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Sample GSM8121295

Query DataSets for GSM8121295

Status

Public on Apr 01, 2024

Title

30K_plex-unc-rep2

Sample type

SRA

Source name

dorsal fin

Organism

Nothobranchius furzeri

Characteristics

tissue: dorsal fin
group: uncut caudal fin

Treatment protocol

Anesthetized fish were amputated using a disposable razor blade at the plane of amputation (Supplementary Figure S1A).

Growth protocol

African killifish Nothobranchius furzeri were reared at the Stowers Institute and all animal procedures were performed with IACUC approval (Protocol ID: 2022-137).

Extracted molecule

total RNA

Extraction protocol

Freshly collected fin tissue was dissociated with 1mg/mL collagenase type II, for 5 mins, 70μm filtered, labeled with CellPlex reagents (10x Genomics), counted, stained with and 1μg/mL DAPI, 500 nM Draq5 at 4e6 cells/mL and sorted on a 6-laser BD S6 FACSymphony with a 100-μm nozzle chilled to 4°C at all times, sorting was done on Draq5 positive DAPI negative gates, samples with post-sort viability >96% were used for library preparation and sequencing.
Libraries were prepared using the Chromium Next GEM Single Cell 3' Reagent Kits v3.1 with Feature Barcode technology for Cell Multiplexing (10x Genomics) according to manufacturer’s directions. Resulting cDNA, short fragment libraries, and CMO libraries were checked for quality and quantity using a 2100 Bioanalyzer (Agilent Technologies) and Qubit Fluorometer (Thermo Fisher Scientific). Multiplexed gene expression and CMO libraries were pooled, as specified by manufacturer, and sequenced to a depth necessary to achieve at least 26,000 mean reads per cell on an Illumina NovaSeq 6000 instrument utilizing RTA and instrument software versions current at the time of processing with the following paired read lengths: 28*10*10*90bp

Library strategy

RNA-Seq

Library source

transcriptomic single cell

Library selection

cDNA

Instrument model

Illumina NovaSeq 6000

Description

uncut dorsal fin belonging to fish with uncut caudal fin

Data processing

Fastqs were demultiplexed using cellranger mkfastq (cellranger 6.0.1) with default settings. Cellranger multi (cellranger 6.0.1) was used to align the fastqs against Ensembl 104 Nfu_20140520 and filter, count barcodes and UMIs
Demultiplexed sample feature count matrices were loaded into R using Seurat (Seurat_4.3.0), mitochondrial percentages were calculated using the PercentageFeatureSet function and a median + 2 sd threshold was use to filter out cells with high mitochondrial gene expression, the resulting percent.mt distribution in the integrated object had a median of 3.9% and the highest value was 21.5%. Low nFeature_RNA count was filtered with median - 1.2 sd and high nFeature_RNA count was filtered with median + 4 sd, the resulting nFeature_RNA distribution in the integrated object had a median of 1536 and the highest value was 5259 nFeature_RNA. We decided to apply relative statistical thresholds to compensate between batch effects and sequencing depth between samples considering doublets were removed during demultiplexing on the previous step. After quality control, all samples were normalized individually using the SCTransform function regressing percent.mt from the model. All the samples for integration were put on a list and anchor features were identified with SelectIntegrationFeatures (nfeatures = 8000), samples were prepared for integration using the function PrepSCTIntegration and the anchor features, and integration anchors were identified with the function FindIntegrationAnchors using the list containing the samples, SCT as the normalization method and the anchor features. Finally, all samples were integrated using the function IntegrateData with the integration anchors and SCT as the normalization method. Once integrated, RunPCA, RunUMAP and FindNeighbors functions were run on the integrated assay, and clusters were computed with the FindClusters function by iteratively changing the cluster resolution from 0.2 to 2 in 0.1 intervals. Cell markers of differential expression analysis was done using the FindMarkers function and selecting the subset of cells to compare to each other.
Differential analysis with MiloR. To understand the enrichment of certain cell types between conditions, differential abundance between the cell neighborhoods for a given condition were tested using MiloR16. We tested for differential abundance between the conditions (proximal vs distal, regenerated vs homeostasis). Using Seurat generated graph, a K-nearest neighborhood (KNN) graph was precomputed which assigned the cells to a neighborhood with the parameters (k=20, d=30, prop = 0.1). Cells within each neighborhood for a given sample were then counted and tested for differential abundance using a generalized linear design framework while accounting for multiple comparison testing using the spatial FDR in MiloR. We then annotated the neighborhoods with the cell clusters and neighborhoods with a fraction less than 0.7 were annotated to be “mixed”.
Assembly: Nfu_20140520
Supplementary files format and content: h5 file containing counts matrix
Supplementary files format and content: per 10x recommendations cell_counts.txt contains cell counts per sample

Submission date

Feb 29, 2024

Last update date

Apr 01, 2024

Contact name

Alejandro Sanchez Alvarado

E-mail(s)

[email protected]

Organization name

Stowers Institute for Medical Research

Lab

Sanchez Alvarado Lab