name: bio-crispr-screens-in-vivo-screens description: Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments. origin: openai4s category: bioskills/crispr-screens metadata: tool_type: mixed primary_tool: MAGeCK third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a license: MIT
Reference examples tested with: MAGeCK 0.5.9+, MAGeCK-VISPR 0.5.6+, pandas 2.2+, numpy 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: mageck --version
- Reference focused libraries: Manguso 2017, Chen 2015, public Addgene aliquots
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Design or analyze an in vivo CRISPR screen" -> Account for the dramatic bottleneck during animal implantation and tumor growth; use focused libraries; recover DNA from tumor explants; analyze with bottleneck-adjusted hit calling.
mageck count + mageck test for standard analysisWhy in vivo screens differ from in vitro:
| Constraint | In vitro | In vivo |
|---|---|---|
| Cells per condition | 10M-100M (unlimited) | Limited by injection volume (1-5M cells typical) |
| Implant -> early tumor cell count | N/A | 10-100x drop typical |
| Late tumor cell count | N/A | Further 5-10x reduction; ~4 sgRNAs/gene retained in late tumors (Scheidmann 2022) |
| Bottleneck per animal | None | Tens of millions of cells fail to engraft |
| Library coverage achievable | 500-1000x | Often 50-100x effective at endpoint |
| sgRNAs survivable | Full library | 66-97% in early (14 d) tumors, strongly cell-line dependent (Lee 2023); by 38-43 d most reads come from the top 1% of guides |
Math: A 70,000-sgRNA library at 500x coverage requires 35M cells in pool. Most syngeneic models can implant 1-5M cells. Result: real coverage is 70x at best; effective coverage at endpoint is even lower after bottleneck.
Solution: Use focused libraries (500-3,000 genes; ~3,000-15,000 sgRNAs) to maintain reasonable coverage despite the bottleneck.
Manguso et al 2017 Nature 547:413 established the canonical in vivo CRISPR screen methodology with a focused library:
Standard focused-library principles:
Public focused libraries: - Manguso 2017 immune library (Addgene) - DepMap focused panels for specific pathways - Custom: order from Twist via CRISPick or CRISPOR
Uijttewaal et al 2025 Nat Biotechnol 43(11):1848 (published online Dec 2024) introduced CRISPR-StAR, which holds sgRNAs inactive until cells have engrafted and re-expanded, then activates each sgRNA in only half the progeny of a clone to generate matched active-vs-inactive internal controls.
How it works: 1. Library is delivered as sgRNAs held in an inactive state, alongside a tamoxifen-inducible CreERT2 recombinase 2. Cells are implanted in animal at MOI 0.3; they engraft and re-expand into single-cell-derived clones (no editing yet); library complexity preserved 3. Tamoxifen induces CreERT2 recombination, which stochastically activates the sgRNA in ~half the cells of each clone 4. Active and still-inactive (wild-type) cells of the same clone, tracked by UMI barcodes, form paired internal active-vs-control comparisons 5. Screen proceeds; tumor harvest, DNA extraction, sequencing 6. Per-clone active-vs-inactive contrast suppresses engraftment and clonal-drift noise
What it buys: CRISPR-StAR enables genome-scale in vivo screens (vs focused libraries) by generating intrinsic per-clone controls; outperforms conventional in vivo screens in therapy-resistant mouse melanoma models (Uijttewaal 2025).
| Model | Immune system | Use case |
|---|---|---|
| Syngeneic (e.g., B16 melanoma in C57BL/6) | Intact mouse immunity | Tumor-immune interaction; checkpoint biology |
| Xenograft (human cancer line in NSG) | Absent / impaired | Tumor cell-intrinsic biology; drug response in human cells |
| PDX (patient-derived xenograft) | Absent / impaired | Patient-specific biology; therapy testing |
| Humanized mouse | Reconstituted human immunity | Tumor-immune in human context (limited) |
| Organoid in vivo | None (in vitro) | Tumor cell-intrinsic in 3D structure |
Decision rule: For immune-targeting drug screens, use syngeneic. For human-cancer cell-intrinsic biology, use xenograft. For patient-specific drug screens, use PDX. Each requires different cell numbers and bottleneck planning.
Goal: Recover sufficient sgRNA-containing DNA from tumor explants for sequencing.
Approach: Dissect tumor; lyse with proteinase K; extract genomic DNA; amplify the sgRNA locus by PCR; sequence on MiSeq / NextSeq / NovaSeq.
# Typical PCR + sequencing parameters for in vivo screens
# Per-tumor DNA: 0.5-5 mg yield from typical syngeneic tumor
# Per-sample sequencing depth: >=500 reads/sgRNA at endpoint (bottleneck-limited libraries need depth, not breadth)
# Multiple animals per condition (n=5-10) to account for clonal variation
# mageck count for in vivo
mageck count \
--list-seq library.csv \
--sample-label Plasmid,Animal1,Animal2,Animal3,Animal4,Animal5 \
--fastq Plasmid.fq.gz A1.fq.gz A2.fq.gz A3.fq.gz A4.fq.gz A5.fq.gz \
--norm-method median \
--output-prefix in_vivo_screen
Goal: Identify per-gene fitness effects despite high inter-animal variability.
Approach: Each animal is a "replicate" with high variance due to clonal dynamics. Use MAGeCK MLE with animal-as-batch covariate, or run MAGeCK RRA per animal and meta-analyze.
# Option A: MAGeCK MLE with batch covariate
cat > in_vivo_design.txt <<EOF
Samples baseline tumor animal_2 animal_3 animal_4 animal_5
Plasmid 1 0 0 0 0 0
Animal1 1 1 0 0 0 0
Animal2 1 1 1 0 0 0
Animal3 1 1 0 1 0 0
Animal4 1 1 0 0 1 0
Animal5 1 1 0 0 0 1
EOF
mageck mle \
--count-table in_vivo_screen.count.txt \
--design-matrix in_vivo_design.txt \
--output-prefix in_vivo_mle
Per-animal RRA + meta-analysis:
import pandas as pd
# Run mageck test on each animal vs plasmid
# Combine with Stouffer's Z method
from scipy.stats import norm
def meta_analyze_animals(per_animal_results):
'''per_animal_results: list of MAGeCK gene_summary.txt per animal.'''
merged = pd.concat([df.assign(animal=i) for i, df in enumerate(per_animal_results)])
grouped = merged.groupby('id')
meta = grouped.apply(lambda g: pd.Series({ # pandas 2.2+: pass include_groups=False
'mean_neg_score': g['neg|score'].mean(),
# clip to keep norm.ppf finite at p=0; negate so positive z = stronger depletion,
# matching examples/per_animal_meta_analysis.py
'stouffer_z': -norm.ppf(g['neg|p-value'].clip(1e-10, 1 - 1e-10)).sum() / (len(g) ** 0.5),
'animals_significant': (g['neg|fdr'] < 0.05).sum(),
'n_animals': len(g)
}))
return meta.sort_values('stouffer_z')
Trigger: Implanted cells lack sufficient library complexity; a few clones dominate the tumor. Mechanism: Inter-animal stochasticity in cell engraftment creates founder effects. Symptom: Per-animal hit lists vary dramatically; no genes appear across all animals. Fix: Use focused library to maintain coverage; increase animals per condition (n=10+); use CRISPR-StAR to delay bottleneck.
Trigger: Wrong library or library not amplified well from tumor DNA. Mechanism: PCR primers don't match the sgRNA flanking; or insufficient DNA template. Symptom: Low mapping rate (<10%); few sgRNAs detected per tumor. Fix: Verify library plasmid sequence; design primers specific to lentiviral cassette; use 10-100 ng input DNA + 25 PCR cycles.
Trigger: Tumor biology differs from in vitro CEGv2 calibration; not all essentials are essential in animal context. Mechanism: Cells in vivo have different growth conditions (nutrients, hypoxia, immune pressure) than in vitro; CEGv2 calibration assumes in vitro context. Symptom: CEGv2 PR-AUC <0.5 in vivo despite high in vitro PR-AUC. Fix: Use cell-type-and-context-specific essentialome (e.g., a corresponding in vitro screen of the same cell type) as a baseline; in vivo essentialome is biology-dependent.
Trigger: Cas9-positive cells were not selected before implantation; library has Cas9-negative escapers. Mechanism: Cas9-negative cells carry sgRNA but no editing; persist in tumor without biological perturbation. Symptom: Specific essentiality signals weak; PR-AUC low. Fix: Always select Cas9-positive cells (FACS or selection) before infection; verify by Cas9 IHC or flow.
Trigger: Limited animals per condition (n=3-5); each has high variance. Mechanism: Per-animal clonal dynamics produce different sgRNA distributions; no consistent signal across few animals. Symptom: MAGeCK p-values inflated; FDR uncalibrated. Fix: Increase animals per condition to 10+; use meta-analysis across animals (Stouffer); validate top hits in arrayed format with n=10 mice each.
Trigger: Spontaneously arising mutations in some tumor regions create non-clonal heterogeneity. Mechanism: Tumor heterogeneity is genuine biology; not all cells in tumor are descendants of original engrafted cells. Symptom: Per-region sequencing shows different sgRNA distributions within same tumor. Fix: Sample multiple tumor regions; or use whole-tumor genomic DNA pooling (averages out heterogeneity).
| Threshold | Value | Source / Rationale |
|---|---|---|
| Cells per animal | 1-5M typical for syngeneic; 5-10M for xenograft | Tumor model dependent |
| sgRNAs per gene in library (focused) | 4-6 | Standard convention |
| Library size for in vivo focused | 3,000-15,000 sgRNAs | Maintainable coverage |
| Coverage at endpoint | ≥50x, ideally 100-200x | Lower than in vitro 500x |
| Animals per condition | 10+ for hit-calling; 5 minimum | Inter-animal variability |
| Animals per condition for arrayed validation | 10 | Tighter signal needed |
| In vivo CEGv2 PR-AUC | >0.4 (context-dependent) | Lower than in vitro 0.7 |
| Late tumor sgRNA-per-gene | ~3.93 mean | Scheidmann 2022 (CTC-derived breast-cancer xenograft; model-dependent) |
| Days to harvest (tumor) | 12-21 days post-implant | Time for selection to manifest |
| Error / symptom | Cause | Solution |
|---|---|---|
| No hits | Library complexity collapsed | Use focused library or CRISPR-StAR |
| Per-animal hit lists differ | Clonal dominance | Use focused library; increase animals |
| Low CEGv2 PR-AUC | Context-specific essentialome | Use in vivo-specific reference set |
| Low mapping rate | Wrong sequencing primers | Verify library lentiviral architecture |
| Coverage at endpoint <50x | Implantation bottleneck | Increase cells implanted; focused library |