MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging

Abstract

Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio ( m / z ) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m / z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample’s molecular composition. Our approach, MSIWarp ( ), finds an m / z recalibration function by maximizing a similarity score that considers both the intensity and m / z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m / z alignment.

Emerging Computational Methods in Mass Spectrometry Imaging

“…Reproduced with permission. [44] Copyright 2020, American Chemical Society. and adopted in major repositories (e.g., METASPACE [32] ) and biomolecular atlas programs, such as HuBMAP.…”

“…[39][40][41] Many computational methods have been developed for spectral alignment in the postacquisition data processing step to reduce mass shifts. [42][43][44] Eriksson et al [44] used a correlation optimized warping (COW) algorithm to reduce mass shifts in MSI data by warping the mass dimension without standards. In this approach, each mass spectrum is recalibrated by maximizing its similarity to the reference spectrum as shown in Figure 3A.…”

“…Ion images generated from D) original spectra and E,F) aligned spectra. Reproduced with permission [44]. Copyright 2020, American Chemical Society.…”

Deep learning model coupled with proteomics to detect yeast epidemic clones across multiple hospitals

“…An alignment technique was used after the preprocessing step, with MSIWarp, a Python package provided with C + + implementation. MSIWarp is a exible tool compatible with multiple instrument types to perform mass alignment of Mass Spectrometry Imaging (MSI) spectra [20]. The alignment approach works on TOF data and reduces the mass range shift by applying recalibration function on mass m/z data and by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra.…”

“…This raises the problem of a possible quality control of the spectra before use for such models. Another aspect to consider is the mass range shift produced by the different machines that will require further data alignment [26] [14], [20], [30], [31].…”