Single cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
To facilitate scalable profiling of single cells, we developed Split Pool Ligation-based Transcriptome sequencing (SPLiT-seq), a single-cell RNA-seq (scRNA-seq) method that labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing and requires no customized equipment. We used SPLiT-seq to analyze 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords. Over 10...
Provenance — who produced it, who reused it
Linked to 10 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- Benchmarking atlas-level data integration in single-cell genomic... 2021 · 1,404 cites
- High-throughput sequencing of the transcriptome and chromatin ac... 2019 · 891 cites
- Mapping single-cell data to reference atlases by transfer learni... 2021 · 625 cites
- An ultra high-throughput method for single-cell joint analysis o... 2019 · 380 cites
- Joint profiling of histone modifications and transcriptome in si... 2021 · 314 cites
- A harmonized atlas of mouse spinal cord cell types and their spa... 2021 · 271 cites
4 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently