Nanoscale TopographyâRigidity Correlation at the Surface of T Cells

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Nanoscale Topography-Rigidity Correlation at the Surface of T Cells Yair Razvag, Yair Neve-Oz, Eilon Sherman, and Meital Reches ACS Nano, Just Accepted Manuscript • DOI: 10.1021/acsnano.8b06366 • Publication Date (Web): 28 Nov 2018 Downloaded from http://pubs.acs.org on December 1, 2018

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Nanoscale Topography-Rigidity Correlation at the Surface of T Cells Yair Razvag, †, ‡ Yair Neve-Oz, ‡ Eilon Sherman, ‡ and Meital Reches*, † †

Institute of Chemistry, The Hebrew University, Jerusalem, 91904, Israel

‡

Racah Institute of Physics, The Hebrew University, Jerusalem, 91904, Israel

ABSTRACT

The mechanical properties of cells affect their function, in sensing, development, and motility. However, the rigidity of the cell surface and its correlation to its local topography remain poorly understood. Here, we applied Quantitative Imaging AFM to capture highresolution force maps at the surface of non-adherent T cells. Using this method, we found a positive topography-rigidity correlation at the cells' surface, as opposed to a negative correlation at the surface of adherent cells. We then used 3D single-molecule localization microscopy of the membrane and cortical actin, and an actin-perturbing drug to implicate actin involvement in the positive rigidity-topography correlation in T cells. Our results clearly reveal the variability of cell-surface rigidity and its underlying mechanism, showing a functional role for cortical actin in the PM protrusions of T cells, since they are locally more rigid than their surroundings. These findings suggest the possible functional role of membrane protrusions as a mechanosensor.

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KEYWORDS: quantitative imaging, topography, rigidity, elastography, microvilli

The nanomechanical properties of living cells play important roles in cellular processes such as mechano-transduction,1 morphogenesis,2 differentiation,3 focal adhesion,4 motility,5 and metastasis.6 Lymphocyte activation was shown to be related to substrate stiffness.7,8 Moreover, different human immune cells were found to display a wide range of viscoelastic properties that varied upon inﬂammatory treatments.9 A straightforward method for measuring the mechanical properties of cell membranes uses Atomic Force Microscopy (AFM), first developed as an imaging tool,10 and for force measurements owing to its high force sensitivity.11,12 Utilizing this method, Radmacher et al. measured the viscoelastic properties of human platelet adsorbed to a glass surface.13 The typical range of stiffness for mammalian cells, as measured with AFM, is between one and hundreds of kPa.14 The Hertz model is often used to describe the cell’s elastic behavior.15 This method relies on measuring the indentation of the AFM cantilever into the cell, and requires the cell to be firmly attached to a surface, to avoid instability and movement of the cell during the measurement. Thus, AFM measurements of non-adherent cells are difficult and uncommon.16 Alternative methods such as micropipette aspiration are typically used to measure the mechanical properties of nonadherent cells.17-19 However, with this method there is little control of the cell material being drawn into the pipette. Using AFM to overcome this challenge, researchers fix the cells 20 or even dehydrate the cells 21,22 of the sample before measuring it. However, fixing the cells with cross linkers, as well as letting the sample dehydrate, significantly alters the elastic properties of the sample and makes it at least 10-fold stiffer.16


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Rosenbluth et al. presented microfabricated wells for immobilizing human leukocytes and showed that lymphoid leukemia cells are stiffer than myeloid cells, implying that this method could serve as a diagnostic tool.23 Other methods use the patch clamp technique as a support for mechanical probing of living cells by AFM.24 Cells are inhomogeneous and anisotropic because they include a cytoskeleton and multiple organelles. Thus, a detailed characterization of cell surface elasticity should be performed over the entire cell surface, rather than at only a few points. Imaging the cell mechanical properties, termed elastography, was first introduced using sonographic techniques.25 However, owing to its high spatial and force resolution, AFM was first presented as a mechanical mapping tool in 1998 26 and ever since, it has emerged as the most widely used technique for force mapping.27,28 Using AFM to demonstrate the plasma membrane (PM) inhomogeneous properties, Docheva et al. utilized force mapping of various live cells in addition to topography imaging.29 Nevertheless, force mapping has a time resolution limitation because it conducts a force curve on every pixel. Overcoming this obstacle, Raman et al. and Wang et al. presented multi-harmonic AFM methods to quantitatively map the nano-mechanical properties at fast rates.30,31 However, these methods are neither robust nor straightforward, and are limited to measurements of adherent cells. Various membrane protrusions can be detected with AFM.32 A typical eukaryotic cell is surrounded by a brush of molecular components of the PM (glycocalyx) as well as protrusions of the PM in the form of microvilli, microridges, cilia, or filopodia.33 Often topography plays a crucial role in functionality. For instance, T cells utilize their membrane protrusions, namely, actin-based cell surface microvilli, loaded with T-cell


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receptor (TCR) proteins as sensors for scanning ligands over the antigen-presenting cell (APC) membrane.34,35 Previous studies have suggested that the microvilli serve as the structural scaffold for clustering TCRs and for the recruitment of microclusters upon TCR engagement.36-38 Although the PM resists deformation owing to its elastic modulus,39 the main factors that dictate the cell shape are the cortex and actomyosin.40 Cell rigidity has been shown to correlate with the stiffness of the actin cytoskeleton, and studies have demonstrated a reduction in stiffness with the use of actin depolymerizers.41-43 Yamane et al. have shown that the cell membranes above ridge-like actin-rich structures are stiffer than their surroundings.44 However, studying the correlation between the topography and actin filaments has usually employed fluorescence intensities that are diffraction limited.45 Here, we employed a commercially available force mapping AFM technique, Quantitative Imaging (QI ; JPK instruments®), which enables the fast and robust force scanning of a biological sample. By using sensitive cantilevers and robust adhesion of cells onto coverslips, we were able to capture high-resolution force maps at the surface of non-adhesive Jurkat CD4+ T cells. Through this technique, we found a positive topography-rigidity correlation at the surface of Jurkats; surprisingly, this correlation was negative at the surface of adherent cells. We then used 3D single-molecule localization microscopy of the membrane and cortical actin, as well as an actin-perturbing drug, and showed actin’s involvement in positive rigidity-topography correlation in T cells.


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RESULTS AND DISCUSSION Quantitative mechanical imaging To study the topography-rigidity correlation in T cells, we used AFM, since it can provide both topographic imaging and measurements of local mechanical forces. We utilized the Quantitative Imaging (QI™) mode of the NanoWizard®3 AFM (JPK), a high force mapping technique. In this technique, force curves are measured on individual pixels as follows. The 2 nm radius AFM tip is brought into contact with the sample, and is kept moving in that direction. Thus, the sample is indented until a certain predefined counter-force is reached (Supp Fig. 1A). The position where the tip starts to sense a counter force defines the contact point. From that data, a topographic image can be extracted (Supp Fig. 1B). At the point where the pre-defined counter force is reached, the direction of the tip movement is reversed and the tip is withdrawn from the sample. Nevertheless, when the tip reaches the point where it should detach from the sample, often, due to non-specific forces between the sample and the tip, the sample is pulled with the tip before complete detachment occurs. The position where the tip detaches can be used to calculate the adhesion force that the sample exerts on the tip (Supp Fig. 1C). The slope of the curve, in both directions of the tip movement, indicates the stiffness of the sample. A stiffer sample is less indented by the tip under the same force, and produces a steeper slope (Supp Fig. 1D). The topography–rigidity correlation in living T cells Recent studies suggest that T-cell membrane protrusions, termed microvilli, could play an essential role in the cell’s decision-making.34,35 It is unclear, however, whether these


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protrusions are more rigid than their membrane surroundings, thus making them a mechano-sensor, or whether the cell rigidity is homogenous and the protrusions act as extended probes, enhancing the effective interface of the cell. Therefore, our first aim was to measure the topography-rigidity correlation in live T cells. T cells are non-adherent cells, and thus are difficult to measure by AFM.23 Moreover, as revealed by electron microscopy (EM),46 the shape of the cells resembles a round sphere with relatively small membrane protrusions. The difference in the cell size and the membrane protrusions creates a scale problem for continuous AFM measurements. Furthermore, the expected bending energy of the PM of live cells is close to the thermal energy (~kBT), thus, creating a problematic signal-to-noise ratio (SNR) in the measurements. To overcome these issues, first we ensured that cells were tightly adhered to coverslips by coating them with multiple antibodies: anti-CD3-, anti-CD45-, and antiCD11a. Second, we started our measurements by taking low-resolution images of relatively wide fields across the cell surface. We then zoomed into regions of interest (ROIs) in the middle of the cell surface, excluding large height differences (Fig. 1A-B). Third, we used very soft cantilevers (MSNL10, Bruker) with a nominal spring constant of 0.01 N/m. Finally, after each pixel, we retracted the AFM tip by an additional 100 nm, to minimize the exerted spatial forces on the membrane protrusions, which can lead to image smearing. The three-dimensional AFM topography resembles the membrane protrusions previously detected by EM (Fig. 1C).46 The adhesion and slope images provide an interesting insight into the membrane material composition, but they seemed quite smooth because the contrast was limited (Fig 1D). However, a more accurate method to study membrane rigidity is to plot the indentation depth versus the tip


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position.11 For a soft sample, the AFM tip will indent significantly more than for a rigid sample at a given tip position (see Sup Fig. 1E and Methods). The indentation analysis revealed a structure that remarkably resembles the height image, with an average Young modulus (Y) of 1.27±0.74 kPa (Fig. 1E). One should bear in mind that when traditional mechanical measurements are conducted, their routine use is with a spherical tip with a diameter of 2-10 µm. Meaning, the obtained value of rigidity is only the average value, and the distribution of values is lost (Supp Fig. 1F). More importantly, when averaging, all correlation with topography is of course lost. To quantify the patterning of the PM rigidity, we focused on high areas (i.e., peaks and ridges) in the height image. We then measured the rigidity as a function of dilations of those areas; i.e., we measured changes in the rigidity as a function of equidistance from the boundaries of the high areas (see Fig. 1F, Supp Fig. 1G and Methods). The analysis (Fig. 1G) showed that the rigidity drops upon receding from the high areas. Thus, the high areas are stiffer than their surroundings by almost 20%. Finally, we wanted to visualize the newly found rigidity-topography correlation. For that purpose, we utilized surface graphs that showed the topography as a grid (Fig. 1Hi, top) and the rigidity values as a heat map (Fig. 1Hi, bottom). The superimposed graphs clearly show the established correlation, since higher areas are more rigid (in red; Fig. 1Hii and 1Hiii). We found similar correlated patterning of the height and rigidity in live T cells when using larger ROIs. The analysis results showed a similar drop in rigidity as a function of the distance from the high area cores (Supp Fig. 2). Thus, the rigiditytopography correlation was robust and displayed decreased rigidity around high areas, with a trough of up to ~500 nm before increasing (Supp Fig. 2F).


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Figure 1. (A) Bright-field image of a live Jurkat cell spread on an antibody adhesioncoated coverslip. (B) Height analysis (left) and zoom in (right) of AFM Quantitative imaging of the cell. (C) 3D visualization of the marked area in B. (D) Adhesion (left) and slope (right) analysis of AFM Quantitative imaging of the area as in B (right). (E) Rigidity analysis of AFM QI of the area as in B (right). (F) Contour analysis for the height as in B (right). Contours are in blue. (G) Rigidity as a function of height dilation analysis. (H) Height 3D presentation edge lines of the area as in B (i up) and the rigidity heat map as in E (i bottom). (ii) Combined presentation of the height 3D edge lines and the rigidity heat map from H. (iii) Zoom into the marked area.

The topography – rigidity correlation in living HEK cells We investigated whether the topography-rigidity correlation that we detected in T cells can be found in additional cell types. For that purpose, we used human embryonic kidney (HEK) cells. These cells are adherent and have a relatively flat topography 47 and a typical size of 30-50 µm (Fig. 2A). For the AFM measurements of HEK cells, we chose ROIs similar to the ROIs we used for T cells (compare Fig. 2B and Fig. 1B). The adhesion and slope images (Fig. 2C) had relatively low contrast; however, the indentation analysis that yielded Y=2.20±0.03 kPa, on average (Fig. 2D), surprisingly revealed, in contrast to the T cell results, an opposite topography-rigidity correlation. The higher areas were softer than their surroundings (Fig. 2E-F; high areas are in deep blue, representing low rigidity).


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Figure 2. (A) Bright-field image of a live HEK 293 cell spread on a coverslip. (B) Height analysis and zoom in of AFM QI of the marked area in A. (C) Adhesion (left) and slope (right) analysis of AFM Quantitative imaging of the area as in B (right). (D) Rigidity analysis of AFM Quantitative imaging of the area as in B (right). (E) Rigidity as a function of height dilation analysis. (F) Height 3D presentation edge lines of the area as in B (up) and a rigidity heat map as in D (bottom). (right) Combined presentation of the height 3D edge lines and the rigidity heat map.


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The physical mechanism underlying the topography-rigidity correlation Our results show a positive correlation between the topography and the rigidity at the PM of T cells, indicating a possible functional role for the high PM areas. For a better insight into the topography-rigidity correlation, we first repeated our measurements with fixed T cells, thus greatly simplifying the AFM measurements and enhancing their contrast (Fig. 3A-B). Indeed, our measurements showed a higher height contrast than for live cells, and allowed the detection of relatively large PM protrusions, as previously found by EM (Supp Fig. 3A-D; 48). Our analysis showed again the topography-rigidity correlation of high areas being more rigid than their surroundings by almost 20% (Fig 3C-E). Interestingly, the relative Young's modulus increased back to its values at the high areas after receding by only 250-300 nm away from these areas (Fig. 3E, Fig. 4A-B and supp Fig. 3 correlation panel). The trend of the Young's modulus seemed constant regardless of the region of interest or the apparent surface pattern under measurement (Supp Fig. 3).


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Figure. 3. (A) Bright-field image of a Jurkat cell spread on an antibody adhesion-coated coverslip 4 min before fixation. (B) Height analysis of AFM Quantitative imaging of the cell (left) and zoom in of the marked area (right). (C) Adhesion (left) and slope (right) analysis of the AFM Quantitative imaging of the area as in B (right). (D) Rigidity analysis of the AFM Quantitative imaging of the area as in B. (E) Rigidity as a function of height dilation analysis.


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To further interpret our results, we turned to a simple physical model of the cell interface. Specifically, we modeled a one-dimensional membrane line being held at its edges by springs (with a spring constant of 1 mN/m), while being held in the middle by a two-fold longer spring; thus with half of the spring constant (0.5 mN/m ; Supp Fig. 4A). The model provided qualitative results similar to those in the experiment, where the rigidity declined when receding from the high point and increased again close to the edges (compare panels Fig. 4B and Supp Fig. 4B, see also methods). Notably, the model’s perception of actin as a spring can explain why the rigidity at the high point is the same or even less than the rigidity at the edges even though the actin in the high areas is denser. The modeling results led us to speculate that the topography-rigidity correlation is related to the cytoskeleton pushing the membrane underneath. To test this hypothesis, we repeated the experiment with cells treated with Latrunculin A (LAT-A). LAT-A is an actin polymerization inhibitor that stabilizes monomeric G-actin.49 We added it to the cells prior to dropping them onto the coverslips (see Methods). As expected by previous studies.35 the cells seemed flat and did not have any protrusions (Supp Fig. 4C-D). The topography of these cells was not correlated with the analyzed rigidity because the rigidity remained flat upon receding from the higher areas (Fig. 4C and Supp Fig. 4E). Taken together, we observed a strong topography-rigidity correlation in T cells, a negative correlation in HEK cells, and no correlation in LatA-treated T cells (Supp Fig. 4F). Young's modulus, Y, and the topography curvature, R, are related to the bending energy of the PM, through:50


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(1) 𝑒𝑏𝑒𝑛𝑑 ~ 𝑌 ×

2

(𝑅1)

The curvature can be extracted from the height images (Supp Fig. 4G and Methods). The bending energy landscape ranged from zero to tens of kBT per 40 nm2 pixel size (at room temperature). It showed that the higher energy values accumulated at the borders of the high membrane areas (Fig. 4D). Figure 4E shows a positive correlation of the membrane bending-energy and the rigidity (red curve) and a negative correlation with membrane curvature (black curve), in support of our model (Supp Fig. 4A).

Figure. 4. (A) Zoomed high-contrast height image (left) and rigidity analysis of the AFM Quantitative imaging (right) of the marked area in Fig 3D. (B) Height (in blue) and rigidity (in red) plots of the cross section depicted by the line in A. (C) Rigidity as a function of height dilation analysis for a LAT A-treated T cell. (D) Membrane bending


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energy landscape calculated by the product of the young modulus and bending. (E) Membrane energy dependence on the curvature (red) and rigidity (black). SMLM correlation of actin and membrane The cell cortex is an actin-rich network that controls the cell shape. It consists of Factin filaments, myosin motors, and actin-binding proteins. Specifically, cortical actin has been shown to increase the PM rigidity.40 Thus, we studied the correlation between membrane protrusions and the location of actin, which indicates areas of higher local rigidity. We used 3D single-molecule localization microscopy of actin of PM T cells (see Methods). Owing to methodological restrictions and to minimize background, we imaged the PM side that adhered to coverslips coated with anti-CD3 antibody. This coating leads to cell activation and tight adherence and spreading of the cells on the coverslips.51 Two-color cell images (Figure 5A; 2D rendering) showed homogenous spreading of the membrane (left panel), in contrast to pronounced clustering of actin (middle panel). A 3D analysis (see Methods) revealed that the membrane was not homogenously spread but instead had significant variations in height (Fig. 5B and Supp Fig. 5B). The cell borders and regions in the middle of the cell were relatively higher than the rest of the PM. Next, we studied the spatial correlation between the PM height variations and the presence of cortical actin. For that purpose, we utilized a coordinate-based colocalization (CBC) analysis,52 in which the membrane molecules were colored according to their Spearman correlation with actin. Figure 5C shows the CBC analysis, in which membrane molecules in blue represent molecules in correlation with actin, whereas membrane molecules in red represent molecules that were anti-correlated with actin. Overall, the


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two maps in Figure 5B and 5C resemble each other, since the coloring of points in the height map correlates with the coloring of points in the CBC map. The actin 3D visualization and CBC (the correlation of actin to membrane molecules; Figure 5D-E) showed similar results, since higher actin clusters in the cell periphery correlated less with the membrane molecules.


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Figure 5.(A) Multicolor 3D SMLM, PALM combined dSTORM images of Jurkat cells expressing PAGFP-actin (green) and membrane tagged with DiD fluorophore (red), fixed after 4 min of spreading on an anti-CD3 coverslip. (B) Whole cell (left) and zoom in (right) membrane molecules colored by their height; molecules that closely adhere to the coverslip (low) are in blue and high molecules above the coverslip are in red. (C) Whole cell (left) and zoom (right) membrane molecules are colored by their CBC score in relation to actin molecules, since molecules in high correlation to actin are in blue and molecules anti-correlated to actin are in red. (D) Whole cell actin molecules are colored by their height, molecules that closely adhere to the coverslip (low) are in blue, and high molecules above the coverslip are in red. (E) Whole cell actin molecules colored by their CBC score in relation to membrane molecules; molecules in high correlation to membrane molecules are in blue and molecules anti-correlated are in red. SMLM dilation analysis To further study the correlation of actin to PM topography, we turned again to dilation analysis. Here, we referred to clusters of actin as markers of rigid areas (Fig. 6A) and analyzed the average height of membrane molecules as a function of the receding distance from the actin-rich rigid areas by sequential dilations (Fig. 6B, Supp Fig. 5A and Methods). Figure 6C shows a positive correlation because the average membrane height rises above the coverslip with receding distance from the rigid areas. We further performed a complementary analysis by plotting the rigidity trend upon receding from lower regions at the PM, which was closer to the coverslip. To determine the PM topography, we interpolated 3D images (Fig. 6D and Supp Fig 5B) and presented them as inverted images, for consistency with the AFM results. Thus, PM regions that were closer


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to the coverslip were presented as higher (Fig. 6E). We then applied the same analysis, by defining higher areas, and calculated the average actin molecule’s density, i.e., rigidity, upon receding from those regions by sequential dilations (Fig. 6F and Supp Fig. 5C-D). Consistently, Fig. 6G shows that the actin density (and thus, rigidity) is reduced with receding distance from the pre-defined higher areas.


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Figure 6. (A) Contour analysis for actin molecules. (B) Zoom of the marked area in A, depicting one actin cluster contour (solid green line) surrounded by membrane molecules colored by their height above the coverslip. Dilation number 10 is denoted by a dashed green line, with a white arrow showing the dilation's trend. (C) Dilation analysis of the membrane height as a function of the distance from the actin contour cores. (D) 3D interpolation height image of the membrane. (E) Reversed height image of D (left) and zoom (right) for the area marked. (F) Contour analysis for a reversed height image. (G) Dilation analysis of actin density as a function of the distance from the height contour cores. In summary, in this study we performed force indentation measurements of non-adherent cells, human leukemic Jurkat CD4+ T cells. Our measurements showed a surprising topography-rigidity correlation, which was opposite in adherent cells. We then showed the involvement of actin in the topography-rigidity correlation via 3D single-molecule localization microscopy of PM and of cortical actin, and via measurements of cells treated with LatA, an actin-perturbing drug. Comparing the extracted Young modulus with published measurements reveals a large difference, since the average elasticity in our measurements is in the kPa range, whereas the reported Young modulus for Jurkat T cells is in the range of 100 Pa.9,23 The reported values were measured by either compressing the cell between two parallel plates or by immobilizing the cell in a microfabricated well. Thus, the cells in the previous studies had some degrees of freedom to move and rotate. It has been shown that a loosely attached cell is more compliant under indenting, which results in an artificially low elastic modulus by a factor of about two.53


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Moreover, previous measurements of cell rigidity have employed AFM with large colloidal spheres. Such measurements can be considered global cell measurements, rather than local, as obtained when using a sharp AFM tip. Specifically, such global measurements include heterogeneous cell components, such as the relatively soft glycocalix matrix. Thus, an effectively reduced value of the elastic module is extracted in such cell measurements without the glycocalix (by a factor of 2-10).54 Furthermore, the AFM cantilever extension rate influences the measured viscoelastic properties because a faster rate increases the measured viscosity.16 Elasticity measurements are comparable if all experimental conditions are kept constant. Here the extension rate was ~30 μm/s, whereas in the reported work the rate was much lower, ranging between 20 and 8000 nm/s. The authors also suggested that at rates higher than 400 nm/s the apparent stiffness increases, since it is significantly influenced by viscosity.9 Although the absolute values of the Young's modulus can differ and must be carefully assessed between different measurement techniques, our results highlight the existence of significant and structured variability in the rigidity of the cell surface. Such variability is expected to affect the overall cell function, e.g., in cell sensing and in activation-related adhesion and spreading. Variations in reports of cellular elasticity values are in part due to the high nonuniformity of the cell surface. Several reports showed local variations of up to several kPa in the cell stiffness due to the presence of internal components, such as actin bundles.28,42 Here, we showed a detailed elastographic analysis of non-adherent cells. Beyond the ability to image the variation of the elastic properties across the membrane, elastography also provides an effective way of reducing errors in stiffness measurements,


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since averaging the standard error of the mean of elastography maps of several cells is more accurate than the traditional single force measurements of each cell (Supp Fig. S6) . Elasticity is known to be uncoupled from topography;26 however, correlation analyses of our measurements have revealed a positive correlation of topography with rigidity. We showed that this positive correlation is peculiar to T cells since it was not found in HEK cells. Cell protrusions, such as T-cell microvilli, may serve for multiple tasks. First, they can act as sensory entities.34 Second, they can facilitate fast flattening of the PM and robust cell spreading, since they store membrane material.55 Such fast spreading is required by T cells upon engagement by cognate antigens on APCs, in order to create a mature IS. Furthermore, we showed a nonhomogeneous membrane energy distribution, where higher energy values accumulated at the borders of the protruded membrane areas. This suggests an additional important energetic role for those protrusions in fast cell spreading because energy is stored at the protrusions' borders and can be released to accelerate cell flattening. Our study sets the stage for exploring this hypothesis via further experiments and physical modeling. We then employed 3D single-molecule localization microscopy of the membrane and actin molecules, and showed a positive correlation of actin with exoplasmic topography, since actin molecules were enriched at membrane ridges. Furthermore, the topographyrigidity correlation was abrogated upon the disruption of actin polymerization. Cell rigidity with actin polymerization is known to be extremely important for changing internal cell signaling and the fate of cells. Thus, the ability to map the elastographic


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properties of the membrane and to correlate them to cortical actin will have a very high impact on the mechanoelastic study of cells. Furthermore, measuring rigidity that way reduces measurement errors; this is highly significant when studying biological functions related to the rigidity of cytoskeleton. CONCLUSIONS Taken together, our results show a surprising rigidity-topography correlation at the surface of T cells. This correlation and its dependence on cortical actin suggest a functional role for PM protrusions, since they are more rigid than their surroundings. Possibly, it can be hypothesized that the membrane protrusions act as a mechanosensor by utilizing actin as a force spring or as a force generator by polymerization.56,57 In this way, the T cell can easily scan the opposing APC with those rigid sensors and upon encountering an antigen, a spreading process is easily achieved by actively reducing the rigidity of these protrusions by actin depolymerization. Thus, our results reveal another level of understanding the molecular mechanisms that govern T-cell sensing and IS creation. Moreover, the rigidity-topography correlation can set the stage for studying the role of microvilli in health and disease. METHODS Cell lines and Cloning Human Embryonic 293 kidney (HEK293 ; ATCC CRL-1573) cells were cultured at 37 °C, 5% CO2 in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum, penicillin, and streptomycin. Jurkat E6.1 (CD4+) T cells were a


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kind gift from the Samelson lab at the NIH. For fluorescence studies, these cells were transfected with DNA using a NEON electroporator (Invitrogen) for determining the expression of actin tagged with PAGFP (MBL International Corporation). Lines of Jurkat E6.1 cells, stably expressing actin-PAGFP, were available for this study from previous work.58 Briefly, in that work the cells were created by selection with Geneticin at 1.5 mg ml−1 (G418, Invitrogen). After 2–3 weeks, the cells were sorted and single clones were grown in 96-well plates. After 3 additional weeks, the extent of protein expression was checked by flow cytometry. Cells were then evaluated using biochemistry assays, flow cytometry, and confocal microscopy. Membrane staining The PM was tagged by incubating the cells in staining solution containing 10 μM DiD (Vybrant® DiD Cell-Labeling Solution, Invitrogen, V22887), in PBS for 0.5–5 min. After having been stained, cells were washed and suspended in imaging buffer. Sample preparation For AFM measurements, preparation of coverslips for imaging spread cells was as follows: Coverslips (#1.5 Cover glass 24mmØ, Marienfeld) were washed with acidic ethanol at room temperature (RT) for 10 min. The liquid was aspirated and coverslips were dried at 37 °C for 1 h. Cleaned coverslips were incubated at RT for 15 min with 0.01% poly-L-lysine (Sigma) diluted in water. Liquid was aspirated and coverslips were dried at 37 °C for 12 h. In the case of Jurkat cell imaging, coverslips were subsequently incubated with purified mouse anti-human CD3ε (clone UCHT1, Affymetrix eBioscience, 160038), purified mouse anti-human CD45 (Bactlab Diagnostics, PMG555480) and


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mouse anti-human CD11a (BD Pharmingen, 555378) at a concentration of 10 μg ml−1 (each) overnight at 4 °C or 2 h at 37 °C. Finally, coverslips were washed with PBS. With HEK293 cell imaging, undifferentiated HEK293 cells were plated at a density of 7.5 × 104/cm2 on poly lysine-coated coverslips and incubated for 24 h. For imaging actin-perturbed cells, Jurkat E61 cells were mixed with LAT-A drug in a final concentration of 2.5 μM 20 minutes prior to imaging. Two cells were imaged consecutively for each LAT-A experiment. For SMLM imaging, glass chambers (#1.5 glass chambers, iBidi) were prepared using the same procedure; however, they were incubated only with purified mouse anti-human CD3ε. A few hours before imaging, Jurkat cells were resuspended in imaging buffer at a concentration of 1 million/1 ml and 100,000–500,000 cells were dropped onto coverslips for SMLM or AFM imaging, incubated at 37 °C. Lastly, cells were either fixed with 2.4% PFA for 30 min at 37 °C after 3 minutes spreading time or used for live cell imaging. Single-molecule localization microscopy Combined SMLM (PALM-dSTORM) imaging was performed in a dSTORM buffer (50 mM TRIS pH = 8, 10 mM NaCl, 0.5 mg ml−1 glucose oxidase, 40 μg ml−1 catalase, 10% glucose, 10 mM MEA) and was performed using a TiE Nikon microscope in EPI mode. A low-intensity laser illumination at 405 nm (~0.5%) was used for activating the photoactivatable fluorescent protein and fluorophores, which were then sequentially imaged in a following frame using laser excitation at 488 or 647 nm. A built-in PerfectFocus system maintained the focus throughout the imaging. The acquisition sequence typically took ~2 min at 100 fps. Three-dimensional PALM was conducted using the astigmatism method.59


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SMLM Data processing SMLM data analysis was performed using a previously described algorithm (ThunderSTORM) 60 for identifying individual peaks. Peaks were further grouped by using a distance threshold of 20 nm and a temporal gap of ~50 ms to account for possible molecular blinking 61 and assigned to individual molecules for rendering of the data. Drift compensation and channel registration were performed using dedicated algorithms from ThunderSTORM software. An astigmatism imaging method 59 was utilized for threedimensional SMLM and was analyzed by ThunderSTORM software. Calibration was conducted using 100 nm Tetraspec fluorescent beads (Invitrogen). Rendering of the SMLM data was with intensities that correspond to the probability density values of their fitted Gaussian with respect to the maximal probability density values detected in the field. Coordinate-based colocalization analysis was performed by an ImageJ plug in implemented in a previously described method.52 AFM data acquisition and analyses A commercial, Nano-Wizard 3® AFM (JPK Instruments AG) equipped with a temperature controller coverslip holder (‘Biocell’, JPK Instruments AG) mounted on an Eclipse Ti-E microscope (Nikon Instruments) was utilized for mechanical mapping of cells. Silicon Nitride (Si3N4) AFM cantilevers with sharp silicon tips (MSNL10, nominal tips radius ~2 nm, Bruker) were used after being cleaned and oxidized using O2 Plasma (Atto, Diener Electronic) for 5 min prior to use. Force constants ranged from 0.009 to 0.05 N m−1, as determined by the thermal noise method for each individual cantilever prior to measurement. To obtain a map of the mechanical properties over the cell surface


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and simultaneously record cell topography with minimal shear forces, the quantitative mode (QI mode®, JPK Instruments AG) of operation was utilized. Usually images were collected with a resolution of 128 × 128 pixels (typically within 5-10 × 5-10 μm2 area) and with the following parameters: a setpoint force of 100 pN, a Z length of 500 nm, a speed of 100 μm s−1 , 700 pixels, and with a frequency of 100 kHz. After each line of scan, an addition 50 nm retract and 30 ms dwell time were implemented for minimal shear force artefacts. Microindentation analysis The Young’s moduli or the rigidity of cells was calculated from the force–indentation curves by fitting them to the Hertz model. JPK data processing software was used for determining the contact point coordinates, Z0 and D0 identification (Supplementary Fig. 1E). The sample deformation, δ, and indenting force, F, were calculated by:

(2) 𝛿 =

{ (𝑧 ― 𝑧 ) ―0 (𝑑 ― 𝑑 )

(3) 𝐹 =

{ 𝑘(𝑑 0― 𝑑 )

0

0

0

𝑧 < 𝑧0 𝑧 ≥ 𝑧0

𝑧 < 𝑧0 𝑧 ≥ 𝑧0

Then linear fitting is applied to fit the F vs. δ2 data in the post-contact region, z ≥ z0, using the Hertz model to extract the Young's modulus, E, of the cell: (4) 𝐹 =

2𝐸 × tan (𝜑) 2 𝛿, 𝜋(1 ― 𝑉)2

where v is the Poisson's ratio and 2φ is the opening angle of the cone tip. Finally, the membrane energy due to bending was calculated by using the relation.50


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(5) 𝑒𝑏𝑒𝑛𝑑 ~ 𝑌 ×

2

(𝑅1)

Physical simulation Membrane lines were held at their edges and middle by springs, with spring constants of 1 mN/m for the edge springs and 0.5 mN/m (the middle spring is a twofold longer spring and thus with half of the spring constant). The line is assumed to be bound to the springs and therefore, in addition to the spring behavior, it also acts as a fulcrum. Each side of the line is calculated separately (between the edge spring to the middle), and by assuming the torques sum to be 0, the calculated indentation is: 𝑓𝑎

1

(6) 𝑥𝑎 = 𝑘 × 𝐿2[𝑙2 + 2(𝐿 ― 𝑙)2] , where fa is the external force applied (i.e., the AFM tip compression) in any spot on the membrane, L is the total membrane line length, and k is the spring constant (of the shorter springs; for that reason there is a factor of 2 before the second expression), and l is the length to the edge. In our simulation the parameters were taken to be fa=200 pN, L=1500 nm , k=1 mN/m , and l=0-1500 nm. SUPPORTING INFORMATION Method scheme, physical model, analysis methods. Figures S1-S6 The Supporting Information is available free of charge on the ACS Publications website


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Visualizing Dynamic Microvillar Search and Stabilization During Ligand Detection by T Cells. Science 2017, 356. 35. Jung, Y.; Riven, I.; Feigelson, S. W.; Kartvelishvily, E.; Tohya, K.; Miyasaka, M.; Alon, R.; Haran, G. Three-Dimensional Localization of T-Cell Receptors in Relation to Microvilli Using a Combination of Superresolution Microscopies. Proc. Natl. Acad. Sci. U. S. A. 2016, 113, E5916-E5924. 36. Bunnell, S. C.; Hong, D. I.; Kardon, J. R.; Yamazaki, T.; McGlade, C. J.; Barr, V. A.; Samelson, L. E. T Cell Receptor Ligation Induces the Formation of Dynamically Regulated Signaling Assemblies. J. Cell Biol. 2002, 158, 1263-75. 37. Yi, J. C.; Samelson, L. E. Microvilli Set the Stage for T-Cell Activation. Proc. Natl. Acad. Sci. U. S. A. 2016, 113, 11061-11062. 38. Sage, P. T.; Varghese, L. M.; Martinelli, R.; Sciuto, T. E.; Kamei, M.; Dvorak, A. M.; Springer, T. A.; Sharpe, A. H.; Carman, C. V. Antigen Recognition is Facilitated by Invadosome-Like Protrusions Formed by Memory/Effector T Cells. J. Immunol. 2012, 188, 3686-3699. 39. Hochmuth, R. M.; Waugh, R. E. Erythrocyte-Membrane Elasticity and Viscosity. Annu. Rev. Physiol. 1987, 49, 209-219. 40. Salbreux, G.; Charras, G.; Paluch, E. Actin Cortex Mechanics and Cellular Morphogenesis. Trends Cell Biol. 2012, 22, 536-545. 41. Charras, G. T.; Horton, M. A. Single Cell Nechanotransduction and Its Modulation Analyzed by Atomic Force Microscope Indentation. Biophys. J. 2002, 82, 2970-2981. 42. Rotsch, C.; Radmacher, M. Drug-Induced Changes of Cytoskeletal Structure and Mechanics in Fibroblasts: An Atomic Force Microscopy Study. Biophys. J. 2000, 78, 520535. 43. Ketene, A. N.; Roberts, P. C.; Shea, A. A.; Schmelz, E. M.; Agah, M. Actin Filaments Play a Primary Role for Structural Integrity and Viscoelastic Response in Cells. Integr. Biol-Uk. 2012, 4, 540-549. 44. Yamane, Y.; Shiga, H.; Haga, H.; Kawabata, K.; Abe, K.; Ito, E. Quantitative Analyses of Topography and Elasticity of Living and Fixed Astrocytes. J. Electron Microsc. 2000, 49, 463-471. 45. Lu, L.; Oswald, S. J.; Ngu, H.; Yin, F. C. Mechanical Properties of Actin Stress Fibers in Living Cells. Biophys. J. 2008, 95, 6060-71. 46. Polliack, A.; Lampen, N.; Clarkson, B. D.; Deharven, E.; Bentwich, Z.; Siegal, F. P.; Kunkel, H. G. Identification of Human B and T Lymphocytes by Scanning ElectronMicroscopy. J. Exp. Med. 1973, 138, 607-624. 47. Chtcheglova, L. A.; Atalar, F.; Ozbek, U.; Wildling, L.; Ebner, A.; Hinterdorfer, P. Localization of the Ergtoxin-1 Receptors on the Voltage Sensing Domain of hERG K+ Channel by AFM Recognition Imaging. Pflug. Arch. Eur. J. Phy. 2008, 456, 247-254. 48. Lin, P. S.; Cooper, A. G.; Wortis, H. H. Scanning Electron-Microscopy of Human T-Cell and B-Cell Rosettes. New Engl. J. Med. 1973, 289, 548-551. 49. Coue, M.; Brenner, S. L.; Spector, I.; Korn, E. D. Inhibition of Actin Polymerization by Latrunculin-A. Febs. Lett. 1987, 213, 316-318. 50. Deserno, M. Fluid Lipid Membranes: From Differential Geometry to Curvature Stresses. Chem. Phys. Lipids 2015, 185, 11-45.


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51. Razvag, Y.; Neve-Oz, Y.; Sajman, J.; Reches, M.; Sherman, E. Nanoscale Kinetic Segregation of TCR and CD45 in Engaged Microvilli Facilitates Early T Cell Activation. Nat. Commun. 2018, 9, 732. 52. Malkusch, S.; Endesfelder, U.; Mondry, J.; Gelleri, M.; Verveer, P. J.; Heilemann, M. Coordinate-Based Colocalization Analysis of Single-Molecule Localization Microscopy Data. Histochem. Cell Biol. 2012, 137, 1-10. 53. Dokukin, M. E.; Guz, N. V.; Sokolov, I. Quantitative Study of the Elastic Modulus of Loosely Attached Cells in AFM Indentation Experiments. Biophys. J. 2013, 104, 21232131. 54. Sokolov, I.; Iyer, S.; Subba-Rao, V.; Gaikwad, R. M.; Woodworth, C. D. Detection of Surface Brush on Biological Cells in vitro with Atomic Force Microscopy. Appl. Phys. Lett. 2007, 91. 55. Figard, L.; Sokac, A. M. A Membrane Reservoir at the Cell Surface: Unfolding the Plasma Membrane to Fuel Cell Shape Change. Bioarchitecture 2014, 4, 39-46. 56. Shin, J. H.; Tam, B. K.; Brau, R. R.; Lang, M. J.; Mahadevan, L.; Matsudaira, P. Force of an Actin Spring. Biophys. J. 2007, 92, 3729-3733. 57. Mogilner, A.; Oster, G. Force Generation by Actin Polymerization II: The Elastic Ratchet and Tethered Filaments. Biophys. J. 2003, 84, 1591-1605. 58. Sherman, E.; Barr, V.; Manley, S.; Patterson, G.; Balagopalan, L.; Akpan, I.; Regan, C. K.; Merrill, R. K.; Sommers, C. L.; Lippincott-Schwartz, J.; Samelson, L. E. Functional Nanoscale Organization of Signaling Molecules Downstream of the T Cell Antigen Receptor. Immunity 2011, 35, 705-720. 59. Huang, B.; Wang, W. Q.; Bates, M.; Zhuang, X. W. Three-Dimensional SuperResolution Imaging by Stochastic Optical Reconstruction Microscopy. Science 2008, 319, 810-813. 60. Ovesny, M.; Krizek, P.; Borkovec, J.; Svindrych, Z. K.; Hagen, G. M. ThunderSTORM: a Comprehensive ImageJ Plug-In for PALM and STORM Data Analysis and Super-Resolution Imaging. Bioinformatics 2014, 30, 2389-2390. 61. Betzig, E.; Patterson, G. H.; Sougrat, R.; Lindwasser, O. W.; Olenych, S.; Bonifacino, J. S.; Davidson, M. W.; Lippincott-Schwartz, J.; Hess, H. F. Imaging Intracellular Fluorescent Proteins at Nanometer Resolution. Science 2006, 313, 1642-1645.


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TOC 177x93mm (150 x 150 DPI)


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Nanoscale TopographyâRigidity Correlation at the Surface of T Cells

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Nanoscale TopographyâRigidity Correlation at the Surface of T Cells