Evidence map›Paper›PMID 26280411›Full record

ArticleNature biotechnology2015

Massively parallel high-order combinatorial genetics in human cells.

Alan S L Wong, Gigi C G Choi, Allen A Cheng, Oliver Purcell, Timothy K Lu

Open access · bronzeAbstract read
In one paragraph

Article in Nature biotechnology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
3.5field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 65 citations in OpenAlex.

  1. Review
  2. Article
  3. A unified genetic perturbation language for human cellular programming.bioRxiv : the preprint server for biology · 2025
    Article
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  9. Review
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  17. Strategies for simultaneous and successive delivery of RNA.Journal of molecular medicine (Berlin, Germany) · 2020
    Article
  18. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Alan S L WongSynthetic Biology Group, MIT Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Gigi C G ChoiSynthetic Biology Group, MIT Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Allen A ChengSynthetic Biology Group, MIT Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Oliver PurcellSynthetic Biology Group, MIT Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Timothy K LuSynthetic Biology Group, MIT Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Synthetic Biologics (United States) · USMassachusetts Institute of Technology · US

Funding

MIT Center for Integrative Synthetic BiologyP50GM098792 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI WEISS, RON · 2013 to 2017
$11.0M
High-Throughput Nanoscale Approaches to Studying and Inhibiting Amyloid ToxicityDP2OD008435 · OD · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI LU, TIMOTHY · 2011 to 2011
$2.4M
NIGMS NIH HHS P50 GM098792NIH HHS DP2 OD008435
6 · The paper itself

Abstract

The systematic functional analysis of combinatorial genetics has been limited by the throughput that can be achieved and the order of complexity that can be studied. To enable massively parallel characterization of genetic combinations in human cells, we developed a technology for rapid, scalable assembly of high-order barcoded combinatorial genetic libraries that can be quantified with high-throughput sequencing. We applied this technology, combinatorial genetics en masse (CombiGEM), to create high-coverage libraries of 1,521 two-wise and 51,770 three-wise barcoded combinations of 39 human microRNA (miRNA) precursors. We identified miRNA combinations that synergistically sensitize drug-resistant cancer cells to chemotherapy and/or inhibit cancer cell proliferation, providing insights into complex miRNA networks. More broadly, our method will enable high-throughput profiling of multifactorial genetic combinations that regulate phenotypes of relevance to biomedicine, biotechnology and basic science.

Indexed as

Base SequenceBiomarkers, TumorCell Line, TumorCombinatorial Chemistry TechniquesGenetic MarkersHigh-Throughput Nucleotide SequencingHumansMicroRNAsMolecular Sequence DataNeoplasms, ExperimentalSequence Analysis, RNABiomarkers, TumorGenetic MarkersMicroRNAs

Identifiers

PMID26280411
PMCPMC4785103
OpenAlexW1159439052

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.