Scils cloud

Manufactured by Bruker

Sourced in Germany

SCiLS Cloud is a web-based software solution for data analysis and visualization of mass spectrometry imaging data. It provides a secure and collaborative platform for researchers to upload, process, and interpret their mass spectrometry imaging datasets.

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Lab products found in correlation

Aperio cs2, by Leica (4 mentions) Scils lab pro, by Bruker (3 mentions) R statistical software version 3, by R Project for Statistical Computing (1 mentions) Scils lab mvs, by Bruker (1 mentions) Scils lab, by Bruker (1 mentions)

4 protocols using scils cloud

Automated Histopathological Annotation of TMA Sections

Cited 1 time

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Following matrix removal, the same TMA sections that were previously measured by MSI were stained with hematoxylin and eosin (H&E) and digitalized, utilizing a slide scanner (Aperio CS2, Leica Bio-systems, Wetzlar, Germany). H&E scans were uploaded, and tumor regions were thoroughly annotated using SCiLS Cloud (discontinued service from Bruker Daltonics) or QPath (v0.2.2) by a board-certified pathologist [61 (link)]. MSI data were processed using SCiLS Lab Pro (Bruker Daltonics) for mass spectrometry and image visualization. Annotations were imported into SCiLS Lab Pro software. The spectra baseline was corrected to the total ion count (TIC).

Gonçalves J.P., Bollwein C., Schlitter A.M., Kriegsmann M., Jacob A., Weichert W, & Schwamborn K. (2022). MALDI-MSI: A Powerful Approach to Understand Primary Pancreatic Ductal Adenocarcinoma and Metastases. Molecules, 27(15), 4811.

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Tumor region annotation and MSI analysis

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TMA sections previously analyzed by MSI were stained with H&E and digitalized utilizing a slide scanner (Aperio CS2, Leica Biosystems, Nussloch, Germany). H&E scans were uploaded, and tumor regions were thoroughly annotated using SCiLS Cloud (discontinued service from Bruker Daltonics). MSI data was processed using SCiLS Lab Pro (Bruker Daltonics) for mass spectrometry and image visualization. Annotations were imported into SCiLS Lab Pro software. Spectra were normalized to the total ion count (TIC).

Martin B., Gonçalves J.P., Bollwein C., Sommer F., Schenkirsch G., Jacob A., Seibert A., Weichert W., Märkl B, & Schwamborn K. (2021). A Mass Spectrometry Imaging Based Approach for Prognosis Prediction in UICC Stage I/II Colon Cancer. Cancers, 13(21), 5371.

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Integrating Histology and Mass Spectrometry Imaging

Cited 1 time

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Following matrix removal, the very same TMA sections measured by MSI, were stained with H&E and digitalized utilizing a slide scanner (Aperio CS2, Leica Biosystems, Nussloch, Germany). H&E scans were uploaded, and tumor regions were thoroughly annotated using SCiLS Cloud (discontinued service from Bruker Daltonics, Bremen, Germany) or QPath (v0.2.2) (Queen’s University, Belfast, United Kingdom) by a pathologist (B.C., B.M., and K.S.) [17 (link)]. MSI data was processed using SCiLS Lab Pro (Bruker Daltonics) for mass spectrometry and image visualization. No damage was inferred on the tissue by the MALDI-TOF laser. Annotations were imported into SCiLS Lab Pro software. Spectra baseline was normalized to the total ion count (TIC). Additionally, the spectra were preprocessed for intensity profile normalization, re-sampling, spatial de-noising, and calculation of a second normalization profile [18 (link),19 (link)]. Subsequently, automated peak picking with weak de-noising was performed, and spectra of individual spots were exported to .csv- format, and imported to R statistical software (version 3.6.3) (R Foundation for Statistical Computing, Vienna, Austria) via RStudio 1.2.5033 [20 ,21 ], for further analysis.

Gonçalves J.P., Bollwein C., Schlitter A.M., Martin B., Märkl B., Utpatel K., Weichert W, & Schwamborn K. (2021). The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging. Metabolites, 11(11), 752.

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Mass Spectrometry Imaging of Tumor Sections

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Tissue sections analyzed by MSI were stained with H&E (hematoxylin and eosin) and digitalized with a slide scanner (Aperio CS2, Leica Biosystems, Wetzlar, Germany). The H&E scans were uploaded and the epithelial tumor regions were thoroughly annotated (B.C. and K.S.) using SCiLS Cloud (discontinued service from Bruker Daltonics; a recommended alternative is the use of QuPath and its integration with SCiLS Lab, from Bruker Daltonics). MSI data was processed using SCiLS Lab MVS (Version 2023a Premium 3D, Bruker Daltonics) for mass spectrometry and image visualization. Annotations were imported into SCiLS Lab MVS software (Version 2023a Premium 3D). Spectra baseline was corrected to the total ion count (TIC). Afterward, spectra were pre-processed for intensity profile normalization, re-sampling, spatial de-noising, and calculation of a second normalization profile [48 (link),49 (link)]. Subsequently, automated peak-picking (376 most intense peaks) in the range of 600–3000 Da range was performed, and the spectra for the individual spots were exported to .csv- format and imported to R statistical software (version 3.6.3) and RStudio 1.2.5033 [50 ].

Gonçalves J.P., Bollwein C., Noske A., Jacob A., Jank P., Loibl S., Nekljudova V., Fasching P.A., Karn T., Marmé F., Müller V., Schem C., Sinn B.V., Stickeler E., van Mackelenbergh M., Schmitt W.D., Denkert C., Weichert W, & Schwamborn K. (2023). Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging. International Journal of Molecular Sciences, 24(3), 2860.

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