from agitation, and the wells were imaged using an Olympus IX71 fluorescence microscope equipped with an Olympus U-CMAD3 camera, with an objective magnification of 20×.

**Particle Analysis.** Particle counting and measurement of particle area (in pixels) from 16 images (covering 0.53 mm²) at each time point were performed with ImageJ, using a fluorescence intensity threshold adjusted to the background for each image set. The selection of the particles was done using the particle analysis command and defining 10 pixels as the minimal particle area. The areas were then converted into µm² using the rate of 0.023 µm² pixel⁻¹.

## ■ RESULTS

**LK Peptides Show Concentration-Dependent Effects on the Lag Time of HI Fibrillation but Have No Influence on the Fibril Growth Rate.** As described in our previous work, HI fibrillation was monitored using ThT fluorescence emission at 482 nm in polystyrene 96 well plates at 37 °C with an orbital agitation of 1200 rpm.¹⁴ In order to study the effect of LK peptides on HI amyloid formation, the peptides, at concentrations between 0.01 and 4.7 µM, were mixed with the HI solution, and the fibrillation kinetics were recorded defining the lag time, elongation, and plateau phases. As a control, no ThT fluorescence increase was observed in wells containing 2.6 µM of peptide alone without HI, showing that the LK peptides do not assemble into amyloid fibers by themselves.

Our results show that, upon incubation with LK5, the shorter peptide, at concentrations up to 3 µM, the lag times of HI fiber formation were not modified and remained similar to those obtained in the absence of peptide (i.e., about 300 min) (Figure 1A, ○). By mixing HI with LK5 at concentrations equal or higher than 3.5 µM, the lag times of HI self-aggregation were decreased about six times to around 75–50 min. The LK7 peptide also shortens the lag times for peptide concentrations higher than 0.7 µM (Figure 1A, ●). For LK7 concentrations from 0.9 to 1.7 µM, the lag times were about 6 times decreased. No effect of the peptide is observed for LK7 concentrations lower than 0.7 µM. Using peptides of longer sequences, LK9 and LK11, the accelerating effect occurs at very low peptide concentrations, lower than 0.3 µM (Figure 1B). The lag time decrease became evident for LK11 concentrations of 0.03 µM, and the minimum lag time, equal to 50 min, was reached for 0.1 µM of LK11 (Figure 1B, inset). Moreover, for LK9 and LK11, a second effect was seen at concentrations higher than 0.3 µM: the lag times increased and reached a value similar to the one obtained without peptide. At 1.5 to 3 µM, LK9 and LK11 seemed to delay the nucleation, as the lag times were higher than the one observed without peptide (see also Figures 3 and 5, white bars). HI fibrillation kinetics is known to be highly variable, and this phenomenon is reflected by the large standard deviations obtained at high peptide concentrations (Figure 1B). In contrast, in the presence of LK peptides in accelerating conditions, the variability is strongly reduced.

For each peptide, the variations of the lag times with their concentration showed a sigmoidal shape suggesting a cooperative effect of the peptides on the acceleration of the nucleation step. Indeed, a critical concentration, corresponding to the one with which the accelerating effect obtained was 50% of the maximum, could be determined. For longer LK peptides, lower concentrations were enough to shorten the lag phase, suggesting that the accelerating effect of the LK peptides is more efficient with increasing peptide length. To assess this point, we plotted the correlation between the length of the

peptides and the logarithm of their critical concentration (Figure 1C). This plot shows that the critical concentration decreases sharply with the length of the LK peptide until 9 amino acids. For LK11 a Hill coefficient of 2.5 and a critical concentration of 0.035 µM are observed (Figure 1C).

In order to check if LK peptides could influence the growth of HI fibrils (elongation phase), the variations of the growth rates in function of the peptide concentration have been investigated (Figure 2). At the different peptide concentrations

<figure><img src="image_84.png" /><figcaption>**Figure 2.** No influence of LK peptides on the growth rate of HI fibrillation. HI and ThT were mixed with LK5 (○), LK7 (●), LK9 (□), and LK11 (■) peptides at different concentrations, and HI fibrillation was monitored by ThT fluorescence.</figcaption></figure>

tested, the growth rates of HI fibrillation remained stable at a value similar to the rate observed without peptide (i.e., about 60 AU min⁻¹), suggesting that the four peptides LK5, LK7, LK9, and LK11 have no significant effect on the elongation step of HI fibrillation. The plateau values recorded during fibrillation experiments remained also unchanged in the presence of LK peptides. Hence LK peptides play a critical role only in the nucleation step. The length of the LKnL peptides in amino acids is important for this process.

**Accelerating HI Fibrillation: LK Peptides Form Complexes Facilitating the Formation of Seeds on the Hydrophobic Surface.** In order to gain insight into the role of the peptides on the hydrophobic surface, we investigated the effect of preadsorption of the peptides on the surface, on the formation of HI aggregation nuclei.

The LK11 peptide was preadsorbed onto hydrophobic surfaces by incubation at 37 °C with rotation at 1200 rpm during 10 min. After the incubation, nonadsorbed peptides were removed by a buffer wash. Then HI solution (86 µM) containing ThT (20 µM) was added to the wells and incubated at 37 °C and 1200 rpm. The lag times of the fibrillation curves, obtained in the presence of preadsorbed peptides, have been compared to the lag times obtained by mixing HI and LK11 simultaneously in the hydrophobic wells. Three concentrations of peptide, 0.01, 0.3, and 3 µM, correlated to three different effects seen on Figure 1B, were compared.

No shortening of the lag time was observed at a peptide concentration of 0.01 µM in comparison with the condition without peptide (Figure 3). In contrast, when mixed with HI, 0.3 µM of LK11 decreased the lag time, and this effect was inversed with a 3 µM concentration of peptide (Figure 3, white bars). These results confirmed the concentration dependency
<figure><img src="image_85.png" /><figcaption>**Figure 3.** Preadsorption of LK11 peptide onto hydrophobic surfaces is enough to decrease the lag time of HI fibrillation. The peptide was incubated with the surface and rinsed before adding HI with ThT to monitor the aggregation kinetics by ThT fluorescence. The influence of different peptide concentrations on the lag time is shown. The effect of the peptide directly mixed with HI (white bars) is compared with the preadsorbed peptide (black bars) and with the preadsorbed peptide in the presence of an HI solution (gray bars).</figcaption></figure>

of the effect of the peptide when mixed with HI as previously shown on Figure 1B.

The preincubation of the peptide at a concentration of 0.01 µM did not influence the HI aggregation kinetics (Figure 3, black column). In contrast, the lag time was greatly shortened when 0.3 or 3 µM of peptide were preadsorbed on the surface before triggering HI fibrillation, when compared to the experiment without peptide (Figure 3, black columns). Indeed, preadsorption of LK11 at 0.3 µM leads to a lag time of about 100 min, while preadsorption of LK11 at 3 µM resulted in a very similar lag time to the one obtained by directly mixing 0.3 µM of LK11 with HI (about 50 min). These results suggest that the accelerating effect of the peptide depends on the amount of adsorbed peptide remaining on the hydrophobic surface and not on the peptide in solution.

Thanks to their high adsorption energy, the peptides could form stable complexes on the surface favoring the growth of amyloid nuclei. This could be achieved in two possible ways: the aggregative complexes could (1) be made of peptides alone and form seeds for HI fibrillation or (2) consist in assemblies of peptides together with HI molecules. In order to assess this point, the influence of HI on the preadsorption of the peptides has been investigated. By adding HI to the peptide solution during the preadsorption step, the decrease of the lag times of fibrillation observed at the peptide concentrations of 0.3 and 3 µM was less pronounced (Figure 3, gray columns). Indeed, for 0.3 µM of preadsorbed LK11, the lag time obtained was about 125 min when HI was added during preadsorption, instead of 100 min for preadsorption of the peptide alone. Similarly, a lag time of about 125 min was also observed, when HI alone was preadsorbed then washed before triggering the formation of the fibrils by adding the peptide at 0.3 µM with HI. These results show that the peptide and HI have no synergistic effect in assembling aggregative prenuclei on the surface in these conditions and that both species, HI and LK peptides, compete for adsorption at the surface.

**LK Peptides Adsorb in a Cooperative Manner on Hydrophobic Surfaces and Remain Stably Adsorbed.**
Because the accelerating effect of LKnL peptides on HI aggregation kinetics is linked to their preadsorption on hydrophobic surfaces, we investigated the adsorption properties of the LK11 peptide in more detail using a fluorescently tagged TAMRA-LK11 peptide (see *Experimental and Theoretical Methods*). As represented in Figure 4A, this peptide shows a similar accelerating effect on the HI aggregation lagtime at low

concentrations (0.07–0.28 µM). Likewise, at high concentrations (>0.7 µM) TAMRA-LK11 strongly inhibits HI aggregation.

To obtain a closer insight into the binding of the TAMRA-LK11 peptide on hydrophobic surfaces, we plotted the concentration of adsorbed peptide after 10 min of incubation as a function of the total peptide concentration (Figure 4B) and the peptide concentration remaining in solution (Figure 4B inset). The results indicate that the TAMRA-LK11 peptide cooperatively adsorbs to the hydrophobic surface and reaches a maximum adsorption after 10 min incubation at 0.07 nMol (eq 0.35 µM in a 200 µL well). Using the Hill equation, described in *Experimental and Theoretical Methods*, we have determined a Hill coefficient of 2.1 and a critical concentration of 0.14 µM for the TAMRA-LK11 peptide adsorption.

We then used this peptide to analyze the stability of its adsorption on hydrophobic surfaces in the presence of HI. TAMRA-LK11 was incubated at 1.4 µM in hydrophobic 96-well plates and shaken at 1200 rpm for 10 min. The solution was then discarded, the well washed with TMN buffer and incubated with 86 µM HI. After the indicated times, the well was rinsed and the adsorbed peptide was desorbed in TMN buffer containing 0.5% SDS before quantification as described in *Experimental and Theoretical Methods*. As can be seen in Figure 4C, the amount of adsorbed peptide decreases slowly over 120 min. Indeed fitting the data points to an exponential function shows that the TAMRA-LK11 peptide has a $t_{1/2}$ of 99 min on the hydrophobic well surface. We can therefore assume that less than 30% of the peptide desorbs from the surface during the lag time.

These results strongly indicate that the cooperative accelerating effect of LK peptides at low concentrations on the lag time of HI aggregation kinetics is driven by a cooperative adsorption mechanism of these peptides on the hydrophobic surface and sustained by the stability of their surface-binding over time.

**Inhibiting HI Fibrillation: LK Peptides in Excess in Solution Block Fibril Formation.**
In solution, LK peptides showed inhibitory effects at high concentrations. Indeed, when an excess of LK11 peptide remained in solution, it delayed the fibrillation kinetics (Figure 3, white bar at 3 µM, Figure 1B for LK11 > 2 µM, and Figure 4A for TAMRA-LK11 > 0.7 µM).

To gain insight into the effect of the peptide in solution, LK11 has been preadsorbed on the hydrophobic surface at three relevant concentrations C (0.01, 0.3, and 3 µM) and
<figure><img src="image_86.png" /><figcaption>**Figure 4.** Stable and cooperative adsorption of TAMRA-LK11 on hydrophobic surfaces drives insulin amyloid aggregation. (A) Evolution of the lag time (circles) of HI aggregation as a function of the TAMRA-LK11 concentration. For easier reading, data points are joined by a hand-drawn dashed line. At 1.4 µM TAMRA-LK11, the lagtime is longer than 24 h. (B) Cooperative adsorption of TAMRA-LK11 on hydrophobic surfaces. The evolution of the amount of adsorbed peptide after 10 min incubation is shown as a function of the initial TAMRA-LK11 peptide concentration. The inset shows a fit using the Hill equation (dotted line) of the adsorbed TAMRA-LK11 peptide as a function of the peptide remaining in solution. (C) Desorption kinetics of TAMRA-LK11. TAMRA-LK11 (1.4 µM) was preadsorbed in TMN buffer (10 min, 1200 rpm, 37 °C), and the concentration of the peptide remaining adsorbed was measured by fluorescence as a function of time. Data were obtained with three replicates.</figcaption></figure>

Indeed, as shown in Figure 4B, at 0.3 µM, most LK11 is adsorbed in oligomers on the surface, while at 3 µM, an excess of peptide remains in solution. Then, the fibrillation has been monitored after adding HI and the peptide at the same concentration C in solution as used for the preadsorption (Figure 5, black bars) or after adding HI and LK11 in excess at

3 µM (Figure 5, gray bars). The lag times obtained were compared to those observed by directly mixing HI with LK11 at concentrations C.

Figure 5 shows that LK11 preadsorbed at 0.3 µM and added at the same concentration in solution can accelerate HI fibrillation. In that case, the lag times obtained are similar to those observed when directly mixing LK11 and HI (Figure 5, white and black bars at 0.3 µM). It suggests that, although insufficient amounts of peptide remain adsorbed on the surface during preadsorption (see Figure 3), the nucleation phase can be accelerated later thanks to the continued presence of the peptide in solution. In contrast, when the peptide is added in excess at 3 µM consecutively to the preadsorption at 0.3 µM, the accelerating effect of LK11 is abolished (Figure 5, gray bar at 0.3 µM).

Figure 5 confirms that, the accelerating effect, obtained by preadsorption of 0.3 µM LK11 peptide, can be blocked by inhibiting peptide concentrations in solution (3 µM). This clearly demonstrates that the peptide can interact with insulin and prevent it from aggregating into growing fibrils. This effect is strongly dependent on the free concentration of peptide in solution (≥3 µM, Figure 5). The inhibitory effect of the LK11 peptide on HI aggregation is probably driven by electrostatic interactions between the positively charged K side chains and HI (negatively charged at pH 7.4). Indeed, high NaCl concentrations (500 mM) reduce this effect drastically but do not affect the acceleration behavior at 0.3 µM (Figure 6).

**Rotation is Not Necessary for the Preadsorption of the Peptide but for the Nucleation Step.** In order to gain insight into the mechanism of LK seed formation, the influence of the rotation during the preadsorption of the peptide has been investigated.

Figure 7 shows that, at LK11 concentrations of 0.3 and 3 µM, agitation was not necessary to obtain the pro-amyloidogenic effect of LK11. Peptides thus efficiently adsorb on the hydrophobic surface by diffusion and form stable complexes. In contrast, rotation is necessary for the nucleation step: no aggregation was observed when the plates were not agitated after the preadsorption step. Moreover, incubation of LK11 at 0.3 µM in the presence of HI for several hours without agitation is not sufficient to create seeds as essayed with a fresh insulin solution. Thus, the transformation of HI and peptide complexes into active nuclei requires agitation.

**Amyloid Prenuclei Can Be Observed by Microscopy.** The nucleation and elongation phases of HI fibrillation can be monitored by different microscopy techniques. We used atomic force microscopy (AFM) to image amyloidogenic prenuclei in situ in the dry state. Glass coverslips were silanized to obtain hydrophobic surfaces and were subsequently incubated at 37 °C under rotation with HI solutions containing LK9 peptide or not (Figure 8).

Using AFM, no aggregates can be observed for the LK9 peptide alone after 2 h of incubation (Figure 8A) or HI alone after 1 h of incubation (Figure 8B). At this same time point, heterogeneous prenuclei can be observed on coverslips incubated with an HI solution mixed with LK9 peptide at 0.2 µM (Figure 8C). These complexes were diverse in shape and size, ranging from 10 nm to at least 35 nm in height and up to several hundred nanometers in width. Their distribution was rather dense after 1 h of incubation, when compared to the coverslips incubated with HI or peptide alone (Figure 8, panels A and B). After HI incubation, the surface rugosity varies from 0.5 nm without peptide to 6 nm in the presence of LK9. The
<figure><img src="image_87.png" /><figcaption>**Figure 5.** Excess of peptide LK11 in solution delays HI fibrillation. The peptide was incubated at concentrations C = 0.01, 0.3, or 3 µM with the hydrophobic surface and rinsed. Then, HI was added together with the peptide at the same concentration C to monitor the aggregation kinetics by ThT fluorescence (black columns). For comparison, HI was also supplemented with LK11 at C = 3 µM and ThT fluorescence was recorded (gray columns). The results have been compared with the effect of the peptide directly mixed with HI (white columns).</figcaption></figure>

<figure><img src="image_88.png" /><figcaption>**Figure 6.** Effect of NaCl concentration on the lag time of HI aggregation. HI and ThT have been mixed with LK11 peptide at different concentrations in Tris buffer containing NaCl at 125 mM (white column), 250 mM (black column), or 500 mM (gray column). HI fibrillation was monitored by ThT fluorescence.</figcaption></figure>

observed prenuclei in Figure 8C highly reduce the lag time of HI aggregation but do not abolish it, relative to the sample shown in Figure 8B.

ThT allows the labeling of amyloid structures and their observation by fluorescence microscopy (Figure 9). No specific fluorescence could be detected in the well incubated with HI and ThT for 120 min (Figure 9A). However, in wells incubated in the presence of LK9 at 0.2 µM, multiple small fluorescent

objects, with different sizes up to 40 µm in diameter, could be observed at 120 min (Figure 9, panels B, C, and D).

Image analysis allows the determination of the size of the particles at the surface of the well with time. Fluorescent particles (up to 15 µm²) were already identified after 30 min of incubation of HI mixed with LK9 (Figure 9C). The amount of such small particles increased dramatically after 120 min incubation (Figure 9D), and concomitantly bigger particles, up to 40 µm², were observed.

Together, the AFM and fluorescence microscopy observations confirm that LK peptides enhance the formation of surface-bound aggregative prenuclei that promote fibril growth.

## ■ DISCUSSION

In this paper, we use a series of (LK)nL peptides of various lengths as tools to investigate the mechanism of HI aggregation on hydrophobic surfaces. The results discussed here shed light on how amyloidogenic peptides cooperate with HI to either form stable aggregation nuclei on material surfaces, or, at higher concentration, block efficient fibril growth at the aggregation nucleus.

**Cooperative Acceleration of Nuclei Formation at Low LK Peptide Concentration.** Peptides (LK)nL, at low substoichiometric concentrations, accelerate the lag time of HI aggregation on hydrophobic surfaces and hence permit faster creation and maturation of amyloidogenic, surface-bound

<figure><img src="image_89.png" /><figcaption>**Figure 7.** The agitation is not an important parameter for the preadsorption of the peptide. The LK11 peptide has been preadsorbed with (white bar) or without (black bar) agitation (1200 rpm) at a concentration of 0.3 or 3 µM and rinsed. HI and ThT were then added to monitor the aggregation kinetics by fluorescence measurements.</figcaption></figure>
<figure><img src="image_90.png" /><figcaption>**Figure 8.** Observation of amyloid prenuclei. AFM (tapping mode) images of silanized glass coverslips incubated at 37 °C, 1200 rpm for 2 h with (A) LK9 peptide alone (0.2 µM), for 1 h with (B) HI solution without peptide, or with (C) HI solution containing 0.2 µM of LK9 peptide. Note that these time points correspond to the lag phase under these experimental conditions. The scale bar represents 500 nm. Images are representative of about 20 independent samples.</figcaption></figure>

<figure><img src="image_91.png" /><figcaption>**Figure 9.** Fluorescence microscopy observation of insulin aggregates. HI was incubated at 37 °C and 1200 rpm and observed by fluorescence microscopy (DAPI filter) every 30 min. Images of the hydrophobic surface at 120 min are shown for (A) HI alone and for (B) HI in the presence of 0.2 µM LK9. The distribution of the particle size over a surface of 0.53 mm² is shown for HI + LK9 (0.2 µM) at (C) *t* = 30 min and (D) *t* = 120 min. Most particles are in focus and firmly adsorbed to the surface, some appear blurred and are not attached to the surface.</figcaption></figure>

prenuclei (Figures 1 and 4A). This effect is most prominent with longer peptides (Figure 1 C), the fastest lag time (40–50 min) being recorded for LK11 and TAMRA-LK11 (Figures 1B and 4A). The increase of the accelerating effect with the length of the peptide is likely to be caused by stronger adsorption of long peptides onto hydrophobic surfaces. Indeed, the hydrophobic contribution to the adsorption energy increases with the number of hydrophobic amino acids. For LK5, which has the smallest hydrophobic contribution to its adsorption energy in the (LK)nL series tested here, higher peptide concentrations are needed to observe an acceleration of the nucleation step (Figure 1A). In line with the assumption that strong LK adsorption promotes faster aggregation, one expects that, for a peptide at a given concentration, the lag time decreases with longer surface contact times. This can be seen for example for LK11, for which the lag time is around 100 min if preadsorbed during 10 min but decreases to 50 min for longer contact times

(Figure 3 black bar at 0.3 µM and Figure 1B). Furthermore, the results of Figures 3 and 4 demonstrate that, to obtain an accelerating effect, it is sufficient that peptides are stably adsorbed on the surface. After their surface adsorption, their presence in solution is no longer required to accelerate HI aggregation. This is due to the stability of their surface adsorption, as demonstrated with the fluorescently labeled TAMRA-LK11 (Figure 4C). This result confirms and strengthens the importance of a stable surface adsorption for peptides/proteins in order to promote the conformational changes that induce nuclei formation and fibrillation (Figure 10).

Plotting the lag time accelerating effect as a function of the LK peptide concentration, shows a sigmoidal behavior indicative of a cooperative effect of the peptides (Figure 1). Using the Hill equation (see *Experimental and Theoretical Methods*), we could determine a critical concentration and the
<figure><img src="image_92.png" /><figcaption>**Figure 10.** Dual effect of LK peptides on HI aggregation kinetics. (A) LK peptide complexes (represented with blue leucine and red lysine residues) cooperatively and stably adsorb on the hydrophobic surface (square) in β-sheet conformation. The hydrogen-bonding axis is represented by a gray transparent double-headed arrow. Upon integration of native insulin, they accelerate the formation of aggregation nuclei. (B) LK peptides in solution inhibit the formation of active aggregation nuclei.</figcaption></figure>

Hill coefficient of each LK peptide concerning its effect on the lag time and for TAMRA-LK11 concerning its surface adsorption (Figure 1C and 4B). All peptides show a positive cooperative behavior and both critical concentrations and Hill coefficients decrease with peptide length (Figure 1C). This holds true for LK peptides that have less than 9 amino acids, and strongly indicates that stable complex formation relies on the local assembly of a defined number of peptides in time. The speed at which such complexes can be created depends on how quickly individual peptides can assemble at a given density on the surface. Using the fluorescently labeled TAMRA-LK11 peptide, we show that the peptide adsorption on the hydrophobic surface itself is positively cooperative (Figure 4B). Together with the demonstrated stability of their surface adsorption (Figure 4C), the cooperative binding of LKNL peptides to the surface is the driving force, accelerating the formation of surface-bound aggregation nuclei. Peptide length, and therefore occupied surface area, and the stability with which they adsorb on the surface determine their cooperative efficiency. For longer peptides less molecules are necessary because they reach higher surface coverage and/or bind more stably than smaller ones. The LK11 peptide shows slightly higher values for the Hill coefficient and the critical concentration than LK9 (Figure 1C), indicating that an optimal peptide length can be defined for efficient aggregation nuclei formation. The cooperative behavior demonstrates the strong potential of aggregative peptides to control aggregation kinetics on material surfaces.

Beyond stable surface adsorption, the conformation and spatial organization of LK peptides on a hydrophobic surface is paramount. DeGrado and Lear¹⁶ have shown by circular dichroism and infrared spectroscopy that LK7 is structurally organized in antiparallel beta sheets in solution and at hydrophobic interfaces for micromolar concentrations and at neutral pH. Matthes et al.¹⁸ determined the conformation of the HI B-chain-derived peptide LVEALYL and showed that it is organized in antiparallel beta sheets and the determination of the crystal structure of the same peptide (PDB: 2OMQ¹⁹) confirms this. The LVEALYL peptide is known to form the spine of the HI fibril¹² and to have accelerating effects similar to those of LK peptides when adsorbed in substoichiometric concentrations on hydrophobic surfaces.¹⁴ We showed that beta-sheet conformation of peptides is essential to obtain accelerating effects, α helix conformations delaying aggregation.¹⁴ Altogether these data strongly support the idea that the establishment of peptide complexes on hydrophobic surfaces effectively triggering nucleation requires the presence of

antiparallel beta sheets (Figure 10). These are either formed by HI upon surface adsorption¹⁵ or provided by adsorbed peptides or a combination of both. It is tempting to speculate that the lag time can be shortened because adsorbed peptides facilitate conformational changes on incoming HI for its incorporation into prenuclei (Figure 10). Electrostatic interactions between HI and solution-exposed K residues probably play only a minimal role. Indeed, in a previous study,¹⁴ we have used a mutant of the B-chain LVEALYL peptide, in which we replaced the negative charge of the glutamate residue (E) by the neutral side chain of threonine (T). When adsorbed to a hydrophobic surface, the resulting LVTALYL peptide exhibits the side chains of amino acids L-T-L-L to the solution, instead of L-E-L-L for the wildtype peptide. Both peptides show similar, accelerated lag times in HI aggregation kinetics, indicating that charges are not the principle driving force for the accelerating effect of these adsorbed peptides. Also, we have used (IS)₂I and (IS)₃I peptides, which adsorb with their hydrophobic isoleucine side chain to the hydrophobic surface and expose a neutral serine side chain to the solution. Both (IS)₂I and (IS)₃I peptides show an accelerating effect,¹⁴ and this effect is cooperative, similar to (LK)nL peptides (data not shown). Moreover high salt concentrations do not affect the accelerating effect of LK peptides (Figure 6).

Our results show that, in order to obtain an accelerating effect on the lag time of HI aggregation, LK peptides do not need to cover the entire material surface but are probably adsorbed in small discrete “islands” scattered over the hydrophobic surface. Indeed, at 0.2 µM, when LK11 reduces the lag time considerably (Figure 1B), there are not enough LK11 molecules in solution to cover the well surface uniformly. Assuming the peptide is a rectangle of width 2.5 Å and length 2 Å per amino acid, one LK11 molecule occupies approximately 55 Å². In order to cover the entire surface of a reaction well filled with 200 µL solution (0.02 dm²) by a monolayer of peptides, 3.6 × 10¹⁴ LK11 molecules are needed. However, at 0.1 nMol (see Figure 4B), the number of LK11 peptide molecules adsorbed is 6 × 10¹³, covering 17% of the surface. Therefore, LK peptides must be adsorbed in a discrete manner on hydrophobic surfaces, at the time when they accelerate nuclei formation. This is also documented by our microscopy results (Figures 8 and 9). In this context, it is relevant to note that DeGrado and Lear demonstrated that the LK7 peptide is organized in aggregates of 31 molecules at the water-air interface.¹⁶ Nayak et al. have measured the kinetics of the formation of insulin aggregation nuclei in solution using small angle neutron scattering and proposed that aggregation nuclei are composed of 3 insulin dimers (6 monomers).²⁰ On a surface, however, this might be different, as peptide/protein adsorption increases their local concentration, and their interaction with surfaces is likely to stabilize the nucleation multimer. The stoichiometry of peptide/HI molecules in surface-bound aggregation nuclei is hitherto unknown, but amyloidogenic peptides are a valuable tool to investigate it.

**Inhibition of HI Aggregation at High LK Peptide Concentration.** The lag time decrease, observed for LK peptides at low concentrations, is reversed at higher peptide concentrations, albeit still substoichiometric (Figures 1 and 4A). For example, LK9 and LK11 lose their accelerating effect gradually when present in concentrations higher than 0.5 µM (Figure 1B). Moreover, we show that the delay in HI aggregation comes from an excess of peptide in solution (Figures 3 and 5). Our results presented in Figure 2 further
show that none of the LK peptides have an effect on the fibrillation growth rate. It is therefore likely that nonadsorbed LK peptides remaining in solution interfere with efficient HI integration at growing prenuclei, either by interacting with preformed nuclei per se or by blocking the conformational changes that HI undergoes when incorporated into an aggregation nucleus (Figure 10). Because this inhibitory effect is observed at peptide concentrations in solution that are largely substoichiometric compared to the concentration of HI (86 µM), it is unlikely that the peptide interacts stably with HI before it reaches an aggregation nucleus. The mechanism of interaction of LK peptides with HI is unknown but probably based on hydrophobic or electrostatic interactions, given the nature of these peptides. Indeed, high ionic concentrations dampen the inhibitory effect (Figure 6), showing that the positively charged K residues and the negatively charged HI (pI = 5.3) could be electrostatically attracted.

The affinity of LK peptides for HI interaction at a growing nucleus is unknown, but the inhibitory effect is predominantly seen with longer peptides LK9 and LK11, suggesting a threshold for the size of the peptide. It is also possible that steric constraints present beyond a certain peptide size interfere with efficient incorporation of HI into a preformed surface-bound prenucleus. Along this line, the TAMRA tag, which is bulky, dramatically enhances the inhibitory effect of the TAMRA-LK11 (Figure 4A) at concentrations >0.7 µM. Finally LK peptides could also interfere with a domain in HI, exposed when HI is being integrated into the prenucleus, thereby hindering HI conformational changes. A comparative shielding mechanism has been shown to be efficient in preventing islet amyloid polypeptide (IAPP) aggregation during expression in *E. coli*.²¹ Indeed, a recombinant protein tag using the HI18-binding domain, known to interact with a hydrophobic sequence in IAAP involved in aggregation, was used to sequester soluble IAAP monomers, thus preventing its aggregation. It is possible that LK peptides interact with partially unfolded HI thereby blocking further transition into the beta-sheet conformation found in the mature amyloid fiber.

## CONCLUSIONS

In this paper, we demonstrate that small peptides consisting of alternate leucine and lysine amino acids of the type (LK)nL are capable to dramatically accelerate HI aggregation but also to inhibit it depending on their concentration. These effects are strictly dependent on the presence of hydrophobic material surfaces and highlight the importance of the impact of materials on protein stability. Indeed, we demonstrate that the cooperative effect on the acceleration of HI aggregation is based on a cooperative binding of the peptides to the surface. Beyond the results presented here, (LK)nL peptides clearly represent a versatile tool to study early stages of material-bound surface aggregation of proteins. As they are incorporated into aggregation nuclei, they are at the same time amyloid aggregation inducers and tools to investigate the aggregates they trigger.

## AUTHOR INFORMATION

### Corresponding Author

*Address: LMGP Phelma Minatec, 3 parvis Louis Néel, CS50257, F-38016 Grenoble cedex1, France. Tel: ++ 33 4 56529335. Fax: ++ 33 4 56529301. E-mail: marianne.weidenhaupt@grenoble-inp.fr.

## Notes

The authors declare no competing financial interest.

## ACKNOWLEDGMENTS

This work was supported by a Ph.D. fellowship from the MENRT to K.C. We thank the CNRS for funding C.V. as a visiting researcher and acknowledge the Nanomonde facility of the CIME Nanotech and Sidali Bessa for assistance with the AFM experiments.

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# Insulin Aggregation at a Dynamic Solid-Liquid-Air Triple Interface

Thibaut Frachon,†,‡ Franz Bruckert,† Quentin Le Masne,‡ Emmanuel Monnin,‡ and Marianne Weidenhaupt*,†,‡

†LMGP, University Grenoble Alpes, CNRS, F-38000 Grenoble, France

‡Eveon S.A.S., Inovallée, F-38330 Montbonnot Saint Martin, France

## S Supporting Information

**ABSTRACT:** Therapeutic proteins are privileged in drug development because of their exquisite specificity, which is due to their three-dimensional conformation in solution. During their manufacture, storage, and delivery, interactions with material surfaces and air interfaces are known to affect their stability. The growing use of automated devices for handling and injection of therapeutics increases their exposure to protocols involving intermittent wetting, during which the solid-liquid and liquid-air interfaces meet at a triple contact line, which is often dynamic. Using a microfluidic setup, we analyze the effect of a moving triple interface on insulin aggregation in real time over a hydrophobic surface. We combine thioflavin T fluorescence and reflection interference microscopy to concomitantly monitor insulin aggregation and the morphology of the liquid as it dewets the surface. We demonstrate that insulin aggregates in the region of a moving triple interface and not in regions submitted to hydrodynamic shear stress alone, induced by the moving liquid. During dewetting, liquid droplets form on the surface anchored by adsorbed proteins, and the accumulation of amyloid aggregates is observed exclusively as fluorescent rings growing eccentrically around these droplets. The fluorescent rings expand until the entire channel surface sweiped by the triple interface is covered by amyloid fibers. On the basis of our experimental results, we propose a model describing the growth mechanism of insulin amyloid fibers at a moving triple contact line, where proteins adsorbed at a hydrophobic surface are exposed to the liquid-air interface.

<figure><img src="image_93.png" /><figcaption>Dewetting triggers aggregation at the triple contact line</figcaption></figure>

## INTRODUCTION

Therapeutic proteins have opened new avenues in disease treatment because their unequaled specific interactions allow drug targeting with unprecedented molecular precision. This property comes at the price of guaranteeing their native conformation, which is inherently far more complex and less stable for proteins than for chemically synthesized drugs. Insulin and monoclonal antibodies (mAbs) are the most produced and administered therapeutic proteins, and their structural stability is determining not only their therapeutic value but also the economic viability of their production.

Besides the multiple environmental factors affecting protein stability (temperature, pH, ionic strength, and so forth), protein aggregation triggered by adsorption at material surfaces or air interfaces is emerging as a crucial parameter commanding conformational stability.¹⁻⁴ Indeed, during their fabrication, storage, transport, and until their delivery, therapeutic protein solutions are constantly in contact with different material surfaces and are very often exposed to air, which can be considered as a smooth and flexible hydrophobic interface. Moreover, in medical devices used for reconstitution or injection, protocols involving intermittent wetting expose the protein solutions to a triple solid-liquid-air interface, which is

often dynamic (pump cavitation, agitation, air bubbles in tubing, and headspace in perfusion bags).

It is well-documented that insulin adsorption at material surfaces, in particular hydrophobic ones, and at the liquid-air interface is detrimental for its stability and leads to accelerated amyloid aggregation. Sluzky and colleagues⁵ pinpointed in a pioneering study that insulin aggregates only in the presence of hydrophobic surfaces when agitated. Since then numerous studies have shed light on kinetic, structural, and nucleation aspects of interfacial insulin aggregate formation, and it is widely accepted that interfacial effects play an important role in insulin stability (for recent examples, see refs 6–9). For antibodies, usually formulated at very high therapeutic concentrations, interface adsorption is one of the parameters guiding formulation strategies. Among many examples, Couston and colleagues¹⁰ have examined the structural organization and surface loading of a mAb under relevant pH and surfactant conditions on silica. A repetitive rupture of the air-liquid interface has been shown to induce the aggregation of mAbs,¹¹ and the effect of a liquid-air interfacial mAb layer

Received: September 8, 2016

Revised: November 14, 2016

Published: November 16, 2016

<figure><img src="image_94.png" /></figure>
on the rheological properties of mAb solutions has been examined.¹²

The above-selected studies underline that a detailed mechanistic understanding on how therapeutic proteins organize at interfaces is a timely subject, which takes all of its importance in the context of today's drug development. In this study, we focus on insulin adsorption and aggregation at a dynamic triple solid-liquid-air interface, mimicking intermittent wetting phenomena encountered in numerous industrial protocols. Although some recent studies are analyzing the effect of a moving air-liquid interface in the context of therapeutic protein aggregation,¹²⁻¹⁴ a detailed real-time assessment of the protein fate at a moving triple contact line is still missing.

It is commonly accepted that proteins have surfactant-like properties and adsorb on hydrophobic interfaces by displacing weakly bound water molecules.²,¹⁵,¹⁶ Secondary structure rearrangements allowing the establishment of hydrophobic interactions with the material or air interface stabilize their adsorption, counterbalancing the surface dehydration with an entropy gain. Subsequently, an interfacial protein layer organizes, in which packing density, lateral interactions, cooperative effects, and aggregation are known to operate at different rates, depending on the nature of the protein.² At a moving triple interface, several interfacial phenomena co-occur, likely potentiating the above effects. Indeed, proteins adsorbed at the solid-liquid and air-liquid interfaces collide at this point inducing higher local concentrations and therefore intensifying intermolecular interactions. Moreover, a moving liquid-air interface submits the locally concentrated proteins to partial dehydration, enhancing conformational instability due to the loss of an integer hydration shell that proteins tend to retain.¹⁷ This is especially true when protein adsorption is strong enough to prevent their dissociation from the surface as the liquid is removed.

We therefore developed a microfluidic setup allowing the movement of an insulin solution back and forth over a hydrophobic surface, thereby displacing the triple solid-liquid-air interface locally and continuously in a controlled way (Scheme 1). Insulin is known to aggregate and form amyloid fibers at hydrophobic surfaces and at the liquid-air interface, following nucleation-based aggregation kinetics.⁹ The α-helix-rich native conformation of the insulin monomer unfolds upon adsorption to form a β-sheet. The stacking of β-sheets between adjacent monomers leads to the rapid formation of amyloid fibers, which can be detected by thioflavin T (THT) fluorescence.¹⁸ Combining reflection interference contrast and fluorescence microscopy, we monitor, in real time and concomitantly, the morphology of the liquid film and the formation of insulin amyloid deposits on the material surface, respectively. We demonstrate that insulin fibers exclusively form in the region of intermittent wetting but not in the region exposed to hydrodynamic shear stress alone, remaining always wet. We show that the triple contact line is the place where the first aggregates are created, and we measure the growth kinetics of insulin amyloid aggregation at the edge of the liquid droplets. Finally, we propose a simple physicomathematical model describing the forces driving these phenomena.

## EXPERIMENTAL SECTION

**Chemicals.** Recombinant human insulin (HI) produced in yeast, bovine serum albumin (BSA), and thioflavin T were purchased from Sigma-Aldrich (ref: I2643, P0914, and T3516, respectively). HI stock solutions were prepared at a concentration of 0.5 mg/mL (86 µM) in

<figure><img src="image_95.png" /><figcaption>Scheme 1. Experimental Setup<sup>a</sup></figcaption></figure>

ᵃ(A) Side view. (B) Top view. Blue represents the protein solution, white represents the air, and gray represents the chip material. Dimensions are in millimeters. The x-axis represents the length of the channel and defines the position at which the images are taken. The upper and lower levels correspond to the far left and far right meniscus positions during a cycle. The displaced liquid volume during each cycle is defined by the pump parameters and equals 2 µL. The cycle duration corresponds to the time that the liquid takes to reach the upper level and to come back to the lower level and equals 15 s. ROI represents the microscope objective position and the field of view during a time-lapse acquisition.

TN buffer (Tris-HCl 25 mM, NaCl 125 mM, pH 7.4). HCl (1 M) was added to completely dissolve HI in the buffer at pH 3.2, and then 1 M NaOH was added to adjust the final pH to 7.4. The concentration was adjusted by UV absorbance at 280 nm using an extinction coefficient of 5.53 $M^{-1} cm^{-1}$. BSA solutions were diluted in pure water to reach a concentration of 0.5 mg/mL. THT stock solutions were prepared in TN buffer at a concentration of 1 mM, adjusted by absorbance measurement (1% THT solution in ethanol, $ε_{416}$ (THT) = 26.6 $M^{-1} cm^{-1}$). Tween 20 and Tween 80 were purchased from Sigma-Aldrich (ref: P1379, MW: 1228 g/mol and P1754, MW: 1310 g/mol, respectively). A polysorbate stock solution was prepared with 10% (v/v) Tween in water, the working concentration was 200 ppm in a standard HI solution (0.5 mL HI at 86 µM + 1 µL Tween 10%). All solutions were stored at 4 °C, conserved less than 2 weeks and filtered through a 0.22 µm Millex-GV filter unit from Merck Millipore (ref: SLGV033SS) before use.

**Experimental Setup.** *Microscopy Setup Specifications.* An Olympus IX71 inverted microscope equipped with an Olympus DP30BW camera, a 60× magnification oil objective with a numerical aperture of 1.25 (ref: UPLanFLN), and a 100 W Hg lamp (ULH100HG and HBO103W/2) were used for fluorescence and reflection interference contrast microscopy (RICM). The camera field of view was 200 by 150 µm. The size of the observed pixels was 150 nm resulting in a lateral resolution of 300 nm. A motorized stage (with a resolution of 0.05 µm and an accuracy of ±3 µm), purchased from Märzhäuser Wetzlar, was used for image acquisitions along the channel. A shutter, purchased from Vincent Associates (ref: Uniblitz VS35S2ZM1R3-NL-24), was used to avoid bleaching during time-lapse fluorescence acquisitions. The camera, the shutter, and the motorized stage were all synchronized and automated with Image Pro Plus 5 (Media Cybernetics) and home-made short programs.

*Fluorescence Microscopy.* The THT dye was used to monitor the amount of amyloid fibers formed at the channel surface. When incorporated into amyloid fibers, THT undergoes a fluorescence shift from ($λ_{ex}$ = 342 nm; $λ_{em}$ = 430 nm) to ($λ_{ex}$ = 450 nm; $λ_{em}$ = 482 nm).¹⁹ This property allows the direct quantification of amyloid fibers
<figure><img src="image_96.png" /><figcaption>**Figure 1.** Image processing of the evolution of a THT-positive ring observed by fluorescence microscopy. (A) Example of a concentric circle drawn around the center of the droplet (white dot). *r* represents the radial distance to the center. Units: gl = gray level. (B) *Integrated fluorescence intensity profile* (*F*, gl × pixel²) as a function of radius *r*. (C) *Radial fluorescence intensity profile* (*I*, gl × pixel) as a function of radius *r*. (D) *Differential fluorescence increase distribution* (Δ*I*, gl × pixel) over a 10 cycle interval as a function of the radius *r*. Continuous line: fit of Δ*I*(*r*) by two Gaussian functions (see eq 4). *ρ*<sub>max</sub><sup>exp</sup> represents the radial position of the maximal fluorescence increase.</figcaption></figure>

by fluorescence recordings at 482 nm. Fluorescence images were
acquired with the microscope described above using a DEAC
fluorescence cube (λ<sub>ex</sub> = 436 ± 10 nm, λ<sub>em</sub> = 480 ± 15 nm). Two
neutral density filters (Olympus, 32ND6 and 32ND12) were used to
reduce the light intensity of the Hg lamp by 92.8%.

RICM. For RICM, a green filter selected the 546 nm Hg emission peak of the lamp, and a semireflecting plate was set at 45° of the optical axis to illuminate the sample.²⁰ A neutral density filter (Olympus, 32ND12) was used to reduce the light intensity of the Hg lamp by 88%.

*Microfluidic Setup.* Straight microfluidic channel chips were purchased from microfluidic ChipShop (ref: 01-0175-0138-02). The channel dimensions were 200 µm deep, 58.5 mm long, and 1 mm large, with a 140 µm thick cover lid compatible with microscope oil objectives. They were equipped with a female mini luer fluidic interface for tubing connections. The chips were made in glass or in cyclo-olefin copolymer (COC, grade: mcs-Topas-03 mcs foil 011), a nonpolar, amorphous, and hydrophobic thermoplastic polymer.

A syringe pump was purchased from World Precision Instruments (ref: AL-6000) and adapted to be used with a 125 µL Distritips syringe body purchased from Socimed (ref: SOC-F164100). Tubing was purchased from Masterflex (ref: Tygon Lab E-3603 L/S 14). Male mini luer fluid connectors and male mini luer plugs were purchased from microfluidic ChipShop (ref: 09-0545-0333-09 and 09-0550-0334-09). A home-made conductive transparent heater was used to uniformly heat the channel at 37 °C, consisting of a silver nanowire network directly deposited on the top of the microchannel and connected to a power generator. Thanks to the Joule effect, when an electrical current is applied through the network, heat is released and transferred to the substrate.²¹ A calibration curve was obtained using a thin Pt500 platinum resistor placed underneath the microchannel to relate the steady-state temperature difference relative to the room temperature to the applied voltage. The room temperature was controlled, and the temperature variation during the entire experiment did not exceed 1 °C.

**Wetting/Dewetting Cycles in a Micro Channel.** The COC microchannels were washed before the experiments with 10 mL of 5% sodium dodecyl sulfate, then with 10 mL of deionized water, 10 mL of 96% ethanol, and finally 10 mL of deionized water. The glass microchannels were washed with 14 M NaOH for 5 min and rinsed with 10 mL of deionized water. A new channel was used for each experiment. The 125 µL syringe was filled with a solution containing HI at 86 µM and THT at 20 µM, previously degased for 10 min. The syringe was fixed on the syringe pump and connected to the

microchannel with tubing using a male mini luer fluid connector. All connections were sealed with parafilm. The chip was firmly clamped on the motorized microscope stage. The channel was completely filled up to the outlet. Then, a plug was introduced in the outlet that made the liquid move back to the upper level (Scheme 1). The plug sealed the device and insured the repeatability of the liquid levels during the cycles by avoiding evaporation. The transparent heater was then placed over the chip to cover the triple interface zone, and the adjacent zone remaining always wet. The syringe pump flow rate was set to 16 µL/min and programmed to repeatedly withdraw and push 2 µL of HI/THT solution, thus moving the liquid-air interface between the upper and the lower levels (see Scheme 1). A complete cycle of wetting-dewetting thus lasted 15 s.

**Image Acquisition.** *Time-Lapse Acquisition.* Before starting the pump, the microscope objective was focused on the cover lid, at 2.4 mm from the upper level and 200 µm from the channel wall (Scheme 1). The pump was programmed to stop for 1 s, every 10 cycles (150 s). The shutter and the camera were synchronized to acquire one image each time the pump stopped. This procedure resulted in a time-lapse image sequence acquisition of one image every 150 s for the entire experiment. Fluorescence or RICM microscope images were acquired as the liquid meniscus was at the upper level or the lower level, respectively. With our microscope setup, it is not possible to simultaneously acquire both fluorescence and RICM time-lapse image series. Consequently, the results based on different imaging mode time-lapse series come from different experiments (such as shown in Figures 5 and 7). Nevertheless, at specific time points, the imaging mode can be switched manually, allowing a fluorescence and RICM image acquisition during the same liquid cycle (see Figures 4 and 6). The gain and acquisition time of the camera were chosen to prevent saturation during the recordings.

*End-Point Fluorescence Distribution.* At the end of the experiment, the protein solution was withdrawn, and the channel was filled with 1 mL of TN buffer supplemented with 20 µM THT. The microscope objective was placed at 200 µm from the channel wall. Fluorescence images were then acquired along the *x*-axis, every 200 µm, using the motorized stage, allowing a total surface coverage along the *x*-axis. The three regions, always dry, triple interface, and always wet, were covered by the image sequence.

RICM Movie. RICM movies were acquired at different times during the experiment in order to determine droplet growth kinetics. The shutter was kept open because there is no photobleaching in RICM recordings. A movie was acquired over several consecutive wetting-
dewetting cycles with the DP Controller software (Olympus) at 15 frames per second.

**Image Analysis.** All image treatments and analyses were performed with ImageJ.

**Fluorescence Quantification of Insulin Aggregation Kinetics and End-Point Distribution of Amyloid Aggregates in Microfluidic Channels.** At any given time point, THT fluorescence was quantified by calculating the mean gray level intensity of the corresponding fluorescence image. Error bars represent the standard deviation of the measured intensities. The same procedure was applied to quantify the THT fluorescence distribution along the microchannel at the end of an aggregation experiment.

**Detailed Growth Kinetics of Amyloid Aggregates around Liquid Droplets Remaining in the Microchannel after Dewetting.** The following image treatment protocol was used to determine the radial distribution of the THT fluorescence increase around the droplets, remaining on the microchannel surface, using fluorescence images recorded every 10 cycles. At each time point, the radial fluorescence intensity profile ($I(r)$) was determined as a function of the distance to the center of the fluorescent ring (Figure 1A-C); then, the differential fluorescence intensity distribution ($\Delta I(r)$) over 10 cycles was calculated and analyzed to obtain the net fluorescence increase ($\Sigma_{\text{exp}}$) and the radial position of the maximum fluorescence increase ($\rho_{\text{max}}^{\text{exp}}$) (Figure 1D).

For each fluorescent image, a circle around the outlines of the fluorescent ring was first drawn (Figure 1A), and the center of this circle was used as the center of the droplet (white dot). Concentric circles of pixel-wise increasing radii were then defined, until they reached another fluorescent ring or the border of the image. The integrated fluorescence intensity profile was calculated by integrating the total fluorescence inside of each circle defined by its radius $r$ (Figure 1B). At every time point $t$, expressed in cycles (1 cycle = 15 s), the integrated fluorescence intensity profile can be analytically expressed as

$$F(r, t) = \int \int_{0,0}^{r,2\pi} I(u, \theta, t) u \, du \, d\theta = \int_{0}^{r} 2\pi I(u, t) u \, du \quad (1)$$

where $I(u, t)$ corresponds to the average fluorescence intensity along the perimeter of the segmentation circle or radius $u$ at a time point $t$.

The radial fluorescence intensity profile $I(r, t)$ at any time point $t$ was obtained by deriving $F(r, t)$ from eq 1 (Figure 1C)

$$I(r, t) = \frac{1}{2\pi r} \frac{dF(r, t)}{dr} \quad (2)$$

The $F(r, t)$ derivative was calculated with the Savitzky and Golay algorithm, using seven points (coefficients used: -3, -2, -1, 0, 1, 2, and 3 normalized by dividing by 28).²²

Then, consecutive radial fluorescence intensity profiles were subtracted to define the differential fluorescence intensity distribution over a 10 cycle interval, $\Delta I$ (Figure 1D).

$$\Delta I(r, t) = I(r, t) - I(r, t - 1) \quad (3)$$

The net fluorescence increase over a 10 cycle interval, $\Sigma_{\text{exp}}$, is obtained by integration of the $\Delta I$ differential profile along $r$.

Finally, each differential profile was fitted with two Gaussian functions and six parameters (Figure 1D).

$$\Delta I(r, t) = A_1 e^{-(r - \rho_{\text{max}}^{\text{exp}})^2 / 2\sigma_1^2} + A_2 e^{-(r - r_2)^2 / 2\sigma_2^2} \quad (4)$$

where $A_1$ and $A_2$ represent the amplitudes, $\rho_{\text{max}}^{\text{exp}}$ and $r_2$ represent the peak positions, and $\sigma_1$ and $\sigma_2$ represent the widths of the two Gaussian functions. The first Gaussian function was used to fit the main peak of the profile, and the second Gaussian function was used to fit the baseline (Figure 1D). The radial position of the maximal fluorescence increase is thus determined by the center of the first Gaussian peak, $\rho_{\text{max}}^{\text{exp}}$ (Figure 1D).

**Droplet Superimposition Index.** At each time point, the superimposition index was determined from two RICM images of droplets obtained after dewetting (Figure 2A), separated by 10 cycles (150 s). For each RICM image, a binary image was created by manually defining a threshold so as to select the dark disk inside of the droplets

<figure><img src="image_97.png" /><figcaption>**Figure 2.** RICM image processing and superimposition index calculation. An RICM time-lapse image (A) is used to create a binary image (B) of the dark disks inside of the droplets. Binary images separated by 10 cycles are then superimposed to form an RGB image (C) from which the superimposition index is calculated (see text). (C) Superimposition index equals 0.58 (calculated at 49 min of experiment). A control is shown in (D), representing the superposition of (B) with itself rotated by 180°. The superimposition index of (D) equals 0.09.</figcaption></figure>

and colored in red for the first image and in green for the second one, obtained 10 cycles later (Figure 2B). Afterward, the 10 cycle-separated colored images were superimposed, resulting in a red, green, and blue (RGB) image (Figure 2C). The superimposition index is calculated from the three RGB images by the ratio of the yellow pixels (red AND green) to the sum of the red and green pixels minus the yellow pixels (red OR green).

$$\text{Superimposition index} = \frac{\text{overlapping droplet area}}{\text{total droplet area}} = \frac{Y}{R + G - Y} \quad (5)$$

The error bars were calculated as the maximal error that could originate from alternative threshold choices.

In order to estimate the probability that droplets overlap randomly, another RGB image was created (Figure 2D), where one image was superimposed with itself, flipped by 180°. The superimposition index is 0.09 in this case. We therefore consider that superimposition indices >0.15 indicate nonrandomness.

**Droplet Reformation Kinetics.** At different time points, the droplet reformation kinetics was determined by analyzing an RICM movie (Movie S1).

First, the first and last frames obtained in the dewetted time interval at ROI were selected. Then, five droplets were selected and their initial and final size was determined using binary images of these two images, as shown in Figure 2B. The reformation kinetics of each droplet was finally calculated as the droplet size difference between the two images divided by the dry time interval. A mean value was obtained by averaging the reformation kinetics of the five droplets and has been calculated at 10, 50, 75, 90, and 120 min of the experiment. Error bars represent the standard deviation of the growth kinetic values.

## RESULTS AND DISCUSSION

We designed a model experiment to be able to observe insulin aggregation at a dynamic triple solid-liquid-air interface. In a microchannel (Scheme 1), an insulin solution is repetitively moved back and forth between two positions (lower and upper
<figure><img src="image_98.png" /></figure>

**Figure 3.** Insulin aggregation is caused by a moving triple interface on a hydrophobic surface. (A) HI aggregation kinetics. 0.5 g/L of HI is cyclically moved between the lower and the upper levels (15 s per cycle), at 37 °C. The mean THT fluorescence at ROI (Scheme 1) is plotted as a function of time. I, II, and III represent the different phases of insulin aggregation kinetics. (B–D) Aggregate distribution along the x-axis (Scheme 1) of the microchannel after 6 h of cycling at 37 °C, measured by THT fluorescence after washing for 0.5 g/L of HI in a COC microchannel (B), 0.5 g/L of BSA in a COC microchannel (C), and 0.5 g/L of HI in a glass microchannel (D). Vertical bars represent the upper (dashed line) and lower levels (solid line). Error bars represent the standard deviation of the average pixel gray value for each image. The results of panels A and B are representative of eight independent experiments.

levels) such that the channel comprises three different regions: a region that is always wet (blue in Scheme 1), a region that is intermittently wet (dotted in Scheme 1), and a region that is always dry (white in Scheme 1). This setup allows a direct in situ analysis of the effect of a moving triple solid–liquid–air interface on protein aggregation. Moreover, a direct comparison between insulin aggregation kinetics at the triple solid–liquid–air interface and at the solid–liquid interface is possible within the same experiment. THT fluorescence is used to monitor the formation of amyloid deposits and RICM to monitor the morphology of the liquid film after liquid receding in real time. A constant 37 °C temperature is maintained in the setup by a transparent heater.

**Insulin Amyloid Aggregates Grow Only in the Intermittently Wet Region Where Triple Interfaces Are Created on a Hydrophobic Surface.** Insulin aggregation kinetics was recorded using THT fluorescence images acquired every 10 cycles (150 s) at the ROI (Scheme 1) during the experiment, when the liquid reached the upper level in the channel. The mean gray level of the images, representing the amount of fibrils created on the surface of the microchannel, is reported in Figure 3A as a function of time. The recorded fluorescence signal follows a standard, nucleation-dependent behavior with three distinct phases: a lag phase (I in Figure 3A), where the fluorescence remains very low, a growth phase (II in Figure 3A), where the fluorescence increases strongly with time and amyloid fibers are forming on the surface, and a plateau phase (III in Figure 3A), where the fluorescence stabilizes indicating that no more fibers are created. The duration of the lag phase varies between 60 and 180 min, as commonly observed for nucleation-dependent insulin aggregation.⁵,²³ When the same experiment was performed at room temperature, amyloid fibers were also formed but on a much longer time scale (6–8 h). Insulin aggregation kinetics is therefore thermally activated, as expected.

When the plateau phase was reached, the HI aggregate distribution was determined using a sequence of consecutive THT images along the x-axis covering the dry, the

intermittently wet, and the constantly immersed regions (Scheme 1). The mean gray level of each image, indicative of the amount of HI fibrils created at each position, is shown in Figure 3B. The fluorescence signal remains very low both in the constantly wet and dry regions, documenting the absence of amyloid aggregates in these areas. Thus, no HI aggregates are desorbed from the surface and deposited randomly during the experiment. On the contrary, in the intermittently wet region, the THT fluorescence is drastically increased, the highest signal being observed at the upper level, in the region remaining dry for the longest time between immersion cycles. Amyloid aggregates are thus only created on the surface in the intermittently wet region, where they remain strongly attached, delimitating this aggregation-prone region sharply from adjacent wet and dry zones. The difference in the fluorescence signal between the always wet and intermittently wet regions shows that the presence of a triple interface is critical to induce HI surface aggregation. At this low flow rate, the shear rate corresponds approximately to $60 \text{ s}^{-1}$, and shear stress alone (due to the liquid movement on the surface) is well below values sufficient to trigger the aggregation of cytochrome c.²⁴ However, the presence of a moving triple interface potentiates the effects of a local shear stress that is enough to trigger HI amyloid aggregation. This observation is in accordance with the literature, where it is well-established that shear stress alone is not detrimental for protein aggregation or inactivation, but the combination of shear stress with interfacial adsorption phenomena is affecting protein stability.²⁵ For mAbs, for example, Biddlecombe and colleagues have demonstrated and quantified in a thorough analysis that mAb monomer depletion is sizeable above shear rates of $5000 \text{ s}^{-1}$ at a solid–liquid interface.²⁶ This indicates that in the triple interface zone, forces that exceed shear stress alone are present and are sufficient to denature proteins.²⁷

The control experiments using BSA (Figure 3C) or insulin in a glass channel (Figure 3D) show that there is no amyloid fiber formation inside of the microchannel in these cases. The mechanism triggering the observed HI aggregation is thus
dependent on the concomitant presence of a hydrophobic surface and a moving air-liquid interface and is not observed for BSA.

**HI Amyloid Aggregates Are Observed Concomitantly with Droplets Left on the Surface after the Liquid Has Receded.** RICM was used to observe and characterize the hydrophobic surface at the ROI when the meniscus of the solution is at the lower level position in the channel (Scheme 1). Characteristic RICM images of the surface at three time points chosen in the lag phase (I in Figure 3A), the growth phase (II in Figure 3A), and the plateau phase (III in Figure 3A) during the HI aggregation kinetics are shown in parallel with THT fluorescence images at the same time points (Figure 4). The black disks surrounded by a white ring seen on RICM

<figure><img src="image_99.png" /><figcaption>**Figure 4.** Position of THT-positive insulin aggregates correlates with the position of droplets on the dewetted surface. Microscopic images at ROI showing THT-positive amyloid aggregates (THT fluorescence, left panels, A, C, and E) and liquid droplets (dark circles surrounded by a bright ring, observed in RICM, right panels, B, D, and F) at the different phases of insulin aggregation (I, II, and III). RICM images are taken when the liquid is at the lower level (channel-dewetted), and fluorescence images are taken when the liquid is at the upper level (channel-filled). The field of view corresponds to 200 × 150 µm². The results shown are representative of eight independent experiments.</figcaption></figure>

images (Figure 4B, D, and F) correspond to the liquid droplets left on the surface when the liquid has receded. The cross shape on the droplets is a camera artifact and is not visible by direct observation in the microscope objective. Fluorescence corresponding to surface-adsorbed HI amyloid aggregates is observed in Figure 4A, C, and E.

Right from the start of the first cycles, liquid droplets appear on the surface (Figure 4B). However, during the whole lag phase, no THT-positive aggregates are observed on the fluorescence images (Figure 4A). When the growth phase is reached, liquid droplets are still visible on the RICM images (Figure 4D), and amyloid aggregates start to appear as circles (Figure 4C). The fluorescent rings extend until the entire channel surface is covered by the amyloid fibers (Figure 4E). At this time, the plateau phase is reached, no more droplets form

on the surface (Figure 4F), but a continuous liquid film remains above the fibers after liquid withdrawal. These observations demonstrate that HI amyloid aggregate growth is intimately correlated with the presence of droplets and that the creation of an aggregate layer modifies the water-retaining properties of the initially hydrophobic material surface.

**Liquid Droplets Are Anchored by Proteins Adsorbed on the Microchannel Surface and Reform at the Same Location Cycle after Cycle.** It should be noted that when a buffer solution was used instead of the protein solution, more and smaller droplets were observed during the dewetting period, at 37 °C (Figure S1A). The presence of insulin therefore has a strong effect on liquid film dewetting. Moreover, we noticed that the liquid droplets remain at the same position during most of the experiment with HI. RICM time-lapse images are used to calculate a droplet superimposition index (see Experimental Section), which represents the percentage of droplets that reform at the same location after 10 wetting-dewetting cycles. For the experiment shown in Figure 5, the lag

<figure><img src="image_100.png" /><figcaption>**Figure 5.** Droplet anchorage and droplet formation kinetics. (A) Droplet superimposition index at ROI, represented as a function of time. (B) Droplet formation kinetics is calculated on a set of representative droplets and is shown as a function of time. I, II, and III indicate the lag phase, growth phase, and plateau phase of insulin aggregation kinetics, respectively. The results shown are representative of three independent experiments.</figcaption></figure>

phase is estimated to extend from 0 to 60 min, the growth phase is estimated to extend from 60 to 100 min, and the plateau phase is situated beyond 100 min (based on fluorescence images taken from time to time during the entire experiment). Plotting the superimposition index as a function of time (Figure 5A) reveals a rapid increase during the first 30 min and then stabilization until 90 min before a sharp decrease. Droplets appear immediately after the first liquid withdrawal. Long before any THT fluorescence is detectable, droplets are anchored to the surface and remain at the same place for much of the growth phase, before disappearing at the end of the growth phase. The same droplet anchorage is also observed when a BSA solution is used in the experiment, indicating that amyloid fibers are not necessary to anchor droplets. In the absence of protein, however, droplets reformed randomly on the surface, and the superimposition index remained below 0.15 (Figure S1B). Droplet anchorage can, thus, be explained by a progressive modification of the surface hydrophobicity during
the lag phase. Indeed, during the initial wetting–dewetting cycles, the gradual adsorption of proteins most likely modifies the nature of the surface locally from hydrophobic to hydrophilic, thus creating energetically favorable wetting zones, where water is preferentially retained.

The adsorbed proteins thus anchor the droplets, which in turn supply further proteins and thereby initiate the aggregation process at their very edge, where the solid material, liquid, and air meet, as is demonstrated hereafter.

A closer observation of the droplet reformation has been performed with the acquisition of RICM movies at the ROI during several cycles (see Movie S1), covering the entire aggregation process. After the liquid recedes, droplets reform by gathering the liquid that remains as a thin film on the microchannel surface around them. Their diameter therefore increases rapidly (Movie S1). An average speed for the reformation of a droplet can thus be calculated by the difference in the droplet radii at the beginning and end of the dry phase during the aggregation kinetics (Figure 5B). The results show a linear decrease in this speed until a complete liquid film is formed on the surface.

All of these observations fit with the idea that amyloid fibers grow at the microchannel surface. As they grow, a porous, proteinaceous zone is created, retaining the liquid and decelerating its movement during the droplet formation. This is reminiscent of the effect of anchored polymers on the dewetting velocity of a liquid thin film.²⁸

**HI Amyloid Aggregates Grow Eccentrically around Droplets at the Triple Contact Line.** The superimposition of RICM and fluorescence images, separated by 10 cycles (Figure 6A), shows that the amyloid aggregates form only around the droplets. Overlaying consecutive fluorescence images, separated by 20 cycles (Figure 6B), shows that amyloid fibers form exclusively at the outer edge of the existing

<figure><img src="image_101.png" /></figure>

**Figure 6.** Progressive accumulation of insulin amyloid aggregates at the edge of the droplet. (A) Superimposition of an RICM image (droplets represented as red circles) and a THT fluorescence image (THT-positive amyloid aggregates in green) taken 10 cycles later at ROI. The right panel shows the magnified view of the region highlighted in A (red square). (B) Superimposition of two THT fluorescence images taken at a 20 cycle interval. THT-positive amyloid aggregates are represented in red for the first image and in green for the following one. Overlapping fluorescent pixels between both images appear in yellow (see yellow arrowhead in the magnified image at the right). The right panel shows a magnified view of the region highlighted in B (red square). The newly formed THT-positive aggregates appear in green (see green arrowhead in the magnified image at the right) at the outer border of the already formed fluorescent aggregate ring.

fluorescent ring. No fibers grow on the surface inside of the droplets or on the one devoid of droplets.

These results demonstrate that the triple contact line, where a droplet edge meets the microchannel surface, is a local trigger for HI aggregation. The presence of a growing amyloid fiber ring around the droplets explains the gradual slowdown of droplet formation kinetics and their stable anchoring at the same place.

**Kinetics of the Formation of HI Amyloid Fibers at a Moving Solid–Liquid–Air Interface.** To study the growth kinetics of HI amyloid fibers near the triple interface, four droplets with different sizes were selected in a time-lapse fluorescence movie corresponding to the experimental results in Figure 3. By quantitative image analysis, the radial distribution of newly formed aggregates was determined (see Experimental Section) during the time course of the aggregation kinetics, from which the total amount of aggregates created over a 10 cycle interval ($\Sigma_{exp}$) and the radial distance at which most of these newly formed aggregates appear ($\rho_{max}^{exp}$) were calculated.

Figure 7A,B represents the evolution of $\Sigma_{exp}$ and $\rho_{max}^{exp}$ with time. During phase II (growth phase, 150–180 min, 120

<figure><img src="image_102.png" /></figure>

**Figure 7.** Detailed growth kinetics of the aggregate ring formed around droplets. Results from four different droplets are represented with square, circle, diamond, and cross symbols. An overall fit of the results is displayed with a continuous line. (A) Net fluorescence increase over a 10 cycle interval, $\Sigma_{exp}$, as a function of the number of cycles. Data are fitted by an exponential function with an amplitude of 915 ± 273 at 600 cycles and an experimental growth rate of $\alpha_{exp} = 0.0226 \pm 0.009 \text{ cycle}^{-1}$. (B) Radial position of the maximal fluorescence increase, $\rho_{max}^{exp}$, as a function of the number of cycles. Data are fitted by a linear function with a y-intercept of 9.00 ± 1.15 µm at 600 cycles and a linear coefficient of $v_{exp} = 0.0282 \pm 0.0061 \text{ µm} \cdot \text{cycle}^{-1}$. Results are representative of three independent experiments.

cycles), $\Sigma_{exp}$ increases quasiexponentially, with an experimental growth rate of $\alpha_{exp} = 0.0226 \pm 0.0009 \text{ cycle}^{-1}$. During the same time interval, $\rho_{max}^{exp}$ increases quasilinearly with time defining an experimental velocity for the amyloid fiber formation $v_{exp} = 0.0282 \pm 0.0061 \text{ µm} \cdot \text{cycle}^{-1}$, confirming that new aggregates accumulate on the outer edge of the ring of previously formed ones. As time goes on, the position of the triple interface moves away from the center of the droplet, driven by the formation of new aggregate fiber layers on the material surface, and the droplets progressively flatten until no more droplets form.
<figure><img src="image_103.png" /><figcaption>**Figure 8.** Physicomathematical model of the amyloid fiber formation at the triple interface. (A) Top: schematic view of the triple interface at the edge of a droplet anchored on an aggregate ring. Bottom: the associated dehydration function. The droplet is represented in blue, and the distribution of amyloid fibers is represented as white bars. The fiber length or density, $L(r)$, depends on $r$, the radial distance to the center of the droplet. The liquid droplet anchors at the radius where the aggregate distribution is maximal ($r_{max}$). The aggregates outside of the droplet are partially wet. $\omega$ represents the characteristic length of the dehydration function. (B–F) Results from a Scilab simulation with $k = 0.81$, $\omega = 0.54$ µm, and $\sigma = 0.76$ µm. Time ($t$) is expressed in wetting–dewetting cycles (1 cycle = 15 s) with respect to the onset of aggregation. The radial position is expressed in micrometers with respect to $r_{max}(0)$. The simulation is performed for 100 cycles. The results are shown at $t = 0$ (blue line), 24 (purple line), 49 (red line), 74 (orange line), and 99 (yellow line) cycles. (B) Amyloid fiber length distribution $L(r, t)$ as a function of radius $r$. (C) Evolution of $\rho_{max}^{th}$, the distance at which most of the amyloid fibers grow with respect to the number of cycles. (D) Dehydration function $D(r, t)$ as a function of radius $r$. (E) Evolution of $\Sigma_{th}$, the amount of new insulin aggregates grown per cycle with respect to the number of cycles. (F) Distribution of new fibers $\Delta L(r, t)$ between cycle $t$ and $t + 1$ as a function of radius $r$.</figcaption></figure>

The formation of protein aggregates at the edge of the droplets during phase II can be explained as follows. The region surrounding the droplet contains surface-bound amyloid fibrils, which (i) anchor the liquid droplet and (ii) act as a porous reservoir of the liquid, bringing further insulin in contact with the aggregated insulin. Insulin from the air–liquid interface is brought into close proximity to the adsorbed fibers during dewetting. Adsorption of these insulin molecules on insulin fibers leads to their incorporation into the fiber. The partial dehydration induced by the receding liquid film favors intermolecular interactions between the adsorbed proteins and the protein fibrils, leading to amyloidal fiber growth. The driving force for the conformational change could in part be provided by the release of solvation water around the insulin molecules concomitant with the formation of intermolecular hydrogen bonds. An induced decrease in the protein hydration layer is well-known to accompany adsorption and aggregation phenomena at hydrophobic interfaces.¹⁶ Our results imply that amyloid aggregates grow at the surface around the droplets rather than being formed elsewhere (e.g., at the liquid–air interface) and being deposited there. Indeed, we observe that during the growth phase, the gain in the THT fluorescence intensity increases by a factor 6 (Figure 7A, between 620 and 700 cycles), whereas the radius of the fluorescence ring (and hence the droplet) increases only about 1.2 times (Figure 7B, between 620 and 700 cycles). It is therefore unlikely that

protein aggregates originate from the air–liquid interface because the amount deposited should only marginally increase with time. By contrast, this strong fluorescence increase better fits with the idea that new insulin fibers grow in contact with the existing ones. The fact that in the presence of nonionic surfactants (Tween 20 or 80), no THT-positive HI aggregates are observed in our system, stresses the importance of interfacial adsorption in HI amyloid aggregation (Figure S2). Indeed polysorbates are added to most of the therapeutic protein formulations in order to prevent interfacial stresses, as these amphiphilic molecules preferentially adsorb at hydrophobic interfaces, thus keeping the proteins in solution.²⁹

**Physicomathematical Model of Insulin Amyloid Fiber Growth at a Moving Triple Interface.** From the above consideration, a simplified model of insulin aggregation is presented below that takes into account the key observations: (i) amyloid fibers form at the surface, (ii) they prevent a full dewetting of the surface, (iii) they allow amyloid fiber growth at the periphery of the droplets, and (iv) no amyloid fiber growth is observed inside of the droplets.

Figure 8A illustrates schematically the growth of insulin amyloid aggregates at the triple interface, located at the edge of a droplet. The distribution of amyloid fibers attached to the surface at a distance $r$ to the center of the droplet after cycle $t$ is called $L(r, t)$. $L$ may represent the size and/or the density of the fibers. We hypothesize that at a given cycle $t$, the droplet
margin anchors at the position r where $L(r, t)$ is maximal, which is called $r_{max}(t)$. We assume (i) that, after a given number of cycles $t$, the formation of new aggregates takes place in the dewetted zone only, for $r > r_{max}(t)$ and (ii) that the growth is governed by a combination of two factors: the hydration of the deposited proteins, and the amount of fibrils already present. Existing fibrils thus play two roles: they constitute a reservoir for insulin solution and a collection of binding sites for incoming insulin molecules.

The growth of amyloid fibrils, $\Delta L(r, t)$, after each wetting-dewetting cycle is thus represented by the following equation

$$ \Delta L(r, t) = k \times D(r - r_{max}(t)) \times L(r, t) \quad (6) $$

where $k$ is a rate constant and $D(r)$ is a nondimensional dehydration function (Figure 8A). The fiber length distribution after a wetting-dewetting cycle, $L(r, t + 1)$ is thus iteratively calculated by

$$ L(r, t + 1) = L(r, t) + \Delta L(r, t) \quad (7) $$

for $r \ge 0$, $t \ge 0$. At each cycle, $r_{max}(t + 1)$ is recalculated and used in the next iteration step. The initial distribution of amyloid aggregates at the edge of a droplet, at $t = 0$, is modeled by a Gaussian function $L(r, 0)$, centered at $r_{max}(0) = 0$, where $\sigma$ is the width of this initial aggregate distribution

$$ L(r, 0) = e^{-(r/\sigma)^2} \quad (8) $$

The dehydration function, $D(r)$ represents the degree of hydration of the protein layer as a function of the distance to $r_{max}(t)$. Because no new amyloid aggregates are created in the wet region ($r < r_{max}$), we assume that for $r < 0$, $D(r) = 0$. We then assume that the dehydration function progressively increases around the droplet to reach one in the fully dehydrated region, with a characteristic distance $\omega$. A simple expression is thus

$$ \text{for } r > 0 \quad D(r) = 1 - e^{-r/\omega} \quad (9) $$

The behavior of this system of coupled differential equations depends on three parameters, $k$, $\sigma$, and $\omega$. For a fixed set of parameters, a numerical simulation is conducted over 100 wetting-dewetting cycles using Scilab. At each cycle, $\Sigma_{th}$, the amount of new protein aggregates created per cycle, is calculated using

$$ \Sigma_{th} = \int_{0}^{+\infty} \Delta L(r, t) dr \quad (10) $$

and $\rho_{max}^{th}$ the distance at which the amyloid fiber growth is maximal, is defined as the radial position $r$ where $\Delta L(r, t)$ is maximal. We also calculate $w(t)$, the width of the zone of newly grown amyloid fibers, defined by the interval where $\Delta L(r, t) > 0.25$.

A wide range of parameters is studied: $0.5 < k < 2$, $0.074 < \omega < 0.74$, and $0.3 < \sigma < 3$. Figure 8 shows a representative example of such a simulation, with $k = 0.81$, $\omega = 0.54$, and $\sigma = 0.76$ µm. After a short transitory period (about 30 cycles), new amyloid material grows at each cycle in the form of an annulus that moves at a constant velocity and increases in amplitude (Figure 8C, E, and F). During this stable period, the width of the zone of fiber formation, $w$, remains constant (Figure S3), whereas $\rho_{max}^{th}$ and $\Sigma_{th}$ increase linearly and exponentially with the number of cycles, respectively, in accordance with the experimental observations. A theoretical aggregation velocity $v_{th}$ is determined by a linear fit of $\rho_{max}^{th}$, and a theoretical

characteristic rate $\alpha_{th}$ is obtained from an exponential fit of $\Sigma_{th}$ between 30 and 99 cycles. The constant growth width, $w$, of the fiber formation zone is defined as $w$ at $t = 99$ cycles. In Figure S4, we represent the effect of $\sigma$, $k$, and $\omega$ on $v_{th}$, $\alpha_{th}$, and $w$. $v_{th}$ is proportional to $\sigma$ and $\ln(k)$ and independent of $\omega$ (Figure S4). $\alpha_{th}$ linearly increases with $k$ and $\sigma$ and exponentially decreases with $\omega$ (Figure S4). $w$ increases as a function of $\ln(k)$, and $\sigma$ decreases as a function of $\omega$ (Figure S4). The rate constant $k$ increases the width of the fiber growth zone, which explains the increased theoretical aggregation velocity $v_{th}$ and the theoretical aggregation rate $\alpha_{th}$. At high $\omega$, fibers that are close to the droplet edge are submitted to a smaller dehydration function and therefore grow less. Nevertheless, $\rho_{max}^{th}$ is attained at the same distance from the droplet edge, which explains the constant value of $v_{th}$.

Using these simulations, it is possible to determine, for each studied droplet, a specific set of parameters ($\sigma$, $\omega$, and $k$) that matches our experimentally determined values for $v_{exp}$ and $\alpha_{exp}$ (results shown in Table S1). First, $\sigma$ is experimentally determined from the first fluorescence image in the movie, where a fluorescent ring is visible around the droplet. The radial fluorescence intensity profile is extracted and fitted with two Gaussian functions to determine the fluorescence ring and the background (see Experimental Section). $\sigma$ is equal to the standard deviation of the Gaussian function that fits the fluorescent peak. Second, $k$ is calculated from $v_{exp}$ and the linear relationship between $v_{th}$ and $\ln(k)$, using the value of $\sigma$ already determined because $v_{th}$ is independent of $\omega$ (Figure S4). Third, $\omega$ is determined from $\alpha_{exp}$ and the exponential relationship between $\alpha_{th}$ and $\omega$ (Figure S4), using the values of $\sigma$ and $k$ previously determined.

Comparing the results obtained for each droplet, the value of the dehydration characteristic distance $\omega$ is remarkably constant, whereas $\sigma$ and $k$ display more variability. The fact that $k$ is close to one indicates that the amount of fibers at the triple interface, most exposed to dehydration, could potentially double at each cycle.

Alternatively, a unique set of parameters can be determined similarly from the fits of $\Sigma_{exp}$ and $\rho_{max}^{exp}$ described in Figure 7 by pooling all data. An average $\sigma$ ($0.53 \pm 0.16$ µm) is used as an input to calculate values of $k$ (1.07 with a 60% confidence interval between 0.59 and 1.92) and $\omega$ ($0.50 \pm 0.01$ µm) that fit all data. The theoretical values of $v_{th}$ and $\alpha_{th}$ are estimated to be $0.0280$ µm·cycle$^{-1}$ and $0.0222$ cycle$^{-1}$, which is close to the experimental values $v_{exp} = 0.0282 \pm 0.0061$ µm·cycle$^{-1}$ and $\alpha_{exp} = 0.0226 \pm 0.0009$. Using this set of parameters, we calculate the width of the fiber formation ring at each cycle, after the transitory period, which equals $0.397 \pm 0.006$ µm (Figure S3). This confirms that the new amyloid fiber growth is restricted to the close proximity of the droplet, in the region where the solid-liquid and liquid-air interfaces meet.

## CONCLUSIONS

In this study, we present the first in situ, real-time analysis of insulin amyloid aggregation at a dynamic triple contact line. Although insulin is likely to adsorb at all of the interfaces present in our setup, the conformational changes and intermolecular interactions necessary to form THT-positive aggregates are triggered preferentially by the forces acting at the triple contact line. Our study reveals a new mechanism for protein aggregation driven by the partial dehydration of surface-adsorbed proteins at the edge of droplets, where the solid-liquid and liquid-air interfaces meet. HI is known to bind THT
when adsorbed at hydrophobic surfaces.⁵,⁷ It has been shown to be monomeric at the air-liquid interface at pH 2.⁶,³⁰ Moreover, it is well-established that the insulin monomer is the most aggregation-prone form at hydrophobic interfaces.⁵,³¹ Therefore, at the triple contact line, three crucial parameters come together: high and possibly monomeric insulin concentration at the air-liquid interface, adsorbed and possibly denatured proteins at the solid-liquid interface, and partial dehydration that favor amyloid fiber growth. Moreover, air-liquid interfaces, in contrast to solid-liquid interfaces, allow for the interfacial diffusion of adsorbed proteins and therefore can contribute to favorable local protein orientations, which combined with the stabilization of adsorption-induced conformational changes, can lead to aggregation nuclei formation and amyloid fiber growth. In addition, mechanical efforts exerted at the droplet edge, where capillary forces are important, may also contribute to de novo fiber formation, by fragmenting existing fibers and therefore creating more elongation sites. A relationship between amyloid fiber fragmentation propensity and growth rate has indeed been documented for yeast Sup35 prion.³²

The role of interfaces in the amyloid aggregation of other proteins has been studied. Jean and colleagues³³ have shown, for islet amyloid polypeptide and amyloid β peptide (Aβ), that their surface-active properties lead to a local protein enrichment at the air-liquid interface above the critical fibrillar concentration, thus enhancing amyloid fibrillation in the presence of an air-liquid interface. Similarly, Morinaga and colleagues³⁴ have demonstrated that, at low concentrations (5 µM), the presence of an air-water interface triggers the fibrillation of Aβ, which does not occur in the absence of such an interface, at this concentration after 3 weeks. For α-synuclein, an air-water interface has been shown to induce surface-catalyzed nucleation and/or branching.³⁵ Altogether these studies highlight the importance of the local protein enrichment as a determining factor in the amyloid aggregation process at air-liquid interfaces. However, in these studies, the presence of solid surfaces, where proteins could adsorb, is not considered.

The role of solid surfaces is evidenced by the fact that agitation strongly enhances aggregation kinetics, which is not explained by the concentration effect at the interfaces alone. Morinaga and colleagues,³⁴ for instance, highlight the effect of agitation on Aβ amyloid aggregation at the air-liquid interface. Although not discussed in their study, it is likely that Aβ amyloids originate at the well border, where the air-liquid interface transiently meets the solid-liquid interface before spreading over the entire air-liquid interface by diffusion.

Compression or rupture of the air-liquid interface strongly enhances mAb aggregate accumulation.¹¹,¹³ In these studies, an intermittently wet surface was created either on a rotating needle or on the walls of a tilted vial but the authors did not analyze the presence of protein aggregates at this place. Similarly, the aggregation of recombinant human growth hormone is strongly enhanced at dynamically renewed air-liquid interfaces by bubble aeration.¹⁴ For the production of air bubbles and for the stirring of the solution, hydrophobic surfaces were also present in this study. The adsorption of proteins at such hydrophobic solid surfaces is therefore likely to create a similar situation of intermittent wetting than the one mimicked in our setup, accelerating protein aggregation by partial dehydration of proteins and by capillary forces acting at the triple contact line.

Despite the accumulating evidence of interfacial effects on protein stability, many solid-liquid or liquid-air interface studies do not observe protein aggregation in real time and do not consider the triple contact line, although it is generally present in most of the conventional setups. Concerning practical applications, our study explains why agitation can be detrimental to protein stability in pharmaceutical containers, where an air headspace or bubbles are often unavoidable. The possible formation of wetting-dewetting zones at interfaces, where proteins strongly adsorb, should thus be carefully examined in industrial processes, as they can be the trigger for aggregate formation.

## ■ ASSOCIATED CONTENT

### ⊕ Supporting Information

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.langmuir.6b03314.

RICM visualization of droplet reformation at the microchannel surface (AVI)

RICM image and RGB superimposition image of liquid droplets using the TN buffer with 20 µM THT. Effect of Tween 20 and 80 on HI aggregation kinetics. Distribution of new fibers and width of the newly amyloid fiber growth zone between 30 and 99 cycles. Effect of the initial distribution width, the dehydration characteristic distance and the rate constant on the theoretical aggregation velocity, the theoretical characteristic rate, and the constant growth width. Summary of the experimentally determined parameters and the deduced values of *k*, *ω*, *v*<sub>th</sub>, and α<sub>th</sub> for four droplets according to the model (PDF)

## ■ AUTHOR INFORMATION

### Corresponding Author

*E-mail: marianne.weidenhaupt@grenoble-inp.fr. Phone: +33 4 56529335. Fax: +33 4 56529301.

### ORCID

Marianne Weidenhaupt: 0000-0001-5180-6758

### Notes

The authors declare no competing financial interest.

## ■ ACKNOWLEDGMENTS

The authors thank Daniel Bellet and Mélanie Lagrange for their contribution concerning the electric heating system and Mikhail Anikin for his dedicated technical assistance. T.F. is supported by ANRT CIFRE PhD grant.

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