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Label-free detection of microvesicles and proteins by the bundling of gliding microtubules Samata Chaudhuri, Till Korten, Slobodanka Korten, Gloria Milani, Tobia Lana, Geertruy te Kronnie, and Stefan Diez Nano Lett., Just Accepted Manuscript • DOI: 10.1021/acs.nanolett.7b03619 • Publication Date (Web): 05 Dec 2017 Downloaded from http://pubs.acs.org on December 5, 2017

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Label-free detection of microvesicles and proteins by the bundling of gliding microtubules Samata Chaudhuri†,‡, Till Korten†,‡, Slobodanka Korten†,‡, Gloria Milani§, Tobia Lana§, Geertruy te Kronnie§, and Stefan Diez†,‡,* †

B CUBE — Center for Molecular Bioengineering, TU Dresden, 01069 Dresden, Germany Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany § Department of Women's and Children's Health, University of Padova, 35128 Padova, Italy * Corresponding author: [email protected] ‡

ABSTRACT Development of miniaturized devices for the rapid and sensitive detection of analyte is crucial for various applications across healthcare, pharmaceutical, environmental, and other industries. Here, we report on the detection of unlabeled analyte by using fluorescently-labeled, antibodyconjugated microtubules in a kinesin-1 gliding motility assay. The detection principle is based on the formation of fluorescent supramolecular assemblies of microtubule bundles and spools in the presence of multivalent analytes. We demonstrate the rapid, label-free detection of CD45+ microvesicles derived from leukemia cells. Moreover, we employ our platform for the label-free detection of multivalent proteins at sub-nanomolar concentrations, as well as for profiling the cross-reactivity between commercially available secondary antibodies. As the detection principle is based on the molecular recognition between antigen and antibody, our method can find general application where it identifies any analyte, including clinically relevant microvesicles and proteins. KEYWORDS: Microtubule, microtubule bundles, kinesin-1, label-free detection, microvesicles, antibody cross-reactivity

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Analyte detection is fundamental to a wide range of applications across different fields, including healthcare1,2, drug discovery3, and environmental safety4. Thereby, the development of fast and sensitive label-free detection methods is becoming increasingly important as analyte labeling – besides being labor-intensive, expensive and time-consuming – often compromises the results5,6. However, so far, most of the available label-free detection methods are based on surface reactions, often lacking the desired sensitivity and speed7,8. To overcome these limitations, we developed a label-free detection platform based on the self-assembly of fluorescently-labeled, antibody-conjugated microtubules (Ab-MTs) in a kinesin-1 gliding motility assay. Owing to their small size, energy efficiency, and robustness in synthetic environments, biomolecular motors (like kinesin-1) and their associated cytoskeletal filaments (like microtubules, MTs) are an attractive choice for a wide range of nanotechnological applications9–12. Over the past decades, biomolecular motors have been extensively used for developing molecular sorting13–17, surface imaging18, and biosensing19–21 devices. Around the same time, considerable research has been focused on studying the dynamic self-assembly of cytoskeletal filaments, driven by the active transport of biomolecular motors, that leads to the formation of highly ordered supramolecular structures, such as bundles and spools

22,23

. While

the latter studies aimed at understanding and tuning the mechanism of biomolecular motordriven self-assembly24–29, engineering applications with this phenomenon have not been reported up to date. Here, we used our platform based on Ab-MTs and kinesin-1 motors for the label-free detection of multivalent analytes (Figure 1). The antibodies on the gliding Ab-MTs capture the analyte by binding to their antigenic sites. Due to their multivalent nature, each analyte can bind to multiple Ab-MTs, thereby cross-linking them. In a motor-driven gliding assay, this results in the formation of supramolecular bundles and spools of Ab-MTs. This behavior of the gliding 2 ACS Paragon Plus Environment

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Ab-MTs enables the label-free detection of the analyte through characterization of Ab-MT bundling and spooling. Utilizing this phenomenon, we demonstrate the label-free detection of microvesicles as well as multivalent proteins, and we profile the cross-reactivity between secondary antibodies. Taken together, our method combines the inherent advantages of biomolecular motors for nanoscale transport with the motor-driven self-assembly of cytoskeletal filaments to successfully engineer a label-free biosensor for analyte detection.

Microvesicles (MVs) are membrane-bound vesicles shed from almost all cell types with size ranging from 100 to 1000 nm. MVs reflect the antigenic and molecular content of the cells of origin32,33, with a relevant role in cell-to-cell communication as vehicles of biological information between cells under both normal and pathological conditions32. In recent years, there has been a growing interest in characterizing MVs as biomarkers for the diagnosis of various diseases33–35. However, the successful detection and development of effective diagnostic platforms for MVs has remained challenging for a long time due to the requirements of extensive sample preparation and MV labeling34,36. Recently, a novel method of MV detection was reported by Kumar et al.35. Specifically, bundles of antibody-conjugated actin filaments were generated by cross-linking with fascin and aggregation of these bundles was used to detect MVs. Despite being a highly promising approach, certain challenges intrinsic to the assay setup were identified; the variability in the number of actin filaments per bundle, the instability of the assembly leading to a loss of actin filaments from the bundles, and a high-degree of uncontrolled filament aggregation upon MV addition impairs the robustness of the detection platform and allows only a qualitative detection of the analyte. To address these challenges, we employed AbMTs driven by kinesin-1 motor proteins. Unlike actin filaments - which are composed of only two protofilaments and thus require filament cross-linking to improve the cargo carrying 3 ACS Paragon Plus Environment

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capacity) - MTs are composed of 13 protofilaments enabling an efficient capture and transport of cargo on single filaments, i.e. without subsequent cross-linking12. Consequently, the assembly of single MTs to supramolecular bundles and spools upon addition of analyte enables the detection mechanism to be quantitative, namely by monitoring the number of MTs assembling into bundles. We generated Ab-MTs by covalently conjugating tetrazine-functionalized monoclonal anti-CD45 mouse IgG antibodies to trans-cyclooctene (TCO) functionalized, rhodamine-labeled MTs, as recently described37. As models, we used two human cell lines for our experiments: an acute lymphocytic leukemia (ALL) patient-derived human T-cell line (DND41, leukemia cell line) and a human osteosarcoma cell line (MG63, control cell line). The leukemia cells have hematopoietic marker CD45 antigens on their surface, while the control osteosarcoma cells lack these surface antigens35. Consequently, the leukemia cells and the control cells release CD45+ MVs (leukemia-MVs) and CD45- MVs (control-MVs), respectively. We isolated MVs from both cell lines (at 1000 cells/µL). A schematic of the assay is shown in Figure 2A. Upon addition of the CD45+ leukemia-MVs to gliding Ab-MTs (comprising anti-CD45 antibodies), the Ab-MTs captured the leukemia-MVs. On encountering other gliding Ab-MTs, these MV-bound-Ab-MTs were cross-linked by the multiple CD45 antigens present on the surface of each leukemia-MV. This resulted in the dynamic self-assembly of MV-bound-Ab-MTs into bundles and spools (Figure 2B). Control-MTs (without conjugated antibodies) showed no supramolecular self-assembly upon addition of leukemia-MVs. As an additional control, we labeled the cell lines with fluorescent dyes to obtain fluorescently-labeled MVs. The fluorescence signal from the leukemia-MVs was found to co-localize with the fluorescent Ab-MT assemblies, confirming that binding of the MVs generated the supramolecular assemblies of the Ab-MTs. In contrast, no change in the gliding of the Ab-MTs was 4 ACS Paragon Plus Environment

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observed upon addition of control-MVs, which did not show any co-localization with the AbMTs, confirming the specificity of our detection method (Figure 2C). Assembly of the Ab-MTs into bundles and spools resulted in an increase in the fluorescence intensity of the Ab-MT signal over time (Figure 2D). While there was no change in the fluorescence intensity of the Ab-MTs upon addition of the control-MVs, a rapid increase was observed after addition of the leukemiaMVs. This allowed us to characterize this distinct behavior as a read-out for analyte detection. Despite the increasing clinical significance of MVs, reliable quantification of MV concentration remains challenging, and no standard protocol is currently available36,38. We, therefore, standardized our assay by quantifying the cell number, which we believe is correlated with the number of MVs shed from the cell surface. Cell number is also a relevant indicator for the diagnosis of various clinical conditions such as, in our example, leukemia. The total number of leukocytes varies between 4500 – 10,000 cells/µl in normal blood, while in leukemia patients the number can increase to 50,000 – 100, 000 cells/µl, or even higher39. In our case, we obtained MVs from 1000 cells/µl (for both control and leukemia cells) and demonstrated the successful detection of MVs from the leukemia cells. This cell number is likely to be relevant for practical clinical setups and will allow analyte detection over a wide range of sample dilutions. We emphasize that our detection is based on the quantification of the fluorescence signal from the Ab-MTs, thus obviating the need to label the MVs.

Next, we employed our Ab-MT platform for the label-free detection of multivalent proteins. We used Ab-MTs (comprising monoclonal mouse antibodies) to detect AlexaFluor488-labeled anti-mouse secondary antibodies. Addition of the protein analyte led to a similar read-out as observed above upon addition of leukemia-MVs, i.e. the formation of characteristic


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supramolecular assemblies of Ab-MT bundles and spools. No such self-assembly was observed for control-MT (without conjugated antibodies) upon analyte addition. The fluorescence signal from the AlexaFluor-488-labeled analyte was found to colocalize with the fluorescent Ab-MT assemblies – but not with the control-MTs – confirming the specificity of the detection (Figure 3A). To investigate the sensitivity of detection, we observed the fluorescence intensity of the AbMTs as a measure of the rate of Ab-MT self-assembly over time for a wide range of analyte concentrations (Figure 3B). We found that the self-assembly rates of the Ab-MTs correlated with the analyte concentration (Figures 3C and 3D), demonstrating the quantitative aspect of the detection method with a sensitivity down to 0.1 nM. It is interesting to note that this detection limit falls well within the dynamic detection range of commercially available ELISA-based IgG detection kits (10 pM to 666 pM; ThermoFisher, Catalog #88-50550-22), underscoring the practical relevance of our proof-of-principle detection platform.

Antibodies recognize and bind to specific regions on the antigen surface, called epitopes. However, antibodies can also bind to similar sequences on other antigens, leading to undesired cross-reactivity40. As secondary antibodies are widely used in biochemical assays like Western blotting, flow cytometry, and immunohistochemistry, the evaluation of cross-reactivity between these antibodies is crucial. Here, using our Ab-MT platform, we tested the following commercially available secondary antibodies for their cross-reactivity to mouse monoclonal antibodies (on the Ab-MTs): anti-mouse, anti-rat, anti-goat and anti-sheep antibodies. Each of these antibodies at 10 nM concentration was added to the Ab-MTs in a gliding assay (Figure 4A). Addition of the anti-mouse antibodies led to rapid supramolecular self-assembly of the AbMTs, as expected. Interestingly, addition of the anti-rat antibodies also resulted in Ab-MT selfassembly. No such self-assembly was observed upon addition of the anti-goat or anti-sheep 6 ACS Paragon Plus Environment

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antibodies. The rate of Ab-MT self-assembly with time was measured (Figure 4B) and fitted to the curve obtained in Figure 3D to estimate the cross-reactivity. The affinity of 10 nM anti-rat secondary antibodies to monoclonal mouse antibodies was found to be comparable to that of 0.5 nM of anti-mouse antibodies, corresponding to a 5% cross-reactivity (using reported quantification methods41,42). In summary, we developed a rapid, ultrasensitive detection platform by covalently conjugating antibodies to MT for analyte detection within ~30 min assay time. As a proof-ofprinciple, we successfully demonstrated the specific capture and detection of MVs for leukemia diagnosis. As MVs are promising biomarkers for various diseases, including various types of cancer and inflammatory diseases, our example demonstrates a proof-of-principle application of a MV-based point-of-care diagnostics platform. Using secondary antibodies as protein analytes, we demonstrated the label-free detection of multivalent proteins in the sub-nanomolar range and profiled the cross-reactivity of commercially available secondary antibodies. The versatility of our method lies in the fact that by changing the antibody conjugated to the MTs, the platform can be easily adapted for the detection of a wide range of analytes. The fact that the detection sensitivity of our proof-of-principle example of IgG detection was comparable to that of commercial ELISA-based IgG detection kits makes our detection platform promising for translation to point-of-care clinical settings. Moreover, we believe that further improvement in the detection sensitivity can be achieved through optimization of the experimental parameters and through nanoseparation43 and molecular-concentration16. The detection principle of our method is based on the supramolecular self-assembly of gliding Ab-MTs upon addition of multivalent analyte. Presumably, the underlying mechanism of the dynamic self-assembly phenomenon is a two-step process based on analyte capture followed by active transport based aggregation44. Details of this mechanism are being extensively 7 ACS Paragon Plus Environment

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studied using gliding biotinylated MTs cross-linked by tetrameric streptavidin24–29. The insights gained from these ongoing studies will improve the understanding of the fundamental aspects of the self-assembly phenomenon described here. Typical to any antibody-based method, the capture and detection of analyte in our assay is influenced by the avidity of the antibody-antigen reaction. As a result, the analyte binding will depend on the binding affinity as well as the analyte valency. Additionally, as Ab-MT cross-linking is brought about by the binding of several analyte entities, the overall binding strength in the generated supramolecular assemblies will significantly increase with the number of bound analyte entities. All these factors will collectively influence the detection limit as well as the dynamic range of detection for a particular analyte-antibody pair. Real-time imaging of the self-assembly of gliding Ab-MTs on analyte addition allows our detection method to be quantitative. We note, that besides detecting multivalent proteins, where a single type of antibody is used to recognize multiple antigenic sites on the protein, the assay can be generalized for the detection of any arbitrary protein by conjugating MTs to two types of antibodies that recognize two different epitopes on the protein. In addition to previously reported biotinylated-MT-streptavidin systems22,23, our examples demonstrate that the dynamic selfassembly of gliding MTs can be generated by any multivalent entity capable of cross-linking the gliding MTs. Therefore, we believe that our assay and quantification method can also be employed for profiling the interaction of different MT-associated-proteins (MAPs) that crosslink MTs45,46, or for characterizing other MT interacting agents47,48, in a simple gliding assay setup.

Methods. Cell Culture and MV isolation: Human T cell leukemic cell line DND41 and human osteosarcoma cell line MG63 were cultured in RPMI 1640 media and DMEM media 8 ACS Paragon Plus Environment

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respectively (media purchased from GIBCO and supplemented with 10% fetal bovine serum, 2 mM L-Glutamine, 100 U/ml penicillin and 10 U/ml streptomycin), and incubated at 37ºC and 5% CO2. To isolate MVs, 50 ml of the culture medium from 1000 cells/µL was centrifuged at 250 g for 5 minutes. The supernatent was taken out and centrifuged at 2500 g for 15 minutes at 4°C. The cell-free supernatent obtained from this step was centrifuged at 18000 g for 2 hours at 4°C to obtain the MVs. The pellet obtained was resuspended in PBS. To obtain fluorescentlylabeled MVs for our control experiments, the DND41 and MG63 cell lines were stained with CellTracker™ Green CMFDA Dye and CellTracker™ Deep Red Dye respectively. 5 µM of the dye was added to the cells in PBS, incubated at 37ºC and 5% CO2 for 30 minutes, followed by removal of the dye-containing PBS media, and resuspension of the cells in their respective culture media. These fluorescently-labeled cells shed fluorescent MVs, which were similarly isolated. Ab-MT preparation and gliding assays: MTs were polymerized in BRB80 (80 mM PIPES, 1 mM EGTA, 1 mM MgCl2; pH 6.9), supplemented with 5 mM MgCl2, 1 mM Mg− GTP, and 5% DMSO, at 37 °C from a 4 mg/mL tubulin mixture and stabilized with 10 mM Taxol in BRB80 buffer solution. The microtubules were covalently conjugated to the tetrazine-modified monoclonal mouse anti-CD45 IgG antibodies (R&D Biosystems, Germany) as recently described37. Full-length Drosophila melanogaster kinesin-1 was expressed in insect cells and purified49. The secondary antibodies (anti-mouse, anti-rat, anti-sheep and anti-rabbit) used were purchased from ThermoFisher Scientific. Gliding motility assays were performed in flow cells by flowing in a solution of 0.5 mg/ml of casein in BRB80, followed by 12.5 nM kinesin-1 solution (each solution incubated for 2 min). Microtubule containing motility solution (1 mM ATP, 20 mM D-glucose, 20 mM glucose oxidase, 10 mM catalase, 10 mM DTT, and 10 µM


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taxol in BRB80) was then added and incubated for 5 min, after which excess MTs were washed off with motility solution. Finally, the analyte (secondary antibodies, microvesicles) was appropriately diluted in the motility solution and added to the gliding assay. Image acquisition and data analysis: Imaging was performed with an inverted fluorescence microscope Nikon Eclipse Ti and PlanApo 100× oil-immersion objective lens N.A., 1.49 with an electron-multiplying charge-coupled device (EMCCD) camera (iXon ultra EMCCD; DU-897U; Andor) in conjunction with NIS-Elements (Nikon) software. The acquired images were analyzed with ImageJ. To quantify the fluorescence intensity of the Ab-MTs with time upon analyte addition, a MATLAB script was used. As the Ab-MTs assembled to bundles and spools, the increase in the fluorescence intensity per pixel shifted the intensity histogram to higher pixel values. This increase was monitored by evaluating the median of the top 1% of the fluorescence intensity pixel values of the Ab-MTs measured for 30-40 different field-of-views. Following data analysis, GraphPad Prism was used for plotting the evaluated results. .


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ACKNOWLEDGMENTS The authors would like to thank Corina Bräuer for technical support, Sumeet Pal Singh for helpful suggestions on the microvesicle labeling protocol, Juliane Beyer for comments on the manuscript, and the entire Diez-Lab for fruitful discussions. Financial support from the German Research Foundation (Cluster of Excellence Center for Advancing Electronics Dresden and the Dresden International Graduate School for Biomedicine and Bioengineering, DIGS-BB), the European Union Seventh Framework and Horizon 2020 Programs (under Grant Agreements 228971 (MONAD), 613044 (ABACUS) and 732482 (Bio4Comp)) are acknowledged. SUPPLEMENTARY MOVIES Movie S1: Fluorescence time-lapse images of gliding rhodamine-labeled Ab-MTs after the addition of leukemia MVs. Time stamps in min:sec.

Movie S2: Fluorescence time-lapse images of gliding rhodamine-labeled Ab-MTs (red) bundled by fluorescent leukemia MVs (green). Images were taken 30 min after the addition of the fluorescent leukemia MVs. Time stamps in min:sec.

Movie S3: Fluorescence time-lapse images of gliding rhodamine-labeled Ab-MTs (comprising monoclonal mouse antibodies) before and after the addition of 10 nM protein analyte (antimouse secondary antibody). Time stamps in min:sec.


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+ Analyte

Ab-MTs gliding on Kinesin-1 coated glass surface

Analyte binds to antibodies on gliding Ab-MTs

Ab-MTs cross-link via bound analyte

Bundling of Ab-MTs

Figure 1: Scheme showing the label-free detection of multivalent analytes. Antibodyconjugated microtubules (Ab-MTs) gliding on a kinesin-1-coated surface bundle due to crosslinking via the multivalent analyte.


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Figure 2: Label-free detection of microvesicles (MVs) as biomarkers for acute lymphocytic leukemia (ALL). (A) Schematic showing the workflow of MV detection. Leukemia-MVs and control-MVs were isolated by serial centrifugations from the supernatant of the cell culture of ALL cells and osteosarcoma cells, respectively. Purified MVs were added directly to a kinesin-1 gliding assay consisting of Ab-MTs and control-MTs. The leukemia-MVs (CD45+) bind to the gliding AbMTs (comprising anti-CD45 antibodies) and cross-link them. The characteristic bundling and spooling of the MV-bound-Ab-MTs into supramolecular assemblies is used as read-out for label-free analyte detection. (B) Fluorescence images of Ab-MTs before and after the addition of the leukemiaMVs showing the time course of Ab-MT bundling due to the analyte. Scale bar 10 µm. (C) Fluorescence images of rhodamine-labeled Ab-MTs (red) and Cy5-labeled control-MTs (cyan) after addition of fluorescently labeled MVs (green). Addition of the leukemia-MVs led to the formation of large supramolecular assemblies of Ab-MT bundles and spools. This Ab-MT self-assembly due to the leukemia MVs was specific; control-MTs continued gliding unaffected in the presence of the MVs. Moreover, no self-assembly was observed upon addition of the control-MVs. Fluorescence labeling of the cell lines prior to MV isolation yielded fluorescently labeled MVs. Exclusive colocalization of the fluorescence signal from the leukemia-MVs to the Ab-MTs, but not to the controlMTs, further confirmed the specificity of the detection. Scale bar 10 µm. (D) Normalized fluorescence intensities (F.I.) of the Ab-MTs versus time after addition of the leukemia-MVs (green line) and the control-MVs (purple line) show a characteristic increase of the F.I. only for the leukemia-MVs.


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Figure 3: Label-free protein detection. (A) Fluorescence images of rhodamine-labeled Ab-MTs (comprising monoclonal mouse antibodies, red) and Cy5-labeled control-MTs (cyan), before and after the addition of 10 nM protein analyte (anti-mouse secondary antibody, green). Addition of protein analyte led to self-assembly of the Ab-MTs, but not of the control-MTs. As the protein analyte used here was fluorescently labeled (Alexa Fluor 488), exclusive co-localization of the fluorescence signal from the analyte to the Ab-MT assemblies confirmed the specificity of the detection method. Scale bar 5 µm. (B) Fluorescence images of rhodamine-labeled Ab-MTs in kinesin-1 gliding assays before and after the addition of the protein analyte. The characteristic bundle and spool formation of the Ab-MTs upon addition of the analyte was observed from 10 nM to 0.1 nM analyte concentrations. Scale bar 5 µm. (C) Normalized fluorescence intensities (F.I.) versus time for the Ab-MTs after addition of different concentrations of the protein analyte. (D) Fitting second order polynomials (F.I. = B0 + B1t + B2t2) to the data in C, the rate of Ab-MT bundling was estimated from the slopes of the curve (B1) at t = 0 for analyte concentrations from 10 nM to 0.1 nM (table). The goodness of the fit, measured from r2 value was 0.96, 0.94, and 0.98 for 10 nM, 1 nM, and 0.1 nM respectively. As the r2 values were less than 0.2 for the 0.01 nM and 0 nM concentrations, they were not used for further evaluation. The reciprocal plot of the rate of Ab-MT bundling versus analyte concentration shows a linear behavior (r2 = 0.99).


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Time (min) Figure 4: Profiling antibody cross-reactivity. (A) Fluorescence images of rhodamine-labeled AbMTs (comprising monoclonal mouse antibodies) before and after the addition of 10 nM of antimouse, anti-rat, anti-rabbit and anti-sheep secondary antibodies. Addition of anti-mouse secondary antibodies led to the characteristic self-assembly of the Ab-MTs. However, cross-reactivity between anti-rat secondary antibodies and the mouse antibodies (on the Ab-MTs) also led to the formation of bundles and spools. No cross-reactivity between anti-rabbit and anti-sheep secondary antibodies was observed with the monoclonal mouse antibodies. Scale bar 5 µm. (B) Normalized fluorescence intensities (F.I.) versus time for the Ab-MTs after addition of different secondary antibodies. (C) The rates of Ab-MT self-assembly (estimated from the slopes of the curve at t = 0) yielded measures for the cross-reactivities between various secondary antibodies and monoclonal mouse antibodies.


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