Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy

Sharma, Ankur; Cao, Elaine Yiqun; Kumar, Vibhor; Zhang, Xiaoqian; Leong, Hui Sun; Wong, Angeline M.L.; Ramakrishnan, Neeraja; Hakimullah, Muhammad; Teo, Hui Min; Chong, Fui Teen; Chia, Shumei; Thangavelu, Matan Thangavelu; Kwang, Xue Lin; Gupta, Ruta; Clark, Jonathan; Periyasamy, Giridharan; Iyer, N. Gopalakrishna; DasGupta, Ramanuj

doi:10.1038/s41467-018-07261-3

Cited by 134 publications

(160 citation statements)

References 45 publications

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“…Human cell lines are a mainstay of cancer research and drug discovery, yet our current knowledge of their ability to recapitulate the expression diversity observed in patient samples is limited. Only a few cancer cell lines have been comprehensively profiled by scRNA-seq so far (Ben-David et al, 2018;Jerby-Arnon et al, 2018;Kim et al, 2015;Sharma et al, 2018). Thus, models are often chosen based on their mutational status, historical popularity, and ease of culturing.…”

Section: Introductionmentioning

confidence: 99%

Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Kinker

Greenwald

Tal

et al. 2019

Preprint

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Cultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors, given the absence of a native tumor microenvironment. Here, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines. We uncovered expression programs that are recurrently heterogeneous within many cancer cell lines and are largely independent of observed genetic diversity. These programs of heterogeneity are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-to-mesenchymal transition, and protein maturation and degradation. Notably, some of these recurrent programs recapitulate those seen in human tumors, suggesting a prominent role of intrinsic plasticity in generating intra-tumoral heterogeneity. Moreover, the data allowed us to prioritize specific cell lines as model systems of cellular plasticity. We used two such models to demonstrate the dynamics, regulation and drug sensitivities associated with a cancer senescence program also observed in human tumors. Our work describes the landscape of cellular heterogeneity in diverse cancer cell lines, and identifies recurrent patterns of expression heterogeneity that are shared between tumors and specific cell lines and can thus be further explored in follow up studies.

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Section: Introductionmentioning

confidence: 99%

Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Kinker

Greenwald

Tal

et al. 2019

Preprint

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“…Our approaches are likely beneficial to characterizing SCCN data derived from single-cell RNA sequencing (scRNA-seq) experiments. To examine that possibility, we collected data obtained from paired primary and metastasis (or relapse) samples of a variety of cancer patients, including 6 head and neck squamous cell carcinoma (HNSCC) 20 , 8 multiple myeloma (MM), 2 oral squamous cell carcinomas (OSCC) 45 and 4 ovarian cancer patients (OV) 46 ( Table S5 ).…”

Section: Resultsmentioning

confidence: 99%

Single-cell copy number lineage tracing enabling gene discovery

Wang

Mohanty

et al. 2020

Preprint

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Aneuploidy plays critical roles in genome evolution.Alleles, whose dosages affect the fitness of an ancestor, will have altered frequencies in the descendant populations upon perturbation.Single-cell sequencing enables comprehensive genome-wide copy number profiling of thousands of cells at various evolutionary stage and lineage. That makes it possible to discover dosage effects invisible at tissue level, provided that the cell lineages can be accurately reconstructed.Here, we present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles. We also present a statistical routine named lineage speciation analysis (LSA), which facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees.We assessed our approaches using a variety of single-cell datasets. Overall, MEDALT appeared more accurate than phylogenetics approaches in reconstructing copy number lineage.From the single-cell DNA-sequencing data of 20 triple-negative breast cancer patients, our approaches effectively prioritized genes that are essential for breast cancer cell fitness and are predictive of patient survival, including those implicating convergent evolution. Similar benefits Recent advances in single-cell DNA sequencing (e.g., tagmentation based approach 15 and single-cell CNV solution by the 10X Genomics) have enabled large-scale acquisition of singlecell copy number (SCCN) profiles in tens of thousands of cells at around 100 kb resolution (~0.1X sequencing coverage per cell) [16][17][18][19] . Other platforms such as single-cell RNA-sequencing 20, 21 and single-cell ATAC-sequencing 22 have also been utilized for SCCN profiling.These SCCN profiles not only present a rich pool of genetic perturbations that are invisible at tissue level, but also potentiate reconstruction of cellular lineage, based on which the impact of an allele on cellular fitness can be measured. Thus, statistical approaches that integrate cellular lineage tracing with population genetic analysis 23 can enable discovery of novel disease genes and mechanisms of disease progression.So far, studies performing retrospective lineage tracing from single-cell data have largely been utilizing phylogenetics approaches designed to model species evolution, which is quite different from cellular evolution in terms of duration, scale, genetics and dynamics 24, 25 . Many existing phylogenetics approaches assume that genomic sites evolve independently and follow the socalled infinite site assumption (ISA) 26 . But in the context of aneuploidy, a genome site can often be altered repeatedly by different CNAs, due partly to constraints on genome and chromatin structures, properties of DNA replication/repairing 27 and functional selection. To apply conventional maximum parsimony approaches on SCCN data, one has to over-segment genomic regions and represent copy numbers as characters in disjoint intervals, which illrepresents the properties of DNAs and dis...

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“…We generated Patient derived cell lines (PDCs) for each patient from lymph node metastatic sites (HN120m, HN137m). We recently demonstrated that these cells all present very distinct genomic profiles 16,17 . They also displayed inherent heterogeneity in migration potential and Epithelial to Mesenchymal transition (EMT) scores 18 .…”

Section: Migratory Sorting Of Metastatic Cells and Correlation With Ementioning

confidence: 99%

A Method to sort heterogenous cell populations based on migration in 2D and 3D environments

Arora¹,

Galeano

Myaing³

et al. 2020

Preprint

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Whereas phenotypic assays such as Boyden chambers and wound healing assays can easily be employed to characterize the migratory potential of cells at the population level, few methods exist that can sort subpopulations of cells based on their migratory behaviour from an initial heterogeneous pool. In this paper, we present an approach to sort migratory cancer and immune cells based on their spontaneous migration in 2D and 3D microenvironments.Using this method, which is easy to implement and readily scalable, millions of live cells can be sorted based on their migratory characteristics and then subjected to downstream genomic, molecular and functional tests. We reveal that enrichment of the most migratory cytotoxic T lymphocytes yields a pool of cells with enhanced cytotoxicity against cancer cells. This new functional sorting method opens new avenues for the precise characterization of the mechanisms underlying hitherto unexplained heterogeneities in migratory phenotypes within a cell population, and for the targeted enrichment of the most potent migratory leukocytes in immunotherapies.

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Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy

Cited by 134 publications

References 45 publications

Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Single-cell copy number lineage tracing enabling gene discovery

A Method to sort heterogenous cell populations based on migration in 2D and 3D environments

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