SCITRAM: a Single-cell Integrated transcription regulation modeler for predicting master transcription factors
Year: 2026
Type: preprint
Abstract: SUMMARY Single-cell omics technologies have provided unprecedented access to molecular cell heterogeneity, revolutionizing our understanding of complex living systems. Although generating such type of data has become a streamlined process, data processing still relies on command-line driven tools, or commercial solutions. Furthermore, single-cell transcriptomics analysis is usually reduced to stratifying cells on the grounds of gene expression signatures, while deconvolving gene regulatory programs (and their associated master transcription factors) responsible for cell heterogeneity are not classically addressed with the available tools, or performed under different data processing platforms. Herein, we present SCITRAM (Single-Cell Integrated TRAnscription regulation Modeler), a user-friendly stand-alone computational solution for predicting master transcription factors from single-cell transcriptomics data. SCITRAM first reconstructs a primary gene regulatory network and subsequently uses this network as template to model transcriptional cascades driven by the in-silico activation of transcription factors identified within the system. We validated SCITRAM performance across a variety of cell fate transition events including T cell exhaustion during solid tumor treatment, lineage stratification during pancreatic endocrinogenesis, as well as nervous tissue formation in cerebral organoid models. Overall, SCITRAM provides an accessible and integrated platform for processing (large amounts of) single-cell transcriptomics data, enabling the investigation of transcriptional regulatory programs and their master transcription factors without requiring a command-line driven environment. Key Points SCITRAM fills a methodological gap by providing an integrated, user-friendly platform for inferring gene regulatory programs and master transcription factors from single-cell transcriptomics data, beyond classical clustering-based analyses. Its workflow combines GRN reconstruction with in-silico TF activation modeling, enabling systematic exploration of transcriptional cascades underlying cellular heterogeneity. The tool eliminates reliance on command-line pipelines or commercial software, making large-scale single-cell regulatory analysis accessible to non-computational users. SCITRAM was validated across diverse biological systems, including T-cell exhaustion, pancreatic endocrine lineage specification, and neural tissue formation in cerebral organoids. SCITRAM provides a unified environment for processing and interpreting single-cell transcriptomics data with a focus on regulatory program discovery. Biographical Note Marco Antonio Mendoza-Parra; PhD/HDR. Research Director (CNRS – France); Head of the team Systems Biology of cell fate decisions (SysFate: https://www.sysfate.org/ ).
Institutions: Centre National de la Recherche Scientifique, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Génomique Métabolique du Genoscope, Genoscope, Université d'Évry Val-d'Essonne, Consorci Institut D'Investigacions Biomediques August Pi I Sunyer, Leibniz Institute for Immunotherapy, University of Bonn, University Hospital Bonn, University of Regensburg
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7216531078