Multiple Statistical Analysis Techniques Corroborate Intratumor Heterogeneity in Imaging Mass Spectrometry Datasets of Myxofibrosarcoma

Abstract

MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information.

Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data

“…MSI has several key characteristics that make it well suited to this task; it is an untargeted analysis that can simultaneously analyze hundreds of molecular ions, it can be directly applied to tissue sections, it is inexpensive, and it is fast. Several previous studies have reported MSI's ability to uncover tumor subpopulations in histologically identical regions of tumor tissue (5,20,21). Here we have used dimensionality reduction based on t-SNE followed by bisecting k-means clustering to automatically segment the tumor-specific MSI data from a patient series into an optimum number of subpopulations.…”

“…This approach was based on the consensus of five different linear multivariate methods (20) to locate tumor subpopulations, but required the user to prespecify the number of subpopulations. The resulting cluster data were then compared with the clinical data to assess their statistical significance.…”

Matrix‐assisted laser desorption/ionization imaging protocol for in situ characterization of tryptic peptide identity and distribution in formalin‐fixed tissue

“…[35] 2.2 Protocol-specific solutions 2.2.1 10 mM citric acid monohydrate pH 6 [29,36] ▪ 1.05 g citric acid monohydrate (Sigma-Aldrich, Japan) ▪ Dissolve in 480 mL H 2 O ▪ pH to 6.0 using 1 M NaOH (~13 mL) (Merck, Darmstadt, Germany) ▪ Make up volume to 500 mL with H 2 O 2.2.2 100 mM ammonium bicarbonate (NH 4 [32] ▪ 0.4 pmol/mL angiotensin I [34][35][36][37][38][39][40][41][42][43] ( Figure 1. Protocol for mapping the distribution and determining the identity of tryptic peptides generated in situ.…”

“…NB: Although multivariate statistics are best used to select peaks of interest, [12,41,42] to keep the workflow as straightforward as possible, peak selection here is manual only and based on operator visualized ion intensity maps for peptides of interest. NB: Steps 6.1 to 6.5 use flexImaging NB: Steps 6.6 onwards use in-house data processing 6.1 When the MALDI-IMS data set loads completely in flexImaging (step 5.10), a sum spectrum will be generated in the Spectrum Display window.…”

High Speed Data Processing for Imaging MS-Based Molecular Histology Using Graphical Processing Units

“…Imaging MS-based molecular histology has been used to differentiate between histologically overlapping/identical tumors [6], found evidence of intratumor heterogeneity that could indicate clonal development [6][7][8] and revealed altered protein expression at tumor interface zones [9,10]. These capabilities offer enormous potential to investigate biomolecular changes that occur prior to (or without) morphological change, or those for which a molecular specific stain is unavailable.…”