Implementation of simultaneous quantitative phase with Raman imaging
© Pavillon and Smith; licensee Springer. 2015
Received: 30 October 2014
Accepted: 4 March 2015
Published: 14 March 2015
We present a technical overview of a multimodal system combining Raman microspectroscopy and quantitative phase microscopy (QPM), which allows two independent and simultaneous measurements of both the local molecular content and dynamic sample morphology. We present in detail the setup implementation and measurement procedure, and show how different features of QPM can be used to ensure optimal Raman measurement conditions and matched fields of view, through off-line calibration procedures such as digital propagation of the measured complex field and analysis of the system’s optical aberrations which can then be employed for numerical compensation and calibration. We present measurements on live cells, where images based both on the quantitative phase signal and on the Raman molecular contrast can simultaneously be retrieved and compared. The dynamic measurements obtained from QPM also enable the monitoring of the cell morphology during the laser scanning of the Raman measurement, making it possible to identify the movements which may occur during the measurement.
PACS codes: 87.64.-t; 87.64.kp; 42.40.Kw
KeywordsRaman spectroscopy Digital holography Microscopy Multimodal imaging Aberration compensation Live cell imaging Label-free
Raman spectroscopy is a valuable tool for non-invasive optical analysis, based on the measurement of the inelastic scattering of a laser excitation, where resonant chemical bonds in the sample possess specific energy shifts corresponding to their various vibrational modes. In the context of biological samples, the Raman spectral information has been used to classify between different cell lines [1,2], or detect cancerous cells or tissue [3,4], among many other applications.
As the excitation light is situated in the visible wavelength range, the technique possesses a spatial resolution which makes spectral imaging possible by coupling the spectral detection with laser-scanning systems. Due to recent advances in the development of low-noise two-dimensional detectors, the number of applications based on Raman microscopy has significantly grown in the past years, to observe, for instance, the distribution of molecules or organelles within cells, such as mitochondria , cytochrome c  or fatty acids .
However, as the Raman signal is usually weak, in particular for biological samples, imaging measurements with extended areas still require rather high excitation power and take a significant amount of time for the complete measurement, which is typically in the range of minutes to tens of minutes, which makes the observation of dynamic phenomena with Raman microscopy very challenging.
To enable the monitoring of dynamic phenomena or the observation of wide areas, Raman microspectroscopy has often been used sequentially with other imaging methods, such as auto-fluorescence , optical coherence tomography , or fluorescence , to cite some of the numerous applications. We present here a measurement approach where the Raman channel is coupled with another imaging modality which can be recorded simultaneously and provides a fast acquisition rate, enabling the dynamic monitoring of the samples during the slower Raman measurement. This second modality is based on quantitative phase microscopy (QPM), implemented as a digital holographic microscope (DHM), a wide-field imaging technique which enables the measurement of the quantitative phase shifts induced by the sample . DHM presents the advantage of retaining the label-free feature of Raman microscopy, while enabling simultaneous measurements based on spectral filtering. While combining other modalities with the Raman signal is not trivial since the Raman scattering is a weak, unpolarised response with a large bandwidth, the combination with DHM can be readily performed by choosing a low power laser for holography at a wavelength outside of the Raman emission range . Additionally, choosing a DHM mode wavelength longer than the Raman region of interest additionally ensures that any Stokes-shifted fluorescence generated remains outside the Raman region of interest.
Quantitative phase signals have recently been used to derive biological indicators for live cell observation, such as the measurement of dry mass , cell differentiation based on morphology , or early indicators of cell death , to cite some possible applications. The multimodal measurements based on Raman microscopy and QPM lead to complimentary information, where Raman can provide information on the molecular content, while QPM enables the dynamic monitoring of the sample morphology. Furthermore, additional correlation between the two signals can be derived, as both are based on different physical interactions with the sample .
We present a methodology and results for this type of approach, first by showing multimodal measurements on latex beads, to characterise the imaging in both channels. We then describe one of the possible procedures that can calibrate such a multimodal system for precise co-localisation in both modes. Measurements on live cells are then presented, which show the possibility of dynamically monitoring the cell morphology by phase during the Raman measurements. Further details on the setup implementation and the image reconstruction procedures are provided in the Methods section.
The combined measurement procedure is presented first through measurements on latex beads, which can be employed to visualise the field of view in both the Raman and QPM channels. This allows us to quantify the degree of spatial mismatch between the two modes, which can then be used as a basis to compensate or calibrate the overall system. In a second part, measurements on live fibroblasts cells are presented to illustrate the combined dynamic and spectral information which can be retrieved through these multimodal measurements.
where ⋆ is the correlation operator, α is a spatial scaling factor, R(θ) is a rotation operator of an angle θ, and x 0 is the position of the maximum value of C(x,y), corresponding to a translation.
The result of the registration procedure as described in Eq. (1) is shown in Figure 1e, where the two channels are merged together with the Raman channel in red and the inverted DHM amplitude in green. The linear transformations lead to a registration accuracy of ± 5 pixels, where the location differences are oriented in different directions across the field of view. This shows that the remaining inaccuracies are induced by distortions such as aberrations in the two detection arms.
One approach to obtain a higher registration accuracy would be to correct the excitation laser locations during acquisition, by using maps as shown in Figure 1e to adjust the driving voltages of the galvano-mirrors, and generate an equally spaced spatial sampling in the object space. However, this approach is not suited for a system based on a slit scanning configuration, as several locations are detected in parallel on the equally-spaced pixels of a 2D detector.
where φ NPL is generated from a polynomial expansion of degree P in both x and y directions, and c x,i ,c y,i are the coefficients to be adjusted to improve the overlay of beads in the two images. The amplitude image can then be extracted from the modified wave front Ψ ′ propagated info focus (see Eq. (4)).
Live cells multimodal measurements
The multimodal measurements are here illustrated by measuring live mouse embryonic fibroblasts cells (MEF) , cultured in Dulbecco’s modified Eagle medium, and plated on quartz-bottom dishes one day prior experiment. Before observation, the culture medium was replaced with a phosphate buffer saline solution supplemented with glucose.
In order to emphasise the changes which can occur during the laser excitation scan, a second set of measurements was performed with approximately two times the power density employed in the other measurements, to 4.34 mW/ μ m 2. The result is shown in the Additional file 2, where it is possible to identify the large morphology changes following the laser scan at higher power, which induces errors in the Raman image, such as the sharp change in the left border of the cell, which results from rapid cell shrinkage. In this case, phase images were recorded every 1.5 s, which is sufficient to follow the morphology changes of the cell, but the acquisition capability is generally limited by the recording speed of the camera. This then typically allows video-rate acquisition, so that faster events could also be observed, while the Raman imaging channel typically requires exposures in the order of seconds per line for biological samples, corresponding to an image acquisition on the order of minutes.
The multimodal approach presented above enables the measurement of two sets of information, with the Raman channel providing spectral information related to the molecular content of the sample, and the phase measurement being a quantitative indicator of the sample dynamic morphology. As shown with the measurements above, the simultaneous acquisition of the two can be employed for samples where the imaging speed of the Raman channel may not be fast enough to follow dynamic changes which can occur during the acquisition.
These combined measurements could also prove useful in other fields such as material science, where speed is of less concern but where both techniques can provide specific features. Raman spectroscopy has been extensively used in this field to characterize material composition and structure , and DHM can be employed in conjunction to provide full-field profilometry with a resolution in the order of 10 nm and below [20,21]. Furthermore, the two measurements are physically complimentary, with QPM and Raman being related respectively to the elastic and inelastic scattering of the excitation light in the sample, enabling the comparison of these two sets of data to derive additional information .
As the presented measurement approach is based on multimodal measurements, the different channels, namely Raman imaging and DHM, are first presented independently. The combination of the two data sets is then discussed in a following section.
The laser delivers an approximate power of 100 mW on the sample, which corresponds to a power density of 480 mW/ μ m 2 at the focal spot. To generate the line excitation, the spot is then rapidly scanned at 100 Hz by the vertical mirror along the measurement zone during the excitation duration in the seconds range, leading to an average power density of 2.47 mW/ μ m 2.
where o,r are respectively the object and reference beams, and ()∗ is the complex conjugate operator. The interferometer is tuned to be in an off-axis configuration, where the reference beam propagation direction is slightly tilted from the optical axis, in order to obtain high frequency interference fringes, and consequently spatially modulate the coherent terms of the hologram. This enables retrieval of the measured complex field, corresponding to the coherent term o r ∗, from the intensity values of the hologram through Fourier filtering, where the coherent term is spectrally separated from the zero-order and conjugate terms . The retrieved complex field can then be demodulated and compensated for aberrations by employing a measurement of the system response .
The use of an interferometer configuration implies that the measurement may be more sensitive to vibrations compared to other types of implementations such as common-path systems . However, in the implementation chosen here, the short exposure time and the one-shot acquisition provided by the off-axis configuration prevent vibrations below kHz frequency from influencing the measurement. Furthermore, this approach provides more flexibility for the implementation of multimodal systems as there is no need to generate a reference wave from the imaging part of the system, as is required in self-referenced common-path configurations. It also makes it possible to independently tune and adjust the object and reference waves to optimize image quality.
Imaging modes combination
The systems presented in the previous subsections can be combined to provide simultaneous multimodal measurements by separating the signals spectrally. The wavelength employed for DHM measurements can be freely chosen and can therefore be selected to be out of the range of the Raman emission, and was selected here in the near-infrared region where absorption is minimal (λ≃ 780 nm), which corresponds to a Raman shift of ∼6000 cm −1 for the excitation wavelength of 532 nm, well outside the Raman range of interest.
In case the spectral ranges for the two channels are fundamentally separate, the two measurements are fully independent, as even stray light or imperfections in spectral separation optics do not influence the signals. On one hand, DHM is wide-field, so that remaining light impinging on the spectrometer is not focused, and is thus strongly filtered by the slit. On the other hand, light from the Raman excitation laser which would be detected by the DHM detector in case of imperfections in the dichroic mirror would not interfere with the reference arm light, and would be filtered out during the Fourier reconstruction process.
We presented the implementation of a multimodal microscope which combines Raman imaging, based on a parallel detection scheme through a line scanning configuration, and quantitative phase microscopy (QPM) based on off-axis digital holographic microscopy (DHM). The two modes can be measured independently and simultaneously through spectral separation of the two wavelength regimes, where the laser line employed in DHM can be freely chosen outside the Raman emission range. This approach makes it possible to combine the specific information from the Raman channel, based on the detection of the intracellular molecular contrast, and the quantitative phase signal, which provides information about the sample morphology and can be measured at video-rate for dynamic monitoring.
As QPM is also based on a label-free contrast, samples can be measured without requiring specific preparation before observation, thus retaining one of the main features of Raman spectroscopy. Furthermore, QPM also employs a reconstruction procedure where several steps are performed off-line after measurement, which can be used in the context of multimodal acquisition to simplify or enhance the measurement procedure. As the measured complex field can be numerically propagated into focus, the actual measurements can be optimised for the Raman channel, whose signal quality is highly sensitive to precise focus of the excitation light within the sample, while allowing post-processing calibration of focus in the QPM mode, thereby ensuring sharp images in both channels without complicated setup adjustments.
As well as providing significant morphological information and temporal dynamics of the sample, the quantitative information retrieved by QPM can also be employed to characterise the setup itself, by determining the amount of aberration present in the system. While aberrations are usually compensated in DHM through a calibration step in order to obtain a flat phase profile, this approach can also be used to estimate the aberrations present in the Raman channel, and pre-compensate for the possible mismatches induced by the aberration differences within the different arms of the microscope system.
Measurements on live cells show that the acquisition rate of the Raman channel, inherently limited by the weak Raman signal and photo-toxicity, may not be fast enough to follow some rapid changes which can occur during the measurement.
The presented approach extends the range of standard Raman measurements, where it becomes possible to extract both spatial and molecular information from spectroscopy, but also dynamic morphological information from the quantitative phase.
The authors would like to thank Dr. T. Saitoh and Dr. S. Akira (Osaka University) who donated the spontaneously immortalized wild-type mouse embryonic fibroblasts.
This work was funded by the Japan Society for the Promotion of Science (JSPS) through the Funding Program for World-Leading Innovative R&D on Science and Technology (FIRST Program), and by the JSPS World Premier International Research Center Initiative Funding Program, and Japan Science and Technology Agency PRESTO program.
- Notingher I, Jell G, Lohbauer U, Salih V, Hench LL. In situ non-invasive spectral discrimination between bone cell phenotypes used in tissue engineering. J Cell Biochem. 2004; 92(6):1180–92.View ArticleGoogle Scholar
- Swain RJ, Kemp SJ, Goldstraw P, Tetley TD, Stevens MM. Assessment of cell line models of primary human cells by Raman spectral phenotyping. Biophys J. 2010; 98(8):1703–11.View ArticleGoogle Scholar
- Haka AS, Shafer-Peltier KE, Fitzmaurice M, Crowe J, Dasari RR, Feld MS. Diagnosing breast cancer by using Raman spectroscopy. Proc Natl Acad Sci U S A. 2005; 102(35):12371–6.View ArticleADSGoogle Scholar
- Lloyd GR, Orr LE, Christie-Brown J, McCarthy K, Rose S, Thomas M, et al. Discrimination between benign, primary and secondary malignancies in lymph nodes from the head and neck utilising Raman spectroscopy and multivariate analysis. Analyst. 2013; 138(14):3900–8.View ArticleGoogle Scholar
- Matthäus C, Chernenko T, Newmark JA, Warner CM, Diem M. Label-free detection of mitochondrial distribution in cells by nonresonant Raman microspectroscopy. Biophys J. 2007; 93(2):668–73.View ArticleGoogle Scholar
- Okada M, Smith NI, Palonpon AF, Endo H, Kawata S, Sodeoka M, et al. Label-free Raman observation of cytochrome c dynamics during apoptosis. Proc Natl Acad Sci U S A. 2012; 109(1):28–32.View ArticleADSGoogle Scholar
- Hedegaard M, Krafft C, Ditzel HJ, Johansen LE, Hassing S, Popp J. Discriminating isogenic cancer cells and identifying altered unsaturated fatty acid content as associated with metastasis status, using K-Means clustering and partial least squares-discriminant analysis of Raman maps. Anal Chem. 2010; 82(7):2797–802.View ArticleGoogle Scholar
- Kong K, Rowlands CJ, Varma S, Perkins W, Leach IH, Koloydenko AA, et al. Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and raman scattering microscopy. Proc Natl Acad Sci U S A. 2013; 110(38):15189–94.View ArticleADSGoogle Scholar
- Patil CA, Kalkman J, Faber DJ, Nyman JS, van Leeuwen TG, Mahadevan-Jansen A. Integrated system for combined Raman spectroscopy–spectral domain optical coherence tomography. J Biomed Opt. 2011; 16(1):011007.View ArticleGoogle Scholar
- van Manen H-J, Kraan YM, Roos D, Otto C. Single-cell Raman and fluorescence microscopy reveal the association of lipid bodies with phagosomes in leukocytes. Proc Natl Acad Sci U S A. 2005; 102(29):10159–64.View ArticleADSGoogle Scholar
- Marquet P, Rappaz B, Magistretti PJ, Cuche E, Emery Y, Colomb T, et al. Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy. Opt Lett. 2005; 30(5):468–70.View ArticleADSGoogle Scholar
- Pavillon N, Fujita K, Smith NI. Multimodal label-free microscopy. J Innov Opt Health Sci. 2014; 7(5):1330009.View ArticleGoogle Scholar
- Rappaz B, Cano E, Colomb T, Kühn J, Simanis V, Magistretti PJ, et al. Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy. J Biomed Opt. 2009; 14(3):034049.View ArticleGoogle Scholar
- Chalut KJ, Ekpenyong AE, Clegg WL, Melhuish IC, Guck J. Quantifying cellular differentiation by physical phenotype using digital holographic microscopy. Integr Biol (Camb). 2012; 4(3):280–4.View ArticleGoogle Scholar
- Pavillon N, Kühn J, Moratal C, Jourdain P, Depeursinge C, Magistretti PJ, et al. Early cell death detection with digital holographic microscopy. PLoS ONE. 2012; 7(1):30912.View ArticleADSGoogle Scholar
- Pavillon N, Hobro AJ, Smith NI. Cell optical density and molecular composition revealed by simultaneous multimodal label-free imaging. Biophys J. 2013; 105(5):1123–32.View ArticleGoogle Scholar
- Colomb T, Cuche E, Charrière F, Kühn J, Aspert N, Montfort F, et al. Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation. Appl Opt. 2006; 45(5):851–63.View ArticleADSGoogle Scholar
- Saitoh T, Fujita N, Hayashi T, Takahara K, Satoh T, Lee H, et al. Atg9a controls dsDNA-driven dynamic translocation of STING and the innate immune response. Proc Natl Acad Sci U S A. 2009; 106(49):20842–6.View ArticleADSGoogle Scholar
- Chalmers JM, Griffiths PR. Handbook of vibrational spectroscopy, vol. 4: Applications in industry, materials and the physical sciences. New York: John Wiley & Sons; 2001.Google Scholar
- Coppola G, Ferraro P, Iodice M, Nicola SD, Finizio A, Grilli S. A digital holographic microscope for complete characterization of microelectromechanical systems. Meas Sci Technol. 2004; 15(3):529–39.View ArticleADSGoogle Scholar
- Kühn J, Charrière F, Colomb T, Cuche E, Montfort F, Emery Y, et al. Axial sub-nanometer accuracy in digital holographic microscopy. Meas Sci Technol. 2008; 19(7):074007.View ArticleADSGoogle Scholar
- Hamada K, Fujita K, Smith NI, Kobayashi M, Inouye Y, Kawata S. Raman microscopy for dynamic molecular imaging of living cells. J Biomed Opt. 2008; 13(4):044027.View ArticleGoogle Scholar
- Cuche E, Marquet P, Depeursinge C. Simultaneous amplitude-contrast and quantitative phase-contrast microscopy by numerical reconstruction of Fresnel off-axis holograms. Appl Opt. 1999; 38(34):6994–7001.View ArticleADSGoogle Scholar
- Colomb T, Kühn J, Charrière F, Depeursinge C, Marquet P, Aspert N. Total aberrations compensation in digital holographic microscopy with a reference conjugated hologram. Opt Express. 2006; 14(10):4300–6.View ArticleADSGoogle Scholar
- Popescu G, Ikeda T, Dasari RR, Feld MS. Diffraction phase microscopy for quantifying cell structure and dynamics. Opt Lett. 2006; 31(6):775–7.View ArticleADSGoogle Scholar
- Goodman JW. Introduction to fourier optics, 2nd edn. New York: Mc Graw Hill Companies, Inc.; 1996.Google Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.