Profiling the immune system of cancer patients at the single-cell level promises to uncover signatures of response and mechanisms of drug action. While single-cell technologies have developed at a rapid pace, applying them to large numbers of clinical samples remains challenging.
To overcome this challenge, researchers at Immunai developed an integrated platform that enables high-quality analysis of single-cell multi-omics data on hundreds of PBMC samples from cancer patients. Leveraging machine learning approaches to correct for sample heterogeneity and mitigate batch effects, this platform enables characterization of the transcriptome, cell surface proteins, and adaptive immune repertoire of large sample numbers at the single-cell level using 10x Genomics technology.
In this webinar, Dr. Tali Raveh, Head of Computational Biology at Immunai, will discuss:
- The power of an integrated platform to elucidate compelling insights from single-cell immune profiling and applications to cancer research
- Efforts to define blood-derived early signatures of patient response and mechanisms of drug action using longitudinal blood samples from cancer patients treated with checkpoint inhibitors
- How to identify and characterize the dynamics and differential gene expression of T-cell clones expanding over time