Next Generation Nanoscale Biosensors using Single Walled

Carbon Nanotubes Corona Phase Molecular Recognition

(CoPhMoRe)

CMC Strategy Forum – Washington D.C., USA

July 17, 2018

Xun Gong, MD, PhDPostdoctoral Associate, Department of Chemical

Engineering

Prof. Michael S. Strano, PhDCarbon P. Dubbs Professor of Chemical

Engineering

Need for more rapid characterization of

pharmaceuticals

2

Mixture of

glycoproteins

Glycans/

glycopeptides

LC, MS,

NMR, etc.

Chandler et al., J Proteome Res 12, 2013.

Reconstruction of glycans

using characterization data

Example: Traditional process for glycan characterization:

Label-free sensor arrays for detection of multiple classes of

therapeutics and responses.

More rapid characterization can improve:

Biopharmaceutical

manufacturing

Process

control

Diagnostics

www.cytofluidix.com

Scientific ResearchOgunniyi, Love et al. Vaccine 32 (2014).

cells

Single Walled Carbon Nanotube (SWNT)

3

nIR Fluorescence

No photo-bleaching1

0

2

4

6

8

10

400 650 900 1150 1400

0

0.2

0.4

0.6

0.8

1

Wavelength (nm)

Abso

rba

nce

(cm

-1)

No

rma

lize

d F

luo

rescen

ce

SWNT

Fluorescence

Blood

Abs.Water

Abs.Water Abs.

Tissue Transparency2 Spatial Information3

1) Angewandte Chemie 2006, 118 ,8318-8321, 2) Biochim et Biophys Acta 1988, 933,

184-192, 3) Nature Nanotech 2013, 8, 959-968

CoPhMoRe in Comparison

4

Antibodies Aptamers Molecular

Imprinting

CoPhMoRe

Route of

Synthesis

Biological Biological Synthetic Synthetic

Stability Poor Poor High High

Selectivity Very High High Medium Medium

Signal

Transduction

No No No Fluorescence

Targets Biomacromolecules Small/Large

Molecules

Low MW

Compounds

(Expanding)

nIR-fluorescent single-walled carbon nanotubes (SWNTs)

as signal transducers

5Boghossian, Strano, et. al., ChemSusChem, 4 (2011) 848–863.

Reuel, Strano et al., ACS Nano, 7, 2013, 7472-7482.

Outline

6

Synthetic molecular recognition using Corona Phase Molecule

Recognition (CoPhMoRe)

Sensors utilizing existing recognition elements

Corona Phase Molecular Recognition - CoPhMoRe

7

Synthetic non-

biological antibody

HydrophilicHydrophobic

Polymer

Fluorescent nanoparticle

(Single walled carbon nanotube - SWNT) Corona

A binding event is translated to a

change in the fluorescence spectra

Polymer wraps SWNT

and forms corona

Polymer modulates

analyte binding

The Solar Corona

Analyte

Hydrophilic

Hydrophobic

SWNT

Hydrophobic

1. Construct library of wrapping polymers to create many corona phases

Analyte

2. Screen all corona phases against analyte library

Hydrophilic

Hydrophobic

SWNT

Hydrophobic

1. Construct library of wrapping polymers to create many corona phases

Analyte

2. Screen all corona phases against analyte library

Hydrophilic

Hydrophobic

SWNT

Hydrophobic

1. Construct library of wrapping polymers to

create many corona phases

Intensity Wavelength

3. Look for changes in

SWNT emission spectra

Corona Phase Molecular Recognition (CoPhMoRe): Towards

synthetic lectins

Nature Nanotech 2013, 8, 959-968

6

Screening Libraries

9

… …

Other Examples (DNA, Surfactants, Synthetic Polymers)

PEG Phospholipids Library for Insulin Detection

Screening Shows CoPhMoRe Phases Sensitive to

Insulin

10

(a)

(b) (c)

(d) (e)

Insulin

Apoliporprotein-AI

o

IR

I

G Bisker, Strano et al ACS Sensors 2018

2

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

Quenching: 62 +/- 2%

The Case for Molecular Recognition

11

Analyte Selectivity

Polymer Interactions (ITC)

G Bisker, et al in submission

Insulin PBS

Molecular Recognition of Whole Insulin

12

FVNQ

HL C G S H L V E A L Y L V C G

ER

GFFYTPKT

GI

V

E

Q C C T S I C S L Y Q L E N Y C N

G Bisker, Strano et al ACS Sensors 2018

Sensor Response Calibration

13

𝑘𝑑 = (0.2 − 0.9) 𝜇𝑀 𝑘𝑑 = (11 − 13) 𝜇𝑀

G Bisker, Strano et al ACS Sensors 2018

CoPhMoRe: Single Molecule Stochastic Binding

14

PBS

0.02 mg/mL Lispro Insulin

100 µM Vitamin C

Lispro Insulin

Vitamin C

-15

-10

-5

0

5

10

15

Wav

elen

gth

Sh

ift

(nm

)

BA-PhO-Dex - SWCNT

-1.0

-0.5

0.0

0.5

1.0

Re

lati

ve

In

ten

sit

y C

ha

ng

e

RITC-PEG-RITC - SWCNT

Small Molecule CoPhMoRe Sensors

15

RITC-PEG-RITC

Fmoc-Phe-PPEG8

-1.0

-0.5

0.0

0.5

1.0

Re

lati

ve

In

ten

sit

y C

ha

ng

e

Fmoc-Phe-PPEG8 - SWCNT

L-thyroxine

estradiol

BA-PhO-Dex

riboflavin

Co

ntr

ol

DM

SO

17-a

lph

a-es

trad

iol

2,4-

Din

itro

ph

eno

l

Ace

tylc

ho

line

chlo

rid

e

alp

ha-

To

cop

her

ol

Ad

eno

sin

e

AT

P

cAM

P

Cre

atin

ine

Cyt

idin

e

D-A

spar

tic

acid

D-F

ruct

ose

D-G

alac

tose

D-G

luco

se

D-M

ann

ose

Do

pam

ine

Gly

cin

e

Gu

ano

sin

e

His

tam

ine

L-A

sco

rbic

aci

d

L-C

itru

llin

e

L-H

isti

din

e

L-T

hyr

oxi

ne

Mel

ato

nin

NA

DH

Qu

inin

e

Rib

ofl

avin

Sal

icyl

ic a

cid

Ser

oto

nin

So

diu

m a

zid

e

So

diu

m p

yru

vate

Su

cro

se

Th

ymid

ine

Try

pto

ph

an

Tyr

amin

e

Ure

a-1.0

-0.5

0.0

0.5

1.0

Rel

ativ

e In

ten

sity

Ch

ang

e

NH2-PPEG8 - SWCNT

Steroid Library

16

Cortisol Testosterone DHEA Pregnenolone

Estradiol Corticosterone Progesterone Cortisone

Aldosterone Prednisolone Prednisone

Tested these steroids against our polymer library

CoPhMoRe: Steroid Sensors - Example Screening

Compare response of SWNT to panel of steroids to see sensitivity and selectivity

17

Cortisol Testosterone

Wavelength (nm) Wavelength (nm)

DHEA Pregnenolone

Wavelength (nm) Wavelength (nm)

Progesterone Cortisone

Wavelength (nm)Wavelength (nm)

Estradiol Corticosterone

Wavelength (nm)Wavelength (nm)

Aldosterone Prednisolone

Wavelength (nm) Wavelength (nm)

Prednisone

Wavelength (nm)

CoPhMoRe: Steroid Sensors - Progesterone

18

0

5

10

15

20

25

% C

ha

ng

e in

Pe

ak A

rea

Chirality Dependent Response

Solution Phase Selectivity

Solution Phase Calibration

Hydrogel Reversible Response

0 µM

100 µM

0 µM

100 µM

0 µM

100 µM

0 µM

Monomer 1 ( PBA )

19

Polymer library for Carbohydrate Detection

Library construction scheme:

Monomer 2

Analyte library #1:

Sugar alcohols

Ahn, Strano et al. Submitted.

20

Only Alternating Polymers Respond to Sugar AlcoholsS

ugar

Alc

ohol Lib

rary

N

anotu

be F

luore

scent R

esponse

Block Random Alternating

Ahn, Strano et al. Submitted.

21

Variation of alternating comonomer revealed selective

arabinose sensor

D-Arabinose

Phenylboronic acid

(PBA)

7

Phenylboronic acid

(PBA)

14

OH

No selectivity

22

Boronic acid position affects its accessibility

Synthesized three polymers using three different PBA monomers.

ortho =2

=meta 3

para =4

23

Position of boronic acid affects strength of enantiomer

binding

=metaortho = para =

Boronic acid in the para position

results in the strongest response to

D-arabinose over L-arabinose

Polymer structure:

Ahn, Strano et al. Submitted.

24

Position of boronic acid affects sensor selectivity

ortho meta paraAhn, Strano et al. Submitted.

Boronic acid-based saccharide sensors as

synthetic lectins

25

Single-walled carbon nanotube sensors designed for monosaccharide

recognition can be used to detect more complex glycan structures

Saccharide concentration: 10 mM Protein concentration: 10 mg/mlConcentration: 10 mM

Outline

26

Synthetic molecular recognition using CoPhMoRe

Sensors utilizing existing recognition elements

Label-free sensors using lectin-functionalized

carbon nanotubes

27

0 50 100 1500

100

200

300

400

500

0 50 100 1500

1000

2000

3000

0 50 100 1500

200

400

600

800

0 50 100 1500

200

400

600

800

0 50 100 1500

500

1000

1500

0 50 100 1500

100

200

300

400

2nd add:

% Modulation

Nu

mb

er

of

Se

ns

ors

1 mg/ml

Chicken IgY

1 mg/ml

Human IgG

0.3 mg/ml

Chicken IgY

Reuel, Strano et al., ACS Nano, 7, 2013, 7472-7482.

Detection scheme: Glycoprotein addition

Glycan detection using PSA-functionalized SWNTs (mannose specific)

Sensors embedded

in a hydrogel

Monitoring IgG hypermannosylation under various

cell culture conditions

28

CHO cells cultured under various solution conditions to induce hypermannosylation

-50 0 500

100

200

300

400

-50 0 500

100

200

300

400

-50 0 500

100

200

300

-50 0 500

100

200

300

400

-50 0 500

200

400

600

-50 0 500

100

200

300

400

Cu

ltu

re A

Days

% Signal Modulation

Extract tested

against PSA-

functionalized

sensors

29

Sensor Form Factors

1. Solution Based Screening

2. Cell Based Assays

3. Animal Studies

4. Multiplex Chip Assays

5. Portable Setups

Drop cast sensor

solution

Advantages of SWNT nanosensor arrays

30Kruss S, …, Strano MS. PNAS 2017.

SWNT nanosensors can provide superior spatiotemporal resolution of cellular efflux

relative to existing state-of-the-art techniques

• Optical transduction enables simple sensor array fabrication

• Millisecond resolution (dependent on optical setup)

• Single molecule sensitivity

Fluorescence intensity trace of a

single (GA)15-ssDNA/SWCNT

imaged on a surface while adding

dopamine (10 µM).

Movie of dopamine release upon stimulation

31Kruss S, …, Strano MS. PNAS 2017.

CoPhMoRe: In Vitro Detection of NO

Intracellular NO based on SWNT fluorescence

32Ulissi ZW, Sen F, …, Strano MS. Nano Letters 2014.

Imaging cellular glycoprotein production using carbon

nanotube sensors

33Reuel, Strano et al., ACS Nano, 7, 2013, 7472-7482.

SW

NT

Flu

ore

sc

en

ce

0.8 1 1.2 1.4

x 104

0

200

400

600

800

1000

0.8 1 1.2 1.4

x 104

0

500

1000

1500

2000

2500

3000

3500

0.8 1 1.2 1.4

x 104

0

500

1000

1500

2000

2500

3000

0.8 1 1.2 1.4

x 104

0

200

400

600

800

1000

0.8 1 1.2 1.4

x 104

0

200

400

600

800

1000

0.8 1 1.2 1.4

x 104

0

500

1000

1500

2000

2500

3000

3500

0.8 1 1.2 1.4

x 104

0

500

1000

1500

2000

2500

3000

0.8 1 1.2 1.4

x 104

0

200

400

600

800

1000

0.8 1 1.2 1.4

x 104

0

200

400

600

800

1000

0.8 1 1.2 1.4

x 104

0

500

1000

1500

2000

2500

3000

3500

0.8 1 1.2 1.4

x 104

0

500

1000

1500

2000

2500

3000

0.8 1 1.2 1.4

x 104

0

200

400

600

800

1000

0.8 1 1.2 1.4

x 104

0

200

400

600

800

1000

0.8 1 1.2 1.4

x 104

0

500

1000

1500

2000

2500

3000

3500

0.8 1 1.2 1.4

x 104

0

500

1000

1500

2000

2500

3000

0.8 1 1.2 1.4

x 104

0

200

400

600

800

1000

IgG production by HEK-TA99 cells monitored by SWNT sensors

Excitation (vis) Emission (nIR)

Nanosensor arrayCells

Label-free, multiplexed nanosensor arrays

34

Printed SWNT sensors enable facile fabrication of label-free microarrays

1 nL spots

500 µm

Dong, Strano et al., ACS Nano 2018.

Mathematical model for the analysis of

complex label-free lectin microarray data

Traditional lectin microarrays require fluorescent labeling and a washing step,

which fundamentally prevents the measurement of weak interactions.

Kuno et al., Nature Methods 2 (2005) 851-856.

Hirabayashi et al., Chem Soc Rev 42 (2013) 4443-4458.

Lectin microarrays offer the ability to multiplex glycan characterization by

measuring a large number of glycan-lectin binding interactions simultaneously.

35

Quantitative glycoprofiling using label-free lectin

microarrays

Label-free lectin microarrays can measure dynamic glycan-lectin binding

data and enable more comprehensive glycan characterization. However,

to date, no formal mathematical model has been developed for extracting

glycan information from large sets of lectin microarray data.

Label-free lectin microarray (2015)2Conventional lectin microarray (2014)1

1Gerlach et al., Anal. Methods 6 (2014) 440-449 2Geuijen et al., Anal. Chem 87 (2015) 8115-8122 36

Mathematical formulation of glycoprofiling

Glycoprofiling involves the elucidation and quantification of all

glycoforms in a mixture

,

,

,

,

,

P A

P B

P C

P D

P E

C

C

C

C

C

PCT

P

A

B

C

D

E

G

G

G

G

G

G P C

1 0 0 0 0

0 1 0 0 1

1 0 0 1 0

0 0 2 0 0

1 0 0 0 1

P

Glycan A Glycoform A

37

Modeling Reversible Interactions

At Steady State:

38

, j

j

j T j j j D j

j

d tk L t G t k K t

dt

max

1

max

2

max

11 ,

1

22 ,

1

,

1

j

j

T j

j D

j

T j

j D

jj

j T j

j D

GL

K

GL

K

GL

K

max

max

max

,

1

1

1

for each lectin,

1

j

j

jj

Tj

j D

j jjj

j D

GL

K

G

K

L

jGj

Solving Steady-State Solution for Two Glycans

0

0

0.115

ss

I I

I

0

0

0.112

ss

I I

I

, ,

, ,

, ,

PNA,

0.20 0.20 0.115

1

PNA A PNA B

PNA A PNA B

A B

D DPNA A PNA B

A BT

D D

G G

K K

G GL

K K

, ,

, ,

, ,

PNA,

0.20 0.20 0.115

1

PNA A PNA B

PNA A PNA B

A B

D DPNA A PNA B

A BT

D D

G G

K K

G GL

K K

0.76

0.54

A

B

G M

G M

After subbing in the KD values:

39

max

10

0 ,

0.20 = 20% sensor response at saturation

j

j

j

T

I I

I L

Model fits using dynamic data

40

LectinGlycan A Glycan B

KD (µM) kf (M-1 s-1) KD (µM) kf (M-1 s-1)

ECL 0.70 32000 3.0 30000

PNA 5.0 80000 0.45 60000

Fitting the model to the dynamic binding data enables us to isolate the contribute

in sensor response from each glycan in time

PNA-SWNTs ECL-SWNTs

Steady state and dynamic analyses yielded similar

estimations of glycan concentrations

Utilizing dynamic binding

data introduces additional

orthogonality into the

microarray

• Decreases number of

lectins needed

• Increases the degree of

glycan characterization

possible using a single

array

41

Nanosensors for Diagnostics and Biomonitoring

42

1) Corona Phase Molecular Recognition:

CoPhMoRe - new method for creating

molecular recognition sites by nanoparticle

templating

2) Multiplexed label free, nanosensor arrays

for process monitoring

Biomolecule detection and characterization using

nanosensor arrays

3) Mathematical model of sensor array

information

Optimization of sensor design and extraction

of additional information

Acknowledgements - Strano Research Group

Professor Michael S. Strano, PhD

Carbon P. Dubbs Professor of Chemical Engineering

Graduate Students

Naveed Bakh Michael Lee Daniel Salem Kevin Silmore Albert Liu Ananth Rajan

Anton Cottrill Minkyung Park Samuel Faucher Tedrick Lew Zhe Yuan Cache Hamilton

Postdoctoral Associates & Fellows

Freddy Nguyen Daichi Kozawa Xun Gong Dorsa Parviz Juyao-Ivy Dong

Volodymyr Koman Pingwei Liu Seonyeong Kwak Manki Son

Alumni

Gili Bisker Nicole Iverson Markita Landry

Jiyoung Ahn Min Hao Wong Song Wang

Punit Mehra Sebastian Kruss

Website: srg.mit.edu

43