist in numerous copies. These compartments constitute a stable core proteome, but at the same time, parts of those proteomes have undergone great diversification in time and space (Jacob, 2001; Holland, 2009). This complex molecular diversity is the basis for the evolution and emergence of new and more sophisticated biological processes. Therefore, to elucidate the mechanisms of complex life phenotypes, it is essential to reveal the deep structure of the subcellular proteome and monitor its spatiotemporal dynamics.

[0014] In the brain, synapses are a prominent example of physiologically important subcellular compartments. They not only connect neurons to each other, but also play a central role in processing, storing, and controlling the information flowing within neural circuits. Common to all chemical synapses is the protein machinery that coordinates the membrane fusion of neurotransmitter-containing vesicles following a presynaptic action potential and the activation of postsynaptic receptors by the released neurotransmitters. However, beyond the essential transmission process and the canonical proteome, synapses are functionally diverse, potentially acting on a wide range of properties of synaptic transmission strength, kinetics, and plasticity (Abbott and Regehr, 2004; O'Rourke et al., 2012). Thus, the anatomical and functional specialization of brain neural circuits is thought to result from molecular diversity in different types of synapses. The deep diversity of the synaptic proteome may underlie cognitive abilities, learning, memory processes, and other complex attributes of the mammalian brain (Emes and Grant, 2012). Indeed, mutations in genes encoding synaptic proteins are frequently associated with mental and neurological disorders in humans (Grant, 2012).

[0015] Combining mass spectrometry (MS)-based proteomics with subcellular fractionation has provided an extensive inventory of protein species, with over 2000 species detected in synaptosomes (Biesemann et al., 2014), ~400 in synaptic vesicles (SVs) (Takamori et al., 2006), ~1500 in postsynaptic densities (Bayes et al., 2012), and ~100 in active sites (AZs) (Boyken et al., 2013) in synaptic fractions. However, current quantitative proteomes of synaptosomes (Wilhelm et al., 2014) and SV proteins (Takamori et al., 2006) are mostly restricted to ubiquitous and abundant proteins, and the number of functionally characterized proteins is scarce. Moreover, these synaptic proteomic inventories are still missing functionally important proteins, although many are differentially expressed in the brain. For example, the synaptotagmin (Syt) family, which is the major Ca2+ receptor for SV exocytosis in neurons, 2+The SV sensor family (SV-sensors) is composed of at least 15 different members beyond the canonical Syt1 and Syt2 (Sudhof, 2002; Chen and Jonas, 2017). However, typically, up to 5 of them are identified in current SV proteomics (Burre et al., 2006; Takamori et al., 2006; Gronborg et al., 2010; Boyken et al., 2013). Missing isoforms include Syt7, which has recently been noted for short-term synaptic plasticity (Jackman et al., 2016; Turecek et al., 2017), asynchronous transmitter release (Li et al., 2017), and / or SV recycling (Liu et al., 2014; Chen et al., 2017). Other notable missing examples include transporters that fill vesicles with non-ubiquitous neurotransmitters (NTs). Besides the canonical (~90% of synapses) NTs glutamate (excitatory synapses) and GABA (inhibitory synapses), the brain uses a variety of less ubiquitous but physiologically essential NTs such as dopamine, serotonin, histamine, and noradrenaline, which are loaded into SVs by venous monoamine transporters (vMAT1 and 2). Similarly, the vesicular acetylcholine transporter (vAChT), used for SV filling with ACh, remains undetected in SV proteomics. Thus, at present, it is not possible to determine how much of the diversified synaptic proteome is hidden. (v) Safe, stable, easily available, and manageable.

[0016] Proteome identification and quantification are highly dependent on MS detectability of specific peptides generated by digesting extracted proteins with sequence-specific enzymes such as trypsin. However, in complex biological mixtures, peptide signals from a few abundant proteins often mask many of the peptide signals from low-abundance proteins. Furthermore, the probability of obtaining peptides with similar masses but different amino acid sequences remains high (Righetti and Boschetti, 2007; Aebersold and Mann, 2016). Thus, many functionally important synaptic proteins remain hidden and undetected by more abundant and / or structurally similar proteins, even with high-resolution MS instruments.

[0017] We explored the peptide characteristics of synaptic proteins hidden in the SV fraction highly purified from rat whole brain tissue homogenate and devised a multienzyme protein digestion method with peptide separation based on multiple biophysical properties of amino acids (Takamori et al., 2006). As a result, we discovered about 1,500 proteins in the SV fraction, which is three times the proteome reported so far. This new proteome contains all canonical SV proteins but also the less ubiquitous synaptotagmins and NT transporters found in a limited SV population in the brain. Furthermore, our approach quantitatively revealed the composition of the SV proteome in synapses, which has two spatially distinct repertoires: proteins present in SVs and proteins that transiently interact with SVs. Most of the newly detected SV proteins in our SV proteomics have an average copy number of less than one per SV, revealing a greater molecular and functional diversity of SVs than previously thought. We constructed a database listing the nomenclature, structural, functional, and pathological information associated with all detected SV fraction proteins. We demonstrated that this resource can be utilized to explore unidentified composition and organization of the SV proteome, including the SV kinome, labome, vacuolar (v)ATPase complex, SV transporters, and novel SV-resident proteins. More importantly, our data analysis found 236 brain disorders associated with 210 proteins detected in the SV fraction; 159 of these proteins (76%) were hidden by previous proteomics approaches. Thus, our study reaches a deep layer of subcellular proteomics by revealing a large-scale, previously hidden SV proteome with a complex and diverse spectrum of functionally important proteins. Our novel method represents an important milestone towards a deep exploration of the (subcellular) synaptic proteome and can reveal their dynamics (variome / variation) during brain development, evolution, and / or disease. Summary of the Invention

[0018] Synapses are specialized cellular structures connecting neurons that are essential for information transmission in the brain. They receive, process, retain and control all the information flowing in the neuronal network. Indeed, alterations in the expression of synaptic proteins often underlie many brain diseases such as Alzheimer's disease, autism, ADHD and schizophrenia. There is therefore great interest in analyzing the whole protein composition or "proteome".

[0019] As current knowledge of the complete proteome of mammalian synapses was still lacking, we recently developed a new synapse proteomics workflow ("UD Proteomics") using an animal model (Taoufiq et al., PNAS 2020, Taoufiq et al.). This protocol allows the identification and quantification of 4,500 synaptic protein species (including 1,500 synaptic vesicle proteins), which is three times more than any species reported so far (Takamori S et al., Cell 2006; Wilhelm BG et al., Science 2014; Koopmans et al., Neuron 2019). This is because the workflow uses an extensive protein digestion step and synapse-specific multidimensional peptide separation during proteomics sample preparation compared to other workflows, especially to separate a large number of overlapping peptide signals in mass spectrometry that have the same mass but different amino acid sequences. The conditions of the UD workflow (number of fractions, solvent pH, ERLIC gradient, chromatography duration, etc.) were optimized for synaptic samples.

[0020] More importantly, our data reveal over 200 synaptic vesicle proteins genetically associated with different brain disorders, 76% of which were undetectable in current published studies.

[0021] Low abundance synaptic proteomes are strongly associated with neurological disorders: (A) Number of synaptic vesicle (SV) proteins identified using Takamori et al Cell 2006 and the SynapseUD proteomics workflow. (B) Quantitative representation in rank abundance plots of the SV proteome. Y-axis: base 10 logarithm of iBAQ score; X-axis: abundance score rank in the proteome. Proteins with disease(s) caused by mutation(s) affecting gene(s) displayed in the database entry are shown (black circles). Of the approximately 1,500 reporting SV proteins, 210 are genetically associated with different brain disorders. Notably, the majority of these are low abundance and "invisible" to previous synaptic proteomics studies (Takamori et al Cell 2006; Wilhelm et al Science 2014; Koopmans et al Neuron 2019).

[0022] By means of our proteomics workflow (Taoufiq et al PNAS 2020), we optimized iPS reprogramming and cell culture media to produce healthier, more "connected" differentiated neurons. UD proteomics can detect and quantify receptors not seen by traditional proteomics. And by adding matching ligands to the media, neuronal proliferation and synapse formation were significantly improved. Neurons are more functional.

[0023] Reprogramming of iPS cells is a relatively new field (less than 10 years old) and is expected to be applied in regenerative medicine and drug discovery. Reprogramming methods were created to simply differentiate cells into basic neurons. However, little research has been done on optimizing neuronal morphogenesis and function. For example, one conventional method is to supplement cell culture medium with BDNF and NT3 growth factors. Using the proteomics data of the present invention, we revealed that the BDNF receptor TrKB is expressed in reprogrammed neurons, but the NT3 receptor TrKC is not. Therefore, NT3 can be excluded from the medium. Furthermore, the proteomics data revealed the presence of receptors for CNTFR, GDNFR1, GDNFR2, GDNFR3, FGFR2, and FGFR3 on reprogrammed neurons. Therefore, adding ligands in the medium matches their ligands (CNTF, GDNF, FGF16, FGF22). As a result, neuronal morphogenesis was significantly improved and synapse formation was enhanced. This process can be further adapted for reprogramming stem cells into any kind of cell type.

[0024] Current proteomic studies have revealed canonical synaptic proteins common to many types of synapses. However, proteins of diverse functions in a subset of synapses remain largely hidden due to their structural similarity to low abundance or abundant proteins. To overcome this limitation, we developed a “super-resolution” (UD) subcellular proteomics workflow. We identified 1,466 proteins using synaptic vesicle (SV) fractions purified from rat brain, which is three times more than previously reported. This refined proteome includes all canonical SV proteins and numerous low-abundance proteins, many of which were newly identified. By comparing UD quantification of SV and synaptosomal fractions, we were able to distinguish between SV-resident and potential SV-visitor proteins. As a result, we discovered 134 SV-resident proteins, including vesicular transporters of non-ubiquitous neurotransmitters in the brain, 86 of which were present at an average copy number of less than one per SV. We provide a fully annotated resource of all newly classified SV resident and potential SV visitor proteins that can be utilized to drive new functional studies, such as the one we characterize here of Aak1 as a novel regulator of synaptic transmission. Furthermore, proteins in the SV fraction have been associated with over 200 different brain diseases. Notably, the majority of these proteins were found in the low abundance proteomic range, highlighting their pathological importance. Our deep SV proteome provides a foundational resource for various future studies on the function of synapses in health and disease.

[0025] Keywords related to the present invention are proteomics, iPS cell differentiation, neurons, growth factors, synapse formation, receptors, ligands, synapses, deep proteomics, synaptic vesicles, brain disorders, neurotransmission, hidden proteome, peptide synthesis, neurological diseases, mass spectrometry identification and quantification.

[0026] Stem cell differentiation methods were created to simply differentiate iPSCs into other cell types. However, optimization of the morphogenesis and function of differentiated cells has been little studied so far. Nevertheless, this may be important in the growing field of regenerative medicine. Based on the present UD proteomics method, new cell culture recipes and virus-based differentiation routes can be created to significantly improve the health and function of iPSC-derived cells. We optimized differentiation to make iPSC neurons in psychiatric patients healthier and more "connected". UD proteomics can detect and quantify numerous receptors and nuclear factors invisible to proteomics. Addition of selected matching ligands to the culture medium significantly improved neural cell proliferation, synapse formation and activity. This process can be adapted for further differentiation of iPSCs into any cell type, including iPSC-derived heart, skin, liver, lung, retina, and pancreatic cells.

[0027] Mammalian intracentral synapses with diverse functions contribute to the complex organization of the brain, yet the molecular basis of synaptic diversity remains to be explored. This is because current synaptic proteomics is limited to the "average" composition of abundant synaptic proteins. Herein, we present a subcellular proteomic workflow that can identify and quantify the deep proteome of synaptic vesicles, including previously missing proteins present in a small fraction of intracentral synapses. This synaptic vesicle proteome newly detects many physiologically and pathologically relevant proteins, especially in the low abundance range, thus providing a resource for future studies of diverse synaptic functions and neuronal dysfunctions.

[0028] According to the disclosure of the present invention, the protein degradation conditions (denaturation conditions, proteolytic enzyme digestion conditions) were optimized, fractions were fractionated using a column called ERLIC, and peptides contained in each fraction were analyzed by LC MS / MS (hereinafter referred to as the UD method). This method itself is an optimization of the conditions for synaptic fraction proteins that have been published in papers and the like. However, as a result of examining these conditions, proteins that were known to exist in synapses but had not been detected by mass spectrometry, or proteins that had been annotated as genes but whose roles and expression sites were unknown, were detected. The number of types of proteins detected by mass spectrometry increased more than threefold compared to 2006 (from 408 types to 1500 types).

[0029] Among the proteins newly detected in the synaptic vesicle fraction, we focused on one protein, SVx, and analyzed it. For the peptide sequence of SVx detected by the UD method, we synthesized stable isotope proteotype peptides containing lysine containing stable isotopes 13C and 15N and having lysine at the C-terminus (peptides after proteolysis by trypsin and LysC have lysine or arginine at the C-terminus) and mixed them in the sample at known concentrations. In addition, we performed the UD method to estimate the concentration in the sample by comparing the signal intensity of the natural peptide sequence with that of the stable isotope proteotype peptide.

[0030] By quantitatively analyzing the proteins contained in synaptic vesicles in this way, we succeeded in estimating the number of molecules per synaptic vesicle. As a result, it was revealed that 236 brain-related diseases are associated with 210 synaptic vesicle proteins, and many proteins with a small number of molecules per synaptic vesicle are involved in brain-related diseases. In the future, it is expected that the localization and dynamics of normal synapses can be clarified by quantitatively analyzing synapse-related proteins in fractions other than synaptic vesicles using the proteotypic peptides obtained in this analysis using the UD method. In addition, further research into the changes in these brain-related diseases is expected to be useful in elucidating the mechanisms and diagnosis of future brain diseases.

[0031] The human brain is capable of complex intelligence because it is made up of billions of neurons assembled into a high-order communication network, connected by trillions of specialized subcellular structures called "synapses". Synapses not only connect nerve cells, but also receive, process, store and control the information flowing within the neural circuits. Therefore, there is great interest in analyzing such specific proteomes.

[0032] At the synapse, a protein machinery coordinates the exocytosis of neurotransmitter-containing synaptic vesicles (SVs) in response to presynaptic action potentials and the subsequent activation of postsynaptic receptors.

[0033] Thus, mutations and abnormalities in many synaptic proteins are associated with brain diseases. However, little is known about how each synaptic protein changes in each disease state. In order to understand the molecular mechanisms underlying each brain disease and develop personalized treatments for each disease, it is essential to understand the changes in various synaptic proteins for each patient's disease.

[0034] To comprehensively and quantitatively understand the changes in synaptic proteins in brain diseases, the UD method, which can comprehensively analyze almost all synaptic proteins, is effective. The UD method includes the steps of (I) purification of synaptic fractions, (II) two-step proteolysis with LysC and trypsin, (III) peptide fractionation with an ERLIC column, and (IV) RPC LC-MS / MS. In Takamori et al., 2006, step (II) uses only trypsin, and step (III) does not exist.

[0035] In order to analyze changes in synaptic proteins in brain diseases, up until now, antibodies against the synaptic proteins to be studied were prepared; diseased and normal states were compared using Western blotting, antibody staining, etc.; and changes in the synaptic proteins to be studied in the diseased state were analyzed. However, it is no exaggeration to say that this method is impossible to capture comprehensive changes in synaptic proteins in diseased states due to constraints such as the time and cost of antibody production and the number of samples that can be processed in one analysis. On the other hand, proteomic analysis using mass spectrometry can analyze a much larger number of proteins at one time than those using antibodies, and these proteins can be quantitatively analyzed. However, there is a problem that the types of proteins that can be detected in the synapse fraction are fewer than the types of proteins present in the synapse. In addition, even proteins that have been revealed to be present in synapses by antibody-based analysis methods cannot be detected in the synaptic fraction by mass spectrometry.

[0036] The inventors have dramatically improved the detection sensitivity of mass spectrometry by using the UD method, making it possible to detect almost all synaptic proteins from synaptic fractions. Such improved detection sensitivity is the result of optimizing each step of the UD method, and is not easily obtained. In the future, it is expected that the UD method will enable comprehensive capture of changes in each synaptic molecule (increase / decrease, change in distribution, post-translational modification) by analyzing human neurons induced from patient cell-derived iPS cells and disease animal models, and will also enable a deeper understanding of the state of disease.

[0037] Peptides detected by the UD method will be able to be used as proteotype peptides of synaptic proteins in the future (see JP-A 2010-085103 "Peptides used for simultaneous protein quantification of metabolic enzymes using a mass spectrometer" since the idea is similar). For example, to know the quantitative change of a specific synaptic protein B in a disease model mouse A, a known amount of proteotype peptide of synaptic protein B is added to each of a synaptic fraction from disease model mouse A and a synaptic fraction from a normal mouse, and analysis is performed by mass spectrometry. The concentration of protein B in the synaptic fractions of disease model mouse A and normal mouse can be determined and compared by comparing the signal intensity of the target peptide with the signal intensity of the added proteotype peptide. Although the sequence of synaptic protein B itself is known, in many cases (especially in the case of a protein present in a small amount in synapses or a protein present in a subset of synapses), it is unclear which part of the peptide therein can be detected by mass spectrometry, and even if it can be detected, how easily it can be detected. Therefore, when proteotype peptides are not known when quantitatively analyzing synaptic proteins by mass spectrometry, there is no choice but to use peptides selected from a list presented by a computer as proteotype peptides, but the probability of actually detecting them is very low. Therefore, if there is information on peptides that can actually be detected by mass spectrometry, it would be possible to reduce the enormous costs involved in synthesizing peptides that may or may not be usable. Therefore, based on the list of peptides of synaptic proteins that can be detected using the UD method, we plan to chemically synthesize proteotype peptides with stable isotope-labeled lysine or arginine at the C-terminus (peptides after proteolysis with LysC and trypsin have lysine or arginine at the C-terminus), and commercialize them for research and diagnosis.

[0038] The disclosed study provides a list of proteotypic peptides of rat synaptic proteins, and while many synaptic proteins are conserved in mammals, the peptides conserved in humans and mice can be selected and used to analyze these species.

[0039] Using the list of peptides, we can design and synthesize the right peptides for identification and absolute quantification of synaptic proteins. The peptides are superior to antibodies in both detection and quantification. Therefore, we thought of starting a company that sells peptides for research of synaptic proteins based on the UD list, which contains about 110,000 peptides to be protected.

[0040] Synaptic dysfunction is a major determinant of neurological diseases. In current proteomic approaches, the synaptic proteome remains largely hidden. We developed a new approach, termed "super-resolution proteomics," for the identification and quantification of the deep subcellular proteome. As a proof of concept, we tripled the known proteome of synaptic vesicles (SVs) already described in Takamori et al Cell 2006 (from 409 to 1483 proteins). Many novel SV accessory protein groups, isoforms, and lower abundance proteins were revealed and quantified. One aspect of the hidden SV proteome analysis was particularly unexpected: 236 different brain diseases associated with 210 SV-interacting proteins were discovered, of which 159 (=76%) were proteins unreported in Takamori et al Cell 2006. This revealed a major challenge for drug targeting of neurological disorders in the 21st century. The "UD peptide list or library" generated by this new method is the key knowledge to further trace, confirm, and quantify the presence of any synaptic protein in a complex sample using highly specific and accurate peptide-based techniques.

[0041] In some embodiments, the present invention relates to: <1> A composition comprising a ligand for modulating the function of a stem cell-derived cell. <2> The ligand is one or more selected from the group consisting of FGF3, FGF7, FGF10, FGF22, FGF8, FGF9, FGF16, FGF17, FGF18, FGF20, BDNF, GDNF, NRTN, PSPN, and CNTF; <1> The composition described in <3> the ligand is FGF22 or a combination of (i) BDNF and GDNF, (ii) BDNF, CNTF and GDNF, (iii) BDNF, CNTF, GDNF and FGF16, or (iv) BDNF, CNTF, GDNF, FGF16 and FGF22; <1> or <2> The composition described in <4> the stem cell-derived cells are selected from the group consisting of stem cell-derived nerve cells, stem cell-derived muscle cells, stem cell-derived hepatic cells, stem cell-derived pancreatic cells, stem cell-derived lung cells, stem cell-derived adipocytes, stem cell-derived cardiomyocytes, stem cell-derived hematopoietic cells, stem cell-derived keratinocytes, stem cell-derived epithelial cells, stem cell-derived endothelial cells, stem cell-derived astrocytes, stem cell-derived oligodendrocytes, stem cell-derived glial cells, stem cell-derived retinal cells, stem cell-derived epidermal cells, stem cell-derived ear cells, stem cell-derived red blood cells, stem cell-derived immune cells, and stem cell-derived germ cells; <1> ~ <3> 13. The composition according to any one of claims 1 to 12. <5> The stem cell-derived cell is a stem cell-derived neural cell. <1> ~ <4> 13. The composition according to any one of claims 1 to 12. <6> Regulating the function of the stem cell-derived cell is to control the differentiation of the stem cell. <1> ~ <5> 13. The composition according to any one of claims 1 to 12. <7> The regulating function of the stem cell-derived cell is enhancing synapse formation, improving neuronal morphogenesis, improving neuronal proliferation, and / or improving neuronal activity. <1> ~ <5> 13. The composition according to any one of claims 1 to 12. <8> The stem cells are induced pluripotent stem cells (iPSCs) and / or embryonic stem cells (ESCs), <1> ~ <7> 13. The composition according to any one of claims 1 to 12. <9> The composition is a pharmaceutical composition for treating a disease and / or disorder related to the nervous system. <1> ~ <8> 13. The composition according to any one of claims 1 to 12. <10> The disease and / or disorder associated with the nervous system comprises Alzheimer's disease, dementia, schizophrenia, autism, ADHD, Parkinson's disease, multiple sclerosis, epilepsy, Charcot-Marie-Tooth disease, retinitis, metabolic neurodegenerative diseases, dystonia, intellectual disability, hearing loss, spinocerebellar ataxia, sleep disorders, cortical dysplasia, dyslexia, microphthalmia, leukodystrophies, and / or movement disorders; <9> The composition described in <11> A method for identifying a protein in a population of proteins in a tissue and / or organ and / or in vitro cell culture and / or purified subcellular fraction, comprising the steps of: (a) digesting the proteins in the population of proteins with one or more enzymes; (b) repeating step (a) one or more times to produce a peptide; (c) separating the peptides into fractions; A method comprising: <12> detecting and sequencing said peptides using mass spectrometry. <11> The method described above. <13> The enzyme comprises lys-C and / or trypsin; <11> or <12> The method described above. <14> The steps (a) and (b) comprise sequential protein digestion steps combining LysC and trypsin-LysC; <11> ~ <13> 2. The method according to claim 1 , <15> The sequential protein digestion steps include a first step of protein digestion with LysC and a second step of protein digestion simultaneously with trypsin and LysC. <14> The method described above. <16> The separation in step (c) is based on electrostatic repulsion / hydrophilic interaction chromatography (ERLIC); <11> ~ <15> 2. The method according to claim 1 , <17> further comprising performing reverse phase chromatography (RPC) of each of said ERLIC fractions; <16> The method according to <18> A method for quantifying a target protein in tissues and / or organs and / or in vitro cell cultures and / or purified subcellular fractions, comprising the steps of: (d) detecting a target protein in a tissue and / or organ and / or in vitro cell culture and / or purified subcellular fraction; (d) <11> ~ <17> selecting a specific protein from the proteins identified by the method according to any one of the preceding claims; (e) <12> identifying proteotypic peptides within said specific protein based on the peptides detected and sequenced by the method of claim 1; (f) isotopically labeling at least one amino acid in the proteotype peptide to produce an isotopically labeled proteotype peptide; (g) adding a constant amount of said isotope-labeled proteotypic peptide to a fraction derived from the sample; (h) subjecting the fraction to mass spectrometry; (i) comparing a signal from a target peptide derived from the target protein with a signal from the isotope-labeled proteotypic peptide added to the fraction in step (g); A method comprising: <19> The in vitro cell culture is an induced pluripotent stem cell (iPSC) culture, an embryonic stem cell (ESC) culture, and / or an iPSC-derived cell culture or an ESC-derived cell culture; <18> The method according to <20> further comprising identifying proteins that are expressed at higher or lower levels in said iPSC-derived cell culture or ESC-derived cell culture than said iPSC culture or ESC culture. <19> The method according to <21> further comprising identifying a ligand or transcription factor for the identified protein that is expressed at a higher or lower level in the iPSC-derived cell culture or ESC-derived cell culture than in the iPSC culture or ESC culture. <20> The method according to <22> The identified protein is a receptor. <20> or <21> The method according to

[0042] In some embodiments, the present invention relates to: [1] A method for identifying a protein in a population of proteins in a tissue and / or organ and / or in vitro cell culture and / or purified subcellular fraction, comprising the steps of: (a) digesting the proteins in the population of proteins with one or more enzymes; (b) repeating step (a) one or more times to produce a peptide; (c) separating the peptides into fractions; A method comprising: [2] The method according to [1], further comprising isolating the protein in the population of proteins from a tissue and / or organ and / or an in vitro cell culture and / or a purified subcellular fraction. [3] The method according to [1] or [2], further comprising denaturing the protein in the population of proteins. [4] The method according to any one of [1] to [3], wherein mass spectrometry is carried out after the step (c). [5] The method according to any one of [1] to [4], further comprising detecting and sequencing the peptide using mass spectrometry. [6] The method according to any one of [1] to [5], further comprising identifying the protein in the population of proteins. [7] The method according to any one of [1] to [6], wherein the tissue and / or the organ comprises the nervous system, heart, lung, liver, spleen, kidney, stomach, small intestine, large intestine, gallbladder, urinary bladder, skin, muscle, blood, lymphatic system, adrenal glands, testes, ovaries, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids. [8] The method according to any one of [1] to [7], wherein the in vitro cell culture is associated with the nervous system, heart, lung, liver, spleen, kidney, stomach, small intestine, large intestine, gallbladder, urinary bladder, skin, muscle, blood, lymphatic system, adrenal gland, testis, ovary, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids. [8bis] The method according to any one of [1] to [7], wherein the purified subcellular fraction is associated with the nervous system, heart, lung, liver, spleen, kidney, stomach, small intestine, large intestine, gallbladder, urinary bladder, skin, muscle, blood, lymphatic system, adrenal glands, testes, ovaries, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids. [9] The method according to any one of [1] to [8bis], wherein the tissue and / or the organ comprises the nervous system.

[10] The method according to any one of [1] to [9], wherein the in vitro cell culture is associated with the nervous system. [10bis] The method according to any one of [1] to [9], wherein the purified subcellular fraction is associated with the nervous system.

[0043]

[11] The method according to any one of [1] to [10bis], wherein the population of proteins is defined as a population of proteins present in synapses.

[12] The method according to any one of [1] to

[11] , wherein the population of proteins is defined as a population of proteins present in synaptic vesicles. [12bis] The method according to any one of [1] to

[11] , wherein the population of proteins is defined as a population of proteins present in synaptic mitochondria, presynaptic membranes, active sites, postsynaptic membrane thickenings, and synaptic clefts.

[13] The method according to any one of [1] to [12bis], wherein the enzyme is defined as a proteinase, a protease, and / or a proteolytic enzyme.

[14] The method according to any one of [1] to

[13] , wherein the enzyme comprises Lys-C, trypsin, Lys-N, Asp-N, Arg-C, Glu-C, chymotrypsin, thermolysin, pepsin, elastase, and / or factor Xa.

[15] The method according to any one of [1] to

[14] , wherein the enzyme comprises lys-C and / or trypsin.

[16] The method according to any one of [1] to

[15] , wherein the separation in step (c) is based on chromatography.

[17] The method according to any one of [1] to

[16] , wherein the separation in step (c) is based on electrostatic repulsion / hydrophilic interaction chromatography (ERLIC).

[18] The method according to

[17] , wherein the ERLIC is carried out using a first solvent and a second solvent.

[19] The method according to

[18] , wherein the first solvent and the second solvent comprise an organic solvent and a carboxylic acid.

[20] The method according to

[18] or

[19] , wherein the first solvent and the second solvent comprise acetonitrile and formic acid.

[0044]

[21] The method according to any one of

[18] to

[20] , wherein the first solvent contains 70 to 100% (V / V) acetonitrile and 0.01 to 1.0% (V / V) formic acid.

[22] The method according to any one of

[18] to

[21] , wherein the first solvent contains 90% (V / V) acetonitrile and 0.1% (V / V) formic acid.

[23] The method according to any one of

[18] to

[22] , wherein the second solvent contains 10 to 50% (V / V) acetonitrile and 0.01 to 1.0% (V / V) formic acid.

[24] The method according to any one of

[18] to

[23] , wherein the second solvent contains 30% (V / V) acetonitrile and 0.1% (V / V) formic acid. [24bis] The method according to any one of

[18] to

[23] , wherein the first solvent contains ammonium hydroxide for adjusting the pH to 4.5. (This is important for creating the pH gradient that is also involved in peptide separation.)

[25] The method according to any one of

[18] to [24bis], wherein the first solvent contains 90% (V / V) acetonitrile and 0.1% (V / V) formic acid, and the second solvent contains 30% (V / V) acetonitrile and 0.1% (V / V) formic acid.

[26] The method according to any one of

[18] to

[25] , wherein the ERLIC is performed using gradient mode.

[27] The gradient mode comprises the steps of (i) and (ii): (i) the first solvent for 1 to 10 minutes; (ii) increasing the second solvent to 1 to 100% in 1 to 30 minutes; The method according to

[26] , comprising:

[28] The method according to

[27] , wherein step (ii) is repeated one or more times.

[29] The gradient mode includes the following steps: 1-10 minutes of the first solvent, 1-15 minutes of the second solvent up to 1-20%, 10-30 minutes of the second solvent up to 10-40%, 5-25 minutes of the second solvent up to 50-90%, 1-10 minutes of the second solvent up to 70-95%, 1-10 minutes of the second solvent up to 70-95%, and 1-10 minutes of the second solvent up to 90-100%. The method according to any one of

[26] to

[28] , comprising:

[30] The method according to any one of

[26] to

[29] , wherein the gradient mode is followed by washing with 10 to 100% of the second solvent for 1 to 10 minutes.

[0045]

[31] The method according to

[30] , wherein the washing is followed by re-equilibration with 90 to 100% of the first solvent for 10 to 30 minutes.

[32] The method according to any one of

[26] to

[31] , wherein the gradient mode is performed at a flow rate of 10 to 100 μL / min.

[33] The gradient mode comprises the following steps: 3 minutes of the first solvent, 7 minutes of 10% of the second solvent, 24 minutes of 25% of the second solvent, 16 minutes of 25% of the second solvent, 6 minutes of 70% of the second solvent, 6 minutes of 81% of the second solvent, 3 minutes of 81% of the second solvent, 6 minutes of 100% of the second solvent, 6 minutes of 100% wash with the second solvent, 20 minutes of 100% re-equilibration with the first solvent, and The method according to any one of

[26] to

[32] , comprising:

[34] Steps (d) and (e) below: (d) selecting a specific protein from the proteins identified by the method according to any one of [1] to

[33] ; (e) identifying a proteotype peptide in the protein based on the peptide detected and sequenced by the method according to any one of [1] to

[33] ; A method for identifying a proteotypic peptide, comprising:

[35] The method according to

[34] , wherein the proteotypic peptide is defined as a peptide having a sequence found only in a single protein and is used to identify the protein in tissues and / or organs and / or in vitro cell cultures and / or purified subcellular fractions.

[36] The method of

[34] or

[35] , wherein the tissue and / or organ comprises the nervous system, heart, lung, liver, spleen, kidney, stomach, small intestine, large intestine, gallbladder, urinary bladder, skin, muscle, blood, lymphatic system, adrenal glands, testes, ovaries, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids.

[37] The method according to any one of

[34] to

[36] , wherein the in vitro cell culture is associated with the nervous system, heart, lung, liver, spleen, kidney, stomach, small intestine, large intestine, gallbladder, urinary bladder, skin, muscle, blood, lymphatic system, adrenal gland, testis, ovary, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids. [37bis] The method according to any one of

[34] to

[36] , wherein the purified subcellular fraction is associated with the nervous system, heart, lung, liver, spleen, kidney, stomach, small intestine, large intestine, gallbladder, urinary bladder, skin, muscle, blood, lymphatic system, adrenal glands, testes, ovaries, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids.

[38] The method according to any one of

[34] to [37bis], wherein the tissue and / or the organ comprises the nervous system.

[39] The method according to any one of

[34] to

[38] , wherein the in vitro cell culture is associated with the nervous system. [39bis] The method according to any one of

[34] to

[38] , wherein the purified subcellular fraction is associated with the nervous system.

[40] The method according to any one of

[34] to [39bis], wherein the population of proteins is defined as a population of proteins present in synapses.

[0046]

[41] The method according to any one of

[34] to

[40] , wherein the population of proteins is defined as a population of proteins present in synaptic vesicles. [41bis] A method according to any one of

[34] to

[40] , wherein the population of proteins is defined as a population of proteins present in synaptic mitochondria, presynaptic membranes, active sites, postsynaptic membrane thickenings, and synaptic clefts.

[42] The method according to any one of

[34] to [41bis], wherein the amino acid sequence of the proteotype peptide is at least 4 amino acids long.

[43] Steps (f) and (g) below: (f) identifying a proteotype peptide using a method according to any one of

[34] to

[42] ; (g) isotopically labeling at least one amino acid in said peptide; A method for producing an isotope-labeled proteotypic peptide, comprising:

[44] The method according to

[43] , wherein the isotope-labeled proteotypic peptide is defined as a peptide having a sequence found only in a single protein, and is used to identify said protein in tissues and / or organs and / or in vitro cell cultures and / or purified subcellular fractions, and is isotopically labeled.

[45] The method according to

[43] or

[44] , wherein lysine and / or arginine in the peptide are isotope-labeled.

[46] An isotope-labeled proteotypic peptide produced by the method described in any one of

[43] to

[45] .

[47] An isotopically labeled peptide derived from a single minor protein of a population in a tissue and / or organ and / or in vitro cell culture and / or purified subcellular fraction for use in quantifying minor proteins in tissues and / or organs and / or in vitro cell cultures and / or purified subcellular fractions, wherein at least one amino acid in the peptide is isotopically labeled.

[48] ​​The isotope-labeled peptide according to

[47] , wherein the trace protein is identified by the method according to any one of [1] to

[33] .

[49] The isotope-labeled peptide according to

[47] or

[48] , wherein the amino acid sequence of the isotope-labeled peptide is detected by the method according to any one of [1] to

[33] .

[50] The isotope-labeled peptide according to any one of

[47] to

[49] , wherein the amino acid sequence of the isotope-labeled peptide is at least 4 amino acids in length.

[0047]

[51] Peptides derived from single, minor proteins in populations of tissues and / or organs and / or in vitro cell cultures and / or purified subcellular fractions.

[52] The peptide according to

[51] , wherein the trace protein is identified by the method according to any one of [1] to

[33] .

[53] The peptide according to

[51] or

[52] , wherein the amino acid sequence of the peptide is detected by the method according to any one of [1] to

[33] .

[54] The peptide according to any one of

[51] to

[53] , wherein the amino acid sequence of the peptide is at least 4 amino acids long.

[55] The peptide according to any one of

[47] to

[54] , wherein the tissue and / or organ comprises the nervous system, heart, lung, liver, spleen, kidney, stomach, small intestine, large intestine, gallbladder, urinary bladder, skin, muscle, blood, lymphatic system, adrenal gland, testis, ovary, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids.

[56] The peptide according to any one of

[47] to

[55] , wherein the in vitro cell culture is associated with the nervous system, heart, lung, liver, spleen, kidney, stomach, small intestine, large intestine, gallbladder, urinary bladder, skin, muscle, blood, lymphatic system, adrenal gland, testes, ovaries, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids. [56bis] The peptide according to any one of

[47] to

[55] , wherein the purified subcellular fraction is associated with the nervous system, heart, lungs, liver, spleen, kidneys, stomach, small intestine, large intestine, gallbladder, urinary bladder, skin, muscle, blood, lymphatic system, adrenal glands, testes, ovaries, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids.

[57] The peptide according to any one of

[47] to [56bis], wherein the tissue and / or the organ comprises the nervous system.

[58] The peptide according to any one of

[47] to

[57] , wherein the in vitro cell culture is associated with the nervous system. [58bis] The peptide according to any one of

[47] to

[57] , wherein the purified intracellular fraction is associated with the nervous system.

[59] The peptide according to any one of

[47] to [58bis], wherein the population of proteins is defined as a population of proteins present in synapses.

[60] The peptide according to any one of

[47] to

[59] , wherein the population of proteins is defined as a population of proteins present in synaptic vesicles.

[0048]

[61] A method for quantifying a target protein in a tissue and / or an organ and / or an in vitro cell culture and / or a purified subcellular fraction, comprising the steps of: (h) adding a predetermined amount of a peptide according to any one of

[46] to

[60] to a fraction derived from the sample; (i) subjecting the fraction to mass spectrometry; (j) comparing a signal from a target peptide derived from the target protein with a signal from a peptide according to any one of

[46] to

[60] added to the fraction in step (h); A method comprising:

[62] The method described in

[61] , further comprising calculating the m / z spectral range of the signal from the target peptide and the m / z spectral range of the signal from a peptide described in any one of

[46] to

[60] added to the fraction in step (h).

[63] The method of

[61] or

[62] , wherein the sample is from a subject having a disease and / or disorder.

[64] The method of

[63] , wherein the disease and / or disorder is associated with the nervous system, heart, lungs, liver, spleen, kidneys, stomach, small intestine, large intestine, gallbladder, bladder, skin, muscle, blood, lymphatic system, adrenal glands, testes, ovaries, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids.

[65] The method according to

[63] or

[64] , wherein the disease and / or disorder comprises a disease of brain cognition, movement, sensory processing, neurodegenerative disease, and / or neurodevelopment.

[66] The method according to any one of

[63] to

[65] , wherein the disease and / or disorder associated with the nervous system comprises Alzheimer's disease, dementia, schizophrenia, autism, ADHD, Parkinson's disease, multiple sclerosis, epilepsy, Charcot-Marie-Tooth disease, retinitis, metabolic neurodegenerative diseases, dystonia, intellectual disability, hearing loss, spinocerebellar ataxia, sleep disorders, cortical dysplasia, dyslexia, microphthalmia, leukodystrophy, and / or movement disorders.

[67] Next steps (k) and (l): (k) performing mass spectrometry on a target peptide derived from the target protein and a peptide according to any one of

[46] to

[60] at a predetermined concentration level to create a calibration curve (the target peptide has the same amino acid sequence as the peptide according to any one of

[46] to

[60] and is unlabeled); (l) obtaining a quantitative value from an area ratio using the calibration curve; The method according to any one of

[61] to

[66] , further comprising:

[68] A method for diagnosing a disease and / or disorder in a subject, comprising comparing a profile of a target protein in a sample obtained from a subject having the disease and / or disorder with a profile of the target protein in a healthy control, the profile comprising an expression level of the target protein quantified by a method according to any one of

[61] to

[67] .

[69] The method of

[68] , wherein the disease and / or disorder is associated with the nervous system, heart, lungs, liver, spleen, kidneys, stomach, small intestine, large intestine, gallbladder, bladder, skin, muscle, blood, lymphatic system, adrenal glands, testes, ovaries, rectum, pancreas, esophagus, thyroid, bone marrow, retina, placenta, and / or body fluids.

[70] The method according to

[68] or

[69] , wherein the disease and / or disorder comprises a disease of brain cognition, movement, sensory processing, neurodegenerative disease, and / or neurodevelopment.

[0049]

[71] The method according to any one of

[68] to

[70] , wherein the disease and / or disorder associated with the nervous system comprises Alzheimer's disease, dementia, schizophrenia, autism, ADHD, Parkinson's disease, multiple sclerosis, epilepsy, Charcot-Marie-Tooth disease, retinitis, metabolic neurodegenerative diseases, dystonia, intellectual disability, hearing loss, spinocerebellar ataxia, sleep disorders, cortical dysplasia, dyslexia, microphthalmia, leukodystrophy, and / or movement disorders.

[72] A composition comprising a peptide according to any one of

[46] to

[60] .

[73] A method for identifying a protein in a population of proteins in a sample, comprising the steps of: (m) digesting the proteins in the population of proteins with one or more enzymes; (n) repeating step (m) one or more times to produce the peptide; (o) separating the peptides into fractions; A method comprising:

[74] A method for quantifying a target protein in a sample, comprising the following steps (p) to (r): (p) adding a predetermined amount of a peptide according to any one of

[46] to

[60] to a fraction derived from a sample; (q) subjecting the fraction to mass spectrometry; (r) comparing a signal from a target peptide derived from the target protein with a signal from a peptide according to any one of

[46] to

[60] added to the fraction in step (p); A method comprising:

[75] A peptide having a sequence defined in the list of peptide information, the peptide being derived from a single protein.

[76] The peptide according to

[75] , wherein the single protein is present in a synapse.

[77] The peptide according to

[75] , wherein the single protein is present in synaptic vesicles.

[78] The method according to any one of

[61] to

[67] , wherein the in vitro cell culture is an induced pluripotent stem cell (iPSC) culture, an embryonic stem cell (ESC) culture, and / or an iPSC-derived cell culture or an ESC-derived cell culture.

[79] The method of

[78] , further comprising identifying proteins that are expressed at higher or lower levels in the iPSC-derived cell culture or ESC-derived cell culture than in the iPSC culture or ESC culture.

[80] The method of

[79] , further comprising identifying a ligand or transcription factor for the protein that has a higher or lower expression level in the iPSC-derived cell culture or ESC-derived cell culture than in the iPSC culture or ESC culture.

[0050]

[81] The iPSC-derived cell culture or the ESC-derived cell culture is an iPSC-derived neuronal cell culture or an ESC-derived neuronal cell culture, an iPSC-derived muscle cell culture or an ESC-derived muscle cell culture, an iPSC-derived hepatic cell culture or an ESC-derived hepatic cell culture, an iPSC-derived pancreatic cell culture or an ESC-derived pancreatic cell culture, an iPSC-derived lung cell culture or an ESC-derived lung cell culture, an iPSC-derived adipocyte culture or an ESC-derived adipocyte culture, an iPSC-derived cardiomyocyte culture or an ESC-derived cardiomyocyte culture, an iPSC-derived hematopoietic cell culture or an ESC-derived hematopoietic cell culture, an iPSC-derived keratinocyte culture or an ESC-derived keratinocyte culture, an iPSC-derived epithelial cell culture or an ESC-derived epithelial cell culture. astrocyte culture derived from iPSC or astrocyte culture derived from ESC; oligodendrocyte culture derived from iPSC or oligodendrocyte culture derived from ESC; retinal cell culture derived from iPSC or retinal cell culture derived from ESC; epidermal cell culture derived from iPSC or epidermal cell culture derived from ESC; ear cell culture derived from iPSC or ear cell culture derived from ESC; erythrocyte culture derived from iPSC or erythrocyte culture derived from ESC; immune cell culture derived from iPSC or immune cell culture derived from ESC; and germ cell culture derived from iPSC or germ cell culture derived from ESC.

[82] The method according to any one of

[78] to

[81] , wherein the iPSC-derived cell culture or the ESC-derived cell culture is an iPSC-derived neuronal culture or an ESC-derived neuronal culture.

[83] The method according to any one of

[79] to

[82] , wherein the protein having a greater expression level in the iPSC-derived cell culture or the ESC-derived cell culture than in the iPSC or ESC culture is selected from the group consisting of FGFR2, FGFR3, TrKB, a GDNF receptor, and CNTFR. A ligand identified by the method described in any one of

[84]

[80] to

[83] .

[85] The ligand described in

[84] , wherein the ligand is selected from the group consisting of FGF3, FGF7, FGF10, FGF22, FGF8, FGF9, FGF16, FGF17, FGF18, FGF20, BDNF, GDNF, NRTN, PSPN, and CNTF.

[86] The ligand according to

[84] or

[85] , wherein the ligand is FGF22 or a combination of (i) BDNF and GDNF, (ii) BDNF, CNTF and GDNF, (iii) BDNF, CNTF, GDNF and FGF16, or (iv) BDNF, CNTF, GDNF, FGF16 and FGF22.

[87] A composition comprising a ligand according to any one of

[84] to

[86] .

[88] The composition described in

[87] , wherein the composition is a pharmaceutical composition.

[89] The composition described in

[88] , further comprising a pharma- ceutically acceptable carrier.

[90] The composition described in any one of

[87] to

[89] , wherein the composition is for regulating the function of the iPSC-derived cell culture or the ESC-derived cell culture.

[0051]

[91] The iPSC-derived cell culture or ESC-derived cell culture is an iPSC-derived neuronal cell culture or an ESC-derived neuronal cell culture, an iPSC-derived muscle cell culture or an ESC-derived muscle cell culture, an iPSC-derived hepatic cell culture or an ESC-derived hepatic cell culture, an iPSC-derived pancreatic cell culture or an ESC-derived pancreatic cell culture, an iPSC-derived lung cell culture or an ESC-derived lung cell culture, an iPSC-derived adipocyte culture or an ESC-derived adipocyte culture, an iPSC-derived cardiomyocyte culture or an ESC-derived cardiomyocyte culture, an iPSC-derived hematopoietic cell culture or an ESC-derived hematopoietic cell culture, an iPSC-derived keratinocyte culture or an ESC-derived keratinocyte culture, an iPSC-derived epithelial cell culture or an ESC-derived iPSC-derived epithelial cell culture or ESC-derived endothelial cell culture, iPSC-derived astrocyte culture or ESC-derived astrocyte culture, iPSC-derived oligodendrocyte culture or ESC-derived oligodendrocyte culture, iPSC-derived retinal cell culture or ESC-derived retinal cell culture, iPSC-derived epidermal cell culture or ESC-derived epidermal cell culture, iPSC-derived ear cell culture or ESC-derived ear cell culture, iPSC-derived erythrocyte culture or ESC-derived erythrocyte culture, iPSC-derived immune cell culture or ESC-derived immune cell culture, iPSC-derived germ cell culture or ESC-derived germ cell culture.

[92] The composition according to

[90] or

[91] , wherein the iPSC-derived cell culture or the ESC-derived cell culture is an iPSC-derived neuronal culture or an ESC-derived neuronal culture.

[93] The composition of

[92] , wherein the modulating function of the iPSC-derived cell culture or the ESC-derived cell culture enhances synapse formation in the iPSC-derived neuronal culture or the ESC-derived neuronal culture.

[94] The composition described in any one of

[87] to

[93] , wherein the composition is for treating a disease and / or disorder related to the nervous system in a subject.

[95] The composition of

[94] , wherein the disease and / or disorder associated with the nervous system comprises Alzheimer's disease, dementia, schizophrenia, autism, ADHD, Parkinson's disease, multiple sclerosis, epilepsy, Charcot-Marie-Tooth disease, retinitis, metabolic neurodegenerative diseases, dystonia, intellectual disability, hearing loss, spinocerebellar ataxia, sleep disorders, cortical dysplasia, dyslexia, microphthalmia, leukodystrophy, and / or movement disorders.

[96] A method for regulating a function of an iPSC-derived cell culture or an ESC-derived cell culture, the method comprising adding a ligand described in any one of

[84] to

[86] or a composition described in any one of

[87] to

[95] to the iPSC-derived cell culture or the ESC-derived cell culture.

[97] The iPSC-derived cell culture or ESC-derived cell culture is an iPSC-derived neuronal cell culture or an ESC-derived neuronal cell culture, an iPSC-derived muscle cell culture or an ESC-derived muscle cell culture, an iPSC-derived hepatic cell culture or an ESC-derived hepatic cell culture, an iPSC-derived pancreatic cell culture or an ESC-derived pancreatic cell culture, an iPSC-derived lung cell culture or an ESC-derived lung cell culture, an iPSC-derived adipocyte culture or an ESC-derived adipocyte culture, an iPSC-derived cardiomyocyte culture or an ESC-derived cardiomyocyte culture, an iPSC-derived hematopoietic cell culture or an ESC-derived hematopoietic cell culture, an iPSC-derived keratinocyte culture or an ESC-derived keratinocyte culture, an iPSC-derived epithelial cell culture or an ESC-derived iPSC-derived epithelial cell culture or ESC-derived endothelial cell culture, iPSC-derived astrocyte culture or ESC-derived astrocyte culture, iPSC-derived oligodendrocyte culture or ESC-derived oligodendrocyte culture, iPSC-derived retinal cell culture or ESC-derived retinal cell culture, iPSC-derived epidermal cell culture or ESC-derived epidermal cell culture, iPSC-derived ear cell culture or ESC-derived ear cell culture, iPSC-derived erythrocyte culture or ESC-derived erythrocyte culture, iPSC-derived immune cell culture or ESC-derived immune cell culture, iPSC-derived germ cell culture or ESC-derived germ cell culture.

[98] The method according to

[96] or

[97] , wherein the iPSC-derived cell culture or the ESC-derived cell culture is an iPSC-derived neuronal cell culture or an ESC-derived neuronal cell culture.

[99] The method of

[98] , wherein modulating a function of the iPSC-derived cell culture or ESC-derived cell culture enhances synapse formation in the iPSC-derived neuronal culture or ESC-derived neuronal culture.

[0100] A method for treating a disease and / or disorder related to the nervous system of a subject, the method comprising administering to the subject an effective amount of a ligand described in any one of

[84] to

[86] or an effective amount of a composition described in any one of

[87] to

[95] .

[0052]

[0101] The method according to

[0100] , wherein the disease and / or disorder associated with the nervous system comprises Alzheimer's disease, dementia, schizophrenia, autism, ADHD, Parkinson's disease, multiple sclerosis, epilepsy, Charcot-Marie-Tooth disease, retinitis, metabolic neurodegenerative diseases, dystonia, intellectual disability, hearing loss, spinocerebellar ataxia, sleep disorders, cortical dysplasia, dyslexia, microphthalmia, leukodystrophy, and / or movement disorders.

[0053] Effect of the Invention According to the present invention, a new approach called "super-resolution proteomics" (UD method) for the identification and quantification of deep subcellular proteomes can be provided. The inventors have dramatically improved the detection sensitivity of mass spectrometry by the UD method, making it possible to detect almost all synaptic proteins from synaptic fractions. By analyzing human neurons induced from patient cell-derived iPS cells and disease animal models using the UD method, it is possible to comprehensively capture changes in each synaptic molecule (increase / decrease, change in distribution, post-translational modification), and it is expected that this will lead to a deeper understanding of the disease state.

[0054] Further, the present invention provides a composition comprising a ligand for reprogramming stem cells and / or regulating the function of stem cell-derived cell cultures. Stem cell reprogramming has great potential for applications in regenerative medicine and drug discovery. Conventional reprogramming methods are designed to simply differentiate cells into basic neurons. However, the stem cell programming of the present invention can optimize neural morphogenesis and function. For example, one conventional method is to add BDNF and NT3 growth factors to cell culture medium. Using the proteomic data of the present invention, it was revealed that the BDNF receptor TrKB is expressed in reprogrammed neurons, but the NT3 receptor TrKC is not expressed. Therefore, NT3 can be excluded from the medium. Furthermore, the proteomic data revealed the presence of receptors for CNTFR, GDNFR1, GDNFR2, GDNFR3, FGFR2, and FGFR3 on reprogrammed neurons. Therefore, adding ligands to the medium matches those ligands (CNTF, GDNF, FGF16, FGF22). The result was significantly improved neuronal morphogenesis and enhanced synaptogenesis, and this process can be further adapted for reprogramming stem cells into any kind of cell type.

[0055] The present invention also provides a pharmaceutical composition for treating diseases and / or disorders associated with the nervous system.

[0056] The method of the present invention can perform comprehensive and quantitative proteomic analysis of cellular proteins. The composition comprising the ligand of the present invention can enhance the cellular health of differentiated stem cells and enhance the cellular function of differentiated stem cells, and can be used in stem cell differentiation, regenerative medicine development, and individualized precision diagnosis for patients. [Brief description of the drawings]

[0057] [Figure 1]Low abundance synaptic proteomes are strongly associated with neurological disorders. (A) Number of synaptic vesicle (SV) proteins identified using Takamori et al Cell 2006 and the SynapseUD proteomics workflow. (B) Quantitative representation of the SV proteome in rank abundance plots. Y-axis: base 10 logarithm of iBAQ score; X-axis: abundance score rank in the proteome. Proteins with disease(s) caused by mutation(s) affecting gene(s) displayed in the database entry are shown (black circles). Of the approximately 1,500 reporting SV proteins, 210 are genetically associated with different brain disorders. Surprisingly, the majority of these are low abundance and "invisible" to previous synaptic proteomics studies (Takamori et al Cell 2006; Wilhelm et al Science 2014; Koopmans et al Neuron 2019). [Diagram 2] Super-resolution proteomics tripled the known SV proteome size. (A) Key steps of the super-resolution (UD) proteomics method: sequential enzymatic digestion steps followed by orthogonal peptide separation using multiple biophysical properties of amino acids (see also Figure 6). (B) Mass spectrometry unique peptide coverage of the active site protein Piccolo (amino acid sequence highlighted) in the Takamori et al. (2006), HD proteomic, and UD proteomic methods. (C) Number of proteins identified in synaptic vesicle (SV) fractions by the Takamori et al. (2006), HD (white), and UD (gray) methods. (D) Members of the synaptotagmin family identified in the SV fraction by the Takamori et al. (2006), HD, and UD methods. (E) EM images of purified synaptosome (P2') and SV fractions, showing clear vesicles with a diameter of about 40 nm. Bottom panel: Representative synaptic structure of the P2' fraction from EM images, showing intact subsynaptic compartments as indicated. (F) SDS-PAGE profile of proteins extracted from the P2' and SV fractions. [Diagram 3]UD proteomics revealed proteins hidden in both high and low abundance ranges of the SV proteome. (A) UD proteomics distinguishes the highly homologous protein isoforms Rab11A and Rab11B. Left panel: Position of Rab11A and Rab11B on the ranked (iBAQ) abundance curve. Center panel: Amino acid sequence alignment of Rab11A and Rab11B showing 91% identity. Right panel: List of unique Rab11 peptides (SEQ ID NOs: 1-17) detected in the SV fraction by HD and UD methods. Rab11A is identified only by UD from a unique peptide located in the C-terminal region of the Rab GTPase. (B) Left panel: V-ATPase-related proteins detected in the SV fraction on the ranked (iBAQ) abundance curve. Right panel: Structural model of the V-ATPase protein in SV. Newly detected proteins in UD proteomics are shown in red. (C) SV-P2' volcano plot (left) and Venn diagram (top right) of newly detected SV-resident transporter proteins (red) in the SV fraction that are known but missing from previous proteomics studies (purple). Bottom right panel: Position of the transporters in the ranked (iBAQ) abundance curve of the SV proteome. [Figure 4]Previously hidden SV-resident proteins show high amino acid sequence homology among mammals. (A) "Unidentified protein RGD1305455" (SEQ ID NO: 18, Uniprot ID: A0A0G2KAX2) was identified in UD proteomics of unique peptides (highlighted in the amino acid sequence) and their location in the ranked (iBAQ) abundance plot (bottom panel) is shown. No unique peptides were detected by HD methods. (B) SV-resident location of RGD1305455 in the SV-P2' volcano plot. (C) Amino acid sequence comparison of RGD1305455 homologs in various animal species. Black and grey shading indicates identical and similar amino acids, respectively. Dashes represent gaps in the sequence. For protein accession numbers and reference sequences, see FIG. 10. Mammalian species are enclosed in dashed red boxes (left panel) and images of species with >97% identity are shown (right panel). (D) Confirmation of the presence of RGD1305455 protein in the SV fraction by targeted proteomics methods. The VLVVEPVK peptide (SEQ ID NO: 19; detected by UD proteomics) was synthesized with a C-terminal "heavier" [13C6 15N2] (predefined shift of +8 neutrons = 8 Da) and used to track the native peptide after mixing with digested SV protein. Left: MS2 spectrum of the heavy VLVVEPVK peptide (SEQ ID NO: 19). Right: MS2 spectrum of the native peptide detected in the SV fraction that matches the MS2 spectrum of the heavy peptide. The red, blue, and black peaks indicate the matched y ion series, b ion series, and mismatched ions, respectively (top panel). Amino acid sequences corresponding to the ion fragments (middle panel). The mass errors of the detected and predicted peptide fragments are plotted (bottom panel). The errors of the native peptide fragments were all less than 0.02 Daltons. The predicted masses of all fragments are shown in Table S5. [Diagram 5]Diversity of neuronal functions and dysfunctions associated with the UD-SV proteome. (A) Functional mosaic of the SV proteome. Each protein detected in the SV fraction by UD proteomics was associated with one or more functional keywords. Top left panel: Sunburst diagram shows the distribution of functional categories (inner circle) and subcategories (outer circle) represented in the total SV fraction proteome and in the SV resident repertoire. List of functional keywords (left) and subcategories (right) with the number of proteins representing each category. (B) Deep low abundance SV proteome is associated with brain diseases. Proteins detected in the SV fraction with "disease(s) caused by mutation(s) affecting gene(s) represented in the entry" are marked and their rank in the iBAQ abundance curve is identified. The analysis was performed manually using the Uniprot and GeneCards databases of human diseases. Markers indicate proteins associated with cognitive disorders (purple), motor disorders (red), and / or sensory processing disorders (yellow). The vertical dashed line indicates the rank 409, which is the number of proteins identified in a previous SV proteomics study (Takamori et al. 2006). Proteins to the right of the dashed line were mostly revealed by UD proteomics. [Figure 6]Methodological advances in SV proteomics. (A) Schematic diagram of the different protocols and MS instruments used by Takamori et al. (2006), the HD method, and the UD method. While the Takamori et al. and HD methods utilize conventional one-step trypsin digestion and online reversed-phase chromatography (RPC), the UD method utilizes sequential protein digestion steps with LysC and a combination of trypsin-LysC, followed by offline electrostatic repulsion hydrophilic interaction chromatography (ERLIC) and online RPC of the respective ERLIC fractions. This protocol allowed the identification of 4,439 proteins (right panel), including 87,931 unique peptides, in the purified synapsomal fraction (P2'), which is 2.5- and 3.7-fold more than those identified by the HD method, respectively. (B) ERLIC fractions separated by C18 reversed-phase chromatography (RPC). Orthogonal peptide separation, with mechanisms based on multiple biophysical properties of amino acids, maximizes the separation of peptides with similar mass but different composition and sequence. ERLIC separates peptides according to their charge, polarity, pI, orientation, post-translational modifications (e.g. phosphorylation), and hydrophobicity according to RPC. The small unmasked peak in the square contains many peptides that were analyzed by LC-MS / MS and sequenced for detailed proteomic identification. (C) Number and distribution of unique peptides identified across the 24 ERLIC fractions (F) in the P2' sample. The number of unique peptides was highest detected in F7-9 and F12-17. [Figure 7]Absolute amount and copy number per SV of selected proteins. Absolute amount quantification was performed by mixing a known amount of heavy isotope synthetic peptide (0.1 mg) with 50 mg of digested SV protein. The exact amount of the peptide of interest (native) was calculated using the intensity of the heavy isotope labeled standard peptide (heavy): absolute amount (mg) = 0.1 × (native / heavy peak area). The average copy number of protein per SV (number / Sv) was estimated using synaptotagmin (Syt1), a transmembrane SV resident protein with molecular weight (MW) = 47 kDa as a reference (estimated at 15 Syt1 per SV by Takamori et al., 2006): number of proteins / SV = absolute amount / MW × [(number of Syt1 / SV) × (MW of Syt1) / (absolute amount of Syt1)]. [Figure 8] Newly detected SV-resident transporters and their putative substrates using UD proteomics. [Figure 9] Reference sequences and accession numbers used for amino acid sequence comparison of RGD1305455 homologs. [Figure 10] Table of predicted fragment ion masses from the heavy VLVVEPVK peptide and the native VLVVEPVK peptide (SEQ ID NO: 19). Masses were generated in silico by PEAK (v7, Bioinformatics Solutions Inc.). Colored masses indicate experimentally observed masses from the match ion MS2 spectrum (FIG. 4C). Note that the detected y ion mass (red), which contains the C-terminal amino acid, is 8 Daltons heavier in the heavy peptide (top table) compared to the native peptide (bottom table). [Figure 11] Heavy peptides used to confirm the presence of newly identified SV resident proteins (SEQ ID NOs: 19-34). As shown in Figure 4C, peptide tracking experiments with these synthesized heavy peptides yielded uniformly positive results, providing further evidence of a previously undetected SV proteome by UD proteomics. [Figure 12] Optimizing proteomics-based human iPS cell reprogramming into healthier and more "connected" neurons. [Figure 13] Rapid and efficient reprogramming of patient-derived iPS cells into neurons. a) DIC image of patient-generated iPS cells depicting flat colony structures with cobblestone morphology. Scale bar: 100 μm. B) DIC image of patient iPS cells differentiated into neurons (iN) 19 days after reprogramming by lentivirus-based delivery of neurogenin-2. iN cells display extensive neuronal morphological features, including branched axons and dendrites. Scale bar: 100 μm. [Figure 14] Differentiated neurons (iNs) in culture form functional synapses. A) DIC image of human iPSC-derived neurons (iNs) in DIV25 culture. Scale bar: 100 µm. B) Immunofluorescence imaging of iNs at DIV25 (neuron-specific marker MAP2 (green), DAPI stained cell nuclei (blue), scale bar: 50 µm). C) Immunofluorescence imaging showing iN synapses (synaptic marker synaptophysin (green), neurite marker MAP2 (white), DAPI cell nuclei (blue), scale bar: 10 µm). D) Action potentials induced in iNs by current injection demonstrate normal basic neuronal membrane properties of iNs. E) Addition of the glutamate neurotransmitter receptor inhibitor CNQX (red output) abolishes spontaneous synaptic currents in iNs (black output), demonstrating the formation of functional excitatory synapses in iN cultures. [Figure 15]UD proteomics reveals a large number of hidden proteins in human iPS cells and induced neuronal (iN) cells. Mass spectrometry-based proteomics was performed using HD and UD workflows. While HD proteomics appears to be limited to about 4,000 identified protein species in both iPS and iN cells, UD proteomics revealed significantly more proteins. Many of the proteins featured in the figure remain "hidden" in the sample when using HD proteomics. ('HD proteomics' = conventional method before using the high-sensitivity Orbitrap Fusion Lumos MS device (Thermo). 'UD proteomics' = our method before using the high-sensitivity Orbitrap Fusion Lumos MS device (Thermo) (Taoufiq et al PNAS 2020).) [Figure 16] UD Proteomics has successfully reprogrammed stem cells from psychiatric patients into neurons. [Figure 17] UD proteomics is used to optimize iPSC-derived neural cell cultures. [Figure 18] Optimizing proliferation and synaptogenesis of human iNeurons through proteomics-based receptor-ligand matching. [Figure 19] UD proteomic information is used to optimize iPSC-derived neural cell cultures. [Figure 20] The BDNF-CNTF-GDNF cocktail of growth factors enhances synaptogenesis in patient iPSC-derived neurons. [Figure 21] The cocktail of growth factors BDNF-CNTF-GDNF-FGF16-FGF22 significantly enhances synaptogenesis in patient iPSC-derived neurons. [Figure 22] Optimizing proliferation and synaptogenesis of human iNeurons by UD proteomics-based receptor-ligand matching. [Diagram 23] UD proteomics run of nuclear proteins in iPS vs. iN. [Figure 24] Optimization of human patient iPS cell differentiation based on super-resolution (UD) proteomics. [Diagram 25] Key aspects of UD proteomics-based optimization of stem cell differentiation. [Figure 26] Key aspects of UD proteomics-based optimization of stem cell differentiation: Step 1 [Figure 27] Key aspects of UD proteomics-based optimization of stem cell differentiation: Step 2 [Figure 28] Proteomics-based receptor-ligand matching. [Figure 29] An innovative culture medium recipe allows stem cells (e.g., from psychiatric patients) to differentiate into healthier, more "connected" and more active neurons. [Diagram 30] "Precise differentiation" of human iPS cells based on nuclear UD proteomics. [Diagram 31] Biochemical purification of nuclei from iPS cells and proteomic analysis. [Diagram 32] Optimization of nuclear protein extraction. [Diagram 33] Identification of new potential master transcription factors for neuronal differentiation. [Diagram 34] Proteomic regulation of newly identified neural transcription factors using iPSC-derived cardiomyocytes (iCMs). [Diagram 35] Confirmation of the identification of potential master transcription factors for neuronal differentiation. [Diagram 36] Overview of the Volcano Plot. [Figure 37] As an example, UD-proteomics optimization of human iPSC-T cell differentiation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0058] As used herein, the term "reprogramming" is used to refer to the process of changing one cell fate to another, for example, converting a mature differentiated cell into a less committed precursor (e.g., stem cell) or converting a less committed precursor (e.g., stem cell) into a differentiated cell. That is, the term "reprogramming" may mean controlling differentiation. The present approach to optimize iPSC-derived T cell differentiation and enhance the functional properties (e.g., cytokine secretion) of differentiated T cells. First, the UD proteomics of the present invention is used to identify and quantify all proteins extracted from the cell membrane and nucleus of iPSC vs. differentiated T cells and (when available) blood T cells. The data generated serves two strategies: 1) Receptor-ligand matching based on UD proteomics: Create unique culture media recipes based on the presence, levels, and / or absence of cell membrane receptors (e.g., growth factor receptors) by supplementing the media with matching ligand combinations. 2) T cell nuclear UD proteomics analysis: By comparing the nuclear proteomes of iPSCs, differentiated T cells, and (when available) blood T cells, we identify T cell type-specific "master transcription factors." We deliver the identified factors to iPS cells by viral vectors to determine which factors contribute most to developmental maturation. Expected Results: Improving the cellular health (e.g., differentiation rate) of differentiated T cells. Enhancing the cellular functions (e.g., immune cell stimulation and secretion of cytokines) of differentiated stem cells. We believe that the results of the project will support and accelerate the development of next-generation therapeutics, such as cancer immunotherapy (Figure 37).

[0059] New workflow with enhanced peptide recovery and separation significantly expands coverage of the synaptic proteome A new workflow was developed to expand the coverage of protein-specific sequences or "unique peptides" before MS identification. First, we introduced Lys-C treatment before and during trypsin digestion to increase the number of accessible cleavage sites (Figures 2A and 6). Second, to improve peptide separation, we introduced offline fractionation using electrostatic repulsion hydrophilic interaction chromatography (ERLIC) based on charge, polarity, pHi, post-translational modification, and orientation before conventional hydrophobic-based reversed-phase chromatography (RPC) (13, 14). To evaluate the impact of this new workflow on expanding protein coverage, we performed a conventional protein digestion-peptide separation protocol in combination with a state-of-the-art mass spectrometer (Q-Exactive Plus) analysis. We call this the "high-resolution" (HD) method, and our new workflow the "super-resolution" (UD) method (Figures 2B and 6). UD-based proteomics revealed 1,466 proteins in the SV fraction (Figure 2C). This is twice as many as the HD method (766) and more than three times as many as previously reported (2). The increased sensitivity of the UD method is also evident from the recovery of unique peptides for individual proteins. For example, 116 unique peptides were identified for the large AZ protein Piccolo, whereas only 14 were recovered by the HD method and only 1 was identified in the previous study (2) (Figure 2B). The UD method not only increased the size of the SV proteome, but also increased the number of isoforms identified within individual protein families, such as synaptotagmin (Figure 2D), where most of the known family members (13 / 15 and extended-Syt1) were detected. The newly detected isoforms included Syt7, which was recently found to regulate multiple modes of neurotransmitter release (8-10). On the other hand, the HD method added only one SYT isoform to the previous SV proteome (2).

[0060] As expected, the UD method detected many more proteins (4,439) in the synaptosome fraction (P2') than the HD method (1,790) (see Figure 6A), indicating that the resolution of the UD method is based on the improvement of the workflow before MS analysis (see Figure 6). Each sample used in the MS analysis of the present invention was confirmed by electron microscopy and electrophoresis. A typical profile of synaptosomes was observed in the P2' sample, but uniform vesicle structures with diameters of 40 to 50 nm were dominant in the SV fraction (Figure 2E). Proteins extracted from the P2' and SV fractions showed different SDS-PAGE profiles (Figure 2F).

[0061] Improved quantification reveals synaptic structure and diversity of the SV proteome In quantitative mass spectrometry, protein abundance can be determined using intensity-based absolute quantification (iBAQ), a label-free approach that divides the sum intensity of all unique peptides of a protein by the total number of unique peptides detected. Thus, the improved peptide recovery by the UD method is expected to improve the accuracy of protein quantification. To test this assumption, we performed immunoblot analysis of 41 proteins in fractions during SV purification and compared the quantitative profiles of the HD and UD methods. As expected, proteins located on the postsynaptic side or in the synaptic cleft were found in the P2' fraction in both immunoblot and MS analysis, but not in the SV fraction. In both Western and UD analysis, SV-resident proteins were detected at higher levels in the SV than in the P2' fraction. On the other hand, the HD method failed to detect SV proteins in the P2' fraction. Similar discrepancies between HD iBAQ data and immunoblot profiles were found in 30% (13 / 41) of cases for AZ, presynaptic membrane, cytoplasm, and total proteins. These results highlight the importance of the UD workflow in quantitative proteomics.

[0062] SVs are purified from synaptosomes (P2'), which contain all SV proteins, but SVs may be free of proteins from other synaptic compartments. Of the 4,424 proteins in the P2' fraction, 3,005 were detected only in the P2' fraction, along with postsynaptic and mitochondrial proteins. Of the 1,466 SV proteins, 1,419 were detected in the P2' fraction. The remaining 47 SV proteins were of low abundance, including VGLUT3, a vesicular glutamate transporter isoform present in a limited set of CNS synapses. To assess the possible contamination of the SV fraction with postsynaptic proteins, we consulted the Synaptic Gene Ontologies (SynGO) resource (15). Of all SV fraction proteins, 97 (7%) were annotated as postsynaptic proteins, whereas 47 of the 97 proteins have been reported to be present and functional in the presynaptic compartment. The contamination of postsynaptic proteins in the SV proteome appears to be minor among the proteins detected in the SynGO database.

[0063] To distinguish between SV-resident proteins and proteins that transiently interact with SV ("visitors"), we determined the iBAQ ratio SV / P2' in a Volcano plot. 134 of the 1,466 SV proteins had an SV / P2' ratio significantly higher than 2 (p<0.05). Using this criterion, we defined the bona fide "SV-resident" protein group, which included all previously established SV proteins (2, 16, 17) as well as novel, hitherto unidentified proteins (see below). On the other hand, the majority of the 1,466 proteins had an SV / P2' ratio lower than 1, suggesting that these proteins occasionally interact with SV. We defined them as potential SV-visitor proteins. This repertoire includes (i) cytoplasmic proteins such as calmodulin, actin, and synaptojanin-1, (ii) AZ proteins such as Piccolo and Bassoon, and (iii) plasma membrane proteins such as syntaxin-1, all of which transiently interact with SVs, for example in the SV trafficking pathway (4, 18, 19). Thus, UD proteomics provides quantitative information to distinguish the synaptic protein repertoires of SV residents from SV visitors.

[0064] We then ranked the 1,466 proteins detected in the SV fraction by their iBAQ abundance. We confirmed that previously reported canonical transmembrane and lipid-anchored proteins were highly abundant. The 180 most abundant protein species accounted for 90% of the total protein mass of SV, accounting for 1.2 × 10 8The remaining 1,286 proteins had iBAQs in the range of E5–E8. Previously, copy numbers per SV were estimated for abundant SV proteins to build an “average SV” model (2,6). We extended the copy number estimates of the newly detected SV proteins using isotope-labeled peptides (see Figure 7). As a calibration standard, we used the previously determined copy number of Syt1: 15 (2). The copy number of Rab3A estimated by this method was 10.5, which is almost consistent with the copy number of 10 previously determined by immunoblotting (2), confirming the accuracy of the method. These analyses showed that the copy number of many SV proteins was less than 1, suggesting that SV proteins are present only in a subpopulation of SVs or that they interact with SVs transiently.

[0065] "Hidden SV proteome" revealed by UD proteomics method To reveal the hidden SV proteome, we tabulated an annotated SV proteome inventory detected by UD proteomics, including a comparison with that by Takamori et al. (2006). This inventory allows for the extraction of new insights into SV structure and function using various filters, such as gene family name, abundance rank, and molecular, structural, or functional categories. The first selected examples from the inventory are Rab GTPases that function in vesicular transport to specific intracellular organelles and membranes (20). These are evolutionarily conserved and show 75–95% amino acid sequence identity. Although such high homology has hindered proteomic detection, we detected and quantified 40 Rabs in SV fractions using UD proteomics, 8 of which are newly reported. Of the 32 Rabs reported so far (2), the abundance of only 18 was quantified using Western blot analysis (21). We found that the majority of high-abundance Rabs (25 / 40) were significantly enriched in the SV fraction. Among them, Rab11A and Rab11B are highly homologous with 91% amino acid sequence identity (Figure 3A). Despite such similarity, they have been reported to function in opposing endosomal sorting pathways (22). Although we found 14 unique peptides common to both Rab11A and B, only UD proteomics could detect Rab11A features in the C-terminal hypervariable region. Thus, UD proteomics can reveal highly homologous but functionally distinct proteins.

[0066] The second example is the vacuolar-type H that acts as an ATP-driven proton pump that activates SVs for neurotransmitter uptake. +The V-ATPase complex is a cytoplasmic domain “V1” that contains eight subunits (A–H) and a transmembrane domain “V0” assembled from four subunits (a, c, d, e) (23) (Figure 3B). Previous proteomics studies estimated the copy number of V-ATPase to be approximately 1–2 / SV, but the complete set of V-ATPase proteins has yet to be identified (2, 4, 17). Interestingly, using UD proteomics, we identified all components of the V-ATPase complex; most of them were found in the high abundance range of the SV proteome (Figure 3B). Furthermore, the V-ATPase accessory proteins Wdr7 and renin receptor (atp6ap2), as well as the previously hidden Dmxl1 and Dmxl2, were all identified (24) (Figure 3B). These low abundance accessory proteins, of which only the renin receptor is classified as SV resident, may regulate the function of the V-ATPase complex in a limited subset of SVs.Thus, the UD method can reveal the complete set of subunits that comprise a large protein complex.

[0067] A third example is SV-resident transporter proteins. Solute carrier ("slc") transporters are transmembrane proteins that control the movement of soluble molecules across the plasma membrane. To date, more than 400 slc genes have been identified in mammals, of which approximately 40% remain uncharacterized with respect to expression profile and function (25). In our UD analysis, slc transporters were detected in both SV-resident and SV-visitor repertoires (Figure 3C). The latter may include transporters that are partially internalized from the plasma membrane into SVs during endocytosis. SV-resident transporters include VGLUT1 (slc17a7) and VGLUT2 (slc17a6), involved in glutamate uptake, and VGAT (slc32a1), involved in GABA and glycine uptake, all of which determine the molecular identity of the major SV populations in the brain (26) and are present at high abundance in the SV proteome (Figure 3C). UD proteomics also detected lower abundance SV-resident transporters that were missing in previous SV proteomics studies. These included VMAT2 (slc18a2; (27)), ChT1 (slc5a7; (28)), VAChT (29), and SVOP (atypical slc subfamily, (30)) of unknown substrate, which are involved in the uptake of monoamines or ACh into SV subpopulations. In addition to these well-known transporters, UD analysis identified nine new SV-resident transporters (Figure 3C), among which slc10a4 has been reported to transport bile acids into SVs to regulate dopamine activity (31). The remaining eight transporters are orphan slcs of unknown function (Table S2). Thus, UD proteomics revealed and quantified both high- and low-abundance hidden transporter proteins in the SV proteome that are ubiquitously or restrictedly present within the SV population.

[0068] The fourth example is a newly discovered protein in the SV fraction (Figure 4). In databanks, this protein is known as RGD1305455 (SEQ ID NO: 18; Uniprot ID: A0A0G2KAX2), or "unidentified protein C7orf43 homolog" and "similar to hypothetical protein FLJ10925". Nothing is known about its tissue expression, developmental profile, or subcellular localization. Six unique peptides derived from RGD1305455 were detected only in UD experiments (Figure 4A). RGD1305455 was discovered as a low-abundance (rank 407; approx. 0.04 copies / SV) SV-resident protein (SV / P2' ratio = 3) (Figures 4A, 4B). It has a conserved DUF domain (DUF4707) and lacks a predicted transmembrane domain. Database searches revealed that this protein is highly conserved among mammals (>97% amino acid identity, Figure 4C, Figure 9). A targeted proteomics approach was used to confirm its presence in the SV fraction. The unique peptide VLVVEPVK (SEQ ID NO: 19, FIG. 4A) in the UD is a 'heavy' C-terminal lysine ( 13 C6 and 15 N2) and mixed with the digested SV protein sample. A parallel reaction monitoring (PRM) assay based on the elution time, ionization, and fragmentation of the heavy peptide detected the matched VLVVEPVK peptide (sequence number 19) in the SV sample (Figure 4D). A detailed comparison of the observed and predicted peptide fragments (Table S4) showed that the mass error of the native fragment was within 0.02 Daltons (Figure 4D). This confirmed with high accuracy that the protein RGD1305455 was indeed present in the SV fraction. Similarly, PRM assays with 15 other heavy peptides against newly identified SV resident proteins confirmed the presence of all tested proteins in the SV fraction (Table S5).

[0069] Functional characterization of the SV-associated kinase protein Aak1 The SV fraction contained numerous non-transmembrane proteins, some of which were present in SVs within the synaptic compartment. These proteins may play a regulatory role in neurotransmission. To address this, we focused on the analysis of protein kinases, which are mainly soluble cytoplasmic proteins. We identified AP2-associated protein kinase 1 (Aak1) as an abundant and SV-resident kinase. The copy number of Aak1 was calculated to be 1.5 / SV (see Figure 7), suggesting that it is ubiquitously present among SVs in central synapses. The enrichment profile of Aak1 in the purified SV fraction was confirmed by Western blot and was in contrast to other cytoplasmic kinases found in P2', such as MARK2 or TNiK. We observed strong colocalization of exogenously expressed Aak1 (TagRFP-Aak1) with the SV marker synaprophysin in cultured hippocampal neurons.

[0070] We used both genetic and pharmacological approaches to clarify the functional role of Aak1, using shRNA knockdown (KD) of Aak1 expression in cultured hippocampal neurons and by direct injection of the Aak1-specific inhibitor LP-935509 (32) into the calyx of Held presynaptic terminals in brainstem slices from rats at postnatal days (P) 13–15. For Aak1-KD, lentivirus targeting Aak1 was applied at day 11 in vitro (DIV11), and synaptophysin became detectable by Western blot. At DIV15, the knockdown effect was maximal, and Aak1 expression was reduced to less than 5%. In hippocampal cultures at DIV15, EPSCS of Aak1-KD neurons underwent rapid short-term depression (STD) during 20-Hz stimulation. The magnitude of STD was significantly greater than that of the control group (p<0.05, n=7). Consistent with this, in LP-935509-loaded HELD calyces, EPSCs underwent stronger STD in 100 Hz trains compared to controls (0.3 s, p<0.05, n=7). Cumulative histograms of EPSC amplitudes yielded pool size and release probability of readily releasable SVs, showing that both Aak1-KD and Aak1 inhibitors reduced pool size without affecting release probability. Furthermore, in Held calyces, Ca 2+ Dependence of fast component (33) and Ca 2+ Both the slower and more dependent components of Aak1 prolonged recovery from STD. Together, these results suggest that Aak1 normally promotes SV recycling, thereby maintaining a releasable SV pool.

[0071] To further investigate whether Aak1 is involved in SV exo-endocytosis, we performed pHluorin assays in cultured hippocampal neurons and capacitance measurements at calyx terminals. The half-decay time of endocytic fluorescence was prolonged by 2-fold (p<0.005, n=51) compared to control (n=20) in pHluorin assays. Similarly, capacitance measurements showed that LP-935509 (1 μm or 10 μm) significantly prolonged the change in endocytic capacitance. No significant decrease in exocytosis was shown in capacitance measurements. Thus, Aak1 likely plays a role in promoting SV endocytosis in both hippocampal and brainstem synapses.

[0072] As the above results suggest the involvement of Aak1 in the SV recycling pathway, we further investigated whether Aak1 plays a physiological role in maintaining neurotransmission. Simultaneous recording of presynaptic and postsynaptic APs showed that an Aak1 inhibitor (1 μM) significantly impaired the fidelity of neurotransmission, assayed as the ratio of postsynaptic APs generated in response to presynaptic APs (p<0.01, n=6). Taken together, the data indicated that Aak1 is a canonical SV-resident protein with an essential functional role in maintaining neurotransmission, especially at high frequencies.

[0073] Many low-abundance SV proteins are associated with a diverse range of physiological functions and neurological disorders UD proteomics newly detected many proteins in both the high and low abundance ranges of the SV fraction proteome, with over 80% detected in the low abundance range. Although SV proteins are expressed at low abundance, they may play important physiological roles. We explored this possibility using the functional and disease annotations in our database by classifying SV proteins into 17 functional categories with 26 subcategories (Figure 5A). Our dataset includes transport proteins, including SNAREs involved in various membrane fusions (26 proteins) (2, 6). It also includes many types of Rab GTPases (40 proteins) and membrane-tethering Trapp complexes (14 proteins). Many of these proteins were identified as SV-resident, suggesting that SVs may be equipped with proteins for various transport pathways to other presynaptic organelles. Other major categories included proteins involved in signal transduction (e.g., kinases, phosphatases), signal transduction, and transport of small molecules. UD proteomics detected many metabolic enzymes (179), including those involved in neurotransmitter metabolism (13), cellular energy production (35), lipid regulation (75), and cyclic nucleotide second messengers (12). These data suggest that metabolic reactions of SVs occur in crowded presynaptic terminals (6). UD proteomics also detected SV proteins classified as autophagy-related proteins (40). The presence of both SV-resident (e.g., snap29, atg9a, trappc8, pik3c3) and SV-visitor autophagy-related proteins (e.g., beclin-1, uvrag, map1lc3a, cisd2) in our SV proteome suggested that autophagic degradation may be involved in maintaining the SV population size in presynaptic terminals.

[0074] To investigate the pathological significance of our UD proteomics data, we searched for genetic information of SV proteins for association with neurological diseases (see "Diseases in the SV proteome") and marked them in the ranked abundance plots of the SV fraction proteome (Figure 5B) and the P2' fraction proteome. We found that 236 different brain diseases are associated with 210 high- and low-abundance proteins in the SV proteome, of which 159 (76%) were newly revealed by the UD method. Similarly, 55% of these SV proteins were found in the low abundance range of the P2' proteome. These results indicate pathological significance regardless of the abundance of SV proteins. SV protein-associated diseases include many motor (145), cognitive (135), and sensory system phenotypes, such as visual (33), and auditory (14) phenotypes. The database also shows SV proteins associated with phenocopied diseases such as mental retardation (28), epilepsy (25), Parkinson's disease (13), amyotrophic lateral sclerosis (4), Alzheimer's disease (4), and cerebellar ataxia (10). UD cross-analysis between function and disease showed that phenocopies may include proteins from both SV resident and SV visitor repertoires, from both high and low abundance ranges, and functionally distinct proteins of the SV life cycle. For example, Parkinson's disease may be associated with mutations in SV resident proteins such as renin receptor (rank 121), dnajc13 (rank 318, SV endocytosis) and sv2c (rank 97, SV transport) involved in SV acidification, or mutations in SV visitor proteins such as synaptojanin-1 (rank 351, SV endocytosis) or pla2g6 (rank 653, lipid composition). In summary, our data analysis reveals the complexity and physiological importance of the repertoire of low-abundance SV proteins newly detected by UD proteomics.

[0075] New generation proteomics with improved peptide preparation techniques reveals a threefold larger SV proteome To achieve a comprehensive SV proteome, maximal visibility of protein-specific sequences or "unique peptides" prior to mass spectrometry (MS) identification seemed essential. Thus, sequential enzymatic protein digestion was achieved by introducing LysC treatment before and during trypsin digestion to increase the number of accessible cleavage sites (Figures 2A and 6A). To maximize the separation of cleaved peptides, in addition to conventional reversed-phase chromatography (RPC), electrostatic repulsion hydrophilic interaction chromatography (ERLIC) was utilized. While RPC separation of peptides is based on hydrophobicity profile, ERLIC allows separation based on multiple biophysical properties, such as amino acid charge, polarity, isoelectric pH, post-translational modifications, and structural orientation (Alpert, 2008; Alpert et al., 2010).

[0076] Technological advances over the past decade have significantly improved the resolution and sensitivity of mass spectrometers. To evaluate how much machine development has contributed to the expansion of SV proteomics, we used a conventional peptide preparation protocol prior to MS analysis using a modern mass spectrometer (Q-Extrative Plus, Figure 6A). We call this the "high-resolution (HD)" method (Gallien et al., 2012). To distinguish between them, we named our new protocol of sample preparation and MS analysis the "super-resolution" (UD) method (Figure 2B, Figure 6). In MS analysis, the UD method was able to detect more unique peptides (e.g., 116 unique peptides of the active site protein Piccolo) than the conventional method (e.g., 1 unique peptide in (Takamori et al., 2006) vs. 14 unique peptides in the HD method (Figure 2B)). Remarkably, the UD method uncovered approximately 1500 proteins in the SV fraction purified from whole adult rat brain, tripling the number previously reported in the SV proteome ( Takamori et al., 2006 ), and also doubled the number of proteins revealed by the HD method ( Figure 2C ).

[0077] The UD method not only increased the size of the SV proteome, but also increased the number of isoforms identified within protein families such as the synaptotagmin family (Figure 2D), where only five isoforms had been previously identified (Takamori et al., 2006). Whereas the HD method identified only one additional isoform, the UD method revealed nine additional isoforms, including synaptotagmin 6, which has unknown function (Chen and Jonas, 2017), and synaptotagmin 7, which has recently been shown to be involved in multiple modes of neurotransmitter release (Liu et al., 2014; Jackman et al., 2016; Luo and Sudhof, 2017; Turecek et al., 2017).

[0078] In parallel, we analyzed synaptosomal fractions (P2') purified from adult rat whole brains using the HD and UD methods and found that the UD method significantly increased the number of proteins and their unique peptide coverage (Figure 6A). Prior to mass spectrometry experiments, we subjected the SV and P2' samples to electron microscopy (EM) analysis, which showed uniform small SV structures in the SV sample and well-preserved subsynaptic structures in the P2' sample (Figure 2E). We also confirmed the protein profiles of the SV and P2' samples in SDS-PAGE analysis (Figure 2F) and Western blot analysis.

[0079] Improved quantitative profiling of proteins in subsynaptic compartments In quantitative mass spectrometry, protein abundance can be measured from the intensity of unique peptides using intensity-based absolute quantification (iBAQ), a label-free approach that divides the sum intensity of all unique peptides of a protein by the total number of unique peptides detected. In this way, the large number of unique peptides obtained by the UD method compared to the HD method can theoretically increase the reliability of protein quantification.

[0080] To test whether this was the case, we performed Western blot analysis of 43 proteins in both the P2' and SV fractions and compared the profiles from the HD and UD methods. As expected, proteins known to reside in the postsynaptic plasma membrane or synaptic cleft were detected in the P2' but not the SV fraction by both Western and MS analysis. Proteins known to reside in SV were detected at higher levels in the SV fraction than in the P2' fraction by both Western and UD analysis. On the other hand, several SV proteins, such as synaptogyrin3, CSPa, SV31, and vMAT2, were not detectable in the P2' fraction by the HD method. Similar discrepancies between the Western profiles and HD iBAQ data were found for active site (AZ) proteins, presynaptic membrane proteins, or cytoplasmic proteins. By the UD method, AZ, presynaptic membrane, cytosolic, and mitochondrial proteins were detected, consistent with the Western profiles of subcellular fractions, as previously reported (Takamori et al., 2006; Ahmed et al., 2013; Boyken et al., 2013). In summary, discrepancies between Western blot profiles and iBAQ data were 30% (13 / 43) using the HD method, but nonexistent using the UD method, indicating that UD proteomics is currently the most reliable quantitative method for synaptic proteomes.

[0081] UD quantification revealed spatial signatures and diversity of the SV proteome In subcellular fractionation experiments, Western blotting has traditionally been utilized for individual synaptic protein profiling. To obtain a large-scale comprehensive distribution profile of SV-associated proteins in the presynaptic compartment, we performed UD quantitative cross-analysis on equal amounts of proteins extracted from the P2' and SV fractions. We identified 4,424 proteins in the P2' fraction and 1,466 proteins in the SV fraction. A large group of 3,005 proteins was detected only in the P2' fraction, and a small group of 47 SV proteins was detected only in the SV fraction (Figure 3A). Proteins detected only in P2' are synaptosomal proteins that do not directly interact with SVs, including postsynaptic and mitochondrial proteins such as PSD-95, gephyrin, and COX-4. The 47 proteins detected only in SVs, including vGluT3, are likely proteins enriched in a subset of SVs revealed by purification, but are thought to be of low abundance or peptide resolution too low to be detected in P2' purified from whole brain. 1,419 proteins were detected in both P2' and SV fractions. Furthermore, we revealed the distribution profile by quantifying their abundance from the SV / P2' ratio using a volcano plot (Figure 3B). We found that 134 proteins had an abundance ratio of SV / P2' significantly higher than 2. We defined this group as the "SV-resident protein repertoire" because it includes proteins such as synaptophysin, synaptotagmin-1, vacuolar ATPase-associated protein, SV2, Scamp1, CSP-A, synapsin-1, and mover, all previously established as SV-resident proteins (Jahn and Sudhof, 1994; Morciano et al., 2005; Burre et al., 2006; Takamori et al., 2006; Ahmed et al., 2013). On the other hand, the majority of 1419 proteins had SV / P2' abundance ratios lower than 1, suggesting that these proteins are abundant in other synaptic compartments but occasionally interact with SVs.We defined these proteins as the "SV-interacting protein" repertoire because they include (I) cytoplasmic proteins such as calmodulin, actin, and tubulin, (II) active site proteins such as piccolo and bassoon, and (iii) plasma membrane proteins such as synaptoyanin-1 and syntaxin 1, all of which are known to transiently interact with SVs (Hirokawa et al., 1989; McPherson et al., 1996; Igarashi and Watanabe, 2007; Jahn and Fasshauer, 2012; Boyken et al., 2013). Thus, UD quantitative proteomics can distinguish between SV-resident and SV-interacting synaptic proteins. These data provide a spatial and functional basis for the large number of proteins newly identified in SV fractions by the UD approach.

[0082] Proteins detected in any of these repertoires may not coexist on SVs in the same abundance. To address this issue, we used UD quantification data to further analyze the abundance and diversity of 1,466 proteins detected in the SV fraction in ranked abundance plots (Figure 3C). As previously reported (Takamori et al., 2006), the transmembrane proteins synaptospisin, synaptotagmin 1, synaptobrevin-2, SV2A, vGluT1, vATPase complex proteins, and the lipid-anchored proteins synapsin-1 and rab3A were highly abundant (word cloud chart in Figure 3C). 180 such proteins account for 90% of the total SV protein mass (ranked abundance plot in Figure 3C). The iBAQ values ​​of these SV proteins are 1.2 × 10 8(1.2E8), whereas the iBAQ values ​​of the other 1,286 proteins ranged from E5 to E8. Copy numbers per SV have been estimated previously for highly abundant SV proteins, constructing an “average SV model” (Takamori et al., 2006). Using isotope-labeled peptides, we extended this estimate not only to low-abundance SV proteins, but also to abundant but previously uncharacterized SV proteins. We therefore utilized the previously determined copy number of synaptotagmin 1 (15, (Takamori et al., 2006)) as a standard. Using this new method, we re-estimated the copy number of rab3A to 10.5 (10 in (Takamori et al., 2006)) and newly estimated the copy numbers of Aak1 (1.5), mover (0.94), Atg9A (0.04), and git1 (0.006) (Table S1). These results suggest that many SV proteins have copy numbers below one and are present only in a subpopulation of SVs (Fig. 3C). Although approximately 10% of SV proteins account for 90% of the SV protein mass (the "canonical repertoire"), it is likely that numerous low-abundance SV-anchored proteins contribute to the functional diversity of SVs.

[0083] In conventional EM imaging, purified SVs appear as uniform membrane structures with diameters of 40–50 nm (Figures 2E, 3D). In conventional EM preparations, contrast-enhancing reagents such as uranyl acetate were used. These reagents mainly bind to membrane phospholipids, thereby obscuring structures around the SVs. To visualize structures anchoring SVs, we used high-angle scattering dark-field scanning electron microscopy (HAADF-STEM, (Krivanek et al., 2010)), which has near-atomic image resolution without staining the specimen. In HAADF-STEM, purified 40 nm SVs were surrounded by giant, heterogeneous structures polarized on one side of the SV (Figure 3E, schematic in Figure 3F). High-magnification images showed protein-like atomic structures. Adaptor protein complex-2 (AP-2) is a presynaptic membrane protein involved in clathrin coating of SVs during endocytosis and may anchor various proteins to SVs. UD proteomics newly identified AP-2 as an SV-resident adaptor protein. Based on our UD proteomic findings, we therefore propose a new SV architecture model that combines SV-anchored proteins with the previously described core proteins (Takamori et al., 2006).

[0084] Power of UD proteomics methods to detect hidden SV proteins To explore hidden aspects of the SV proteome, we tabulated all proteins detected in SV fractions by UD proteomics with annotation (see supplementary Excel database). Our new UD-SV proteomics data were compared with those reported in Takamori et al. (2006) and can be filtered by gene family, abundance rank, structural information such as transmembrane proteins, lipid-anchored proteins, or soluble proteins, and cellular function categories such as metabolic enzymes, small GTPase-related proteins, and autophagy-related proteins.

[0085] In our dataset, we first investigated the rab subfamily of small GTPases using the functional keyword “rabs”. Rab proteins play a central role in docking and targeting of transport vesicles to specific organelles and membranes (Stenmark, 2009). Rabs are evolutionarily conserved, exhibiting 75–95% amino acid sequence identity (Diekmann et al., 2011). Such high homology may complicate proteomic detection. Using UD proteomics, we detected 40 rabs in the SV fraction, 32 of which have been previously reported but without abundance quantification (Takamori et al., 2006). We found many rabs (29 / 40) in the SV resident protein repertoire and in the high abundance range, suggesting that SVs may have focused and variable transport pathways within the presynaptic compartment other than endo-exocytosis. Rab11A and rab11B are some of the most identical isoforms, with 91% amino acid sequence identity (Figure 4A). However, they are involved in opposite endosomal sorting pathways (Grimsey et al., 2016). We found 14 unique peptides common to both rab11A and B. However, only UD proteomics was able to detect 14 unique peptides of rab11A in the C-terminal hypervariable region (Figure 4A). Thus, UD methods with improved peptide coverage can reveal highly homologous but functionally distinct proteins.

[0086] We next searched for subunits of the vacuolar (v)ATPase protein complex (gene family: atp6, functional keyword in the dataset: “vATPase”). vATPase functions as an ATP-driven proton pump for vesicle acidification required for neurotransmitter uptake by synaptic vesicles. The vATPase complex is composed of two domains: a peripheral domain “V1” composed of eight different proteins (A, B, C, D, E, F, G, H) and a membrane-embedded domain “V0” assembled from four different proteins (a, c, d, e) (Forgac, 2007) (Figure 4B). Previous proteomic studies estimated the copy number of vATPase to be >1 per SV, but failed to identify the complete set of vATPase proteins present in the SV fraction (Morciano et al., 2005; Burre et al., 2006; Takamori et al., 2006; Boyken et al., 2013). Here, we used UD proteomics to identify all components of the vATPase complex, which were predominantly detected in the high abundance range of the SV proteome (Figure 4B, supplementary Excel database). Furthermore, in the low abundance range, we identified previously detected vATPase accessory proteins, such as Wdr7 and renin receptor (atp6ap2), as well as newly detected proteins, such as Dmxl1 and Dmxl2 (Merkulova et al., 2015) (Figure 4B). These low abundance accessory proteins likely modulate the function of the vATPase complex through transient interactions and / or in a limited subpopulation of SVs. Thus, the UD method can reveal a complete inventory of protein complexes.

[0087] Next, UD analysis of SV resident transporter proteins was performed. Solute carrier (''slc'') transporters are transmembrane proteins that control the movement of soluble molecules across the cell membrane. Currently, over 400 SLC genes have been identified in mammals, and approximately 40% of them remain uncharacterized with respect to their expression profile and / or function(s) (Cesar-Razquin et al., 2015). In UD analysis, transporters detected in the SV fraction are either SV resident (i.e., iBAQ score SV > P2'), or synaptic proteins of the cell membrane (i.e., iBAQ score SV < P2'), and synaptic proteins of the cell membrane are most likely to be ''caught up in the traffic'' of vesicles during the cycle of exocytosis and endocytosis (Figure 4C). SV resident transporters include vGluT1 (slc17a7) and vGluT2 (slc17a6) for glutamate neurotransmitter uptake, and vGAT (slc32a1) for GABA and glycine uptake, which define the molecular identity of the major SV populations in the brain (Takamori et al., 2000a; Takamori et al., 2000b), and for this reason, are present in high abundance in the SV proteome (Figure 4C). UD proteomics also detected SV resident transporters established in the low abundance range that were missing in previous SV proteomics studies. These include vMAT2 (slc18a2, (Nirenberg et al., 1995)), ChT1 (slc5a7, (Ferguson et al., 2003)), vAchT (slc18a3, (Weihe et al., 1996)), and SVOP (atypical SLC subfamily, (Janz et al., 1998)), which are involved in the uptake of monoamine neurotransmitters (dopamine, serotonin, norepinephrine, histamine) or acetylcholine into relatively minor SV populations.In addition to these well-known transporters, UD analysis identified nine new SV-resident transporters (Figure 4C), of which Slc10a4 has been reported to import bile acids into SVs to regulate dopamine activity (Larhammar et al., 2015). The remaining eight transporters are entirely novel and have unknown functions (Table S2). Taken together, UD proteomics revealed hidden proteins of both high and low abundance in the SV proteome present in canonical and subpopulations of SVs in the brain.

[0088] Identification and validation of novel mammalian SV proteins discovered by UD proteomics We found a completely novel protein in the SV fraction of adult rat brain. This protein with unknown function is named RGD1305455 (Uniprot ID: A0A0G2KAX2), "unidentified protein C7orf43 homolog", or "similar to hypothetical protein FLJ10925" in the databank. LC-MS / MS analysis of the UD method detected six peptides generated from RGD1305455, but not in either the SV or P2' HD experiments. Our UD analysis showed that the product of this unidentified gene is an SV resident protein, possibly present in a subset of vesicles or synapses in the brain. To date, the gene databank does not provide any information on gene expression in tissues, developmental profile, or subcellular localization of the protein. RGD1305455 is a non-transmembrane protein with a DUF domain (DUF4707) conserved in eukaryotes. Comparison with homologs reported in other vertebrates revealed high amino acid sequence conservation among mammals (>97% identity) (Figure 9), suggesting that the structure and function of RGD1305455 are evolutionarily conserved even in animals with high brain function integrity.

[0089] In a complex biological mixture, on average, a total of 500,000 peptides may be detected and sequenced by mass spectrometry. LC-MS / MS analysis can produce identification artifacts due to overlapping peptide spectra and false-positive protein assignments (Chen et al., 2009; Bogdanow et al., 2016). To exclude the possibility that RDG1305455 detected by UD proteomics is an artifact, we used a targeted proteomics strategy with synthetic stable isotope-labeled peptides to confirm the presence of the native peptides that compose this protein in digested SV protein samples.

[0090] The unique peptide sequence VLVVEPVK (SEQ ID NO: 19) detected by UD proteomics was chemically synthesized with full labeling (13C6 and 15N2) at the C-terminal lysine (K), resulting in a mass shift of +8 Da ("heavy peptide"). This heavy peptide was analyzed by LC-MS / MS before mixing with the digested SV protein sample, providing information on elution time, ionization pattern, and fragmentation pattern. A parallel reaction monitoring (PRM) assay based on these parameters (Peterson et al., 2012) detected the matched native VLVVEPVK peptide (SEQ ID NO: 19) in the SV sample. A detailed comparison of the observed and predicted peptide fragments (Table S4) showed that the mass error of the native fragment was within 0.02 Daltons. This confirmed with high accuracy that the RGD1305455 protein was present in the SV fraction. We also performed PRM assays with 15 other heavy peptides of newly identified SV resident proteins (Table S5). All tested proteins were confirmed to be present in the SV fraction. These results provide strong evidence for a "hidden SV proteome" with potentially important functions.

[0091] Functional identification of SV-associated kinase protein Aak1 The SV fraction contained numerous transmembrane or lipid-anchored proteins, as well as “soluble accessory” proteins that remain attached to SVs in the synaptic compartment (see Supplementary Excel database). These SV accessory proteins may play important regulatory roles in neurotransmission. To address this possibility, we focused on UD data analysis of protein kinases, which are primarily soluble cytoplasmic proteins. Using the keywords “signal transduction” and “kinase”, we identified Aak1 as the kinase that most strongly bound to purified SVs from our database (Figure 5A). The copy number of Aak1 was calculated to be 1.5 / SV (Figure 7), suggesting its ubiquitous presence among SVs in central synapses (Figure 5B). The profile of Aak1, highly enriched in the purified SV fraction compared to the P2′ fraction, was confirmed by Western blot and was in contrast to the profiles of other cytoplasmic kinases found in synapses (P2′), such as MARK2 and TNiK. In cultured hippocampal neurons, strong colocalization of both exogenously expressed Aak1 (TagRFP-Aak1) and the major SV component synaptophysin (sypHy) was observed in nerve terminals using confocal fluorescence microscopy (Figure 5C). These data support the idea that Aak1 is a soluble kinase protein, but localizes to synaptic vesicles in the presynaptic compartment.

[0092] We used both molecular / genetic and pharmacological approaches to clarify the functional role of Aak1 using shRNA knockdown (KD) of Aak1 expression in cultured hippocampal neurons and by directly loading the Aak1-specific inhibitor LP-935509 (Kostich et al., 2016) into the calyx of Held presynaptic terminals in brainstem slices from rats on postnatal days (P) 13–15. For shRNA knockdown of Aak1, lentivirus targeting exon 11 of Aak1 (Ultanir et al., 2012) was applied at day 11 in vitro (DIV11), when synaptophysin becomes detectable by Western blot. An ineffective shRNA targeting exon 20 of Aak1 was used as a negative control (Figure 5D). At DIV15, the knockdown effect was maximal, and Aak1 expression was reduced to less than 5% in Western blot analysis. We recorded EPSCs from hippocampal neurons at DIV15 and induced short-term depression (STD) with a train of 20 stimuli at 20 Hz. Compared with controls, EPSCs from Aak1-KD neurons showed faster and stronger STD, which was evident at several stimuli, and their amplitudes were reduced to significantly lower levels (p<0.05, n=7, Fig. 5E). Similarly, in HELD cupules loaded with LP-935509, EPSCs underwent stronger STD during 100 Hz trains (0.3 s, p<0.05, n=7). In HELD cupules, recovery after STD was significantly enhanced by Ca 2+ Fast component of Ca dependence 2+ The Aak1 signaling pathway follows a quadratic exponential time course with an independent slow component (Wang and Kaczmarek, 1998). Aak1 inhibitors significantly prolonged the time constants of both components (Figure 5F). Taken together, these results suggest that Aak1 normally plays a facilitative role in SV recycling and reuse in presynaptic terminals.

[0093] To further investigate whether Aak1 is involved in SV exo-endocytosis, we expressed the pH-sensitive fluorescent markers pHluorin and synaptophysin (sypHy) in cultured hippocampal neurons and measured intravesicular pH changes associated with SV exocytosis and endocytosis (Miesenbock et al., 1998). In Aak1-KD cultured hippocampal neurons, the extent of exocytosis measured by SV pH neutralization was unchanged, but the reacidification time of endocytosis was prolonged by two-fold in half-life compared to control cells (p<0.01, n=51 in Aak1-KD, n=20 in control) (Figure 5G). In the cupule of Held, exo-endocytosis can be directly monitored using membrane capacitance measurements (Sun and Wu, 2001; Yamashita et al., 2005). In the presence of LP-935509 (1 μM or 10 μM), the change in endocytic capacitance in the presynaptic terminal was significantly prolonged (p<0.05, n=6), but presynaptic Ca 2+ Current charge (Q ca ) or change in exocytosis capacitance (ΔC m ) was unchanged (Figure 5H). Thus, the consistent results at hippocampal and brainstem synapses suggest that Aak1 plays a facilitatory role in SV endocytosis and recycling.

[0094] The above results indicate that reduction or inhibition of Aak1 expression does not directly affect exocytosis but impairs SV recycling. We then examined whether Aak1 plays a physiological role in the maintenance of high-frequency neurotransmission. To this end, we simultaneously recorded APs from presynaptic terminals (input) and postsynaptic neurons (output) and assessed the fidelity of neurotransmission (output / input AP ratio) in the cupule of Held (Fig. 5I). During 100 Hz stimulation, presynaptic APs did not become dysfunctional at all (data not shown), whereas postsynaptic APs became dysfunctional gradually. In the presence of LP-935509 (1 μm) at the presynaptic terminal, postsynaptic APs began to become dysfunctional earlier than controls, with a significant decrease in transmission fidelity (approximately 50%) within 40 s (p<0.01, n=6). In summary, Aak1 is a canonical SV-resident protein in synapses and is shown to play an essential functional role in the maintenance of high-frequency neurotransmission.

[0095] The largely hidden (deep) proteome may underlie brain disease phenocopy UD proteomics newly detected many proteins in both high and low abundance ranges of the SV proteome, with most of them (~80%) found in the low range (position 410 or higher, iBAQ < 2.7E7, see supplementary database in Excel). Low abundance proteins that may not be ubiquitously present in the SVs of central synapses could potentially play important physiological roles. To address this possibility, we verified the functional annotations and disease associations reported in our dataset. Approximately 1500 SV fraction proteins may be involved in various cellular functions. We classified the proteins into 17 categories with 26 subcategories. Our dataset contains a large number of transport proteins, including a diverse set of SNAREs involved in various membrane fusions, as previously reported (Takamori et al., 2006; Burre and Volknandt, 2007; Wilhelm et al., 2014). It also contains a large number of rab GTPases (40) and the membrane tethering complex trappc (14). The majority of these proteins were identified as SV-resident in UD proteomics, suggesting that SVs are adapted for various trafficking pathways and communication with other presynaptic organelles. Other major categories included proteins involved in signal transduction (e.g., kinases, phosphatases), signal transduction (e.g., trimeric GTPases), and transport of small molecules. UD proteomics detected a large number of metabolic enzymes (179), including enzymes involved in neurotransmitter metabolism (13), cellular energy production (35), (phospho)lipid regulation (75), and cyclic nucleotides (12). These data suggest that metabolic reactions at crowded presynaptic terminals likely occur locally by direct interaction with SVs (Wilhelm et al., 2014). UD proteomics also detected SV proteins classified as autophagy-related proteins (40). Some of these proteins have been reported to be associated with neuropsychiatric disorders such as schizophrenia or Alzheimer's disease (Salminen et al., 2013; Vijayan and Verstreken, 2017).The presence of both SV-resident SV proteins (e.g., snap29, atg9a, trappc8, pik3c3) and transiently interacting SV proteins (e.g., beclin-1, uvrag, map1lc3a, cisd2) across the SV proteome indicates that control of SV population size by autophagy may play an important physiological role at synapses.

[0096] To reveal pathological associations in the UD proteomics data, we searched for proteins detected in the SV fractions for which neurological disease(s) caused by mutation(s) in those genes have been reported (see the Supplementary Excel database of "Diseases in the SV Proteome") and marked them in the ranked abundance plot. We found 236 brain diseases associated with 210 proteins detected in both the high and low abundance ranges of the SV proteome. 159 (76%) proteins out of the 210 SV proteins were newly revealed by the UD method. The SV protein-associated diseases included many motor (145) and / or cognitive (135) diseases, and a relatively small number of sensory diseases, including visual (33) and hearing (14) dysfunctions. The database also contains SV proteins associated with a number of phenocopying diseases, such as mental retardation (28), epilepsy (25), Parkinsonism (13), amyotrophic lateral sclerosis (4), Alzheimer's disease variants (4), and ataxia (10). UD cross-analysis between function and disease showed that phenocopy could be caused by proteins from the SV resident repertoire and the repertoire that transiently interacts with SVs, proteins from both high and low abundance ranges, and proteins involved in different functions of the SV life cycle. For example, parkinsonism has been associated with mutations in SV resident proteins such as renin receptor (rank 121, SV acidification, (Korvatska et al., 2013)), dnajc13 (rank 318, SV endocytosis (Vilarino-Guell et al., 2014)), and sv2c (rank 97, SV trafficking (Hill-Burns et al., 2013)), or SV transient interacting proteins such as synaptojanin-1 (rank 351, SV endocytosis (Quadri et al., 2013)), and pla2g6 (rank 653, SV lipid composition (PaisanRuiz et al., 2009)) (see supplemental Excel database of "Diseases in the SV proteome").In summary, these data analyses revealed the complexity and functional importance of the protein repertoire newly detected by UD proteomics in the low abundance range of the SV proteome. EXAMPLES

[0097] The present invention will now be described in more detail with reference to the following examples. It should be noted that the present invention is not limited to these embodiments.

[0098] Materials and Methods All animal experiments were performed in accordance with the guidelines of the Physiological Society of Japan, the German Animal Welfare Act, and regulations at the Okinawa Institute of Science and Technology Graduate University, the Max Planck Institute for Biophysical Chemistry, and Doshisha University.

[0099] Purification of brain synaptosomes and synaptic vesicles: Synaptosomes (P2') and synaptic vesicles (SV) were purified from whole brains of 4- to 6-week-old Sprague-Dawley rats following the same protocols used in (2), as previously described in (34) for SV and in (4) for P2'. The quality of all P2' and SV purification methods was controlled by Western blots of synaptic protein markers and electron microscopy. A detailed description of the biochemical, imaging, and electrophysiological procedures and analyses is provided in SI Materials and Methods.

[0100] UD Proteomics Sample Preparation and Mass Spectrometry: Sequential protein digestion: Fifty micrograms of protein extracted from P2' or SV were resuspended in 200 μL of a buffer containing 8 M urea, 100 mM Tris-HCl (pH=8) ("urea buffer") and placed on a Pall Nanosep® 10K Omega filter (Sigma). After shaking at 850 rpm for 1 min at room temperature (Eppendorf Thermomixer), the sample was centrifuged at 6,400×g for 13 min (optimal conditions to remove all liquid from the upper chamber using a TOMY Kintaro KT-24 centrifuge). After repeating these steps twice, the protein was resuspended in 200 μL of urea buffer containing 50 mM iodoacetamide and incubated at room temperature for 1 h in the dark. The alkylation was then stopped by the above centrifugation and resuspending the protein sample in 200 μL of urea buffer containing 25 mM DTT. The unfolded protein was then washed with 20 mM ammonium bicarbonate. Proteolytic enzymes were used at a ratio of 1:50 with protein. To generate peptides, a first digestion step ("trimming") was performed using endoproteinase lys-C (Promega) at 37 °C for 6 h, followed by a second digestion step using a trypsin / lys-C combination (Promega) at 37 °C overnight (16-18 h). After centrifugation as above, the digested peptides were acidified with 1% TFA and concentrated to dryness using an EZ-2 Elite evaporator (SP Scientific).

[0101] Orthogonal peptide separation: To separate the peptides, an offline electrostatic repulsion hydrophilic interaction chromatography or ERLIC-based separation was performed (Alpert et al., 2010). The following conditions were adapted and optimized to obtain the maximum number of identified proteins from the P2' and SV samples. Preparation of mobile phase solvents: Solvents were freshly prepared for each experiment using C / MS grade acetonitrile (ACN), formic acid (FA), and water from Thermo Fisher Chemicals. Solvent A: 90% ACN, 0.1% FA. Ammonium hydroxide (NH4OH, 25% w / w in water, Fluka) was then added to adjust the pH to 4.5. Solvent B: 30% ACN, 0.1% FA. The digested peptide mixture was resuspended in 20 μl of solvent A and injected onto a weak anion exchange PolyWax LP column (PolyLC Inc.; 1 mm ID × 150 mm, 5 particle size, 300A pore size) using a PAL HTC autosampler (CTC Analytics) for automated injection and fraction collection at a flow rate of 40 μL / min in gradient mode (3 min solvent A, to 10% B in 7 min, 10% B to 25% B in 24 min, 25% B to 70% B in 16 min, 70% B to 81% B in 6 min, 81% B to 100% B in 3 min, final wash with 100% B for 6 min, re-equilibration with 100% A for 20 min). Twenty-four fractions were collected every 3 min from 0 to 72 min and then concentrated to dryness using a speed vacuum Genevac EZ-2 Elite (SP Scientific).

[0102] Mass spectrometry: Dried peptides were resuspended in 30 μL of 0.1% formic acid and analyzed using an Ultimate 3000 nano-high pressure liquid chromatography (nano-HPLC) system (Dionex), an HTC-PAL autosampler (CTC Analytics), and a Q-Exactive Plus Orbitrap hybrid mass spectrometer (Thermo Scientific) equipped with a nanoelectrospray ion source. Five μL of each sample was injected onto a Zorbax 300SB C18 capillary column (0.3 × 150 mm, Agilent Technologies) heated at 40 °C. 0.1% formic acid in distilled water as solvent A and 0.1% formic acid in acetonitrile as solvent B were used with a 1-h HPLC gradient (1% B to 32% B in 45 min, 32% B to 45% B in 15 min, final wash at 75% B for 5 min, re-equilibration at 1% B for 10 min). A flow rate of 3.5 μL / min was used for peptide separation. The temperature of the heated capillary was 300 °C, and a spray voltage of 1.9 kV was applied to all samples. Mass spectrometer settings were full MS scan range of 350-1500 m / z with mass resolution of 70,000, scan time of 30 μs, automatic ion amount control set to 1.0E6 ions, and fragmentation MS2 of the 20 most intense ions.

[0103] Protein Identification: Protein identification was performed using Proteome Discoverer software v2.1 (Thermo Scientific) and Mascot 2.6 (Matrix Science) as the search engine. The UniprotKB database downloaded from Rattus norvegicus (Proteome ID: UP000002494) was used with the following search parameters: trypsin enzyme with a maximum of two false cleavages and precursor and fragment mass tolerances were set at 10 ppm and 0.02 Da, respectively. Cysteine ​​carbamidomethylation, methionine oxidation, asparagine and glutamine deamidation, and N-terminal protein acetylation were set as variable modifications. Results were filtered using a false discovery rate of less than 1% as the cutoff threshold, determined by the Percolator algorithm in the Proteome Discoverer software.

[0104] Statistical analysis of quantitative proteomics data: For Volcano Plots, intensity-based absolute quantification (iBAQ) data obtained from Proteome Discoverer were used for statistical analysis using R software, version 3.2.5 (R Project for Statistical Computing). Quasi-Poisson generalized linear models (y~1, y~treatment) were generated and compared using analysis of deviance (Anova) of generalized linear model fits with F-tests, and p-values ​​were obtained and corrected with the Benjamini-Hochberg method.

[0105] Data availability. Proteomic raw data files have been deposited in the Japan Proteome Standard Repository database (accession number: JPST000968 ). All other study data are included in the article, Dataset S1 , and SI Appendix .

[0106] Supplementary Information Text SI Materials and Methods Purification of synapses and synaptic vesicles from rat brain: Synaptosomes (P2') and synaptic vesicles (SVs) were purified from 4-6 week old rat brains. All steps were performed at 4 °C. Nine brains were dissected and homogenized in 60 mL of ice-cold sucrose buffer (0.32 M sucrose, 4 mM HEPES NaOH (pH 7.4) supplemented with 1 μg / mL pestatin, and 0.2 mM PMSF protease inhibitor) in a glass-Teflon homogenizer using 9 up-and-down strokes at 900 rpm. The brain homogenate (BH) was centrifuged for 10 min at 2,700 rpm at 4 °C (Sorvall SS34 rotor). The resulting pellet (P1: cell debris, nuclei) was discarded and the supernatant (S1) was collected and centrifuged for 15 min at 10,000 rpm at 4 °C (Sorvall SS34 rotor) to obtain the cytoplasmic fraction (S2) and crude synaptic body fraction (P2). Ficoll gradients were prepared in sucrose buffer with the following layers (from bottom to top): 4 mL of 13% Ficoll, 1 mL of 9% Ficoll, and 4 mL of 6% Ficoll. The P2 fraction (3 mL in each tube) was layered on top of the Ficoll gradient and centrifuged at 22,500 rpm for 35 min at 4°C (SW41 rotor, Beckman). The fraction at the interface between the 13% and 9% Ficoll layers was collected, diluted with sucrose buffer, and centrifuged at 11,000 rpm for 12 min at 4°C (Sorvall SS34 rotor). The pellet was then resuspended in sucrose buffer to obtain the synaptosomal fraction (P2'). For SV purification, the P2 suspension was further centrifuged at 10,500 rpm for 15 min at 4°C (Sorvall SS34 rotor) and the pellet was resuspended in 13 mL of sucrose buffer. This suspension, termed the "well-washed" crude synaptosome fraction, was then transferred to a glass-Teflon homogenizer. Osmotic lysis was immediately performed by the addition of 117 mL of ice-cold water and three up-and-down strokes at 3,000 rpm. The resulting synaptosome lysate was buffered with 1 mL of 1 M HEPES-NaOH (pH 7.4) and centrifuged (Sorvall SS34 rotor) at 16,500 rpm for 20 min at 4 °C to obtain a lysate pellet (LP1, synaptic membrane-enriched fraction) and a lysate supernatant (LS1, synaptic cytoplasmic contents).LS1 was then collected and transferred to twelve 10 mL polycarbonate tubes and centrifuged at 50,000 rpm for 2 h at 4 °C (50Ti rotor, Beckman). The supernatant (LS2) was removed and the pellet (LP2, the "crude synaptic vesicle" fraction) was resuspended in 3 mL of 40 mM sucrose. The suspension was then layered on top of a 2%-22% (w / v) sucrose gradient in 5 mM HEPES pH 8.0 (generated using an automated gradient mixer, Biocomp Instruments) and centrifuged at 25,000 rpm for 4 h at 4 °C (SW28 rotor, Beckman). Fractions corresponding to the sucrose region enriched for synaptic vesicles (banding of material in 7%-13% sucrose) were collected, pooled and layered on top of a controlled pore glass chromatography column (2 cm i.d. × 150 cm) (glass beads: average pore size 300 nm, size 74-125 μm (120 / 200 mesh)). Size exclusion chromatography was performed overnight in glycine buffer (0.3 M glycine, 5 mM HEPES-NaOH pH 7.2, 0.02% sodium azide) at a flow rate of 40 mL / h. Fractions containing mainly heterogeneous membranes with diameters >100 nm were excluded. Fractions containing small vesicles with uniform shape, diameters 40-45 nm, were collected and centrifuged (SW50.1 rotor, Beckman) at 50,000 rpm for 1 h at 4 °C to obtain the "pure synaptic vesicles" fraction (SV). The quality of all P2' and SV purification methods was controlled by Western blots of synaptic protein markers and electron microscopy (see "Western blotting characterization" and "Electron microscopy imaging").

[0107] Protein extraction and immunoblotting characterization: Proteins were extracted using lysis buffer containing 100 mM Tris-HCl (pH = 8), 4% SDS, 100 mM DTT, and protease inhibitor cocktail (Sigma). Protein concentrations were analyzed using both NanoDrop™ 2000 (Thermo Scientific) and Direct Detect® (Millipore) spectrophotometers. Proteins from each fraction were loaded equally (10 μg per lane) on a 4-12% BIS-Tris SDS gel (NuPAGE™, Thermo Scientific) and transferred to a supported nitrocellulose membrane (Bio-Rad). The transferred membrane was blocked with 5% skim milk TBST for 1 h at room temperature. All primary antibodies (see antibody table) were used at a dilution of 1:1000 in 1% skim milk TBST and incubated with the membrane overnight at 4°C. After washing three times with TBST, secondary antibody-HRP conjugates were used at a dilution of 1:2000. After washing, blots were developed using Clarity™ Western ECL Substrate (Bio-Rad Laboratories) and imaged on a ChemiDoc™ XRS+ system using Image Lab™ software (Bio-Rad Laboratories).

[0108] Targeted Proteomic Analysis: Peptide selection criteria Peptide sequences for targeted proteomics were selected according to the following criteria: 1) unique peptides from synaptic proteins detected by HD and / or UD proteomics; 2) peptides that do not contain any amino acid modifications such as acetylation or carbamidomethylation; 3) Peptides less than 16 amino acids in length that can be synthesized rapidly with high purity without an additional liquid chromatography purification step.

[0109] Peptide synthesis Peptides were synthesized at 1 μmole scale in 96-well plates using a high-throughput automated peptide synthesizer, ResPep SL (Intavis Bioanalytical Instruments), with preloaded Fmoc- 13 C6 15 N2 Lysine or Fmoc- 13 C6 15 The peptides were synthesized via conventional 9-fluoroenylmethyloxycarbonyl (Fmoc) solid-phase peptide synthesis (SPPS) on N4 arginine TCP resin (Intavis Bioanalytical Instruments). All Fmoc-amino acids were purchased from Watanabe Chemical Industries and prepared at 0.5 M in N-methylpyrrolidone (NMP, Wako Pure Chemical Industries). For the automated synthesis, the following reagents (Wako Pure Chemical Industries) were dissolved in N,N-dimethylformamide (DMF) and used according to the Intavis Bioanalytical Instruments SPPS protocol: 0.5 m HBTU coupling reagent, 44% N-methyl-morpholine (NMM) base, 5% acetic anhydride capping reagent, and 20% piperidine deprotection reagent. After synthesis, peptides were cleaved with (v / v / v) 92.5% TFA, 5% TIPS, and 2.5% water for 2 h, precipitated with t-butyl-methyl-ether at -30°C, pelleted, resuspended in water, and lyophilized overnight (EYELA FDS-1000). Prior to use, weighted peptides were redissolved in water and their concentrations were confirmed using a Direct Detect® infrared spectrophotometer (Millipore), and a calibration curve was generated using peptide standards (6x5 LC-MS / MS peptide reference mix, Promega). The purity and sequence of all synthesized peptides were confirmed by LC-MS / MS (see "Mass Spectrometry"), and their elution times, m / z values, main charge states, and fragmentation information were collected using parallel reaction monitoring (PRM).

[0110] De novo sequencing and correlation with isotope-labeled references To confirm the presence of novel SV proteins in the samples, de novo sequencing analysis of peptides from MS / MS data and correlation with synthetic peptides (spectral annotation, ion match table, plotted mass error) was performed using PEAKS software (v 7.0, Bioinformatics Inc).

[0111] Targeted proteomic analysis The PRM acquisition method combined two scan events, corresponding to one full scan and one PRM event, targeting doubly and triply charged precursor ions of the synthesized peptide (154 reaction mass). The full scan event used a mass range of 350–1200 m / z, an orbitrap resolution of 70,000, a target automatic ion quantity control (AGC) value of 5E5, and a maximum injection time of 30 ms in profile mode. The full scan event was followed by a PRM involving three multiplex scan events. This PRM employed an Orbitrap resolution of 17,500, a target AGC value of 1E6, and a maximum injection time of 50 ms. Precursor ions of each targeted peptide were isolated using a 1.6-m / z unit window and a positive offset of 0.4-m / z. Fragmentation was performed with stepped collision energies of 15, 22, and 27, and MS / MS scans were acquired with a start mass of 300 m / z, and the end mass was automatically defined by the charge state of the precursor ions. The generated MS / MS scan library was uploaded to Skyline software (version 3.6.0.10493) (1) and all assigned fragment ions were extracted. For each SRM chromatogram, automatic peak integration within the peak boundaries and natural abundance to weight ratios were calculated using the software.

[0112] Absolute quantification Absolute quantification was performed by mixing known amounts of synthetic heavy peptides (0.1 or 0.05 μg) with 50 μg of digested SV protein. The absolute amount of native peptide was calculated using the abundance signal from the corresponding heavy peptide. Absolute amount (μg) = (0.1 or 0.05) × (abundance ratio of native / heavy). The average copy number of protein per SV was estimated as a reference to synaptotagmin (Syt1), a transmembrane SV resident protein (estimated at 15 Syt1 per SV by Takamori et al., 2006). Number of proteins / SV = absolute amount / molecular weight × [(number of Syt1 / SV) × (molecular weight of Syt1) / (absolute amount of Syt1)].

[0113] Protein annotation in the SV proteome dataset: For each protein identified in the SV fraction, a search of the scientific literature (PubMed, Google Scholar) and databases (UniprotKB, NCBI) was performed manually. The most significant biological function(s) were reported and functional keyword(s) were assigned for data filtering purposes. Structural annotations related to protein-membrane interactions were generated using UniprotKB. Proteins were indicated as having or not having transmembrane domain(s) (TM), and membrane-anchored proteins were indicated with lipid type. A comparison of our data with the previously described SV proteome by Takamori et al. was performed. Since designation of a particular protein by multiple names is frequent in protein taxonomy and scientific literature (e.g., tumor protein p63-regulated gene 1-like protein (Tprg1l) is also called mossy fiber terminal-associated vertebrate-specific presynaptic protein (Mover), and Family with sequence similarity 79 member A protein (Fam79A)), a rigorous cross-comparison was performed using the gi numbers listed by Takamori et al. and amino acid sequence searches from NCBI RefSeq with Uniprot ID-related amino acid sequences and gene symbols from our SV protein dataset. Proteins were then indicated whether they were previously detected in Takamori et al. Disease annotations were made using the Uniprot and GeneCards databases of human diseases. Proteins detected in the SV fractions were indicated with "disease(s) caused by mutation(s) affecting the gene(s) represented in the entry". Symptom descriptions were used to classify the reported diseases into cognitive, motor, and / or sensory neurological disorders. Additionally, we searched for potential contamination with postsynapse-specific proteins in the SV fractions in the recently published Synaptic Gene Ontologies resource SynGo (2), after which proteins were mentioned whether they were reported in SynGo or not, and SynGo cellular components were indicated.

[0114] "Word Cloud" Representation: A script was used in Python to generate a list of iBAQ values ​​and Uniprot names for the 400 most abundant proteins in the SV-1 experiment, each normalized by the iBAQ value of synaptophysin (ranked 1 for SV-1). This list was uploaded into the web interface of the online word cloud generator WordArt (formerly TagUl) to generate an SV proteome word cloud image.

[0115] Amino acid sequence alignment: Protein amino acid sequences were obtained from Ensembl and aligned using the ClustalW2 program from EMBL-EBI. Amino acid alignment output and shading were processed using the Boxshade program (ExPASy Bioinformatics Resource Portal).

[0116] Electron Microscopy Imaging: Synaptosome (P2') sample Purified synapses were resuspended in 2.5% glutaraldehyde, 0.1 M cacodylate buffer and fixed for 30 min at room temperature. After washing three times with 0.1 M cacodylate buffer and centrifuging at 13,000 rpm (Eppendorf Centrifuge 5418) for 5 min, synapses were stained with 1% osmium, 0.1 M cacodylate buffer for 30 min and centrifuged at 3,000 rpm (Eppendorf Centrifuge 5810 R) for 5 min. Synapses were then washed three times with 2 ml of pure water (Otsuka Distilled Water) and centrifuged at 3,000 rpm for 5 min, followed by successive dehydration steps in 70%, 80%, 90%, 95% ethanol and three times dehydrated in 100% ethanol. The day before use, EPON resin solution was prepared by mixing TAAB812, DDSA, MNA, and DMP30 (TAAB Laboratories) in a ratio of 20:10:10:1, respectively, for 12 h. The resin solution was then mixed with 100% ethanol in a ratio of 1:1 and used to resuspend the synapses and incubate at room temperature for 30 min. After centrifugation as above, the synapses were resuspended in the resin solution and mixed thoroughly for 10 min. To remove microbubbles, centrifugation at 1,000 rpm for 1 min and incubation in a low vacuum chamber for 1 h and on the bench overnight were performed. The samples were then centrifuged at 5,000 rpm for 30 min to collect the largest synapses at one end of the resin and placed in an oven at 60 °C for 2 days (Electron Microscope Oven TD-700, Dosaka EM co. Ltd.). Sections (50 nm thick) of the samples were prepared using a Leica UC6 ultramicrotome. Synaptic sections were mounted on copper grids (HF34 Maxtaform Grids 200 mesh, Nisshin EM Co. Ltd.) pretreated with 100% acetone.Sections were then stained with 4% uranyl acetate for 30 min, washed four times with pure water (Otsuka Distilled Water), stained with lead solution (1% lead(II) nitrate, 1% lead acetate trihydrate, 1% lead citrate n-hydrate from Wako Pure Chemical Industries Ltd, pH 7 adjusted with NaOH) using a Nalgene® 171-0045 syringe filter for 5 min, and washed four times as above. Images of synapses were collected with a JEM-1230R electron microscope (JEOL) operating at 100 keV and processed with digital microscopy software (GATAN).

[0117] Synaptic vesicle (SV) samples Carbon-copper grids were prepared as follows: a 5-15 nm carbon film (resistance of 4 ohms / cm, purity 99.9999%, Nisshin EM co.Ltd.) was attached onto a copper grid (HF34 Maxtaform Grids 200 mesh, Nisshin EM co.Ltd.) using a JEOL IB-29510VET device, and pretreated by glow discharge treatment in an ion coater (DII-29020HD, JEOL) to make it hydrophilic and prevent particle aggregation during drying. Purified synaptic vesicles (3 μl of a 20-fold dilution from the original sample solution to observe individual vesicles) were then placed onto the grid, immediately dried with paper, and stained with 1% uranyl acetate (phospholipid contrast agent). SV images were collected with a JEM-1230R electron microscope (JEOL) operating at 100 keV and processed with digital microscopy software (GATAN).

[0118] Fluorescence Microscopy Imaging: SypHy and Aak1 cloning SypHy-P2A-TagRFP and SypHy-P2A-TagRFP-Aak1 were expressed in a neuron-specific manner using lentiviral-based vectors in combination with the Tet-Off system (3). Two vectors were used: a "control" vector expressing the processive tetracycline transactivator (tTAad) under the control of the human synapsin 1 promoter (STB), and a "response" vector (TGB) expressing either SypHy-P2A-TagRFP or SypHy-P2A-TagRFP-Aak1 under the control of a modified tetracycline response element (TRE) composite promoter. To construct SypHy-P2A-TagRFP-Aak1, full-length mouse Aak1 (accession number NM_001040106) was amplified by PCR and subcloned into the StuI site of pCR blunt-end vector (Thermo Fisher Scientific) according to the manufacturer's instructions, and the sequence was verified. The full length of Aak1 was excised by BamHI / EcoRI double digestion and cloned in frame into the BglII / EcoRI sites of the pTagRFP-C vector (Evrogen). Independently, a DNA fragment encoding sypHy lacking the stop codon (4) and a DNA fragment encoding the self-cleaving P2A peptide (5) were amplified by PCR and cloned into the TGB vector using the In-Fusion cloning kit (Clontech) according to the manufacturer's instructions. Finally, a fragment encoding SypHy-P2A and a fragment encoding TagRFP-Aak1 were PCR amplified and cloned into the TGB vector using the In-Fusion cloning kit. To generate SypHy-P2A-TagRFP, essentially the same procedure was performed, except that the TagRFP fragment amplified by PCR was combined with SypHy-P2A using the In-Fusion cloning kit.

[0119] Lentivirus-mediated expression of SypHy and Aak1 Lentiviruses were generated from HEK293T cells transfected with 3.4 μg of lentiviral backbone vectors (SypHy-P2A-TagRFP / TagRFP-Aak1 and either STB or TGB) and helper plasmids (2 μg of pCAG-kGP1, 1 μg of pCAG4-RTR2, and 1 μg of pCAG-VSVG) (3) using the calcium phosphate method (6). Cultures were infected with STB lentivirus at 0–1 DIV, with TGB lentivirus at 7 DIV, and used for experiments at 14–16 DIV.

[0120] Image analysis Live imaging was performed at room temperature (~24 °C) on an inverted microscope (Olympus) equipped with a 60x (1.35 NA) oil immersion objective and a 75 W xenon lamp. Images (1024 × 1024 pixels) were acquired with a cMOS camera (ORCA-FLASH 4.0, Hamamatsu Photonics) with an exposure time of 100 ms, under the control of MetaMorph software (Molecular Devices). SypHy was imaged with 470 / 22 nm excitation and 514 / 30 nm emission filters, whereas TagRFP fluorescence was imaged with 556 / 20 nm excitation and 600 / 50 nm emission filters. Acquired images were analyzed using MetaMorph software. Quantification of TagRFP and SypHy fluorescence used the line scan function of the MetaMorph software. A 5-pixel wide line was manually drawn along the axon and the fluorescence signal was normalized by the highest signal of either TagRFP or SypHy fluorescence in the selected region.

[0121] pHluorin-Based Live Imaging: Cloning For pHluorin assays with knockdown of Aak1 expression, GFP in pLVTHM was replaced with the red fluorescent protein FusionRed (FusRed) by restriction ligation cloning. FusRed was amplified from the pCAG-FusRed template by PCR using the KOD-plus-Neo DNA polymerase kit (Toyobo), and the restriction sites MauB1 and Spe1 were added to the following primers: 5'-TCGACGCGCGCGGCCACCATGGTGAGCGAGCTG-3' (forward; SEQ ID NO: 35), 5'-TATGACTAGTAT TTACCTCCATCACCAG-3' (reverse; SEQ ID NO: 36). The amplified DNA fragment was run on an agarose gel before purification with the Monarch DNA Gel Extraction Kit (New England BioLabs), ligated (T4 DNA ligase, New England BioLabs) into MauB1-Spe1 digested pLVTHM, and cloned in the HST08 E. coli strain (Stellar™ Competent Cells, Clontech). Plasmids confirmed by sequencing were purified from E. coli using the Plasmid Maxi Kit (Qiagen). pLVTHM-FusRed-shRNA lentivirus was then prepared as described in "Lentivirus Preparation and Titering".

[0122] Phluorin assay Dissociated hippocampal neurons were transfected at DIV0 with 0.8 μg of pCAG-SypHy2x plasmid by electroporation (one pulse at 1360 V for 24 ms, 10 μL suspension of 100,000 cells) using the Neon Transfection System (Invitrogen). After growth, cells were infected at DIV11–12 with pLVTHM-FusRed lentivirus expressing shRNA (see "shRNA cloning and knockdown assay"). Before imaging, cells cultured on glass coverslips (DIV15–19) were placed on the built-in imaging chamber of a confocal microscope and continuously perfused with standard extracellular solution at 25 °C containing the following (in mM): 140 NaCl, 2.4 kCl, 10 HEPES, 10 glucose, 2 CaCl2, 1 MgCl2, 0.01 CNQX (pH 7.4). SypHy imaging was performed with a Zeiss LSM780 confocal microscope containing a C-Apochromat 40x / 1.2 W Korr M27 objective. Neurons were identified using SypHy resting fluorescence (neurons expressing synaptofluorin) and FusRed fluorescence (neurons expressing shRNA) with a GFP bandpass filter 488 nm excitation and 493–586 nm emission and an mCherry bandpass filter 561 nm excitation and 578–697 nm emission, respectively. A concentric bipolar electrode (FHC) was placed 80–100 μm away from the neuron and subjected to a train of pulses (1 ms, 8 V) at 10 Hz for 10 s. Image acquisition of a portion of the axon of stimulated neurons was performed by Zen software v2.1 (Zeiss) in time-lapse mode at 1–2 frames per second. Baseline fluorescence was recorded for 1 min before each stimulation.

[0123] Image analysis Image analysis was performed using ImageJ (National Institutes of Health) and OriginPro2017 (OriginLab Corporation). In ImageJ, a 1.6 μm square region of interest was manually placed at the center of the fluorescent spot, and the corresponding fluorescent data were extracted in Origin. Fluorescence time courses of raw traces were corrected for photobleaching by OriginPro2017 using the baseline fluorescence intensity F0, which was fitted as ((F-F0) / F0) at the respective time point. Half-decay times were measured as the time it took for the signal to reach half of its peak intensity after stimulation. Mean half-life data from 51 boutons of 16 neurons in 5 independent experiments for Aak1 knockdown were compared with mean half-life data from 20 boutons of 10 neurons in 4 independent experiments.

[0124] Electrophysiological assays: Brain slice preparation and solutions Wistar rats of either sex (postnatal days 13–15) were killed by decapitation under isoflurane anesthesia. Transverse brainstem slices (175–200 μm thick) containing the medial trapezoid nucleus (MNTB) were cut using a vibroslicer (VT1200S, Leica) in an ice-cold solution containing (in mM): 200 sucrose, 2.5 KCl, 26 NaHCO3, 1.25 NaH2PO4, 6 MgCl2, 10 glucose, 3 myo-inositol, 2 sodium pyruvate, and 0.5 sodium ascorbate (pH 7.4, 310–320 mOsm when aerated with 95% O2 and 5% CO2). Prior to recording, slices were incubated for 1 h at 37°C and then maintained at room temperature (24–26°C) in a standard aCSF solution containing (in mM): 125 NaCl, 2 KCl, 26 NaHCO3, 1.25 NaH2PO4, 2 CaCl2, 1 MgCl2, 10 glucose, 1 myo-inositol, 2 sodium pyruvate, 0.5 sodium ascorbate (pH 7.4, 310–320 mOsm when aerated with 95% O2 and 5% CO2). Principal neurons of the MNTB and the cupules of Held presynaptic terminals were visually identified using a 40x water-immersion objective mounted on an upright microscope (BX51WI, Olympus).

[0125] Membrane capacitance measurement Membrane capacitance measurements were performed at room temperature (RT, 26–27 °C) from the cupule of Held presynaptic terminals in the whole-cell configuration. Data were acquired at a sampling rate of 50 KHz using an EPC-10 patch-clamp amplifier controlled by PatchMaster software (HEKA) after online filtering at 5 kHz. The cupule of the Held terminal was voltage-clamped at a holding potential of -80 mV and a sinusoidal voltage command of 60 mV peak-to-peak voltage was applied at 1 kHz. The Aak1 inhibitor LP935509 (Axon MedChem) was dissolved in DMSO (0.1%), which was also included in the pipette solution and injected from the whole-cell pipette into the cupule terminal by diffusion. Care was taken to keep the access resistance below 14 MΩ to allow the drug to diffuse into the terminal within 5 min after whole-cell rupture. Presynaptic voltage-dependent calcium charge transfer (Q CaTo isolate α-aspartate synaptic vesicles, aCSF contained 10 mM tetraethylammonium chloride (TEA, Tokyo Chemical Industry Ltd.), 0.5 mM 4-aminopyridine (4-AP, Nacalai Tesque), 1 mM tetrodotoxin (TTX, Nacalai Tesque), 10 mM bicuculline methiodide (Santa Cruz Biotechnology), and 0.5 mM strychnine hydrochloride (Tokyo Chemical Industry Ltd.). The intracellular solution for presynaptic terminals contained (in mM): 125 Cs-methanesulfonate, 30 CsCl, 10 HEPES, 0.5 EGTA, 12 Na2-phosphocreatine, 3 MgCl2, and 0.3 Na2GTP (315–320 mOsm, pH 7.3 adjusted with CsOH). The tip of the recording pipette was coated with dental wax (GC Corporation) to reduce stray capacitance (4–6 pF). Presynaptic Q was measured using a single-pulse step depolarization to +10 mV for 20 ms. Ca The membrane capacitance (C) was measured within 450 ms after the rectangular pulse stimulation. m Changes in extracellular C were excluded from the analysis to avoid contamination by conductivity-dependent capacitance artifacts. Data were acquired within 20 min after whole-cell rupture. m Amplitude of change (ΔC m ) is the baseline and C m The difference between the values ​​was measured. m Recordings are shown as the average of 50 data points (for 50 ms) plotted every 50 ms (for short time scales) or every 500 ms (for long time scales). The half-life of endocytosis is ΔC m Measurements were taken from the midpoint of collapse.

[0126] Recording of EPSCs For recording of evoked EPSCS, simultaneous presynaptic and postsynaptic whole-cell recordings were made from the cupule nerve terminal and the postsynaptic cell. Throughout the experiments, presynaptic recordings were made in current clamp mode and postsynaptic recordings were made in voltage clamp mode at a holding potential of -70 mV. Pipette solution for recording of presynaptic action potentials (AP) contained (mM): 110 K-gluconate, 10 L-glutamate, 30 KCl, 10 HEPES, 0.5 EGTA, 12 Na2-phosphocreatine, 3 MgATP, 1 MgCl2, 0.3 Na2GTP (315 mOsm, pH 7.3 adjusted with KOH). Pipette solution for postsynaptic recordings contains (in mM): 110 CsF, 30 CsCl, 10 HEPES, 5 EGTA, 1 MgCl2, 5 QX3 14-Cl (300 mOsm, pH 7.3 adjusted with CsOH). EPSCSs were evoked by current injection (0.5–1 nA, 1 ms) into the presynaptic terminal via a recording glass electrode in the presence of bicuculline methiodide (10 μM) and strychnine hydrochloride (0.5 μM).

[0127] Action potential recording To record postsynaptic action potentials (APs), simultaneous presynaptic and postsynaptic whole-cell recordings were performed from the cupule nerve terminal and the postsynaptic MNTB principal neuron both in current-clamp mode and in the presence of bicuculline methiodide (10 μM) and strychnine hydrochloride (0.5 μM). The pipette solution for postsynaptic AP recordings contained (mM): 120 K-gluconate, 30 KCl, 5 EGTA, 12 Na2-phosphocreatine, 3 MgATP, 1 L-arginine, 1 MgCl2, 0.3 Na2GTP (315 mOsm, pH 7.3 adjusted with KOH). Presynaptic APs were induced by injecting square pulse currents into the cupule in current-clamp mode through a recording glass electrode filled with a K-gluconate-based internal solution (described above).

[0128] Data statistical analysis Data were analyzed using Igor Pro 6 (WaveMetrics), Excel 2011 (Microsoft), and SigmaPlot 12 (Systat Software Inc.). All values ​​are given as mean ± SEM, and p < 0.05 was considered to indicate significant differences by Student's t test, one-way ANOVA, and Bonferroni post hoc test.

[0129] Hippocampal cell culture electrophysiology Whole-cell patch clamp recordings were made from hippocampal cultures isolated at DIV15 (the earliest time at which Aak1 knockdown is complete in neuronal cells infected with lentivirus at DIV11-12 and the culture developmental stage at which endogenous expression of the major SV protein synaptophysin is detectable). Pipette solution contained (in mM): 110 CsF, 30 CsCl, 10 HEPES, 5 EGTA, 1 MgCl2, 5 QX314-Cl (300 mOsm, pH 7.3 adjusted with CsOH). Cells were continuously perfused with a standard aCSF solution containing the following (in mM): 125 NaCl, 2 KCl, 26 NaHCO3, 1.25 NaH2PO4, 2 CaCl2, 1 MgCl2, 10 glucose, 1 myo-inositol, 2 sodium pyruvate, 0.5 sodium ascorbate (pH 7.4, 310-320 mOsm when aerated with 95% O2 and 5% CO2), 0.05 D-AP5, 0.01 bicuculline methiodide. The stimulating bipolar electrode was placed close to the afferent neuron at a distance of approximately 80-100 µm from the target neuron visualized by GFP under a fluorescent microscope. Neurons were voltage clamped at -70 mV by an EPC-10 amplifier (HEKA Electronics, Germany). Only cells with a series resistance below 15 milliohms and with 70-80% of this resistance compensated were analyzed. Currents were acquired using PATCHMASTER software (HEKA Electronics), filtered at 5 kHz and digitized at 10 kHz. Data were analyzed using AxographX ​​(Axograph Inc., USA) and IgorPro (WaveMetrics Inc., USA). All experiments were performed at room temperature. All values ​​are given as mean ± SEM and p < 0.05 was considered significantly different by Student's t test, paired t test, one-way ANOVA and Bonferroni post-hoc test.

[0130] Dissociated hippocampal cell culture: Primary hippocampal cell culture was performed as described in (7). Neonatal mice (P1) (ICR CD-1, Charles River Laboratories) were sacrificed by decapitation, and hippocampi were dissected at 4°C in filter-sterilized HBSS buffer containing 0.1% glucose (Thermo Scientific), 1 mM sodium pyruvate (Thermo Scientific), and 10 mM HEPES (Sigma). Hippocampal tissue was then digested using a papain-based Neuron Dissociation Solutions S kit (Wako Pure Chemical Industries Ltd). The collected cells were seeded on poly-L-lysine-coated μ-Dish 35 mm low (Ibidi) containing Basal Medium Eagle (Thermo Scientific) containing 0.45% glucose, 1 mM sodium pyruvate, 2 mM L-glutamine, and supplemented with 10% (v / v) fetal bovine serum (Thermo Scientific), and placed at 37°C under 5% CO2. After 3 hours, the seeding medium was replaced with the following maintenance medium: Neurobasal™ medium (Thermo Scientific) containing 2 mM L-glutamine and 2% (V / V) B27™ supplement (Thermo Scientific). On the fourth in vitro day, half of the culture medium was replaced with freshly prepared maintenance medium, and similarly changed every 3 or 4 days.

[0131] HEK293T cells: Human Embryonic Kidney (HEK) 293T cells (Lenti-X™ 293T cell line, Clontech) were seeded at 25% confluence on 100 mm BioCoat™ collagen I pre-coated culture dishes (Corning) and cultured in DMEM high glucose (Thermo Scientific) containing 1 mM sodium pyruvate (Thermo Scientific) and 10% (V / V) fetal bovine serum (Biological Industries) at 37° C. under 5% CO2. Cells were passaged at 90% confluence by trypsinization and reseeding.

[0132] Lentivirus preparation and titration: Upon reaching 90% confluence, HEK293T cell dishes were transfected with 7.3 μg each of pLVTHM (transfer plasmid containing the shRNA of interest and a GFP reporter gene (8)), 5 μg of psPAX2, and 2.3 μg each of pMD2.G (lentiviral packaging plasmid, a gift from Didier Trono, Addgene, plasmids #12247, #12260, and #12259, respectively) using 75 μg of Polyethylenimine Max 40K (Polysciences Inc.) in 1 mL of Opti-MEM™ (Thermo Scientific) added to the culture dish. The cells were incubated at 37° C. under 5% CO2 for 7 h, after which the medium was replaced with 8 mL of fresh medium. After 48 hours, cell culture supernatants (containing lentivirus) were collected, filtered through a 0.45 μm syringe filter, and ultracentrifuged at 87,000 g for 2 hours at 4° C. (JS-24.15 rotor, Beckman Coulter). The lentivirus pellet was then resuspended using PBS, placed on ice for 2 hours, aliquoted (3 × 5 μL of lentivirus suspension from each HEK293T cell culture dish), and stored at −80° C.

[0133] To titer the pLVTHM-based lentivirus, primary hippocampal cultures at day in vitro culture (DIV) 6 were used and infected with serial dilutions (1:1,000 to 1:500,000) of frozen lentivirus aliquots in fresh maintenance medium. After 24 h, the lentivirus-containing medium was replaced with fresh maintenance medium. Infected culture dishes were observed using a confocal microscope (LSM 780, Zeiss) on DIV11–12. Images were acquired and analyzed with brightness and contrast set at the appearance limit of autofluorescence on control non-infected dishes. The total number of neurons and GFP-positive neurons were counted to calculate the biological titer (BT) in transducing units (TU) per mL according to the formula described in (9). BT = (F × N × D) / V, where F is the percentage of GFP-positive cells, N is the number of cells plated in the culture dish, D is the dilution factor, and V is the dilution volume in ml. All lentiviruses used in the experiments were 3 × 10 at a 1:1,000 dilution. 9 The biological titer exceeded TU / mL, and the GFP-positive infection rate exceeded 95%.

[0134] shRNA cloning and knockdown assays: RNA interference knockdown was performed by plasmid-based short hairpin RNA (shRNA) with the sequence 5'-CAGTCAACCTCTTCAGTCA-3' (SEQ ID NO: 37), which efficiently targeted mouse Aak1 at nucleotide positions 1808-1826 (exon 11, previously used and described in (10)). The sequence 5'-ACCCTATTCCTGTACTAATTA-3' (SEQ ID NO: 38), which targets a region of exon 20 of Aak1 and showed no interference effect on Aak1 expression in Western blot experiments, was used as a negative control (shRNA control). Specific forward and reverse shRNA oligonucleotides flanked by the restriction sites Mlu1 and Cla1 were designed to contain the sense strand of the target sequence of 19 or 21 nucleotides, followed by a short spacer (TTCAAGAGA) (SEQ ID NO: 39) and the reverse complement of the sense strand. Five thymidines were added to the ends of the oligonucleotides as RNA transcription stop signals (see table for full custom sequence). The customized oligos (Fasmac) were annealed at 2 μM in annealing buffer (OriGene) at 95 °C for 5 min, followed by incubation at 70 °C for 10 min and slow cooling to room temperature. The annealed oligos were then inserted downstream of the H1 promoter of pLVTHM lentiviral vector digested with Mlu1 and Cla1. All constructs were confirmed by sequencing services from Fasmac. Hippocampal neuronal cultures at DIV11-12 were infected with GFP and shRNA expressing lentiviruses (see "Lentivirus preparation and titration"). At DIV15, neuronal cells were scraped, proteins were extracted, and the expression of GFP (lentivirus infection reporter) and aak1 (RNAi target) was monitored by Western blot (see "Western blot characterization").

[0135] Proteomics Data Repository The proteomics raw data files of this study are available from the Japan Proteome Standard Repository Database (11). The accession numbers are PXD021549 for ProteomeXchange and JPST000968 for jPOST.

[0136] The quality of all P2' and SV purification methods was controlled by Western blots of synaptic protein markers and electron microscopy (see "Western blotting characterization" and "Electron microscopy imaging").

[0137] Proteomics sample preparation and mass spectrometry Sequential protein digestion Fifty micrograms of protein (volume less than 30 μl) extracted from P2' or SV was resuspended in 200 μL of a buffer containing 8 M urea, 100 mM Tris-HCl (pH=8) ("urea buffer") and placed on a Pall Nanosep® 10K Omega filter (Sigma). After shaking at 850 rpm for 1 min at room temperature (Eppendorf Thermomixer), the sample was centrifuged at 6,400×g for 13 min (optimal conditions to remove all liquid from the upper chamber using a TOMY Kintaro KT-24 centrifuge). After repeating these steps twice, the protein was resuspended in 200 μL of urea buffer containing 50 mM iodoacetamide and incubated at room temperature for 1 h in the dark. The alkylation was then stopped by the above centrifugation and resuspending the protein sample in 200 μL of urea buffer containing 25 mM DTT. The unfolded protein was then washed with 20 mM ammonium bicarbonate and centrifuged three times as above. Protease was used at a ratio of 1:50 to protein.

[0138] To generate as many unique peptides as possible, a first digestion step ("trimming") was performed using endoproteinase lys-C (Promega) at 37 °C for 6 h, followed by a second digestion step using a trypsin / lys-C combination (Promega) at 37 °C overnight (16-18 h). After centrifugation as above, the digested peptides were acidified with 1% TFA and concentrated to dryness using an EZ-2 Elite evaporator (SP Scientific).

[0139] Orthogonal peptide separation To separate as many of the resulting peptides as possible, electrostatic repulsion hydrophilic interaction chromatography or ERLIC-based separation was performed (Alpert et al Anal.Chem.2010). The following conditions were adapted and optimized to obtain the maximum number of identified proteins from the P2' and SV samples. Preparation of mobile phase solvents: Solvents were freshly prepared for each experiment using Optima® LC / MS grade acetonitrile (ACN), formic acid (FA), and water from Fisher Chemicals. Solvent A: 90% ACN, 0.1% FA. Ammonium hydroxide (NH4OH, 25% w / w in water, Fluka) was then added to adjust the pH to 4.5. Solvent B: 30% ACN, 0.1% FA. Using a PAL HTC autosampler (CTC Analytics) for automated injection and fraction collection, the digested peptide mixture was fractionated on a weak anion exchange PolyWax LP™ column (PolyLC Inc.; 1 mm ID x 150 mm, 5 particle size, 300A pore size) at a flow rate of 40 μL / min in gradient mode (3 min solvent A, to 10% B in 7 min, 10% B to 25% B in 24 min, 25% B to 70% B in 16 min, 70% B to 81% B in 6 min, 81% B to 100% B in 3 min, final wash at 100% B for 6 min, re-equilibration at 100% A for 20 min). 24 fractions were collected every 3 min from 0 to 72 min and subsequently concentrated to dryness using an EZ-2 Elite evaporator (SP Scientific).

[0140] Mass spectrometric detection (including a second separation based on RPC LC-MS C18) Peptide samples were resuspended in 30 μL of 0.1% formic acid and analyzed using an Ultimate 3000 nano-HPLC system (Dionex), an HTC-PAL autosampler (CTC Analytics), and a Q-Exactive Plus Orbitrap hybrid mass spectrometer (Thermo Scientific) equipped with a nanoelectrospray ion source. Five μL of each sample was injected onto a Zorbax 300SB C18 capillary column (0.3 × 150 mm, Agilent Technologies) heated at 40 °C. 0.1% formic acid in distilled water as solvent A and 0.1% formic acid in acetonitrile as solvent B were used with a 1-h HPLC gradient (1% B to 32% B in 45 min, 32% B to 45% B in 15 min, final wash at 75% B for 5 min, re-equilibration at 1% B for 10 min). A flow rate of 3.5 μL / min was used for peptide separation. The temperature of the heated capillary was 300 °C, and a spray voltage of 1.9 kV was applied to all samples. Mass spectrometer settings were full MS scan range of 350–1500 m / z with mass resolution of 70,000, scan time of 30 μs, automatic ion amount control set to 1 × E6 ions, and fragmentation MS2 of the 20 most intense ions.

[0141] Protein identification Protein identification was performed using Proteome Discoverer software v2.1 (Thermo Scientific) and Mascot 2.6 (Matrix Science) as the search engine. The UniprotKB database downloaded from Rattus norvegicus (Proteome ID: UP000002494) was used with the following search parameters: trypsin enzyme with a maximum of two false cleavages and precursor and fragment mass tolerances were set at 10 ppm and 0.02 Da, respectively. Cysteine ​​carbamidomethylation, methionine oxidation, asparagine and glutamine deamidation, and N-terminal protein acetylation were set as variable modifications. Results were filtered using a false discovery rate of less than 1% as the cutoff threshold, determined by the Percolator algorithm in the Proteome Discoverer software.

[0142] Analysis of proteomics data (Excel tables and Volcano plots) The results obtained from Proteome Discoverer, peak area scores, peptide spectrum match counts (PSMs), were used for statistical analysis using R software version 3.2.5 (R Project for Statistical Computing). Quasi-Poisson generalized linear models (y~1, y~treatment) were generated and compared using analysis of deviation (Anova) of generalized linear model fits using F-tests, and p-values ​​were obtained and corrected with the Benjamini-Hochberg method.

[0143] Discussion In this study, we used SVs purified from rodent brains as a model for the identification and quantification of the “deep proteome” applying a newly established proteomics workflow. SVs isolated from mammalian brains are morphologically homogenous (34) and share a common set of proteins, with over 90% containing the major SV protein synaptophysin (2). However, they are heterogeneous with respect to synapse type and neurotransmitter content. With the novel proteomics workflow disclosed herein, we identified approximately 1,500 proteins in SVs, more than three times as many proteins as previously reported (2, 4, 7). Among them, we found 134 SV-resident proteins, 86 of which are low abundance (<1 copy / SV). Thus, the previous lack of vesicular transporters for monoamines and acetylcholine suggests that these proteins may be restricted to SV subsets and are present in only a small proportion of brain synapses. Of the approximately 1,500 proteins in the SV fraction, more than 200 proteins have genetic associations with CNS diseases, highlighting the importance of a deep, diverse and previously hidden proteome for proper functioning of the brain.We constructed a resource database that contains all data on the identification, quantitative distribution, and structure and function annotation of each protein detected in the SV fraction.

[0144] The increased peptide coverage of the "UD workflow" is based on two main improvements that were combined: (i) enhanced cleavage with sequential proteases and (ii) the introduction of offline orthogonal peptide separation prior to reversed-phase LC-MS / MS. These steps significantly increased unique peptide detection and significantly expanded the inventory of proteins in SVs, including proteins with high homology within their families. For example, 40 Rab proteins with high sequence homology (75-95%) but distinct transport functions (20) were identified. Similarly, functionally characterized but cryptic synaptic tagmins such as Syt7 (8-10) were detected together with other family members with unknown functions. Moreover, the high peptide yield of UD proteomics allows unprecedented label-free and reliable quantification of most proteins in the dataset. For the first time, the copy numbers of hundreds of proteins could now be assessed, giving quantitative coverage of the entire SV proteome organization. Compared with previous quantitative studies (2, 6), our results largely confirm the average copy number per vesicle, with the exception of three proteins, SNAP29, vti1a, and ClC3, whose abundance scores are too low to be considered as additional major SV proteins. On the other hand, most of the detected proteins were found to have, on average, less than one copy number per SV, revealing a much greater SV heterogeneity than previously assumed.

[0145] Our label-free quantification also allowed quantitative comparison of the proteomes of isolated nerve terminals and purified synaptic vesicles. This was not only the basis for identifying bona fide SV residents but also for the first time to distinguish between SV resident and potential SV visitor proteins. Remarkably, about 50% of SV residents are non-transmembrane proteins, highlighting the high organization of the proteome despite the dense molecular population at synapses (6). For example, UD proteomics revealed that among non-transmembrane proteins, Aak1 is the major SV resident protein, with an SV / P2' ratio of about 4 and copies / SV of 1.5. This kinase was found to be essential for maintaining high-frequency neurotransmission by promoting SV recycling. Thus, the new classification of the SV protein repertoire may facilitate functional studies and lead to the identification of key regulators of synaptic transmission.

[0146] It must be kept in mind that SVs are isolated solely based on their size and density, starting from enriched synaptosomes. The heterogeneity could therefore be caused, at least in part, by the presence of membranes derived from different transport steps, such as vesicles that are partially non-clathrin-coated, small endosomal vesicles, and SVs from axonal compartments towards nerve terminals. These compartments are part of the same recycling pathway and are predicted to share vesicle membrane resident proteins, although the "visitor" proteins are likely to be different. This could explain the presence of endosome-associated proteins (e.g., Stx7, AP3) or active site proteins (e.g., Piccolo, Bassoon) in the SV proteome. Moreover, we could not exclude the possibility that the SV preparation was even slightly contaminated with vesicles from other sources, for example, small vesicles artificially generated from larger membranes during homogenization or vesicles on the postsynaptic side. Indeed, analysis of the UD-SV proteome using the SynGO resource (15) revealed a postsynaptic contamination of at least 4% based on 691 proteins annotated in SynGO. The presence of contaminations among the 775 SV proteins in this study cannot be definitively calculated because they are not annotated in the SynGO database.

[0147] A careful look at the defined SV-resident repertoire (proteins with SV / P2' iBAQ ratio >2) provides important clues to better understand SV molecular and functional heterogeneity. Of the 134 SV-resident proteins, 86 have a copy number / SV <1. The 40 most abundant SV-resident proteins (ranks 1–180) include all subunits of the V-ATPase, vesicular "tetraspanins" including SCAMPs, synaptophysin and synaptogyrin, Syts and SV2 proteins, as well as membrane-associated synapsins and CSPs. Also included in this list are VGLUT1 / 2 and VGAT, the vesicular transporters of the two main neurotransmitters in the brain: glutamate (excitatory synapses) and GABA (inhibitory synapses). All of these proteins are likely present in SVs throughout the nervous system.

[0148] Minor SV residents (<1 copy per SV, >180 rank) include proteins commonly involved in membrane trafficking, such as additional SNAREs, Rab GTPases, phospholipid kinases, tethering complexes, and autophagy-related proteins. Their low abundance suggests that they reside in a subset of vesicles in synapses. For example, the copy number of Atg9a, a transmembrane protein, was 1 per 25 SVs (see Figure 7). This suggests that 4% of vesicles in synaptic compartments may be recruited to the autophagy pool. Another possibility is that these proteins may be specifically expressed in a small subset of synapses in certain brain regions. Indeed, this list includes known rare neurotransmitter vesicular transporters VMAT2, VAChT, Slc5a7, and VGLUT3, reflecting the functional heterogeneity of synapses (Figure 3C, Figure 8). Interestingly, our list also includes about a dozen novel transporter proteins.

[0149] Many SV proteins, whether classified as resident or potential visitors, may have specific functions to regulate or maintain synaptic performance. Indeed, our UD proteomics detected over 200 proteins in the SV fraction known to be genetically associated with neurological (mental, motor, and sensory processing) disorders. Notably, the majority of these proteins (76%) were found in the low abundance range, with copy numbers below 0.04 / Sv. These neurological disorders are believed to result from various synaptic dysfunctions specific to discrete neuronal populations in the nervous system. Indeed, recent evidence supports the idea of ​​“synaptopathy” as a polygenic mechanism underlying psychiatric disorders (35–38). During evolution, abundant canonical proteins are often ancestral components, while low abundance proteins tend to emerge for novel functions (39). In this regard, the deep diversity of the synaptic proteome may be responsible for mammalian or human-specific neurological disorders. This may be the main reason why, despite technical difficulties, investigation of the deep subcellular proteome beyond the “average model” is necessary.

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Claims

1. A composition for regulating a cellular function, comprising BDNF, CNTF, and GDNF.

2. The composition of claim 1 , further comprising FGF16 or FGF22, or FGF16 and FGF22.

3. 2. The composition of claim 1, further comprising one or more selected from the group consisting of FGF3, FGF7, FGF10, FGF22, FGF8, FGF9, FGF16, FGF17, FGF18, FGF20, NRTN, and PSPN.

4. The composition of claim 1 further comprising an NRTN.

5. The composition of claim 1 , wherein the cells are stem cells or stem cell-derived neural cells.

6. The composition according to claim 1 , wherein regulating the function of the cell is controlling the differentiation of the cell.

7. The composition of claim 1 , wherein the stem cells are induced pluripotent stem cells (iPSCs) and / or embryonic stem cells (ESCs).

8. The composition of claim 1, wherein the cells are stem cell-derived neurons, and regulating the function of the stem cell-derived neurons comprises enhancing synapse formation, improving neuronal morphogenesis, improving neuronal proliferation, and / or improving neuronal activity.

9. The composition according to any one of claims 1 to 8, wherein the composition is a pharmaceutical composition for treating diseases and / or disorders related to the nervous system.

10. 10. The composition of claim 9, wherein the disease and / or disorder associated with the nervous system comprises Alzheimer's disease, dementia, schizophrenia, autism, ADHD, Parkinson's disease, multiple sclerosis, epilepsy, Charcot-Marie-Tooth disease, retinitis, metabolic neurodegenerative diseases, dystonia, intellectual disability, hearing loss, spinocerebellar ataxia, sleep disorders, cortical dysplasia, dyslexia, microphthalmia, leukodystrophy, and / or movement disorders.

11. 1. A method for regulating a cell function, comprising: contacting said cell with BDNF, CNTF, and GDNF.

12. 12. The method of claim 11, further comprising contacting the cells with FGF16 or FGF22, or FGF16 and FGF22.

13. 12. The method of claim 11, further comprising contacting the cells with one or more selected from the group consisting of FGF3, FGF7, FGF10, FGF22, FGF8, FGF9, FGF16, FGF17, FGF18, FGF20, NRTN, and PSPN.

14. 12. The method of claim 11, further comprising contacting the cell with NRTN.

15. The method of claim 11 , wherein the cell is a stem cell or a stem cell-derived neural cell.

16. The method of claim 11, wherein regulating the function of the cell comprises controlling the differentiation of the cell.

17. 12. The method of claim 11, wherein the stem cells are induced pluripotent stem cells (iPSCs) and / or embryonic stem cells (ESCs).

18. The method of claim 11, wherein the cells are stem cell-derived neurons, and modulating the function of the stem cell-derived neurons comprises enhancing synapse formation, improving neuronal morphogenesis, improving neuronal proliferation, and / or improving neuronal activity.

19. A kit for modulating a cellular function, comprising one or more vectors containing nucleic acid sequences encoding BDNF, CNTF, and GDNF, or BDNF, CNTF, and GDNF.

20. The kit of claim 19, further comprising FGF16 or FGF22, or FGF16 and FGF22, or wherein the one or more vectors comprise a nucleic acid sequence encoding FGF16 or FGF22, or wherein the one or more vectors comprise a nucleic acid sequence encoding FGF16 and FGF22.

21. 20. The kit of claim 19, further comprising one or more of FGF3, FGF7, FGF10, FGF22, FGF8, FGF9, FGF16, FGF17, FGF18, FGF20, NRTN, and PSPN, or wherein the one or more vectors comprise a nucleic acid sequence encoding one or more of FGF3, FGF7, FGF10, FGF22, FGF8, FGF9, FGF16, FGF17, FGF18, FGF20, NRTN, and PSPN.

22. 20. The kit of claim 19, further comprising NRTN, or wherein said one or more vectors comprise a nucleic acid sequence encoding NRTN.

23. The kit of any one of claims 19 to 22, further comprising a container.

24. The kit of any one of claims 19 to 22, further comprising a package insert containing instructions for use of the kit.

Images