Title: Golgi‐associated microtubules are fast cargo tracks and required for persistent cell migration
Abstract: Report27 January 2020free access Source DataTransparent process Golgi-associated microtubules are fast cargo tracks and required for persistent cell migration Huiwen Hao State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Jiahao Niu State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Boxin Xue State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Qian Peter Su orcid.org/0000-0001-7364-3945 State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Menghan Liu Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China Search for more papers by this author Junsheng Yang State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Jinshan Qin State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Shujuan Zhao State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Congying Wu Corresponding Author [email protected] orcid.org/0000-0002-3223-7884 Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China Search for more papers by this author Yujie Sun Corresponding Author [email protected] orcid.org/0000-0002-9489-4820 State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Huiwen Hao State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Jiahao Niu State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Boxin Xue State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Qian Peter Su orcid.org/0000-0001-7364-3945 State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Menghan Liu Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China Search for more papers by this author Junsheng Yang State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Jinshan Qin State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Shujuan Zhao State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Congying Wu Corresponding Author [email protected] orcid.org/0000-0002-3223-7884 Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China Search for more papers by this author Yujie Sun Corresponding Author [email protected] orcid.org/0000-0002-9489-4820 State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China Search for more papers by this author Author Information Huiwen Hao1, Jiahao Niu1, Boxin Xue1, Qian Peter Su1,†, Menghan Liu2, Junsheng Yang1, Jinshan Qin1, Shujuan Zhao1, Congying Wu *,2 and Yujie Sun *,1 1State Key Laboratory of Membrane Biology & Biomedical Pioneer Innovation Center (BIOPIC) & School of Life Sciences, Peking University, Beijing, China 2Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China †Present address: Institute for Biomedical Materials & Devices (IBMD), Faculty of Science, the University of Technology Sydney, Ultimo, NSW, Australia *Corresponding author. Tel: +86 10 62757265; E-mail: [email protected] *Corresponding author. Tel: +86 10 62744060; E-mail: [email protected] EMBO Rep (2020)21:e48385https://doi.org/10.15252/embr.201948385 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Microtubules derived from the Golgi (Golgi MTs) have been implicated to play critical roles in persistent cell migration, but the underlying mechanisms remain elusive, partially due to the lack of direct observation of Golgi MT-dependent vesicular trafficking. Here, using super-resolution stochastic optical reconstruction microscopy (STORM), we discovered that post-Golgi cargos are more enriched on Golgi MTs and also surprisingly move much faster than on non-Golgi MTs. We found that, compared to non-Golgi MTs, Golgi MTs are morphologically more polarized toward the cell leading edge with significantly fewer inter-MT intersections. In addition, Golgi MTs are more stable and contain fewer lattice repair sites than non-Golgi MTs. Our STORM/live-cell imaging demonstrates that cargos frequently pause at the sites of both MT intersections and MT defects. Furthermore, by optogenetic maneuvering of cell direction, we demonstrate that Golgi MTs are essential for persistent cell migration but not for cells to change direction. Together, our study unveils the role of Golgi MTs in serving as a group of “fast tracks” for anterograde trafficking of post-Golgi cargos. Synopsis This study reveals that Golgi MTs are more stable and polarized than non-Golgi MTs. Golgi MTs support cell migration persistence without affecting directionality of motile cells. Post-Golgi cargos are enriched on Golgi MTs and move much faster than on non-Golgi MTs. Cargos frequently pause at the sites of both MT intersections and MT defects. Compared to non-Golgi MTs, Golgi MTs are more stable and morphologically more polarized towards the cell leading edge. Golgi MTs exhibit significantly fewer inter-MT intersections and defect repair sites. Introduction Cell migration requires efficient cargo trafficking 1-4. Previous studies have demonstrated the importance of retrograde trafficking in cell motility 5-8. Interestingly, disrupting the ER to Golgi trafficking 9 or the TGN budding 10 also hinders cell migration, suggesting that the anterograde trafficking is also important for cells to move. Despite these findings, the specific cellular structures ensuring the post-Golgi cargo to function in sustaining persistent cell migration have been under debate. Cargo trafficking is largely dependent on the MT network and its associated regulators and motors. The MT array is composed of centrosome-anchored MTs which symmetrically distribute in the cell in a radial fashion, as well as asymmetric MTs which originate from or are stabilized at subcellular locations other than the centrosome. The Golgi apparatus has been reported to be a major hub for non-centrosome MTs to nucleate from. Nucleation and stabilization of the Golgi MTs are under the regulation of A-kinase-anchoring protein (AKAP450) 11, 12, CLIP-associated proteins (CLASPs) 13-15, and calmodulin-regulated spectrin-associated protein 2 (CAMSAP2) 16-18. Previous reports claimed that the Golgi MTs functioned to maintain the crescent-moon-shaped Golgi ribbon 13, 19-21. However, a recent study using electron microscopy by Wu et al 16 has argued that loss of Golgi MTs by depletion of AKAP450 and CAMSAP2 did not alter the Golgi stacks. Meanwhile, accumulated evidence suggested that another role of the Golgi MTs was the juxtanuclear positioning of the Golgi apparatus 2, 22-24. The improper positioning of Golgi caused by loss of Golgi MTs has been reported to be detrimental in wound healing, suggesting that the Golgi MTs may be required in directional cell migration 22. In line with these observations, Golgi MTs have also been implicated to be essential for cargo trafficking based on the observation that loss of these MTs led to vesicle retention around the Golgi 16, 25 and crucial for insulin secretion 25. However, the underlying mechanisms remain elusive, partially due to the lack of direct observation of Golgi MT-dependent vesicular trafficking and its relevance to cell migration. Here, taking advantage of super-resolution stochastic optical reconstruction microscopy (STORM) and live-cell single-particle analysis, we were able to track cargo trafficking on individual MTs and trace the originations of these tracks in motile cells. Based on the spatial association with the Golgi apparatus, we classified MTs into Golgi-associated MTs (GaMTs) and non-GaMTs. Interestingly, we discovered that post-Golgi cargo trafficking was much faster on GaMTs than on non-GaMTs. Compared to non-GaMTs, GaMTs were morphologically more polarized toward the cell leading edge with significantly fewer inter-MT intersections and lattice repair sites. Our STORM/live-cell imaging showed that cargos frequently paused at MT intersections and MT lattice repair sites. Furthermore, using an optogenetic system, we demonstrated that the GaMTs were essential for persistent cell migration but not required for cells to change direction. Taken together, we proved that the Golgi MTs serve as a group of “fast tracks” for anterograde trafficking of post-Golgi cargos to support persistent cell migration. Results Super-resolution STORM imaging reveals interphase Golgi MTs To understand how Golgi MTs contribute to cargo trafficking and how that links to cell motility, we applied super-resolution STORM imaging to reveal the detailed infrastructure of Golgi MTs in an interphase cell (Appendix Fig S1A and B), which were unable to be resolved by conventional fluorescence imaging due to the high density 20. In order to avoid artifacts caused by the 3D projection into the 2D plane, we performed 3D STORM imaging of MTs in 600-nm-thick sections (Appendix Fig S1C and D). Our strategy was able to resolve individual MTs and allow the tracing of MT originations (Fig 1, Appendix Fig S1E and F, and Movie EV1). The MTs with clear spatial association with the Golgi apparatus in our STORM images were referred to as Golgi-associated MTs (GaMTs) hereafter (Fig 1B and Appendix Fig S1E). GaMTs, marked in red, had one or more contact sites with the Golgi membrane (Appendix Fig S1E), while non-GaMTs, marked in green (Appendix Fig S1E), showed no connections with the Golgi. Figure 1. Classification of GaMTs and non-GaMTs based on super-resolution imaging Cartoon description of the segmentation of a polarized cell into four quadrants. The red dots indicate the center of the nucleus and the cell leading edge membrane, and the black arrow indicates the cell front-rear direction. The red dashed line box indicates the STORM imaging region. Left: representative conventional image of interphase MT network in the 1st quadrant of a human retinal pigment epithelium (HRPE) cell. Gray: α-tubulin; yellow: TGN-46; yellow dashed line: Golgi boundary. Right: the classified MT subgroups. Red: GaMTs; green: non-GaMTs. Scale bar: 5 μm. Box–whisker plot presents the proportion of GaMT and non-GaMTs (data were pooled from three independent experiments and n = 36 cells). The ends of whiskers set as the 10 and 90% of the entire population. ***P < 0.001, two-tailed Mann–Whitney. Six representative images of MT networks extracted from MT STORM images of HRPE cells under combinative KDs. Cells were rotated to orient their leading edges locating in the 1st quadrant. Upper: control cell; CLASPs KD cell; GCC185 KD cell. Lower: CLASPs + GCC185 KD cell; CAMSAP2 KD cell; AKAP450 KD cell. Red: GaMT; green: non-GaMT. Scale bar: 5 μm. Box–whisker plot presents the ratio of GaMT/non-GaMT under combinative KDs (one representative of three independent experiments and n = 12 cells). The ends of the whiskers are set at 10 and 90% of the entire population. *P < 0.05, **P < 0.01, ***P < 0.001, ns, no significant difference, unpaired t-test. Time series show EB1 movement on individual GaMTs. Red: GaMT; green: non-GaMT; white: EB1 tracks. Scale bar: 1 μm. Time series show EB1 movement on individual non-GaMTs. Red line: GaMT; green line: non-GaMT; white line: EB1 tracks. Scale bar: 0.5 μm. EB1 velocity distributions of GaMTs and non-GaMTs. Red curve and green curve represent the Gaussian fittings (data were pooled from three independent experiments and n = 92 tracks). ***P < 0.001, unpaired t-test. Source data are available online for this figure. Source Data for Figure 1 [embr201948385-sup-0026-SDataFig1.xlsx] Download figure Download PowerPoint To spatially characterize individual MTs, we defined a Cartesian coordinates for each cell with the migrating cell leading edge as the 1st quadrant (plus and minus 45° from the dashed line passing the leading edge middle point and the nuclear center) (Fig 1A) 26. The originations of 78% MTs in the leading edge quadrant of the cell were retrieved from STORM images, in which the ratio of GaMTs/non-GaMTs was 1.5. The remaining 22% MTs with unclear originations were left unclassified (Fig 1C and Appendix Fig S1E). Similar results were observed when we used different Golgi markers-TGN46, Man-II, and GM130 to label trans-, media-, or cis-Golgi (Appendix Fig S1G and H). It is worth noticing that such analyses indeed require super-resolution imaging as conventional imaging showed a much higher ratio of GaMTs/non-GaMTs than that of STORM imaging (Appendix Fig S1F). Moreover, when we disrupted Golgi with brefeldin A (BFA), the GaMTs disappeared rapidly, indicating their dependence on intact Golgi structure (Appendix Fig S1I–K and Movie EV2). It has been shown that CLASPs, recruited to the Golgi membrane by trans-Golgi network (TGN) GRIP-domain-containing protein GCC185, CAMSAP2, and A-kinase anchor protein 450 (AKAP450), serve to stabilize MTs nucleated at or derived from the Golgi 12, 16, 18. In order to confirm whether the previously identified genes regulate the MTs that spatially in contact with the Golgi, we examined the GaMTs upon knocking down (KD) of these genes. Reduced MT number in the 1st quadrant was observed (Appendix Fig S1L and M) and the GaMT/non-GaMT ratio dropped dramatically (Fig 1D and 1E), confirming the critical role of these genes in maintaining the MTs that are in spatial contact with the Golgi. We also observed that CAMSAP2 KD showed stronger effect in reducing the GaMT population than KD of GCC185 or CLASPs (Fig 1E). In a previous study, CAMSAP2 depletion was shown to reduce non-centrosome MTs without changing the Golgi-derived MTs 16. In conjunction with this observation, our data indicated that CAMSAP2 may regulate a subpopulation of non-centrosomal MTs which are derived from cellular origins other than the Golgi, and later captured by the Golgi apparatus. Therefore, in our experiment, CAMSAP2 KD reduced the MTs that were in contact with but did not derive from the Golgi. Previous findings revealed that Golgi-derived MTs were more resistant to ice or nocodazole-induced depolymerization 13, 26. We found that after ice or nocodazole treatment, most of the remaining stable MTs located in the 1st quadrant (Appendix Fig S1N and O), and meanwhile, the GaMT/non-GaMT ratio increased from 1.5 to above 5 (Appendix Fig S1P and Q). These observations indicated that GaMTs are more stable than non-GaMTs. We next evaluated MT dynamics by analyzing the trajectories of the growing end-binding protein EB1, which indicate the growing MTs 27. EB1 trajectories were polarized and enriched in the 1st quadrant (Appendix Fig S1R–T). EB1 velocity was significantly increased in CAMSAP2 or CLASPs/GCC185 KD cells (Appendix Fig S1U), suggesting that the growth rate of GaMTs was lower than non-GaMTs. In order to confirm it, we registered EB1 trajectories onto individual MTs via superimposing live-cell single-particle trajectories onto STORM images of MTs (Fig 1F and G, Movies EV3 and EV4). We observed that the growth rate of GaMTs (348.33 ± 22.54 nm/s) was significantly slower than that of non-GaMTs (434.93 ± 19.39 nm/s) (Fig 1H). Taken together, we have revealed the MT population in spatial contact with the Golgi in a migrating cell during interphase. Using ice/nocodazole treatments, genetic disruptions, and EB1 tracking, we showed that GaMTs are nearly functionally equivalent to other Golgi MTs previously defined based on different basis. Importantly, as the classification of GaMTs is based on the spatial association with Golgi, this will be a valuable addition to genetic manipulation or in vitro reconstitution approaches to dissect the specific roles of centrosomal vs. non-centrosomal microtubules, linking the subcellular localization of MTs with their functions. MTs associated with the Golgi are fast tracks for post-Golgi cargos We next investigated the role of Golgi MTs in vesicle trafficking. Via the retention using selective hooks (RUSH) system 28, we synchronized and visualized E-cadherin (Ecad) cargo trafficking in HRPE cells (Fig 2A). Based on analysis of 45 cells and 4,443 cargos, we observed that the 1st quadrant contained significantly more cargos compared to the other quadrants (Fig 2B). Interestingly, when assigning cargo speed into four intervals (0–100 nm/s, 100–200 nm/s, 200–400 nm/s, and > 400 nm/s), we found that, among all four quadrants, 90% of the cargos faster than 400 nm/s appeared in the 1st quadrant (Fig 2C). This result revealed a distinct feature of the leading edge quadrant in dominating fast cargo trafficking. Figure 2. Cargo velocity on GaMTs and non-GaMTs Representative trajectories of Ecad cargos tracked over 120 s in an HRPE cell. Colors represent different average velocity ranges. Red: > 400 nm/s; purple: 200–400 nm/s; green: 100–200 nm/s; yellow: 0–100 nm/s. Scale bar: 20 μm. Total number of cargos within different velocity ranges in the four quadrants, respectively (data were pooled from three independent experiments and n = 45 cells). Proportions of cargos with different velocity in the four quadrants. Red: the 1st quadrant; dark gray: the 2nd quadrant; light gray: the 3rd quadrant; white: the 4th quadrant (data were pooled from three independent experiments and n = 45 cells). Flow chart shows the method of combining STORM imaging with single-particle tracking in live cells. Red lines indicate the cargo trajectories. Lower right, MT networks (gray) were extracted and present in purple. Representative time series show one fast cargo moving on a GaMT and a slow cargo moving on a non-GaMT. Upper, conventional imaging; lower, STORM imaging. Red line: GaMT; green line: non-GaMT; white circle: cargo position. Scale bar: 2 μm. Marginal distributions show the velocity on GaMT (red dots) or non-GaMTs (green dots). Red curve and green curve, Gaussian fitting curves of velocity distribution on GaMT or non-GaMT, respectively (data were pooled from three independent experiments and n = 69 MTs). Peak of red curve (431.14 ± 9.7 nm/s) and peak of purple curve (188.96 ± 22.03 nm/s). ***P < 0.001, unpaired t-test. Upper panel: Time series show an example of several fast cargos present on GaMT. Lower panel: Time series show one slow cargo on non-GaMT switching onto a GaMT and becoming faster. Red line: GaMT; green line: non-GaMT; white circle: cargo position. White line, cargo trajectory. Scale bar: 2 μm. Box–whisker plot presents the averaged acetylation level of fast or slow MT tracks (data were pooled from two independent experiments and n = 63 MTs). The ends of the whiskers are set at 10 and 90% of the entire population, ns, no significant difference, unpaired t-test. Marginal distributions of the mean velocity of cargos on segments of Ac-MTs (orange dots) and non-Ac-MTs (purple dots). Orange curve and purple curve represent the Gaussian fittings (data were pooled from two independent experiments and n = 63 MTs). ns, no significant difference, unpaired t-test. Source data are available online for this figure. Source Data for Figure 2 [embr201948385-sup-0027-SDataFig2.xlsx] Download figure Download PowerPoint Following the same strategy, we also examined two other post-Golgi cargos, the tumor necrosis factor-α (TNF) and the vesicular stomatitis Indiana virus G protein (VSVG), as well as one recycling cargo marked by Rab5 (Rab5 cargos, associated with early endosomes) (Appendix Fig S2A–I). In contrast to all three post-Golgi cargos (Ecad, TNF, and VSVG) displaying polarized distribution of cargo velocity, the Rab5 cargos showed no obvious difference among all four quadrants (Appendix Fig S2I). Collectively, these data suggested that certain trafficking-supporting system exists in the cell leading edge quadrant, affecting post-Golgi cargo behavior in a specific way. The spatially biased post-Golgi cargo trafficking prompted us to pinpoint the role of GaMT tracks. To directly test the hypothesis that efficiently directed post-Golgi cargo trafficking via specialized MTs was required for cell motility, we systematically analyzed cargo behavior on individual MTs by superimposing live-cell single-particle trajectories of trafficking cargos onto STORM images of MTs 29. This allowed us to track cargo motility along individual MT tracks (Fig 2D). We captured events where the cargo on the GaMT transported over 5 μm during 8 s with an average velocity of ~600 nm/s (Fig 2E and Movies EV5). In contrast, we also observed cargos on the non-GaMT that moved back-and-forth and only traveled less than 1 μm during 20 s (Fig 2E and Movie EV6). We analyzed 36 cargos on GaMTs and 33 cargos on non-GaMTs in 36 cells and concluded that the cargo velocity on GaMTs (431.1 ± 9.7 nm/s) was significantly higher than that on non-GaMTs (188.9 ± 22.03 nm/s; Fig 2F). This difference could be caused by certain intrinsic properties distinct for the GaMTs, or due to differed cargo properties. Interestingly, we observed events in which a slowly transporting cargo on a non-GaMT switched track onto a GaMT and immediately reached a high speed (Fig 2G, Movies EV7 and EV8). These observations strongly suggested that biased cargo velocity between GaMTs and non-GaMTs was mainly due to certain intrinsic properties of the tracks rather than cargo properties such as associated motor types and numbers. Some post-translational modifications (PTMs) of MT are known to affect cargo trafficking. For instance, acetylated MTs (Ac-MTs) and detyrosinated MTs (Detyr-MTs) have been reported to promote binding and motility of kinesin-1 motors 30. We stained tyrosinated (Tyr-MTs), detyrosinated, and acetylated tubulin for GaMTs and non-GaMTs. We found that both Ac-MTs and Detyr-MTs were enriched in the first quadrant, while Tyr-MTs were evenly distributed in all quadrants (Appendix Fig S2J and K). As GaMTs are enriched in the first quadrant, we then focused on whether acetylation or detyrosination contributes to the difference between GaMTs and non-GaMTs. After STORM imaging and MT grouping, we found both Ac-MTs and Detyr-MTs were slightly enriched on GaMTs (32.9% vs. 15.9% for Ac-MTs; 26.2% vs. 13.7% for Detyr-MTs; Appendix Fig S2L–O). We then interrogated the role of MT PTMs in differed cargo velocity. Ac-MTs were specifically labeled, and two-color STORM imaging was applied to monitor MT acetylation level between fast and slow cargo trafficking tracks. We were able to detect only very marginal difference in the acetylation level between the GaMT tracks (26.8%) and the non-GaMT tracks (23.1%) (Fig 2H). As acetylation often appeared segmented along MTs, we then carefully examined cargo velocity on Ac-MT segments and non-Ac-MT segments. Likewise, we were only able to detect minor difference in cargo velocity between Ac-MT segments (387.05 ± 15.18 nm/s) and non-Ac-MT segments (317.66 ± 40.67 nm/s) (Fig 2I). In addition, we performed orthogonal analysis by carefully comparing cargo speed on Ac-GaMT, non-Ac-GaMT, Ac-non-GaMT, and non-Ac-non-GaMT. The results showed that the average cargo velocities of Ac-GaMTs and non-Ac-GaMTs were far faster than that on non-GaMTs, regardless of their PTMs (Appendix Fig S2R and Movies EV9–EV12). We also carefully examined the detailed motility behavior of cargos on different MTs. The results showed that cargos on non-GaMTs underwent more frequent pausing and reversing events than GaMTs, also independent of their PTMs (Appendix Fig S2T and U). Similar with acetylation, the average cargo velocities of Detyr-GaMTs and non-Detyr-GaMTs were faster than that on non-GaMTs, regardless of their PTMs (Appendix Fig S2S and Movies EV13–EV16). Thus, we conclude that acetylation and detyrosination have limited contribution to the differential cargo speed on GaMTs and non-GaMTs. GaMTs contain less MT repair sites that benefit fast cargo trafficking The MT defects have been shown in vitro to affect kinesin-based cargo transporting 31-33. To evaluate the MT defects level in GaMTs vs. non-GaMTs, we applied the established GTP-tubulin perfusion assay to identify MT defects (MT lattice repair sites) in live cells 34. Interestingly, upon KD of CLASPs, CAMSAP2, or AKAP450, MT repair ratio was significantly increased (Fig 3A and B). Meanwhile, after Centrinone-B treatment to deplete centrosomal MTs 35, MT repair level remained unchanged compared with control cells (Fig 3B). To investigate the repair sites in more detail, we then used STORM imaging to analyze the MT repair level on individual GaMTs and non-GaMTs (Fig 3C and D). We discovered that GaMTs harbored much fewer MT repair sites compared to non-GaMTs (Fig 3E and F). We then interrogated whether MT repair sites would halt cargo trafficking in vivo by applying live-cell imaging combined with STORM super-resolution imaging. We captured events where cargos paused at the GTP-tubulin-labeled MT repair sites (Fig 3G and H). Quantification of cargo motility revealed that cargos paused and reversed more frequently on MT repair segments than non-repair segments (Fig 3I and J, Movies EV17 and EV18), lending an explanation to the observation that cargos on non-GaMTs underwent more frequent pausing and reversing events than GaMTs (Appendix Fig S2T and U). This is the first evidence, to our knowledge, supporting that MT defects affect cargo transporting in cells. These observations suggest that the difference in MT defects between GaMTs and non-GaMTs serves as the major mechanism for fast and slow cargo transport. Figure 3. Non-GaMT harbors more MT repair sites where cargos frequently pause and reverse A. Five representative images of MT repair sites in HRPE cells under combinative KDs. Cells were rotated to orient their leading edges locating in the 1st quadrant. From left to right, control cell, CLASPs&GCC185 KD cell, CAMSAP2 KD cell, AKAP450 KD cell, and centrinone-B-treated cell. Gray: α-tubulin; red: GTP-tubulin. Scale bar: 20 μm. B. Box–whisker plot presents intensity analysis of MT repair sites in HRPE cells under combinative KDs (one representative of three independent experiments and n = 20 cells). ***P < 0.001, unpaired t-test. C. Conventional image (left) and STORM image (right) of MTs and MT repair sites. Gray: α-tubulin; yellow: GTP-tubulin; green: Golgi. Scale bar: 5 μm. D. MT repair sites on GaMTs and non-GaMTs extracted from (C). Red: GaMTs; green: non-GaMTs; yellow: GTP-tubulin. E. MT repair sites on GaMTs and non-GaMTs separately presented in detail. White arrows indicate GTP-tubulin-labeling sites. Scale bar: 5 μm. F. Box–whisker plot presents the ratio of MT repair sites on GaMTs and non-GaMTs separately (one representative of three independent experiments and n = 8 cells). ***P < 0.001, unpaired t-test. G. Representative time series show one fast cargo moving on a GaMT and slowing down at the MT repair site. Red: GaMT; green: non-GaMT; yellow: GTP-tubulin; white circle: cargo position. Scale bar: 2 μm. H. Representative time series show one cargo moving fast between two MT repair sites and slowing down at the MT repair sites. Red: GaMT; green: non-GaMT; yellow: GTP-tubulin; white circle: cargo position. Scale bar: 2 μm. L, J. Pause (I) and reverse (J) events of cargos on MT repair segment and non-MTs repair segment, respectively (data were pooled from two independent experiments and n = 22 cells). ***P < 0.001, ns, no significant difference, unpaired t-test. Data information: The ends of the whiskers are set at 10 and 90% of the entire population. Source data are available online for this figure. Source Data for Figure 3 [embr201948385-sup-0028-SDataFig3.xlsx]