Automated Analysis of SEM X-Ray Spectral Images: A Powerful 
New Microanalysis Tool

Kotula, Paul Gabriel; Keenan, Michael R.; Michael, Joseph R.

doi:10.1017/s1431927603030058

Cited by 316 publications

(247 citation statements)

References 9 publications

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“…15, June 2004cross-linked Fc⑀RI, GM1, and many signaling proteins is markedly reduced in the absence of osmium (Wilson et al, 2000). To validate the nomenclature, we stained membrane sheets with osmium tetroxide but not uranyl acetate and imaged them using a FEI/Philips Electron Optics Tecnai F30-ST, 300-kV TEM/STEM equipped with an energy dispersive x-ray spectrometer, electron energy-loss spectrometer/imaging filter, and Automated eXpert Spectral Image Analysis (AXSIA) software (Kotula et al, 2003). Figure 5A is a STEM bright-field image of a membrane sheet prepared from antigen-stimulated RBL-2H3 cells.…”

Section: Microdomain Organization In Mast Cell Membranesmentioning

confidence: 99%

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Markers for Detergent-resistant Lipid Rafts Occupy Distinct and Dynamic Domains in Native Membranes

Wilson

Steinberg

Liederman

et al. 2004

MBoC

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187

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Lipid rafts isolated by detergent extraction and sucrose gradient fractionation from mast cells are enriched for the glycosylphosphatidylinositol-linked protein Thy-1, the ganglioside GM1, palmitoylated LAT, and cross-linked IgE receptors, Fc⑀RI. This study addresses the relationship of fractionation data to the organization of raft markers in native membranes. Immunogold labeling and electron microscopy shows there is little or no colocalization of the raft markers Thy-1, GM1, and LAT with each other or with Fc⑀RI on native membrane sheets prepared from unstimulated cells. External cross-linking of Thy-1 promotes coclustering of Thy-1 with LAT, but not with GM1. Thy-1 and LAT clusters occur on membrane regions without distinctive features. In contrast, external cross-linking of Fc⑀RI and GM1 causes their redistribution to electron-dense membrane patches independently of each other and of Thy-1. The distinctive patches that accumulate cross-linked Fc⑀RI and GM1 also accumulate osmium, a stain for unsaturated lipids, and are sites for coated vesicle budding. Electron microscopy reveals a more complex and dynamic topographical organization of membrane microdomains than is predicted by biochemical analysis of detergent-resistant membranes. INTRODUCTIONOrdered regions of membrane, known as microdomains, lipid rafts, detergent-resistant membranes (DRMs), and other abbreviations are thought to be critical sites of signal propagation and membrane trafficking (Edidin, 1997(Edidin, , 2001Simons and Ikonen, 1997;Anderson, 1998; London, 1998, 2000;Jacobson and Dietrich, 1999;Langlet et al., 2000;Anderson and Jacobson, 2002). Analysis of these regions typically begins with detergent solubilization of whole cells followed by sucrose density gradient centrifugation and the recovery of detergent-resistant membranes from the light fractions of the gradient. The DRMs are enriched for caveolin, glycosylphosphatidylinositol-linked (GPI-linked) proteins, glycosphingolipids, GM1 ganglioside, and cholesterol, suggesting that these components are associated in the liquid-ordered (l o ) phase of the lipid bilayer (Schroeder et al., 1994;Ahmed et al., 1997). The interpretation of gradient centrifugation experiments remains controversial. There is evidence that detergents may force associations between components that are not colocalized in intact cells (Mayor and Maxfield, 1995), and fractionation results are known to be dramatically altered by varying the concentration of Triton X-100 (Field et al., 1999;Parolini et al., 1999), by use of alternative detergents (Montixi et al., 1998;Surviladze et al., 1998) or by omission of detergent altogether (Ilangumaran et al., 1999;Harder and Kuhn, 2000;Surviladze et al., 2001). Methods to observe membrane segregation in situ have also generated controversy. Results based on light and fluorescence microscopy of proteins and lipids, including refinements of the established fluorescence recovery after photobleaching methods and new single particle tracking methods, have led some investigators to co...

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Section: Microdomain Organization In Mast Cell Membranesmentioning

confidence: 99%

“…Spectral images were collected with the Tecnai Imaging and Analysis software at a resolution of 128 ϫ 128 pixels (16,384 spectra) at 1024 channels/spectrum. Details of multivariate statistical analysis methods are provided in Kotula et al (2003). …”

mentioning

confidence: 99%

Markers for Detergent-resistant Lipid Rafts Occupy Distinct and Dynamic Domains in Native Membranes

Wilson

Steinberg

Liederman

et al. 2004

MBoC

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187

183

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“…Over a decade, our research group has been developing an alternative method to nonnegative matrix factorization (NMF), or multivariate curve resolution (MCR) for the analysis of EELS-HSI [12,13]. We consider this approach to be successful because NMF naturally restricts both the spatial intensities and basis spectra to nonnegative values.…”

Section: Introductionmentioning

confidence: 99%

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

Shiga

Muto

2018

Nanoinformatics

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Recent advances in scanning transmission electron microscopy (STEM) techniques have enabled us to obtain spectroscopic datasets such as those generated by electron energy-loss (EELS)/energy-dispersive X-ray (EDX) spectroscopy measurements in a PC-controlled way from a specified region of interest (ROI) even at atomic scale resolution, also known as hyperspectral imaging (HSI). Instead of conventional analytical procedures, in which the potential constituent chemical components are manually identified and the chemical state of each spectral component is successively determined, a statistical machine-learning approach, which is known to be more effective and efficient for the automatic resolution and extraction of the underlying chemical components stored in a huge three-dimensional array of an observed HSI dataset, is used. Among the statistical approaches suitable for processing HSI datasets, methods based on matrix factorization such as principal component analysis (PCA), multivariate curve resolution (MCR), and nonnegative matrix factorization (NMF) are useful to find an essential low-dimensional data subspace hidden in the HSI dataset. This chapter describes our developed NMF method, which has two additional terms in the objective function, and which is particularly effective for analyzing STEM-EELS/EDX HSI datasets: (i) a soft orthogonal penalty, which clearly resolves partially overlapped spectral components in their spatial distributions and (ii) an automatic relevance determination (ARD) prior, which optimizes the number of components involved in the observed

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“…This problem is being attacked with different approaches. One powerful method employs multivariate curve resolution as the basis for image processing tools that capture the variance between spectral components (Kotula et al 2003). We are pursuing the approach we describe as "derived spectra."…”

Section: Introductionmentioning

confidence: 99%

“Derived spectra” software tools for detecting spatial and spectral features in spectrum images

Newbury

Bright

2006

Scanning

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Summary:A spectrum image is recorded as an x-y array of beam locations at each of which a spectrum of radiation is recorded as stimulated by the beam. The large database or "datacube" that results from a single image presents a significant challenge to the analyst to recover information efficiently, especially in the case where a true unknown is examined. This paper describes a class of "derived spectra" software tools that can aid the analyst in recognizing both common and rare features within the datacube. A derived spectrum tool creates a spectrum-like display (intensity vs. channel number) in which the intensity (e.g., xray counts) at a particular channel (e.g., x-ray photon energy) is calculated from all of or a subset of the pixel intensities measured for that channel. Derived spectra tools considered include the SUM, MAXIMUM PIXEL, RUN-NING SUM, and RUNNING MAXIMUM. The SUM-derived spectrum is useful for recognizing common features of the datacube, while the MAXIMUM PIXEL-and RUN-NING MAXIMUM-derived spectra can locate rare, unanticipated features, which may occur as infrequently as being present at a single pixel in the original datacube.

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Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool

Cited by 316 publications

References 9 publications

Markers for Detergent-resistant Lipid Rafts Occupy Distinct and Dynamic Domains in Native Membranes

Markers for Detergent-resistant Lipid Rafts Occupy Distinct and Dynamic Domains in Native Membranes

High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

“Derived spectra” software tools for detecting spatial and spectral features in spectrum images

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