RDL logo
About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide
​
​
Sign inGet started
​
​

About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide

Sign inGet started
RDL logo

Verified research datasets. Instant access. Built for collaboration.

Navigation

About

Aims and Scope

Advisory Board Members

More

Who We Are?

Add Raw Data

User Guide

Legal

Privacy Policy

Terms of Service

Support

Got an issue? Email us directly.

Email: info@rawdatalibrary.netOpen Mail App
​
​

© 2025 Raw Data Library. All rights reserved.
PrivacyTerms
  1. Raw Data Library
  2. /
  3. Publications
  4. /
  5. Cut–dip–budding delivery system enables genetic modifications in plants without tissue culture

Verified authors • Institutional access • DOI aware
50,000+ researchers120,000+ datasets90% satisfaction
Article
en
2022

Cut–dip–budding delivery system enables genetic modifications in plants without tissue culture

0 Datasets

0 Files

en
2022
Vol 4 (1)
Vol. 4
DOI: 10.1016/j.xinn.2022.100345

Get instant academic access to this publication’s datasets.

Create free accountHow it works

Frequently asked questions

Is access really free for academics and students?

Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.

How is my data protected?

Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.

Can I request additional materials?

Yes, message the author after sign-up to request supplementary files or replication code.

Advance your research today

Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.

Get free academic accessLearn more
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration
Access Research Data

Join our academic network to download verified datasets and collaborate with researchers worldwide.

Get Free Access
Institutional SSO
Secure
This PDF is not available in different languages.
No localized PDFs are currently available.
Jian Kang Zhu
Jian Kang Zhu

Institution not specified

Verified
Xuesong Cao
Hongtao Xie
Minglei Song
+10 more

Abstract

Of the more than 370 000 species of higher plants in nature, fewer than 0.1% can be genetically modified due to limitations of the current gene delivery systems. Even for those that can be genetically modified, the modification involves a tedious and costly tissue culture process. Here, we describe an extremely simple cut-dip-budding (CDB) delivery system, which uses Agrobacterium rhizogene to inoculate explants, generating transformed roots that produce transformed buds due to root suckering. We have successfully used CDB to achieve the heritable transformation of plant species in multiple plant families, including two herbaceous plants (Taraxacum kok-saghyz and Coronilla varia), a tuberous root plant (sweet potato), and three woody plant species (Ailanthus altissima, Aralia elata, and Clerodendrum chinense). These plants have previously been difficult or impossible to transform, but the CDB method enabled efficient transformation or gene editing in them using a very simple explant dipping protocol, under non-sterile conditions and without the need for tissue culture. Our work suggests that large numbers of plants could be amenable to genetic modifications using the CDB method.

How to cite this publication

Xuesong Cao, Hongtao Xie, Minglei Song, Jinghua Lu, Ping Ma, Boyu Huang, Mugui Wang, Yifu Tian, Fan Chen, Jun Peng, Zhaobo Lang, Guofu Li, Jian Kang Zhu (2022). Cut–dip–budding delivery system enables genetic modifications in plants without tissue culture. , 4(1), DOI: https://doi.org/10.1016/j.xinn.2022.100345.

Related publications

Why join Raw Data Library?

Quality

Datasets shared by verified academics with rich metadata and previews.

Control

Authors choose access levels; downloads are logged for transparency.

Free for Academia

Students and faculty get instant access after verification.

Publication Details

Type

Article

Year

2022

Authors

13

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1016/j.xinn.2022.100345

Join Research Community

Access datasets from 50,000+ researchers worldwide with institutional verification.

Get Free Access