Water-assisted laser desorption/ionization mass spectrometry for minimally invasive in vivo and real-time surface analysis using SpiderMass

Ogrinc, Nina; Saudemont, Philippe; Balog, Júlia; Robin, Yves‐Marie; Gimeno, Jean-Pascal; Pascal, Quentin; Tierny, Dominique; Takáts, Zoltán; Fournier, Isabelle

doi:10.1038/s41596-019-0217-8

Cited by 56 publications

(73 citation statements)

References 44 publications

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“…1). Surprisingly, a population of ions associated with the presence of lipids were detected albeit at lower intensity (10e 3 -10e 4 TIC range) than in case of fresh-frozen tissue samples analyzed with the WALDI-MS (10e 5 -10e 6 TIC range) (16). The detected species correspond to speci c fatty acids (m/z 200-400) and phospholipids species (m/z 700-900) in positive ( Fig.…”

Section: Optimization Of Waldi-ms For Analyzing Unprocessed Ffpe Tissmentioning

confidence: 87%

“…In a new approach, some methods have even been developed to enable the analysis of fresh tissue specimens or for in vivo analysis. These encompass of the Rapid Evaporative Ionisation Mass Spectrometry (REIMS) which is a direct combination of an electrosurgical diathermy with on-line mass spectrometric analysis (13,14); the SpiderMass minimally invasive technology that is based on micro-sampling by laser resonant excitation of the endogenous water molecules in the mid-infrared (λ = 2.94 µm) and on-line analysis by MS (15)(16)(17)and the MassSpec Pen which is based on water micro-extraction of tissues and the direct analysis of the extract by MS (18).…”

Section: Introductionmentioning

confidence: 99%

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Sarcoma Molecular Pathological Diagnosis of FFPE Tissues without Dewaxing based on Lipidomic Profiles using SpiderMass Technology

Ogrinc¹,

Caux²,

Robin³

et al. 2021

Preprint

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Background :Formalin-fixation and paraffin-embedding (FFPE) is the worldwide gold standard for tissue preservation and routine pathology for general and cancer diagnostics. Despite robust histopathology methods, accurate diagnostic remains difficult for certain cases. Overall, the entire process is time-consuming, labor-intensive and doesn’t reach the right sensitivity and specificity. Lipids are central players in cancer, but they are not routinely used in diagnostics because of the sample processing which limits their detection, and lipid analysis directly from unprocessed FFPE tissues has never been reported. Methods :We present a new method for cancer diagnostics based on tissue-lipidomic signatures through rapid screening of FFPE without dewaxing using Water-Assisted Laser Desorption Ionization Mass Spectrometry (WALDI-MS) and deep-learning. Results :We show on a cohort of canine and human sarcoma tissues that classification for sarcoma sub-typing is possible and enables diagnosing blind samples in less than five minutes, with over 95% probability to discriminate specific subtypes. Conclusion: We have revealed the possibility to create a rapid diagnostic platform to screen clinical FFPE tissues with minimal sample preparation for molecular pathology.

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Section: Optimization Of Waldi-ms For Analyzing Unprocessed Ffpe Tissmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Sarcoma Molecular Pathological Diagnosis of FFPE Tissues without Dewaxing based on Lipidomic Profiles using SpiderMass Technology

Ogrinc¹,

Caux²,

Robin³

et al. 2021

Preprint

Self Cite

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show abstract

“…These large and medium datasets are used to investigate the transfer and cumulative learning approaches. SpiderMass is based on the WALDI process which corresponds to MALDI with water as matrix 45 . We first train the CNNs with the MALDI dataset (rat brain dataset) and then perform the classification of canine sarcoma and microorganism datasets based on this training.…”

Section: The Microorganism Dataset Contains a Five Human Pathogen Colmentioning

confidence: 99%

Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification

et al. 2020

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Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these models require time-consuming preprocessing steps to remove artifacts, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn representations from raw data. However, their effectiveness decreases when the number of available training samples is small, which is a common situation in medicine. In this work, we investigate transfer learning on 1D-CNNs, then we develop a cumulative learning method when transfer learning is not powerful enough. We propose to train the same model through several classification tasks over various small datasets to accumulate knowledge in the resulting representation. By using rat brain as the initial training dataset, a cumulative learning approach can have a classification accuracy exceeding 98% for 1D clinical MS-data. We show the use of cumulative learning using datasets generated in different biological contexts, on different organisms, and acquired by different instruments. Here we show a promising strategy for improving MS data classification accuracy when only small numbers of samples are available.

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“…The basic design of the instrument setup is described in detail elsewhere 4,7 In the following experiments, the prototype was equipped with a fibered tunable wavelength…”

Section: The Spidermass Systemmentioning

confidence: 99%

“…mini-invasive IR laser-based MS technique designed for in vivo real-time molecular analysis 3,4 and has been showcase at the veterinary surgery room for tissue profiling 7 . The method is based on the resonant excitation of the endogenous water molecules and provides metabo-lipidomic molecular profiles specific to different cell phenotypes very similarly to the others MSI modalities (e.g.…”

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confidence: 99%

Robot-Assisted SpiderMass forin vivoReal-Time Topography Mass Spectrometry Imaging

Ogrinc

Kruszewski

Chaillou

et al. 2020

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Mass Spectrometry Imaging (MSI) has shown to bring invaluable information for biological and clinical applications. However, conventional MSI is generally performed ex vivo from tissue sections. Here, we develop a novel MS-based method for in vivo mass spectrometry imaging. By coupling the SpiderMass technology, that provides in vivo minimally invasive analysis, to a robotic arm of high accuracy, we demonstrate that images can be acquired from any surface by moving the laser probe above the surface. By equipping the robotic arm with a sensor, we are also able to both get the topography image of the sample surface and the molecular distribution, and then and plot back the molecular data, directly to the 3D topographical image without the need for image fusion. This is shown for the first time with the 3D topographic MS-Based whole-body imaging of a mouse. Enabling fast in vivo MSI bridged to topography pave the way for surgical applications to excision margins.

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Water-assisted laser desorption/ionization mass spectrometry for minimally invasive in vivo and real-time surface analysis using SpiderMass

Cited by 56 publications

References 44 publications

Sarcoma Molecular Pathological Diagnosis of FFPE Tissues without Dewaxing based on Lipidomic Profiles using SpiderMass Technology

Sarcoma Molecular Pathological Diagnosis of FFPE Tissues without Dewaxing based on Lipidomic Profiles using SpiderMass Technology

Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification

Robot-Assisted SpiderMass forin vivoReal-Time Topography Mass Spectrometry Imaging

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