Understanding how gene expression in single cells progresses over time is vital for revealing the mechanisms governing cell fate transitions. RNA velocity, which infers immediate changes in gene expression by comparing levels of new (unspliced) versus mature (spliced) transcripts, represents an important advance to these efforts. A key question remaining is whether it is possible to predict the most probable cell state backward or forward over arbitrary time scales.

In this webinar, Xiaojie Qiu of the University of California, San Francisco, shares an inclusive model, termed Dynamo, capable of predicting cell states over extended time periods. The model incorporates promoter state switching, transcription, splicing, translation, and RNA/protein degradation by taking advantage of single-cell RNA-seq data and transcriptome/proteome co-assay measurements.

Dr. Qiu demonstratea how the Dyamo model can be used to infer the entire kinetic behavior of a cell and will show that it is possible to analytically reconstruct the transcriptomic vector field from sparse and noisy vector samples generated by single-cell experiments, especially those produced from metabolic labeling based scRNA-seq (i.e scSLAM-seq, NASC-seq, sci-fate or scNT-seq).

In this webinar you can learn:

• How to use Dynamo to perform RNA velocity analysis and vector field reconstruction
• Details of an inclusive model capable of predicting cell states over extended time periods
• How to analytically reconstruct the transcriptomic vector field from sparse and noisy vector samples generated by single-cell experiments

RNA-sequencing (RNA-seq) provides information on gene expression and transcript structure. RNA-seq is widely used to study molecular mechanisms underlying development and disease in both humans and model organisms. Such studies benefit from large sample sizes that include biological variables such as sex. However, manual preparation of RNA-seq libraries is limited in scale and remains a potential source of unwanted variation.

In this webcast, the speakers present their experience and insights with automating RNA-seq library production. They will discuss the application of their first automated RNA-seq protocol, which profiles the 3’ ends of transcripts. Using this method, they studied the gene expression dynamics of the mouse pituitary gland during postnatal development to identify sex-specific gene expression modules. Finally, they discuss their ongoing experience with this method, as well as other automated RNA-seq library prep methods that have diagnostic potential in a clinical setting.

In this webinar you can learn:

• How to establish and evaluate automated RNA-seq library methods.
• How you can take advantage of automated 3’ UTR-seq library preparation to scale up the study of gene expression in model organisms.
• Important considerations when implementing automated RNA-seq library methods in a clinical setting.

In this webinar, you will learn about Lexogen’s CORALL Total RNA-Seq workflow and ready-to-use data analysis on the BlueBee Genomics Platform.

• Fast and cost-efficient generation of UMI-labelled stranded libraries for whole transcriptome analyses using Illumina® NGS platforms
• Facilitated data residency
• Fast and user-friendly data mapping and calculation of gene expression values
• Streamlined workflow and simplified data analysis
• GDPR and other data regulations compliance
• On-demand access to computational resources and storage for any scale
• Secure sharing of data with collaborators
• Access to BlueBee’s large-scale data aggregation and mining tools

This webinar discusses a study that compared nine different transcriptomic analysis technologies with matched fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) cancer tissues. Cost and tissue availability normally preclude processing samples across multiple technologies, making it difficult to directly evaluate performance, reliability, and to what extent gene expression data from different platforms can be compared or integrated. In order to explore the feasibility of integrating gene expression data from different platforms, Dr. Arran K. Turnbull of the Cancer Research UK Edinburgh Center and colleagues explored nine technologies, which varied in resolution, cost, and RNA requirements. The study used sequential tumor biopsies from 11 postmenopausal women with estrogen receptor positive breast cancer treated with three months of neoadjuvant anti-estrogen therapy. Half of each sample was snap frozen in liquid nitrogen and half was FFPE. Transcriptomic analyses were performed using the Illumina Beadarray, Affymetrix U133A, Affymetrix Clariom S, NanoString nCounter, AmpliSeq Transcriptome, Lexogen QuantSeq and IonXpress RNAseq, Tempo-Seq BioSpyder, and Qiagen UPX 3’. Dr. Turnbull details the study’s findings, which include: – Robust gene expression profiles can be reliably generated from FFPE tissues and are comparable to those derived from FF tissue using established transcriptomic approaches. – A range of new technologies are available for the study of FFPE tissues; these vary in cost, resolution, and RNA requirements to fit the user’s needs. – Gene expression data from biologically similar studies, generated using different technologies, can be reliably integrated for robust meta-analysis, subject to appropriate batch correction analysis.

This webinar outlines a study that combined genome-wide and classical molecular approaches to demonstrate that translation strongly affects mRNA stability in a codon-dependent manner, ultimately influencing mRNA and protein levels in higher organisms.

Ribosomes are the most abundant RNA-binding structures in the cell, and while their main function is to decode nucleotides into amino acid sequences, translation can also affect mRNA stability depending on codon composition. This regulatory pathway is different from codon usage or bias and is known as “codon optimality” defined as the property of given codons to regulate mRNA stability in a translation-dependent manner.

In this webinar, Ariel Bazzini of the Stowers Institute for Medical Research details a study that took three independent approaches in different human cells. The team measured the decay of existing mRNAs by performing time-course RNA-seq; measured mRNA stability independent of untranslated regions (UTRs) using a vector-based library termed ORFome; and measured mRNA stability without blocking mRNA transcription using a method called SLAM-seq.

Dr. Bazzini discusses the study’s findings, which provide valuable insights into a novel and powerful regulatory pathway that may be an underlying cause of misregulated gene expression in human conditions and diseases.

This webinar discusses novel long-read transcript sequencing (LRTseq) methods for transcriptome annotation that could increase the efficiency and accuracy of future sequencing projects.

LRTseq technologies, such as Pacific Biosciences’ Iso-Seq and Oxford Nanopore’s cDNA sequencing, have the power to provide rapid high-quality de novo transcriptome annotations. However, standard cDNA library preparation methods for LRTseq capture a significant amount of degraded RNA and are often overpopulated with highly expressed genes.

Degraded RNA reduces the efficiency of sequencing and introduces uncertainty with respect to predicted transcription start sites. Highly expressed genes can dominate LRTseq data, resulting in a loss of coverage for lower expressed genes. This typically results in missing genes and alternative transcripts in the final genome annotation.

While conventional RNA sequencing allows comprehensive transcriptome analyses at steady state, its utility for probing transcriptional responses to cell perturbations is limited by the vast diversity of messenger RNA (mRNA) and protein half-lives. At early time points, detectable changes in mRNA abundance are inevitably biased toward short-lived transcripts, while analyses at later time points do not allow for distinguishing direct from secondary effects. We have developed a simple, scalable method for enabling direct detection of 4-thiouridine (4sU)-labeled transcripts within the total RNA pool, which can be combined with standard RNA sequencing methods for investigating dynamic changes in gene expression. We have named this technique “thiol (SH)-Linked Alkylation for the Metabolic sequencing of RNA” (SLAMseq). One key application of SLAMseq is to directly quantify specific or global changes in mRNA output following pharmacological or chemical–genetic gene perturbation, and to thereby define primary transcriptional functions of genes and drugs.

During the webinar, the speakers will:

• Explain the basic principles and experimental implementation of SLAMseq
• Outline the method’s utility for probing direct transcriptional targets of regulatory genes as well as primary transcriptional responses to drug treatment
• Discuss future applications.

This webinar outlines a study that sought to characterize the landscape of alternative polyadenylation (APA) in the lung cancer transcriptome in order to gain insight into the role of APA in cancer progression.

APA involves the selection of an alternate poly(A) site on the pre-mRNA that leads to generation of isoforms of various length. In cancer, APA is emerging as an alternative mechanism for proto-oncogene activation in the absence of somatic mutations. Recent studies show a correlation of APA profiles with cancer prognosis, suggesting that APA is an important mechanism of cancer progression. In addition, environmental exposures such as temperature and exogenous hormones can also induce APA as a stress-response mechanism.

In this webinar, Dr. Adriana Zingone of the National Cancer Institute, Center for Cancer Research discusses her team’s work to characterize APA in the lung cancer transcriptome and to test a hypothesis that smoking modulates differential usage of polyadenylation sites within mRNA transcripts.

Biobanks consisting of formalin-fixed, paraffin-embedded (FFPE) patient samples, collected over decades, present unique opportunities for studying gene expression in large cohorts of patients with a given disease.This approach, however, has been limited by the high degree of RNA degradation in FFPE-derived samples, in some cases leading to more than half of the biobank samples being discarded.

This webinar will introduce a method for successfully generating libraries and analyzing FFPE-derived RNA samples so degraded that less than 20 percent of the RNA fragments have a length above 200 nucleotides. Our speaker, Anne-Margrethe Krogsdam Christensen of Innsbruck Medical University, will discuss a comparison of the results from FFPE samples and matched fresh-frozen samples, and finally across a cohort of patient cancer samples.

Dr. Krogsdam Christensen will explain how her team has been able to generate viable libraries and quality sequencing data, regardless of the degree of degradation, thereby strongly pushing the limits for FFPE samples that can be included in analysis.

Tris(1,3-dichloro-2-propyl) phosphate (TDCIPP) is a high-production volume organophosphate flame retardant that is used worldwide and detected within human populations, including children. Within zebrafish, we previously showed that initiation of TDCIPP exposure at 0.75 h post-fertilization (hpf) results in epiboly disruption at 6 hpf, and 76% of epiboly-arrested embryos are strongly dorsalized by 24 hpf – a phenotype that mimics the effects of dorsomorphin (DMP), a bone morphogenetic protein (BMP) antagonist that dorsalizes embryos in the absence of epiboly defects. Therefore, the primary objective of this study was to investigate the potential role of aberrant BMP signaling in TDCIPP-induced toxicity during early embryogenesis. Using epiboly and dorsalization as read-outs, we found that embryos were more susceptible to TDCIPP when exposures were initiated by 2-3 hpf, a maternal-to-zygotic transition characterized by genome activation and initiation of cell motility. We also found that 4’-hydroxychalcone (a BMP agonist) significantly mitigated TDCIPP-induced epiboly arrest as well as TDCIPP- and DMP-induced dorsalization. However, phospho-Smad1/5/9 detection in situ revealed that DMP – but not TDCIPP – blocked the normal progression of BMP signaling gradients at 8 hpf. Therefore, we relied on mRNA-sequencing to identify other signaling pathways that may be altered by TDCIPP during the first 24 h of development. We initiated treatments at 0.75 hpf and collected embryos at 3, 4, 5, 6, 8, 10, 12 and 24 hpf, totaling 90 RNA samples. Libraries were then prepared using the Lexogen Quantseq FWD library prep kit and their qualities were checked using our Agilent Bioanalyzer and quantified using a Qubit fluorometer. The libraries were then sequenced across 8 Illumina High Output (1X75 cycle) flow cells using our Illumina Miniseq and the resulting fastq files were processed and data analyzed using BlueBee

’s Quantseq analysis platform. These data revealed that, despite phenotypic similarities starting at 10 hpf, there was minimal overlap in DMP- and TDCIPP-induced effects on the transcriptome, a finding supported by the absence of TDCIPP-induced effects on BMP signaling. However, unlike DMP, TDCIPP exposure resulted in a decrease in transcripts that regulate mesoderm differentiation (tbx16, tbx6, tbx6l, msgn1) at the beginning of segmentation (10-12 hpf), as well as hematopoiesis-specific transcripts (hbae1.3, hbae3, hbbe1.2, hbbe3) at the beginning of pharyngula (24 hpf). Therefore, as red blood cells are derived from the mesoderm during hematopoiesis, we exposed embryos to TDCIPP from 0.75 to 8 hpf and, using o-dianisidine staining, revealed that embryonic hemoglobin levels were significantly decreased at 72 hpf. Overall, our results suggest that initiation of TDCIPP exposure during early-blastula (2-3 hpf) alters the normal trajectory of epiboly, dorsoventral patterning, and hematopoiesis.

Reference: Dasgupta S, Cheng V, Vliet SMF, Mitchell CA, Volz DC. 2018. TDCIPP exposure during early-blastula alters the normal trajectory of zebrafish development. Environmental Science and Technology. 52 (18), pp 10820–10828.

The Lexogen QuantSeq expression profiling library prep kits enable fast, easy, and cost-effective sequencing by gene counting and are an exceptional alternative to standard RNA-Seq protocols. Using the QuantSeq 3′ mRNA-Seq pipeline in Partek Flow, QuantSeq data processing is fast and easy. By combining powerful statistics and interactive visualizations in an intuitive graphical user interface, Partek Flow helps you get the most biology out of your data.

In this webinar, you will see how quick and easy it is to go from raw QuantSeq data to biological insights using the QuantSeq pipeline in Partek Flow.

Topics Include:

• Overview of the QuantSeq technology and data analysis
• Introduction to Partek Flow
• Live demo of QuantSeq pipeline in Partek Flow
• Ask us anything session

Gene expression profiling by high-throughput sequencing reveals qualitative and quantitative changes in RNA species at steady state but obscures the intracellular dynamics of RNA transcription, processing and decay. We developed thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAMseq), an orthogonal-chemistry-based RNA sequencing technology that detects 4-thiouridine (s4U) incorporation in RNA species at single-nucleotide resolution. In combination with well-established metabolic RNA labeling protocols and coupled to standard, low-input, high-throughput RNA sequencing methods, SLAMseq enabled rapid access to RNA-polymerase-II-dependent gene expression dynamics in the context of total RNA. We validated the method in mouse embryonic stem cells by showing that the RNA-polymerase-II-dependent transcriptional output scaled with Oct4/Sox2/Nanog-defined enhancer activity, and we provide quantitative and mechanistic evidence for transcript-specific RNA turnover mediated by post-transcriptional gene regulatory pathways initiated by microRNAs and N6-methyladenosine. SLAMseq facilitates the dissection of fundamental mechanisms that control gene expression in an accessible, cost-effective and scalable manner.

Many RNA binding proteins (RBPs) regulate the selection of alternative polyA sites. To understand their regulatory principles, we developed expressRNA, a web platform encompassing computational tools for integration of the QuantSeq 3´ mRNA-Seq, iCLIP and RNA motif analyses. This reveals at nucleotide resolution the ‘RNA maps’, which demonstrate that RBPs bind to specific positions on pre-mRNAs to regulate the polyA sites. Our RNAmotifs2 software also identifies clustered sequence motifs that mediate the regulation of these sites. We used this approach to show that TDP-43, an RBP involved in several neurodegenerative diseases, binds close to the polyA site to repress, and further downstream to enhance their use. We conclude that TDP 43 directly regulates diverse types of pre-mRNA processing events according to common positional principles.

Learning Objectives:

• How to analyse 3´ mRNA-Seq QuantSeq data
• How to use the expressRNA platform
• Why is it helpful to derive an RNA map, and what does it show us about regulatory mechanisms?

Alzheimer’s Disease (AD) is the result of complex interactions between risk factors that cause pleiotropic changes in molecular networks linking a host of biological processes. A variety of genetic factors have been shown to contribute to risk with varying degrees of penetrance: the identification of mutations in the amyloid-beta precursor protein (APP), presenilin (PSEN1 and PSEN2) genes that cause Mendelian forms of AD represented key milestones for understanding the initial mechanisms and pathways involved in AD pathogenesis. Remarkably, variants in these genes confer a different transcriptomic profiles, and mutation carriers clustered separately from their non-carrier siblings. New evidences provide support for both neuronal and glial specific pathways contributing to pathogenesis. However, little is understood about how the genetic loci and molecular changes are organized into common networks. We combined transcriptomic cell-type profiling and network co-expression analyses to study a unique collection of human postmortem brain tissue ascertained to represent the AD Mendelian mutations.
Using novel digital deconvolution approaches, we derived cell-type specific expression. We ascertain the distribution of neuros, microglia, oligodendrocytes and astrocytes in a collection of more than 1500 AD and non-demented subjects. We derived gene regulatory networks employing the expression corrected for the distinct cell-type distributions, and identified modules that cluster genes that harbor variants usually associated with both early-onset autosomal dominant (PSEN1) and late-onset sporadic classifications of AD (SOD1, BACE1, PICALM, SLC4A2).
Understanding variant-specific effects is of an immense importance for the elucidation of the underlying biology of the Alzheimer Disease. Our initial analysis reveals a transcriptional regulation module that link that early-onset autosomal dominant and late-onset sporadic genes.

Learning Objectives:

• Identify confounding factors that can affect transcriptomic analyses and learn how to address them
• Familiarize with machine learning techniques that allow to validate results when analyzing dataset with a reduced number of samples
• Learn digital deconvolution approaches to infer cell composition from RNA-seq data
• Learn how transcriptomic profiles can reveal gene co-expression networks

Hardly anyone would run an RNA gel without a ladder, but transcriptomes are mostly sequenced without the use of external standards. The added layer of transcript isoform complexity in eukaryotes as well as incomplete or incorrect gene annotations further challenge RNA-Seq pipelines to correctly calculate and compare gene expression values. Lexogen, a specialized transcriptomics company, addresses this problem by providing Spike-In RNA Variant Control Mixes (SIRVs) to the RNA-Seq community. These controls are processed together with the RNA sample to allow for an evaluation of the RNA-Seq workflow and, in particular, of transcript isoform detection and gene expression quantification. The mixes contain 69 transcript variants that map to 7 human model genes and mirror the native transcriptome complexity by comprehensively representing splicing isoforms, transcription start-site and end-site variants, overlapping transcripts and antisense transcription. Lukas Paul, Head of Services at Lexogen, describes in this webinar how the SIRVs have been used to estimate absolute accuracy and consistency, as well as concordance in gene expression measurements at the level of workflows, experiments, and samples. A “SIRVs dashboard” is introduced that brings together spike-in derived NGS data, annotations and data evaluation in an easily navigable way, and the webinar will highlight how condensed SIRVs data can function as a “RNA-Seq fingerprint” that enables comparisons across experiments, samples and platforms.

A large fraction of the RNA transcribed in eukaryotic cells is rapidly degraded in the nucleus. A poly-adenylation complex distinct from the canonical poly(A) machinery is responsible for initiating 3´-5´ degradation of nuclear RNAs. This non-canonical poly(A) machinery, termed the Trf4/5-Air1/2-Mtr4 or TRAMP complex, catalyzes the addition of 3-4 adenosines on target RNA 3´-ends. This tags the transcript for 3´-5´ exonuclease digestion by the nuclear RNA exosome, which can either degrade or trim the RNA in a manner dependent on the presence of RNA structures or RNA-binding proteins. Inactivating the nuclear exosome stabilizes these otherwise short-lived RNAs, and subsequent cellular polyadenylation lengthens the oligo(A) tails to >30 adenosines.The majority of these poly(A)+ 3´-ends arise from non-coding and pervasive RNA polymerase II (Pol II) transcripts undergoing transcription termination by the Nrd1-Nab3-Sen1 (NNS) complex. 3´-sequencing of RNAs from exosome-inactivated cells enabled mapping the precise 3´-ends of these unstable RNAs, providing a high-resolution view of NNS termination genome-wide.Surprisingly, different NNS-dependent terminators display substantial heterogeneity in the width of the termination window, with some genes terminating the majority of transcripts in a window of 500 bp. Further analysis of NNS-terminators with a narrow termination window revealed that a particular set of DNA-binding proteins cooperate with NNS by roadblocking Pol II to promote efficient transcription termination genome-wide. Using the QuantSeq 3´ mRNA-Seq library prep kits, we were able to multiplex >40 samples per sequencing lane and obtain between 2 to 5 million reads per sample. This enabled us to analyze numerous different strains with various exosome and roadblocking factors inactivated, showing that inactivating roadblocks shifted the window of NNS termination downstream. Strikingly, disabling NNS enabled elongation of Pol II through the same roadblocks.These results explain how RNA processing signals control the outcome of collisions between Pol II and DNA binding proteins.

RNA sequencing (RNA-seq) has revolutionized the study of gene expression in animals, plants and microorganisms. However, because of its high cost, this technology has been mainly used in experiments with limited number of samples. To examine a cost-effective alternative, we used a method, which confines sequencing to the 3’-end of mRNA and produces just one fragment per transcript, resulting in a dramatic decrease in sequencing cost. Total RNA isolated from chicken adipose tissue samples was used for cDNA library preparation using QuantSeq 3’mRNA-seq library Prep Kit. Sixty-one uniquely indexed cDNA libraries were pooled and sequenced on one lane on the Illumina Hiseq 2500. On average, 2.24 million reads per sample were generated, 90.1% of which were mapped to the chicken reference genome (Ensembl Galgal4). For more than 70% of the genes with detectable expression, we redefined the 3’-end and identified alternative polyadenylation sites within the 3’-untranslated regions. To compare gene expression measures between 3’-RNA-seq and RNA-seq technologies, we used data from a subset of 20 samples that were previously used in a RNA-seq study of feed efficiency. The correlation of the log10(fold-change) for gene expression (high- vs. low-feed efficiency birds) between these two methods was 0.90. In conclusion, 3’-RNA-seq is a cost effective method amenable to global gene expression studies at population-level, e.g., expression QTL (eQTL) mapping.  Also, it allows for accurate detection of the 3’-end of transcripts, enabling verification of the current gene model annotations and global characterization of alternative polyadenylation.