Spatial Mapping of Mouse Brain Aging through Indexed Sequencing
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1126287 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR29481300_1.fastq.gz | Fastqsanger.gz | |||
| SRR29481300_2.fastq.gz | Fastqsanger.gz | |||
| SRR29481301_1.fastq.gz | Fastqsanger.gz | |||
| SRR29481301_2.fastq.gz | Fastqsanger.gz | |||
| SRR29481302_1.fastq.gz | Fastqsanger.gz |
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Project description:Here, we have developed a novel methodology called IRIS (Imaging Reconstruction using Indexed Sequencing) that enables cost-effective spatial transcriptomics profiling without relying on optical imaging. Through neighborhood interaction-based reconstruction, IRIS allows extensive analysis of large tissue sections and many replicates with adjustable mapping resolution at only a fraction of the cost of other commercial platforms. With the IRIS platform, we reconstructed a large area spatial area with two whole mouse brain coronal sections. Moreover, we also created a spatially resolved transcriptome atlas of the mouse brain and identified aging-associated changes in gene expression and spatial organization across various brain cell types. Further analysis of cell-cell interaction changes identified aging-associated foci in white matter regions enriched with inflammatory subtypes of microglia and oligodendrocytes. Overall, the IRIS methodology cost-effective and ease-of-use approach makes it broadly applicable to the studies of spatial gene expression changes in various systems.
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Project description:These data were used in the spatial transcriptomics analysis of the article titled \\"Single-Cell and Spatial Transcriptomics Analysis of Human Adrenal Aging\\".
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2024-02-20 | GSE193107 | GEO
Project description:Spatial transcriptomic profiling of the aging mouse brain [spatial scRNA-seq]
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Project description:Spatial transcriptomics has revolutionized our understanding of cellular network dynamics in aging and disease by enabling the mapping of molecular and cellular organization across various anatomical locations. Despite these advances, current methods face significant challenges in throughput and cost, limiting their utility for comprehensive studies. To address these limitations, we introduce IRISeq (Imaging Reconstruction using Indexed Sequencing), a optics-free spatial transcriptomics platform that eliminates the need for predefined capture arrays or extensive imaging, allowing for the rapid and cost-effective processing of multiple tissue sections simultaneously. Its capacity to reconstruct images based solely on sequencing local DNA interactions allows for profiling of tissues without size constraints and across varied resolutions. Applying IRISeq, we examined gene expression and cellular dynamics in thirty brain regions of both adult and aged mice, uncovering region-specific changes in gene expression associated with aging. Further cell type-centric analysis further identified age-related cell subtypes and intricate changes in cell interactions that are distinct to certain spatial niches, emphasizing the unique aspects of aging in different brain regions. The affordability and simplicity of IRISeq position it as a versatile tool for mapping region-specific gene expression and cellular interactions across various biological systems.
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Project description:Single-Cell and Spatial Transcriptomics Analysis of Human Adrenal Aging (spatial transcriptomics)
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Project description:Genetic analyses suggest that alterations in gene expression at the molecular and tissue levels can have profound effects on aging for multi-cellular organisms. However, much remains unknown about the normal pattern of genetic changes in different tissues and how these tissues interact during aging. To investigate tissue-specific aging systematically, we measured expression profiles of aging in Drosophila melanogaster in seven tissues representing nervous, muscular, digestive, renal, reproductive, and storage systems. In each tissue, we identified hundreds of age-related genes mostly showing gradual changes of transcript levels with age. Age-relatedgenes showed clear tissue-specific transcriptional patterns; less than 10% of age-related genes in each tissue shared expression patterns with any other tissue; less than 20% of age-related biological processes were shared between tissues. A significant portion of tissue-specific age-related genes are those involved in physiological functions regulated by the corresponding tissue. However, limited overlaps of age-related function groups among tissues particularly those involved in proteasome function suggest some common mechanisms of transcription regulation in aging across tissues. This study defined global, temporal and spatial changes associated withaging at the molecular and tissue levels. Analyses indicated that different tissues might age in different patterns or at different rates. This study addressed comprehensively the relationship of age-related changes among different tissues in one organism, providing a foundation to address tissue-specific regulation in aging. RNA was then amplified by a one-step linear amplification protocol to generate amplified RNA (aRNA). Experiment aRNA refers to amplified RNA from flies of 15, 20, 30, 45 and 60 days old, and reference aRNA refers to amplified RNA from flies of 3 days old, and experiment and reference aRNAs were labeled with fluorescent dye Cy3 and Cy5, respectively. For each tissue, RNA from the corresponding tissue of 3-day old flies was used as the reference RNA and expression profiles at each of the five age-points was measured twice by using independently prepared duplicated samples. Seven types of tissues or organs of the male fly strain w1118 , accessory gland, testis, brain, gut, malpighian tubule, dorsal thoracic muscle and abdominal fat body were hand dissected out of flies at age of 3, 15, 20, 30, 45 and 60 days old. Tissues or organs from four males of the same age were pooled together and used for each RNA sample preparation.
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Project description:Background: Traumatic brain injury (TBI) often results in diverse molecular responses, challenging traditional proteomic studies that measure average changes at tissue levels and fail to capture the complexity and heterogeneity of the affected tissues. Spatial proteomics offers a solution by providing insights into sub-region-specific alterations within tissues. This study focuses on the hippocampal sub-regions, analyzing proteomic expression profiles in mice at the acute (1 day) and subacute (7 days) phases of post-TBI to understand subregion-specific vulnerabilities and long-term consequences. Methods: Three mice brains were collected from each group including Sham, 1-day post-TBI and 7-day post-TBI. Hippocampal subregions were extracted using Laser Microdissection (LMD); and subsequently analyzed by label-free quantitative proteomics. Results: The spatial analysis reveals region-specific protein abundance changes, highlighting the elevation of FN1, LGALS3BP, HP, and MUG-1 in the stratum moleculare (SM), suggesting potential immune cell enrichment post-TBI. Notably, established markers of chronic traumatic encephalopathy, IGHM and B2M, exhibit specific upregulation in the dentate gyrus bottom (DG2) independent of direct mechanical injury. Metabolic pathway analysis identifies disturbances in glucose and lipid metabolism, coupled with activated cholesterol synthesis pathways enriched in SM at 7-Day post-TBI and subsequently in deeper DG1 and DG2 suggesting a role in neurogenesis and onset of recovery. Coordinated activation of neuroglia and microtubule dynamics in DG2 suggest recovery mechanisms in less affected regions. Cluster analysis revealed spatial variations post-TBI, indicative of dysregulated neuronal plasticity and neurogenesis and further predisposition to neurological disorders. TBI-induced protein upregulation (MUG-1, PZP, GFAP, TJP, STAT-1 and CD44) across hippocampal sub-regions indicates shared molecular responses and links to neurological disorders. Spatial variations were demonstrated by proteins dysregulated in both or either of the time-points exclusively in each subregion (ELAVL2, CLIC1 in PL, CD44 and MUG-1 in SM, and SHOC2, LGALS3 in DG). Conclusions: Utilizing advanced spatial proteomics techniques, the study unveils the dynamic molecular responses in distinct hippocampal subregions post-TBI. It uncovers region-specific vulnerabilities and dysregulated neuronal processes, and potential recovery-related pathways that contribute to our understanding of TBI’s neurological consequences and provides valuable insights for biomarker discovery and therapeutic targets.
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Project description:The spatial organisation of Cellular protein expression profiles within tissues determines cellular function and are key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially resolved, quantitative proteomics of tissue that generates maps of protein expression across a tissue slice derived from a human atypical teratoid-rhabdoid tumour (AT/RT). We employ spatially-aware statistical methods that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with varying spatial abundance patterns. We identified PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response driven, spatially organized protein networks of the extracellular tumour matrix. Overall, this work informs on methods for spatially resolved deep proteo-phenotyping of tissue heterogeneity, which will push the boundaries of understanding tissue biology and pathology at the molecular level.
2023-10-10 | PXD039159 | Pride
Project description:The spatial organisation of Cellular protein expression profiles within tissues determines cellular function and are key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially resolved, quantitative proteomics of tissue that generates maps of protein expression across a tissue slice derived from a human atypical teratoid-rhabdoid tumour (AT/RT). We employ spatially-aware statistical methods that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with varying spatial abundance patterns. We identified PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response driven, spatially organized protein networks of the extracellular tumour matrix. Overall, this work informs on methods for spatially resolved deep proteo-phenotyping of tissue heterogeneity, which will push the boundaries of understanding tissue biology and pathology at the molecular level.
2023-10-10 | PXD039398 | Pride