H. Georg Schulze

Assessing differentiation status of human embryonic stem cells noninvasively using Raman microspectroscopy.

Raman microspectroscopy is an attractive approach for chemical imaging of biological specimens, including live cells, without the need for chemi-selective stains. Using a microspectrometer, near-infrared Raman spectra throughout the range 663 cm(-1) to 1220 cm(-1) were obtained from colonies of CA1 human embryonic stem cells (hESCs) and CA1 cells that had been stimulated to differentiate for 3 weeks by 10% fetal bovine serum on gelatin. Distributions and intensities of spectral bands attributed to proteins varied significantly between undifferentiated and differentiated cells. Importantly, compared to proteins and lipids, the band intensities of nucleic acids were dominant in undifferentiated cells with a dominance-reversal in differentiated cells. Thus, we could identify intensity ratios of particular protein-related bands (e.g., 757 cm(-1) tryptophan) to nucleic acid bands (784 cm(-1) DNA/RNA composite) that were effective in discriminating between spectra of undifferentiated and differentiated cells. We observed no discernible negative effects due to the laser exposure in terms of morphology, proliferation, or pluripotency of the stem cells. We conclude that Raman microscopy and complementary data processing procedures provide a rapid, noninvasive approach that can distinguish hESCs from differentiated cells. This is the first report to identify specific Raman markers for the differentiation status of hESCs.

Hollow-core photonic crystal fiber-optic probes for Raman spectroscopy

We have implemented a new Raman fiber-optic probe design based on a hollow-core photonic-crystal excitation fiber surrounded by silica-core collection fibers. The photonic-crystal fiber offers low attenuation at the pump radiation wavelength, mechanical flexibility, high radiation stability, and low background noise. Because the excitation beam is transmitted through air inside the hollow-core fiber, silica Raman scattering is much reduced, improving the quality of the spectra obtained using probes of this design. Preliminary results show that the new probe design decreases the Raman background from the silica by approximately an order of magnitude compared to solid-core silica Raman probes.

Fully Automated High-Performance Signal-to-Noise Ratio Enhancement Based on an Iterative Three-Point Zero-Order Savitzky—Golay Filter

The automated processing of data from high-throughput and real-time collection procedures is becoming a pressing problem. Currently the focus is shifting to automated smoothing techniques where, unlike background subtraction techniques, very few methods exist. We have developed a filter based on the widely used and conceptually simple moving average method or zero-order Savitzky–Golay filter and its iterative relative, the Kolmogorov–Zurbenko filter. A crucial difference, however, between these filters and our implementation is that our fully automated smoothing filter requires no parameter specification or parameter optimization. Results are comparable to, or better than, Savitzky–Golay filters with optimized parameters and superior to the automated iterative median filter. Our approach, because it is based on the highly familiar moving average concept, is intuitive, fast, and straightforward to implement and should therefore be of immediate and considerable practical use in a wide variety of spectroscopy applications.

A Small-Window Moving Average-Based Fully Automated Baseline Estimation Method for Raman Spectra

A fully automated and model-free baseline-correction method for vibrational spectra is presented. It iteratively applies a small, but increasing, moving average window in conjunction with peak stripping to estimate spectral baselines. Peak stripping causes the area stripped from the spectrum to initially increase and then diminish as peak stripping proceeds to completion; a subsequent increase is generally indicative of the commencement of baseline stripping. Consequently, this local minimum is used as a stopping criterion. A backup is provided by a second stopping criterion based on the area under a third-order polynomial fitted to the first derivative of the current estimate of the baseline-free spectrum and also indicates whether baseline is being stripped. When the second stopping criterion is triggered instead of the first one, a proportionally scaled simulated Gaussian baseline is added to the current estimate of the baseline-free spectrum to act as an internal standard to facilitate subsequent processing and termination via the first stopping criterion. The method is conceptually simple, easy to implement, and fully automated. Good and consistent results were obtained on simulated and real Raman spectra, making it suitable for the fully automated baseline correction of large numbers of spectra.

Absolute Quantification of Intracellular Glycogen Content in Human Embryonic Stem Cells with Raman Microspectroscopy

We present a method to perform absolute quantification of glycogen in human embryonic stem cells (hESCs) in situ based on the use of Raman microspectroscopy. The proposed quantification method was validated by comparison to a commonly used commercial glycogen assay kit. With Raman microspectroscopy, we could obtain the glycogen content of hESCs faster and apparently more accurately than with the kit. In addition, glycogen distributions across a colony could be obtained. Raman spectroscopy can provide reliable estimates of the in situ glycogen content in hESCs, and this approach should also be extensible to their other biochemical constituents as well as to other cell types.

A Model-Free, Fully Automated Baseline-Removal Method for Raman Spectra

We present here a fully automated spectral baseline-removal procedure. The method uses a large-window moving average to estimate the baseline; thus, it is a model-free approach with a peak-stripping method to remove spectral peaks. After processing, the baseline-corrected spectrum should yield a flat baseline and this endpoint can be verified with the χ2-statistic. The approach provides for multiple passes or iterations, based on a given χ2-statistic for convergence. If the baseline is acceptably flat given the χ2-statistic after the first pass at correction, the problem is solved. If not, the non-flat baseline (i.e., after the first effort or first pass at correction) should provide an indication of where the first pass caused too much or too little baseline to be subtracted. The second pass thus permits one to compensate for the errors incurred on the first pass. Thus, one can use a very large window so as to avoid affecting spectral peaks—even if the window is so large that the baseline is inaccurately removed—because baseline-correction errors can be assessed and compensated for on subsequent passes. We start with the largest possible window and gradually reduce it until acceptable baseline correction based on the χ2-statistic is achieved. Results, obtained on both simulated and measured Raman data, are presented and discussed.

SNR Enhancement and Deconvolution of Raman Spectra Using a Two-Point Entropy Regularization Method

A new method for Raman signal recovery, the two-point maximum entropy method (TPMEM), based on a regularization method using two-point entropy is presented. The method can be used for signal-to-noise ratio (SNR) enhancement in very low SNR measurements or for deconvolution, in order to remove the effects of the instrumental line shape on the measured spectrum. Unlike most SNR enhancement schemes, TPMEM requires no filter parameters and no a priori knowledge of the expected signal. A rigorous test on a randomly produced set of convolved and/or noise-corrupted simulated Raman spectra is presented in order to validate the method and compare it to Savitzky-Golay filtering and the maximum entropy method. The method is evaluated on the basis of the root mean square (rms) error and correlation coefficients of the recovered data with the original data, as well as on the basis of SNR improvement, and showed significant improvements in both performance and speed over conventional methods. The method is demonstrated in an application involving fiber-optic-linked Raman and resonance Raman spectroscopy.

Label-free determination of the cell cycle phase in human embryonic stem cells by Raman microspectroscopy.

The cell cycle is a series of integrated and coordinated physiological events that results in cell growth and replication. Besides observing the event of cell division it is not feasible to determine the cell cycle phase without fatal and/or perturbing invasive procedures such as cell staining, fixing, and/or dissociation. Raman microspectroscopy (RMS) is a chemical imaging technique that exploits molecular vibrations as a contrast mechanism; it can be applied to single living cells noninvasively to allow unperturbed analysis over time. We used RMS to determine the cell cycle phase based on integrating the composite 783 cm(-1) nucleic acid band intensities across individual cell nuclei. After correcting for RNA contributions using the RNA 811 cm(-1) band, the measured intensities essentially reflected DNA content. When quantifying Raman images from single cells in a population of methanol-fixed human embryonic stem cells, the histogram of corrected 783 cm(-1) band intensities exhibited a profile analogous to that obtained using flow-cytometry with nuclear stains. The two population peaks in the histogram occur at Raman intensities corresponding to a 1-fold and 2-fold diploid DNA complement per cell, consistent with a distribution of cells with a population peak due to cells at the end of G1 phase (1-fold) and a peak due to cells entering M phase (2-fold). When treated with EdU to label the replicating DNA and block cell division, cells with higher EdU-related fluorescence generally had higher integrated Raman intensities. This provides proof-of-principle of an analytical method for label-free RMS determination in situ of cell cycle phase in adherent monolayers or even single adherent cells.

Raman Microscopy-Based Cytochemical Investigations of Potential Niche-Forming Inhomogeneities Present in Human Embryonic Stem Cell Colonies

Measuring spatial and temporal patterns of cytochemical variation in human embryonic stem cell (hESC) colonies is necessary for understanding the role of cellular communication in spontaneous differentiation, the mechanisms of biological niche creation, and structure-generating developmental processes. Such insights will ultimately facilitate directed differentiation and therewith promote advances in tissue engineering and regenerative medicine. However, the patterns of cytochemical inhomogeneities of hESC colonies are not well studied and their causes are not fully understood. We used Raman spectroscopic mapping to contrast supra-cellular variations in cytochemical composition across pluripotent and partly differentiated hESC colonies to gain a better understanding of the early-stage (i.e., 5 days) effects of the differentiation process on the nature and evolution of these patterns. Higher protein-to-nucleic acid ratios, a differentiation status indicator observed previously using Raman spectroscopy, confirmed reported results that spontaneous differentiation is more pronounced on the edges of a colony than elsewhere. In addition, pluripotent and partly differentiated colonies also showed higher lipid concentrations relative to nucleic acids at colony edges in contrast to relative glycogen concentrations, which were up to 400% more pronounced in the colony centers compared to their edges. Pluripotent and partly differentiated colonies differed, with the latter having higher average protein-to-nucleic acid and lipid-to-nucleic acid ratios but a lower glycogen-to-nucleic acid ratio. In both cases, cell density, pluripotency, and high glycogen appeared to vary in tandem. Spatial variations in glycogen- and protein-to-nucleic acid ratios have features on the order of 100 μm and larger. These dimensions are consistent with those reported for stem cell niches and suggest that cytochemical inhomogeneities may provide colony-level information about niches and niche formation. These results demonstrate Raman mapping to be a potentially useful technique for revealing the complexities in the spatial organization of hESC cultures and thus for monitoring the evolution of engineered hESC niches.

Automated estimation of white Gaussian noise level in a spectrum with or without spike noise using a spectral shifting technique.

Various tasks, for example, the determination of signal-to-noise ratios, require the estimation of noise levels in a spectrum. This is generally accomplished by calculating the standard deviation of manually chosen points in a region of the spectrum that has a flat baseline and is otherwise devoid of artifacts and signal peaks. However, an automated procedure has the advantage of being faster and operator-independent. In principle, automated noise estimation in a single spectrum can be carried out by taking that spectrum, shifting a copy thereof by one channel, and subtracting the shifted spectrum from the original spectrum. This leads to an addition of independent noise and a reduction of slowly varying features such as baselines and signal peaks; hence, noise can be more readily determined from the difference spectrum. We demonstrate this technique and a spike-discrimination variant on white Gaussian noise, in the presence and absence of spike noise, and show that highly accurate results can be obtained on a series of simulated Raman spectra and consistent results obtained on real-world Raman spectra. Furthermore, the method can be easily adapted to accommodate heteroscedastic noise.