Use of Multiphoton FLIM as an Assay Technique for ... of Multiphoton FLIM as an Assay Technique for Carcinogenesis and Pharmacological Efficacy Outline • Review of technical aspects

Use of Multiphoton FLIM as an Assay Technique for Carcinogenesis and Pharmacological Efficacy

Outline

• Review of technical aspects of FLIM and implementation in Bruker FM multiphoton systems. Muhammad Nazir, PhD, Applications Engineer, Bruker FM

• Cellular-Level Metabolic Imaging to Predict Anti-Cancer Drug Response, Dr. Melissa Skala, Vanderbilt University

• Assistant Professor Biomedical Engineering

• Development and translation of optical imaging and spectroscopy techniques to quantify changes in optical biomarkers including cellular metabolism, blood oxygenation and concentration, microvessel network structure and perfusion, and collagen content

• Focus on cancer and cardiovascular disease

• Questions

• Type in questions at any time during presentation

9/11/2014 2 Bruker Confidential

Fluorescence

• Emission of a photon from a molecule in ‘singlet’ excited state

• Properties:

• Intensity (photon flux)

• Wavelength

• Lifetime

• Polarization state of molecule


http://www.shsu.edu/~chm_tgc/chemilumdir/JABLON.GIF

Lifetime

• Measure of how long a molecule stays in the excited state

• Independent of Concentration

• Altered by Micro-environment

• Frequency-domain and Time-domain measurements


Single Exponential Decay


0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25 30 35

Flu

ore

sce

nce

/A

U

Time/nsec

Fluorescence Decay Curve

TCSPC (Time Correlated Single Photon Counting)

• Time of arrival of every single photon measured with respect to some reference pulse


TCSPC

• By sampling the single photon emission after a large number of excitation pulses, the experiment constructs a probability distribution


TCSPC

• Very good at low light level signals

• Fitting an exponential decay curve to the data

9/11/2014 8


Components of a TCSPC based FLIM system

Constant Fraction Discriminator (CFD)

Straight Threshold Detection Constant Fraction Detection

FLIM

• Lifetime measurements with a laser scanning microscope

• Two-dimensional image of spatially distributed lifetimes shown in false color


Integration of FLIM to Bruker’s Multiphoton systems

• TCSPC based FLIM systems are generally stand alone systems

• LSM software runs on one computer and the FLIM software runs on another computer

• FLIM data is stored on a different computer than the intensity data

• Capabilities of LSM software can not be extended to FLIM

• Bruker has developed a FLIM system that is fully integrated with our MP systems

• One computer and one software (PrairieView) for intensity and FLIM acquisition

• Switching between intensity and FLIM is transparent to user

• System features for intensity are available in FLIM mode as well


Bruker’s implementation

• Elegant and simple

• SPC-150 (housed in the same computer as other scanning cards)

• GaAsP PMTs (or Hybrid PMTs) used for detection

• FIFO mode

• Spatial information and Triggering signals are provided by signals from the scanning system

• Lifetime data streamed directly to shared memory accessible by PrairieView

• Data saved in B&H sdt format

• Switch box

• Curve fitting and analysis is done offline with Becker & Hickl SPCImage software

• Other features offered by SPC-150 are still available


Software Implementation

• Raster Scanned Image

• ROIs

• Z-series

• T-series

• Line scans

• Tiled Images

(Montages)

• Control over key FLIM parameters


Analysis software: SPCImage from Becker & Hickl


Cellular-Level Metabolic Imaging to Predict Anti-

Cancer Drug Response

Alex J. Walsh, Rebecca S. Cook, Melinda E. Sanders, Carlos L. Arteaga,

Melissa C. Skala

Assistant Professor

Department of Biomedical Engineering

Vanderbilt University

Nashville, TN

Streamlined Vision of Care

Current Standard of Care for

Breast Cancer

Screening Diagnosis Surgery &

Staging Therapy Resistance

determined

after months

Neoadjuvant chemotherapy

• >10 standard-of-care drugs

• 3-4 chosen by IHC

• 30-40% of patients non-responsive

(>100,000/year)

• >50 FDA approved drugs for breast cancer

1. Smith et al. J Clin Oncol 2002 2. Chang et al., Clin Cancer Res 2000 3. Vogel et al, J Clin Oncol 2002

Optical Screens for Optimal

Clinical Treatment

Breast tumor

biopsy

Apply multiple drug

combinations

Optical metabolic

imaging

Efficacy of drugs &

drug combinations

Optimal treatment

regimen

Remission-free

survival

0

0.5

1

1.5

Cell

ula

r M

eta

bo

lism

Untreated Treated

* *

**

0%

20%

40%

60%

80%

100%

Pro

ba

bili

ty o

f tr

ansitio

n

to n

ext

sta

ge

Paul et al., Nat Rev Drug Disc. 2010

0

500

1000

1500

2000Submission

to launch Phase III

Phase II

Phase I Preclinical

Lead optimization

Hit-to-lead Target-to-hit

Co

st

(mill

ion

s $

)

$1.8B

Goal: Leverage functional

optical imaging to monitor

tumor response to therapy

in pre-clinical models

Demand for biomarkers

that improve selection of

drugs for clinical trials

The Challenges of Drug

Development

Tumor Metabolism as a Marker

of Drug Response

• Cellular-resolution images of metabolism

may identify sub-populations responsible

for relapse

• Therefore, microscopic metabolic imaging

is an attractive assessment tool

Locasale et al., Mol Systems Biol 2012

Sensitive cells Resistant cells

Kleppe et al., Nat Med 2014

Therapy

• Most anti-cancer

therapies disrupt

cellular metabolism

• Metabolic changes

likely precede

changes in tumor size

(standard measure)

Some Metabolic Imaging

Technologies

1. Ueda S, et al. Cancer Res. 2012;72(17):4318-28. 2. O’Sullivan et al. Breast Cancer Res. 2013;15:R14

Photoacoustic imaging, MRI, etc…

Method Endpoint Advantages Limitations

IHC

Protein

expression

- High resolution

- Molecularly

specific

- Ex vivo; no dynamics

- Protein expression, not

function

FDG-

PET

FDG uptake - In vivo

- Requires radiotracers

- Low sensitivity and

resolution

DOT1,2 Tissue

Optical Index

- In vivo

- No contrast agent

- Low resolution

TOI = ctHHb × water / (% lipid)

Optical Metabolic Imaging (OMI)

Method Endpoint Advantages Limitations

IHC

Protein

expression

- High resolution

- Molecularly

specific

- Ex vivo; no dynamics

- Protein expression, not

function

FDG-

PET

FDG uptake - In vivo

- Requires radiotracers

- Low sensitivity and

resolution

DOT1,2 Tissue

Optical Index

- In vivo

- No contrast agent

- Low resolution

Optical

Metabolic

Imaging

NAD(P)H

and FAD

fluorescence

- In vivo

- Cellular

resolution

- No contrast agent

- Low penetration depth

1. Ueda S, et al. Cancer Res. 2012;72(17):4318-28. 2. O’Sullivan et al. Breast Cancer Res. 2013;15:R14

FADH2 FAD

Optical Metabolic Imaging (OMI):

Redox Ratio

1. Chance et al., J Biol Chem 1979 2. Georgakoudi, Ann. Rev Biomed. Eng. 2012 3. Varone, Georgakoudi, Cancer Res 2014

Fatty Acid

Oxidation

NAD+

FADH2

FAD

NADH

Lipid & Amino

Acid Synthesis

NADPH

NADP+

Pentose Phosphate

Pathway

2NADP+

2NADPH

ROS

scavenging

NADP+ NADPH

IFAD

INAD(P)H Redox Ratio =

FADH2 FAD

Optical Metabolic Imaging (OMI):

Redox Ratio

Fatty Acid

Oxidation

NAD+

FADH2

FAD

NADH

Lipid & Amino

Acid Synthesis

NADPH

NADP+

Pentose Phosphate

Pathway

2NADP+

2NADPH

ROS

scavenging

NADP+ NADPH

FAD NADH

autofluorescent

molecules

NADPH

IFAD


1. Chance et al., J Biol Chem 1979 2. Georgakoudi, Ann. Rev Biomed. Eng. 2012 3. Varone, Georgakoudi, Cancer Res 2014

OMI: Fluorescence Lifetimes of

NAD(P)H and FAD

Data

Fit

System

Response

𝐼 𝑡 = 𝛼1𝑒𝑥𝑝−𝑡/𝜏1 + 𝛼2𝑒𝑥𝑝−𝑡/𝜏2 + 𝐶

𝜏𝑚 = 𝛼1𝜏1 + 𝛼2𝜏2

1.7 ns

0.5 ns

Example NAD(P)H tm Images:

Fluorescence lifetime reports

microenvironment (protein binding) of

NAD(P)H and FAD :

NAD(P)H t1 Free NAD(P)H1

NAD(P)H t2 Bound NAD(P)H1

FAD t1 Bound FAD2

FAD t2 Free FAD2

a Fractional

component

3. Nakashima N, et al. J Biol Chem. 1980;255(11):5261-3.

4. Blacker, Duchen et al., Nature communications 5 (2014).

1. Lakowicz JR, et al. P Natl Acad Sci USA. 1992;89(4):1271-5.

2. Konig K, et al. J Biomed Opt, 2003; 8:432–439.

OMI Endpoints

IFAD


𝐼 𝑡 = 𝛼1𝑒𝑥𝑝−𝑡/𝜏1 + 𝛼2𝑒𝑥𝑝−𝑡/𝜏2 + 𝐶

𝜏𝑚 = 𝛼1𝜏1 + 𝛼2𝜏2

𝑂𝑀𝐼 𝐼𝑛𝑑𝑒𝑥 =𝑅𝑅𝑖

𝑅𝑅+

𝑁𝐴𝐷(𝑃)𝐻𝜏𝑚𝑖

𝑁𝐴𝐷(𝑃)𝐻𝜏𝑚

−𝐹𝐴𝐷𝜏

𝑚𝑖

𝐹𝐴𝐷𝜏𝑚

Relative amounts of

NAD(P)H and FAD

Enzyme activity of

NAD(P)H

Enzyme activity of

FAD

Walsh et al. Cancer Res 2013

Resistant

Responsive

[glucose uptake]/[lactate secreted]

OMI Instrumentation

2-Photon Fluorescence Microscope by Bruker FM

• 40X/1.3 NA oil immersion objective

• 750 nm excitation (400-480 nm emission) for NAD(P)H fluorescence

• 890 nm excitation (500-600 nm emission) for FAD fluorescence

• 7-9 mW power at the sample

• TCSPC for FLIM imaging

• B&H SPC150 board; GaAsP PMT

Scanning Galvos

PMT

Emission Filter

Objective

(40X oil, 1.3 NA)

Laser Laser

Use mask to extract for each cell:

1) NAD(P)H t1

2) NAD(P)H t2

3) NAD(P)H a1

4) FAD t1

5) FAD t2

6) FAD a1

7) NAD(P)H photon count

8) FAD photon count

Calculate combination variables:

Redox ratio, NAD(P)H tm , FAD tm

Single Cell Analysis

1 2 3 4 5

1. NAD(P)H

intensity image

2. Threshold to

identify nuclei

3. Set nuclei as

primary objects

4. Propagate out

from nuclei to

identify cells

5. Cytoplasm =

cell - nuclei

1. Cell Profiler. www.cellprofiler.org 2. Walsh et al. Proc SPIE 2014;8948

Redox Ratio

Num

ber

of

Cells

Akaike H. IEEE Transactions on. 1974;19(6):716-23.

OMI Index

Identify cell sub-populations

with AIC criteria

http://www.cellprofiler.org/

In vivo validation studies

• Trastuzumab: HER2 inhibitor given to ~20% of patients that

over-express HER2

• 30-40% of patients de novo resistant to trastuzumab

• Xenograft models: HER2+ human breast tumor cells that are

responsive (BT474) and resistant (HR6) to trastuzumab

BT474:

HER2+ trastuzumab-

responsive

HR6:

HER2+ trastuzumab-

resistant

50

150

250

0 5 10 15

Tum

or

Siz

e (

mm

3)

Days Post Treatment

* * *

2.5 ns

0.5 ns

FD

G U

pta

ke

(%

ID/g

)

2 5 1 2

0

2

4

6

D a y s P o s t T re a tm e n t

100 mm

In vivo Tests of Anti-HER2

Drug Response BT474 (responsive)


0

0.5

1

1.5

2

2 5 14

OM

I In

dex

Days of Treatment

**

** *

T

bladder

heart

T

* p<0.05 vs. Control IgG

0

100

200

300

400

0 5 10 15

Tum

or

Siz

e (

mm

3)

Days Post Treatment

2.5 ns

0.5 ns 100 mm

HR6 (non-responsive)

0

0.5

1

1.5

2 5 14

OM

I In

dex

Days of Treatment


In vivo Tests of Anti-HER2

Drug Response

FD

G U

pta

ke

(%

ID/g

)

2 5 1 2

0

2

4

6

D a y s P o s t T re a tm e n t

*

T T

MDA-MB-361

(partially-responsive)

0

0.4

0.8

1.2

1.6

-1 4N

orm

aliz

ed

Num

ber

of C

ells

OMI Index

*

*

* p<0.05 vs. Control IgG

High-throughput drug screening:

validation in animal models

Primary Tumor Organoids

• Include cancer cells as well as native stromal components – Enable communication between cell types

– Fibroblasts and immune cells 1, 2

• Grow in 3D structure – More closely mimic in vivo conditions

– Microenvironment replicates tumor-like gradients

of oxygen, glucose, and pH 3

• Non-invasive cellular-level imaging can identify sub-populations of response within intact organoids

• Patient tissue can be grown in organoids, incubated with several therapies, and measured with OMI to predict optimum treatment regimens

1. Birgersdotter, et al. Semin Cancer Biol (2005) 2. Bates, et al. Molec Biol Cell (2003) 3. Sutherland, et al. Science (1988)

170 µm

Mince

Partial

Digest

Organoids mammary

gland

(epithelia

& stroma)

Mince

Partial

Digest

-Ad.H2BmCherry

Fluorescent

imaging of cell division

-Embed

in 3D

matrix

Optical imaging of metabolism

-Embed in 3D

matrix

-Wash

-Pellet

-Filter

Single cell suspensions

Phospho-

Flow/CyTOF

-Enrich for

epithelial

cells

Organoids

-Wash

-Pellet

Complete

Digest

mammary

gland

(epithelia

& stroma)

Mince

Partial

Digest

-Ad.H2BmCherry

Fluorescent


-Embed

in 3D

matrix


-Embed in 3D

matrix

-Wash

-Pellet

-Filter


Phospho-

Flow/CyTOF

-Enrich for

epithelial

cells

Organoids

-Wash

-Pellet

Complete

Digest

mammary

gland

(epithelia

& stroma)

Mince

Partial

Digest

-Ad.H2BmCherry

Fluorescent


-Embed

in 3D

matrix


-Embed in 3D

matrix

-Wash

-Pellet

-Filter


Phospho-

Flow/CyTOF

-Enrich for

epithelial

cells

Organoids

-Wash

-Pellet

Complete

Digest

Mammary

gland (epithelia

& stroma) Wash

Pellet

Embed in

3D matrix

Organoid Generation Sample Estrogen

Receptor

Progesterone

Receptor

HER2

Trastuzumab-

responsive xenograft

+ + +

Trastuzumab-

resistant xenograft

+ + +

Patient 1 + + -

Patient 2 + + -

Patient 3 + + -

Patient 4 + + -

Patient 5 - - +

Patient 6 - - -

Treat with

Anti-cancer

drugs

Paclitaxel (chemotherapy)

Tamoxifen (anti-ER)

Trastuzumab (anti-HER2)

XL147 (anti-PI3K) Image 24, 48, 72 hrs

For in vivo validation, see:



Trastuzumab-Responsive

(BT474) Xenograft

72 hours 72 hours 72 hours

N=21 mice

N=1 mouse

In vivo validation

* p<0.05 vs. Control IgG Walsh et al. Cancer Res 2014

Trastuzumab-Resistant (HR6)

Xenograft

24 hours 72 hours 72 hours

In vivo validation


Cellular-level Response in

Trastuzumab-Resistant Xenograft

0

1

2

3

-1 1 3

Norm

aliz

ed N

um

ber

of

Cells

OMI Index

Control

Trastuzumab (H)

0

1

2

3

-1 1 3

Norm

aliz

ed N

um

ber

of

C

ells

OMI Index

24 hours 72 hours




0

1

2

3

-1 1 3

Norm

aliz

ed N

um

ber

of

Cells

OMI Index

Control

Paclitaxel (P)

Trastuzumab (H)

0

1

2

3

-1 1 3

Norm

aliz

ed N

um

ber

of

C

ells

OMI Index

24 hours 72 hours




0

1

2

3

-1 1 3

Norm

aliz

ed N

um

ber

of

Cells

OMI Index

Control

Paclitaxel (P)

Trastuzumab (H)

H + P + X

0

1

2

3

-1 1 3

Norm

aliz

ed N

um

ber

of

C

ells

OMI Index

24 hours 72 hours


Multi-drug screens in patients

0.3 ns

1.7 ns

0.3 ns

1.7 ns

ER+ HER2+ TNBC

0

10 Redox

Ratio

(NAD(P)H

/FAD)

NAD(P)H

tm

FAD tm

Human Tissue Derived Organoids

6 organoids/group imaged; n ~=50-150 cells/group

100mm


0

0.5

1

1.5

2

2.5

3

TNBC ER+ HER2+

OM

I In

dex

*

*

0

0.2

0.4

0.6

0.8

1

1.2

1.4

TNBC ER+ HER2+

OM

I In

dex *

*

OMI Differentiates BC Subtypes

Primary Tumor Organoids Immortalized Cell Lines

* p<0.05, T-test, n ~=300-600 cells/group 𝑂𝑀𝐼 𝐼𝑛𝑑𝑒𝑥 =

𝑅𝑅𝑖

𝑅𝑅+

𝑁𝐴𝐷(𝑃)𝐻𝜏𝑚𝑖

𝑁𝐴𝐷(𝑃)𝐻𝜏𝑚

−𝐹𝐴𝐷𝜏

𝑚𝑖

𝐹𝐴𝐷𝜏𝑚


A

Patient 1: Estrogen Receptor +

P = Paclitaxel (chemotherapy) T = Tamoxifen (anti-ER)

H = Trastuzumab (anti-HER2) X = XL147 (anti-PI3K) 72hr

Redox Ratio NAD(P)H tm FAD tm

0

1

2

3

4

5

-0.5 1.5

No

rma

lize

d N

um

ber

o

f C

ells

OMI Index

Control

H+P+X

H+P+T+X

H+P+X has

two peaks

0

0.5

1

1.5

2

OM

I In

dex

*

* * *

* *

*

• OMI detects drug-induced changes in

human organoids

• Single cell analysis allows identification

of cellular subpopulations

• Population curves are broader for

human vs. xenograft tumors greater

heterogeneity in human tumors


Validation for Patient 1

% C

lea

ve

d C

as

pa

se

-3 +

Contr

ol

Paclit

axel (P

)

Tam

oxifen (

T)

Tra

stu

zum

ab (

H)

XL147 (

X)

H +

X

H +

P +

T

H +

P +

X

H +

P +

T +

X

0 .0

0 .1

0 .2

0 .3

0 .4

0 .5

* *

*

*

*

* *

0

0.5

1

1.5

2

OM

I In

dex

*

* * *

* *

*

OMI measured drug response

correlates with cleaved caspase 3

staining of cell death

P = Paclitaxel (chemotherapy) T = Tamoxifen (anti-ER)

H = Trastuzumab (anti-HER2) X = XL147 (anti-PI3K)

72hr

72hr

* p<0.05, T-test, n ~=100-300 cells/group

* p<0.05, T-test, n= 5 images; quantified as number

+ stained cells / total number cells in image Walsh et al. Cancer Res 2014

0

0.5

1

1.5

2

OM

I In

dex

* * *

*

* *

Patients 2-4: Estrogen

Receptor +

0

0.5

1

1.5

OM

I In

dex

* * * * *

0

0.5

1

1.5

2

2.5

OM

I In

dex

*

*

* *

• No response of any

ER+ tumor to

trastuzumab (anti-

HER2)

• Varied response to

Paclitaxel

(chemotherapy) and

tamoxifen (anti-ER)

suggests de novo

resistance

• Combination therapies

induce greatest change P = Paclitaxel (chemotherapy) T = Tamoxifen (anti-ER)

H = Trastuzumab (anti-HER2) X = XL147 (anti-PI3K)

HTS2-72hr HTS3-24hr

HTS4-72hr

* p<0.05, T-test, n ~=75-150 cells/group

100mm


Conclusions

• OMI detects early molecular changes in organoids that can

streamline drug development

– Faster read-outs, fewer animals to measure drug

response

• Single cell analysis reveals tumor heterogeneity and could

identify sub-populations of drug-resistance in vivo

• An organoid-OMI screen could predict optimal drugs for

patients

Walsh et al. Cancer Res 2013; Walsh et al. Cancer Res 2014

Acknowledgments

Collaborators:

Rebecca Cook, Charles Manning,

Carlos Arteaga, Donna Hicks, Nipun

Merchant, Jason Castellanos,

Jonathan Irish

Lab Members: Alex Walsh, Wesley

Sit, Alec Lafontant, Kristin Poole,

Amy Shah, Jason Tucker-Schwartz,

Chetan Patil, Devin McCormack,

Constance Lents, Joe Sharick,

Maryse Lapierre-Landry

We are currently recruiting graduate students and postdocs;

please email [email protected] if interested.

research.vuse.vanderbilt.edu/skalalab

NCI R01 CA185747

Breast Cancer SPORE

P50 CA098131

Documents

Use of Multiphoton FLIM as an Assay Technique for ... of Multiphoton FLIM as an Assay Technique for Carcinogenesis and Pharmacological Efficacy Outline • Review of technical aspects