The global pandemic has increased focus and scrutiny on molecular diagnostic assay development, resulting in a need for assays that provide quick results while delivering expected performance and quality. The need for these types of assays in infectious disease diagnostics has become apparent on a wide scale, but molecular diagnostic testing is also poised to grow in other areas such as genetic testing, prenatal testing, oncology, and pharmacogenomics over the next several years.

Growth in the amount of testing being done in these areas brings additional challenges due to a number of issues such as various sample types, presence of inhibitors, and low sample amounts—all of which contribute to potential poor results due to less than ideal amplification reactions.

Learning Objectives:

1. Demonstrate how lyo-ready enzymes can overcome many of these issues and provide a viable solution for assay development.

2. Discuss advantages of lyophilization compared to traditional methods.

3. Explain why the quality and manufacturing considerations are crucial when selecting a commercialization partner.

Up until now, Big Data in healthcare has failed to drive any real progress in curing disease. However, we have just now reached a cost and throughput threshold in sequencing and bioinformatic technology that is enabling the collection and analysis of whole exome and transcriptome data from blood, not just in oncology, but in diseases where no molecular data has been available before. This vast amount of data offers opportunities for discovery. Collected over time across hundreds of thousands of patients, this data is finally unlocking a true molecular classification of disease that will drive vast innovation in treatments and cures.

Learning Objectives:

1. Explain why Big Data in healthcare has not led to any revolutionary clinical discoveries.

2. Discuss how the serial collection of exome and transcriptome data from blood empower new possibilities in understanding disease.

3. Discuss how AI applied to this data make discoveries that were not possible before.

A hypoxic tumour microenvironment is a feature of solid tumours. Many tumour types contain high fractions of hypoxic tissue. The relationship between high levels of tumour hypoxia and a poor patient outcome (prognosis) is firmly established. Patients with the most hypoxic tumours have the worst prognosis and the tumours are more likely to spread to other sites in the body. The most hypoxic tumours are also more likely to recur following surgical removal and hypoxia leads to resistance to many treatments including radiotherapy, chemotherapy, and immunotherapy. Approximately 50% of solid tumours are hypoxic. Head and neck, prostate, bladder, and soft-tissue cancers are highly impacted by hypoxic microenvironments, and this reduces survival rates. Identification of patients for potential treatment with hypoxia targeted therapies could increase cancer survival rates by 30% in some patient populations, with hypoxia scoring required to make informed patient-specific treatment decisions. The use of precision medicine (treatment according to hypoxic-status) would reduce the need for trial-and-error approaches, improve patient outcomes, reduce the risk of exposure to cancer therapy complications while also improving cost-efficiency and patient quality-of-life.

Currently there is no easy, accurate, reproduceable way to clinically measure tumour hypoxia. To address this issue we have derived, developed, and validated a tissue specific test that can measure hypoxia by the expression of a number of hypoxia-associated genes – a signature. We developed signatures for different cancers (head and neck, prostate, bladder, sarcoma). The signatures, each made up of 24-28 genes, can be measured together in a single test (pan-cancer Next Generation Sequencing-NGS panel). 

The test is taken from the original diagnostic biopsy at the start of the cancer care pathway. The value of our test is to enable clinicians to make informed decisions on patient-specific precision treatment pathways according to tumour hypoxia-status.

Learning Objectives:

1. Define the impact of hypoxia on patient treatment response.

2. Explain the derivation, development and delivery of a hypoxia associated gene expression signature.

3. Evaluate the best clinical delivery route to improve cancer patient outcomes.

In 2013, a separate diagnosis of PTSD was introduced into the DSM-V to mitigate the well-known issue of accurately differentiating between PSTD and other psychiatric disorders.  In addition to these advancements, the field continuous to develop more quantifiable methodologies, such as biological markers to strengthen the tool set available to clinicians enabling them to distinguish between overlapping conditions. For example, PTSD symptom profile overlaps with those of common mental conditions, such as mood disorders, anxiety disorders, and substance abuse, the most common being Major Depressive Disorder. Reasons for why it is important to make a clearer distinction between these two disorders is due to the treatment options available to both. For PTSD, Cognitive Behavior Therapy (CBT) has consistently been found to be the most effective treatment both in the short term and the long term. Whereas for MDD, monotherapy with antidepressants is recognized by current guidelines as an effective first-line treatment for patients with mild, moderate or even severe major depression.  Since PSTD has shown moderate heritability accountability, identifying the genetic factors that contribute to this susceptibility is of great interest for developing a biological diagnostic tool as the similarities of symptoms between PTSD and MDD may be due in part to shared genetic liability. Nevertheless, manifestation of these underlying mechanisms may also vary due to epigenetic factors. Polaris genomics has developed genetic test panels - PTS-ID DNA and Trugen-1 RNA  for PTSD susceptibility and diagnosis, based on the data from prior research studies. Each panel aims to compliment the other by encompassing a multi-omics approach to study the flow of biological information at multiple levels (in this case: genome; epigenome; transcriptome), thus, unraveling the mechanisms underlying the biological condition of interest, resulting in identification of a reliable predictive and diagnostic biomarker tools for more accurate diagnosis and treatment of mental and behavioral health conditions.

Learning Objectives: 

1. Discuss genetic biomarkers related to Post-Traumatic Stress Disorder.

2. Discuss pharmacogenomic biomarkers that impact anti-depressant treatment.

3. Explain how genomics can improve diagnosis and treatment of mental and behavioral health disorders.

Accurate classification of genetic alterations is important to ensure the provision of high-quality clinical data. For interpretation of germline alterations, there are standardized guidelines dating back to 2000 with the most recent version being a joint publication from the American College of Medical Genetics (ACMG) and the Association of Molecular Pathology (AMP) (Richards et al., 2015, DOI: 10.1038/gim.2015.30). For somatic alterations, guidelines from the Association of Molecular Pathology (AMP), the American Society of Clinical Oncology (ASCO), and the College of American Pathology (CAP) were more recently published to assess and tier variants based on clinical significance (Li et al., 2017, DOI: 10.1016/j.plabm.2020.e00170).

In an effort to enhance the existing guidelines, we developed a formal structured approach for the classification of somatic variants to not only account for a variant's clinical significance but also its ability to drive tumorigenesis. This approach is largely based on the structure of the ACMG/AMP germline guidelines, which has been shown to fit a Bayesian framework (Tavtigian et al., 2018, DOI: 10.1038/gim.2017.210), and adopts elements from both the ACMG/AMP germline guidelines and AMP/ASCO/CAP somatic guidelines.

Join this webinar to learn a method for somatic variant curation.

Learning Objectives:

1. Discuss key elements from the AMP/ASCO/CAP guidelines for somatic variant interpretation and the ACMG/AMP guidelines for germline variant interpretation.

2. Review certain evidence criteria in this newly developed framework and how they may be combined into a classification.

3. Compare the performance of this approach with other classification models.

Genomics has had rapid implementation over the past decade, with recent growth in application in oncology, mental health, maternal fetal medicine, solid organ transplantation, and more. Much of the focus has been in perinatal care, ranging from NIPT to rapid whole genome sequencing of NICU patients.  This is now expanding to cardiology, neurology, and autoimmune disease, where genomic explanations for so called general maladies are being understood.  There has also been much focus on specific drug-gene-action relationships, such that there are now a number of actionable pharmacogenomics examples. With the application of broad sequencing panel testing, there are often additional opportunities to impact a patient’s care, beyond the specific disease focus area.  This shift to understanding the broad population and the opportunities to alter a populations disease trajectory is making population health genomics an exciting area for impact.

Learning Objectives:

1. Identify various challenges in implementing genomics from research to practice.

2. Define the impact of pharmacogenomics on a patient’s risk:benefit decision.

3. Explain current approaches to applying genomics panel testing in the clinic 

The COVID-19 pandemic has taken a dramatic toll worldwide. Its impact possibly has been the most palpable amongst uniquely vulnerable groups of patients, such as patients with a cancer diagnosis whose outcomes suffered in addition as a result of disrupted healthcare services. Imminent need for knowledge as to best means to protect patients with a cancer diagnosis remains an acute need with new variants and waves. This keynote talk will address knowledge obtained since the start of the pandemic as to the impact of COVID-19 and in particular immunity against COVID-19.  Adverse outcomes in the face of COVID-19 clearly highlight the notable impact of a cancer diagnosis and even more prominently that of advanced age, frailty, co-morbidities as well as certain malignancy types, such as hematological malignancies and thoracic cancers. While case fatality rates have dropped significantly during the Omicron crisis, these key predictors remain pertinent to identify patients with a worse prognosis. Robust efforts have been undertaken to understand correlates of immunity amongst patients with a malignant diagnosis and demonstrate generally robust immune response to COVID-19 infections as well as COVID-19 vaccines with the exception of definable subsets of patients with high levels of baseline immune suppression, such as B-cell malignancies or following receipt of highly immune suppressive therapies, in particular anti-CD20 monoclonal antibody therapy, CAR-T cell therapy and stem cell transplantation. Booster vaccinations can enhance detectable immunity in general as well as amongst some of the most immune suppressed patients, however further need remains for alternate strategies, such as anti-COVID-19 antibody therapy for stronger protection. Immune correlates of vaccine response, including serological testing, neutralization assay and T cell assays provide surrogate markers of immune response and might be helpful for risk assessment and patient enrichment for clinical studies.

Learning Objectives:

1. Discuss risk factors for poor outcomes amongst patients with a cancer diagnosis and COVID-19.

2. Discuss measures of immunity post-COVID-19 infection and following COVID-19 vaccinations.

3. Discuss emerging strategies, such as booster vaccinations and prophylactic antibody therapies to enhance anti-COVID-19 immunity amongst the most immune suppressed.

Esophageal adenocarcinoma [EAC] is the fastest growing in incidence cancer in the US over the last 25 years, while also having a 5 year survival of less than 20%. Barrett’s Esophagus [BE] which occurs in up to 5% of the population, is the only known precursor of EAC and caused by gastric reflux. Once identified, patients with BE undergo lifelong endoscopic surveillance with multiple biopsies taken for pathology review– even though their individual lifetime risk of EAC is small. This is an extremely costly and inconvenient process which, among other issues, is also associated with pathology ‘overcalling’ of precancerous changes leading to significant overtreatments.

Aneuploidy has long been proposed as a tool of EAC progression risk assessment in BE patients but due to a number of issues has not been implemented as a clinical test. We recently assessed whether an esophageal brushing that widely sampled the esophagus could be combined with massively parallel sequencing to interrogate ~350,000 genome-spanning regions and identify chromosome arm alterations to characterize aneuploidy and identify patients with disease progression to dysplasia or cancer.

Our results demonstrate the feasibility of combining aneuploidy with chromosomal changes of a limited gene panel to accurately define BE patients with BE-associated cancer and pre-cancerous changes. The clinical implications of this approach are now being proposed for prospective trials. 

Learning Objectives:

1. Discuss the increasing healthcare burden of esophageal cancer.

2. Discuss the morphologic and molecular basis for esophageal cancer.

3. Identify the opportunities for use of molecular changes to identify patients at high risk of esophageal cancer.

Objective : Evaluation of EDTA-gel blood collection tubes from remote locations for NIPS (Non-invasive prenatal testing).

Design: Paired comparisons

Population: 61 pregnant women from British Columbia aged 19 or over, 10 to 14 weeks gestation undergoing NIPS.

Methods: Participants were phlebotomized with both Streck and EDTA-gel tubes. EDTA-gel tubes were centrifuged prior to shipping. Libraries prepared from cfDNA extracted from both types of tubes were sequenced on Illumina NextSeq 500 and fetal fraction estimated using SeqFF. EDTA-gel tube libraries were further size-selected on agarose gel and resequenced. The three results from each patient were compared.
Main outcome measure: Fetal fraction (FF) expressed as a percentage of total cfDNA sequenced calculated from sequence read counts (SeqFF).

Results: Streck tubes samples showed an average 1% higher FF than centrifuged EDTA-gel tubes without size selection kept at 4°C after centrifugation and shipped on frozen packs. This difference increases with temperature. When EDTA-gel samples’ libraries were size-selected by gel electrophoresis to eliminate cfDNA fragments larger than 160bp, the mean FF increased from 7% to 13% with no samples having a FF < 4%.

Conclusions: EDTA-gel tubes reduces NIPS sampling cost and reduces tube processing time in the laboratory. In addition, after adding a size-selection step of the libraries, the FF is increased without degradation of cfDNA. The step reduces the number of test failures, increases NIPS clinical performance, and may be helpful in situations asking for a higher FF such as twin pregnancies or screening for sub-chromosomal imbalances.

Learning Objectives:

1. Discuss the importance and impact of fetal fraction on NIPS assay performance. 

2. Explain how gel-based size selection must be applied to enrich maternal sample for cfDNA fragments of fetal origin.

Follow us on a journey of single cell data exploration.  See how you can Process, Analyze, Compare, and Contextualize, your results to identify novel biological relationships and drive new hypotheses. In this presentation, Dr. Jean-Noel Billaud, PhD, will take you from data to biological insight using human single cell fetal liver data.

Learning Objectives:

1. Explain whiich tools and workflows are best suited to process scRNA-seq data.

2. Analyze, highlight and identify cell types of interest.

3. Compare and discover similar biology phenomena.

4. Discuss context from other tissues and diseases to unlock hidden biology.

MicroRNAs (miRNAs) are small RNAs with an average size of 22 nucleotides that regulate gene expression through various mechanisms. During the last decade, researchers have identified over 2600 mature microRNAs in humans. Today, researchers are focusing on identifying key miRNAs that can serve as biomarkers for early detection of diseases, stratification of disease states and selection of potential therapies.

This webinar will focus on miRNA profiling from biofluids, such as serum and plasma, while looking at solutions across fresh and FFPE samples. Our webinar will highlight best practices in miRNA isolation, NGS library construction, data analysis and downstream biological verification.

Regardless of method, single cell RNA-seq only captures a small fraction of the transcriptome of each cell. Often, this is due to inherent limitations of the methodology as reads ‘dropout’ at each step of library preparation. These dropout events are then confounded with noise, outliers, and stochastic genetic variation, resulting in the daunting computational task to parse out the true signal. Almost all computational algorithms have evolved to address this zero-inflation issue through a multitude of approaches, typically through various dimensionality reduction or imputation techniques. While consensus for a standardized computational approach has yet to be met, we present a turnkey molecular solution that drastically reduces dropout events attributable to technical noise, statistically enhancing biological interpretation. 

Traditionally, single cell data processing incorporates certain filtering and normalization steps prior to canonical clustering and downstream interpretation. Instead of removing those reads in-silico, our universally incorporated molecular solution (CRISPRclean) removes those reads in-vitro, redistributing sequencing clusters to unique biologically relevant transcripts. By tailoring guides to deplete un-annotated genomic intervals in addition to the highest expressed ribosomal and mitochondrial genes, we have exhibited the ability to redistribute 50% of reads through in-silico depletion across single cell data from 14 tissue types. 

This webinar will review the results of our preliminary in-vitro studies in immune cells. At a standard sequencing depth, CRISPRclean enables the recovery of an additional 300 genes per cell. In addition, roughly 5,000 genes have a two-fold enrichment in unique molecular counts. Of those 5,000 genes, greater than 90% are lowly expressed. This added resolution of rare transcripts translates to increased characterization of distinct cell states. In fact, in PBMCs, CRISPRclean identifies two additional cell types, including a rare dendritic subtype. Furthermore, by redistributing 50% of sequenced reads to biologically relevant transcripts, an additional 2,600 ligand-receptor interactions are observed as a part of the cell-to-cell communication. Consequently, there is a two-fold enrichment in the immune interaction network. In essence, CRISPRclean recovers more information per cell. In essence, CRISPRclean recovers more information per cell.  CRISPRclean enhances signal to noise and provides additional confidence in finding solutions to biologically relevant problems.

Learning Objectives:

1. Discuss the signal-to-noise problem of single cell RNA-seq methods.

2. Explain how to improve RNA-seq sensitivity using CRISPR technology.

3. Explain how enhanced resolution allowed increased characterization of distinct cell states.

Targeted sequencing has many applications in cancer biomarker research, carrier screening and inherited disorders, drug development, mitochondrial DNA variant detection, human ID and paternity testing and the detection of rare variants in human samples.

With the introduction of our QIAseq NGS Panels in 2016, we set a new standard for targeted amplicon-based genotyping via NGS. New features such as error-correcting molecular barcodes known as Unique Molecular Indices (UMIs) and Single Primer Extension (SPE) allowed the detection of rare variants with high confidence, all from a single tube. Our panels enabled the enrichment of up to 20,000 targets while ensuring high uniformity and complete coverage.

We have now further enhanced QIAseq chemistry to improve your NGS workflow even more. New QIAseq Targeted DNA Pro panels leverage major innovations that provide you with the next level of performance. The panels ensure superior ease of use and increased workflow efficiency while facilitating high-fidelity variant analysis.

This webinar explores how the new QIAseq Targeted DNA Pro workflow fast-tracks and streamlines library preparation so that you can accelerate your time-to-result and get deeper insights faster.

Learning Objectives:

1. Discuss NGS applications and challenges in biomarker research.

2. Discuss technical overview and benefits of new QIAseq Targeted DNA Pro Panels.

3. Discuss performance data in detection of somatic and germline variants.

While early genetic diagnosis can guide critical care management, the turnaround time for whole genome based diagnostic testing is months. Recent programs in neonatal populations have reduced turnaround to days and shown that rapid genetic diagnosis enhances patient care and reduces healthcare costs. Yet, most critical care decisions need to be made in hours.We developed a whole genome sequencing approach designed to provide a genetic diagnosis within hours. Optimized highly parallel nanopore sequencing was coupled with high-performance cloud computing to implement near real-time basecalling and alignment followed by accelerated variant calling. A custom scheme for variant prioritization took minutes to rank variants likely to be deleterious allowing efficient manual review and classification. We performed whole genome sequencing on 12 patients from the critical care units of Stanford hospitals. In 10 cases, the pipeline produced diagnostic results faster than all previously published clinical genome analyses. Per patient, DNA extraction, library preparation, and nanopore sequencing across 48 flow cells generated 173–236 GigaBases of sequencing data in as little as 1:50 hours. After optimization, the average turnaround time was 7:58 hours. A pathogenic or likely pathogenic variant was identified in five out of 12 patients (42%). After confirmation in a CLIA laboratory, clinical management was altered in every case. We developed an approach to make a genetic diagnosis from whole genome sequencing in hours, returning actionable, cost-saving diagnostic information on critical care timescales. 

Learning Objectives:

1. Explain how genetic diagnosis can impact clinical care.

2. Discuss advances in sequencing technologies and how they can be applied in a clinical setting.

3. Discuss the basics of Nanopore sequencing.

Pathogen detection and surveillance have become a high priority in both healthcare and environmental settings for the safety of patients and the general public. The COVID-19 pandemic highlighted the need for strategies to identify and differentiate pathogen variants of concern. Municipalities employ NGS-based wastewater analysis to identify not only SARS-CoV-2 variants but also other viral and bacterial species as early alert systems of community health. In clinical settings where co-infections of two or more pathogens can complicate patient outcomes, such as with SARS-CoV-2 and influenza, microbial surveillance of patient areas may help circumvent disease outbreaks. Thus, there is a widespread need for targeted and sensitive variant detection through whole genome characterization of a wide variety of microbial pathogens.

This webinar will discuss new probe-based NGS enrichment solutions. We’ll introduce our QIAseq xHYB Viral and Bacterial Panels, which use hybrid capture technology to provide high-sensitivity targeted whole genome enrichment for detecting and sequencing:

Respiratory viruses
Sexually transmitted pathogens
Antimicrobial-resistant (AMR) genes from bacteria 
Adventitious agents 

Details of the content of these new panels and their performance across several different applications and sample types will be presented.

Target enrichment workflows for Next Generation Sequencing are well-established methods that enable labs to achieve relatively low sequencing costs by specifically sequencing regions of interest. Often we make trade-off decisions between performance and turnaround time depending on which method they decide to use in their labs. 

KAPA HyperPETE is a novel target enrichment technology that combines the performance of hybrid-capture with the speed and simplicity of amplicon-based workflows. In addition, it is optimized to detect all major somatic variant types (SNV, CNV, InDel, MSI status, and novel fusion transcript partners), and can be performed on tissue DNA, plasma DNA, and tissue RNA samples. 

Learning Objectives:

1. Discuss the science behind Primer Extension Target Enrichment (PETE) technology.

2. Explain how KAPA HyperPETE reduces bias and increases the accuracy of sequencing data.

3. List performance metrics for KAPA HyperPETE panels for somatic tissue DNA, somatic tissue RNA, and cfDNA inputs.

The combination of the wealth of genomic and phenotypic datasets to the measurement of the circulating proteome enables a snapshot of real-time biology and important results for determining underlying mechanisms of disease, identifying promising novel drug targets, and opening up new opportunities for companion diagnostics for personalized medicine. This presentation will cover the underlying technology to interrogate up to 3,000 protein biomarkers simultaneously using next-generation sequencing as a readout from a variety of biofluids. Importantly, we will also cover the application of measuring thousands of circulating protein biomarkers to genetic variation data and disease phenotype to determine causality across several diseases with ready application to drug target validation, drug safety and repurposing, and determining biological mechanism of drug action.

In addition, several large collaborative efforts with the UK Biobank and the SCALLOP consortium will illustrate the power of this approach to leverage large cohort studies that combine genetic and phenotypic data with protein biomarker information. 

Learning Objectives:

1. Discuss how genomic variant data can be used in combination with disease phenotype data and circulating proteome data to determine disease causality using a method called Mendelian Randomization

2. Discuss how powerful large cohorts with whole-exome and whole-genome datasets combined with collaborative efforts such as the UK Biobank and the SCALLOP Consortium can drive a new era in discovery of disease mechanisms, novel drug targets, and novel diagnostics

3. Explain the direct connection the plasma proteome offers in the drug discovery process, whether drug safety, drug repurposing, or determining the biological mechanism of drug action

Long read Structural Variation calling remains a challenging but highly accurate way to identify complex genomic alterations. To address this challenge, we developed Sniffles2, a successor to SV-detection method Sniffles. Sniffles2 increases both accuracy (e.g genotype, insertion sequence), speed (higher parallelism) and has more advanced functionality to further promote new insights into the organism or diseases. In the well-established GIAB benchmark, Sniffles2 outperforms its closest competitors (e.g. SVIM, CuteSV) both in terms of accuracy and efficiency.

Furthermore, Sniffles2 solves the problem of family to population level SV calling to produce fully genotyped VCF files by introducing a gVCF file concept. Sniffles2 population mode is designed to be more efficient and accurate at identifying SV across a collection of samples from familial trios and up to large-scale cohorts of thousands of individuals. Sniffles2 furthermore enables the detection of mosaic SV in bulk long read data. We used Sniffles2 somatic mode on the genomic data from the brain of a patient with multiple system atrophy, a rare sporadic neurodegenerative condition similar to Parkinson’s disease, with 55x WGS ONT coverage. We were able to identify multiple somatic SVs for this brain region. Thus overall demonstrating the utility and versatility of Sniffles2 to identify SV from mosaic to population levels.

Learning Objectives:

1. Discuss long read sequencing in complex genomic alterations.

2. Discuss structural variation in complex genomic alterations.

3. Discuss structural variation and its importance in human health/disease.

Next-generation sequencing (NGS) has become a foundational tool for both biological research and in-vitro diagnostics, particularly in oncology, immunology, and detection of genetic disorders. Despite its success, there is a need for new DNA sequencing platforms that combine high accuracy, speed, and flexible throughput to provide timely results and cost-effective operations for research and clinical applications. In this webinar we will review how rapid sequencing and flexible throughput can reduce turnaround time in research and clinical NGS. We will then describe the sequencing-by-synthesis process, highlighting the chemistry and engineering innovations enabling rapid sequencing-by-synthesis. Finally, we will characterize the performance of the novel Singular Genomics G4 Sequencing Platform for rapid sequencing-by-synthesis.

Learning Objectives:

1. Discuss the importance of rapid sequencing and flexible throughput in research and clinical next-generation sequencing.

2. Discuss the chemistry and engineering innovations enabling rapid sequencing-by-synthesis.

3. Evaluate the performance of the novel Singular Genomics G4 platform for rapid sequencing-by-synthesis.

Understanding the order in which mutations accumulate during cancer progression could help us identify effective therapeutic targets. Yet, our insights are limited by the fact that we can only observe a single snapshot of a specific cancer tissue and that we do not know its position on the cancer evolution time scale. In this talk, I will share with you our recent progress that makes the estimate of order as well as disease progression time more accurate by scaling up continuous-time Markov chains to hundreds of mutations.

Learning Objectives:

1. List some important limitations of the data that is available on cancer patients.

2. Discuss how even seemingly independent items (mutations) provide valuable information about disease progression time.

3. Explain the main idea how we scale up continuous time Markov chains to take this relevant information into account.

Recurrently emerging SARS-CoV-2 variants of concern (VOC) present increasingly sophisticated immune escape strategies that fuel global infection rates, as exemplified by the raging B.1.1.529 variant known as omicron, rendering ending this pandemic notoriously challenging. It is therefore paramount to rapidly adapt research strategies to understand the rich repertoire of both viral behavior and anti-viral responses to VOCs in order to develop timely and universal interventions. Readily adaptable spatial transcriptomics approaches facilitate such dynamic investigations and allow to map the severe heterogeneity presented by SARS-CoV-2 VOCs. To draw on maximum sensitivity and specificity at subcellular resolution, we resorted to the novel spatial multiplexed single-cell mRNA in-situ hybridization technology called Molecular Cartography™ (MC) developed by Resolve Biosciences. Using MC, we dissect transcriptional responses to SARS-CoV-2 in various cell lines of pulmonary, colorectal, and hepatocellular origin and compare immune escape strategies of relevant VOCs including B.1.617.2 (delta) and B.1.1529. Furthermore, spatially discriminating higly infected from bystander cells allowed discerning primary from secondary anti-viral responses and pinpoint molecular signatures unique to individual VOCs. Interestingly, virus-induced dysregulation of the JAK/STAT and NFB signaling pathways by all tested VOCs highlighted their importance as therapeutic targets. We further show that SARS-CoV-2 nucleocapsid protein transcripts majorly locate  vicinal to the cell nucleus. Finally, by spatially tracking antiviral response dynamics emanating from the primary infected cell, we  elucidate differences in temporally diverging infection sites and evaluate responses to virus propagation. Overall, MC allowed gaining a thorough comparative understanding of host response dynamics to diverse SARS-CoV-2 VOCs that is imperative to prepare for future VOCs and aid in ending this pandemic.

Learning Objectives:

1. Discuss the importance of spatial analysis in SARS-CoV-2 research.

2. Explain the complex heterogeneity in host-pathogen responses to SARS-CoV-2 variants of concern (VOC).

3. Identify major anti-viral signaling pathways hijacked by SARS-CoV-2 VOCs.

Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost. Tracking low frequency intra-host variants provides important insights with respect to elucidating within- host viral population dynamics and transmission. However, given the higher error rate of ONT, accurate identification of intra-host variants with low allele frequencies remains an open challenge with no viable computational solutions available. In response to this need, we present Variabel, a novel approach and first method designed for rescuing low frequency intra-host variants from ONT data alone. We evaluate Variabel on both synthetic data (SARS-CoV-2) and patient derived datasets (Ebola virus, norovirus, SARS-CoV-2); our results show that Variabel can accurately identify low frequency variants below 0.5 allele frequency, outperforming existing state-of-the-art ONT variant callers for this task. Variabel is open-source and available for download at: www.gitlab.com/treangenlab/variabel. 

Learning Objectives:

1. Introduce variant detection of viral genomes. 

2. Explain consensus-level vs within-host variation of SARS-CoV-2.

3. Identify the current challenges of variant detection in Oxford Nanopore sequencing data.

4. Introduce a computational method to distinguish between ONT sequencing errors and ture biological variants. 

The CYP2D6 enzyme metabolizes approximately 25% of commonly used pharmaceuticals and is of intense pharmacogenetic interest. Polymorphisms in CYP2D6 can alter an individual’s response to a wide range of commonly used medicines. Accurate allele typing of CYP2D6 has proved challenging due to frequent copy number variants (CNVs) and the paralogous CYP2D7 pseudogene. SNP-arrays, qPCR and short-read sequencing have been employed to interrogate CYP2D6, however these technologies are unable to capture longer range information. Long-read sequencing using the Single Molecule Real Time (SMRT) sequencing platform has yielded promising results for CYP2D6 allele typing. Previous studies have been limited in scale and have employed nascent data processing pipelines. We have developed a robust data processing pipeline “PLASTER” for accurate allele typing of SMRT sequenced amplicons. We demonstrate the pipeline by typing CYP2D6 alleles in a large cohort of 377 Solomon Islanders. PLASTER is implemented in Nextflow and is available at https://github.com/bahlolab/PLASTER.
 
Learning Objectives:

1. Explain how pharmacogenetic variation can alter responses to drug treatment, and how drug efficacy may vary from population to population.

2. Explain why CYP2D6 screening is useful for treatment of Plasmodium vivax Malaria.

3. Discuss the relative advantages and disadvantages of short and long read sequencing technologies for pharmacogenetic screening.

Sex differences play an important role in development and progression of many diseases, and understanding how a disease differs between the sexes is essential for the advancement of prevention, diagnosis, and treatment. For most cancer types, males have a higher risk of developing the disease and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood, and most current approaches to clinical management, as well as therapeutics development and selection, are sex-independent. We developed a sex-specific gene regulatory network approach to integrate genomic data and model gene regulation in males and females. We demonstrated that a repertoire of transcription factors drives sex-biased gene regulation, and that differentially regulated genes are enriched for tissue-related functions and diseases with sex-biased manifestations. In colon cancer, we found strong sex-biased regulation of the drug metabolism pathway, which was associated with higher survival outcomes in women treated with chemotherapy. Our findings uncovered patterns of gene regulation that differentiate males and females and may provide functional basis to explain sex differences in clinical observation. Our sex-specific regulatory network approach can be used to understand how sex influences progression and therapeutic responses in cancer and other complex diseases and may help accelerate the development and implementation of sex-specific precision medicine.

Learning Objectives:

1. Discuss conceptual and methodological gaps to incorporating sex in genomics research.

2. Discuss how sex differences in disease arise due to a combination of environmental, genetics, epigenetics, and transcriptional regulation.

3. Explain how integrative network analyses help in understanding the interplay of sex and genomics in diseases.
 

The human genome encodes 25,000 genes. But the biological complexity inherent in polygenic traits is a clear sign that the whole is greater than the sum of its parts. Genes, gene products, and other cellular components don't act in isolation, but rather in networks that allow us to look beyond the individual genes and to embrace the complex, adaptive nature of the regulatory process. 

Learning Objectives:

1. Discuss what Gene Regulatory Networks (GRNs) are-and are not.

2. Discuss one way that one can infer GRNs from data by introducing constraints on our models.

3. Explain how GRNs can provide insight beyond simple differential expression or differential co-expression.