Single-Clone Multi-omics Sequencing for Cancer Diagnosis

Introduction

Cancer led to one in six deaths worldwide. By nature, cancer is characterized by abnormal and uncontrolled cellular growth caused primarily by genetic mutations. Despite the high cellular heterogeneity, only a small number of somatic mutations have been shown to be directly associated with tumorigenesis, hence driving cancer growth.

Hurdles in Cancer Diagnosis

The first hurdle in cancer diagnosis is to identify rare populations that have the potential to develop drug resistance. A second hurdle is to associate mutations at the genetic level with the RNA expression level, which will improve the chances of pinpointing a specific mutation with the relevant activity.

Importance of Multi-Omics Sequencing

It is of crucial importance to develop a single-cell multi-omics sequencing tool to simultaneously study the heterogeneity of expression and DNA-based regulation of cancer-associated genes. Recently, several multi-omics tools have emerged that allow for the interrogation of both gene expression and open chromatin regions.

Limitations of Current Tools

However, there are some notable limitations that constrain the capability and applicability of these tools:

1. The expensive and disposable apparatuses and reagents of these methods are extremely costly, limiting their extensive application and potential as a diagnostic tool.
2. The transcriptome derived from all these technologies only captures the 3' RNA termini of polyadenylated transcripts. This excludes the capture of non-polyadenylated transcripts, which play a vital role in cancer development, and the interrogation of somatic mutations from full-length RNA.
3. The low input of single-cell material leads to sparse data.

Our Novel Technology

To overcome these hurdles, our novel technology is based on three methods that we have developed, leveraging the power of microfluidics:

1. Single-clone sequencing
2. Full-length RNA
3. A cost-effective open chromatin extraction method

MultiCloneSeq will be the first method providing low-cost and full-scale multi-omic profiling with high resolution.