Seq-Well: A portable, low-cost platform for single-cell RNA-seq of low-input samples
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ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE92495
PROVIDER: PRJNA374551 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Single-cell RNA -seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA -seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We characterize Seq-Well extensively and use it to profile thousands of primary human macrophages exposed to tuberculosis.
2017-02-17 | GSE92495 | GEO
Project description:Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols
| PRJNA271674 | ENA
Project description:Despite the precipitous decline in the cost of genome sequencing over the last few years, library preparation for RNA-seq is still laborious and expensive for high throughput screening for drug discovery. Limited availability of RNA generated by some experimental workflows poses an additional challenge and typically adds to the cost of RNA library preparation. In a search for low cost, automation-compatible RNA library preparation kits that also maintain strand specificity and are amenable to low input RNA quantities, we systematically tested two recent commercial technologies – Swift and Swift Rapid – using the Illumina TruSeq stranded mRNA, the de facto standard workflow for bulk transcriptomics, as our reference. We used the Universal Human Reference RNA (UHRR) (composed of equal quantities of total RNA from 10 human cancer cell lines) to benchmark differential gene expression in these kits, at input quantities ranging between 10 ng to 500 ng. Read quality and alignment metrics revealed high mapping efficiency and uniform read coverage through genes for all samples across all three kits. Normalized read counts between all treatment groups were in high agreement, with pairwise Pearson correlation coefficients >0.97. Compared to the Illumina TruSeq stranded mRNA kit, both Swift RNA library kits are cost effective and offer shorter workflow times enabled by their patented Adaptase technology. Furthermore, the Swift RNA kit allows for a relatively broader (and lower) input range, producing consistent results across diverse samples. The Swift Rapid RNA method is the fastest and most cost effective NGS workflow that is best suited for higher RNA yields, with the exact same RNA input range as the Illumina TruSeq kit. We also found the Swift RNA kit to produce the fewest number of differentially expressed genes and pathways attributable to input mRNA concentration.
2021-02-23 | GSE167300 | GEO
Project description:Clinical samples promise unparalleled insights into the cellular mechanisms that underlie pathological conditions and therapeutic responses. However, they often can be precious, with few cells available for single-cell analysis, necessitating effort to maximize the amount of information that can be garnered from each. Here, we introduce a low material input, cost-effective protocol for conducting multi-omic analyses and sample hashing on single-cell suspensions using the Seq-Well S3 platform. Our protocol, designed to be both accessible and affordable, leverages readily available reagents and standard laboratory equipment, significantly lowering barriers to entry for researchers in low- and middle-income countries. The method detailed herein offers a streamlined and efficient workflow for: (1) precise staining of single-cell suspensions with antibody-oligo conjugates for accurate cell surface protein identification and effective sample multiplexing; (2) reliable generation of Seq-Well S3 sequencing libraries; (3) optional generation of bulk-RNA sequencing libraries through an optimized SMART-seq2 protocol; and, (4) robust computational pipelines for in-depth multi-omic data analysis. This protocol works with fragile and limited cell inputs (here, outlined for 15,000 cells per 200 µL - a fraction of the input required by most commercial methods). It also offers significant cost and time savings, with the entire process from cell isolation to sequencing taking only 3-6 days, plus an additional 1-2 days for data processing. In sum, this generally applicable pipeline empowers researchers around the globe to apply single-cell multiomics to advance their own research agendas.
2024-05-02 | GSE266385 | GEO
Project description:We developed a new method on sequencing low-input RNA. This method shows much low-bias with the advantage of semiconductor while competing with smart-seq2. In order to analyze the low-input RNA datasets sensitively, we also develop FANSe2splice with high experimental verification rate as the analysis tool in our method.
2017-06-12 | GSE87660 | GEO
Project description: Not available
| S-EPMC4380159 | biostudies-literature
Project description:We sequenced mRNA according to several library prep protocols with known mixtures of two species of Drosophila in order to establish linear response in each protocol.
2015-01-07 | GSE64673 | GEO
Project description:Here, we report an enrichment-based ultra-low input cfDNA methylation profiling method using methyl-CpG binding proteins capture, termed cfMBD-seq. We optimized the conditions of cfMBD capture by adjusting the amount of MethylCap protein along with using methylated filler DNA. Our data showed that cfMBD-seq performs equally to the standard MBD-seq (>1000 ng input) even when using 1 ng DNA as the input. cfMBD-seq demonstrated equivalent sequencing data quality as well as similar methylation profile when compared to cfMeDIP-seq. We showed that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of CpG islands. This new bisulfite-free ultra-low input methylation profiling technology has a great potential in non-invasive and cost-effective cancer detection and classification.
2021-01-01 | GSE161331 | GEO
Project description:Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion
| PRJNA629575 | ENA
Project description:RNA sequencing is a powerful approach to quantify the genome-wide distribution of mRNA molecules in a population to gain deeper understanding of cellular functions and phenotypes. However, unlike eukaryotic cells, mRNA sequencing of bacterial samples is more challenging due to the absence of a poly-A tail that typically enables efficient capture and enrichment of mRNA from the abundant rRNA molecules in a cell. Moreover, bacterial cells frequently contain 100-fold lower quantities of RNA compared to mammalian cells, which further complicates mRNA sequencing from non-cultivable and non-model bacterial species which are often present in low abundance. To overcome these limitations, we report EMBR-seq (Enrichment of mRNA by Blocked rRNA), a method that efficiently depletes 5S, 16S and 23S rRNA using blocking primers to prevent their amplification. EMBR-seq results in greater than 80% of the sequenced RNA molecules deriving from mRNA. We demonstrate that this increased efficiency provides a deeper view of the transcriptome without introducing technical amplification-induced biases. Moreover, compared to recent methods that employ a large array of oligonucleotides to deplete rRNA, EMBR-seq employs a single oligonucleotide per rRNA, thereby making this new technology significantly more cost-effective, especially when applied to varied bacterial species. Finally, compared to existing commercial kits, we show that EMBR-seq can be used to successfully quantify the transcriptome from more than 500-fold lower starting total RNA. Thus, EMBR-seq provides an efficient and cost-effective approach to quantify global gene expression profiles from low input bacterial samples.
2020-10-22 | GSE149666 | GEO