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Biomedical Optics & Medical Imaging

Holographic tissue dynamics spectroscopy for profiling new drugs

Screening digital holograms of living tissues in response to drug treatment indicates mechanism of action.
8 June 2011, SPIE Newsroom. DOI: 10.1117/2.1201105.003746

In early drug discovery, new candidate compounds are applied to living cells to see if they have desirable—such as cytostatic or cytotoxic—effects. Once a compound is identified as a ‘hit’, it is important to determine how it affects the many different cellular functions. This is achieved with phenotypic profiling,1 which seeks to capture patterns of cellular response to drugs. Conventional profiling typically uses vital dyes to distinguish viable and unviable cells, or labels specific molecular signalling pathways to measure protein expression levels. Such labels and dyes must be directly administered, and are limited to monitoring pre-defined molecular targets. Alternatively, a label-free approach could measure functional changes as cells respond to drugs (which can be done without any need of foreign agents) or pre-bias of the expected response. This is particularly important when profiling new drug candidates whose mechanisms of action (MOAs) are unknown. Here, we describe our use of holographic tissue dynamics spectroscopy (TDS) for label-free phenotypic profiling.

Cells live by the maxim ‘keep moving or die’. All processes of life involve movement of cellular constituents on all scales, from electron transport across mitochondrial membranes and organelles shuttling enzymes on molecular motors, to active changes in the cell membrane. Each of these activities has a specific function, and the health of a cell—and its response to drugs—can be defined by how they change.

Coherence-gated digital holography can be used to record the unique spectrographic fingerprints of drug action in living cells. The technique captures the ‘signatures’ of different types of motion by measuring the fluctuations of light scattered from within the tissue.2 Additionally, the dynamically fluctuating laser speckle obtained can then be converted into time-frequency spectrograms. Using light of short coherence length isolates the signals from small-volume elements in 3D, similar to optical coherence tomography.3 However, using digital holography as the coherence detector, we can capture information from a full plane inside the tissue.4,5 Once signals from a selected depth are isolated, the fluctuations of these signals over time are decomposed into their component frequencies and plotted as a drug response spectrogram.6

Figure 1 shows two drug response spectrograms, where the frequency ranges across three orders of magnitude (0.005–5Hz). The top spectrogram is for cytochalasin D, which inhibits the formation of actin filaments in the cell. The cellular response showed an enhancement of the mid-range frequencies for about 3h, after which both low and high frequencies were enhanced. In contrast, the drug response for iodoacetate—a metabolic inhibitor of glycolysis—showed a different pattern. There was pronounced, low-frequency enhancement with oscillations over time, which may be related to the known biochemical oscillations of the glycolytic pathway.7 The differing drug response spectrograms can each be considered fingerprints—or voiceprints—of the specific drug.


Figure 1. Drug response spectrogram for (top) cytochalasin D and (bottom) iodoacetate. The fluctuation frequency is plotted as a function of time after drug administration. Enhancement and suppression of spectral content is represented by red and blue, respectively. The two drugs exhibit very different responses.

We built a library of drug response spectrograms by applying many drugs to different tumor cell lines under different conditions. Within the library we found some drugs displayed spectroscopic similarities, which may indicate they share common MOAs. We constructed a similarity matrix for 28 different drugs, doses, and conditions. We then applied clustering techniques to the matrix to group and classify the drugs according to their common responses (see Figure 2). Each element of the array is the cross-correlation coefficient between the individual drug spectrograms. We clearly observed clustering in the similarity matrix—in approximate block-diagonal form—indicating close correspondences among groups of spectrograms. This means that the drugs within these groups share common responses that distinguish them from the other groups, and indicate common MOAs.


Figure 2. Similarity matrix, after hierarchical clustering, of cross-correlation coefficients among the drug response spectrograms. The nearly block-diagonal form shows groups of similar response. Noc: nocodazole. Colch: colchicine. Cyto: cytochalasin D. Osm: osmolarity. Cyclo: cycloheximide. Iod: iodoacetate. KCN: potassium cyanide. Tax: Taxol. TNF: tumor necrosis factor.

In summary, TDS is a label-free approach to phenotypic profiling of tissue. It captures the functional motion of the cells and its change in response to drug administration, without the need for exogenous dyes. The clear differences among the drug response spectrograms for different compounds indicates the high specificity of the technique, much like voiceprint spectrograms are used for security identification purposes. Additionally, drug response correlations between a new drug candidate and a known drug may allow early identification of its MOA. Our future work will expand this technique into early drug discovery pipelines.

This work was performed in collaboration with professors John Turek (Basic Medical Sciences, Purdue University, West Lafayette, IN) and Kwan Jeong (Korean Military Academy, Seoul, South Korea). It was funded by the Chemical, Bioengineering, Environmental, and Transport Systems division of the National Science Foundation.


David Nolte
Department of Physics
Purdue University
West Lafayette, IN 

David Nolte is a professor of physics in the fields of biophotonics and condensed matter. His special area of interest is biointerferometry of molecular films and living tissues.


References:
1. Y. Feng, T. J. Mitchison, A. Bender, D. W. Young, J. A. Tallarico, Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds, Nat. Rev. Drug Discovery 8, pp. 567-578, 2009. doi:10.1038/nrd2876
2. K. Jeong, J. J. Turek, D. D. Nolte, Volumetric motility-contrast imaging of tissue response to cytoskeletal anti-cancer drugs, Opt. Express 15, pp. 14057-14064, 2007. doi:10.1364/OE.15.014057
3. A. F. Fercher, W. Drexler, C. K. Hitzenberger, T. Lasser, Optical coherence tomography—principles and applications, Rep. Prog. Phys. 66, pp. 239-303, 2003. doi:10.1088/0034-4885/66/2/204
4. P. Yu, M. Mustata, L. Peng, J. J. Turek, M. R. Melloch, P. M. W. French, D. D. Nolte, Holographic optical coherence imaging of rat osteogenic sarcoma tumor spheroids, Appl. Opt. 43, pp. 4862-4873, 2004. doi:10.1364/AO.43.004862
5. P. Yu, L. Peng, M. Mustata, J. J. Turek, M. R. Melloch, D. D. Nolte, Time-dependent speckle in holographic optical coherence imaging and the health of tumor tissue, Opt. Lett. 29, pp. 68-70, 2004. doi:10.1364/OL.29.000068
6. K. Jeong, J. J. Turek, D. D. Nolte, Speckle fluctuation spectroscopy of intracellular motion in living tissue using coherence-domain digital holography, J. Biomed. Opt. 15, pp. 030514, 2010. doi:10.1117/1.3456369
7. A. Betz, B. Chance, Phase relationship of glycolytic intermediates in yeast cells with oscillatory metabolic control, Arch. Biochem. Biophys. 109, pp. 585-594, 1965. doi:10.1016/0003-9861(65)90404-2