At HS Analysis, we specialise in unveiling the future of cancer research with Deep Learning. We are at the forefront of integrating cutting-edge technology with life sciences. Our latest innovation bridges the realm of proteomics and oncology, providing groundbreaking tools for the scientific community.
Cancer research has increasingly embraced mass spectrometry-based profiling of clinical specimens (such as tissues and biofluids). A frequent strategy is to describe differences in protein expression between tumor and healthy tissue or between blood from sick and healthy people.
HSA KIT: The Toolkit Revolutionizing MS Data Analysis
Our flagship product, HSA KIT, offers versatile solutions for visualizing and understanding Mass Spectrometry (MS) data. Compatible with .d (Bruker) and .mzML files, HSA KIT simplifies data handling and ensures seamless integration with other analytical tools and workflows. Regular updates enhance its functionality, ensuring users benefit from the latest features and capabilities in MS data analysis.
Spec2PepNet (MS Viewer): Decoding Mass Spectrometry Data
The journey begins with our advanced deep learning module, Spec2PepNet. This powerful tool is designed to analyze raw mass spectrometry data, combining it with FASTA files to provide detailed information about proteins identified in your samples. Key functionalities include:
- Real-time Visualization: View peaks and spectra of peptides along with corresponding proteins from the spectral library.
- Protein Interaction Analysis: Gain insights into biological pathways and networks through detailed protein-protein interaction studies.
- Sample Comparison: Compare multiple samples to identify differences in protein expression levels or modifications, essential for uncovering key insights in cancer research.
Moreover, Spec2PepNet generates detailed reports, encapsulating information about each peptide’s abundance and other factors. Comparative quantification highlights significant differences observed between samples, such as healthy vs. cancerous tissues.
MS Analyser: Deep Insights from Predictive Data
Following the detailed analysis by Spec2PepNet, the MS Analyser module takes center stage. Focusing on studying protein structures and functions, it allows for comprehensive exploration of peptide positions, mapping them onto whole protein sequences. Highlighted capabilities include:
- Peptide Mapping: Compare experimental results with theoretical expectations to validate findings and guide future research.
- Post-Translational Modifications: Determine these modifications to gain further insights into protein functions and regulatory mechanisms.
- Network Analysis: Understand protein interactions within networks and signaling pathways.
By analyzing crucial data from the .csv file produced by Spec2PepNet, MS Analyser uncovers patterns and relationships between proteins, providing a holistic view of protein function and regulation within biological systems.
Experimental results correspond to the input .csv file which was produced as a result by our MS Viewer, It contains important information about quality and quantity of peptides. This data can then be further analyzed using bioinformatics tools to identify patterns and relationships between proteins. By integrating multiple sources of data, a comprehensive understanding of protein function and regulation within biological systems can be obtained.
As for the theoretical results, these are the expected behaviour of peptides once their respective protein is digested with trypsin. This provides valuable insights into expected protein structure and its behavior. Additionally, this data can be used to validate experimental results and guide future research directions in the field of proteomics.
Most viable use case of this module is to check which peptides are missing or in higher concentration that expected in experimental results.
Now that the actual and expected peptide positions are obtained, a detailed overview can be found automatically in the analysis tab. Most importantly the comparison of this type of result can further be compared for two different files, for example, healthy vs cancerous tissues.
In summary, the MS Analyser model has shown a capability to differentiate between normal and tumor tissue at a molecular level, with variations in protein and peptide counts being noted. Some proteins manifest with higher peptide counts, although this may not be consistent across all cases of CRC. Lastly, graphical representations suggest certain peptides’ absence in tumor tissues compared to normal tissues, pointing towards specific molecular changes associated with cancerous transformations.
The abundance of peptides alone cannot reveal much information about their behavior unless it is studied with their intensity. Theoretically, the peptides belonging to the same protein must have the same stoichiometry i.e. they must be present in equal molar amounts. However, their detection in Mass Spectrometers is influenced by the presence of other precursor ions.
Diving deep into peptide intensities
During one of our recent studies, we found some differences for MCM proteins in tumor samples when compared to normal ones. Specifically, MCM3 (Minichromosome Maintenance Complex Component 3) protein, an essential player in DNA replication and cell cycle regulation.
The objective was to explore the differential peptide intensities of the MCM3 protein in normal versus tumor tissues using various data transformation methods. By comparing raw, normalized, and log-transformed intensities, we aimed to identify specific peptides whose expression levels differ significantly between these conditions.
Examining the differences in peptide intensities and their positions in normal and tumor tissues uncovers vital aspects of protein regulation and function that are pivotal in cancer development. This analysis not only advances our understanding of tumor biology but also opens up new avenues for diagnostic and therapeutic innovations.
Leading the Charge in Oncological Proteomics
Our project stands as a testament to the relevance of proteomics in cancer research. By utilizing these sophisticated deep learning models, HS Analysis is driving forward a new era where the prediction and interpretation of proteomic data can lead to actionable insights in oncology. We believe that this project will not only underscore the vital role of proteomics but also foster advancements that bring us closer to effective cancer treatments.
Join us on this innovative journey to revolutionize cancer research through proteomics and deep learning. Discover how HS Analysis is making strides towards a future where precision medicine and personalized treatments are within reach.
Explore our vision and learn more about our pioneering work at HS Analysis
Research is a time-consuming process that necessitates long-term answers. The use of such libraries restricts the ability to integrate with future ambitions. Furthermore, relying only on pre-existing libraries may limit one’s ability to adapt to and incorporate new technologies or approaches that arise in the future. It is critical for researchers and developers to be able to experiment with new methodologies and tailor their solutions to changing research needs.
Open source softwares has certain drawbacks, such as limited compatibility with certain data formats, a difficult user interface, and a lack of thorough technical support.
HSA KIT offers seamless integration with existing laboratory equipment and software systems, streamlining the workflow and maximizing efficiency. This allows researchers and developers to focus more on their scientific goals rather than technical challenges.
In addition to streamlining the analysis process for mass spectrometry, HSA KIT offers an adaptable platform for incorporating deep learning techniques. This extends its potential beyond conventional mass spectrometry applications by enabling the modification of modules to suit a variety of applications like illness detection, food toxicity certification, or principal component analysis. Not to mention the capability to work with various data types and, if needed, carry out automatic file conversions.