Sample introduction example 2 (tech paper, annotated)

Nice declarative title. Perhaps a bit long.

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

Abstract

Molecular differences between individual cells can lead to dramatic differences in cell fate following an applied treatment, such as the difference between death versus survival of cancer cells upon receiving anti-cancer drugs. However, current strategies to retrospectively identify the cells that give rise to distinct rare behaviors and determine their distinguishing molecular characteristics remain limited. Here we describe Rewind, a methodology that combines genetic barcoding with an RNA-based readout to directly capture rare cells that give rise to cellular behaviors of interest, specifically the emergence of resistance to targeted cancer therapy. Using Rewind, we analyzed over 5 million cells (!) to identify differences in gene expression and MAP-kinase signaling in single melanoma cells that mark a rare subpopulation of drug-naive cells (initial frequency of ~1:1000-1:10,000 cells) that ultimately gives rise to drug resistant clones. We further show that even within this rare subpopulation, molecular differences between single cells before the application of drug predict future differences in drug resistant behavior. 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 cryptic variability that can underlie a range of distinct rare-cell phenotypic outcomes upon drug exposure. Applying Rewind to other rare biological phenomena, such as cancer metastasis, tissue regeneration, and stem cell reprogramming, may provide a means to map rare cellular states to the unique cellular fates to which they give rise.

Introduction

Start with a general introduction to the field. This sentence sets the "level"; in this case, it's about single cell biology, and at a pretty high level (i.e., not assuming much detailed knowledge).

Individual cells—even those of ostensibly the same cell type—can differ from each other in a number of ways. Some of these differences can result in a “primed” cellular state that can, in a particular context, ultimately lead to biologically distinct behaviors (Symmons and Raj 2016; Raj and van Oudenaarden 2008). This cellular priming underlies a number of important single cell phenomena. , when anti-cancer therapeutics are applied to clonally derived cancer cells, most of the cells die; however, a small number of cells survive and proliferate, and these cells drive therapy resistance (Gupta et al. 2011; Sharma et al. 2010; Roesch et al. 2010; Shaffer et al. 2017).

(Shaffer et al. 2018; Gupta et al. 2011). What remains largely unknown, outside of a few cases (Shaffer et al. 2017; Cohen et al. 2008; Spencer et al. 2009), is how this variability maps to distinct cellular outcomes following a treatment. As a result, several questions remain unanswered. Is molecular variability in the initial state of cells inconsequential because all cells ultimately funnel into the same cell fate? Can different cell fates arise from otherwise indistinguishable initial molecular states? Or can most differences in ultimate fate be traced back to measurable differences in the initial states of cells?

(i.e., “barcoding”) (Sigal et al. 2006; Cohen et al. 2008; Biddy et al. 2018; Raj et al. 2018). Time lapse microscopy allows one to directly follow cells over time, providing a definitive cellular lineage, high resolution temporal information, and a direct connection to fate (Sigal et al. 2006; Cohen et al. 2008). , monitoring the molecular processes occurring in these cells by time-lapse remains challenging: fluorescent reporters of gene expression and signaling activity are difficult to introduce and validate, and the ability to multiplex them is generally limited. Barcoding, in which cells are labeled by unique and sometimes mutable nucleic acid sequences (Frieda et al. 2016; Raj et al. 2018; Alemany et al. 2018; McKenna et al. 2016; Kalhor et al. 2018), allows one to track cells by sequencing. When combined with single cell RNA sequencing methods, one can connect cellular lineages to transcriptomic profiles (Biddy et al. 2018; Weinreb et al. 2020; Al’Khafaji et al. 2019; Hurley et al. 2020). These techniques, however, are difficult to combine with assays that measure molecular features outside of gene expression levels, which may be important to distinguish key subpopulations (Weinreb et al. 2020; Wu et al. 2020). A key challenge for both of these methodologies is the detection of rare cells (1:1000 or even more rare), for which neither time-lapse nor single cell RNA sequencing is particularly effective (new techniques aim to circumvent these limitations; (Al'Khafaji et al. 2018; Feldman et al. 2019).

Usually a short description of the work here. Shorter and different than abstract. Keep it focused on answering precisely the questions posed in the introduction. Highlight the single most important point.

Here, we combine cellular barcoding with RNA Fluorescent In Situ Hybridization (RNA FISH) to selectively isolate rare cells that adopt distinct cellular fates. We apply this methodology, which we call Rewind, to targeted therapy resistance in melanoma, revealing prospective expression markers of cells primed for resistance upon BRAFV600E inhibition. Further, we show that differences in resistance outcome can be traced to distinct cell subpopulations that can be discriminated on the basis of their transcriptome profiles. These findings suggest that cryptic single cell variability within otherwise homogeneous cells can lead to important differences in ultimate cellular behavior in response to a treatment.