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. 2022 Apr 26;94(16):6180-6190.
doi: 10.1021/acs.analchem.1c05238. Epub 2022 Apr 12.

'On the Spot' Digital Pathology of Breast Cancer Based on Single-Cell Mass Spectrometry Imaging

Eva Cuypers  1 , Britt S R Claes  1 , Rianne Biemans  2 , Natasja G Lieuwes  2 , Kristine Glunde  3   4 , Ludwig Dubois  2 , Ron M A Heeren  1
Affiliations

'On the Spot' Digital Pathology of Breast Cancer Based on Single-Cell Mass Spectrometry Imaging

Eva Cuypers et al. Anal Chem. .

Abstract

The molecular pathology of breast cancer is challenging due to the complex heterogeneity of cellular subtypes. The ability to directly identify and visualize cell subtype distribution at the single-cell level within a tissue section enables precise and rapid diagnosis and prognosis. Here, we applied mass spectrometry imaging (MSI) to acquire and visualize the molecular profiles at the single-cell and subcellular levels of 14 different breast cancer cell lines. We built a molecular library of genetically well-characterized cell lines. Multistep processing, including deep learning, resulted in a breast cancer subtype, the cancer's hormone status, and a genotypic recognition model based on metabolic phenotypes with cross-validation rates of up to 97%. Moreover, we applied our single-cell-based recognition models to complex tissue samples, identifying cell subtypes in tissue context within seconds during measurement. These data demonstrate "on the spot" digital pathology at the single-cell level using MSI, and they provide a framework for fast and accurate high spatial resolution diagnostics and prognostics.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Experimental workflow from cell preparation to digital pathology. (A) Cell preparation on poly-l-lysine-coated ITO slides. (B) Sample sublimation and MSI analysis. (C) Data analysis encompassing ROI selection for every single cell, determination of the mean mass spectrum of every cell after rms normalization, model building using PCA/LDA analysis, and applying the generated method offline and online for pathological identification. H&E-based staining is considered the gold standard to which results should be compared.
Figure 2
Figure 2
Example of the spatial distribution of PC 34:1 for all 14 analyzed cell subtypes after rms normalization. The scale bar represents 200 μm. Measurements were performed using timsTOF fleX MALDI-2 with a 5 μm pixel size.
Figure 3
Figure 3
DDA identification of lipids measured on Orbitrap Elite. (A) Sample full MS spectrum in the positive ion mode and (B) MS2 of m/z 788.61 (PC 36:1), both measured in MDA-MB-231 cells. (C) Heat map of 79 identified lipids based on DDA analysis of single cells from the 14 breast cancer cell lines. Lipids identified are shown for three representative individual cells from three different cell cultures of the same cell line.
Figure 4
Figure 4
Classification models in AMX Model Builder (Waters) of (A) different molecular breast cancer subtypes based on ER, PR, and HER2 status and (B) different human breast cancer cell lines, based on single-cell profiles obtained from timsTOF fleX imaging experiments.
Figure 5
Figure 5
Comparison of mean normalized mass profiles of 10 differentiating lipids of single MDA-MB-231 cells after rms normalization measured on six different MSI instruments (positive mode). Error bars represent standard deviations from three randomly chosen cell profiles.
Figure 6
Figure 6
Automatic recognition of human breast tumor MDA-MB-231 xenografts from timsTOF fleX MALDI-1. (A) Annotated H&E staining (left), optical image of section (middle), and the distribution of identified cells combined with the overlayed optical image (right). (B) Separated images showing the distribution of the identified cells. (C) Comparison of cell recognitions measured on timsTOF fleX MALDI-1 and Synapt G2-Si HDMS. Percentages are calculated as the percentage of pixels classified (100% is the full measured region).
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