posted on 2021-07-23, 16:44authored byTobias Boskamp, Rita Casadonte, Lena Hauberg-Lotte, Sören Deininger, Jörg Kriegsmann, Peter Maass
Matrix-assisted
laser desorption/ionization mass spectrometry imaging
(MALDI MSI) is an established tool for the investigation of formalin-fixed
paraffin-embedded (FFPE) tissue samples and shows a high potential
for applications in clinical research and histopathological tissue
classification. However, the applicability of this method to serial
clinical and pharmacological studies is often hampered by inevitable
technical variation and limited reproducibility. We present a novel
spectral cross-normalization algorithm that differs from the existing
normalization methods in two aspects: (a) it is based on estimating
the full statistical distribution of spectral intensities and (b)
it involves applying a non-linear, mass-dependent intensity transformation
to align this distribution with a reference distribution. This method
is combined with a model-driven resampling step that is specifically
designed for data from MALDI imaging of tryptic peptides. This method
was performed on two sets of tissue samples: a single human teratoma
sample and a collection of five tissue microarrays (TMAs) of breast
and ovarian tumor tissue samples (N = 241 patients).
The MALDI MSI data was acquired in two labs using multiple protocols,
allowing us to investigate different inter-lab and cross-protocol
scenarios, thus covering a wide range of technical variations. Our
results suggest that the proposed cross-normalization significantly
reduces such batch effects not only in inter-sample and inter-lab
comparisons but also in cross-protocol scenarios. This demonstrates
the feasibility of cross-normalization and joint data analysis even
under conditions where preparation and acquisition protocols themselves
are subject to variation.