Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation) Identifies Markers of Resistance to Targeted BRAF Inhibitors in Melanoma Cell Populations

Yu-Jui Ho, Naishitha Anaparthy, David Molik, Toby Aicher, Ami Patel, James Hicks, View ORCID ProfileMolly Hammell
doi: https://doi.org/10.1101/239319
Yu-Jui Ho
1Watson School of Biological Science, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Naishitha Anaparthy
2Department of Molecular and Cellular Biology, Stony Brook University, NY, 11794
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
David Molik
3Department of Biological Science, University of Notre Dame, IN, 46556
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Toby Aicher
4Middlebury College, Middlebury, VT 05753
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ami Patel
5Mount Sinai Health System, New York, NY 10003
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
James Hicks
6Department of Biological Science, University of Southern California, CA, 90089
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Molly Hammell
1Watson School of Biological Science, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Molly Hammell
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Single-cell RNA-Seq’s (scRNA-Seq) unprecedented cellular resolution at a genome wide scale enables us to address questions about cellular heterogeneity that are inaccessible using methods that average over bulk tissue extracts. However, scRNA-Seq datasets also present additional challenges such as high transcript dropout rates, stochastic transcription events, and complex population substructures. Here, we present SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation): a robust method for scRNA-Seq analysis that provides quantitative statistical metrics at each step of the scRNA-Seq analysis pipeline including metrics for: the determination of the number of clusters present, the likelihood that each cell belongs to a given cluster, and the association of each gene marker in determining cluster membership. Comparing SAKE to multiple single-cell analysis methods shows that most methods perform similarly across a wide range cellular contexts, with SAKE outperforming these methods in the case of large complex populations. We next applied the SAKE algorithms to identify drug-resistant cellular populations as human melanoma cells respond to targeted BRAF inhibitors. Single-cell RNA-Seq data from both the Fluidigm C1 and 10x Genomics platforms were analyzed with SAKE to dissect this problem at multiple scales. Data from both platforms indicate that BRAF inhibitor resistant cells can emerge from rare populations already present before drug application, with SAKE identifying both novel and known markers of resistance. In addition, we compare integrated genomic and transcriptomic markers to show that resistance can arise stochastically within multiple distinct clonal populations.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted December 24, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation) Identifies Markers of Resistance to Targeted BRAF Inhibitors in Melanoma Cell Populations
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation) Identifies Markers of Resistance to Targeted BRAF Inhibitors in Melanoma Cell Populations
Yu-Jui Ho, Naishitha Anaparthy, David Molik, Toby Aicher, Ami Patel, James Hicks, Molly Hammell
bioRxiv 239319; doi: https://doi.org/10.1101/239319
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
SAKE (Single-cell RNA-Seq Analysis and Klustering Evaluation) Identifies Markers of Resistance to Targeted BRAF Inhibitors in Melanoma Cell Populations
Yu-Jui Ho, Naishitha Anaparthy, David Molik, Toby Aicher, Ami Patel, James Hicks, Molly Hammell
bioRxiv 239319; doi: https://doi.org/10.1101/239319

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4654)
  • Biochemistry (10300)
  • Bioengineering (7615)
  • Bioinformatics (26192)
  • Biophysics (13448)
  • Cancer Biology (10620)
  • Cell Biology (15345)
  • Clinical Trials (138)
  • Developmental Biology (8453)
  • Ecology (12755)
  • Epidemiology (2067)
  • Evolutionary Biology (16765)
  • Genetics (11356)
  • Genomics (15400)
  • Immunology (10548)
  • Microbiology (25042)
  • Molecular Biology (10152)
  • Neuroscience (54101)
  • Paleontology (398)
  • Pathology (1655)
  • Pharmacology and Toxicology (2877)
  • Physiology (4314)
  • Plant Biology (9197)
  • Scientific Communication and Education (1581)
  • Synthetic Biology (2541)
  • Systems Biology (6752)
  • Zoology (1452)