bio-crispr-screens-in-vivo-screens

What this skill does

## Version Compatibility

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.

## In Vivo CRISPR Screen Analysis

**"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.

- CLI: `mageck count` + `mageck test` for standard analysis
- Special handling: bottleneck-adjusted coverage thresholds; per-tissue per-animal replicate structure

## The In Vivo Bottleneck Problem

**Why 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 (~94% library complexity may survive in CD45+ TILs) |
| Late tumor cell count | N/A | Further 5-10x reduction; final ~3.93 sgRNAs/gene |
| 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 | 80-94% in early tumors; 60-80% in late tumors |

**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.

## Focused Library Design for In Vivo

**Manguso et al 2017 *Nature* 547:413** established the canonical in vivo CRISPR screen methodology with a focused library:

- 2,368 genes (kinases + cell surface proteins + immune factors)
- 4 sgRNAs per gene (~9,500 sgRNAs total)
- Targeted at immune-evasion biology in syngeneic mouse melanoma
- Identified PTPN2, NLRC5, CD47 as immune-evasion genes

**Standard focused-library principles:**

1. **Gene selection:** Define the biology to be tested (e.g., immune evasion, metastasis); restrict library to genes plausibly involved (kinases, surface proteins, regulators)
2. **Library size:** 500-3,000 genes; 3-6 sgRNAs/gene; total 3,000-18,000 sgRNAs
3. **Coverage achievable:** With 5M cells implanted, 100-300x coverage is achievable

**Public focused libraries:**
- Manguso 2017 immune library (Addgene)
- Joung 2017 pooled screen reference
- DepMap focused panels for specific pathways
- Custom: order from Twist via CRISPick or CRISPOR

## CRISPR-StAR (Temporal Activation; Uijttewaal 2025)

**Uijttewaal et al 2025 *Nat Biotechnol* 43(11):1848** (published online Dec 2024) introduced CRISPR-StAR (Staggered Activation Reporter), which uses inducible Cas9 expression to delay gene editing until after cells have engrafted in the animal.

**How it works:**
1. Library is delivered into cells with inducible (Dox-controlled) Cas9
2. Cells are implanted in animal at MOI 0.3
3. Cells engraft into tumor (no editing yet); library complexity preserved
4. Days 5-7 post-implantation: induce Cas9 with doxycycline
5. Editing begins; screen proceeds for additional weeks
6. Tumor harvest, DNA extraction, sequencing

**Quantified gain:** 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).

## Syngeneic vs Xenograft vs PDX

| 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.

## Tumor DNA Extraction and Sequencing

**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.

```bash
# 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 (lower than in vitro 300+)
# 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
```

## Hit Calling for In Vivo

**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.

```bash
# 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:**

```python
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({
        'mean_neg_score': g['neg|score'].mean(),
        'stouffer_z': norm.ppf(g['neg|p-value']).sum() / (len(g) ** 0.5),
        'animals_significant': (g['neg|fdr'] < 0.05).sum(),
        'n_animals': len(g)
    }))
    return meta.sort_values('stouffer_z')
```

## Failure Modes

### Clonal dominance from low complexity

**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.

### Tumor DNA extraction yields no sgRNA reads

**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%); fe