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| Status |
Public on Jan 18, 2023 |
| Title |
Single-cell analysis reveals inflammatory interactions driving macular degeneration |
| Organism |
Homo sapiens |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
Due to commonalities in pathophysiology, age-related macular degeneration represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 17 retinas with age-related macular degeneration and controls. We create a machine learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease. Examining single-cell data from Alzheimer’s disease and progressive multiple sclerosis with our pipeline, we find a similar glial activation profile enriched in the early phase of these neurodegenerative diseases. In late-stage age-related macular degeneration, we identify a microglia-to-astrocyte signaling axis mediated by interleukin-1beta which drives angiogenesis characteristic of disease pathogenesis. Thus, due to shared glial states, the retina provides a system for investigating therapeutic approaches in neurodegenerative diseases.
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| Overall design |
Samples of retina were isolated from the macula in the central retina of post-mortem globes, located away from the optic disc and major arterioles. For each punch of tissue, the retina was mechanically separated from the underlying retinal pigment epithelium-choroid, nuclei were isolated and purified using the Nuclei EZ Prep Nuclei Isolation Kit (Sigma), following the manufacturer’s protocol. The nuclei suspensions were counted with trypan blue prior to loading on the microfluidics platform. Isolated nuclei from each macular sample were processed through microfluidics-based single nuclear RNA-seq. Single-cell libraries were prepared using the Chromium 3' v2 and v3 platforms (10X Genomics) following the manufacturer’s protocol.
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| Contributor(s) |
Hafler BP, Zheng L, DiStasio M |
| Citation(s) |
37147305 |
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| Submission date |
Dec 15, 2022 |
| Last update date |
Jan 19, 2024 |
| Contact name |
Marcello DiStasio |
| E-mail(s) |
marcello.distasio@yale.edu
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| Organization name |
Yale School of Medicine
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| Department |
Pathology
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| Street address |
300 George St. Rm 353D
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| City |
New Haven |
| State/province |
CT |
| ZIP/Postal code |
06511 |
| Country |
USA |
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| Platforms (1) |
| GPL18573 |
Illumina NextSeq 500 (Homo sapiens) |
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| Samples (17)
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| GSM6841146 |
HC2229, Healthy Control, snRNAseq |
| GSM6841147 |
A19-0065, Healthy Control, snRNAseq |
| GSM6841148 |
1941W, Healthy Control, snRNAseq |
| GSM6841149 |
A0233, Wet AMD, snRNAseq |
| GSM6841150 |
A1098, Wet AMD, snRNAseq |
| GSM6841151 |
A1435, Wet AMD, snRNAseq |
| GSM6841152 |
A1547, Wet AMD, snRNAseq |
| GSM6841153 |
A19-2161, Wet AMD, snRNAseq |
| GSM6841154 |
A20-1232, Wet AMD, snRNAseq |
| GSM6841155 |
A20-1257, Wet AMD, snRNAseq |
| GSM6841156 |
G1850, Dry AMD, snRNAseq |
| GSM6841157 |
A19-2617, Dry AMD, snRNAseq |
| GSM6841158 |
AMD19-1891, Dry AMD, snRNAseq |
| GSM6841159 |
A18-1974, Dry AMD, snRNAseq |
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| Relations |
| BioProject |
PRJNA912653 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE221042_RAW.tar |
2.0 Gb |
(http)(custom) |
TAR (of MTX, TSV) |
| GSE221042_data.mtx.gz |
422.5 Mb |
(ftp)(http) |
MTX |
| GSE221042_genes.csv.gz |
256.0 Kb |
(ftp)(http) |
CSV |
| GSE221042_sample_annotations.csv.gz |
768.8 Kb |
(ftp)(http) |
CSV |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
| Processed data are available on Series record |
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