Variations in the Abundance of Lipid Biomarker Ions in Mass

Nov 9, 2016 - STTARR Innovation Centre, Princess Margaret Cancer Centre, 101 ... variations in the abundances of cancer biomarker ions seen in MS ...
0 downloads 0 Views 5MB Size
Download PDF
Article pubs.acs.org/ac

Variations in the Abundance of Lipid Biomarker Ions in Mass Spectrometry Images Correlate to Tissue Density Jade Bilkey,‡,∇ Alessandra Tata,†,∇ Trevor D. McKee,‡ Andreia M. Porcari,§ Emma Bluemke,† Michael Woolman,† Manuela Ventura,† Marcos N. Eberlin,§ and Arash Zarrine-Afsar*,†,∥,⊥,# †

Techna Institute for the Advancement of Technology for Health, University Health Network, Toronto, Ontario M5G-1P5, Canada STTARR Innovation Centre, Princess Margaret Cancer Centre, 101 College Street, Toronto, Ontario M5G 1L7, Canada § ThoMSon Mass Spectrometry Laboratory, Institute of Chemistry, University of Campinas, Campinas, SP Brazil ∥ Department of Medical Biophysics, University of Toronto,101 College Street Suite 15-701, Toronto, Ontario M5G 1L7, Canada ⊥ Department of Surgery, University of Toronto, 149 College Street, Toronto, Ontario M5T-1P5, Canada # Keenan Research Centre for Biomedical Science, Li Ka-Shing Knowledge Institute, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario M5B-1W8, Canada ‡

ABSTRACT: While mass spectrometry (MS) imaging is widely used to investigate the molecular composition of ex vivo slices of cancerous tumors, little is known about how variations in the cellular properties of cancer tissue can influence cancer biomarker ion images. To better understand the basis for variations in the abundances of cancer biomarker ions seen in MS images of relatively homogeneous ex vivo tumor samples, sections of snap frozen human breast cancer murine xenografts were subjected to desorption electrospray ionization mass spectrometry (DESI-MS) imaging. Serial sections were then stained with hematoxylin and eosin (H&E) and subjected to detailed morphometric cellular analysis, using a commercial digital pathology platform augmented with custom-tailored image analysis algorithms developed inhouse. Gross morphological heterogeneities due to stroma, vasculature, and noncancer cells were mapped in the tumor and found to not correlate with the areas of suppressed cancer biomarker abundance. Instead, the ion abundances of major breast cancer biomarkers were found to correlate with the cytoplasmic area of cancer cells that comprised the tumor tissue. Therefore, detailed cellular analyses can be used to rationalize subtle heterogeneities in ion abundance in MS images, not explained by the presence of gross morphological heterogeneities such as stroma.

M

ions that are consistently detectable in the cancerous tissue. Significant progress has been made in determining m/z values that characterize cancers, even allowing for discrimination between tumor subtypes, which may enable more accurate prognosis and aid in directing further therapy.4 Human tumors, however, are highly heterogeneous in nature, often comprised of vasculature and significant amounts of noncancer cells such as stroma, necrotic, or hypoxic cancer regions that all lead to variations in both protein and small molecule MS signatures of the target tissue.5,6 In MS imaging studies, mass profile heterogeneity within the tumor is common, as the biological and thus chemical construction of the tumor is heterogeneous. Considering MS signal is accumulated over a fixed area within the tissue surface (i.e., pixel, or region of interest (ROI)), the density of cells in that area within the tumor tissue is another rather unexplored aspect, which may further influence the observed abundance of m/z values.

ass spectrometry (MS) imaging is now widely used to characterize the molecular composition of cancer using ex vivo slices of tissue. Since detection with MS often requires little sample preparation, ambient mass spectrometry imaging has great potential to be integrated into surgical oncology as a reliable, highly sensitive cancer site characterization tool.1−4 The highly sensitive multiplexed capabilities of MS imaging methods allow simultaneous detection of many cancer and healthy tissue biomarker ions. This provides new opportunities to rapidly identify chemical signatures of cancerous tissues or reveal cancer regions on the basis of detecting known cancer biomarker ions on the surface of a tissue slice.4 The imaging process generates a significant body of data in the form of mass lists formed by mass to charge (m/z) values of ions and their relative abundances characteristic to the target tissue.4 With a high degree of dimensionality, these m/z values are the very experimental data points that ultimately define the features that characterize the tissue molecular content, allowing tissue differentiation in spatially resolved MS images. In order to implement MS imaging for cancer diagnostic purposes, it is necessary to obtain reliable cancer biomarker © XXXX American Chemical Society

Received: July 19, 2016 Accepted: November 9, 2016 Published: November 9, 2016 A

DOI: 10.1021/acs.analchem.6b02767 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

geneity is a variable that can only be addressed at the image interpretation step. In this work, cancer cells within tumor tissue grown from the same batch of tumor precursor cells in two independent mice were subjected to detailed morphometric analysis. Both tissues were subjected to identical sample preparation, and experimental conditions such as solvent, collection geometry, instrument calibration, and tuning. Variations in the instrument performance and data acquisition were monitored through analysis of the coefficient of variation in relative m/z intensities and are ruled out as a major cause of dataset variability.32 Thus, we hypothesized that detailed morphometric cellular analysis of the cancer cells would provide a test bed to determine cellular parameters that best rationalize subtle variations in cancer biomarker ions seen in DESI-MS images of these otherwise fairly homogeneous tumor samples.

These variations in MS imaging datasets arising as a consequence of sample heterogeneity must be understood and subsequently taken into consideration. In the quest to discover robust cancer biomarker ions, this intrinsic heterogeneity warrants a detailed interrogation of MS ion images, since this heterogeneity often manifests itself in variations in cancer biomarker ion abundance across the tissue slice and could result in a misinterpretation of data. The need for this detailed examination manifests itself in the recent wave of reported disease biomarker ions around systematic changes in abundance, accessed through sophisticated multivariate statistical analysis methods designed to tease out most subtle changes in ion abundance between healthy and diseased tissue. In instances where cancer site heterogeneity is visible in histopathological investigations, the areas of heterogeneity can be easily identified and excluded from further analyses aimed at determining cancer biomarker ions. These regions are identified using supervised, pathology-guided image alignment and analysis techniques, which is an approach consistent with the movement toward better contextualization of the MS imaging results.7−9 As supervised digital image analysis, image registration, and alignment approaches mature over time, unsupervised statistical methods that segment MS images on the basis of the average profile in each sample subregion10−13 are also being developed. These unsupervised methods seek unknown latent m/z variables in the MS imaging data sets to rationalize and take into consideration their variations, including those that may arise due to sample heterogeneity. Xenograft models of human tumors, widely used in MS studies of human cancers, produce tumors that are significantly more homogeneous than their patient-derived counterparts. While stroma and vasculature, among other structures essential for biological function, are sometimes seen in xenograft models, they provide a good test bed to examine the potential influence of cancer cell density on the heterogeneity of MS profiles. Variations in cancer biomarker ion abundances are still seen in these relatively homogeneous xenograft tumors.14 To better understand the basis for variations in the abundances of cancer biomarker ions seen in MS images of ex vivo tumor samples, sections of snap frozen human breast cancer murine xenografts were subjected to desorption electrospray ionization−mass spectrometry (DESI-MS) imaging, with serial sections stained with hematoxylin and eosin (H&E) and subjected to detailed morphometric cellular analysis using a digital pathology platform. DESI-MS is an ambient MS imaging technique that uses a spray of charged microdroplets to induce desorption and ionization of analytes directly from the surface of an ex vivo tissue slice. DESI-MS imaging has been extensively used to determine cancer specific biomarker ions, in combination with supervised statistical methods.2,8,15−27 In particular, pathologic transformations that are accompanied by changes in structural and metabolic lipids profiles are most easily distinguished with DESI-MS imaging.2,4,8,9,15−22,24,26−31 The experimental set up for DESI-MS imaging contains a number of variables, such as ion collection geometry and stability of the solvent spray, each of which could contribute to variations in MS data. The factors that contribute to variations in MS imaging datasets could be classified into categories of (i) instrument performance, (ii) sample preparation, and (iii) intrinsic sample heterogeneity discussed above. Whereas the experimental setup and tissue preparation methods could be (and ideally must be) kept constant between image acquisitions, intrinsic sample hetero-

■

EXPERIMENTAL SECTION This study focuses on better understanding of previously seen variations in cancer biomarker ion abundance of breast cancer and thus repurposes some of the tumor samples and DESI-MS data generated for a previous work from our laboratory.14 Some of the DESI-MS images and H&E images of the tumor slices used for the tissue density map calculations in this study have been reported previously14 and are reproduced here for the sake of clarity of our discussion regarding morphometric analysis of cancer tissue. The previous work that inspired this project focused on the utility of polarimetric imaging of tissue slices for accelerated acquisition of DESI-MS data. The details of the experimental protocol reported previously14 are reproduced here to facilitate the readers’ access to the experimental material. In instances where previously published H&E images are reproduced for the sake of clarity of our discussion regarding tissue density, reference to the original work14 is given. Human breast cancer xenografts were prepared in two mice by inoculating identical aliquots of the same batch of tumor precursor cells.14 Tumors were harvested, and sections from each independent tumor were subjected to DESI-MS imaging to reveal the distribution of lipids and small metabolites in the tissue. MS imaging was performed after the optimization of the source parameters and ion collection geometry. All tissue slices analyzed in this study are thus subjected to identical sample preparation and data collection conditions such as DESI geometry, instrument parameter, calibration, and tuning. The distribution maps of two major lipid biomarker ions of breast cancer, m/z 303.2 for deprotonated arachidonic acid [FA(20:4)-H]− and m/z 331.2 for deprotonated adrenic acid [FA(22:4)-H]−, were determined.9,27,28 For each of the tissue sections imaged with DESIMS, a consecutive slice of 5 μm thickness was subjected to H&E staining to provide a parallel assessment of the histopathological features that rationalize the lipid distribution maps. Animal Study. All animal studies were approved by the animal ethics and use committee (Animal Use Protocol at the University Health Network, Toronto, Canada). Two female Severe Combined ImmunoDeficient (SCID) mice (from Harlan) were inoculated with human MDA-MB-231-LUC breast cancer cells in their quadriceps muscles (40 μL of 4 × 106). The mice were housed for a few weeks (3−4 weeks) to allow tumor growth up to 5−7 mm in diameter, mimicking a breast tumor infiltrating the pectoralis muscles. B

DOI: 10.1021/acs.analchem.6b02767 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry Tissue Sample Preparation. Mice were euthanized with an overdose of isoflurane and tumors surgically removed, with a wide 2−3 mm margin containing some muscle tissue. Extracted tissues were subsequently frozen in vapor from liquid N2 and stored at −80 °C. To prevent OCT material from contaminating the area being sectioned, flash frozen tissues were mounted onto a metal specimen holder with a small amount of Tissue-Tek OCT compound (Sakura Finetek USA, Inc.). Using a CM1950 cryostat (Leica), serial sections with thicknesses of 20 μm (for DESI-MS imaging) and 5 μm (for histological analysis) were sectioned and mounted onto standard Superfrost microscope glass slides. Slides were stored at −80 °C until analyzed. Laboratory Histology Analysis. Sections were thawed at room temperature for 5 min, fixed in 2% paraformaldehyde for 15 min, and washed in running tap water for 5 min. Tissue sections were then immersed in Harris Hematoxylin (Leica Biosystems) for 3 min, washed in warm running tap water for an additional 3 min prior to differentiating in 1% acid alcohol. Sections were washed in warm running tap water for 3 min before being immersed in Eosin (Leica Biosystems) for 40 s. Sections were washed briefly in water (10 dips) before dehydrating through a series of alcohol solutions from 70% to 100%, cleared through 4 changes of xylene, and coverslipped using Permount mounting media. Digital images were captured using an AperioScanScope XT (Leica Biosystems, Nußloch, Germany). DESI-MS and DESI-MS Imaging Experiments. All MS experiments were performed using a LTQ mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA). The glass slides containing tumor tissues 20 μm thick were mounted on a homemade two-dimensional (2D) moving stage (as described elsewhere33). DESI-MS imaging was carried out in the negative ion mode (mass range of m/z 260 to m/z 1000). A 1:1 mixture of acetonitrile and dimethylformamide (both HPLC-MS grade; Sigma−Aldrich, Oakville, Ontario, Canada) was used as the charged spray solvent and delivered at a flow rate of 1.5 μL/ min. The sprayer-to-surface distance was ∼1.0 mm, the sprayerto-inlet distance was 6−8 mm, an incident spray angle was set at 52°, and a collection angle of 10° was used. Source parameters were as follows: capillary voltage, 5 kV; capillary temperature, 275 °C; and pressure of nitrogen spray, 120 psi. Tissue slices were rastered in horizontal rows separated by 150 μm vertical steps until the entire sample was imaged. The line scans were performed at a constant velocity in the range of 203 μm/s and the scan time was 0.87 s. The software platform Image Creator version 3.0 (created in house) was used to convert the Xcalibur 2.0 mass spectra files (.raw) into a format compatible with BioMap (http://www.maldi-msi.org/), which was used to process the mass spectral data and generate 2D spatially resolved ion images. The assignment of lipid biomarkers in the negative ion mode of the tumor samples was made by DESI-MS/MS, corroborated using published breast cancer biomarker ions.28,9,27 Image Analysis. Digital pathology pyramid files containing 20× magnification images of the H&E stained slides, with a pixel resolution of 0.5 μm/pixel, were loaded into Definiens Developer software (Definiens AG, Munich, Germany) and manually annotated to exclude any muscle surrounding the tumor region from analysis. A procedural algorithm was developed to analyze cellular and morphometric features in tumor ROIs and applied to these sections. The algorithm followed a straightforward segmentation methodology of (i)

separating H&E signals using color deconvolution, (ii) subtracting background regions, and (iii) defining tissue and nuclear areas using the eosin and hematoxylin signals via intensity and area thresholds. With this regional segmentation into tissue and nuclear areas, the ROIs were divided using a regular, square grid to approximate the resolution of the DESIMS images (100 μm2). Cellular statistics were sampled at slightly higher resolution than DESI-MS images to account for resampling during the registration process. For each square of the grid, aggregate statistics for the tissue and cells within that square were calculated and exported, along with spatial information, as .csv files, to enable alignment with the DESIMS images. Of the statistics collected, the two image features of greatest interest were cytoplasm area (defined here as the total area of tissue within a square minus the total area of all nuclei within a square) and approximated cell count (defined as the number of nuclei detected within a square). Pseudo-images were generated from these data to allow visual alignment with DESI-MS images. This was performed by an in-house MATLAB script that uses MATLAB’s Image Toolbox registration algorithms, along with manual QC, to compute 2D affine transformation matrices that match the pixel pitch and orientation of each pseudo-image to the corresponding DESI-MS ion image. These transformation matrices were then applied to the original, non-pseudo-image, aggregate statistical data. We found that this technique facilitates a highly robust, multimodal analysis without a loss of accuracy, because of image manipulation artifacts. Once suitable transformations were computed, another inhouse script, coded in the Python language, imported both the image analysis data and DESI-MS images. The recorded transformations were applied to the data to produce a pixelaligned measure of cytoplasm area for each pixel in the ion image. The aligned cytoplasm area map was sampled using bicubic interpolation at the center of each corresponding pixel in the DESI-MS experiment, generating an aligned cytoplasm area image at the effective resolution of 150 μm × 176 μm. To obtain an ion image normalized for cytoplasm area, we followed a naı̈ve method where each pixel in the ion image was multiplied by the intensity complement of the area map’s corresponding pixel. An ordinary least-squares regression was performed on aligned pixel pairs for each ion peak image aligned with image analysis data. Plots showing the correlation of ion abundance to cytoplasm area and cell count were generated by binning each image analysis measurement by the corresponding pixel’s ion abundance in the aligned ion image and averaging the resulting group. The results were summarized in scatter plot form, using Excel software with cubic polynomial trendlines. Ion abundance and cytoplasm area were, respectively, the dependent and independent variables and coefficient of determination (R2) values were reported. Misaligned image data were simulated by mirroring the image analysis data across the y-axis and R2 generated using the same procedure. Segmentation of H&E Images. H&E pathology (pyramid files, 20× magnification, 0.5 μm/pixel) were imported into Definiens Tissue Studio (Definiens AG, Munich, Germany). A machine-learning classification algorithm was then used to classify tumor cells from necrotic tissue, vasculature, muscle, nerve cells, stroma, and empty spaces, using a limited dataset of manually assigned ROIs. The program applied these classifications to the entire tissue section, using a deconvolution of the hematoxylin signal. This provides a segmentation that C

DOI: 10.1021/acs.analchem.6b02767 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

Figure 1. Histopathology and cellularity analysis of breast cancer infiltrating the muscle: (A) H&E image (this image has been previously published14 and is reproduced here for the sake of clarity of our discussion regarding cellular analysis; the cancer region appears very homogeneous in this image); (B) morphometric analysis of the H&E image, revealing the spatial distribution of intratumoral heterogeneity; (C) DESI-MS ion images for m/z 303.2 (arachidonic acid [FA(20:4)-H]− and m/z 331.2 (adrenic acid [FA(22:4)-H]−) (dark areas of low abundance of cancer biomarker ions are highlighted); (D) down-sampled tissue density image at resolutions approaching that of a typical DESI-MS image revealing areas of low tissue density corresponding to similar locations in the DESI-MS image shown in panel (C) (the 150 μm × 176 μm image is adjusted to match the aspect ratio of the other tissue density images); and (E, F) a normalized image is calculated for each ion shown in panel (C) by performing pixel-by-pixel multiplication of the m/z image of said ion with the inverted cytoplasm area image. The coefficient of determination (R2) through pixel-wise regression analysis is given, along with the R2 values corresponding to the same regression analysis using a misaligned mirror image. Pixel-wise intensity relationship plots, along with empirical cubic function fits as trend lines, indicating the relationship between cytoplasmic area and cell counts (from nuclear recognition). The total number of data points arising from binning of a given number of pixels is also presented, along with the R2 values of the cubic polynomial fit of the cell count versus m/z abundance data. As illustrated here, both cytoplasmic area and cell count showed positive correlations with the ion abundance data. Images from reference 14 are reproduced with permission from the Royal Society of Chemistry.

illustrates the spatial distribution of cancer cells and intratumoral heterogeneity.

■

of breast cancer tissue and does contain some level of gross heterogeneity, because of the presence of noncancer cells. Figure 1C shows the ion abundance image of the two known breast cancer markers of m/z 303.2 (arachidonic acid, [FA(20:4)-H]−) and m/z 331.2 (adrenic acid, [FA(22:4)H]−) in grayscale. A close inspection of these ion images suggests heterogeneity in the abundance of biomarker ions that characterize the breast cancer spectrum across the surface of the tissue slice. The areas of suppressed cancer biomarker ion abundance (Figure 1C) do not coincide with areas of known gross tumor heterogeneity (Figure 1B). In fact, the areas that appear “dark” (low abundance) in DESI-MS ion images localize to sites that are essentially comprised of breast cancer cells (see Figure 1B, which shows an overlay of these areas on the tumor heterogeneity map). A possible cause for this heterogeneity could be an unstable MS signal, either as a consequence of unstable solvent spray or instrument performance variations resulting in fluctuations in ion abundance over the sample and in these areas. Coefficient

RESULTS AND DISCUSSION

Without the help of specific immunostaining methods on serial sections or detailed digital pathology assessment of the stained image, a routine microscopy examination of the H&E images of breast cancer tumors14 does not immediately reveal significant histological heterogeneities that could explain the fairly inhomogeneous distribution of cancer biomarkers across the tumor region observed.14 Figure 1A reproduces an example H&E image published previously14 for the sake of clarity of our discussion. Despite the apparent homogeneity in the H&E image, detailed morphometric analysis of the H&E image (Figure 1B) reveals and maps the areas of gross heterogeneity, such as stroma, vasculature, muscle, and nerve tissues, as well as empty spaces in tissue architecture exposed as a consequence of sectioning. As can be seen here, this tumor is largely comprised D

DOI: 10.1021/acs.analchem.6b02767 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

inverted cytoplasm area. After this treatment, the normalized image intensity range is adjusted linearly for visualization. As can be seen in Figures 1E and 1F, the normalization process accounts for the majority of the hotspots of ion abundances observed within the sample, except at the edges where imperfection in the alignment leads to incomplete signal correction. Through quantitatively correlating the tissue density values to DESI-MS ion abundances for m/z 303.2 and m/z 331.2, we can show a correlation between DESI-MS and cytoplasmic area. The R2 values obtained through pixel-wise regression analysis, as described in the Experimental Section, are shown on the normalized images. The statistical significance of these correlation coefficients (R2 = 0.79 for m/z 331.2 and R2 = 0.82 for m/z 303.2) is evident, when compared to those that used misaligned images (R2 = 0.14 and R2 = 0.38, respectively). This supports the specificity of the alignment approach for proper normalization, because, by mirroring the tissue density image to create misalignment, we show that very weak correlation coefficients are obtained. We further investigated the cellular parameters that best rationalize tissue density hotspots. Capitalizing on the fact that cell nuclei could easily be identified through automated object recognition and morphometric analysis using Definiens, in Figures 1E and 1F, we show the relationship between tissue density hotspots and m/z intensity (i.e., ion abundance) for the two prominent breast cancer markers of m/z 303.2 and m/z 331.2. Empirical trend lines using fits to the cubic function that best describe the intensity relationship data are also presented to guide the eye. Two parameters of cytoplasmic area and cell density, both determined through nuclear recognition from digitized images, are shown to correlate with biomarker ion intensities over the entire image. Variations in the strength of ion abundance correlation between cell count and cytoplasmic area were noted (see Figures 1 and 3, presented later in this work) that are not currently understood. Since it is the structural membrane and metabolic cytoplasmic lipids that largely dominate the DESIMS lipid profiles of tissues, this work further investigated the correlation between cytoplasmic area and cancer biomarker ion abundances. The areas of suppressed cancer biomarker ion abundance did not contain stroma or other forms of heterogeneity (Figure 1B). To cross-validate our findings, we show that the DESI-MS spectrum of the dark regions from the ion abundance maps (Figure 2) predominantly contains features known to populate the MS profile of breast cancer tissue for this tumor model.14 No significant m/z values indicative of other molecular species beyond those expected to be present in the breast cancer sample are seen. The very small size of these regions hampered independent validation using laser capture microdissection liquid chromatography−mass spectrometry analysis (LC-MS). Figure 3 shows the same quantitative comparisons between DESI-MS images and tissue density discussed above for two additional slices of the same murine xenograft tumor 20 and 170 μm apart from the slice shown in Figure 1. Corroborations made using an independent tumor in a second mouse are also presented. The correlation (cubic fit) between tissue density hotspots and maximal cancer biomarker ion abundances for aligned (versus misaligned) images of R2 = 0.79 (0.14), R2 = 0.82 (0.38), R2 = 0.39 (0.17), R2 = 0.44 (0.01), R2 = 0.60 (0.08), R2 = 0.74 (0.01), R2 = 0.51 (0.25), R2 = 0.36 (0.17) seen across independent samples over two independent breast cancer biomarker ions examined in this study, implicates

of variance (CV) analysis is routinely used to assess data reproducibility in analytical sciences,32 and it has also been used in DESI-MS studies for the same purpose.34,35 A CV value of 24% was determined from the ratios of the ions of m/z 281.2 to m/z 283.2, the two most abundant ions in tissue as done by other investigators.34 This value suggests reproducible MS line scans across the DESI-MS image, producing only a small variation in the ion abundance. To evaluate the stability of the solvent spray, the ion intensities of the two major ions of m/z 311.2 and m/z 325.2 observed on the glass slide were monitored before the acquisition of the imaging data commenced. The CV value of 2% from the ratio of the intensities for these two ions was calculated over 8 min of acquisition and strongly suggests that a stable solvent spray likely persisted during data acquisition. Previous studies from other groups have implicated varying cancer cell density across tumors as a possible cause for unexplained inhomogeneous distribution of cancer biomarkers within a tumor.36,37 To investigate the influence that tissue density may have on DESI-MS ion abundance maps, we subjected the digitized H&E images of our tumors to morphometric analysis using the digital pathology platform package from the Definiens software (Definiens Tissue Studio). The morphometric examination of these tissue sections revealed different and distinct regions of increased tissue density within each tumor slice. Figure 1D shows the results of the morphometric analysis of a digitized H&E (5 μm tissue slice shown in Figures 1A and 1B, which is consecutive to the slice imaged with DESI-MS in Figure 1C) indicating heterogeneity in tissue density from determining cytoplasmic area across the surface of the slice, as detailed in the Experimental Section. In Figure 1D, we also show how features visible in the tissue heterogeneity image evolve as the highresolution density image is deliberately down-sampled to 150 μm × 176 μm over a step range, which is the effective resolution of the DESI-MS images. This allowed us to better visualize how tissue heterogeneity manifested itself on a resolution length scale typical of the DESI-MS image. While the tissue slice may appear fairly homogeneous, according to visual inspections of the optical H&E images shown in this figure and our previous study,14 it nevertheless contains significant heterogeneities in its density. We hypothesized that this heterogeneity in tissue density may explain the heterogeneity observed in the ion abundance of breast cancer biomarker ions and moved to investigate this by examining the correlation between hotspots of tissue density and those of cancer marker abundance using quantitative analyses. We then correlated tissue density to DESI-MS biomarker ion abundance in an attempt to rationalize variations observed in the ion abundance maps of these seemingly homogeneous tumor tissue slices. The DESI-MS images shown in Figure 1C suggest that these biomarkers have a heterogeneous distribution within the tumor slice. After rigid body alignment between DESI-MS images and a 100 μm square sampled tissue density image from cytoplasmic area, we resample the aligned cytoplasmic density map at the center of each pixel in the DESI-MS experiment using bicubic interpolation, generating an aligned image at the effective resolution of 150 μm × 176 μm (Figure 1D, shown with the pixel size adjusted, to maintain a square aspect ratio). A normalized image for each m/z ion abundance image is then generated by normalizing the cytoplasm area image (intensity/ max intensity), inverting the normalized cytoplasmic area image and then multiplying m/z image with the rigid body aligned, E

DOI: 10.1021/acs.analchem.6b02767 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

variations in tissue density, as a possible contributing factor to variations in the ion abundance observed in DESI-MS images. For the comparisons provided in Figures 1 and 3, a cubic polynomial function was chosen empirically, based on the observation of a breakdown in linear model over the range of signals examined in this study. Investigation of data points in these scatter plots shows that tissue density reached saturation at cytoplasmic areas of ∼4000 μm2 over all slices examined (corresponding to an approximate cell count of 40). We speculate that this threshold in cytoplasmic area/cell count, beyond which DESI-MS signal does not correlate with these measures, may be related both to limitations in cellular density measurements for closely packed tumor cells and complexities in sample heterogeneity beyond that saturation limit. Below this saturation limit, a linear correlation does seem to exist, but a simple consideration would indicate a saturation limit for closely packed cells within the sampled grid, for average breast cancer cell sizes of ∼15−20 μm, to be ∼30−60 cells per grid region, corresponding to a rough cytoplasmic area of 3000− 5000 μm2, based on average cytoplasmic area per cell values. Therefore, an increase in tissue density within this fixed pixel size of DESI-MS solvent spray is expected to result in a rise in DESI-MS signal until saturating cytoplasmic area values are reached. After saturation, the complex effects of sample heterogeneity within the effective area of DESI-MS pixel size become more pronounced than the amount of substrate lipids present in the cytoplasmic area interrogated in a single DESIMS pixel, and a range of ion abundance values are observed based on the intrinsic heterogeneity of the sample. A cubic polynomial function is one of the simplest complex functions that can relate abundance to tissue density, while also fitting a saturation level around the cytoplasmic area value of 3000− 4000 μm2 for all datasets examined in this study. However, we also further performed linear fits of the scatter plot data over the entire range of normalized cancer biomarker ion abundances. While correlation coefficients of the linear relationships were slightly weaker than those observed for fits to the cubic polynomial function, aligned images consistently showed a stronger correlation coefficient of linear dependence compared to misaligned controls. The R2 values for aligned (misaligned) images (linear model) were R2 = 0.78 (0.01), R2 = 0.73 (0.24), R2 = 0.28 (0.16), R2 = 0.33 (0.01), R2 = 0.55 (0.08), R2 = 0.66 (0.01), R2 = 0.39 (0.18), and R2 = 0.33(0.11). The linear fit trend lines are not shown in Figures 1 and 3. Similar trends held true for more complex s-shaped functions with a saturating component (fits and data not shown). Overall, we have observed correlated variations in DESI-MS ion abundance and tissue density that must be taken into consideration for robust interpretation of ion abundance maps in MS image experiments. A potential caveat that must be emphasized here is that we have used a consecutive tissue slice for our morphometric analysis. DESI-MS analysis, in the absence of extensive optimization, resulted in significant removal of tissue material from the slice that prevented post DESI-MS staining of the same slice to increase robustness of the comparison, as in other MSI approaches or interfaces.38 Our reliance on H&E information from consecutive tissue slices creates an element of uncertainly that must be noted. Although the consecutive sections used for H&E analysis and DESI-MS are only 5 μm apart, the uncertainty inherent to this analysis cannot be underestimated. In addition, detailed morphometric analyses performed here require very thin tissue sections and cannot be

Figure 2. MS spectra of dark and light areas of tissue density. Here, we show MS spectra of areas of low tissue density and compare them to MS spectra of areas of elevated tissue density. In all of the spectra shown here, biomarker ions known to be associated with breast cancer are noted. Namely, m/z 281.2 [FA(18:1)-H]−, m/z 303.2 [FA(20:4)H]−, m/z 331.2 [FA(22:4)-H]−, and m/z 885.5 [PI(38:4)-H]− (all known breast cancer markers) are seen.9,27,28 This figure repurposes the same tissue density image presented in Figure 1D at a resolution of 150 μm × 176 μm to illustrate the location of the ROIs (averaged over 10 pixels) used to determine the DESI-MS spectra. F

DOI: 10.1021/acs.analchem.6b02767 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry

Figure 3. Correlation between DESI-MS ion images and cellularity analysis of breast cancer tumors. Panels (A) and (B) shows DESI-MS images, cytoplasmic area images, H&E images, normalized DESI-MS images, and the intensity relationships for two additional slices of the same tumor specimen analyzed in Figure 1. Panel (C) reports the same for an independent breast tumor specimen. The caption in Figure 1 contains more details with regard to how the results shown in each panel here are obtained and analyzed. In summary, correlations between tissue density and cancer biomarker ion abundance is observed in repeat measurements from the same specimen and in an independently produced tumor. Note that folding of the tissue slice creates additional uncertainties, especially at the edges in panel (C). Nevertheless, good correlations between tissue density and biomarker ion abundance are observed. Images from reference 14 are reproduced with permission from the Royal Society of Chemistry. G

DOI: 10.1021/acs.analchem.6b02767 Anal. Chem. XXXX, XXX, XXX−XXX

Analytical Chemistry

■

completed on slices 10−15 μm thick, suitable for MS imaging. This technical limitation would preclude the use of a tissue slice of intermediate thicknesses suitable for both MS and H&E imaging even if we had succeeded in optimizing DESI-MS conditions that would minimally disrupt our samples or allow the use of the same thin tissue slice for MS and optical assessments.

REFERENCES

(1) Calligaris, D.; Norton, I.; Feldman, D. R.; Ide, J. L.; Dunn, I. F.; Eberlin, L. S.; Cooks, R. G.; Jolesz, F. A.; Golby, A. J.; Santagata, S.; Agar, N. Y. J. Mass Spectrom. 2013, 48, 1178. (2) Santagata, S.; Eberlin, L. S.; Norton, I.; Calligaris, D.; Feldman, D. R.; Ide, J. L.; Liu, X.; Wiley, J. S.; Vestal, M. L.; Ramkissoon, S. H.; Orringer, D. A.; Gill, K. K.; Dunn, I. F.; Dias-Santagata, D.; Ligon, K. L.; Jolesz, F. A.; Golby, A. J.; Cooks, R. G.; Agar, N. Y. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 11121. (3) Kumar, V. S.; Webster, M. Clin. Chem. 2016, 62, 300. (4) Ifa, D. R.; Eberlin, L. S. Clin. Chem. 2016, 62, 111. (5) Mirnezami, R.; Spagou, K.; Vorkas, P. A.; Lewis, M. R.; Kinross, J.; Want, E.; Shion, H.; Goldin, R. D.; Darzi, A.; Takats, Z.; Holmes, E.; Cloarec, O.; Nicholson, J. K. Mol. Oncol. 2014, 8, 39. (6) Guo, S.; Wang, Y.; Zhou, D.; Li, Z. Sci. Rep. 2014, 4, 5959. (7) Gholami, B.; Norton, I.; Eberlin, L. S.; Agar, N. Y. IEEE J. Biomed. Health Informatics 2013, 17, 734. (8) Dill, A. L.; Eberlin, L. S.; Costa, A. B.; Zheng, C.; Ifa, D. R.; Cheng, L.; Masterson, T. A.; Koch, M. O.; Vitek, O.; Cooks, R. G. Chem.Eur. J. 2011, 17, 2897. (9) Guenther, S.; Muirhead, L. J.; Speller, A. V.; Golf, O.; Strittmatter, N.; Ramakrishnan, R.; Goldin, R. D.; Jones, E.; Veselkov, K.; Nicholson, J.; Darzi, A.; Takats, Z. Cancer Res. 2015, 75, 1828. (10) Paine, M. R.; Kim, J.; Bennett, R. V.; Parry, R. M.; Gaul, D. A.; Wang, M. D.; Matzuk, M. M.; Fernandez, F. M. PLoS One 2016, 11, e0154837. (11) Bemis, K. D.; Harry, A.; Eberlin, L. S.; Ferreira, C.; van de Ven, S. M.; Mallick, P.; Stolowitz, M.; Vitek, O. Bioinformatics 2015, 31, 2418. (12) Chaurand, P.; Schwartz, S. A.; Billheimer, D.; Xu, B. J.; Crecelius, A.; Caprioli, R. M. Anal. Chem. 2004, 76, 1145. (13) Parry, R. M.; Galhena, A. S.; Gamage, C. M.; Bennett, R. V.; Wang, M. D.; Fernandez, F. M. J. Am. Soc. Mass Spectrom. 2013, 24, 646. (14) Tata, A.; Gribble, A.; Ventura, M.; Ganguly, M.; Bluemke, E.; Ginsberg, H. J.; Jaffray, D. A.; Ifa, D. R.; Vitkin, A.; Zarrine-Afsar, A. Chem. Sci. 2016, 7, 2162. (15) Eberlin, L. S. Methods Mol. Biol. 2014, 1198, 299. (16) Eberlin, L. S.; Dill, A. L.; Costa, A. B.; Ifa, D. R.; Cheng, L.; Masterson, T.; Koch, M.; Ratliff, T. L.; Cooks, R. G. Anal. Chem. 2010, 82, 3430. (17) Eberlin, L. S.; Dill, A. L.; Golby, A. J.; Ligon, K. L.; Wiseman, J. M.; Cooks, R. G.; Agar, N. Y. Angew. Chem., Int. Ed. 2010, 49, 5953. (18) Eberlin, L. S.; Ferreira, C. R.; Dill, A. L.; Ifa, D. R.; Cooks, R. G. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 2011, 1811, 946. (19) Eberlin, L. S.; Gabay, M.; Fan, A. C.; Gouw, A. M.; Tibshirani, R. J.; Felsher, D. W.; Zare, R. N. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 10450. (20) Eberlin, L. S.; Norton, I.; Dill, A. L.; Golby, A. J.; Ligon, K. L.; Santagata, S.; Cooks, R. G.; Agar, N. Y. Cancer Res. 2012, 72, 645. (21) Eberlin, L. S.; Norton, I.; Orringer, D.; Dunn, I. F.; Liu, X.; Ide, J. L.; Jarmusch, A. K.; Ligon, K. L.; Jolesz, F. A.; Golby, A. J.; Santagata, S.; Agar, N. Y.; Cooks, R. G. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 1611. (22) Eberlin, L. S.; Tibshirani, R. J.; Zhang, J.; Longacre, T. A.; Berry, G. J.; Bingham, D. B.; Norton, J. A.; Zare, R. N.; Poultsides, G. A. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 2436. (23) Wu, C.; Dill, A. L.; Eberlin, L. S.; Cooks, R. G.; Ifa, D. R. Mass Spectrom. Rev. 2013, 32, 218. (24) Calligaris, D.; Feldman, D. R.; Norton, I.; Brastianos, P. K.; Dunn, I. F.; Santagata, S.; Agar, N. Y. Int. J. Mass Spectrom. 2015, 377, 690. (25) Cooks, R. G.; Ifa, D. R.; Sharma, G.; Tadjimukhamedov, F.; Ouyang, Z. Eur. Mass Spectrom. 2010, 16, 283. (26) Dill, A. L.; Ifa, D. R.; Manicke, N. E.; Costa, A. B.; Ramos-Vara, J. A.; Knapp, D. W.; Cooks, R. G. Anal. Chem. 2009, 81, 8758. (27) Dill, A. L.; Ifa, D. R.; Manicke, N. E.; Ouyang, Z.; Cooks, R. G. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2009, 877, 2883.

■

CONCLUSION This work, to the best of our knowledge, is the first demonstration of the utility of a simple H&E image analyzed with morphometric methods to correlate cancer tissue density from a cellular readout to DESI-MS images to rationalize the heterogeneities in DESI-MS ion abundance maps. However, our finding is not the first call for attention to tumor tissue density in cancer imaging. In keeping with our findings, a proteomic study of histone variants with matrix-assisted laser desorption ionization−mass spectrometry (MALDI-MS) imaging has reported correlations between histone variant abundances and nuclear density.39 Another example involves magnetic resonance imaging (MRI),40 where diffusion tensor MRI has found conflicting correlations concerning the effect of tumor tissue density on two of the key values that diffusion tensor imaging provides.41−43 Thus, a similar consideration for the effects of tissue density in MS imaging would not be unique, and once these biomarker measurements are to be used for diagnostic purposes, as has been suggested, the effects must be taken into account to ensure accurate diagnosis of tumor grade and type. Therefore, our preliminary results may provide a note of caution for biomarker discovery efforts using MS imaging methods. In the absence of gross morphological heterogeneity, even subtle differences such as fluctuations in cancer tissue density may lead to statistical discrimination of otherwise identical imaging datasets. This may potentially lead to false positives in image-based biomarker discovery efforts, further highlighting the need for independent validation of marker significance using complementary techniques. Therefore, for rigorous interpretation of DESI-MS images, ion abundance maps should be corrected for variations in the tissue density.

■

Article

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Arash Zarrine-Afsar: 0000-0002-8013-6893 Author Contributions ∇

These authors contributed equally to this work.

Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We are grateful to Prof. Demian Ifa (York University) for guidance, advice as well as for access to DESI-MS interface, and the mass analyzer used in this study. The authors would like to acknowledge the Spatio-Temporal Targeting and Amplification of Radiation Response (STTARR) program and its affiliated funding agencies. We acknowledge technical support from Milan Ganguly (STTARR Innovation Centre) for tissue sectioning and pathology and are grateful to Mark Zaidi and Sehrish Butt (STTARR Innovation Centre) for assistance and advice in image analysis. H

DOI: 10.1021/acs.analchem.6b02767 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry (28) Calligaris, D.; Caragacianu, D.; Liu, X.; Norton, I.; Thompson, C. J.; Richardson, A. L.; Golshan, M.; Easterling, M. L.; Santagata, S.; Dillon, D. A.; Jolesz, F. A.; Agar, N. Y. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 15184. (29) Balog, J.; Sasi-Szabo, L.; Kinross, J.; Lewis, M. R.; Muirhead, L. J.; Veselkov, K.; Mirnezami, R.; Dezso, B.; Damjanovich, L.; Darzi, A.; Nicholson, J. K.; Takats, Z. Sci. Transl. Med. 2013, 5, 194ra93. (30) Dill, A. L.; Eberlin, L. S.; Zheng, C.; Costa, A. B.; Ifa, D. R.; Cheng, L.; Masterson, T. A.; Koch, M. O.; Vitek, O.; Cooks, R. G. Anal. Bioanal. Chem. 2010, 398, 2969. (31) Gerbig, S.; Golf, O.; Balog, J.; Denes, J.; Baranyai, Z.; Zarand, A.; Raso, E.; Timar, J.; Takats, Z. Anal. Bioanal. Chem. 2012, 403, 2315. (32) Department of Health and Drug Administration. Guidance for Industry: Bioanalytical Method Validation, 2013. Available via the Internet at: http://www.fda.gov/downloads/drugs/ guidancecomplianceregulatoryinformation/guidances/ucm368107.pdf. (33) Manicke, N. E.; Kistler, T.; Ifa, D. R.; Cooks, R. G.; Ouyang, Z. J. Am. Soc. Mass Spectrom. 2009, 20, 321. (34) Dill, A. L.; Eberlin, L. S.; Costa, A. B.; Ifa, D. R.; Cooks, R. G. Anal. Bioanal. Chem. 2011, 401, 1949. (35) Abbassi-Ghadi, N.; Jones, E.; Veselkov, K.; Huang, J.; Kumar, S.; Strittmatter, N.; Golf, O.; Kudo, H.; Goldin, R. D.; Hanna, G.; Takats, Z. Anal. Methods 2014, 7, 71−80. (36) Jones, E. A.; van Remoortere, A.; van Zeijl, R. J.; Hogendoorn, P. C.; Bovée, J. V.; Deelder, A. M.; McDonnell, L. A. PLoS One 2011, 6, e24913. (37) Willems, S. M.; van Remoortere, A.; van Zeijl, R.; Deelder, A. M.; McDonnell, L. A.; Hogendoorn, P. C. J. Pathol. 2010, 222, 400. (38) Thiele, H.; Heldmann, S.; Trede, D.; Strehlow, J.; Wirtz, S.; Dreher, W.; Berger, J.; Oetjen, J.; Kobarg, J. H.; Fischer, B.; Maass, P. Biochim. Biophys. Acta, Proteins Proteomics 2014, 1844, 117. (39) Lahiri, S.; Sun, N.; Solis-Mezarino, V.; Fedisch, A.; Ninkovic, J.; Feuchtinger, A.; Gotz, M.; Walch, A.; Imhof, A. Proteomics 2016, 16, 437. (40) Kinoshita, M.; Hashimoto, N.; Goto, T.; Kagawa, N.; Kishima, H.; Izumoto, S.; Tanaka, H.; Fujita, N.; Yoshimine, T. NeuroImage 2008, 43, 29. (41) Beppu, T.; Inoue, T.; Shibata, Y.; Kurose, A.; Arai, H.; Ogasawara, K.; Ogawa, A.; Nakamura, S.; Kabasawa, H. J. Neuro-Oncol. 2003, 63, 109. (42) Beppu, T.; Inoue, T.; Shibata, Y.; Yamada, N.; Kurose, A.; Ogasawara, K.; Ogawa, A.; Kabasawa, H. Surg. Neurol. 2005, 63, 56. (43) Stadlbauer, A.; Ganslandt, O.; Buslei, R.; Hammen, T.; Gruber, S.; Moser, E.; Buchfelder, M.; Salomonowitz, E.; Nimsky, C. Radiology 2006, 240, 803.

I

DOI: 10.1021/acs.analchem.6b02767 Anal. Chem. XXXX, XXX, XXX−XXX