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Raman and Infrared Spectroscopy Distinguishing Replicative Senescent from Proliferating Primary Human Fibroblast Cells by Detecting Spectral Differences Mainly Due to Biomolecular Alterations Katharina Eberhardt,†,‡ Claudia Beleites,†,§ Shiva Marthandan,⊥,∥ Christian Matthaü s,†,‡ Stephan Diekmann,⊥ and Jürgen Popp*,†,‡ †

Leibniz Institute of Photonic Technology e. V., Albert-Einstein-Str. 9, 07745 Jena, Germany Institute for Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany § Chemometric Consulting and Chemometrix GmbH, Södeler Weg 19, 61200 Wölfersheim, Germany ⊥ Department of Molecular Biology, Leibniz Institute on Aging − Fritz Lipmann Institute (FLI), Beutenbergstr. 11, 07745 Jena, Germany ‡

S Supporting Information *

ABSTRACT: Cellular senescence is a terminal cell cycle arrested state, assumed to be involved in tumor suppression. We studied four human fibroblast cell strains (BJ, MRC-5, IMR-90, and WI-38) from proliferation into senescence. Cells were investigated by label-free vibrational Raman and infrared spectroscopy, following their transition into replicative senescence. During the transition into senescence, we observed rather similar biomolecular abundances in all four cell strains and between proliferating and senescent cells; however, in the four aging cell strains, we found common molecular differences dominated by protein and lipid modifications. Hence, aging induces a change in the appearance of biomolecules (including degradation and storage of waste) rather than in their amount present in the cells. For all fibroblast strains combined, the partial least squares-linear discriminant analysis (PLS-LDA) model resulted in 75% and 81% accuracy for the Raman and infrared (IR) data, respectively. Within this validation, senescent cells were recognized with 93% sensitivity and 90% specificity for the Raman and 84% sensitivity and 97% specificity for the IR data. Thus, Raman and infrared spectroscopy can identify replicative senescence on the single cell level, suggesting that vibrational spectroscopy may be suitable for identifying and distinguishing different cellular states in vivo, e.g., in skin.

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may have complex cumulative effects on heterogeneous cell populations. Cellular senescence can be either stress induced or due to replicative exhaustion.7 Several biomarkers can be used for senescence detection, for instance, a high senescenceassociated β-galactosidase (SA-β-Gal) activity5 or the appearance of SASP. The expression of these markers is not uniform but varies distinctively between cells and tissues. So far, no single marker is entirely specific for unambiguous identification of senescent cells and for differentiation from various cellular states.9 Hence, in order to identify senescent cells and distinguish them from proliferating cells in vitro, combinations of different markers are used for molecular-specific (but invasive) bioanalytical assays and protocols, for instance,

ext to proliferation, human diploid primary cells can adopt several cellular states, for instance, quiescence, senescence, apoptosis, terminal differentiation, or cancer. Cellular senescence is a terminal phase, and cells stop dividing after a finite number of cell divisions (the “Hayflick limit”, here counted in population doublings, PDs, as numbers of doubling times of the total cell amounts in culture).1,2 As a consequence, cells develop a number of phenotypes comprising several common features including flat, enlarged, often multinucleated morphology,3 secretion of proteins, such as growth factors or chemokines with paracrine or autocrine effects (referred to as “senescence-associated secretory phenotype”, SASP),4 and accumulation of lipofuscin as an aging pigment and granular particles5 as well as an increased mitochondrial and lysosomal mass.6,7 In general, with time, the integrity and function of tissues decline, resulting in an increased susceptibility to ageassociated diseases.8 A combination of various stress factors © 2017 American Chemical Society

Received: November 1, 2016 Accepted: January 27, 2017 Published: January 27, 2017 2937

DOI: 10.1021/acs.analchem.6b04264 Anal. Chem. 2017, 89, 2937−2947

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Analytical Chemistry

and detached by adding 0.05% Trypsin-EDTA. Fibroblasts were subcultivated in the ratio of 1:4 and therefore counted as +2 PDs to the former PD, until they were senescent in culture. For the experiments, the trypsinized cells were cultivated on calcium fluoride coverslips (CaF2, Crystal, Berlin, Germany), in order to avoid background scattering observed when using regular glass slides. After 1 day, adherent cells were washed three times with 1× PBS and fixed with 4% paraformaldehyde in 1× PBS at room temperature for 10 min, according to standard protocols. The washing steps were repeated in order to remove embedding compounds; with this, spectral contaminations were minimized. For stock purposes, cryoconservation of cell strains at various PDs was done.31 The SA βGal assay was performed for the fibroblast cells in culture.5 Data were taken at every four PDs, and the mean values ± standard deviations were analyzed of 60 cells, three times each (data were partially presented in ref 29). Paraformaldehyde fixed cells were stored in PBS at 4 °C for up to 2 weeks. Once RS images were completed, cells were studied by FT-IR measurements. Raman Spectroscopy. Raman microscopic images were recorded using a confocal Raman microscope (model alpha 300 R, WITec GmbH, Ulm, Germany). The system was equipped with a 488 nm argon ion excitation laser (Lasos, Jena, Germany). Laser light was focused onto the sample with a power of ca. 10 mW by using a 60× water immersion objective (Nikon NIR Apo 60×/NA 1.0 W, Tokyo, Japan). Raman and FT-IR spectroscopy are label-free. Both methods can principally be noninvasive. Optical measurements are particularly harmless to cells when longer wavelengths are applied. We therefore Raman analyzed fibroblast cells at 785 nm; however, we detected only weak signals. We switched to shorter wavelengths and carried out experimental tests at 488 nm with less than 3 mW laser power and measurement times of less than a few hours. These Raman live cell imaging conditions were nondestructive for fibroblast cells, consistent with ref 32. However, these conditions resulted in weak Raman signals which hardly enabled cell identification. We therefore increased laser power to 10 mW. We also fixed the cells to allow for measurement times long enough to obtain high quality Raman and FT-IR data. Cell areas were scanned in a raster pattern at a constant stage speed. Spectra were collected with a 0.5 μm resolution and an integration time of 0.5 s. Spectral range was 200−4500 cm−1; a 600 lines/mm grating leads to a spectral resolution of approximately 6 cm−1.The 1600 × 200 pixels CCD was operated at −67 °C. A minimum of ten cells per PD was imaged for each fibroblast cell strain. Cells were imaged as follows: 87, within the young and proliferating group; 85, in the intermediate midaged group; 91, in the senescent group. From the total of 263 single cells by RS, the mean spectrum of each cell was calculated. The overview is summarized in Table S-1. Fourier Transform Infrared Spectroscopy. After the Raman measurements, samples were washed with distilled water. Our FT-IR measurements of cells in an aqueous environment resulted in very weak cellular signals dominated by water spectra. Thus, for FT-IR analysis, cells were dried in order to avoid water bands. Samples were measured under initial drying of 30 min and ongoing purging with dried air. The FT-IR cell measurements were performed on a Varian 670-IR spectrometer (Agilent, Santa Clara, CA, USA) via in transmission mode. Single cell spectra were collected at 4 cm−1 spectral resolution and 64 coadditions in the wavenumber range from 900 to 4000 cm−1 using the Mercury Cadmium Telluride (MCT) detector. The microscope was equipped with

primary antibodies for immune-fluorescence or immuneblotting analysis,10 SA-β-Gal, or lipofuscin autofluorescence.10 Cellular senescence is physiologically important and plays a role in medical diagnostics, as it is involved in, e.g., suppression of tumorigenesis,11 degenerative diseases, organismal aging,12 or tissue repair.13 The distinction of senescent cells from healthy and cancer cells is essential for tissue diagnosis. For the detection of senescence in vivo, alternative label-free (and noninvasive) identification methods are required. Vibrational spectroscopic methods are label-free, can in principle be noninvasive and nondestructive, and require minimal sample preparation. Here, we studied human primary fibroblasts by vibrational spectroscopy: Raman (RS) and Fourier transform infrared (FT-IR) spectroscopy. By combining both techniques, complementary data were obtained, resulting in an increased validity of the spectroscopic studies.14,15 In combination with optical microscopy, vibrational spectroscopic techniques have become well-established and powerful methodologies for labelfree characterization of single cells. The obtained spectral information offers a unique molecular fingerprint of a complex and intact biological sample. With the aid of chemometric methods, the spectral information can be interpreted on the basis of the overall chemical composition, and alterations within samples can be observed. Using RS, individual cells can be characterized,16,17 as well as cell phenotypes18 and biochemical differences related to cell cycle and proliferation.19,20 Imaging by RS offers high spatial (90% confluence until they were subcultivated. For that, cells were washed once with 1× phosphate buffered saline (PBS, pH 7.4) 2938


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human fibroblast cell strains: BJ, IMR-90, MRC-5, and WI-38. All cells were obtained at PDs of about 30. At low PDs, the cells showed exponential growth (linear increase in PD versus days) with SA-β-Gal values of 0 (Figure S-1). In our experiments, the proliferating IMR-90 and WI-38 cells stopped dividing at about 59 PDs while MRC-5 and BJ cells grew until PD 72 and 73, respectively (Figure S-1A), in quantitative agreement with earlier results.30,31 As a marker for senescence in these cells, we determined the relative number of SA-β-Gal positive cells. In IMR-90 cells, this marker increased already at low PDs while in BJ cells the marker increased only at high PDs, with WI-38 and MRC-5 cells in between (Figure S-1B), in agreement with published data.31 As an additional senescence marker, after long-term cell cultivation, we observed a significant modification of shape and size of the senescent fibroblasts in transmission light microscopy. Compared to young proliferating cells, high PD cells lose their spindle shaped structure, the number of nuclei per cell and the cell size increased by more than half. According to their PDs (growth curve) and SA-β-Gal positive staining, cells were categorized into three general age groups: young, midaged, and senescent (Tables S-1 and S-2). For the aging process, we assumed a similar water concentration in senescent compared to proliferating cells. Characterization of Replicative Senescence by Raman Spectroscopy. After brief inspection under a light microscope, all four fibroblast cell strains were investigated by Raman spectroscopy (RS) recording full images for a number of cells (in total 263). In order to obtain high quality data, cells were fixed (see the Experimental Section). We determined spectral information associated with the phenotype and molecular composition as well as biochemical changes within individual cells during their transition from proliferation into replicative senescence. Single point cell spectra at 0.5 μm resolution showed typical intracellular variations. However, due to the short spectral integration time, required for the acquisition of representative data sets of the intrinsically flat and elongated cell morphology of senescent cells, the signal-to-noise ratio was low. A previous study used single point measurements at different cell positions and could not identify senescent trends in respect to increasing cell passages.40 Low throughput and time-consuming measurements due to high spatial resolution can be overcome by averaging the point spectra of the whole cell.41 Single mean Raman spectra for the investigated cells are the basis for the data analyses presented in the following. Examples of typical Raman images of young and senescent BJ cells are displayed (Figure 1A,B). The images indicate morphological changes associated with the senescence process, e.g., an increase in size and elongation or droplet-like accumulation of lipids within the cytoplasm. The mean Raman spectra of all cell strains show typical spectral features of human cells (Figure 1C,D). The major Raman bands of the 500 to 1800 and 2800 to 3020 cm−1 spectral regions can be assigned to the main cell constituents: proteins, lipids, and nucleic acids. Band assignments of Raman spectra of individual cells have been described in great detail,42 and an overview of the most prominent assignments is listed at the top of Table S-3. The mean spectra of all cells for the age groups, young, midaged, and senescent, were plotted (Figure 1C). Overall, the three Raman spectra indicated considerable similarities in biochemical compositions of the age groups. However, small but reproducible spectral changes were observed. They reflected selected minute alterations in the overall biochemical composition associated with differences in

a 15×/NA 0.4 objective. Background measurements were performed regularly to minimize noise and water vapor signals. Cells were investigated by using an aperture size of ca. 50 × 50 μm. The total of 1544 measured single cells by FT-IR spectroscopy is summarized in Table S-2. Cells were measured as follows: 599, within the young group; 442, in the midaged group; 503, in the senescent group. Data Analysis. All Raman and infrared data were preprocessed and analyzed within “R”, a free software for statistical computing and graphics.33 The freely available packages “hyperSpec” and “ggplot2” were used for data import and preprocessing and analysis, respectively.34,35 For Raman data, cosmic spikes were removed from Raman images by a spike identification, with the threshold chosen for separation from other peaks and data interpolation after spike removal.36 Wavenumber deviations due to small fluctuations in the excitation wavelength were corrected by aligning the phenylalanine band to 1003 cm−1. All spectra underwent a smoothing interpolation (spc.loess) onto an evenly spaced wavenumber axis from 400 to 3100 cm−1 with data point spacing of 2 cm−1. Baseline correction was performed by extended multiplicative signal correction (EMSC) to remove offset, linearity, and baseline terms as well as water contribution. Spectra were truncated to the regions of 500− 1800 and 2800−3020 cm−1. Finally, the mean spectrum of each cell was calculated, and the spectra were area normalized. For FT-IR data, spectra were cut into the fingerprint and high wavenumber regions from 900 to 1800 and 2800 to 3100 cm−1. Subsequently a polynomial baseline was fitted automatically to all spectra in the separated wavenumber regions (spc.fit.poly.below). For noise reduction, the collapsed data were interpolated by a smoothing with data points every 4 cm−1. This lowered the number of variates, in order to stabilize the model. Finally, also the FT-IR spectra were area normalized. Partial least squares-linear discriminant analysis (PLS-LDA) was chosen as a classifier, implemented in the package “cbmodels”37 based on the combination of the packages “pls”38 for PLS and “MASS”39 for LDA. PLS describes the data by a small number of latent variables (lv) that covary with the property of interest (here the senescence progression) and provides regularization for the subsequent LDA. “Cbmodels” PLS-LDA retains the bilinear properties and provides combined coefficients due to an appropriate centering of both PLS and LDA. The RS were modeled using 4 PLS lv, and FT-IR models used 6 lv. The performance of the PLS-LDA model was checked for the RS and FT-IR data by a 100× iterated 5-fold cross-validation (CV) leaving out PDs (relative to measurement days). In addition to the PLS-LDA calculations for the single cell strains, calculations for all cell strains together were done using the same number of lv. This classification was validated by a leave-one-PD-out CV using half of the spectral data of the young and senescent group without iterations. For the extremely small training sets, we restricted the spectra for training and prediction to be at most half the total number of spectra.

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RESULTS The transition into senescence of four primary human fibroblast cell strains was identified by SA-β-Gal staining and analyzed by Raman and infrared spectroscopy. Cellular Growth of Fibroblasts and Observation of Senescence. We measured the growth curve of the four 2939


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2A). PLS-LDA grouped the different age levels in different regions of the PLS-LDA scatter plot. In all four cases, a good separation of young from senescent cells was already achieved by PLS-LD1. PLS-LD2 further discriminated the midaged from young and senescent cells. In BJ and WI-38 cell strains, some overlap was observed between the cells mainly for intermediate PDs, indicating heterogeneity in the temporal response of the individual cells during their transition into senescence, supporting earlier observations.30 The coefficient vectors of PLS-LD1 and PLS-LD2 (Figure 2B) reflected the spectral features associated with the differentiation of the young, intermediate midaged, and senescent age groups for each cell strain. These could be interpreted in terms of band intensities and band positions, reflecting changes in relative concentration or composition, respectively. Different regions within the spectra or coefficients could be roughly assigned to different classes of biological molecules, such as proteins, lipids, or carbohydrates. Clear changes were found in all spectral regions of all four cell strains (Figure 2B). The CH stretching between 2800 and 3100 cm−1 could be indicative for compositional changes either in the protein or in the lipid composition. Maximum intensities of proteins are normally centered around 2930 cm−1, whereas Raman intensities of lipids are highest at slightly lower frequencies (around 2850 cm−1). Within this spectral region, all four coefficient vectors associated with progressing senescence showed both, changes in intensities rather attributed to changes in overall abundance as well as shifts in intensity maxima due to compositional changes of proteins and lipids present in aging vs young cells. The amide I Raman intensities are particularly sensitive to conformational changes of proteins. Although distinct assignments to individual proteins or families of proteins could not be made, taking the overall abundance into account, we presume the changes likely to be due to alterations in conformations of the structural proteins. Distinct spectral differences were further reflected in the broad amide III region between 1200 and 1350 cm−1. The spectral region below 1200 cm−1, being indicative for carbohydrates, polyphosphates, and smaller molecules, for instance, nucleic acids, also showed significant changes for all four cell strains. In the coefficients of BJ, MRC-5, and WI-38 cells, spectral extrema of around 990 cm−1 were obvious, but for IMR-90, these were completely absent. For the MRC-5 strain, this common spectral feature was accompanied by an extremum of opposite sign at 1056 cm−1. The coefficient of the IMR-90 cell strain did not exhibit any striking spectral features in this region. In comparison with the other cell strains, IMR-90 showed the least distinct features for all regions. In general, the changes in the region below 1200 cm−1 did not resemble any of the well-known carbohydrates, polyphosphates, or smaller biomolecules, excluding a more distinct spectral assignment. Apart from the applied PLS-LDA differentiation that resulted in the scores and coefficient vectors described above, the algorithm could be employed as a classifier by projecting the scores into a discriminant space and afterward for a prediction of class membership probabilities. The resulting classification model of each cell strain was tested for the age groups. Sensitivity and specificity of correct allocated senescent cells within the confusion matrix was 95% and 95% for BJ, 94% and 98% for MRC-5, 90% and 96% for IMR-90, and 89% and 99% for WI-38 cells, respectively (Table S-6). In order to further investigate the spectral contrast between young and senescent cells, a classification model was built to

Figure 1. Raman images based on intensities of the CH-stretching vibrations observed from 2800 to 3020 cm−1, plotted for (A) young BJ cells (PD 28) and (B) a senescent BJ cell (PD 70). In (C), the mean spectra of the three age groups: young (blue), midaged (green), and senescent (red), were plotted for all cell strains. In (D), the mean spectra of each cell strain for all ages is shown. Arrows in (C) and (D) indicate small differences within the spectra for transition into senescence and fibroblast cell strains. For better visualization, the low wavenumber region from 500 to 1800 cm−1 was plotted, enhanced by a factor of four.

the metabolism of young versus aging cells: We detected nucleic acids (1580 cm−1) and proteins (1658 cm−1) slightly down-regulated and lipids (1732, 2850, 2930 cm−1) slightly upregulated in aged fibroblast cells (Figure 1C). Correspondingly, also the spectra of the four cell strains (mean over all ages) showed considerable similarity (Figure 1D) indicating a similar biochemical composition of the cell strains. However, small but reproducible spectral changes were observed for the cell strains (indicated by arrows in Figure 1D). These minute but significant differences in spectral band positions and intensities were detected properly by using chemometric data analysis. In addition, the mean spectra of the age groups and of the four cell strains are plotted along with the respective standard deviations (SDs) (Figure S-2A,C). Furthermore, the difference spectra of young versus senescent cells and of all cell strains are shown (Figure S-2B,D). Classification was performed using cell-mean Raman spectra from 263 individual fibroblast cells. The first four PLS components described 86% of the variance in the preprocessed spectra. By plotting the first (PLS-LD1) against the second PLSlinear discriminant (PLS-LD2), young, midaged, and senescent cells could be separated for each of the four cell strains (Figure 2940


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Figure 2. Classification of the RS spectra from young, midaged, and senescent cells by a PLS-LDA with four PLS components. (A) PLS-LD1 plotted versus PLS-LD2 for each cell strain separately. The different age levels were indicated by colors (young: blue; midaged: green; senescent: red). Individual PDs are depicted by different symbols (see below plots). (B) Mean Raman spectra (“mean”) and PLS-LD1 and PLS-LD2 coefficients of the four cell strains. For better visualization, the y-scale of the different spectral regions has been adjusted. The wavenumber region between 500 and 1800 cm−1 of the mean spectra was stretched 4-fold.

Figure 3. Prediction of young and senescent cells by leave-one-PD-out CV by a PLS-LDA testing and training calculation for all cell strains together, measured by RS. In (A), density plot of the PLS-LDA scores. Young and senescent cells were tested (intense color). The density of training cells is shown in the pale color. Median of the age groups is shown as dashed lines. (B) 16 PLS-LDA coefficients of all models calculated during leave-onePD-out cross validation.

distinguish these two groups, leaving out the midaged PDs, by a leave-one-PD-out cross validation. Figure 3A shows density plots of PLS-LD scores for both training and test spectra. By comparing individual cell strains, minimally different predictions of young and senescent cells were seen by testing and training data. Particularly, BJ cells showed distinct spatial separations between young and senescent groups. More age group superpositions were seen for MRC-5 and WI-38 cells, in particular the training data of IMR-90 cells showed large overlaps. PLS-LDA coefficients of in total 16 combinations

from all cell strains and from PDs classified as young and senescent ages were observed (Figure 3B). Fifteen combinations were tested, and the respective 16th was trained and plotted. Compared to the calculation for all cell strains together (Figure S-3B), weak bands were enhanced and spectral differences in the fingerprint region were more obvious, especially between 900 and 1300 cm−1. Overall accuracy was 75% (Table S-7). Misclassifications due to displacements occurred mostly at PDs for the oldest “young” (PD 31 and 36) and the youngest “old” cells (PD 53 and 68): excluding 2941


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Analytical Chemistry those data, overall CV accuracy would increase (data not shown). Senescent cells were recognized with 93% sensitivity and 90% specificity. Thus, by Raman spectra, senescent cells can be distinguished from young proliferating cells with high reliability. For comparison, a general spectral classification of proliferating versus senescent cells for all cell strains treated as one data set showed less distinct clustering of young, midaged, and senescent cells (Figure S-3A). Nevertheless, with these coefficient values (Figure S-3B) from the calculation of all cell strains treated as a single data set, we were able to address the general molecular changes (Table S-4) due to the senescence process (based on ref 42). Spectral differences compared to single cell strain calculations were observed in the PLS-LD1 coefficients at 1122 and 1164 cm−1 assigned to proteins and at 1720 and 2970 cm−1 related to lipids. Overall, however, the data confirmed the senescent trend observations for single cell strains since most dominant bands coincided (Table S-4, “in total”). Testing cell strain specificities separately for the age groups, spectral differences were observed within PLS-LD1 and PLSLD2. BJ and IMR-90 cells were clearly separated in all age groups by PLS-LD1 (Figure S-5). MRC-5 and WI-38 cells showed more superpositions but nevertheless were parted by PLS-LD1. In turn, both cell strains were separated with some overlaps against BJ and IMR-90 cells by PLS-LD2. Within the midaged group, the most distinct differences were seen compared to the other age groups, indicating a molecular variance in the cell strain behavior during the transition into senescence. Overall, minimal spectral differences within the coefficients caused separations between cell strains, indicating few variations in biochemical compositions. For all PLS-LD1 coefficients, obvious bands within the fingerprint region were assigned to nucleic acids at 1056, 1484, and 1574 cm−1 and to proteins at 1012 cm−1 as well as to amide I from 1656 to 1672 cm−1 and to lipids at 1056, 1440, 1656, and 1668 cm−1. Thus, by RS, we observed minor biomolecular differences in abundance and composition among the four human fibroblast cell strains. When these strains transited into senescence, we identified molecular changes defined by molecular refolding and modification and, only to a small extent, by molecular abundance. Characterization of Replicative Senescence by FT-IR Spectroscopy. Next, we investigated the transition into senescence of the same four cell strains by FT-IR spectroscopy. Here, in order to avoid otherwise dominating water bands, the fixed cells were dried. FT-IR is complementary to RS in the quantum mechanical selection rules as well as different physical constrains on sample preparation. Compared to RS, data acquisition was faster, allowing collection of more data points per unit time. Major FT-IR bands are assigned in the literature (Table S-3, bottom).43 The FT-IR microscope allows one to position the aperture as well as visually inspect the cell morphology. An example of cell enlargement due to senescence is shown (Figure 4A,B). In contrast to Raman data acquisition, representative FT-IR spectra of whole cells were recorded as mean spectra in one measurement by placing an appropriate size of aperture over the selected cells. We observed no Mie scattering effects due to rather flat and adherent cell morphologies. Mie scattering has often been observed on spherical cells.44 Mean spectra of all fibroblast cell strains of the individual age groups were plotted (Figure 4C). Minute but reproducible spectral changes were observed for the young,

Figure 4. Microscopic images (A, B), utilizing the reflectance mode of the FT-IR spectroscope, also showing the aperture size (black lines) used for the measurements (ca. 50 × 50 μm). From each sample slide, single spectra of ca. 50 cells were measured, using an aperture of 50 × 50 μm at different slide positions. Displayed are (A) young BJ cells (PD 28) and (B) a senescent BJ cell (PD 70, circled in white). In (C), the mean spectra over all cell strains for the three age groups, young (blue), midaged (green), and senescent (red), were plotted. In (D), the mean spectra over all ages is shown for each cell strain. Arrows visualize small differences within the spectra for (C) the transition into senescence and (D) the fibroblast cell strains.

midaged, and senescent groups reflecting, in analogy to the Raman data sets, minor changes in the overall molecular composition of the cells transiting into senescence. Quantitatively, the three infrared spectra were rather similar, indicating considerable correspondence between the biochemical compositions of the three age groups (combined for all four fibroblast cell strains). Nevertheless, small but reproducible spectral changes were observed: The observed spectral changes reflected selected minute alterations in the overall biochemical composition associated with differences in the metabolism of young proliferating versus aging cells: We detected nucleic acids (1080 cm−1) and proteins (1652 cm−1) slightly downregulated and lipids (1732, 2852, 2920 cm−1) slightly upregulated in aged fibroblast cells (Figure 4C) confirming our Raman results. In parallel, also the spectra of the four cell strains (mean over all ages) showed considerable quantitative agreement (Figure 4D) indicating similarities in the biochemical composition. However, small but reproducible spectral changes were observed (indicated by arrows in Figure 4D). Additionally, the mean FT-IR spectra of the age groups and of the four cell strains are plotted with the respective standard deviations (SDs) given (Figure S-2E,G). Also, the difference spectra of young versus senescent cells and of all cell strains are displayed (Figure S-2F,H). 2942


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Figure 5. Classification of the spectra collected by FT-IR from young, midaged, and senescent cells by a PLS-LDA with six PLS components. For the score plot shown in (A), the calculation was performed for each cell strain separately. The different age levels are indicated by the colors (young: blue; midaged: green; senescent: red). The individual PDs are depicted by different symbols. (B) Mean spectra (“mean”) of each cell strain are shown in comparison to the PLS-LD1 and PLS-LD2 coefficients. The y-scale of the different spectral region has been adjusted for better visualization.

Figure 6. Prediction of young and senescent cells by leave-one-PD-out CV by a PLS-LDA testing and training calculation for all cell strains together, measured by FT-IR. In (A), a density plot of the PLS-LDA scores was shown. Young and senescent cells were tested (intense color). The density of training cells is shown in the pale color. Median of the age groups is shown as dashed lines. (B) PLS-LDA coefficients of all models calculated during leave-one-PD-out cross validation.

The biochemical transition from proliferation to senescence could be well followed by our FT-IR measurements. The coefficients associated with differentiations of proliferating against senescent cells were displayed (Figure 5B). In contrast to Raman spectra of proteins, FT-IR spectra exhibit very pronounced absorptions due to C−N stretching vibrations, referred to as the amide II band which, due to the associated selection rules, are not observed by Raman spectroscopy. Instead, the amide III region is inactive for transition in the infrared. In the CH stretching region, indicative for changes in the protein as well as lipid compositions, distinct spectral

All FT-IR data sets were subjected to the same multivariate statistical PLS-LDA data analysis as the Raman data. Six PLS components were identified as an optimum for the PLS-LDA model which described 80% of the data. The PLS-LDA-based FT-IR classification was carried out for all four cell strains individually. Plotting PLS-LD1 against PLS-LD2 clearly separated all cell strains in their young, midaged, and senescent state (Figure 5A). PLS-LD1 segregated young from senescent cells, whereas PLS-LD2 further differentiated the midaged cells from the two other groups. The absolute position of the groups within each plot was invariant for the applied calculation model. 2943


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groups (Figure S-6). In particular, young and midaged BJ, IMR90, and MRC-5 cells were well separated. Similar to the RS results, cell strain properties were found to vary in senescence: Spectral differences between the coefficients indicated variations in biochemical compositions between cell strains. All coefficient vectors showed obvious band differences within the whole analyzed wavenumber region, assigned to many cellular macromolecules: Bands between 900 and 1200 cm−1 were assigned to carbohydrates (1020, 1076, 1100, and 1150 cm−1) as well as nucleic acids (including 1230 cm−1), bands between 1300 and 1500 cm−1 to proteins (phenylalanine 1486 cm−1) and lipids (phospholipids 1384 cm−1), and prominent amide I and II bands between 1500 and 1800 cm−1 to proteins (tyrosine 1520 cm−1). Further vibrations due to functional ester groups in lipids were seen at 1740 cm−1 and due to CH3 and CH2 groups between 2848 and 2992 cm−1, mainly assigned to phospholipids. Taken together, mainly for proteins and lipids, but also for nucleic acids and carbohydrates, we observed age-induced differences mainly in composition and to a minor extent also in abundance for the four human fibroblast cell strains which allowed us to identify the cellular state as being either proliferating or senescent. These age-induced differences were mainly defined by biomolecular refolding and modification.

features for all four cell strains were detected. The region in the infrared is sensitive to spectral changes in the composition of fatty acid side chains. Fatty acids like phospholipids are amphiphilic molecules present in membranes.45 This observation was supported by spectral characteristics around 1750 cm−1, which are associated with the CO stretching vibrations of the carboxyl groups of esters also typical for membrane lipids. Especially, the coefficients of BJ, MRC-5, and WI-38 exhibited distinct features around the ester band. All four coefficients had zero crossings within the amide I region while the amide II around 1575 cm−1 was apparently less affected. Typically, the amide II is very sensitive to changes in β-sheet formation which seems not to play a predominant role in senescence progression. Further similarities among the cell strains were found in the low wavenumber region. These are dominated by ring vibrations of carbohydrates or of symmetric stretch vibrations of C−O−P or P−O−P groups, present in various oligo- and polysaccharides or nucleic acids. Typical DNA and RNA bands were observed due to the asymmetric stretching vibrations of phosphodiesters. However, none of the spectral patterns within the coefficients could be assigned distinctly to these macromolecules. Due to the relatively small amount of DNA or RNA per cell mass (∼10% w/w) compared to the higher amount of proteins (∼60% w/w), the region around 1652 cm−1 is generally more pronounced by the conformation-sensitive amide I band of αhelical structures and revealed variations in the protein contents. In summary, for the cell strain transition into senescence, FT-IR spectroscopy identified changes in biomolecular composition and modification but only minor changes in molecular abundance, consistent with and extending our Raman results. Sensitivity and specificity for recognition of senescent cells was around 82% and 100% for BJ, 87% and 97% for MRC-5, 88% and 99% for IMR-90, and 86% and 94% for WI-38 cells, respectively (Table S-6). Figure 6A shows the probability density of the PLS-LD scores for both training and testing data of the young and senescent groups. The test scores were in excellent agreement with the training scores, as expected due to the excellent stability of the PLS-LDA coefficients (Figure 6B). Compared to RS, FT-IR achieved a better separation between young and senescent cells. The overall accuracy was 81%. Senescent cells were recognized with 84% sensitivity and 97% specificity (Table S-7). Additionally, senescence classification was calculated for all cell strains treated as one data set (Figure S-4). A larger overlap was observed among young, midaged, and senescent cells in comparison to the classification of individual cell strains (Figure S-4A). Generally, PLS-LD1 separated young from senescent cells, while PLS-LD2 separated midaged cells from proliferating and senescent cells. The associated coefficient values were plotted (Figure S-4B). In agreement with single cell strain calculations, we were able to identify similar indicative band positions, especially at 1032, 11252, 1736, and 2868 cm−1 (Table S-5, “in total”). Due to the low number of cells measured in the midaged group of WI-38, the overlap with the senescent group appeared to be larger. In general, we could interpret these results, in combination with those of the RS calculation, as common molecular changes due to the cellular transition into senescence. Spectral differences between cell strains were observed when plotting PLS-LD1 against PLS-LD2 separately for the age

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DISCUSSION We demonstrated here that label-free Raman and FT-IR spectroscopy can well identify senescence in single cells and clearly differentiate senescent from proliferating states. Our results gave a profound novel insight into cellular age-induced biochemical changes. Despite the very small spectral differences during this transition, senescent cells were discriminated within four different cell strains. Because intensities of Raman spectra depend on the amount of molecules within the sampled volume, quantitative analyses of cells with large morphological differences are challenging. Quantitative differences can also relate to morphological changes within cells during their transition into senescence. Spectral differences in senescent cells can therefore arise either from molecular differences or from topographical changes. By means of calibration models for molecules, RS mapping had been combined with atomic force microscopy, demonstrating the potential of measuring concentration maps for cells with large morphological differences.46 Adequate spectral sampling effects were found to be sufficient to reproduce Raman spectra of whole cells from a minimum of small local sample sizes, demonstrating that the same degree of discrimination can be achieved as within full Raman images.47 By our experimental approach, therefore (i.e., within the narrow raster scans during RS imaging, averaging of the Raman spectra, and the amount of measured cells), morphological and chemical changes within the cells were correctly taken into account. Although the spectral differences were observed to be small during the transition into senescence, these differences were repeatable and consistent. As shown here, on the basis of these differences, multivariate models were able to discriminate between cells. Subsequent FT-IR measurements provided complementary biochemical information, which confirmed the results of the Raman experiments. In the Raman and infrared spectra, the transition into senescence was not associated with a single or very few selected marker bands. Instead, senescence was found to be a multifactorial complex process with minor changes in abundance but considerable compositional modifications of 2944


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lipids (CH2) are also regulated by cellular growth or apoptosis.28,63 In addition, cancer stimulation of fibroblasts induced significant changes in lipids and nucleic acids.64 With both techniques, RS and FT-IR, distinct spectral differences associated with senescence were obvious in all four fibroblast cell strains. Spectral differences between young and aged cells were determined for each cell strain individually as well as combined for all cells studied here. These findings identify a common senescent process, as also identified by ageinduced changes of mRNA expression levels.65 The transcriptome studies showed a considerable overlap of differentially expressed genes and their common protein expression profiles in different cell strains, indicating that a selected number of genes and pathways possess key relevance for the transition into senescence. Protein levels in these fibroblast strains also indicated strong age-associated changes with only some strain-specific contributions.31 Interestingly, no significant senescent trends with respect to ascending cell passages were identified when using point measurements at different cell positions.40 Imaging or spectral registration of the whole cell is thus essential for the statistical analysis when studying the transition into senescence. Drawbacks, such as the low throughput or time-consuming measurements due to high spatial resolution, can be overcome by using an integrated acquisition mode.66 For a solid statistical analysis, a high number of analyzed cells are crucial for data reliability. Classifications by RS achieved higher values than those by FT-IR. On the other hand, higher signal-to-noise ratios, no fluorescence, faster measurements, and therefore more data were obtained by FT-IR. However, other than for RS, FT-IR measurements usually require drying of the cells. Thus, principally, RS is better suited for clinical issues due to its in vivo applicability while FT-IR is superior for the identification and classification of in vitro samples. A more detailed molecular understanding of the transition into senescence would improve from a closer investigation of (i) individual cell compartments (for instance, nuclei and lipid vesicles) and (ii) the influence of the microenvironment on aging cells. Such studies, together with our transcriptome31 and spectroscopic analyses (here), will help to reveal the biomolecular processes triggering the contribution of senescent cells to the decline of tissue integrity and function.

mainly proteins and lipids (constituting around 75% of the cellular components) and only to a minor extent nucleic acids and carbohydrates (making up around 25%).43 Principally, the detailed interpretation of the spectral differences, arising from changes in bulk classes of cellular components and not from individual (bio)chemical species, is challenging as differences can originate from a multitude of substances. However, senescence induced protein and lipid alterations were also found by other analytical approaches,48−51 for example, being due to intracellular degradation. Furthermore, reactive oxygen species (ROS) triggers intracellular replicative senescence.52 By staining with oxidant-sensitive dyes and using laser-based techniques (like microscopy or flow cytometry), higher ROS levels were found in senescent compared to proliferating cells,53 indicating the important aging-related cellular influence of ROS, in particular on biomembranes. An increase in Raman intensity and infrared absorbance has been previously observed in senescent cells due to accumulations of modified molecules, e.g., due to DNA damage.54,55 In mesenchymal stem cells, the spectral ratio of RS bands from 1157 to 1174 cm−1, assigned to vibrations in the aromatic amino acids tyrosine and phenylalanine, was discussed as a possible senescence marker.56 PLS-LDA calculations of the individual cell strains resulted in weak alterations at around 1170 cm−1 (except for IMR-90 cells) which can be tentatively assigned to tyrosine and phenylalanine. Both increase during senescence and were discussed to be autofluorescent,56 as observed in our Raman spectra. Additionally, protein band alterations within our data may indicate lysosomal accumulations of waste materials and misfolded proteins. The ageinduced significant up-regulation of lysosome pathways in the same fibroblast cell strains is consistent with our spectroscopic data.31 Age-induced aggregation of biological waste material induces lipofuscin accumulation inside lysosomes10,57 which therefore can serve as senescence biomarker. These granules consist primarily of oxidized or misfolded proteins and lipids, for instance, triglycerides, free fatty acids, or cholesterol. Lipofuscin has cytotoxic effects in cell cycle arrested cells and thus may limit their lifespan. The amplification of mitochondrial and lysosomal alterations results in further formation of aggregates, due to the development of aberrant intermolecular interactions, additional oxidations, and cross-linkages.58 Also, mitochondrial DNA damages correlated with ROS production and were causal for cellular senescence.59 We observed age-induced changes in lipid conformation, as indicated by shifts at 2850 and 2882 cm−1 assigned to the CH2 symmetric and asymmetric stretch of lipids and proteins, respectively. When cellular senescence was induced by oncogene activation (treatment of a breast cancer cell line with doxycycline hyclate), cellular changes in phospholipids of nuclear membranes were observed by RS at 1656 cm−1, consistent with our findings.60 These differences were assigned to CC stretch vibrations of -cis isomers, indicating mainly unstable structural modifications during senescence. Also NMR spectroscopy detected an increased phospholipid metabolism in replicative as well as in induced senescent cells.61 In older patients, FT-IR identified increased lipid peroxidation, possibly due to membrane reactions with ROS and, as a consequence, induction of oxidative stress on cell levels.62 This general senescence-associated increase in structural modification and disorganization in biomembranes is confirmed by the spectral changes observed here. Concentration changes in acyl chains of

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CONCLUSIONS Here, we showed that label-free RS and FT-IR spectroscopy, in combination with multivariate analysis, can discriminate the proliferating from the senescent state in several human fibroblast strains on the single-cell level. We identified molecular differences among the four cell strains and in their individual transition into senescence, in agreement with earlier findings.30,65 These differences are not due to the amount of biomolecules present since we observed rather similar biomolecular abundance in all four cell strains and between proliferating and senescent cells. Instead, when the four cell strains transit into senescence, we found common molecular differences dominated by protein and lipid modifications. This biochemical finding supplements our recent observations that in these cell strains aging induces a shift in the mRNA transcription profile, indicating a change in cellular activities.31 Thus, aging induces a change in biomolecular, mainly protein and lipid, biochemical appearance (including degradation and storage as waste) rather than in their amount present in the cells. 2945


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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b04264. Overviews of the analyzed cells and their grouping into the three age groups together with their growth curves and positive staining for senescent cells; standard deviations of mean spectra and difference spectra of the age groups and cell strains; analysis of all cell strains to determine the behavior of the age groups independently from cell strain differences by PLS-LDA classification; Raman and FT-IR data; results of a general proliferation or senescence behavior and differences among cell strains (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Tel: +49 (0) 3641 206 300. Fax: +49 (0) 3641 206 399. Email: [email protected]. ORCID

Claudia Beleites: 0000-0003-1626-154X Jürgen Popp: 0000-0003-4257-593X Present Address ∥

S.M.: PAREXEL International GmbH, Am Bahnhof Westend 11, 14059 Berlin, Germany. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the research program of the Jena Centre for Systems Biology of Ageing - JenAge. We acknowledge JenAge funding by the German Ministry for Education and Research (Bundesministerium für Bildung und Forschung − BMBF; support code: 0315581). The research has been supported by grants from the Carl-Zeiss-Stiftung.

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