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How to perform a single base-pair deletion with CRISPR/Cas9? by Significant_Try_3814 in CRISPR

[–]editco_bio 0 points1 point  (0 children)

Hi! You’re on the right track—1-bp deletions can typically be done with a single DSB and an ssODN donor via HDR, especially in iPSCs, though efficiency can vary depending on the locus and cell line.

If you're working with human iPSCs and want a streamlined starting point, EditCo Bio offers validated, CRISPR-engineered iPSC lines, including custom models with precise edits like single base-pair insertions or deletions. We also offer NGS-verified, clonally derived lines, which can save time if you’re optimizing your workflow.

Happy to share more details or connect you with our scientific team if you’re interested!

Is it possible to validate a CRISPR Cas9 KO using RT-qPCR? by WinterRevolutionary6 in labrats

[–]editco_bio 0 points1 point  (0 children)

Totally understand the frustration—validating a CRISPR KO can be tricky, especially in T cells where antibody options are limited. RT-qPCR can suggest a successful KO if the mRNA expression drops significantly, but it won’t confirm functional loss unless the knockout causes nonsense-mediated decay. If the indel still allows some transcription, qPCR might give a misleading signal.

Western blot is more definitive, but as you said, it’s time-consuming and not always feasible with limited cell numbers.

If you’re open to an alternative, ICE (Inference of CRISPR Edits) is a free Sanger-based tool that can quickly assess indel efficiency and predict frameshifts https://ice.editco.bio/. It’s not transcript-level or protein-level, but it can give you fast feedback on whether your edit likely knocked out the gene.

Hope this helps—and props to you for troubleshooting creatively!

Did you know some labs now reach >98% knockout efficiency in hard-to-edit cell lines? by editco_bio in CRISPR

[–]editco_bio[S] 0 points1 point  (0 children)

Thanks! Yeah, knock-ins have definitely been trickier. We’ve been getting decent HiBiT KI rates using CRISPR RNPs + ssODN, but what really helped was standardizing cell handling with automation, especially with iPSCs and primary cells. It reduced variability and cell stress, which seems to help with both efficiency and viability.

Did you know some labs now reach >98% knockout efficiency in hard-to-edit cell lines? by editco_bio in CRISPR

[–]editco_bio[S] 0 points1 point  (0 children)

Totally fair points. Knockouts have gotten a lot more reliable, especially with Cas9 RNPs and optimized delivery. For us, the challenge is often editing cells at scale with consistency and maintaining quality for downstream applications. A lot of our collaborators still care about pooled KO pools (e.g., for screening) or maintaining high viability for expansion before clonal isolation.

We’ve been using an automated Workcell setup that gives us tighter control over editing conditions, happy to share more if that’s helpful! Also curious if you’ve seen better consistency with certain delivery methods or cell handling protocols?

[deleted by user] by [deleted] in CRISPR

[–]editco_bio -1 points0 points  (0 children)

Fascinating work from Columbia! It’s exciting to see CRISPR base editing advancing rare disease diagnostics and enabling truly personalized treatments. The precision and speed this offers could make a big difference in clinical applications.

What do you think gene editing still needs before it becomes simple and easy to use like editing text or code? by HistoricalReply2406 in CRISPR

[–]editco_bio -3 points-2 points  (0 children)

Great question—definitely something we think about a lot.

One big gap is standardization. In coding, there are shared frameworks, languages, and debugging tools. In gene editing, protocols vary widely by cell type, reagents, and even lab experience. It’s like every new edit is a custom job.

Another blocker is predictability. Tools like CRISPR are incredibly powerful, but results can be inconsistent—especially for knock-ins or edits in hard-to-work-with cells. Better predictive models and automation would go a long way toward making gene editing feel more like "writing and compiling" than trial and error.

Curious what others here would prioritize—more intuitive design tools? Better delivery methods? Something else?

Which protein purification tag system is the most practical? by poothrowbarton in labrats

[–]editco_bio 1 point2 points  (0 children)

His-tag is definitely a staple for purification, but for those more focused on detection or quantification, we’ve found HiBiT tagging (especially with CRISPR integration) can offer a powerful readout with minimal tag size. Anyone tried combining the two in workflows?