Mapping and Profiling Lipid Distribution in a 3D Model of Breast Cancer Progression

Abstract

Aberrant lipid accumulation and marked changes in cellular lipid profiles are related to breast cancer metabolism and disease progression. In vitro , these phenomena are primarily studied using cells cultured in monolayers (2D). Here, we employ multicellular spheroids, generated using the MCF10A cell line series of increasing malignancy potential, to better recapitulate the 3D microenvironmental conditions that cells experience in vivo . Breast cancer cell lipid compositions were assessed in 2D and 3D culture models as a function of malignancy using liquid chromatography coupled with mass spectrometry. Further, the spatial distribution of lipids was examined using Raman chemical imaging and lipid staining. We show that with changes in the cellular microenvironment when moving from 2D to 3D cell cultures, total lipid amounts decrease significantly, while the ratio of acylglycerols to membrane lipids increases. This ratio increase could be associated with the formation of large lipid droplets (>10 μm) that are spatially evident throughout the spheroids but absent in 2D cultures. Additionally, we found a significant difference in lipid profiles between the more and less malignant spheroids, including changes that support de novo sphingolipid production and a reduction in ether-linked lipid fractions in the invasive spheroids. These differences in lipid profiles as a function of cell malignancy and microenvironment highlight the importance of coupled spatial and lipidomic studies to better understand the connections between lipid metabolism and cancer.

Lipidomic study of cell lines reveals differences between breast cancer subtypes

“…The conventional 2D cultures are a further limitation. Cell shape and microenvironment influence cellular lipid content and composition [64] and 2D cultures do not bring out site-specific transcriptomic profiles of metastases [65]. The added serum is the main source of lipids on the culture medium, and this was the same for all cell lines.…”

Tissue expansion of ‘cleared’ whole brains reduces contrast in ex vivo MRI

“…We therefore proceeded to analyze lipid-content by Raman spectroscopy (RS) that does not require any labeling. RS is used to measure composition and relative abundance of biomolecules, notably proteins and lipids, by their characteristic chemical bonds [67][68][69] . Though lipids signatures span across the entire RS spectrum 69 , we focused on the ratio between peaks appearing at 2850 cm -1 (representing CH 2 bonds of long aliphatic lipid chains) and 2940 cm -1 (attributed to CH 3 bonds as typically found in proteins) to assess protein/lipid ratio, as previously employed 70,71 , including on a couple of cleared samples 62 .…”

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

“…Huang et al developed a graphical data processing pipeline for MSI based spatially resolved metabolomics ( Huang et al, 2019 ), which could achieve multivariate statistical results in an intuitive and simple way as well as discovery low-abundant but reliable biomarkers in heterogeneous tumors. MSI has been employed to different cancers including brain ( Jarmusch et al, 2016 ; Clark et al, 2018 ), breast ( Guenther et al, 2015 ; Abdelmoula et al, 2016 ; Angerer et al, 2016 ; Wang S. et al, 2016 ; Torata et al, 2018 ; Vidavsky et al, 2019 ), lung ( Calligaris et al, 2015 ; Li T. et al, 2015 ; Carter et al, 2017 ; Holzlechner et al, 2018 ), ovarian ( Dória et al, 2016 ; Briggs et al, 2019 ), prostrate ( Wang et al, 2017 ), esophageal ( Guo et al, 2014 ; Abbassi-Ghadi et al, 2016 ; Sun et al, 2019a ), colon ( Hiraide et al, 2016 ; Inglese et al, 2017 ), oral ( Uchiyama et al, 2014 ; Bednarczyk et al, 2019 ), skin ( Xu et al, 2017 ; Margulis et al, 2018 ), adrenal gland ( Sun et al, 2019b ) and gastrointestinal stromal tumors ( Abu Sammour et al, 2019 ) for spatial metabolomics analysis. MSI is also used to determine the metabolite changes in the 3D osteosarcoma cell culture model upon drug treatments ( Palubeckaitė et al, 2020 ).…”