1
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Yuan Z, Zhao F, Lin S, Zhao Y, Yao J, Cui Y, Zhang XY, Zhao Y. Benchmarking spatial clustering methods with spatially resolved transcriptomics data. Nat Methods 2024; 21:712-722. [PMID: 38491270 DOI: 10.1038/s41592-024-02215-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 02/16/2024] [Indexed: 03/18/2024]
Abstract
Spatial clustering, which shares an analogy with single-cell clustering, has expanded the scope of tissue physiology studies from cell-centroid to structure-centroid with spatially resolved transcriptomics (SRT) data. Computational methods have undergone remarkable development in recent years, but a comprehensive benchmark study is still lacking. Here we present a benchmark study of 13 computational methods on 34 SRT data (7 datasets). The performance was evaluated on the basis of accuracy, spatial continuity, marker genes detection, scalability, and robustness. We found existing methods were complementary in terms of their performance and functionality, and we provide guidance for selecting appropriate methods for given scenarios. On testing additional 22 challenging datasets, we identified challenges in identifying noncontinuous spatial domains and limitations of existing methods, highlighting their inadequacies in handling recent large-scale tasks. Furthermore, with 145 simulated data, we examined the robustness of these methods against four different factors, and assessed the impact of pre- and postprocessing approaches. Our study offers a comprehensive evaluation of existing spatial clustering methods with SRT data, paving the way for future advancements in this rapidly evolving field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
| | - Fangyuan Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Senlin Lin
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yu Zhao
- Tencent AI Lab, Shenzhen, China
| | | | - Yan Cui
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - Xiao-Yong Zhang
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China
| | - Yi Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
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2
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González-Fernández A, Dexter A, Nikula CJ, Bunch J. NECTAR: A New Algorithm for Characterizing and Correcting Noise in QToF-Mass Spectrometry Imaging Data. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2443-2453. [PMID: 37819737 PMCID: PMC10623552 DOI: 10.1021/jasms.3c00116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/08/2023] [Accepted: 09/28/2023] [Indexed: 10/13/2023]
Abstract
A typical mass spectrometry imaging experiment yields a very high number of detected peaks, many of which are noise and thus unwanted. To select only peaks of interest, data preprocessing tasks are applied to raw data. A statistical study to characterize three types of noise in MSI QToF data (random, chemical, and background noise) is presented through NECTAR, a new NoisE CorrecTion AlgoRithm. Random noise is confirmed to be dominant at lower m/z values (∼50-400 Da) while systematic chemical noise dominates at higher m/z values (>400 Da). A statistical approach is presented to demonstrate that chemical noise can be corrected to reduce its presence by a factor of ∼3. Reducing this effect helps to determine a more reliable baseline in the spectrum and therefore a more reliable noise level. Peaks are classified according to their spatial S/N on the single ion images, and background noise is thus removed from the list of peaks of interest. This new algorithm was applied to MALDI and DESI QToF data generated from the analysis of a mouse pancreatic tissue section to demonstrate its applicability and ability to filter out these types of noise in a relevant data set. PCA and t-SNE multivariate analysis reviews of the top 4000 peaks and the final 744 and 299 denoised peak list for MALDI and DESI, respectively, suggests an effective removal of uninformative peaks and proper selection of relevant peaks.
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Affiliation(s)
| | - Alex Dexter
- National
Physical Laboratory, Teddington, Middlesex TW11 0LW, U.K.
| | | | - Josephine Bunch
- National
Physical Laboratory, Teddington, Middlesex TW11 0LW, U.K.
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3
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Wittek O, Jahreis B, Römpp A. MALDI MS Imaging of Chickpea Seeds ( Cicer arietinum) and Crab's Eye Vine ( Abrus precatorius) after Tryptic Digestion Allows Spatially Resolved Identification of Plant Proteins. Anal Chem 2023; 95:14972-14980. [PMID: 37749896 PMCID: PMC10568532 DOI: 10.1021/acs.analchem.3c02428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023]
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) imaging following in situ enzymatic digestion is a versatile analytical method for the untargeted investigation of protein distributions, which has rarely been used for plants so far. The present study describes a workflow for in situ tryptic digestion of plant seed tissue for MALDI MS imaging. Substantial modifications to the sample preparation procedure for mammalian tissues were necessary to cater to the specific properties of plant materials. For the first time, distributions of tryptic peptides were successfully visualized in plant tissue using MS imaging with accurate mass detection. Sixteen proteins were visualized and identified in chickpea seeds showing different distribution patterns, e.g., in the cotyledons, radicle, or testa. All tryptic peptides were detected with a mass resolution higher than 60,000 as well as a mass accuracy better than 1.5 ppm root-mean-square error and were matched to results from complementary liquid chromatography-MS/MS (LC-MS/MS) data. The developed method was also applied to crab's eye vine seeds for targeted MS imaging of the toxic protein abrin, showing the presence of abrin-a in all compartments. Abrin (59 kDa), as well as the majority of proteins visualized in chickpeas, was larger than 50 kDa and would thus not be readily accessible by top-down MS imaging. Since antibodies for plant proteins are often not readily available, in situ digestion MS imaging provides unique information, as it makes the distribution and identification of larger proteins in plant tissues accessible in an untargeted manner. This opens up new possibilities in the field of plant science as well as to assess the nutritional quality and/or safety of crops.
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Affiliation(s)
| | - Bastian Jahreis
- Bioanalytical Sciences and
Food Analysis, University of Bayreuth, Universitaetsstrasse 30, D-95447 Bayreuth, Germany
| | - Andreas Römpp
- Bioanalytical Sciences and
Food Analysis, University of Bayreuth, Universitaetsstrasse 30, D-95447 Bayreuth, Germany
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4
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Klaila G, Vutov V, Stefanou A. Supervised topological data analysis for MALDI mass spectrometry imaging applications. BMC Bioinformatics 2023; 24:279. [PMID: 37430224 DOI: 10.1186/s12859-023-05402-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/26/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) displays significant potential for applications in cancer research, especially in tumor typing and subtyping. Lung cancer is the primary cause of tumor-related deaths, where the most lethal entities are adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Distinguishing between these two common subtypes is crucial for therapy decisions and successful patient management. RESULTS We propose a new algebraic topological framework, which obtains intrinsic information from MALDI data and transforms it to reflect topological persistence. Our framework offers two main advantages. Firstly, topological persistence aids in distinguishing the signal from noise. Secondly, it compresses the MALDI data, saving storage space and optimizes computational time for subsequent classification tasks. We present an algorithm that efficiently implements our topological framework, relying on a single tuning parameter. Afterwards, logistic regression and random forest classifiers are employed on the extracted persistence features, thereby accomplishing an automated tumor (sub-)typing process. To demonstrate the competitiveness of our proposed framework, we conduct experiments on a real-world MALDI dataset using cross-validation. Furthermore, we showcase the effectiveness of the single denoising parameter by evaluating its performance on synthetic MALDI images with varying levels of noise. CONCLUSION Our empirical experiments demonstrate that the proposed algebraic topological framework successfully captures and leverages the intrinsic spectral information from MALDI data, leading to competitive results in classifying lung cancer subtypes. Moreover, the framework's ability to be fine-tuned for denoising highlights its versatility and potential for enhancing data analysis in MALDI applications.
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Affiliation(s)
- Gideon Klaila
- Institute for Algebra, Geometry, Topology and their Applications (ALTA), University of Bremen, 28359, Bremen, Germany.
| | - Vladimir Vutov
- Institute for Statistics, University of Bremen, 28359, Bremen, Germany
| | - Anastasios Stefanou
- Institute for Algebra, Geometry, Topology and their Applications (ALTA), University of Bremen, 28359, Bremen, Germany
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5
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Vutov V, Dickhaus T. Multiple multi-sample testing under arbitrary covariance dependency. Stat Med 2023. [PMID: 37173292 DOI: 10.1002/sim.9761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/23/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023]
Abstract
Modern high-throughput biomedical devices routinely produce data on a large scale, and the analysis of high-dimensional datasets has become commonplace in biomedical studies. However, given thousands or tens of thousands of measured variables in these datasets, extracting meaningful features poses a challenge. In this article, we propose a procedure to evaluate the strength of the associations between a nominal (categorical) response variable and multiple features simultaneously. Specifically, we propose a framework of large-scale multiple testing under arbitrary correlation dependency among test statistics. First, marginal multinomial regressions are performed for each feature individually. Second, we use an approach of multiple marginal models for each baseline-category pair to establish asymptotic joint normality of the stacked vector of the marginal multinomial regression coefficients. Third, we estimate the (limiting) covariance matrix between the estimated coefficients from all marginal models. Finally, our approach approximates the realized false discovery proportion of a thresholding procedure for the marginal p-values for each baseline-category logit pair. The proposed approach offers a sensible trade-off between the expected numbers of true and false findings. Furthermore, we demonstrate a practical application of the method on hyperspectral imaging data. This dataset is obtained by a matrix-assisted laser desorption/ionization (MALDI) instrument. MALDI demonstrates tremendous potential for clinical diagnosis, particularly for cancer research. In our application, the nominal response categories represent cancer (sub-)types.
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Affiliation(s)
- Vladimir Vutov
- Institute for Statistics, University of Bremen, Bremen, Germany
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6
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Mogilenko DA, Sergushichev A, Artyomov MN. Systems Immunology Approaches to Metabolism. Annu Rev Immunol 2023; 41:317-342. [PMID: 37126419 DOI: 10.1146/annurev-immunol-101220-031513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.
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Affiliation(s)
- Denis A Mogilenko
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Current affiliation: Department of Medicine, Department of Pathology, Microbiology, and Immunology, and Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Alexey Sergushichev
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia
| | - Maxim N Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
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7
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Spatial segmentation of mass spectrometry imaging data featuring selected principal components. Talanta 2023; 253:123958. [PMID: 36179560 DOI: 10.1016/j.talanta.2022.123958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 12/13/2022]
Abstract
Spatial segmentation aims to find homogeneous/heterogeneous subgroups of spectra or ion images in mass spectrometry imaging (MSI) data. The maps it generated inform researchers of vital characteristics of the data and thus provide the basis for strategizing further biological analysis. Dimensional reduction and clustering are two basic steps of segmentation. Due to the variations in the quality, resolution, density of spectral information, and sizes, not all datasets could be segmented ideally with combinations of different dimensional reduction and clustering algorithms. Here, we proposed a segmentation pipeline that utilized pattern compression by principal component analysis (PCA) and represented by principal components. Instead of preprocessed or raw MSI data, normalized principal components were used for the segmentation process. Multiple datasets of rat brains and mouse kidneys were tested, and the proposed segmentation pipeline presented the obvious advantage of easy-to-use and can be readily intergraded with other existing innovative pipelines.
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8
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Spatially aware dimension reduction for spatial transcriptomics. Nat Commun 2022; 13:7203. [PMID: 36418351 PMCID: PMC9684472 DOI: 10.1038/s41467-022-34879-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022] Open
Abstract
Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex.
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9
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Baquer G, Sementé L, Mahamdi T, Correig X, Ràfols P, García-Altares M. What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2022:e21794. [PMID: 35822576 DOI: 10.1002/mas.21794] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.
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Affiliation(s)
- Gerard Baquer
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Lluc Sementé
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Toufik Mahamdi
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Xavier Correig
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - Pere Ràfols
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - María García-Altares
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
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10
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Quantifying Spatial Heterogeneity of Tumor-Infiltrating Lymphocytes to Predict Survival of Individual Cancer Patients. J Pers Med 2022; 12:jpm12071113. [PMID: 35887610 PMCID: PMC9317291 DOI: 10.3390/jpm12071113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Tumor-infiltrating lymphocytes (TILs), identified on HE-stained histopathological images in the cancer area, are indicators of the adaptive immune response against cancers and play a major role in personalized cancer immunotherapy. Recent works indicate that the spatial organization of TILs may be prognostic of disease-specific survival and recurrence. However, there are a limited number of methods that were proposed and tested in analyses of the spatial structure of TILs. In this work, we evaluated 14 different spatial measures, including the one developed for other omics data, on 10,532 TIL maps from 23 cancer types in terms of reproducibility, uniqueness, and impact on patient survival. For each spatial measure, 16 different scenarios for the definition of prognostic factor were tested. We found no difference in survival prediction when TIL maps were stored as binary images or continuous TIL probability scores. When spatial measures were discretized into a low and high category, a higher correlation with survival was observed. Three measures with the highest cancer prognosis capability were spatial autocorrelation, GLCM M1, and closeness centrality. Most of the tested measures could be further tuned to increase prediction performance.
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11
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Tuck M, Grélard F, Blanc L, Desbenoit N. MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives. Front Chem 2022; 10:904688. [PMID: 35615316 PMCID: PMC9124797 DOI: 10.3389/fchem.2022.904688] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/14/2022] [Indexed: 01/22/2023] Open
Abstract
Multimodal imaging is a powerful strategy for combining information from multiple images. It involves several fields in the acquisition, processing and interpretation of images. As multimodal imaging is a vast subject area with various combinations of imaging techniques, it has been extensively reviewed. Here we focus on Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) coupling other imaging modalities in multimodal approaches. While MALDI-MS images convey a substantial amount of chemical information, they are not readily informative about the morphological nature of the tissue. By providing a supplementary modality, MALDI-MS images can be more informative and better reflect the nature of the tissue. In this mini review, we emphasize the analytical and computational strategies to address multimodal MALDI-MSI.
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12
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A versatile deep learning architecture for classification and label-free prediction of hyperspectral images. NAT MACH INTELL 2021; 3:306-315. [PMID: 34676358 PMCID: PMC8528004 DOI: 10.1038/s42256-021-00309-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Hyperspectral imaging is a technique that provides rich chemical or compositional information not regularly available to traditional imaging modalities such as intensity imaging or color imaging based on the reflection, transmission, or emission of light. Analysis of hyperspectral imaging often relies on machine learning methods to extract information. Here, we present a new flexible architecture, the U-within-U-Net, that can perform classification, segmentation, and prediction of orthogonal imaging modalities on a variety of hyperspectral imaging techniques. Specifically, we demonstrate feature segmentation and classification on the Indian Pines hyperspectral dataset and simultaneous location prediction of multiple drugs in mass spectrometry imaging of rat liver tissue. We further demonstrate label-free fluorescence image prediction from hyperspectral stimulated Raman scattering microscopy images. The applicability of the U-within-U-Net architecture on diverse datasets with widely varying input and output dimensions and data sources suggest that it has great potential in advancing the use of hyperspectral imaging across many different application areas ranging from remote sensing, to medical imaging, to microscopy.
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13
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Guo L, Hu Z, Zhao C, Xu X, Wang S, Xu J, Dong J, Cai Z. Data Filtering and Its Prioritization in Pipelines for Spatial Segmentation of Mass Spectrometry Imaging. Anal Chem 2021; 93:4788-4793. [PMID: 33683863 DOI: 10.1021/acs.analchem.0c05242] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Mass spectrometry imaging (MSI) could provide vast amounts of data at the temporal-spatial scale in heterogeneous biological specimens, which challenges us to segment accurately suborgans/microregions from complex MSI data. Several pipelines had been proposed for MSI spatial segmentation in the past decade. More importantly, data filtering was found to be an efficient procedure to improve the outcomes of MSI segmentation pipelines. It is not clear, however, how the filtering procedure affects the MSI segmentation. An improved pipeline was established by elaborating the filtering prioritization and filtering algorithm. Lipidomic-characteristic-based MSI data of a whole-body mouse fetus was used to evaluate the established pipeline on localization of the physiological position of suborgans by comparing with three commonly used pipelines and commercial SCiLS Lab software. Two structural measurements were used to quantify the performances of the pipelines including the percentage of abnormal edge pixel (PAEP) and CHAOS. Our results demonstrated that the established pipeline outperformed the other pipelines in visual inspection, spatial consistence, time-cost, and robustness analysis. For example, the dorsal pallium (isocortex) and hippocampal formation (Hpf) regions, midbrain, cerebellum, and brainstem on the mouse brain were annotated and located by the established pipeline. As a generic pipeline, the established pipeline could help with the accurate assessment and screening of drug/chemical-induced targeted organs and exploration of the progression and molecular mechanisms of diseases. The filter-based strategy is expected to become a critical component in the standard operating procedure of MSI data sets.
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Affiliation(s)
- Lei Guo
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Zhenxing Hu
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Chao Zhao
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China.,Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiangnan Xu
- School of Mathematics and Statistics, The University of Sydney, Camperdown Sydney, NSW 2006, Australia
| | - Shujuan Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing 102206, China
| | - Jingjing Xu
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Jiyang Dong
- National Institute for Data Science in Health and Medicine, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
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14
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Grélard F, Legland D, Fanuel M, Arnaud B, Foucat L, Rogniaux H. Esmraldi: efficient methods for the fusion of mass spectrometry and magnetic resonance images. BMC Bioinformatics 2021; 22:56. [PMID: 33557761 PMCID: PMC7869484 DOI: 10.1186/s12859-020-03954-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 12/30/2020] [Indexed: 11/29/2022] Open
Abstract
Background Mass spectrometry imaging (MSI) is a family of acquisition techniques producing images of the distribution of molecules in a sample, without any prior tagging of the molecules. This makes it a very interesting technique for exploratory research. However, the images are difficult to analyze because the enclosed data has high dimensionality, and their content does not necessarily reflect the shape of the object of interest. Conversely, magnetic resonance imaging (MRI) scans reflect the anatomy of the tissue. MRI also provides complementary information to MSI, such as the content and distribution of water. Results We propose a new workflow to merge the information from 2D MALDI–MSI and MRI images. Our workflow can be applied to large MSI datasets in a limited amount of time. Moreover, the workflow is fully automated and based on deterministic methods which ensures the reproducibility of the results. Our methods were evaluated and compared with state-of-the-art methods. Results show that the images are combined precisely and in a time-efficient manner. Conclusion Our workflow reveals molecules which co-localize with water in biological images. It can be applied on any MSI and MRI datasets which satisfy a few conditions: same regions of the shape enclosed in the images and similar intensity distributions.
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Affiliation(s)
- Florent Grélard
- UR BIA, INRAE, 44316, Nantes, France. .,BIBS Facility, INRAE, 44316, Nantes, France.
| | - David Legland
- UR BIA, INRAE, 44316, Nantes, France.,BIBS Facility, INRAE, 44316, Nantes, France
| | - Mathieu Fanuel
- UR BIA, INRAE, 44316, Nantes, France.,BIBS Facility, INRAE, 44316, Nantes, France
| | - Bastien Arnaud
- UR BIA, INRAE, 44316, Nantes, France.,BIBS Facility, INRAE, 44316, Nantes, France
| | - Loïc Foucat
- UR BIA, INRAE, 44316, Nantes, France.,BIBS Facility, INRAE, 44316, Nantes, France
| | - Hélène Rogniaux
- UR BIA, INRAE, 44316, Nantes, France.,BIBS Facility, INRAE, 44316, Nantes, France
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15
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Murta T, Steven RT, Nikula CJ, Thomas SA, Zeiger LB, Dexter A, Elia EA, Yan B, Campbell AD, Goodwin RJA, Takáts Z, Sansom OJ, Bunch J. Implications of Peak Selection in the Interpretation of Unsupervised Mass Spectrometry Imaging Data Analyses. Anal Chem 2021; 93:2309-2316. [PMID: 33395266 DOI: 10.1021/acs.analchem.0c04179] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t-distributed stochastic neighbor embedding and k-means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.
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Affiliation(s)
- Teresa Murta
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Rory T Steven
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Chelsea J Nikula
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Spencer A Thomas
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Lucas B Zeiger
- Cancer Research UK Beatson Institute, Glasgow G61 1BD, U.K
- Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Glasgow G61 1QH, U.K
| | - Alex Dexter
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Efstathios A Elia
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | - Bin Yan
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
| | | | - Richard J A Goodwin
- Imaging and AI, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB4 0WG, U.K
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, U.K
| | - Zoltan Takáts
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
| | - Owen J Sansom
- Cancer Research UK Beatson Institute, Glasgow G61 1BD, U.K
- Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Glasgow G61 1QH, U.K
| | - Josephine Bunch
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington TW11 0WL, U.K
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, U.K
- The Rosalind Franklin Institute, Oxfordshire OX11 0FA, U.K
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16
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Lieb F, Boskamp T, Stark HG. Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers. J Proteomics 2020; 225:103852. [DOI: 10.1016/j.jprot.2020.103852] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/13/2020] [Accepted: 05/29/2020] [Indexed: 12/23/2022]
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17
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Alexandrov T. Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence. Annu Rev Biomed Data Sci 2020; 3:61-87. [PMID: 34056560 DOI: 10.1146/annurev-biodatasci-011420-031537] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, USA
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18
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Geier B, Sogin EM, Michellod D, Janda M, Kompauer M, Spengler B, Dubilier N, Liebeke M. Spatial metabolomics of in situ host-microbe interactions at the micrometre scale. Nat Microbiol 2020; 5:498-510. [PMID: 32015496 DOI: 10.1038/s41564-019-0664-6] [Citation(s) in RCA: 108] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 12/16/2019] [Indexed: 11/09/2022]
Abstract
Spatial metabolomics describes the location and chemistry of small molecules involved in metabolic phenotypes, defence molecules and chemical interactions in natural communities. Most current techniques are unable to spatially link the genotype and metabolic phenotype of microorganisms in situ at a scale relevant to microbial interactions. Here, we present a spatial metabolomics pipeline (metaFISH) that combines fluorescence in situ hybridization (FISH) microscopy and high-resolution atmospheric-pressure matrix-assisted laser desorption/ionization mass spectrometry to image host-microbe symbioses and their metabolic interactions. The metaFISH pipeline aligns and integrates metabolite and fluorescent images at the micrometre scale to provide a spatial assignment of host and symbiont metabolites on the same tissue section. To illustrate the advantages of metaFISH, we mapped the spatial metabolome of a deep-sea mussel and its intracellular symbiotic bacteria at the scale of individual epithelial host cells. Our analytical pipeline revealed metabolic adaptations of the epithelial cells to the intracellular symbionts and variation in metabolic phenotypes within a single symbiont 16S rRNA phylotype, and enabled the discovery of specialized metabolites from the host-microbe interface. metaFISH provides a culture-independent approach to link metabolic phenotypes to community members in situ and is a powerful tool for microbiologists across fields.
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Affiliation(s)
- Benedikt Geier
- Max Planck Institute for Marine Microbiology, Bremen, Germany.
| | - Emilia M Sogin
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Dolma Michellod
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Moritz Janda
- Max Planck Institute for Marine Microbiology, Bremen, Germany
| | - Mario Kompauer
- Institute of Inorganic and Analytical Chemistry, Justus Liebig University Giessen, Giessen, Germany
| | - Bernhard Spengler
- Institute of Inorganic and Analytical Chemistry, Justus Liebig University Giessen, Giessen, Germany
| | - Nicole Dubilier
- Max Planck Institute for Marine Microbiology, Bremen, Germany
- MARUM, University of Bremen, Bremen, Germany
| | - Manuel Liebeke
- Max Planck Institute for Marine Microbiology, Bremen, Germany.
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19
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Tortorella S, Tiberi P, Bowman AP, Claes BSR, Ščupáková K, Heeren RMA, Ellis SR, Cruciani G. LipostarMSI: Comprehensive, Vendor-Neutral Software for Visualization, Data Analysis, and Automated Molecular Identification in Mass Spectrometry Imaging. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:155-163. [PMID: 32881505 DOI: 10.1021/jasms.9b00034] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Mass Spectrometry Imaging (MSI) is an established and powerful MS technique that enables molecular mapping of tissues and cells finding widespread applications in academic, medical, and pharmaceutical industries. As both the applications and MSI technology have undergone rapid growth and improvement, the challenges associated both with analyzing large datasets and identifying the many detected molecular species have become apparent. The lack of readily available and comprehensive software covering all necessary data analysis steps has further compounded this challenge. To address this issue we developed LipostarMSI, comprehensive and vendor-neutral software for targeted and untargeted MSI data analysis. Through user-friendly implementation of image visualization and co-registration, univariate and multivariate image and spectral analysis, and for the first time, advanced lipid, metabolite, and drug metabolite (MetID) automated identification, LipostarMSI effectively streamlines biochemical interpretation of the data. Here, we introduce LipostarMSI and case studies demonstrating the versatility and many capabilities of the software.
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Affiliation(s)
- Sara Tortorella
- Molecular Horizon Srl, Via Montelino 30, 06084 Bettona, Perugia, Italy
- Consortium for Computational Molecular and Materials Sciences (CMS)2, Via Elce di Sotto 8, 06123 Perugia, Italy
| | - Paolo Tiberi
- Molecular Discovery Ltd., Centennial Park, WD6 3FG Borehamwood, Hertfordshire, United Kingdom
| | - Andrew P Bowman
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Britt S R Claes
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Klára Ščupáková
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Shane R Ellis
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Gabriele Cruciani
- Consortium for Computational Molecular and Materials Sciences (CMS)2, Via Elce di Sotto 8, 06123 Perugia, Italy
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
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20
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Inglese P, Correia G, Takats Z, Nicholson JK, Glen RC. SPUTNIK: an R package for filtering of spatially related peaks in mass spectrometry imaging data. Bioinformatics 2019; 35:178-180. [PMID: 30010780 PMCID: PMC6298046 DOI: 10.1093/bioinformatics/bty622] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 07/10/2018] [Indexed: 11/14/2022] Open
Abstract
Summary SPUTNIK is an R package consisting of a series of tools to filter mass spectrometry imaging peaks characterized by a noisy or unlikely spatial distribution. SPUTNIK can produce mass spectrometry imaging datasets characterized by a smaller but more informative set of peaks, reduce the complexity of subsequent multi-variate analysis and increase the interpretability of the statistical results. Availability and implementation SPUTNIK is freely available online from CRAN repository and at https://github.com/paoloinglese/SPUTNIK. The package is distributed under the GNU General Public License version 3 and is accompanied by example files and data. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Paolo Inglese
- Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Gonçalo Correia
- Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Zoltan Takats
- Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Jeremy K Nicholson
- Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Robert C Glen
- Computational and System Medicine, Department of Surgery and Cancer, Imperial College London, London, UK
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21
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Eriksson JO, Rezeli M, Hefner M, Marko-Varga G, Horvatovich P. Clusterwise Peak Detection and Filtering Based on Spatial Distribution To Efficiently Mine Mass Spectrometry Imaging Data. Anal Chem 2019; 91:11888-11896. [PMID: 31403280 PMCID: PMC6751525 DOI: 10.1021/acs.analchem.9b02637] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
![]()
Mass
spectrometry imaging (MSI) has the potential to reveal the
localization of thousands of biomolecules such as metabolites and
lipids in tissue sections. The increase in both mass and spatial resolution
of today’s instruments brings on considerable challenges in
terms of data processing; accurately extracting meaningful signals
from the large data sets generated by MSI without losing information
that could be clinically relevant is one of the most fundamental tasks
of analysis software. Ion images of the biomolecules are generated
by visualizing their intensities in 2-D space using mass spectra collected
across the tissue section. The intensities are often calculated by
summing each compound’s signal between predefined sets of borders
(bins) in the m/z dimension. This
approach, however, can result in mixed signals from different compounds
in the same bin or splitting the signal from one compound between
two adjacent bins, leading to low quality ion images. To remedy this
problem, we propose a novel data processing approach. Our approach
consists of a sensitive peak detection method able to discover both
faint and localized signals by utilizing clusterwise kernel density
estimates (KDEs) of peak distributions. We show that our method can
recall more ground-truth molecules, molecule fragments, and isotopes
than existing methods based on binning. Furthermore, it automatically
detects previously reported molecular ions of lipids, including those
close in m/z, in an experimental
data set.
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Affiliation(s)
| | - Melinda Rezeli
- Lund University , Department of Biomedical Engineering , Lund , Sweden
| | - Max Hefner
- Lund University , Department of Biomedical Engineering , Lund , Sweden
| | | | - Peter Horvatovich
- Lund University , Department of Biomedical Engineering , Lund , Sweden.,University of Groningen, Department of Analytical Biochemistry , Groningen Research Institute of Pharmacy , Antonius Deusinglaan 1 , 9713 AV Groningen , The Netherlands
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22
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C Silva AS, Palmer A, Kovalev V, Tarasov A, Alexandrov T, Martens L, Degroeve S. Data-Driven Rescoring of Metabolite Annotations Significantly Improves Sensitivity. Anal Chem 2018; 90:11636-11642. [PMID: 30188119 DOI: 10.1021/acs.analchem.8b03224] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
When analyzing mass spectrometry imaging data sets, assigning a molecule to each of the thousands of generated images is a very complex task. Recent efforts have taken lessons from (tandem) mass spectrometry proteomics and applied them to imaging mass spectrometry metabolomics, with good results. Our goal is to go a step further in this direction and apply a well established, data-driven method to improve the results obtained from an annotation engine. By using a data-driven rescoring strategy, we are able to consistently improve the sensitivity of the annotation engine while maintaining control of statistics like estimated rate of false discoveries. All the code necessary to run a search and extract the additional features can be found at https://github.com/anasilviacs/sm-engine and to rescore the results from a search in https://github.com/anasilviacs/rescore-metabolites .
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Affiliation(s)
- Ana S C Silva
- VIB-UGent Center for Medical Biotechnology , Ghent , 9000 , Belgium.,Department of Biochemistry, Faculty of Medicine , Ghent University , Ghent , 9000 , Belgium.,Bioinformatics Institute Ghent , Ghent University , Ghent , 9000 , Belgium
| | - Andrew Palmer
- Structural and Computational Biology Unit , European Molecular Biology Laboratory , Heidelberg , 69117 Germany
| | - Vitaly Kovalev
- Structural and Computational Biology Unit , European Molecular Biology Laboratory , Heidelberg , 69117 Germany
| | - Artem Tarasov
- Structural and Computational Biology Unit , European Molecular Biology Laboratory , Heidelberg , 69117 Germany
| | - Theodore Alexandrov
- Structural and Computational Biology Unit , European Molecular Biology Laboratory , Heidelberg , 69117 Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology , Ghent , 9000 , Belgium.,Department of Biochemistry, Faculty of Medicine , Ghent University , Ghent , 9000 , Belgium.,Bioinformatics Institute Ghent , Ghent University , Ghent , 9000 , Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology , Ghent , 9000 , Belgium.,Department of Biochemistry, Faculty of Medicine , Ghent University , Ghent , 9000 , Belgium.,Bioinformatics Institute Ghent , Ghent University , Ghent , 9000 , Belgium
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23
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Sandrin TR, Demirev PA. Characterization of microbial mixtures by mass spectrometry. MASS SPECTROMETRY REVIEWS 2018; 37:321-349. [PMID: 28509357 DOI: 10.1002/mas.21534] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 03/09/2017] [Accepted: 03/09/2017] [Indexed: 05/27/2023]
Abstract
MS applications in microbiology have increased significantly in the past 10 years, due in part to the proliferation of regulator-approved commercial MALDI MS platforms for rapid identification of clinical infections. In parallel, with the expansion of MS technologies in the "omics" fields, novel MS-based research efforts to characterize organismal as well as environmental microbiomes have emerged. Successful characterization of microorganisms found in complex mixtures of other organisms remains a major challenge for researchers and clinicians alike. Here, we review recent MS advances toward addressing that challenge. These include sample preparation methods and protocols, and established, for example, MALDI, as well as newer, for example, atmospheric pressure ionization (API) techniques. MALDI mass spectra of intact cells contain predominantly information on the highly expressed house-keeping proteins used as biomarkers. The API methods are applicable for small biomolecule analysis, for example, phospholipids and lipopeptides, and facilitate species differentiation. MS hardware and techniques, for example, tandem MS, including diverse ion source/mass analyzer combinations are discussed. Relevant examples for microbial mixture characterization utilizing these combinations are provided. Chemometrics and bioinformatics methods and algorithms, including those applied to large scale MS data acquisition in microbial metaproteomics and MS imaging of biofilms, are highlighted. Select MS applications for polymicrobial culture analysis in environmental and clinical microbiology are reviewed as well.
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Affiliation(s)
- Todd R Sandrin
- School of Mathematical and Natural Sciences, Arizona State University, Phoenix, Arizona
| | - Plamen A Demirev
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
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24
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BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology. Sci Rep 2018; 8:4053. [PMID: 29511258 PMCID: PMC5840264 DOI: 10.1038/s41598-018-22499-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 02/23/2018] [Indexed: 12/18/2022] Open
Abstract
Mass Spectrometry Imaging (MSI) holds significant promise in augmenting digital histopathologic analysis by generating highly robust big data about the metabolic, lipidomic and proteomic molecular content of the samples. In the process, a vast quantity of unrefined data, that can amount to several hundred gigabytes per tissue section, is produced. Managing, analysing and interpreting this data is a significant challenge and represents a major barrier to the translational application of MSI. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets. Here, we present a computational platform (pyBASIS) capable of optimized and scalable processing of MSI data for improved information recovery and comparative analysis across tissue specimens using machine learning and related pattern recognition approaches. The proposed solution also provides a means of seamlessly integrating experimental laboratory data with downstream bioinformatics interpretation/analyses, resulting in a truly integrated system for translational MSI.
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25
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Picard de Muller G, Ait-Belkacem R, Bonnel D, Longuespée R, Stauber J. Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2017; 28:2635-2645. [PMID: 28913742 DOI: 10.1007/s13361-017-1784-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 07/28/2017] [Accepted: 08/10/2017] [Indexed: 06/07/2023]
Abstract
Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric - number of objects and total surface - information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. Graphical Abstract ᅟ.
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Affiliation(s)
| | - Rima Ait-Belkacem
- ImaBiotech SAS, Parc Eurasanté, 885 rue Eugène Avinée, 59120, Loos, France
| | - David Bonnel
- ImaBiotech SAS, Parc Eurasanté, 885 rue Eugène Avinée, 59120, Loos, France
| | - Rémi Longuespée
- Mass Spectrometry Laboratory (LSM), Systems Biology and Chemical Biology, GIGA-Research, University of Liège, Allée du 6 août 11, 4000, Liège, Belgium
- Institute of Pathology, University of Heidelberg, Im Neuenheimer Feld 224, 69120, Heidelberg, Germany
| | - Jonathan Stauber
- ImaBiotech SAS, Parc Eurasanté, 885 rue Eugène Avinée, 59120, Loos, France.
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26
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FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nat Methods 2016; 14:57-60. [PMID: 27842059 DOI: 10.1038/nmeth.4072] [Citation(s) in RCA: 253] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Accepted: 10/13/2016] [Indexed: 02/08/2023]
Abstract
High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics.
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27
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An approach to optimize sample preparation for MALDI imaging MS of FFPE sections using fractional factorial design of experiments. Anal Bioanal Chem 2016; 408:6729-40. [DOI: 10.1007/s00216-016-9793-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 07/01/2016] [Accepted: 07/12/2016] [Indexed: 12/29/2022]
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28
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Bemis KD, Harry A, Eberlin LS, Ferreira CR, van de Ven SM, Mallick P, Stolowitz M, Vitek O. Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments. Mol Cell Proteomics 2016; 15:1761-72. [PMID: 26796117 PMCID: PMC4858953 DOI: 10.1074/mcp.o115.053918] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Indexed: 11/24/2022] Open
Abstract
Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments and supervised classification of images into predefined classes. In both cases, the important secondary goals are to characterize the uncertainty associated with the segmentation and with the classification and to characterize the spectral features that define each segment or class. Recent analysis methods have focused on the spatial structure of the data to improve results. However, they either do not address these secondary goals or do this with separate post hoc procedures. We introduce spatial shrunken centroids, a statistical model-based framework for both supervised classification and unsupervised segmentation. It takes as input sets of previously detected, aligned, quantified, and normalized spectral features and expresses both spatial and multivariate nature of the data using probabilistic modeling. It selects informative subsets of spectral features that define each unsupervised segment or supervised class and quantifies and visualizes the uncertainty in spatial segmentations and in tissue classification. In the unsupervised setting, it also guides the choice of an appropriate number of segments. We demonstrate the usefulness of this framework in a supervised human renal cell carcinoma experimental dataset and several unsupervised experimental datasets, including a pig fetus cross-section, three rodent brains, and a controlled image with known ground truth. This framework is available for use within the open-source R package Cardinal as part of a full pipeline for the processing, visualization, and statistical analysis of mass spectrometry imaging experiments.
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Affiliation(s)
| | | | - Livia S Eberlin
- §Department of Chemistry, Purdue University, West Lafayette, IN 47907
| | | | - Stephanie M van de Ven
- ¶Canary Center at Canary Foundation, Stanford University School of Medicine, Palo Alto, CA 94304; College of Science and
| | - Parag Mallick
- ¶Canary Center at Canary Foundation, Stanford University School of Medicine, Palo Alto, CA 94304; College of Science and
| | - Mark Stolowitz
- ¶Canary Center at Canary Foundation, Stanford University School of Medicine, Palo Alto, CA 94304; College of Science and
| | - Olga Vitek
- **College of Computer and Information Science, Northeastern University, Boston, MA 02115
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29
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Elsner C, Abel B. Mass Spectrometric Imaging of Gold Nanolayer Coated Latent Fingermarks: Deciphering Overlapping Features by Statistical Analysis. ACTA ACUST UNITED AC 2016. [DOI: 10.4236/aces.2016.65050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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30
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Winderbaum LJ, Koch I, Gustafsson OJR, Meding S, Hoffmann P. Feature extraction for proteomics imaging mass spectrometry data. Ann Appl Stat 2015. [DOI: 10.1214/15-aoas870] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Palmer A, Ovchinnikova E, Thuné M, Lavigne R, Guével B, Dyatlov A, Vitek O, Pineau C, Borén M, Alexandrov T. Using collective expert judgements to evaluate quality measures of mass spectrometry images. Bioinformatics 2015; 31:i375-84. [PMID: 26072506 PMCID: PMC4765867 DOI: 10.1093/bioinformatics/btv266] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Motivation: Imaging mass spectrometry (IMS) is a maturating technique of molecular imaging. Confidence in the reproducible quality of IMS data is essential for its integration into routine use. However, the predominant method for assessing quality is visual examination, a time consuming, unstandardized and non-scalable approach. So far, the problem of assessing the quality has only been marginally addressed and existing measures do not account for the spatial information of IMS data. Importantly, no approach exists for unbiased evaluation of potential quality measures. Results: We propose a novel approach for evaluating potential measures by creating a gold-standard set using collective expert judgements upon which we evaluated image-based measures. To produce a gold standard, we engaged 80 IMS experts, each to rate the relative quality between 52 pairs of ion images from MALDI-TOF IMS datasets of rat brain coronal sections. Experts’ optional feedback on their expertise, the task and the survey showed that (i) they had diverse backgrounds and sufficient expertise, (ii) the task was properly understood, and (iii) the survey was comprehensible. A moderate inter-rater agreement was achieved with Krippendorff’s alpha of 0.5. A gold-standard set of 634 pairs of images with accompanying ratings was constructed and showed a high agreement of 0.85. Eight families of potential measures with a range of parameters and statistical descriptors, giving 143 in total, were evaluated. Both signal-to-noise and spatial chaos-based measures performed highly with a correlation of 0.7 to 0.9 with the gold standard ratings. Moreover, we showed that a composite measure with the linear coefficients (trained on the gold standard with regularized least squares optimization and lasso) showed a strong linear correlation of 0.94 and an accuracy of 0.98 in predicting which image in a pair was of higher quality. Availability and implementation: The anonymized data collected from the survey and the Matlab source code for data processing can be found at: https://github.com/alexandrovteam/IMS_quality. Contact:theodore.alexandrov@embl.de
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Affiliation(s)
- Andrew Palmer
- European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Ekaterina Ovchinnikova
- European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Mikael Thuné
- European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Régis Lavigne
- European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Blandine Guével
- European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Andrey Dyatlov
- European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Olga Vitek
- European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Charles Pineau
- European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Mats Borén
- European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Theodore Alexandrov
- European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany, Denator, Uppsala, Sweden, Protim, Inserm U1085 - Irset, University of Rennes 1, Rennes, France, SCiLS GmbH, Bremen, Germany, College of Computer and Information Science, Northeastern University, Boston, MA, USA and Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA European Molecular Biology Laboratory, Heidelberg, Germany, Center for Industrial Mathematics, University of Bremen, Bremen, Germany, High Performance Humanoid Technologies Lab, Institute for Anthropomatics, Karlsruhe Institute of Technolo
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Wijetunge CD, Saeed I, Boughton BA, Spraggins JM, Caprioli RM, Bacic A, Roessner U, Halgamuge SK. EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data. Bioinformatics 2015; 31:3198-206. [DOI: 10.1093/bioinformatics/btv356] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2014] [Accepted: 06/04/2015] [Indexed: 11/13/2022] Open
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Affiliation(s)
- Bernhard Spengler
- Justus Liebig University Giessen, Institute of Inorganic and Analytical
Chemistry, Schubertstrasse
60, Building 16, 35392 Giessen, Germany
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Aram P, Shen L, Pugh JA, Vaidyanathan S, Kadirkamanathan V. An efficient TOF-SIMS image analysis with spatial correlation and alternating non-negativity-constrained least squares. ACTA ACUST UNITED AC 2014; 31:753-60. [PMID: 25452330 DOI: 10.1093/bioinformatics/btu734] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
MOTIVATION Advances in analytical instrumentation towards acquiring high-resolution images of mass spectrometry constantly demand efficient approaches for data analysis. This is particularly true of time-of-flight secondary ion mass spectrometry imaging where recent advances enable acquisition of high-resolution data in multiple dimensions. In many applications, the distribution of different species from a sampled surface is spatially continuous in nature and a model that incorporates the spatial correlation across the surface would be preferable to estimations at discrete spatial locations. A key challenge here is the capability to analyse the high-resolution multidimensional data to extract relevant information reliably and efficiently. RESULTS We propose a framework based on alternating non-negativity-constrained least squares which accounts for the spatial correlation across the sample surface. The proposed method also decouples the computational complexity of the estimation procedure from the image resolution, which significantly reduces the processing time. We evaluate the performance of the algorithm with biochemical image datasets generated from mixture of metabolites.
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Affiliation(s)
- Parham Aram
- Department of Automatic Control and Systems Engineering and Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - Lingli Shen
- Department of Automatic Control and Systems Engineering and Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - John A Pugh
- Department of Automatic Control and Systems Engineering and Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - Seetharaman Vaidyanathan
- Department of Automatic Control and Systems Engineering and Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - Visakan Kadirkamanathan
- Department of Automatic Control and Systems Engineering and Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
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