run_metadata: 32375

This data as json

rowid	run.accession	experiment.accession	sample.accession	study.accession	bioproject	study.title	study.alias	study.type	study.abstract	study.attributes	study.PMIDs	sample.description	sample.title	sample.alias	sample.centername	sample.attributes	GEOsample.title	GEOsample.dataprocessing	GEOsample.source	GEOsample.treatmentprotocol	GEOsample.extractprotocol	GEOsample.growthprotocol	GEOsample.characteristics	GEOsample.accession	experiment.title	experiment.alias	experiment.library_name	experiment.design_description	experiment.library_construction_protocol	experiment.attributes	experiment.library_strategy	experiment.library_source	experiment.library_selection	experiment.library_layout	experiment.platform	experiment.instrument_model	experiment.spot_descriptor	experiment.study_ref	run.title	run.attributes	run.filename	run.semantic_name	run.total_bases	run.total_spots	run.alias	run.read_lengths	run.base_counts	run.r1_length	run.r2_length	run.r3_length	run.r4_length	run.Acount	run.Ccount	run.Gcount	run.Tcount	run.Ncount	run.experiment	run.pool_member	submission.accession	submission.srasource	submission.bioprojectsource	seqdetective.n_mates	seqdetective.mapping_rate.mate1	seqdetective.mapping_rate.mate2	seqdetective.nofeature_rate.mate1	seqdetective.nofeature_rate.mate2	seqdetective.sparsity.mate1	seqdetective.sparsity.mate2	seqdetective.pos_strand_rate.mate1	seqdetective.pos_strand_rate.mate2	seqdetective.readlen.mate1	seqdetective.readlen.mate2	seqdetective.judgement.mate1	seqdetective.judgement.mate2	seqdetective.judgement.reason	platform_family	instrument_generation	read_bias	selection_class	prep_kit	sc_or_bulk	tech_class	technology	tech_variant	submission.bioprojectsource.country	earliest_date	devstage_curation	devstage_curation_coarse	tissue_curation	tissue_curation_coarse
32375	SRR29181679	SRX24701870	SRS21429399	SRP509892	PRJNA1116548	Barcoding Notch signaling in the developing brain	GSE268356	Other	Developmental signaling inputs are fundamental for shaping cell fates and behavior. However traditional fluorescent based signaling reporters have limitations in scalability and molecular resolution of cell types. We present SABER seq a CRISPR Cas molecular recorder that stores transient developmental signaling cues as permanent mutations in cellular genomes for deconstruction at later stages via single cell transcriptomics. We applied SABER seq to record Notch signaling in developing zebrafish brains. SABER seq has two components: a signaling sensor and a barcode recorder. The sensor activates Cas9 in a Notch dependent manner with inducible control while the recorder obtains mutations in ancestral cells where Notch is active. We combine SABER seq with an expanded juvenile brain atlas to identify cell types derived from Notch active founders. Our data reveals rare examples where differential Notch activity in ancestral progenitors is detected in terminally differentiated neuronal subtypes. SABER seq is a novel platform for rapid scalable and high resolution mapping of signaling activity during development. Overall design: 10x Genomics libraries of single cell transcriptomes and SABER seq data and genomic DNA SABER data		pubmed:39575683		SABERb4 brainB2 2 brain4 transcriptome	GSM8290056		source name:zebrafish whole brain\|tissue:zebrafish whole brain\|developmental stage:21 dpf 23 dpf Juvenile\|genotype:WT/TLAB\|geo loc name:missing\|collection date:missing	SABERb4 brainB2 2 brain4 transcriptome	Raw FASTQ reads were aligned against zebrafish genome built from GRCz.109.gtf and the GRCz11.fa files using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly all count matrices were separately loaded into R. Seurat objects were then created with modified parameters requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline similar to the transcriptomic reads. A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. post loading filtered barcode matrices into R error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously7 35. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. scRNA seq data was analyzed using the following protocol. Raw FASTQ reads were aligned against zebrafish genome built from GRCz.109.gtf and the GRCz11.fa files using the Cell Ranger count pipeline to generate gene by cell count matrices filtered barcode matrix. The count matrices were processed using the standard workflow of the Seurat v5.0.2 R package with modifications. Briefly all count matrices were separately loaded into R. Seurat objects were then created with modified parameters requiring features to be detected in at least 3 cells and cells to contain at least 300 genes. Subsequently the Seurat objects were merged to create a single Seurat object. Cells with RNA counts between 300 and 4000 and a mitochondrial gene percentage of less than 9% were selected for downstream analysis. Gene expression values were log normalized with the default parameters and scaled for the top 3000 variable genes. The scaled data was then used to compute the 50 principal components. Cells were iteratively clustered using the Louvain algorithm post identifying neighboring cells. Differentially expressed genes for each cluster were identified using genes that are detected in at least 10% of cells in either of the two clusters and genes showing a minimum difference of 10% between the two clusters. Cell types were annotated using known marker genes. Finally the data was visualized using UMAP. SABER seq barcode analysis was performed using the following protocol. The SABER seq FASTQ reads were processed using the Cell Ranger count pipeline.A bash script was then used to generate a text file containing all FASTQ headers and error corrected cell barcodes for SABER seq data. Error corrected cell barcodes from transcriptomic reads were isolated and matched against those in the text file to identify common cell barcode that are shared between the transcriptomic and SABER seq datasets. The resulting text file contained redundant error corrected cell barcodes associated with different UMIs for the same cell. Thus unique error corrected cell barcodes were isolated to create a text file containing unique FASTQ headers and error corrected barcodes. Subsequently FASTQ reads matching the FASTQ headers in this file were extracted from the SABER seq raw reads using a Python script. SABER seq barcodes were analyzed using the scGESTALT pipeline available on Github as described previously. To analyze differences in barcode detection and editing frequencies between cell types a binomial test was used. genome build/assembly: GRCz11 processed data files format and content: *.h5 files contain filtered features/genes and barcode data generated from cell ranger pipeline. processed data files format and content: LinC1 Filtered Fastq Headers CB.txt LinC2 Filtered Fastq Headers CB.txt LinD1 Filtered Fastq Headers CB.txt LinD2 Filtered Fastq Headers CB.txt LinD3 Filtered Fastq Headers CB.txt files contain filtered and unique fastq headers and cell barcodes post matching the trasncriptomic cell barcodes. processed data files format and content: SABERb1 linC1 S4 BrainC1 S6.CB.xlsx SABERb1 linC2 S5 BrainC2 S7.CB.xlsx SABERb2 linD1 S1 BrainD1 S8.CB.xlsx SABERb2 linD2 S2 BrainD2 S9.CB.xlsx SABERb2 linD3 S3 BrainD3 S10.CB.xlsx SABERb4 linB2.1 13.123.2.With.Primer.stats.xlsx SABERb4 linB2.2 14.124.2.With.Primer.stats.xlsx files contain the SABER Seq output post matching reads to the reference sequence.	zebrafish whole brain		Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018 Nature Biotechnology with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 °C for 20 min. Cells were resuspended with 150 μl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer’s instructions Single Cell 3’ v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes 5 µl of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 °C 30 s; [98 °C 10 s; 68 °C 25 s; 72 °C 15 s] x 14 cycles; 72 °C 2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 µl elution buffer EB buffer. A second PCR was carried out using 3 µl of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point 2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1 the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 µl elution buffer EB buffer similar to PCR1. For iteration 2 an optimization was trialed and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 µl EB. Finally adapters and sample indices were incorporated in a third PCR reaction using 5 7 µl of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT 10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT where x represents index bases from the 10X Dual Index Kit TT SetA H index entries. For iteration 1 the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 µl elution buffer EB to generate final libraries. For iteration 2 the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles Read2 151 cycles Index1 10 cycles Index2 10 cycles. Standard sequencing primers were used.	This work was performed under protocol numbers 807110 and 807259 which were approved by the University of Pennsylvania’s Office of Animal Welfare of Institutional Animal Care and Use Committee IACUC. All zebrafish work in this study follows the University of Pennsylvania Institutional Animal Care and Use Committee regulations.	tissue:zebrafish whole brain\|developmental stage:21 dpf 23 dpf Juvenile\|genotype:WT/TLAB	GSM8290056	GSM8290056: SABERb4 brainB2 2 brain4 transcriptome; Danio rerio; RNA Seq	GSM8290056 r1	GSM8290056	1	Wild type and SABER x her4.3:switchCas9 zebrafish brains were harvested and dissociated at 21 dpf 23 dpf as described previously Raj et. al 2018 Nature Biotechnology with the following modifications. All microcentrifuge tubes were treated with 1% BSA/DPBS overnight to prevent cell loss due to adhesion. Brains were dissociated with 1 mL of 20 units/ml papain in EBSS media and incubated at 37 °C for 20 min. Cells were resuspended with 150 μl ice cold 1%BSA/DPBS solution. Transcriptome libraries were processed according to the manufacturer's instructions Single Cell three prime v3 kit. Transcriptome libraries were sequenced using NovaSeq S4 300 cycle kits Novogene and Novaseq SP 100 and 200 cycle kits MedGenome. To generate SABER seq libraries samples post cDNA amplification and prior to fragmentation were split into two parts. One part was processed for transcriptome libraries as instructed by the manufacturer. The other part was processed for SABER seq libraries as follows. To enrich for SABER barcodes 5 µl of the whole transcriptome cDNA was PCR amplified with 10X p140v1UP GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT CGTCGTAGATCTTCATCGTGATG and 10XPCR1F CTACACGACGCTCTTCCGATCT primers and Q5 polymerase. The reaction conditions were: 98 °C 30 s; [98 °C 10 s; 68 °C 25 s; 72 °C 15 s] x 14 cycles; 72 °C 2 min. The reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 µl elution buffer EB buffer. A second PCR was carried out using 3 µl of PCR1 product and 10XPCR1F and 10X p140v2Sa GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT GAAACTACATGGGGTcaGCTC primers. Reaction conditions were same as PCR1 except only 13 cycles were completed. At this point 2 different clean up conditions were used: iteration 1 SABER seq v0 and iteration 2 SABER seq v1. For iteration 1 the reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 µl elution buffer EB buffer similar to PCR1. For iteration 2 an optimization was trialed and the reaction was cleaned up with right side size selection using SPRIselect beads 0.6X beads used initially and supernatant was rebound with 1.5X beads. The product was eluted in 20 µl EB. Finally adapters and sample indices were incorporated in a third PCR reaction using 5 7 µl of a 1:10 dilution of PCR2 product. Reactions conditions were same as PCR1 except 9 10 cycles were completed. Primers used: 10XP5Dual Hx AATGATACGGCGACCACCGAGATCTACAC xxxxxxx xxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT 10XP7Dual H5 AATGATACGGCGAC CACCGAGATCTACAC xxxxxxxxxx ACACTCTTTCCCTACACGACGCTCTTCCGATCT where x represents index bases from the 10X Dual Index Kit TT SetA H index entries. For iteration 1 the third PCR reaction was cleaned up with 0.8X SPRISelect beads and eluted in 20 µl elution buffer EB to generate final libraries. For iteration 2 the third PCR reaction was cleaned up with right side size selection using SPRIselect beads 0.65X beads used initially and supernatant was rebound with 1.5X beads. SABER seq libraries were sequenced using Novaseq SP 200 cycle kits MedGenome with 5 10% PhiX spike in. Sequencing parameters: Read1 28 cycles Read2 151 cycles Index1 10 cycles Index2 10 cycles. Standard sequencing primers were used.		RNA-Seq	TRANSCRIPTOMIC SINGLE CELL	cDNA	PAIRED	ILLUMINA	Illumina NovaSeq 6000		SRP509892		loader:fastq load.py	SABERb4_brainB2_2_S4_L001_R2_001.fastq.gz SABERb4_brainB2_2_S4_L001_R1_001.fastq.gz SABERb4_brainB2_2_S4_L001_I2_001.fastq.gz SABERb4_brainB2_2_S4_L001_I1_001.fastq.gz	fastq fastq fastq fastq	22587540571.0	113505229.0	GSM8290056 r1	0:10 1:10 2:28 3:151	A:5299086616;C:3418923384;G:3717477937;T:4703769200;N:32442	10	10	28	151	5299086616	3418923384	3717477937	4703769200	32442	SRX24701870	SRS21429399	SRA1989588	University of Pennsylvania	University of Pennsylvania	1	0.88382		0.22459		0.76524		0.54792		151		B		usable mapping rate	illumina	novaseq_era	full_length	random_priming	unknown	sc	single_cell_droplet	10x		United States	2024-05-25	Larval	Larval	Brain	Nervous System