Biology subjects

Torre, E. A.

Publications and source records attributed to Torre, E. A..

3 recordsLinked to original sources

bioRxiv2020

Retrospective identification of rare cell populations underlying drug resistance connects molecular variability with cell fate

Molecular differences between individual cells can lead to dramatic differences in cell fate, such as death versus survival of cancer cells upon drug treatment. These originating differences remain largely hidden due to difficulties in determining precisely what variable molecular features lead to which cellular fates. Thus, we developed Rewind, a methodology that combines genetic barcoding with RNA FISH to directly capture rare cells that give rise to cellular behaviors of interest. Applied to BRAFV600E melanoma, we trace drug-resistant cell fates back to single-cell gene expression differences in their drug-naive precursors (initial frequency of [~]1:1000-1:10,000 cells) and relative persistence of MAP-kinase signaling soon after drug treatment. Within this rare subpopulation, we uncover a rich substructure in which molecular differences between several distinct subpopulations predict future differences in phenotypic behavior, such as proliferative capacity of distinct resistant clones following drug treatment. Similarly, we show that treatments that modify the frequency of resistance can allow otherwise non-resistant cells in the drug-naive population to become resistant, and that these new populations are marked by the variable expression of distinct genes. Together, our results reveal the presence of hidden, rare-cell variability that can underlie a range of latent phenotypic outcomes upon drug exposure.

systems biology↗

bioRxiv2019

Genetic screening for single-cell variability modulators driving therapy resistance

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.

cell biology↗

bioRxiv2017

A Comparison Between Single Cell RNA Sequencing And Single Molecule RNA FISH For Rare Cell Analysis

The development of single cell RNA sequencing technologies has emerged as a powerful means of profiling the transcriptional behavior of single cells, leveraging the breadth of sequencing measurements to make inferences about cell type. However, there is still little understanding of how well these methods perform at measuring single cell variability for small sets of genes and what \"transcriptome coverage\" (e.g. genes detected per cell) is needed for accurate measurements. Here, we use single molecule RNA FISH measurements of 26 genes in thousands of melanoma cells to provide an independent reference dataset to assess the performance of the DropSeq and Fluidigm single cell RNA sequencing platforms. We quantified the Gini coefficient, a measure of rare-cell expression variability, and find that the correspondence between RNA FISH and single cell RNA sequencing for Gini, unlike for mean, increases markedly with per-cell library complexity up to a threshold of [~]2000 genes detected. A similar complexity threshold also allows for robust assignment of multi-genic cell states such as cell cycle phase. Our results provide guidelines for selecting sequencing depth and complexity thresholds for single cell RNA sequencing. More generally, our results suggest that if the number of genes whose expression levels are required to answer any given biological question is small, then greater transcriptome complexity per cell is likely more important than obtaining very large numbers of cells.

genomics↗