Integration of Single Cell DNA/RNA for Intratumor Heterogeneity Analysis with MaCroDNA

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Summary

In the evolving landscape of cancer research, scientists can now precisely track mutations and their impacts at the cellular level, thanks to single-cell DNA and RNA sequencing technologies. They introduced MaCroDNA, a novel technique for aligning single-cell DNA and RNA data by employing a sophisticated matching process of gene read counts. This method has shown superior accuracy and efficiency, particularly in colorectal cancer studies, compared to existing approaches. Notably, MaCroDNA's insights into Barrett's esophagus transitioning into esophageal cancer underscore the growing influence of significant genomic mutations on cell differentiation, corroborating earlier research findings.

Research Criteria

The research criteria for this study involve the development and evaluation of the MaCroDNA algorithm for integrating single-cell DNA and RNA sequencing data. The algorithm aims to accurately assign single-cell RNA sequencing (scRNA-seq) cells to cancer clones identified from single-cell DNA sequencing (scDNA-seq) data. The performance of MaCroDNA is compared with other methods like clonealign and Seurat in terms of cell-to-clone assignment accuracy and predictive accuracy for clonal prevalences.

Sample Type

The samples used for the single-cell experiments in this study are derived from biopsies of patients with Barrett's esophagus (BE). The study specifically examines the progression from non-dysplastic BE to low-grade dysplasia, high-grade dysplasia, and esophageal adenocarcinoma. The single cells sequenced for either DNA or RNA are from these biopsies.

Result—MaCroDNA is an Innovative Tool Designed for Analyzing Single-Cell Sequencing Data

MaCroDNA, a novel tool, functions by merging data from single-cell RNA and DNA sequencing from identical tissues. Its key role is to link each cell in the RNA dataset with one in the DNA dataset, using Pearson correlation coefficients to find the best matches. The approach varies based on dataset size; if RNA cells outnumber DNA cells, MaCroDNA implements a heuristic method for multiple sequential bipartite matching, ensuring maximum unique pairings. Conversely, with fewer RNA than DNA cells, each RNA cell is paired uniquely. This process enables MaCroDNA to align RNA cells with identified clones in the DNA data, offering insights into the tissue's cellular make-up and genetic variance.

Fig.1 Overview of MaCroDNA¹.

Result—Genomic Variability Influences Cancer Gene Expression Differently in Advanced Barrett Esophagus and Esophageal Adenocarcinoma

In their investigation, researchers delved into the genetic diversity's impact on Barrett's esophagus (BE) evolving into esophageal adenocarcinoma (EAC). They employed MaCroDNA to scrutinize biopsies at different BE and EAC stages, aiming to link mutations with gene expression alterations. The hypothesis centered on mutations increasingly influencing transcriptomic diversity during BE advancement. By integrating single-cell DNA and RNA sequencing with cell lineage data and applying the K* statistic to assess gene expression and cell lineage relationships, they discovered a notable correlation in higher-grade dysplasia and EAC. This correlation was less pronounced in milder or normal tissues, underscoring the pivotal role of genetic alterations in esophageal cancer progression.

Fig.2 Distribution of the K* indices for all genes in each biopsy¹.

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At Creative Biolabs, we excel in offering microdroplet-based single cell DNA sequencing services, focusing on high-throughput examination of chosen gene regions. Our technique shines in dissecting cell populations and identifying single-cell genetic differences, although it requires a predefined detection panel. Our expertise lies in the detailed exploration of cellular diversities through cutting-edge single-cell RNA sequencing. Our services span from meticulous sample handling to thorough data interpretation, ensuring quick, flexible, and accurate results. We are committed to enriching your research projects with our tailored, in-depth analytical approach.

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Reference

1. Edrisi, Mohammadamin, et al. "Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA." Nature Communications 14.1 (2023): 8262.