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Combined ultra-low input mRNA and whole-genome sequencing from human embryonic stem cells

ABSTRACT: Next Generation Sequencing has proven to be an exceptionally powerful tool in the field of genomics and transcriptomics. With recent development it is nowadays possible to analyze ultra-low input sample material down to single cells. Nevertheless investigating such sample material still limits the analysis to either the genome or transcriptome, hence a combined analysis of both types of nucleic acids from the same sample material is still in demand.We developed a protocol which enables the combined analysis of the genome as well as the transcriptome by Next Generation Sequencing from ultra-low input samples. The protocol was evaluated by sequencing sub-colony structures from human embryonic stem cells containing 150 to 200 cells. The method can be adapted to any available sequencing system (e.g. Illumina, SOLiD, 454, etc.).To our knowledge, this is the first report where sub-colonies of human embryonic stem cells have been analyzed both at the genomic as well as transcriptome level. The method of this proof of concept study may find useful practical applications for cases where only a limited number of cells are available, e.g. for tissues samples from biopsies, circulating tumor cells and cells from early embryonic development. The results we present demonstrate that a combined analysis of genomic DNA and messenger RNA from ultra low input samples is feasible and can readily be applied to other cellular systems with limited material available.

ORGANISM(S): Homo sapiens

PROVIDER: GSE69471 | GEO | 2015/11/17

SECONDARY ACCESSION(S): PRJNA285681

REPOSITORIES: GEO

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Homo sapiens

Project description:Combined ultra-low input mRNA and whole-genome sequencing from human embryonic stem cells

| PRJNA285681 | ENA

Comparative evaluation of RNA-Seq library preparation methods for strand-specificity and low input

Project description:Library preparation is a key step in gene expression quantification. There are considerable advantages to both strand specific sequencing and the ability to sequence samples with very small amounts of starting material. Until recently there was no kit available that allowed both simultaneously. The standard Illumina-TruSeq stranded mRNA Sample Preparation kit requires abundant starting quantity while the Takara Bio-SMART-Seq® v4 Ultra® Low Input RNA kit allows for ultra low starting quantities but sacrifices strand specificity. Recently a kit that can do both, SMARTer® Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian by Takara Bio, has become available. Evaluating the performance and effects of these sample preparation kits is a critical determinant for selecting the appropriate sequencing protocol, but a comprehensive comparison is currently missing. To address this we performed a detailed comparative analysis of sequencing libraries prepared with the three kits. We prepared a set of samples representing two experimental conditions with each kit, allowing for comparison of the kits in a standard realistic differential expression analysis. We find substantial differences in the levels of alignment and differential gene expression. Using differential expression analysis we show that using Pico results in identifying 55% less differentially expressed genes than TruSeq. Nevertheless, using gene pathway enrichment analysis we find similar results for all three kits, indicating that ultimately comparable functional results can be reached.

2019-09-17 | GSE124167 | GEO

To identify potential ubiquitin ligases that regulate BIK1 homeostasis

Project description:To identify potential ubiquitin ligases that regulate BIK1 homeostasis,A total amount of 3 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. We using an optimized data analysis workflow,

2023-06-14 | GSE227628 | GEO

RNA sequencing (RNA-seq) for identifing differentially expressed genes for mitochondrial unfolded protein response in Arabidopsis

Project description:The purpose of this study is to identify differentially expressed expressed genes for mitochondrial unfolded protein response in Arabidopsis. A total amount of 3 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. We using an optimized data analysis workflow,

2022-03-21 | GSE198496 | GEO

An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations

Project description:The development of combined chromatin immunoprecipitation and next generation sequencing (ChIP-seq) technologies has enabled genome-wide epigenetic profiling of numerous cell lines and tissue types. A major limitation of ChIP-seq, however, is the large number of cells required to generate high quality datasets, precluding the study of rare cell populations. Here, we present an ultra-low-input micrococcal nuclease-based native ChIP (ULI-NChIP) and sequencing method, to generate genome-wide histone mark profiles with high resolution and reproducibility from as few as one thousand cells. Using ULI-NChIP, we generated high quality maps of several covalent histone marks from 10^3-10^6 embryonic stem cells. To further validate our procedure we demonstrate that genome-wide H3K27me3 profiles generated from 10^3 E13.5 primordial germ cells (PGCs) using ULI-NChIP-seq show high similarity to recently generated datasets using 50-180× more material, illustrating the utility of this method for generating high quality libraries with improved complexity from rare cell populations.

2014-12-31 | GSE63523 | GEO

Comprehensive analysis of RNA-seq kits for standard, low and ultra-low quantity samples

Project description:High-throughput RNA-sequencing has now become the gold standard method for whole-transcriptome gene expression analysis. It is widely used in a number of applications studying various transcriptomes of cells and tissues. It is also being increasingly considered for a number of clinical applications, including expression profiling for diagnostics or alternative transcripts detection. However, RNA sequencing can be challenging in some situations, for instance due to low input quantities or degraded RNA samples. Several protocols have been proposed to overcome some of these challenges, and many are available as commercial kits. Here we perform a comprehensive testing of three recent commercial technologies for RNA-seq library preparation (Truseq, Smarter and Smarter Ultra-Low) on human reference tissue preparations, for standard (1ug), low (100 and 10 ng) and ultra-low (< 1 ng) input quantities, and for mRNA and total RNA, stranded or unstranded. We analyze the results using read quality and alignments metrics, gene detection and differential gene expression metrics. Overall, we show that the Truseq kit performs well at 100 ng input quantity, while the Smarter kit shows degraded performances for 100 and 10 ng input quantities, and that the Smarter Ultra-Low kit performs quite well for input quantities < 1 ng. All the results are discussed in details, and we provide guidelines for the selection of a RNA-seq library preparation kits by biologists.

2019-05-10 | GSE124198 | GEO

RNA sequencing comparing the transcriptome of a severe congenital neutropenia (SCN) patient who progressed to AML (SCN-AML) with the SCN phase and healthy controls

Project description:CD34+ hematopoietic stem progenitor cells (HSPCs) from cryo-preserved blood or bone marrow were FACS sorted in TriZol and RNA was isolated according to the manufacturer’s protocol. SMARTer Ultra Low Input RNA kit for sequencing (Clontech, v4 Cat# 634891) was used to generate cDNA. Sequencing libraries were generated using TruSeq Nano DNA Sample Preparation kits (Illumina, Cat# 20015964), according to the low sample protocol and paired-end sequenced on a HiSeq 2500 or Novaseq 6000 (both Illumina).

2020-08-24 | E-MTAB-9381 | biostudies-arrayexpress

Project description:PacBio Ultra Low Input HiFi Reads

| PRJNA657245 | ENA

Establishment of Transposase-assisted Low-input m6A Sequencing Technique

Project description:As the most abundant and best-characterized internal mRNA modification, N6-methyladenosine (m6A) emerges to play a critical regulatory role in wide range of physiological and pathological processes, including gametogenesis, neuronal development, obesity and tumorigenesis. Methylated RNA immunoprecipitation coupled with next-generation sequencing (MeRIP-seq) facilitates transcriptome-wide m6A profiling, also is the most widely used technique to understand the biological significance of m6A. However, it typically requires over 100 μg of total RNA or 107 cells as input materials, hampering its application in limited samples. Here, we develop tMeRIP-seq, a transposase assisted MeRIP-seq method to achieve m6A profiling using ultra-low amount of input RNA. By marrying Tn5 tagmentation to m6A-specific immunoprecipitation, tMeRIP-seq largely improves the efficiency of library construction and reduces the input materials to as little as 60 ng total RNA or 103 cells. We apply this method on a small droplet of human blood and recapitulate the m6A profile previously reported using conventional protocol. We find tMeRIP-seq is a convenient and powerful method to examine m6A in ultra-low input material, potentially providing m6A as a new layer of bio-marker for liquid biopsy.

2021-09-07 | GSE167307 | GEO

Comparative proteome signatures of trace samples by multiplexed Data-Independent Acquisition

Project description:Single cell transcriptomics have revolutionized fundamental understanding of basic biology and disease. Since transcripts often do not correlate with protein expression, it is paramount to complement transcriptomics approaches with proteome analysis at single cell resolution. Despite continuous technological improvements in sensitivity, mass spectrometry-based single cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultra-low input samples. While conventional, data-dependent shotgun proteomics (DDA) of ultra-low input samples critically suffers from the accumulation of missing values with increasing sample-cohort size, data-independent acquisition (DIA) strategies do usually not to take full advantage of isotope-encoded sample multiplexing. We also developed a novel, identification-independent proteomics data analysis pipeline to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin to identify cell types and single protein knockouts. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. These data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultra-low input samples, such as single cells.

2021-05-17 | PXD023574 | Pride

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