Evidence map›Paper›PMID 34322133›Full record

ReviewFrontiers in immunology2021

Exploiting Single-Cell Tools in Gene and Cell Therapy.

Daniel Bode, Alyssa H Cull, Juan A Rubio-Lara, David G Kent

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
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

27 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Particle-Assisted Optoelectronic Tweezers for Manipulating Single Cells and Microparticles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  7. Advances in CRISPR-Cas9 in lineage tracing of model animals.Animal models and experimental medicine · 2025
    Review
  8. Decoding the NRF2-NOTCH Crosstalk in Lung Cancer-An Update.Antioxidants (Basel, Switzerland) · 2025
    Review
  9. Review
  10. Review
  11. Article
  12. Contextual AI models for single-cell protein biology.bioRxiv : the preprint server for biology · 2024
    Article
  13. Review
  14. Article
  15. Review
  16. Review
  17. Article
  18. Review
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Daniel BodeWellcome Medical Research Council (MRC) Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom.
Alyssa H CullYork Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom.
Juan A Rubio-LaraYork Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom.
David G KentYork Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom.

Funding

Medical Research Council MR/V005502/1Wellcome Trust
6 · The paper itself

Abstract

Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.

Indexed as

AnimalsCell- and Tissue-Based TherapyGenetic TherapyHumansSingle-Cell AnalysisCAR T cell therapycell therapydisease heterogeneitygene therapymultimodal omicsmultiomicsscRNA-seqsingle-cell sequencing

Identifiers

PMID34322133
PMCPMC8312222

What OpenQuestion holds

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LicenceCC BY
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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.