Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH

Torre, Enrique; Dueck, Hannah; Shaffer, Sydney M.; Gospočić, Janko; Gupte, Rohit; Bonasio, Roberto; Kim, Junhyong; Murray, John I.; Raj, Arjun

doi:10.1016/j.cels.2018.01.014

Cited by 126 publications

(154 citation statements)

References 27 publications

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“…Before normalization, a series of filtering steps was performed. Only those cells showing at least 1,500 detected genes and 5,000 UMIs were considered for further analyses (Torre et al, 2018). Reads mapping on mitochondrial genes were excluded.…”

Section: Resultsmentioning

confidence: 99%

Exploiting evolutionary herding to control drug resistance in cancer

Acar

Nichol

Fernandez-Mateos

et al. 2019

Preprint

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Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased growth rate or increased sensitivity to another drug due to evolutionary trade-offs. This weakness can be exploited in the clinic using an approach called 'evolutionary herding' that aims at controlling the tumour cell population to delay or prevent resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here we present a novel approach for evolutionary herding based on a combination of single-cell barcoding, very large populations of 10 8 -10 9 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary herding in non-small cell lung cancer, showing that herding allows shifting the clonal composition of a tumour in our favour, leading to collateral drug sensitivity and proliferative fitness costs. Through genomic analysis and single-cell sequencing, we were also able to determine the mechanisms that drive such evolved sensitivity. Our approach allows modelling evolutionary trade-offs experimentally to test patient-specific evolutionary herding strategies that can potentially be translated into the clinic to control treatment resistance.Here, we present a novel experimental approach to study evolutionary herding quantitatively and demonstrate the evolutionary determinants of collateral drug sensitivity by clonal herding of cancer cell populations. Results Evolutionary herding of resistant cells through fitness landscapesThe relationship between heritable information, whether genetic or epigenetic, and the corresponding cellular phenotype, can be represented by the classical fitness landscape model

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Section: Resultsmentioning

confidence: 99%

Exploiting evolutionary herding to control drug resistance in cancer

Acar

Nichol

Fernandez-Mateos

et al. 2019

Preprint

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“…Therapies such as vemurafenib designed to inhibit particular oncogenic targets can often kill most of the tumor cells, but a few remaining cells can continue to proliferate, ultimately repopulating the tumor. While the mechanisms underlying this therapy resistance can sometimes be the result of a genetic mutation, many recent studies, both in melanoma and other cancers, suggest that cellular plasticity may also dictate which cells are able to survive drug treatment, with rare primed cells being reprogrammed by the addition of drug into a stably resistant state 8,[11][12][13][14][15][16][17][18][19][20][21] . In melanoma, this rare primed cellular state, which we have also previously referred to as the pre-resistant cellular state, is often marked by transiently high expression of several resistance marker genes, such as EGFR , NGFR and AXL (Fig.…”

Section: Introductionmentioning

confidence: 99%

Genetic screening for single-cell variability modulators driving therapy resistance

Torre

Arai

Bayatpour

et al. 2019

Preprint

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Cellular plasticity describes the ability of cells to transition from one set of phenotypes to another. In the context of cancer therapeutics, plasticity refers to transient fluctuations in the molecular state of tumor cells, driving the formation of rare cells primed to survive drug treatment and ultimately reprogram into a stably resistant fate. However, the biological processes governing this cellular plasticity remain unknown. We used CRISPR/Cas9 genetic screens to reveal genes that affect cell fate decisions by altering cellular plasticity across a range of functional categories. We found that cellular plasticity and cell fate decision making can be decoupled in that factors can affect cell fate decisions in both plasticity-dependent and independent manners. We discovered a novel mode of altering resistance based on cellular plasticity that, contrary to known mechanisms, pushes cells towards a more differentiated state. We further confirmed our prediction that manipulating cellular plasticity before the addition of the main therapy would result in changes in therapy resistance more than concurrent administration. Together, our results indicate that identifying pathways modulating cellular plasticity has the potential to alter cell fate decisions and may provide a new avenue for treating drug resistance.

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“…This equation predicted that over a relatively large range of reasonable parameters, (CV ~ 0.5-2, fraction of time on ~ 0.01), the predicted memory was mostly in the range of 5-10 divisions, matching our experimental observations. Motivated by our previous work in this cell line, we also looked for correspondences between the degree of heritability and the rarity of gene expression as measured by the Gini coefficient, a metric for inequality (Jiang et al, 2016;Shaffer et al, 2017;Torre et al, 2018) . We observed that indeed the two metrics were correlated (adjusted R 2 = 0.4898) ( Fig.…”

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confidence: 99%

Memory sequencing reveals heritable single cell gene expression programs associated with distinct cellular behaviors

Shaffer

Emert

Sizemore

et al. 2018

Preprint

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Non-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. Yet it remains unclear whether these fluctuations can persist for much longer than the time of one cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the duration of gene expression fluctuations or cellular memory difficult to measure. Here, we report a method combining Luria and Delbrück's fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several cell divisions. MemorySeq revealed multiple gene modules that are expressed together in rare cells within otherwise homogeneous clonal populations. Further, we found that these rare cell subpopulations are associated with biologically distinct behaviors, such as the ability to proliferate in the face of anti-cancer therapeutics, in different cancer cell lines. The identification of non-genetic, multigenerational fluctuations has the potential to reveal new forms of biological memory at the level of single cells and suggests that non-genetic heritability of cellular state may be a quantitative property. Main text:Cellular memory in biology, meaning the persistence of a cellular or organismal state over time, occurs over a wide range of timescales and can be produced by a variety of mechanisms. Genetic differences are one form of memory (Ben-David et al., 2018) , encoding variation between organisms on multi-generational timescales. Within an organism, mechanisms involving the regulation of gene expression encode the differences between cell types in different tissues, with cells retaining memory of their state over a large number of cell divisions (Bonasio et al., 2010) . In contrast, recent measurements suggest that the expression of many genes in single cells may have very little memory, displaying highly transient fluctuations in transcription. These rapid fluctuations have been referred to as gene expression "noise" and have generally been difficult to associate with physiological distinctions between single cells

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Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH

Cited by 126 publications

References 27 publications

Exploiting evolutionary herding to control drug resistance in cancer

Exploiting evolutionary herding to control drug resistance in cancer

Genetic screening for single-cell variability modulators driving therapy resistance

Memory sequencing reveals heritable single cell gene expression programs associated with distinct cellular behaviors

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