Multimodal mass spectrometry imaging identifies cell-type-specific metabolic and lipidomic variation in the mammalian liver.

Spatial single-cell omics provides a readout of biochemical processes. It is challenging to capture the transient lipidome/metabolome from cells in a native tissue environment. We employed water gas cluster ion beam secondary ion mass spectrometry imaging ([H2O]n>28K-GCIB-SIMS) at ≤3 µm resolution using a cryogenic imaging workflow. This allowed multiple biomolecular imaging modes on the near-native-state liver at single-cell resolution. Our workflow utilizes desorption electrospray ionization (DESI) to build a reference map of metabolic heterogeneity and zonation across liver functional units at tissue level. Cryogenic dual-SIMS integrated metabolomics, lipidomics, and proteomics in the same liver lobules at single-cell level, characterizing the cellular landscape and metabolic states in different cell types. Lipids and metabolites classified liver metabolic zones, cell types and subtypes, highlighting the power of spatial multi-omics at high spatial resolution for understanding celluar and biomolecular organizations in the mammalian liver.

Multiplexed ion beam imaging (MIBI) for characterization of the tumor microenvironment across tumor types.

An ability to characterize the cellular composition and spatial organization of the tumor microenvironment (TME) using multiplexed IHC has been limited by the techniques available. Here we show the applicability of multiplexed ion beam imaging (MIBI) for cell phenotype identification and analysis of spatial relationships across numerous tumor types. Formalin-fixed paraffin-embedded (FFPE) samples from tumor biopsies were simultaneously stained with a panel of 15 antibodies, each labeled with a specific metal isotope. Multi-step processing produced images of the TME that were further segmented into single cells. Frequencies of different cell subsets and the distributions of nearest neighbor distances between them were calculated using this data. A total of 50 tumor specimens from 15 tumor types were characterized for their immune profile and spatial organization. Most samples showed infiltrating cytotoxic T cells and macrophages present amongst tumor cells. Spatial analysis of the TME in two ovarian serous carcinoma images highlighted differences in the degree of mixing between tumor and immune cells across samples. Identification of admixed PD-L1+ macrophages and PD-1+ T cells in an urothelial carcinoma sample allowed for the detailed observations of immune cell subset spatial arrangement. These results illustrate the high-parameter capability of MIBI at a sensitivity and resolution uniquely suited to understanding the complex tumor immune landscape including the spatial relationships of immune and tumor cells and expression of immunoregulatory proteins.

Fully convolutional neural network for removing background in noisy images of uranium bearing particles.

A fully convolutional neural network (FCN) was developed to supersede automatic or manual thresholding algorithms used for tabulating SIMS particle search data. The FCN was designed to perform a binary classification of pixels in each image belonging to a particle or not, thereby effectively removing background signal without manually or automatically determining an intensity threshold. Using 8000 images from 28 different particle screening analyses, the FCN was trained to accurately predict pixels belonging to a particle with near 99% accuracy. Background eliminated images were then segmented using a watershed technique in order to determine isotopic ratios of identified particles. A comparison of the isotopic distributions of an independent data set segmented using the neural network with a commercially available automated particle measurement (APM) program developed by CAMECA was performed. This comparison highlighted the necessity for effective background removal to ensure that resulting particle identification is not only accurate, but preserves valuable signal that could be lost due to improper segmentation. The FCN approach improves the robustness of current state-of-the-art particle searching algorithms by reducing user input biases, resulting in an improved absolute signal per particle and decreased uncertainty of the determined isotope ratios.

Cluster secondary ion mass spectrometry imaging of interfacial reactions of TiO2 microspheres embedded in ionic liquids.

RATIONALE: Our goal is to develop protocols for the elucidation of the identity and structure of reaction products embedded in a reaction medium. Results should find significance in a variety of disciplines ranging from the study of biological cells and tissues, to the steps associated with the functionalization of nanoparticles. METHODS: We utilize cluster secondary ion mass spectrometry (cluster-SIMS) to acquire three-dimensional (3D) information about 5-30 µm TiO2 microspheres imbedded into an ionic liquid. The method allows molecular depth profiling with submicron spatial resolution and depth profiling with a resolution of several tens of nanometers. The ionic liquid matrix enshrouds the spheres, allowing them to be introduced into the vacuum environment of the mass spectrometer. RESULTS: The results provide 3D chemical information about these microspheres as they are synthesized by interfacial sol-gel reactions. We show that with 40 keV C60 (+) , it is possible to erode through the reaction medium and map the distribution of those embedded TiO2 microspheres. Moreover, we demonstrate that it is possible to monitor surface modification of the particles and, via ion beam drilling, elucidate their internal structure. CONCLUSIONS: Using cluster-SIMS imaging, we are able to elucidate the identity and structure of reaction products embedded in a reaction medium, a problem of long-standing interest for materials characterization. With this strategy, we have provided a new approach that may be especially useful for the characterization of biological tissue and cells within the vacuum confines of the mass spectrometer. Copyright © 2015 John Wiley & Sons, Ltd.

Multimodal image fusion with SIMS: Preprocessing with image registration.

In order to utilize complementary imaging techniques to supply higher resolution data for fusion with secondary ion mass spectrometry (SIMS) chemical images, there are a number of aspects that, if not given proper consideration, could produce results which are easy to misinterpret. One of the most critical aspects is that the two input images must be of the same exact analysis area. With the desire to explore new higher resolution data sources that exists outside of the mass spectrometer, this requirement becomes even more important. To ensure that two input images are of the same region, an implementation of the insight segmentation and registration toolkit (ITK) was developed to act as a preprocessing step before performing image fusion. This implementation of ITK allows for several degrees of movement between two input images to be accounted for, including translation, rotation, and scale transforms. First, the implementation was confirmed to accurately register two multimodal images by supplying a known transform. Once validated, two model systems, a copper mesh grid and a group of RAW 264.7 cells, were used to demonstrate the use of the ITK implementation to register a SIMS image with a microscopy image for the purpose of performing image fusion.

Improving secondary ion mass spectrometry image quality with image fusion.

The spatial resolution of chemical images acquired with cluster secondary ion mass spectrometry (SIMS) is limited not only by the size of the probe utilized to create the images but also by detection sensitivity. As the probe size is reduced to below 1 µm, for example, a low signal in each pixel limits lateral resolution because of counting statistics considerations. Although it can be useful to implement numerical methods to mitigate this problem, here we investigate the use of image fusion to combine information from scanning electron microscope (SEM) data with chemically resolved SIMS images. The advantage of this approach is that the higher intensity and, hence, spatial resolution of the electron images can help to improve the quality of the SIMS images without sacrificing chemical specificity. Using a pan-sharpening algorithm, the method is illustrated using synthetic data, experimental data acquired from a metallic grid sample, and experimental data acquired from a lawn of algae cells. The results show that up to an order of magnitude increase in spatial resolution is possible to achieve. A cross-correlation metric is utilized for evaluating the reliability of the procedure.