csf assays to detect patients with seeding- competent ...competent aggregates of abeta, tau and...

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


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


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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%


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)

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***

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

***


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.


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


86% sensitivity

Results of a blinded study in CSF

Parkinson’s Disease

Disease Controls

#

#

***

#

#

# 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


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).


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.


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

csf assays to detect patients with seeding- competent ...competent aggregates of abeta, tau and...

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