Dataset Information

Accelerated Lysis and Proteolytic Digestion of Biopsy-level Fresh Frozen and FFPE Tissue Samples Using Pressure Cycling Technology

ABSTRACT: We developed an accelerated PCT method with doubled throughput while maintaining digestion efficiency, the number of proteins/peptides identified, as well as reproducibility of protein quantitation, compared to the previously reported method. The accelerated PCT method does not require new chemical reagents or increase the cost of reagents. This accelerated PCT method simplifies the manual sample handling steps, and thereby reduces user error and technical variability. The method has been benchmarked and validated using both FFPE and FF mouse liver tissue samples.

ORGANISM(S): Homo Sapiens

SUBMITTER: Tiannan Guo

PROVIDER: PXD020353 | iProX | Thu Jul 16 00:00:00 BST 2020

REPOSITORIES: iProX

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Publications

Accelerated Lysis and Proteolytic Digestion of Biopsy-Level Fresh-Frozen and FFPE Tissue Samples Using Pressure Cycling Technology.

Gao Huanhuan H Zhang Fangfei F Liang Shuang S Zhang Qiushi Q Lyu Mengge M Qian Liujia L Liu Wei W Ge Weigang W Chen Chen C Yi Xiao X Zhu Jiang J Lu Cong C Sun Ping P Liu Kexin K Zhu Yi Y Guo Tiannan T

Journal of proteome research 20200330 5

Pressure cycling technology (PCT)-assisted tissue lysis and digestion have facilitated reproducible and high-throughput proteomic studies of both fresh-frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tissue of biopsy scale for biomarker discovery. Here, we present an improved PCT method accelerating the conventional procedures by about two-fold without sacrificing peptide yield, digestion efficiency, peptide, and protein identification. The time required for processing 16 tissue samples ...[more]

PMID: 32182071

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Project description:This study was performed to evaluated RNA extraction and gene expression analysis of FFPE specimen stored for more than 20 years. Using long time stored FFPE material; large retrospective studies correlating molecular features with therapeutic response and clinical outcome, can be performed. Quantitative PCR (qPCR) was used to evaluate RNA extraction methods and to compare gene expression profiles of FFPE and fresh frozen (FF) tissue. Extracted RNA was subsequently subjected to microarray analysis and compared to qPCR data. The Ambion RecoverAll kit appears to be particularly suited for RNA extraction of long time stored FFPE tissues. Gene expression analysis using Affymetrix platform displayed a high degree of correlation for endogenous control genes comparing FF and FFPE tissues. We conclude that high quality gene expression signatures can be recognized using Affymetrix gene expression platform on FFPE tissue stored for more than 20 years. However, a general interpretation must be done with caution as different FFPE procedures have varying effects on RNA quality. Three RNA extraction methods from Roche (Basel, Switzerland), Ambion (Austin, TX, USA) and Qiagen (Hilden, Germany), designed for FFPE material was used. The methods were compared using qPCR. For the qPCR analysis, two different concentrations of input cDNA were used (20ng and 100ng) and 32 human endogenous control genes were examined. RNA from the Ambion FFPE kit was further analyzed by using microarray. Amplification of RNA prior to microarray analysis was performed using Nugen technologies (San Carlos, CA, USA). Nugen has developed amplification kits both for FFPE and FF materials: WT- ovation FFPE RNA amplification System V2 (Nugen FFPE), Ovation FF RNA Amplification System V2 (Nugen V2 FF) and Ovation FF Pico RNA Amplification System (Nugen PICO FF). The Nugen FFPE kit, designed for FFPE material was only used for FFPE tissue. The Nugen Pico FF kit, designed to target small amounts of FF RNA (>500 pg) and the Nugen V2 FF kit designed to target total FF RNA, were only used for FF tissue. Affymetrix standard amplification protocol (Affy FF) designed for FF RNA was also included as the standard method for amplification.

Project description:Purpose: The goals of this study is to determine the best method of gene expression quantification (RNA-seq, Microarray, NanoString) and amplification kits adapted to low-input and/or low-quality RNA samples (FFPE samples) Methods: Mouse bladder cancer cell line (mouse bladder cancer cell line, BC57) and mouse normal mouse normal urothelium were fixed in formalin and embedded in paraffin (FFPE), andfesh frozen (FF) in liquid nitrogen. The total RNA of these 4 samples were tested by 3 technologies (NanoString, RNA-seq and Microarray) and the results were compared to its reference (high-quality and high-input RNA of mouse bladder cancer cell line and mouse normal mouse normal urothelium). For NanoString with low-input RNA samples, each sample was tested by NanoString quantification after amplification by SMARTer Stranded Total RNA-Seq Kit - Pico Input mammelian and Ovation SoLo NuGEN RNA-seq System, and NanoString based on PCR approach with three input quantities: 50pg, 250pg and 2ng of total RNA, except for NanoString quantification after amplification by SMARTer Stranded Total RNA-Seq Kit - Pico Input mammelian kit for which the minimum recommended quantity was 250pg of total RNA. NanoString direct quantification was also done for FF and FFPE samples at high amount (50ng of total RNA) and results obtained from FF samples were considered as the reference. To determine which is the method for NanoString technology, low-input and low-quality RNA samples, we performed NanoString control quality metrics, principal component analysis, and a differential analysis between the mouse bladder cancer cell lines and the mouse normal mouse normal urothelium for each input quantity, amplification method and method of sample preservation (FF or FFPE). Results: The NanoString based PCR based approach is recommended for quantification of gene expression of FFPE and FF samples from 250pg of total RNA. However, NanoString quantification after amplification by SMARTer Stranded Total RNA-Seq Kit - Pico Input mammelian and Ovation SoLo NuGEN RNA-seq System is not recommended for FF and FFPE from low-input samples.

Project description:Microarray data analysis Intensity of 21,939 gene features per array was extracted from scanned microarray images using Feature Extraction 5.1.1 software (Agilent Technologies), which performs background subtractions and dye normalization. This normalization method is targeted at detecting changes in relative expression of individual genes rather than global expression. Global expression change would require external normalization controls (van de Peppel et al., 2003). Text output was processed using an application developed in-house to perform ANOVA analysis (http://lgsun.grc.nia.nih.gov/ANOVA/). Intensity of features measured with a >50% error were replaced with missing values except features with very low intensity. Surrogate values equal to mean error were inserted for values that were negative or less than the probe error. Data were analyzed using ANOVA with embryonic stage as a factor. The small number of biological replications typical in expression profiling experiments results in a highly variable error variance, and this problem is usually addressed by log-ratio thresholds (Schena et al., 1995) that require subjective decisions about biological significance, or by Bayesian adjustment of error variance (Baldi and Long, 2001), which may still underestimate error variance and result in false positive results. To reduce false-positives, we opted for a very conservative error model in which error variance that is used for estimating F-statistics is the maximum of the actual error variance for this gene and the average error variance in 500 genes with similar average intensity. Statistical significance was determined using the False Discovery Rate (FDR = 10%) method (Benjamini and Hochberg, 1995). Pair-wise mean comparison was done with t-statistics and FDR=10%. Further data processing including scatter plots, hierarchical clustering, and principal component analysis (PCA) were also performed through NIA microarray analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/). The input file for NIA microarray analysis tool is available at http://lgsun.grc.nia.nih.gov/microarray/data.html

Project description:Feature Extraction 5.1.1 software (Agilent Technologies) performs background subtractions and dye normalization. This normalization method is targeted at detecting changes in relative expression of individual genes rather than global expression. Global expression change would require external normalization controls (van de Peppel et al., 2003). Text output was processed using an application developed in-house to perform ANOVA analysis (http://lgsun.grc.nia.nih.gov/ANOVA/). Intensity of features measured with a >50% error were replaced with missing values except features with very low intensity. Surrogate values equal to mean error were inserted for values that were negative or less than the probe error. Data were analyzed using ANOVA with embryonic stage as a factor. The small number of biological replications typical in expression profiling experiments results in a highly variable error variance, and this problem is usually addressed by log-ratio thresholds (Schena et al., 1995) that require subjective decisions about biological significance, or by Bayesian adjustment of error variance (Baldi and Long, 2001), which may still underestimate error variance and result in false positive results. To reduce false-positives, we opted for a very conservative error model in which error variance that is used for estimating F-statistics is the maximum of the actual error variance for this gene and the average error variance in 500 genes with similar average intensity. Statistical significance was determined using the False Discovery Rate (FDR = 10%) method (Benjamini and Hochberg, 1995). Pair-wise mean comparison was done with t-statistics and FDR=10%. Further data processing including scatter plots, hierarchical clustering, and principal component analysis (PCA) were also performed through NIA microarray analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/). Details are described in the publication (Hamatani T. et al., Developmental Cell. Published online Dec. 18, 2003).

Dataset Information

Accelerated Lysis and Proteolytic Digestion of Biopsy-level Fresh Frozen and FFPE Tissue Samples Using Pressure Cycling Technology

Publications

Accelerated Lysis and Proteolytic Digestion of Biopsy-Level Fresh-Frozen and FFPE Tissue Samples Using Pressure Cycling Technology.

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