Evidence map›Paper›PMID 35752172›Full record

ArticleMolecular cell2022

Machine-learning-optimized Cas12a barcoding enables the recovery of single-cell lineages and transcriptional profiles.

Nicholas W Hughes, Yuanhao Qu, Jiaqi Zhang, Weijing Tang, Justin Pierce, Chengkun Wang, Aditi Agrawal, Maurizio Morri, Norma Neff, Monte M Winslow and 2 more

Abstract read
In one paragraph

Article in Molecular cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Advances in CRISPR-Cas9 in lineage tracing of model animals.Animal models and experimental medicine · 2025
    Review
  9. Loss of YTHDC1 mExperimental & molecular medicine · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Clonal tracking in cancer and metastasis.Cancer metastasis reviews · 2024
    Review
  14. Article
  15. Review
  16. Review
  17. 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

12 authors.

Nicholas W HughesDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Wu Tsai Neuroscience Institute, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
Yuanhao QuDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Jiaqi ZhangDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Laboratory of Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Weijing TangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Justin PierceDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Chengkun WangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Aditi AgrawalChan Zuckerberg Biohub, Stanford, CA 94305, USA.
Maurizio MorriChan Zuckerberg Biohub, Stanford, CA 94305, USA.
Norma NeffChan Zuckerberg Biohub, Stanford, CA 94305, USA.
Monte M WinslowDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Mengdi WangDepartment of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA; Center for Statistics and Machine Learning, Princeton University, Princeton, NJ 08544, USA. Electronic address: mengdiw@princeton.edu.
Le CongDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Wu Tsai Neuroscience Institute, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: congle@stanford.edu.

Funding

Recombineering-based no-cleavage gene-editing toolkit for large-scale genome engineering and functional screeningR01GM141627 · NIGMS · STANFORD UNIVERSITY · PI Le Cong · 2021 to 2026
$3.3M
Towards Robust Multiplex Genome Engineering Beyond CRISPR-Cas9R35HG011316 · NHGRI · STANFORD UNIVERSITY · PI CONG, LE · 2020 to 2025
$2.8M
(PQ4) Quantitative and multiplexed analysis of gene function in cancer in vivoR01CA231253 · NCI · STANFORD UNIVERSITY · PI PETROV, DMITRI, WINSLOW, MONTE MEIER · 2018 to 2022
$2.3M
Computational appliance: a supercomputer for modern biomedical researchS10OD023452 · OD · STANFORD UNIVERSITY · PI DATTA, SOMALEE · 2017 to 2017
$600k
NCI NIH HHS R01 CA231253NHGRI NIH HHS R35 HG011316NIGMS NIH HHS R01 GM141627NIH HHS S10 OD023452
6 · The paper itself

Abstract

The development of CRISPR-based barcoding methods creates an exciting opportunity to understand cellular phylogenies. We present a compact, tunable, high-capacity Cas12a barcoding system called dual acting inverted site array (DAISY). We combined high-throughput screening and machine learning to predict and optimize the 60-bp DAISY barcode sequences. After optimization, top-performing barcodes had ∼10-fold increased capacity relative to the best random-screened designs and performed reliably across diverse cell types. DAISY barcode arrays generated ∼12 bits of entropy and ∼66,000 unique barcodes. Thus, DAISY barcodes-at a fraction of the size of Cas9 barcodes-achieved high-capacity barcoding. We coupled DAISY barcoding with single-cell RNA-seq to recover lineages and gene expression profiles from ∼47,000 human melanoma cells. A single DAISY barcode recovered up to ∼700 lineages from one parental cell. This analysis revealed heritable single-cell gene expression and potential epigenetic modulation of memory gene transcription. Overall, Cas12a DAISY barcoding is an efficient tool for investigating cell-state dynamics.

Indexed as

CRISPR-Cas SystemsDNA Barcoding, TaxonomicCell LineageHumansMachine LearningPhylogenyCas12aCRISPR barcodinghigh throughput screeninglineage trackingmachine learningmelanomaonline learning optimizationPRC2single cell genomicstranscriptional memory

Identifiers

PMID35752172
PMCPMC10599400

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.