Quantitative phase imaging

An expanded version can be found here

 

Gabriel Popescu

Spectroscopy Laboratory, MIT

 

1. Cells as “phase” objects

Ever since the invention of the microscope in the 17th century, it has been observed that live cells are transparent under visible illumination, i.e. they do not absorb (or scatter) light significantly. Thus, a magnified image of cells will generally exhibit low contrast. In order to overcome this difficulty, researchers have used exogenous (extrinsic) contrast agents that bind at various sites in the cell and render the structures visible. In the 1930s, Zernike realized that, although cells are not highly absorbing, they produce significant changes to the wavefront of the incident light due to refractive index variations across the cell, i.e. cells are phase objects. This simply means that light travels with different velocities in different regions of the cell, or, equivalently, that light experiences different time (phase) delays across the cell.

 

Zernike developed phase contrast microscopy that used this phase information to generate high-contrast images of unstained cells. He received the 1953 Nobel Prize in Physics for this invention. Differential interference contrast microscopy is another technique that uses phase delay to generate contrast.

 

 

2. Qualitative vs. quantitative phase information

Both phase contrast (PC) and differential interference contrast (DIC) microscopy have been used extensively to infer morphometric features of live cells without the need for exogenous contrast agents. The optical phase shift through a given sample can be regarded as a powerful endogenous (intrinsic) contrast agent. However, PC and DIC are qualitative in terms of optical path-length measurement, i.e. the relationship between the irradiance and phase of the image field is generally nonlinear.

       Quantifying the optical phase shifts associated with cells gives access to information about morphology and dynamics at the nanometer scale. Scanning electron microscopy can produce quantitative images of cellular components with nanometer scale accuracy. However, this method requires heavy preparation, which prevents its applicability to live cells.

 

 

3. Quantitative phase imaging (QPI)

In order to obtain nanometer scale information from unperturbed live cells, we employed the principle of optical interferometry, where a probe light beam is compared (interfered) with a reference beam. Quantifying the phase difference between the imaging and the reference beams, detailed knowledge about cell motions is obtained. We developed several techniques for quantitative phase imaging: Fourier phase microscopy (FPM)1-3, Hilbert phase microscopy (HPM)4, 5, and diffraction phase microscopy (DPM)6, 7. Currently, the stability of our instrument is 0.2 nm in normal laboratory conditions, i.e. with no isolation or stabilization of the instrument. All of these methods incorporate interferometry with commercial microscopes.

 

4. Applications of QPI

We have applied QPI to obtain the refractive index of live cells8, and to study highly dynamic phenomena such as cell motility9, cell growth, and membrane dynamics2, 10. Quantifying the nanometer level fluctuations of red blood cells, we quantified for the first time membrane tension in a completely non-perturbing way10.

 

5. Outlook

Molecules are elusive to optical imaging because they are much below the resolving power of microscopes. However, quantifying with QPI nanometer scale motions in cells may provide a direct way to detect the work of a single active molecule and reveal the machinery of cells at the molecular level. QPI grants many applications for basic and clinical research. Investigation of tissue slices will provide quantitative information about the tissue architecture and how that may change with the progression of cancer11. Cell membrane integrity plays an important role in many diseases, including malaria and sickle cell anemia. We anticipate that QPI can become a clinical tool for studying the progression of these diseases and the effects of various drugs on membrane properties. With improved automation and packaging, this type of instrumentation will be suitable for commercialization.

 

6. References (PDFs can be downloaded here)

1.    G. Popescu, L. P. Deflores, J. C. Vaughan, K. Badizadegan, H. Iwai, R. R. Dasari and M. S. Feld, Fourier phase microscopy for investigation of biological structures and dynamics, Opt. Lett., 29, 2503-2505 (2004).

2.    G. Popescu, K. Badizadegan, R. R. Dasari and M. S. Feld, Observation of dynamic subdomains in red blood cells, J. Biomed. Opt. Lett., 11, 040503 (2006).

3.    N. Lue, W. Choi, G. Popescu, R. R. Dasari, K. Badizadegan and M. S. Feld, Quantitative phase imaging of live cells using fast Fourier phase microscopy, Appl. Opt., in press).

4.    T. Ikeda, G. Popescu, R. R. Dasari and M. S. Feld, Hilbert phase microscopy for investigating fast dynamics in transparent systems, Opt. Lett. , 30, 1165-1168 (2005).

5.    G. Popescu, T. Ikeda, C. A. Best, K. Badizadegan, R. R. Dasari and M. S. Feld, Erythrocyte structure and dynamics quantified by Hilbert phase microscopy, J. Biomed. Opt. Lett., 10, 060503 (2005).

6.    Y. K. Park, G. Popescu, K. Badizadegan, R. R. Dasari and M. S. Feld, Diffraction phase and fluorescence microscopy, Opt. Exp., 14, 8263 (2006).

7.    G. Popescu, T. Ikeda, R. R. Dasari and M. S. Feld, Diffraction phase microscopy for quantifying cell structure and dynamics, Opt Lett, 31, 775-777 (2006).

8.    N. Lue, G. Popescu, T. Ikeda, R. R. Dasari, K. Badizadegan and M. S. Feld, Live cell refractometry using microfluidic devices, Opt. Lett., 31, 2579 (2006).

9.    G. Popescu, K. Badizadegan, L. P. Deflores, R. R. Dasari and M. S. Feld, Characterization of cell motility by Fourier phase microscopy, Biophys. J., manuscript in preparation).

10.  G. Popescu, T. Ikeda, K. Goda, C. A. Best-Popescu, M. Laposata, S. Manley, R. R. Dasari, K. Badizadegan and M. S. Feld, Optical measurement of cell membrane tension, Phys. Rev. Lett., in press).

11.  M. Hunter, V. Backman, G. Popescu, M. Kalashnikov, C. W. Boone, A. Wax, G. Venkatesh, K. Badizadegan, G. D. Stoner and M. S. Feld, Tissue Self-Affinity and Light Scattering in the Born Approximation: A New Model for Precancer Diagnosis, Phys. Rev. Lett., 97, 138102 (2006).