Newsletter October: How can AI simplify cell painting?

Welcome to the October edition of the AI for Live Cell Insights Newsletter, bringing you the latest live cell analyses powering drug discovery and cosmetics development. Each month, we will explore a new application of AI-based cellular analysis for label-free live cell imaging, with publication highlights and news from Nanolive. This month, we ask, “How can AI simplify cell painting?” and look at how holotomography provides a unique canvas for AI analysis of cellular phenotypes.  

Finally, we feature recent publications spanning metabolism, mitochondria, nanomaterials, and drug delivery. 

How can AI simplify cell painting?  

Phenotypic screening has re-emerged as a powerful approach for identifying and characterizing drug candidates, especially in an era where mechanism-based screening often overlooks complex biological responses. Yet, conventional cell painting methods rely on fluorescent staining and endpoint imaging, which introduce variability, limit biological relevance, and complicate data analysis. 

Nanolive now puts forward its alternative phenotypic screening approach to cell painting which uses AI to automate analysis of cellular responses at population, cellular, and organelle levels by segmenting organelles and other cellular structures without staining, then carrying out multiparametric analyses and data visualization for phenotypic fingerprinting and predictive modeling, accelerating drug discovery and mechanism-of-action elucidation. 

There is no pipeline-building or raw data handling involved, enabling researchers to make data-driven decisions and focus on discovery. 

Step 1: Eliminate dyes

In contrast to the complex multi-stain and fixation protocols required to visualize these structures with chemical dyes, Nanolive’s label-free assays enable scientists to get straight to acquiring images.  

Rather than staining organelles, Nanolive integrates morphological, dry mass, and kinetic data through advanced AI analysis to deliver structural and functional information for more meaningful results which help to elucidate drug mechanism of action; for example, detecting cell death mechanism, lipid droplet biogenesis, or mitochondrial network fragmentation. 

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An overview of Nanolive’s phenotypic analysis solution, illustrating how a single image acquisition is transformed into a wide range of actionable biological insights.

Step 2: Make it dynamic 

By eliminating phototoxicity and the toxic effects of dyes, long-term, dynamic imaging becomes possible! Cells can be imaged in their native states for as long as required, and timelapse acquisitions allow monitoring of cell growth, health, and cytotoxicity kinetics. 

Understanding a compound’s impact on cell viability and death pathways is a fundamental step in drug discovery, yet conventional assays often rely on fluorescent stains or endpoint measurements that can bias results and miss dynamic cellular events. For biotech and pharma researchers seeking to accelerate phenotypic screening, Nanolive’s label-free approach delivers an unprecedented window into cell health, morphology, and death kinetics, all captured dynamically from a single dataset. 

Dynamics of cytotoxicity are illustrated in this screenshot from a timelapse acquisition using Nanolive’s label-free solution to quantify the mechanism of ebastine-induced cell death. 

This example UMAP clustering analysis was produced in EVE Explorer using data from a 96-well plate drug screen, incorporating label-free metrics from Nanolive’s digital assays.

Download our one-pager here for more details on Nanolive’s AI-powered phenotypic screening.

Did you miss us at ELRIG Drug Discovery last week?

It’s not too late to download our poster “AI-driven mitochondria and organelle analysis in label-free images” to find out how Nanolive are combining label-free holotomographic microscopy with Artificial Intelligence to visualize and quantify subcellular changes triggered by infection.

See how virus-induced remodeling of lipid droplets, mitochondria, nuclei, and nucleoli, as well as shifts in organelle cross-regulation networks were quantified label-free, and revealed in detail. 

AI-powered segmentation of nuclei (cyan), nucleoli (pink), mitochondria (red), lipid droplets (green), and cell membrane (gray), shown next to the raw refractive index imaging data of U2OS cells (right). 

Latest publication highlights with Nanolive imaging:

• Metabolism: In their latest publication, a team at Amgen used Nanolive’s Smart Lipid Droplet AssayLIVE to characterize a variant predisposing to metabolic dysfunction-associated steatotic liver disease (MASLD). In Cell Reports, their paper notably used the assay to detect significant changes in lipid droplet dry mass over time. 

• Sherman, D. et al, (2025) ‘PNPLA3-I148M is a neomorph that interferes with two primary hepatic triglyceride clearance pathways’, Cell Reports https://doi.org/10.1016/j.celrep.2025.116371  

• Drug delivery: Waglewska, E. et al, (2025) ‘Harnessing Cationic Bilosomes to Create a Green Light-Triggered Nanoplatform for Skin Melanoma Treatment’ Nanotechnology, Science and Applications https://doi.org/10.2147/NSA.S531026  

• Mitochondrial transfer: Li, C.-J. et al, (2025) ‘Reprogramming the aging ovarian microenvironment via mitochondrial sharing and structural remodeling’, Theranostics https://doi.org/10.7150/thno.119957  

• Nanomaterials: Klebowski, B. et al, (2025) ‘Comparison of cytotoxicity, radiosensitizing properties and cellular uptake of palladium nanoparticles stabilized with commercial chitosan and chitosan isolated from honey bees’ Research Square https://doi.org/10.21203/rs.3.rs-7643260/v1  

• Oncology: Sarelo, P. et al, (2025) ‘Effects of DNA Methylation Inhibitors on Molecular and Structural Changes in Chromatin Organization in Leukemia Cells’ Molecular and Cellular Biology https://doi.org/10.1080/10985549.2025.2560965 

Find over 350 publications featuring Nanolive imaging here.

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