Multivariate curve resolution techniques try to estimate physically and/or chemically meaningful profiles underlying a set of chemical or related measurements. However, the estimation of profiles is not generally unique and it is often complicated by intensity and rotational ambiguities. Constraints as further information of chemical entities can be imposed to reduce the extent of
Rationale
Identification of subregions under different pathological conditions on cancerous tissue is of great significance for understanding cancer progression and metastasis. Infrared matrix‐assisted laser desorption electrospray ionization mass spectrometry (IR‐MALDESI‐MS) can be potentially used for diagnostic purposes since it can monitor spatial distribution and abundance of metabolites and lipids in biological tissues. However, the large size and high dimensionality of hyperspectral data make analysis and interpretation challenging. To overcome these barriers, multivariate methods were applied to IR‐MALDESI data for the first time, aiming at efficiently resolving mass spectral images, from which these results were then used to identify normal regions within cancerous tissue.
Methods
Molecular profiles of healthy and cancerous hen ovary tissues were generated by IR‐MALDESI‐MS. Principal component analysis (PCA) combined with color‐coding built a single tissue image which summarizes the high‐dimensional data features. Pixels with similar color indicated similar composition. PCA results from healthy tissue were further used to test each pixel in cancerous tissue to determine if it is healthy. Multivariate curve resolution‐alternating least squares (MCR‐ALS) was used to obtain major spatial features existing in ovary tissues, and group molecules with the same distribution patterns simultaneously.
Results
PCA as the predominating dimensionality reduction approach captured over 90% spectral variances by the first three PCs. The PCA images show the cancerous tissue is more chemically heterogeneous than healthy tissue, where at least four regions with different m/z profiles can be differentiated. PCA modeling assigns top regions of cancerous tissue as healthy‐like. MCR‐ALS extracted three and four major compounds from healthy and cancerous tissue, respectively. Evaluating similarities of resolved spectra uncovered the chemical components that were distinct in some regions on cancerous tissue, serving as a supplementary way to differentiate healthy and cancerous regions.
Conclusions
Two unsupervised chemometric methods including PCA and MCR‐ALS were applied for resolving and visualizing IR‐MALDESI‐MS data acquired from hen ovary tissues, improving the interpretation of mass spectrometry imaging results. Then possible normal regions were differentiated from cancerous tissue sections. No prior knowledge is required using either chemometric method, so our approach is readily suitable for unstained tissue samples, which allows one to reveal the molecular events happening during disease progression.
Self‐modeling curve resolution (SMCR) techniques are widely applied for resolving chemical data to the pure‐component spectra and composition profiles. In most circumstances, there is a range of mathematical solutions to the curve resolution problem. The mathematical solutions generated by SMCR obey the applied constraints coming from a priori physicochemical information about the system under investigation. However, several studies demonstrate that a unique solution can be obtained by implementing some constraints such as trilinearity, equality, zero concentration region, correspondence, local‐rank, and non‐negativity under data‐based uniqueness (DBU) condition. In this research, a general rule for uniqueness (GRU) is proposed to unify all the different information that lead to a unique solution in one framework. Moreover, GRU can be a guide for developing new constraints in SMCR to get more accurate solutions.The authors are delighted to dedicate this manuscript to Professor Paul J. Gemperline in recognition of his significant contributions to the field of chemometrics. We believe that the chemometrics society's success in addressing its mission owes a great deal to his vision, passion for learning and teaching, and extensive scientific efforts over the years. We honor his friendship and generous supports.
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