Proteomics is a rapidly growing field that involves the systematic, large-scale analysis of proteins and provides a global perspective on how these molecules interact to create a functioning biological system. With our analyses, we enable proteomic research on your samples, focusing on flow-cytometry, mass cytometry (CyTOF), imaging mass cytometry (IMC), mass spectrometry and more.
The proteome is a complex entity that can be defined by the sequence, structure, abundance, localization, modification, interaction, and biochemical function of each of its components.
Proteomics can identify and monitor biomarkers by analyzing proteins in body fluids such as urine, serum, exhaled air, and cerebrospinal fluid. In addition, proteomic insights enable it to facilitate drug development by providing a comprehensive map of protein interactions associated with disease progression.
Our suite of analytical modules enables in-depth analysis of medical data, providing valuable insights. We combine relevant analysis modules to build and run the best possible analysis for you. A selection of analytical modules can be found here.
Dimensionality reduction is a technique used to reduce the number of features or variables in a dataset while maintaining the information content. Some techniques for dimensionality reduction used in our pipleine include Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP).
Clustering is a technique used to group similar cells or samples together in a dataset. The goal of clustering is to divide the data into distinct groups, called clusters, such that the cells or samples within a cluster are more similar to each other than to those in other clusters. Clustering is a useful tool to discover hidden patterns in the data.
Marker molecule identification is the process of identifying specific biomolecules, such as proteins or RNA, that can be used to distinguish between different biological states or cell types.
Cell-type annotation refers to the process of identifying and labeling the different types of cells present in a biological sample. Cell-type annotation is an important step for obtaining accurate and specific information about the biological system, and it is useful for understanding gene expression patterns, identifying new cell types and markers, and for understanding the cellular interactions in the sample.
Perturbation analysis is a technique used to study how small changes/perturbations affects the whole system. In biology, a perturbation can be introduced at the genetic level, for example through CRISPR technology or administration of specific drugs, and the resulting phenotype is then studied. This helps to understand the function of the gene that has been perturbed in the cell.
Differential expression analysis is a statistical method used to identify genes, transcripts or other features that have different expression levels between two or more groups of samples. The goal of DEA is to identify the features (i.e. genes or transcripts) that are differentially expressed between the groups of samples, and to quantify the magnitude of the difference in expression.
Transcriptional regulator prediction is a computational technique used to identify and predict the proteins or other molecules (often referred to as transcription factors) that regulate the transcription of genes. Transcriptional regulator prediction is useful to understand the molecular mechanisms that control gene expression and to identify potential drug targets. It can also aid in understanding the genetic regulation of diseases, and in the design of new therapies.
Time series analysis is a statistical technique that is used to analyze and model time-dependent data. It involves identifying patterns, trends, and dependencies in a sequence of data points, collected at regular intervals over time. The goal of time series analysis is to understand the underlying patterns and processes in the data.
The combination of high-dimensional omics data and data collected in the clinics provides a more holistic view on patients and opens up the possibility to better identify and characterize patient groups.
Functional enrichment analysis is a bioinformatics method used to identify the biological pathways, processes, or functions that are over-represented among a set of genes or proteins. The goal of functional enrichment analysis is to understand the biological mechanisms that underlie a particular phenotype or disease state.
This analysis uses e.g. gene signatures from the public domain and statistically evaluates whether the 'signature' is enriched between two or more groups of samples.
Feature selection is a process in machine learning and statistics used to identify the most relevant features or variables in a dataset, with the goal of improving the performance of a model or algorithm. The idea is that by removing irrelevant or redundant features, the model can be simpler and more efficient, while also reducing overfitting and the risk of false discovery.
The goal of patient classification is to identify subgroups of patients who have similar characteristics, such as similar gene expression profiles, and to use this information to guide treatment decisions or develop personalized medicine strategies.
Flow cytometry and mass cytometry (e.g. CyTOF) are powerful methods for high-throughput protein analysis, allowing the simultaneous detection of multiple antigens for single cell proteomic profiling. These methods provide valuable insights into steady-state and pathological processes.
We provide proteomic research analyses on your samples, offering evaluation of various proteomic technologies, including:
Mass cytometry (CyTOF)
Imaging mass cytometry (IMC)
With our analytical modules we tackle a wide range of possible research questions.
Identifying specific molecules or biological pathways that are involved in disease and that can be targeted with drugs are crucial for the development of new therapeutics. The goal is to find new targets that can be used to develop new and more effective drugs to treat a variety of diseases.
Understanding the mechanism of action of a substance (e.g. drug) can help scientists and researchers understand how it works, and can also inform the improvement or the development of new drugs or treatments. Importantly, understanding the mechanism of action of a substance can also help in understanding its potential risks and side effects.
Biomarkers can be used to diagnose and monitor disease, to evaluate the effectiveness of a treatment, or to predict the likelihood of developing a certain condition. Therefore, a proper biomarker is an essential tool for the diagnosis and treatment of diseases.
Investigating drug responses refers to the study of how a specific drug or treatment interacts with the body and produces its effects. It is closely related to the understanding of mechanism of action.
Companion diagnostics refer to diagnostic tests that are used in conjunction with specific therapeutic drugs to help identify patients who are most likely to benefit from the treatment, or to predict which patients may be at risk for side effects. The development of companion diagnostics is an important aspect of personalized medicine, which aims to tailor treatments to individual patients based on their unique characteristics, such as their genetic makeup or biomarkers.
Patient stratification refers to the process of classifying or grouping patients based on certain characteristics that are relevant to the disease or condition they are being treated for. This can include characteristics such as genetic makeup, biomarkers, or other clinical or demographic factors. The goal of patient stratification is to identify subgroups of patients who are most likely to benefit from a specific treatment, or to predict which patients may be at risk for side effects.
Exploration of cellular heterogeneity refers to the study of the diversity of cells within a tissue or organism. Exploring cellular heterogeneity is important for understanding the functioning of different cell types within a tissue and how they interact with each other, as well as for understanding the diversity of diseases and disorders.
With the characterization of diseases you gain an understanding of the underlying molecular causes of a specific disease or condition. This includes identifying risk factors and biomarkers associated with the disease, as well as understanding the mechanisms that lead to the development and progression of the disease.
Transcriptional regulation refers to the control of gene expression. It is mainly controlled by transcription factors, which in turn are controlled by signalling pathways. Understanding these mechanisms is important for understanding how cells and organisms develop and respond to their environment.
Identifying and characterising different subgroups of patients or cellular subtypes within a particular disease diagnosis can help to better understand the underlying causes of the disease and can lead to more effective treatment strategies.
Cellular processes include cell growth and division, metabolism, communication between cells, and many more. The characterization of cellular processes is important for understanding how cells work, both individually and in conjunction with other cells, and how they interact with their environment.