Open LabBot and RmsiGUI: Community development kit for sampling automation and ambient imaging

Rosas-Román, Ignacio; Ovando-Vázquez, Cesaré; Moreno-Pedraza, Abigail; Guillén‐Alonso, Héctor; Winkler, Robert

doi:10.1016/j.microc.2019.104343

“…The algorithm was implemented in the programming language R (R Core Team 2018) and integrated into the graphical user interface of RmsiGUI (https://bitbucket.org/lababi/ rmsigui/, Rosas-Román et al (2020)).…”

Section: Methodsmentioning

confidence: 99%

Contrast Optimization of Mass Spectrometry Imaging (MSI) Data Visualization by Threshold Intensity Quantization (TrIQ)

Winkler¹,

Rosas-Román²

2020

Preprint

Self Cite

0

View full text Add to dashboard Cite

<div>Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential</div><div>mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient</div><div>discovery of ‘regions of interest’ (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values (‘outliers’) and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https://bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https://bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.</div><div><br></div>

show abstract

“…A 3D-LTP ionisation probe enabled measurement times of 10 s per sample [24]. The Open LabBot and its control software RmsiGUI were released as a community development kit [19]. Mounting a 3D-LTP probe and a continuous wave ultraviolet (CW-UV) diode laser for laser desorption (LD) enabled the imaging of alkaloids in plant tissues with a lateral resolution of 50 µm (Figure 4A) [30].…”

Section: Robotics and Imagingmentioning

confidence: 99%

“…Moreover, 3D printing provides a cheap and fast way to manufacture diverse custom pieces and assemble experimental prototypes for ion mobility and mass spectrometry [18]. Besides, components and software of 3D printers serve for building custom AIMS robots [19].…”

mentioning

confidence: 99%

Build, Share and Remix: 3D Printing for Speeding Up the Innovation Cycles in Ambient Ionisation Mass Spectrometry (AIMS)

García-Rojas

¹

,

Guillén‐Alonso

²

,

Martínez‐Jarquín

³

et al. 2022

Self Cite

View full text Add to dashboard Cite

Ambient ionisation mass spectrometry (AIMS) enables studying biological systems in their native state and direct high-throughput analyses. The ionisation occurs in the physical conditions of the surrounding environment. Simple spray or plasma-based AIMS devices allow the desorption and ionisation of molecules from solid, liquid and gaseous samples. 3D printing helps to implement new ideas and concepts in AIMS quickly. Here, we present examples of 3D printed AIMS sources and devices for ion transfer and manipulation. Further, we show the use of 3D printer parts for building custom AIMS sampling robots and imaging systems. Using 3D printing technology allows upgrading existing mass spectrometers with relatively low cost and effort.

show abstract

“…Several programs and workflows use the statistical language R, such as MSI.R (Gamboa-Becerra et al 2015), Cardinal (Bemis et al 2015), and the Galaxy MSI module (Föll, Moritz, Wollmann, Stillger, Vockert, Werner, Bronsert, Rohr, Grüning, and Schilling 2019). Recently, we published an R-based platform for MSI data processing with a graphical user interface, RmsiGUI (Rosas-Román, Ovando-Vázquez, Moreno-Pedraza, Guillén-Alonso, and Winkler 2020), which provides modules for the control of an open hardware imaging robot ('Open LabBot'), the processing of raw data, and the analysis of MSI data. We integrated the TrIQ algorithm into the RmsiGUI, but also provide the R code snippets to facilitate its adoption into other programs.…”

Section: Organism Tissuementioning

confidence: 99%

Contrast Optimization of Mass Spectrometry Imaging (MSI) Data Visualization by Threshold Intensity Quantization (TrIQ)

Winkler¹,

Rosas-Román²

2020

Preprint

Self Cite

0

View full text Add to dashboard Cite

<div>Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential</div><div>mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient</div><div>discovery of ‘regions of interest’ (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values (‘outliers’) and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https://bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https://bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.</div><div><br></div>

show abstract

Open LabBot and RmsiGUI: Community development kit for sampling automation and ambient imaging

Cited by 21 publications

References 30 publications

Contrast Optimization of Mass Spectrometry Imaging (MSI) Data Visualization by Threshold Intensity Quantization (TrIQ)

Contrast Optimization of Mass Spectrometry Imaging (MSI) Data Visualization by Threshold Intensity Quantization (TrIQ)

Build, Share and Remix: 3D Printing for Speeding Up the Innovation Cycles in Ambient Ionisation Mass Spectrometry (AIMS)

Contrast Optimization of Mass Spectrometry Imaging (MSI) Data Visualization by Threshold Intensity Quantization (TrIQ)

Contact Info

Product

Resources

About