Enabling multiplexed CRISPR/Cas9 screening via massively parallel in-library ligation
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA630673 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11704888_1.fastq.gz | Fastqsanger.gz | |||
| SRR11704888_2.fastq.gz | Fastqsanger.gz | |||
| SRR11704889_1.fastq.gz | Fastqsanger.gz | |||
| SRR11704889_2.fastq.gz | Fastqsanger.gz | |||
| SRR11704890_1.fastq.gz | Fastqsanger.gz |
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Project description:Here we developed a massively parallel in-library ligation methodology to simultaneously perturb four pre-designed targets in CRISPR/Cas9 screening. Thousands of pairs of sequences precisely ligated with their counterparts in library, which enabled simultaneous expression of four gRNAs from each single vector. We demonstrated this novel method with 6,236 4-gene combinations targeting 1,599 immune response related genes, and generated a plasmid library with 1,400x coverage. The library performance was evaluated in a canonical T cell activation experiment, and combinations involved in TCR signaling pathway or TCR complex were successfully identified as positive regulators. Novel combination that is reflecting a potential pathway crosstalk was also verified. This new methodology expands the capacity of the perturbation in CRISPR screening and provided a powerful tool for researches in broad fields to study the combinatorial outcomes from coordinated gene behaviors.
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Project description:Single-cell proteomics by mass spectrometry (MS) allows the quantification of proteins with high specificity and sensitivity. To increase its throughput, we developed nano-proteomic sample preparation (nPOP), a method for parallel preparation of thousands of single cells in nanoliter-volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation using the plexDIA MS multiplexing method, which uses non-isobaric mass tags to barcode peptides from different samples for data-independent acquisition, demonstrates accurate quantification of ~3,000-3,700 proteins per human cell. A separate implementation with isobaric mass tags and prioritized data acquisition demonstrates analysis of 1,827 single cells at a rate of >1,000 single cells per day at a depth of 800-1,200 proteins per human cell. The protocol is implemented by using a cell-dispensing and liquid-handling robot-the CellenONE instrument-and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 d to prepare >3,000 single cells. We provide metrics and software (the QuantQC R package) for quality control and data exploration. QuantQC supports the robust scaling of nPOP to higher plex reagents for achieving reliable and scalable single-cell proteomics.
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Project description:BackgroundCell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood.ResultsHere, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance.ConclusionsOur results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.
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