The field of single-cell omics has transformed our understanding of biological processes and is constantly advancing both experimentally and computationally. One of the most significant developments is the ability to measure the transcriptome of individual cells by single-cell RNA-seq (scRNA-seq), which was pioneered in higher eukaryotes. While yeast has served as a powerful model organism in which to test and develop transcriptomic technologies, the implementation of scRNA-seq has been significantly delayed in this organism, mainly because of technical constraints associated with its intrinsic characteristics, namely the presence of a cell wall, a small cell size and little amounts of RNA. In this review, we examine the current technologies for scRNA-seq in yeast and highlight their strengths and weaknesses. Additionally, we explore opportunities for developing novel technologies and the potential outcomes of implementing single-cell transcriptomics and extension to other modalities. Undoubtedly, scRNA-seq will be invaluable for both basic and applied yeast research, providing unique insights into fundamental biological processes.
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http://dx.doi.org/10.1002/yea.3934 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, ON K1H 8L6.
Although chromatin remodelers are among the most important risk genes associated with neurodevelopmental disorders (NDDs), the roles of these complexes during brain development are in many cases unclear. Here, we focused on the recently discovered ChAHP chromatin remodeling complex. The zinc finger and homeodomain transcription factor ADNP is a core subunit of this complex, and de novo mutations lead to intellectual disability and autism spectrum disorder.
View Article and Find Full Text PDFCancer Res
January 2025
Karolinska Institutet, Stockholm, Stockholm, Sweden.
Transgenic mice and organoid models, such as three-dimensional tumoroid cultures, have emerged as powerful tools for investigating cancer development and targeted therapies. Yet, the extent to which these preclinical models recapitulate the cellular identity of heterogeneous malignancies, like neuroblastoma (NB), remains to be validated. Here, we characterized the transcriptional landscape of TH-MYCN tumors by single-cell RNA sequencing (scRNA-seq) and developed ex vivo tumoroids.
View Article and Find Full Text PDFAnim Cells Syst (Seoul)
January 2025
Department of Genome Medicine and Science, Gachon University College of Medicine, Incheon, Republic of Korea.
Dynamic modeling of cellular states has emerged as a pivotal approach for understanding complex biological processes such as cell differentiation, disease progression, and tissue development. This review provides a comprehensive overview of current approaches for modeling cellular state dynamics, focusing on techniques ranging from dynamic or static biomolecular network models to deep learning models. We highlight how these approaches integrated with various omics data such as transcriptomics, and single-cell RNA sequencing could be used to capture and predict cellular behavior and transitions.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Stem Cell and Regenerative Medicine, Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
Background: It is worthwhile to establish a prognostic prediction model based on microenvironment cells (MCs) infiltration and explore new treatment strategies for triple-negative breast cancer (TNBC).
Methods: The xCell algorithm was used to quantify the cellular components of the TNBC microenvironment based on bulk RNA sequencing (bulk RNA-seq) data. The MCs index (MCI) was constructed using the least absolute shrinkage and selection operator Cox (LASSO-Cox) regression analysis.
Respir Res
January 2025
Department of Regenerative and Infectious Pathology, Hamamatsu University School of Medicine, 1-20-1 Handayama Chuo-ku, Hamamatsu, Shizuoka, 431-3192, Japan.
Background: Recent advances in comprehensive gene analysis revealed the heterogeneity of mouse lung fibroblasts. However, direct comparisons between these subpopulations are limited due to challenges in isolating target subpopulations without gene-specific reporter mouse lines. In addition, the properties of lung lipofibroblasts remain unclear, particularly regarding the appropriate cell surface marker and the niche capacity for alveolar epithelial cell type 2 (AT2), an alveolar tissue stem cell.
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