A flow cytometry-based assay system for the in vitro screening of anti-steatotic compounds.

Authors

Kajal Deshmukh, Navya Malladi, Sanjay K Banerjee

Published in

In vitro models. Volume 5. Issue 3. Pages 205-217. Epub Mar 16, 2026.

Abstract

The rising prevalence of MASLD and the absence of approved therapies highlight the need for reliable, quantifiable in vitro assays to screen compounds targeting hepatic lipid accumulation. Current methods are often semi-quantitative and lack sensitivity. This study aimed to develop and validate a flow cytometry-based assay using HepG2 cells to assess lipid accumulation, a key early marker of hepatic steatosis and toxicity.
Steatosis was induced using varying concentrations of oleic acid (0-800 μM) for 12-48 h. Lipid accumulation was quantified using Nile Red staining and flow cytometry. The data were compared with two other conventional methods, the absorbance/fluorescence-based and the image-based systems. The developed assay system was tested against various clinically used drugs known to have anti-hepatosteatosis properties.
Compared to the conventional methods, Oleic acid-induced fat accumulation, measured by flow cytometry in the hepatocyte-specific HepG2 cell line, was observed to be dose-dependent and time-dependent, with significant lipid accumulation at 400 μM oleic acid after 24 h. The assay was validated using known anti-steatotic drugs, where Saroglitazar, Pioglitazone, Empagliflozin, and Atorvastatin significantly reduced lipid accumulation in HepG2 cells. Further, the sensitivity of the assay was checked by calculating the IC₅₀ values, where Saroglitazar and Triacsin C showed IC₅₀ values of 0.16 μM and 0.15 μM, respectively. This study successfully developed a flow cytometry-based in vitro assay using HepG2 cells to screen drugs against MASLD.
The present flow cytometry assay is a human cell-based and animal-free system that offers a sensitive, accurate, and high-throughput platform for evaluating anti-steatotic compounds, with clear advantages over conventional methods for early-stage drug discovery.
The online version contains supplementary material available at https://doi.org/10.1007/s44164-026-00110-4.

PMID:
42763509
Bibliographic data and abstract were imported from PubMed on 20 Sep 2026.

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