Discriminating and Imaging Different Phosphatidylcholine Species within Phase-Separated Model Membranes by Principal Component Analysis of TOF-Secondary Ion Mass Spectrometry Images

Abstract

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) enables chemically imaging the distributions of various lipid species in model membranes. However, discriminating the TOF-SIMS data of structurally similar lipids is very difficult because the high intensity, low mass fragment ions needed to achieve submicrometer lateral resolution are common to multiple lipid species. Here, we demonstrate that principal component analysis (PCA) can discriminate the TOF-SIMS spectra of four unlabeled saturated phosphatidylcholine species, 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC), 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC), 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), and 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC) according to variations in the intensities of their low mass fragment ions (m/z ≤ 200). PCA of TOF-SIMS images of phase-separated DSPC/DLPC and DPPC/DLPC membranes enabled visualizing the distributions of each phosphatidylcholine species with higher contrast and specificity than that of individual TOF-SIMS ion images. Comparison of the principal component (PC) scores images to atomic force microscopy (AFM) images acquired at the same membrane location before TOF-SIMS analysis confirmed that the PC scores images reveal the phase-separated membrane domains. The lipid composition within these domains was identified by projection of their TOF-SIMS spectra onto PC models developed using pure lipid standards. This approach may enable the identification and chemical imaging of structurally similar lipid species within more complex membranes.

Structural determinants of protein partitioning into ordered membrane domains and lipid rafts

“…Another interesting approach is to study the lateral molecular composition of bilayers by Time of Flight Secondary Ion Monitoring (Tof-SIMS) (Kraft, Weber et al 2006, Zheng, McQuaw et al 2008. Modern instruments can detect submicroscopic lipid domains, in addition to detailing the molecular composition of those membranes (Vaezian, Anderton et al 2010), and this approach has recently been extended to live cells to study both lipid (Frisz, Klitzing et al 2013) and protein (Wilson, Frisz et al 2015) distributions.…”

Quantifying the Molar Percentages of Cholesterol in Supported Lipid Membranes by Time-of-Flight Secondary Ion Mass Spectrometry and Multivariate Analysis

“…However, our previous studies demonstrate that, using our instrumentation, ion doses within the static limit do not yield sufficient ion signals to permit detecting compositional changes within model membranes with multivariate techniques. 38 Instead, an ion dose of approximately 3 × 10 13 ions/cm 2 , which exceeds the static limit, yields the higher counts of fragment ions that are beneficial for extracting compositional information from model lipid membranes using multivariate analysis. 38 Data Analysis.…”

“…38 Instead, an ion dose of approximately 3 × 10 13 ions/cm 2 , which exceeds the static limit, yields the higher counts of fragment ions that are beneficial for extracting compositional information from model lipid membranes using multivariate analysis. 38 Data Analysis. Unit mass binned spectra were extracted from sample locations that corresponded to the membrane, and not the chrome grid, using Wincadence software (Physical Electronics, Incorporated, Chanhassen, MN).…”

Image and Spectral Processing for ToF-SIMS Analysis of Biological Materials

“…8) Many nice examples of lipid analysis with ToF-SIMS have been shown. 3,[18][19][20][21][22][23][24][25] Control spectra for many common lipids have been published and used to study cells and tissues. 3,19,26) However, many of the higher mass, more chemically specific, lipid peaks are not detected, especially when one collects high spatial resolution images using ToF-SIMS.…”

“…1,[3][4][5]8,[18][19][20][21][23][24][25][26] For this work, ToF-SIMS analysts have used a variety of methods to process and understand their data. These include a combination of traditional manual analysis and the application of multivariate analysis (MVA) methods.…”