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GEO help: Mouse over screen elements for information. |
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| Status |
Public on Feb 26, 2024 |
| Title |
Crispr Mouse with ReDeeM, Batch2, RNA, sample 15 |
| Sample type |
SRA |
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| Source name |
Lung cancer
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| Organism |
Mus musculus |
| Characteristics |
tissue: Lung cancer
cell type: Kras;Trp53(KP)-drive lung adenocarcinoma
library type: mRNA
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| Extracted molecule |
polyA RNA |
| Extraction protocol |
Mouse experiments were approved by the Massachusetts Institute of Technology Institutional Animal Care and Use Committee (IACUC) (institutional animal welfare assurance no. A-3125-01). A male mouse embryonic stem cell (mESC) line harbouring the conditional alleles KrasLSL-G12D/+ and Trp53fl/fl (KP) was engineered with the lineage tracer cassettes. The detailed engineering process, including vector information, tumour harvest and single-cell suspension were prepared as described in Yang et al. Two independent mESC lines were used for batch-1 and batch-2 experiments.
The single cells of batch-2 from multiple tumors were labelled with Cell Hash and were profiled using ReDeeM except the following modification. Additional Target Site libraries were needed. The amplified cDNA libraries were further amplified with Target Site-specific primers containing Illumina-compatible adapters and sample indices (oDYT023-oDYT038, forward:5′CAAGCAGAAGACGGCATACGAGATNNNNNNNNGTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGAATCCAGCTAGCTGTGCAGC; reverse:5′-AATGATACGGCGACCACCGAGATCTACACNNNNNNNNTCTTTCCCTACACGACGCTCTTCCGATCT; “N” denotes sample indices) using Kapa HiFi ReadyMix (Roche), as described.
For sequencing scRNA, scATAC, mtDNA libraries, the same strategy as described for ReDeeM were used, except that four sets of mouse specific probes were designed to enrich mitochondrial fragments. For sequencing Target Site libraries, 15,000 total reads per cell were expected, and the following read lengths were used: Read1: 26 cycles, i7: 8 cycles, Read2: 290 cycles).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic single cell |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
3' mRNA library (Cell barcodes are matchable with ATAC, mtDNA,TargetSite and CellHash lib in sample 15): read1 (1-28nt) file contains cell barcode and UMI; read2 file contains transcript.
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| Data processing |
Standard ReDeeM pipeline was used for pre-processing mtDNA data as described above. The Target Site data was pre-processed using Python package Cassiopeia. Briefly, reads with identical cellBC and UMI were collapsed into a single, error-corrected consensus sequence representing a single-expressed transcript. Consensus sequences were identified within a cell based on a maximum of 10 high-quality mismatches (PHRED score greater than 30) and an edit distance less than 2 (default pipeline parameters). UMIs within a cell reporting more than one consensus sequence were resolved by selecting the consensus sequence with more reads. Each consensus sequence was aligned to the wild-type reference Target Site sequence using a local alignment strategy, and the intBC and indel alleles were called from the alignment. Cells with fewer than 2 reads per UMI on average or fewer than 10 UMIs overall were filtered out. These data were summarized in a molecule table which records the cellBC, UMI, intBC, indel allele, read depth, and other relevant information. Then, character matrices were formed summarizing indel information across the N cells in a population and their M cut-sites. Characters (i.e., cut-sites) with more than 80% missing information or containing a mutation that was reported in greater than 98% of cells were filtered out for downstream analysis
Assembly: mm10
Supplementary files format and content: 10x Genomics output files: filtered_feature_bc_matrix.h5, atac_fragments.tsv.gz
Supplementary files format and content: mtDNA mutation raw genotype and depth: mtDNA.RawGenotypes.Total.StrandBalance, mtDNA.depth
Supplementary files format and content: Analyzed Indels per cell by Cassiopeia: indel_alleles.tar.gz
Supplementary files format and content: TotalSeq-A Cell hash analyzed: Hash_calls.tsv, Hash_annotation.tsv
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| Submission date |
Feb 26, 2024 |
| Last update date |
Mar 01, 2024 |
| Contact name |
Chen Weng |
| E-mail(s) |
cweng@wi.mit.edu
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| Organization name |
Broad Institute and Whitehead Institute
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| Department |
Genetics
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| Lab |
Vijay Sankaran lab and Jonathan Weissman lab
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| Street address |
415 Main St, Cambridge
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| City |
Boston |
| State/province |
OH |
| ZIP/Postal code |
02139 |
| Country |
USA |
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| Platform ID |
GPL24247 |
| Series (2) |
| GSE219015 |
Deciphering cell states and genealogies of human hematopoiesis with single-cell multi-omics |
| GSE259285 |
Deciphering cell states and genealogies of human hematopoiesis with single-cell multi-omics [Crispr_Mouse_Batch2] |
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| Relations |
| BioSample |
SAMN40149255 |
| SRA |
SRX23810669 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM8113086_Crispr_Mouse_Batch2.filtered_feature_bc_matrix.h5 |
161.0 Mb |
(ftp)(http) |
H5 |
SRA Run Selector |
| Raw data are available in SRA |
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