Watch our webinar on using single-cell data to deconstruct a human autoimmune disease
Watch the webinar with Prof. Brenner on the following link: https://youtu.be/H4RuwhcO32g
Single cell/single nucleus analyses including scRNA-seq, snATAC-seq, scTCR-seq, flow cytometry and CyTOF provide a new dimension in understanding human inflammatory diseases. They provide an unbiased approached to determine both the type and state of inflammatory cells and the tissue parenchymal cells and how they may interact in the pathologic state. We will examine all of the cell types and states found in the synovium in rheumatoid arthritis as a prototypical inflammatory disease. We will delineage (>20) the T cell types/states found and how they differ from what might have been expected based on past literature. This will include the major finding of a new T helper cell population that is implicated in B cell differentiation to antibody producing cells that dominates among the CD4+ T cell states. Further, we will reveal the nature of the major tissue CD8+ T cell as granzyme K expressing, rather than the granzyme B expressing CD8+ T cells known in viral infections. B cell states and plasma cells will be enumerated with implications on the likely relevance of follicular and extrafollicular B cell differentiation. Inflammatory and non-inflammatory macrophages will be outlined, including our finding of a newly described super-activated macrophage state. Finally, the type and state of stromal fibroblasts that interact with the inflammatory leukocytes will be revealed as they directly mediate tissue damage. Besides the application of standard clustering algorithms, we will also employ recently described algorithms to assess neighborhoods of cells that co-vary with implications for cell-cell interactions. We will then show how many of the interacting cells cooperate to produce the end organ pathology in rheumatoid arthritis, many of which are shared across inflamed tissues.
The format is the following:
- 5 mins – introduction
- 20 mins – main discussion
- 20 min+ – Q&A
Scailyte is an ETH Zürich spin-off with a best-in-class artificial intelligence platform for the discovery of complex disease patterns from single-cell data. Our solution provides unprecedented insight into the disease and patients’ biology and enables the discovery of new clinically-relevant biomarker signatures by uncovering human’s hidden “single-cell” secrets.
Scailyte’s proprietary best-in-class data analysis platform ScaiVision™ associates multimodal single-cell datasets (RNA-/TCR-/BCR-seq, proteomics, etc.) with clinical endpoints, such as disease diagnosis, progression, severity, treatment response, and toxicity response to identify ultra-sensitive biomarker signatures and cell functionality states. The performance and clinically-relevant applications of Scailyte’s platform ScaiVision have been demonstrated in well established CAR-T cell therapies and various clinical projects in Oncology and Immunology.