Single Cell Multi-omics: Solving the Jigsaw

Since the introduction of single cell sequencing techniques to the genomics community, interest in its use has grown exponentially and the number of single cell publications has exploded. The first single cell application was single cell RNA-Seq, which enabled scientists to focus on the gene expression profiles of individual cells rather than on tissue averages. As new single cell applications have been developed the trend is now the integration of two or more omics data sets known as single cell multi-omics. Currently, the most common is the integration of gene and protein expression data such as feature barcoding and CITE-Seq. Another exciting development is tissue transcriptomics, where gene expression data is collected together with histological information. There are many bioinformatic tools and analysis strategies available that can pose significant research challenges and easily shift the focus from biological questions to “how do I” questions. 

Learning Objectives:

1. An ECCITE-Seq project (cell hashing, CRISPR, gene expression, and protein expression) and

2. A spatial transcriptomics project (transcriptomics and histology)