{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"omics_type":["Metabolomics","Unknown","Transcriptomics","Genomics","Proteomics"],"submitter":["Jacob Insua-Rodríguez"],"instrument_platform":["10X Genomics Chromium v3.1 with multiplexing kit","Illumina NovaSeq 6000"],"study_type":["RNA-seq of total RNA"],"organism":["Mus musculus"],"species":["Mus musculus"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-16621"],"description":["Metastasis frequently affects multiple organs in  stage IV breast cancer patients and is associated with dismal outcomes. The mechanisms enabling cancer cell colonization across diverse anatomical sites remain poorly understood, mainly due to lack of models of systemic disease and of studies in clinical samples involving multiple metastatic sites. Here, we combined metastatic niche labeling with single-cell RNA sequencing (scRNA-seq) in a mouse model of breast cancer multi-organ metastasis, and generated a cellular and molecular atlas of metastatic ecosystems encompassing >70,000 cells across brain, lung, liver, and bone."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Nucleic Acid Extraction - Droplet generation and scRNA-seq library preparation Cell pools were processed following the Chromium Next GEM Single Cell 3’ user guide instructions (10X Genomics, CG000388 Rev B). In brief, cells were resuspended in chilled PBS + 10% FBS at a concentration of approximately 1,000 cells/µl and loaded onto the 10X Genomics Chromium Controller for generating single-cell droplets.","Library Construction - Single-cell RNA-seq gene expression and cell multiplexing libraries were generated following the 10X Genomics Chromium v3.1 guideline.","Sample Collection - Tissue digestion for generation of single-cell suspensions To generate a niche-labeled scRNA-seq dataset of multi-organ synchronous metastasis, single cell suspensions were generated from brain, lung, liver and bones from lower limbs tissues of FVB mice injected with VO-PyMT cells. One mouse was processed each time, and a total of 3 mice were used for this dataset. To generate single cell suspensions, tissue dissociation protocols were optimized to ensure the best possible representation of cellular diversity for each organ, cell yield and viability, considering technical feasibility as well. For each iteration of these experiments, a cohort of 3-5 mice were injected i.c. with VO-PyMT cells, and on days 10-11 post-i.c. injections, systemic metastatic burden was monitore","Sequencing - Multiplexed, single cell gene expression libraries were sequenced on the Illumina HiSeq. Alignment of 3′ end counting libraries from scRNA-seq analyses was completed utilizing 10x Genomics CellRanger v.6.1.2. Each library was aligned to an indexed mm10 genome using cellranger count."],"figure_sub":["Organization","MINSEQE Score","Assays and Data","MAGE-TAB Files"],"pubmed_authors":["Kai Kessenbrock","Devon Lawson","Jacob Insua-Rodríguez"],"additional_accession":[]},"is_claimable":false,"name":"Single-cell RNA-sequencing of multi-organ metastasis in the VO-PyMT mouse model of breast cancer","description":"Metastasis frequently affects multiple organs in  stage IV breast cancer patients and is associated with dismal outcomes. The mechanisms enabling cancer cell colonization across diverse anatomical sites remain poorly understood, mainly due to lack of models of systemic disease and of studies in clinical samples involving multiple metastatic sites. Here, we combined metastatic niche labeling with single-cell RNA sequencing (scRNA-seq) in a mouse model of breast cancer multi-organ metastasis, and generated a cellular and molecular atlas of metastatic ecosystems encompassing >70,000 cells across brain, lung, liver, and bone.","dates":{"release":"2026-07-31T00:00:00Z","modification":"2026-07-31T13:26:31.697Z","creation":"2026-02-10T12:30:24.992Z"},"accession":"E-MTAB-16621","cross_references":{"ENA":["ERP188936"],"EFO":["EFO_0002944","EFO_0004170","EFO_0009653","EFO_0005518","EFO_0004184"]}}