Analysis of single algal cells by combining mass spectrometry with Raman and fluorescence mapping

Fagerer, Stephan R.; Schmid, Thomas; Ibáñez, Alfredo J.; Pabst, Martin; Steinhoff, Robert; Jefimovs, Konstantins; Urban, Pawel L.; Zenobi, Renato

doi:10.1039/c3an01135f

Cited by 53 publications

(54 citation statements)

References 27 publications

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“…MS profiling of adhered cells provides advantages in data fusion by simplifying data processing and allowing sequential analysis of the same cell. Microarrays for mass spectrometry [15–17] (MAMS) have demonstrated such capabilities by combining Raman microspectroscopy with MS [18]. As an alternative to MAMS, cells may be randomly seeded on a substrate, greatly relaxing fabrication requirements, but at the expense of a necessarily gentle sample extraction.…”

Section: Introductionmentioning

confidence: 99%

microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling

Comi

Neumann

Thanh

et al. 2017

J. Am. Soc. Mass Spectrom.

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Image-guided mass spectrometry (MS) profiling provides a facile framework for analyzing samples ranging from single cells to tissue sections. The fundamental workflow utilizes a whole-slide microscopy image to select targets of interest, determine their spatial locations, and subsequently perform MS analysis at those locations. Improving upon prior reported methodology, a software package was developed for working with microscopy images. microMS, for microscopy-guided mass spectrometry, allows the user to select and profile diverse samples using a variety of target patterns and mass analyzers. Written in Python, the program provides an intuitive graphical user interface to simplify image-guided MS for novice users. The class hierarchy of instrument interactions permits integration of new MS systems while retaining the feature-rich image analysis framework. microMS is a versatile platform for performing targeted profiling experiments using a series of mass spectrometers. The flexibility in mass analyzers greatly simplifies serial analyses of the same targets by different instruments. The current capabilities of microMS are presented, and its application for off-line and on-line analysis of single cells on three distinct instruments is demonstrated. The software has been made freely available for research purposes.

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Section: Introductionmentioning

confidence: 99%

microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling

Comi

Neumann

Thanh

et al. 2017

J. Am. Soc. Mass Spectrom.

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“…5,6 Our laboratories have employed time-of-flight (ToF) secondary ion mass spectrometry (SIMS) and CRM to study lignin and cellulose distributions in the perennial grass Miscanthus x giganteus , 7 and matrix assisted laser desorption ionization (MALDI) and CRM to characterize the distribution of lipids and peptides across 3-D cell cultures, 8 while Zenobi and coworkers have successfully correlated Raman and MALDI to study isolated algal cells. 9 …”

Section: Introductionmentioning

confidence: 99%

Multimodal chemical imaging of molecular messengers in emerging Pseudomonas aeruginosa bacterial communities

Baig

Dunham

Morales-Soto

et al. 2015

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Two label-free molecular imaging techniques, confocal Raman microscopy (CRM) and secondary ion mass spectrometry (SIMS), are combined for in situ characterization of the spatiotemporal distributions of quinolone metabolites and signaling molecules in communities of the pathogenic bacterium Pseudomonas aeruginosa. Dramatic molecular differences are observed between planktonic and biofilm modes of growth for these bacteria. We observe patterned aggregation and a high abundance of N-oxide quinolines in early biofilms and swarm zones of P. aeruginosa, while the concentrations of these secreted components in planktonic cells and agar plate colonies are below CRM and SIMS detection limits. CRM, in conjunction with principal component analysis (PCA) is used to distinguish between the two co-localized isomeric analyte pairs 4-hydroxy-2-heptylquinoline-N-oxide (HQNO)/2-heptyl-3-hydroxyquinolone (PQS) and 4-hydroxy-2-nonylquinoline-N-oxide (NQNO)/2-nonyl-hydroxyquinolone (C9-PQS) based on differences in their vibrational fingerprints, illustrating how the technique can be used to guide tandem-MS and tandem-MS imaging analysis. Because N-oxide quinolines are ubiquitous and expressed early in biofilms, these analytes may be fundamentally important for early biofilm formation and the growth and organization of P. aeruginosa microbial communities. This study underscores the advantages of using multimodal molecular imaging to study complex biological systems.

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“…97 Further experiments correlated fluorescence and Raman microspectroscopy acquired from the freshwater algae Haematococcus pluvialis and combined the images with MS measurements to discriminate between encystment stages. 100 In addition, using Saccharomyces cerevisiae as a model organism, Zenobi and co-workers 98 investigated the metabolic consequences of environmental and genetic perturbations on several metabolites, recapitulating population-level changes and discriminating genotypic differences.…”

Section: Dissociated and Cultured Cell Samples: Searching For Cell Sumentioning

confidence: 99%

Categorizing Cells on the Basis of their Chemical Profiles: Progress in Single-Cell Mass Spectrometry

et al. 2017

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The chemical differences between individual cells within large cellular populations provide unique information on organisms’ homeostasis and the development of diseased states. Even genetically identical cell lineages diverge due to local microenvironments and stochastic processes. The minute sample volumes and low abundance of some constituents in cells hinder our understanding of cellular heterogeneity. Although amplification methods facilitate single-cell genomics and transcriptomics, the characterization of metabolites and proteins remains challenging both because of the lack of effective amplification approaches and the wide diversity in cellular constituents. Mass spectrometry has become an enabling technology for the investigation of individual cellular metabolite profiles with its exquisite sensitivity, large dynamic range, and ability to characterize hundreds to thousands of compounds. While advances in instrumentation have improved figures of merit, acquiring measurements at high throughput and sampling from large populations of cells are still not routine. In this Perspective, we highlight the current trends and progress in mass-spectrometry-based analysis of single cells, with a focus on the technologies that will enable the next generation of single-cell measurements.

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Analysis of single algal cells by combining mass spectrometry with Raman and fluorescence mapping

Cited by 53 publications

References 27 publications

microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling

microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling

Multimodal chemical imaging of molecular messengers in emerging Pseudomonas aeruginosa bacterial communities

Categorizing Cells on the Basis of their Chemical Profiles: Progress in Single-Cell Mass Spectrometry

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