Experiment / E3ECG835BAAV-MPRA / in vivo MPRA

PESCA pooled AAV9 single-nucleus MPRA screen in mouse primary visual cortex

A scalable platform for the development of cell-type-specific viral drivers

A pooled AAV9 library of 287 candidate genomic gene regulatory elements (GREs), each represented by three unique 10-bp barcodes, was injected into primary visual cortex of two adult C57BL/6J mice. A modified inDrops single-nucleus RNA-seq assay with PCR enrichment of viral transcripts measured GRE-barcode UMI expression across ten cortical cell types, with SST-versus-non-SST enrichment reported.

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Organism: Mouse
Taxonomy ID: NCBITaxon:10090
Biosample: UBERON:0002436
Reference genome: mm10
Design focus: Region-focused
Region of interest: Not reported / not applicable

Perturbation & assay details

Basal / Untreated

PESCA combines an in vivo AAV9 reporter library with a single-cell readout. Each wild-type genomic GRE was placed 5′ of a human beta-globin minimal promoter driving GFP, and three independent 10-bp barcodes were placed in the 3′ UTR of the reporter transcript; the library contained 861 GRE-barcode pairs covering 287 GREs. The pooled library was injected into V1 of two 6-week-old C57BL/6J mice. Five approximately 3,000-nucleus inDrops libraries were generated per animal; a PCR-enriched viral-transcript fraction retained the inDrops cell barcode, viral GRE barcode, and UMI. Published PESCA specificity is the ratio of mean GRE expression in Sst+ nuclei to mean expression in Sst− nuclei with a 0.01 pseudocount, rather than a conventional bulk RNA/DNA plasmid ratio.

Processed data

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Visible columns (34 of 34)

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Filters apply to this table only. The CSV download contains the complete processed table; filtered rows are available through the API.

Column dictionary · 34 definitions

gre_id: Unique GRE identifier assigned to one of the 287 genomic regulatory elements in the PESCA library.
source_region_id: Original master-region identifier from the authors' genomic annotation.
chromosome: Mouse chromosome containing the GRE.
start: Start coordinate of the GRE interval on mm10/GRCm38.
end: End coordinate of the GRE interval on mm10/GRCm38.
region_length_bp: Interval length in base pairs calculated as end minus start.
annotation: Genomic annotation supplied by the authors, such as intergenic or intronic.
distance_to_tss_bp: Signed distance in base pairs to the nearest annotated transcription start site, as supplied by the authors.
nearest_promoter_id: Nearest transcript/promoter identifier supplied by the authors.
nearest_gene: Nearest gene name supplied by the authors.
barcode_1: First 10-bp viral barcode paired with the GRE.
barcode_2: Second 10-bp viral barcode paired with the GRE.
barcode_3: Third 10-bp viral barcode paired with the GRE.
animal1_barcode_1_viral_umi: Sum of viral barcode 1 UMI counts across the five animal-1 inDrops libraries after the >200 non-viral UMI filter.
animal1_barcode_2_viral_umi: Sum of viral barcode 2 UMI counts across the five animal-1 inDrops libraries after the >200 non-viral UMI filter.
animal1_barcode_3_viral_umi: Sum of viral barcode 3 UMI counts across the five animal-1 inDrops libraries after the >200 non-viral UMI filter.
animal2_barcode_1_viral_umi: Sum of viral barcode 1 UMI counts across the five animal-2 inDrops libraries after the >200 non-viral UMI filter.
animal2_barcode_2_viral_umi: Sum of viral barcode 2 UMI counts across the five animal-2 inDrops libraries after the >200 non-viral UMI filter.
animal2_barcode_3_viral_umi: Sum of viral barcode 3 UMI counts across the five animal-2 inDrops libraries after the >200 non-viral UMI filter.
animal1_total_viral_umi: Sum of all three GRE barcode UMI counts across animal 1 after cell QC.
animal2_total_viral_umi: Sum of all three GRE barcode UMI counts across animal 2 after cell QC.
total_viral_umi: Sum of all six barcode-by-animal viral UMI counts after cell QC.
animal1_barcode_1_detected_cell_libraries: Number of nonzero cell-library observations for barcode 1 across animal 1's five count matrices; a cell with multiple barcodes contributes once per barcode.
animal1_barcode_2_detected_cell_libraries: Number of nonzero cell-library observations for barcode 2 across animal 1's five count matrices.
animal1_barcode_3_detected_cell_libraries: Number of nonzero cell-library observations for barcode 3 across animal 1's five count matrices.
animal2_barcode_1_detected_cell_libraries: Number of nonzero cell-library observations for barcode 1 across animal 2's five count matrices.
animal2_barcode_2_detected_cell_libraries: Number of nonzero cell-library observations for barcode 2 across animal 2's five count matrices.
animal2_barcode_3_detected_cell_libraries: Number of nonzero cell-library observations for barcode 3 across animal 2's five count matrices.
total_barcode_detection_events: Sum of the six barcode-specific detected-cell-library counts; this is a barcode detection-event total, not a deduplicated nucleus count.
barcode_animal_aggregates_with_nonzero_qc_umi: Number of the six barcode-by-animal aggregate groups with at least one post-QC viral UMI.
atac_specificity_published: Published ATAC-seq specificity score for the GRE from eLife Supplementary file 2.
pesca_sst_specificity_published: Published PESCA SST specificity fold-enrichment from eLife Supplementary file 2: mean GRE expression in Sst+ nuclei divided by mean expression in Sst− nuclei with the paper's 0.01 pseudocount.
log2_pesca_sst_specificity_published: Base-2 logarithm of pesca_sst_specificity_published, calculated for this package.
pesca_sst_specificity_geo_annotation: PESCA specificity value in the GEO S2 annotated-GRE file; retained as a provenance field because it differs from the final eLife Supplementary file 2 value for some GREs.

Quality control

The authors used marker-based Seurat clustering after removing viral features, retaining nuclei with more than 200 non-viral UMIs and classifying them into ten cell types; viral reads were uniquely assigned to custom viral contigs and condensed by cell barcode/UMI. The ten released count matrices contained 47,348 rows, of which 32,335 passed the >200 non-viral UMI filter (17,656 from animal 1 and 14,679 from animal 2), matching the paper. The authors also report 0.3 ± 0.06% ambiguous/unassigned viral reads and library-complexity recovery of 802/861 barcodes corresponding to 285/287 GREs. Package processing applied the >200 non-viral UMI filter before summing viral counts across the five libraries per animal. All 287 GRE rows were retained because each has valid coordinates, three assigned barcodes, a finite published specificity score, and positive post-QC viral coverage (12–15,649 viral UMIs per GRE); no significance cutoff was applied so the complete screen remains available.

Curation notes

The experiment combines the two animals represented by count files 6-19-1-1 through 6-19-1-5 and 6-19-2-1 through 6-19-2-5 in GSM4058339/GSE136802. The processed table joins the three barcode assignments from GSE136802_S2_annotated_gres.csv.gz, published genomic/ATAC/PESCA fields from eLife Supplementary file 2, and post-QC viral UMI summaries computed from the ten released count matrices. The GEO annotation and final eLife Supplementary file 2 contain materially different PESCA specificity values while agreeing on GRE identities, coordinates, annotations, and barcode assignments; the eLife value is treated as the canonical published score and the GEO value is retained explicitly rather than silently discarded. The series also contains upstream PV, VIP, and SST INTACT ATAC-seq samples; those selection data are preserved in raw_data but are not modeled as separate MPRA experiments here. No raw FASTQ/SRA reads were included, per the packaging request.

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