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The contribution of RNA to the FTIR spectrum of eukaryotic cells Paolo Zucchiatti, Elisa Mitri, Sasa Kenig, Fulvio Billè, George Kourousias, Diana Eva Bedolla, and Lisa Vaccari Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b02744 • Publication Date (Web): 14 Nov 2016 Downloaded from http://pubs.acs.org on November 15, 2016
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The contribution of RNA to the FTIR spectrum of eukaryotic cells Paolo Zucchiatti1,2, Elisa Mitri1, Saša Kenig1, Fulvio Billè1, George Kourousias1, Diana Bedolla1 and Lisa Vaccari1* 1 Elettra – Sincrotrone Trieste, S.S. 14 Km 163.5, 34151, Trieste, Italy 2 Università degli Studi di Trieste, Dipartimento di Fisica, via Valerio 2, 34127 Trieste, Italy ABSTRACT: We report on an optimized protocol for the digestion of cellular RNA, which minimally affects the cell membrane integrity, maintaining substantially unaltered the vibrational contributions of the other cellular macromolecules. The design of this protocol allowed us to collect the first FTIR spectra of intact hydrated B16 mouse melanoma cells deprived of RNA and to highlight in-cell diagnostic spectral features of it. Complementing the cellular results with the FTIR analysis of extracted RNA, ds-DNA and ss-cDNA and isolated nuclei, we verified that the spectral component centered at ~1220 cm-1 is a good qualitative and semi-quantitative marker of cellular DNA, since minimally affected by cellular RNA removal. Conversely, the band centered at ~1240 cm-1, conventionally attributed to RNA, is only a qualitative marker of it, since its intensity is majorly influenced by other macromolecules containing diverse phosphate groups, such as phospholipids and phosphorylated proteins. On the other hand, we proved that the spectral contribution centered at ~1120 cm-1 is the most reliable indicator of variations in cellular RNA levels, that better correlates with cellular metabolic activity. The achievement of these results have been made possible also by the implementation of new methods for baseline correction and automated peak fitting, presented in this paper.
Introduction: The possibility offered by Fourier Transform InfraRed Microspectroscopy (FTIRM) to get spatially resolved biochemical information from the sample vibrational profile in a label-free and non-destructive manner boosted its application for life sciences in the last few decades. The hybridization of FTIRM field with biomedical microelectromechanical systems (bio-MEMS) enabled exciting opportunities for the analysis of biological samples, such as cells and tissues under physiological conditions1,2. Real-time FTIR monitoring of cellular events was first proposed for prokaryotic cells3,4 and later on for eukaryotic ones, providing information, for example, on protein phosphorylation during neuronal differentiation5 and protein aggregation in living mammalian cells6,7. However, despite the well-documented diagnostic and prognostic capabilities of FTIRM, the interpretation of cellular spectra is still not exhaustive. While the inter- and intra-sample heterogeneity prevents a unified picture of the finest vibrational details, the complexity of the biological matrix only partially accounts for their fragmentary comprehension. Until now, the interpretation of the cellular spectra has been predominantly based on a wide and detailed collections of FTIR spectral frequencies that originate from studies on model systems, such as individual molecules (often of synthetic origin), and simple mixtures, mostly collected under dry conditions8,9. Cell infrared spectra have been therefore deciphered as the sum of individu-
al spectral contributions, roughly weighted on the relative abundance of the most relevant cellular constituents. This reductionist approach is obviously an over-simplification of the cellular milieu, since biomolecules in cells adopt higher order structures that guide their functionality, as a consequence of the interaction with other molecules and their micro-environment10. Instead, holistic strategies should be designed, and results should be integrated with the acquired background knowledge for shedding new light on the complexity of the cellular vibrational profile. The paramount role of the physiological environment in FTIRM of cells is the clearest evidence of the aforementioned paradigm. The fact that DNA geometry is sensitive to humidity is known from the fifties11. The extent of BDNA double helix to the A-form conversion depends on the degree of dehydration, and it is mainly reflected as a shift of the asymmetric stretching band of phosphates moieties from ~1225 cm-1 to ~1240 cm-1, in both isolated ds-DNA and cellular FTIR spectra12. However, DNA is only one of the two cellular nucleic acids, and actually ribonucleic acid (RNA) comprises approximately 50% of the total cellular nucleic acid content in all the progressive stages of the cellcycle13. Despite its constitutive contribution and its essential part in cell life, RNA has been thus far almost ignored in the analysis of cellular spectra. Since RNA can only assume the A-form, the spectral contribution at ~1240 cm-1 in hydrated cells has been on a first approximation considered diagnostic of the RNA cellular content14, and its contribution to cell cycle progression has already been point-
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ed out by the authors15,16. However, there is up-to-date no clear attribution of cellular spectral features of the RNA. With special regard to its phosphate backbone, little is known on the extent of the overlapping of the asymmetric and symmetric stretching modes with the spectral features of other molecules containing the same moieties, such as DNA, but also phospholipids and phosphorylated proteins. In the present study, we present an original approach for the in-cell assessment of RNA infrared peaks, based on its removal from B16 mouse melanoma cells, without compromising cellular integrity and modifying the cellular environment. Cellular data have been integrated with the study of the vibrational features of ds-DNA, ss-cDNA, ssRNA and nuclei extracted from the same cell line. The experimental design, implemented with newly developed methods for spectral baseline correction and automated band fitting, allowed us to identify in an original and reliable manner the spectral features most sensitive to RNA variations at cellular level, on the base of the residual intensity of its characteristic spectral features upon RNA cellular digestion. Methods Preparation of B16 mouse melanoma cellular samples We used B16 mouse melanoma cell line as a model for the study. Cells were grown in Dulbecco’s modified Eagle’s medium (DMEM – PAA Laboratories GmbH) supplemented with 10% FBS (EuroClone SpA) at 37°C in 5% CO2 atmosphere and routinely passaged every 2-3 days. Cells were collected by trypsinization and extensively washed in PBS (Control cells, Ctrl hereafter). Cell cycle phase distribution was monitored by flow cytometry after PI staining. Subconfluent B16 cells were used unless stated otherwise. The average cell cycle phase distribution for such B16 culture reflects an asynchronous population: 57.4±2.6% G1/G0; 29.8±1.5% S; 12.8±1.4% G2/M. RNA, ds-DNA and ss-cDNA purification from B16 cells RNA was extracted by Isol-RNA lysis reagent (5-Prime) and following TRIzol® Plus RNA Purification Kit protocol (Thermo Fisher Scientific). All passages were performed maintaining the temperature of 4°C to avoid RNA degradation. RNA was resuspended in 20 µL of RNase and DNase free water. ds-DNA was extracted using QIAmp® DNA Blood Mini Kit (Qiagen) and purified by mini-spin columns of the kit. Purified DNA was eluted from the columns with 50 µL of RNase and DNase free water. Complementary DNA (cDNA) was collected and purified in order to obtain ss-DNA. cDNA was obtained with retrotranscription of extracted B16 RNA by M-MLV Reverse Transcriptase Kit (Invitrogen Corp. – Thermo Fisher Scientific). After retrotranscription, RNA was digested with 2 µL of 20 mg/mL RNase A at 37°C for 30 minutes. ss-DNA was then purified by using QIAquick® PCR Purification Kit (Qi-
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agen) using a microcentrifuge. cDNA was resuspended in 35 µL of DNase free water. Concentration and purity of all nucleic acids solutions were estimated using NanoDrop 2000 UV-Vis spectrophotometer (Thermo Fisher Scientific). Samples of ds-DNA (~0.9μg/μL and A260/280~1.9), ss-DNA (~0.12 μg/μL and A260/280~1.92) and RNA (~4.5μg/μL and A260/280 ~2.01) were used for further studies. In addition, the integrity of extracted RNA was tested on 1% agarose gel. Degradation of B16 cellular RNA To enable RNase A to enter cells and degrade the RNA, cells first had to be permeabilized. To test the effect of Triton X100 and find its optimal concentration B16 cells were treated with Triton X-100 in PBS at different concentrations, 0.010% (sample name T0.01 hereafter), 0.025% (T0.025 hereafter) or 0.100% (T0.100 hereafter) for 5 minutes at room temperature. Upon treatment, cells were extensively washed in PBS. To degrade cellular RNA, cells were immediately treated with 0.2 mg/mL of RNase A for 30 minutes at room temperature (RNase cells hereafter) and washed in PBS. To verify efficient RNA digestion, RNA was isolated from an aliquot of Ctrl and RNase cells and visualized on 1% agarose gel. Cells were then fixed in 3.7% of PFA. Each sample was prepared in duplicate. B16 Nuclei Extraction For the nuclei extraction, 107 B16 cells were collected by scrapping and washed in ice-cold PBS. Cells were then resuspended in Lysis buffer (10mM Tris, pH7.5, 10 mM NaCl, 3mM MgCl2, 0.05% NP40) supplemented with protease inhibitor cocktail (Roche) and incubated on ice for 15 min. Cytoplasmic fraction was removed by centrifugation. For FTIR measurements, nuclei were fixed in 2% PFA and resuspended in physiological solution. Western blot analysis of α-tubulin and topoisomerase IISanta Cruzwas run in parallel to confirm efficient cytoplasm removal. For our purposes, nuclei were lysed in CEB buffer (HEPES 10 mM, KCl 60 mM, EDTA 1 mM, NP40 1%). FTIR measurements: setup and data collection The spectroscopic experiments were carried out at the infrared beamline SISSI (Synchrotron Infrared Source for Spectroscopic and Imaging) at Elettra-Sincrotrone Trieste, Italy17. RNA, ds-DNA and ss-DNA solutions were measured by ATR-FTIR spectroscopy using the MIRacle™ Single Reflection ATR box (PIKE Technologies) with Germanium (Ge) IRE. Spectra were acquired using the Vertex 70 interferometer (Bruker Corporation) equipped with a deuterated triglycine sulfate (DTGS) detector. FTIR-ATR spectra were collected from 3900 to 650 cm-1 in double side, forward/backward acquisition mode with a scanner velocity of 5 kHz. For each spectrum, 128 scans were averaged with
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a spectral resolution of 4 cm-1. Fourier transform was carried out with Mertz phase correction, Blackman-Harris 3terms apodization function. Background was collected on clean Ge IRE. 5μL of RNA, ds-DNA and ss-DNA were dropped onto the IRE crystal, and ATR-FTIR spectra continuously acquired up to complete dehydration. FTIR microspectra of control and treated cells, both live and fixed in hydrated conditions, were measured using microfluidic devices, manufactured at LILIT (Laboratory for Interdisciplinary LIThography) of IOM-CNR Trieste, Italy, following a procedure detailed elsewhere18. CaF2 windows 0.5 mm thick, were used for the fabrication of the devices with an optical path of ~ 7.5 μm. Upon treatments, cells were washed twice by centrifugation (155 x g, 3’ at RT) and resuspended in a sterile physiological solution 0.9% NaCl, achieving a final concentration of approximately 1x106 cells/mL. 2 μL of cell suspension were then dropped within the device. Spectra were collected using a Bruker Hyperion 3000 Vis-IR microscope equipped with a midband HgCdTe detector having a 100 µm sensitive element, coupled with a Bruker Vertex 70 interferometer. Spectra were collected in transmission mode using a 15x Schwarzschild condenser and objective and setting knife-edge apertures to 40 x 40 µm in order to collect groups of three to four cells. For each spectrum, collected from 4000 to 800 cm-1 in double side, forward/backward acquisition mode with a scanner velocity of 40 kHz, 265 scans were averaged with a spectral resolution of 4 cm-1. Non linearity correction for HgCdTe detector was applied. Fourier transform was carried out with Mertz phase correction, Blackman-Harris 3-terms apodization function. For each condition, spectra of 80 to 100 cells were collected. The spectrum of a buffer point close to the cells was also collected every 10 groups of cells. Both cells and buffers spectra were rationed against a background taken on a 1mm thick CaF2 window. B16 hydrated cell nuclei were measured with the same procedure. FTIR data analysis Raw spectra were corrected for carbon dioxide and water vapor using OPUS 7.5 routines (Bruker Optics GmbH). No further treatments have been done for ATR-FTIR data. For cell, nuclei and buffer spectra, the IBI method (see below) was used for buffer subtraction and baseline correction in the spectral region 1350-900 cm-1. Subsequently, vector normalized second derivative were computed (Savitzky-Golay filter, 13 smoothing points) in the region of interest by OPUS 7.5 routines and the rapid automated fitting was done (see below). PCA analysis was done using HyperSpec and stats packages in R HyperSpec19 on subtracted absorbance cellular spectra14, normalized in the spectral region (30002800&1595-945 cm-1) and baseline corrected in the same spectral range. A new Iterative method for local Baseline Identification
In the spectral region 1350-900 cm-1, there is virtually no need for water subtraction for highlighting cellular spectral bands, since this region is free from water features (see Fig. 1). However, subtraction of the buffer contribution is fundamental for getting rid of potential buffer contaminants20. As a consequence of the severe cut in transmission introduced by CaF2 below ~1150 cm-1 and to the slightly different slope of the raw cell and buffer curves, determined by the small differences in refractive indexes of the two media, the subtraction procedure produces partially negative spectra, that cannot be properly baseline corrected using standard implemented baseline correction routines (see Fig. 1C). BaF2 based microfluidic chips would be better for nucleic acid analysis, due to the higher transparency of this material in the spectral region of interest. The problem of solubility of BaF2, and its potential cellular cytotoxicity, has been previously addressed by the authors via the modification of the BaF2 surface properties by sputtering a thin layer of Si on the window surfaces in contact to water media21. Unfortunately, this approach is more time- and money-consuming, while CaF2 can be used as it is purchased upon sterilization, showing a toxicity comparable to conventional plastic and glass materials1. Aiming to exploit the acquired knowledge for the analysis of dynamic events in live unfixed mammalian cells, we therefore devoted time to the optimization of a suitable baseline correction routine and we developed an Iterative method for Baseline Identification (IBI). The proposed method assumes certain anchor points in the raw cell spectrum, set manually where the subtracted signal should be zero. We refer to these anchor points as knots. Those knots have been set at 1350, 1181, 1150, 1000, 945, 900 cm-1, according to the zero baseline points on dried B16 cellular spectra collected as a densely packed monolayer deposited on a 0.5 mm thick CaF2 window. In these conditions, water problems are eliminated, while CaF2 cut and Mie scattering effects are minimized. These knots are used for a subsequent interpolation that implies an iterative procedure, which moves the knots ±1cm-1/iteration depending on the intermediate results. Number of iterations can vary from 4 to 14 depending on the sample. The final solution requires that the produced optimized baseline when subtracted from the measured spectra does not result in negative spectral values within the region of interest (See Fig. 1). The method utilizes an interpolation algorithm. Since the most commonly used algorithms, like those based on linear and spline approaches, produced suboptimal results, a specific type of interpolation proposed by Akima22 has been identified as the most suitable for this method (See Fig. S1 in Supporting Information for more details). Rapid Automated Fitting IBI method has been optimized in order to provide an optimal baseline for corrected spectra prior to the subsequent step of peak fitting. Specifically, spectra bands in the regions 1350-1181 cm-1 and 1150-945 cm-1 were deconvolved, using a number of 8 Gaussian components for each. The number and position of the Gaussian peaks have been selected on the base of second derivative analysis of the
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spectra. For the rapid analysis of hundreds of individual spectra, we developed an automated peak fitting procedure. Using the Powell method of minimization23, both bands were fitted using 24 parameters (Gaussian curve height, width and position for each band component), with the quadratic norm of residuals as quality function. Band position was let to vary of ±4 cm-1 (according to the spectral resolution), bandwidth was let to vary within 10 to 40 cm-1 range, while band height was imposed positive and let to vary freely. This new method is very fast in terms of computation and requires no user input, with the exception of the initial peak positions. Both the Iterative Baseline Identification method and the automated fitting have been implemented in Python. ANOVA test (OriginPro2016) was used to determine whether there were any statistically significant differences between band fitting results for B16 Ctrl, T001, RNase and extracted nuclei groups. Results and Discussion ATR-FTIR spectra of RNA, ds-DNA and ss-DNA from B16 cells A vast amount of literature exists on the determination of the spectral features of isolated ds-DNA and RNA strands, and the reader is referred to it for a punctual band assignment of the spectral features within the entire MIR spectral range24–27. However, in most of the cited papers, authors analyzed isolated ds-DNA and RNA filaments, mostly collected under dry conditions and often of synthetic origin28,29. Few studies focused on simplified mixture of dsDNA and hemoglobin30 and ds-DNA, RNA and Human Serum Albumin (HSA)31,32, all systems very different from the cellular complexity.
Fig. 1. Application of the Iterative Baseline Identification method. (A): Raw cell spectrum (continuous black line) and corresponding buffer spectrum (dotted black line). (B): Subtracted spectra not corrected for the baseline (continuous black line). Knots points are visu-
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alized as well as their shift during interpolation procedure with 6 interactions (dark grey for iteration 1, light grey for iterations 2 to 5, black for final iteration 6). The baseline trend is also shown, with the same color code of knots. (C): The result of the IBI procedure utilizing the Akima interpolation algorithm is plot (continuous black line). Spectral profiles obtained by applying baseline routines of OPUS 7.5 software are shown for comparison: rubberband correction method with 6 baseline points (dashed black line) and concave rubberband correction method with 6 baseline points and 6 interactions (dotted black line).
The purpose of our work is to establish a finer correlation between DNA and RNA contributions to the spectra of the whole cell. As a first preliminary step, RNA, ds-DNA and ssDNA were extracted directly from B16 mouse melanoma cells. In Fig. 2, the ATR-FTIR spectra of RNA, ds-DNA and ss-DNA are reported at comparable level of hydration, estimated on the base of the intensity of the combination band of bending and vibrational modes of water in the 2650-1750 cm-1 spectral region. More details on the hydration dependent spectral behavior within the 1800-900 cm1 spectral range are given in Supporting Information (see Fig. S2). Nevertheless, aiming to extend our results to cellular level, we focused our attention to the 1350-900 cm-1 sub-range, since it is freer from spectral contributions of other biomolecules in complex cellular systems than the higher wavenumber regions, i.e. phospholipids, Amide I and II bands in the 1750-1500 cm-1 range. In addition, the latter range is the most affected by any buffer subtraction procedure33, and therefore less reliable than the 1350-900 cm-1 one when considering spectral intensities. For the RNA spectrum, two clear spectral components can be seen in Fig. 2, centered at 1240 and 1220 cm-1, while the ds-DNA band in the same region is sharper and peaked at 1226 cm-1. The shift of the asymmetric stretching band of nucleic acid phosphate moieties of ds-DNA from ~1225 to ~1240 cm-1 as a consequence of dehydration was the subject of several studies and a clear correlation was established between the band position and the two main conformations of ds-DNA12. In particular, the component peaked at ~1225 cm-1 was related to the B-form of hydrated ds-DNA while the one at ~1240 cm-1 to the A-form of dehydrated ds-DNA. The close relation between position/intensity of this band with the helical 3D arrangement of nucleic acids is further supported by the analysis of ssDNA spectrum (see Fig. 2), that is one of the few spectra of ss-DNA extracted from mammalian cells reported in literature to the best of our knowledge34. The contamination of the ss-DNA sample with proteins can be excluded by the measured UV absorbance ratio A260/280 ~ 1.92, but residual ds-DNA strains and RNA-DNA duplexes are possibly present in our sample. Nevertheless, it can be easily appreciated that the intensity of the asymmetric stretching band of phosphates is greatly suppressed in comparison with the symmetric one, and differently structured compared to the corresponding RNA and ds-DNA bands (band components are peaked at ~1247 cm-1, ~1233 cm-1 and ~1219 cm-1). With few exceptions, such as ds-RNA of some viruses and synthetic siRNA, in nature RNA is found as a single-
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strand35,36. However, it often folds onto itself in functional units characterized by intra-pairing of short sequences (510 bp) folded in A-form DNA like, due to the steric hindrance of the hydroxyl group at C2’of ribose. Despite short and not easily quantifiable, the number of these sequences is for sure not negligible, and they are responsible for the 1240 cm-1 component of the ATR-FTIR RNA spectrum. However, we cannot assume that secondary and tertiary RNA cellular structures are entirely maintained under denaturing conditions used for RNA extraction. Moreover, RNA is very sensitive to degradation37. Despite RNA integrity was tested upon each extraction (see Fig. S3) and samples were stored at -80 °C until the measurements, RNA fragmentation during data collection time (about 30 minutes) cannot be excluded. These considerations directly correlates with the observation that isolated RNA spectra reported in literature31,34 have different weights of the 1240 cm-1 and 1220 cm-1 components. The latter has been attributed by many authors to ribose38, and should be more directly correlated with the total RNA concentration. A different extent of DNA-like A-form of RNA subdomains directly affects the relative weight of the aforementioned contributions.
Fig. 2 ATR-FTIR spectra of RNA, ss-DNA and ds-DNA extracted from asynchronous B16 mouse melanoma cells. ATR spectra are shifted by
The symmetric stretching band of phosphates is centered at ~1086 cm-1 for all the measured nucleic acid forms, and it is much less sensitive to helical folding than the asymmetric, as already reported in litteraure12. Conversely, the ribose nature of the phosphate-sugar backbone of RNA is clearly appreciable from the presence of a well-defined spectral band centered at ~1119 cm-1 (stretching vibration of the skeletal structure around the C2’-OH group of ribose)39. Instead, spectral features of the skeletal C-O fall close to each other for ribose (spectral component at 1059 cm-1) and deoxyribose (spectral component at 1053 cm1)24. The spectral bands centered at ~993 cm-1 and ~914 cm-1 are present only in the spectra of RNA extracted from B16 asynchronous cell, diagnostic of the vibrational modes involving C2’-O and C-C ring vibrations of ribose25. Conversely, the spectral band centered at ~970 cm-1, assigned to the backbone vibrations of nucleic acids40, is almost invariant in position for all the measured nucleic acid forms. The correlation between its molar absorptivity and the helical form of nucleic acids can be deduced comparing RNA and ss-DNA spectra versus ds-DNA. Finally, a spectral component centered at ~940 cm-1 can be seen only for ds-DNA, representing AT base pairs of B-form helices, but at very low intensity41.
0.2 a.u. for comparison purposes.
Therefore, by comparing the spectral shapes of ds-DNA, ssDNA and RNA extracted from B16 mouse melanoma cells in the 1350-900 cm-1 region, four bands can be considered sufficiently well-separated and intense enough to be diagnostic of cellular RNA compared to ds-DNA isolated from B16 mouse melanoma cells. They are centered at about 1240 cm-1, 1119 cm-1, 994 cm-1 and 914 cm-1. However, a straightforward extrapolation of this information to cellular analysis is not possible without further verifications, mainly for two reasons. First, it has to be taken into account that in a complex system vibrational bands of other biomolecules could overlap with the selected ones. Second,
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as already highlighted, the molar absorptivity of the nucleic acid is very sensitive to their secondary and tertiary structure, which we expect to be different at cellular level with respect to isolated molecules. Total removal of cellular RNA To characterize the spectral contributions of RNA to the complete cellular profile, we used RNase A, an enzyme commonly employed for the digestion of both single and double stranded chains of RNA, paying attention to preserve the cell integrity. To enable the efficient entering of RNase A into the cell, we optimized a protocol on unfixed B16 cells with Triton X100. Three different concentrations of detergent were tested (0.010% 0.025% and 0.100%). For each concentration, cell integrity was preliminary evaluated by visible microscopy (See Figure S4). A dramatic cellular shrinkage was observed for T0.100 cells, symptomatic of a significant loss of cytosolic material due to an extensive membrane poration. Therefore, the T0.100 condition was excluded a priori. The assessment of the cellular biochemical integrity for the other two concentrations was done by FTIR microscopy, measuring small group of asynchronous cells. PCA analysis of the collected spectra shows that Ctrl and T0.010 cells are scarcely separated in the PC space (PC1 versus PC2), while T0.025 cells form a well separated group (see Fig. 3A). The interpretation of the loading 1 (see Fig. 3B) suggests that the separation between T0.025 and the other conditions (T0.010 and Ctrl) is mostly due to the increase in the lipid and nucleic acid content relative to cellular proteins, as suggested by the strong negative peaks associated to symmetric and asymmetric stretching of methyl and methylene groups (3000-2800 cm-1) and to phosphate moieties (~1300-950 cm-1). This result seems to be inconsistent with respect to the detergent effect of Triton but it could be explained by the fact that the loss of membrane phospholipids is minor compared to the loss of cytosolic material. In addition, cellular lipids are not localized only in the external membrane, but they are also the main components of the internal membrane network that has a total surface larger than the surface of plasma membrane. At the same time, the major part of DNA and RNA molecules is stored in large organelles, such as nucleus and ribosomes, that cannot pass through the little pores formed on the cellular membrane. Conversely, cytosolic proteins, that are the major constituents of the cytosol, flow out from the cell through the pores, as can be deduced by the positive peak in the Am ide II region in PC1 plot. This event induces T0.025 cell shrinkage (See Figure S4) and the morphological variations can partially account for different scattering properties, and therefore band position, among Ctrl and T0.010 versus T0.025 cells42. Fig. 3 Effects of Triton and RNase. (A) Scatter plot of the Principal Component Analysis (PCA) scores: Black dots represent Ctrl cells, Light Grey dots represent T0.010 cells, and Dark Grey dots represent T0.025 cells. (B) Profile of the PC1 loading in the spectral region of PCA analysis (3000-2800, 1595-945 cm-1). (C) Agarose Gel Electrophoresis of RNA. m=RNA markers, 1=Ctrl, 2=T0.010, 3=RNase
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In summary, we can conclude that the membrane damage induced by T0.010 does not perturb extensively the B16 cells (PC2 accounts for 3.6% of the total variance of the system), while the cell membrane poration is much more extensive for T0.025 (PC1 accounts for 90.0% of the total variance of the system). Therefore, 0.01% Triton X100 was selected as an optimal concentration, that does not induce major morphological and spectral changes but enables efficient RNA digestion. Digestion efficiency was assessed by agarose gel electrophoresis (see Fig. 3C). The cellular RNA content strongly depends on the development stage and cell type. However, a canonical mammalian cell contains about 10-30 pg of RNA35, and ribosomal RNA (rRNA) constitutes 80-85% of the total RNA. rRNA is mainly localized in the ribosomes, cellular organelles designated for protein synthesis, composed by rRNA for about 2/3 and ribosomal proteins for the remaining weight fraction. 28S and 18S rRNA are the major constituents of ribosomes. As can be seen from the Fig. 3C, the RNA extracted from control cells shows two main bands, corresponding to the aforementioned rRNA constituents. In treated cells, only the digested RNA is visible (as a smear at the end of RNase lane in the gel), confirming that a concentration of 0.010% of Triton is sufficient for the efficient cellular uptake of RNase and consequent extensive fragmentation of cellular RNA. In-cell RNA spectral features: qualitative and quantitative analysis Once treated with RNase A, B16 mouse melanoma cells have been fixed with 3.7% PFA in PBS. We have decided to fix cells, because live RNase cells had very low absorbance signals compared to Ctrl cells, progressively decreasing during the time of measurement (data not shown). This behavior can be safely ascribed to cell death by necrosis, induced by the total block of the cellular machinery. It is however known from literature14, and it is evident from the spectral similarities between live and fixed Ctrl B16 cells (see Fig. S5 of Supporting Information and related comments), that PFA fixation does not introduce significant spectral distortions, making PFA fixed hydrated cells a reliable and stable model for in-cell evaluation of RNA spectral features. The most relevant spectral changes associated to RNA digestion in eukaryotic B16 cells have been highlighted in the spectral region below 1400 cm-1, as documented by the second derivative average spectra reported in Fig. 4. In the same figure, the average second derivative spectrum of B16 cells treated with Triton 0.010% is also reported for comparison. The total removal of RNA determines the regression of the spectral weight of four band components compared to control and Triton treated cells. These are centered at ~1240 cm-1, ~1120 cm-1, ~996 cm-1 and ~916 cm-1. At first glance this is not surprising, on the basis of the results obtained on extracted nucleic acids. The variation of the 1240 cm-1 component, to a first approximation assigned to the helical A-form of nucleic acids, is consistent with the removal of cellular RNA. However,
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Analytical Chemistry
although reduced, this contribution does not disappear completely, revealing the presence of other cellular components absorbing at the same frequencies. Moreover, it is interesting to notice that the band component centered at ~1224 cm-1 for Ctrl and T0.010 red-shifts of about 4 cm-1 in RNase cells, possibly reflecting the decreased contribution to this spectral region of the 1220 cm-1 RNA component of ribose. The decrease of the component peaked at ~1120 cm-1 is indeed more evident, but again it does not vanish completely. In some works31,39, the trend of this peak was related to the one at 1160 cm-1, considered diagnostic for the RNA content in cells. In our spectra, we did not notice any variation at this spectral position. We could not even detect it in ATR-FTIR spectra of extracted RNA, despite the high concentration and excellent purity. Conversely, the second derivative analysis pointed out that the band centered at 1052 cm-1 became sharper upon RNA removal, possibly reflecting the decreased contribution to this spectral region of the 1059 cm-1 RNA component of ribose. Finally, upon RNA removal, we found the reduction of the spectral component centered at 996 cm-1, as well as the parallel decrement of the band at 916 cm-1. These considerations are only qualitative, since the contribution of the other biomolecules to the cellular vibrational profile architecture cannot be entirely assessed by the analysis of the second derivatives only, from which the quantitative information is partially lost. Conversely, the fitting of the spectral components of the bands centered at about 1230 cm-1 and 1090 cm-1, called Band I and Band II respectively hereafter, could support a more comprehensive assignment of the RNA contribution to cell spectra. To this aim, we developed an Iterative method for Baseline Identification (IBI) described in Methods. Moreover, to be able to fit thousands of spectra, a rapid automated fitting procedure has been developed, and proposed here for the first time. Band I and Band II have been independently deconvolved for the Ctrl and RNase datasets. T0.010 dataset has been also deconvolved for comparison purposes, producing results comparable to Ctrl dataset (data not shown). Band I has been fitted from 1350-1181 cm-1 with 8 Gaussians components, initialized at 1338, 1318, 1298, 1282, 1260, 1243, 1223, 1206 cm-1, as chosen upon analysis on second derivative spectra. Exemplificative results of the deconvolution are shown in Fig. 5 A,B.
Fig. 4. Average second derivative spectra in the 1480-900 cm-1 spectral region for control B16 cells (Ctrl, dark), B16 cells treated with 0.010% Triton (T0.010, dark grey) and RNase treated B16 cells (RNase, grey). Line thickness is proportional to the standard deviation of the mean.
Conventional fitting of this spectral region considers shorter spectral ranges, from approximately 1270 to 1180 cm-1. For B16 cells this range is too restrictive, since important vibrational components between ~1350 and ~1230 cm-1 significantly contribute to the spectral shape. These components probably belong to the Amide III band (1350-1200 cm-1), and they overlap with the spectral contributions of cellular DNA and RNA43. Indeed, this observation is supported by the work of Benedetti et al.31, from which it can be clearly deduced that the higher wavenumber components in this spectral regions can be ascribed to the protein contribution in DNA/RNA/HSA mixtures. The sensitivity of Amide III band position to secondary protein structure and the negligible effects on its shape of the water bending mode tails are well-known. However, its relatively low intensity limited the exploitation of its beneficial characteristics for the assessment of protein folding states also for simple systems like protein solutions or films. Probably due to the lack of literature, the Amide III contribution has been often ignored also in the analysis of cellular spectra. Based on our results, we suggest that it should not be so. Actually, spectral components centered at 1318, 1298, 1282 and 1260 cm-1 contribute approximately 35±3% to the total area of Band I of Ctrl Cells. For what concerns the spectral contributions at 1240 and 1220 cm-1, it is clearly visible that the relative weight of the two components changes upon RNase treatment (1240/1220 Gaussian band area ratio: 1.09±0.12 vs 0.90±0.12, p
