How to Screen a Non-Proliferative Phenotype

Four readouts work when growth is not the phenotype:

1. FACS sorting on a marker or reporter, comparing sorted populations.
2. Survival or competition under a challenge, if the phenotype affects viability under stress.
3. Imaging-based (optical pooled) screens, where the phenotype is morphological or spatial.
4. Single-cell transcriptomic readouts (Perturb-seq), where the phenotype is a program.

In each case, the crucial design element is a separable, quantitative definition of the phenotype before the screen begins.

Design. Build or identify a line with a fluorescent readout that tracks the phenotype — a reporter gene, a surface marker, or a dye whose retention reflects function. Sort the top and bottom fractions, sequence guides in each.

Requirements.

• • A bimodal or clearly separable distribution; overlapping distributions produce noise.
• • Sort gates defined in advance, ideally by pilot experiments on positive and negative controls.
• • Enough cells per gate to preserve coverage.

Controls. Non-targeting guides in both gates; a known regulator of the phenotype as a positive control.

If the phenotype only manifests under a stress — drug, hypoxia, immune pressure — a survival screen under that challenge behaves like a dropout screen, with the same coverage requirements.

The trap: the challenge itself changes growth, so a hit may reflect general stress resistance rather than the specific phenotype. Include an orthogonal stress arm to distinguish.

Optical pooled screens read guide identity in situ, so the phenotype can be morphological, spatial, or intensity-based.

Strengths. Rich phenotypes that no sorting strategy captures; single-cell resolution.

Requirements. An image feature that is measurable and stable; a way to link guides to cells (in situ sequencing); substantial image analysis capacity.

Limitation. Throughput per experiment is lower than sequencing-based screens, and feature selection is a modelling choice that can be over-fitted.

Perturb-seq converts the phenotype into a transcriptional program. It is the right choice when the phenotype is "which program changed", and the wrong choice when the phenotype is a specific protein level you could measure by FACS.

The constraint is cells per perturbation: 20 cells is noisy, 100 is workable, and genome-wide coverage at that depth is rarely affordable.

The fourth row is where most non-proliferative screens quietly fail: gates set after seeing the data convert noise into hits.

EDITGENE builds reporter and marker lines for FACS-based screens, validates separability with positive controls, and supports imaging-compatible formats.

Tell us the phenotype and how you would measure it in a single cell. Contact EDITGENE to request a project consultation or quote.

Readout performance depends on the marker, its dynamic range, and the cell type; separability must be established empirically before screening.

Non-proliferative screens still require coverage and controls comparable to dropout screens; hits require individual validation.

• • Shalem O, et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science. 2014;343:84–87.
• • Feldman D, et al. Optical pooled screens in human cells. Cell. 2019;179:787–799.
• • Dixit A, et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell. 2016;167:1853–1866.
• • Doench JG. Am I ready for CRISPR? A user's guide to genetic screens. Nature Reviews Genetics. 2018;19:67–80.