CORTEXA
← Browse
arxivq-bio.GNcs.AI2026-06-26

Reconstructing the Developmental Trajectory of Adipocytes in Human Adipose Tissue Using Single-Cell RNA Sequencing

Weny S. M Sitinjak, Humasak Tommy Argo Simanjuntak

Obesity is a global health crisis associated with metabolic disorders such as type 2 diabetes and cardiovascular disease. This study employed single-cell RNA sequencing to reconstruct the developmental trajectory of human adipocytes from adipose tissue samples. Our analysis identified 15 transcriptionally distinct cell clusters, including 7 transitional states, revealing the dynamic process of adipocyte differentiation. We detected 16 functionally active signaling pathways mediating cellular communication between adipocytes and their progenitors. Among these, insulin-like growth factor (IGF) and fibroblast growth factor (FGF) pathways emerged as the most prominent networks, showing consistent activity across differentiation stages (p<0.05). The study revealed depot-specific differences, with visceral adipocytes undergoing additional extracellular matrix remodeling absent in subcutaneous differentiation. Spatial analysis further showed that IGF signaling was particularly active in perivascular niches, while FGF activity dominated in mature adipocyte zones. These results provide the first comprehensive map of human adipocyte development, highlighting IGF and FGF pathways as potential therapeutic targets. The identified signaling networks offer new insights for developing interventions to promote healthy adipose expansion or inhibit pathological fat accumulation. This work advances our fundamental understanding of adipose tissue biology while providing clinically relevant data for metabolic disorder treatments.

View free PDFSource page

Related papers

arxivq-bio.GNcs.AIcs.LG2026-07-20

Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min-Max Selection

Yaodi Luo, Peize He, Bowen Han, Lingbei Mengg

Single-cell datasets are increasingly costly to store, audit, and reuse for model training. Dimensionality reduction and dataset distillation can reduce this burden, but conventional distillation methods often produce synthetic expression profiles that cannot be traced to an assa…

View free PDFSource page
arxivcs.LGcs.AIq-bio.GN2026-07-04

SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data

Muhammet Sami Yavuz, Ayhan Can Erdur, Sabri Mustafa Kahya, Benedikt Wiestler, Jana Lipkova

Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment. Existing approaches to this challenge typically restrict analysis to genes shared across cohorts, exclude pat…

View free PDFSource page
arxivq-bio.GNcs.AIcs.LG2026-07-21

Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models

Sarwan Ali

Genomic language models achieve strong performance across regulatory-genomics tasks, yet what these models internally represent remains opaque, and the field lacks a principled procedure for verifying that an apparent ``concept'' inside a model is real rather than an artifact of…

View free PDFSource page
arxiveess.IVcs.AIq-bio.GN2026-06-29

Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction

Dominik Winter, Dominik Vonficht, Loïc Le Bescond, Christian Gebbe, Marco Rosati, Richard J. Chen, et al.

H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability. We show that training a lightweight alignment modul…

View free PDFSource page
arxivq-bio.GNcs.AI2026-06-25

GRAFT: Biological Graph and Hypergraph Benchmarks for Linked Gene Expression and Phenotypic Trait Prediction in Arabidopsis thaliana

Manuel Serna-Aguilera, Vanshika Jindal, Fiona L. Goggin, Jiamei Li, Aranyak Goswami, Alexander Bucksch, et al.

Understanding which genes control which traits in an organism remains one of the central challenges in biology. Despite significant advances in data collection technology, our ability to map genes to traits is still limited. This genome-to-phenome (G2P) challenge spans several pr…

View free PDFSource page
arxivcs.LGcs.AIq-bio.BMq-bio.GN2026-06-26

Two-Stage Fine-Tuning for Protein Sequence Generation with Targeted Amino-Acid Composition

Violeta Basten-Romero, Rubén Muñoz-Tafalla, Anna María Díaz-Rovira, Bertran Miquel-Oliver, Isaac Filella-Merce, Víctor Guallar

Protein language models are standard priors for biological sequence generation, but steering them toward explicit distributional design targets remains largely unexplored. We study a constrained protein generation problem in which sequences must match a desired amino-acid (AA) co…

View free PDFSource page