Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Seq-Well: A portable, low-cost platform for single-cell RNA-seq of low-input samples

ABSTRACT: 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.

OTHER RELATED OMICS DATASETS IN: PRJNA374551

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE92495 | GEO | 2017/02/17

SECONDARY ACCESSION(S): PRJNA374551

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Seq-Well: A portable, low-cost platform for single-cell RNA-seq of low-input samples

Project description:Seq-Well: A portable, low-cost platform for single-cell RNA-seq of low-input samples

| PRJNA374551 | ENA

Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols

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

Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols.

Project description:Recently, a number of protocols extending RNA-sequencing to the single-cell regime have been published. However, we were concerned that the additional steps to deal with such minute quantities of input sample would introduce serious biases that would make analysis of the data using existing approaches invalid. In this study, we performed a critical evaluation of several of these low-volume RNA-seq protocols, and found that they performed slightly less well in per-gene linearity of response, but with at least two orders of magnitude less sample required. We also explored a simple modification to one of these protocols that, for many samples, reduced the cost of library preparation to approximately $20/sample.

| S-EPMC4380159 | biostudies-literature

Well-paired-seq: a Size-exclusion and Locally Quasi-static Hydrodynamic Microwell Chip for Single-cell RNA-seq

Project description:High throughput single-cell RNA sequencing (scRNA-seq) is a powerful technology for revealing new cell types and cellular states. However, the existing droplet or well-based methods are primarily based on the stochastic pairing of single cells and barcoded beads, resulting in a large number of empty microreactors and a low cell/bead utilization efficiency. Here, we present Well-paired-seq, consisting of thousands of size-exclusion and quasi-static hydrodynamic dual wells to address these limitations. The size-exclusion based dual-well structure allowed one cell and one bead to be trapped in the bottom well (cell-capture-well) and the top well (bead-capture-well), respectively, while the quasi-static hydrodynamic principle ensured that the trapped cells were unable to escape from cell-capture-wells, achieving cumulative capture of cells and buffer exchange in the Well-paired-seq chip. By the integration of quasi-static hydrodynamic and size-exclusion principles, the structure of the dual-well ensures one cell and one bead pairing with high density, acheiving high efficiency of cell/bead pairings (~80%) and low cell doublets. The high utilization of microreactors and single cells/beads enable us to achieve an ultra-high throughput (~105 cells ) with low collision rates (6.2%). We demonstrate the technical performance of Well-paired-seq by collecting transcriptome data from around 200,000 cells across 21 samples including various cell lines, peripheral blood mononuclear cells, and drug-treated cells, successfully revealing the heterogeneity of single cells and showing the wide applicability of Well-paired-seq for basic or clinical biomedical research.

2022-01-04 | GSE192708 | GEO

Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion

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

Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols

Project description:Low-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols

| PRJNA271674 | ENA

Well-ST-seq: cost-effective and cellular level spatial transcriptomics with high RNA capture efficiency

Project description:Spatial transcriptomic technologies are promising tools to reveal fine anatomical profiles of tissues. In the case of methodologies utilizing barcoded probe arrays, achieving a balance among probe barcoding complexity, cost, gene capture sensitivity, and spatial resolution is crucial for accelerating the spreading speed of spatial transcriptomic in basic science and clinical work. Here, we developed spatially cellular-level RNA-capture probe arrays using miniaturized microfluidic and microarray technologies. By leveraging the predetermined and cost-effective probe fixation characteristics of this methodology, we significantly reduced the consumable cost of the probe array to $0.31/mm2 and fabrication time to approximately 2 hours. Furthermore, the modification of the RNA-capture probe on sequencing slides by microfluidic chip does not rely on large imaging or printing instruments. Notably, the efficiency of the transcript captured by the probe array is even comparable to conventional single-cell RNA sequencing. Based on this technology, the stacked three-dimensional transcriptome atlas and the spatial cell heterogeneity of mouse brains were successfully visualized. Taken together, we present an experimental and analytical framework for the spatial investigation of mouse brain structures and cell phenotypes.

2024-05-01 | GSE247834 | GEO

Cell-free DNA methylome profiling by MBD-seq with ultra-low input

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

A scalable, low cost, multi-omic interrogation and sample hashing workflow for single-cell analysis using the Seq-Well S3 platform

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

Low-cost, low-bias and low-input RNA-seq with High Experimental Verifiability based on Semiconductor Sequencing

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

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data