Spectral Analysis and Quantitation in MALDI-MS Imaging.

Transcription

1 Tina Memo No Internal, Second Year Reoort. Spectral Analysis and Quantitation in MALDI-MS Imaging. Somrudee Deepaisarn. Last updated 2 /12 / 2016 Imaging Science and Biomedical Engineering Division, Medical School, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT.

2 SPECTRAL ANALYSIS AND QUANTITATION IN MALDI-MS IMAGING SECOND YEAR CONTINUATION REPORT SOMRUDEE DEEPAISARN DIVISION OF INFORMATICS, IMAGING AND DATA SCIENCES FACULTY OF BIOLOGY, MEDICINE AND HEALTH THE UNIVERSITY OF MANCHESTER 2016 SUPERVISORS DR ADAM MCMAHON DR NEIL THACKER

3 Abstract The most standard method for MALDI-MS quantitation to date uses a variation of one specific m/z peak in predicting sample quantities. We have shown in this work a more accurate method of quantifying MALDI-MS data using linear Poisson ICA which takes into account all useful information from entire spectral data set. Our in-house linear Poisson ICA together with the pre-processing software developed can achieve down to a 4.7% error on predicting proportion of biological mixture samples, which yields an improvement from using the standard method by at least 40%. The results pave a way for quantitative MALDI- MS imaging where is currently very limited with other techniques. 1. Introduction Matrix assisted laser desorption/ionisation (MALDI) is an ionisation technique used in mass spectrometry (MS), enabling ionisation of large non-volatile molecules such as lipids, peptides, proteins, etc. Data acquired using MALDI-MS are recorded as mass spectrum-i.e. a plot of ion signal intensity against mass-to-charge ratio (m/z). MALDI-MS features in molecular image acquisition of sample surfaces formed from mass spectral data at spatial locations (See Figure 1), mainly to produce in-vitro images of biological tissue sample which leads to applications, including medical imaging. However, the technique is relatively new (Tanaka et al., 1988; Karas and Hillenkamp, 1988) where ionisation machanisms are complicated and not yet fully understood, variety sources of chemical and physical noise can cause signal-noise variations and repeatability problems. Hence, a quantitative approach for analysing MALDI-MS data is still under development (Duncan et al., 2008) m/z [PC 32:1 + H] + %int Figure 1 Mass spectra and mass spectrometry imaging Most of the previously published articles around quantitation of MALDI-MS have used a single peak quantitation technique (Szajli et al., 2008; Liland et al., 2009). Where, each of the mass spectra in a data set is normalised to a reference peak or a total ion 2

4 current of a mass spectrum, and the change in normalised signal quantity of one specific peak of interest across specimens would then predict a relative amount of the associated molecule within a mixture, which represents a proportion of underlying sample characterised by that molecule. The MALDI-MS image in Figure 1 is also obtained by this single peak method. There are also some attempts to interpret MALDI-MS data using multivariate statistical method such as principal component analysis (PCA), independent component analysis (ICA), hierarchical clustering, Non-negative matrix factorization (NMF), multivariate curve resolution alternating least squares (MCR ALS) (Mantini et al., 2008; Alexandrov, 2012; Shao et al., 2012; Gut et al., 2015; Kehrmann et al., 2016). People tend to use available software packages to perform such analyses which are limited to the assumptions made for specific algorithms and not necessarily suited to the intended MALDI- MS data analysis application. To the best of our knowledge, statistical analysis methods were only used for classification purposes, but no quantitative/numerical measure of absolute or relative signal quantity was previously reported, except the work done by Gut et al. (2015) where the level of accuracy achieve was very limited. Independent Component Analysis (ICA) is a statistical method of manipulating multivariate data into fewer numbers of components that can describe the original data system, given that the hidden components are linear and independent to each other. Our in-house ICA software (TINA vision : uses the linear Poisson s statistical assumption which is suitable for dealing with some experimental data in science, such as those acquired from counting based measurements. With our in-house linear Poisson ICA algorithm, not only the componential signal quantities but also the errors associated to these quantities can be obtained. This software is shown to apply successfully on planetary image analysis (Tar et al., 2015; Tar et al., 2016). The process of acquiring MALDI-MS signals should as well obey linear Poisson s statistics. MALDI mass spectra are known to be noisy. Signals and noise might be recorded simultaneously along a spectrum, adding the complexity to data analysis. Therefore, the ICA algorithm is ideal for applying to these data, to extract independent components that could then be determined as genuine signals or noise. Here, underlying sample quantity in mixture might be best described by linear combination of some components, thus, introducing a novel method for MALDI-MS quantitation and possibly extending to mass spectrometry imaging. This automatic technique will be developed for separating and quantitating tissue types in biological MALDI-MS data with a focus on lipid quantitation, towards the final aim in identifying and quantifying lipid biomarkers in lipid-rich tissue, e.g. brain tissue. This second year continuation report contains, optimised sample preparations and MALDI-MS data acquisition protocols, spectral pre-processing procedure, and ICA analysis. 3

5 This is followed by results, discussions and key conclusions. Finally, future plans for experimental and analysis work are also discussed. 2. Method 2.1 Sample Preparation and MALDI-MS Data Acquisition As a step towards imaging quantitation, 2 MALDI mass spectral data sets were obtained in a way that we know the true quantity of samples mixed in every specimen, and then used to test for the performance of the linear Poisson ICA algorithm. One mass spectral data set was acquired from mixtures of lipid extracts from homogenised (lamb) brain and liver tissues, the other set from lipid extracts from cow s and goat s milk mixtures. Both selected sets of mixtures have high lipid-content to ensure similar spectral characteristics to that expected in further lipid-rich tissue imaging. First of all, brain and liver tissue samples were homogenised separately. Each of them underwent lipid extraction using methanol:chloroform method (Bligh and Dyer, 1959), followed by a further wash with deionised water to desalt the lipid extract solutions (dissolved in chloroform). Then, the brain and liver lipid extracts were mixed in proportion where the ratio of one in a mixture was varied by an increment of 10%. This gave the total of 11 specimens of different brain-liver concentrations measured by weight. Same method was done for the milk sample apart from that the cow s and goat s milk were mixed in 11 different weight proportions before the lipid extraction process. In order to prepare the MALDI-MS specimens, the matrix substance, 2,5- dihydroxybenzoic acid (DHB) was made into solution of 10 mg/ml of acetonitrile with 0.1% trifluoroacetic acid (TFA). The matrix solution was then pre-mixed with each concentration of the lipid mixture extract at the ratio of 1.5 : 1 before depositing on MALDI-MS sampling wells of stainless steel target plate. A pre-mixed specimen was deposited onto 8 sampling wells, each of which required 2-layer depositions with 1 µl per layer. For every single well, a MALDI mass spectrum was acquired. Therefore, a data set contains 88 mass spectra, with 8 repeats per concentration. Note that the MALDI-MS instrument used for spectra acquisition was an Axima CFR+ ToF 2 model from Kratos (a Shimadzu group company). This instrument had been upgraded to have a 1 khz laser, effectively making it equivalent to the more recent Performance. A mass spectrum was created from 200 accumulated MS profiles with 5 laser shots per profile. Laser power was adjusted to optimise s/n and repeatability in mass spectra as much as possible and was kept constant when acquiring a same data set. Due to the lack of signal stability in mass spectra across the data sets, we can only keep spectra with similar total signal quantity to the rest of the data set in order to ensure 4

6 the pre-processing and ICA modelling would be performed efficiently. To reduce variability, we filtered out spectra with most uncommon characteristics which might be caused by insufficient s/n (too low signal quantity spectra), or saturation effects and electrical ringing (effects too high signal quantity spectra). 10% and 25% of spectra in brain-liver and milk data set, respectively, that carries furthest total signal intensity away from the median values were removed and only the remaining data used in further analysis. 2.2 Pre-processing of Mass Spectral Data We have developed an in-house TINA spectral pre-processing software to ensure data are in the right format for our linear Poisson ICA analysis. Mass spectral data were converted into histogram form where the binning was re-sized to down the mass resolution to 1, 1 and 1 times the original mass resolution to reduce correlation between adjacent bins The reduced mass resolution spectra at a selected mass range were pre-processed using optimised parameterisation. The pre-processing steps including alignment, background subtraction and peak picking, respectively, were performed on every spectrum in the data sets. Firstly, every spectrum in an entire data set is allowed to shift horizontally until they are well aligned. Secondly, background subtraction was done by hysteresis thresholding (Seepujak et al., 2015). Finally, peak picking was performed by selecting only spectral peaks that sit above a certain threshold to be carried forward for ICA analysis. Figure 2 shows the results of preprocessing steps. a b Figure 2 Pre-processing steps (a) a spectra (from a brain-liver data set) after binning re-sized to half the original mass resolution, alignment, background subtraction, and (b) a peakpicked spectra 5

7 Note that the mass range and number of peaks picked are specific to a data set, decided by information-contained region. The (lipid) mass ranges selected for brain-liver and milk data sets were m/z and m/z, with 76±1 and 103±4 picked peaks, respectively. 2.3 Spectral Analysis Standard Single Peak Analysis Every pre-processed mass spectra in a data set was normalised to a reference (its highest intensity) peak. The changes in normalised signal quantity of every peak according to varying concentration across specimens were observed. The most influential peak which gave best concentration prediction i.e. determined by the best achievable least square error of the fitted linear trend line from the measured values, was used to represent a performance of the method (see Equations (1) and (2)). Least square = n i=1 (linearfit i concentration i ) 2 (1) Least square Error = n (2) Where n is a number of spectra in a data set. For an ith spectrum, concentration is a true concentration of sample of interest calculated from a ratio of measured quantity of that sample in a mixture (by weight), e.g. a ratio of measured quantity of brain lipid extract in a mixture of brain-liver lipid extracts ICA Analysis Our in-house developed ICA software (TINA) assumes linearity in the statistically independent components with Poisson distributed errors. The algorithm can automatically model different sources of variation that exists in each sub-sampling. For every training peak-picked spectrum as a result of the pre-processing in Section 2.2, an ICA model, M x is built for each peak as expressed in Equation (3). M x = k=1 P(x k)q k (3) Where x represents a peak, P(x k) is a probability density of seeing a peak, x in the kth component (common value across all training spectra), N is the number of component extracted, and Q k is a quantity associated to the kth component on a specific training spectrum. In order to select the best ICA model, initial model selection was based on the model fitting performance across an entire data set, χ 2 per degree of freedom, (see Figure 3) to suggest approximately the a suitable number of ICA components to be extracted. 6 N

8 Figure 3 Model selection curve suggesting ICA fitted performance of this particular data set does not improve much with around 7-8 or more components extracted The main criterion of finding the correct model for our analysis was to assess the accuracy of the model in predicting the concentration of underlying samples contained in mixture specimens for a MALDI mass spectral data set. In other words, a least square variance was computed for each of the predicted sample proportions from ICA models of different numbers of components while varying linear combinations between the extracted components, in comparison with the known sample proportions. The minimum least square out of all these possible component numbers and combinations test represents the method s performance (see also Equations (1) and (2)). The least square calculation of all possible component combinations were done in MATLAB. 3. Results and Discussion Mass spectral data analyses were found to be optimised when 1 or 1 times the 2 3 original mass resolution was used as incoming bin entry. The results from half entry resolution will be illustrated and discussed in this section as prime examples. 3.1 Brain-liver Analysis The least square calculation of varied ICA component numbers and combinations describe in Section suggested the 8-component model shown in Figure 4(a). Where 7 out of 8 components are predicted to be genuine signals (4 contain brain lipid information and the other 3 contain liver lipid information) plotted as the bottom 4 and top 3 components in Figure 4(b), respectively, and the 1 remaining components is determined as background noise to be discarded. Note that the growing linear trend from one concentration end to another can be observed in Figure 4(b) at the boundary between the light green bars and the red bars. 7

9 a b Figure 4 Plots of fraction quantity for (a) 8 extracted components, and (b) 7 out of 8 signal components, against spectrum in brain lipid concentration order (0-100%) Given that 2 different samples (brain and liver lipid extracts) were known to exist in each specimen with varied concentration, we expected the linear Poisson ICA to model at least 2 sources of variation of the spectral data set. In reality, there is also background noise that meant to be random throughout specimens. Moreover, even signal contribution from brain or liver sample itself cannot be described perfectly by only one component due to within-sample variation. The extracted spectral components of the above results are plotted in Figure 5(b)s and 5(c)s, where characteristics of these extracted components suggested to be classed as of either brain (Figure 5.1(b)) or liver (Figure 5.2(b)) agree well with the pure brain and liver spectra in Figure 5.1(a) and 5.2(a). Background noise contamination in the spectral data is also modelled with no similar appearance to one of the samples (see Figure 5(c)s the same plot of the only one background noise component determined is put into Figure 5.1(c) and 5.2(c) for comparisons to brain and liver components). The main source of within-sample modelling variation was observed coming from strong sodiated and potassiated m/z peaks that vary in intensities from measurement to measurement. Therefore, this suggests that we need right selection of linear combination of right component in order to quantify linearity between 2 different samples in mixtures proportion. Figure 4(b) plot is simplified into Figure 6 where all sampling from same concentration are averaged with linear fitting shown. 8

10 a b c Figure 5.1 (a) Averaged spectrum for pure brain spectra (red), (b) four extracted spectral ICA components for brain (red), (c) a background noise spectral component (black) 9

11 a b c Figure 5.2 (a) Averaged spectrum for pure liver spectra (blue), (b) three extracted spectral ICA components for liver (blue), (c) a background noise spectral component (black) 10

12 Figure 6 Plots of fraction quantity for signal components against brain lipid concentration with linear fitting indicating 4 brain and 3 liver components To compare the performances of our linear Poisson ICA model with the standard single peak method (Section 2.3.1) in predicting the concentration brain-liver lipid sample, Figure 8(c) and 8(a) were plotted, with addition of Figure 8(b) being the single peak analysis of ICA modelling peak illustrated as spikes in Figure 7. Where the most influential peak assessed was at the m/z. Note that all fittings and errors were calculated from 80 data points. a b Figure 7 Averaged ICA fitted model of picked-peak ( m/z) brain-liver spectral data set of half entry resolution for (a) brain and (b) liver spectra with the bar being histograms for actual quantities and the spikes being ICA fitted models 11

13 a b c Figure 8 Plots of measured brain concentration against changes in quantity (blue spots) for (a) single peak signal intensity ratio of peaks m/z vs m/z, (b) single peak intensity ratio calculated from ICA model of same peaks, and (c) linear Poisson ICA predicted brain componential fraction, with the linear fitted line The scatter plots from Figure 8(a), (b) and (c) indicate a significant improvement in quantitating MALDI-MS data using the linear Poisson ICA method. Where the ICA predicted peak ratio in Figure 8(b) resulted in smaller distribution between data points. The result improve even more when the full ICA analysis was performed in Figure 8(c) where all peaks from the model contributed to the linearity predicted by combination of ICA components, and the concentration for brain in the brain-liver mixes are well-predicted with the dynamic range spans from close to 0% to 100%. 12

14 Table 1 Errors in quantitating brain lipid concentrations in brain-liver MALDI-MS data set using different analysis methods (assessed from 80 spectra) Analysis method Error (%) Single peak 7.8 ± 0.6 Single ICA peak 6.2 ± 0.5 ICA model prediction 4.7 ± 0.4 The numerical figures of these results are shown in Table 1. The ICA model prediction bring the error down to 4.7% whereas the prediction from most influential single peak has the 7.8% error. That is our linear Poisson ICA leads to approximately a 40% quantitation improvement from a standard method. 3.2 Milk Analysis The analyses of cow-goat milk spectral data were done in the same way as the brainliver analysis and the expression of results follows Section 3.1. The least square calculation of varied ICA component numbers and combinations suggested 6 components to be extracted with 4 genuine signal components (2 contain cow s milk information and the other 2 contain goat s milk information), and the 2 remaining components are background noise. The linearity of averaged ICA componential plot of varied milk concentrations can be seen in Figure 9. For the milk data set, all fittings and errors were calculated from 66 data points. Figure 9 Plots of fractional quantity for signal components against cow s milk concentration with linear fitting indicating 2 cow s milk (lower) and 2 goat s milk (upper) components In the case of goat s and cow s milk mixture, the selected peak was m/z, where the peak s signal intensity varied most with milk type proportions as seen in Figure 10, the ICA model of the two milk types. In contrast with the brain and liver spectra which shown 13

15 clearly unique characteristics between distinct types (see Figure 7(a) and (b)), a great similarity between the two types of milk spectra are presented and the m/z seems to be the only peak to differentiate between the two. Therefore, the change in ICA modelled components was driven mostly by the only peak and does not give as great accuracy in Figure 9 compared to Figure 6. However, quantitation using the linear Poisson ICA method was far more accurate than the standard single peak analysis (see Table 2). Figure 10 Averaged ICA fitted model of picked-peak cow s (red) and goat s (blue) milk spectral data of mass range m/z Table 2 Errors in quantitating cow s milk lipid concentrations in cow-goat milk MALDI-MS data set using different analysis methods (assessed from 66 spectra) Analysis method Error (%) Single peak 14.4 ± 1.2 Single ICA peak 12.7 ± 1.1 ICA model prediction 8.7 ± Conclusions Least square values for different quantitative analysis methods representing how far the calculated proportion value obtained from a prediction method is away from the true concentration value, were used to assess the method s accuracy and compared the quantitating performance between methods. In term of accuracy, the results obtained by using our in-house linear Poisson ICA along with our pre-processing software improves on results from the standard method about 40%. The use of building ICA model of MALDI-MS data should improve the accuracy of quantifying mixes in specimens as taking into account all significant signals from entire spectra in the whole data set rather than only using a single peak to separate the measured quantity into finite number of components that could be 14

16 estimated and classified as signals or noise/background/contaminants. Another strength of using the ICA is the method can be further optimised such as adjusting numbers of components to suit a data set. In contrast, not much alteration could be done to achieve a better accuracy on a standard single peak analysis once the most influential peak are found. With the ICA analysis, the more changing peaks across spectral data usually give the better accuracy of predicting underlying components. However, many signal variations introduced non-linearity in the incoming spectra, e.g. suppression and signal-to-noise problems. Overall, MALDI-MS has turned into a more quantitative tool by applying our linear Poisson ICA as the degree of accuracy achievable was as good as <5% error in the brain-liver data set where enough biological variations were observed. This strongly suggests application of the technique for MALDI-MS imaging. 5. Future Work To finish off the ICA results in this document, error bars will be computed from modelled peak fits of every spectrum for each extracted components. The error characteristics for each analysis will then be described as pull distributions. Also, important peaks that characterise brain, liver, cow s milk and goat s milk sample will be identified by tandem mass spectrometry (MS/MS). The quantitation of MALDI-MS data limitation is believed to be due to the uncontrolled variability of the acquired data. This might be improved with experimental adjustments. We plan to obtain also the results for brain-liver mixture with additional internal standards, and another setting with added sodium salt. Applying the linear Poisson ICA concept for the imaging aspect is the final goal of this work. Where the ICA model s components composed in spectra stored at different spatial location throughout imaging sample can be plotted. The constructed ICA image would represent the distribution of bunch of molecules contained in some tissue type, and useful biological/pathological information could then be extracted. This could lead to quantitative analysis of biological systems using biochemical molecules which are specific to tissues and/or might be used as biomarkers to investigate abnormality. Hence, a novel method of quantification of MALDI-MS imaging can be introduced. Final aims : To apply the linear Poisson ICA in (brain) imaging approach of MALDI-MS To automatically extract useful components from MALDI-MS images and able to see (lipid) distribution in tissue To reduce noise in MALDI-MS images and make quantitative measure out of them To quantitatively determine (lipid) biomarkers in brain dementia and/or cancers 15

17 Challenges : We also need to overcome numbers of challenges in imaging aspect since MALDI-MS images would contain huge data set (in the order of ten thousands of pixels), with limited s/n on spectra acquired at each pixel location. Also, there should be more variation in ions signals that contribute to the mass spectra as a result of local biological molecules which possibly lead to more components needed in building an ICA model. The most important challenge is that there will be no ground truth for the imaging sample as to the brain-liver or milk analyses; therefore, the same accuracy test is not available. Potential solutions : Entropy and mutual information (MI) concepts could help automate data mining. Where smaller set of possible information-rich ICA component combinations could be used to construct useful MALDI-MS images. This possibly can guide biological and/or pathological structure in (brain) tissue. Please see Table 3 for a future work plan to be undertaken in the third year of the PhD programme. Note that the work done in this document will be used as a basis for a paper publication. 16

18 Nov 17 Oct 17 Sep 17 Aug 17 Jul 17 Jun 17 May 17 Apr 17 Mar 17 Feb 17 Jan 17 Dec 16 Table 3 A third year plan of the PhD research project Month Plan For brain-liver and milk MS data analyses, compute error bars for ICA model fits / generate pull distributions and Bland-Altman plot to observe noise characteristics Use tandem MS to identify important peaks that characterise brain, liver, cow s milk and goat s milk samples Acquire and analyse MS data for brainliver mixtures with added internal standards (PCs and/or PEs) / with added sodium (and/or lithium) salt Generate more MSI data from biological (eg. rat brain, diseased model) tissue samples using the 7090 MALDI-MS instrument / find optimised MSI sample preparation protocols and acquisition parameters / understand spectral behaviours e.g. signal-to-noise, suppression effects / be able to upload acquired MSI data onto TINA software Apply the linear Poisson ICA to MS images and extract useful components with assistance of entropy and mutual information concepts Construct ICA images containing information for some types of (lipidic) tissues / subtract background noise component to enhance MSI images / quantify signal components with quoted errors Use the ICA method to obtain useful biochemical/pathological information from MALDI-MSI on biological systems / investigate (lipid) biomarkers in brain dementia and/or cancers Perform tandem MS to identify molecules of interest and describe tissue compositions on MS images Thesis writing 17

19 References Alexandrov, T. MALDI imaging mass spectrometry: statistical data analysis and current computational challenges. 13(16), 2012 Bligh, E.G. and Dyer, W.J. "A Rapid Method of Total Lipid Extraction and Purification." Can J Biochem Physiol, 37(8), 1959: Duncan, M. W., Roder, H., & Hunsucker, S. W. Quantitative matrix-assisted laser desorption/ionization mass spectrometry. Briefings in Functional Genomics and Proteomics, 7(5), 2008: Gut, Y., Boiret, M., Bultel, L., et al. Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets. J Pharm Biomed Anal. 105, 2015: Karas, M., and Hillenkamp, F. "Laser Desorption Ionization of Proteins with Molecular Masses Exceeding 10,000 Daltons." Anal Chem, 60(20), 1988: Kehrmann, J., Wessel, S., Murali, R., Hampel, A., Bange, F.-C., Buer, J., & Mosel, F. Principal component analysis of MALDI TOF MS mass spectra separates M. abscessus (sensu stricto) from M. massiliense isolates. BMC Microbiology, 16(24), Liland, K.H., Mevik, B., Rukke, E., Almøy T. and Isaksson, T. Quantitative whole spectrum analysis with MALDI-TOF MS, Part II: Determining the concentration of milk in mixtures, Chemometrics and Intelligent Laboratory Systems, 99(1), 2009: Mantini, D. Petrucci, F. Del Boccio, P., et al. Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra. Bioinformatics, 24(1), 2008: Seepujak, A., Thacker, N.A. and Tar, P.D. Background Subtraction and Noise Modelling for Spectroscopy Data. Accessible from [TINA Memo : ] Shao, C., Tian, Y., Dong, Z., et al. The Use of Principal Component Analysis in MALDI-TOF MS: a Powerful Tool for Establishing a Mini-optimized Proteomic Profile. American Journal of Biomedical Sciences, 4(1), 2012: Szajli, E. Feher, T. and Medzihradszky, K.F. Investigating the quantitative nature of MALDI- TOF MS. Mol Cell Proteomics, 7(12), 2008: Tanaka, K., Waki, H., Ido, Y., Akita, S., Yoshida, Y. and Yoshida, T. "Protein and Polymer Analyses up to M/Z by Laser Ionization Time-of-Flight Mass Spectrometry." In Rapid Communications in Mass Spectrometry, 3,

20 Tar, P.D., Thacker, N.A., Gilmour, J.D. and Jones, M.A., Automated Quantitative Measurements and Associated Error Covariances for Planetary Image Analysis, Advances in Space Research, 56(1), 2015: Tar, P.D., Bugiolacchi, R., Thacker, N.A. Gilmour, J.D. and MoonZoo Team. Estimating False Positive Contamination in Crater Annotations from Citizen Science Data. Earth Moon & Planets, doi: /s