SCiLS™ Software Solutions

Analysis and visualization of MALDI Imaging data has never been easier with the software from Bruker.

Powerful visualization and reporting

• Explore every feature in every spectrum 
  
• Study molecular images directly in histopathological context
• Compelling statistical charts and reports
• Volumetric visualization of 3D MS imaging studies (optional)
  

Advanced data processing and analysis

• Advanced machine learning algorithms for data sets of virtually unlimited size
• Comparative analysis for uncovering discriminative and correlated spectral features
• Unsupervised spatial segmentation and component analysis for untargeted clustering and data mining
• Metadata annotations for characterizing regions in clinical and pre-clinical studies
• Classification models for categorization of unlabeled samples
• Analyte quantitation based on dilution series
  

Workflow and extensibility

• Comprehensive tools for all kinds of targeted and untargeted SpatialOMx^® workflows
• Integration with histological annotations from digital pathology
• Extensive application programming interface (API) for automated reporting and advanced workflow integration
• Export MS imaging data to the open industry standard imzML
• Import MS imaging datasets from imzML or from various third party vendors’ imaging data formats (optional)

System requirements​
The following system requirements are recommended:

• Intel Core i7 processor or Intel Xeon processor (or equivalent) with at least 4 cores
• Microsoft Windows 10 64 Bit operating system
• 64 GB of RAM (128 GB recommended for large datasets)
• 1 TB disk space (SSD recommended, see below)
• Full HD display (1920 x 1080) or larger
• Graphics adapter supporting OpenGL 3.2, at least 4 GB GPU memory recommended

For best performance, a solid state drive (SSD) should be used to host the SCiLS™ Lab data files.

SCiLS™ Scope lowers the barriers for sharing and viewing MALDI HiPLEX-IHC and MALDI Imaging data. With the easy-to-use, lightweight interface and data in the open OME-TIFF format, it is ideal for sharing data between collaborators who want to focus on examining images. Intuitive review of images within a pathological context provides more insight per pixel.

Visualization of MALDI HiPLEX-IHC data

SCiLS™ Scope enables easy image review, including overlay, adjustment and analysis of individual channels. It provides an entry point into MALDI Imaging by focusing on targeted protein panels.

SCiLS™ Scope offers essential functionality for quickly assessing acquired MALDI HiPLEX-IHC targeted spatial proteomics data. Each mass-tag released from an antibody is represented as a channel that can be visualized individually or in combination. Other important features are:

• Stepless zooming 
• Distance measurement between biologically relevant structures
• Image export by copying to clipboard or saving as PNG or TIFF files
• Global brightness and contrast adjustments to all channels, or channel-independent to accommodate for difference in signal strength.

Close integration with MALDI HiPLEX-IHC workflow

The SCiLS™ Scope software completes the targeted spatial proteomics workflow provided by MALDI HiPLEX-IHC. Data acquisition is based on the SCiLS™ autopilot, using a default acquisition method and the HiPLEX mass-tags for online calibration ensuring high data quality. Data import, pre-processing and export to the open OME-TIFF format are performed by SCiLS™ Lab automatically and in the background, and provides immediate results after analysis.

System requirements

The following system requirements are recommended:

• Hard disk: at least 100 GB of free disk space.
• Main Memory: minimum 32 GB RAM.
• Operating System: Windows 10 64-Bit. 
• Graphics: AMD Radeon or NVIDIA higher-end graphics 4 GB VRAM
• Graphic resolution: 1920x1024 pixels with true colors. 

SCiLS™ Lab integrates data from all major mass spectrometry vendors, including Bruker’s FLEX series and MRMS, as well as datasets provided in the open imzML format. For timsTOF fleX ion mobility data, SCiLS™ Lab displays mass-mobility ion images allowing CCS-enabled data interpretation.
 
Comparative analysis of multiple samples can be visualized in both 2D and 3D, enabling a multitude of applications in pharmaceutical drug development, tissue-resolved biomarker discovery, and translational pathology research to complement immunohistochemistry.
 
SCiLS™ Lab supports intuitive workflows for MALDI Imaging quantitation of target molecules directly from tissue. It integrates with the SpatialOMx^® workflow from MetaboScape^® and visualizes the annotated MALDI molecular tissue images of detected features (metabolites, lipids, glycans). For integration with histology, SCiLS™ Lab provides an interface to the powerful open source software QuPath widely used in digital pathology.

Large sample cohorts in clinical research studies
In clinical biomarker studies for the discovery of discriminating ions, large sample cohorts and huge amounts of data need to be analyzed. SCiLS™ Lab makes it easy to link sample metainformation to MALDI MSI data, saving time and avoiding errors. Histological annotations from digital pathology are integrated using an interface to the powerful and widely used open source application QuPath.

Automatic elucidation of spectral features discriminating pathophysiological alterations are easily performed. With just one click, SCiLS™ Lab automatically quantifies how well ions discriminate between different biological states. The resulting list of novel biomarker candidates can be easily exported for subsequent analysis.

Discover the powerful data mining capabilities that are now just a single click away!

Whole body imaging
The visualization of drug and metabolite distributions within whole-body tissue sections  remains a challenging methodology with respect to sample processing, acquisition, and computational data analysis.  

SCiLS™ Lab offers tools for the entire MALDI MSI data analysis workflow. Multiple data sets composed of millions of spectra can be processed using one software, from the registration of multiple individual data sets over spectral preprocessing to comprehensive statistical analysis.