Mining the Archives: A Cross-Platform Analysis of Gene Expression Profiles in Archival Formalin-Fixed Paraffin-Embedded Tissues

Comparison of RNA-Sequencing Methods for Degraded RNA

RNA sequencing (RNA-Seq) is a powerful technique and is increasingly being used in clinical research and drug development. Currently, several RNA-Seq methods have been developed. However, the relative advantage of each method for degraded RNA and low-input RNA, such as RNA samples collected in the field of clinical setting, has remained unknown. The Standard method of RNA-Seq captures mRNA by poly(A) capturing using Oligo dT beads, which is not suitable for degraded RNA. Here, we used three commercially available RNA-Seq library preparation kits (SMART-Seq, xGen Broad-range, and RamDA-Seq) using random primer instead of Oligo dT beads. To evaluate the performance of these methods, we compared the correlation, the number of detected expressing genes, and the expression levels with the Standard RNA-Seq method. Although the performance of RamDA-Seq was similar to that of Standard RNA-Seq, the performance for low-input RNA and degraded RNA has decreased. The performance of SMART-Seq was better than xGen and RamDA-Seq in low-input RNA and degraded RNA. Furthermore, the depletion of ribosomal RNA (rRNA) improved the performance of SMART-Seq and xGen due to increased expression levels. SMART-Seq with rRNA depletion has relative advantages for RNA-Seq using low-input and degraded RNA.

The comparative performance of a custom Canine NanoString® panel on FFPE and snap frozen liver biopsies

Formalin-Fixed Paraffin Embedded (FFPE) biopsies would provide a critical mass of cases to allow investigation of canine liver disease, however their use is often limited by challenges typically associated with transcriptomic analysis. This study evaluates the capability of NanoString® to measure the expression of a broad panel of genes in FFPE liver samples. RNA was isolated from matched histopathologically normal liver samples using FFPE (n = 6) and snap frozen in liquid nitrogen (n = 6) and measured using a custom NanoString® panel. Out of the 40 targets on the panel, 27 and 23 targets were above threshold for non-diseased snap frozen and FFPE tissue respectively. The binding density and total counts were significantly reduced in the FFPE samples relative to the snap frozen samples (p = 0.005, p = 0.01, respectively), confirming a reduction in sensitivity. The concordance between the snap frozen and FFPE samples was high, with correlations (R) ranging between 0.88 and 0.99 between the paired samples. An additional 14 immune-related targets, undetectable the non-diseased FFPE liver, were above threshold when the technique was applied to a series of diseased samples, further supporting their inclusion on this panel. This use of NanoString® based analysis opens up huge opportunity for retrospective evaluation of gene signatures in larger caseloads through harnessing the capacity of archived FFPE samples This information used alongside clinical and histological data will not only afford a way to explore disease etiopathogenesis, it may also offer insight into sub-types of liver disease in dogs, which cannot be discerned using more traditional diagnostic methods.

Selection and Evaluation of mRNA and miRNA Reference Genes for Expression Studies (qPCR) in Archived Formalin-Fixed and Paraffin-Embedded (FFPE) Colon Samples of DSS-Induced Colitis Mouse Model

The choice of appropriate reference genes is essential for correctly interpreting qPCR data and results. However, the majority of animal studies use a single reference gene without any prior evaluation. Therefore, many qPCR results from rodent studies can be misleading, affecting not only reproducibility but also translatability. In this study, the expression stability of reference genes for mRNA and miRNA in archived FFPE samples of 117 C57BL/6JOlaHsd mice (males and females) from 9 colitis experiments (dextran sulfate sodium; DSS) were evaluated and their expression analysis was performed. In addition, we investigated whether normalization reduced/neutralized the influence of inter/intra-experimental factors which we systematically included in the study. Two statistical algorithms (NormFinder and Bestkeeper) were used to determine the stability of reference genes. Multivariate analysis was made to evaluate the influence of normalization with different reference genes on target gene expression in regard to inter/intra-experimental factors. Results show that archived FFPE samples are a reliable source of RNA and imply that the FFPE procedure does not change the ranking of stability of reference genes obtained in fresh tissues. Multivariate analysis showed that the histological picture is an important factor affecting the expression levels of target genes.

Improper preanalytical processes on peripheral blood compromise RNA quality and skew the transcriptional readouts of mRNA and LncRNA

Genetic and epigenetic reprogramming caused by disease states in other tissues is always systemically reflected in peripheral blood leukocytes (PBLs). Accurate transcriptional readouts of Messenger RNA (mRNA) and Long non-coding RNA (lncRNA) in peripheral blood leukocytes are fundamental for disease-related study, diagnosis and treatment. However, little is known about the impact of preanalytical variables on RNA quality and downstream messenger RNA and Long non-coding RNA readouts. In this study, we explored the impact of RNA extraction kits and timing of blood placement on peripheral blood leukocyte-derived RNA quality. A novel enhanced evaluation system including RNA yields, purity, RNA integrity number (RIN) values and β-actin copies was employed to more sensitively identify RNA quality differences. The expression levels of informative mRNAs and Long non-coding RNAs in patients with chronic obstructive pulmonary disease (COPD) or triple-negative breast cancer (TNBC) were measured by Quantitative reverse transcription polymerase chain reaction (qRT-PCR) to investigate the impact of RNA quality on transcriptional readouts. Our results showed that the quality of RNA extracted by different kits varies greatly, and commercial kits should be evaluated and managed before batch RNA extraction. In addition, the quality of extracted RNA was highly correlated with the timing of blood placement, and the copy number of β-actin was significantly decreased after leaving blood at RT over 12 h. More importantly, compromised RNA leads to skewed transcriptional readouts of informative mRNAs and Long non-coding RNAs in patients with chronic obstructive pulmonary disease or triple-negative breast cancer. These findings have significant implications for peripheral blood leukocyte-derived RNA quality management and suggest that quality control is necessary prior to the analysis of patient messenger RNA and Long non-coding RNA expression.

Assessment of 2-Year Storage Conditions on Protein, RNA, and DNA in Unstained Human Tissue Sections, Including a Novel Multiplex Digital Gene Expression Profiling Method with Implications for Biobanking

Background: Formalin-fixed, paraffin-embedded (FFPE) tissues are a valuable resource for clinical and basic science research. Paraffin blocks and the resulting unstained sections (USS) are often stored for years before being used. Previous studies have evaluated the effects of time, temperature, humidity, and inert gases on preservation of USS; however, no study has examined all four variables together. Methods: In the current work, we prospectively and blindly assessed time points from 0 to 24 months, room versus refrigerated temperature, and presence of a desiccant and/or nitrogen atmosphere on a variety of benign and malignant tissues from North America and Africa. End points included immunohistochemistry (IHC), in situ hybridization (ISH), extracted RNA and DNA quantity and quality, and messenger RNA performance in a novel, multiplexed digital gene expression profiling assay of both housekeeping and tumor-specific genes. Results: We found that using current methods of antigen retrieval, staining, and extraction, the end points of IHC, ISH, RNA, and DNA were well preserved under the various conditions tested, with implications that pre-embedding factors contribute to variability in subsequent tissue integrity. We also document that spectrophotometric estimations of nucleic acid concentrations were in general estimated to be higher than with fluorimetric methods, which may be pertinent to end assay development. We further describe a new multiplex assay, the PlexSet digital gene expression assay, suitable for evaluating RNA quality in FFPE tissues. Conclusion: Altogether, these results may provide helpful guidance with regard to approaches for long-term storage conditions for USS.

Quality control recommendations for RNASeq using FFPE samples based on pre-sequencing lab metrics and post-sequencing bioinformatics metrics

Background

Formalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data.

Methods

We optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics.

Results

Samples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes (11,400 # of detected genes with TPM > 4). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.9 ng/ul and 2.08 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.82 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.848 in predicting QC status (pass/fail) of FFPE samples.

Conclusions

We provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.7 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01355-0.

Evaluating the Influences of Confounding Variables on Benchmark Dose using a Case Study in the Field of Ionizing Radiation

Purpose A vast amount of data regarding the effects of radiation stressors on transcriptional changes has been produced over the past few decades. These data have shown remarkable consistency across platforms and experimental design, enabling increased understanding of early molecular effects of radiation exposure. However, the value of transcriptomic data in the context of risk assessment is not clear and represents a gap that is worthy of further consideration. Recently, benchmark dose (BMD) modeling has shown promise in correlating a transcriptional point of departure (POD) to that derived using phenotypic outcomes relevant to human health risk assessment. Although frequently applied in chemical toxicity evaluation, our group has recently demonstrated application within the field of radiation research. This approach allows the possibility to quantitatively compare radiation-induced gene and pathway alterations across various datasets using BMD values and derive meaningful biological effects. However, before BMD modeling can confidently be used, an understanding of the impact of confounding variables on BMD outputs is needed. Methods: To this end, BMD modeling was applied to a publicly available microarray dataset (Gene Expression Omnibus #GSE23515) that used peripheral blood ex-vivo gamma-irradiated at 0.82 Gy/min, at doses of 0, 0.1, 0.5 or 2 Gy, and assessed 6 hours post-exposure. The dataset comprised six female smokers (F-S), six female non-smokers (F-NS), six male smokers (M-S), and six male non-smokers (M-NS). Results: A combined total of 412 genes were fit to models and the BMD distribution was noted to be bi-modal across the four groups. A total of 74, 41, 62 and 62 genes were unique to the F-NS, M-NS, F-S and M-S groups. Sixty-two BMD modeled genes and nine pathways were common across all four groups. There were no differential sensitivity of responses in the robust common genes and pathways. Conclusion: For radiation-responsive genes and pathways common across the study groups, the BMD distribution of transcriptional activity was unaltered by sex and smoking status. Although further validation of the data is needed, these initial findings suggest BMD values for radiation relevant genes and pathways are robust and could be explored further in future studies.

Benchmark dose modeling of transcriptional data: a systematic approach to identify best practices for study designs used in radiation research

PURPOSE

Benchmark dose (BMD) modeling is a method commonly used in chemical toxicology to identify the point of departure (POD) from a dose-response curve linked to a health-related outcome. Recently, it is being explored on transcriptional data and in adverse outcome pathways (AOPs). As AOPs are informed by diverse data types, it is important to understand the impact of study parameters such as dose selection, number of replicates and dose range on BMD outputs for radiation induced genes and pathways.

MATERIALS AND METHODS

Data were selected from the Gene Expression Omnibus (GSE52403) that featured gene expression profiles of peripheral blood samples from C57BL/6 mice 6 hours post-exposure to ¹³⁷Cs gamma-radiation at 0, 1, 2, 3, 4.5, 6, 8 and 10.5 Gy. The dataset comprised a broad dose-range over multiple dose-points with consistent dose spacing and multiple biological replicates. This dataset was ideal for systematically transforming across three categories: (1) dose-range, (2) dose-spacing and (3) number of controls/replicates. Across these categories, 29 transformed datasets were compared to the original dataset to determine the impact of each transformation on the BMD outputs.

RESULTS

Most of the experimental changes did not impact the BMD outputs. The transformed datasets were largely consistent with the original dataset in terms of number of reproduced genes modeled and absolute BMD values for genes and pathways. Variations in dose selection identified the importance of the absolute value of the lowest and second dose. It was determined that dose selection should include at least two doses <1 Gy and two >5 Gy to achieve meaningful BMD outputs. Changes to the number of biological replicates in the control and non-zero dose groups impacted the overall accuracy and precision of the BMD outputs as well as the ability to fit dose-response models consistent with the original dataset.

CONCLUSION

Successful application of transcriptomic BMD modeling for radiation datasets requires considerations of the exposure dose and the number of biological replicates. Most important is the selection of the lowest doses and dose spacing. Reflections on these parameters in experimental design will provide meaningful BMD outputs that could correlate well to apical endpoints of relevance to radiation exposure assessment.

Development of Methods to Extract RNA From Archived Pediatric Needle Liver Biopsies to Produce Sequencing Data

ABSTRACT

Genetic susceptibility has been proposed as etiopathogenic in several pediatric liver diseases including autoimmune hepatitis (AIH). High throughput sequencing (HTPS) has been applied to archived needle liver biopsies obtained from adults but rarely to pediatric biopsies. For conclusive diagnosis of AIH, most subjects have an initial formalin-fixed paraffin embedded (FFPE) needle liver biopsy that is eventually archived and may be stored for decades.

OBJECTIVE

Our goal was to develop methods to utilize tissue from archived needle liver biopsies for extraction of RNA sufficient to produce HTPS data.

METHODS

We extracted total RNA from 45 FFPE needle liver biopsy samples (24 AIH type 1 patients and 21 controls [ages 15_11 and 19_10]; biopsy storage time 0.5-20 years) and constructed cDNA libraries that were then sequenced on an Illumina HiSeq2000 platform.

RESULTS

Forty (89%) of the libraries produced high-quality sequences for further analyses. The average number of sequences obtained per library from HTPS was 55,136,519 (range 14,914,291-184,027,499). There was a significant inverse relationship between the number of human reads obtained and the age of the specimen (P < 2_10_7). It was possible to classify more than 90% of the reads as known genes in samples that had been stored for less than 10 years.

CONCLUSIONS

Archived needle liver biopsies can be used for sequence based interrogation of the etiologic origins of complex liver diseases of young subjects, such as AIH.

Integration of whole-exome and anchored PCR-based next generation sequencing significantly increases detection of actionable alterations in precision oncology

BACKGROUND

Frequency of clinically relevant mutations in solid tumors by targeted and whole-exome sequencing is ∼30%. Transcriptome analysis complements detection of actionable gene fusions in advanced cancer patients. Goal of this study was to determine the added value of anchored multiplex PCR (AMP)-based next-generation sequencing (NGS) assay to identify further potential drug targets, when coupled with whole-exome sequencing (WES).

METHODS

Selected series of fifty-six samples from 55 patients enrolled in our precision medicine study were interrogated by WES and AMP-based NGS. RNA-seq was performed in 19 cases. Clinically relevant and actionable alterations detected by three methods were integrated and analyzed.

RESULTS

AMP-based NGS detected 48 fusions in 31 samples (55.4%); 31.25% (15/48) were classified as targetable based on published literature. WES revealed 29 samples (51.8%) harbored targetable alterations. TMB-high and MSI-high status were observed in 12.7% and 1.8% of cases. RNA-seq from 19 samples identified 8 targetable fusions (42.1%), also captured by AMP-based NGS. When number of actionable fusions detected by AMP-based NGS were added to WES targetable alterations, 66.1% of samples had potential drug targets. When both WES and RNA-seq were analyzed, 57.8% of samples had targetable alterations.

CONCLUSIONS

This study highlights importance of an integrative genomic approach for precision oncology, including use of different NGS platforms with complementary features. Integrating RNA data (whole transcriptome or AMP-based NGS) significantly enhances detection of potential targets in cancer patients. In absence of fresh frozen tissue, AMP-based NGS is a robust method to detect actionable fusions using low-input RNA from archival tissue.

Direct formalin fixation induces widespread transcriptomic effects in archival tissue samples

Sequencing technologies now provide unprecedented access to genomic information in archival formalin-fixed paraffin-embedded (FFPE) tissue samples. However, little is known about artifacts induced during formalin fixation, which could bias results. Here we evaluated global changes in RNA-sequencing profiles between matched frozen and FFPE samples. RNA-sequencing was performed on liver samples collected from mice treated with a reference chemical (phenobarbital) or vehicle control for 7 days. Each sample was divided into four parts: (1) fresh-frozen, (2) direct-fixed in formalin for 18 h, (3) frozen then formalin-fixed, and (4) frozen then ethanol-fixed and paraffin-embedded (n = 6/group/condition). Direct fixation resulted in 2,946 differentially expressed genes (DEGs) vs. fresh-frozen, 98% of which were down-regulated. Freezing prior to formalin fixation had ≥ 95% fewer DEGs vs. direct fixation, indicating that most formalin-derived transcriptional effects in the liver occurred during fixation. This finding was supported by retrospective studies of paired frozen and FFPE samples, which identified consistent enrichment in oxidative stress, mitochondrial dysfunction, and transcription initiation pathways with direct fixation. Notably, direct formalin fixation in the parent study did not significantly impact response profiles resulting from chemical exposure. These results advance our understanding of FFPE samples as a resource for genomic research.

Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment

PURPOSE

Benchmark dose (BMD) modeling is used to determine the dose of a stressor at which a predefined increase in any biological effect above background occurs (e.g. 10% increase from control values). BMD analytical tools have the capacity to model transcriptional dose-response data to derive BMDs for genes, pathways and gene ontologies. We recently demonstrated the value of this approach to support various areas of radiation research using predominately 'in-house' generated datasets.

MATERIALS AND METHODS

As a continuation of this work, transcriptomic studies of relevance to ionizing radiation were retrieved through the Gene Expression Omnibus (GEO). The datasets were compiled and filtered, then analyzed using BMDExpress. The objective was to determine the reproducibility of BMD values in relation to pathways and genes across different exposure scenarios and compare to those derived using cytogenetic endpoints. A number of graphic visualization approaches were used to determine if BMD outputs could be correlated to parameters such as dose-rate, radiation quality and cell type.

RESULTS

Curated studies were diverse and derived from experiments with varied design and intent. Despite this, common genes and pathways were identified with low and high dose thresholds. The higher BMD values were associated with immune response and cell death, while transcripts with lower BMD values were generally related to the classic DNA damage response/repair processes, centered on TP53 signaling. Analysis of datasets with relatively similar dose-ranges under comparable experimental conditions showed a bi-modal distribution with a high degree of consistency in BMD values across shared genes and pathways, particularly for those below the 25th percentile of total distribution by dose. The median BMD values were noted to be approximately 0.5 Gy for genes/pathways that comprised mode 1. Furthermore, transcriptional BMD values derived from a subset of genes using in vivo and in vitro datasets were in accord to those using cytogenetic endpoints.

CONCLUSION

Overall, the results from this work highlight the value of the BMD methodology to derive meaningful outputs that are consistent across different models, provided the studies are conducted using a similar dose-range.

Extensive Changes in Transcriptomic "Fingerprints" and Immunological Cells in the Large Organs of Patients Dying of Acute Septic Shock and Multiple Organ Failure Caused by Neisseria meningitidis

Background: Patients developing meningococcal septic shock reveal levels of Neisseria meningitidis (10⁶-10⁸/mL) and endotoxin (10¹-10³ EU/mL) in the circulation and organs, leading to acute cardiovascular, pulmonary and renal failure, coagulopathy and a high case fatality rate within 24 h. Objective: To investigate transcriptional profiles in heart, lungs, kidneys, liver, and spleen and immunostain key inflammatory cells and proteins in post mortem formalin-fixed, paraffin-embedded (FFPE) tissue samples from meningococcal septic shock patients. Patients and Methods: Total RNA was isolated from FFPE and fresh frozen (FF) tissue samples from five patients and two controls (acute non-infectious death). Differential expression of genes was detected using Affymetrix microarray analysis. Lung and heart tissue samples were immunostained for T-and B cells, macrophages, neutrophils and the inflammatory markers PAI-1 and MCP-1. Inflammatory mediators were quantified in lysates from FF tissues. Results: The transcriptional profiles showed a complex pattern of protein-coding and non-coding RNAs with significant regulation of pathways associated with organismal death, cell death and survival, leukocyte migration, cellular movement, proliferation of cells, cell-to-cell signaling, immune cell trafficking, and inflammatory responses in an organ-specific clustering manner. The canonical pathways including acute phase response-, EIF2-, TREM1-, IL-6-, HMBG1-, PPAR signaling, and LXR/RXR activation were associated with acute heart, pulmonary, and renal failure. Fewer genes were regulated in the liver and particularly in the spleen. The main upstream regulators were TNF, IL-1β, IL-6, RICTOR, miR-6739-3p, and CD3. Increased numbers of inflammatory cells (CD68+, MPO+, CD3+, and CD20+) were found in lungs and heart. PAI-1 inhibiting fibrinolysis and MCP-1 attracting leukocyte were found significantly present in the septic tissue samples compared to the controls. Conclusions: FFPE tissue samples can be suitable for gene expression studies as well as immunostaining of specific cells or molecules. The most pronounced gene expression patterns were found in the organs with highest levels of Neisseria meningitidis DNA. Thousands of protein-coding and non-coding RNA transcripts were altered in lungs, heart and kidneys. We identified specific biomarker panels both protein-coding and non-coding RNA transcripts, which differed from organ to organ. Involvement of many genes and pathways add up and the combined effect induce organ failure.

Next generation sequencing data for use in risk assessment

Next generation sequencing (NGS) represents several powerful platforms that have revolutionized RNA and DNA analysis. The parallel sequencing of millions of DNA molecules can provide mechanistic insights into toxicology and provide new avenues for biomarker discovery with growing relevance for risk assessment. The evolution of NGS technologies has improved over the last decade with increased sensitivity and accuracy to foster new biomarker assays from tissue, blood and other biofluids. NGS sequencing technologies can identify transcriptional changes and genomic targets with base pair precision in response to chemical exposure. Further, there are several exciting movements within the toxicology community that incorporate NGS platforms into new strategies for more rapid toxicological characterizations. These include the Tox21 in vitro high throughput transcriptomic screening program, development of organotypic spheroids, alternative animal models, mining archival tissues, liquid biopsy and epigenomics. This review will describe NGS-based technologies, demonstrate how they can be used as tools for target discovery in tissue and blood, and suggest how they might be applied for risk assessment.

Demodifying RNA for Transcriptomic Analyses of Archival Formalin-Fixed Paraffin-Embedded Samples

Archival formalin-fixed paraffin-embedded (FFPE) tissue samples offer a vast but largely untapped resource for genomic research. The primary technical issues limiting use of FFPE samples are RNA yield and quality. In this study, we evaluated methods to demodify RNA highly fragmented and crosslinked by formalin fixation. Primary endpoints were RNA recovery, RNA-sequencing quality metrics, and transcriptional responses to a reference chemical (phenobarbital, PB). Frozen mouse liver samples from control and PB groups (n = 6/group) were divided and preserved for 3 months as follows: frozen (FR); 70% ethanol (OH); 10% buffered formalin for 18 h followed by ethanol (18F); or 10% buffered formalin (3F). Samples from OH, 18F, and 3F groups were processed to FFPE blocks and sectioned for RNA isolation. Additional sections from 3F received the following demodification protocols to mitigate RNA damage: short heated incubation with Tris-Acetate-EDTA buffer; overnight heated incubation with an organocatalyst using 2 different isolation kits; or overnight heated incubation without organocatalyst. Ribo-depleted, stranded, total RNA libraries were built and sequenced using the Illumina HiSeq 2500 platform. Overnight incubation (± organocatalyst) increased RNA yield >3-fold and RNA integrity numbers and fragment analysis values by > 1.5- and >3.0-fold, respectively, versus 3F. Postsequencing metrics also showed reduced bias in gene coverage and deletion rates for overnight incubation groups. All demodification groups had increased overlap for differentially expressed genes (77%-84%) and enriched pathways (91%-97%) with FR, with the highest overlap in the organocatalyst groups. These results demonstrate simple changes in RNA isolation methods that can enhance genomic analyses of FFPE samples.

Fundamentals of RNA Analysis on Biobanked Specimens

Compared to DNA, analysis of RNA is one step closer on the central dogma of biology to assessing cellular function. This makes it an extremely valuable target for research and clinical testing in nearly all areas of molecular biology. Most RNA molecules are ephemeral by nature. They exist as temporary intermediates, ostensibly enabling data transfer between the genome and the organism. Their ribose backbone renders them sensitive to simple degradation over time and they are the target molecule for numerous and abundant ribonucleases which have evolved to chop them to pieces with extreme efficiency. At the biochemical level, this means that they degrade rapidly in most physiological and laboratory conditions and are thus challenging to study. When considering specimen banking, it is critical to keep this reality in mind, as some commonly used banking modalities will not adequately preserve the relevant RNA molecules in a measureable state.In this chapter, we explore the broad range of RNA testing methodologies in current use, with particular focus on how specimen preparation impacts analysis. Following an overview in the introduction, Subheading 2 covers the major specimen types amenable to RNA analysis in the context of biobanking. Subheading 3 discusses the applications of various RNA analysis modalities to research and clinical testing.

Small Nucleolar RNA Score: An Assay to Detect Formalin-Overfixed Tissue

Although there are millions of formalin-fixed paraffin-embedded (FFPE) tissue blocks potentially available for scientific research, many are of questionable quality, partly due to unknown fixation conditions. We analyzed FFPE tissue biospecimens as part of the NCI Biospecimen Preanalytical Variables (BPV) program to identify microRNA (miRNA) markers for fixation time. miRNA was extracted from kidney and ovary tumor FFPE blocks (19 patients, cold ischemia ≤2 hours) with 6, 12, 24, and 72 hours fixation times, then analyzed using the WaferGen SmartChip platform (miRNA chip with 1036 miRNA targets). For fixation time, principal component analysis of miRNA chip expression data separated 72 hours fixed samples from 6 to 24 hours fixed samples. A set of small nuclear RNA (snRNA) targets was identified that best determines fixation time and was validated using a second independent cohort of seven different tissue types. A customized assay was then developed, based on a set of 24 miRNA and snRNA targets, and a simple “snoRNA score” defined. This score detects FFPE tissue samples with fixation for 72 hours or more, with 79% sensitivity and 80% specificity. It can therefore be used to assess the fitness-for-purpose of FFPE samples for DNA or RNA-based research or clinical assays, which are known to be of limited robustness to formalin overfixation.

Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals

Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.

Dried Blood Spot RNA Transcriptomes Correlate with Transcriptomes Derived from Whole Blood RNA

Obtaining RNA from clinical samples collected in resource-limited settings can be costly and challenging. The goals of this study were to 1) optimize messenger RNA extraction from dried blood spots (DBS) and 2) determine how transcriptomes generated from DBS RNA compared with RNA isolated from blood collected in Tempus tubes. We studied paired samples collected from eight adults in rural Tanzania. Venous blood was collected on Whatman 903 Protein Saver cards and in tubes with RNA preservation solution. Our optimal DBS RNA extraction used 8 × 3-mm DBS punches as the starting material, bead beater disruption at maximum speed for 60 seconds, extraction with Illustra RNAspin Mini RNA Isolation kit, and purification with Zymo RNA Concentrator kit. Spearman correlations of normalized gene counts in DBS versus whole blood ranged from 0.887 to 0.941. Bland-Altman plots did not show a trend toward over- or under-counting at any gene size. We report a method to obtain sufficient RNA from DBS to generate a transcriptome. The DBS transcriptome gene counts correlated well with whole blood transcriptome gene counts. Dried blood spots for transcriptome studies could be an option when field conditions preclude appropriate collection, storage, or transport of whole blood for RNA studies.

Biomedical analysis of formalin-fixed, paraffin-embedded tissue samples: The Holy Grail for molecular diagnostics

More than a century ago in 1893, a revolutionary idea about fixing biological tissue specimens was introduced by Ferdinand Blum, a German physician. Since then, a plethora of fixation methods have been investigated and used. Formalin fixation with paraffin embedment became the most widely used types of fixation and preservation method, due to its proper architectural conservation of tissue structures and cellular shape. The huge collection of formalin-fixed, paraffin-embedded (FFPE) sample archives worldwide holds a large amount of unearthed information about diseases that could be the Holy Grail in contemporary biomarker research utilizing analytical omics based molecular diagnostics. The aim of this review is to critically evaluate the omics options for FFPE tissue sample analysis in the molecular diagnostics field.

Robust detection of immune transcripts in FFPE samples using targeted RNA sequencing

Current criteria for identifying cancer patients suitable for immunotherapy with immune checkpoint blockers (ICBs) are subjective and prone to misinterpretation, as they mainly rely on the visual assessment of CD274 (best known as PD-L1) expression levels by immunohistochemistry (IHC). To address this issue, we developed a RNA sequencing (RNAseq)-based approach that specifically measures the abundance of immune transcripts in formalin-fixed paraffin embedded (FFPE) specimens. Besides exhibiting superior sensitivity as compared to whole transcriptome RNAseq, our assay requires little starting material, implying that it is compatible with RNA degradation normally caused by formalin. Here, we demonstrate that a targeted RNAseq panel reliably profiles mRNA expression levels in FFPE samples from a cohort of ovarian carcinoma patients. The expression profile of immune transcripts as measured by targeted RNAseq in FFPE versus freshly frozen (FF) samples from the same tumor was highly concordant, in spite of the RNA quality issues associated with formalin fixation. Moreover, the results of targeted RNAseq on FFPE specimens exhibited a robust correlation with mRNA expression levels as measured on the same samples by quantitative RT-PCR, as well as with protein abundance as determined by IHC. These findings demonstrate that RNAseq profiling on archival FFPE tissues can be used reliably in studies assessing the efficacy of cancer immunotherapy.

Data-intensive drug development in the information age: applications of Systems Biology/Pharmacology/Toxicology

Pharmaceutical companies continuously face challenges to deliver new drugs with true medical value. R&D productivity of drug development projects depends on 1) the value of the drug concept and 2) data and in-depth knowledge that are used rationally to evaluate the drug concept's validity. A model-based data-intensive drug development approach is a key competitive factor used by innovative pharmaceutical companies to reduce information bias and rationally demonstrate the value of drug concepts. Owing to the accumulation of publicly available biomedical information, our understanding of the pathophysiological mechanisms of diseases has developed considerably; it is the basis for identifying the right drug target and creating a drug concept with true medical value. Our understanding of the pathophysiological mechanisms of disease animal models can also be improved; it can thus support rational extrapolation of animal experiment results to clinical settings. The Systems Biology approach, which leverages publicly available transcriptome data, is useful for these purposes. Furthermore, applying Systems Pharmacology enables dynamic simulation of drug responses, from which key research questions to be addressed in the subsequent studies can be adequately informed. Application of Systems Biology/Pharmacology to toxicology research, namely Systems Toxicology, should considerably improve the predictability of drug-induced toxicities in clinical situations that are difficult to predict from conventional preclinical toxicology studies. Systems Biology/Pharmacology/Toxicology models can be continuously improved using iterative learn-confirm processes throughout preclinical and clinical drug discovery and development processes. Successful implementation of data-intensive drug development approaches requires cultivation of an adequate R&D culture to appreciate this approach.

Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment

There is increasing interest in the use of quantitative transcriptomic data to determine benchmark dose (BMD) and estimate a point of departure (POD) for human health risk assessment. Although studies have shown that transcriptional PODs correlate with those derived from apical endpoint changes, there is no consensus on the process used to derive a transcriptional POD. Specifically, the subsets of informative genes that produce BMDs that best approximate the doses at which adverse apical effects occur have not been defined. To determine the best way to select predictive groups of genes, we used published microarray data from dose–response studies on six chemicals in rats exposed orally for 5, 14, 28, and 90 days. We evaluated eight approaches for selecting genes for POD derivation and three previously proposed approaches (the lowest pathway BMD, and the mean and median BMD of all genes). The relationship between transcriptional BMDs derived using these 11 approaches and PODs derived from apical data that might be used in chemical risk assessment was examined. Transcriptional BMD values for all 11 approaches were remarkably aligned with corresponding apical PODs, with the vast majority of toxicogenomics PODs being within tenfold of those derived from apical endpoints. We identified at least four approaches that produce BMDs that are effective estimates of apical PODs across multiple sampling time points. Our results support that a variety of approaches can be used to derive reproducible transcriptional PODs that are consistent with PODs produced from traditional methods for chemical risk assessment.

Transcriptional benchmark dose modeling: Exploring how advances in chemical risk assessment may be applied to the radiation field

Recent advances in "-omics" technologies have simplified capacity to concurrently assess expression profiles of thousands of targets in a cellular system. However, compilation and analysis of "omics" data in support of human health protection remains a challenge. Benchmark dose (BMD) modeling is currently being employed in chemical risk assessment to estimate acceptable levels of exposure. Although typically applied to conventional endpoints, newer software has enabled this application to be extended to transcriptomic datasets. BMD analytical tools now have the capacity to model transcriptional dose-response data to derive meaningful BMD values for genes, pathways and gene ontologies. In this report, radiation data obtained from the Gene Expression Omnibus (GEO) were analyzed to generate BMD values for transcriptional responses. The datasets comprised microarray analyses of human blood gamma-irradiated ex vivo (0-20 Gy) and human-derived cell lines exposed to alpha particle radiation (0.5-1.5 Gy). The distributions of BMDs for statistically significant genes and pathways in response to radiation exposure were examined and compared across studies. BMD modeling could identify pathway/gene sensitivities across wide radiation dose ranges, experimental conditions (time-points, cell types) and radiation qualities. BMD analysis offered a new approach to examine transcriptional data. The results were shown to provide information on transcriptional thresholds of effects to support refined risk assessments for low dose ionizing radiation exposures, derive gene-based values for relative biological effectiveness and identify pathways involved in radiation sensitivities across cell types which may extend to applications a clinical setting. Environ. Mol. Mutagen. 57:589-604, 2016. © 2016 Wiley Periodicals, Inc.

Editor's Highlight: Dose-Response Analysis of RNA-Seq Profiles in Archival Formalin-Fixed Paraffin-Embedded Samples

Use of archival resources has been limited to date by inconsistent methods for genomic profiling of degraded RNA from formalin-fixed paraffin-embedded (FFPE) samples. RNA-sequencing offers a promising way to address this problem. Here, we evaluated transcriptomic dose responses using RNA-sequencing in paired FFPE and frozen (FROZ) samples from 2 archival studies in mice, one <2 years old and the other >20 years old. Experimental treatments included 3 different doses of di(2-ethylhexyl)phthalate or dichloroacetic acid for the recently archived and older studies, respectively. Total RNA was ribo-depleted and sequenced using the Illumina HiSeq platform. In the recently archived study, FFPE samples had 35% lower total counts compared to FROZ samples but high concordance in fold-change values of differentially expressed genes (DEGs) (r²  =0.99), highly enriched pathways (90% overlap with FROZ), and benchmark dose estimates for preselected target genes (<5% difference vs FROZ). In contrast, older FFPE samples had markedly lower total counts (3% of FROZ) and poor concordance in global DEGs and pathways. However, counts from FFPE and FROZ samples still positively correlated (r² = 0.84 across all transcripts) and showed comparable dose responses for more highly expressed target genes. These findings highlight potential applications and issues in using RNA-sequencing data from FFPE samples. Recently archived FFPE samples were highly similar to FROZ samples in sequencing quality metrics, DEG profiles, and dose-response parameters, while further methods development is needed for older lower-quality FFPE samples. This work should help advance the use of archival resources in chemical safety and translational science.

Toxicogenomics Case Study: Furan

Development of pragmatic methodologies for human health risk assessment is required to address current regulatory challenges. We applied three toxicogenomic approaches—quantitative, predictive, and mechanistic—to a case study in mice exposed for 3 weeks to the hepatocarcinogen furan. We modeled the dose response of a variety of transcriptional endpoints and found that they produced benchmark doses similar to the furan-dependent cancer benchmark doses. Meta-analyses showed strong similarity between furan-dependent gene expression changes and those associated with several hepatic pathologies. Molecular pathways facilitated the development of a molecular mode of action for furan-induced hepatocellular carcinogenicity. Finally, we compared transcriptomic profiles derived from formalin-fixed and paraffin-embedded (FFPE) samples with those from high-quality frozen samples to evaluate whether archival samples are a viable option for toxicogenomic studies. The advantage of using FFPE tissues is that they are very well characterized (phenotypically); the disadvantage is that formalin degrades biomacromolecules, including RNA. We found that FFPE samples can be used for toxicogenomics using a ribo-depletion RNA-seq protocol. Our case study demonstrates the utility of toxicogenomics data to human health risk assessment, the potential of archival FFPE tissue samples, and identifies viable strategies toward the reduction of animal usage in chemical testing.

Transcriptome Sequencing (RNAseq) Enables Utilization of Formalin-Fixed, Paraffin-Embedded Biopsies with Clear Cell Renal Cell Carcinoma for Exploration of Disease Biology and Biomarker Development

Formalin-fixed, paraffin-embedded (FFPE) tissues are an underused resource for molecular analyses. This proof of concept study aimed to compare RNAseq results from FFPE biopsies with the corresponding RNAlater^® (Qiagen, Germany) stored samples from clear cell renal cell carcinoma (ccRCC) patients to investigate feasibility of RNAseq in archival tissue. From each of 16 patients undergoing partial or full nephrectomy, four core biopsies, such as two specimens with ccRCC and two specimens of adjacent normal tissue, were obtained with a 16g needle. One normal and one ccRCC tissue specimen per patient was stored either in FFPE or RNAlater^®. RNA sequencing libraries were generated applying the new Illumina TruSeq^® Access library preparation protocol. Comparative analysis was done using voom/Limma R-package. The analysis of the FFPE and RNAlater^® datasets yielded similar numbers of detected genes, differentially expressed transcripts and affected pathways. The FFPE and RNAlater datasets shared 80% (n = 1106) differentially expressed genes. The average expression and the log2 fold changes of these transcripts correlated with R² = 0.97, and R² = 0.96, respectively. Among transcripts with the highest fold changes in both datasets were carbonic anhydrase 9 (CA9), neuronal pentraxin-2 (NPTX2) and uromodulin (UMOD) that were confirmed by immunohistochemistry. IPA revealed the presence of gene signatures of cancer and nephrotoxicity, renal damage and immune response. To simulate the feasibility of clinical biomarker studies with FFPE samples, a classifier model was developed for the FFPE dataset: expression data for CA9 alone had an accuracy, specificity and sensitivity of 94%, respectively, and achieved similar performance in the RNAlater dataset. Transforming growth factor-ß1 (TGFB1)-regulated genes, epithelial to mesenchymal transition (EMT) and NOTCH signaling cascade may support novel therapeutic strategies. In conclusion, in this proof of concept study, RNAseq data obtained from FFPE kidney biopsies are comparable to data obtained from fresh stored material, thereby expanding the utility of archival tissue specimens.

Advanced Applications of RNA Sequencing and Challenges

Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.