Scils lab 2016b

Manufactured by Bruker

Sourced in Germany

SCiLS Lab 2016b is a software package for the analysis and visualization of mass spectrometry imaging data. It provides tools for processing, analyzing, and interpreting mass spectrometry imaging datasets.

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Lab products found in correlation

Fleximaging 5, by Bruker (2 mentions) Smartbeam 3d laser, by Bruker (1 mentions) Flexanalysis 4, by Bruker (1 mentions) Rapiflex tissuetyper, by Bruker (1 mentions) Rapiflex maldi tof tof system, by Bruker (1 mentions) Flexcontrol, by Bruker (1 mentions)

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SCiLS Lab (24 citations) SCiLS 2016b (2 citations) SCiLS lab software 2016b (2 citations)

5 protocols using scils lab 2016b

MALDI Imaging Mass Spectrometry Protocol

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MALDI MSI data were acquired with a rapifleX tissuetyper in single TOF mode (Bruker Daltonik GmBH, Bremen, Germany), equipped with a SmartBeam 3D laser. Mass spectra were the sum of 1000 individual laser shots, with a 90% laser intensity. Mass spectral peptidomic (m/z range 800 Da–5 kDa) images were obtained in positive reflector mode with a reflector voltage of 3005 V, a sample rate of 0.63 GS/s, a laser resolution of 50 µm and a raster width of 50 µm × 50 µm.
All the spectra are preprocessed with a Top Hat baseline algorithm for baseline subtraction and the resulting overall average spectrum of the ion image is TIC normalized in flexImaging 5.0 (Bruker Daltonik GmBH, Bremen, Germany) after recalibration in flexAnalysis 4.0 with external calibration standard. The results will be further processed in SCiLS lab 2016b (Bruker Daltonik GmBH, Bremen, Germany) and R software (Cardinal) [42 (link)].

Berghmans E., Van Raemdonck G., Schildermans K., Willems H., Boonen K., Maes E., Mertens I., Pauwels P, & Baggerman G. (2019). MALDI Mass Spectrometry Imaging Linked with Top-Down Proteomics as a Tool to Study the Non-Small-Cell Lung Cancer Tumor Microenvironment. Methods and Protocols, 2(2), 44.

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Metabolite and Lipid Annotation in MALDI-MSI

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The m/z features present in both MALDI-TOF-MSI and MALDI-FTICR-MSI datasets with similar tissue distributions were used to assign metabolites and lipid species. The m/z values from MALDI-FTICR-MSI were imported into the Human Metabolome Database (https://hmdb.ca/)^{36 (link)}. Metabolite and lipid species annotation was performed with an error of ≤ ± 10 ppm. For small molecules only detected in MALDI-TOF, annotation was performed with an error of ≤ ± 20 ppm. MSI data were exported and processed in SCiLS Lab 2016b (Bruker Daltonics). All MALDI-TOF-MSI data was baseline corrected and normalized using the root mean square (RMS). Peaks from the average spectrum with a signal-to-noise-ratio higher than 3 (SNR ≥ 3) were selected and all matrix peaks were excluded. A total of 389 m/z features (containing small molecules and lipids) were selected by peak intensities for further data analysis.

Rietjens R.G., Wang G., van der Velden A.I., Koudijs A., Avramut M.C., Kooijman S., Rensen P.C., van der Vlag J., Rabelink T.J., Heijs B, & van den Berg B.M. (2023). Phosphatidylinositol metabolism of the renal proximal tubule S3 segment is disturbed in response to diabetes. Scientific Reports, 13, 6261.

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Spatial Metabolomics for Grapevine-Pathogen Interactions

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Data were analysed using the MSI software SCiLS™ Lab 2016b (Bruker Daltonics, Bremen, Germany) for unsupervised spatial identification of discriminative features between control, 96 hpi, and 96 hpi with visible sporulation of P. viticola sporangiophores. For each pixel, data was normalised by the total ion count. MALDI FT-ICR-MS chemical images were generated from a colour scale, which represents the normalised intensity of specific ions. Each pixel of the image is associated with the original mass spectrum that is acquired in a particular position. A spatial localisation of the analytes is provided as a function of the m/z values. For metabolite identification, selected m/z values were submitted to MassTRIX 3 (Suhre and Schmitt-Kopplin, 2008 (link)) server (http://masstrix.org, accessed in March 2022) considering the following parameters: positive scan mode; the adducts [M+H]⁺, [M+K]⁺ and [M+Na]⁺ were considered; a maximum m/z deviation of 2 ppm was considered; Vitis vinifera was selected; search was performed in the databases “KEGG (Kyoto Encyclopaedia of Genes and Genomes)/HMDB (Human Metabolome DataBase)/LipidMaps without isotopes”.

Maia M., McCann A., Malherbe C., Far J., Cunha J., Eiras-Dias J., Cordeiro C., Eppe G., Quinton L., Figueiredo A., De Pauw E, & Sousa Silva M. (2022). Grapevine leaf MALDI-MS imaging reveals the localisation of a putatively identified sucrose metabolite associated to Plasmopara viticola development. Frontiers in Plant Science, 13, 1012636.

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MALDI-TOF/TOF-MSI of Metabolites and Lipids

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MALDI-TOF/TOF-MSI was performed using a RapifleX MALDI-TOF/TOF system (Bruker Daltonics). Negative-ion-mode mass spectra were acquired at a pixel size of 5 × 5 µm² over a mass range of m/z 80–1000. Prior to analysis, the instrument was externally calibrated using red phosphorus. Spectra were acquired with 15 laser shots per pixel (for 5 × 5 µm² measurements) at a laser repetition rate of 10 kHz. Data acquisition was performed using flexControl (Version 4.0, Bruker Daltonics) and flexImaging 5.0 (Bruker Daltonics). Sections present on the same slide were measured in a randomized order. The m/z features present in MALDI-TOF-MSI dataset were further used for identity assignment of metabolites and lipid species. The m/z values were imported into the Human Metabolome Database^{47 (link)} (https://hmdb.ca/) after re-calibration in mMass and annotated for metabolites and lipids species with an error ≤ ±20 ppm. The ¹³C-labeled peaks were selected by comparing the spectrum of control and ¹³C-labeling experiments, and annotated based on the presence of un-labeled metabolites and their theoretical m/z values. Peak intensities of the selected features were exported for all the measured pixels from SCiLS Lab 2016b (version 2016b, Bruker Daltonics), which were used for the following analysis. Single ion visualizations were also obtained from SCiLS Lab.

de Haan M.J., Jacobs M.E., Witjas F.M., de Graaf A.M., Sánchez-López E., Kostidis S., Giera M., Calderon Novoa F., Chu T., Selzner M., Maanaoui M., de Vries D.K., Kers J., Alwayn I.P., van Kooten C., Heijs B., Wang G., Engelse M.A, & Rabelink T.J. (2024). A cell-free nutrient-supplemented perfusate allows four-day ex vivo metabolic preservation of human kidneys. Nature Communications, 15, 3818.

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Preprocessing and Statistical Analysis of Mass Spectrometry Data

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Data files containing the individual spectra of each entire measurement region (115,068 for DN and 96,539 for HN, respectively) were then imported into SCiLS Lab 2016b software (Bruker Daltonics) to perform preprocessing: baseline subtraction (convolution algorithm), normalization (total ion current algorithm), and spatial denoizing. Average (avg) spectra, representative of the whole measurement regions were generated to display differences in the protein profiles. Peak picking and alignment were performed as feature extraction for statistical analysis and this resulted in the detection of 281 m/z features within the dataset. Unsupervised principal component analysis (PCA) was also performed to reduce the high complexity of the data. Finally, receiver operating characteristic (ROC) analysis was performed, with an area under the curve (AUC) of ≥0.80 and a p value ≤0.05 being required for a peak to be considered as statistically significant. These signals were curated to only include m/z values representative of the monoisotopic mass of a tryptic peptide.

Smith A., Iablokov V., Mazza M., Guarnerio S., Denti V., Ivanova M., Stella M., Piga I., Chinello C., Heijs B., van Veelen P.A., Benediktsson H., Muruve D.A, & Magni F. (2020). Detecting Proteomic Indicators to Distinguish Diabetic Nephropathy from Hypertensive Nephrosclerosis by Integrating Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging with High-Mass Accuracy Mass Spectrometry. Kidney & blood pressure research, 45(2).

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