Live-cell Vibrational Spectroscopy for Nanotoxicity and ...
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Live-cell Vibrational Spectroscopy for Nanotoxicity and Nanomedicine
S. K. Sundaram, B. J. Riley, T. J. Weber, C. A. Sacksteder, B. J. Harrer
Pacific Northwest National LaboratoryRichland, WA 99352, USA
J. M. PetermanSimplex Scientific
Middleton, WI 53562, USA
Keynote at NanotechInsight2009, Barcelona, SpainMarch 28 – April 2, 2009
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• Introduction– Nanotoxicity– Need rapid screening– Biomarker discovery
• ATR - FTIR spectroscopy– Transmission vs. ATR– Challenges and solutions
• Particokinetics - Silica settling• Silica settling on C-10 cell and interacting• Connection to high level cellular processes• Summary• Acknowledgements
Outline
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• The adverse health effects of particulate matter (PM) are well documented– Pulmonary– Cardiovascular
• Nanoparticle toxicity studies encompass complex processes – Partico/chemico/biokinetics– Composition-dependent
- Intentional functionalization- Unintentional functionalization and contaminants
• Impossible to test every variable - needs a rapid screening tool
Health Effects
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Biomarkers of particulate matter toxicity may not adequately predict nanoparticle toxicity (Warheit et al., Toxicol. Sci. 77:117-125, 2004).
What method can be used to rapidly detect biologically active nanoparticles?
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Need for Rapid and Cost Effective Global Profiling Technologies: Mode of Action is Unclear
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Comparison of Modalities
– IR Spectroscopy Modality - Can track IR-observable changes in viable cells in
real-time without the need for a specific probe- Expected to provide a semi-quantitative if not
quantitative way to know when to look for emerging biomarkers based on real-time monitoring of IR spectra
- Expected to provide new supporting bonding data by correlating IR spectral data to fluorescence imaging data of known cell responses to model toxicants
–Fluorescence Imaging Modality- Real-time sensitivity- Dependent on availability of probes for pre-determined
pathways/responses
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Why Combine IR Spectroscopy and Fluorescence Microscopy Capabilities?
–Difficult to interpret IR spectra when the origin of the IR-observable signal is unknown
–Fluorescence capability can exploit a battery of established fluorescence-based assays to improve our understanding of IR observable signals- e.g., changes in pH, membrane fluidity, organelle
status, etc…
–Fluorescence imaging modality will train the IR spectroscopy modality
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Electromagnetic Radiation
Wavelength
Ultraviolet Visible Near Infrared Infrared
200 nm 400 nm 700 nm 2500 nm 10000 nm
4000 cm-114400 cm-125000 cm-1
50000 cm-1
Frequency
High Energy Low Energy
1000 cm-1
• Infrared region: – Low energy radiation– Causes net change in the dipole moment– Changes amplitude of rotation, stretching, and bending of
molecules– Non-symmetric bonds (H2O, CH, CH2, NH, NH3)
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Monitoring Cellular Response By FTIR Spectroscopy
• Cells make a good model system.- Respond to a wide range of materials- Provide complex response- Rapid screening- Cost effective- Animal welfare
• Emphasis placed on detecting biological activity of nanomaterial– No bias in the interpretation of biological activity– Detect biologically active nanoparticles without knowledge of
cellular targets a priori– Do not need a contrast agent or specific reagent to detect
biological activity– Near real-time (minutes)
• Prioritize materials for further investigation
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Transmission vs. ATR Modes of FTIR Spectroscopy
cell cellcell
Transmission Attenuated Total Reflectance (ATR)
cell cellcell
IR Detector IR Detector
IR Source
IR Source
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ATR-FTIR Spectroscopy
Material n (RT)ZnSe 2.40ZnS 2.20AMTIR 2.50Ge 4.0Water 1.330Silica 1.460
( ) 212
2122
1
0
sin2 nndP
−=
θπλ
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Light Source
Light Detector Sample holder
ZnSe crystal
ZnSe crystal
ATR-FTIR Spectroscopy (continued)
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Unheated plate
Heated plate
ATR-FTIR Spectroscopy(continued)
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A Dedicated FTIR Facility
Thermo Nicolet 4700 FTIR bench
Thermo Continuµm IR microscope
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Problems in ATR Measurements: How to Improve?
ZnSe Crystal
Cell Membrane
1 µm3-10 nm
Cytosol
Functionalization layer (~ 5 Å)
IR SourceIR Detector
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Particokinetics - In Vitro
• Shape, size, and density affect settling rate.
• Settling time impacts timing/magnitude of response
• Media “dose” is different than dose to the cell
Time
Res
pons
e
Screen Compare Prioritize
In vitro
J. G. Teeguardian, P. M. Hinderliter, G. Orr, B. D. Thrall, and J. G. Pounds, “Particokinetics In Vitro: Dosimetry Considerations for In Vitro Nanoparticle Toxicity Assessments,” Toxicol. Sci., 95, pp 300-312 (2007).
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Particokinetics - Silica Particles
1 µm silicasettling experiment
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Particokinetics - Silica Particles(continued)
0
20
40
60
80
100
120
0 50 100 150 200Time (minutes)
Exp
erim
enta
l (pe
ak a
rea
unde
r 110
7 cm
-1)
0.E+00
1.E+07
2.E+07
3.E+07
4.E+07
5.E+07
6.E+07
Sim
ulat
ion
(num
ber o
f set
tled
parti
cles
)
1µm silica (measured)1µm silica (calculated)
1 µm silica settling model
J. G. Teeguardian, P. M. Hinderliter, G. Orr, B. D. Thrall, and J. G. Pounds, “Particokinetics In Vitro: Dosimetry Considerations for In Vitro Nanoparticle Toxicity Assessments,” Toxicol. Sci., 95, pp 300-312 (2007).
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Nano Silica Particles SettlingHigh dose of 10 nm silica after 2 hrs of settling
Low dose of 10 nm silica after 2 hrs of settling
Primary silica peak~ 1107 cm-1
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1
2
(1) Asymmetric CH3 stretch(2) Asymmetric CH2 stretch(3) Symmetric CH3 stretch(4) Symmetric CH2 stretch
Scans show a negative absorbance ofcells following exposure to silica particlesas compared to untreated cells.
Zoom in!
~2 hrs after exposure to high dose of 10nm silica
~22 hrs after exposure to low dose of 10nm silica
The changing of the CH2 andCH3 stretching peaks revealthat the membrane isbecoming soluble. This wasalso reported by Bureau, et al.
B. Bureau, X. H. Zhang, F. Smektala, J.-L. Adam, J. Troles, H.-l. Ma, C. Boussard-Ple`del, J. Lucas, P. Lucas, D. Le Coq, M. R. Riley, J. H. Simmons, “Recent advances in chalcogenide glasses,” Journal of Non-Crystalline Solids, 345&346, pp 276-283 (2004).
Nano Silica Particles Settling
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21A. M. Melin, A. Allery, A. Perromat, C. Bébéar, G. Déléris, B. Barbeyrac. Fourier transform infrared spectroscopy as a new tool for characterization of mollicutes. Journal of Microbiological Methods. 2004, 56, 73-82.
A. Pacifico, L. A. Chiriboga, P. Lasch, M. Diem. Infrared spectroscopy of cultured cells: II. Spectra of exponentially growing, serum-deprived and confluent cells. Vibrational Spectroscopy. 2003, 32, 107-122.
1400 cm-1
-COO- symmetric stretch (Pacifico et al, 2003)
1455 cm-1
CH2/CH3 deformation modes (Pacifico et al, 2003)
1546 cm-1
N-H bending vibration (amide II) (Melin et al, 2004)
1625, 1640, 1657 cm-1
β-sheets, α-helices (Melin, 2001)
A. M. Melin, G. Perromat, G. Déléris. 2001. Effect of radical attach on bacteria: an application of FT-IR spectroscopy. Applied Spectroscopy 55:23-28.
Zoom in!
~2 hrs after exposure to high dose of 10nm silica
~22 hrs after exposure to low dose of 10nm silica
Nano Silica Particles Settling
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Can We Detect Induced Biological Activity In Live Cells By FTIR?
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Ratio Spectra To Identify Differences
No difference
numerator
denominator
Difference Spectra
TargetWavenumber
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-20
-10
0
10
20
30
40
165016521654165616581660
Wavenumbers (cm-1)
LPS
:Con
trol r
atio
1652:C=O stretch vibration
2 Hr6 Hr8 Hr24 Hr
Temporal Changes in RAW 264.7 Cells Treated With LPS
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Macrophage Activation
• Wavenumber 1652 frequently modulated by endotoxin treatment– C=O
• Endotoxin induces an inflammatory response in macrophages– reactive oxygen and nitrogen species (oxidative/
nitrative stresses) - Cox-2- iNOS
• Oxygen free radical damage to proteins results in protein carbonyl formation– C=O
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Hypothesis
Inflammatory processes can be detected in live cells by FTIR.
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Wavenumber 1652 cm-1 Temporal Profile Following LPS-Treatment
Time [Hr]
Fold
Incr
ease
J J
J
JJ J
J
0
5
10
0 2 4 6
Wavenumber 1652
iNOS
Cox-2
DAPI
Control LPS
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Indomethacin Effects on Peak 1652 cm-1
-37
17
165016521654-327
1217
165016521654
Fold
Cha
nge
LPSLPS +
Indomethacin
Wavenumbers (cm-1)
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Cox-2 Transfected Cells Treated With Arachidonic Acid
1652 cm-1
Time [Hr]
J JJ
J
J
0
2
4
6
8
0 2 4
Fold
Incr
ease + AA
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Carbonyl bond frequencies found in different functional groups
Group Frequency (cm-1) Functional Group Figure Reference
1610-1550 / 1420-1300 Carboxylate (carboxylic acid salt) 1
1680-1630 Amide 2
1690-1675/(1650-1600)a Quinone or conjugated ketone 3
1725-1700 Carboxylic acid 4
1725-1705 Ketone 5
1740-1725/(2800-2700)b Aldehyde 6
1750-1725 Ester 7
1735 Six-membered ring lactone 8
1760-1740 Alkyl carbonate 9
1815-1770 Acid (acyl) halide 10
1820-1775 Aryl carbonate 11
1850-1800/(1790-1740) Open-chain acid anhydride 12
1870-1820/(1800-1775) Five-membered ring anhydride 13
2100-1800 Transition metal carbonyls 14
a Lower frequency band is from the conjugated double bondb Higher frequency band characteristic of aldehydes, associated with the terminal aldehydic C-H stretch
REFERENCE: J. Coates. Interpretation of infrared spectra, a practical approach in Encyclopedia of Analytical Chemistry. R. A. Meyers (Ed.), pp. 10815-10837. Copyright © John Wiley & Sons Ltd, Chichester. 2000.
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MWCNT-Treated RAW 264.7 Cells
Exp 1
Exp 2
Exp 3
1083 O-P=O symmetric stretchphosphates
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Transformation and Analysis
Data
Information
Knowledge
Analysis
Model
Chemometrics• Discipline concerned with the
application of statistics and mathmatical methods of chemistry
• Complex, interrelated data sets
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Qualitative vs. Quantitative
• Mostly qualitative or semi-quantitative• Quantitative analysis
–Water absorption–Base-line shift–Temperature control–Atmosphere
• Chemometrics• Mechanisms• Integration for rapid screening
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Summary• Live cell FTIR spectroscopy is a promising tool to identify
biologically active nanomaterials– Intuitive temporal patterns of regulation– Changes are complex
- Complementary to genomic and proteomic approaches
• Functionalization monolayer chemistries support cell attachment and proliferation– Goal is to identify chemistry that is biologically inert or non-perturbing
to improve ATR spectroscopy in live cell experiments
• Particochembiokinetics – Can be studied in near real-time
• Development of pathway- or process-specific spectral profiles is needed to enable spectra interrogation– Cell cycle, Cell death, Target pathways, Toxicants with well defined
modes of action, etc.
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Acknowledgements
• Cheryl Cejka for support under TechnologyMaturation program
• Joel G. Pounds, Ellyn Murphy, EnvironmentalBiomarker Initiative (EBI)– Support of LDRD project
• Pacific Northwest National Laboratory (PNNL) is amultiprogram national laboratory operated byBattelle Memorial Institute for the United StatesDepartment of Energy under DE-AC06-76RLO1830.