Analysis of ion currents in mass spectrometric profiles using glioblastoma tissue

Introduction

New techniques for rapid tissue profiling in mass spectrometry have made it possible to develop methods for express analysis of biological substances^1–7. Many approaches to integrate mass spectrometry into the clinical routine have been published^8–12. Interpretation and analysis of mass spectra of complex mixtures of biological molecules are still a rather difficult task due to the enormous variety of molecule types contained in biological tissues and their concentrations^13,14. The LC-MS/MS analysis, which is considered a gold standard between methods for the accurate identification of complex mixture components, is time-consuming and cannot be used as a method for rapid analysis¹⁵. Therefore, for rapid analysis, one has to use direct mass spectrometry without preliminary chromatographic separation of mixtures. Neurosurgery is one of the areas that requires rapid analysis of biological tissues dissected during procedure¹⁶ to determine the boundaries of the tumor and control the volume of the excised tissue. It was shown that this problem could be solved using the mass spectrometric approach¹⁷.

Primary brain tumors account for 1.4% of all cancers and 2.4% of all cancer-related mortality in the United States. Approximately 20,500 newly diagnosed cases and 12,500 deaths are associated with primary malignant brain tumors each year¹⁸. According to recent reports, glial tumors are considered the most common primary brain tumors. They represent 31% of all brain and central nervous system (CNS) tumors diagnosed in the United States, and 81% of all malignant brain and CNS tumors^19,20. Glial tumors can be divided into different subtypes based on morphological and genetic data following the WHO classification²¹.

It should be noted that all glial tumors can be very heterogeneous at the morphological level not only in between patients but even in within-patient due to the relatively random mixture of more benign and more aggressive parts²². This variability can become a serious problem for the rapid, accurate, and objective diagnosis of both the tumor border and its level of malignancy. The task of detecting tumor cells is most important in the border area since the undissected tumor cell may contribute to recurrence in the future²³.

During the transfer of the results of biological experiments into clinical practice a number of questions needs to be addressed²³, including a study of the stability of the mass spectrometric characteristics of the sample and the reproducibility of the results under variable external (mode, range, temperature, resolution, time) or internal (sample characteristics, biological variability) conditions. For this analysis, the stability of the whole spectrum structure and the analysis of the dynamics of individual ion currents are of interest.

The study of the dynamics of individual ion currents is widely used in the analysis of mass spectrometric images^24–27. In this work, using the analysis of the dynamics of ion currents, we investigated the influence of the internal heterogeneity of the sample, as well as the processes of ion suppression and extraction on the interpretation of mass spectrometric profiles of glioblastoma samples.

Methods

Results

Correlation matrices of ion currents are shown in Figure 1 and Figure 2 for negative and positive ion modes, respectively. The top and the right panels of the figure show the average spectrum for all measurements, the left panel represents the similarity matrices. So, the right and the top panels are the aggregated mass spectrum of the spectra of all 6 measurements normalized to the total ion current. The m/z range on the axis of the spectra corresponds to the range on the axes of the similarity matrix, i.e. the values of the axes of the similarity matrix and the spectra coincide.

Figure 1. Similarity matrix of ion currents in negative mode with aggregated mass spectra.

Figure 2. Similarity matrix of ion currents in positive mode with aggregated mass spectra.

Several regions of the high ion correlation represented by compact red squares could be seen in the negative ion current correlation matrix (Figure 1). Within these regions, ion currents have a correlation close to unity, which indicates the similarity in dynamics regardless of the relative intensity of the ion current in samples. Correlation values close to the negative unity indicate a negative linear dependence of the intensities of a pair of ions in all samples.

Interestingly, there are two groups of ions in the approximate ranges of 700–900m/z and 1500–1700m/z. Ions in these groups are correlated both within and among themselves. The blue stripes at the boundaries of these red squares indicate a negative correlation of these groups with ions in the ranges of 100–700m/z and 900–1500m/z.

The correlation matrix for positive ions (Figure 2) contains significantly fewer areas of high correlation on the main diagonal of the similarity matrix. This means that ions close to each other in mass in the positive ion mode behave more independently than negative ions. Nevertheless, several correlated squares on the main diagonal with a correlation between 0.2 and 0.4 can be recognized in Figure 2. The highest correlated ions group in all samples are located around 1500–1600m/z. Contrary to the small number of high positive correlation values, the high negative correlation values are more common in the matrix of similarity of ion currents in positive polarity spectra.

Correlation matrices in Figure 1 and Figure 2 were built using all 6 fragments of the biological sample. In this case, the orientation of the fragment in the cartridge during measurement, as well as the internal heterogeneity of the sample, can affect the value of the correlation. Figure 3 and Figure 4 demonstrate the dynamics of the currents of several pairs of negative and positive ions, respectively. Figure 3 and Figure 4 represent the mutual distribution of ion currents. Figure 3 and Figures 4 A,C,E,G show a time course for pairs of ion currents grouped by fragments, where dashed lines separate repetitions of measurements for each fragment, whereas different glioblastoma fragments are separated by vertical lines. Ion currents are shifted to zero mean and normalized to the standard deviation (z-normalization). Figure 3 and Figures 4 B,D,F,H are scatter-plots for the corresponding pairs of ion currents. The coordinate of each point is determined by the value of the ion intensities normalized to the total ion current in a single mass spectrum.

Figure 3. Representations of ion currents in negative mode.

Data correlations are above the graphs.

Figure 4. Representations of ion currents in positive mode.

Data correlations are above the graphs.

Both pairs of ions with a high positive correlation (“correlating”, Figures 3A, 3C, Figures 4A, 4C) and with a noticeable negative correlation (“anti-correlating”, Figures 3E, 3G, Figures 4E, and 4G) were selected. For clarity, the measurements for individual fragments are separated by a solid line, and repetitions within one fragment are separated by a dotted line. Figure 3A and Figure 4A demonstrate the dynamics of pairs of ion currents whose correlations are positive in all fragments. Figure 3B and Figure 4B prove this statement since the cloud of points is strongly elongated along the y=k*x line. Ion pairs that are shown in Figure 3C and Figure 4C also have a positive total correlation. However, the correlation of ion currents in the fifth and sixth fragments in Figure 3C and the second and fifth fragments in Figure 4C is negative.

Unlike the previous two pairs, the ion currents of the pair shown in Figure 3E and Figure 4E have a negative total correlation, which can be seen from the descending shape of the point clouds in Figure 3F and Figure 4F. In this case, the correlation of currents in the first and fourth fragments in Figure 3E and the third fragment in Figure 4E is positive.

The spread matrices shown in Figures 5B and 5D were constructed for visual assessment of the presence of ions with alternating correlations in various fragments in addition to the correlation matrices (Figures 5A and 5C the same as Figure 1 and Figure 2). Most of the anticorrelating regions for negative ions (blue stripes in Figure 5A) are alternating signs (corresponding red stripes in Figure 5B). Some of the low-mass ions have spread close to 1, despite the relatively high overall correlation. This means that these ions showed a correlation close to zero for some of the fragments. The similarity matrices of ion currents for individual fragments, which were used to calculate the spread of correlations, are presented in Figures S3 and S4.

Figure 5. Matrices of correlation values spread for ion currents in different fragments’ mass spectra.

Discussion

The ions in the matrices (Figure 1 and Figure 2) are ordered by the m/z ratio (along the axis of matrices). The understanding of conformity of ion currents with the obtained mass spectrometric profiles is convenient in Figure 1 and Figure 2. However, ion currents can be reordered, for example, using hierarchical clustering methods so that the most similar ions are found in the closest rows (and columns) (Figures S1 and S2). This method of analysis allows for exploring different groups of ions. For example, groups of isotopes and adducts can be combined into one feature, hence, the dimension of the mass spectrum feature space can be reduced. Such reduction is of crucial importance for the application of machine learning techniques since the performance of many algorithms is degraded with the growth of the number of features and the presence of highly correlated features. It also should be taken into account that both adducts and isotopes represent the same pool of molecules, so redistribution of the concentration into a number of features obscured the overall dynamics of the ion current and makes interpretation of the spectra more difficult. The most common distance between the closest ions is equal to one for positive ions and one or two for negative ions (Figures S1C and S2C). The presence of chlorine adducts in negative mode causes this effect, as chlorine adducts give a significant isotopic peak at a distance of 2m/z from the monoisotopic peak. Such peaks with similar ion currents dynamics and 1 or 2 m/z difference form about 10% of all peaks in both polarities.

Some of the pairs of ion currents have persistent signs of correlation, i.e. the correlation has the same sign for all measured fragments (Figure 3A, Figures 4A,E). Adducts and isotopic peaks belong to such pairs. At the same time, ions that are neither isotopes nor adducts, but have a high positive persistent correlation of ion currents (Figure 3A, Figure 4A), can be a result of coordinated metabolic processes, for example, intermediates of one metabolic pathway. The identification and detailed analysis of such groups of ions in different fragments of the sample can provide additional information for the characterization of the sample itself. Thus, groups of ions with a high positive correlation of ion currents, whose sign does not change from fragment to fragment, are functionally coupled: either as adducts/isotopes or as biochemically related molecules.

Strictly negative persistent values of the correlation of ion currents (Figure 4E) indicate the ion suppression, the matrix effect. Such groups of ions could be associated with the measuring characteristics of the mass spectrometer.

Ion pairs with alternating signs of correlation values are associated with sample heterogeneity and extraction specifics of corresponding substances. So, two substances in one fragment were extracted in the same way from similar locations, whereas the regions of high concentration of these substances could be located differently in the other fragment, for example, on the surface and deeper inside the fragment. In this case, in the second fragment, the first substance might have been exhausted by the time the second substance could just start to be actively extracted. Examples of such pairs can be seen in Figures 3E, 3G in the second fragment, when the ion intensities change take place at the repetition border. It should be noticed that due to the peculiarities of the measurement protocol, the time between two repeated measurements of one sample is equal to five minutes, which is enough for a significant change in the ion concentration during the extraction process. The study of the internal heterogeneity of the sample would be very interesting since the sample was located at the border of the tumor and thus contained both tumor tissues and reactively modified brain tissues of the transitional region.

Ion currents of a pair of molecules can be either correlated or anticorrelated within one fragment, whereas the trend can be stable or alternating in different fragments. These factors allow the statement of the reason for the dynamics of ion currents: ion suppression, the sample position, or the functional relationship of molecules.

The alternating-sign correlation is rare in the positive mode compared to the negative ion mode, if we compare the correlation of the corresponding ion currents in different fragments (Figure 5D). Thus, the effects associated with the specifics of extraction, heterogeneity of the sample, and the location of the sample in the ion source are quite rare for the positive mode. At the same time, the alternating correlation of ion currents between different fragments dominates in the negative mode, which indicates the manifestation of such effects. Consequently, the biopsy tissues contain large quantities of molecules that are detected in the positive mode, and these molecules are rather evenly distributed within the sample. The effects associated with the geometry of the sample and its internal heterogeneity are visible in the negative ion mode. This confirms earlier observations that positive ions better describe inter-patient variability, while negative ions reflect intra-sample variability²⁹.

Thus, all pairs of ions between that have a significant correlation value can be divided into three groups:

We will refer to the three types of relationships described above as measure-, biochemical-, extraction-related coupling. All types of these couplings are present in mass spectra, but they can be distinguished. For example, if the difference m/z is equal to 1 or the mass of the adduct and the overall correlation is positive and has a constant sign, then this is a positive measure-related coupling; positive persistent correlation with an unobvious difference in mass — is a biochemical-related coupling candidate; negative persistent correlation with an unobvious difference in mass — negative measure-related coupling; alternating correlation in different fragments — extraction-related coupling.

Thus, we have three types of coupling, two signs for each coupling type. Four of these six cases can be identified with high accuracy by analysis of ion currents using mass spectrometric profiling, and the other two require additional identification of molecules and their mapping to metabolic networks. For example, the difference between a positive measure-related coupling due to the presence of a chlorine adduct and a biochemical-related coupling due to the presence of two lipid species, which fatty acid residues differ by one double bond, can be reliably revealed only by the identification of molecules through the LC-MS/MS analysis.

The study of biochemical-related coupling is of great interest. The ratio of saturated and unsaturated lipids could be changed due to metabolic reprogramming during the development of the tumor¹⁷. So, the differences in the population of fatty acids between healthy and tumor tissues become noticeable. This can lead to significant ion currents correlations due to common precursors — an altered set of fatty acids. For example, if stearate is present in excess, lipids containing C18 will be at sufficiently high concentrations and, hence, the correlation between the peaks will reflect an excess of C18 within a particular tissue type, despite the actual class of lipids (phosphoserines, cardiolipins, ethanolamines, etc.). If we assume that the microextraction processes for similar molecules have similar dynamics, then their ion currents will have a high correlation. Thus, biochemical specifics appear as a positive relationship of ion currents. Moreover, these ion currents can be affected by the extraction and geometric variability, which leads to a low absolute value of the overall correlation, although the correlation of ion currents in different fragments stays positive.

The mass spectrometric profiles of glial tumors in the negative mode consist mainly of ions, whose dynamics reflect the specifics of microextraction and geometry of a particular sample. In the positive mode, mass spectra consist mainly of evenly distributed ions that are not significantly affected by the geometry of the sample, and processes of microextraction are constant in various fragments of tumor tissue. It is worth noting that ions in the positive and negative modes correspond to different molecules and even classes of molecules.

Methods created in the laboratory should be proven to be stable for the transition from laboratory studies to routine clinical mass spectrometric analysis. Routine clinical practice requires a reduction in the analysis time, therefore, recently, ambient ionization MS methods have been widely developed. Ambient ionization, unlike traditional LC-MS/MS analysis, allow measurements in the shortest possible time (minutes for the analysis vs hours for chromatography). At the same time, markers of a particular tissue found using LC-MS/MS can disappear from the ambient MS data due to the ion suppression, which can be found through analysis of Figures 5A and 5B or Figures 5C and 5D. The indicator of ion suppression is the case, where both values of ion currents correlation and correlation spread matrices are closed to zero (twice blue pixels). Thus, an understanding of the processes of ion suppression makes it possible to predict the behavior of markers found by LC-MS/MS analysis under conditions of ambient ionization setup. Aggregation of ions that behavior correlated due to measurement procedure (isotopes, adducts, etc.) is another possibility to simplify the analysis of mass spectrometric profiles. Such groups of ions are well distinguished using hierarchical clustering (Figures S1 and S2): Figures S1C and S2C demonstrate high peaks at 1m/z and 2m/z, which indicates the isotopic structure and the presence of chlorine adducts in the negative ion mode or saturated bonds in lipid tails.

Conclusion

The currents of ions forming the mass spectrometric profiles obtained from glial tumors are grouped according to their similarity with each other due to several factors that include the specifics of measuring, extraction, and biochemical processes.

The dynamics of ions in the mass spectrometric profiles of glioblastoma samples obtained in the positive mode proves that the bulk of ions corresponds to molecules uniformly distributed in the sample volume in a relatively stable concentration. At the same time, the profiles obtained in the negative mode contain ions with high heterogeneity of the spatial distribution, which is confirmed by a large number of pairs with an alternating sign of correlation. This makes the negative range the preferred choice when looking for markers of glial tumors. On the other hand, this fact allows us to conclude that marker lipids are differently distributed within the sample.

Data availability