Magnus Kjaergaard1,2, Alexander J Dear1, Franziska Kundel1, Seema Qamar3, Georg Meisl1, Tuomas P J Knowles1,4, David Klenerman1,5. 1. Department of Chemistry , Cambridge University , Lensfield Rd , Cambridge CB2 1EW , United Kingdom. 2. Aarhus Institute of Advanced Studies , Aarhus University , Høegh-Guldbergs Gade 6B , DK-8000 Aarhus C , Denmark. 3. Cambridge Institute for Medical Research , University of Cambridge , Hills Road , Cambridge CB2 0XY , United Kingdom. 4. Cavendish Laboratory, Department of Physics , University of Cambridge , Cambridge CB3 0HE , United Kingdom. 5. UK Dementia Research Institute , University of Cambridge , Cambridge CB2 0XY , United Kingdom.
Abstract
The molecular mechanism of protein aggregation is of both fundamental and clinical importance as amyloid aggregates are linked to a number of neurodegenerative disorders. Such protein aggregates include macroscopic insoluble fibrils as well as small soluble oligomeric species. Time-dependent resolution of these species is prerequisite for a detailed quantitative understanding of protein aggregation; this remains challenging due to the lack of methods for detecting and characterizing transient and heterogeneous protein oligomers. Here we have used single molecule fluorescence techniques combined with mechanistic modeling to study the heparin-induced aggregation of the repeat region of tau, which forms the core region of neurofibrillary tangles found in Alzheimer's disease. We distinguish several subpopulations of oligomers with different stability and follow their evolution during aggregation reactions as a function of temperature and concentration. Employment of techniques from chemical kinetics reveals that the two largest populations are structurally distinct from fibrils and are both kinetically and thermodynamically unstable. The first population is in rapid exchange with monomers and held together by electrostatic interactions; the second is kinetically more stable, dominates at later times, and is probably off-pathway to fibril formation. These more stable oligomers may contribute to other oligomer induced effects in the cellular environment, for example, by overloading protein quality control systems. We also show that the shortest growing filaments remain suspended in aqueous buffer and thus comprise a third, smaller population of transient oligomers with cross-β structure. Overall our data show that a diverse population of oligomers of different structures and half-lives are formed during the aggregation reaction with the great majority of oligomers formed not going on to form fibrils.
The molecular mechanism of protein aggregation is of both fundamental and clinical importance as amyloid aggregates are linked to a number of neurodegenerative disorders. Such protein aggregates include macroscopic insoluble fibrils as well as small soluble oligomeric species. Time-dependent resolution of these species is prerequisite for a detailed quantitative understanding of protein aggregation; this remains challenging due to the lack of methods for detecting and characterizing transient and heterogeneous protein oligomers. Here we have used single molecule fluorescence techniques combined with mechanistic modeling to study the heparin-induced aggregation of the repeat region of tau, which forms the core region of neurofibrillary tangles found in Alzheimer's disease. We distinguish several subpopulations of oligomers with different stability and follow their evolution during aggregation reactions as a function of temperature and concentration. Employment of techniques from chemical kinetics reveals that the two largest populations are structurally distinct from fibrils and are both kinetically and thermodynamically unstable. The first population is in rapid exchange with monomers and held together by electrostatic interactions; the second is kinetically more stable, dominates at later times, and is probably off-pathway to fibril formation. These more stable oligomers may contribute to other oligomer induced effects in the cellular environment, for example, by overloading protein quality control systems. We also show that the shortest growing filaments remain suspended in aqueous buffer and thus comprise a third, smaller population of transient oligomers with cross-β structure. Overall our data show that a diverse population of oligomers of different structures and half-lives are formed during the aggregation reaction with the great majority of oligomers formed not going on to form fibrils.
Entities:
Keywords:
aggregation mechanism; amyloid oligomers; kinetic modeling; single-molecule FRET; tau
Assembly of proteins into large
insoluble fibrils is a characteristic hallmark, and possibly a cause,
of several neurodegenerative diseases such as Parkinson’s and
Alzheimer’s diseases.[1] In Alzheimer’s
disease, two distinct types of protein aggregates are found in the
brain post mortem: extracellular plaques composed
of the amyloid-β peptide and neurofibrillary tangles composed
of the protein tau. Of these two types of fibrils, the neurofibrillary
tangles correlate more strongly with the extent of pathology, leading
to an interest in the mechanism of their formation.[2]Tau is an intrinsically disordered protein that is
thought to bind
and stabilize microtubules, thus assisting intracellular transport.[3] The microtubule binding site is located in the
C-terminal half of the protein and contains three or four repeats
with high β-sheet propensity, which form most of the core of
the neurofibrillary tangles.[4,5] As this repeat region
contains the regions required for filament formation, it is widely
used for biophysical studies of the aggregation of tau. The repeat
region is also the site of a number of point mutations, such as ΔK280
and P301L, which both cause early onset frontotemporal dementia.[6,7]In vitro aggregation of tau is slow as the repeat
region is highly positively charged, leading to intermolecular repulsion.
This repulsion can be overcome to trigger aggregation, which has been
suggested to occur either by excessive phosphorylation of tau or by
addition of negatively charged cofactors such as RNA or heparin sulfate.[8,9]In addition to neurofibrillary tangles, tau can form a range
of
oligomeric forms with different properties. Oligomers of amyloid forming
proteins are heterogeneous and rare relative to the monomer and fibrillar
forms. This complicates the characterization of oligomers, especially
the transient species occurring during fibrillation that cannot be
purified. In cases where oligomers have been characterized, a range
of species coexist. For example in the case of α-synuclein,
oligomers fall into two broad classes, where globular oligomers are
formed initially and gradually convert into cytotoxic oligomers with
amyloid-like cross-β structure.[10] These oligomers include different segments of the protein chain,[11] differ in population between pathological mutants,[12] and undergo two unimolecular conversion steps
before fibrillation.[13] For many amyloidogenic
proteins, both the monomers and the fibrils are not particularly toxic
to cells, which is also the case for tau. The cytotoxic agents are
thus typically soluble oligomeric aggregates.[14] Due to the heterogeneity of oligomers only some of these oligomers
can grow into fibrils, and only some are cytotoxic.We recently
developed a single molecule Förster resonance
energy transfer (smFRET) assay detecting tau oligomer populations
during filament formation.[15] We showed
that tau oligomers are populated at low concentrations during the
aggregation reaction and require an intramolecular conversion step
before growth into filaments. Mutants associated with frontotemporal
dementia have much higher oligomeric populations, primarily due to
faster oligomer formation. In previous studies of α-synuclein
using the same methodology,[10,12,13] we observed two populations of oligomers based on FRET efficiency,
which had different stabilities. In our previous studies of tau, we
did not observe distinct FRET populations.[15] In this study, we revisit single molecule FRET studies of tetra
repeat (K18) tau aggregation by using the buffer conditions to resolve
species of different stabilities. Where possible, we determine the
activation barriers for the key reactions and gain insight into the
range of oligomers formed during aggregation using detailed mechanistic
models fitted over a range of temperatures and concentrations.
Results
Single-Molecule
Techniques Identify Three Different Classes
of Oligomers
To monitor rare oligomeric species during the
aggregation reaction, we use an engineered tau labeled with a single
fluorophore per molecule. By mixing aliquots labeled with different
dyes, we ensure that half of the tau molecules are labeled with a
FRET donor and half with a FRET acceptor. Monomeric tau shows no FRET,
but in oligomers, the fluorophores are brought into close proximity
and FRET occurs (Figure A). It should be noted that the labeling requires us to remove the
two native cysteines in K18tau, and thus we do not observe the disulfide
bond formation suggested to affect some forms of in vitro aggregation reactions. The FRET is detected one particle at a time
using a confocal single molecule FRET instrument. This allows us to
simultaneously quantify populations of oligomers in a vast excess
of monomeric tau.[16] The oligomeric population
of wild-type K18tau is low and close to the limit of detection for
this assay; however the pathological mutation ΔK280 gave rise
to higher oligomer concentrations that can be quantified robustly.
In our previous study, we verified that the fluorophore labeling only
minimally affected the kinetics of the process and the morphology
of the filaments formed.[15] We therefore
used this mutant as a model system for studying events occurring in
tau oligomers.
Figure 1
(A) Sketch of molecule assay for detection of oligomers.
The experiments
use tau K18 ΔK280 labeled just outside the amyloidogenic core
with either Alexa488 or Alexa647 in equal amounts. The dyes are excited
with a diffraction limited 488 nm laser spot and detected one by one
in a microfluidic flow channel using a confocal microscope. (B) The
need for heparin to initiate the reaction suggests a dependence on
electrostatic interactions. The ionic strength of the dilution buffer
can thus be used to discriminate between oligomers with differential
dependence on electrostatic interactions with heparin. (C) Samples
from an early (15 min) and a late (2 h) time point in an aggregation
reaction were diluted into buffers with different ionic strength.
The number of observed oligomers decreases dramatically for the early
time point, whereas the late time point is independent of ionic strength.
(D) Samples are taken from an aggregation reaction at 37 °C and
diluted into a dilution buffer with either 500 mM NaCl or no NaCl.
The number of oligomers detected at low ionic strength peaks at the
first time point but drops rapidly to similar numbers as that observed
at high ionic strength suggesting initial formation of electrostatically
stabilized oligomers in kinetic experiments. (E) The number of oligomers
detected increases with increasing heparin concentration, where there
is a decrease in the fraction of bright oligomer events (F) (defined
as bursts with more than ten times the photons of an average monomer
burst). This suggests that higher heparin concentration leads to more
and smaller oligomers. Error bars represent SEM from three repeats.
(A) Sketch of molecule assay for detection of oligomers.
The experiments
use tauK18 ΔK280 labeled just outside the amyloidogenic core
with either Alexa488 or Alexa647 in equal amounts. The dyes are excited
with a diffraction limited 488 nm laser spot and detected one by one
in a microfluidic flow channel using a confocal microscope. (B) The
need for heparin to initiate the reaction suggests a dependence on
electrostatic interactions. The ionic strength of the dilution buffer
can thus be used to discriminate between oligomers with differential
dependence on electrostatic interactions with heparin. (C) Samples
from an early (15 min) and a late (2 h) time point in an aggregation
reaction were diluted into buffers with different ionic strength.
The number of observed oligomers decreases dramatically for the early
time point, whereas the late time point is independent of ionic strength.
(D) Samples are taken from an aggregation reaction at 37 °C and
diluted into a dilution buffer with either 500 mM NaCl or no NaCl.
The number of oligomers detected at low ionic strength peaks at the
first time point but drops rapidly to similar numbers as that observed
at high ionic strength suggesting initial formation of electrostatically
stabilized oligomers in kinetic experiments. (E) The number of oligomers
detected increases with increasing heparin concentration, where there
is a decrease in the fraction of bright oligomer events (F) (defined
as bursts with more than ten times the photons of an average monomer
burst). This suggests that higher heparin concentration leads to more
and smaller oligomers. Error bars represent SEM from three repeats.The single molecule FRET assay
has previously shown that different
classes of oligomers of synuclein could be distinguished based on
shifting FRET efficiencies.[10] TauK18 displayed
a broad FRET peak with no clear development during the reaction[15] suggesting that another strategy is needed to
separate any putative different species of tau oligomers. Single molecule
FRET detection of oligomers requires dilution to the picomolar level,
well below the concentrations needed to sustain a fibrillation reaction
(Figure A). This approach
only detects oligomers that are stable during the duration of the
measurement (6 min). The low concentrations used therefore pose both
a challenge to detecting transient oligomers, and an opportunity to
use differential stability to distinguish oligomer populations. We
speculated that we could tune the dilution buffer to destabilize certain
species causing them to dissociate before detection.K18tau
requires the addition of a cofactor for efficient in vitro aggregation, usually polyanions such as RNA or
heparin.[17,18] The polyanionic nature of the cofactors
suggests that their role is to help cationic tau overcome charge–charge
repulsion, for example, by electrostatically assisting oligomer formation
and thus seeding the reaction. This suggests that ionic strength provides
a promising way to distinguish different subpopulations of oligomers,
by selectively destabilizing heparin-dependent oligomers with high
ionic strength. To test this hypothesis, we diluted samples from an
aggregation reaction into buffers with either higher or lower ionic
strength than the reaction conditions (Figure B). It should be noted that while we record
the FRET data under extreme conditions of ionic strength in order
to distinguish high- and low-stability oligomers, the reaction conditions
where the oligomers are formed is near physiological ionic strength.
We found that early tau oligomers, which can be detected after 15
min of aggregation,[15] disassociated with
increasing ionic strength, while later oligomer populations are largely
independent of ionic strength (Figure C). At physiological ionic strength, we still detected
a considerable population of the electrostatically stabilized oligomers,
suggesting that these could play a role in vivo.The dependence of oligomer stability on buffer conditions showed
that the dilution buffer can be used to separate different classes
of oligomers in kinetic experiments. Therefore, we performed aggregation
reactions where the aliquots were diluted in either high (containing
0.5 M NaCl) or low ionic strength buffer (no NaCl). When the reaction
mixture was analyzed in low ionic strength buffer, the oligomer population
peaked at the first time point and subsequently decayed (Figure D). When the reaction
was analyzed in high ionic strength buffer, the oligomer population
is smaller and built up slowly to a peak after 45 min. Notably, the
oligomer populations converged at later time points, showing that
the population shifts to more stable oligomers that cannot be disassociated
in high ionic strength buffer. This implies the presence of a population
of electrostatically stabilized oligomers early in the reaction that
later gives way to more stable species, presumably stabilized through
hydrophobic interactions. In the following, we refer to the oligomers
that do or do not depend on electrostatic interactions as type A and
B oligomers, respectively. We may estimate the concentrations of these
oligomer populations directly by comparing the burst rates to samples
of labeled tau at known concentrations. We determined the concentration
of type A oligomers by subtracting the concentration of type B oligomers
from the total concentration.If the type A oligomers are indeed
nucleated by direct interactions
with the anionic cofactors, then the oligomer ensemble should be sensitive
to the ratio of tau and heparin. We tested this by performing aggregation
experiments with varying heparin/tau ratios. In ThT monitored reactions
at different heparin/tau ratios, the final amount of ThT positive
aggregates scales with the heparin concentration, although there is
no appreciable effect on the half-life (Figure S1). The population of tau oligomers increases with increasing
heparin concentration although it reaches a plateau around a ratio
of 1:2 (Figure E).
This ratio corresponds to the fastest fibril formation rate in previous
studies.[19] With increasing heparin concentration,
the average size of oligomers decreased for both transient and stable
oligomers, which is shown by the reduced fraction of oligomers above
a given brightness threshold (Figure F). These results show that heparin participates directly
in the formation of early oligomers and that both type A and type
B oligomers are present in a range of sizes.Filaments generated
in aggregation reactions are present in a range
of lengths and can form a colloidal suspension. Species detectable
by smFRET that meet the IUPAC definition of oligomers therefore include
short elongation-competent filaments (“fibrillar oligomers”)[20] as well as possible prefibrillar intermediates
of nucleation. In our experiments, we have identified two distinct
classes of oligomers (types A and B). Combining average fibril lengths
derived from TEM (∼2000 monomers)[15] with the total amount of tau incorporated into fibrils based on
monomer depletion data (∼8.5 μM), we estimate that the
final fibril concentration (∼4.25 nM) is far lower than the
maximum observed concentrations of either oligomer class (e.g., 104
and 37 nM at 37 °C for types A and B, respectively), which must
therefore both be distinct prefibrillar intermediates. Fibrillar oligomers
must thus comprise a third class of oligomer in this system. They
are likely to be stable and thus detected with the type B oligomers
in the FRET assay; however, since their concentration is comparatively
negligible, the measured type B oligomer concentration effectively
corresponds to prefibrillar oligomers. Furthermore, almost all type
A and B oligomers are depleted by the end of the reaction, therefore
most of them must dissociate or otherwise deplete rather than becoming
fibrils.Oligomeric species may be on and some off the main
reaction pathway
to insoluble fibrils. On-pathway intermediates should shorten the
lag phase in aggregation reactions by circumventing part of the nucleation
process. The lag phase of tauK18 ΔK280 is absent under the
conditions used for smFRET experiments, so to study the effect of
seeding, we changed ionic strength and temperature to lengthen the
lag phase to 4 h (Figure A). The seeds were drawn from an aggregation reaction identical
to that used in Figure , and added to a fresh reaction at slower aggregation conditions.
It should be noted that all time points were drawn from the same reaction
and that early samples were stored on ice until analysis. The assumption
behind this experimental design is that there is little development
in the oligomer population during the incubation on ice. All seeding
samples shorten the lag phase dramatically (Figure A,B), and the effect is almost identical
regardless of whether the seeding sample is taken after 15, 45, or
90 min, despite the aggregation reaction being almost complete by
90 min. This demonstrates that elongation-competent fibrillar species
are responsible for the observed seeding effect and that most fibrillar
seeds are formed almost instantaneously after addition of heparin,
such that nucleation of new filaments ceases after very early times.
The seeding reaction was then repeated where larger aggregates were
removed from the seeding solution by centrifugation. The early time
points have a similar ability to seed fibril formation (Figure B), but most of the growth
competent fibrils are removed at later time points. This demonstrates
that most fibrillar species are small enough at early time points
to still be suspended after centrifugation; it is therefore reasonable
to label them fibrillar oligomers.
Figure 2
Seeding and single oligomer imaging using
an amyloid-specific dye.
(A) To allow observation of the effect of seeding, the aggregation
reaction was slowed down by reducing the temperature (30 °C)
and changing the buffer conditions. Aliquots of the reactions were
taken at different time points of the reaction, similar to Figure D, and added to a
fresh reaction (10% volume). The aliquots were taken from either the
whole solution (A) or a solution cleared of larger fibrillar species
by centrifugation (B). The reaction mixture effectively seeds the
new reaction, although the number of smaller fibrillar seeds decrease
after an initial peak. (C–F) Aliquots were taken at different
time points from a reaction of K18 tau ΔK280 (10 μM tau,
37 °C, 1:4 heparin/tau) and adsorbed to a cover slide. The amyloid-like
species were imaged using TIRF microscopy with the amyloid specific
fluorophore pFTAA. The time course (C) shows a gradual build-up and
eventual disappearance of particles with cross-β structure.
Error bars represent SEM from three repeats.
Seeding and single oligomer imaging using
an amyloid-specific dye.
(A) To allow observation of the effect of seeding, the aggregation
reaction was slowed down by reducing the temperature (30 °C)
and changing the buffer conditions. Aliquots of the reactions were
taken at different time points of the reaction, similar to Figure D, and added to a
fresh reaction (10% volume). The aliquots were taken from either the
whole solution (A) or a solution cleared of larger fibrillar species
by centrifugation (B). The reaction mixture effectively seeds the
new reaction, although the number of smaller fibrillar seeds decrease
after an initial peak. (C–F) Aliquots were taken at different
time points from a reaction of K18tau ΔK280 (10 μM tau,
37 °C, 1:4 heparin/tau) and adsorbed to a cover slide. The amyloid-like
species were imaged using TIRF microscopy with the amyloid specific
fluorophore pFTAA. The time course (C) shows a gradual build-up and
eventual disappearance of particles with cross-β structure.
Error bars represent SEM from three repeats.Previous studies of amyloid forming proteins have shown the
coexistence
of oligomers with and without a cross-β structure.[10] To test whether this is the case for the tau
oligomers observed here, we used a recently developed assay (SAVE)[21,22] that allows single aggregate detection of the fluorescence from
an amyloid specific dye. Aliquots were taken during the reaction of
tau, and oligomers were adsorbed on a glass surface. Oligomers were
detected via the amyloid specific dye pFTAA[23] and total internal reflection fluorescence (TIRF)
microscopy (Figure C–F). pFTAA has been demonstrated to recognize a range of
earlier intermediates of amyloidogenic proteins and was thus preferred
over the more traditional ThT. The population of oligomers builds
up to a peak concentration and gradually disappears (Figure C). Notably, this oligomeric
population has a different kinetic profile than either the type A
or type B oligomers, showing that it represents a third species. Fibrillar
oligomers have a high cross-β content, so they are likely the
species being reported here. It should, however, be noted that due
to the adsorption to a glass surface in this experiment, larger oligomers
are likely preferentially detected, and so the population of this
species cannot be accurately quantified. Therefore, we do not include
this third class of oligomers in the subsequent modeling.
Heparin-Mediated
Tau Fibril Formation Proceeds via an Unusual Mechanism
The aggregation kinetics of many amyloid-forming
peptides have been modeled over the past two decades, with formation
of new fibrillar species from soluble peptide usually treated as a
single coarse-grained nucleation reaction step.[24−26] In all these
models, fibrillar species undergo rapid growth by monomer addition
(“elongation”). The difference in time scales between
nucleation and elongation gives rise to extended fibrils.To
permit global fitting,[27] we varied the
concentration of tau in the aggregation reaction at a heparin/tau
ratio of 1:4. Bulk ThT fluorescence experiments yielded identical
normalized reaction trajectories (Figure A). This implies that the rate of new filament
formation is independent of the monomer concentration. We repeated
these experiments with the single molecule FRET assay and diluting
the aliquots into high and low ionic strength buffers as above. This
thus allowed us to follow the concentrations of monomeric tau (Figure B), type A oligomers
(Figure C), and type
B oligomers (Figure D) during the reaction. Similarly to the bulk assays, the reaction
trajectories of monomers and both types of oligomers deduced from
single molecule observations superimpose when normalized by the initial
monomer concentrations. The seeding experiment described above provides
evidence that fibril nucleation occurs almost instantaneously at the
start of the reaction and then ceases. By testing a series of bulk
aggregation models (Figure ), we verified that the only simple kinetic model capable
of reproducing the monomer reaction trajectory to within most of the
error bars is such an “initial nucleation” model similar
to that described previously for an unrelated protein.[28] We further verified that the number of fibrils
formed at the start of the reaction is independent of the initial
monomer concentration and is thus likely mediated by a surface, for
instance, the air–water interface. In such a scenario, the
rate-limiting factor would be the availability of the interface, resulting
in no monomer dependence for the nucleation rate. Furthermore, the
ceasing of nucleation after an early time could represent a lack of
availability of free interface at later times.
Figure 3
Concentration dependence
of tau K18 ΔK280 aggregation. (A)
ThT assay of K18 tau ΔK280 at different tau concentrations with
1:4 heparin/tau ratio. (B–D) Single-molecule FRET experiments
were used compare aggregation reactions run at different concentrations
of tau (1:4 heparin/tau). Based on burst rates, we estimate the concentrations
of monomeric tau (B), type A oligomers (C), and stable type B oligomers
(D). The populations of the different species evolve with similar
time constants in the different experiments, and the oligomer population
roughly scales with the total monomer concentration. Error bars represent
SEM from three repeats.
Figure 4
Testing different bulk kinetic models reveals the mechanism of
filament formation. (A) Traditional models of biofilament formation
cannot reproduce the data; misfits reveal the absence of secondary
processes and of a conventional primary nucleation step. (B) Using
the “initial nucleation” mechanism inferred from the
seeded experiments captures the curve shape but overestimates the
concentration dependence of the aggregation. (C) Allowing saturation
of the elongation reaction step cannot match the concentration dependence
while retaining the correct curve shape at late times. (D) Allowing
instead the “initial nucleation” process to be fully
saturated yields good fits. The equations for the misfit models are
given in the SI, in addition to an outline
of model selection theory and estimation of fit quality. Initial monomer
concentrations: black, m0 = 20 μM;
blue, m0 = 10 μM; green, m0 = 5 μM.
Concentration dependence
of tauK18 ΔK280 aggregation. (A)
ThT assay of K18tau ΔK280 at different tau concentrations with
1:4 heparin/tau ratio. (B–D) Single-molecule FRET experiments
were used compare aggregation reactions run at different concentrations
of tau (1:4 heparin/tau). Based on burst rates, we estimate the concentrations
of monomeric tau (B), type A oligomers (C), and stable type B oligomers
(D). The populations of the different species evolve with similar
time constants in the different experiments, and the oligomer population
roughly scales with the total monomer concentration. Error bars represent
SEM from three repeats.Testing different bulk kinetic models reveals the mechanism of
filament formation. (A) Traditional models of biofilament formation
cannot reproduce the data; misfits reveal the absence of secondary
processes and of a conventional primary nucleation step. (B) Using
the “initial nucleation” mechanism inferred from the
seeded experiments captures the curve shape but overestimates the
concentration dependence of the aggregation. (C) Allowing saturation
of the elongation reaction step cannot match the concentration dependence
while retaining the correct curve shape at late times. (D) Allowing
instead the “initial nucleation” process to be fully
saturated yields good fits. The equations for the misfit models are
given in the SI, in addition to an outline
of model selection theory and estimation of fit quality. Initial monomer
concentrations: black, m0 = 20 μM;
blue, m0 = 10 μM; green, m0 = 5 μM.Finally, we were able to infer (see SI) that the fibril disaggregation rate is negligible, and the monomeric
tau remaining at the end of the reaction is inactive and unable to
aggregate into fibrils, possibly due to changes in heparin stoichiometry
during the reaction. With insufficient data to determine exactly why
this occurs, we do not explicitly model this inactive monomer population.
Characterizing the Kinetics of Tau Oligomers
We previously
proposed a minimal kinetic model of K18tau aggregation involving
formation of a population of growth incompetent prefibrillar oligomers
that convert into growth competent fibrillar species.[15] With the expanded data set recorded here, we thus sought
to elaborate on this model, through quantitative modeling of the time
evolution of monomeric, oligomeric, and fibrillar species. Having
determined the mechanism of fibril formation, we next tested a series
of mechanisms featuring different reaction steps between the oligomeric
species (Figure ),
to eliminate inconsistent mechanisms and uncover the most likely ones
for quantitative analysis (see SI for outline
of model selection theory).
Figure 5
Testing of different oligomer kinetic models
reveals the mechanisms
of oligomer formation. (A–C) Type A oligomers are found to
form very rapidly, directly from monomers; the formation step is partially
saturated with a reaction order of 1. (D, E) Type B oligomers form
from the reactant ensemble of monomers and type A oligomers with a
reaction order of 1. The equations for the misfit models are given
in the SI, in addition to an outline of
model selection theory and estimation of the fit quality. Initial
monomer concentrations: black, m0 = 20
μM; blue, m0 = 10 μM; green, m0 = 5 μM.
Testing of different oligomer kinetic models
reveals the mechanisms
of oligomer formation. (A–C) Type A oligomers are found to
form very rapidly, directly from monomers; the formation step is partially
saturated with a reaction order of 1. (D, E) Type B oligomers form
from the reactant ensemble of monomers and type A oligomers with a
reaction order of 1. The equations for the misfit models are given
in the SI, in addition to an outline of
model selection theory and estimation of the fit quality. Initial
monomer concentrations: black, m0 = 20
μM; blue, m0 = 10 μM; green, m0 = 5 μM.We first demonstrated that type A oligomers form directly
from
monomers and dissociate so rapidly that they are almost at equilibrium
with monomers on the measurement time scale of our experiments, especially
at higher temperatures. We further showed that the reaction order
of type A oligomer association with respect to monomers is unity.
Note the reaction order is not in general the same as the critical
nucleus size;[27,29] its value here indicates a saturation
effect, possibly due to association occurring at surfaces.The
saturated nature of the new filament formation process meant
it was not possible from the kinetic data to determine conclusively
whether type B oligomers were on-pathway to filament formation; however,
given that the kinetics of type B oligomer formation appear substantially
slower than those of filament formation, it seems unlikely that they
are on-pathway.The rapidity of exchange between monomers and
type A oligomers
relative to the time scale of monomer depletion prevented us from
determining whether type B oligomers are formed directly from monomers
or from conversion of type A oligomers. Indeed, from a kinetic modeling
perspective it is more appropriate to treat monomers and type A oligomers
as an ensemble of reactant species, rather than considering type A
oligomers as an intermediate species.[30] Whether type A oligomers are on-pathway intermediates of type B
oligomer formation is therefore not a meaningful question under these
conditions, as the type A oligomers are part of the reactant ensemble.
We therefore instead modeled the total flux of type B oligomers from
monomers. The reaction order of formation of type B oligomers with
respect to monomers was then found to be unity.
Quantitative
Modeling
The minimal coarse-grained kinetic
model capable of reproducing the time evolution of all the species
we observe has been established above. The two oligomer species, SA and SB, form through monomer association with
rate constants koA and koB and disappear with rate constants kdA and kdB. Fibrils all form
effectively instantaneously at the start of the reaction, yielding
a constant fibril concentration [P]; these fibrils grow by monomer
addition with rate constant k+. The model
explicitly considers the concentration of tau monomers [m](t), the concentrations of oligomers [SA](t), and [SB](t), and the mass
concentration of fibrils [M](t).Rate equations
describing the system can be written as follows:These equations may be solved analytically
(see Methods). In principle, further complexity
could be added to this coarse-grained kinetic modeling framework,
for instance, differentiating between additional hypothetical subpopulations
of oligomers or explicit modeling of heparin concentration; this would,
however, require greater experimental constraints than currently available,
to avoid overfitting. The purpose of the kinetic modeling is instead
to discover the dynamical relationships between those coarse-grained
species that our experiments are capable of resolving.Using
this model, we sought to characterize the temperature dependence
of the aggregation reaction. We used the single molecule aggregation
experiment to follow the populations of monomers and of the two different
categories of prefibrillar oligomers formed during aggregation reactions
from 22 to 42 °C (Figure ). The temperature series extends slightly beyond physiologically
relevant temperatures in both directions; however, this is necessary
to determine the activation energies of the underlying reaction steps.
To do so requires that only the rate constants change over this temperature
range and not the reaction mechanisms. This is justified by the relatively
narrow temperature range and validated by our successful global fitting
of a single model across many temperatures and concentrations.
Figure 6
Modeling of
the aggregation reaction based on temperature-dependence
of the aggregation reaction. (A) The simplest model that describes
the total data set involves a probable off-pathway, stable “type
B” oligomer and an unstable “type A” oligomer
in rapid exchange with the monomeric state. (B) Numerical fitting
of concentration of monomers and the two classes of oligomers estimated
from the single molecule aggregation assay. The model adequately describes
the total data set and allows us to extract the thermodynamic parameters
found in Table .
Modeling of
the aggregation reaction based on temperature-dependence
of the aggregation reaction. (A) The simplest model that describes
the total data set involves a probable off-pathway, stable “type
B” oligomer and an unstable “type A” oligomer
in rapid exchange with the monomeric state. (B) Numerical fitting
of concentration of monomers and the two classes of oligomers estimated
from the single molecule aggregation assay. The model adequately describes
the total data set and allows us to extract the thermodynamic parameters
found in Table .
Table 1
Thermodynamic
Parameters Extracted
from Modeling
rate constants
H (kJ/mol)
TS (37 °C) (kJ/mol)
G (37 °C) (kJ/mol)
k+
69 ± 7
56 ± 7
12 ± 1
koB
55 ± 5
17 ± 5
37 ± 1
kdB
46 ± 17
19 ± 17
27 ± 1
The data at 27 °C and above
may be fitted to the integrated
rate laws to determine the rate parameters in Table S1. However, at 37 and 42 °C, the exchange between
monomers and type A oligomers is too rapid to determine the magnitudes
of koA and kdA; instead, only their ratio (equilibrium constant KE = koA/kdA) may be determined with any accuracy, averaged over
these two temperatures (see Methods). Finally,
at 22 °C a classical lag phase in the monomer depletion kinetics
appears, and we must explicitly include the formation of new filaments
in the model in order to reproduce this effect (see Methods). The kinetic equations for oligomers remain unchanged,
and we may still directly compare the oligomer formation and dissociation
rate constants across temperatures. Examining the data, we see that
the reaction rates all tend to increase with temperature, as expected.
The population at 27 °C of type A oligomers appears anomalously
low, resulting in an anomalously low koA and KE.Similarly to protein folding,[31,32] a simplified
view of the dynamics of protein aggregation may be arrived at by considering
the individual reaction steps as diffusion processes across free energy
landscapes. Kramers’ theory[33,34] then provides
an expression for each rate constant k in terms of
the highest free energy barrier ΔG⧧⊖ on the lowest possible free energy pathway crossing the landscape
between the relevant reactants and products:where T is the temperature, R is the gas constant,
and A is a prefactor.A has
previously been estimated for the filament
elongation reaction step by considering the peptide as a Gaussian
polymer diffusing across a free energy surface.[35] It is given as A = Dreff, where D is a characteristic diffusion
constant for polypeptides,[36] equal to 5
× 10–10 m2 s–1, and reff is a characteristic distance
related to the curvature of the free energy surface, given by 2 ×
10–11 m for tauK18. Note that a highly accurate
value for A is not needed to determine free energies
from rate constants since errors in the prefactor enter only logarithmically.
The same estimate generalized for application to nonunity reaction
orders was shown to be a reasonable approximation for A for the other reaction steps of protein aggregation.[37]Given this estimate for the prefactor
(A = 2 ×
104 μM–1 h–1)
and rate constants over a range of temperatures, we may estimate both
enthalpies and entropies of activation. We were able to extract enthalpies
and entropies of activation for elongation, k+ based on rate constants from 27–42 °C, and oligomer
type B formation, koB and kdB, using the entire temperature series (Table ). The rate constants of oligomer
type A formation, koA and kdA, cannot be determined with sufficient accuracy to permit
a thermodynamic analysis of the energy barrier. To estimate k+, we used an average fibril length, L, of ∼2000 monomers per fibril as estimated previously
from TEM.[15] Dividing the mass concentration
of fibrils formed by this value yields [P], which is unlikely to have
a significant temperature dependence, since it is likely surface-limited.
In turn, this permits extraction of a temperature-dependent k+. Any errors in L only affect
the results of the barrier analysis logarithmically.
Discussion
Using single particle approaches, we have identified and characterized
three major categories of oligomers formed during the aggregation
of K18tau. It is unlikely that this represents the full diversity
of oligomers, however, as some classes of oligomers may be too rare
to be detected or will be lumped together in our measurements. Several
studies have previously demonstrated significant heterogeneity in
the oligomers formed by amyloidogenic proteins. This heterogeneity
translates into differences in functional properties such as toxicity.[38,39] Notably, a recent study of the yeastprion protein Ure2 demonstrates
the formation of two separate oligomer species with different kinetic
and structural properties. Similar to our results, both of these oligomers
are unstable and predominantly dissociate; unlike our study, however,
both are positively identified as on-pathway to fibril formation.[40] Previous studies of tetrarepeat tau aggregation
have also suggested the existence of stable off-pathway oligomers,
which were found to resemble short rod-like protofibrils.[41] This study also suggested the existence of heparin-stabilized
oligomers, that are both in rapid exchange with monomers and on-pathway
to fibrils. While numbers are not directly comparable due to differences
in the reaction conditions, this model is qualitatively similar to
the one we propose here. The advantage of the approach taken here
is that we can follow the appearance and eventual fate of the different
oligomeric species and thus use these data for mechanistic modeling.
These models revealed that the most frequently occurring oligomer
is transient in nature. A significant energetic rearrangement would
be needed for these to convert to growth-competent, fibril-like oligomers,
which are present at far lower concentrations. These initial transient
oligomers are replaced by more stable type B oligomers, that seem
to be off-pathway to fibril formation. The kinetics of oligomer formation
observed in the SAVE experiment is slower than that observed by FRET,
and therefore, this experiment likely reports on a separate group
of oligomers not separated in our FRET experiments due to their lower
incidence. Given that the SAVE experiment is believed to report on
cross-β structure, the difference in kinetics observed between
the different experiments suggest that the type B oligomers do not
have the mature cross-β structure.Several other groups
have estimated activation enthalpies of the
different steps of the fibrillation process. Typically, these estimations
have been done from bulk studies and determine the energy barriers
of nucleation and of elongation. Comparatively few, however, have
estimated transition state entropies. We calculate both the enthalpy
and the entropy of activation for elongation, and find they are of
a similar magnitude to those reported in other proteins.[42] The moderately large enthalpic barrier is likely
due to breakage of hydrogen bonds and other interactions associated
with solvation and with internal structure, and the highly favorable
transition state entropy is likely due to desolvation of hydrophobic
segments of the monomer and the fibril end.It is difficult
to infer the energy barriers to formation and depletion
of prefibrillar oligomeric intermediates in bulk studies, as oligomer
formation is accompanied by the subsequent appearance of fibrils that
dominate bulk measurements. A major advantage of the present study
is that the quantitative observation of individual oligomer populations
permits the decomposition of such rates, and we thus report the kinetic
and thermodynamic signatures of formation of two oligomeric species
formed in parallel. In agreement with other studies on filament nucleation,
we find that the type B oligomer formation reaction has an enthalpic
barrier of 55 kJ/mol but is driven by a small favorable entropic contribution
to the transition state. The type A oligomer formation and depletion
free energy barriers are too low to be characterized under present
conditions. Studies of the nucleation of Aβ1–42 indicate a much larger enthalpy of activation of 144 kJ/mol.[37] If the mechanism of tau oligomer formation is
typical of amyloidogenic peptides, this would imply that fewer rearrangements
and loss of bonds to solvent are needed for amyloid oligomer formation
than for fibril formation and that the main enthalpic barrier for
fibril nucleation lies in the conformational conversion step that
transforms on-pathway oligomers to growing fibrils. The enthalpy of
activation of tau oligomerization is instead approximately of the
same magnitude as elongation by one monomer for other amyloid-forming
proteins, which in a large systematic study was found to range from
27 to 107 kJ/mol for proteins without a rigid tertiary structure.[42] The transition state is unlikely to solely involve
dissociation of heparin, whose enthalpy of binding to tau is −6
kJ/mol.[43] Given its similarity to enthalpic
barriers for elongation, it probably instead predominantly consists
of breaking intraprotein and protein–solvent interactions.
Candidates for such intramolecular interactions have been identified
by NMR spectroscopy, which shows both transient local and long-range
structure in both K18 and full-length tau.[44−46] The favorable
entropy term for the type B oligomer formation transition state is
significantly smaller than that for elongation and moreover is approximately
symmetrical with respect to dissociation, suggesting that it is not
due solely to desolvation of hydrophobic regions of the peptides.
Combined with the n = 1 reaction order, we infer
that the highest transition state associated with the barrier represents
a conformational rearrangement, either of heparin-bound tau monomer
or of a type A oligomer (the latter possibly involving heparin dissociation)
and not the association of two monomers.Our results show that
primary nucleation is saturated with respect
to monomer and ceases rapidly as the reaction progresses. A simple
physical mechanism, which can result in such behavior, is heterogeneous
nucleation at surfaces. This type of behavior has been found to dramatically
enhance the primary nucleation of new amyloid filaments from some
important peptides.[28,47,48] Furthermore, similar behavior has been observed for tau in vitro(49) and suggested to occur in vivo.[50] An avenue for future
research would be to test this explanation by varying the properties
of the interfaces in the experimental setup. Microdroplet-based aggregation
experiments would permit particularly fine control of the interfaces.[51,52]Due to the heterogeneity of the aggregate species assembled
from
amyloidogenic proteins, it is usually difficult to assign specific
functions to certain classes of aggregates. Information may be inferred
by comparing the physiological effects of samples taken from different
stages in an aggregation reaction to the oligomer and fibril concentrations
at the same time points, either measured directly or calculated using
our model. Our study thus provides a window into the putative physiological
functions of different classes of oligomers. In type B oligomers,
the protein is likely trapped in conformations that do not allow them
to reorganize to form a fibril-like oligomer. Due to the relatively
slow dissociation time, the stable type B oligomers have a longer
persistence time and hence have more potential to cause cellular damage
than type A oligomers. Notably, we did not see any direct toxicity
of these oligomeric preparations in cell culture,[15] but recent in vitro experiments have demonstrated
that they can cause calcium influx into lipid vesicles and bind tightly
to chaperones thus challenging the cell’s quality control systems.[53] Although this study did not distinguish between
different classes of oligomers, the sample was withdrawn at the peak
of type B oligomer population, so it is likely that these effects
are due to the more stable oligomeric species. Overload of the quality
control systems may be connected to neurodegenerative diseases,[54,55] which underscores the importance of efficient chaperone sinks even
if they are off-pathway to fibril formation.
Methods
Protein
Expression and Labeling
A synthetic gene encoding
I260C/C291A/C322AtauK18 codon optimized for expression in E. coli(9) in a pJ414 vector was
purchased from DNA2.0 (Menlo Park, CA). This construct enables site-specific
fluorophore-labeling at cysteine 260 by removing the two native cysteine
residues.[15] The ΔK280 mutation was
introduced using Quikchange site-directed mutagenesis. Protein expression
and purification was performed as described previously.[15] Before fluorophore labeling, the protein was
incubated with 10 mM DTT in PBS for 30 min and subsequently buffer
exchanged into degassed PBS using a CentriPure P25 column. A 1.5 molar
excess of maleimide derivatives of either Alexa 488 or 647 (Molecular
Probes) was added to tau typically with a concentration of 200–300
μM and allowed to react for 2 h at 37 °C. The reaction
was quenched by addition of 10 mM DTT, and the monomeric protein was
subsequently separated from unreacted dye and oligomeric species by
gel filtration on a Superdex75 10–300 column. TauK18 was concentrated
to a concentration of 200–300 μM using a spin filter
and frozen in liquid nitrogen in aliquots. Samples were used without
refreezing of aliquots as this was found to cause preaggregation of
the protein.
ThT Measurements and Seeding
Samples
for ThT measurements
were prepared from unlabeled protein in 50 mM ammonium acetate, pH
7, 1 mM DTT, and 20 μM ThT. Prior to the experiment, the sample
was spun at 14000g for 5 min, and only the top two-thirds
of the supernatant were used for the reaction to remove aggregated
proteins, which is crucial to the reproducibility of the experiment.
The reaction was started by addition of low molecular weight heparin
(average MW = 5000, Fisher Scientific) at a molar ratio of one heparin
to four tau. Multiple reactions were run in parallel in a 96-well
plate (Corning 3881) using a sample volume of 60 μL. ThT was
excited through a 440 nm/10 nm filter, and the fluorescence emission
was selected with a 480 nm/10 nm filter. Measurements were done under
quiescent conditions and at 37 °C. Aliquots were taken out from
an aggregation reaction identical to that described above after 0,
15, 45, and 90 min of incubation and stored on ice until the last
time point. Half the samples were spun for 5 min at 14000g to pellet fibrils. A 10% volume of the aliquots was added to new
tauK18 ΔK280 aggregation reactions in 10 mM Na2HPO4/NaH2PO4, pH 7, 100 mM NaCl, 1 mM DTT,
20 mM ThT, 2.5 mM heparin. The ThT fluorescence was monitored as described
above except the reaction was carried out at 30 °C.
Single-Molecule
FRET Measurements
Aggregation reactions
were carried out using a mixture of 5 μM tau–Alexa488
and 5 μM tau–Alexa647. The reaction was initiated by
addition of heparin and aliquots were taken out as indicated and diluted
12.500 times before smFRET analysis. The diluted sample was pumped
through a microfluidic channel[12,56] at a flow rate of 0.5
cm/min. A 488 nm laser beam was focused to a diffraction limited spot
in the center of the channel by a 60× NA 1.4 microscope objective.
Fluorescence emission was collected by the same objective and passed
through a FF500/646-Di01 dichroic filter. The fluorescence emission
was split into two channels by a FF605-Di02 dichroic filter, and selected
with a 535/50 bandpass filter for the donor signal and FF01-697/75
filter for the acceptor. Photon counts were collected in 200 μs
time bins for 6 min per time point.
Analysis of smFRET Data
Monomer and oligomer events
were identified as time bins having more than 10 photons in the donor
and acceptor channel, respectively. Photon counts were corrected for
cross-talk and background fluorescence as described previously.[15] Concentrations were estimated by normalizing
the burst rate relative to the known concentration of monomer in the
first time-point. Bursts were classified as large if they had more
than ten times the average burst count of a monomer event, which equals
approximately 180 photons in total.
SAVE Experiments
SAVE experiments were performed as
described previously.[21,22] In summary, borosilicate glass
coverslips (VWR international, Ø 50 mm) were cleaned using an
argon plasma cleaner (PDC-002, Harrick Plasma) for 30 min to remove
any fluorescent residues. Multiwell slide chambers (CultureWell chambered
cover glass 50 well, Grace Bio-Laboratories) were separated from the
original cover glass and affixed to the cleaned cover slides. To stain
aggregates for imaging, samples were diluted into 30 nM pFTAA to a
final protein concentration of 25 nM tau. Then 10 μL of each
sample was adsorbed to the cover slides for 15 min before imaging.
The samples were imaged using a home-built total internal reflection
fluorescence microscope as described previously.[22]
Quantitative Modeling at 27–42 °C
We developed
a kinetic model of the aggregation reactions based on eqs –4. These equations may be solved exactly, yielding:We observe
a large kdA in practice. We see that,
if kdA ≫ 2k+γ, monomers and type
A oligomers are effectively in equilibrium after a short initial time,
with equilibrium constant KE = koA/kdA. Indeed,
above 32 °C formation and dissociation of type A oligomers occurs
so rapidly that this occurs by the first time point and the rate constants koA and kdA cannot
be determined at all; we may instead only determine an average KE over these 2 temperatures. All we may infer
about the magnitudes of the component rate constants is that they
are larger than those at 32 °C. At lower temperatures, this does
not occur quite so rapidly; nonetheless, the transition time is still
short enough that the rate constants cannot be determined with great
accuracy. However, at any temperature we may infer from the form of eq lower bounds for kdA and therefore for koA. The data at 27–42 °C were fitted to the analytical
expressions eqs –7 using code from the online fitting platform Amylofit;[27] and the best-fit rate constants were recorded
(Table S1).
Quantitative Modeling at
22 °C
At 22 °C,
new filament formation is slow enough that a lag phase is visible,
and nucleation is no longer effectively instantaneous. We must modify
our model to reproduce this behavior, yielding the following equations
instead of eq :These equations are related to those employed
in ref (40). The rate
constants at 27 °C were used to develop trial parameters of sufficient
accuracy to enable an efficient numerical fit of the 22 °C data
to eqs , 2, 3, 8, and 9) to be carried out. The resultant rate constants
for oligomer formation and dissociation may be directly compared with
those determined at higher temperatures. In all cases, the fits were
of high enough quality to verify the consistency of our model with
the experimental data.
Authors: Georg Meisl; Julius B Kirkegaard; Paolo Arosio; Thomas C T Michaels; Michele Vendruscolo; Christopher M Dobson; Sara Linse; Tuomas P J Knowles Journal: Nat Protoc Date: 2016-01-07 Impact factor: 13.491
Authors: Anđela Šarić; Thomas C T Michaels; Alessio Zaccone; Tuomas P J Knowles; Daan Frenkel Journal: J Chem Phys Date: 2016-12-07 Impact factor: 3.488
Authors: Georg Meisl; Xiaoting Yang; Erik Hellstrand; Birgitta Frohm; Julius B Kirkegaard; Samuel I A Cohen; Christopher M Dobson; Sara Linse; Tuomas P J Knowles Journal: Proc Natl Acad Sci U S A Date: 2014-06-17 Impact factor: 11.205
Authors: G Perry; S L Siedlak; P Richey; M Kawai; P Cras; R N Kalaria; P G Galloway; J M Scardina; B Cordell; B D Greenberg Journal: J Neurosci Date: 1991-11 Impact factor: 6.167
Authors: Franziska Kundel; Liu Hong; Benjamin Falcon; William A McEwan; Thomas C T Michaels; Georg Meisl; Noemi Esteras; Andrey Y Abramov; Tuomas J P Knowles; Michel Goedert; David Klenerman Journal: ACS Chem Neurosci Date: 2018-04-08 Impact factor: 4.418
Authors: Marija Iljina; Gonzalo A Garcia; Mathew H Horrocks; Laura Tosatto; Minee L Choi; Kristina A Ganzinger; Andrey Y Abramov; Sonia Gandhi; Nicholas W Wood; Nunilo Cremades; Christopher M Dobson; Tuomas P J Knowles; David Klenerman Journal: Proc Natl Acad Sci U S A Date: 2016-02-16 Impact factor: 11.205
Authors: Anthony W P Fitzpatrick; Benjamin Falcon; Shaoda He; Alexey G Murzin; Garib Murshudov; Holly J Garringer; R Anthony Crowther; Bernardino Ghetti; Michel Goedert; Sjors H W Scheres Journal: Nature Date: 2017-07-05 Impact factor: 49.962
Authors: Samuel I A Cohen; Risto Cukalevski; Thomas C T Michaels; Anđela Šarić; Mattias Törnquist; Michele Vendruscolo; Christopher M Dobson; Alexander K Buell; Tuomas P J Knowles; Sara Linse Journal: Nat Chem Date: 2018-03-26 Impact factor: 24.427
Authors: Samuel I A Cohen; Sara Linse; Leila M Luheshi; Erik Hellstrand; Duncan A White; Luke Rajah; Daniel E Otzen; Michele Vendruscolo; Christopher M Dobson; Tuomas P J Knowles Journal: Proc Natl Acad Sci U S A Date: 2013-05-23 Impact factor: 11.205
Authors: Alexander J Dear; Thomas C T Michaels; Georg Meisl; David Klenerman; Si Wu; Sarah Perrett; Sara Linse; Christopher M Dobson; Tuomas P J Knowles Journal: Proc Natl Acad Sci U S A Date: 2020-05-15 Impact factor: 11.205
Authors: William E Arter; Catherine K Xu; Marta Castellana-Cruz; Therese W Herling; Georg Krainer; Kadi L Saar; Janet R Kumita; Christopher M Dobson; Tuomas P J Knowles Journal: Nano Lett Date: 2020-10-20 Impact factor: 11.189
Authors: Phuong H Nguyen; Ayyalusamy Ramamoorthy; Bikash R Sahoo; Jie Zheng; Peter Faller; John E Straub; Laura Dominguez; Joan-Emma Shea; Nikolay V Dokholyan; Alfonso De Simone; Buyong Ma; Ruth Nussinov; Saeed Najafi; Son Tung Ngo; Antoine Loquet; Mara Chiricotto; Pritam Ganguly; James McCarty; Mai Suan Li; Carol Hall; Yiming Wang; Yifat Miller; Simone Melchionna; Birgit Habenstein; Stepan Timr; Jiaxing Chen; Brianna Hnath; Birgit Strodel; Rakez Kayed; Sylvain Lesné; Guanghong Wei; Fabio Sterpone; Andrew J Doig; Philippe Derreumaux Journal: Chem Rev Date: 2021-02-05 Impact factor: 60.622
Authors: Chih Hung Lo; Colin Kin-Wye Lim; Zhipeng Ding; Sanjula P Wickramasinghe; Anthony R Braun; Karen H Ashe; Elizabeth Rhoades; David D Thomas; Jonathan N Sachs Journal: Alzheimers Dement Date: 2019-10-22 Impact factor: 21.566