Related Experiment Video
Updated: Aug 23, 2025

12:04
Engineering Oncogenic Heterozygous Gain-of-Function Mutations in Human Hematopoietic Stem and Progenitor Cells
Published on: March 10, 2023
3.7K
Combinatorial gene targeting in primary human hematopoietic stem and progenitor cells
Alexandra Bäckström1, David Yudovich1, Kristijonas Žemaitis1
1Division of Molecular Medicine and Gene Therapy, Lund Stem Cell Center, Lund University, BMC A12, 221 84, Lund, Sweden.
Scientific Reports
|October 28, 2022
Summary
This study introduces a CRISPR/Cas9 method for multiplexed gene editing in primary human hematopoietic stem and progenitor cells (HSPCs). The system efficiently targets multiple genes simultaneously, enabling new research into gene interactions and diseases.
Area of Science:
- Molecular Biology
- Gene Editing Technology
- Hematopoiesis Research
Background:
- CRISPR/Cas9 gene editing is versatile but challenging in primary human cells.
- Developing efficient multiplexed gene editing in hematopoietic stem and progenitor cells (HSPCs) is crucial for functional genomics.
Purpose of the Study:
- To establish a robust system for simultaneous, multiplexed gene editing in primary human HSPCs.
- To enable tracking and functional analysis of single- and double-edited cells.
Main Methods:
- Co-delivery of lentiviral sgRNA vectors (for EGFP or KuO) and Cas9 mRNA into primary human HSPCs.
- Simultaneous targeting of two genetic loci using CRISPR/Cas9.
- Utilizing fluorescent markers for tracking edited cells.
Main Results:
- Achieved robust double knockout of CD45 and CD44 in HSPCs with ~70% efficiency.
- Demonstrated modeling of gene dependencies for cell survival by targeting cohesin genes (STAG1, STAG2).
- Showed potential synergistic effects for HSPC expansion by targeting AHR and CoREST complex members.
Conclusions:
- The developed traceable multiplexed CRISPR/Cas9 system is effective for primary HSPCs.
- This system facilitates studies on genetic dependencies and cooperation in HSPCs.
- Has significant implications for modeling polygenic diseases and understanding gene interaction mechanisms.

