Single-Cell Transcriptomics of Wnt1 Tumor Models

Whole Wnt1 (tamoxifen injected, Cre negative), DN-Wnt1, and K8iKOR-Wnt1 tumors were dissociated as described above and tumor cells were filtered with a 70 μm filter directly after dissociation to collect single cells from the entire tumor. Cells were captured using the 10X Chromium system (10X Genomics) and sequenced with the NextSeq 500 (Illumina). Raw reads were barcode deconvoluted and aligned to the reference genome (mm10) via cellranger (v3.1.0). All subsequent processing was performed using the Seurat package within R (v3.1.5). Low quality cells (cells with percentage of reads of mitochondrial origin >10%, with percentage of reads of ribosomal origin >45%, with <1000 feature counts, with >6000 feature counts) were filtered from the dataset, and read counts were normalized using the scTransform method (28 (link)). Samples were integrated with the Seurat integrate function (29 (link)) and clustered via UMAP according to nearest neighbors. Re-clustering was performed as above on subset clusters based on common annotation types.

Free full text: Click here

Obr A.E., Bulatowicz J.J., Chang Y.J., Ciliento V., Lemenze A., Maingrette K., Shang Q., Gallagher E.J., LeRoith D, & Wood T.L. (2022). Breast tumor IGF1R regulates cell adhesion and metastasis: alignment of mouse single cell and human breast cancer transcriptomics. Frontiers in Oncology, 12, 990398.

Publication 2022

10X Chromium system NextSeq 500

Cells Chromium Genome Mitochondrial origin Reads Ribosomal Tamoxifen Tumor Wnt1

Corresponding Organization : Rutgers, The State University of New Jersey

Other organizations : Icahn School of Medicine at Mount Sinai

Top 5 similar protocols

Variable analysis

independent variables

Whole Wnt1 (tamoxifen injected, Cre negative)
DN-Wnt1
K8iKOR-Wnt1

dependent variables

Tumor cell characteristics (e.g., transcriptome profile) after dissociation and single-cell sequencing

control variables

Filtering tumor cells with a 70 μm filter after dissociation to collect single cells from the entire tumor
Capturing cells using the 10X Chromium system and sequencing with the NextSeq 500
Filtering low-quality cells based on mitochondrial read percentage, ribosomal read percentage, feature counts, and normalization using the scTransform method
Integrating samples and clustering cells via UMAP according to nearest neighbors
Re-clustering of subset clusters based on common annotation types

Annotations

Based on most similar protocols

Etiam vel ipsum. Morbi facilisis vestibulum nisl. Praesent cursus laoreet felis. Integer adipiscing pretium orci. Nulla facilisi. Quisque posuere bibendum purus. Nulla quam mauris, cursus eget, convallis ac, molestie non, enim. Aliquam congue. Quisque sagittis nonummy sapien. Proin molestie sem vitae urna. Maecenas lorem.

As authors may omit details in methods from publication, our AI will look for missing critical information across the 5 most similar protocols.

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!