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. Author manuscript; available in PMC: 2020 Nov 1.
Published in final edited form as: J Neurochem. 2019 Jan 31;151(4):397–416. doi: 10.1111/jnc.14635

Mass Spectrometry: A Platform for Biomarker Discovery and Validation for Alzheimer’s and Parkinson’s Diseases

Eugene M Cilento 1, Lorrain Jin 1, Tessandra Stewart 1, Min Shi 1, Lifu Sheng, Jing Zhang 1,2,#
PMCID: PMC6535381  NIHMSID: NIHMS998870  PMID: 30474862

Abstract

Accurate, reliable, and objective biomarkers for Alzheimer’s disease (AD), Parkinson’s disease (PD), and related age-associated neurodegenerative disorders are urgently needed to assist in both diagnosis, particularly at early stages, and monitoring of disease progression. Technological advancements in protein detection platforms over the last few decades have resulted in a plethora of reported molecular biomarker candidates for both AD and PD; however, very few of these candidates are developed beyond the discovery phase of the biomarker discovery pipeline, a reflection of the current bottleneck within the field. In this review, the expanded use of selected reaction monitoring (SRM) targeted mass spectrometry will be discussed in detail as a platform for systematic verification of large panels of protein biomarker candidates prior to costly validation testing. We also advocate for the coupling of discovery-based proteomics with modern targeted-MS based approaches (e.g. SRM) within a single study in future workflows to expedite biomarker development and validation for AD and PD. It is our hope that improving the efficiency within the biomarker development process by use of an SRM pipeline may ultimately hasten the development of biomarkers that both decrease misdiagnosis of AD and PD and ultimately lead to detection at early stages of disease and objective assessment of disease progression.

Graphical Abstract

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Accurate, reliable, and objective biomarkers are urgently needed to assist in both early disease diagnosis and monitoring of disease progression for Alzheimer’s disease (AD) and Parkinson’s disease (PD). Technological developments have resulted in a plethora of biomarker candidates, yet very few become clinically useful. In this review, we highlight the application of selected reaction monitoring (SRM) targeted mass spectrometry as an effective platform for decreasing the bottleneck between biomarker discovery and clinical validation.

Introduction

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are progressive, age-related neurodegenerative disorders with substantial impacts on society (Chan et al. 2016, Coon & Edgerly 1999, Findley 2007). The two hallmark pathologies of AD are β-amyloid (Aβ) plaques and neurofibrillary tangles (NFTs) (reviewed in (Masters et al. 2015)). Aβ peptides, derived from the amyloid precursor protein (APP), are deposited in neuritic plaques (Cummings & Cole 2002) found in the brains of AD patients and appear to be central to AD pathogenesis. Tau protein is also crucially involved in AD pathogenesis: hyper phosphorylation, truncation, and oligomerization of tau are critical in the formation of NFTs (Cummings & Cole 2002, Semla 2007, Alonso et al. 2001, Alonso et al. 1996, Basurto-Islas et al. 2008, de Calignon et al. 2010). Lewy bodies, the defining pathological characteristic of Lewy body diseases including PD, contain α-synuclein (α-syn) (Kalia & Kalia 2015), particularly modified (e.g., phosphorylated) forms, the role of which in PD pathogenesis is also highlighted by the genetic link between α-syn and PD (Klein & Westenberger 2012). Additionally, they are associated with damage to multiple types of neurons, including dopaminergic neurons (DA) in the substantia nigra (SN) (Poewe et al. 2017, Braak et al. 2003, Halliday et al. 2011). Currently, there is no cure for either disease, and their diagnosis, which requires a combination of clinical assessments, neuropsychological testing, imaging, and exclusion of other neurological disorders (Dubois et al. 2007, Tolosa et al. 2006), most commonly occurs when symptoms develop during later stages of disease progression. Unfortunately, symptoms often do not present before significant, irreversible damage has already occurred in the brain (more than 50% of hippocampal neurons (West et al. 2004, Price et al. 2001) for AD and 60–80% of DA neurons within the SN for PD (Miller & O’Callaghan 2015, Cheng et al. 2010) have already been damaged or lost), resulting in poor prognosis. Additionally, current therapies are primarily focused on alleviating symptoms (cognitive for AD, motor-based for early PD, though cognitive decline is common with progression of PD), but no therapies capable of altering the course of disease exist (Olanow & Stocchi 2017) (Cummings & Cole 2002, Semla 2007). Moreover, objective analysis and monitoring of disease progression or treatment effects is complicated by the lack of suitable markers. Therefore, improvements are urgently needed for diagnosis and progression of AD and PD, particularly at the earliest stages where disease modifying drugs are likely to be more effective, such as through the development of more accurate and accessible biomarkers.

Biomarkers for AD and PD

Advantages of protein biomarkers

Biomarkers are biological characteristics that can be objectively measured to evaluate normal or pathological status of biological systems (Biomarkers Definitions Working 2001, Strimbu & Tavel 2010). Ideally, efficient biomarkers can 1) reveal characteristics of risk, presence, and state of a particular disease (Poste 2011, Puntmann 2009, Zhang et al. 2007, Rifai et al. 2006, Mayeux 2004), 2) be used clinically to predict, diagnose, and monitor disease progression, and 3) assess stage, response to treatment, or prognosis (McDermott et al. 2013). Among the best biomarkers available for neurodegenerative diseases are alterations detected by neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) (Ballard et al. 2011, Ghidoni et al. 2013, Risacher & Saykin 2013). For example, both structural and functional neuroimaging have shown promising results in identifying patients with mild cognitive impairment (MCI) who are at a high risk of developing AD (Jack et al. 1999, Wang et al. 2006, Desikan et al. 2010, Forsberg et al. 2008, Koivunen et al. 2010, Hampel et al. 2012), while PET imaging of amyloid burden has potentially improved accuracy of AD diagnosis (Beach et al. 2014, Boccardi et al. 2016). Similarly, PET imaging is quite effective in detecting nigrostriatal degeneration at pre-motor stages of PD (Tzoulis et al. 2013, Nurmi et al. 2001, Broussolle et al. 1999). Unfortunately for both AD and PD, imaging techniques are associated with relatively high costs to healthcare providers and patients, and access to these tools is generally limited to large medical centers (Blennow et al. 2010, Hampel et al. 2008, Hampel et al. 2012, Piccini & Whone 2004), making early diagnostic screening as well as routine monitoring by these tools difficult (Loane & Politis 2011, Schilling et al. 2016). Instead, biomarkers based on differential expression of DNA (Anderson & Kodukula 2014, Podlesniy et al. 2013, Ziegler et al. 2012), RNA (Jeromin & Bowser 2017, Shah et al. 2017, Sheinerman et al. 2017), or proteins (Weiner et al. 2012, Blennow et al. 2010, Hampel et al. 2010, Trojanowski et al. 2010) can be used to determine risk or diagnosis of the disease, while functionally related differences, such as post-translational modifications (PTMs) (Barrett & Timothy Greenamyre 2015), have potential as prognostic predictors of disease progression (Cooper et al. 2006, Breydo et al. 2012, Ferreon & Deniz 2007, Hartl 2017, Sweeney et al. 2017, Robinson 2008). Proteins, which are the major functional units in biological and physiological events, are highly abundant in both tissue and biological fluids (e.g. cerebral spinal fluid [CSF] and blood) and can be detected rather well using protein assays established over the last several decades (Costa et al. 2018). Further, aggregated forms of pathological proteins in both AD and PD are secreted into body fluids and thought to be indicative of pathological development (Musiek & Holtzman 2015, Jouanne et al. 2017, Giraldez-Perez et al. 2014). Thus, protein-based biomarkers have become the most practical and well-studied candidates for molecular biomarkers of AD and PD.

Current protein biomarkers for AD

Currently, amyloid beta (Aβ) and tau proteins/peptides within the CSF comprise the only clinically validated biomarkers for AD (see (Olsson et al. 2016) for a systemic review of these markers). In the CSF, increased total tau or phosphorylated tau protein along with decreased Aβ1–42 (Aβ42) has been demonstrated to provide both high sensitivity and specificity in differentiating AD from controls (Hulstaert et al. 1999, Blennow et al. 2010, Tang & Kumar 2008). Additionally, it has been shown that the combination of total tau and Aβ42 perform well in predicting the conversion of patients with prodromal AD (MCI) to full AD dementia (Hansson et al. 2006, Mattsson et al. 2009, Hertze et al. 2010, Shaw et al. 2009) and also future cognitive decline in asymptomatic older adults (Soldan et al. 2016, Fagan et al. 2007). Additionally, the Aβ42/ Aβ40 ratio seems to show a higher concordance with amyloid load in the brain as assessed by PET than Aβ42 alone (Niemantsverdriet et al. 2017) (Janelidze et al. 2016b) (Lewczuk et al. 2017) and may be of particular use when CSF results are ambiguous (Sauvee et al. 2014). Still, the efficacy of these markers in the CSF during these stages remains to be validated, and, while Aβ42 and tau have demonstrated clinical utility for AD diagnosis, limitations remain for both early detection and differential diagnosis of various dementia and monitoring AD progression (Ritchie et al. 2017).

Current biomarkers for PD

Like Aβ and tau for AD, α-syn, a critical protein involved in PD pathogenesis (Kalia & Kalia 2015), has been extensively evaluated as a likely candidate biomarker for PD given its known involvement within PD pathology, and has shown some promise (Tokuda et al. 2006, Mollenhauer et al. 2011, Parnetti et al. 2011, Wang et al. 2012, Hong et al. 2010, Parnetti et al. 2014, Hall et al. 2015, Korff et al. 2013, Stewart et al. 2015, Fairfoul et al. 2016, Shahnawaz et al. 2017, Groveman et al. 2018). Unfortunately, unlike AD pathological proteins, CSF α-syn has not yet been clinically validated as a biomarker for PD. Various studies have demonstrated that total α-syn protein appears generally lower in CSF of PD patients than compared to healthy controls (Tokuda et al. 2006, Mollenhauer et al. 2011, Parnetti et al. 2011, Wang et al. 2012, Hong et al. 2010, Parnetti et al. 2014, Hall et al. 2015). More recently, α-syn also appears clearly linked to other diseases, including AD (Mollenhauer et al. 2011, Korff et al. 2013). Modified α-syn, including phosphorylated and aggregated forms, have been reported to be more promising candidates (Stewart et al. 2015, Fairfoul et al. 2016, Shahnawaz et al. 2017, Groveman et al. 2018) though further validation is still needed. In addition to α-syn, the AD biomarker tau has become another attractive candidate biomarker for PD, as studies have implicated its association with PD risk and participation in PD pathogenesis based on genome-wide association studies (Zabetian et al. 2007, Simon-Sanchez et al. 2009, Healy et al. 2004, Zhang et al. 2005). Its levels have been reported to be decreased in the CSF of PD patients; however, conflicting studies, showing no change in tau levels, also exist (Kang et al. 2013, Mollenhauer et al. 2011, Hall et al. 2012, Shi et al. 2011, Montine et al. 2010, Abdo et al. 2007). As with tau, the AD marker Aβ42 has also been a useful tool for PD, as studies have reported an association between reduced CSF Aβ42 and cognitive decline in PD (Montine et al. 2010, Skogseth et al. 2015, Terrelonge et al. 2016, Siderowf et al. 2010, Compta et al. 2009). Lastly, DJ-1, another protein that has been associated with an early-onset recessive form of PD (Bonifati et al. 2003), has also generated interest as a candidate marker. However, researchers have struggled to demonstrate consistency between studies (Hong et al. 2010, Waragai et al. 2006).

Additional limitations related to current AD and PD biomarkers

Another limitation related to CSF biomarkers, whether for AD or PD, is the fact that CSF is not suitable for routine clinical practice, as collection requires a lumbar puncture procedure which is often perceived as invasive and painful to patients. As such, patients with minimal or no discernible clinical symptoms are often unwilling to undergo CSF collection, which severely limits access to the prodromal or preclinical population. Thus, both fields would benefit from a collective transition to testing within more clinically-practical biological sources such as blood, which is routinely collected in clinical settings. However, robust diagnostic or predictive blood markers for AD remain to be defined (Hampel et al. 2012, Song et al. 2009, Blennow & Zetterberg 2015, Henriksen et al. 2014), with conflicting findings of minimally decreased or unchanged levels of Aβ42 in AD vs controls (Hampel et al. 2012, Song et al. 2009, Blennow & Zetterberg 2015, Henriksen et al. 2014, Janelidze et al. 2016a) and difficult detection of tau in MCI and AD (Henriksen et al. 2014) without expensive detection platforms (Shi et al. 2016b, Zetterberg et al. 2013). One recent development, reported by two independent studies, is the significantly different Aβ42/ Aβ40 ratio among amyloid positive vs negative patients which may provide improve diagnosis for patients with ambiguous CSF Aβ42 or tau results, which has sparked some hope back into plasma-derived biomarked development for AD (Ovod et al. 2017, Nakamura et al. 2018). Reports on α-syn and other candidates for PD markers in blood are even less consistent and promising (Shi et al. 2016a, Pan et al. 2014, Maita et al. 2008, Waragai et al. 2007, An et al. 2018, Shi et al. 2010, Duran et al. 2010)(El-Agnaf et al. 2006, Lee et al. 2006, Li et al. 2007, Maita et al. 2008, Waragai et al. 2007).

Recent studies by our lab and other groups have suggested blood based but central nervous system (CNS)-specific extracellular vesicles (EVs) might be another alternative biomarker source for neurodegenerative diseases (Howitt & Hill 2016, Malm et al. 2016, Goetzl et al. 2018, Zheng et al. 2017). EVs, which include exosomes and other small membrane-contained vesicles (Lee et al. 2011, Chivet et al. 2012, Rufino-Ramos et al. 2017), have been implicated as important vessels for transportation of cargo proteins between the CNS and periphery (reviewed recently in (Matsumoto et al. 2017a)), and recent work suggests this can also include key proteins involved with AD and PD such as tau (Shi et al. 2016a), Aβ42(Takahashi et al. 2002), and α-syn (Budnik et al. 2016, Coleman & Hill 2015, Thompson et al. 2016, Matsumoto et al. 2017b). Further, we have recently used immunoassay detection platforms to report that CNS-derived EVs within plasma contain altered levels of α-syn and tau in PD 95 (Shi et al. 2014). These studies hint at the potential utility of plasma EVs as a future biomarker resource that maintains the clinical ease of blood collection but avoids many of the complications involved with blood alone. However, isolation of CNS-derived EVs from plasma is a time-consuming and challenging process, and optimization is still needed in both EV isolation methods and assay development to assess the feasibility as a biomarker resource alternative.

Alternative platforms for protein biomarkers

Measurements of nearly all of the existing AD and PD molecular biomarker candidates identified in CSF and blood are carried out based on known pathogenically important proteins with antibody-based assays, which are often associated with relatively high variability, particularly when different detection techniques (different antibodies, sample preparation, calibrators, etc.) are used, leading to discrepant results across laboratories (Zetterberg 2015, Olsson et al. 2016, Chaloupka et al. 1996). Additionally, inter-laboratory standardization of immunoassay measurements of CSF tau and Aβ42 has proven difficult (Mattsson et al. 2011, Mattsson et al. 2013). Similar difficulties with immunoassay variability have been encountered in PD and related disorders (Kruse et al. 2015, Schapira 2013). Furthermore, development of quantitative immunoassays for most novel candidates identified by various exploratory approaches to look for protein candidates beyond known Aβ, tau and α-syn is limited by the lack of available (and specific) antibodies, which are further challenged by interference with antibody binding within complex solutions (e.g. biofluids such as CSF and blood) due to matrix effects. Additional obstacles are that de novo assay development is time consuming, prohibitively expensive for screening multiple markers, and very difficult to multiplex (Hoofnagle & Wener 2009). Thus, although a large library of potential protein biomarkers has been identified through proteomic studies (see below), the vast majority of candidates never reach the stage of validation (biomarker pipeline illustrated in Figure 1) and clinical testing (referred to as the “bottleneck” in biomarker development (Witkowska et al. 2012, Makawita & Diamandis 2010, Paulovich et al. 2008)). One approach to minimize the biomarker bottleneck could be use of alternate platforms, not limited by these challenges. As an antibody-free platform with high sensitivity, accuracy, and excellent multiplex capability, targeted mass spectrometry (MS) techniques such as selected reaction monitoring (SRM) may be an effective alternative for candidate validation. Or, alternatively as highlighted in this review, SRM can also work to reduce the bottle neck in biomarker development by providing a platform for efficient, and cost effective verification of large sets of candidates simultaneously.

Figure 1: Pipeline for biomarker development.

Figure 1:

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A pipeline for proteomics-based biomarker development typically proceeds through three preclinical stages: discovery, verification, and validation (Frantzi et al. 2014, Paulovich et al. 2008, Parker & Borchers 2014, Surinova et al. 2011, Rifai et al. 2006, Chan et al. 2016) with an inverse relationship between the numbers of subject samples and candidate markers investigated. Identification in the discovery phase is often determined by fold differences (i.e. up or down fold changes) in relative expression between disease and control samples within a few, often pooled samples. Then, candidates can enter the verification stage, where they can be further evaluated in a small set (10–50) of patient samples and critically analyzed for reproducibility and assay development. Once verified, a smaller panel of candidates (typically ≤ 10) can enter the validation stage, where they are validated within a much larger set (100–500) of samples (Parker & Borchers 2014) to assess the sensitivity and specificity as well as utility within a larger cohort. This is the final preclinical stage, and chosen candidates can be further evaluated for useful clinical application.

Mass Spectrometry

MS application to proteomics

In the last few decades, development and application of MS techniques have made essential contributions to proteomic studies involving protein identification (Altelaar et al. 2013), structure characterization (Konermann et al. 2011), as well as quantitation (Mallick et al. 2007, Peng et al. 2012, Angel et al. 2012). Until recently, MS has mostly been incorporated into large scale biomarker studies primarily through bottom-up, “shotgun” proteomic approaches. In these studies, proteins obtained from a sample are proteolytically cleaved into small peptides (classically by trypsin), which are fragmented within the MS to develop mass spectra that are then matched against a database, to ultimately deduce their protein of origin (Figure 2, see (Tsiatsiani & Heck 2015) for a descriptive review on MS technology). Peptide fragmentation patterns generated from tandem MS (often referred to as MS/MS, but will simply be referred to as MS in this review) add greatly to the reliability of protein/peptide identification and structure characterization. Coupling the MS system with liquid chromatography (LC) allows proteomic analysis of complex mixtures, as the linked LC system provides separation of proteins (and therefore improved resolution of MS) with significantly decreased sample purification and preparation time. MS-based proteomic analysis can also be quantitative, a fundamental necessity for identifying differences between biological samples in disease state characterization studies. Applications and limitations of the widely used quantitation strategies in LC-MS analysis have been reviewed in detail previously (see (Demmers 2012)). Briefly, peptides and proteins can be quantified by coupling isotope labels to amino acids by metabolic (Langen et al. 2000, Conrads et al. 2001, Ong et al. 2002, Beynon & Pratt 2005, Oda et al. 1999, Chahrour et al. 2015), chemical (Jin et al. 2016, Ross et al. 2004, Thompson et al. 2003), or enzymatic labeling (Heller et al. 2003, Yao et al. 2003, Mirgorodskaya et al. 2000, Bantscheff et al. 2004) strategies as well as more cost-effective label-free strategies (Neilson et al. 2011) such as ion intensity (Bondarenko et al. 2002, Ono et al. 2006) or spectrum counting analysis (Gilchrist et al. 2006, Liu et al. 2004, Otsuki et al. 2001) (See Figure 3A for quantitative MS approaches directly applicable to biomarker discovery workflows). Collectively, modern MS quantitation of proteins/peptides has been achieved in many proteomic studies, including neurodegenerative disorder related studies (Kitsou et al. 2008, Jin et al. 2006), which provided comparable, yet improved conclusions to those drawn from similar studies using conventional techniques (e.g., two-dimensional gel electrophoresis (2-DE)) (Basso et al. 2004), while also achieving greater depth of informative proteome analysis (Han et al. 2008).

Figure 2: Biomarker identification by top-down “shotgun” proteomics.

Figure 2:

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A: Typical process for extracting peptides from proteins within complex biological mixtures. After extracting proteins from biological sources, they are often separated (e.g. electrophoresis or column fractionation methods) before cleavage of proteins into peptide components by enzymatic digestion (most commonly by trypsin). B: Peptide identification by LC-MS. Following digestion, peptides are then further separated by liquid chromatography, ionized, and detected by the MS. Peptides are then further selected for fragmentation within the MS, and the individual components result in a fragmentation spectra identified by the detector (MS2). C: Process of protein identification. Fragmentation spectra can then be searched against spectral libraries of known peptide spectra to determine their peptide identity. Further bioinformatics can be applied to assess the validity of the peptides and resulting proteins identified by the study.

Figure 3: Select methods to quantify relative expression of protein biomarker candidates.

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A: Isobaric chemical labeling for exploratory profiling. Isobaric labeling allows improved detection of peptides with fold difference in expression between a disease and control in complex biological mixtures for exploratory profiling in the discovery phase. Samples from each condition, which are digested and labeled separately prior to mixing and LC-MS analysis, are identified by MS2 fragmentation of isobaric labels, and the relative, quantitative difference can be used to determine differentially expressed peptide targets. B: Label-free methods for targeted approaches. SRM and PRM allow peptide fragmentation for improved relative quantitation of targeted species in complex biological mixtures for targeted-based biomarker verification. Samples containing digested peptides are analyzed separately by triple quadrapole LC-MS separately, where precursor ions are selected in the first quadrapole (Q1), fragmented within the second quadrapole (Q2), and analyzed within the third (Q3). For PRM, all transitions are measured within a HRAM Q-Orbitrap, while for SRM, specific combinations of the precursor ion with a product ion fragment (“transitions”) are selected for detection within the third quadrapole.

Advantages of MS as a platform for biomarker workflows

In addition to the high sensitivity and minimal analytical variation, protein identification by MS provides several technical advantages for protein biomarker workflows. First, it allows high-throughput generation of in-depth information on protein/peptide detection, even within complex mixtures like biological fluids. Second, peptides detected can include modified forms such as truncations and PTMs, which are thought to be important for AD, PD, and related diseases(Oueslati et al. 2010, Schmid et al. 2013, Barthelemy et al. 2016, Inoue et al. 2014, Vosseller 2007), and often require specialized and separate detection methods when using other platforms. Third, peptide detection is not reliant on affinity binding or antibody recognition, and therefore is both free of many analytical limitations by immunoaffinity based assays (e.g. ELISA), and can be additionally used in an unbiased approach for screening. Finally, further advantages of MS include its ability to distinguish between very closely related protein species, its ability to allow precise monitoring of biological and analytical variability due to sample handling, and its high multiplexing capacity (Hale 2013). Altogether, MS provides a powerful and highly sensitive peptide detection platform that is free of many analytical variations associated with other common antibody-based platforms.

Targeted MS technologies

Proteomic profiling techniques, while excellent for identifying potential biomarker candidates, are subject to their own difficulties in biomarker development (Frantzi et al. 2014), specifically that the widely used “shotgun” profiling techniques result in large pools of candidates including false discoveries (which may include false positive MS identifications and/or differences only detected in test cohorts) (Levitsky et al. 2017, Gupta & Pevzner 2009) so that each candidate requires further validation using independent methods. To meet this demand, modern MS techniques that allow selection and precise quantification of unique peptides, e.g., accurate inclusion mass screening (AIMS) (Jaffe et al. 2008) and selected reaction monitoring (SRM) (Whiteaker et al. 2011, Addona et al. 2009, Selevsek et al. 2011), have been developed. These targeted proteomic technologies have been proposed as the basis of a viable biomarker pipeline (Whiteaker et al. 2011) (Figure 1) and have become powerful tools in biomarker discovery due to their high sensitivity, accuracy and specificity. SRM, in particular, has emerged as an alternative to immunoaffinity-based measurements of defined protein sets with excellent reproducibility across different laboratories and instrument platforms (Addona et al. 2009, Selevsek et al. 2011).

SRM and PRM targeted MS approaches

SRM and parallel reaction monitoring (PRM) are two targeted MS approaches with powerful applications for biomarker detection and quantification (Figure 3B). In SRM analysis, precursor ions of different charge states are chosen as representative of their respective peptides for further investigation. Targeted precursor ions are initially filtered by the first quadrupole and then fragmented in the collision cell (second quadrupole). Then, in the third quadrupole, only specific product ions are selected for detection. Specific combinations of a precursor ion with a product ion are referred to as “SRM transitions”, which are transformed into a detectable signal for targeted peptide identification and quantification. Because fragmentation occurs within milliseconds, the collection window of a transition can be manipulated for each target (often referred to as “scheduled-SRM”) to increase the total number of targets added within a single run. Then, through careful extrapolation of the parent ions, the scheduled SRM approach can be applied for the identification and quantitation of hundreds of peptides in a single MS run (Kiyonami et al. 2011) (see (Calvo et al. 2011, Kuzyk et al. 2013, Boja & Rodriguez 2012, Gallien et al. 2011, Picotti & Aebersold 2012, Meng & Veenstra 2011, Vidova & Spacil 2017) for reviews of SRM technology and assay designs).

The PRM method performs comparably to SRM, but requires much less assay development (Ronsein et al. 2015). Using a high resolution accurate mass (HRAM) Q-Orbitrap MS, PRM filters and fragments precursor ions similarly to SRM but uses an Orbitrap mass analyzer to detect all resulting product ions (Wildsmith et al. 2014, Gallien et al. 2012, Bourmaud et al. 2016). As PRM has the advantages of acquiring full MS/MS spectrum of all fragment ions generated from the targeted peptides/proteins with high resolution and high mass accuracy, this approach has recently emerged as an alternative method of targeted quantitation (Rauniyar 2015, Peterson et al. 2012). This is especially useful for confirming the identity of the targeted peptides and characterizing associated PTMs. However, despite the improved resolution, PRM has a longer scan time for each targeted precursor compared to SRM, and therefore it is less efficient for targeting large sets of peptides simultaneously. Thus, to date, SRM remains the most common targeted MS approach for biomarker studies, and likely the most practical for large-scale candidate investigations.

To aid the targeted-MS assay development process, SRM is commonly supplemented with label-based quantitative approaches. This method provides the advantage of adding “heavy” isotope labeled internal standards (e.g. total 13C/15N labeled protein or specific peptides), which are chemically equivalent to the “light” endogenous counterparts and provide identical yield post preparation procedure, allowing easier identification of target peptides in complex mixtures (including those of low abundance or otherwise technically difficult to detect). These standards also function to further minimize analytical variation, as they maintain similar ionization and fragmentation behavior in the mass spectrometer, altogether minimizing impact on quantification errors due to variations in sample preparation and LC performance (Pannee et al. 2013). Finally, “spike-in” of heavy labeled standards also provides the ability to quantify (relative or absolute) endogenous peptides based on the light/heavy ratio within samples and directly compare relative expression across samples. All of these applications provide strong advantages for biomarker studies which utilize the SRM platform.

Application of SRM in biomarker studies

Altogether, targeted MS approaches like SRM convey the principle advantages of carrying out identification and quantitation of hundreds of protein targets, possess the capability of substantial multiplexing in a single run to increase the throughput, and, most importantly, maintain the ability to provide structural specificity, (Bereman et al. 2012) characteristics that make it valuable for biomedical research. Moreover, SRM allows measurement of relative and absolute peptide levels through detection of well-defined targeted peptides with high selectivity and sensitivity. Additionally, compared to routine immunoassays, SRM assays are highly flexible, more quickly developed, cost effective, and capable of distinguishing highly similar proteoforms such as PTMs (Vidova & Spacil 2017). And, like immunoassays, standard reference approaches have recently been developed and recognized for selective, accurate, and reproducible measurements of Aβ42 in CSF (Leinenbach et al. 2014, Korecka et al. 2014), comparable to the development and performance of AlzBio3 kit for immunoassay measurements (with potentially improved variability (Mattsson et al. 2009, Kuhlmann et al. 2017). Together, these advantages suggest SRM may be a suitable and cost effective platform for large scale candidate testing within the verification stage of biomarker development, as some studies have already demonstrated.

Application of SRM in AD biomarker studies

Application of SRM to verify previously reported candidate biomarkers is relatively new, but some studies have begun to utilize this technology in AD (Table 1). Commonly, workflows begin by identifying a list of potential targets based on previous reports demonstrating differential expression between disease and control, followed by assay development utilizing heavy isotope labeled standards to generate a panel of peptide targets for SRM targeting (see (Spellman et al. 2015) for an extensive collaborative development of multiplexed SRM peptide assays prior to follow up studies using heavy-labeled peptide standards). For example, Chen et al. applied SRM quantification along with stable isotope-labeled heavy protein internal standards to AD and control brain tissue samples and determined differences in concentrations of previously reported AD-relevant proteins, both between groups (Chen et al. 2013, Chen et al. 2012) and between sub-regions of the brain (Chen et al. 2012). Another group also looked across different brain regions in AD vs control tissue in an attempt to validate a panel of previously reported synaptic proteins, and reported differences in relative abundances between hippocampus and motor cortex for both AD and control (Chang et al. 2014). Similar to the tissue studies, other groups have begun using SRM aided by heavy labeled standards to verify candidate AD biomarkers within biofluids such as CSF. For example, Paterson et al. used SRM with heavy labeled peptides to assess a panel of 54 CSF biomarkers in two independent cohorts (which they grouped into ‘AD’ and ‘non-AD’ based on immunoassay measurement of total tau and Aβ42), and determined five proteins to be significantly elevated in AD in both cohorts (Paterson et al. 2016). A similar approach was taken in another recent study by Begcevic et al. attempting to verify a panel of 30 peptides in CSF of MCI, and AD patients further separated by AD dementia severity based on Mini-Mental State Examination (MMSE) and clinical dementia rating (CDR) scores, which reported several candidates that may correlate with AD severity, and possibly also disease progression (Begcevic et al. 2018). Another study by Wildsmith et al. quantified a panel of 39 peptides (from 30 proteins) within the CSF of control, MCI, and AD patients, and determined four targets were significantly different between AD and control, while four markers also demonstrated significant longitudinal change within AD (Wildsmith et al. 2014). Together, these studies provide examples of the practical application of SRM to verify panels of AD biomarker candidates. Each study began with a larger collection of previously reported candidates and utilized SRM to refine the candidate panels and highlight those which demonstrated practical biomarker applications.

Table 1:

Biomarker studies using SRM on human AD clinical samples.

Source MS Method Purpose of MS in study Finding by MS Cohort Reference
Brain tissue: Frontal and temporal cortex SRM + heavy labeled quantification concatemer (QconCAT) Quantification of clusterin in control vs severe AD. Level of clusterin was significantly increased in AD vs control in frontal but not temporal cortex. Frontal: 6 AD, 6 control
Temporal: 6 AD, 6 control		 Chen et al. 2012
Brain tissue: Frontal cortex SRM+ heavy labeled quantification concatemer (QconCAT) Quantification of total APP, APP695, and Aβ in control vs severe AD. Level of Aβ was significantly increased in the AD vs control. Level of total APP was similar in AD vs control while APP695 was significantly lower AD vs control. 10 AD, 7 control Chen et al. 2013
Brain tissue: Temporal cortex SRM + internal heavy labeled peptide standards Demonstrate the utility of SRM compared to other platforms, while also demonstrate histone acetylation changes within AD. Histone acetylation significantly decreased in AD temporal lobe vs control. 10 AD, 3 control Zhang et al. 2012
Brain tissue: Hippocampus and motor cortex SRM + enolase as internal standard Quantitative analysis of synaptic proteins in control vs AD. Relative abundance of different subtypes of synaptic proteins in hippocampus vs motor cortex for both AD and control. 6 AD, 6 control Chang et al 2014
CSF SPE + SRM + heavy isotope labeled peptides Absolute quantification of Aβ38, Aβ40, and Aβ42 in control vs AD. Ratio of Aβ42/Aβ40 increased the separation performance between AD vs control as compared to Aβ42 alone. Two sets of 15 AD vs 15 control Pannee et al. 2013
CSF SRM + heavy stable isotope labeled peptides Identification of longitudinally dynamic biomarkers in AD. 4 CSF proteins were significantly different in AD vs control, while 4 demonstrated significant longitudinal changes in AD. 45 AD, 5 MCI, 10 control Wildsmith et al. 2014
CSF dMS + SRM Discovery of candidate markers for AD progression. Neurosecretory protein VGF and neuronal pentraxin receptor 1 were both significantly lower in AD vs controls. Discovery cohort: 10 AD, 10 control
Longitudinal cohort: 30 AD, 30 control		 Hendrickson et al. 2015
CSF SRM + heavy isotope labeled peptides Validation of candidate biomarkers for AD progression in MCI vs AD (mild, moderate, and severe). Neuronal pentraxin receptor 1 was significantly lower in AD vs MCI, as well as progressively lower in severe AD. Set 1: 8 MCI, 11 Mild AD, 24 Moderate AD, 15 Severe AD
Set 2: 6 MCI, 8 Mild AD, 16 Moderate AD, 13 Severe AD		 Begcevic et al. 2018
CSF SRM + high pH fractionation Parallel Tau and Aβ42 detection by SRM. Significant negative correlation between ratio of tau and Aβ peptides with disease severity. 10 AD, 10 control, 10 MCI Pottiez et al. 2017
CSF SRM + heavy isotope labelled peptide or yeast enolase peptide Validation of candidate AD biomarkers. 9 markers significantly elevated in AD patients vs control. Cohort 1: 35 AD, 31 control.
Cohort 2: 46 AD, 36 control		 Paterson et al. 2016
CSF SRM Validation of a large peptide panel to discriminate AD-disease states. 320 of 567 peptides in the MRM panel were detectable in > 10% of ADNI cohort used. 66AD, 134 MCI, 85 control Spellman et al. 2015
Blood serum SRM Validation of serum ApoE protein levels in relation to AD. ApoE level was lower in AD vs control. 45 AD, 43 control Han et al. 2014
Tear fluid LC-MS/MS profiling + SRM + heavy isotope labeled peptides Discovery and verification of AD biomarkers within tear fluids. 6 markers significantly altered in AD vs control, of which a panel of four targets was reported with greatest potential diagnostic utility. 14 AD, 9 control Kalló et al. 2016
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*

Aβ = amyloid beta, AD = Alzheimer’s disease, ApoE = Apolipoprotein E, APP = amyloid precursor protein, CSF = cerebral spinal fluid, dMS = differential MS, LC-MS/MS = liquid chromatography tandem mass spectrometry, MCI = mild cognitive impairment, SPE = solid phase extraction, SRM = selected reaction monitoring.

In addition to marker verification, some studies have also used SRM coupled with heavy labeled standards to examine current AD biomarker peptides closely and determine their absolute quantification within groups. Pannee et al. determined the absolute quantification of Aβ peptides (Aβ38, Aβ40, Aβ42) in the CSF of AD and control patients by coupling solid-phase peptide extraction to SRM with heavy labeled peptide standards, and reported similar disease-association efficacy of SRM identified Aβ42 to that of traditional ELISA assays (which was further enhanced when taking the Aβ42/ Aβ40 ratio) (Pannee et al. 2013). A study by Han et al. validated serum ApoE protein levels in relation to AD using SRM MS-based analysis from the blood serum of control and AD patients, and further compared these results to a parallel measure using fluorescence-activated cell sorting (FACS) analysis, and reported comparably decreased levels in AD vs control (Han et al. 2014). Finally, we applied a high pH fractionation peptide separation method (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing, a.k.a. PRISM) (Shi et al. 2012) to an SRM with heavy peptide standard workflow to determine the absolute CSF concentrations of tau and Aβ42 in parallel within control, MCI, and AD groups, and demonstrated their efficacy to separate groups. We found equivalent diagnostic utility to immunoassay measurements within the same sample set, with particular strength in the ratio of Aβ42 to global Aβ expression (Pottiez et al. 2017). Together, these studies illustrate the capacity of SRM to apply both relative and absolute quantification of peptide targets for verification of AD candidate biomarkers.

Applications of SRM for PD biomarker verification

To a lesser extent than AD, some studies have also attempted to verify biomarker candidates for PD (Table 2), using workflows similar to those described above. Building off previous proteomic profiling, bioinformatics analysis, and SRM assay optimization, we developed a refined panel of protein peptide targets differentially expressed between disease and control which were further verified by SRM to provide useful marker panels for PD diagnosis (5 peptide panel) and disease severity correlation (2 peptide panel)(Shi et al. 2015), however larger cohort and longitudinal validation studies are needed. In a separate study, we also developed a list of potential glycopeptide candidate targets based on previous reports of differential expression within brain tissue and CSF, and developed assays for SRM targeting within plasma samples of PD and control (Pan et al. 2014, Hwang et al. 2010). Along with demonstrating the practicality of SRM for detecting low abundance biomarkers in the periphery with the aid of heavy labeled standard peptides, differential comparison of PD and healthy controls, along with Unified Parkinson’s Disease Rating Scale (UPDRS) scores, revealed potential verification of glycosylation PTMs as markers of PD progression, although further studies in larger, independent cohorts are needed for more complete interpretation (independent cohort validation of both studies is currently ongoing in our lab) (Pan et al. 2014). In another study, after previously reporting a panel of 13 proteins extracted from T-lymphocytes and identified by 2-DE as a discriminant signature for PD (Alberio et al. 2012), control, or atypical parkinsonism, Alberio et al. also used SRM to verify this molecular signature in an independent cohort and platform. Using 9 selected peptides (from 7 proteins), 8 displayed a fold change in agreement with 2-DE. Additionally, using a discriminant classification system to adequately compare the two methods (with respect to modified and total protein detection), 16 of the 18 patients were properly classified (Alberio et al. 2014). Although the sample size was low for verification as we and others have described (Rifai et al. 2006, Surinova et al. 2011, Parker & Borchers 2014), this study particularly highlights the additional practicality of utilizing SRM to verify biomarker candidates discovered by multiple platforms.

Table 2:

Biomarker studies using SRM on human PD clinical samples.

Source MS Method Purpose of MS in study Finding by MS Cohort Reference
CSF 2D-LC-MS/MS + iTRAQ + SRM 2D-LC-MS/MS + iTRAQ: Identify biomarker candidates differentially regulated between PD, PDD, and control.
SRM: Verify identified candidates.		 16 differentially regulated proteins between PD and PDD by ITRAQ, only 2 PD vs control or PDD vs control could be verified by SRM. 12 PD, 12 PDD, and 12 control (pooled) Lehnert et al. 2012
Plasma SRM + heavy isotope labeled peptides
 Discover and verify panel of glyocopeptides which may correlate with PD diagnosis or severity. 3 glycopeptides differentially expressed between diagnostic groups; panel of four correlated with diagnosis, panel of 2 with PD severity. Initial validation: 15 PD, 15 AD, 15 control (pooled from 75 PD, 15 AD, 30 control)
Final validation: 98 PD, 15 AD, 49 control		 Pan et al. 2014
Blood SRM + single (pre-digested) standard spike in Verify PD-protein signature in T-lymphocytes identified by 2-DE with SRM in an independent cohort. 8 of 9 selected peptides displayed fold change concordant with 2-DE discovery study. 9 PD vs 9 control Alberio et al. 2014
CSF 2D-LC-MS/MS + SRM + heavy peptide spike in Identify and verify peptides which could correlate with PD diagnosis or severity. ~1400 peptides differentially expressed in PD vs normal in training set,
Five peptide panel able to differentiate PD vs control or AD, two which correlated with PD severity.		 Training: 30 PD vs 30 control (pooled)
Validation: 40 PD vs 38 AD vs 40 control		 Shi et al. 2015
CSF SRM + fractionation + heavy protein spike in of α-syn Measure absolute α-syn and validate immunoassay reported differences among PD vs related diseases. αSyn, βSyn and γSyn concentrations were increased in AD and Creutzfeldt-Jakob disease but not altered in PD, PDD, LBD and atypical parkinsonian syndromes. 37 Control, 23 PD, 17 PDD, 10 LBD, 20 PSP, 10 CBS, 19 AD, 10 CJD Oeckl et al. 2016
CSF SRM + heavy protein spike in of α-syn Measure absolute α-syn in CSF and identify novel peptide markers. Six peptides of α-syn identified, one novel peptide correlated with PD severity, and changes significantly tracked progression longitudinally. Cross sectional: 15 PD vs 15 control
Longitudinal: 15 PD for 2 points		 Yang et al. 2017
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2-DE = two-dimensional gel electrophoresis α-syn = alpha-synuclein, AD: Alzheimer´s disease, CBS: corticobasal syndrome, CJD: Creutzfeldt-Jakob disease, CSF = cerebral spinal fluid, LBD: Lewy body dementia, LC-MS/MS = liquid chromatography tandem mass spectrometry, PD: Parkinson´s disease, PDD: Parkinson’s disease with dementia, PSP: progressive supranuclear palsy, SRM = selected reaction monitoring.

As with Aβ42 and tau for AD studies, SRM has also been utilized to closely monitor the absolute quantification of α-syn in relation to PD studies. Using a peptide fractionation method followed by SRM targeting aided by heavy standards, Oeckl. et al. measured α-syn primarily in its unmodified form (spanning roughly 70% of the protein) within various neurodegenerative disease groups, and found no alterations in α-syn expression within PD or PD related diseases compared to control, a result that contradicts most immunoassay studies, possibly resulting from the lack of C-terminal and/ or modified peptides included in the study (Oeckl et al. 2016). We similarly reported an SRM assay for measuring absolute α-syn quantification without fractionation (Yang et al. 2017), and found one peptide was particularly strong in differentiating PD from control with similar power to immunoassay detection (Hong et al. 2010, Wang et al. 2012). Like the Aβ peptide study by Panne et al. and our study with parallel Aβ and tau detection, these studies demonstrate the potential utility of absolute quantification by SRM to closely monitor biomarker peptides with both well-established roles in pathological development as well as prior studies using traditional detection platforms for comparative assessment. Further studies are still needed to confirm the validity of these peptide targets as accurate reporters of total protein presence. Additionally, more studies measuring these targets by both traditional and SRM platforms in parallel are needed to accurately assess the advantages of utilizing this approach moving forward. Further, the potential to develop assays that specifically target proteoforms relevant for protein aggregation and or pathological development, such as aggregated or modified α-syn (e.g. phosphorylated serine 129), is a strong advantage for SRM over immunoassay development, perhaps providing a platform for parallel detection of multiple unmodified, modified, and aggregated targets of a marker without the limitations associated with antibody capture.

Bridging biomarker discovery and verification with SRM

Another advantage of MS approaches in the biomarker pipeline is that platforms for discovery and verification can be coupled together within a single study (see Figure 4 for an illustrated workflow). Indeed, some studies have worked to combine the strengths of deep exploratory profiling, quantitative labeling, and targeted-MS approaches to both discover and further verify biomarkers for AD and PD. For example, after applying iTRAQ isobaric labeling to CSF samples from healthy controls and PD patients with or without dementia, Lehnert et al. first used shotgun proteomics in a discovery phase to determine differentially expressed peptides for candidate targets, then applied targeted SRM proteomic analysis in a separate verification phase within a single study. After identifying a panel of differentially expressed proteins between groups in the discovery study, the panel was fine-tuned to only a few candidates following SRM verification (Lehnert et al. 2012). As mentioned above, a study in our lab presented a staged pipeline for biomarker development taking a similar approach, coupling unbiased data-dependent acquisition (DDA) analysis of MS with the high sensitivity, accuracy and specificity of SRM analysis to identify and subsequently verify biomarkers from CSF samples of PD patients compared to healthy controls (Shi et al. 2015). Similarly to Lehnert et al., we discovered a list of candidates that were substantially altered in PD subjects, which was further fine-tuned following SRM verification within the single study. In a slightly different approach, Hendrickson et al applied a high resolution open discovery platform (coined ‘differential MS’), which, rather than isobaric labeling, takes into account the variability of measured intensities in both high and low abundance ions (Wiener et al. 2004) and allows relative quantification without an internal standard, first within a small discovery cohort of AD and control to identify differentially expressed candidate markers. Again, in the same study, two markers were further selected for a SRM targeting in a larger, longitudinal cohort, and the authors reported an estimated annual rate of differential expression per year in AD (Hendrickson et al. 2015). Altogether, these studies demonstrate the practical application of coupled discovery and verification through MS platforms, and provide a path for future biomarker studies moving forward. Although advancements in discovery platforms (including MS) has greatly enhanced sensitivity of candidate identification, these and other studies highlight the critical need for systematic workflows that subsequently challenge and refine such candidates prior to moving onto costly stages of validation testing. Targeted MS approaches like SRM provide such a platform, which can be efficiently utilized to verify previously reported panels of targets, or ideally, coupled within a discovery study to refine the final panel reported.

Figure 4: Proposed workflow of coupled unbiased discovery and targeted verification of biomarkers by MS within a single study.

Figure 4:

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In the first discovery phase (Phase 1), a small set of samples are isobarically labeled and processed for deep exploratory profiling to quantitatively identify peptides which display increased or decreased fold differences in a disease vs control setting. Then, in a follow-up verification phase (Phase 2) in a larger cohort, a panel of candidates from the discovery phase is rigorously tested for verification by a targeted SRM approach supplemented by “spike-in” of heavy standard peptides for both detection aid and relative quantification.

Future perspective

Utilizing SRM to improve AD and PD biomarker specificity

Previous efforts have identified panels of promising markers for improved diagnostic separation of AD or PD from their respective controls. As diagnosis is currently heavily dependent on symptomatic presentation, confirmation of diagnosis at autopsy demonstrates a high rate of misdiagnosis based on clinical symptoms 8,172–178, particularly at early disease stages (Tolosa et al. 2006, Dubois et al. 2016). Other forms of dementia such as frontotemporal dementia (FTD) and those associated with PD and cerebrovascular disease, are often confused with AD, and sufficient evidence to confirm the diagnosis is lacking, as 12–23% of patients classified as AD during life are not confirmed at autopsy (Gaugler et al. 2013, Klatka et al. 1996, Pearl 1997, Ranginwala et al. 2008, Lim et al. 1999). Similarly, other parkinsonian diseases and motor disorders are often falsely diagnosed as PD, with up to 25% of PD diagnosed patients alternatively diagnosed post-mortem (Tolosa et al. 2006, Hughes et al. 1992, Marsili et al. 2018). Therefore, in addition to developing improved markers for early detection and tracking of progression, improvements are also needed to increase their specificity to decrease the chance of misdiagnosis (Dubois et al. 2016). The utility of currently available molecular biomarkers (e.g., Aβ42 and tau for AD and α-syn for PD) in such differential diagnosis has shown to be limited, probably due to overlap in the underlying primary pathologies, and introduction of additional biomarkers reflecting other types of pathologies could be of value to optimize the differential diagnosis. With its antibody free, highly multiplex capacity, SRM provides a powerful platform for the simultaneous detection of panels of markers, which, when measured together, may improve differentiation between AD and other dementias or PD and related disorders (an approach currently underway in our laboratory).

SRM to improve pathological biomarkers in the blood through EVs

As discussed prior, the most promising biomarkers for both AD and PD are measured within CSF, an impractical resource for pre-clinical or prodromal patients within a clinical setting. Blood, which is a practical alternative, has its own challenges (reviewed in (Thambisetty & Lovestone 2010)), as extensive studies have indicated the potential challenges of diagnostic or predictive blood-based biomarkers for AD and PD due to extraordinarily low-abundance of disease-specific proteins able to be detected (Hong et al. 2010, Lin et al. 2012, Snyder et al. 2014, Henriksen et al. 2014, Rembach et al. 2014, Swaminathan et al. 2014, Lovheim et al. 2017). Thus, transitioning to blood remains challenging and likely dependent on technological advances. However, as also mentioned above, isolation of blood based but CNS-derived EVs may be a more efficient application of blood as a biomarker resource. Along with allowing multiple markers to be measured in a single preparation, another major advantage SRM could provide is the detection of PTM modified cargo such as α-syn thought to be highly abundant within EVs (reviewed in (Moreno-Gonzalo et al. 2014)). As proteins like α-syn and tau are thought to be highly modified by PTMs during aggregation (Jho et al. 2010, Morris et al. 2011, Andringa et al. 2004, Paleologou et al. 2010, Lashuel et al. 2013), SRM may provide a powerful platform for the parallel and simultaneous detection of multiple pathological forms (PTM, truncated, aggregated, etc.) originating from the CNS but accessible within the blood. Although sample isolation and assay developments still require optimization (currently ongoing in our lab), this SRM application has the capacity to utilize the blood as a resource for detecting pathological progression well beyond the capability of immunoassays.

Conclusions

Improvements are needed in both the sensitivity and specificity of current biomarkers for AD and PD. Application of exploratory MS-techniques has increased the quantity of reported candidate biomarkers for AD, PD and many other diseases; however, a notable distinction between the discovery and validation phases of biomarker development still remains. Much of the criticism in the biomarker field results from the discrepancy between discovery and validation, in which a validated marker has a defined clinical utility demonstrated across multiple patient cohorts (Crutchfield et al. 2016). Both phases are challenging and expensive, and validation requires an extra depth of resources (Horvatovich & Bischoff 2010). Targeted MS methods like SRM may provide a platform for large-scale candidate verification prior to costly validation testing (such as the studies highlighted in Tables 1 and 2). Further, we propose the use of parallel exploratory and targeted-MS approaches within a single study as a method for improving the bottleneck to biomarker validation within the AD and PD biomarker fields.

Acknowledgements and conflict of interest disclosure

The authors were supported by grants from the National Institutes of Health (NIH) (U01 NS082137, U01 NS091272, R01 AG056711, and R56 AG057417 to J.Z). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.Nothing to disclose.

Abbreviations used

2-DE

Two-dimensional gel electrophoresis

α-syn

Alpha-synuclein

Aβ

Amyloid beta

AD

Alzheimer’s disease

CNS

Central Nervous System

CSF

Cerebral spinal fluid

DA

Dopaminergic

DLB

Dementia with Lewy body

ELISA

Enzyme-linked immunosorbent assay

EV

Extracellular vesicle

FTD

Frontotemporal dementia

LC-MS/MS

Liquid chromatography tandem mass spectrometry

ND

Neurodegenerative diseases

MCI

Mild cognitive impairment

MS

Mass spectrometry

MRI

Magnetic resonance imaging

MRM

Multiple reaction monitoring

PD

Parkinson’s disease

PET

Positron imaging tomography

PRISM

High-pressure, high-resolution separations with intelligent selection and multiplexing

PRM

Parallel reaction monitoring

PTM

Post-translational modification

SN

Substantia nigra

SRM

Selected reaction monitoring

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