Raman and Infrared Spectroscopy Distinguishing Replicative

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Raman and infrared spectroscopy distinguish replicative senescent from proliferating primary human fibroblast cells by detecting spectral differences mainly due to biomolecular alterations Katharina Eberhardt, Claudia Beleites, Shiva Marthandan, Christian Matthäus, Stephan Diekmann, and Juergen Popp Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b04264 • Publication Date (Web): 27 Jan 2017 Downloaded from http://pubs.acs.org on January 27, 2017

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Analytical Chemistry

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Raman and infrared spectroscopy distinguish replicative senescent from proliferating primary human fibroblast cells by detecting spectral differences mainly due to biomolecular alterations Katharina Eberhardt 1,2, Claudia Beleites 1,3, Shiva Marthandan 4,5, Christian Matthäus 1,2, Stephan Diekmann 5, Jürgen Popp 1,2* 1

Leibniz Institute of Photonic Technology e. V., Albert-Einstein-Str. 9, 07745 Jena (Germany)2

Institute for Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena (Germany) 3

Chemometric Consulting and Chemometrix GmbH, Södeler Weg 19, 61200 Wölfersheim

(Germany) 4

current address: PAREXEL International GmbH, Am Bahnhof Westend 11, 14059 Berlin (Germany) 5 Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Department of Molecular Biology, Beutenbergstr. 11, 07745 Jena (Germany) *Corresponding Author: Jürgen Popp Albert-Einstein-Straße 9 07745 Jena, Germany Tel: +49 (0) 3641 206 300 / fax: +49 (0) 3641 206 399 / e-mail: [email protected] e-mail of authors: [email protected]; [email protected]; [email protected] [email protected]; [email protected]; [email protected] Keywords: ageing, dermal cells, vibrational spectroscopy, FTIR, PLS-LDA, CV ACS Paragon Plus Environment


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Summary 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 PLS-LDA model resulted in 75 % and 81 % accuracy for the Raman and 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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Apart from proliferation, human diploid primary cells can adopt several cellular states, as 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 multi-nucleated morphology 3

, secretion of proteins, such as growth factors or chemokines with paracrine or autocrine

effects (referred as “senescence-associated secretory phenotype”, SASP) 4, accumulation of lipofuscin as an aging pigment and granular particles 5 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 age-associated diseases 8. A combination of various stress factors 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 senescence-associated β-galactosidase (SA-β-Gal) activity 5 or the appearance of SASP. The expression of these markers is not uniform but varies distinctively between cells or 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, as for instance primary antibodies for immune-fluorescence or immune-blotting analysis 10, SA-β-Gal or lipofuscin auto-fluorescence 10. Cellular senescence is physiologically important, and plays a role in medical diagnostics, as it is involved in e.g. suppression of tumorigenesis

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, degenerative diseases, organismal aging

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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 non-invasive and non-destructive, and require minimal sample preparation. Here, we studied human primary fibroblasts by vibrational spectroscopy: Raman (RS) and Fourier transform infrared (FTIR) spectroscopy. By combining both techniques, complementary data were obtained, resulting in an increased validity of the spectroscopic studies ACS Paragon Plus Environment

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. In


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combination with optical microscopy, vibrational spectroscopic techniques have become wellestablished and powerful methodologies for label-free 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 based on the overall chemical composition, and alterations within samples can be observed. Using RS, individual cells can be characterized 16,17, as well as cell phenotypes 18 and biochemical differences related to cell cycle and proliferation

19,20

. Imaging by RS offers high

spatial (90 % confluence until they were sub-cultivated. For that, cells were washed once with 1x phosphate buffered saline (PBS, pH 7.4) and detached by adding 0.05 % Trypsin-EDTA. Fibroblasts were sub-cultivated in the ratio 1:4, 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 cover slips (CaF2, Crystal, Berlin, Germany), in order to avoid background scattering observed when using regular glass slides. After one day, adherent cells were washed three times with 1x PBS and fixed with 4 % paraformaldehyde in 1x PBS at room temperature for 10 min, according to standard protocols. The washing steps were repeated in order to remove embedding compounds; by this, spectral contaminations were minimized. For stock purposes cryoconservation of cell strains at various PDs were done

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. SA ߚ-Gal 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 partial presented in 29). Paraformaldehyde fixed cells were stored in PBS at 4 °C for up to two weeks. Once RS images were completed, cells were studied by FTIR 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 60x water immersion objective (Nikon NIR Apo 60x/NA 1.0 W, Tokyo, Japan). Raman and FTIR 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 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 non-destructive for fibroblast cells, consistent with

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. 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 FTIR data. Cell areas were scanned in in a raster pattern at a constant stage speed. Spectra were collected with a 0.5 µm resolution and an ACS Paragon Plus Environment


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integration time of 0.5 sec. Spectral range was 200 to 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. Within the young and proliferating group 87 cells, in the intermediate mid-aged group 85 cells and in the senescent group 91 cells were imaged. 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 FTIR measurements of cells in an aqueous environment resulted in very weak cellular signals dominated by water spectra. Thus, for FTIR 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 FTIR 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 co-additions in the wavenumber range from 900-4000 cm-1 using the Mercury Cadmium Telluride (MCT) detector. The microscope was equipped with a 15x/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 x 50 µm. The total of 1,544 measured single cells by FTIR spectroscopy are summarized in Table S-2. Within the young group 599 cells, in the mid-aged group 442 cells and in the senescent group 503 cells were measured. 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, preprocessing and analysis, respectively 34,35. Raman data: Cosmic spikes were removed from Raman images by a spike identification, threshold choosing 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 ACS Paragon Plus Environment

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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 500–1800 and 2800-3020 cm-1. Finally, the mean spectrum of each cell was calculated and the spectra were area normalized. FTIR data: Spectra were cut into the fingerprint and high wavenumber regions from 900 to 1800 cm-1 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 FTIR spectra were area normalized. Partial Least Squares - Linear Discriminant Analysis (PLS-LDA) was chosen as a classifier, implemented in the package “cbmodels”

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based on the combination of the packages “pls”

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for PLS and “MASS” 39 for LDA. PLS describes the data by a small number of latent variables (lv) that co-vary 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, FTIR models used 6 lv. The performance of the PLS-LDA model was checked for the RS and FTIR data by a 100x 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. 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 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 ACS Paragon Plus Environment


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growth (linear increase in PD versus days) with SA-β-Gal values of 0 (Fig. 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 (Fig. 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 (Fig. S-1B), in agreement with published data 31. As an additional senescence marker, after longterm 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, mid-aged and senescent (Table 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 Materials and Methods). 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 (Fig. 1A and B).


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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.

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), mid-aged (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) visualize 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 four. The mean Raman spectra of all cell strains show typical spectral features of human cells (Fig 1C and 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 ACS Paragon Plus Environment


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the age groups young, mid-aged and senescent were plotted (Fig. 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 the metabolism of young versus aging cells: We detected nucleic acids (1580 cm-1) and proteins (1658 cm-1) slightly down- and lipids (1732, 2850, 2930 cm-1) slightly up-regulated in aged fibroblast cells (Fig. 1C). Correspondingly, also the spectra of the four cell strains (mean over all ages) showed considerable similarity (Fig. 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 Fig. 1D). These minute but significant differences in spectral band positions and intensities were detected properly by using chemometric data analysis. Supplementing, the mean spectra of the age groups and of the four cell strains are plotted along with the respective standard deviations (SDs) (Fig. S-2A and C). In addition, the difference spectra of young versus senescent cells and of all cell strains are shown (Fig. S-2B and 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 PLS-linear discriminant (PLS-LD2), young, midaged and senescent cells could be separated for each of the four cell strains (Fig. 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 mid-aged 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 (Fig. 2B) reflected the spectral features associated with the differentiation of the young, intermediate mid-aged 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. ACS Paragon Plus Environment

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Figure 2: Classification of the RS spectra from young, mid-aged 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, mid-aged: 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 to 1800 cm-1 of the mean spectra was stretched four-fold. Clear changes were found in all spectral regions of all four cell strains (Fig. 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 ACS Paragon Plus Environment


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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 as 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 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 afterwards 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 distinguish these two groups, leaving out the mid-aged 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 (Fig. 3B). 15 combinations were tested and the respective 16th was trained and plotted. Compared to the calculation for all cell strains together (Fig. S-3B), weak bands were enhanced and spectral differences in the ACS Paragon Plus Environment

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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 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.

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 pale color. Median of the age groups is shown as dashed lines. B) 16 PLS-LDA coefficients of all models calculated during leave-one-PD-out cross validation. 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, mid-aged and senescent cells (Fig. S-3A). Nevertheless, with these coefficient values (Fig. 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 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”).



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Testing cell strain specificities separately for the age groups, spectral differences were observed within PLS-LD1 and PLS-LD2. BJ and IMR-90 cells were clearly separated in all age groups by PLS-LD1 (Fig. 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 mid-aged 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 seen at 1056, 1484 and 1574 cm-1, to proteins seen at 1012 cm-1 as well as from 1656 to 1672 cm-1 (amide I) 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 FTIR spectroscopy Next, we investigated the transition into senescence of the same four cell strains by FTIR spectroscopy. Here, in order to avoid otherwise dominating water bands, the fixed cells were dried. FTIR 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 FTIR bands are assigned in the literature (Table S-3, bottom) 43. The FTIR microscope allows positioning of the aperture as well as visual inspection of cell morphology. An example of cell enlargement due to senescence is shown (Fig. 4A and B). In contrast to Raman data acquisition, representative FTIR 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 (Fig. 4C). Minute but reproducible spectral changes were observed for the young, mid-aged and senescent groups


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reflecting, in analogy to the Raman data sets, minor changes in the overall molecular composition of the cells transiting into senescence.

Figure 4: Microscopic images (A, B), utilizing the reflectance mode of the FTIR spectroscope, also showing the aperture size (black lines) used for the measurements (ca. 50 x 50 µm). From each sample slide, single spectra of ca. 50 cells were measured, using an aperture of 50x50 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), mid-aged (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. 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 downACS Paragon Plus Environment


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and lipids (1732, 2852, 2920 cm-1) slightly up-regulated in aged fibroblast cells (Fig. 4C) confirming our Raman results. In parallel, also the spectra of the four cell strains (mean over all ages) showed considerable quantitative agreement (Fig. 4D) indicating similarities in the biochemical composition. However, small but reproducible spectral changes were observed (indicated by arrows in Fig. 4D). Supplementing, the mean FTIR spectra of the age groups and of the four cell strains are plotted with the respective standard deviations (SDs) given (Fig. S-2E and G). Also the difference spectra of young versus senescent cells and of all cell strains are displayed (Fig. S-2F and H). All FTIR 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 FTIR classification was carried out for all four cell strains individually. Plotting PLS-LD1 against PLS-LD2 clearly separated all cell strains in their young, mid-aged and senescent state (Fig. 5A). PLS-LD1 segregated young from senescent cells, whereas PLS-LD2 further differentiated the mid-aged cells from the two other groups. The absolute position of the groups within each plot was invariant for the applied calculation model. The biochemical transition from proliferation to senescence could be well followed by our FTIR measurements. The coefficients associated with differentiations of proliferating against senescent cells were displayed (Fig. 5B). In contrast to Raman spectra of proteins, FTIR spectra exhibit very pronounced absorptions due to C-N stretching vibrations, referred to as 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 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.


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Typically, the amide II is very sensitive to changes in β-sheet formation which seems not to play a predominant role in senescence progression.

Figure 5: Classification of the spectra collected by FTIR from young, mid-aged 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, mid-aged: 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. 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 PO-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.



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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 α-helical structures and revealed variations in the protein contents. In summary, for the cell strain transition into senescence, FTIR 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 (Fig. 6B). Compared to RS, FTIR 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).


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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 FTIR. In 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 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. Additionally, senescence classification was calculated for all cell strains treated as one data set (Fig. S-4). A larger overlap was observed among young, mid-aged and senescent cells in comparison to the classification of individual cell strains (Fig. S-4A). Generally, PLS-LD1 separated young from senescent cells, while PLS-LD2 separated mid-aged cells from proliferating and senescent cells. The associated coefficient values were plotted (Fig. 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 mid-aged 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 groups (Fig. S-6). In particular, young and mid-aged BJ, IMR-90 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



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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. Discussion We demonstrated here that label-free Raman and FTIR 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


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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, based on these differences multivariate models were able to discriminate between cells. Subsequent FTIR 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 multi-factorial complex process with minor changes in abundance but considerable compositional modifications of mainly proteins and lipids (constituting around 75 % of the cellular components) and only to 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 auto-



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fluorescent 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 age-induced 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 lysosomes 10,57 which therefore can serve as senescence biomarker. These granules consist primarily of oxidized or misfolded proteins and lipids, as 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 FTIR

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 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 FTIR, 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 age-induced ACS Paragon Plus Environment

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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 ageassociated changes with only some strain-specific contributions 31. Interestingly, no significant senescent trends in 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 as for instance 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 by FTIR. On the other hand, higher signal-to-noise ratios, no fluorescence, faster measurements and therefore more data were obtained by FTIR. However, other than for RS, FTIR measurements usually require drying of the cells. Thus principally, RS is better suited for clinical issues due to its in vivo applicability while FTIR 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 (as for instance nuclei and lipid vesicles) and (ii) the influence of the microenvironment on aging cells. Such studies, together with our transcriptome

31

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. Conclusions Here we showed that label-free RS and FTIR 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. ACS Paragon Plus Environment


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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. Associated Content Supporting Information Overviews of the analyzed cells and their grouping into the three age groups together with their growth curves and positive staining for senescent cells were given. Standard deviations of mean spectra and difference spectra of the age groups and cell strains were plotted. In comparison to the single cell strain classification of the age groups, all cell strains were analyzed together to determine the behavior of the age groups independently from cell strain differences. This classification by PLS-LDA was carried out with the same parameters as described in the article. Furthermore, Raman and FTIR data were analyzed to examine cell-strain specificities. Results of a general proliferation or senescence behavior and differences among cell strains were plotted in the Supporting Information but described and discussed in the article. This material is available free of charge via the Internet at http://pubs.acs.org. Author Information Corresponding Authors *E-mail: [email protected]. Notes The authors declare no competing financial interest. Acknowledgements 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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For TOC only



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Raman and Infrared Spectroscopy Distinguishing Replicative

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