Three steps towards comparability and standardization among molecular methods for characterizing insect communities.

Molecular methods are currently some of the best-suited technologies for implementation in insect monitoring. However, the field is developing rapidly and lacks agreement on methodology or community standards. To apply DNA-based methods in large-scale monitoring, and to gain insight across commensurate data, we need easy-to-implement standards that improve data comparability. Here, we provide three recommendations for how to improve and harmonize efforts in biodiversity assessment and monitoring via metabarcoding: (i) we should adopt the use of synthetic spike-ins, which will act as positive controls and internal standards; (ii) we should consider using several markers through a multiplex polymerase chain reaction (PCR) approach; and (iii) we should commit to the publication and transparency of all protocol-associated metadata in a standardized fashion. For (i), we provide a ready-to-use recipe for synthetic cytochrome c oxidase spike-ins, which enable between-sample comparisons. For (ii), we propose two gene regions for the implementation of multiplex PCR approaches, thereby achieving a more comprehensive community description. For (iii), we offer guidelines for transparent and unified reporting of field, wet-laboratory and dry-laboratory procedures, as a key to making comparisons between studies. Together, we feel that these three advances will result in joint quality and calibration standards rather than the current laboratory-specific proof of concepts. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

A Robust Closed-Tube Method for Resolving tp53M214K Genotypes.

The tp53M214K zebrafish mutant is a versatile platform with which to model a diverse spectrum of human diseases. However, currently available genotyping methods for this mutant require lengthy hands-on processes such as restriction digests and outsourced Sanger sequencing. To address this deficiency, we leveraged high-resolution melting analysis technology in conjunction with a parallel, in-tandem wild-type spike-in approach to develop a robust genotyping protocol capable of discriminating tp53M214K zygosity. In this study, we describe our method in detail. We anticipate that our genotyping protocol will benefit researchers utilizing the tp53M214K zebrafish mutant by offering reliable results with a shorter turnaround time, lower personnel involvement, and higher throughput than traditional methods, thereby decreasing the burden of genotyping and maximizing research efficiency.

Analysis of sRNAs and Their mRNA Targets in Sinorhizobium meliloti: Focus on Half-Life Determination.

Regulation of gene expression at the level of RNA and/or by regulatory RNA is an integral part of the regulatory circuits in all living cells. In bacteria, transcription and translation can be coupled, enabling regulation by transcriptional attenuation, a mechanism based on mutually exclusive structures in nascent mRNA. Transcriptional attenuation gives rise to small RNAs that are well suited to act in trans by either base pairing or ligand binding. Examples of 5'-UTR-derived sRNAs in the alpha-proteobacterium Sinorhizobium meliloti are the sRNA rnTrpL of the tryptophan attenuator and SAM-II riboswitch sRNAs. Analyses addressing RNA-based gene regulation often include measurements of steady-state levels and of half-lives of specific sRNAs and mRNAs. Using such measurements, recently we have shown that the tryptophan attenuator responds to translation inhibition by tetracycline and that SAM-II riboswitches stabilize RNA. Here we discuss our experience in using alternative RNA purification methods for analysis of sRNA and mRNA of S. meliloti. Additionally, we show that other translational inhibitors (besides tetracycline) also cause attenuation giving rise to the rnTrpL sRNA. Furthermore, we discuss the importance of considering RNA stability changes under different conditions and describe in detail a robust and fast method for mRNA half-life determination. The latter includes rifampicin treatment, RNA isolation using commercially available columns, and mRNA analysis by reverse transcription followed by quantitative PCR (RT-qPCR). The latter can be performed as a one-step procedure or in a strand-specific manner using the same commercial kit and a spike-in transcript as a reference.

Exogenous spike-in mouse RNAs for accurate differential gene expression analysis in barley using RT-qPCR.

Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) followed by the 2-ΔΔCt method is the most common way to measure transcript levels for relative gene expression assays. The quality of an RT-qPCR assay is dependent upon the identification and validation of reference genes to normalize gene expression data. The so-called housekeeping genes are commonly used as internal reference genes because they are assumed to be ubiquitously expressed at stable levels. Commonly, researchers do not validate their reference genes but rely on historical reference genes or previously validated genes from an unrelated experiment. Using previously validated reference genes to assess gene expression changes occurring during malting resulted in extensive variability. Therefore, a new method was tested and validated to circumvent the use of internal reference genes. Total mouse RNA was chosen as the external reference RNA and a suite of primer sets to putatively stable mouse genes was created to identify stably expressed genes for use as an external reference gene. cDNA was created by co-amplifying total mouse RNA, as an RNA spike-in, and barley RNA. When using the external reference genes to normalize malting gene expression data, standard deviations were significantly reduced and significant differences in transcript abundance were observed, whereas when using the internal reference genes, standard deviations were larger with no significant differences seen. Furthermore, external reference genes were more accurate at assessing expression levels in malting and developing grains, whereas the internal reference genes overestimated abundance in developing grains and underestimated abundance in malting grains.

Pathogen quantitative efficacy of different spike-in internal controls and clinical application in central nervous system infection with metagenomic sequencing.

IMPORTANCE: Metagenomic next-generation sequencing (mNGS) has been used broadly for pathogens detection of infectious diseases. However, there is a lack of method for the absolute quantitation of pathogens by mNGS. We compared the quantitative efficiency of three mNGS internal controls (ICs) Thermus thermophilus, T1 phages, and artificial DNA sequence and developed the most applicable strategies for pathogen quantitation via mNGS in central nervous system infection. The IC application strategy we developed will enable mNGS analysis to assess the pathogen load simultaneously with the detection of pathogens, which should provide critical information for quick decision-making of treatment as well as clinical prognosis.

A metagenomics method for the quantitative detection of bacterial pathogens causing hospital-associated and ventilator-associated pneumonia.

IMPORTANCE: The management of ventilator-associated pneumonia and hospital-acquired pneumonia requires rapid and accurate quantitative detection of the infecting pathogen. To this end, we propose a metagenomic sequencing assay that includes the use of an internal sample processing control for the quantitative detection of 20 relevant bacterial species from bronchoalveolar lavage samples.

A new spike-in-based method for quantitative metabarcoding of soil fungi and bacteria.

Metabarcoding is a powerful tool to characterize biodiversity in biological samples. The interpretation of taxonomic profiles from metabarcoding data has been hindered by their compositional nature. Several strategies have been proposed to transform compositional data into quantitative data, but they have intrinsic limitations. Here, I propose a workflow based on bacterial and fungal cellular internal standards (spike-ins) for absolute quantification of the microbiota in soil samples. These standards were added to the samples before DNA extraction in amounts estimated after qPCRs, to target around 1-2% coverage in the sequencing run. In bacteria, proportions of spike-in reads in the sequencing run were very similar (< 2-fold change) to those predicted by the qPCR assessment, and for fungi they differed up to 40-fold. The low variation among replicates highlights the reproducibility of the method. Estimates based on multiple bacterial spike-ins were highly correlated (r = 0.99). Procrustes analysis evidenced significant biological effects on the community composition when normalizing compositional data. A protocol based on qPCR estimation of input amounts of cellular spikes is proposed as a cheap and reliable strategy for quantitative metabarcoding of biological samples.

Interlaboratory evaluation of quality control methods for circulating cell-free DNA extraction.

Analysis of circulating cell-free DNA (ccfDNA) isolated from liquid biopsies is rapidly being implemented into clinical practice. However, diagnostic accuracy is significantly impacted by sample quality and standardised approaches for assessing the quality of ccfDNA are not yet established. In this study we evaluated the application of nucleic acid "spike-in" control materials to aid quality control (QC) and standardisation of cfDNA isolation for use in in vitro diagnostic assays. We describe an approach for the design and characterisation of in-process QC materials, illustrating it with a spike-in material containing an exogenous Arabidopsis sequence and DNA fragments approximating to ccfDNA and genomic DNA lengths. Protocols for inclusion of the spike-in material in plasma ccfDNA extraction and quantification of its recovery by digital PCR (dPCR) were assessed for their suitability for process QC in an inter-laboratory study between five expert laboratories, using a range of blood collection devices and ccfDNA extraction methods. The results successfully demonstrated that spiking plasmid-derived material into plasma did not deleteriously interfere with endogenous ccfDNA recovery. The approach performed consistently across a range of commonly-used extraction protocols and was able to highlight differences in efficiency and variability between the methods, with the dPCR quantification assay performing with good repeatability (generally CV <5%). We conclude that initial findings demonstrate that this approach appears "fit for purpose" and spike-in recovery can be combined with other extraction QC metrics for monitoring the performance of a process over time, or in the context of external quality assessment.

2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Proteomics Study.

Despite the advantages of fewer missing values by collecting fragment ion data on all analytes in the sample as well as the potential for deeper coverage, the adoption of data-independent acquisition (DIA) in proteomics core facility settings has been slow. The Association of Biomolecular Resource Facilities conducted a large interlaboratory study to evaluate DIA performance in proteomics laboratories with various instrumentation. Participants were supplied with generic methods and a uniform set of test samples. The resulting 49 DIA datasets act as benchmarks and have utility in education and tool development. The sample set consisted of a tryptic HeLa digest spiked with high or low levels of 4 exogenous proteins. Data are available in MassIVE MSV000086479. Additionally, we demonstrate how the data can be analyzed by focusing on 2 datasets using different library approaches and show the utility of select summary statistics. These data can be used by DIA newcomers, software developers, or DIA experts evaluating performance with different platforms, acquisition settings, and skill levels.

High-quality single amplicon sequencing method for illumina MiSeq platform using pool of 'N' (0-10) spacer-linked target specific primers without PhiX spike-in.

BACKGROUND: Illumina sequencing platform requires base diversity in the initial 11 cycles for efficient cluster identification and colour matrix estimation. This limitation yields low-quality data for amplicon libraries having homogeneous base composition. Spike-in of PhiX library ensures base diversity but reduces the overall number of sequencing reads for data analysis. To overcome such low diversity issues during amplicon sequencing on illumina platforms, we developed a high throughput single amplicon sequencing method by introducing 'N' (0-10) spacers in target gene amplification primers that are pooled for simple handling. RESULT: We evaluated the efficiency of 'N' (0-10) spacer-linked primers by targeting bacterial 16S V3-V4 region, demonstrating heterogeneous base library construction. The addition of 'N' (0-10) spacers causes sequencing frameshift at every base that leads to base diversity and produces heterogeneous high quality reads within a single amplicon library. We have written a python based command-line software,"MetReTrim", to trim the 'N' (0-10) spacers from the raw reads ( https://github.com/Mohak91/MetReTrim ). We further demonstrated the accuracy of this method by comparative mock community analysis with standard illumina V3-V4 primer method. The ZymoBIOMICS™ microbial community DNA standard was used as a control for this study. We performed data analysisusing the DADA2 pipeline where taxonomy was assigned using SILVA database as reference. We observed no difference between the communities represented by our method and standard illumina V3-V4 primer method. CONCLUSION: This method eliminates the need for PhiX spike-in for single amplicon sequencing on illumina MiSeq platform. This allows for sequencing of more number of samples in a run and a reduction in the overall cost. Given that Illumina sequencing works on SBS chemistry irrespective of the platform (such as HiSeq, MiSeq, NextSeq, NovaSeq, etc.) we propose that this strategy of using 'N' (0-10) spacer-linked primer design can be adopted for generating high-quality single locus amplicon sequencing in a high throughput manner across the illumina platform subject to further validation.

Quantitative Comparison of Multiple Chromatin Immunoprecipitation-Sequencing (ChIP-seq) Experiments with spikChIP.

The chromatin immunoprecipitation coupled with the next-generation sequencing (ChIP-seq) is a powerful technique that enables to characterize the genomic distribution of chromatin-associated proteins, histone posttranslational modifications, and histone variants. However, in the absence of a reference control for monitoring experimental and biological variations, the standard ChIP-seq scheme is unable to accurately assess changes in the abundance of chromatin targets across different experimental samples. To overcome this limitation, the combination of external spike-in material with the experimental chromatin is offered as an effective solution for quantitative comparison of ChIP-seq data across different conditions. Here, we detail (i) the experimental protocol for preparing quality control spike-in chromatin from Drosophila melanogaster cells and (ii) the computational protocol to compare ChIP-seq samples with spike-in based on the use of the spikChIP software.

CUT & RUN to Profile Chromatin-Bound Proteins in Primary Mouse Neural Progenitor Cells.

Cleavage under targets and release using nuclease (CUT & RUN) is an innovative method to profile histone modifications and chromatin-bound proteins genome-wide. CUT & RUN offers two distinct advantages of requiring much fewer cells and providing strong signal-to-noise ratios in deep-sequencing data. Here, we describe a workflow starting from dissociation and sorting of mouse embryonic brains, CUT & RUN, and DNA library preparation to deep sequencing. With our workflow, researchers can obtain high-quality sequencing data to profile histones and chromatin-associated proteins by using as few as 100,000 neural progenitor cells (NPCs).

Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls.

Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cell-free DNA (cfDNA). We developed a set of synthetic spike-in DNA controls for cfMeDIP-seq to provide a simple and inexpensive reference for quantitative normalization. We designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G + C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). Using 0.01 ng of spike-in controls enables training a generalized linear model that absolutely quantifies methylated cfDNA in MeDIP-seq experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases.

Dataset containing physiological amounts of spike-in proteins into murine C2C12 background as a ground truth quantitative LC-MS/MS reference.

In this article, we present a data dependent acquisition (DDA) dataset which was generated as a reference and ground truth quantitative dataset. While initially used to compare samples measured with DDA and data independent acquisition (DIA) (Barkovits et al., 2020), the presented dataset holds potential value as a benchmark reference for any workflows working on DDA data. The entire dataset consists of 15 LC-MS/MS measurements composed of five distinct spike-in-states, each with three replicates. To generate the data set, a C2C12 (immortalized mouse myoblast) cell lysate was used as a complex background for five different states which were simulated by spiking 13 defined proteins at different concentrations. For this purpose, the cell lysate was used in a constant amount of 20 µg for all samples and different amounts of the 13 selected proteins ranging from 0.1  to 10 pmol were added, reflecting physiological amounts of proteins. Afterwards, all samples were tryptically digested using the same method. From each sample 200 ng tryptic peptides were measured in triplicates on a Q Exactive HF (Thermo Fisher Scientific). The mass range for MS1 was set to 350-1400 m/z with a resolution of 60,000 at 200 m/z. HCD fragmentation of the Top10 abundant precursor ions was performed at 27% NCE. The fragment analysis (MS2) was performed with a resolution of 30,000 at 200 m/z. Additionally to the raw files, the dataset contains centroided mzML files and spectrum identification results for peptide identifications performed by Mascot (Perkins et al., 1999), MS-GF+ (Kim et al., 2010) and X!Tandem (Craig and Beavis, 2004) for each separate MS analysis. The corresponding FASTA containing protein sequences as well as a combination of all identification runs performed by PIA (Uszkoreit et al., 2019, 2015) and a peptide and protein quantification performed by OpenMS (Pfeuffer et al., 2017) is included. All data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (Perez-Riverol et al., 2018) with the dataset identifier PXD012986.

miRNA Expression in Ovarian Cancer in Fresh Frozen, Formalin-fixed Paraffin-embedded and Plasma Samples.

BACKGROUND/AIM: MicroRNAs (miRNAs) are small noncoding RNAs involved in gene expression regulation and have been investigated as potential biomarkers for various diseases, including ovarian cancer (OC). However, lack of standardized protocols regarding e.g., RNA isolation, cDNA synthesis, spike-in controls for experimental steps, and data normalization, impacts cross validation of results across research groups and hinders implementation of miRNAs as clinical biomarkers. MATERIALS AND METHODS: RNA was isolated from matching fresh-frozen tissue (FF), formalin-fixed paraffin embedded (FFPE) tissue, and plasma samples from twenty women diagnosed with OC using three commercial kits: miRNeasy Tissue/Cells, miRNeasy FFPE, and miRNeasy Serum/Plasma (Qiagen, Copenhagen, Denmark). RNA isolation, cDNA synthesis, and PCR performance were tested using miRCURY LNA miRNA Quality Control PCR (QC) Panels (Qiagen). Finally, miRNA stability was assessed using five algorithms: BestKeeper, Normfinder, GeNorm, comparative delta-Ct and comprehensive ranking provided by a web-based RefFinder tool. RESULTS: RNA from FF, FFPE and plasma was extracted using commercially available kits and the differences in yield and purity were examined. We developed a simple method for identifying and potentially excluding samples based on their crossing point values from RT-qPCR data, which could improve existing manufacture guidelines. Moreover, we discussed how assessment of miRNA stability differs between algorithms, possibly leading to inconsistent results. CONCLUSION: We present guidelines for RNA isolation, cDNA synthesis, and data normalization for successful miRNA expression profiling using RT-qPCR in corresponding biological OC specimens. We recommend QC panels in combination with spike-in controls and interplate controls to monitor process efficiencies.

Quantitative DNA-RNA Immunoprecipitation Sequencing with Spike-Ins.

R-loops are three-stranded nucleic acid structures, comprising an RNA-DNA hybrid and a displaced strand of ssDNA. R-loops have important physiological roles in cells, but deregulation of R-loop dynamics can also have harmful cellular outcomes. The genome-wide mapping of R-loops offers an unbiased approach to study R-loop biology in a wide range of contexts. Here we present a protocol to sequence RNA-DNA hybrids genome-wide with strand-specificity and high resolution. We also include information on how to prepare and incorporate into the workflow appropriate internal spike-in standards which facilitate accurate normalization of the sequencing signal, thereby providing quantitative insights into R-loop formation between different experimental samples.

Genome-Wide Analysis of DNA-RNA Hybrids in Yeast by DRIPc-Seq and DRIP-Seq.

DNA-RNA hybrids are required for several natural processes in the cell, such as replication and transcription. However, the misregulation of its metabolism is an important source of genetic instability, a hallmark of diseases including cancer. For this reason, genome-wide detection of DNA-RNA hybrids is becoming essential to identify new factors that play a role in its formation or resolution and to understand the global changes in its dynamics because of genetic alterations or chemical treatments. Here, we describe two different immunoprecipitation-based procedures for the genome-wide profiling of DNA-RNA hybrids in the yeast Saccharomyces cerevisiae: DRIP-seq and DRIPc-seq.

Normalized Ribo-Seq for Quantifying Absolute Global and Specific Changes in Translation.

Ribosome profiling (Ribo-Seq) is a highly sensitive method to quantify ribosome occupancies along individual mRNAs on a genome-wide scale. Hereby, ribosome-protected fragments (= footprints) are generated by nuclease digestion, isolated, and sequenced together with the corresponding randomly fragmented input samples, to determine ribosome densities (RD). For library preparation, equal amounts of total RNA are used. Subsequently, all transcript fragments are subjected to linker ligation, cDNA synthesis, and PCR amplification. Importantly, the number of reads obtained for every transcript in input and footprint samples during sequencing depends on sequencing depth and library size, as well as the relative abundance of the transcript in the sample. However, the information pertaining to the absolute amount of input and footprint sequences is lost during sample preparation, hence ruling out any conclusion whether translation is generally suppressed or activated in one condition over the other. Therefore, the RD fold-changes determined for individual genes do not reflect absolute regulation, but have to be interpreted as relative to bulk mRNA translation. Here, we modified the original ribosome profiling protocol that was first established by Ingolia et al. (2009), by adding small amounts of yeast lysate to the mammalian lysates of interest as a spike-in. This allows us to not only detect changes in the RD of specific transcripts relative to each other, but also to simultaneously measure global differences in RD (normalized ribosome density values) between samples. Graphic abstract: Global changes in translation efficiency can be detected with polysome profiling, where the proportion of polysomal ribosomes is interpreted as a proxy for ribosome density (RD) on bulk mRNA. Ribo-Seq measures changes in RD of specific mRNAs relative to bulk mRNA. The addition of a yeast-lysate, as a spike-in for normalization of read counts, allows for an absolute measurement of changes in RD.

A MicroRNA Next-Generation-Sequencing Discovery Assay (miND) for Genome-Scale Analysis and Absolute Quantitation of Circulating MicroRNA Biomarkers.

The plasma levels of tissue-specific microRNAs can be used as diagnostic, disease severity and prognostic biomarkers for chronic and acute diseases and drug-induced injury. Thereby, the combination of diverse microRNAs into biomarker signatures using multivariate statistics seems especially powerful from the perspective of tissue and condition specific microRNA shedding into the plasma. Although next-generation sequencing (NGS) technology enables one to analyse circulating microRNAs on a genome-scale level, it suffers from potential biases (e.g., adapter ligation bias) and lacks absolute transcript quantitation as well as tailor-made quality controls. In order to develop a robust NGS discovery assay for genome-scale quantitation of circulating microRNAs, we first evaluated the sensitivity, repeatability and ligation bias of four commercially available small RNA library preparation protocols. The protocol from RealSeq Biosciences was selected based on its performance and usability and coupled with a novel panel of exogenous small RNA spike-in controls to enable quality control and absolute quantitation, thus ensuring comparability of data across independent NGS experiments. The established microRNA Next-Generation-Sequencing Discovery Assay (miND) was validated for its relative accuracy, precision, analytical measurement range and sequencing bias and was considered fit-for-purpose for microRNA biomarker discovery. Summarized, all these criteria were met, and thus, our analytical platform is considered fit-for-purpose for microRNA biomarker discovery from biofluids in the setting of any diagnostic, prognostic or patient stratification need. The established miND assay was tested on serum, cerebrospinal fluid (CSF), synovial fluid (SF) and extracellular vesicles (EV) extracted from cell culture medium of primary cells and proved its potential to be used across different sample types.

TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences.

T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to misdiagnosis if diagnostically relevant TCRs remain undetected. To address this issue, we developed TCRpower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth and read cutoff. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones (TCCs) as spike-in TCRs. We sequenced the spike-in TCRs from TCCs, together with TCRs from peripheral blood, using a 5' RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cutoff, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0% and TCR ß-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease-relevant TCRs for diagnostic applications.