Evidence map›Paper›PMID 41479046›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

The Cell Painting PLUS Method: Phenotypic Profiling Using Iterative Staining-Elution Cycles.

Elena von Coburg, Sebastian Dunst

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Elena von CoburgGerman Centre for the Protection of Laboratory Animals (Bf3R) and Experimental Toxicology, German Federal Institute for Risk Assessment (BfR), Berlin, Germany.
Sebastian DunstGerman Centre for the Protection of Laboratory Animals (Bf3R) and Experimental Toxicology, German Federal Institute for Risk Assessment (BfR), Berlin, Germany. Sebastian.Dunst@bfr.bund.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell Painting (CP) is a widely used imaging-based method for untargeted high-throughput phenotypic profiling (HTPP) of compounds or genetic perturbations that lead to morphological changes in cells. Here, we describe the Cell Painting PLUS (CPP) method, an efficient, robust, and broadly applicable HTPP approach that overcomes certain boundaries of the standard CP method. In CPP, an elution buffer enables iterative staining, elution, and re-staining of the same cells with at least seven fluorescent dyes labeling nine cellular compartments and organelles. In this way, CPP provides higher flexibility in selecting and combining various fluorescent dyes for customized multiplexing of phenotypic profiling screens. The separate imaging and analysis of single dyes in individual channels ensures a high specificity of the generated phenotypic profiles and thus enables precise insights into cellular processes and dysfunction. This chapter describes the process of generating CPP phenotypic profiling data that is based on automated staining and imaging procedures as well as a customized image analysis pipeline using the Harmony software. The resulting data can be further processed and visualized using customized KNIME software-based pipelines as described in von Coburg et al. (Nat Commun 16:3857, 2025) for unbiased and in-depth characterization of the modes-of-action of compounds or genetic perturbations in toxicology and biomedical research.

Indexed as

High-Throughput Screening AssaysImage Processing, Computer-AssistedStaining and LabelingFluorescent DyesHumansMicroscopy, FluorescencePhenotypeSoftwareFluorescent DyesCell Painting PLUSHarmony softwareHigh-content screeningHigh-throughputMorphological fingerprintsPhenotypic profiling

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.