A Computational Approach for Cell Characterization 1 Without Prior Isolation: Advances in scRNA-seq
Bruno Sime Ferreira Nunes1,2*, Marco Antônio Zanata Alves1, Tarcio Teodoro Braga2*
1Department of Informatics, Federal University of Paraná (UFPR), Brazil.
2Department of Basic Pathology, Federal University of Paraná (UFPR), Brazil.
Correspondence to: Bruno Sime Ferreira Nunes, Department of Informatics, Federal University of Paraná (UFPR), Brazil. E-mail: brunosime@ufpr.br
Correspondence to: Tarcio Teodoro Braga, Department of Basic Pathology, Federal University of Paraná (UFPR), Brazil. E-mail: tarcio.braga@ufpr.br
Received: July 22, 2025; Peer Review Completed: August 12, 2025; Revised: August 13, 2025; Published: N/A
Citation: Nunes BSF, Alves MAZ, Braga TT. A Computational Approach for Cell Characterization 1 Without Prior Isolation: Advances in scRNA-seq. J Genomics Proteomics Bioinformatics. 2025;1(1).
Copyright: © 2025 Nunes BSF, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
ABSTRACT
Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but traditional cell isolation methods like flow cytometry and laser microdissection often suffer from limitations in efficiency, viability, and bias. To overcome these challenges, computational tissue deconvolution approaches have emerged as effective alternatives. In this work, we introduce a high-performance computational pipeline for scRNA-seq data analysis that identifies and segregates cell populations based on marker gene expression. Our method incorporates advanced preprocessing, normalization, and clustering techniques, optimized for scalability and reproducibility in high-performance computing (HPC) environments. Compared to related tools, our pipeline offers enhanced adaptability across diverse datasets and experimental settings. We validated its performance using zebrafish ventricular tissue post-injury, effectively identifying key regenerative cell types such as immune cells, including macrophages. This approach supports in-depth biological discovery without prior physical cell separation and expands the potential of scRNA-seq applications in regenerative biology, immunology, and single-cell transcriptomics.
KEYWORDS
Single-cell RNA sequencing (scRNA-seq); Cell characterization; Computational deconvolution; High-performance computing (HPC); Marker gene expression; Zebrafish heart regeneration; Immune cell profiling; Macrophages; Tissue heterogeneity; Unbiased cell type identification.
This article is currently under peer review and is expected to be published soon in the Journal of Genomics, Proteomics & Bioinformatics.
Note: "A digitally signed author cover letter has been submitted as part of the manuscript submission to the Journal of Genomics, Proteomics & Bioinformatics. All contributing authors have confirmed that the manuscript represents original work, has not been published previously, and is not being submitted simultaneously to any other journal for consideration."
