Nov 30, 2023
CRISPRa tiling screens
- Sean R. McCutcheon1,
- Adam M. Swartz1,
- Michael C. Brown1,
- Alejandro Barrera1,
- Christian McRoberts Amador1,
- Keith Siklenka1,
- Lucas Humayun1,
- Maria A. ter Weele1,
- James M. Isaacs1,
- Andrea R Daniel1,
- Timothy E. Reddy1,
- Andrew S. Allen1,
- Smita K. Nair1,
- Scott J. Antonia1,
- Charles A. Gersbach1
- 1Duke University
- Andrea R Daniel: This protocol was adapted from the work of Sean McCutcheon and colleagues in the Gersbach lab at Duke University.
- Gersbach Lab
Protocol Citation: Sean R. McCutcheon, Adam M. Swartz, Michael C. Brown, Alejandro Barrera, Christian McRoberts Amador, Keith Siklenka, Lucas Humayun, Maria A. ter Weele, James M. Isaacs, Andrea R Daniel, Timothy E. Reddy, Andrew S. Allen, Smita K. Nair, Scott J. Antonia, Charles A. Gersbach 2023. CRISPRa tiling screens. protocols.io https://dx.doi.org/10.17504/protocols.io.q26g7p9bqgwz/v1
Manuscript citation:
McCutcheon, S.R., Swartz, A.M., Brown, M.C. et al. Transcriptional and epigenetic regulators of human CD8+ T cell function identified through orthogonal CRISPR screens.Nat Genet (2023). https://doi.org/10.1038/s41588-023-01554-0
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: November 30, 2023
Last Modified: November 30, 2023
Protocol Integer ID: 91655
Keywords: CRISPRa, dSaCas9, Jurkat cells, IL2RA, tiling screen
Funders Acknowledgements:
NIH
Grant ID: HG012053
Abstract
This protocols describes methods for characterizing the activity of dSaCas9 as a activator using promoter tiling guide RNA screens in Jurkat cells.
Construction of CRISPRa Jurkat lines
Construction of CRISPRa Jurkat lines
Polyclonal dSaCas9VP64 and VP64dSaCas9VP64 Jurkat cell lines were generated by transducing Jurkat cells with lentivirus encoding for either dSaCas9VP64–2A–PuroR or VP64dSaCas9VP64–2A–PuroR.
Cells were selected for 5 days using 0.5 µg ml−1 of puromycin.
IL2RA CRISPRa tiling screen
IL2RA CRISPRa tiling screen
After selection, 1 × 106 dSaCas9VP64 and VP64dSaCas9VP64 Jurkat cells were plated and transduced in triplicate with the IL2RA gRNA library lentivirus at a low multiplicity of infection (MOI).
Cells were expanded for 10 days, selected for Thy1.1 using a CD90.1 Positive Selection Kit (StemCell Technologies), and stained for Thy1.1 and IL2RA.
Transduced cells in the lower and upper 10% tails of IL2RA expression were sorted for subsequent gRNA library construction and sequencing.
All replicates were maintained and sorted at a minimum of 500× coverage.
gRNA sequencing
gRNA sequencing
Genomic DNA was isolated using Qiagen’s DNeasy Blood and Tissue Kit. Genomic DNA was split across 100 μl PCR reactions (25 cycles at 98 °C for 10 s, 60 °C for 30 s, and 72 °C for 20 s) with Q5 2× Master Mix and up to 1 μg of genomic DNA per reaction.
PCRs were pooled together for each sample and purified using double-sided (SPRI)bead selection at 0.6× and 1.8×.
Libraries were run on a High Sensitivity D1000 tape (Agilent) to confirm amplicon size and quantified using Qubit’s dsDNA High Sensitivity assay.
Libraries were diluted to 2 nM, pooled together at equal volumes, and sequenced using Illumina’s MiSeq Reagent Kit v2 (50 cycles).
Primers are available in Supplementary Table 5 of McCutcheon et al. Nature Genetics, 2023. https://doi.org/10.1038/s41588-023-01554-0
Processing gRNA sequencing and gRNA analysis
Processing gRNA sequencing and gRNA analysis
FASTQ files were aligned to custom indexes for each gRNA library (generated from the bowtie2-build function) using Bowtie 2 (ref. 67).
Counts for each gRNA were extracted and used for further analysis in R.
Individual gRNA enrichment was determined using the DESeq2 (ref. 68) package to compare gRNA abundance between groups for each screen.
Protocol references
67. Lagmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
68. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).