bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.01.26.701682

Label-free quantitative phenotyping of hepatic stellate cell activation using holotomography with AI-enabled subcellular segmentation

Hong, S.-h.·Park, J.·Kim, H.·Moon, H.·Lee, K.·Lee, H.·Lee, S.·Park, Y.
View original source ↗Export metadata ↓

Abstract

Hepatic stellate cell (HSC) activation is a central driver of liver fibrosis, yet its quantitative characterization in living cells remains limited by endpoint assays that rely on fixation and labeling. Here we introduce a label-free framework that combines three-dimensional holotomography (HT) with automated, AI-assisted analysis to study HSC activation dynamics in live cells. Using refractive index tomography, we non-invasively visualize hallmark structural features of HSC activation, including lipid droplet depletion, cytoskeletal remodeling, and changes in cellular morphology. Correlative fluorescence imaging validates the biological relevance of HT-derived features, while longitudinal imaging reveals continuous activation trajectories at single-cell resolution. Automated segmentation of whole cells and subcellular organelles enables scalable extraction of multi-parametric biophysical descriptors, defining a quantitative phenotypic fingerprint that distinguishes quiescent and activated states. Together, this work establishes holotomography-based quantitative phenotyping as a powerful approach for studying HSC activation and, more broadly, dynamic cell-state transitions in living systems.

Explore related subjects

cell biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hong, S.-h., Park, J., Kim, H., Moon, H., Lee, K., Lee, H., Lee, S., Park, Y.. 2026-01-27. Label-free quantitative phenotyping of hepatic stellate cell activation using holotomography with AI-enabled subcellular segmentation. https://doi.org/10.64898/2026.01.26.701682

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Not all TOP RNAs are created equal: 3'UTR length and TSS selection predict the translational regulation of LARP1-bound mRNAs in CD4+ T cells

Naive T cells are poised for activation and contain a pool of translationally repressed ribosomal protein (RP) mRNA prepared to induce ribosome biogenesis to support protein synthesis, cell growth and proliferation. RP mRNA are the prototypical members of a class of transcripts initiating at cytosine followed by a CU rich element called terminal oligo pyrimidine (TOP) RNAs. TOP RNAs are regulated by an RNA binding protein LARP1, which promotes transcript stabilisation and translational repression. We investigated LARP1 function in T cell activation by generating cross-linking immunoprecipitation (CLIP) datasets detailing the LARP1-RNA interactions in naive and activated CD4+ T cells and identifying novel TOP RNAs. TOP RNAs identified by this analysis were functionally diverse. RP mRNAs were typified by high stability, and translational repression in naive T cells followed by MTORC1-dependent translation increases following T cell activation. However, other TOP RNAs varied in these aspects of their regulation. Notably, TOP RNAs with longer 3'UTRs had a relaxed dependency on LARP1 for stability and a reduced dependency on MTORC1 for their translation. Transcription start site heterogeneity also impacted TOP RNA regulation by generating a mixture of transcript isoforms with different TOP motif lengths. Longer terminal oligo pyrimidine stretches were associated with a greater dependency on MTORC1 for translation. Differential regulation of TOP RNAs may allow tuneable translational responses to MTORC1 and indicates potential roles for LARP1 beyond translation regulation and stability.

cell biology↗
bioRxiv2026

Sex-specific metabolic regulation by the Drosophila RNA-binding protein Nab2

Conserved RNA binding proteins (RBPs) regulate key steps of gene expression including mRNA processing, export, localization, stability and translation. Human ZC3H14 is a conserved RBP that regulates pre-mRNA processing in neurons and loss of ZC3H14 leads to neurological defects. Studies of Nab2, the Drosophila orthologue of ZC3H14, have identified potential target RNAs involved in metabolism, suggesting Nab2 may influence neurometabolic circuitry. Here, we show a female-specific increase in dilp2 and dilp5 mRNA levels. The dilps encode insulin-like peptides that signal from the brain insulin producing cells (IPCs) to peripheral tissues. Nab2null females have enlarged lipid droplets in the fat body, a tissue analogous to human adipose tissue and liver. Notably, neuronal depletion of Nab2 increases lipid droplet size while neuronal expression of Nab2 in Nab2null female rescues this phenotype supporting a role for Nab2 in a neuronal circuit that regulates dilp levels. Furthermore, depletion of dilp2 or dilp5 from IPCs rescues the enlarged lipid droplet phenotype in Nab2null females indicating that elevated dilp2/dilp5 contributes to enlarged lipid droplets. Together, these data support a female-specific role for Nab2 in brain neurons to support insulin signaling and fat storage, expanding the known functions of RBPs linking neuronal function and metabolic homeostasis.

cell biology↗
bioRxiv2026

Deep generative embeddings of gene expression and splicing reposition the interpretation of single-cell transcriptomic signatures

Single-cell transcriptomic analysis predominantly derives cell identity from gene expression analysis, while alternative splicing is processed separately despite its fundamental role for cell homeostasis. To overcome the limits of separate investigations, we developed a probabilistic deep learning framework, Crecerelle, enabling resolution of the contributions of gene expression and alternative splicing in each cell. Crecerelle learns cell embeddings from gene expressions and alternative splicing isoforms, to decipher their mutually dependent impact on the functional characterisation of cells in a data-driven manner, exemplified for the Tabula Muris dataset. This is enabled through a zero-and-N-inflated Dirichlet-Multinomial for a variational autoencoder that learns cell embeddings solely from splicing profiles, as well as a bi-modal variational autoencoder with a relevance-weighted mixture-of-experts variational posterior to consolidate the modality-specific contribution at single-cell level. Crecerelle reveals cell-type-specific isoform markers as well as subpopulations with unique isoforms and uncovers regulatory and disease-associated pathways not detected by gene expression analyses alone. This scalable and interpretable framework thus allows a more holistic study of transcriptomic regulation and will open a route to modality-relevance-weighted investigations across single-cell multiomics datasets and their influence on cellular homeostasis, tissue development and disease phenotypes.

cell biology↗