Unsupervised cell identification on multidimensional X-ray fluorescence datasets

Abstract

A novel approach to locate, identify and refine positions and whole areas of cell structures based on elemental contents measured by X-ray fluorescence microscopy is introduced. It is shown that, by initializing with only a handful of prototypical cell regions, this approach can obtain consistent identification of whole cells, even when cells are overlapping, without training by explicit annotation. It is robust both to different measurements on the same sample and to different initializations. This effort provides a versatile framework to identify targeted cellular structures from datasets too complex for manual analysis, like most X-ray fluorescence microscopy data. Possible future extensions are also discussed.

Development of a single-cell X-ray fluorescence flow cytometer

“…The experimentally determined mean Fe content for individual bRBCs, $ 50 fg, is in good agreement with previous findings (Herring et al, 1960a;Kakkar & Makkar, 2009;Shamberger, 2003). Although the data are significantly noisier, the average Zn content ($ 0.4 fg cell À1 ) is again in good agreement with previous measurements (Herring et al, 1960a;Wang et al, 2014). Even with these noisy data, we can see the importance of single-cell measurements: the distribution of Fe is much larger than would be expected simply from experimental uncertainty.…”

“…With the development of intense, third-generation synchrotron sources, XRF imaging of intact cells using either dried or cryogenically frozen samples has become a widely used technique. Recent work (Wang et al, 2014) used raster scanning of air-dried cells in fly scan mode with unsupervised cell identification to separate cell types and quantify the elemental content of individual cells from XRF datasets. This work successfully identified hundreds of cells using imaging techniques.…”

2015 Atomic Spectrometry Update – a review of advances in X-ray fluorescence spectrometry and their applications

“…Performing SR based X-ray nano-CT and XRF techniques on a section of a SOFC Ni-YSZ (yttria-stabilised zirconia) anode, the XRF results provided elemental identication and coarse spatial mapping, further the nano-CT was used to map the detailed 3D spatial distribution of Ni, YSZ, and a nickel sulfur poisoning phase. Wang et al 58 introduced a versatile framework to identify targeted cellular structures from datasets, too complex for manual analysis like most XRF microscopy datasets. The results provided strong evidence of the detrimental effects of 100 mg g À1 hydrogen sulde on typical Ni-YSZ anode materials.…”

X-ray nanoprobes and diffraction-limited storage rings: opportunities and challenges of fluorescence tomography of biological specimens

“…One of the ways in which one can exploit the capabilities of diffraction-limited storage rings is to study not single examples of biological specimens but populations, so that one can gain real statistics on variations that occur in nature. A first example of this approach has been the automatic identification of individual cell types in a mixed sample, allowing one to generate statistical measures of the variation in metal content in cells separated by type (Wang et al, 2014). One could imagine extending this approach from 2D to 3D, and performing statistical analysis not just on cell types but on organelle types within cells.…”