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Graham McVicker

@grahammcvicker.bsky.social
98 followers 117 following 12 posts

Associate Professor at the Salk Institute

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Reposted by Graham McVicker
Jacob Schreiber @jmschreiber91.bsky.social · 16/02/2025
Comprehensive dissection of cis-regulatory elements in a 2.8 Mb topologically associated domain in six human cancers www.nature.com/articles/s41...
nature.com
Comprehensive dissection of cis-regulatory elements in a 2.8 Mb topologically associated domain in six human cancers - Nature Communications
The oncogene MYC plays a key role in cancer initiation and progression. Using thousands of CRISPR perturbations, the authors investigate regulators of MYC in six different cancers. These tumor-specifi...
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Reposted by Graham McVicker
Hilary Martin @hilarycmartin.bsky.social · 16/02/2025
Our new paper about rare variant contributions to sex differences in autism is out at AJHG, led by Mahmoud Koko with @vw1234.bsky.social and Kyle Satterstrom. Biggest analysis of exome data in autism to date including SPARK and ASC. www.sciencedirect.com/science/arti...
sciencedirect.com
Contribution of autosomal rare and de novo variants to sex differences in autism
Autism is four times more prevalent in males than females. To study whether this reflects a difference in genetic predisposition attributed to autosom…
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Reposted by Graham McVicker
Gonzalo Benegas @gonzalobenegas.bsky.social · 13/02/2025
Can DNA sequence models predict mutations affecting human traits? We introduce TraitGym, a curated benchmark of causal regulatory variants for 113 Mendelian & 83 complex traits, and evaluate functional genomics and DNA language models. Joint work w/ Gökcen Eraslan and @yun-s-song.bsky.social 🧵👇
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
Superb-seq is also EASY TO PERFORM. Protocol uses standard kits, equipment, and NO virus. We also provide free software, SHERIFF, to analyze Superb-seq data. We hope this will enable wide-spread adoption and use in diverse cell types!
On-target edit events detected for different cell types (K562, primary T cells, GM12878, Jurkats) and guides using TIDE. Most tested guides resulted in efficient insertions of the 34bp donor sequence containing the T7 promoter.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
Superb-seq could be used to readily distinguish benign from potentially risky Cas9 off-target edit profiles for therapeutic applications. Perturb-seq and Guide-seq lack this capability, since they do not jointly detect edit sites and transcriptomes at single cell resolution.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
Guide presence is an unreliable indicator of on-target guide editing. Other methods that use guide-only read outs may be confounded by off-target edits that are sometimes even more frequent than the on-target edit.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
All off-targets were non-coding, so paired scRNA-seq was crucial to determine their functional effects using differential expression, as shown for the USP9X intronic edit above.
Edit events colored by the corresponding guide. Only one guide had no detected off target events. Y-axis shows homology between genome and guide spacer. X/I/O indicates whether edit is exonic, intronic, or intergenic. Most off-target edits are intronic.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
Many of the off-target edit sites (total = 36) were not predicted by popular in silico tools. These tools appear to underestimate the tolerance of guide bulges and mismatches with off-target edit sites, yet also predict many off-target edits that do not occur.
Venn diagram showing overlap between off-target edit sites identified with Superb-seq and those predicted by in silico tools Cas-OFFinder, COSMID and E-CRISP. The overlap is poor and 11 off-targets were not predicted by any computational tool.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
Off-target sites had clear guide homology and PAMs. There was however a surprising tolerance of guide bulges and mismatches with off-targets, including mismatches in the “seed” region.
Pairwise alignments of SMARCA4 g22 sequence and the genome site at the edit. All off-target sites have PAMS, but have various mismatches to the guide spacer sequence.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
One guide had an off-target edit that was 34x more frequent than the on-target edit! A total of 5 off-target sites were observed in more cells than the on-target for this particular guide.
Detected edits for SMARCA4 guide 22. 13 edits were detected. Y axis is the frequency of the edit (number of cells) and X axis is the homology between the genome and the guide spacer sequence. Some off target events are more frequent than the intended on-target.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
We quantified Cas9 edits per cell (‘edited alleles’), and associated edit alleles with gene expression. One intronic off-target edit perturbed the expression of USP9X and >100 downstream genes!
Sheriff counts edit alleles per cell at each edit site. An off-target edit within the first intron of USP9X is associated with differential expression of USP9X and >100 downstream genes.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
SURPRISING RESULT. We applied Superb-seq to 10k K562 cells and detected pervasive off-target Cas9 edits, with an average of 6 off-target sites per guide, ranging in frequency from 0.03-18.6% of cells!
Genome positions and frequencies (number of cells) of on and off-target events detected with Superb-seq.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
Superb-seq detects edits and cell RNA by labelling nuclease cleavage sites with homology-free insertion of a T7 promoter, followed by “Zombie” in situ transcription with T7-pol to generate edit-site marking barcoded RNA. T7 and cell RNAs are jointly read out with scRNA-seq.
A schematic of Superb-seq showing 3 steps: (1) edit labeling with donor sequence containing T7 promoter; (2) In situ transcription with T7 polymerase; (3) combinatorial single-cell RNA-seq + computational analysis with Sheriff.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
Unlike single-cell Perturb/CROP-seq methods that read guide RNAs as a proxy of edits, Superb-seq captures edit events directly. This enables functional assessment of edits including off-target events that confound analyses and raise gene therapy risk.
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Graham McVicker @grahammcvicker.bsky.social · 11/02/2025
Excited to announce our preprint describing SUPERB-SEQ 🦸, a new method to measure Cas9 edits and their effects on gene expression in single cells. Led by @micklorenzini.bsky.social and @bradbalderson.bsky.social www.biorxiv.org/content/10.1...
biorxiv.org
Joint single-cell profiling of CRISPR-Cas9 edits and transcriptomes reveals widespread off-target events and their effects on gene expression
A longstanding barrier in genome engineering with CRISPR-Cas9 has been the inability to measure Cas9 edit outcomes and their functional effects at single-cell resolution. Here we present Superb-seq , ...
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Reposted by Graham McVicker
Molly Przeworski @mollyprz.bsky.social · 02/02/2025
“The distribution of highly deleterious variants across human ancestry groups”. Preprint with Anastasia Stolyarova and @gcbias.bsky.social: www.biorxiv.org/content/10.1...
biorxiv.org
The distribution of highly deleterious variants across human ancestry groups
A major focus of human genetics is to map severe disease mutations. Increasingly that goal is understood as requiring huge numbers of people to be sequenced from every broadly-defined genetic ancestry...
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Reposted by Graham McVicker
Zixuan (Eleanor) Zhang @elezzx.bsky.social · 27/01/2025
Excited to present our work on developing jaxQTL, a fast single-cell eQTL mapping tool that improves power and robustness in identifying sc-eQTLs using count-based models. See details in threads 🧵 www.medrxiv.org/content/10.1...
medrxiv.org
Efficient count-based models improve power and robustness for large-scale single-cell eQTL mapping
Population-scale single-cell transcriptomic technologies (scRNA-seq) enable characterizing variant effects on gene regulation at the cellular level (e.g., single-cell eQTLs; sc-eQTLs). However, existi...
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Reposted by Graham McVicker
David A Knowles @davidaknowles.bsky.social · 09/12/2024
Excited to share our first foray into (noncoding) rare variant association testing: a probabilistic model that learns functional annotation importance and finds associations missed by existing methods. Anjali did a fantastic job with model assessment and scaling! www.medrxiv.org/content/10.1...
medrxiv.org
Leveraging functional annotations to map rare variants associated with Alzheimer's disease with gruyere
The increasing availability of whole-genome sequencing (WGS) has begun to elucidate the contribution of rare variants (RVs), both coding and non-coding, to complex disease. Multiple RV association tes...
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