|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Feldman, D, Tsai, FN, Garrity, AJ, O'Rourke, R, Brenan, L, Ho, P, Gonzalez, E, Konermann, S, Johannessen, CM, Beroukhim, R, Bandopadhayay, P, Blainey, PC|
|Date Published||2020 11 24|
BACKGROUND: Many biological processes, such as cancer metastasis, organismal development, and acquisition of resistance to cytotoxic therapy, rely on the emergence of rare sub-clones from a larger population. Understanding how the genetic and epigenetic features of diverse clones affect clonal fitness provides insight into molecular mechanisms underlying selective processes. While large-scale barcoding with NGS readout has facilitated cellular fitness assessment at the population level, this approach does not support characterization of clones prior to selection. Single-cell genomics methods provide high biological resolution, but are challenging to scale across large populations to probe rare clones and are destructive, limiting further functional analysis of important clones.
RESULTS: Here, we develop CloneSifter, a methodology for tracking and enriching rare clones throughout their response to selection. CloneSifter utilizes a CRISPR sgRNA-barcode library that facilitates the isolation of viable cells from specific clones within the barcoded population using a sequence-specific retrieval reporter. We demonstrate that CloneSifter can measure clonal fitness of cancer cell models in vitro and retrieve targeted clones at abundance as low as 1 in 1883 in a heterogeneous cell population.
CONCLUSIONS: CloneSifter provides a means to track and access specific and rare clones of interest across dynamic changes in population structure to comprehensively explore the basis of these changes.
|Alternate Journal||BMC Biol|
|PubMed Central ID||PMC7687773|
|Grant List||CA201592-02 / NH / NIH HHS / United States |
1U54CA224068-01 / NH / NIH HHS / United States
CA188228 / NH / NIH HHS / United States
CA219943 / NH / NIH HHS / United States
HG009283 / NH / NIH HHS / United States