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Multidimensional biomarkers for multiple system atrophy: an update and future directions

Translational Neurodegeneration volume 12, Article number: 38 (2023) Cite this article

Abstract

Multiple system atrophy (MSA) is a fatal progressive neurodegenerative disease. Biomarkers are urgently required for MSA to improve the diagnostic and prognostic accuracy in clinic and facilitate the development and monitoring of disease-modifying therapies. In recent years, significant research efforts have been made in exploring multidimensional biomarkers for MSA. However, currently few biomarkers are available in clinic. In this review, we systematically summarize the latest advances in multidimensional biomarkers for MSA, including biomarkers in fluids, tissues and gut microbiota as well as imaging biomarkers. Future directions for exploration of novel biomarkers and promotion of implementation in clinic are also discussed.

Introduction

Multiple system atrophy (MSA) is a rare, rapidly progressing neurodegenerative disease first reported in 1969 [1]. The pathological hallmark of MSA is glial cytoplasmic inclusions (GCIs) consisting of misfolded α-synuclein (α-syn) in affected brain regions, including striatonigral and olivopontocerebellar systems. Due to the presence of α-syn aggregation in the brain, MSA is identified as a synucleinopathy, together with Parkinson's disease (PD) and Dementia with Lewy bodies (DLB). MSA is an orphan disease with an average annual incidence of 0.6–0.7 per 100,000 person-years in Western countries [2]. Clinically, MSA is characterized by various combinations of progressive autonomic failure, cerebellar ataxia, parkinsonian syndrome and pyramidal features. According to the predominant motor symptom, MSA can be divided into the parkinsonian subtype (MSA-P) and the cerebellar subtype (MSA-C). The average survival of MSA patients is 6–10 years from symptom onset to death and very few patients can survive more than 15 years [3]. Currently, there is no effective treatment strategy for MSA.

Based on the Movement Disorder Society Criteria for the diagnosis of MSA, the diagnostic certainty includes four levels: neuropathologically established MSA, clinically established MSA, clinically probable MSA and possible prodromal MSA. The autopsy confirmation is still the gold standard for definite diagnosis of MSA [4]. Because of its clinical heterogeneity and overlapping manifestations with other diseases, including PD, progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and sporadic adult-onset ataxia of unknown aetiology (SAOA), the clinical diagnosis for MSA is quite challenging [5, 6]. Therefore, biomarkers facilitating the precise diagnosis is necessary in clinical practice. In addition, when evaluating the disease severity and therapeutic effect, the unified multiple system atrophy rating scale (UMSARS) remains the main tool [7]. However, the semiquantitative rating pattern and the ceiling effect have limited its reliability [8]. Thus, there is also an urgent demand for sensitive biomarkers of MSA for monitoring disease severity and therapeutic effects.

The Biomarker Definitions Working Group has defined a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacologic response to a therapeutic intervention” [9, 10]. In previous studies of MSA, researchers have explored the potential value of biomarkers from different sample sources such as fluids (including cerebrospinal fluid, saliva, blood and urine), tissues, stool and imaging [11]. However, few biomarkers are available in the clinical practice. In this review, we systematically summarize the multidimensional candidate biomarkers for MSA and discuss their prospects in clinical practice, aiming to provide directions for future research (Fig. 1).

Fig. 1
figure 1

Schematic overview of multidimensional biomarkers for MSA, including fluid, tissue, gut microbiota and imaging biomarkers. Illustration created with BioRender.com

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Fluid biomarkers

α-Syn

α-Syn is composed of 140 amino acids and present at presynaptic terminals. The precise function of α-syn is obscure, but currently it is considered as a regulator of neurotransmitter release and synaptic integrity [12]. There are three main forms of α-syn examined in MSA, i.e., total α-syn (t-α-syn), phosphorylated α-syn (pS129-α-syn) and α-syn oligomers (o-α-syn) [13, 14]. Although the deposition of aggregated α-syn in the cytoplasm of oligodendroglia is considered as a pathologic hallmark of MSA (Fig. 2), whether it can serve as a reliable biomarker of MSA is still controversial.

Fig. 2
figure 2

Pathological mechanisms involved in MSA and related biomarkers. Illustration was created with BioRender.com

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Quite a few researchers have measured α-syn in cerebrospinal fluid (CSF). Most of the studies found lower CSF levels of t-α-syn in MSA patients compared to healthy controls (HCs) [15,16,17,18,19,20,21,22] and three studies found no significant difference [23,24,25]. One autopsy study showed an increasing tendency of t-α-syn in the postmortem CSF of MSA patients [26]. The conflicting conclusions might be attributed to the neuronal release of α-syn into the CSF and peripheral blood. As for the differences among MSA and other neurodegenerative diseases, some studies illustrated significantly decreased levels of CSF t-α-syn in MSA compared to PSP and CBD [14, 27]. However, the majority of studies found no significant differences of CSF t-α-syn among MSA and other α-synucleinopathies, PSP or CBD [17, 21, 23, 24, 26, 28], suggesting limited value in differential diagnosis (Table 1).

Table 1 Potential fluid protein and metabolite biomarkers for MSA
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Phosphorylation at serine 129 is a common posttranslational modification of α-syn, which is more disease-specific and might improve the toxicity of α-syn by promoting its aggregation [29]. However, some studies in recent years have also implied a potential protective role for pS129-α-syn as it apparently inhibits further aggregation [30]. Although the exact role of pS129-α-syn remains undetermined, its value as a biomarker has been explored. One study found significantly decreased CSF pS129-α-syn levels in MSA than in HC and PD [20]. However, other studies did not replicate the results, showing no remarkable differences of CSF pS129-α-syn between MSA and HC or PD [14, 26]. Moreover, the pS129-α-syn/t-α-syn ratio in the CSF of MSA patients is much higher than that in HC, but does not significantly differ from that in PD patients [14, 20]. Another form of α-syn, oligomeric α-syn (o-α-syn), shows no significantly different postmortem CSF levels among patients with MSA, PD, PSP and HC [26]. Thus, the oligomeric form of α-syn might have limited value as a biomarker for MSA (Table 1).

The values of α-syn levels in blood are also undetermined. Two studies showed significantly higher levels of plasma t-α-syn in MSA patients than in HCs [31, 32], while another study found no significant difference in serum t-α-syn [17]. A meta-analysis reported that the plasma levels of t-α-syn in MSA patients are significantly increased compared with HC; however, the analysis only included two studies [22]. For comparison between MSA and PD, one study demonstrated decreased plasma t-α-syn levels in MSA [31], while another reported no significant difference [17]. In addition, recently, different types of α-syn in red blood cells (RBCs) have been investigated since erythrocytes are the major source of peripheral α-syn. Generally, levels of oligomeric and phosphorylated α-syn in RBCs have been reported to be significantly increased in MSA patients than in HCs [33,34,35]. One of these studies showed that pS129-α-syn levels in RBCs could distinguish MSA patients from HCs with a sensitivity of 80.37%, a specificity of 88.64% and an area under the curve (AUC) of 0.91, suggesting a high diagnostic value of pS129-α-syn in RBCs for MSA (Table 1). However, the ability of α-syn in RBCs to differentiate MSA from other neurodegenerative diseases including PD, PSP and CBD still need further exploration [35].

Recently, seeding aggregation assays (SAAs), including real-time quaking-induced conversion (RT-QuIC) technology and protein-misfolding cyclic amplification (PMCA), have been applied to measure the seeding activity of pathological α-syn [36,37,38]. These two assays were originally used for the detection of pathological prion proteins in body fluids and skin tissues [39,40,41]. Increasing evidence suggests that pathological α-syn spreads throughout the nervous system in a prion-like manner, acting as seeds to induce further aggregation of native α‐syn [42, 43]. Thus, seed amplification assays may be used for the detection of pathological α-syn, even a very small amount of α-syn, by amplifying the abnormal protein to detectable levels [38, 44, 45]. In CSF samples, three studies revealed good performance of seed amplification assays in differentiating MSA from HC [45,46,47]. However, another two studies found quite low rates of positive RT-QuIC results in MSA patients [48, 49]. This discrepancy may be caused by differences in specific experimental conditions. By contrast, CSF seed amplification assay results consistently showed good performance in differentiating MSA from PD and DLB, suggesting the presence of different conformational strains of α-synuclein among α-synucleinopathies [45,46,47,48,49]. The correlation analysis showed that all RT-QuIC parameters were associated with worse clinical progression of MSA [47] (Table 2).

Table 2 Seeding activity of pathological α-syn in different samples from MSA patients measured by seeding aggregation assays
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Due to the invasive nature of CSF collection, more easily accessible fluids are in need to investigate the seeding activity of pathological α-syn. Only one study assessed the use of salivary samples and found that salivary α-syn RT-QuIC assay had a detection sensitivity of 61.1% in MSA patients [50] (Table 2). Overall, there has been increasing evidence for the potentials of humoral α-syn seeding activity as a reliable biomarker for MSA. Further validation in larger cohorts including various samples using established uniform experimental procedures is necessary.

Coenzyme Q10 (CoQ10)

CoQ10 is a large, lipid-soluble molecule located at the cell membrane. It is a powerful cellular antioxidant and can transport electrons from complexes I & II to complex III in the mitochondrial electron transport chain. One of the essential enzymes involved in CoQ10 biosynthesis is hydroxbenzoate polyprenyltransferase (CoQ2) encoded by the COQ2 gene [51]. Previous studies have found that homozygous and compound heterozygous mutations in COQ2 may be the etiology of familial and sporadic MSA, and several mutation sites in COQ2 have been identified to be associated with increased susceptibility to MSA. In addition, the level of CoQ10 and its activity have been proven to be decreased in MSA patients with COQ2 mutation [52,53,54,55]. Subsequent studies also showed impaired mitochondrial function and significant reduction of CoQ10 levels in lymphoblastoid cells, fibroblasts and cerebellum of MSA patients [56,57,58], suggesting the relationship of CoQ10 with MSA pathogenesis.

Only one study has investigated the diagnostic value of CSF CoQ10 levels in MSA patients so far. Compared with HC, PD or PSP patients, the level of CSF CoQ10 in MSA patients was significantly decreased, but did not differ between MSA-C and MSA-P subtypes [59]. The conclusions deserve further consideration due to the small sample size employed in the study. Most of the studies investigating CoQ10 levels in blood revealed lower CoQ10 levels in MSA patients compared to HCs [60,61,62]. Clinical association analysis results showed a weak negative correlation between plasma CoQ10 levels and UMSARS-II scores in MSA-C patients [60]. None of these studies found significant differences between patients with MSA and PD, nor between different subtypes of MSA (Table 1). Although the conclusions of current studies are consistent, the reliability still needs to be further validated, because the CoQ10 level can be affected by many confounders, including dietary habits, lipoprotein levels, hyperthyroidism and drugs [61].

Neurofilament light protein (NfL)

Neurofilament is a neuronal cytoskeletal protein, which is released upon axonal damage, into the extracellular space and subsequently to CSF and blood. NfL is the smallest subunit of neurofilament protein and a marker for central neuronal damage, particularly sensitive to axonal destruction of long fibre tracts [63, 64]. Since axonal injury is a specific pathogenic process in neurodegenerative diseases, theoretically, NfL may serve as a biomarker for neurodegenerative diseases. The sensitivity of traditional enzyme linked immunosorbent assay (ELISA) to detect blood NfL is limited due to the low concentration (picograms per milliliter). In addition, repeated detection of CSF is inconvenient, even if the concentration of CSF NfL is relatively high. Recently, the development of single-molecule array (Simoa) technique with ultra-sensitivity has allowed quantification of the analyte at levels < 1 pg/ml, paving the way for evaluating NfL levels in both central and peripheral fluids [63, 65]. NfL has been proposed as a sensitive diagnostic or prognostic marker for various neurodegenerative diseases, including PD, Alzheimer’s disease (AD), spinocerebellar ataxia type 3 (SCA3), amyotrophic lateral sclerosis (ALS) and Huntington’s disease (HD) [66,67,68,69,70]. Likewise, NfL may also be a potential biomarker for MSA since neuronal damage is a crucial aspect of MSA pathogenesis as well (Fig. 2).

Among the studies of CSF NfL, both ELISA and Simoa technique showed markedly elevated CSF NfL levels in MSA patients compared to HC and PD with satisfactory discrimination [16, 21, 71,72,73,74]. Meanwhile, the high correlation between CSF and blood NfL levels has been acknowledged since NfL could penetrate through the blood–brain barrier [73, 75, 76]. Compared with HCs and PD patients, MSA patients show significantly increased serum and plasma levels of NfL, both of which display strong differentiating power with an AUC > 0.9 against HC and > 0.8 against PD [66, 73, 74]. In addition, serum NfL is able to differentiate MSA-C from SAOA and SCA according to a pilot study, with significantly higher levels in MSA patients [64]. These findings suggest that humoral NfL might serve as a valuable biomarker in diagnosis and differential diagnosis of MSA. However, studies on its performance in discriminative diagnosis of MSA from other neurodegenerative diseases are needed (Table 1). As for the association with disease progression, previous studies consistently showed that blood NfL levels are significantly correlated with disease severity and the baseline concentration is a good predictor for MSA progression [73, 74, 77]. These results suggest that the blood NfL is a reliable biomarker for monitoring the disease severity and the therapeutic effect on MSA.

YKL-40

YKL-40, also known as chitinase-3-like-1, is a member of the 18-glycosyl hydrolase family and extensively expressed in various cells such as chondrocytes, synovial cells, neutrophils, macrophages, astrocytes and microglia. Previous studies have verified the participation of YKL-40 in inflammation. Since neuroinflammation with activation of microglia and astrocytes is an important pathogenic process in MSA, YKL-40 may be a potential biomarker of MSA [16, 78] (Fig. 2). However, the conclusions of published studies are inconsistent. Two studies using CSF samples showed no significant difference between MSA patients and HCs [79, 80], while another study demonstrated significantly higher CSF YKL-40 levels in MSA patients than in HCs [16]. A meta-analysis based on the above two studies [79, 80] found no significant difference of CSF YKL-40 levels between MSA patients and HCs [81]. There is also inconsistency for the comparison of MSA vs PD patients. Two studies found that the level of YKL-40 in the CSF of MSA patients was significantly higher than that of PD patients [79, 80], but another study found no significant difference [16]. Only one study compared serum YKL-40 levels in MSA patients with HCs and PD patients, and no significant difference was found [80] (Table 1). Based on the existing findings, YKL-40 may not be a reliable biomarker for MSA, despite the limited number of relevant studies.

Glial fibrillary acidic protein (GFAP)

GFAP is another marker of glial cell activation and plays an important role in the communication between glial cells and Purkinje cells. Because the activation of glial cells is a vital pathological process in MSA, GFAP may serve as a potential biomarker for MSA (Fig. 2). When comparing MSA patients with HCs, current studies all show that CSF concentrations of GFAP tend to be higher in MSA patients, but without statistical significance [82,83,84]. When comparing MSA with PD, studies have reported higher or similar CSF GFAP levels in MSA [83, 84] (Table 1). In addition, a meta-analysis concluded that the CSF levels of GFAP in MSA patients are significantly higher than that in HCs; however, this meta-analysis included limited number of studies [21]. In brief, the use of GFAP as a biomarker of MSA needs to be further explored.

Myelin basic protein (MBP)

MBP is the second most abundant protein in the central nervous system (CNS) myelin and is bound to the cytosolic surface of the oligodendrocyte membrane. MBP is essential for the formation of CNS myelin. It also plays vital roles in signaling, interacting with the cytoskeleton, regulating the expression of other myelin proteins and binding polynucleotides in the nucleus [85]. The levels of MBP may be elevated in the CSF of MSA patients since demyelination is an important part of the pathogenesis of MSA [86] (Fig. 2). Researchers have analyzed MBP levels in the CSF and showed significantly increased concentrations of MBP in MSA when compared with HCs and PD [84, 87]. Moreover, one study demonstrated that the CSF MBP could differentiate MSA and PD at early stages with high accuracy [84] (Table 1). Thus, CSF MBP has the potential to be a diagnostic biomarker of MSA. Validation in larger sample cohorts is needed.

Homocysteine (Hcy)

Hcy is a sulfur-containing amino acid generated during methionine metabolism. Previous studies have indicated that Hcy is involved in the modulation of N-methyl-D-aspartate (NMDA) receptor, and is associated with neuroinflammation, oxidative stress, neuronal apoptosis, signaling pathway regulation, and mitochondrial dysfunction [88, 89]. These physiological processes play vital roles in the pathogenesis of neurodegenerative diseases, and hyperhomocysteinemia has been proven to increase the risk of AD and PD [90,91,92,93,94]. Besides, a randomized controlled trial showed that the serum level of Hcy is positively correlated with the rate of brain atrophy [95]. Therefore, Hcy may serve as an indicator of neurodegenerative disorders.

Serum Hcy is gaining increasing attention in the field of MSA. Most studies have revealed that the serum levels of Hcy in MSA patients are significantly increased compared with HCs, especially in male patients [96,97,98,99]. A meta-analysis based on three studies also showed higher serum Hcy levels in MSA patients than in HCs [81]. When compared with PD patients, a study found that the serum Hcy level in male MSA patients is significantly higher than that in male PD patients, while no significant difference was observed in females [99] (Table 1). In addition, the serum Hcy levels are positively correlated with the Hoehn-Yahr stage, the UMSARS-IV score, and the nonmotor symptoms score burden—cardiovascular, and negatively correlated with the MMSE score of MSA patients, indicating that Hcy is associated with aggravation of motor and non-motor dysfunction in MSA [97, 99]. However, the specificity of Hcy requires further evaluation since it can be involved in the pathophysiology of various human diseases.

RNA

Transcription of genes is a critical process as it is the driver of protein expression and other cellular activities, which can adapt rapidly to physiological or pathological changes. Thus, transcriptional profiles provide possibilities as promising biomarkers for diseases. Besides messenger RNAs (mRNAs) which account for 1%–2% of human transcriptome, non-coding RNAs, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), are also receiving increasing attention due to their regulatory functions in transcription, splicing and translation [100, 101]. In addition, the emerging transcriptomics investigating the comprehensive RNA transcripts in a high-throughput manner offers an encouraging approach for transcriptional biomarker discovery [102].

mRNAs

The biomarker value of mRNAs has been proven in various neurodegenerative diseases, such as AD and PD where several classifiers consisting of different blood RNA profiles show high distinguishing accuracies [103,104,105,106]. For MSA, both microarrays and RNA sequencing have been performed to investigate the differentially expressed genes (DEGs) in brain tissues compared to HCs [107,108,109,110]. However, only one study has assessed the transcriptomic landscape in the peripheral blood of MSA patients, which found distinct expression profiles of MSA compared to HC and PD, including DEGs involved in the nervous system development, cytoskeleton, protein modification, etc. Besides, the discrepancy between MSA subtypes has also been revealed, that MSA-P patients possess more DEGs than MSA-C patients when compared to HCs [111]. Another study analyzed the levels of several targeted gene transcripts from nasal fluid cells in MSA and found reduced mRNA levels of parkin and AIMP2 in MSA patients compared to HC [112]. Although none of the studies directly explored the distinguishing accuracy of altered gene transcripts, the existing evidence suggests the biomarker potential of mRNAs for MSA. Thus, more transcriptomics studies of different clinical samples are needed to provide additional evidence for the biomarker value of mRNAs in MSA.

MiRNAs

MiRNAs are small single-stranded non-coding RNAs found in almost all eukaryotic cells and several viruses [113]. MiRNAs negatively regulate the expression of target genes by promoting the inhibition and degradation of mRNAs. On the one hand, previous studies have observed that miRNAs are widespread in the CNS and that aberrant expression of miRNAs may cause neurodegeneration by various mechanisms including mitochondrial dysfunction, oxidative stress, autophagy, α-syn accumulation, synaptic transmission and neuroinflammation [114,115,116]. On the other hand, several disease-related proteins in neurodegenerative disorders have been proven to influence miRNAs [117,118,119]. Thus, increasing attentions are being paid to miRNAs and their functions in neurodegenerative diseases. MiRNAs are widely distributed in biofluids, highly stable and quantifiable [114, 120,121,122,123]. Besides, existing transcriptomics studies have reported aberrant miRNAs in MSA, so miRNAs are considered as promising MSA biomarkers.

In the CSF of MSA patients, several miRNAs have been found to be significantly increased (including miR-184, miR-218-5p, and miR-7-5p) or decreased (including miR-19a, miR-19b, miR-24, and miR-34c) compared with HCs, and a combination of these miRNAs presents an improved diagnostic accuracy. There are also significant differences between MSA and PD patients. MiR-184 and miR-7-5p are much higher while miR-106b-5p and miR-let-7b-5p are lower in MSA compared with PD. However, no combined miRNA panel has been found to discriminate MSA from PD with high accuracy [124, 125]. Among these miRNAs, miR-7-5p is more disease-specific, which has been reported to target α-syn mRNA and subsequently regulate α-syn expression and neuron survival [126].

In the blood, a variety of differentially expressed miRNAs have been found in MSA patients compared with HCs and PD patients, including miR-16-5p, miR-24-3p, miR-7641, miR-191, miR-671-5p and miR-19b-3p [127,128,129,130,131,132]. A combination of serum miR-141, miR-193a-3p and miR-30c has been verified to well discriminate between MSA and HC with an AUC of 0.895, and a diagnostic panel consisting of five miRNAs (miR-31, miR-141, miR-181c, miR-193a-3p and miR-214) accurately differentiated MSA from PD with an AUC of 0.951 [129]. Besides, the expression levels of plasma miR-671-5p, miR-19b-3p and miR-24-3p showed significant differences between the two subtypes of MSA [127].

In conclusion, existing findings indicate that miRNA transcriptomics might be an ideal biomarker for MSA, but the lack of a uniform detection method has limited its application. In addition, only a small number of MSA patients were included in published studies, so large-cohort studies based on multi-center cooperation may be a future direction to better investigate the biomarker value of miRNAs in MSA patients.

Tissue biomarkers

Skin

α-Syn

The aberrant aggregation of α-syn is considered as the initiating factor for α-synucleinopathies. Previous studies have proven that pathological α-syn is not restricted to the CNS, but could also be detected in multiple peripheral organs and tissues including skin, salivary glands, sympathetic ganglia, vagus nerve, gastrointestinal tract, genitourinary tract, and heart [133, 134]. Such peripheral pathological changes may be a precursor to central pathology and associated with complex clinical symptoms [41].

Skin biopsy is a relatively safe and highly acceptable way to measure α-syn deposition in the peripheral nervous system [135]. Existing studies demonstrate that pS129-α-syn deposition within dermal nerves could be detected in skin of 67%-86% of MSA patients, but could not be found in HCs [136,137,138]. The results indicate that cutaneous pS129-α-syn might be a reliable biomarker to help identify MSA from HC. Moreover, the location of pS129-α-syn deposition also provides evidence for the differential diagnosis among α-synucleinopathies. Skin biopsies of MSA patients display a prevalent abnormal pS129-α-syn deposition in somatic sensory fibers, whereas PD and DLB patients mainly show a widespread involvement of autonomic fibers [136,137,138,139]. Thus, the specific aggregation of pS129-α-syn in skin somatic sensory fibers may help differentiate MSA from other α-synucleinopathies.

The RT-QuIC technology and PMCA have also been applied to measure pathological α-syn in skin and demonstrated significantly higher aggregation seeding activity in individuals with α-synucleinopathies than non-synucleinopathies and HC [41, 140]. However, limited investigations have been conducted in MSA. Only one study included three MSA autopsy skin samples and two of them showed positive seeding activity [41]. Therefore, the seeding activity of α-syn in skin needs to be validated in more cases including both postmortem and antemortem samples. Moreover, the study found variable sensitivity and specificity of RT-QuIC in different body regions, with posterior cervical region exhibiting higher and earlier α-syn seeding activity than abdominal region or leg [41] (Table 2). Thus, standardized procedure and uniform sampling region should be taken into account when detecting cutaneous α-syn.

Peripheral skin innervation

Autonomic dysfunction is an important clinical manifestation of MSA. However, autonomic impairment such as orthostatic hypotension, sphincter dysfunction and constipation is also commonly observed in PD and pure autonomic failure (PAF) patients and is not sufficient to distinguish between these disorders [141]. Recently, some scholars have explored the dermal innervations and speculated that the denervation of peripheral nerves may be a useful marker for differentiating among MSA, PD and PAF. Epidermal nerve fiber density (ENFD) reflects the number of unmyelinated fibers per linear millimetre of epidermis. Previous studies have found a pronounced decrease of ENFD in these three disorders, but ENFD is much lower in patients with PD or PAF compared with MSA [136, 139, 142,143,144]. Besides, studies also revealed different skin autonomic innervations among MSA, PD and PAF. Though distal autonomic innervation of the sweat glands in MSA patents is slightly decreased when compared with HCs, it is relatively preserved in MSA compared to PD and PAF, particularly for the cholinergic and adrenergic autonomic innervation [136, 142]. Accordingly, peripheral skin innervation is helpful for differential diagnosis of MSA, but the diagnostic utility needs further verification due to the limited investigation.

Olfactory mucosa (OM)

Olfactory dysfunction is a common feature of α-synucleinopathies and α-syn is reported to be more abundant synuclein compared with β- and γ-synuclein in OM [145]. In contrast to CSF, OM samples can be collected non-invasively and repeatedly. Thus, the pathological α-syn in OM may offer as a promising biomarker.

RT-QuIC analysis of OM samples from MSA and PD patients showed higher aggregation seeding activity than non-synucleinopathies and HC [146,147,148]. Besides, the biochemical and morphological properties of α-syn fibrils are significantly different between PD and MSA [146, 148]. A study further revealed that the seeding activity was present in 90% MSA-P patients whereas no seeding activity was found in almost all MSA-C OM samples [147]. The results suggest that α-syn aggregation seeding activity in OM may be a promising biomarker for discriminating between these two subtypes. Moreover, the seeding activity of α-syn in OM has been reported to be positively related with rigidity and postural instability and shows a tendency of inverse association with disease duration [147] (Table 2). However, whether COVID-19 infection might influence the properties of the OM samples and then alter the results of α-syn seeding activity is unknown, so additional investigations in post-infection population will be needed.

Gut microbiota

Gut microbiota refers to the microorganisms residing in the human digestive tract, including bacteria, fungi, viruses and bacteriophage. The number of gut microorganisms is between 1013 and 1014 orders of magnitude, with a range of 500 to 1000 species [149]. It has been reported that gut microbiota plays important roles in regulating immunity, digestion, intestinal endocrine, neurological signaling, and drug and toxin metabolism of the host, as well as producing numerous compounds affecting the physiological status of the host [150]. In particular, the proposed microbiome-gut-brain axis suggests that gut microbiota may participate in the homeostasis and diseases of CNS through immune, neurological and neuroendocrinal pathways [151, 152]. Therefore, the disorganized gut microbiota has been considered as a potential therapeutic target and a possible biomarker for neurodegenerative disorders, such as AD, PD and multiple sclerosis (MS) [153,154,155,156,157].

At present, two methods are mainly used for the detection of gut microbiota: marker-gene studies such as 16S rDNA sequencing and multi-omics technology such as metagenomic sequencing. Both methods have been applied in MSA patients. Increased abundance of putative pro-inflammatory bacteria such as Bacteroides has been reported in MSA patients compared to HC, while the abundance of putative anti-inflammatory bacteria such as Ruminococcaceae and Coprobacillaceae is lower in MSA [158,159,160]. One study revealed significantly different abundance of fecal and blood microbiota between MSA patients and HC, and that a combination of six genera was predictive of MSA with an AUC of 0.853. In addition, a combined model of five genera from feces and blood was able to differentiate between two subtypes of MSA with an AUC of 0.898 [161]. Another study constructed a set of 25 gut microbial gene markers to discriminate MSA from PD patients with an AUC of 0.831 [155]. Gut microbiota dysbiosis provides a new direction for searching MSA biomarkers. However, due to the limited sample size and inherent discrepancy between 16S rDNA and metagenomic sequencing, the reliability of gut microbiota as a biomarker for MSA needs further exploration. Besides, targeted validation and multi-omics technology are required for obtaining more detailed information of microbiotic function in MSA patients.

Imaging biomarkers

Magnetic resonance imaging (MRI)

Morphological MRI is an important auxiliary tool in the clinical diagnosis of MSA. Typical imaging features in brain MRI, such as the “hot-cross bun” sign and the “putaminal slit” sign, are clinically suggestive of MSA, which are recommended in the second consensus statement on the diagnosis of MSA [162, 163]. In clinically or neuropathologically diagnosed cases, the “hot-cross bun” sign has almost 100% specificity for MSA against HC, PD, PSP and CBD, but the sensitivity is much lower (around 50%) [162, 164,165,166]. In the new Movement Disorder Society (MDS) criteria for the diagnosis of MSA, the “hot-cross bun” sign is still recommended as a supportive biomarker for MSA. However, its diagnostic specificity should be considered with caution when distinguishing MSA from non-degenerative parkinsonism or SCA [4]. As for the “putaminal slit” sign, high specificity (around 90%) and low sensitivity (around 30%) have been reported in differentiating MSA from PD, but the differential diagnostic potential in separating MSA from PSP is limited [162, 164, 165]. In addition, the “putaminal slit” sign is a common feature on 3.0 T MRI images of HC [167]. Therefore, the clinical value of this sign appears to be limited and it is omitted from the new MDS criteria for the diagnosis of MSA [4]. Based on the limitation of conventional imaging biomarkers, more advanced imaging techniques are needed for the development of reliable MSA biomarkers.

Recently, researchers have applied multimodal imaging and machine learning techniques to systematically evaluate MRI abnormalities in MSA patients. The machine learning approach based on volumetry of regions including putamen and infratentorial regions, has been shown to accurately classify MSA, PD and PSP with an accuracy of about 97% [168,169,170,171,172]. In addition to volumetric parameters, other parameters such as fractional anisotropy, mean diffusivity and iron deposition in multimodal imaging approach are also helpful in the diagnosis of MSA [173,174,175,176,177,178,179]. A logistic regression model based on fractional anisotropy in the cerebellum, brain stem and bilateral superior corona radiata, together with the mean diffusivity in the right superior frontal gyrus, has been shown to discriminate MSA from PD patients with an accuracy  > 95% [173]. In general, multimodal MRI parameters and artificial intelligence analysis have the potential to further improve the performance of MRI in the diagnosis of MSA.

Positron emission tomography (PET)

Molecular imaging based on hypometabolism, decreased activity of dopaminergic and cholinergic systems and neuroinflammation has also been used in MSA patients [5]. 18F-FDG PET has revealed reduced glucose metabolic activity in the frontal cortex, striatum, cerebellum, and brainstem of MSA patients [180,181,182,183,184]. 18F-dopa, 18F-FP-CIT, and 11C-DTBZ PET which assess the function of dopaminergic system, have shown significantly decreased activity in the putamen, caudate nucleus, ventral striatum, globus pallidus externa and red nucleus in MSA patients [181, 185]. 11C-PMP PET which assesses the activity of cholinergic systems, revealed reduction of brain acetylcholinesterase activities in the thalamus and cerebellum in MSA-C patients, while significant decreases in cortical and subcortical cholinergic activity are present in MSA-P patients [186, 187].

Recently, PET using a novel radiotracer 11C-UCB‐J that binds to synaptic vesicle protein 2A (SV2A), a synapse-specific protein, has shown high sensitivity in various neurodegenerative diseases, including PD, AD, DLB, PSP [188,189,190,191]. The SV2A-targeting PET can also assist the diagnosis of MSA due to the neurodegenerative pathology in MSA. However, since decreased synaptic density is widely present among neurodegenerative diseases, comparative analysis of its performance on different MSA-mimicking disorders is a necessary direction for future research.

For α-synucleinopathies, developing α-syn-specific PET technique is promising as it offers in vivo monitoring of development of α‐syn pathologies and directly reflects the effects of anti-synuclein therapies in living patients compared with other imaging techniques [192]. Moreover, the conformation and distribution of α-syn aggregates in MSA patients are different from those in other synucleinopathies [45, 193], indicating the potential advantages of disease-specific PET ligands in differentiating these diseases. Up to now, nearly 40 different ligands have been developed for α-syn [194, 195], among which more than 20 compounds have been radiolabeled and evaluated in animal models [196,197,198,199,200,201] or postmortem tissues of patients [202,203,204,205,206,207,208,209]. The PET ligand [11C]PBB3 shows distinct affinity to α-syn pathology in different synucleinopathies [204]. Even more encouraging, PET scans with two α-syn ligands 11C-BF-227 and 18F‐SPAL‐T‐06 in living MSA patients show high distribution in brain regions coincident with the predominant distributions of GCIs, compared with HCs [205, 210]. However, the current radiotracers are still not able to overcome the disadvantages of low values of brain uptake, affinity, selectivity and disease-specificity. Thus, preclinical studies of α-syn-specific PET need to be further conducted before clinical application.

Conclusions and future perspectives

The clinical diagnosis, severity monitoring and prognosis for MSA are quite challenging, which also restrict the development of novel disease-modifying strategies. Significant efforts have been made to explore biomarkers for MSA in recent years. Here, we comprehensively review the latest progress on the multidimensional biomarkers for MSA, including biomarkers in fluids and tissues, gut microbiota and imaging biomarkers (Fig. 1). Among these biomarkers, α-syn in the CSF, blood and tissues is the most focused target, although inconsistency exists among studies on the exact levels of α-syn. The SAAs targeting the CSF or tissue α-syn are a great step forward and show high differentiating accuracy, including in distinguishing among α-synucleinopathies in particular. The different conformational strains and reaction kinetics of α-syn among α-synucleinopathies might underlie this differentiating accuracy [211]. Exploring the seeding activity of α-syn in other samples such as the saliva may be an alternative since lumbar puncturing and tissue biopsy are a challenge for clinical extension. Furthermore, developing PET imaging ligands to noninvasively detect original α-syn pathology facilitates in vivo studies of the distinct conformations and regional spread of α-syn pathology among α-synucleinopathies, as well as exploration of targets for anti-syn therapies [192]. In addition, NfL levels in fluids have been used as an outcome measure to evaluate the efficacy of drug trials in MSA (NCT05104476, NCT05109091), based on their significant correlation with disease severity. In addition, multimodal imaging combined with machine learning techniques is also a breakthrough in this field.

Although significant progress has been made on biomarker discovery for MSA, there are still great gaps in the application of these biomarkers in clinic. Several reasons may explain the challenges in this field. Since autopsy confirmation is the only gold standard for definite diagnosis and previous studies revealed that the antemortem diagnostic accuracy is only about 62% for MSA [6], the presence of misdiagnosis in current biomarker studies limits the accuracy of conclusions, which might underlie the discrepancy among different studies. Besides, the limited sample size due to the low prevalence of MSA also partially accounts for the inconsistency among studies. Furthermore, the intrinsic heterogeneity between two MSA subtypes sets up obstacles for biomarker identification. Finally, the lack of standard methodologies may be another important reason why results are not always reproducible in this field.

For future explorations, large multi-center cohort studies are necessary. Validation in neuropathologically established MSA cohorts and explorations between different MSA subtypes are needed. Furthermore, unified standards and methodologies are also mandatory to improve the replicability of biomarkers for MSA. In recent years, progresses in high-throughput technologies and artificial intelligence have provided new opportunities for exploring disease biomarkers [212]. Integrating multi-omics data through artificial intelligence technology, including genomics, transcriptomics, proteomics, metabolomics, metagenomics, and radiomics, is able to draw a multi-omics characteristic spectrum of MSA patients and develop a multi-dimensional biomarker system. Constructing diagnostic or predictive panels through the combination of multidimensional biomarkers, based on the novel techniques and artificial intelligence, is quite promising for this field.

In conclusion, great efforts have been made in the field of multidimensional biomarkers for MSA and several potential biomarkers have been identified in the past years. However, there are still quite a few challenges before implementation of these biomarkers in clinic. Applying advanced detection techniques such as SAAs and multi-omics analysis in large-sample cohorts including neuropathologically established MSA cases, will pave the way for exploration of MSA biomarkers.

Availability of data and materials

Not applicable.

Abbreviations

α-syn:

α-Synuclein

AD:

Alzheimer's disease

ALS:

Amyotrophic lateral sclerosis

AUC:

Area under the curve

CBD:

Corticobasal degeneration

CNS:

Central nervous system

CoQ10:

Coenzyme Q10

CSF:

Cerebrospinal fluid

DEG:

Differentially expressed gene

DLB:

Dementia with Lewy bodies

ENFD:

Epidermal nerve fiber density

GCI:

Glial cytoplasmic inclusion

GFAP:

Glial fibrillary acidic protein

HC:

Healthy controls

Hcy:

Homocysteine

HD:

Huntington's disease

MBP:

Myelin basic protein

MRI:

Magnetic resonance imaging

MS:

Multiple sclerosis

MSA:

Multiple system atrophy

NfL:

Neurofilament light protein

OM:

Olfactory mucosa

PAF:

Pure autonomic failure

PD:

Parkinson's disease

PET:

Positron emission tomography

PMCA:

Protein-misfolding cyclic amplification

PSP:

Progressive supranuclear palsy

RBCs:

Red blood cells

RT-QuIC:

Real-time quaking-induced conversion

SAA:

Seeding aggregation assay

SAOA:

Sporadic adult-onset ataxia of unknown aetiology

SCA:

Spinocerebellar ataxia

Simoa:

Single-molecule array

SV2A:

Synaptic vesicle protein 2A

References

  1. Graham JG, Oppenheimer DR. Orthostatic hypotension and nicotine sensitivity in a case of multiple system atrophy. J Neurol Neurosurg Psychiatry. 1969;32(1):28–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Fanciulli A, Wenning GK. Multiple-system atrophy. N Engl J Med. 2015;372(3):249–63.

    Article  PubMed  Google Scholar 

  3. Krismer F, Wenning GK. Multiple system atrophy: insights into a rare and debilitating movement disorder. Nat Rev Neurol. 2017;13(4):232–43.

    Article  PubMed  Google Scholar 

  4. Wenning GK, Stankovic I, Vignatelli L, Fanciulli A, Calandra-Buonaura G, Seppi K, et al. The movement disorder society criteria for the diagnosis of multiple system atrophy. Mov Disord. 2022;37(6):1131–48.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Koga S, Dickson DW. Recent advances in neuropathology, biomarkers and therapeutic approach of multiple system atrophy. J Neurol Neurosurg Psychiatry. 2018;89(2):175–84.

    Article  PubMed  Google Scholar 

  6. Koga S, Aoki N, Uitti RJ, van Gerpen JA, Cheshire WP, Josephs KA, et al. When DLB, PD, and PSP masquerade as MSA: an autopsy study of 134 patients. Neurology. 2015;85(5):404–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wenning GK, Tison F, Seppi K, Sampaio C, Diem A, Yekhlef F, et al. Development and validation of the Unified Multiple System Atrophy Rating Scale (UMSARS). Mov Disord. 2004;19(12):1391–402.

    Article  PubMed  Google Scholar 

  8. Palma J-A, Vernetti PM, Perez MA, Krismer F, Seppi K, Fanciulli A, et al. Limitations of the Unified Multiple System Atrophy Rating Scale as outcome measure for clinical trials and a roadmap for improvement. Clin Auton Res. 2021;31(2):157–64.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Trojanowski JQ, Growdon JH. A new consensus report on biomarkers for the early antemortem diagnosis of Alzheimer disease: current status, relevance to drug discovery, and recommendations for future research. J Neuropathol Exp Neurol. 1998;57(6):643–4.

    Article  CAS  PubMed  Google Scholar 

  10. Laurens B, Constantinescu R, Freeman R, Gerhard A, Jellinger K, Jeromin A, et al. Fluid biomarkers in multiple system atrophy: A review of the MSA Biomarker Initiative. Neurobiol Dis. 2015;80:29–41.

    Article  PubMed  Google Scholar 

  11. Vargas AJ, Harris CC. Biomarker development in the precision medicine era: lung cancer as a case study. Nat Rev Cancer. 2016;16(8):525–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kawahata I, Finkelstein DI, Fukunaga K. Pathogenic impact of α-Synuclein phosphorylation and its kinases in α-synucleinopathies. Int J Mol Sci. 2022;23(11):6216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Eusebi P, Giannandrea D, Biscetti L, Abraha I, Chiasserini D, Orso M, et al. Diagnostic utility of cerebrospinal fluid α-synuclein in Parkinson’s disease: a systematic review and meta-analysis. Mov Disord. 2017;32(10):1389–400.

    Article  CAS  PubMed  Google Scholar 

  14. Constantinides VC, Majbour NK, Paraskevas GP, Abdi I, Safieh-Garabedian B, Stefanis L, et al. Cerebrospinal fluid-synuclein species in cognitive and movements disorders. Brain Sci. 2021;11(1):119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hall S, Öhrfelt A, Constantinescu R, Andreasson U, Surova Y, Bostrom F, et al. Accuracy of a panel of 5 cerebrospinal fluid biomarkers in the differential diagnosis of patients with dementia and/or parkinsonian disorders. Arch Neurol. 2012;69(11):1445–52.

    Article  PubMed  Google Scholar 

  16. Magdalinou NK, Paterson RW, Schott JM, Fox NC, Mummery C, Blennow K, et al. A panel of nine cerebrospinal fluid biomarkers may identify patients with atypical parkinsonian syndromes. J Neurol Neurosurg Psychiatry. 2015;86(11):1240–7.

    Article  CAS  PubMed  Google Scholar 

  17. Mollenhauer B, Locascio JJ, Schulz-Schaeffer W, Sixel-Döring F, Trenkwalder C, Schlossmacher MG. α-Synuclein and tau concentrations in cerebrospinal fluid of patients presenting with parkinsonism: a cohort study. Lancet Neurol. 2011;10(3):230–40.

    Article  CAS  PubMed  Google Scholar 

  18. Mondello S, Constantinescu R, Zetterberg H, Andreasson U, Holmberg B, Jeromin A. CSF α-synuclein and UCH-L1 levels in Parkinson’s disease and atypical parkinsonian disorders. Parkinsonism Relat Disord. 2014;20(4):382–7.

    Article  PubMed  Google Scholar 

  19. Shi M, Bradner J, Hancock AM, Chung KA, Quinn JF, Peskind ER, et al. Cerebrospinal fluid biomarkers for Parkinson disease diagnosis and progression. Ann Neurol. 2011;69(3):570–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wang Y, Shi M, Chung KA, Zabetian CP, Leverenz JB, Berg D, et al. Phosphorylated α-synuclein in Parkinson’s disease. Sci Transl Med. 2012;4(121):121ra20.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Schulz I, Kruse N, Gera RG, Kremer T, Cedarbaum J, Barbour R, et al. Systematic assessment of 10 biomarker candidates focusing on α-synuclein-related disorders. Mov Disord. 2021;36(12):2874–87.

    Article  CAS  PubMed  Google Scholar 

  22. Yang F, Li WJ, Huang XS. Alpha-synuclein levels in patients with multiple system atrophy: a meta-analysis. Int J Neurosci. 2018;128(5):477–86.

    Article  CAS  PubMed  Google Scholar 

  23. Aerts MB, Esselink RAJ, Abdo WF, Bloem BR, Verbeek MM. CSF α-synuclein does not differentiate between parkinsonian disorders. Neurobiol Aging. 2012;33(2):430.e1-430.e3.

    Article  CAS  PubMed  Google Scholar 

  24. Tateno F, Sakakibara R, Kawai T, Kishi M, Murano T. Alpha-synuclein in the cerebrospinal fluid differentiates synucleinopathies (Parkinson Disease, dementia with Lewy bodies, multiple system atrophy) from Alzheimer disease. Alzheimer Dis Assoc Disord. 2012;26(3):213–6.

    Article  CAS  PubMed  Google Scholar 

  25. Seino Y, Nakamura T, Kawarabayashi T, Hirohata M, Narita S, Wakasaya Y, et al. Cerebrospinal fluid and plasma biomarkers in neurodegenerative diseases. J Alzheimers Dis. 2019;68(1):395–404.

    Article  CAS  PubMed  Google Scholar 

  26. Foulds PG, Yokota O, Thurston A, Davidson Y, Ahmed Z, Holton J, et al. Post mortem cerebrospinal fluid α-synuclein levels are raised in multiple system atrophy and distinguish this from the other α-synucleinopathies, Parkinson’s disease and Dementia with Lewy bodies. Neurobiol Dis. 2012;45(1):188–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sako W, Murakami N, Izumi Y, Kaji R. Reduced alpha-synuclein in cerebrospinal fluid in synucleinopathies: evidence from a meta-analysis. Mov Disord. 2014;29(13):1599–605.

    Article  CAS  PubMed  Google Scholar 

  28. Magdalinou N, Lees AJ, Zetterberg H. Cerebrospinal fluid biomarkers in parkinsonian conditions: an update and future directions. J Neurol Neurosurg Psychiatry. 2014;85(10):1065–75.

    Article  PubMed  Google Scholar 

  29. Fujiwara H, Hasegawa M, Dohmae N, Kawashima A, Masliah E, Goldberg MS, et al. alpha-Synuclein is phosphorylated in synucleinopathy lesions. Nat Cell Biol. 2002;4(2):160–4.

    Article  CAS  PubMed  Google Scholar 

  30. Ghanem SS, Majbour NK, Vaikath NN, Ardah MT, Erskine D, Jensen NM, et al. α-Synuclein phosphorylation at serine 129 occurs after initial protein deposition and inhibits seeded fibril formation and toxicity. Proc Natl Acad Sci USA. 2022;119(15):e2109617119.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lee PH, Lee G, Park HJ, Bang OY, Joo IS, Huh K. The plasma alpha-synuclein levels in patients with Parkinson’s disease and multiple system atrophy. J Neural Transm. 2006;113(10):1435–9.

    Article  CAS  PubMed  Google Scholar 

  32. Sun ZF, Xiang XS, Chen Z, Zhang L, Tang BS, Xia K, et al. Increase of the plasma α-synuclein levels in patients with multiple system atrophy. Mov Disord. 2014;29(3):375–9.

    Article  CAS  PubMed  Google Scholar 

  33. Liu G, Tian C, Gao L, Cao Z, Feng T. Alpha-synuclein in erythrocyte membrane of patients with multiple system atrophy: a pilot study. Parkinsonism Relat Disord. 2019;60:105–10.

    Article  PubMed  Google Scholar 

  34. Wang X, Yu S, Li F, Feng T. Detection of α-synuclein oligomers in red blood cells as a potential biomarker of Parkinson’s disease. Neurosci Lett. 2015;599:115–9.

    Article  CAS  PubMed  Google Scholar 

  35. Li XY, Yang W, Li X, Li XR, Li W, Song Q, et al. Phosphorylated alpha-synuclein in red blood cells as a potential diagnostic biomarker for multiple system atrophy: a pilot study. Parkinsons Dis. 2020;2020:8740419.

    PubMed  PubMed Central  Google Scholar 

  36. Kang UJ, Boehme AK, Fairfoul G, Shahnawaz M, Ma TC, Hutten SJ, et al. Comparative study of cerebrospinal fluid α-synuclein seeding aggregation assays for diagnosis of Parkinson’s disease. Mov Disord. 2019;34(4):536–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Concha-Marambio L, Farris CM, Holguin B, Ma Y, Seibyl J, Russo MJ, et al. Seed amplification assay to diagnose early Parkinson’s and predict dopaminergic deficit progression. Mov Disord. 2021;36(10):2444–6.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Russo MJ, Orru CD, Concha-Marambio L, Giaisi S, Groveman BR, Farris CM, et al. High diagnostic performance of independent alpha-synuclein seed amplification assays for detection of early Parkinson’s disease. Acta Neuropathol Commun. 2021;9(1):179.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Saborio GP, Permanne B, Soto C. Sensitive detection of pathological prion protein by cyclic amplification of protein misfolding. Nature. 2001;411(6839):810–3.

    Article  CAS  PubMed  Google Scholar 

  40. Coysh T, Mead S. The future of seed amplification assays and clinical trials. Front Aging Neurosci. 2022;14:872629.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wang Z, Becker K, Donadio V, Siedlak S, Yuan J, Rezaee M, et al. Skin α-synuclein aggregation seeding activity as a novel biomarker for Parkinson disease. JAMA Neurol. 2020;78(1):1–11.

    PubMed  PubMed Central  Google Scholar 

  42. Bernis ME, Babila JT, Breid S, Wüsten KA, Wüllner U, Tamgüney G. Prion-like propagation of human brain-derived alpha-synuclein in transgenic mice expressing human wild-type alpha-synuclein. Acta Neuropathol Commun. 2015;3:75.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Brandel JP, Corbillé AG, Derkinderen P, Haïk S. Is Parkinson’s disease a prion disease? Rev Neurol. 2015;171(12):812–24.

    Article  PubMed  Google Scholar 

  44. Nakagaki T, Nishida N, Satoh K. Development of α-synuclein real-time quaking-induced conversion as a diagnostic method for α-synucleinopathies. Front Aging Neurosci. 2021;13:703984.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Shahnawaz M, Mukherjee A, Pritzkow S, Mendez N, Rabadia P, Liu X, et al. Discriminating α-synuclein strains in Parkinson’s disease and multiple system atrophy. Nature. 2020;578(7794):273–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Singer W, Schmeichel AM, Shahnawaz M, Schmelzer JD, Boeve BF, Sletten DM, et al. Alpha-synuclein oligomers and neurofilament light chain in spinal fluid differentiate multiple system atrophy from Lewy body synucleinopathies. Ann Neurol. 2020;88(3):503–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Poggiolini I, Gupta V, Lawton M, Lee S, El-Turabi A, Querejeta-Coma A, et al. Diagnostic value of cerebrospinal fluid alpha-synuclein seed quantification in synucleinopathies. Brain. 2022;145(2):584–95.

    Article  PubMed  Google Scholar 

  48. Rossi M, Candelise N, Baiardi S, Capellari S, Giannini G, Orrù CD, et al. Ultrasensitive RT-QuIC assay with high sensitivity and specificity for Lewy body-associated synucleinopathies. Acta Neuropathol. 2020;140(1):49–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Quadalti C, Calandra-Buonaura G, Baiardi S, Mastrangelo A, Rossi M, Zenesini C, et al. Neurofilament light chain and α-synuclein RT-QuIC as differential diagnostic biomarkers in parkinsonisms and related syndromes. NPJ Parkinsons Dis. 2021;7(1):93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Luan M, Sun Y, Chen J, Jiang Y, Li F, Wei L, et al. Diagnostic value of salivary real-time quaking-induced conversion in Parkinson’s disease and multiple system atrophy. Mov Disord. 2022;37(5):1059–63.

    Article  CAS  PubMed  Google Scholar 

  51. Foti SC, Hargreaves I, Carrington S, Kiely AP, Houlden H, Holton JL. Cerebral mitochondrial electron transport chain dysfunction in multiple system atrophy and Parkinson’s disease. Sci Rep. 2019;9(1):6559.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Multiple-System Atrophy Research Collaboration. Mutations in COQ2 in familial and sporadic multiple-system atrophy. N Engl J Med. 2013;369(3):233–44.

    Article  Google Scholar 

  53. Ogaki K, Fujioka S, Heckman MG, Rayaprolu S, Soto-Ortolaza AI, Labbé C, et al. Analysis of COQ2 gene in multiple system atrophy. Mol Neurodegener. 2014;9:44.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Ronchi D, Di Biase E, Franco G, Melzi V, Del Sorbo F, Elia A, et al. Mutational analysis of COQ2 in patients with MSA in Italy. Neurobiol Aging. 2016;45:213.e1-213.e2.

    Article  CAS  PubMed  Google Scholar 

  55. Jeon BS, Farrer MJ, Bortnick SF. Mutant COQ2 in multiple-system atrophy. N Engl J Med. 2014;371(1):80.

    Article  CAS  PubMed  Google Scholar 

  56. Barca E, Kleiner G, Tang G, Ziosi M, Tadesse S, Masliah E, et al. Decreased coenzyme Q10 levels in multiple system atrophy cerebellum. J Neuropathol Exp Neurol. 2016;75(7):663–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Monzio Compagnoni G, Kleiner G, Bordoni A, Fortunato F, Ronchi D, Salani S, et al. Mitochondrial dysfunction in fibroblasts of multiple system atrophy. Biochim Biophys Acta Mol Basis Dis. 2018;1864(12):3588–97.

    Article  CAS  PubMed  Google Scholar 

  58. Schottlaender LV, Bettencourt C, Kiely AP, Chalasani A, Neergheen V, Holton JL, et al. Coenzyme Q10 levels are decreased in the cerebellum of multiple-system atrophy patients. PLoS ONE. 2016;11(2):e0149557.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Compta Y, Giraldo DM, Muñoz E, Antonelli F, Fernández M, Bravo P, et al. Cerebrospinal fluid levels of coenzyme Q10 are reduced in multiple system atrophy. Parkinsonism Relat Disord. 2018;46:16–23.

    Article  PubMed  Google Scholar 

  60. Du J, Wang T, Huang P, Cui S, Gao C, Lin Y, et al. Clinical correlates of decreased plasma coenzyme Q10 levels in patients with multiple system atrophy. Parkinsonism Relat Disord. 2018;57:58–62.

    Article  PubMed  Google Scholar 

  61. Mitsui J, Matsukawa T, Yasuda T, Ishiura H, Tsuji S. Plasma coenzyme Q10 levels in patients with multiple system atrophy. JAMA Neurol. 2016;73(8):977–80.

    Article  PubMed  Google Scholar 

  62. Kasai T, Tokuda T, Ohmichi T, Ishii R, Tatebe H, Nakagawa M, et al. Serum levels of coenzyme Q10 in patients with multiple system atrophy. PLoS ONE. 2016;11(1):e0147574.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Khalil M, Teunissen CE, Otto M, Piehl F, Sormani MP, Gattringer T, et al. Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol. 2018;14(10):577–89.

    Article  CAS  PubMed  Google Scholar 

  64. Wilke C, Bender F, Hayer SN, Brockmann K, Schöls L, Kuhle J, et al. Serum neurofilament light is increased in multiple system atrophy of cerebellar type and in repeat-expansion spinocerebellar ataxias: a pilot study. J Neurol. 2018;265(7):1618–24.

    Article  PubMed  Google Scholar 

  65. Rohrer JD, Woollacott IOC, Dick KM, Brotherhood E, Gordon E, Fellows A, et al. Serum neurofilament light chain protein is a measure of disease intensity in frontotemporal dementia. Neurology. 2016;87(13):1329–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Hansson O, Janelidze S, Hall S, Magdalinou N, Lees AJ, Andreasson U, et al. Blood-based NfL: a biomarker for differential diagnosis of parkinsonian disorder. Neurology. 2017;88(10):930–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Mattsson N, Andreasson U, Zetterberg H, Blennow K. Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 2017;74(5):557–66.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Byrne LM, Rodrigues FB, Blennow K, Durr A, Leavitt BR, Roos RAC, et al. Neurofilament light protein in blood as a potential biomarker of neurodegeneration in Huntington’s disease: a retrospective cohort analysis. Lancet Neurol. 2017;16(8):601–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Peng Y, Zhang Y, Chen Z, Peng H, Wan N, Zhang J, et al. Association of serum neurofilament light and disease severity in patients with spinocerebellar ataxia type 3. Neurology. 2020;95(22):e2977–87.

    Article  CAS  PubMed  Google Scholar 

  70. Weydt P, Oeckl P, Huss A, Müller K, Volk AE, Kuhle J, et al. Neurofilament levels as biomarkers in asymptomatic and symptomatic familial amyotrophic lateral sclerosis. Ann Neurol. 2016;79(1):152–8.

    Article  CAS  PubMed  Google Scholar 

  71. Constantinescu R, Rosengren L, Johnels B, Zetterberg H, Holmberg B. Consecutive analyses of cerebrospinal fluid axonal and glial markers in Parkinson’s disease and atypical Parkinsonian disorders. Parkinsonism Relat Disord. 2010;16(2):142–5.

    Article  PubMed  Google Scholar 

  72. Herbert MK, Aerts MB, Beenes M, Norgren N, Esselink RAJ, Bloem BR, et al. CSF neurofilament light chain but not FLT3 ligand discriminates parkinsonian disorders. Front Neurol. 2015;6:91.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Chelban V, Nikram E, Perez-Soriano A, Wilke C, Foubert-Samier A, Vijiaratnam N, et al. Neurofilament light levels predict clinical progression and death in multiple system atrophy. Brain. 2022;145(12):4398–408.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Marques TM, van Rumund A, Oeckl P, Kuiperij HB, Esselink RAJ, Bloem BR, et al. Serum NFL discriminates Parkinson disease from atypical parkinsonisms. Neurology. 2019;92(13):e1479–86.

    Article  PubMed  Google Scholar 

  75. Bacioglu M, Maia LF, Preische O, Schelle J, Apel A, Kaeser SA, et al. Neurofilament light chain in blood and CSF as marker of disease progression in mouse models and in neurodegenerative diseases. Neuron. 2016;91(1):56–66.

    Article  CAS  PubMed  Google Scholar 

  76. Lin CH, Li CH, Yang KC, Lin FJ, Wu CC, Chieh JJ, et al. Blood NfL: a biomarker for disease severity and progression in Parkinson disease. Neurology. 2019;93(11):e1104–11.

    Article  CAS  PubMed  Google Scholar 

  77. Zhang L, Cao B, Hou Y, Gu X, Wei Q, Ou R, et al. Neurofilament light chain predicts disease severity and progression in multiple system atrophy. Mov Disord. 2022;37(2):421–6.

    Article  CAS  PubMed  Google Scholar 

  78. Bonneh-Barkay D, Bissel SJ, Wang G, Fish KN, Nicholl GCB, Darko SW, et al. YKL-40, a marker of simian immunodeficiency virus encephalitis, modulates the biological activity of basic fibroblast growth factor. Am J Pathol. 2008;173(1):130–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Hall S, Janelidze S, Surova Y, Widner H, Zetterberg H, Hansson O. Cerebrospinal fluid concentrations of inflammatory markers in Parkinson’s disease and atypical parkinsonian disorders. Sci Rep. 2018;8(1):13276.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Olsson B, Constantinescu R, Holmberg B, Andreasen N, Blennow K, Zetterberg H. The glial marker YKL-40 is decreased in synucleinopathies. Mov Disord. 2013;28(13):1882–5.

    Article  CAS  PubMed  Google Scholar 

  81. Cong S, Xiang C, Wang H, Cong S. Diagnostic utility of fluid biomarkers in multiple system atrophy: a systematic review and meta-analysis. J Neurol. 2021;268(8):2703–12.

    Article  PubMed  Google Scholar 

  82. Brouillette AM, Öz G, Gomez CM. Cerebrospinal fluid biomarkers in spinocerebellar ataxia: a pilot study. Dis Markers. 2015;2015:413098.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Canaslan S, Schmitz M, Villar-Piqué A, Maass F, Gmitterová K, Varges D, et al. Detection of cerebrospinal fluid neurofilament light chain as a marker for alpha-synucleinopathies. Front Aging Neurosci. 2021;13:717930.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Santaella A, Kuiperij HB, van Rumund A, Esselink RAJ, Bloem BR, Verbeek MM. Cerebrospinal fluid myelin basic protein is elevated in multiple system atrophy. Parkinsonism Relat Disord. 2020;76:80–4.

    Article  PubMed  Google Scholar 

  85. Boggs JM. Myelin basic protein: a multifunctional protein. Cell Mol Life Sci. 2006;63(17):1945–61.

    Article  CAS  PubMed  Google Scholar 

  86. Yoon YS, Ahn WJ, Ricarte D, Ortiz D, Shin CY, Lee SJ, et al. Alpha-synuclein inclusion formation in human oligodendrocytes. Biomol Ther (Seoul). 2021;29(1):83–9.

    Article  CAS  PubMed  Google Scholar 

  87. Abdo WF, van de Warrenburg BPC, Kremer HPH, Bloem BR, Verbeek MM. CSF biomarker profiles do not differentiate between the cerebellar and parkinsonian phenotypes of multiple system atrophy. Parkinsonism Relat Disord. 2007;13(8):480–2.

    Article  CAS  PubMed  Google Scholar 

  88. Finkelstein JD. Methionine metabolism in mammals. J Nutr Biochem. 1990;1(5):228–37.

    Article  CAS  PubMed  Google Scholar 

  89. Kruman II, Culmsee C, Chan SL, Kruman Y, Guo Z, Penix L, et al. Homocysteine elicits a DNA damage response in neurons that promotes apoptosis and hypersensitivity to excitotoxicity. J Neurosci. 2000;20(18):6920–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Irizarry MC, Gurol ME, Raju S, Diaz-Arrastia R, Locascio JJ, Tennis M, et al. Association of homocysteine with plasma amyloid beta protein in aging and neurodegenerative disease. Neurology. 2005;65(9):1402–8.

    Article  CAS  PubMed  Google Scholar 

  91. Zoccolella S, Lamberti P, Iliceto G, Dell’Aquila C, Diroma C, Fraddosio A, et al. Elevated plasma homocysteine levels in L-dopa-treated Parkinson’s disease patients with dyskinesias. Clin Chem Lab Med. 2006;44(7):863–6.

    Article  CAS  PubMed  Google Scholar 

  92. Ray L, Khemka VK, Behera P, Bandyopadhyay K, Pal S, Pal K, et al. Serum homocysteine, dehydroepiandrosterone sulphate and lipoprotein (a) in Alzheimer’s disease and vascular dementia. Aging Dis. 2013;4(2):57–64.

    PubMed  PubMed Central  Google Scholar 

  93. Fan X, Zhang L, Li H, Chen G, Qi G, Ma X, et al. Role of homocysteine in the development and progression of Parkinson’s disease. Ann Clin Transl Neurol. 2020;7(11):2332–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Morris MS. Homocysteine and Alzheimer’s disease. Lancet Neurol. 2003;2(7):425–8.

    Article  CAS  PubMed  Google Scholar 

  95. Smith AD, Smith SM, de Jager CA, Whitbread P, Johnston C, Agacinski G, et al. Homocysteine-lowering by B vitamins slows the rate of accelerated brain atrophy in mild cognitive impairment: a randomized controlled trial. PLoS ONE. 2010;5(9):e12244.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Zhang S, Shi C, Mao C, Song B, Hou H, Wu J, et al. Plasma homocysteine, vitamin B12 and folate levels in multiple system atrophy: a case-control study. PLoS ONE. 2015;10(8):e0136468.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Guo Y, Zhuang XD, Xian WB, Wu LL, Huang ZN, Hu X, et al. Serum Klotho, vitamin D, and homocysteine in combination predict the outcomes of Chinese patients with multiple system atrophy. CNS Neurosci Ther. 2017;23(8):657–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Zhou L, Jiang Y, Zhu C, Ma L, Huang Q, Chen X. Oxidative stress and environmental exposures are associated with multiple system atrophy in Chinese patients. Can J Neurol Sci. 2016;43(5):703–9.

    Article  PubMed  Google Scholar 

  99. Chen D, Wei X, Zou J, Wang R, Liu X, Xu X, et al. Contra-directional expression of serum homocysteine and uric acid as important biomarkers of multiple system atrophy severity: a cross-sectional study. Front Cell Neurosci. 2015;9:247.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Riedmaier I, Pfaffl MW. Transcriptional biomarkers–high throughput screening, quantitative verification, and bioinformatical validation methods. Methods. 2013;59(1):3–9.

    Article  CAS  PubMed  Google Scholar 

  101. Pérez-Soriano A, Martí MJ. Mini-Review: the MSA transcriptome. Neurosci Lett. 2021;743:135586.

    Article  PubMed  Google Scholar 

  102. Hampel H, Nisticò R, Seyfried NT, Levey AI, Modeste E, Lemercier P, et al. Omics sciences for systems biology in Alzheimer’s disease: State-of-the-art of the evidence. Ageing Res Rev. 2021;69:101346.

    Article  CAS  PubMed  Google Scholar 

  103. Fehlbaum-Beurdeley P, Sol O, Désiré L, Touchon J, Dantoine T, Vercelletto M, et al. Validation of AclarusDx™, a blood-based transcriptomic signature for the diagnosis of Alzheimer’s disease. J Alzheimers Dis. 2012;32(1):169–81.

    Article  CAS  PubMed  Google Scholar 

  104. Lunnon K, Sattlecker M, Furney SJ, Coppola G, Simmons A, Proitsi P, et al. A blood gene expression marker of early Alzheimer’s disease. J Alzheimers Dis. 2013;33(3):737–53.

    Article  CAS  PubMed  Google Scholar 

  105. Sood S, Gallagher IJ, Lunnon K, Rullman E, Keohane A, Crossland H, et al. A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status. Genome Biol. 2015;16(1):185.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Jiang F, Wu Q, Sun S, Bi G, Guo L. Identification of potential diagnostic biomarkers for Parkinson’s disease. FEBS Open Bio. 2019;9(8):1460–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Mills JD, Kim WS, Halliday GM, Janitz M. Transcriptome analysis of grey and white matter cortical tissue in multiple system atrophy. Neurogenetics. 2015;16(2):107–22.

    Article  CAS  PubMed  Google Scholar 

  108. Piras IS, Bleul C, Schrauwen I, Talboom J, Llaci L, De Both MD, et al. Transcriptional profiling of multiple system atrophy cerebellar tissue highlights differences between the parkinsonian and cerebellar sub-types of the disease. Acta Neuropathol Commun. 2020;8(1):76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Mills JD, Ward M, Kim WS, Halliday GM, Janitz M. Strand-specific RNA-sequencing analysis of multiple system atrophy brain transcriptome. Neuroscience. 2016;322:234–50.

    Article  CAS  PubMed  Google Scholar 

  110. Langerveld AJ, Mihalko D, DeLong C, Walburn J, Ide CF. Gene expression changes in postmortem tissue from the rostral pons of multiple system atrophy patients. Mov Disord. 2007;22(6):766–77.

    Article  PubMed  Google Scholar 

  111. Pérez-Soriano A, Arnal Segura M, Botta-Orfila T, Giraldo D, Fernández M, Compta Y, et al. Transcriptomic differences in MSA clinical variants. Sci Rep. 2020;10(1):10310.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Kim H, Kang SJ, Jo YM, Kim MS, Lee Y, Cho SH, et al. Quantitative analysis of nasal transcripts reveals potential biomarkers for Parkinson’s disease. Sci Rep. 2019;9(1):11111.

    Article  PubMed  PubMed Central  Google Scholar 

  113. Treiber T, Treiber N, Meister G. Regulation of microRNA biogenesis and its crosstalk with other cellular pathways. Nat Rev Mol Cell Biol. 2019;20(1):5–20.

    Article  CAS  PubMed  Google Scholar 

  114. Arshad AR, Sulaiman SA, Saperi AA, Jamal R, Mohamed Ibrahim N, Abdul Murad NA. MicroRNAs and target genes as biomarkers for the diagnosis of early onset of Parkinson disease. Front Mol Neurosci. 2017;10:352.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Bian S, Sun T. Functions of noncoding RNAs in neural development and neurological diseases. Mol Neurobiol. 2011;44(3):359–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Valera E, Spencer B, Mott J, Trejo M, Adame A, Mante M, et al. MicroRNA-101 modulates autophagy and oligodendroglial alpha-synuclein accumulation in multiple system atrophy. Front Mol Neurosci. 2017;10:329.

    Article  PubMed  PubMed Central  Google Scholar 

  117. Rinchetti P, Rizzuti M, Faravelli I, Corti S. MicroRNA metabolism and dysregulation in amyotrophic lateral sclerosis. Mol Neurobiol. 2018;55(3):2617–30.

    Article  CAS  PubMed  Google Scholar 

  118. Kawahara Y, Mieda-Sato A. TDP-43 promotes microRNA biogenesis as a component of the Drosha and Dicer complexes. Proc Natl Acad Sci USA. 2012;109(9):3347–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Kinoshita C, Kubota N, Aoyama K. Interplay of RNA-binding proteins and microRNAs in neurodegenerative diseases. Int J Mol Sci. 2021;22(10):5292.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Dewan R, Traynor BJ. Plasma microRNA signature as biomarker for disease progression in frontotemporal dementia and amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2021;92(5):458.

    Article  PubMed  Google Scholar 

  121. Ramaswamy P, Christopher R, Pal PK, Yadav R. MicroRNAs to differentiate Parkinsonian disorders: advances in biomarkers and therapeutics. J Neurol Sci. 2018;394:26–37.

    Article  CAS  PubMed  Google Scholar 

  122. Yuen SC, Liang X, Zhu H, Jia Y, Leung SW. Prediction of differentially expressed microRNAs in blood as potential biomarkers for Alzheimer’s disease by meta-analysis and adaptive boosting ensemble learning. Alzheimers Res Ther. 2021;13(1):126.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Xiang C, Han S, Nao J, Cong S. MicroRNAs dysregulation and metabolism in multiple system atrophy. Front Neurosci. 2019;13:1103.

    Article  PubMed  PubMed Central  Google Scholar 

  124. Marques TM, Kuiperij HB, Bruinsma IB, van Rumund A, Aerts MB, Esselink RAJ, et al. MicroRNAs in cerebrospinal fluid as potential biomarkers for Parkinson’s disease and multiple system atrophy. Mol Neurobiol. 2017;54(10):7736–45.

    Article  CAS  PubMed  Google Scholar 

  125. Starhof C, Hejl AM, Heegaard NHH, Carlsen AL, Burton M, Lilje B, et al. The biomarker potential of cell-free microRNA from cerebrospinal fluid in Parkinsonian Syndromes. Mov Disord. 2019;34(2):246–54.

    Article  CAS  PubMed  Google Scholar 

  126. Junn E, Lee KW, Jeong BS, Chan TW, Im JY, Mouradian MM. Repression of alpha-synuclein expression and toxicity by microRNA-7. Proc Natl Acad Sci USA. 2009;106(31):13052–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Uwatoko H, Hama Y, Iwata IT, Shirai S, Matsushima M, Yabe I, et al. Identification of plasma microRNA expression changes in multiple system atrophy and Parkinson’s disease. Mol Brain. 2019;12(1):49.

    Article  PubMed  PubMed Central  Google Scholar 

  128. Vallelunga A, Ragusa M, Di Mauro S, Iannitti T, Pilleri M, Biundo R, et al. Identification of circulating microRNAs for the differential diagnosis of Parkinson’s disease and Multiple System Atrophy. Front Cell Neurosci. 2014;8:156.

    Article  PubMed  PubMed Central  Google Scholar 

  129. Yan JH, Hua P, Chen Y, Li LT, Yu CY, Yan L, et al. Identification of microRNAs for the early diagnosis of Parkinson’s disease and multiple system atrophy. J Integr Neurosci. 2020;19(3):429–36.

    Article  PubMed  Google Scholar 

  130. Kume K, Iwama H, Deguchi K, Ikeda K, Takata T, Kokudo Y, et al. Serum microRNA expression profiling in patients with multiple system atrophy. Mol Med Rep. 2018;17(1):852–60.

    CAS  PubMed  Google Scholar 

  131. Pérez-Soriano A, Bravo P, Soto M, Infante J, Fernández M, Valldeoriola F, et al. MicroRNA deregulation in blood serum identifies multiple system atrophy altered pathways. Mov Disord. 2020;35(10):1873–9.

    Article  PubMed  Google Scholar 

  132. Vallelunga A, Iannitti T, Dati G, Capece S, Maugeri M, Tocci E, et al. Serum miR-30c-5p is a potential biomarker for multiple system atrophy. Mol Biol Rep. 2019;46(2):1661–6.

    Article  CAS  PubMed  Google Scholar 

  133. Beach TG, Adler CH, Sue LI, Vedders L, Lue L, White Iii CL, et al. Multi-organ distribution of phosphorylated alpha-synuclein histopathology in subjects with Lewy body disorders. Acta Neuropathol. 2010;119(6):689–702.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Pouclet H, Lebouvier T, Coron E, Rouaud T, Flamant M, Toulgoat F, et al. Analysis of colonic alpha-synuclein pathology in multiple system atrophy. Parkinsonism Relat Disord. 2012;18(7):893–5.

    Article  PubMed  Google Scholar 

  135. Kim JY, Illigens BM, McCormick MP, Wang N, Gibbons CH. Alpha-synuclein in skin nerve fibers as a biomarker for alpha-synucleinopathies. J Clin Neurol. 2019;15(2):135–42.

    Article  PubMed  PubMed Central  Google Scholar 

  136. Donadio V, Incensi A, Rizzo G, De Micco R, Tessitore A, Devigili G, et al. Skin biopsy may help to distinguish multiple system atrophy-parkinsonism from Parkinson’s disease with orthostatic hypotension. Mov Disord. 2020;35(9):1649–57.

    Article  CAS  PubMed  Google Scholar 

  137. Doppler K, Weis J, Karl K, Ebert S, Ebentheuer J, Trenkwalder C, et al. Distinctive distribution of phospho-alpha-synuclein in dermal nerves in multiple system atrophy. Mov Disord. 2015;30(12):1688–92.

    Article  CAS  PubMed  Google Scholar 

  138. Donadio V, Incensi A, El-Agnaf O, Rizzo G, Vaikath N, Del Sorbo F, et al. Skin α-synuclein deposits differ in clinical variants of synucleinopathy: an in vivo study. Sci Rep. 2018;8(1):14246.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Haga R, Sugimoto K, Nishijima H, Miki Y, Suzuki C, Wakabayashi K, et al. Clinical Utility of Skin Biopsy in Differentiating between Parkinson’s Disease and Multiple System Atrophy. Parkinsons Dis. 2015;2015:167038.

    PubMed  PubMed Central  Google Scholar 

  140. Donadio V, Wang Z, Incensi A, Rizzo G, Fileccia E, Vacchiano V, et al. In vivo diagnosis of synucleinopathies: a comparative study of skin biopsy and RT-QuIC. Neurology. 2021;96(20):e2513–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Riley DE, Chelimsky TC. Autonomic nervous system testing may not distinguish multiple system atrophy from Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2003;74(1):56–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Donadio V, Cortelli P, Elam M, Di Stasi V, Montagna P, Holmberg B, et al. Autonomic innervation in multiple system atrophy and pure autonomic failure. J Neurol Neurosurg Psychiatry. 2010;81(12):1327–35.

    Article  CAS  PubMed  Google Scholar 

  143. Novak P, Marya NB, Whren K, Bhawan J. Dermal sheet preparations in the evaluation of dermal innervation in Parkinson’s disease and multiple system atrophy. J Cutan Pathol. 2009;36(3):296–301.

    Article  PubMed  Google Scholar 

  144. Nolano M, Provitera V, Estraneo A, Selim MM, Caporaso G, Stancanelli A, et al. Sensory deficit in Parkinson’s disease: evidence of a cutaneous denervation. Brain. 2008;131(Pt 7):1903–11.

    Article  PubMed  Google Scholar 

  145. Duda JE, Shah U, Arnold SE, Lee VM, Trojanowski JQ. The expression of alpha-, beta-, and gamma-synucleins in olfactory mucosa from patients with and without neurodegenerative diseases. Exp Neurol. 1999;160(2):515–22.

    Article  CAS  PubMed  Google Scholar 

  146. De Luca CMG, Elia AE, Portaleone SM, Cazzaniga FA, Rossi M, Bistaffa E, et al. Efficient RT-QuIC seeding activity for α-synuclein in olfactory mucosa samples of patients with Parkinson’s disease and multiple system atrophy. Transl Neurodegener. 2019;8:24.

    Article  PubMed  PubMed Central  Google Scholar 

  147. Bargar C, De Luca CMG, Devigili G, Elia AE, Cilia R, Portaleone SM, et al. Discrimination of MSA-P and MSA-C by RT-QuIC analysis of olfactory mucosa: the first assessment of assay reproducibility between two specialized laboratories. Mol Neurodegener. 2021;16(1):82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. De Luca CMG, Consonni A, Cazzaniga FA, Bistaffa E, Bufano G, Quitarrini G, et al. The alpha-synuclein RT-QuIC products generated by the olfactory mucosa of patients with Parkinson’s disease and multiple system atrophy induce inflammatory responses in SH-SY5Y cells. Cells. 2021;11(1):87.

    Article  PubMed  PubMed Central  Google Scholar 

  149. Kim S, Jazwinski SM. The gut microbiota and healthy aging: a mini-review. Gerontology. 2018;64(6):513–20.

    Article  CAS  PubMed  Google Scholar 

  150. Fan Y, Pedersen O. Gut microbiota in human metabolic health and disease. Nat Rev Microbiol. 2021;19(1):55–71.

    Article  CAS  PubMed  Google Scholar 

  151. Nagpal J, Cryan JF. Microbiota-brain interactions: Moving toward mechanisms in model organisms. Neuron. 2021;109(24):3930–53.

    Article  CAS  PubMed  Google Scholar 

  152. Quigley EMM. Microbiota-brain-gut axis and neurodegenerative diseases. Curr Neurol Neurosci Rep. 2017;17(12):94.

    Article  PubMed  Google Scholar 

  153. Li B, He Y, Ma J, Huang P, Du J, Cao L, et al. Mild cognitive impairment has similar alterations as Alzheimer’s disease in gut microbiota. Alzheimers Dement. 2019;15(10):1357–66.

    Article  PubMed  Google Scholar 

  154. Marizzoni M, Cattaneo A, Mirabelli P, Festari C, Lopizzo N, Nicolosi V, et al. Short-chain fatty acids and lipopolysaccharide as mediators between gut dysbiosis and amyloid pathology in Alzheimer’s disease. J Alzheimers Dis. 2020;78(2):683–97.

    Article  CAS  PubMed  Google Scholar 

  155. Qian Y, Yang X, Xu S, Huang P, Li B, Du J, et al. Gut metagenomics-derived genes as potential biomarkers of Parkinson’s disease. Brain. 2020;143(8):2474–89.

    Article  PubMed  Google Scholar 

  156. Miyauchi E, Kim SW, Suda W, Kawasumi M, Onawa S, Taguchi-Atarashi N, et al. Gut microorganisms act together to exacerbate inflammation in spinal cords. Nature. 2020;585(7823):102–6.

    Article  CAS  PubMed  Google Scholar 

  157. Yan Z, Yang F, Cao J, Ding W, Yan S, Shi W, et al. Alterations of gut microbiota and metabolome with Parkinson’s disease. Microb Pathog. 2021;160:105187.

    Article  CAS  PubMed  Google Scholar 

  158. Engen PA, Dodiya HB, Naqib A, Forsyth CB, Green SJ, Voigt RM, et al. The potential role of gut-derived inflammation in multiple system atrophy. J Parkinsons Dis. 2017;7(2):331–46.

    Article  CAS  PubMed  Google Scholar 

  159. Tan AH, Chong CW, Song SL, Teh CSJ, Yap IKS, Loke MF, et al. Altered gut microbiome and metabolome in patients with multiple system atrophy. Mov Disord. 2018;33(1):174–6.

    Article  PubMed  Google Scholar 

  160. Barichella M, Severgnini M, Cilia R, Cassani E, Bolliri C, Caronni S, et al. Unraveling gut microbiota in Parkinson’s disease and atypical parkinsonism. Mov Disord. 2019;34(3):396–405.

    Article  PubMed  Google Scholar 

  161. Du J, Huang P, Qian Y, Yang X, Cui S, Lin Y, et al. Fecal and blood microbial 16s rRNA gene alterations in chinese patients with multiple system atrophy and its subtypes. J Parkinsons Dis. 2019;9(4):711–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Schrag A, Good CD, Miszkiel K, Morris HR, Mathias CJ, Lees AJ, et al. Differentiation of atypical parkinsonian syndromes with routine MRI. Neurology. 2000;54(3):697–702.

    Article  CAS  PubMed  Google Scholar 

  163. Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ, et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology. 2008;71(9):670–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Massey LA, Micallef C, Paviour DC, O’Sullivan SS, Ling H, Williams DR, et al. Conventional magnetic resonance imaging in confirmed progressive supranuclear palsy and multiple system atrophy. Mov Disord. 2012;27(14):1754–62.

    Article  PubMed  Google Scholar 

  165. Lee EA, Cho HI, Kim SS, Lee WY. Comparison of magnetic resonance imaging in subtypes of multiple system atrophy. Parkinsonism Relat Disord. 2004;10(6):363–8.

    Article  PubMed  Google Scholar 

  166. Pellecchia MT, Stankovic I, Fanciulli A, Krismer F, Meissner WG, Palma JA, et al. Can autonomic testing and imaging contribute to the early diagnosis of multiple system atrophy? A systematic review and recommendations by the movement disorder society multiple system atrophy study group. Mov Disord Clin Pract. 2020;7(7):750–62.

    Article  PubMed  PubMed Central  Google Scholar 

  167. Mahlknecht P, Hotter A, Hussl A, Esterhammer R, Schocke M, Seppi K. Significance of MRI in diagnosis and differential diagnosis of Parkinson’s disease. Neurodegener Dis. 2010;7(5):300–18.

    Article  PubMed  Google Scholar 

  168. Huppertz H-J, Möller L, Südmeyer M, Hilker R, Hattingen E, Egger K, et al. Differentiation of neurodegenerative parkinsonian syndromes by volumetric magnetic resonance imaging analysis and support vector machine classification. Mov Disord. 2016;31(10):1506–17.

    Article  PubMed  Google Scholar 

  169. Chougar L, Faouzi J, Pyatigorskaya N, Yahia-Cherif L, Gaurav R, Biondetti E, et al. Automated categorization of Parkinsonian syndromes using magnetic resonance imaging in a clinical setting. Mov Disord. 2021;36(2):460–70.

    Article  CAS  PubMed  Google Scholar 

  170. Morisi R, Manners DN, Gnecco G, Lanconelli N, Testa C, Evangelisti S, et al. Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines. Parkinsonism Relat Disord. 2018;47:64–70.

    Article  PubMed  Google Scholar 

  171. Krismer F, Seppi K, Göbel G, Steiger R, Zucal I, Boesch S, et al. Morphometric MRI profiles of multiple system atrophy variants and implications for differential diagnosis. Mov Disord. 2019;34(7):1041–8.

    Article  PubMed  PubMed Central  Google Scholar 

  172. Scherfler C, Göbel G, Müller C, Nocker M, Wenning GK, Schocke M, et al. Diagnostic potential of automated subcortical volume segmentation in atypical parkinsonism. Neurology. 2016;86(13):1242–9.

    Article  PubMed  Google Scholar 

  173. Péran P, Barbagallo G, Nemmi F, Sierra M, Galitzky M, Traon AP-L, et al. MRI supervised and unsupervised classification of Parkinson’s disease and multiple system atrophy. Mov Disord. 2018;33(4):600–8.

    Article  PubMed  Google Scholar 

  174. Archer DB, Bricker JT, Chu WT, Burciu RG, McCracken JL, Lai S, et al. Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study. Lancet Digit Health. 2019;1(5):e222–31.

    Article  PubMed  PubMed Central  Google Scholar 

  175. Beliveau V, Müller C, Steiger R, Gizewski ER, Poewe W, Seppi K, et al. Characterization and diagnostic potential of R* in early-stage progressive supranuclear palsy variants. Parkinsonism Relat Disord. 2022;101:43–8.

    Article  CAS  PubMed  Google Scholar 

  176. Lancione M, Cencini M, Costagli M, Donatelli G, Tosetti M, Giannini G, et al. Diagnostic accuracy of quantitative susceptibility mapping in multiple system atrophy: the impact of echo time and the potential of histogram analysis. Neuroimage Clin. 2022;34:102989.

    Article  PubMed  PubMed Central  Google Scholar 

  177. Lim SJ, Suh CH, Shim WH, Kim SJ. Diagnostic performance of T2* gradient echo, susceptibility-weighted imaging, and quantitative susceptibility mapping for patients with multiple system atrophy-parkinsonian type: a systematic review and meta-analysis. Eur Radiol. 2022;32(1):308–18.

    Article  PubMed  Google Scholar 

  178. Sugiyama A, Sato N, Kimura Y, Fujii H, Maikusa N, Shigemoto Y, et al. Quantifying iron deposition in the cerebellar subtype of multiple system atrophy and spinocerebellar ataxia type 6 by quantitative susceptibility mapping. J Neurol Sci. 2019;407:116525.

    Article  PubMed  Google Scholar 

  179. Zhang P, Chen J, Cai T, He C, Li Y, Li X, et al. Quantitative susceptibility mapping and blood neurofilament light chain differentiate between parkinsonian disorders. Front Aging Neurosci. 2022;14:909552.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Taniwaki T, Nakagawa M, Yamada T, Yoshida T, Ohyagi Y, Sasaki M, et al. Cerebral metabolic changes in early multiple system atrophy: a PET study. J Neurol Sci. 2002;200(1–2):79–84.

    Article  PubMed  Google Scholar 

  181. Niccolini F, Politis M. A systematic review of lessons learned from PET molecular imaging research in atypical parkinsonism. Eur J Nucl Med Mol Imaging. 2016;43(12):2244–54.

    Article  PubMed  PubMed Central  Google Scholar 

  182. Otsuka M, Ichiya Y, Kuwabara Y, Hosokawa S, Sasaki M, Yoshida T, et al. Glucose metabolism in the cortical and subcortical brain structures in multiple system atrophy and Parkinson’s disease: a positron emission tomographic study. J Neurol Sci. 1996;144(1–2):77–83.

    Article  CAS  PubMed  Google Scholar 

  183. Juh R, Kim J, Moon D, Choe B, Suh T. Different metabolic patterns analysis of Parkinsonism on the 18F-FDG PET. Eur J Radiol. 2004;51(3):223–33.

    Article  PubMed  Google Scholar 

  184. Brajkovic L, Kostic V, Sobic-Saranovic D, Stefanova E, Jecmenica-Lukic M, Jesic A, et al. The utility of FDG-PET in the differential diagnosis of Parkinsonism. Neurol Res. 2017;39(8):675–84.

    Article  CAS  PubMed  Google Scholar 

  185. Lewis SJ, Pavese N, Rivero-Bosch M, Eggert K, Oertel W, Mathias CJ, et al. Brain monoamine systems in multiple system atrophy: a positron emission tomography study. Neurobiol Dis. 2012;46(1):130–6.

    Article  CAS  PubMed  Google Scholar 

  186. Hirano S, Shinotoh H, Arai K, Aotsuka A, Yasuno F, Tanaka N, et al. PET study of brain acetylcholinesterase in cerebellar degenerative disorders. Mov Disord. 2008;23(8):1154–60.

    Article  PubMed  Google Scholar 

  187. Gilman S, Koeppe RA, Nan B, Wang CN, Wang X, Junck L, et al. Cerebral cortical and subcortical cholinergic deficits in parkinsonian syndromes. Neurology. 2010;74(18):1416–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  188. Matuskey D, Tinaz S, Wilcox KC, Naganawa M, Toyonaga T, Dias M, et al. Synaptic changes in Parkinson disease assessed with in vivo imaging. Ann Neurol. 2020;87(3):329–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Mecca AP, Chen MK, O’Dell RS, Naganawa M, Toyonaga T, Godek TA, et al. In vivo measurement of widespread synaptic loss in Alzheimer’s disease with SV2A PET. Alzheimers Dement. 2020;16(7):974–82.

    Article  PubMed  PubMed Central  Google Scholar 

  190. Andersen KB, Hansen AK, Damholdt MF, Horsager J, Skjaerbaek C, Gottrup H, et al. Reduced synaptic density in patients with lewy body dementia: an [C]UCB-J PET imaging study. Mov Disord. 2021;36(9):2057–65.

    Article  CAS  PubMed  Google Scholar 

  191. Holland N, Jones PS, Savulich G, Wiggins JK, Hong YT, Fryer TD, et al. Synaptic loss in primary tauopathies revealed by [C]UCB-J Positron emission tomography. Mov Disord. 2020;35(10):1834–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Hansson O. Biomarkers for neurodegenerative diseases. Nat Med. 2021;27(6):954–63.

    Article  CAS  PubMed  Google Scholar 

  193. Schweighauser M, Shi Y, Tarutani A, Kametani F, Murzin AG, Ghetti B, et al. Structures of α-synuclein filaments from multiple system atrophy. Nature. 2020;585(7825):464–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  194. Korat Š, Bidesi NSR, Bonanno F, Di Nanni A, Hoàng ANN, Herfert K, et al. Alpha-synuclein PET tracer development-an overview about current efforts. Pharmaceuticals. 2021;14(9):847.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. Haider A, Elghazawy NH, Dawoud A, Gebhard C, Wichmann T, Sippl W, et al. Translational molecular imaging and drug development in Parkinson’s disease. Mol Neurodegener. 2023;18(1):11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  196. Ferrie JJ, Lengyel-Zhand Z, Janssen B, Lougee MG, Giannakoulias S, Hsieh CJ, et al. Identification of a nanomolar affinity α-synuclein fibril imaging probe by ultra-high throughput in silico screening. Chem Sci. 2020;11(47):12746–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  197. Kaide S, Watanabe H, Shimizu Y, Iikuni S, Nakamoto Y, Hasegawa M, et al. Identification and evaluation of bisquinoline scaffold as a new candidate for α-Synuclein-PET imaging. ACS Chem Neurosci. 2020;11(24):4254–61.

    Article  CAS  PubMed  Google Scholar 

  198. Kuebler L, Buss S, Leonov A, Ryazanov S, Schmidt F, Maurer A, et al. [11C]MODAG-001-towards a PET tracer targeting α-synuclein aggregates. Eur J Nucl Med Mol Imaging. 2021;48(6):1759–72.

    Article  CAS  PubMed  Google Scholar 

  199. Levigoureux E, Lancelot S, Bouillot C, Chauveau F, Verdurand M, Verchere J, et al. Binding of the PET radiotracer [18F]BF227 does not reflect the presence of alpha-synuclein aggregates in transgenic mice. Curr Alzheimer Res. 2014;11(10):955–60.

    Article  CAS  PubMed  Google Scholar 

  200. Maurer A, Leonov A, Ryazanov S, Herfert K, Kuebler L, Buss S, et al. 11C radiolabeling of anle253b: a putative PET tracer for parkinson’s disease that binds to α-synuclein fibrils in vitro and crosses the blood–brain barrier. Chem Med Chem. 2020;15(5):411–5.

    Article  CAS  PubMed  Google Scholar 

  201. Zhang X, Jin H, Padakanti PK, Li J, Yang H, Fan J, et al. Radiosynthesis and in vivo evaluation of two PET radioligands for imaging α-synuclein. Appl Sci. 2014;4(1):66–78.

    Article  PubMed  Google Scholar 

  202. Fodero-Tavoletti MT, Mulligan RS, Okamura N, Furumoto S, Rowe CC, Kudo Y, et al. In vitro characterisation of BF227 binding to alpha-synuclein/Lewy bodies. Eur J Pharmacol. 2009;617(1–3):54–8.

    Article  CAS  PubMed  Google Scholar 

  203. Gaur P, Galkin M, Kurochka A, Ghosh S, Yushchenko DA, Shvadchak VV. Fluorescent probe for selective imaging of α-synuclein fibrils in living cells. ACS Chem Neurosci. 2021;12(8):1293–8.

    Article  CAS  PubMed  Google Scholar 

  204. Koga S, Ono M, Sahara N, Higuchi M, Dickson DW. Fluorescence and autoradiographic evaluation of tau PET ligand PBB3 to α-synuclein pathology. Mov Disord. 2017;32(6):884–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  205. Matsuoka K, Ono M, Takado Y, Hirata K, Endo H, Ohfusa T, et al. High-contrast imaging of α-synuclein pathologies in living patients with multiple system atrophy. Mov Disord. 2022;37(10):2159–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  206. Miranda-Azpiazu P, Svedberg M, Higuchi M, Ono M, Jia Z, Sunnemark D, et al. Identification and in vitro characterization of C05–01, a PBB3 derivative with improved affinity for alpha-synuclein. Brain Res. 2020;1749:147131.

    Article  CAS  PubMed  Google Scholar 

  207. Verdurand M, Levigoureux E, Lancelot S, Zeinyeh W, Billard T, Quadrio I, et al. Amyloid-beta radiotracer [18F]BF-227 does not bind to cytoplasmic glial inclusions of postmortem multiple system atrophy brain tissue. Contrast Media Mol Imaging. 2018;2018:9165458.

    Article  PubMed  PubMed Central  Google Scholar 

  208. Watanabe H, Ariyoshi T, Ozaki A, Ihara M, Ono M, Saji H. Synthesis and biological evaluation of novel radioiodinated benzimidazole derivatives for imaging α-synuclein aggregates. Bioorg Med Chem. 2017;25(24):6398–403.

    Article  CAS  PubMed  Google Scholar 

  209. Watanabe H, Ono M, Ariyoshi T, Katayanagi R, Saji H. Novel Benzothiazole derivatives as fluorescent probes for detection of β-amyloid and α-synuclein aggregates. ACS Chem Neurosci. 2017;8(8):1656–62.

    Article  CAS  PubMed  Google Scholar 

  210. Kikuchi A, Takeda A, Okamura N, Tashiro M, Hasegawa T, Furumoto S, et al. In vivo visualization of alpha-synuclein deposition by carbon-11-labelled 2-[2-(2-dimethylaminothiazol-5-yl)ethenyl]-6-[2-(fluoro)ethoxy]benzoxazole positron emission tomography in multiple system atrophy. Brain. 2010;133(Pt 6):1772–8.

    Article  PubMed  Google Scholar 

  211. Iranzo A, Fairfoul G, Ayudhaya ACN, Serradell M, Gelpi E, Vilaseca I, et al. Detection of α-synuclein in CSF by RT-QuIC in patients with isolated rapid-eye-movement sleep behaviour disorder: a longitudinal observational study. Lancet Neurol. 2021;20(3):203–12.

    Article  CAS  PubMed  Google Scholar 

  212. Montaner J, Ramiro L, Simats A, Tiedt S, Makris K, Jickling GC, et al. Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat Rev Neurol. 2020;16(5):247–64.

    Article  PubMed  Google Scholar 

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Acknowledgements

Not applicable.

Funding

This review was funded by the National Key R&D Program of China (2021YFA0805200 to H Jiang), the National Natural Science Foundation of China (81974176 and 82171254 to H Jiang), the Innovation Research Group Project of Natural Science Foundation of Hunan Province (2020JJ1008 to H Jiang), the Scientific Research Foundation of Health Commission of Hunan Province (B2019183 to H Jiang), the Key Research and Development Program of Hunan Province (2020SK2064 to H Jiang), the Innovative Research and Development Program of Development and Reform Commission of Hunan Province to H Jiang, the Natural Science Foundation of Hunan Province (2022JJ20094 and 2021JJ40974 to Z Chen), the Central South University Research Programme of Advanced Interdisciplinary Study (2023QYJC010 to H Jiang), the Project Program of National Clinical Research Center for Geriatric Disorders, Xiangya Hospital (2020LNJJ12 to H Jiang), and the Science and Technology Innovation Program of Hunan Province (2022RC1027 to Z Chen).

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Author notes

  1. Linlin Wan and Sudan Zhu contributed equally to this work.

Authors and Affiliations

  1. Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China

    Linlin Wan, Sudan Zhu, Zhao Chen, Beisha Tang & Hong Jiang

  2. Department of Neurology, The Third Xiangya Hospital, Central South University, Changsha, 410013, China

    Hong Jiang

  3. Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, 410008, China

    Linlin Wan, Zhao Chen, Beisha Tang & Hong Jiang

  4. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China

    Linlin Wan, Zhao Chen, Beisha Tang & Hong Jiang

  5. Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, China

    Linlin Wan

  6. Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, 410008, China

    Zhao Chen, Beisha Tang & Hong Jiang

  7. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Rong Qiu

  8. National International Collaborative Research Center for Medical Metabolomics, Central South University, Changsha, 410008, China

    Linlin Wan & Hong Jiang

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  1. Linlin Wan
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Contributions

LLW, SDZ and HJ: Design; LLW and SDZ: Writing of the first draft; ZC, RQ, BST and HJ: Editing of final version of the manuscript.

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Correspondence to Hong Jiang.

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Wan, L., Zhu, S., Chen, Z. et al. Multidimensional biomarkers for multiple system atrophy: an update and future directions. Transl Neurodegener 12, 38 (2023). https://doi.org/10.1186/s40035-023-00370-0

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Keywords

Translational Neurodegeneration

ISSN: 2047-9158

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