Letter pubs.acs.org/NanoLett
3D Structures of Responsive Nanocompartmentalized Microgels Arjan P. H. Gelissen,† Alex Oppermann,† Tobias Caumanns,‡ Pascal Hebbeker,† Sarah K. Turnhoff,† Rahul Tiwari,§ Sabine Eisold,∥ Ulrich Simon,∥ Yan Lu,⊥ Joachim Mayer,‡ Walter Richtering,† Andreas Walther,§ and Dominik Wöll*,† †
Institute of Physical Chemistry, RWTH Aachen University, Landoltweg 2, D-52056 Aachen, Germany GFE Central Facility for Electron Microscopy, RWTH Aachen University, Mies-van-der-Rohe-Straße 59, D-52074 Aachen, Germany § DWI − Leibniz-Institute for Interactive Materials, Forckenbeckstraße 50, D-52074 Aachen, Germany ∥ Institute of Inorganic Chemistry, RWTH Aachen University, Landoltweg 1, D-52056 Aachen, Germany ⊥ Soft Matter and Functional Materials, Helmholtz-Zentrum Berlin für Materialien und Energie, Hahn-Meitner-Platz 1, D-14109 Berlin, Germany ‡
S Supporting Information *
ABSTRACT: Compartmentalization in soft matter is important for segregating and coordinating chemical reactions, sequestering (re)active components, and integrating multifunctionality. Advances depend crucially on quantitative 3D visualization in situ with high spatiotemporal resolution. Here, we show the direct visualization of different compartments within adaptive microgels using a combination of in situ electron and super-resolved fluorescence microscopy. We unravel new levels of structural details and address the challenge of reconstructing 3D information from 2D projections for nonuniform soft matter as opposed to monodisperse proteins. Moreover, we visualize the thermally induced shrinkage of responsive core−shell microgels live in water. This strategy opens doors for systematic in situ studies of soft matter systems and their application as smart materials. KEYWORDS: Nanoscale soft materials, super-resolved localization microscopy, in situ transmission electron microscopy, cryogenic transmission electron microscopy, microgels, nanoparticles
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for complex biological systems, only a few studies address soft matter and material systems.19−23 Revealing 3D information based on the 2D images is of crucial importance.12,24 The structure of monodisperse proteins can be reconstructed from 2D cryo-TEM projections, which is, however, not applicable to nonuniform soft matter.25,26 Alternative ways to extract 3D information rely on reconstruction from tilted 2D projections (tomography).25,27,28 Besides being technically demanding, it is inapplicable to study dynamic processes with temporal resolution in situ. 3D information in SRFM can for instance be obtained by sophisticated optical engineering of the point spread function,29−31 which, however, sacrifices accuracy in lateral localization.18 Here, we demonstrate a strategy to recover detailed 3D information on the compartmentalization of responsive core− shell particles of increasing complexity using combinations of cryo-TEM, in situ TEM, and SRFM, particularly direct
esponsive complex polymer colloids with anisotropic distribution of functionalities, nonspherical geometries, and, in particular, different compartments have moved into the focus for fundamental nanoscience and applications.1−4 Their tailored design and the analysis of their function require powerful visualization on different scales, whereby the nanoscale possesses the most severe experimental challenges. Waterswollen microgels with complex architectures are emerging polymer colloids for advanced materials.5−8 Being soft,9 deformable10 and with smooth interfaces,11 they provide ideal challenges to fundamentally advance imaging techniques for compartmentalized soft matter. Cryogenic transmission electron microscopy (cryo-TEM) has emerged as powerful technique with which colloidal systems of low intrinsic contrast in quasi-native conditions can be studied.12 Its major drawback is the restriction to static pictures as the particles are embedded in a vitrified water layer. Dynamic, time-dependent processes can be investigated by liquid-cell in situ TEM,13 which yet requires contrast-rich systems. 14,15 Super-resolution fluorescence microscopy (SRFM) complements TEM techniques and can resolve nanoscale structures.16−18 Although SRFM is advancing quickly © XXXX American Chemical Society
Received: September 20, 2016 Revised: October 4, 2016
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DOI: 10.1021/acs.nanolett.6b03940 Nano Lett. XXXX, XXX, XXX−XXX
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Figure 1. Radial distributions of 2D projections. (a) 2D projection of simulated points randomly distributed within the shell for a core−shell particle. The points represent the NPs or dye molecules in our experiments. (b) The radial distributions of 2D projections of core−shell architectures with labeled shell and sharp core−shell transition for different core radii. The radius r is normalized to the particle radius R. (c) Influence of different gradients at the boundary between core and shell, characterized by the corresponding radial 3D density profiles in the inset, on the radial distribution of NPs. The difference between the sharp transition in (b) and the gradual transition in (c) is visualized by the cartoons of core−shell particles (black: sharp transition; blue: gradual transition) in the inset of (c).
stochastic optical reconstruction microscopy (dSTORM).32 We develop algorithms for quantitative image analysis (see the Software section in the Supporting Information) to reconstruct 3D information from 2D projections. Importantly, we harness the symmetry of the objects to counterbalance nonuniform size distributions. As a proof-of-concept of the capabilities of the approach, we will showcase the in situ temporal monitoring of the temperature-induced collapse of a core−shell microgel in water using liquid-cell TEM. We start with our concept of reconstructing 3D information from 2D projections. Simulated core−shell particles with a homogeneous distribution of labels and stains in the shell (Figure 1a) yield radial 2D distributions, as shown in Figure 1b. They can be described explicitly as a function of the core size (see Note 1 and eq 1 in the Supporting Information ). Additionally, the transition between core and shell (hard or gradient) influences the shape of the 2D distributions. A sharp transition leads to a more pronounced edge in the 2D projections, while a gradient smears out the edge (Figure 1c, black → blue). To apply this concept to microscopy data, we determine the 2D radial distributions from recorded images and recover 3D densities using two different methods: (A) fit with a core−shell model33 with adjustable smoothness of the core−shelltransition and at the particle edge due to dangling chains and (B) model-free evaluation of 3D densities using discrete inversion (Section 1 and Figures S4 and S5 in the Supporting Information).34 We analyze particles with different core−shell architectures: (1) a hard core with surface-bound hard particles, (2) a hard core with a soft microgel shell, and (3) soft core and soft shell microgels with different architectures and labeling. The key to a proper visualization relies on appropriate labels. We use nanoparticles (NPs) in TEM and fluorophores in SRFM.
Gold NPs (AuNPs) coupled to larger silica nanoparticles (SiO2NPs) present a simple system to demonstrate the methodology because it only consists of hard particles with well-defined distances between the center of the SiO2NP and the surrounding AuNPs (1; Figure 2a). The analysis of the distances between these centers clearly reveals that the AuNPs are adsorbed onto the spherical SiO2NP (Figure 2b). The density, ρ3D, of the AuNPs is zero inside the SiO2NPs and has a constant value at their edge (Figure 2c). Higher complexity occurs for particles with soft compartments as demonstrated for a core−shell microgel with a hard nonstained polystyrene (PS) core and a soft poly(Nisopropylacrylamide) (PNIPAM) shell (2). The cationic charges introduced by the initiator 2,2′-azobis(2-methylpropionamidine)·2 HCl (AAPH) during the shell synthesis can be stained by AuNPs via reduction of HAuCl4 with NaBH4 because AuCl4− coordinates to the cationic functions.35 The PS core has a higher native contrast than the water-swollen PNIPAM shell, allowing for its clear distinction in Figure 2d. Our analysis yields the expected AuNP density of 0 in the PS core (r/R < 0.5) and an increasing density in the shell. Surprisingly, the AuNP density in the shell is not homogeneous, but, as shown from both the fit with the fuzzy sphere model and the model-free inversion method, it increases from the PS−PNIPAM interface to the exterior of the PNIPAM shell (Figure 2e, f). The possibility of quantifying such a heterogeneous distribution of AuNPs is of paramount importance, as it is highly relevant for the catalytic activity of such hybrid structures.35 Our most complex systems are soft core−soft shell microgels (3). We start with PNIPAM-based core−shell microgels with a cationic core and an anionic shell. The cationic and anionic functions are incorporated by copolymerization and amount both to 10 mol %, respectively. Staining is achieved by B
DOI: 10.1021/acs.nanolett.6b03940 Nano Lett. XXXX, XXX, XXX−XXX
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Figure 2. Quantitative 3D structure for three systems of increasing complexity. (a) Scanning electron microscopy image of SiO2NPs with AuNPs on their surface (1). (b) Projected 2D radial distribution of AuNPs as a function of relative radius r/R (2444 AuNPs in 67 silica particles evaluated); green line: fit to the core−shell model in Figure S3. (c) Relative particle density, ρ3D, calculated with inversion method (blue circles) and fitted with the core−shell model (green line). (d) Cryo-TEM image of PS−core−PNIPAM−shell microgels 2 stained with AuNPs. (e) Projected 2D radial distribution of AuNPs (4461 AuNPs in 36 microgels evaluated); green line: fit to the core−shell model in Figure S3. (f) ρ3D calculated with inversion method (blue circles) and fitted with the core−shell model (line). (g) Cryo-TEM image of cationic core-anionic shell microgels 3 stained with AgNPs in the shell (P(NIPAM-co-DMAPMA)−core−P(NIPAM-co-MIA)−shell microgel; DMAPMA = N-[3-(dimethylamino)propyl]methacrylamide; MIA indicates itaconic acid monomethyl ester). (h) Projected 2D radial distribution of AgNPs (10422 AgNPs in 216 microgels evaluated); green line: fit to the core−shell model in Figure S3. (i) ρ3D, calculated with inversion method (blue circles) and the core−shell model (line). Regularization parameter used for all inversions of this figure: λ = 2.043. Scale bars: 200 nm. The cartoons in (c,f,i) sketch the distribution of labels as obtained from our analysis. The radius R is drawn as dashed black circle and was manually adjusted for each particle to account for their inherent polydispersity. Data analysis performed with the developed software “SoMaCoFit”, available online (Section 1 in the Supporting Information).
for a statistically sound compartment analysis already on the level of single microgels. Core−shell microgels have not been studied with SRFM previously, and therefore, we develop appropriate functionalization schemes. We introduce amines in the core and disulfides in the shell of PNIPAM−core−poly(N-isopropylmethacrylamide) (PNIPMAM)−shell microgels (4; see Section 2d in the Supporting Information). Selective labeling of the core proceeds via active ester chemistry at the primary amines. After the reduction of the disulfides to thiols, the shell can be labeled via Michael addition. We use Alexa 647 derivatives in both cases. Figure 3 displays a comparison between core-andshell, core-only, and shell-only labeled microgels in water (4c+s, 4c, and 4s). The radial distributions based on the 2D projected localizations (Figure 3c,f,i) are clearly different and reveal 3D density profiles reflecting the different spatial functionalization
formation of AgNPs from AgNO3 with NaBH4, which is known to selectively label the anionic shell (for details, see Section 2c in the Supporting Information). This selectivity is not obvious from inspection of the cryo-TEM images (Figure 2g). However, quantification of the 3D radial distribution of the labeling density (Figure 2i) reveals the majority of AgNPs to be indeed localized within the shell, while the formation of some AgNPs in the core cannot totally be avoided, as is evident from the remaining density of ca. 0.1 in the plateau region at r/R < 0.6. The 3D reconstruction routine is also applicable to dSTORM of core−shell microgels. This requires labeling of the corresponding compartments with suitable fluorophores and applying appropriate blinking conditions. One of the main benefits of dSTORM is the possibility for significantly higher labeling densities compared to NP labeling in TEM. This allows C
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Figure 3. Super-resolution fluorescence microscopy and structural analysis of PNIPAM−core−PNIPMAM−shell microgels in water using dSTORM. (a) Synthesis and labeling of the core−shell microgels. (b) Super-resolved and diffraction-limited (left upper corner) image of core-andshell labeled microgels (particle type 4c+s). The dashed circle represents the microgel size determined by DLS. (c) Relative number of localizations as a function of relative radius r/R (137232 localizations, 56 microgels); orange and green line: fits according to a homogeneous profile or, respectively, the core−shell models presented in Figure S3. (d) Relative labeling density ρ3D as a function of r/R for the inversion method (blue circles, regularization parameter λ = 1.1037), a homogeneous profile (orange line) and the core−shell model (green line). (e) Super-resolved image of coreonly labeled microgels (particle type 4c). (f) Relative number of localizations as a function of r/R (91379 localizations, 142 microgels); green line: fit according to the core−shell model. (g) Relative labeling density ρ3D for the inversion method (blue circles, λ = 3.7827) and the core−shell model (green line). (h) Super-resolved image of shell-only labeled microgels (particle type 4s). (i) Relative number of localizations as a function of relative radius r/R (357089 localizations, 226 microgels); green line: fit according to the core−shell model. (j) Relative localization density ρ3D for the inversion method (blue circles, λ = 1.1037) and the core−shell model (green line). (k,l) Distributions and their Gaussian fits of core radius and shell thickness, respectively, of 154 individual particle type 4s microgels. (m) Correlation between the analyzed core and shell radius. Scale bars: 500 nm. D
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Figure 4. Direct quantification of thermoresponsive collapse of single microgels from in situ TEM of a core−shell microgel with a stained shell (see also Supporting Video S1). (a) Bright-field cryo-TEM image of a microgel 3 with core−shell architecture stained with AgNPs in the shell. (b) In situ high-angle annular dark-field scanning TEM image of the same microgel. (c) Comparison of the 2D radial distributions of AgNPs resulting from in situ TEM and cryo-TEM images, respectively. (d) Temperature-dependent collapse of a PNIPAM-based microgel stained in the shell with AgNPs (particle type 5; see the Supporting Information) at pH 10. The lack of additional comonomers in the microgel core ensured a pronounced collapse of the microgel at T = VPTT (32 °C). At T < VPTT, the size of the microgel was unaffected, which underlines that the observed collapse is exclusively caused by a change in solvent quality due to heating. The profiles calculated from a greyscale analysis of the 2D projections were fitted with the core−shell model presented in Figure S3. The 2D profiles and the 3D distributions recovered with the inversion method are presented in Figure S17. All scale bars are 100 nm.
microgels using liquid-cell in situ TEM. In contrast to cryoTEM with vitrified, immobile microgels, in situ TEM is able to provide deeper understanding of interactions and dynamics of and between particles and their response to external stimuli; however, it typically faces lower contrast and poorer spatial resolution. Therefore, we determined the radial 2D distribution of the grayscale values, instead of label positions, of single shelllabeled microgels and cross-checked that the same distributions are yielded for cryo and in situ TEM images (Figures 4a−c and S16 and Section 5 in the Supporting Information). The temperature-induced collapse of the AgNP-labeled shell at T > VPTT (32 °C) could be observed in situ on the singleparticle level (Figure 4d and Movie 1) and the evolution of the 3D labeling density fitted to the model presented in Figure S3 in the Supporting Information. This experiment demonstrates for the first time that compartmentalization and responsive behavior of soft materials like microgels can be visualized and analyzed simultaneously in their native environment. In summary, we demonstrated a new concept for quantifying compartmentalization within soft matter systems via in situ electron and localization-based super-resolved fluorescence microscopy. On the basis of the 2D projection of the positions of nanoparticles or fluorophores specifically labeling the nanoscale compartments of interest, we unravel the internal 3D nanostructure of complex responsive microgels. The use of symmetry is crucial to overcome the adverse effects of polydispersity inherent to synthetic soft matter. In contrast to tomography, the approach moreover allows for an observation of structural changes of the internal structure in situ and enables an unprecedented time-dependent visualization and
of the microgels. The homogeneously labeled microgel (4c+s; Figure 3d) displays a constant labeling density, whereas an increased dye concentration is observed in the center for the core-labeled microgels (4c; Figure 3g). Due to unspecifically physisorbed dyes, some localizations occur in the microgel shell. As an advantage, these signals enable us to determine the particle size, R, needed for the normalization to the outer dimensions. Alternatively, this size is available from dynamic light scattering (DLS; dashed circles in Figures 3b,e,h, and Figure S10 and Section 2d in the Supporting Information) or by labeling the shell with a different fluorophore. In case of the shell-labeled microgels, a clear core−shell structure with a higher labeling density in the shell is obvious in Figure 3j. The core also contains some physisorbed dyes due to the attractive Coulomb interaction between the negatively charged Alexa 647 label and the partially positively charged amine-functions in the microgel core (4s; Section 2d in the Supporting Information). The high labeling density allows for an analysis of the compartmentalization of single microgels and, thus, a statistical evaluation of an ensemble. Figure 3k,l list the distributions of the core radius and shell thickness for ca. 150 individual core− shell microgels. The average radius of the full microgels agrees reasonably with the hydrodynamic radius of 250 nm, as determined by DLS. Because the transition between core and shell is smooth in microgels, the assignments of core radius and shell thickness are not independent from each other but anticorrelated, as shown in Figure 3m; i.e., smaller cores typically have larger shells. To capitalize on the gained understanding, we investigated the dynamics during the collapse of single thermoresponsive E
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D.W., A.W., J.M., W.R., and U.S. supervised the work. A.P.H.G., A.O., T.C., A.W., and D.W. wrote the manuscript with input of all other authors. A.P.H.G., A.O., and T.C. contributed equally.
evaluation of responsive and adaptive systems, as showcased for the temperature-induced collapse of PNIPAM microgels. Overall, our results underpin the high potential of electron and super-resolved fluorescence microscopy in combination with sophisticated analysis to visualize the delicate nanoscale internal structure of soft matter systems as well as their dynamics. This is of high importance for systems beyond the investigated microgel architectures, such as layer-by-layer modified liposomes, vesicular polymersomes, polyelectrolyte complexes, and porous silica particles. While we herein focused on core−shell architectures, we foresee that the methods can be extended to more complex models of anisometric or patchy particles. This would allow us to bring quantitative in situ imaging with temporal resolution closer to the capabilities of highly developed scattering experiments.
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Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors thank the Deutsche Forschungsgemeinschaft for financial support within SFB 985 “Functional microgels and microgel systems”. A.O. and P.H. thank the “Fonds der Chemischen Industrie” (FCI) for a scholarship. A.G. thanks Larissa Laurini for help with data treatment, Udit Shah for developing a graphic user interface (GUI) in MATLAB, and Otto Virtanen and Prof. Andrij Pich for fruitful discussion. Furthermore, support of the fluorescence competence center FLAMENCO of the RWTH Aachen University is appreciated.
ASSOCIATED CONTENT
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S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.nanolett.6b03940. Additional details on experimental procedures, particle synthesis and staining, electron microscopy, SRFM, DLS, SLS, and NMR. Figures showing geometric parameters used for modelling the 2D projections of core-shell particles, a flow chart to demonstrate our method to recover the 3D radial labelling density from 2D projections, a density profile of inhomogeneous microgels with core−shell architecture, effect of noise on inversion, stability plots to find the right regularization parameter, evaluation of PS−core−NIPAM−shell particles from a cryo-TEM image, a cryo-TEM image of a PS−core−NIPAM−shell particle, 1H-NMR spectra of the microgel core and the core−shell before and after saponification, dependence of the hydrodynamic radius Rh of the core and core−shell microgels on temperature, dependence of the hydrodynamic radius of the core and core−shell microgels on temperature, dependence of the form factor of the core−shell microgels, radial density profiles according to different methods, dependence of the hydrodynamic radius of the core−shell microgel stained with AgNPs on temperature, experimental setup for in situ experiments, EEL spectrum of a water-filled liquid-cell, example of a radial distribution analysis, and quantification of thermoresponsive collapse. Tables showing the composition of microgels and used amounts of chemicals in synthesis. (PDF) Temperature-induced collapse of the AgNP-labeled shell microgels. (MPG)
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Author Contributions
All authors have given approval to the final version of the manuscript. A.W. and D.W. designed the project. A.P.H.G., A.O., T.C., and S.K.T. carried out all of the experiments and analyzed the data. All authors discussed the data. A.O., A.P.H.G., and D.W. wrote the SoMaCoFit software. P.H. coded the inversion method. R.T. recorded cryo-TEM results, and S.E. synthesized the SiO2NPs with AuNPs on their surface. Y.L. provided data on PS−core−PNIPAM−shell microgels. F
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