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Identification and Correction of Amplification Protocol Bias in Microarray Studies

ABSTRACT: mRNA abundance microarrays are the most widely used high-throughput technology for functional genomics. But despite this extensive characterization, and despite their wide-spread adoption in discovery research, the uptake of microarrays as clinical diagnostic tools remains relatively limited due to sample degradation. Recently, the NuGEN WT-Ovation Pico kit was developed to resolve this problem. Here, we comprehensively evaluate its utility on two separate biological problems. First, we evaluate its effect on 12 malignant and 2 normal cervix samples. Second, we evaluate its effect on 2 normal kidney samples. In each case, we directly compare aliquots of the same RNA sample that are prepared by standard and low-input/low-RIN protocols to address three key questions. First, does the use of a low-input/degraded-sample protocol alter transcriptional profiles either globally or of specific genes? Second, do these changes alter biological conclusions drawn from the dataset? Third, are these changes biased towards specific classes of genes, either in terms of their sequence characteristics (e.g. length, GC-content) or their biological function?

ORGANISM(S): Homo sapiens

PROVIDER: GSE42764 | GEO | 2014/01/01

SECONDARY ACCESSION(S): PRJNA183102

REPOSITORIES: GEO

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Identification and Correction of Amplification Protocol Bias in Microarray Studies

Project description:mRNA abundance microarrays are the most widely used high-throughput technology for functional genomics. But despite this extensive characterization, and despite their wide-spread adoption in discovery research, the uptake of microarrays as clinical diagnostic tools remains relatively limited due to sample degradation. Recently, the NuGEN WT-Ovation Pico kit was developed to resolve this problem. Here, we comprehensively evaluate its utility on two separate biological problems. First, we evaluate its effect on 12 malignant and 2 normal cervix samples. Second, we evaluate its effect on 2 normal kidney samples. In each case, we directly compare aliquots of the same RNA sample that are prepared by standard and low-input/low-RIN protocols to address three key questions. First, does the use of a low-input/degraded-sample protocol alter transcriptional profiles either globally or of specific genes? Second, do these changes alter biological conclusions drawn from the dataset? Third, are these changes biased towards specific classes of genes, either in terms of their sequence characteristics (e.g. length, GC-content) or their biological function? To achieve these goals, we selected 12 frozen cervix cancers and 2 unmatched normal samples. RNA was extracted and was split into two aliquots: one was prepared using the Nugen WT-Ovation Pico kit and the other using the standard Affymetrix Express array method. This approach allows us to directly assess the effect of sample preparation, in isolation of any other effects. These data were then pre-processed and the filtered genes were subject to linear-modeling to evaluate technical and biological variability. The resulting gene sets were then subject to pathway and sequence analysis, and LTR analysis. Lastly, to generalize these conclusions we repeated this experimental approach identically in two normal kidney samples and one RNA mixture control sample.

2014-01-01 | E-GEOD-42764 | biostudies-arrayexpress

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

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

ChIP-chip from Drosophila egg chambers using ORC2 antibody

Project description:Chromatin immunoprecipitation of the Origin Recognition Complex (ORC) followed by hybridization to genome-wide tiling microarrays demonstrated that ORC does not localize to DAFC-34B after stage 10, despite a second round of replication initiation Comparison of ORC2 ChIP sample compared to input DNA for Drosophila egg chambers

2011-09-06 | E-GEOD-31890 | biostudies-arrayexpress

Preserving biological heterogeneity with personalized genomics batch correction

Project description:Motivation: Sample source, procurement process, and other technical variations introduce batch effects into genomics data. Algorithms to remove these artifacts enhance differences between known biological covariates, but also carry potential concern of removing intra-group biological heterogeneity and thus any personalized genomic signatures. As a result, accurate identification of novel subtypes from batch corrected genomics data is challenging using standard algorithms designed to remove batch effects for class comparison analyses. Nor can batch effects be corrected reliably in future applications of genomics-based clinical tests, in which the biological groups are by definition unknown a priori. Results: Therefore, we introduce new algorithm, personalized-SVA (pSVA), blind to biological covariates corrected technical artifacts while retaining biological heterogeneity in genomic data. This algorithm facilitated accurate subtype identification in head and neck cancer from gene expression data in both formalin fixed and frozen samples. When applied to predict HPV status, pSVA improved cross- study validation even if the sample batches were highly confounded with HPV status in the training set. Availability: All analyses were performed using R version 2.15.0. The code and data used to generate the results of this manuscript is available from https://sourceforge.net/projects/psva.

2016-12-12 | GSE53355 | GEO

The use of linear ion traps in data independent acquisition methods benefits low-input proteomics

Project description:Here we revisited the use of linear ion traps mass analyzers in DIA methods to evaluate their potential to boost peptide and protein identifications in low input and single-cell proteomics applications

2021-09-09 | PXD025766 | Pride

Lambdavirus lambda

Project description:PacBio Low Input Sequencing

| PRJNA272509 | ENA

Low-input Hi-C library construction

Project description:To evaluate the robustness of CtG, we constructed Hi-C libraries of varying quantities of cell inputs utilizing a low-input Hi-C technique.

2023-07-19 | GSE233166 | GEO

df8375ac-142f-4e34-b744-42f587e7a7bc - samples

Project description:Low-input RNA-Seq (~200 cells per sample) of CD4+ T cells in patients with kidney transplants, dialysis, or healthy controls after the second dose of Tozinameran COVID-19 vaccine. RNA-Seq was performed using the Smart-Seq v4 ultra-low input protocol. The dataset includes 4 healthy control samples, 3 kidney transplant recipients, and 4 patients undergoing dialysis.

| EGAD00001007689 | EGA

Differences between fibroblasts derived from normal and IPF lungs

Project description:Objective: To evaluate the characteristics of IPF lungs in fibroblasts, we performed RNA-Sequencing of fibroblasts derived from normal and IPF lungs Method:　NHLF（Normal human lung fibroblast）　and DHLF-IPF (DIPF; Diseased human lung fibroblasts derived from idiopathic pulmonary fibrosis)　were purchased from Lonza (Walkersville, MD, USA) .Total RNA was extracted from fibroblasts (NHLF and DIPF) using an RNeasy® Mini Kit (#74106; Qiagen, Valencia, CA, USA). Preparation of a next-generation sequencing library was performed using a SMARTer® Stranded Total RNA Sample Prep Kit–Pico Input Mammalian (TaKaRa, Shiga, Japan). Sequencing was performed on an Illumina HiSeq 2500 platform in 75-base single-end mode with Illumina Casava 1.8.2 software for base calling. Sequenced reads were mapped to the human reference genome sequence (hg19) using TopHat v.2.0.13 software, in combination with Bowtie 2 v.2.2.3 and SAMtools v.0.1.19. Result: Of the 26,257 genes analyzed, for NHLF and DHLF-IPF (DIPF), FPKM was required to be greater than or equal to 0.3. Under these conditions, 764 genes were up regulated inDHLF-IPF (DIPF) and 691 genes were down regulated.

2021-10-06 | GSE185259 | GEO

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