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Single-Cell Technologies: From Research to Application
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Division of Hematology and Oncology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei 10449, Taiwan
Prof. Dr. Chung-Der Hsiao
Epidermal Stem Cell Lab, Department of Bioscience Technology, Chung Yuan Christian University, Chung-Li 32023, Taiwan
Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
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Single-Cell Technologies: From Research to Application

Abstract submission deadline
31 October 2025
Manuscript submission deadline
31 December 2025
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Topic Information

Dear Colleagues,

Recent advancements in single-cell sequencing, imaging, and analytical technologies have revolutionized our understanding of cellular heterogeneity, providing unparalleled insights into biological processes such as development, differentiation, and disease progression. These innovations enable researchers to analyze cellular dynamics with remarkable precision, uncovering critical mechanisms and biomarkers essential for health and disease. Additionally, single-cell technologies are driving transformative progress in personalized medicine, oncology, and immunology through precise diagnostics, targeted therapies, and innovative data analysis tools. Furthermore, the development of sophisticated computational and bioinformatics tools is vital for managing and interpreting the complex data generated from single-cell studies. These tools facilitate data integration, visualization, and interpretation, making them indispensable for extracting meaningful biological insights. This Topic seeks contributions from leading researchers across various disciplines to foster innovative applications and collaborative efforts. By integrating technological advancements with biological and clinical challenges, this Topic aims to drive progress in basic, translational, and clinical research. The ultimate goal is to enhance our understanding of cellular processes, improve diagnostic and therapeutic strategies, and translate these insights into improved health outcomes. Original research papers and review articles related to this Topic are welcomed.

Dr. Ken-Hong Lim
Prof. Dr. Chung-Der Hsiao
Prof. Dr. Pei-Ming Yang
Topic Editors

Keywords

  • single-cell technologies
  • personalized medicine
  • diagnostics
  • therapeutics
  • computational biology
  • bioinformatics
  • cellular heterogeneity

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
BioTech
biotech 		3.1 4.8 2012 22.3 Days CHF 1600 Submit
DNA
dna 		- - 2021 20.5 Days CHF 1000 Submit
Genes
genes 		2.8 5.5 2010 14.6 Days CHF 2600 Submit
International Journal of Molecular Sciences
ijms 		4.9 9.0 2000 20.5 Days CHF 2900 Submit
Current Issues in Molecular Biology
cimb 		3.0 3.7 1999 17.8 Days CHF 2200 Submit

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Published Papers (1 paper)

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Article
A New Tool to Decrease Interobserver Variability in Biomarker Annotation in Solid Tumor Tissue for Spatial Transcriptomic Analysis
by Sravya Palavalasa, Emily Baker, Jack Freeman, Aditri Gokul, Weihua Zhou, Dafydd Thomas, Wajd N. Al-Holou, Meredith A. Morgan, Theodore S. Lawrence and Daniel R. Wahl
Curr. Issues Mol. Biol. 2025, 47(7), 531; https://doi.org/10.3390/cimb47070531 - 9 Jul 2025
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Abstract
Integrating spatial transcriptomic data with immunofluorescence image data is challenging using existing tools due to their differences in spatial resolution. Immunofluorescence provides information about protein expression at the cellular or subcellular level, whereas spatial transcriptomic platforms typically rely on multicellular “spots” for RNA [...] Read more.
Integrating spatial transcriptomic data with immunofluorescence image data is challenging using existing tools due to their differences in spatial resolution. Immunofluorescence provides information about protein expression at the cellular or subcellular level, whereas spatial transcriptomic platforms typically rely on multicellular “spots” for RNA profiling. Our study coupled spatial transcriptomics of irradiated glioblastoma tissues with immunofluorescence for γH2AX, a marker of DNA damage within the nuclei of cells. We then compared gene expression in γH2AX-positive and negative regions within the tissue. There was significant interobserver variability in manual annotation of γH2AX positivity in multicellular spots by three different researchers (Kappa statistic = 0.345), despite all of them being familiar with γH2AX immunofluorescence and having predefined imaging parameters for annotation. This variability led to different researchers nominating different genes as being associated with DNA repair. To overcome this problem, we have developed a new tool using MATLAB. This tool performs “spot”-wise image analysis and uses researcher-defined parameters such as immunofluorescent marker intensity threshold and number of positive cells to annotate the “spots” as γH2AX positive or negative. The tissue with the most variability in manual annotation was annotated reproducibly by our MATLAB tool, leading to reproducible downstream analysis. Full article
(This article belongs to the Topic Single-Cell Technologies: From Research to Application)
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