Microtubule density and landing rate as parameters to analyze tau protein in the MT-kinesin “gliding” assay

Sensors and Actuators B 238 (2017) 954–961

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Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb

Microtubule density and landing rate as parameters to analyze tau protein in the MT-kinesin “gliding” assay Subhathirai Subramaniyan Parimalam a , Mehmet C. Tarhan b,c , Stanislav L. Karsten c,d , Hiroyuki Fujita c , Hirofumi Shintaku a , Hidetoshi Kotera a , Ryuji Yokokawa a,∗ a

Department of Micro Engineering, Kyoto University, Kyoto, Japan Laboratory for Integrated Micro Mechatronic Systems (LIMMS), Institute of Industrial Science (IIS), The University of Tokyo, Tokyo, Japan c Center for International Research on Micronano Mechatronics (CIRMM), Institute of Industrial Science (IIS), The University of Tokyo, Japan d NeuroInDx Inc., Signal Hill, CA, USA b

a r t i c l e

i n f o

Article history: Received 25 March 2016 Received in revised form 15 June 2016 Accepted 16 July 2016 Available online 18 July 2016 Keywords: Tauopathies gliding assay tau detection microtubules kinesin biomarker

a b s t r a c t Microtubule-associated protein (MAP) tau is a well-established hallmark of a large group of age related neurodegenerative diseases collectively called tauopathies. Under pathological conditions the equilibrium of tau binding to the MTs is perturbed, either by misregulation in the expression levels of specific tau isoforms or by MAPT gene mutations. Preclinical detection of such misregulated tau proteins in cerebrospinal fluid (CSF) is desirable for differential diagnosis and effective prognosis of neurodegeneration. Conventional tau protein detection methods utilize tau isoform-specific antibodies. Such immuno-based protocols, including enzyme-linked immunosorbent assay (ELISA) and Western blots have appropriate sensitivity and specificity, but often show high variability and are time consuming. Here, we established a non-immuno tau protein detection method utilizing microtubule (MT)-kinesin “gliding”assay. All the six tau isoforms expressed in the human brain (0N3R, 1N3R, 2N3R, 0N4R, 1N4R and 2N4R) and five MAPT gene mutants (V248L, G272V, P301L, V337M and R406W) were studied. The landing rate, binding density and gliding velocity of MTs with respect to each tau type were determined and are proposed as tau detection parameters. The detection parameters depicted the type of tau bound to the MTs. Furthermore, MT landing rate and density were found to be superior to gliding velocity in differentiating tau isoforms and mutants. The 3R vs. 4R isoforms, their admixtures, wild vs. mutant 2N4R and specific mutants were differentiated. Our data show that MT-kinesin gliding assay provides a convenient, lab-on-a-chip (LOC) compatible and antibody-free protocol for tau protein analysis. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Tauopathies including Alzheimer’s disease (AD), progressive supranuclear palsy (PSP), Pick’s disease, and frontotemporal dementia with Parkinsonism (FTDP) are age-related neurodegenerative diseases characterized by the pathological accumulation of tau protein [1,2]. The identification of mutations in the microtubule associated protein tau (MAPT) gene encoding tau protein associated with familial FTDP linked to chromosome17q21 (FTDP17) (for review see van Slegtenhorst et al., 2000) and functional studies in transgenic mice [3] confirmed the essential role(s) of tau in the development of neurodegenerative diseases. MAPT gene

∗ Corresponding author at: Department of Micro Engineering, Kyoto University, C3−c2S18, Kyoto daigaku−Katsura, Nishikyo−ku, Kyoto, 615−8540 Japan. E-mail address: [email protected] (R. Yokokawa). http://dx.doi.org/10.1016/j.snb.2016.07.082 0925-4005/© 2016 Elsevier B.V. All rights reserved.

mutations that induce hyperphosphorylation [4] and misregulation in the expression of tau isoforms are associated with different tauopathies [5,6]. The presence of hyperphosphorylated tau [7,8] and alteration of different tau isoform’s ratio, for example, 3R:4R ratio, in cerebrospinal fluid (CSF) prior to disease development makes it an important clinical biomarker for presymptomatic diagnosis of tauopathies [9,10]. Currently only immunological methods are being used for tau analysis [11,12]. The results of these tests often demonstrate high variability that complicates data interpretation between different laboratories [13]. Therefore a rapid, antibody-free, inexpensive, and highly reproducible method compatible with a lab-on-a-chip (LOC) format and capable of detecting specific tau isoforms is in high demand. Tau has a complex developmentally regulated expression pattern that can result in the production of six different isoforms in the human brain, which differ by the number of projection domains and microtubule binding repeats (MTBRs; R1-R4 repeats) at N-

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ent MAPs ratio on MT density and velocity over the kinesin-coated surface [24,27]. Tau’s isoform specific effects on MTs gliding velocity have already been reported, yet they are not conclusive mainly because these investigations are limited only to two or three isoforms [20,24–28]. The effect of all the isoforms, their admixtures and mutants on MT-gliding behavior was not previously studied. Moreover, no convenient readout methodology compatible with a LOC format was introduced so far. Here, we demonstrate that MT density and landing rate are superior parameters to MT gliding velocity in detecting various tau species in a short assay time (∼ 5 min). Proposed detection method is LOC compatible and not limited to tau protein, but allows to examine the effects of other MAPs on MT-kinesin interaction. 2. Materials and Methods 2.1. Preparation of solutions and proteins Fig. 1. Schematic representation of tau isoforms and mutants. (a) Six tau isoform expressed in human brain − 0N3R, 1N3R, 2N3R, 0N4R, 1N4R and 2N4R, which differ by the number of projection domains and microtubule binding repeats (MTBRs; R1R4 repeats) at N-terminal and C-terminal regions, respectively. Projection domain contains up to two (2N) negatively charged 29-residue sequences, and MTBR region has either three (3R) or four positively charged MTBRs (4R). (b) The five FTDP-17 mutations in 2N4R tau isoform-V248L, G272V, P301L, V337M and R406W. The red line indicates the position of the missense mutation.

terminal and C-terminal regions, respectively (Fig. 1). Projection domain may contain up to two (2N) negatively charged 29-residue sequences, and MTBR region has either three (3R) or four positively charged MTBRs (4R) (Fig. 1a) [14,15]. Composition of tau isoforms directly affects their interaction with microtubules (MTs), i.e. isoforms with 4R bind to MTs with greater affinity than those with 3R [14]. Moreover, tau-MT interaction also affects kinesin movement along the MTs [16–20]. The effect of the different tau isoforms on the MT-kinesin system using both kinesin motility and MT gliding assays [21] have been extensively studied [16–18,22–28]. It was shown that kinesin-based anterograde axonal transport may be regulated by tau isoforms at multiple levels [16,19] including steric interaction between MT and kinesin [24] and/or binding-site competition [27]. These observations triggered several attempts to develop tau detection methods which use tau-regulated binding of kinesin to MTs rather than traditional immuno-based methods of tau detection. The kinesin motility assay was employed in developing such a tau detection method and was successfully demonstrated over a suspended MT [18]. However, the observed effect of tau on kinesin velocity greatly depends on a particular experimental design that is reflected in the frequently contradicting results. For example, some studies report that tau has no effect on kinesin velocity [16,26] whereas others report the opposite [17,18,20]. Despite of the inconsistent observations with respect to tau effect on kinesin translocation, kinesin motility assay allows for the study of individual or countable number of kinesin molecules [19]. However, the molecules are subjected to thermal fluctuations; the run length tends to be relatively short, and consequently, they often do not encounter an MT-bound tau. In addition, the kinesin motility assays require a technically complex visualization system, for example, total internal reflection fluorescence (TIRF) microscopy [16], precise control of the number of kinesin molecules per bead, and specially fabricated nanostructures to suspend the MTs to achieve sufficient sensitivity in a microchip format [18]. The technical limitations for the kinesin motility assay systems described above preclude their application as an easy-to-use tau detection method. Multiple kinesin motors involved in a MT gliding assay provide stable gliding surface for MTs. Earlier studies using the gliding assay required long assay time (> 30 min) to reveal the effect of differ-

The motility solution used for all assays was BRB80 (80 mM PIPES, 1 mM ethylene glycol tetraacetic acid, 1 mM MgCl2 ), to which 1 mM ATP, 1 mM MgCl2 , an antifade system (25 mM glucose, 20 mM dithiothreitol, 216 ␮g ml−1 glucose oxidase (product No. G2133), 36 ␮g ml−1 catalase (product No. C9322), 1% (v/v) 2-mercaptoethanol (␤-ME) (catalog No. 138-14342; Wako Pure Chemical Industries, Osaka, Japan) 10 ␮M paclitaxel (product No. T1912), and casein (product No. C5890) at a final concentration of 0.5 mg ml−1 was added. All the above reagents except ␤-ME were purchased from Sigma-Aldrich, St. Louis, US. The His6-tagged kinesin was purified by Ni-NTA affinity. Recombinant Homo sapiens kinesin (residues 1–573) was ligated into the pET30b plasmid to have His6-tag for the following purification, and expressed, isolated, and purified from Escherichia coli Rosetta (DE3) (catalog No. 70956; Merck Millipore, Darmstadt, Germany) cells as described [29], then stored in liquid nitrogen, and before each assay, was diluted to 30 ␮g ml−1 with 2.5 mg ml−1 casein, 1 mM ATP, and 1 mM MgCl2 . Tubulin was purified from porcine brains obtained from a local slaughterhouse (Ikeda Food, Kyoto, Japan) by two-cycles of assembly-disassembly procedure and phosphocellulose chromatography [30]. A portion of the tubulin preparation was labeled with TAMRA by standard protocols [31]. Tubulin was stored in liquid nitrogen until use. MTs were prepared by polymerizing labeled tubulin and unlabeled tubulin (1:10 molar ratio) at 37 ◦ C for 30 min in BRB80 containing 1 mM MgSO4 and 1 mM GTP. The polymerized MTs were stabilized by the presence of 40 ␮M paclitaxel (final concentration). Recombinant human tau isoforms and mutants were purchased from rPeptide (Georgia, US), then diluted in DIW and stored at −80 ◦ C. For the assay 5 ␮M MT solution in BRB80 containing 10 ␮M paclitaxel was prepared and MTs were sheared for 30-35 times by a needle (22 S gauge, 51 mm in length, Hamilton, Nevada, US) to obtain uniform distribution in MT length. Their lengths are summarized in Fig. S1. 2.2. MT binding and gliding assay The glass substrates were first immersed in 10 N KOH for 12 hr and then rinsed in an ultrasonicator (As One-USD 2) with deionized water. A flow cell was constructed using a glass slide (24 mm × 36 mm; Matsunami Glass, Kyoto, Japan) and a cover slip (12 mm × 18 mm; Matsunami Glass, Kyoto, Japan), which were sandwiched together with paraffin tapes (0.127 mm thickness) (Fig. 2a). One of tau isoforms (0N3R, 1N3R, 2N3R, 0N4R, 1N4R, or 2N4R) or tau mutants (V248L, G272V, P301L, V337M, or R406W) and MTs were incubated at a final concentration of 10 nM/100 nM/1 ␮M and 0.5 ␮M, respectively, at 37 ◦ C for 30 min (Fig. 2b). For the assay with different ratios of 3R:4R isoforms, MTs were incubated with

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Fig. 2. Schematic representation of tau protein detection using MT gliding assay over kinesin-coated surface. (a) Preperation of the flow cell. A 2 mm wide flow cell was formed by two cover glasses sandwiched together with Paraffin strips (0.127 mm in thickness) in-between. Casein and kinesin solutions were sequentially injected into the flow cell. (b) Preparation of tau-bound MTs. Labeled (TAMRA) and unlabeled tubulins were polymerized, stabilized and sheared. These MTs were incubated with each of tau species respectively. Control MTs were prepared without tau binding (no-tau MTs). The MTs were finally injected into the kinesin-coated flow cell. The flow cell was immediately sealed and incubated for 5 min. (c) Observation and measurement of density, landing rate and gliding velocity of MTs.

2N3R:2N4R ratio of 1:3, 1:1 and 3:1 with keeping the total tau concentration at 1 ␮M. Then, those tau-MT solutions were diluted with the motility solution (1:4 ratio). Casein (10 ␮l, 0.5 mg ml−1 ) was flushed into the flow cell and incubated for 3 min at room temperature. Next, 10 ␮l of the kinesin solution was introduced into the flow cell and incubated for 5 min. Finally, one of 10 ␮l tau-bound MTs or no-tau MTs (control) were added into the flow cell, and that was then immediately sealed with vacuum grease (8009-03-8, APIEZON) (t = 0 min) (Fig. 2c). After incubation for 5 min, images were acquired as described below (Supplementary movies 1–3).

where, A is a saturation coefficient that defines the number of MTs on the kinesin surface, and  is a time constant. The landing rate was defined as the number of MTs that had landed per min per ␮m2 , i.e., D per min. Density, landing rate and gliding velocity were measured using in-house-written MATLAB routines. For the velocity measurement, FIESTA tracking algorithms determined x and y positions of MT leading tips and the center coordinate between two consecutive images. The displacement of the leading tip, dLk , between two consecutive frames, k−1 and k, was calculated by the equation: dLk =



(xk − xk−1 )2 + (yk − yk−1 )2

1/2

(2)

2.3. Measurement of three detection parameters on kinesin-coated surface

By integrating dL, the total displacement of the MT filament and the velocity were determined.

Tau-bound MTs were visualized using a fluorescent microscope (IX71, Olympus) equipped with a 100 × oil-immersed objective and a charge-coupled device camera (ORCA-R2, Hamamatsu, Hamamatsu, Japan). Fluorescent images were stored using HDR35 recording software (Hamamatsu). Images and movies were processed using ImageJ, FIESTA [32] and MATLAB software (MathWorks). The movies were analyzed by dividing the viewing area into four quadrants and the number of MTs were counted to reduce a possible bias caused by the nonuniformity of kinesin coating. Control MTs, V337M-bound MTs, and 2N4R-bound MTs were assessed by recording a 1 min video at t = 5, 10, 15, 20, 25 and 30 min after the start of the assay (exposure time: 500 ms (2 fps); view area: 86.7 ␮m × 66.0 ␮m). Once the assay time was optimized to 5 min, all other data was obtained at t = 5 min. MT density was defined as the number of surface-attached MTs per ␮m2 . With time, t, the MT density, D, increased. The relationship between D and t was fit to:

2.4. Statistical analysis



t

D = A 1 − e− 

 (1)

Student’s t-test was performed to calculate the significance of two different conditions (Table S1-S3). Analysis of variance (ANOVA) was applied to estimate if the difference between the groups was statistically significant. If a difference was found to be significant, then Tukey-Kramer method was applied to two groups [33]. 3. Results and Discussion Although stabilized MTs were utilized, we hypothesized that the mutant tau could depolymerize MTs as in the case of in vivo tau pathology [5]. The depolymerization of MTs will drastically affect the MT concentration in an assay solution and has a potential to affect the tau detection parameters. In particular, the MT length can affect the attachment and detachment of MTs on the kinesincoated surface: the shorter MTs detach more rapidly due to less number of kinesins interacted [34]. The correlation between MT

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Fig. 3. Time course of MT density and MT landing rate. (a) Density vs. time. Curves were fit to an exponential curve expressed by Eq. 1. (Materials and Methods). (b) Landing rates vs. time. A significant difference in the landing rates (***: P < 0.001) of 2N4R- and V337M-bound MTs was found at 5 min. Diamonds −control (no-tau); triangles −V337M; squares −2N4R tau, Means ± SEM; N > 100.

length and MT binding phenomenon lead to the measurement of MT length after tau incubation (Fig. S1). MTs length did not show significant difference when incubated with 2N4R and five of its mutated variants. In an effort to shorten the assay time for a LOC format, we evaluated a time course of MT density (Fig. 3a) and landing rate (Fig. 3b). Having excessive amount of MTs in the flow cell the MT density on the surface gradually increased until the dynamic landing to and detachment from the surface of the flow cell were equilibrated (Fig. 3a). The landing rate on the other hand decreased with time and approached below 5 × 10−5 filaments ␮m−2 min−1 at 10 min after starting the assay (Fig. 3b). Although the landing rate values were similar among three different conditions (control, 2N4R and V337M mutant) after 10 min (Fig. 3b), initially higher landing rate for both control and V337M (Fig. 3b) resulted in higher and rapidly saturated MT densities (Fig. 3a). On the other hand, lower landing rate and thus, lower MT density of 2N4R required a longer assay time to achieve complete saturation. In both densities and landing rates, significant differences (student’s t test P < 0.001) were observed for 2N4R vs. V337M and 2N4R vs. control even at t = 5 min. Therefore, five minutes incubation time was selected to examine the effect of tau species on MT density, landing rate and velocity. MT densities of 2N3R and 2N4R were measured (Fig. 4a) for lower tau concentrations (10 − 100 nM), although MT landing and binding kinetics were evaluated at saturated tau concentration of 1 ␮M (Fig. 3). MT density decreased with the increase of tau concentration in both 2N3R and 2N4R cases, and the difference between two isoforms became prominent at 100 nM of tau (Fig. 4a). The significant difference in MT density (P < 0.01) between 2N3R and control was detected only when MTs were incubated with 1 ␮M 2N3R. Therefore, 1 ␮M tau was used for the rest of the study.

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Three detection parameters were measured for MTs incubated with one of tau isoforms-0N3R, 1N3R, 2N3R, 0N4R, 1N4R, and 2N4R (Fig. 4b–d). Fluorescent MTs in three representative assays are shown in Fig. 4e–g, and corresponding movies (Movies S1-S3). Two groups of tau isoform-bound MTs were clearly distinguished in any detection parameter: those that contain three MTBRs (0N3R, 1N3R, and 2N3R) and those that contains four MTBRs (0N4R, 1N4R, and 2N4R). The difference in density and landing rate between two groups is more significant (P < 0.001; Fig. 4b–c) than in velocity (P < 0.05; Fig. 4d), which demonstrates that density and landing rate are more sensitive detection parameters than velocity. Previous studies demonstrated that 4R isoforms have greater affinity for MTs than 3R isoforms due to the presence of R2 and R1-R2 inter repeat region [14,18,35] and overall higher net positive charge [36]. In addition, there is speculation that 4R isoforms and 3R isoforms bind to MTs in different conformations due to the uneven distribution of MT binding affinity along their binding domain [14,37]. These reasons make 4R isoform-bound MTs less accessible to the kinesin-coated surface than 3R isoform-bound MTs, resulting in the lower values of three detection parameters (Fig. 4). No significant differences in all three parameters were found between the isoforms with different numbers of N-inserts in the projection domain (Fig. 4b–d). Some of the previous studies suggested that a projection domain enhances MT-kinesin interaction by increasing the binding and decreasing the unbinding of kinesin to and from MTs, respectively [18,26] Whereas another study demonstrate that only the MTBRs are predominant in modulating kinesin motility [16]. Further, the effect of a projection domain on the MT movement in the gliding assay might have also been masked, because it involves much higher number of kinesin molecules than previously reported studies (> 100 kinesins for a 10 ␮m MT, Ikuta et al., 2014). Overall, the changes in all three detection parameters clearly depended on the number of MTBRs which was in agreement with previous studies [18,25] but were not significantly affected by the number of N-inserts (partly in agreement with the results by Schmidt et al., 2012). Next, we assayed admixtures of different ratios of 3R and 4R isoforms corresponding to the human physiological and pathological conditions. In a healthy human brain, equal amount of 3R and 4R isoforms are expressed, but altered in pathological condition [38]. In particular, pathological condition such as AD the 4R isoform level is increased (∼2:1 4R:3R) [9], while in Pick’s disease the 3R isoform expression is increased. The CSF tau levels were found to indicate alteration in tau isoform expression [10]. Therefore, while keeping the total tau concentration at 1 ␮M, we tested the following 2N3R:2N4R isoform ratios: 1:3, 1:1 and 3:1. The MT density decreased significantly (P < 0.001) with relative increase of 2N4R isoform (Fig. 5). A mixture with the same amount of 2N3R and 2N4R showed characteristics closer to a pure 2N4R solution (P > 0.05) than a pure 2N3R solution (P < 0.001) because 2N4R has higher affinity to MTs when both 2N4R and 2N3R isoforms coexist in a solution [39]. Finally, effects of five 2N4R FTDP-17 mutations (V248L, G272V, P301L, V337M, and R406W) were studied. All of the mutations are located in C-terminal that harbour the MTBRs, contributing in different ways to the loss of MT integrity in vivo [40] and in vitro [41,42]. Any mutant-bound MTs demonstrated significantly higher MT density (P < 0.001; Fig. 6a) and landing rate (P < 0.05; Fig. 6b) when compared to 2N4R-bound MTs. Whereas, P301L and V337M showed no significant difference in the densities and landing rates (P > 0.05), while V248L, G272V and R406W had significantly lower densities and landing rates (P < 0.05 or P < 0.01) when compared to control. Mutants could be grouped into two: G272V-, P301L- and V337M-bound MTs had significantly greater densities and landing rates than V248L- and R406W-bound MTs (P < 0.05). However, there were no significant differences in densities and landing rates

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Fig. 4. Densities, landing rates, and velocities of MTs bound with tau isoforms on a kinesin-coated surface. (a) Density of MTs incubated with 10-1000 nM of 2N3R and 2N4R isoforms. Density decreased with the increase of tau concentration, and 2N3R and 2N4R were distinguished over 100 nM. At 1 ␮M, both 2N3R and 2N4R showed significantly low density compared to control (no-tau). (b) Densities, (c) landing rates, and (d) velocities for MTs bound with 0N3R, 1N3R, 2N3R, 0N4R, 1N4R, or 2N4R. The values were normalized with respect to no-tau MTs. The 3R isoform-bound MTs have significantly larger values for three parameters than did the 4R isoform-bound MTs. No significant differences were found among the 3R isoforms or among the 4R isoforms. Means ± SEM; *: P < 0.05; **: P < 0.01; ***: P < 0.001; n.s.: P > 0.05 (ANOVA); N > 300 (Over a hundred MTs were examined per flow cell for three experiments). (e-g) Fluorescent images of MTs on a kinesin surface. (e) No-tau MTs (control), (f) 2N3R-MTs, and (g) 2N4R-MTs; scale bar: 20 ␮m.

Fig. 5. Densities of MTs incubated with different ratio of 2N3R to 2N4R. MTs were incubated in mixture of 2N3R and 2N4R isoforms in ratio of 1:0, 3:1, 1:1, 1:3, and 0:1. At ratios when 2N4R constituted more than 50% of the mixture (1:1 and 1:3), the binding density were similar to that of MTs bound only with 2N4R (0:1), and there was no significant difference among them. At ratios when 2N4R constituted below 50% (3:1), the density did not have significant difference from that of MTs bound only with 2N3R (1:0). Means ± SEM; ***: P < 0.001; n.s.: P > 0.05 (ANOVA); N > 300 (Over a hundred MTs were examined per flow cell for three experiments).

among each group (P > 0.05 for both groups). Consequently, the first group (G272V, P301L, and V337M) showed relatively larger MT densities and landing rates, and the second group (V248L and R406W) showed relatively smaller MT densities and landing rates. In contrast to density and landing rate, the velocity did not show significant differences among mutants (P > 0.05), although R406W and V248L decreased velocity compared to the control (P < 0.001), and G272V, P301L, and V337M did not (P > 0.05; Fig. 6c). In addition, there was a significant difference between the wild-type 2N4R MTs and any of the mutants (P < 0.001 for each case). Therefore, velocity can be used to differentiate mutants from wild-type 2N4R, but fails to identify the mutants. This result clearly shows that density and landing rate provide higher sensitivity and are more competent in examining tau effect on MT-kinesin interaction. The presented results directly reflect the binding affinities of the five tau mutants for MTs because the relative densities and landing rates follow the order of binding affinities of tau to MTs. All these mutations lower the binding affinity of tau to MT as reported previously: P301L having the strongest effect, G272V and V337M having

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Fig. 6. Densities, landing rates, and velocities of MTs bound with tau mutants on a kinesin-coated surface. (a) Densities, (b) landing rates, and (c) velocities of MTs bound with one of the five 2N4 R mutants. The values were normalized with respect to no-tau MTs. The densities and landing rates of the MTs bound with the mutant 2N4Rs were significantly greater than those for wild-type 2N4R. The V248L- and R406W-bound MTs had significantly smaller densities and landing rates than did the other three mutants. In contrast, difference among mutants were not found in velocity, although wild-type 2N4R and mutants were distinguished. Means ± SEM; ***: P < 0.001; *: P < 0.05; n.s.: P > 0.05 (ANOVA); N > 300 (Over a hundred MTs were examined per flow cell for three experiments).

intermediate effects, and R406W having the least effect [6,43,44]. The effect of V248L on the tau-MT interaction is reported for the first time. The positions of these FTDP-17 mutations explain the effect tau‘s binding affinity to MTs: V248L and G272V are located in R1, P301L in R2, V337M between R3 and R4, and R406W distal to the MTBR (Fig. 1b). Between two mutations located in the same MTBR (R1 region), the density and landing rate of G272Vbound MTs are significantly greater than those of V248L-bound MTs (P < 0.05; Fig. 6a, b). This may be explained by two facts: (i) The G272V mutation is found in the MT-binding motif PGGG of R1 region, which is known to cause a reduction in the binding affinity to MTs in comparison with that of 2N4R [45]. (ii) The G272V is located at the MTBR region that interacts with proline rich flanking region, which results in enhancement of tau-MT binding [46]. P301L mutation in R2 and V337M mutation between R3-R4 dramatically reduce binding affinity to MTs [42] and thus, did not show significant difference compared to control. R406W, which is not located in MTBRs, induces hyperphosphorylation, and later results in the loss of binding affinity [47]. Therefore, immediate effect of R406W was not seen in our assay. The density and landing rate of R406W-bound MTs were much smaller than those of G272V-, P301L-, and V337M-bound MTs (Fig. 6a, b). Our assay system utilizes collaborative work of multiple kinesin motors allowing stable MT gliding. However, the large number of surface kinesin molecules hinders the clear detection of the tau effect. We found that density and landing rate measurements have several advantages to differentiate the tau effect when compared with gliding velocity. First of all, the former ones provide higher sensitivity (Figs. 4 and 6). Secondly, velocity measurements require a more complex setup. Finally, specific software is essential for the data analysis of the gliding velocity. Among those three parameters, the MT density demands the simplest setup and analysis with a single image at t = 5 min. Therefore, for an easy and fast tau analysis of future on-chip applications, MT density is a more suitable parameter compared to others reported here. Our method can be an initial step towards developing such a platform that is capable of mimicking events that occur during in vivo axonal transport, such as tau-MT interaction and MT-kinesin interaction. In particular, the poor binding of mutant tau to MTs in the presence of paclitaxel [43] is also verified through our assay

(Fig. 6), which otherwise needs cellular level or animal model studies [48]. In an in vivo study, the complex cellular and/or intercellular background influences the detection signals from the targeted molecules; this makes it challenging to perform certain assays. In addition, the interference from the other cellular components makes it further difficult to perform isolated assays, such as MTkinesin binding assay to observe the effect of tau or some other MAPs. In a cell free in vitro model such as our assay, the scope of examining molecular level interaction isolated form other intracellular events provides a suitable platform to clearly understand the individual protein interactions. 4. Conclusion In summary, employing an MT gliding assay that incorporates a kinesin-coated surface, we have examined three detection parameters and found that densities and landing rates are superior to velocity. They are capable of differentiating 3R and 4R isoforms, different 3R:4R ratios, tau mutant groups, and tau mutants from wild-type tau. Because various in vivo studies have shown that the presence of each mutants used in our study strongly correlates with neurodegenerative conditions [6,42,44] and an imbalance in the ratio of 3R:4R isoforms is sufficient to cause neurodegeneration [9], our method has a potential to be adopted as a tau assay platform for tauopathy-related conditions. The degree to which mutation affects the tau binding affinity is determined directly through our gliding-assay, which cannot be accomplished through any other conventional methods, such as enzyme-linked immunosorbent assay. Moreover, parameters measured in our assay are straightforward and advantageous when the gliding assay-based tau detection will be incorporated with a LOC format. Our system is a promising approach towards the development of a non-immune-based method for detecting wild-type tau isoforms, mutants and other MAPs, which may aid in the differential diagnosis of tauopathies. Acknowledgements This work is supported by Nakatani foundation (RY); Japan Society for the Promotion of Science (JSPS) (06035-123332) (RY); JSPS and NSF under the Japan U.S. Cooperative Science Pro-

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gram (11033011-000121) (RY); NIH/NIMH 2R44MH091909 (SLK), JSPS L-15536 (SLK), Grant-in-Aid for Scientific Research (KAKENHI) 26790030 (MCT), Kyoto University Supporting Program for Interaction-Based Initiative Team Studies (SPIRITS) (SPS, RY) as part of the Program for Promoting the Enhancement of Research Universities, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. We thank Mr. Kazuya Fujimoto for tubulin and kinesin preparation. We also thank Mr. Tsubasa Ikeuchi for the MATLAB routine. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.snb.2016.07.082. References [1] L. Buee, T. Bussiere, V. Buee-Scherrer, A. Delacourte, P.R. Hof, Tau protein isoforms, phosphorylation and role in neurodegenerative disorders, Brain Res. Rev. 33 (2000) 95–130. [2] M. Goedert, Tau protein and neurodegeneration, Semin. Cell Dev. Biol. 15 (2004) 45–49. [3] J. Lewis, E. McGowan, J. Rockwood, H. Melrose, P. Nacharaju, M. Van Slegtenhorst, et al., Neurofibrillary tangles, amyotrophy and progressive motor disturbance in mice expressing mutant (P301L) tau protein, Nat. Genet. 25 (2000) 402–405. [4] H. Hampel, K. Buerger, R. Zinkowski, S.J. Teipel, A. Goernitz, N. Andreasen, et al., Measurement of phosphorylated tau epitopes in the differential diagnosis of Alzheimer disease: a comparative cerebrospinal fluid study, Arch. Gen. Psychiatry 61 (2004) 95–102. [5] G. Lee, C.J. Leugers, Tau and tauopathies, Prog. Mol. Biol. Transl. Sci. 107 (2012) 263–293. [6] M. van Slegtenhorst, J. Lewis, M. Hutton, The molecular genetics of the tauopathies, Exp. Gerontol. 35 (2000) 461–471. [7] K. Blennow, Cerebrospinal fluid protein biomarkers for Alzheimer’s disease, NeuroRx 1 (2004) 213–225. [8] K. Ishiguro, H. Ohno, H. Arai, H. Yamaguchi, K. Urakami, J.M. Park, et al., Phosphorylated tau in human cerebrospinal fluid is a diagnostic marker for Alzheimer’s disease, Neurosci. Lett. 270 (1999) 91–94. [9] S. Chen, K. Townsend, T.E. Goldberg, P. Davies, C. Conejero-Goldberg, MAPT isoforms: differential transcriptional profiles related to 3R and 4R splice variants, J. Alzheimer Dis. 22 (2010) 1313–1329. [10] C. Luk, Y. Compta, N. Magdalinou, M.J. Martí, G. Hondhamuni, H. Zetterberg, et al., Development and assessment of sensitive immuno-PCR assays for the quantification of cerebrospinal fluid three- and four-repeat tau isoforms in tauopathies, J. Neurochem. 123 (2012) 396–405. [11] M. Herrmann, S. Golombowski, K. Krauchi, P. Frey, C. Mourton-Gilles, C. Hulette, et al., ELISA-quantitation of phosphorylated tau protein in the Alzheimer’s disease brain, Eur. Neurol. 42 (1999) 205–210. [12] C. Luk, G. Giovannoni, D.R. Williams, A.J. Lees, R. de Silva, Development of a sensitive ELISA for quantification of three- and four-repeat tau isoforms in tauopathies, J. Neurosci. Methods 180 (2009) 34–42. [13] J. Bordeaux, A.W. Welsh, S. Agarwal, E. Killiam, M.T. Baquero, J.A. Hanna, et al., Antibody validation, Biotechniques 48 (2010) 197–209. [14] B.L. Goode, M. Chau, P.E. Denis, S.C. Feinstein, Structural and functional differences between 3-repeat and 4-repeat tau isoforms – implications for normal tau function and the onset of neurodegenerative disease, J. Biol. Chem. 275 (2000) 38182–38189. [15] K.J. Rosenberg, J.L. Ross, H.E. Feinstein, S.C. Feinstein, J. Israelachvili, Complementary dimerization of microtubule-associated tau protein: implications for microtubule bundling and tau-mediated pathogenesis, Proc. Natl. Acad. Sci. U. S. A. 105 (2008) 7445–7450. [16] R. Dixit, J.L. Ross, Y.E. Goldman, E.L.F. Holzbaur, Differential regulation of dynein and kinesin motor proteins by tau, Science 319 (2008) 1086–1089. [17] D.P. McVicker, L.R. Chrin, C.L. Berger, The nucleotide-binding state of microtubules modulates kinesin processivity and the ability of Tau to inhibit kinesin-mediated transport, J. Biol. Chem. 286 (2011) 42873–42880. [18] M.C. Tarhan, Y. Orazov, R. Yokokawa, S.L. Karsten, H. Fujita, Biosensing MAPs as roadblocks: kinesin-based functional analysis of tau protein isoforms and mutants using suspended microtubules (sMTs), Lab Chip 13 (2013) 3217–3224. [19] M. Vershinin, B.C. Carter, D.S. Razafsky, S.J. King, S.P. Gross, Multiple-motor based transport and its regulation by Tau, Proc. Natl. Acad. Sci. U. S. A. 104 (2007) 87–92. [20] D. Yu, N.E. LaPointe, E. Guzman, V. Pessino, L. Wilson, S.C. Feinstein, et al., Tau proteins harboring neurodegeneration-linked mutations impair kinesin translocation in vitro, J. Alzheimer Dis. 39 (2014) 301–314. [21] S.L. Rogers, J.M. Scholey, Motility Assays for Microtubule Motor Proteins, eLS, John Wiley & Sons, Ltd, 2001. [22] A. Ebneth, R. Godemann, K. Stamer, S. Illenberger, B. Trinczek, E.M. Mandelkow, et al., Overexpression of tau protein inhibits kinesin-dependent

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Biographies Subramaniyan Parimalam Subhathirai received her Ph.D degree in Micro Engineering from Kyoto University, Japan, 2016. She is currently a Researcher Associate at Department of Micro Engineering, Kyoto University. She holds a Bachelor degree in Siddha medicine and Surgery from The Tamil Nadu Dr. M.G.R. Medical University, India, 2010 and Master of Science degree in Nanoscience and Nanotechnology

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from SRM University, India, 2012. Her research interest includes microfluidic device fabrication and Biosensors.

and applications to optics, hard-disk drives, and biotechnology/nanotechnology. He is also interested in autonomous distributed microsystems.

Mehmet C. Tarhan received his Ph.D degree in Electrical Engineering from The University of Tokyo, Japan, 2010. He is currently an Associate Professor at HEI ISA ISEN (Yncréa Group), Lille, France. His research area involves micro/nano technologies, biological applications and integration of them especially with motor proteins and microtubule.

Hirofumi Shintaku received his Ph.D. degree in the Mechanical Engineering from Kyoto University, Japan in 2006, and is currently an Assistant Professor at Department of Micro Engineering, Kyoto University. He has worked on research and development of micro/nano fluidics, electrokinetic phenomena and acoustics. His current research interest involves microfluidics for single cell studies.

Stanislav L. Karsten received his Ph.D degree in Medical Genetics and Pathology from Uppsala University, Sweden in 2000. Dr. Karsten held a faculty position at UCLA and served as a Chief of the Division of Neuroscience at Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center from 2007 to 2011. At that time, his research was focused on functional neurogenomics, neurodegenerative diseases and methods development. Currently, Dr. Karsten is President and CEO of NeuroInDx, Inc., a company developing methods and instrumentation for the analysis of single cells and heterogeneous tissues. Dr. Karsten is an author of over 50 peer reviewed publications, reviews, book chapters and patent applications.

Hidetoshi Kotera received his Ph.D. degree in Mechanical Engineering from Kyoto University in 1992, and is a Professor at Department of Micro Engineering, Kyoto University. He was Vice President (2008–2012) and Executive Vice President (2012–2014) of Kyoto University. Now He is the Advisor to MEXT, Bureau of the CSTP/OECD and Special Assistant to the President, RIKEN, Japan, since 2015. His current research areas are nano technology, cell biology and nano medicine.

Hiroyuki Fujita received the B.S., M.S., and Ph.D. degrees in Electrical Engineering from The University of Tokyo, Japan, in 1975, 1977, and 1980, respectively. He has been with the Institute of Industrial Science, The University of Tokyo, where he was a Lecturer, in 1980 and 1981, was an Associate Professor in 1981–1993, and has been a Professor, since 1993. He has also been the Director of the Center for International Research on Micronanomechatronics, Institute of Industrial Science, The University of Tokyo, since 2003. He is currently engaged in the investigation of microelectromechanical systems fabricated by integrated-circuit-based processes

Ryuji Yokokawa received his Ph.D. degree from the Electrical Engineering Department at The University of Tokyo, in 2005. He is an Associate Professor at Department of Micro Engineering, Kyoto University, Japan, since 2011. He was an Assistant Professor at Department of Micro System Technology, Ritsumeikan University, Japan (2005–2009), and Department of Micro Engineering, Kyoto University (2009–2011). He was a Project Researcher of Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency (PRESTO, JST), and an Adjacent Faculty of World Premier International Research Center (WPI) Initiative, Integrated Cell Material Sciences (iCeMS), Kyoto University. His current research areas are micro/nano fabrications with focus on biophysical properties of motor proteins and cell culture platforms.