Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq

Abstract

A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline, Sleeping Beauty (SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumor’s major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes.

Transposons As Tools for Functional Genomics in Vertebrate Models

“…Thus, “trunk” mutations that occurred early in tumor development can be distinguished from “branch” mutations that may have caused progression. Single cell transposon insertion site recovery allows even better resolution for studies like these [133]. RNA sequencing-based transcriptomics is being applied to SB transposon-based models, and this reveals the tumor gene expression profile as well as the identity of genes altered by transposon insertion [134].…”

Optical Barcoding for Single-Clone Tracking to Study Tumor Heterogeneity

“…Unfortunately, in vivo tracking of single-cell clones has been technically challenging. Recent efforts have applied a variety of techniques, including genetic DNA barcoding, 3 transposon-based mutagenesis, 4 fluorescentlabeling approaches, or combinations thereof. [5][6][7] Although genetic DNA barcoding allows labeling of highly complex subpopulations, it is largely restricted to the analysis of lysed cells and requires multiple time-consuming and costly steps, including next-generation sequencing and biostatistical analysis, before the different clonal populations can be quantified.…”

The Coevolution of Promoters and Transcription Factors in Animal and Plant Cells

“…Taken together, the altruistic interactions between promoters and TFs engage in a mutualistic coevolution in the plant kingdom. The evolution of molecular interaction networks drives the diversity of traits and species in biology (Guimaraes et al, 2017;Mann et al, 2016;Paterson et al, 2010) through continual natural selection for adaptation and counter-adaptation. In the animal kingdom, the strengthening of the positive electrical property enhances the DNA binding activity of TFs, and therefore makes more proteins be DNA-binding proteins or TF proteins (Extended Data Figure 1B).…”

“…Coevolutionary theories are now well developed and have been applied in many fields, such as cosmology (Heckman and Kauffmann, 2011;Martin-Navarro et al, 2018;Wu et al, 2015). Sociology (Henrich et al, 2006), computer science (Liu and Lu, 2015), biology (Bottery et al, 2019;Chen et al, 2020;Cross et al, 2019;Garcia-Bayona and Comstock, 2018), biomedicine (Bottery et al, 2017;Loftie-Eaton et al, 2016;Single et al, 2007), and molecular biology (Barreto et al, 2018;Jin et al, 2017b;Lord et al, 2019;Mann et al, 2016). The interactions between promoters and transcription factors (TFs) are the beginning of gene transcription, and it has been acknowledged that such interactions are vital for transcription regulation and could affect all stages of cell lifecycle (Mirny, 2010).…”