Loss of synaptophysin, one of the major synaptic vesicle membrane proteins, is surprisingly well tolerated in knockout mice. To test whether compensatory gene transcription accounts for the apparent lack of functional deficiencies, comparative transcriptome analyses were carried out. The retina was selected as the most suitable tissue since morphological alterations were observed in mutant photoreceptors, most notably a reduction of synaptic vesicles and concomitant increase in clathrin-coated vesicles. Labeled cRNA was prepared in triplicate from retinae of age- and sex-matched wild-type and mutant litter mates and hybridized to high-density microarray chips. Only three differentially expressed RNAs were identified in this way, one of which was synaptophysin. Further validation by quantitative RT-PCR could only corroborate the results for the latter. We therefore conclude that, despite the distinct morphological phenotype, no significant changes in gene expression are detectable in synaptophysin-deficient animals and that therefore compensatory mechanisms are either pre-existent and/or act at the posttranscriptional level.
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http://dx.doi.org/10.1016/j.brainres.2006.01.080 | DOI Listing |
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.
View Article and Find Full Text PDFGigascience
January 2025
School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
Background: The accurate deciphering of spatial domains, along with the identification of differentially expressed genes and the inference of cellular trajectory based on spatial transcriptomic (ST) data, holds significant potential for enhancing our understanding of tissue organization and biological functions. However, most of spatial clustering methods can neither decipher complex structures in ST data nor entirely employ features embedded in different layers.
Results: This article introduces STMSGAL, a novel framework for analyzing ST data by incorporating graph attention autoencoder and multiscale deep subspace clustering.
Invest Ophthalmol Vis Sci
January 2025
Southwest Hospital/Southwest Eye Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
Purpose: Previous studies have reported divergent sexual responses to aging; however, specific variations in gene expression between aging males and females and their potential association with age-related retinal diseases remain unclear. This study collected data from public databases and developed a comprehensive comparison of retina between aging females and males.
Methods: Single-cell RNA (scRNA) and bulk RNA sequencing data of the aging retina from females and males in public databases were utilized for integrated analysis to investigate sex-biased expression in retina.
Microbiol Spectr
January 2025
Asia-Pacific Centre for Animal Health, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Victoria, Australia.
Previous studies have demonstrated the safety and efficacy of a live-attenuated glycoprotein G (gG) deletion mutant vaccine strain of ILTV (∆gG-ILTV). In the current study, transcriptional profiles of chicken tracheal organ cultures (TOCs), 24 h post inoculation with ∆gG-ILTV or the gG-expressing parent wild-type strain, CSW-1 ILTV were explored and compared with the mock-infected TOCs using RNA-seq analysis. Transcriptomes of the vaccine and wild-type ILTV were also compared with each other.
View Article and Find Full Text PDFSpatial transcriptomics data analysis integrates gene expression profiles with their corresponding spatial locations to identify spatial domains, infer cell-type dynamics, and detect gene expression patterns within tissues. However, the current spatial transcriptomics analysis neglects the multiscale cell-cell interactions that are crucial in biology. To fill this gap, we propose multiscale cell-cell interactive spatial transcriptomics (MCIST) analysis.
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