csf assays to detect patients with seeding- competent ...competent aggregates of abeta, tau and...
TRANSCRIPT
Claudio Soto, PhD
Mitchell Center for Alzheimer’s disease and Related Brain
Disorders, Dept of Neurology
McGovern Medical School, University of Texas at Houston
and Amprion, Inc
CSF assays to detect patients with seeding-
competent aggregates of Abeta, tau and alpha-
synuclein
Prion diseases
Protein Misfolding
and aggregation
Misfolded Aggregates deposited in the brain
Alzheimer’s disease Parkinson’s disease
Huntington’s disease
Amyothropic lateral sclerosisSoto (2003) Nature
Rev Neurosci. 4:49-60
Soluble protein
Misfolded oligomers
Protein misfolding in Neurodegenerative diseases
Amyloid fibrils
Amyloid plaques
Cellular dysfunction
Tissue damage
Protofibrils
Detection of oligomers: Opportunities and challenges
Formation of misfolded protein oligomers is possibly the earliest
pathological event in neurodegenerative diseases and likely begins
decades before clinical symptoms.
Misfolded oligomers are thought to be the most biologically active
structures in neurodegeneration.
Soluble oligomers are likely circulating in biological fluids, offering
an opportunity for non-invasive detection.
Misfolded oligomers are highly heterogeneous in size, structure and
biological activity.
Some oligomers might be on-pathway and others off-pathway in the
amyloid fibrillization process.
Misfolded oligomers are likely transient, unstable and exist in a
much lower concentration than the respective normal monomeric
proteins.
Opportunities
Challenges
How to detect small quantities of misfolded
proteins in biological fluids of patients affected by
neurodegenerative diseases?
Our strategy is to use the ability of misfolded protein aggregates to seed the
conversion of the normal protein to enable their high sensitive and specific
detection in biological fluids.
Our strategy is to use the ability of misfolded oligomers to seed polymerization
of monomeric protein to enable their high sensitivity detection.
No seeds
Patient’s samples containing seeds
Time
Ag
gre
gati
on
No seeds
+ patient’s samples
Our strategy for sensitive detection
Normal protein
Incubation
Growing of units
Incubation
Growing of units
+
Protein Misfolding Cyclic Amplification (PMCA)
Seeds
Soto et al. (2002) Trends Neurosci. 25:390-394
Fragmentation
Multiplication of units
Multiplication of units
Fragmentation
Applications of PMCA
For Prion diseases
Application of PMCA for sensitive detection of prions
Applications of PMCA
for Detection of Amyloid-beta
Oligomers in Alzheimer’s disease
Alzheimer’s disease neurological alterations
Macroscopic changes
Brain atrophy
Microscopic changes
0h 5h 10h 24h
200 nm
4KDa
170Kda
0 5 Time (h)
Preparation of Synthetic Ab Oligomers
Salvadores et al. (2014) Cell Reports 7: 261
Current status of Aβ-PMCA
Limit of detection below 10 atto-moles
Alzheimer’s disease (AD)
Non-Neurodegenerative Controls (NND)
Non-AD Neurodegenerative Controls (NAND)
Detection of Ab Oligomers by Aβ-PMCA in CSF
Salvadores et al. (2014) Cell Reports 7: 261
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******
***
Sensitivity and Specificity in CSF samples
AD n=50
NND n=37
NAND n=41 (7 PD, 5 ALS, 6 FTD, 5 PSP, 4 HD, 4 DLB, 5 SCA, 5 PPA)
Estimation of sensitivity, specificity and predictive value for Aβ-PMCA using CSF samples
Groups Sensitivity2 Specificity2
PositivePredictive
Value2
Negative Predictive
Value2
AD vs NAND 100.0% 94.6% 96.2% 100.0%
AD vs NND 90.0% 84.2% 88.2% 86.5%
AD vs All3 90.0% 92.0% 88.2% 93.2%
Salvadores et al. (2014) Cell Reports 7: 261
FTD: Frontotemporal dementia (Tau aggregates)
PD: Parkinson disease and Lewy bodies dementia (α-synuclein aggregates)
HD: Huntington’s disease (Huntingtin aggregates)
ALS: Amyotrophic lateral sclerosis (SOD and TDP43 aggregates)
***
***
Specificity against samples that may contain other seeds
Studies of Aβ-PMCA specificity using synthetic aggregates implicated in the
two most prevalent protein misfolding diseases besides AD, i.e. Parkinson
disease and type 2 diabetes associated to the aggregation of α-synuclein and
amylin, respectively.
Specificity of Aβ-PMCA assay against cross-seeding
Blood represents the most convenient fluid for a
biochemical diagnosis of Alzheimer’s disease
Why?
✓ Blood offers the best option for a routine, non-invasive test
✓ It is very well accepted that infectious prions are present in blood of animals and
humans and can be detected by PMCA
✓ Aβ has been shown to be present in blood and contribute to brain pathology
✓ Aβ can cross the blood-brain barrier in both directions
✓ Labeled Aβ injected in blood can be retrieved in brain plaques
But.. It is technically very challenging
✓ Blood is a very complex fluid with many other component that interfere with Aβ
aggregation assay
✓ It is likely that the amount of misfolded Aβ oligomers circulating in blood is very
low and its detection will be confounded by the larger concentration of soluble Aβ
✓ Misfolded Aβ oligomers are presumably bound to other proteins, making difficult
its detection
Towards a blood-based diagnosis of AD
Plasma Aβ-PMCA requires a pre-capture step
Pre clearing the
Blood Plasma
3000 rpm X 15 min
1:1 dilution in
PBS T(0.1%) + PI
100 µl/well in duplicates
Aβ-PMCA
200 µl BPELISA plates coated
with sequence or
conformational
antibodies
1:1 dilution
in 2X PBS +
PI + 1% NP40
Incubation with antibody
(sequence or conformational)
coated beads
16 h at 22 °C
500 µl BP
Pre-clearing the
Blood Plasma
Beads washed re-suspended in 20 ul
of aggregation buffer
10 ul added
Into two wells
Aβ-PMCA
Strategy 1: Using antibody-coated plates
Strategy 2: Immuno-precipitation and concentration by antibody-coated beads
Minimum detectable amount of Aβ oligomers = 20pg,
equivalent to 1.1 x 10-16 moles (assuming an average molecular
weight of 170KDa for the oligomers) and extent of seeding is
proportional to the quantity of seeds
Sensitivity of Aβ-PMCA in spiked plasma
Ag
gre
gati
on
, %
Time, h
Alzheimer’s disease patients
93.3% sensitivity; 90% specificity
Detection of Aβ oligomers in AD plasma
***
Applications of PMCA
for Detection of Tau
Oligomers
T 50, h
ou
rs
Cyclic Amplification of Tau Misfolding (Tau-PMCA)
Initial detection limit 0.125 pg of Tau seeds, which is equivalent to 1 atto-mol (assuming a MW of 135K). Direct relationship between the amount of oligomers and the parameters of Tau-PMCA
Specificity of Tau-PMCA assay against cross-seeding
Reproducibility of the Tau-PMCA assay
Experiments were done in triplicate with 2 different Tau seeds, at 4 distinct times, in buffer or CSF, with or without freezing/thawing, and with 5 different concentrations of seeds. No significant differences were observed in any condition.
**
**
0
1 0 0 0
2 0 0 0
3 0 0 0
c o n t r o l
A D
r e c s e e d s ( p o s i t i v e c o n t r o l )
N o s e e d s ( n e g a t i v e c o n t r o l )
T a u o p a t h i e s
Ma
xi
mu
m
ag
gr
eg
at
io
n,
fl
uo
re
sc
en
ce
u
ni
ts
Preliminary results with human CSF samples
(4 PSP, 1 FTD, 5 CBD and 1 CTE)
Applications of PMCA for Detection of α-Synuclein
Oligomers
Brain alterations in Parkinson’s disease
α-synuclein aggregates in Lewy bodies
Large
oligomers
Monomer17
22
120135
75
KDa
Preparation of Synthetic α-syn aggregates
O h 96 h 240 h
50 nm 50 nm50 nm
Shahnawaz et al. (2017) JAMA Neurol 74: 163
αSyn-PMCA in automatic machine
Detection limit below 2 pg (15 atto-mol, assuming a MW of 135K). Direct relationship between the amount of oligomers and the parameters of αSyn-PMCA.This system allow continuous monitoring of the entire plate and using a plate stacker we can run up to 20 plates per machine.
Specificity of αSyn-PMCA
No signal was detectable when the reaction was incubated with Aβ or Tau oligomers. The concentration of seeds added was very high (higher than the highest amount shown in the previous graph). These seeds produce a very large signal in the respective Aβ-PMCA and Tau-PMCA assays. The results indicate that αSyn-PMCA is very specific to detect αSynoligomers.
Shahnawaz et al. (2017) JAMA Neurol 74: 163
αSyn-PMCA in CSF
Using optimized conditions, we can detect as little as 0.02 pg of αSynoligomers in CSF, which translate to around 0.15 atto-mols (assuming a MW of 135 KDa). Clear signal was observed in the samples from patients affected by Parkinson’s disease and no signal in controls. αSyn-PMCA signal can be reduced by immuno-depletion of oligomers.
Agg
rega
tio
n, %
Time, hA
ggre
gati
on
, %Time, h
Shahnawaz et al. (2017) JAMA Neurol 74: 163
86% sensitivity
Results of a blinded study in CSF
Parkinson’s Disease
Disease Controls
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***
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# These two patients developed symptoms of PD 1 and 4 years after sample was collected, one of them was confirmed by autopsy.
Flu
ore
scen
ce
Shahnawaz et al. (2017) JAMA Neurol 74: 163
Sensitivity, specificity and predictive values
Parameter Value 95% confidence intervals
Sensitivity for PD 88.5% 79.2 – 94.6%
Sensitivity for DLB 100.0% 94.9-100.0%
Sensitivity for MSA 80% 79.5-94.6%
Specificity against disease controls
96.9% 89.3-99.6%
Specificity against controls and neurodegenerative diseases
94.0% 86.5-98.0%
Positive predictive value 94.7% 88.0-98.3%
Negative predictive value 87.6% 78.7-93.7%
Sensitivity, Specificity and predictive value for αSyn-PMCA in CSF samples
Data was analyzed by ROC curves using results from 76 samples from PD patients, 10 DLB, 10
MSA and 65 control patients affected by unrelated diseases and 18 from other neurodegenerative
diseases (except AD). Two samples originally provided as controls were later confirmed to be
taken at the pre-clinical stage of PD or DLB. These samples were included in the disease group
for the purpose of the analysis.
Predictive positive and negative values were determined considering all synucleinopathies
samples and controls affected by other neurological and neurodegenerative diseases (except
AD).
Shahnawaz et al. (2017) JAMA Neurol 74: 163
aSyn-PMCA correlate with disease progression
Hoehn &Yahr scale
rs = -0.5354
P= 0.0058
T50, h
rs = -0.3608
P=0.0189
Japanese Cohort German Cohort
Data suggest a relationship between disease severity at the moment of CSF collection and the time to reach 50% aggregation in the αSyn-PMCA assay. Further studies need to be done to confirm this result, hopefully with longitudinal samples.
Shahnawaz et al. (2017) JAMA Neurol 74: 163
PD=109
Controls= 82
Sensitivity=95.41%
Specificity= 90.24%
Positive Predictive Value=92.85%
Negative Predictive Value= 93.67%
False positives
False negatives
Parkinson’s disease
Control samples
Max
imu
m f
luo
resc
ence
Max
imu
m f
luo
resc
en
ce
Validation of aSyn PMCA with MJF Samples
Agg
rega
tio
n,
ThT
Flu
ore
sce
nce
Time
T50 : time required to reach 50% of maximum aggregation.Provides information about the amount of seeds present in the mixture
Maximum fluorescence : Signal at plateau level.Provides information about the presence or absence of seeds (positive/negatives). It is also dependent on the structure of the aggregates in terms of their accessibility for ThT binding.
Explanation of PMCA analysis
Interpretation of αSyn-PMCA results
Agg
rega
tio
n, %
Time, h
0 . 0 1 0 . 1 1 1 0 1 0 0 1 0 0 0
4 0
6 0
8 0
α-synuclein oligomers, pg
T50
, h
Quantification of αSyn oligomers
Agg
rega
tio
n, %
Differentiation between Parkinson’s disease
and Multiple system atrophy by the
characterization of α-Synuclein
conformational strains
Parkinson’s disease ControlsMultiple System Atrophy
Ma
xim
um
flu
ore
sce
nce
Distinguishing PD and MSA by αSyn-PMCA
0 50 100 150 200 250 300 350 4000
1000
2000
3000
4000
5000
6000
7000MSA
PD
Time (h)
Fluo
resc
ence
(AU
)
5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 8 0 0
0
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
W a v e l e n g h t , n m
Fl
uo
re
sc
en
ce
(
AU
)
L C O 5 - P D
L C O 5 - M S A
5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 8 0 0
0
5 0 0
1 0 0 0
W a v e l e n g h t , n m
Fl
uo
re
sc
en
ce
(
AU
)
L C O 7 - P D
L C O 7 - M S A
Differentiating PD and MSA strains: amyloid binding dyes
PD MSA1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
PD MSA1 2 3 4 5 1 2 3 4 5
PD MSAKDa KDa KDa
10
15
25
35
40
10
15
25
35
40
10
15
25
35
40
3
6
14
1 2 3 1 2 3
PD MSA
KDa
Differentiating PD and MSA strains: proteolytic resistance
Differentiating PD and MSA strains: structural features
PD MSA
Data 5
1600162016401660168017000.0
0.5
1.0
1.5
2.0
2.5PD
MSA
Alpha-Helix Beta-Sheet Beta-Turn Other
PD 0% 51% 29% 20%
MSA 0% 61% 20% 19%
Wavenumber [cm-1]
Ab
s
Wavenumber (cm-1)
1700 1680 1660 1640 1620 1600
2.5
2.0
1.5
1.0
0.5
0
Ab
so
rba
nc
e
PDMSA
2 1 0 2 2 5 2 4 0
- 5 0
- 2 5
0
2 5
5 0
P D
M S A
Mo
la
r
el
li
pt
ic
it
y
✓ Cyclic amplification of protein misfolding (PMCA) is a platform technology that
can be adapted to detect disease-relevant aggregates implicated in various protein
misfolding disorders.
✓ PMCA has been optimized for detection of PrPSc, Aβ, Tau and α-synuclein in
biological fluids of patients affected by diverse neurodegenerative diseases.
✓ PrP-PMCA enables highly sensitive and specific detection of infectious prions in
human samples of blood and urine, as well as during all the pre-symptomatic phase
of the disease in a primate model of human vCJD.
✓ Optimized Aβ-PMCA enables detection of Aβ misfolded oligomers in CSF and
plasma of patients affected by AD with high sensitivity and specificity.
✓ Optimized αSyn-PMCA permit high sensitive and specific detection of α-
Synuclein misfolded oligomers in CSF of PD patients. Signal correlates with disease
progression.
✓ PMCA may be useful to detect the presence of misfolded oligomers in biological
fluids, determine their quantity, and identify the type of conformational strains. This
might be useful for disease diagnosis, monitor disease progression, pre-clinical
diagnosis, evaluate efficacy of treatments, target engagement in clinical trials and
personalized medicine.
Conclusions
AcknowledgmentsRodrigo Morales, PhDInes Moreno-Gonzalez, PhDSandra Pritzkow, PhDMohammad Shahnawaz, PhDAbhisek Mukherjee, PhDEnrique Armijo, PhDLuis Concha, PhDKarina Cuanalo, PhDFei Wang, PhDMarcelo Chacon, PhDThomas Eckland, PhDGeorge EdwardsCarlos KrammNicolas MendezJonathan SchulzAdam LyonRuben Gomez GutierrezNazaret Gamez RuizKaterine DoPaulina SotoDamian GorskiMichelle PinhoPrakruti RabadiaGloria GalvanJennifer Bales
Former lab members
Funding
Joaquin Castilla, PhD
Gabriela Saborio, MD
Claudio Hetz, PhD
Lisbell Estrada, PhD
Fabio Moda, PhD
Claudia Duran-Aniotz, PhD
Paula Saa, PhD
Celine Adessi, PhD
Bruno Permanne, PhD
Kinsey Maundrell, PhD
Sylvain Bieler, PhD
Leoncio Vergara, MD
Manuel Camacho, PhD
Kristi Green, PhD
Veer Gupta, PhD
Raphaele Buser, PhD
Milene Russelaskis, PhD
Macarena Lolas, MD
Veronica Garcia, PhD
Dennisse Gonzalez
Marcelo Barria, PhD
Natalia Salvadores, PhD
Baian Chen, PhD
Zane Martin, PhD
Rodrigo Diaz, PhD
Kyung-Won Park, PhD
Ping Ping Hu, PhD
Diego Morales, PhD
Javiera Bravo, PhD
Charles Mays, PhD
Abha Sood, PhD
Andrea Flores
Uffaf Khan
Jorge De Castro
Laurence Anderes
NIH (NINDS, NIA, NIAID, NIGMS), US
Department of Defense, Mitchell
Foundation, CART Foundation,
Alzheimer’s Association, PrioNet
Canada/Merck Serono, Michael J.
Fox Foundation, ALS Association,
Huffington Foundation