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

Benjamin L. Emert, Christopher Coté, Eduardo A. Torre, Ian P. Dardani, Connie L. Jiang, Naveen Jain, Sydney M. Shaffer, Arjun Raj

Preprint posted on 19 March 2020 https://www.biorxiv.org/content/10.1101/2020.03.18.996660v1

Pioneering a new technique called Rewind, Emert et al. connect differences in gene expression and MAPK signaling with cell fate outcomes to retrospectively identify the rare cell populations that arise during targeted cancer therapy.

Selected by NYUPeerReview

Context

Resistance to cancer drugs remains a significant hurdle for effective treatment across many cancer types. This primarily stems from poor molecular understanding of how a subset of cancer cells survives treatment and facilitates cancer recurrence, while most cells are killed by drug treatment. While previous work shows that drug resistance is conferred by new mutations arising during treatment, existing heterogeneity among cells is believed to contribute as well, as rare populations can exist in a state “primed” for drug resistance. Thus, identification of molecular differences that distinguish these rare “primed” cancer cells can inform strategies for alternative therapeutic intervention or identify biomarkers that better predict prognosis following drug treatment. However, attempts to do so have largely been challenging from a technical standpoint, including a limited ability to (1) detect and isolate rare cells, and (2) monitor molecular processes over an extended time scale (such as in time lapse microscopy). 

 

Impact 

This preprint caught our attention because it described a clever system capable of overcoming the technical limitations highlighted above. The authors’ method, Rewind, not only allows for identification and tracking of rare cells of interest at different timepoints, but also enables their isolation for use in molecular assays. By introducing unique DNA barcodes into tens of thousands of cells, the authors can identify resistant cells surviving anti-cancer drug treatment through sequencing. They then can “rewind” and isolate clonally related cells in the pre-treatment population by FACS sorting for FISH probes targeting those same barcodes. To demonstrate how Rewind can be used, the authors asked if these resistant cells expressed different genes compared to the rest of the population, reflecting a “primed” state. They confirmed that “primed” cells express known markers associated with resistance, but they were also able to identify many new markers.  

 

From a broad perspective, Rewind has the potential to address questions beyond initial events driving drug resistance and cancer model systems. Rewind could be used for instance to trace immune cell development or understand molecular details of senescence. The possibilities are endless! Rewind also comprises simple molecular techniques that are available to most research labs, making it a relatively accessible approach. We commend the authors for supporting reproducibility by providing highly detailed methods and protocols supplemented with links to pipelines and their raw data. 

 

Key Findings

  1. The authors present Rewind, which is able to identify and isolate rare cells primed for chemotherapy resistance, present in only 0.05% of the cellular population, by tracking unique DNA barcodes through sequencing. These primed cells expressed previously identified resistance markers, like EGFR and AXL, thus validating their method. They additionally identified about 200 new resistance markers, such as ITGA3. 
  2. Single molecule FISH analysis showed that about 87% of primed cells expressed many resistance markers simultaneously, indicating a coordinated primed signature rather than sporadic expression of resistance markers in the population.
  3. Using Rewind, the authors outlined transcriptional differences between populations of “highly resistant” primed cells, “less resistant” primed cells, and unprimed cells. This highlights the heterogeneity that exists within these rare primed populations, which may inform more effective interventions to prevent cancer recurrence.
  4. The authors explored the broad applicability of Rewind by asking several other questions relevant to vemurafenib resistance in a melanoma cell line. These include exploring induced resistance by DOT1L inhibition and how vemurafenib affects phosphorylation levels of kinases implicated in proliferation. 

 

Questions for Authors

  1. The authors assayed the transcriptomes of the rare primed cells before vemurafenib treatment. What else can be done with these cells beyond RNA-Seq? Would it be possible to adapt this protocol for mass spectrometry to understand proteomic differences, or to use ATAC-Seq and ChIP-Seq to look into chromatin states and interactions? 
  2.  What is the sensitivity limit for tagging rare cells? How low can you go? How does it compare to other competing approaches?
  3. The authors define cells contributing the top 60 ranked barcodes after vemurafenib treatment as their resistant population, and further classify the barcodes ranked 1-30 as highly resistant, and barcodes 31-60 as less resistant. Was there specific rationale behind setting these thresholds, or were these chosen arbitrarily?

Tags: chemotherapy, noise, single cell, variability

Posted on: 14 April 2020

doi: https://doi.org/10.1242/prelights.18604

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1 comment

3 years

BENJAMIN EMERT

Wow, thank you for the thoughtful summary and discussion. Regarding your questions for the authors:
1. We’re hopeful that the approach can be combined with additional downstream assays (beyond RNA-seq, RNA-FISH and immunofluorescence) that are compatible with fixed cells (formaldehyde or methanol). With a first attempt at ATAC-seq (using the ATAC-SEE protocol) the tagmentation looked incomplete on the bioanalyzer and I haven’t gone back to try optimizing. I suspect that you could selectively amplify and sequence integrated barcodes from an ATAC-seq library so if the phenotype is not too rare, you could try connecting chromatin accessibility to fate using scATAC-seq (analogous to what was done in Biddy et al and Weinreb and Rodriguez-Fraticelli et al.).
We have not tried ChIP-seq with Rewind due to the cell number requirement. Also, another student in lab has had trouble doing ChIP-seq on cells post smFISH. CUT & RUN/CUT & TAG might be a good alternative for rare cell populations although from what I’ve read, you may need to start with a larger number of cells if they’re fixed.
We haven’t seriously considered combing proteomics with Rewind and I don’t know if our fixation and FISH protocols would need to be modified or are simply incompatible with mass spec.
2. In the DOT1L inhibitor experiments (figures 5 and 6) we isolated (by FACS) and identified (by imaging) primed cells that were at a frequency of <1:10,000 cells. That number is based on using probes targeting 30 barcodes out of 400,000 barcodes introduced at the start and is consistent with the frequency of FISH-positive cells we found by FACS and by imaging. I haven't tried isolating anything more rare than that. I believe this is slightly more sensitive than COLBERT or the method described in Feldman et al.
If you need to increase sensitivity/specificity further, you could try the following: A. Increase the length of the barcodes to fit more FISH probes. We have some designs to keep sequencing costs down. B. Use combinations of fluors to detect different barcodes (a la SeqFISH/MERFISH or Feldman et al 2019). Computationally shouldn't be too difficult since you should only need to register cells round to round, not spots. C. Enrich for the cells of interest ahead of time using some marker with high sensitivity but possibly low specificity D. Tolerate greater false positives in the Rewind sort then perform scRNA-seq to determine which cells actually carry the barcodes of interest.
3. That first threshold for defining which barcodes to probe was based on A. The frequency of colonies emerging from cells treated with the chosen concentration of drug. In general we wanted the expected frequency of resistance to be greater the the fraction of barcodes we probed. B. How many barcodes can we probe simultaneously while still feeling confident in our detection. Since the cells we were interested in are so rare initially, we optimized protocols and set thresholds to minimize false positives as much as possible. Other application may require limiting the false negatives.
In terms of splitting the barcodes into "highly resistant" and "less resistant", that decision was mostly arbitrary. In retrospect (or in future experiments), it might've made more sense to separate the rankings further or probe additional quantiles.
I hope I understood your questions and addressed them at least somewhat. Thank you again for taking the time to review our work 🙂

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