Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of in vitro evolution

Abstract

Cancer cell lines are not homogeneous nor are they static in their genetic state and biological properties. Genetic, transcriptional and phenotypic diversity within cell lines contributes to the lack of experimental reproducibility frequently observed in tissue-culture-based studies. While cancer cell line heterogeneity has been generally recognized, there are no studies which quantify the number of clones that coexist within cell lines and their distinguishing characteristics. We used a single-cell DNA sequencing approach to characterize the cellular diversity within nine gastric cancer cell lines and integrated this information with single-cell RNA sequencing. Overall, we sequenced the genomes of 8824 cells, identifying between 2 and 12 clones per cell line. Using the transcriptomes of more than 28 000 single cells from the same cell lines, we independently corroborated 88% of the clonal structure determined from single cell DNA analysis. For one of these cell lines, we identified cell surface markers that distinguished two subpopulations and used flow cytometry to sort these two clones. We identified substantial proportions of replicating cells in each cell line, assigned these cells to subclones detected among the G0/G1 population and used the proportion of replicating cells per subclone as a surrogate of each subclone's growth rate.

Modeling cancer progression using human pluripotent stem cell-derived cells and organoids

“…Single-cell RNA sequencing (scRNA-seq) technology has been used to identify the subpopulations of a range of cancers, including hematopoietic ( Jaitin et al, 2014 ), pancreatic ( Peng et al, 2019 ), liver ( Halpern et al, 2017 ), colorectal ( Dai et al, 2019 ), and lung cancers ( Kim et al, 2020 ). Moreover, single-cell DNA sequencing (scDNA-seq) is also useful for the discovery of copy-number ( Gao et al, 2016 ; Laks et al, 2019 ) and branching evolution in cancers ( Yu et al, 2014 ; Wang et al, 2017 ; Andor et al, 2020 ). Recently, single-cell-based transcriptomics or epigenomics have been applied to deconstruct heterogeneity present within hPSC-derived brain organoids ( Tanaka et al, 2020 ) and kidney organoids ( Wu and Humphreys, 2020 ; Subramanian et al, 2019 ).…”

Colorectal cancer metastases in the liver establish immunosuppressive spatial networking between tumor associatedSPP1+macrophages and fibroblasts

“…To identify clonal CNVs, we performed whole genome single cell sequencing on a subset of four mCRC samples (P5915, P6593, P6335 and P6198). We modeled per-cell read counts per genomic bin as a Poisson distribution dependent on both the GC content and the copy number as previously described (Andor et al 2020), using Cell Ranger. The GC bias was modeled as a quadratic function with fixed intercept and correction on a cell-by-cell basis was performed.…”

“…cellular state) of resident cells for any given tumor (Lim et al 2020;Sathe et al 2020). Single cell DNA sequencing studies have revealed a broad spectrum of genomic alterations such as copy number variation that distinguishes different clonal populations (Leung et al 2017;Andor et al 2020;Velazquez-Villarreal et al 2020).…”

Alleloscope: Integrative single cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer

“…Such events include the pervasively occurring copy-neutral loss of heterozygosity (LOH) [5][6][7][8] , intriguing "mirrored events" 9,10 where a given tumor may have cancer cells carrying amplification of one haplotype are intermingled with cancer cells carrying amplification of the other haplotype, and the even more complex alterations that are only detectable through allele-specific analysis 11 . While the importance of allelespecific copy number has been emphasized in bulk DNA sequencing analysis [5][6][7][8]11 , most single-cell CNV analysis considers only total copy number due to low per-cell coverage [12][13][14][15][16][17][18][19] . Recently, Zaccaria et al developed CHISEL 10 , a method for single-cell allele-specific copy number analysis, but requires externally phased haplotypes based on large reference cohorts.…”