MOLECULAR AND CELLULAR NEUROSCIENCE | DECEMBER 07, 2018

Schöll M, Maass A, Mattsson N, Ashton N, Blennow K, Zetterberg H and Jagust W

Moll Cell Neurosci. 2018.

DOI: https://doi.org/10.1016/j.mcn.2018.12.001 

ABSTRACT

The aggregation of fibrils of hyperphosphorylated and C-terminally truncated microtubule-associated tau protein characterizes 80% of all dementia disorders, the most common neurodegenerative disorders. These so-called tauopathies are hitherto not curable and their diagnosis, especially at early disease stages, has traditionally proven difficult. A keystone in the diagnosis of tauopathies was the development of methods to assess levels of tau protein in vivo in cerebrospinal fluid, which has significantly improved our knowledge about these conditions. Tau proteins have also been measured in blood, but the importance of tau-related changes in blood is still unclear. The recent addition of positron emission tomographyligands to visualize, map and quantify tau pathology has further contributed with information about the temporal and spatial characteristics of tau accumulation in the living brain. Together, the measurement of tau with fluid biomarkers and positron emission tomography constitutes the basis for a highly active field of research.

This review describes the current state of biomarkers for tau biomarkers derived from neuroimaging and from the analysis of bodily fluids and their roles in the detection, diagnosis and prognosis of tau-associated neurodegenerative disorders, as well as their associations with neuropathological findings, and aims to provide a perspective on how these biomarkers might be employed prospectively in research and clinical settings.

1. Introduction

Proteinopathies are diseases where pathogenic post-translational modifications (PTMs), such as excess phosphorylation, misfolding, and aggregation of proteins are unregulated and misfolded proteins accumulate, harming cells and their environment (Fontaine et al., 2015; Ren et al., 2014; Walker and LeVine 3rd, 2012). Disorders associated with the accumulation of the microtubule-associated protein tau are thus termed tauopathies. Tau, among other physiological roles, stabilizes microtubules and maintains synaptic integrity and function. Under pathological conditions, however, its amyloidic (“starch-like”) nature (Sipe et al., 2016) results in self-aggregation into aberrant fibrillar β-sheet structures, which accumulate intracellularly and cause synaptic dysfunction and degeneration (Iqbal et al., 2016). Six isoforms of tau are expressed in the human brain, resulting from alternate splicing of the MAPT gene on chromosome 17 and differing in the number of amino terminal inserts (0/1/2N) and the number of microtubule-binding domain repeats, either three (3R) or four (4R) (Goedert et al., 1989). The neuropathology of different tauopathies exhibits varying isoform composition of the different intracellular tau inclusions as well as distinct neuroanatomical distribution and relative amounts of tau inclusions. Common tauopathies include certain variants of frontotemporal lobe dementia (FTD) – frontotemporal lobar degeneration-tau (FTLD-tau) (Mackenzie and Neumann, 2016) – such as autosomal-dominant FTLD-17, where mutations in MAPT cause mixed 3R/4R tau inclusions whose grey and white matter distribution can overlap with sporadic tauopathies; Pick’s disease(PiD), characterized by so-called Pick bodies, round inclusions formed almost exclusively by 3R tau aggregating along spatial phases in neocortical layers II-IV and certain cell populations of the hippocampus (Irwin et al., 2016); progressive nuclear palsy (PSP), characterized by predominant 4R tau inclusions mainly in specific nuclei of the basal ganglia, diencephalon, brainstem and cerebellum, with limited involvement of the neocortex(Dickson et al., 2010); corticobasal degeneration (CBD) with 4R-dominant tau pathology grossly overlapping with PSP with a tendency to greater involvement of the cerebral cortex and especially white matter (Dickson et al., 2011; Ouchi et al., 2014); and Alzheimer’s disease (AD), which accounts for the vast majority of tauopathy cases and is characterized by mixed 3R/4R mixed tau pathology accumulating in distinct spatial and temporal stages (Braak stages, see below) (Braak and Braak, 1991; Braak et al., 2011).

In 1906, Alois Alzheimer presented what he had found in the brain of the alleged first patient to be diagnosed with AD. What he described as “Eigentümliche Veränderungen der Hirnrinde” (“Peculiar changes of the brain’s cortex”) at a conference in Tübingen, Germany, was likely the first description of AD-typical neurofibrillary tangle pathology, which together with plaques, composed of fibrillar β-amyloid (Aβ) protein, constitute the macropathological hallmarks of AD. It would take another 80 years until hyperphosphorylated tau was identified as a core component of AD-related paired helical filaments (PHF), the main components of neurofibrillary tangles (NFT) (Grundke-Iqbal et al., 1986a; Grundke-Iqbal et al., 1986b; Ihara et al., 1986; Kosik et al., 1986; Wood et al., 1986). Another decade later, in vivo assessment of tau became possible through the development of assays for the detection of total (T-tau) and phosphorylated tau (P-tau) in cerebrospinal fluid (CSF) (Blennow et al., 1995). Five years ago, positron emission tomography (PET) ligands binding to tau with high affinity were introduced, enabling the visualization, mapping, and quantification of tau in the living brain (Chien et al., 2013).

Ongoing treatment trials targeting tau pathology now call for reliable and quantifiable tau biomarkers for the early identification of eligible study participants and the in vivoquantification of potential treatment effects (Cummings et al., 2018; Khanna et al., 2016). Furthermore, the National Institute on Aging and Alzheimer Association (NIA-AA) research criteria in the diagnosis of AD (Jack Jr. et al., 2018a) have implemented the use of tau biomarkers derived from PET and CSF, however, it remains unclear how the different biomarkers may be optimally operationalized.

Here, we summarize the current state of fluid- and imaging-derived markers for tau pathology.

2. Fluid biomarkers of tau

2.1. Tau in CSF

Over three decades ago, the finding that P-tau was the major component of tangles (Grundke-Iqbal et al., 1986b) made tau proteins in CSF prime candidates for quantitative immunoassays. Today, both CSF T-tau and P-tau assays (along with low Aβ42) are central to the Internal Working Group (IWG) (Dubois et al., 2014) and the NIA-AA (Jack Jr. et al., 2018a) research criteria in the diagnosis of AD. In AD, neurons release more tau proteins to the extracellular space, which is reflected in CSF from patients as increased concentrations of both T-tau, measured using antibodies against mid-domain tau epitopes that are not phosphorylated, and P-tau that is measured using antibody combinations that specifically recognize mid-domain P-tau epitopes (Olsson et al., 2016). These biomarker changes correlate with each other, however, this association is most pronounced in AD and less pronounced for isolated increases in T-tau (Skillback et al., 2015). CSF T-tau and P-tau correlate with AD-type neurodegeneration and tangle pathology in autopsy and biopsy studies (Buerger et al., 2006; Seppala et al., 2012; Tapiola et al., 2009), but the correlations are modest, and not reported in all studies (Engelborghs et al., 2007). This appears to be different from correlation studies between tau PET imaging and CSF tau biomarkers (see below), and immediately suggest differences in how these different biomarker modalities reflect the underlying processes in AD.

CSF T-tau has been postulated to reflect the severity of acute injury and/or on-going neurodegeneration (Blennow and Hampel, 2003). CSF T-tau is likely increased very early in the disease process since increased levels can be seen already in Aβ-positive cognitively unimpaired individuals (CU) (while other markers of injury, including neurogranin and neurofilament light protein were not increased until the prodromal stage in the same cohort (Mattsson et al., 2016a)). However, the increase in CSF T-tau is not specific to AD, since CSF T-tau levels are the highest in conditions with the most severe neurodegeneration, including Creutzfeldt-Jakob disease, which has been shown to be >10-times higher in T-tau concentrations than dementia (Skillback et al., 2014). Following acute brain injury, a rapid spike in CSF T-tau is observed and maintained for a number of weeks before slowly declining to normal levels (Hesse et al., 2001; Zetterberg et al., 2006). In AD, higher T-tau values predict a more rapid cognitive decline (Buchhave et al., 2012; Wallin et al., 2010), and relate to more rapid hippocampal atrophy and reductions in FDG-PET binding (Mattsson et al., 2016a; Chiaravalloti et al., 2018), which supports the idea that T-tau levels are related to the intensity of neurodegeneration.

Several validated commercially available immunoassays targeting threonine 181 (P-tau181) consistently demonstrate an increase in AD. However, other mid-domain P-tau residues (threonine 231, serine 199 and 231) and C-terminal residues (Serine 396 and 404) are also increased in AD (Hu et al., 2002; Ishiguro et al., 1999; Kohnken et al., 2000). A limited number of studies seem to demonstrate co-linearity but specificity in P-tau181, 199 and 231 to distinguishing AD from other neurodegenerative disorder and aged healthy controls (Hampel et al., 2004).

A major outstanding research question is why other tauopathies, including some forms of FTD and associated disorders like PSP, do not show increased P-tau concentration in the CSF, at least not as robustly as in AD (Zetterberg, 2017). It is possible that disease-specific phosphorylation of tau occurs in these disorders, or that tau is processed or truncated in a way that is not recognized by the available assays. Another potential explanation for why increased CSF T-tau and P-tau are specific to AD is that this particular pathological change is simply more extensive and severe in AD than in other tauopathies. However, this seems contradicted by the fact that some non-AD tauopathy patients, most notably PSP patients, even have decreased CSF P-tau levels compared to control populations (Hall et al., 2012; Meeter et al., 2018; Wagshal et al., 2015).

Yet another possibility is that CSF T-tau and P-tau increase reflects a neuronal response to Aβ pathology, which precedes neurodegeneration and tangle pathology, as suggested by mouse model studies (Maia et al., 2013) and tau kinetics studies in humans (Sato et al., 2018). This is in agreement with the poor correlation between CSF T-tau and P-tau with [18F]Flortaucipir (FTP) imaging in early stages of AD, as discussed further below.

It is known that the major proportion of tau is cleaved to N-terminally truncated or N-terminalto mid domain fragments before being secreted to the CSF, while C-terminal fragments are much less abundant (Barthelemy et al., 2016; Meredith Jr. et al., 2013). Recent studies have identified tau species cleaved by asparagine endopeptidase (AEP), that generate tau fragments ending at amino acid 368 (based on tau 2 N/4R tau numbering), which are up-regulated in aging and AD, and which have an increased tendency for aggregation into tangles (Zhang et al., 2014). Future research to evaluate if tau368 or other C-terminally extended tau fragments measured in CSF may serve as biomarkers for tau pathology is warranted.

2.2. Tau in plasma

Both imaging (see below) and CSF biomarkers work well to identify AD pathophysiology. Nonetheless, PET imaging is costly and access is restricted to specialized centers. Gradually, CSF sampling for analysis of AD biomarkers is becoming increasingly used in clinical practice for management of neurodegenerative diseases, yet there remains a level of perceived invasiveness or complexity attached to a lumbar puncture in many countries. The accessibility and ease of blood sampling provides significant practical advantages for measuring AD biomarkers, for both clinical assessment or screening and repeated sampling in therapeutic trials.

Blood communicates with the brain across the blood-brain barrier, via lymph vessels and through the glymphatic system (Plog and Nedergaard, 2018; Zetterberg and Blennow, 2018). Despite this, it has proven difficult to establish the core CSF biomarkers for AD pathology in blood. Firstly, due to its continuous and uninhibited exchange with the brain, biomarkers in CSF will be considerably higher in concentration than biomarkers in blood. Furthermore, once entering the bloodstream, brain-derived proteins will compete in a complex matrix of highly abundant plasma proteins (e.g. albumin, IgG, transferrin, haptoglobin, and fibrinogen) that span >10 orders of magnitude. Secondly, a brain-derived biomarker may undergo protease degradation, metabolized by the liver and cleared by the kidneys or simply have substantial expression by peripheral tissues. Lastly, analytical factors such as interference from heterophilic antibodies or variations in blood collection, processing and storage can have a major influence on an individual result. All these factors will introduce a high degree of variability that is unrelated to the disease pathogenesis and will be difficult to normalize.

In the advent of emerging ultrasensitive techniques that resolve confounding matrix effects, the femtomolar detection of proteins in blood has been demonstrated (Rissin et al., 2010). Indeed, increased plasma levels of T-tau in AD compared to CU and/or MCI (mild cognitive impairment) have been exhibited by immunomagnetic reduction (IMR) (Tzen et al., 2014) and Single molecule array (Simoa) (Zetterberg et al., 2013). More recently, in the ADNI and BioFINDER cohorts, this finding was confirmed but with large overlaps between the clinical groups, which almost certainly concludes that plasma T-tau is not diagnostically useful in a cross-sectional manner (Mattsson et al., 2016b). Conversely, baseline plasma T-tau might be useful in predicting future cognitive decline (Mattsson et al., 2016b; Mielke et al., 2017), atrophy and hypermetabolism (Mattsson et al., 2016b). Likewise, in cardiac arrest, with much greater acute neuronal injury than AD, baseline plasma T-tau may be used for prognosis of patients with poor neurological outcome (Mattsson et al., 2017c). Plasma T-tau, however, correlates poorly with CSF T-tau (Mattsson et al., 2016b; Zetterberg et al., 2013), which might have several explanations, e.g. rapid peripheral degradation of tau or that blood tau levels are confounded by expression of tau in peripheral tissues. As with CSF, assays directed towards truncated fragments of tau in plasma may yield greater disease specificity.

At this time there is no validated assay for P-tau in blood. Nonetheless, recent pilot data have reported encouraging findings. The Simoa and Meso Scale Discovery (MSD) platforms have both been utilized to demonstrate an increase of P-tau181 concentration in AD patients compared to healthy controls (Mielke et al., 2018; Tatebe et al., 2017). Importantly, however, both these studies reported weak but significant correlations with either CSF P-tau181 (Tatebe et al., 2017) or FTP (Mielke et al., 2018). Intriguingly, the stated concentrations of P-tau181 between the two methods differ substantially (Simoa assay reported a mean concentration of 0.171 pg/mL in AD patients versus 12 pg/mL on the MSD platform). In another recent paper, plasma P-tau231 was measured using a fiber optics technique in which antibody-based detection was combined with rolling circle amplification (Rubenstein et al., 2017). Increased concentrations of plasma P-tau231 in patients with traumatic brain injury (TBI) were observed but as of yet no data has been reported in AD. This promising data, albeit preliminary, on P-tau makes it one of the most anticipated areas of AD biomarker research alongside plasma Aβ42/Aβ40 (Ashton et al., 2018a, Ashton et al., 2018b) and neurofilament light chain (Mattsson et al., 2017a).

Other areas of ongoing investigation for peripheral tau include, but are not limited to, exosomes and saliva. Increases in T-tau and P-tau within neuronal-derived exosomes in plasma have been a developing area of research (Fiandaca et al., 2015; Winston et al., 2016). As it stands, further validation of these results and methodologies are needed. It has previously been shown that T-tau is readily measurable in saliva (Ashton et al., 2018a). While concentrations are noticeably higher than plasma, no statistically significant difference across diagnostic groups was observed. Preliminary proteomic data suggests that certain P-tau fragments derived in saliva may be of importance in clinical AD, however this is yet to be determined using a robust platform in large sample sizes.

3. Tau positron emission tomography (PET)

Given the molecular diversity of tauopathies, it cannot be assumed that one PET ligand or even one class of chemically similar PET ligands will be useful in the research of all disorders. Several companies and academic groups have developed and are continuously developing ligands presumably binding to tau (please refer to Fig. 1 for the chemical structures of current alleged tau PET ligands). Based on the recently published cryo-EM structure for the tau fibril (Fitzpatrick et al., 2017), four binding sites have been suggested for current PET ligands, and different candidates show differential preference for each of these sites (Murugan et al., 2018).

3.1. First-generation tau PET ligands

The first PET ligand used to visualize the molecular underpinnings of AD was [¹⁸F]FDDNP (Shoghi-Jadid et al., 2002), which binds to both Aβ fibrils and tau NFT. Due to its relative nonspecificity for one or the other it has largely been replaced by tau or Aβ-specific ligands.

One of the earliest agents developed specifically for tau imaging was the carbon-11 labelled PBB3, a pyrinated phenyl- and pyridinyl-butadienyl-benzothiazole with a 50-fold higher affinity to tau over Aβ deposits (Maruyama et al., 2013). [¹¹C]PBB3 was initially found to exhibit affinity to tau in AD, PSP and CBD tissue samples (Ono et al., 2017b), however its utility was hampered by high white matter uptake, low target to white matter ratio on autoradiography in AD tissue, its fast metabolism in vivo (<8% remaining 3 min after injection) (Hashimoto et al., 2014), photo-isomerization upon exposure to fluorescent light, and a dominant brain-penetrant metabolite that complicated quantification of ligand uptake (Hashimoto et al., 2015). Finally, carbon-11 labelling with its short half-life (~20 min) limits potential use of any PET ligand as compared to fluorine-18 labelling (half-life ~ 110 min). To address these issues, the fluorine-18 analogues AM-PBB3 and PM-PPB3 were developed (Ono et al., 2017a; Shimada et al., 2017a) but limited data have been published to date.

A series of compounds have been developed at Tohuku University, Japan, the most widely used being the arylquinoline derivatives [¹⁸F]THK5117 (Okamura et al., 2013) and [¹⁸F]THK5351. Of these two, [¹⁸F]THK5351 demonstrated favorable imaging characteristics, generally demonstrating higher grey matter and lower white matter uptake but lower lipophilicity (Betthauser et al., 2017a). The S-enantiomer form of [¹⁸F]THK5117, [¹⁸F]THK5317, has also been assessed in vivo (Chiotis et al., 2016; Jonasson et al., 2016). However, high off-target binding of [¹⁸F]THK5351 was observed especially in the thalamus, and blocking studies with the monoamine oxidase B (MAO-B) inhibitor selegiline showed dramatic reduction in thalamic [¹⁸F]THK5351 uptake (Ng et al., 2017a). Cortical uptake was also reduced significantly, indicating that this off-target binding also affected what had been previously assumed to be tau specific uptake in in vivo studies (see also below).

The radioligand [¹⁸F]Flortaucipir (FTP, previously AV1451 or T807), a benzimidazolepyrimidine derivative, is the by far most widely studied tau PET tracer to date. It has been shown to bind with high affinity (25–27 fold larger than to Aβ) to 3R and 4R tau isoforms in PHF of AD patients (Chien et al., 2013; Lowe et al., 2016; Marquie et al., 2015). Smith and colleagues furthermore showed that in vivo FTP-binding and post-mortem PHF load were highly correlated in one subject with a MAPT R406W mutation causing AD-like tau pathology (Smith et al., 2016). However, large inter- and intraindividual differences were observed in a recent study in post mortem tissue from several neurodegenerative disorders (Wren et al., 2018), calling for further investigation of the binding characteristics. Nonetheless, on a group level, FTP demonstrated clinical usefulness when its discriminative accuracy between AD dementia and non-AD neurodegenerative disorders was examined in a large multisite study, yielding 89.9% (95% CI, 84.6%–93.9%) sensitivity and 90.6% (95% CI, 86.3%–93.9%) specificity or 96.8% (95% CI, 92.0%–99.1%) sensitivity and 87.9% (95% CI, 81.9%–92.4%) specificity based on different thresholds applied to medial-basal and lateral temporal cortex ligand uptake (Ossenkoppele et al., 2018).

The in vivo kinetics of [¹⁸F]FTP have been investigated in a number of studies, aiming at validating semi-quantitative estimates such as the standardized uptake value ratio (SUVR), which are more appropriate for clinical research than fully dynamic and quantitative approaches (Baker et al., 2017a; Barret et al., 2016; Golla et al., 2017; Hahn et al., 2017; Wooten et al., 2017). In those studies including blood sampling and metabolite analysis, 20–30% of [¹⁸F]FTP was found to remain 60 min after injection, with the main metabolites being polar and thus not entering the brain. Free fraction in plasma was low (0.19%) (Barret et al., 2016). The different studies came to different conclusions regarding the most appropriate kinetic model to describe ligand uptake. Whereas the two-tissue compartment model was found to best describe the data, also in the cerebellar cortex for all subjects including controls (Barret et al., 2016), others found that in controls the uptake was best described by the 1-tissue compartment model (Golla et al., 2017), indicating that model preference depended on the underlying volume of distribution. A third study found that none of the compartment models adequately described the data (Hahn et al., 2017). Most importantly, however, all studies found that reference-based quantification of dynamic data correlated well with arterial blood-based quantification supporting the use of an 80–100 (75–105) min SUVR as an acceptable, yet not ideal, method for clinical studies (see also (Heurling et al., 2018) for assessment of how regionally and temporally different times to transient equilibria influence SUVR reliability). Regional increase of ligand uptake past 180 min in certain high-binding patients might also influence semi-quantification by SUVR in earlier time frames and needs to be evaluated further. Finally, analysis of in vivo test-retest reliability of FTP yielded low variability in SUVR (standard deviation of mean percent change 1.46–3.27% depending on brain region) (Devous Sr. et al., 2018).

3.2. Second-generation tau PET ligands

Numerous publications have applied FTP in clinical studies, as described in the next section. However, concerns about off-target binding issues (see below) have triggered the continuous development of a second generation of tau PET ligands. As data on most of these novel compounds have only been published or presented very recently, no head-to-head comparison has been conducted including any of these. Fig. 2 therefore displays current tau PET ligand uptake patterns in different representative cases of cognitively healthy elderly individuals and patients with early-onset and late-onset AD, respectively. The figure is intended to provide an impression of dynamic range and potential off-target binding patterns without claiming completeness.

Fig. 2. Representative PET scans using different current ligands presumably specific for tau in cognitively healthy elderly individuals (upper rows), patients with early-onset (EOAD, middle rows) and late-onset Alzheimer’s disease (LOAD, lower rows). All images were processed in a unified manner including co-registration of PET to its corresponding T1-weighted magnetic resonance imaging (MRI) scan, spatial normalization to Montreal Neurological Institute (MNI) template space and creation of standardized uptake value ratios (SUVR) using an inferior cerebellum reference region (see (Baker et al., 2017b) for methods). Note that this is not a head-to-head comparison but merely a display of sample scans from different representative cases, obtained at various sites using scanners with different spatial resolution, and non-harmonized image reconstruction. FTP images (80–100 min SUVR) were derived from the Berkeley Aging Cohort Study (BACS; University of California, Berkeley/San Francisco, Drs. William Jagust and Gil Rabinovici), [¹⁸F]GTP1 (60–90 min SUVR) images were kindly provided by Genentech (Drs. Robby Weimer and Sandra Sanabria Bohorquez), RO6958948 images (60–90 min SUVR) by Roche (Drs. Gregory Klein and Edilio Borroni) and Drs. Dean Wong and Hiroto Kuwabara from Johns Hopkins University, Baltimore, Maryland, MK6240 images (90–110 min SUVR) by Drs. Pedro Rosa-Neto and Tharick Ali Pascoal at McGill University, Montreal, Canada, and PI2620 scans (60–90 min SUVR) by Drs. André Müller and Santaigo Bullich at Life Molecular Imaging GmbH (former Piramal Imaging). Aβ pos/neg: Amyloid status positive/negative based on Aβ PET scans; MMSE = Mini Mental StateExamination.

After screening three final candidates (RO6958948, RO6931643, and RO6924963) (Gobbi et al., 2017), Roche decided on [¹⁸F]RO6958948 as their lead compound (Wong et al., 2018). Chemically similar to [¹⁸F]FTP (Fig. 1) and presumably binding to the T808 binding site, it is anticipated to exhibit higher affinity to mixed 3R/4R over isolated 3R or 4R tau pathology. [¹⁸F]RO6958948 was reported to have a lipophilicity of log D = 3.22 and rather low plasma free fraction of 7%. Low non-specific binding was shown using autoradiography in tissue from healthy controls, and high grey/white matter ratio in tissue samples from AD subjects with tau pathology corresponding to Braak Stage V. No significant binding was observed in vitro in PSP, CBD or PiD tissue samples (Honer et al., 2018). In a first-in-man study, 90 min dynamic scans were performed with arterial blood sampling, demonstrating clear discrimination between healthy controls and AD patients both using SUVR and volume of distribution estimates (Wong et al., 2018). For SUVR creation, a 60–90 min frame was proposed, correlating well with arterial input-based quantification. After 15 min, 30% remained as parent fraction, and the uptake could be described by a two-tissue compartment model. No radiolabeled metabolites suspected to cross the blood-brain barrierwere detected. Test-retest variability was 6–10% depending on brain region and quantification method (SUVR or DVR). [¹⁸F]RO6958948 is currently employed in a longitudinal study, preliminary results showed increased ligand uptake over a <1 year follow-up period (Wong et al., 2017).

Following extensive preclinical validation (Walji et al., 2016), Merck put forward [¹⁸F]MK-6240 as lead compound (Hostetler et al., 2016), demonstrating a K_i = 0.36 nM in NFT-rich AD brain homogenate versus the high-affinity NFT ligand [³H]-NFT-355, to be compared with a K_i = 10 μM in amyloid plaque rich AD homogenate versus the amyloid plaque ligand [³H]-MK-3328 (Hostetler et al., 2016). Compared with [³H]FTP, [³H]-MK-6240 was found to have a two- to five-fold higher binding potential. Non-human primate blocking studies showed no apparent off-target binding. Three recently published studies have evaluated [¹⁸F]MK-6240 in vivo (Betthauser et al., 2018; Lohith et al., 2018; Pascoal et al., 2018). Ligand bindingpatterns were described in regions associated with NFT deposition. All studies included dynamic scanning for between 90 and 180 min, demonstrating rapid brain delivery and washout, and tracer kinetics were investigated in healthy controls and AD patients using reference- or blood-input based modelling approaches. Distribution volumes correlated well with SUVR over either 70–90 (Betthauser et al., 2018; Lohith et al., 2018) or 90–110 min (Pascoal et al., 2018). Whereas non-AD tauopathies have not yet been investigated with [¹⁸F]MK-6240, it presumably shares the same binding site as [¹⁸F]FTP and is thus unlikely to exhibit high affinity to isolated 3R or 4R tau pathology.

Data for the novel [¹⁸F]GTP-1 (Genentech Tau Prope-1) and [¹⁸F]PI2620 (Life Molecular Imaging, formerly Piramal Imaging) compounds have thus far only been presented at conferences. [¹⁸F]GTP1 is currently evaluated in a longitudinal study, preliminary cross-sectional results demonstrated clearly increased uptake in expected regions in AD patients as well as an association between ligand uptake and cognitive impairment. Images showed increased retention in the basal ganglia relative to background in some subjects but kinetic analysis suggest the measured SUVR may not necessarily reflect specific tracer binding (Sanabria Bohorquez et al., 2016; Sanabria Bohorquez et al., 2017; Weimer et al., 2017).

[¹⁸F]PI2620 is structurally similar to FTP (Fig. 1), again suggesting similar binding preferences to 3R/4R tau. In vitro binding characteristics were reported for PHF tau deposits (IC₅₀ = 3.9 nM), K18 fibrils (4R, IC₅₀ = 8.4 nM), homogenates of AD brain (IC₅₀ = 1.9 nM), Pick’s Disease (3R, IC₅₀ = 2.6 nM), and PSP (4R, IC₅₀ = 10.7 nM), indicating high affinity to 3R/4R tau deposits but potentially also 3R inclusions (Stephens et al., 2018). No binding to β-amyloid or MAO-A/B was reported. [¹⁸F]PI2620 is currently evaluated in several clinical trials, including a test/retest study with arterial sampling. Initial in vivo data showed robust brain uptake and fast wash-out in non-target regions, no lipophilic metabolites, and 20% of the intact tracer in blood at 60 min p.i. (Seibyl et al., 2017). SUVR over a 60–90 min p.i. time frame were highly correlated with DVR and showed clear discrimination between AD patients and control individuals, as well as high correlation between test-retest results (Stephens et al., 2018).

4. Tau PET imaging in aging and Alzheimer’s disease

4.1. Topography of tau ligand retention

Based on years of neuropathological evidence from autopsies, the presence and location of pathological tau accumulations in the human brain are well established (Braak and Braak, 1991). These studies have proposed a progression of stages of tau deposition based on cross sectional autopsy data (see Fig. 3A). These stages begin with tau deposits in the entorhinal cortex (Braak stages I/II), moving to inferolateral temporal cortex and parts of the medial parietal lobe (stages III/IV), and eventually throughout association cortex (V/VI). Interestingly, stages III/IV can be seen in both CU older people and those with AD while stages I/II are common in those who are cognitively normal and stages V/VI are almost universally associated with dementia (Bennett et al., 2006; Hyman et al., 2012).

Fig. 3. Tau tracer uptake patterns resemble ex vivo Braak stages.

A. Schematic display of Braak stages in the development of Alzheimer’s disease-associated tau pathology based on post mortem data. Reprinted from “Stages of the Pathologic Process in Alzheimer Disease: Age Categories From 1 to 100 Years,” (Braak et al., 2011). Copyright 2011 by Oxford University Press B. Voxel-wise two-sample t-tests on Flortaucipir SUVR images between subjects assigned to contiguous tau-PET based Braak stages. Results are Family-Wise Error (FWE) corrected at voxel level (p_voxel < 0.05, k > 100). Individuals included cognitively normal adults and AD patients. See (Maass et al., 2017) for more information.

The relatively recent advent of relevant PET radioligands has permitted the in vivo detection of tau which largely parallels these autopsy findings (see Fig. 3B). The radioligand [¹⁸F]FTP has been most widely studied so far and binds with high affinity to PHF tau in AD patients (Chien et al., 2013; Lowe et al., 2016; Marquie et al., 2015). Tau tracer retention in aging and AD appears to follow a particular topography (e.g. Cho et al., 2016a) and although longitudinal data are sparse (Jack Jr. et al., 2018b; Southekal et al., 2018), the distribution can be captured as a regional pattern that appears to begin in entorhinal cortex, spreading through inferolateral temporal lobes and medial parietal lobes and eventually to wide areas of the neocortex. In contrast to Aβ, which is often found throughout neocortex in a regionally less specific manner using PET imaging, the quantitation of tau deposition requires careful selection of region-specific measures of tracer retention. A number of different approaches have been suggested included regional and global measures for binary categorization (Jack Jr. et al., 2017; Maass et al., 2017; Mishra et al., 2017; Wang et al., 2016) as well as topographical staging approaches that recapitulate ex vivo pattern of hierarchical tau spread (Maass et al., 2017; Schöll et al., 2016; Schwarz et al., 2016). Quantification from large regions may be sufficient to capture AD-related tau PET signal and progressive within-person accumulation of pathologic tau (Jack Jr. et al., 2018b; Maass et al., 2017). The high regionality of brain tau load as visualized with PET is further emphasized by studies employing data-driven approaches without prior definition of anatomical regions (Sepulcre et al., 2017a; Whitwell et al., 2018b).

4.2. Effects of Aβ and age on patterns of tau PET ligand uptake

Elevated tau tracer binding in the medial temporal lobe (MTL) is commonly seen in CU elderly, whereas widespread binding in neocortical regions usually requires the presence of aggregated Aβ (Cho et al., 2016a; Gordon et al., 2016; Johnson et al., 2016; Lockhart et al., 2017b; Maass et al., 2017; Pontecorvo et al., 2017; Schöll et al., 2016; Schwarz et al., 2016; Sepulcre et al., 2016).

Interestingly, although there is an overall correlation between the amount of Aβ in the brain and the amount of tau (Johnson et al., 2016), the spatial locations of these two aggregated proteins are discordant. Specifically, Sepulcre and colleagues (Sepulcre et al., 2016) demonstrated strong positive local-to-local tau versus Aβ PET correlations in lateral temporal, frontal and parietal lobes across 88 CU elderly, but tau in these regions further correlated with Aβ throughout the brain. Data from Lockhart and colleagues (Lockhart et al., 2017b) confirmed these findings in 46 CU showing that tau PET signal in the temporal lobe and some frontal areas correlated with Aβ measures among widespread neocortical regions (see Fig. 4). Together these data indicate a regional vulnerability of temporal brain regions to tau regardless of where Aβ is deposited. Notably, associations between global Aβ and regional tau PET signal seems to be strongest in the entorhinal cortex (Vemuri et al., 2017) as demonstrated in in large sample of 420 CU elderly, suggesting that this “early tau region” should be included in meta regions for detection of AD-related tau PET signal.