What is Immunomics?

Immunomics is the study of immune systems by integrating the omics technologies.

Understanding the immune system’s function benefits from a holistic approach to omics disciplines. Viewing single immunological components within a cross-linked network reveals how they interact and function together.

Traditionally, high-throughput, high-resolution techniques have been utilized in immunology on a single technique basis. Increasingly, data from these techniques is being combined. Researchers are producing a 360-degree view of the immune system function by integrating immunology, genomics, proteomics, transcriptomics, epigenetics and bioinformatics.

Immunology

Traditional immunology involves the detection of proteins (epitopes) on the cell surface or intracellularly.  The most common technique for multiple protein detection in immunology is classical flow cytometry. High-throughput and high-resolution techniques are breaking through to mainstream use. These include multi-parameter characterization using spectral flow cytometry or Cytometry Time of Flight (CyTOF) techniques for larger antibody panels.  Cytokine responses can be detected using multiplexed ELISA techniques and T- or B-cell reactivity can be measured using various immunoassays.

Proteomics

Protein microarrays and mass spectrometry can be used to analyze multiple proteins simultaneously. It is mainly used for biomarker discovery.

Genomics

Next Gen Sequencing (NGS) of mRNA (or Total RNA) allows for transcriptomic analyses at the transcript isoform level. It remains the gold standard for unbiased genome wide assessment of gene expression. 

Transcriptomics

Targeted single cell transcriptomics (scRNAseq) focuses on the heterogeneity of cell populations in health and disease or cellular differentiation and developmental trajectories. Oligonucleotide-based cellular indexing of transcriptomes and epitopes (CITE-seq) enables the combination of scRNA-seq with analysis of surface protein expression in the same cell.

Epigenomics

The epigenome can be studied using several techniques to determine DNA methylation using Bisulfite treatment of DNA before regular NGS, Protein/DNA interactions using CHIP-seq which Crosslinks DNA and protein for immunoprecipitation and NGS-based analysis, or Chromatin structure using ATAC-seq where double-stranded DNA is cut and oligonucleotides are introduced for library prep and NGS.

Bioinformatics

For each of these techniques collecting dense technical and clinical metadata on participants in clinical trials when using omics technologies is becoming more important.  In addition, reviewing the data compared to other techniques allows researchers to form a complete picture of the entire immune system from a holistic perspective.

Challenges

The analysis challenges for the immunomics analysis are multiple:  the heterogeneous formats of the different readouts (e.g. flow, scRNA-seq, ELISA), the wide variety of analysis workflows for each type of readout. This includes innovating new algorithms to answer specific challenges for each technology (quality control, normalization, batch correction). In addition, each type of technology generates different data sizes (from GigaBytes right up to PetaBytes ). Each technology has its specific technical annotations requirement (e.g. run annotation, sample annotation), thus leading to an explosion of annotation approaches.  All the current solutions analysis pipelines are written in pipelines from different code frameworks (Python, R, C++, Matlab, etc.) resulting in higher costs.

About Tercen

Tercen is an analysis platform that integrates multiple technological platforms, such as Flow Cytometry, scRNA-seq, ELISA, Microarray and Mass Spectrometry. It does this with the flexibility and extensibility that is needed to handle the data, pipelines, algorithms, and visuals that are required.

With Tercen, immunologists can upload genomic and proteomic readouts and select relevant analysis pipelines to be executed. They can add clinical annotations to the experiments, and rapidly explore their integrated results.  Statistical algorithms can be quickly added to the pipelines to cater to any innovative immunology question.  All analysis, data, and results are in one central location and easily shareable with colleagues across multiple research groups and sites.

 

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