bio-crispr-screens-in-vivo-screens

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.
tool_type	mixed
primary_tool	MAGeCK

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
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.

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:

• 2,398 genes covering kinases, phosphatases, cell-surface proteins, antigen presentation, immune regulation and chromatin remodeling; the 2,368 expressed in the melanoma line were the ones scored
• 4 sgRNAs per gene, delivered as four sub-pools of one sgRNA per gene plus 100 non-targeting controls each (9,992 sgRNAs total)
• Targeted at immune-evasion biology in syngeneic mouse melanoma
• Recovered the known immune-evasion genes Cd274 (PD-L1) and Cd47, and identified Ptpn2 loss as sensitizing tumors to immunotherapy through increased IFN-gamma signaling and antigen presentation

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)
• DepMap focused panels for specific pathways
• Custom: order from Twist via CRISPick or CRISPOR

CRISPR-StAR (Stochastic Activation by Recombination; Uijttewaal 2025)

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).

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.

# 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

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.

# 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')

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%); 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.

In vivo PR-AUC against CEGv2 is poor

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.

Pre-screen Cas9 selection failure

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.

Inter-animal variability dominates hit calling

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.

Tumor heterogeneity destroys screen signal

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).

Quantitative Thresholds

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

Common Errors

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

References

• Manguso RT et al. 2017. Nature 547:413. In vivo CRISPR screen for immune evasion; canonical focused-library design.
• Chen S et al. 2015. Cell 160:1246. Original in vivo Cas9 screening methodology.
• Lee TW et al. 2023. Cancer Gene Ther 30:1610. Clonal dynamics limit detection of selection in tumour xenograft CRISPR/Cas9 screens.
• Scheidmann MC et al. 2022. Cancer Res 82:681. In vivo CRISPR screen in a CTC-derived xenograft; late-tumor sgRNA-per-gene retention.
• Uijttewaal ECH et al. 2025. Nat Biotechnol 43:1848 (online Dec 2024). CRISPR-StAR intrinsic-control screening for in vivo models.

Related Skills

• crispr-screens/library-design - Focused library design for in vivo
• crispr-screens/mageck-analysis - MAGeCK MLE with animal-as-batch covariate
• crispr-screens/hit-calling - Per-animal meta-analysis strategies
• crispr-screens/screen-qc - In-vivo-specific QC thresholds
• crispr-screens/batch-correction - Animal cohort as batch in MLE
• crispr-screens/combinatorial-screens - In vivo combinatorial screens
• crispr-screens/copy-number-correction - Cancer-line in vivo screens
• pathway-analysis/go-enrichment - Functional analysis of in vivo hits