IGATech Contest 2020: #scRNAseq Winner Project Presentation

10 March 2021

Pleased to present the winning proposal “Understanding tumor and microenvironmental heterogeneity in primary and secondary cancers by single-cell RNA-seq” sent by the 31 y/o post-doctoral research fellow Elena Campaner from the University of Trieste.

Here is the brief summary of the project: 

The dissemination of cancer cells from the primary tumor to distant organs (metastatic colonization) is the primary cause of cancer mortality. Indeed, while the clinical management of primary tumors is usually under control, therapy resistance is frequently observed in metastatic patients. Several efforts have been made to understand the working principles of metastatic dissemination, revealing, however, that there is no significant difference in genetic alterations between primary and metastatic lesions. This fact suggests that other attributes of tumors may account for their progression. 

Single-cell expression profiling studies offer a powerful method to study functional heterogeneity in metastatic tumorsSingle cell sequencing provides the sequence information from individual cells, offering a better picture of tumors in terms of cellular composition and cellular differences in the context of their microenvironment. In the present study, we aim at performing single-cell profiling of a human colorectal primary tumor and matched liver metastasis to unveil cellular dynamics and molecular features associated with tumor progression. 

The result of this analysis will help in facilitating the identification of key drivers of cancer progression and programs involved in drug resistanceThis analysis will also help in highlighting the complex cellular ecosystem with active communication between malignant and non-malignant cells in primary and secondary tumors. 



Figure 1. Workflow shows collection and processing of fresh samples of primary colorectal carcinoma (CRC) and matched liver metastasis for single cell RNA-sequencing. Cell type identification in tumor masses is achieved by the analysis of differentially expressed genes. The figure was created with BioRender.com. 


We are really excited and eagerly look forward to starting with the experiment!


Stay tuned for updates!