Adult lake trout held at two temperatures were interbred to study the influence of parental thermal environments on the next generation’s thermal physiology. Offspring performance reflected both their own rearing environment and parental influences, although parental effects on offspring physiology were limited and not always beneficial. The capacity of ectotherms to cope with rising temperatures associated with climate change is a significant conservation concern as the rate of warming is likely too rapid to allow for adaptative responses in many populations. Transgenerational plasticity (TGP), if present, could potentially buffer some of the negative impacts of warming on future generations. We examined TGP in lake trout to assess their inter-generational potential to cope with anticipated warming. We acclimated adult lake trout to cold (10°C) or warm (17°C) temperatures for several months, then bred them to produce offspring from parents within a temperature treatment (cold-acclimated and warm-acclimated parents) and between temperature treatments . At the fry stage, offspring were also acclimated to cold (11°C) or warm (15°C) temperatures. Thermal performance was assessed by measuring their critical thermal maximum (CTM) and the change in metabolic rate during an acute temperature challenge. From this dataset, we also determined their resting and peak  metabolic rates. There was little variation in offspring CTM or peak metabolic rate, although cold-acclimated offspring from warm-acclimated parents exhibited elevated resting metabolic rates without a corresponding increase in mass or condition factor, suggesting that transgenerational effects can be detrimental when parent and offspring environments mismatch. These results suggest that the limited TGP in thermal performance of lake trout is unlikely to significantly influence population responses to projected increases in environmental temperatures. Populations are being forced to respond to climate change as environmental temperatures continue to increase towards their viable limits , but thoPopulations may be able to compensate for long-term changes in temperature by preconditioning their offspring for harsher environments . Though Salvelinus namaycush) is a cold-adapted, stenothermal salmonid  to an acute temperature increase. Our predictions were based on the evidence that standard metabolic rate in warm-acclimated adult lake trout would be elevated due to temperature acclimation  MO2 would increase in offspring with warm-acclimated parents, similar to the maximum metabolic rate findings of 2-temperature relationship of offspring from warm-acclimated parents compared to those from cold-acclimated parents. Lastly, offspring with warm-acclimated parents would also have a higher CTM compared to those from cold-acclimated parents. Provided that resources are not limited, a higher peak MO2 and CTM would benefit the offspring overall, allowing them to tolerate warmer environments, though the trade-off would be an increased resting MO2 associated with a higher cost of living. If the hypothesis that the parents additively contribute to the transgenerational effect is correct, then the acclimation temperature of both parents will have an effect on the offspring’s response.We looked for evidence of TGP in the offspring’s upper thermal tolerance  and metabolic response  and the Ontario Ministry of Natural Resources and Forestry (OMNRF) Aquatic Animal Care Committee (Protocol # 136).The strain of lake trout used in this experiment originated from Seneca Lake, which is a glacial lake located in the Finger Lakes region in central New York State . This strain has been kept in the OMNRF hatchery system for five generations since 1990 and has been maintained using rotational line crossing  to maintn = 8 and 9, mixed sex) and acclimated to two different temperatures (10 ± 0.5°C and 17 ± 0.5°C) beginning in July 2015, by increasing temperatures 1°C per day until target temperatures were reached. The lower target temperature was based on lake trout temperature requirements for spawning and the elevated temperature was chosen to exceed their typical range but remaining within physiological limits  were held at the OMNRF White Lake Fish Culture Station  where they were individually PIT tagged , divided into two groups  where 140 ml of eggs were stripped from each female, divided evenly among four jars and fertilized by pipetting milt directly onto them. Families were produced by a full factorial 4 × 4 mating cross using two males and two females from each of the two temperature treatments  so that resultant offspring were from parents who had been acclimated to either the same or different temperatures prior to spawning. This resulted in four offspring families from each of the four parental treatment groups  for a total of 16 families  tanks. Each tank was separated into four sections to keep the families separate; however, due to space constraints, two families were kept in each tank section where the offspring from the two families sharing a section were half-siblings by their father. The individuals would later be identified to family using microsatellite genotyping . Two tanks received a cold/optimal temperature (11°C) and the other two received a warm temperature (15°C) so that each family had seven representatives acclimated to each temperature. The lower acclimation temperature was selected based on the optimal growth temperature for lake trout , and theAfter transferring the fry to the larger tanks, we changed the water temperature at a rate of 1°C per day until the target temperatures (11 and 15°C) were reached, and the fish were acclimated for 3–4 weeks before the experiments began. The fish were fed 5–6 times a day at 2–3% their mass; however, fish were fasted for at least 12 hours prior to experimentation so that the physiological effects from recent feeding did not influence experimental results (2), using closed respirometry during an acute temperature increase (+2°C·h−1). From this dataset, we determined each individual offspring’s resting rate of oxygen consumption (MO2) and peak  MO2. The resting MO2 was recorded as the MO2 at the fish’s acclimation temperature before temperature began to rise with the acute temperature challenge, and the peak MO2 was recorded as the highest, thermally induced MO2 achieved during the trial. We distinguish peak MO2 from maximum MO2 (reported for exhaustive exercise protocols) because highest MO2 observed due to temperature may not necessarily represent the absolute maximum rate possible for each offspring. For this reason, we do not calculate aerobic scope. To determine the upper thermal tolerance in the offspring, we measured the CTM, which is the highest temperature that can be tolerated by the fish. This was recorded as the temperature at which the fish lost equilibrium as temperature increased, identified as a loss of dorsoventral orientation with the inability to right itself after 5–10 seconds.To test for potential transgenerational effects of the parental environment on offspring physiology, we measured and compared the metabolic rate of offspring of parents acclimated to matched or mismatched temperature conditions. To do so, we first measured the metabolic rate of offspring as the rate of oxygen consumption (MO3 volume) sealed at one end and fitted with an acrylic lid. Each lid had an inlet and outlet valve to allow water to flow through the chambers using a submersible pump that circulated water through the respirometers at 4.5 l min−1. The valves were situated on either side of a fitting that held a dissolved oxygen probe  in place. The respirometers were contained in clear plastic tubs (two respirometers per tub) atop two side-by-side stir plates so that each respirometer was positioned over a stir plate. A magnetic stir bar in each respirometer was set to spin at ~60 RPM to keep water circulating in the chamber and a perforated stainless-steel grid separated the fish from the stir bar. The containers received aerated freshwater from a source tank that was temperature controlled using three 500 W titanium heaters  with digital temperature controllers . The plastic tubs were covered in a sheet of thin, black plastic to minimize visual disturbance to the fish.Respirometers consisted of custom-built glass cylinders  interfaced with LoggerPro software  so that the reduction in oxygen concentration could be recorded. Measurement of MO2 began after a 30 second wait period, then the drop in O2 was recorded for 10 minutes, after which the valves were opened to allow fresh water to flush the chamber until the next oxygen consumption measurement was made (~30 minutes). We observed the activity of the fish during each trial and an MO2 value was excluded from the analysis if a fish was active during the measurement period.The night before the experiment, eight fish were individually transferred into separate respirometers where they received a continuous flow of fresh water maintained at their acclimation temperature and were left to adjust to the experimental apparatus overnight. Resting MO2, fish were subjected to an acute temperature challenge of +2°C per hour by raising the temperature of the water in the source tank that fed the tubs housing the respirometers. We chose this rate to be consistent with previous studies that measured metabolic rate via oxygen consumption in related species . The focal fish was blotted dry on a paper towel so that mass  and fork length  could be measured, and a caudal fin tissue sample (~0.25 cm2) was preserved in 95% ethanol for subsequent genotyping to identify offspring individuals to their respective family .After measurement of resting MO−1 at the start of the measurement period; O2 saturation by the end of the measurement period varied depending on the temperature during the acute temperature challenge, where it ranged 4.5–5.5 mg l−1. The measurement period was shortened if oxygen concentration in the respirometers began to approach the critical limit of 3.5 mg O2 l−1 to attempt to minimize hypoxia-related responses in the fish  was calculated using the following formula:2]) is the decline in water oxygen concentration during the 10-minute measurement period, RV is the volume (l) of the respirometer, FV is the volume of the fish (l) and h is the time in hours.Reduction in oxygen concentration was recorded as mg OCondition factor was calculated as follows:2 during the temperature challenge and the resting and peak MO2 were conducted using R (v. 3.5.2) with the ‘MuMIn’ . Statistical analyses of the MO ‘MuMIn’ , ‘lme4’  ‘MuMIn’  packagesThe complexity of the experimental design (large number of fixed effects and interaction terms) and logistic limitations on sample size (number of independent crosses and available rearing space) prevented using conventional statistical analyses to assess the relative contributions of within and TGP. We tested a large number of fixed effects to determine how offspring upper thermal tolerance and metabolic rate changed with offspring acclimation temperature (within-generation plasticity), parental acclimation temperature (TGP) and their potential interactions. The list of fixed effects was further expanded by splitting parental acclimation temperature into maternal and paternal components to assess the relative parental contributions. Along with the interaction terms, this unavoidably gave rise to a complex global model.♀xC♂, C♀xW♂, W♀xC♂ or W♀xW♂ (C = cold acclimation and W = warm acclimation). To test for whether parental acclimation temperature yielded differential effects on mass and condition of offspring reared in cold or warm water, an interaction between offspring and parental treatment group was also included as a fixed effect predictor. Degree days was included as a random intercept to control for effects of age on mass and condition, since the experiment lasted ~5 weeks and most of the cold-acclimated offspring were tested in the first half of the experimental period. Here, degree days were calculated for each fish as the cumulative temperature experienced above 0°C (M and IDF) were also included as random intercepts to account for statistical non-independence between offspring that were sired or dammed from the same parents.To test for effects of maternal, paternal and offspring acclimation temperatures on offspring condition factor and mass, we used two separate general linear mixed effects models (GLMM) in JMP, with mass and condition factor as Gaussian-distributed response variables. These models both included offspring acclimation temperature (cold or warm) and parent acclimation temperature as fixed effect predictors, where parents were treated as a single explanatory variable with mother and father acclimation temperature combined and represented as one of four fixed effects: C2) response of the offspring to an acute temperature challenge, we again used a GLMM, using the ‘nlme’ package in R , offspring acclimation temperature  and acclimation temperatures of the mothers  and fathers  as fixed effect predictors, along with all possible interactions between these terms. Additionally, Mass (fixed term) was included in the model as a continuous predictor because metabolic rate scales with mass and the warm-acclimated offspring grew heavier than cold-acclimated ones. Similar to our previous models, both mother and father ID (IDM and IDF) were included as random intercepts to account for relatedness among offspring, and offspring ID was included as a random intercept to control for statistical non-independence between measurements drawn from the same individual.To test the effect of maternal, paternal and offspring acclimation temperature on the metabolic , we first modelled the relationship between MO2 and acute challenge temperature alone using a cubic regression spline in a general additive model (GAM), using three knots to appropriately capture the shape of the relationship while avoiding over-fitting. Predicted MO2 at each challenge temperature was extracted for each offspring and used in place of acute challenge temperature within our GLMM to account for the variation in the response variable (observed MO2) due to acute challenge temperature. This approach permitted us to (i) remove the complex, curvilinear relationship between MO2 and acute challenge temperature, (ii) test whether the remaining variation in MO2  can be explained by the other terms in the GLMM and (iii) include multi-level interactions between a previously non-linear predictor  and additional factorial predictors, which cannot be accomplished simply with current additive models. Finally, to account for heterogeneity of variance in MO2 across acute challenge temperature (and detected across predicted MO2) and to correct for autocorrelation between measurements drawn at adjacent time-points, we weighted our model by acute challenge temperature and included a type I autoregressive correlation structure, with an estimated ρ of 0.397.Because the relationship between MO2, peak MO2 and CTM of offspring was analyzed using three independent linear mixed models in R, each with the ‘lme4’ package (MT), father acclimation temperature (FT) and offspring acclimation temperature (OT) as fixed effect predictors, with all possible interactions between each of these factors, along with offspring mass as a covariate. Unfortunately, however, our total number of observations per experimental group (n = 157) was too few to support a robust approach such as an analysis of variance to test the individual effects of each predictor  and father ID (IDF) as random intercepts to account for relatedness among offspring. The best models were considered as those with a ΔAIC ≤ 2 as recommended by An effect of parental acclimation temperature on the resting MOF1,21.28 = 93.58, P < 0.001; ♀xC♂, C♀xW♂, W♀xC♂, W♀xW♂) , but there was an interaction between offspring acclimation and parental group .On average, warm-acclimated offspring were nearly twice as heavy compared with cold-acclimated offspring . Offspring condition factor was not affected by parent acclimation temperatures  nor by the interaction between offspring acclimation and parental group .Warm-acclimated offspring had significantly higher body condition than cold-acclimated offspring with means of 0.93 ± 0.01 versus 0.89 ± 0.01 , respect2 with increasing body mass . Offspring acclimation temperature (OT) had a significant effect on MO2 with warm-acclimated offspring having a higher MO2 , the interaction between offspring and paternal acclimation temperature did influence MO2 . There was no significant interaction between mother and father acclimation temperature on the offspring’s metabolic response . Significant two-way interactions occurred between aT and OT  demonstrating that some remaining variation in MO2 that was not explained by challenge temperature could be explained by offspring acclimation temperature; more specifically, offspring reared at warm temperatures appeared to respond differently to the thermal challenges than did cold-acclimated offspring. The acclimation temperature of the parents interacted significantly with the acute temperature challenge to determine the offspring’s MO2  had a higher metabolic rate at the beginning of the temperature challenge compared with the other parental acclimation groups  that best predicted the trends in the data with Mass appearing in each of these models. The first model contained Mass as the only fixed variable, but this model was only 1.23 times more likely  than model 2, which included maternal (MT) and paternal (FT) acclimation temperature with their interaction, to best explain the variation in the data (OT) appeared only twice among the six models. Maternal (MT) and paternal (FT) acclimation temperature appeared most frequently among the six models, with an interaction between these terms appearing in two of these. An interaction between offspring and paternal acclimation temperature (O · TFT) appeared in only one of the models  to visually explore trends within and between the offspring and parental acclimation groups , while the resting MO2 of the other three groups ranged between 148.3 ± 11.08 and 162.1 ± 10.51 mg O2 kg−1 h−1  containing mass as the only fixed parameter (MT) and paternal (FT) acclimation temperature, and the interaction term between the two, occurred in the second-best model, which had a 23% (iW) chance of being the top model  was also plotted to visually explore trends within and between the offspring and parental acclimation groups (2 (mass-specific) than warm-acclimated offspring  and parental acclimation temperature (MT and FT). The top model (iW = 0.39) was the global model containing Mass as a covariate with the offspring (OT), maternal (MT) and paternal (FT) acclimation temperature as fixed effects and all two-way and three-way interaction terms between them  times more likely to explain variation in CTM compared to model 2 (Mass and offspring acclimation temperature (OT), whereas as the fourth model contained only OT (Four AIC models best explained the trends in the CTM data (ΔAIC ≤ 2). Models 1 and 2 together suggest that CTM depended on a complex interaction between offspring (een them . The sec model 2 . The thi only TO .♀xW♂ parents had a CTM 1.17°C higher than that of cold-acclimated offspring (open boxes) from the same parental group , CTM was comparable .Lake trout families exhibited evidence of limited TGP, although the effects of TGP on MO2 was most strongly influenced by mass and acclimation temperature. This is not surprising given that warm-acclimated offspring were heavier than their cold-acclimated siblings and thus had higher whole-animal O2 consumption rates. The effect of allometric scaling was apparent when the MO2 was expressed mass specifically, where mean MO2 was higher in cold-acclimated offspring overall compared with warm-acclimated offspring  was the highest among of the cold-acclimated offspring (2 (2 was elevated in cold-acclimated offspring from warm acclimated parents in our study. We interpret the high resting MO2 in this group (11°C acclimated offspring from warm acclimated parents) to be due to an increased effort into surviving elevated temperature. It was evident that this group was not diverting energy into increased growth considering that the mass and condition factor were no greater in this group compared to the others.At lower challenge temperatures (11–20°C), the average MO parents , suggest parents  at theseded 24°C . This diffspring , and parpring  funding to CCW.ConservPhysiol_SupplementaryInfo_Final_clean_coab021Click here for additional data file.


The zero-field cooled (ZFC), thermoremanent (TRM), and isothermal remanent magnetization (IRM) were recorded after specific cooling protocols and compared to those of archetypal spin glasses and their dimensionality. The system is found to display glassy magnetic features. We illustrate in detail, by a number of experiments, the dynamical properties of the low-temperature superspin glass phase. We observe that these glassy features are quite similar to those of atomic spin glasses. Some differences are observed, and interestingly, the non-atomic nature of the superspin glass is also reflected by an observed superspin dimensionality crossover. Monte Carlo simulations—that explicitly take into account core and surface contributions to the magnetic properties of these ultrasmall nanoparticles in direct contact, as well as interparticle interactions—evidence effects of the interplay between (intraparticle) core/surface exchange coupling and (interparticle) dipolar and exchange interactions.The non-equilibrium dynamics of the superspin glass state of a dense assembly of ~ 2 nm MnFe Aging reflects a slow equilibration of the spin configuration at a constant temperature in the spin glass phase after a quench from high temperature. Such an aged spin configuration is kept in memory upon further cooling while at the same time new spin configurations are imprinted at shorter lengths scales (rejuvenation)4. To probe the intrinsic response of the spin glass phase, magnetometry experiments are performed in low magnetic fields, so that a linear response to field changes is achieved and the principle of superposition applies to the magnetic relaxation4. Interacting magnetic nanoparticles have been found to display glassy dynamics and undergo (super)spin glass phase transitions, e.g. concentrated ferrofluids6, dense assemblies of nanoparticles9 and nanocomposites11. The magnetic properties of magnetic nanoparticles are affected by the nature and strength of the inter-particle magnetic interaction11, magnetic anisotropy12, the nanoparticle size and shape distribution9, as well as the interplay between inter- and intraparticle interactions stemming from surface14.Spin glasses2O415 nanoparticle systems of various sizes17. In the current study, we have investigated the superspin glass state of a dense assembly (i.e. particles are in close contact) of ultra-small (diameter 2 nm) MnFe2O4 nanoparticles. This implies a significant role of the disordered surface affecting both intra (core/surface exchange coupling) and interparticle (dipolar and exchange interactions) effects. We observe that the system undergoes a superspin glass phase transition and we provide evidence, by means of time and temperature dependent memory experiments, that the low field magnetization dynamics is similar to that of conventional (atomic) spin glasses. Monte Carlo simulations, using a mesoscopic model of the nanoparticle system, reproduce the experimental findings, and confirm, accounting for core-surface and interparticle contributions, the significance of the interplay between intra- and interparticle effects. The relative contribution of dipolar and exchange (involving the surface shells) interparticle interactions is determined by simulating the memory experiment in absence of one of them. The simulations indicate that both types of interactions contribute to the observed memory effect and that the contribution of dipolar interactions is much stronger than that of exchange ones, which alone would produce a much smaller effect.Glassy magnetic states have been reported in MnFe2O4 powder19 showed that the mean crystallite size obtained by XRD analysis is ~ 2 nm, whereas the value obtained by powder specific area (278 m2/g) is about 4 nm. This discrepancy suggests that the particles consist of a few aggregated crystallites. Remanence magnetization measurements by means of DCD and IRM protocols recorded at 5 K clearly show the presence of long range dipolar interparticle interactions. Detailed discussion of these results is reported in the Supplementary Materials.Earlier studies on the MnFe57Fe Mössbauer spectrometry under intense external magnetic field (MSMF) was performed; all the spectra were analyzed using the program Mosfit20. MSMF allows a more reliable distinction between Fe ions located in interstitial sites with tetrahedral (A) and octahedral (B) oxygen coordination 22. Furthermore, MSMF spectra can also give information about the magnetic structure of the nanoparticles. In the presence of an external magnetic field parallel to the gamma ray direction, the relative areas of the six lines give information about the degree of alignment of the magnetic moments with the applied field.To further investigate the magnetic structure of these small particles, 57Fe Mössbauer spectrum O2/3(OH)4/3, probably arranged in a core–shell structure19. MnFe2O4 has ferrimagnetic structure, whilst a high frustration due to a reduced symmetry23 induces an AF-like behavior.The rum Fig.  has a co25. The superparamagnetic onset above Tmax is also confirmed by thermoremanent magnetization, which approaches zero at the temperature where FC and ZFC curves merge. MZFC shows a maximum at Tmax ~ 45 K; below this temperature MFC shows a maximum (~ 43 K), then becomes temperature independent and finally shows a slight upturn at the lowest temperature. This behavior resembles that of spin glasses3 and has been observed in monodisperse systems of strongly interacting magnetic particles9. As discussed in Supplementary Materials (Sect. 5), the presence of strong magnetic interaction between the particles is confirmed by magnetic-field dependent remanent magnetization measurements by means of DCD and IRM protocols recorded at 5 K.In order to investigate magnetization dynamics of the nanoparticles, AC susceptibility and DC magnetization measurements and zero field Mössbauer spectrometry at different temperature have been performed. Figure 57Fe Mössbauer spectra recorded at different temperatures in the range 4–65 K. Mössbauer spectra of magnetic nanoparticle assemblies typically consist of a superposition of a sextet due to particles with long relaxation time compared to the time scale (⁓ 5 × 10−9 s) of Mössbauer spectrometry and a doublet due to particles with shorter relaxation time compared to it. The relative area of the doublet increases with increasing temperature. The blocking/freezing temperature in Mössbauer spectroscopy TMoss, may be defined as the temperature at which the spectral areas of the sextet and the doublet are equal. From analyses of our Mössbauer spectra at different temperatures , and thus the freezing/blocking temperature Tmax estimated using the two techniques are expected to differ considerably. In the literature, it is reported that the ratio TMoss/Tmax decreases with increasing interparticle interactions27. For the blocking temperature of non-interacting particles, obeying Arrhenius dynamics, the ratio TMoss/Tmax is about 6, whilst lower values are reported for spin glass and superspin glass systems28, where critical dynamics govern the behavior. For our sample the ratio TMoss/Tmax is ⁓ 1.4.Figure le ⁓ 5 × 0−9 s of Tf for each frequency, below which the longest relaxation time (τ) of the system exceeds the observation time of the measurement, τ ~ 1/(2πf).  datasets which may be analyzed using various scaling laws. Data obtained from Mössbauer spectroscopy experiments  is added using an open marker. In spin glass systems, the critical slowing down implies that the time necessary to reach equilibrium will become longer and longer when approaching the spin glass phase transition Tg (glass temperature) and τ diverges at Tg according to the power law τ/τ0 = ε−zν, where ε is the reduced temperature (T − Tg)/Tg and z, ν critical exponents2. As seen in Fig. Tf data follows a power law behavior on a pretty wide frequency window, with physical values of critical exponents (zν = 9(1)) and flipping times, (τ0 = 10−12 (1) s) indicating a (super)spin glass transition at Tg = 46 (1) K in the system. The error bars on the parameters derived from the scaling analysis are quite large. Yet, the obtained zν value is comparable to those of 9–11 reported for superspin glasses9, and in the range of those observed for "isotropic" Heisenberg atomic spin glasses (zν ~ 6–8) and "anisotropic" Ising ones (zν ~ 10–12)29. The value of τo determined in our analysis is lower than typical values for superspin glasses5, possibly due the very small size and strong magnetic interaction of the particles.The ac-susceptibility was recorded for different frequencies (f) and the in-phase component of the susceptibility χ’ is shown in Fig. t, τ ~ 1/πf. (See 2O4 is investigated by means of time-dependent relaxation (not shown) and temperature-dependent memory experiments performed in small magnetic fields (10 Oe), described in the Supplemental Materials . In the TRM case the field is thus kept to its H = 10 Oe value, while for the ZFC, it remains zero. The memory curves show a dip (ZFC) or bump (TRM) illustrating the memory and rejuvenation effects. The FC magnetization experiences a minute downward relaxation during such a halt in a constant magnetic field. The principle of superposition connects the response to magnetic field changes and relaxation as MZFC = MFC − MTRM3, provided that the field change yields linear response. Using a specific heating rate in ZFC/TRM experiments corresponds to probing the system at a specific observation time (order of 10 s). The similarity of the difference plots between reference and ZFC/TRM memory curves plotted in Fig. 3 is valid also for our sample. The excess magnetization at Th in MTRM (T) is equal to the magnetization loss in MZFC (T) in absolute values.The existence of non-equilibrium dynamics of MnFec fields 0 Oe, des13. The energy parameters given in the Supplementary Material (equation S1) are based on the bulk values of MnFe2O4 (MS = 5 × 105 A/m and K = 3 × 103 J/m3), and their modifications are established considering the nanoparticles morphology (e.g. reduced symmetry and reduced size) using a mean field approach. The three spin model was employed and the effective intra-particle exchange coupling constants among the core spin and the surface spins were taken as jc1 = 0.5, jc2 = 0.45, jsrf = − 1.0 and the effective anisotropy constants of the core as kC = 0.05 and the surface ksrf = 1.0. We take the inter-particle exchange coupling constant as jinter = − 0.50 as a free parameter, the dipolar strength is calculated and found g = 3. Figure w = 5 × 106 and their difference ΔΜ. The dip of the memory ZFC, the bump of the memory TRM and the similarity of the ΔΜ curves of the two memory experiments confirms that the dynamic properties of the simulated system are characterized by the existence of a superspin glass state. The validity of the model, accounting for interparticle contributions and intraparticle , demonstrates that the observed memory effects result from an interplay between interparticle interactions and surface disorder.Memory experiments have been also performed using MC-simulations, considering a mesoscopic model for a dense assembly of ferrimagnetic nanoparticles showing superspin glass characteristics in ZFC-FC curves, as described in Refinter or g, respectively. The results . Weak rejuvenation effects have been reported in many cases for atomic spin glasses in simulations based on the Edwards Andersson model30. Similar results have been obtained for Ising models of dipolarly interacting nanoparticles31. However the present Heisenberg model takes into account the interparticle exchange interactions and the anisotropy energies of the weakly anisotropic Mn ferrite nanoparticles together with strong surface spin disorder, and hence may display stronger memory and rejuvenation features.In order to determine the relative contribution of dipolar and exchange (involving the surface shells) interparticle interactions, we have simulated the memory experiment in absence of one of them, i.e. switching off either jlts Fig.  indicateh. The influence of aging (wait time tw dependence) on the magnetic relaxation can be directly monitored in low field isothermal remanent magnetization vs. temperature experiments (MIRM(T)) using a similar temperature protocol as in the memory experiments including a field application after different wait times (tw) during the halt32. (tw is the time the sample has been kept at constant temperature before the field change). The excess magnetization attained during the halt freezes in when the field is cut off and cooling resumes after the halt. The inset of Fig. IRM(T) recorded on heating using tw = 0 and tw = 3000 s and in both cases a hold time for the magnetic field of 3000 s. As seen in the figure, the magnitude of MIRM depends on the wait time at Th (= 25 K) before the field application, which reflects a wait time dependence (aging) of the zero field cooled magnetization MZFC. Similar experiments may be devised, considering a zero-field stop (ZFS) procedure for the TRM, in which the magnetic field is switched to zero during the halt3. Sketches of the evolution of the temperature and magnetic field with time in the various protocols are included in supplementary materials  measurements using the same wait times and hold times as in the IRM experiments are shown in Fig. The memory experiments described above corresponds to field stop (FS) experiments in which the magnetic field is kept to its initial value (zero for ZFC and H for TRM) during the time tals Fig. . The resw = 0) or including a wait time (tw = 3000 s) at the halt temperature before the field application, one may observe how the IRM curves recorded with tw = 3000 s lie significantly lower than the tw = 0 one32. The difference curves between the experiments performed with and without wait times, denoted ΔMTRMZFS, ΔMTRMFS and ΔMIRM are plotted in Fig. 3 that ΔMTRMZFS(T), ΔMIRM(T), and ΔMTRMFS(T) reflect MTRM, MZFC, and MFC respectively, at an observation time given by the heating rate . The superposition of relaxations MZFC = MFC − MTRM would then imply the relation ΔMIRM(T) = ΔMTRMFS(T) − ΔMTRMZFS(T). This seems to be satisfied for the excess magnetization of the MnFe2O4 superspin glass, as we observe that ΔMTRMZFS(T) (plotted as − ΔMTRMZFS(T)) is quite similar to ΔMIRM(T), except in the vicinity of Th, around which ΔMTRMFS(T) is non-zero. As seen in Fig. TRMZFS(T), ΔMIRM(T), and ΔMTRMFS(T) curves for MnFe2O4 (main frame) are quite similar to those of the archetypal Ag(Mn) spin glass (inset)3.Both the IRM and TRM data (ZFS and FS conditions), reflect, as expected, the aging phenomenon. Comparing the curves collected without wait time  spin glass, for which an upturn of the magnetization is observed below Th33. In the latter case, the upturn has been related to the spin dimensionality (Heisenberg-like), as Ising spin glasses show IRM curves without upturn, akin to MnFe2O432. Interestingly, it was observed in Ref.33 that the IRM curves of maghemite nanoparticles had different superspin dimensionality, depending on Th. The “Heisenberg character” of the IRM curves at temperatures near Tg (with upturn below Th) is gradually replaced by an “Ising character” as Th becomes lower and lower. Interestingly a similar crossover is observed in the present MnFe2O4 system , thermoremanent (TRM), and isothermal remanent magnetization (IRM) were recorded, using specific protocols, to compare the low field magnetization dynamics of that system to that of archetypal spin glasses. Mössbauer spectra were recorded as a function of temperature and magnetic field, yielding information on the dynamical magnetic properties and magnetic structure, respectively. The low-field magnetization dynamics was found similar to that of archetypal spin glasses in spite of the inherent characteristics of nanosystems , reflected in the observed superspin dimensionality crossover. The time and temperature dependence of the ZFC and TRM magnetization were well reproduced by Monte Carlo simulations, using a mesoscopic model of a nanoparticle assembly, with core/shell morphology, accounting for all the intraparticle  and interparticle (dipolar and exchange interactions) effects. This provides an evidence of the interplay between the above effects. The simulation of the memory experiments clearly evidences that the contribution of dipolar interactions is much stronger than that of exchange interactions.The dynamical magnetic properties of a superspin glass consisting of a dense assembly of ultra-small MnFe2O4 nano-powders have been synthesized by coprecipitation of Fe3+ and Mn2+ from water-in-toluene reverse micelle system and subsequent thermal treatment at 320°C. Detailed synthesis procedure, structural and morphological characterization are reported elsewhere19.MnFemax = 5 T). The temperature-dependent ZFC, FC, TRM, and IRM magnetization measurements, as well as the magnetic field-dependent direct current demagnetization (DCD) and IRM are described in more details in the Supplemental Materials. To avoid any movement of the nanoparticles during the measurements, the samples, in the form of powders, were immobilized in epoxy resin. The ac-susceptibility χ data was recorded as a function of the temperature T and frequency ω = 2πf on the same system as well as on a Quantum Design physical property measurement system (PPMS).DC magnetization measurements were performed in zero-field-cooled (ZFC), field-cooled (FC), thermo-remanent (TRM) and isothermal remanent (IRM) conditions using a Quantum Design SQUID magnetometer equipped with a superconducting coil  are considered for each particle, yielding exchange interaction (with strengths Jc and Jsrf) and anisotropy terms (Kc and Ksrf). The interparticle interaction is considered by including exchange (Jinter) and dipolar (g) interaction. For the calculation of the Reference and Memory ZFC and TRM curves we follow the experimental procedure: (1) the system was first cooled at a constant temperature rate from T = 0.8 in zero field in the ZFC magnetization procedure and by applying a low field H = 0.08 for the calculation of the TRM curves. (2) hold-and-wait at temperature Th = 0.18 for a waiting time (ZFC and TRM) tw = 5 × 106 Monte Carlo Steps per Spin (MCSS). (3) Continue cooling down the system to the lowest temperature T = 0.002 (4) heating the sample in the presence of a magnetic field H = 0.08 for ZFC; for the TRM, the field was removed. The magnetization was monitored in the step (4) for each temperature. Detailed description of the model is reported in Supplementary Materials.We have used the Monte Carlo (MC) simulations technique to calculate the memory behavior of the dense assembly of ultra-small Mn ferrite nanoparticles. A detailed description of our mesoscopic model that includes the core/surface morphology of each particle and the interparticle interaction is given in Ref.Supplementary Information.


Outer membrane proteins (OMPs) are crucial for the survival of bacteria. The two chaperones 17-kilodalton protein (Skp) and survival factor A (SurA) play key roles in OMP maturation by keeping unfolded OMP proteins soluble in the periplasm. However, their functionalities are incompletely understood. Here, we establish connections between structural and energetic features employed by the two chaperones when interacting with unfolded OmpX. We find that expansion, accompanied with fast polypeptide chain reconfiguration, prevents unfolded OmpX from misfolding and aggregating. Moreover, chaperone interaction with unfolded OmpX is thermodynamically calibrated, allowing for a fine-tuned association of chaperones with OMPs in the adenosine triphosphate-depleted periplasm. We further discovered that Skp and SurA act together as disaggregases and are able to disassemble oligomeric OMP aggregates, revealing remarkable functionalities of this periplasmic chaperone system. Periplasmic chaperones 17-kilodalton protein (Skp) and survival factor A (SurA) are essential players in outer membrane protein (OMP) biogenesis. They prevent unfolded OMPs from misfolding during their passage through the periplasmic space and aid in the disassembly of OMP aggregates under cellular stress conditions. However, functionally important links between interaction mechanisms, structural dynamics, and energetics that underpin both Skp and SurA associations with OMPs have remained largely unresolved. Here, using single-molecule fluorescence spectroscopy, we dissect the conformational dynamics and thermodynamics of Skp and SurA binding to unfolded OmpX and explore their disaggregase activities. We show that both chaperones expand unfolded OmpX distinctly and induce microsecond chain reconfigurations in the client OMP structure. We further reveal that Skp and SurA bind their substrate in a fine-tuned thermodynamic process via enthalpy–entropy compensation. Finally, we observed synergistic activity of both chaperones in the disaggregation of oligomeric OmpX aggregates. Our findings provide an intimate view into the multifaceted functionalities of Skp and SurA and the fine-tuned balance between conformational flexibility and underlying energetics in aiding chaperone action during OMP biogenesis. Molecular chaperones are key cellular components that play fundamental roles in maintaining cellular proteostasis , 2. Esse3One network where chaperone activity is of particular relevance is the biogenesis of outer membrane proteins (OMPs) , 7. OMPs812In Gram-negative bacteria, OMPs are translated in the cytoplasm, from where they are translocated across the inner bacterial membrane via the Sec machinery to the periplasmic space , 5. WithTwo chaperones which have been shown to be indispensable for the biogenesis of bacterial OMPs are the 17-kilodalton protein (Skp)  and surv2427Structural studies of the eight β-stranded protein outer membrane protein X (OmpX) in the presence of Skp using NMR spectroscopy have found that unfolded OmpX (uOmpX) shows submillisecond backbone dynamics  in compl34In addition to the well-described holdase activities of Skp and SurA that protect OMPs from misfolding or aggregating, a recent study has suggested that Skp disaggregates oligomeric uOMP structures . While STo gain insights into the multifaceted functionalities of Skp and SurA and their action mechanisms, we study here the conformational dynamics and thermodynamics of the eight β-stranded protein OmpX in the presence of the chaperones and explore their disaggregation activities. Using single-molecule Förster resonance energy transfer (smFRET), we resolve the heterogeneities, structural dynamics, and thermodynamics underlying the different states of uOmpX at near-native conditions. Strikingly, we find that both chaperones expand the unfolded polypeptide chain upon binding. The degree of expansion is concentration dependent for SurA, but not for Skp. Probing structural changes and chaperone interaction at different temperatures, we gain insights into the enthalpic and entropic contributions of complex formation and find that the interaction modes of both chaperones differ strongly and are dictated by entropy–enthalpy compensation. Finally, we use fluorescence correlation spectroscopy (FCS) to probe the disaggregation capabilities of Skp and SurA and find synergistic activity of both chaperones in the disassembly reaction of oligomeric OmpX aggregates. Our findings provide fundamental insights into the structural and energetic mechanisms underlying Skp and SurA chaperone–OMP interactions and their role in OMP biogenesis.Probing chaperone effects and conformational dynamics of uOMPs under native-like conditions  has been difficult to achieve due to the aggregation-prone nature of OMPs in aqueous environments , 7, 40. 41451,149) furnished with donor and acceptor dyes (N-oxide (LDAO)  at micromolar concentrations and subsequently diluted it in the presence of 6 M GdmCl to a concentration of 20 nM. This was followed by a 1,000-fold dilution step into GdmCl-free buffer or into GdmCl-free buffer supplemented with the chaperones Skp or SurA. The resultant [GdmCl] after the final dilution step was 6 mM and the remaining [LDAO] was <100 nM, thus yielding uOmpX at 20 pM in a micelle-free native-like aqueous buffer that is largely devoid of denaturant (denoted as uOmpXaq). smFRET measurements were performed directly after the dilution protocol, thus mimicking the scenario when newly secreted OMPs enter the periplasm and encounter their interacting chaperones. Subsequently, fluorescence bursts were recorded from a large number of individual protein molecules diffusing through the confocal detection volume to generate FRET efficiency (E) histograms  and provide information on intrachain dynamics  located at Êaq ∼ 0.77 and a tail spreading toward low transfer efficiencies (Left). Broadened E distributions are generally ascribed to conformational heterogeneity with interconversion dynamics on timescales comparable to or longer than the observation time (∼1 to 2 ms). Indeed, the width of the unfolded state population is in excess of that expected for a distribution that is limited only by detection shot noise , we observed a broadened E distribution further indicative of a rough energy landscape of the uOmpXaq state . We also observed a modest expansion of the uOmpXaq chain on the timescale of 4 to 6 h , in line with reports which suggest that this mechanism prevents aggregation in the absence of both a membrane mimetic environment and chaperones , as indicated by a displacement of the uOmpXaq population from the static FRET line in the relative donor lifetime (τD(A)/τD(0), i.e., the ratio of the fluorescence lifetime of the donor in the presence and absence of acceptor, respectively) versus E plot , indicating that uOmpXaq’s fast reconfiguration dynamics vanish over time. This points toward the formation of long-range interactions in the unfolded-state and a dedynamization of the uOmpXaq polypeptide chain while preserving the broad conformational heterogeneity. Such a behavior was absent in the case of the measurement performed at 37 °C  and was reversible upon temperature increase from 25 °C to 37 °C , likely due to temperature-induced chain dynamics. Of note, in all experiments, an additional minor population (∼10%) at a high FRET efficiency (E ∼ 0.92) was observed. This very compact state is particularly observed after long measurement times of 4 to 6 h  and likely constitutes a misfolded state, denoted as uOmpXcompact throughout the article .Interestingly, for the measurement conducted at 25 °C, at late time points (4 to 6 h), the aq adopts a broad, heterogeneous ensemble of structures and populates a rugged energy landscape leading to a multitier dynamical behavior with both fast submillisecond chain reconfiguration dynamics and slow conformational interconversion. An overall expansion of the uOmpXaq polypeptide chain is observed, in line with a conformational loosening mechanism that likely serves to alleviate uOMP aggregation  gradually arose, being already visible at 0.5 nM [Skp3]  and uOmpXcompact (compactÊ ∼ 0.92) states. This suggests that the presence of Skp3 does not affect the conformational properties of these states and implies that these do not directly interact with Skp3. We therefore identify these states, even in presence of chaperones, as uOmpXaq and uOmpXcompact . The FRET efficiency peak of Skp3-bound uOmpX was centered around ÊSkp3–uOmpX ∼ 0.45, a considerably larger interfluorophore distance than the unbound uOmpX  state. A careful analysis of the donor lifetime, the fluorophore anisotropies, and the rotational correlation times of the fluorophores reassured that the lower E values reflect a global conformational change arising from the increased end-to-end distance of the uOmpX polypeptide chain upon binding to Skp3 . Moreover, Gaussian fitting revealed that, at [Skp3] = 2.5 µM, ∼80% of uOmpX molecules were in a Skp3–uOmpX complexed state, suggesting that the presence of Skp shifts the equilibrium from the unbound to the bound form.To extract populations from the FRET efficiency histograms, we found that three Gaussian distributions were sufficient to describe the overall shape and change of shape upon chaperone titration . Gaussian fitting allowed us to identify the peak position and fraction of SurA-bound uOmpX with ÊSurA–uOmpX ∼ 0.6 at a concentration of ∼6 µM, which is close to the intracellular concentration of SurA  thus suggest that they themselves occupy a distinguishable configurational space likely due to the difference in the interaction mechanisms between the chaperone and its substrate . The FRET efficiencies of the unbound (uOmpXaq) and compact uOmpX (uOmpXcompact) states did not vary markedly across the chaperone concentration series performed at different temperatures . For the chaperone-bound fraction of uOmpX, ÊSkp3–uOmpX remained constant, within error, across nearly three orders of magnitude of [Skp3] at 25 °C (Left). This suggests a concentration-independent, possibly 1:1 stoichiometry, of Skp3 and uOmpX, in accordance with previous studies . Interestingly, at elevated temperatures, the dependency on [SurA] was enhanced and we observed a strong decrease of ÊSurA–uOmpX with increasing [SurA] at 37 °C from ÊSurA–uOmpX (1.2 µM) ∼ 0.66 to ÊSurA–uOmpX (25 µM) ∼ 0.55. This suggests that uOmpX is more expanded at elevated [SurA], which can be explained by either a different interaction mode or that uOmpX is sequestered by multiple SurAs as also suggested by previous studies  and the expected interdye distance ⟨Rinterdye⟩  of the donor–acceptor distance . We determined the peak positions for E and τD(A)/τD(0) for each condition and plotted the values in a two-dimensional (2D) diagram . The bound FRET efficiencies (ÊSkp3–uOmpX and ÊSurA–uOmpX) across different chaperone concentrations were offset in distinct clusters from the static FRET line. The offset indicates that the polypeptide chain undergoes fast configurational changes on the submillisecond timescale while being bound to chaperones.In a last step, to gain further insights into the structural and dynamical properties of chaperone-bound uOmpX, we analyzed the underlying dynamics of the chaperone-complexed uOmpX polypeptide chain. To this end, we extracted single-molecule events of the chaperone-bound population using a combined FRET efficiency and relative donor fluorescence lifetime filter (T) and a window size of 2 × ΔΔT  in the 2D emission-delay histogram . The timescale of interconversion dynamics between different molecular states were then extracted by comparing slices of the 2D histogram with either short or long initial microtimes  , 61 and  2 × ΔΔT A were sostograms B. The amp3–uOmpX C and Surp3–uOmpX D complex3 and SurA leads to an increased end-to-end distance of the uOmpX polypeptide chain. Skp3-complexed uOmpX showed a decreased conformational heterogeneity, while binding of SurA increased the conformational heterogeneity of uOmpX, indicating very distinct interaction mechanisms of the chaperones on their substrates. Complexed uOmpX exhibited fast chain reconfiguration dynamics on timescales <10 µs, unlike uOmpXaq alone, which showed multitier dynamics on a large range of timescales (submillisecond to ≥100 ms) originating from its rugged energy landscape. The fast dynamics and the possible expansion of chaperone-bound uOmpX may aid in preventing uOmpX from getting trapped in a fortuitous conformation which might be folding incompetent or inaccessible to the BAM complex. A wealth of different conformations also indicates a small entropic penalty for complexing uOmpX to Skp3 or SurA.To conclude, we found that binding of the chaperones SkpH, and entropic, ΔS, contributions to the free energy change of interaction, ΔG, which are connected to the association constant, Ka, for a bimolecular association reaction via:We next set out to obtain insights into the energetic contributions of Skp and SurA chaperone–uOmpX interactions. Interaction isotherms at different temperatures allow for the extraction of enthalpic, ΔH and ΔS and considering the fraction of the compact state (fc(T)), which is likely unable to be chaperone complexed, as well as the maximum fraction of the bound state ) and minimizing the reduced χ2, the best estimators for ΔH and ΔS were obtained. Here, we chose a Hill coefficient of 1 for Skp3 and 1.5 for SurA, based on earlier reports . As c1/2 (inverse of Ka) is dependent on temperature, at 37 °C, we obtained a c1/2-value of 359 ± 0.1 nM and 407 ± 0.1 nM for Skp3–uOmpX and SurA–uOmpX, respectively. This would convert to dissociation constants, KD ∼ 359 nM and KD ∼ 8.2 µM for Skp3–uOmpX and SurA–uOmpX, respectively. At 25 °C, we obtained a c1/2-value of 3.554 ± 0.001 nM and 102.89 ± 0.02 nM for Skp3–uOmpX and SurA–uOmpX, respectively. This would convert to KD ∼ 3.6 nM and KD ∼ 1.0 µM for Skp3–uOmpX and SurA–uOmpX, respectively, which is in very good agreement with previously reported values for the chaperone–OMP interactions  = –298 ± 19 kJ mol−1 and ΔS (Skp3–uOmpX) = –0.84 ± 0.06 kJ mol−1 K−1 (G (Skp3–uOmpX) = (–38.3 ± 0.2) kJ mol−1 at 37 °C. The change of enthalpy upon binding is large, which is in agreement with recent findings demonstrating a rich interaction of the uOmpX polypeptide chain with the cavity formed by Skp3  = –132 ± 24 kJ mol−1 and ΔS (SurA–uOmpX) = –0.24 ± 0.08 kJ mol−1 K−1 (G (SurA–uOmpX) = (–57 ± 1) kJ mol−1 at 37 °C, which is slightly more favorable than that for Skp3. The change of enthalpy was twofold lower than that for Skp3, likely due to more transient binding as suggested in recent studies , they agree well with our finding of an increased coefficient of variation  for SurA–uOmpX as compared to Skp3–uOmpX. Thus, binding of SurA to its client uOmpX is markedly different in nature than Skp3 binding to uOmpX. The reduction of conformational freedom of uOmpX in the cavity of Skp3 needs to be compensated by an increased interaction enthalpy  and devised an FCS-based assay that allows us to probe the disaggregase efficacy of the two chaperones. To this extent, we diluted labeled and denatured uOmpX at pM concentrations with unlabeled and denatured uOmpX in buffer to yield a final OmpX concentration of 1 µM (aq (∼10 pM) indicates that the fast-diffusing component in the correlation curve of the aggregation sample stems from monomeric uOmpXaq, with a characteristic diffusion time of τDiff = (0.27 ± 0.01) ms . The second, slowly diffusing species in the OmpXAgg measurements showed a τDiff = (31.6 ± 1.3) ms , which is about two orders of magnitude slower than that of single uOmpXaq molecules. Notably, OmpXAgg is possibly an ensemble of differently sized aggregates; hence, values reported here reflect average values. We determined that in our aggregation mixture, about 24 ± 2% of the labeled uOmpX molecules are part of aggregates .To address this question, we investigated the action of Skp of 1 µM A. The re of 1 µM B, green.3 or 5.8 µM SurA to the mixture. We recorded FCS curves at 37 °C directly after the dilution and obtained multimodal curves, indicating the presence of more than two components in solution. In addition to the fast-diffusing species with τDiff = (0.27 ± 0.01) ms reflecting free uOmpXaq and the aggregated OmpXAgg species with very high diffusion times (τDiff ∼ (32 ± 1) ms), we expected to also observe chaperone-complexed uOmpX species with intermediate diffusion times. By comparison with an FCS curve recorded from a highly dilute sample of labeled uOmpXaq (∼10 pM), we assigned diffusion times of τDiff = (1.79 ± 0.05) ms and (1.38 ± 0.03) ms to Skp3- and SurA-bound monomeric uOmpX, respectively . To quantify the fractional amounts of the three components , we fitted the autocorrelation data of our mixture experiment with three components. Here, the respective diffusion times were kept constant to extract the amplitudes of each component as they are inversely proportional to the concentrations and fractional amounts of the species. The addition of Skp3 to the aggregation mixture reduced the apparent fraction of OmpXAgg to 20 ± 2% and the addition of SurA even to 13 ± 1%. A reduction of apparent OmpXAgg was only observed at chaperone concentrations above 1 µM . Interestingly, in both cases, the disaggregation of OmpX was found to be accompanied by an increase in the free uOmpXaq population  and only negligibly in the chaperone–uOmpX complex. Assuming uniform mixing of the labeled and unlabeled uOmpX sample, the increase of the free uOmpX population suggests that the chaperones show a higher affinity toward OmpXAgg as compared to uOmpX. This finding is significant, as it suggests that the careful adaptation of affinities is crucial during physiological stress conditions. In such a scenario, disaggregation activity might be more important than holdase activity to prevent the formation of large toxic OMP aggregates in the periplasm.Next, we studied the effect that the chaperones Skp and SurA have on OmpX aggregation. To this end, we first incubated the mixture containing 1 µM unlabeled uOmpX and 10 pM labeled uOmpX for 10 min and then added either 2.5 µM SkpAgg and labeled uOmpX at pM concentrations after a 10-min incubation at 37 °C. Strikingly, the fraction of OmpXAgg was reduced to ∼5%  in the presence of both Skp and SurA could originate from handing over of uOmpX between the chaperones or from a dynamic adaptation of the chaperone functionalities between disaggregation and chaperoning activities.Finally, we asked the question whether there are synergistic effects on the disaggregation reaction, given that both chaperones are present in the periplasm. We performed our disaggregation assay in which both chaperones were added simultaneously to the mixture containing OmpXd to ∼5% D. TakingPeriplasmic chaperones Skp and SurA are essential players in OMP biogenesis. They act as holdases, preventing uOMP substrates from misfolding and exhibit disaggregase functionalities, aiding in the clearance of OMP aggregates , 38. Und3-bound uOmpX state exhibits a greater end-to-end distance as compared to the SurA–uOmpX state over the concentration range tested, suggesting that uOmpX interacts with Skp3 at multiple sites by spreading across the inner surface of the chaperone cavity and appears conformationally expanded within the chaperone cavity  without a signal sequence was expressed as inclusion bodies in Escherichia coli BL21(DE3) cells and purified following standard procedures including anion exchange chromatography, as previously reported . Details are given in SI Appendix.A tag-free double-cysteine variant of OmpX  fusion proteins in E. coli BL21 (DE3) cells and purified using immobilized metal affinity chromatography under denaturing conditions, as previously reported .A multistep dilution protocol, as shown in SI Appendix. Data analysis was performed with custom-written Matlab scripts (Mathworks). Single-molecule events were identified from the acquired photon stream by a burst search algorithm. Details about smFRET and FCS analysis procedures, including burst selection, data reduction, calculation of FRET efficiencies, and fluorescence, are given in SI Appendix.Experiments were carried out using a single-molecule confocal fluorescence microscope as previously described  and detaSupplementary File


Traditional 3D printing based on Digital Light Processing Stereolithography (DLP-SL) is unnecessarily limiting as applied to microfluidic device fabrication, especially for high-resolution features. This limitation is due primarily to inherent tradeoffs between layer thickness, exposure time, material strength, and optical penetration that can be impossible to satisfy for microfluidic features. We introduce a generalized 3D printing process that significantly expands the accessible spatially distributed optical dose parameter space to enable the fabrication of much higher resolution 3D components without increasing the resolution of the 3D printer. Here we demonstrate component miniaturization in conjunction with a high degree of integration, including 15 μm × 15 μm valves and a 2.2 mm × 1.1 mm 10-stage 2-fold serial diluter. These results illustrate our approach’s promise to enable highly functional and compact microfluidic devices for a wide variety of biomolecular applications. The ever-growing need for highly functional, compact, and integrated microfluidic devices often incurs lengthy and expensive manufacturing processes. Here, authors introduce a generalized 3D printing process that enables fast parallel fabrication of miniaturized, high resolution 3D components. Traditional methods to create such voids rely on either planar fabrication using cleanroom processes or plastic forming methods such as hot embossing or injection molding. In each case, individual layers are fabricated that must be aligned and bonded, with one or more layers having surface relief features that result in voids when stacking layers in the bonded device3. Using such processes for microfluidic device development incurs significant delays in the iterative design/fabricate/test cycle required to create successful devices, too often making device development a lengthy and expensive process. One reason 3D printing has received so much attention in recent years for microfluidic device fabrication12 is that it offers the potential to dramatically speed up device development by reducing the fabrication part of the development cycle to the minutes timescale. Moreover, 3D printing affords the additional benefit of moving away from planar or stacked designs, enabling actual 3D layout of microfluidic elements with complex yet compact 3D geometries, resulting in the fabrication of devices that contain elements not possible with conventional planar methods14.The overarching characteristic of fabricating microfluidic devices is creating negative space, i.e., voids, in an otherwise solid material16, Digital Light Processing Stereolithography (DLP-SL) offers a particularly attractive approach to 3D print microfluidic devices because at least tenfold higher resolution can be realized than with the closest competitor method, inkjet-based material jetting (compare Refs. 17), and much larger build sizes can be fabricated in much less time than with submicron resolution two-photon polymerization19. However, current commercial DLP-SL 3D printing practice uses an overly limiting process comprised of the following steps: (1) design device in 3D CAD tool, (2) export design as STL (Standard Tessellation Language) file, (3) slice STL file into a stack of 2D images in which each image represents a layer of identical thickness, (4) perform 3D printing by exposing each image to the same thickness of resin using the same exposure time for all images above the initial burn-in layers, (5) post-print flush to remove unpolymerized resin and thereby reveal the interior features (voids) that comprise the device, and  (6) post-flush cure to drive further polymerization to increase material strength.Based on previous workThe above process can be effective for non-microfluidic designs with positive features where the minimum feature size is 5–10 pixels (100–200 μm) or larger. However, a critical consequence of Steps 3 and 4 is that the entire 3D print represents a complex set of trade-offs involving layer thickness and exposure time that can be impossible to satisfy, especially for high-resolution microfluidic device features involving only a few pixels. For example, the need for adequate green (as-printed) material strength mandates longer layer exposure times, which is vital so that the overall printed part can be connected to a vacuum and/or pressure source to flush unpolymerized resin from void regions, and so that otherwise fragile membranes in valve structures can withstand the forces inherent in the flushing process. However, longer exposure times result in deeper optical penetration into previously fabricated layers during exposure, making it impossible to create the smallest z features because trapped unpolymerized resin in negative spaces becomes polymerized. Hence, standard DLP-SL 3D printing imposes severe fabrication limitations on the size and type of microfluidic device structures, which artificially limits the potential of 3D printing for 10–30 μm feature microfluidic device fabrication.16.The purpose of this paper is to introduce transformational changes to traditional 3D printing as described above, enabling negative structures as small as a few pixels to be created, including active features such as valves. Our generalization of the 3D printing process includes the following characteristics: (1) each layer can be composed of an arbitrary number of overlapping and/or spatially distinct images such that arbitrary position-dependent optical exposure is achieved within each layer, (2) each layer can have an arbitrary thickness, independent of all other layers, and (3) multiple stacked layers of smaller thicknesses and limited spatial extent can be embedded in surrounding thicker layers. These features make it possible to mix and match arbitrary layer thicknesses and exposure regions to access a much larger x/y/z/dose photopolymerization parameter space than traditional 3D printing and thereby break its unnecessarily restrictive tradeoffs to achieve much smaller active elements for a given raw resolution of the 3D printing system. We note that our previous demonstration of 18 μm × 20 μm 3D printed passive channels made with a custom 3D printer and resin employed only Characteristic 120 to 6 pixels (46 μm). In addition, we introduce a few-picoliter dead volume 3D printed valve, called a squeeze valve. We show that squeeze valves can have an active area as small as 2 × 2 pixels (15 μm × 15 μm). We use both types of valves to create compact pumps. We then integrate multiple pumps and valves into compact, fast, diffusion-driven, 1:1 mixers, and sequentially connect ten individual mixer units to create a serial dilution system with ten simultaneous discrete outputs having relative concentrations that span three orders of magnitude. When squeeze valves are used, the ten-stage serial diluter is exceptionally small, having an x–y footprint of only 2.2 mm × 1.1 mm. Finally, we illustrate the utility of an on-chip serial dilution system by demonstrating the dose-dependent permeabilization of A549 cells in different concentrations of digitonin. The microfluidic device miniaturization and integration shown in this paper demonstrate the transformational potential of our re-envisioned 3D printing approach.Using our generalized 3D printing approach, we demonstrate dramatic miniaturization of active components. For example, we show that 3D printed membrane valves can be reduced from a membrane diameter of 40 pixels (300 μm)21. Therefore, there has been a strong motivation to endow 3D printed microfluidics with a similar valve fabrication capability. The first demonstration of 3D printed valves was shown in 2015 by our group7, closely followed by the Folch group8. In both cases the valves were not particularly small, 2 and 3 mm diameter and 10 mm diameter, respectively. Since then, there has been a continual drive to reduce the size of 3D printed valves. In 2016 we reported 1.08 mm diameter valves using a commercial 3D printer and custom resin22. With the advent of our first custom 3D printer and associated resin16, we showed in 2018 the realization of 300 μm diameter valves20. In 2019 Folch et al. demonstrated 500 μm diameter valves using a commercial 3D printer and a custom resin11. In this paper we use our approach to 3D printing to demonstrate 3D printed membrane valves as small as 46 μm in diameter. Moreover, we show that this 3D printing approach enables the fabrication of squeeze valves that are even smaller, down to ~15 μm × 15 μm.On-chip integrated valves are critical components to control fluid flow in microfluidic devices and are an important element in the popularity of polydimethylsiloxane (PDMS) microfluidics in which it is straightforward to create valves based on the elastomeric nature of PDMS22. When the control chamber is pressurized, the membrane deflects until it covers the channel connected to the center of the bottom of the fluid chamber, blocking fluid flow and therefore closing the valve , the fluid chamber height (h), the cross-section area (w2) of the channel to be blocked, and the membrane thickness. The latter is contingent on the optical dose the membrane receives, since there is unpolymerized resin in the region behind the membrane that polymerizes as the dose increases16. The dose is proportional to the time the membrane is exposed by the 3D printer light source. For the fabrication parameters given, Table The pressure required to close a membrane valve depends on the membrane’s area  and lower their response time, which we require to be fast (≤15 ms) if we want to use them as part of pumps, which we show in the next section.Integrated 10-stage 2-fold serial dilution devices are based on 20 pixel valves, since the investigation of 12 and 6 pixel valves occurred after testing the serial diluter. We have included videos of membrane valve operation as Supplementary Videos 20.The membrane-based pumps and mixers used for serial dilution in Sect. ∘ relative to each other. This arrangement makes the squeezed region a cuboid with the top and bottom defined by square membranes.The geometry of our squeeze valve is shown in Fig. p–r. As iWhen the control channel is pneumatically pressurized, the thin top and bottom membranes deflect, as illustrated in Fig. As shown in Table Effectively fabricating such 3D geometries is enabled by our ability to independently specify layer thicknesses and pixel doses. Figure When opening an initially closed valve, a specific volume of fluid is drawn from the attached fluid channels into the enlarged fluid chamber volume created by movement of the valve membrane. The opposite happens when closing an initially open valve. We can use this fluid displacement phenomenon to create a pump by connecting an additional valve to the fluid input channel and another to the fluid output channel, and appropriately synchronizing the opening and closing of these valves in conjunction with the center valve to control which fluid channel the central valve draws fluid from and to which it expels fluid.Pump measurements) form the first pump in Fig. Pump measurements. For example, a 50 ms phase time (250 ms pump cycle) results in a volumetric flow rate of close to 0.1 μL/min. Supplementary Video Two 20-pixel diameter membrane valves and a 20-pixel DC  along the flow channel. Commensurate with its small size, its generated flow rate is also low. For example, Figs. 23. Operation of a squeeze valve pump is demonstrated in Supplementary Video The third pump Fig. k–o consi25. Over several decades, various passive and active mixing strategies have been demonstrated. Comprehensive reviews of such strategies can be found in Refs. 26. In this paper, we take a different approach to mixing that relies solely on one-dimensional (1D) diffusion in a narrow channel that is specifically designed for fast mixing times (~1 s) in a short length (<1 mm).As is well-known, fluid mixing is a challenge at the low Reynolds numbers typical of microfluidic device operation since fluid flow is laminartD, over a distance, l, is given by27D is the diffusion coefficient of the molecular species in solution. Clearly, the shorter the diffusion distance, l, the shorter the time it takes to mix. For our mixer, we therefore choose to use a tall, high aspect ratio channel, as shown schematically in Fig. l = w/2, over which molecules from one fluid need to diffuse into the second fluid is small. If the dwell time of the fluids in the channel is greater than tD, diffusion-based mixing will occur. For example, Fig. w, is 30.9 μm, which results in a diffusion length of just over 15 μm, corresponding to diffusion times ranging from 0.12 to 1.2 s for the diffusion coefficients shown in Fig. The time required to diffuse, 22, which is the inlet valve, Vin, to the diffusion mixing channel. An extra valve, Vout, is added after the diffusion channel to ensure that the already mixed fluid that has exited the diffusion channel does not interact with the fluids inside the channel while they are still mixing. The diffusion channel is 950 μm long and 370 μm tall with tapered entrance and exit heights. The total diffusion channel volume is 8.95 nL. Section Mixer measurements describes the timing sequence and mixing time tradeoffs.We are particularly interested in mixing two fluids in equal amounts to create a 1:1 mixture. We can create a simple but effective 1:1 diffusion-driven mixer by connecting the outputs of two identical pneumatically actuated pumps to the high aspect ratio diffusion channel such that the fluids form adjacent sheets as schematically illustrated in Fig. Mixer measurements to measure the fluorescence of the mixed fluids (90–120 s). At 120 s, we stop the pumps and close all valves. This approach allows us to monitor the standard deviation of the mixed fluorescein concentration in the ROI as a function of time (120–140 s). The evolution of the standard deviation over time is a measure of how well mixed the fluid is, with a well-mixed fluid showing no change over time and a poorly mixed fluid showing a decrease over time13. Note that the standard deviation  was used . Previously, various researchers reported a range of digitonin concentrations used in cell membrane permeabilization experiments. Experiments on bovine chromaffin cells reported the use of 30 μM digitonin to achieve 90% cell permeabilization35. Moreover, in astrocytes32, 20–30 μM digitonin was used while 20–40 μM digitonin was used in A549 cells34 to achieve at least 75% plasma membrane permeabilization. Though, to our reading, definitive EC50 derivation for digitonin-based permeabilization in A549 has not yet been established. Alternatively, an EC50 value of 65.79 μM for digitonin has been shown in CHO cells using an Aequorin reporter assay36.As a biological proof-of-concept using this two-chip dose-response assay system, we show selective permeabilization using digitonin, a steroidal saponin. The use of digitonin in selective permeabilization has been shown in chromaffin cells10. Moreover, in principle any DLP-SL 3D printer manufacturer could implement the generalized 3D printing approach discussed in this paper as long as the raw projected image resolution is sufficient (7.6 μm in our case). To facilitate the broad adoption of our generalized 3D printing approach and the ability to share device designs, we have open-sourced a 3D print file specification based on the standard JSON (javascript object notation) format that incorporates all of the features of our 3D printing approach, which we have made freely available on github.com 37.Results reported in this paper show that our generalized 3D printing approach is effective for fabricating tiny active components such as valves and pumps on a size scale that until now has only been available using conventional lithographic methods. Moreover, we have shown that our approach is also effective in integrating such small active components into more sophisticated 3D functional structures such as active mixers and serial dilutors. This approach opens the door to replacing expensive and time-consuming cleanroom processes and equipment with fast and much less expensive 3D printing, which would revolutionize microfluidic device development38. With this approach, microfluidic devices the size of a well plate could be possible. However, for many applications, this may be unnecessary. We argue that it may be more attractive to make 3D printed microfluidic devices as small as possible so that many devices can be fabricated in a single 3D print run to take advantage of parallel fabrication for manufacturing. For example, consider Supplementary Fig. 20 to facilitate integration with a more extensive reusable 3D printed chip with the required bulky chip-to-world interconnects and additional microfluidic functionality. Focusing on parallel printing of such small chips but with sophisticated functionality offers a path to manufacturability and componentized assembly of more complex devices. Examples include well plate-sized constructs in which a number of highly compact 3D printed devices are integrated onto a larger, lower resolution piece that combines with a well plate, or devices that integrate directly into the well plate itself. In summary, our generalized 3D printing approach opens many new possibilities for microfluidics beyond those available with traditional lithography-based fabrication methods. We hope that our approach will be broadly adopted to rapidly advance microfluidics research and applications.A common concern regarding high resolution DLP-SL for microfluidics is the trade-off between resolution and image area: the higher the resolution , the more limited the image area and therefore the smaller the maximum device x–y footprint. This perceived shortcoming can be marginally compensated using a larger format micromirror array, such as a 4 megapixel (MP) array instead of a 2 MP array. We use a 4 MP micromirror array in our custom 3D printers, resulting in an image area of 19.5 mm × 12.2 mm, which is twice as large as for a 2 MP format, but may still be too small for some applications. A straightforward method to overcome this trade-off is to stitch images into a larger area multi-image mosaic for each layer20.For 3D printing we use a custom photopolymerizable resin which consists of poly(ethylene glycol) diacrylate  with a 1% (w/w) phenylbisphosphine oxide (Irgacure 819) photoinitiator and a 2% (w/w) 2-nitrophenyl phenyl sulfide (NPS) UV absorber, details of which are provided in Refs. Uranine powder (40%) was procured from Fischer Science, and Macron Fine Chemicals supplied sodium hydroxide pellets.39). It is the next generation of custom 3D printer originally reported in Ref. 16.The custom 3D printer used in this paper has a 385 nm LED light source and a pixel pitch of 7.6 μm in the projected image plane. We refer to it as the High Resolution 2 3D printer  and then immersed in toluene mixed with 10% 3-(trimethoxysilyl)propyl methacrylate for 2 h. After silanization, we store the glass slides in fresh toluene inside a closed container until use, ranging from under an hour to several weeks. Unless otherwise noted, all 3D prints reported in this paper are fabricated with a layer thickness of 10 μm and an exposure time of 900 ms. The image plane irradiance is 21.2 mW⋅cm22. A graphic illustration of the pump sequence is shown in Supplementary Fig. t = ti+1 − ti, is defined as the time for a single phase in the five-phase pump sequence in Supplementary Table Pumps are operated with the five-phase valve opening and closing sequence shown in Supplementary Table 122. We call this modified structure a displacement chamber (DC). It defines the fluid volume pumped in each pump cycle13.Since at any single phase of the pumping cycle either V1 or V2 is closed, the center valve does not have to block fluid flow when it is actuated. For pumps based on membrane valves, we therefore position both fluid channels at the edges of the center valve’s fluid chamber so that the structure can only displace fluid and not block fluid flow13. The inlet fluid and the pump outlet were kept at the same height as the pump to permit the determination of fluid flow for zero back pressure.To measure pump volumetric flow rate as a function of phase time, we recorded video at 240 frames per second using a cell phone camera attached to a microscope while pumping a dye solution through an initially empty microfluidic channel with known dimensions. This approach allowed us to track the fluid meniscus frame-by-frame with a custom Python script to determine the volumetric flow rate22. As shown in Fig. Data are shown in Fig. A, followed by Fluid B into DCB, after which Vin and Vout are opened, and fluid from both DCs is simultaneously pushed into the diffusion channel, then the process repeats. For a 50 ms phase time, a single nine-phase mixer period is 450 ms. We deliberately designed the volume of the diffusion channel to be nearly four times the volume pumped into it by both pumps during a single nine-phase period such that the average fluid dwell time in the channel is nearly four nine-phase mixer periods . Note that this permits mixing even for proteins that are 10’s of kDa. Thus, mixing larger molecules with smaller diffusion coefficients can be performed in the same mixer structure by increasing the dwell time in the diffusion channel. This adjustment can be accomplished by decreasing the flow rate into the diffusion channel by increasing the phase time. Alternatively, the diffusion channel dwell time can be increased by redesigning the 1:1 mixer module with a larger volume diffusion channel having an increased height or length (or both) and/or by decreasing the size of the pump DCs to reduce the effective flow rate into the diffusion channel for a given phase time.Table 13. We used 100 μM fluorescein in 0.1M NaOH and 0.1M NaOH as fluid inputs A and B. A phase interval of 50 ms (450 ms per complete mixer cycle) was sufficient to mix the fluorescein solution thoroughly. In addition, pressures of 25 PSI and 10 PSI were used to actuate valves and DCs, respectively. An Olympus IX73 fluorescence microscope and ORCA-Flash 4.0 camera (Hamamatsu) were used for fluorescein fluorescence measurements.To quantitatively measure mixer performance, we used fluorescence measurements with a dilute fluorescein solution. We focused a microscope on the output channel region of interest (ROI) just to the right of the diffusion channel in Fig. Serial diluter characterization was also done with 100 μM fluorescein in 0.1M NaOH diluent and 0.1M NaOH as fluid inputs A and B, respectively. Fluorescence in the serial diluter output channels is normalized to that of the undiluted sample fluid and converted to percent.t = 0) is diluent fluid in all the diluent pumps for all stages and outlet channels, and undiluted fluorescein solution (denoted subsequently as 100% concentration) in all the fluorescein solution pumps in each stage. This situation occurred because the experimental sequence was (1) pump 100% fluorescein solution through the fluorescein pumps and into the output channels to get a maximum fluorescence baseline, followed by (2) pump 100% diluent through the diluent pumps and into the outlet channels to get a minimum fluorescence baseline, and then (3) normal operation of the serial diluter in which all the pumps are used. The net result is that right after starting step (3) at t = 0, there is a spike in fluorescence as the 100% fluorescein solution is cleared from each stage’s fluorescein pump, followed by a decrease in fluorescence, and then a rise until steady state is reached.For the experiment shown in Fig. Microscopy at non-normal incidence was performed with a Keyence VHX-970 Digital Microscope.2 plasma for 6 min using a parallel-plate plasma etcher (Technics PlanarEtch II) at 200 W with 10 sccm O2. Cell plates were then cleaned with 100% IPA for 1 h and dried overnight at 55 °C in an oven.Cell plates were exposed to O4 cells were seeded in each microwell. The cells were maintained on the cell plate for 2 or 3 days prior treatment.A549 adenocarcinoma cells (ATCC CCL-185) were maintained in DMEM/F12 media  supplemented with 10% FBS  and 1X Antibiotic-Antimycotic . Cells were detached from the cell plates using 0.25% trypsin-EDTA . Approximately 1.65 × 103D printed chips with the 5-stage serial diluter were primed with TWEEN-80 (0.05% in deionized water) for 10 min to eliminate bubbles inside the device. Then both Fluid 1  and Fluid 2 (200 μg/mL digitonin + 3 μg/mL propidium iodide in DMEM/F12) were introduced into the device using two syringe pumps at 40 μL/min (Fluid 1) and 10 μL/min (Fluid 2) while agitating the inputs until there were no bubbles inside the chip. The syringe pump was then disconnected and the serial diluter was turned on and operated with a phase interval of 50 ms. After equilibration, the chip with cell-seeded wells was mounted and clamped on top of the serial diluter chip. The cells were treated for ~20 min, following which the cell plate was separated from the serial diluter. The cell plate was then immediately fixed using 1% paraformaldehyde in PBS  for 15 min at 37 °C with mild agitation then incubated for 5 min with a PBS wash three times. The cell plate was then imaged using an Olympus IX73 microscope and ORCA-Flash 4.0 camera (Hamamatsu). A separate cell plate was used for the ethanol control. Each cell plate was soaked in 3 μg/mL propidium iodide diluted in 100% ethanol for 20 min at room temperature followed by an incubation of 5 min with a PBS wash three times.Fluorescence images were processed using Fiji  using the background removal function. Intensity signals were quantified as the area of positive cells using Fiji thresholding and area measurement. Visible light images were stitched using Adobe Photoshop CC 2018 (Adobe Systems Incorporated). Data were processed using Graphpad Prism Version 8.4.2 . Limits were set to the 100% ethanol as the 100% permeabilization response and 0 μg/mL digitonin as the baseline response.Microchip wells and channels were filled with stabilized gram iodine  to provide contrast inside fluid channels. Chips were then scanned using a QuantumGX2 μCT scanner  at the following settings: 4 min High resolution, Al 0.5 mm + Cu 0.06 mm filter, 36 mm FOV, 90 kV, 88 μA. After initial scan images were reconstructed to a final voxel size of 25 μm and 9 μm. 3D images were then reconstructed as a Maximum Intensity Projection using Caliper Analyze 12.0 . False coloring was achieved using Adobe Photoshop CC 2018. Photomicrographs of the chips are taken using a Canon EOS Rebel SL2 camera mounted on an Olympus BX51 microscope with UPlanFL N 4x objective. Image stitching basic light correction was done in Adobe Photoshop CC 2018.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Supplementary Movie 6Supplementary Movie 7Supplementary Movie 8