Genetics Virtual Week 2021

The ability of single-cell RNA-Seq (scRNA-Seq) to measure the transcriptional basis of heterogeneity between individual cells is transforming biomedical research. It enables the precise ‘molecular dissection’ of the multiple cell types present within a complex tissue. This makes it possible to determine how a pathology affects each cell type, rather than just the overall effect upon the bulk tissue. As the technology matures and the range and scope of analytical tools expands, ever greater insights into cellular characteristics and their relationships to one another are becoming possible. However, each biological system presents unique challenges and customised experimental design and analyses are required. In this presentation I will outline recent and ongoing projects from my lab and collaborators which are applying scRNA-Seq to investigate the basis of the major eye diseases Diabetic Retinopathy, Age-related Macular Degeneration and Glaucoma. This work employs both in vivo models of the retina and in vitro cell culture systems with retinal endothelial, retinal pigment epithelial and lamina cribrosa cells. The results from a range of analytical approaches will be described, with examples chosen to help others maximise the pertinent information gained from their own experiments.

Learning Objectives:

1. Evaluate the potential of alternative technical and analytical single cell RNA-Seq approaches to provide relevant biological insights into a given model system.

2. Provide examples of how single cell RNA-Seq is being applied in vision science to understand the basis of eye diseases.

Biological systems are comprised of numerous cell types, intricately organized to form functional tissues and organs. Cell atlas initiatives with single-cell RNA sequencing have begun to characterize cell types based on their RNA expression profiles. However, the tissue organization is lost when cells are dissociated for single-cell sequencing, making it difficult to study how the cellular heterogeneity is contributing to the function of the tissue. Multiplexed error robust in situ hybridization (MERFISH) technology  enables the direct profiling of the spatial organization of intact tissue with genomic-scale throughput. Through combinatorial labeling, sequential imaging, and error-robust barcoding, MERFISH technology provides the highest detection efficiency and resolution available for spatial genomics. In a single experiment, hundreds of thousands of cells can be spatially profiled with subcellular resolution with high accuracy and reproducibility. In this presentation, we used the technology to profile gene expression in situ across large tissue areas in mouse brain and human colon cancer, discover and map cell types in complex tissue in response to external stimuli in animal models, and analyze the intracellular organization of transcriptome with sub-cellular resolution in individual cells.

Learning Objectives:

1. Learn about the benefits of spatially resolved, single-cell transcriptomics for further characterizing cell types based on their RNA expression profiles.

2. Learn about different research applications of MERFISH technology for the direct profiling of the spatial organization of intact tissues with genome-scale throughput.

Traditional genomics approaches look at bulk signatures from often heterogeneous samples or tissues. These approaches do not have the requisite resolution to fully uncover the true complexity of biology. Single cell and spatial transcriptomics approaches provide the ability to build a more comprehensive multidimensional understanding of complex biological systems. In order to fully utilize such technologies, there are several key experimental design and sample preparation considerations that when followed will help lead to success.  Both single cell and spatial transcriptomics provide high resolution views, and we will discuss how combining both of these technologies work synergistically to uncover an even greater understanding.

Learning Obejctives:

1. Understand the key requirements for sample preparation in a single cell and/or spatial transcriptomics experiment

2. Learn the synergies between a combined single cell and spatial transcriptomics approach

Traumatic brain injury (TBI) is best characterized as brain dysfunction caused by an outside force, usually a violent blow to the head, often occurring as a result of a severe sports injury or car accident. It is a major global health concern, affecting 69 million people worldwide per year, and is one of the most common causes of disability and death in adults. Nearly 50% of TBI patients experience long term cognitive impairment, linked to hippocampal neuronal damage and death. At the same time a deregulation of the hippocampal neurogenic processes is observed which impedes possible functional recovery of the brain. In particular, immunohistochemistry in mouse hippocampus has shown that cortical TBI affects neural stem and progenitor cells (NSPCs) residing in sub-granular zone of dendrite gyrus. Moreover, severe changes in the states of astrocytes, cells regulating axonal growth and synaptogenesis, has been observed throughout hippocampus. Here, in a collaborative project between VIB-KU Leuven, University of Amsterdam and the Bioinformatics CRO, we explore the cellular response in hippocampus following cortical TBI, exploiting cutting edge single cell RNA sequencing (10X Genomics) and highly multiplexed in situ hybridization (Resolve Biosciences) technologies. We systematically assess the sensitivity of NSPCs, immature astrocytes and immature neurons located in the sub-granular layer of dentate gyrus to TBI-induced pathology at the molecular level. We further characterize molecular changes within the hippocampus and the surrounding tissue, providing a spatial map of gene expression changes with single cell resolution.

Learning Objectives:

1. Single cell and spatial transcriptomics to investigate cell type heterogeneity at the highest resolution.

2. The impact of cortical traumatic brain injury on hippocampus and the surrounding tissue.

3. Effects of traumatic brain injury on neural stem and progenitor cells.

We are entering into an exciting era of genomics where truly complete, high-quality assemblies of human chromosomes are available end-to-end, or from ‘telomere-to-telomere’ (T2T). Recently, the Telomere-to-Telomere (T2T) consortium announced our v1.0 assembly that includes more than 150 Mbp of novel sequence compared to GRCh38, achieves near-perfect sequence accuracy, and unlocks the most complex regions of the genome to functional study. This technological advance, crediting the confluence of new assembly methods with long read sequencing technologies, offers a new opportunity to comprehensively the genomic structure and epigenetic organization in the most repeat-dense regions of our chromosomes. In particular, I will focus on the release of initial genetic and epigenetic reference of all human centromeric regions. High-resolution study of the pericentromeric sequence content and organization reveals new satellite families, sites of transposable element insertion, segmental duplications, and pericentromeric gene predictions. Using unique markers (marker-assisted method) to anchor ultra-long nanopore reads to human centromeric regions regions we report hypomethylated dips at every centromeric region, as previously described for the T2TX centromere. These sites are shown to coincide with regions enriched in centromere protein A (CENP-A) and may provide a signature of sites of kinetochore assembly genome-wide.

Learning objectives:

1. Understand the incomplete nature of the human reference genome, which sequences are missing and how that impacts our understanding for basic/translational research

2. Explain the differences in long read technologies in repeat assembly

3. Describe sequences and epigenetic patterns that are in the part of our genome that are not represented in current reference maps (e.g. GRCh38).

Nanopore sequencing has enormous potential in epigenetic applications; unlike traditional sequencing-by-synthesis technologies, it can distinguish covalently modified nucleotides directly through their modulation of the electrolytic current. We can take advantage of the long read lengths generated by nanopore sequencing to precisely call methylation patterns, and to obtain phased methylation information across the genome. The promise of nanopore sequencing has already been demonstrated by the accurate calling of 5-methylcytosine in a CG context using hidden Markov models (nanopolish) and neural networks (Megalodon). We have now trained our models on other modified (non-canonical) nucleotides, focussing here on dam-mediated adenosine methylation (N6-mA). Methylated adenosine sequencing has applications in bacterial epigenetics as well as exogenous labeling in mammalian systems. In particular, the DamID method uses a dam enzyme fused to a protein of interest, e.g. nuclear lamina, resulting in labeling DNA proximal to the protein. We are able to detect this N6-mA modification simultaneously with 5mC, providing two distinct epigenetic signatures along long single DNA molecules. We applied this to mammalian cells treated by DamID to characterize lamin-associated domains (LADs). LADs showed on average significantly higher methylation profiles than other regions in our pipeline. Applying this model we can simultaneously measure phased nuclear positioning information (LADs) with DNA cytosine methylation (5-mC) using long reads. With this method, we have explored CTCF binding sites and CpG islands on the border of LADs to investigate their mechanism.

Learning Objectives:

1. Base modification detection using nanopore sequencing

2. Simultaneous profiling of DNA methylation and lamina-associated domains

The Genome in a Bottle Consortium has published benchmarks for variant calling, but some challenging medically-relevant genes have been partially or fully excluded due to mapping challenges, structural variation, and issues in the reference genomes. Here, we use a trio-based, long-read, phased diploid assembly to form phased small variant and structural variant benchmarks for 273 out of 396 autosomal genes covered <90% by mapping-based benchmarks. We curated >1000 variants to exclude errors in the assembly, mostly in homopolymers and highly homozygous regions, and to ensure the benchmark accurately identifies false positives and false negatives across call sets. The new benchmark for challenging medically relevant genes will improve the characterization of challenging variants, leading to better insights for clinical genomics in the near future.

Learning Objectives:

1. Learn about applications of long read sequencing technologies for variant detection in medical genes

2. Clarify use of Genome in a Bottle benchmarks and how a medical gene focused benchmark improves clinical application

Alternative RNA isoforms are defined by promoter choice, alternative splicing, and polyA site selection. Although differential isoform expression is known to play a large regulatory role in eukaryotes, it has proved challenging to study with standard short-read RNA-seq because of uncertainties it leaves about the full-length structure and precise termini of transcripts. The rise in throughput and quality of long-read sequencing now offers the possibility to unambiguously identify most transcript isoforms from beginning to end. However, the application of long-read sequencing to single-cell RNA-seq in particular remains limited by low throughput and high expense. Here, we develop and characterize long-read Split-seq (LR-Split-seq), which utilizes a combinatorial barcoding-based method for sequencing single cells and nuclei with long reads. We demonstrate that LR-Split-seq can associate isoforms to cell types with relative economy. We characterize LR-Split-seq for whole cells and nuclei by using the well-studied mouse C2C12 system in which mononucleated myoblast cells differentiate and fuse into multinucleated myotubes. We show that the overall results are consistent with existing knowledge of the system and are reproducible when comparing long- and short-read data from the same cell or nuclear samples. We find substantial evidence of differential isoform expression during differentiation including alternative transcription start site (TSS) usage. Moreover, we validate our known and novel isoforms by integration of isoform expression dynamics with snATAC-seq chromatin accessibility at their TSSs. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells that can be matched to short-read sequencing of an expanded pool of the same cells as well as integrated with additional single-cell omics data modalities.

Learning Objectives:

1. Demonstrate the utility of long-read RNA-sequencing in studying alternative isoforms

2. Define the long-Split-seq approach to profiling the transcriptomes of single-cells

Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. While the role of promoters as driver elements in cancer has been recognized, the contribution of alternative promoters to regulation of the cancer transcriptome remains largely unexplored. Here I will present how active promoters can be identified using RNA-Seq data, enabling the analysis of promoter activity in more than 18,000 samples. In order to study novel promoters, we generated long read RNA-Seq data in 10 different gastric cancer cell lines. Our results show that alternative promoters are frequently used, that they are predictive of patient survival, and that promoters are a major contribution to the diversity of isoforms in cancer.

Learning Objectives:

1. Learn about alternative promoters and their role in cancer

2. Learn how long read RNA-Seq helps to study the transcriptome

Microbial communities include distinct lineages of closely related organisms which have proved challenging to separate in metagenomic assembly. Challenges include the existence of highly related organisms that exist the same environment, or several species that exist at very low abundances and are therefore inconsistently sampled. Many of these challenges are exacerbated by limitations in DNA sequencing technologies, where some platforms produce reads that are too short or reads that have too many errors. The advent of long, accurate “HiFi” reads presents a possible means to address the challenge of metagenome assembly by disambiguating individual reads or contigs within metagenomic bins.  We present a metagenomic assembly of a complex microbial community from parasite-infested sheep fecal material.  We found that assemblies made from HiFi reads were able to properly assemble low abundance species in our sample, and could often assemble microbial genomes into single, circular contigs.  Taking advantage of the low error rates of HiFi reads, we developed a method that can track linked single nucleotide polymorphisms (SNPs), which is termed “phasing,” and identify individual SNP haplotypes that indicate subpopulations of microbes in a sample. This method is sensitive and scalable, and was able to phase haplotypes as long as 309 SNPs across hundreds of thousands of bases in the microbial genome.  We also report successful characterization of multiple, closely-related microbes within a sample with potential to improve precision in assigning mobile genetic elements to candidate host genomes within complex microbial communities.

Learning Objectives:

1. Identify the challenges of metagenome assembly

2. Quantify the benefits of low error rate long reads on metagenome sequencing and assembly

3. Understand concepts such as “phasing” and “haplotyping” as it relates to metagenome assembly

Somatic mutations that arise during development are increasingly recognized as contributors to neurodevelopmental disease. One challenge has been to determine how mosaic mutations contribute to cell lineages and affect the function of individual cells, which could have implications for disease manifestation. We integrated high-throughput single-cell 3’-RNA-sequencing with targeted Iso-Seq long-read RNA sequencing on the PacBio Sequel II platform to overlay single-cell genotype with cell-type and gene expression analyses. We applied this strategy to study the contribution of somatic mutations to developmental brain malformations in surgically-resected patient brain tissue.

Learning objectives:

1. Understand how long-read sequencing can be useful for haplotype phasing of variants.

2. Understand how long-read sequencing can be integrated with single-cell 3’-RNA-sequencing.

Precise diagnosis of neurodevelopmental disorders (NDDs), which often have genetic causes, is a challenging and important problem.  Here we describe the results of a recent pilot study using PacBio CCS sequencing reads, which are both long and accurate the basepair level, to analyze 6 proband-parent trios affected by an NDD that remains undiagnosed after extensive testing, including short-read genome sequencing.  Our results suggest that long-read genomes may reveal biologically and clinically relevant information in many families affected by unexplained NDDs.

Learning Objectives:

1. Understand the role of genome sequencing for neurodevelopmental disorder diagnosis

2. Define the types of genetic variation that can be uncovered more effectively by long reads than short reads

Recently, long-read sequencing with high accuracy has become a reality. Previous technologies allowed for the detection of particular classes of genetic variation and/or have focused on pre-defined regions of the genome. De novo assembly of an individual’s complete genome has also been difficult and time-intensive with the drastically shorter reads that have been the standard in our field. Long-read sequencing is a ground-breaking advance that enables us to perform analyses to assemble genomes within one day and to detect nearly all forms of variation in one single technology. In this talk, I will discuss the application of Pacific Biosciences (PacBio) HiFi long-read sequencing to human genetics. First, I will describe our analysis workflow for long-read sequencing that includes de novo assembly and mapping-based strategies to detect variation in a single individual. Second, I will detail the utility of public long-read sequencing data for filtering of variation detected within individuals. This approach helps narrow into relevant rare variation within an individual and is necessary for variation not detected by previous technologies. Third, I will discuss the benefit of long-read sequencing for phasing of variation regarding detection of compound heterozygous variants. Finally, I will show examples of our utilization of long-read sequencing to understand complex structural variation in individuals we have assessed in our lab. Overall, my talk will introduce the concept of long-read sequencing and its application in human genetics.

Learning Objectives:

1. Understand the concept of long-read sequencing

2. Learn applications of long-read sequencing to human genetics

The complete assembly of each human chromosome is essential for understanding human biology and evolution. Using complementary long-read sequencing technologies, we complete the first linear assembly of a human autosome, chromosome 8. Our assembly resolves the sequence of five previously long-standing gaps, including a 2.08 Mbp centromeric α-satellite array, a 644 kbp β-defensin copy number polymorphism important for disease risk, and an 863 kbp variable number tandem repeat at chromosome 8q21.2 that can function as a neocentromere. We show that the centromeric α-satellite array is generally methylated except for a 73 kbp hypomethylated region of diverse higher-order α-satellite enriched with CENP-A nucleosomes, consistent with the location of the kinetochore. Additionally, we confirm the overall organization and methylation pattern of the centromere in a diploid human genome. Using a dual long-read sequencing approach, we complete high-quality draft assemblies of the orthologous chromosome 8 centromeric regions in chimpanzee, orangutan, and macaque for the first time to reconstruct its evolutionary history. Comparative and phylogenetic analyses show that the higher-order α-satellite structure evolved specifically in the great ape ancestor, and the centromeric region evolved with a layered symmetry, with more ancient higher-order repeats located at the periphery adjacent to monomeric α-satellites. Additionally, sequence and assembly of two other primate centromeres confirms the overall organization and evolution of these regions. We estimate that the mutation rate of centromeric satellite DNA is accelerated at least 2.2-fold, and this acceleration extends beyond the higher-order α-satellite into the flanking sequence.

Learning Objectives:

1. Explain why centromeres, segmental duplications, and other repeat-rich regions of the human genome have been historically challenging to resolve

2. Identify recent advances in long-read DNA sequencing technology and assembly algorithms that are now allowing scientists to accurately assemble complex regions of the genome

3. Describe genetic and epigenetic features of newly resolved regions in chromosome 8 and how the chromosome 8 centromere has evolved over the last 25 million years

Structural variants (SVs) are essential in human evolution and genetic disease but remain understudied. This is especially the case for non-Caucasian ethnicities. We report, for the first time, a comprehensive study of SVs across the genomes of 499 Han Chinese, the largest ethnicity group, using ~15x Oxford Nanopore technology. We identify a total of 81,752 SVs, of which 46.86% are novel when compared to the SV calls in the dbVar and many can be successfully genotyped in short read data. Using these we were able to identify and compare Han Chinese specific SV hotspots across the genome and identified novel sequences that can be place in the human genome.  We found that SV hotspots not only affected the well-known amylase locus, but also seven carbohydrate metabolic processes in the Han Chinese genomes. Further, we uncovered 355 unreported natural knockout genes in the Han Chinese genomes. These data provide the first sequence map of structural variation map of Han Chinese and are a valuable resource for both the population genetics and medical communities.

Learning Objectives:

1. Attendees will learn about variability and complexity of SV across the Han Chinese population and its implication on phenotypes

2. Attendees will learn about what we are missing on GRCH38/37 and why ethnicity specific reference/ pan genomes are necessary

3. We will review approaches and sequencing technologies to scale up the study of hundreds to thousands of long read genomes

Telomere length (TL) is widely considered a molecular/cellular hallmark of the aging process with implications for multiple diseases. While there has been success in epidemiology and genomewide association studies (GWAS) to understanding telomere biology and genetics, there is a gross under-representation of minority and admixed populations in these efforts, limiting our understanding of health disparities pertaining to diseases related to aging, inflammation, and cellular senescence that are related to TL. High throughput technologies with decreasing sequencing cost per sample has enabled the generation of whole genome sequencing (WGS) at an unprecedented scale with large, well-phenotyped resources of subjects having WGS in epidemiology, precision medicine and biobank endeavors nationally and internationally. With large and well-powered sample sizes, multi-ethnic representation, and WGS data, we have an unprecedented opportunity address critical gaps in TL genetics through NHLBI's Trans-Omics for Precision Medicine (TOPMed) Program on heart, lung, blood, and sleep disorders. Similar to the novel gains in focusing on this ‘functionally relevant’ part of the dynamic human genome, is the added value of calling clonal hematopoiesis from the same genomes, and exploring relationships between the two. This talk will focus on the opportunities from of out-of-the-box and non-traditional approaches to leveraging pre-existing data and the opportunities at scale from large multi-ethnic resources such as TOPMed to bridge major gaps with health disparities research within a Precision Medicine framework.

Learning objectives:

1. Calling variation corresponding to the dynamic genome - CHIP and telomere length

2. The importance of multi-ethnic representation in large Precision Medicine Efforts

The recent explosion in the sample sizes and diversity of omics assays has created exciting new opportunities for biomedical scientists. However, connecting these omics data types in an interpretable way remains a challenge, especially when the data come from different sources and cohorts. One approach to this problem is to build regulatory networks that reflect the support of the data. In this talk, I will discuss a new method for estimating the effects of germline genetic variants on transcription factor (TF) regulatory events. This method, EGRET, integrates an individual's genotype with regulatory data to produce an individual-specific regulatory network. When we applied EGRET to RNA-seq and genotype data collected from 119 individuals in three cell types, we find TF regulatory disruptions that are disease-specific and occur only in the cell type relevant to the disease. Together this suggests that EGRET provides valuable insight into regulatory processes influencing disease.

Learning Objectives:

1. Describe the effect size distribution and typical genomic locations of GWAS variants

2. Explain which data types the EGRET method uses as input

In mammals, DNA methylation is an epigenetic mark that regulates gene expression by serving as a maintainable mark whose absence marks promoters and enhancers. Recent epidemiological studies demonstrate that DNA methylation levels are associated with aspects of biological aging in humans. We present universal mammalian epigenetic clocks that lend themselves for measuring age across mammalian species. We demonstrate that DNA methylation levels are strongly associated with maximum lifespan. These epigenetic clocks are expected to be useful for in vitro studies, animals models, and ultimately human clinical trials.

Learning Objectives:

1. Define the concept of an epigenetic clock

2. Review evidence that epigenetic clocks are indicators of epigenetic age in mammals

3. Explain what is meant by universal mammalian clock

Many diseases show sex differences in incidence or progression, suggesting that one sex has inherent biological factors that protect from or exacerbate disease. Historically the root causes of all sex differences in tissue phenotypes were thought to be the gonadal hormones. The advent of specialized mouse models has uncovered non-hormonal origins of sex bias in disease, caused by inherent inequality of XX vs. XY sex chromosomes. For example, the Four Core Genotypes and XY* models allow the investigator to compare mice with different sex chromosomes, but with the same type of gonad. Use of these mouse lines in several models of disease has now progressed far enough that specific X or Y genes have been identified that give rise to molecular cascades causing sex differences in disease. The number of X chromosomes, one vs. two, causes sex differences in models of metabolism and adiposity, of autoimmune disease, of Alzheimer’s disease, and bladder cancer. The underlying genes that cause the X chromosome effects are X-linked Kdm5c and Kdm6a, two lysine demethylases acting on H3K4 to activate or reduce gene expression elsewhere in the genome. The genes escape X inactivation and are expressed from all X chromosomes, so that XX cells express a higher dose than XY cells. This inherent inequality influences diverse tissues and diseases. Just as the discovery of gonadal hormonal sex-biasing factors, such as testosterone and estradiol, catalyzed waves of discovery of downstream mechanisms leading to sexual dimorphism in many tissues, we now expect the discovery of new sex chromosomal sex-biasing factors to lead to new research on novel pathways causing sex differences in diverse tissues related to numerous diseases. Supported by NIH grants OD026560, HD100298, HD076125, DK083561, HL1311820

Learning Objectives:

1. Understand mouse models that test inherent sex differences in X and Y gene dose that cause sex differences in disease.

2. Appreciate new evidence about specific X genes that are the origins of sex bias in the genome.

Sex and gender differences are apparent in health and disease and aduring aging. Chronic obstructive pulmonary disease is a leading cause of death with pronounced sex and gender differences associated with disease susceptibility and severity. Studying genetics and (epi)genomics will allow the development of holistic approaches to modeling the molecular differences that may be further exacerbated for COPD as a disease of accelerated aging. The hope is that big data and sex and gender aware systems and network medicine approaches will inform innovations for diagnostic, therapeutic and preventative approaches to COPD as a disease of aging.

Learning Objectives:

1. Overview associations with sex/ gender and lung disease

2. Note a role for the (epi)genome in sex differeneccs in the lung

3. Propose a role for Networks to integrate sex and gender differences in understanding COPD

NGS Bioinformatics software has come of age. Our workflows need software solutions that don’t only carry out the correct calculations to give the correct results reliably and unambiguously, but that are architected and designed with safety, confidentiality and usability as key principles throughout. Real-world software must be able to integrate cleanly and reliably with laboratory and clinical systems, and also be able to scale with users’ needs. In this presentation, we review the role of software in NIPT and related NGS test workflows, and consider how it can be built to create turnkey solutions for analysis and sample and clinical test data management that integrate seamlessly with test workflows.

Learning Objectives:

1. Understand the roles of software in sequencing-based test workflows.

2. Describe the foundation user needs that software in sequencing-based workflows must meet, and explain the importance of each.

3. Give examples of how the key software user needs might be met in a real-world example of a test workflow, such as for non-invasive aneuploidy testing.

Engineered cell therapy is an emerging field of science to target and treat cancer. Current strategies include utilizing immune cells such as T cells, NK cells and Macrophages or other cells such as RBC, induced pluripotent stem cells. These cells are genetically engineered to express tumor antigen-specific protein on the cell surface along with gene modifications to make these cells more tolerant and persist longer in the patient. The field is moving towards combination therapy and making marked improvements in manufacturing and logistics of this therapy. Combination therapy include CAR-T cells with immune modulators such as PD-1 inhibitors or with other cell therapies such as CAR-NK cells. Manufacturing improvements include shortening the culture period thereby reducing the time from bench-to-bedside, making the cells off-the-shelf (allogeneic) and logistics of sourcing raw materials. Gene modification technologies including CRISPR, zincfinger, MegaTALs can knockdown genes that can cause graft versus host disease and knock-in genes that encode for CAR or factors such as cytokines that increase the persistence of these cells. Despite these scientific advancements, we are still at the beginning of these therapeutic milestones. Targeting solid tumor with this therapy has been a challenge and modulating the tumor microenvironment to accentuate the function of CAR-cells are being explored.

Learning objectives:

1. Understand the gene engineered cell therapy field

2. Areas that need improvement in this field

3. Manufacturing objectives in cell therapy

As viral threats continue to emerge, viral detection assays are important not only for recognizing if a virus is present, but also for surveillance and variant characterization. Accurate detection and monitoring are especially important as the SARS-CoV-2 virus mutates, requiring robust tools capable of new variants. This has been made especially clear with the emergence of new SARS-CoV-2 variants that cannot be detected by conventional PCR tests. In this presentation we will review the different types of virus detection assays. We will focus on hybrid capture followed by Next Generation Sequencing (NGS) for viral detection. The technology behind hybrid capture will be discussed. We will then demonstrate how hybrid capture is capable of distinguishing viruses causing similar respiratory illness symptoms. We will also demonstrate how hybrid capture can be used for detecting and characterizing SARS-CoV-2.

Learning Objectives:

1. The differences in diagnostic viral detection assays

2. The basics of hybrid capture and how it can be used for viral recognition and characterization.

As part of Healthy Davis Together, we have implemented rapid, inexpensive, high throughput testing for SARS-Cov-2 using technology repurposed from the agricultural biotechnology sector.  This is providing at least weekly testing for the campus and the City of Davis.   This may have contributed to the consistently low prevalence of Covid-19 in the community. The workflow involves testing saliva samples from asymptomatic individuals without RNA extraction; more than 4,000 samples are routinely processed per day with over 90% of results reported within the day after sample collection.  This throughput required the deployment of a superplex assay for N1 and N2.  One key step is treating the saliva with papain protease to reduce viscosity.  This has a sensitivity comparable to EUA approved tests of nasal swabs.  To date, we have processed over 400,000 tests and identified more than 1,700 positive individuals.  The IntelliQube qPCR machine is not the rate limiting step, which provides the opportunity for high throughput genotyping of all positive samples for variants of SARS-CoV-2.  Genotyping using SNPs that discriminate between known variants of concern is more efficient, less expensive, technically less demanding, and importantly much more rapid than sequencing.  It will not, however, discover new variants and is therefore complementary to sequencing.  In collaboration with LGC, we have developed and deployed SNP assays that can distinguish all the current variants of concern and interest.  All positive samples are assayed and genotypes assigned within a few days of sample collection.  Non-wildtype genotypes are validated by sequencing.  This provides rapid resulting to the county health authorities. Robust genotyping technology is available for monitoring large numbers of positive samples on a global scale; this will be particularly important in geographical areas where sequencing is not feasible.  The regulatory framework for this is more challenging than the technical aspects.

Learning Objectives:

1. What were the key elements to the development of high throughput testing for Sars-CoV-2?

2. How can SNP genotyping be used for rapid monitoring of variants of SARS-CoV-2?

A One-Step RT-qPCR assay based on the CDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel has been developed which can effectively detect SARS-CoV-2 particles from saliva samples which undergo both heat inactivation and direct lysis with detergent, eliminating the need to extract RNA. Human RNAseP detection ensures accessibility of challenging saliva samples, acting as a true internal control in a multiplexed reaction. 6ul reactions have been validated with positive clinical samples and perform well with a sensitivity of 94.7% or higher. Low volume testing increases throughput, decreases cost, and decreases total specimen volume requirement to 0.375ul. After the collection process is described in writing and through video instructions accessible online, participants are able to perform the collection at home without oversight, and failure to submit a quality sample is below 1%.

Learning Objectives:

1. To describe the relative advantages and disadvantages of a saliva-based assay to detect upper respiratory pathogens, specifically SARS-CoV-2.

2. To describe the relative advantages and disadvantages of an RNA extraction-free protocol to detect upper respiratory pathogens, specifically SARS-CoV-2.

3. To explore designing flexible and agile PCR-based protocols

Historically, nasopharyngeal sampling has been considered the gold standard for the detection of most upper respiratory tract infections, however major medical and laboratory supply chain issues limited testing capacity early in the COVID-19 pandemic. The need for alternate mass testing strategies placed saliva- based diagnostics into the spotlight. We have shown that SARS-CoV-2 RNA can be detected in saliva from COVID-19 symptomatic and asymptomatic individuals with comparable sensitivity to nasopharyngeal swabs. Additionally, this viral RNA has shown to be stable at room temperature for prolonged periods in saliva. In response to the urgent need for increased capacity for affordable SARS-CoV-2 testing, we developed SalivaDirect protocol, an open source, saliva-based, nucleic-acid-extraction-free, qRT-PCR method for SARS-CoV-2 detection. Raw saliva is used for this protocol, allowing for the use of generic tubes for sample collection. The exclusion of nucleic acid extraction and flexibility in reagents, instruments and sample collection devices required reduces the testing costs to approximately $1.21–4.39 per sample. SalivaDirect offers a sensitive, noninvasive, low-cost alternative to traditional testing, allowing for increased testing capacity, especially in economically challenged environments.

Learning Objectives:

1. Assess the pros and cons of saliva-based COVID-19 testing.

2. Explore different saliva collection and processing workflows.

Controlling the spread of the 2019 novel coronavirus SARS-CoV-2 and ensuring a return to 'normal' activities in a post-COVID world requires two complementary efforts: 1) Widespread availability of reliable and accurate testing at massive scale to identify and isolate those infected, and 2) Continued identification and monitoring of emerging variants of concern to identify and avoid spread of resistant or vaccine-bypassing strains.

During the height of the coronavirus pandemic, a group of leading experts came together to launch the Pandemic Response Lab (PRL) which was announced by NYC’s Mayor De Blasio in August 2020. The lab is a collaboration between Brooklyn-based Opentrons, a laboratory automation company, and scientists from NYU, Harvard, and beyond. PRL developed a novel platform for performing gold standard RT-qPCR COVID testing for upwards of 30,000 samples per day for the city of New York, with 24-hour turnaround time. We are now growing our footprint to new labs across the country to bring increased testing capacity where it’s needed most, as well as expanding our test capabilities to widespread surveillance of school children and businesses to allow safe reopening.

Most recently, we have further leveraged our scientific talent to rapidly develop a from-scratch high-throughput platform for COVID genome sequencing. The pipeline is low-cost and designed to be directly integrated into our PCR testing platform, such that we are capable of rapid turnaround of sequences within days of a positive test result. This platform is currently delivering over 1,000 COVID genomes per week within NYC, and we are scaling to an anticipated capacity of 10s of thousands per week very soon.

In this talk, I will discuss the origins and continued development of the PRL test and sequencing platforms, as well as some of the future vision for PRL in a post-COVID world.

Learning Objectives:

1. What can the Ct value of a PCR-based test tell you about the collected specimen?

2. Explain the (current) primary goal of covid genome sequencing.

3. What are the pros and cons of using a PCR-based variant detection method vs. sequencing?

As the SARS-CoV-2 continues to spread and evolve and vaccination efforts are under way, countries around the world are confronting new public health challenges due to the emergence and rapid dominance of novel genetic variants. Evidently, there is great need for robust genomics tools to support viral detection and surveillance efforts. While RT-PCR is a great and inexpensive tool for diagnostics, only Next Generation Sequencing (NGS) solutions facilitate the genetic characterization of the virus and provide insights of the viral variant dynamics.  In this talk, you will discover how a hybrid capture based NGS approach can efficiently and reliably call SARS-CoV-2 genetic variants with high sensitivity and fewer false negatives.

Learning Obectives: 

1. Gain knowledge and understanding about a hybrid capture NGS workflow

2. Understand how Next Generation Sequencing (NGS) is crucial for tracking viral evolution to support pandemic surveillance.

3. Discover how a hybrid capture based Next Generation Sequencing approach enables more robust and reliable variant detection

Given the onset of extensive school closures, we are facing widespread scenarios of stunted learning, social isolation, and most troubling, an unprecedented surge in student suicides, all which are amplified in underserved communities. Stringent restrictions imposed upon businesses, in the name of public health and safety, have provoked irreparable consequences for society at large. We are facing surges in unemployment, homelessness, social isolation and a troublesome widening of the socio-economic divide. The need for a fast, cost-effective, and scalable testing solution to facilitate the reopening of both schools as well as businesses, could not be urgent, as we attempt to reopen during a time of increasing cases across the globe. Here, we look at various diagnostic measures that can be leveraged to instantaneously inform infected individuals and interrupt chains of transmission.

Learning Objectives:

1. Identify distinct types of molecular diagnostics for COVID-19

2. Determine when to use one diagnostic modality over another

3. Define novel approaches to detect SARS-CoV-2

According to the American Cancer Society, in there were an estimated 110,070 new cases diagnosed and approximately 32,120 deaths from gynecologic cancers in the U.S. in 2018. Of the five most common types of gynecologic cancer (cervical, ovarian, uterine, vaginal, and vulvar), ovarian cancer is associated with the highest mortality rate and is the fifth leading cause of cancer death. With the exception of pap smear for cervical cancer, there is no specific screening test for any gynecologic cancer. If detected early, mortality rate of ovarian cancer can be reduced by as much as 90% and this presents a huge unmet need. Among many potential biomarkers, circulating tumor DNA (ctDNA) has emerged in the last few years as a major tool for precision medicine in oncology. ctDNA exhibits genetic and epigenetic alterations from its cell of origin and is therefore becoming a key biomarker in non-invasive early detection using liquid biopsy. In this talk, I will highlight several challenges that still exist, despite rapid progress, in widespread clinical usage of ctDNA for cancer screening, notably for the early detection. These include limited sensitivity of current technology to detect small tumors with very low levels of mutant ctDNA present, a priori unknown somatic mutations and inability to distinguish ctDNA from cfDNA from normal cells released by non-malignant cells during normal cellular turnover. I will also review some of the techniques that are being used to overcome these challenges such as ultra-deep next-generation sequencing, customized bioinformatics tools and, machine learning to build classification models by integrating multiple analytes (such as proteins, transcriptome, miRNA and exosomes). I will conclude by providing a brief commentary on the future prospects of this promising technology.

Learing Objectives:

1.    What is the current level of detection (LOD) for mutant allele fraction that most liquid-biopsy technologies/test can offer? A: 0.5%

2.    What is the challenge of using a larger sequencing panel for the detection of ctDNA? A: requires a greater input of DNA and higher sequencing costs due to lower throughput of sample

Laboratory implementation of clinical genomics in children requires customization of analytical tools tailored to profile the divergent mutational landscape in childhood tumors. The relative paucity of point mutations and the higher abundance of fusion genes, structural variation, and epigenomic alterations in pediatric tumors raise different challenges in clinical diagnostics. Multimodal genomic technologies combining DNA and RNA sequencing with methylation profiling is rapidly becoming the standard of care in correctly classifying tumors and identifying actionable alterations in childhood tumors. Join this webinar to learn the challenges and benefits of clinical genomics in pediatric oncology.

Learning Objectives:

1. Understand the mutational landscape of childhood cancers

2. Explain the different types of fusion genes driving cancers

3. Understand the utility of unbiased broad testing in children

5-fluorouracil (5-FU) and its oral analog capecitabine are fluoropyrimidine chemotherapy agents used in several solid tumors. Approximately 5% of patients inherit diminished or null activity genetic variants in DPYD, which encodes the enzyme that metabolizes fluoropyrimidine, leading to higher systemic drug exposure and increased risk of severe and fatal toxicity. Many European countries recommend DPD/DPYD screening prior to fluoropyrimidine treatment and there are guidelines from Clinical Pharmacogenetics Implementation Consortium (CPIC) that recommend safe fluoropyrimidine starting doses in known DPYD carriers. However, DPYD testing is not recommended by the FDA or clinical oncology practice guidelines, which has led to minimal testing in the United States. One of the main reasons for the lack of clinical uptake is the cost of testing large numbers of patients to find the relatively uncommon (~5%) DPYD carriers. A potential solution that has not been explored, to our knowledge, is to use existing research-only genetic data to identify suspected carriers of actionable genotypes who should undergo confirmatory testing prior to treatment. This talk will summarize the evidence of the clinical benefit of pre-treatment DPYD testing and the reasons that testing is rarely conducted in the US. I will then describe ongoing work to use an institutional genetic data repository for ultra-efficient DPYD implementation at the University of Michigan Rogel Cancer Center. Finally, I will describe our efforts to get oncology clinical guidelines and the FDA to recommend pre-treatment DPYD testing and give the audience a simple way to support our effort. 

Learning Objectives:

1. Identify the five actionable DPYD variants that increase risk of fluoropyrimidine toxicity

2. Describe the main reasons that pre-treatment DPYD testing is not conducted in the United States

3. Recognize the benefits of using existing institutional genetic data for efficient pharmacogenetic implementation

Phenoconversion is a mismatch between an individual’s genotype-based prediction of a drug metabolism and its true capacity. In other words, there is a mismatch between the clinically observed phenotype and the phenotype you expect to see from a patient’s genotype. This is a great example of why we don’t want to interpret PGX results in isolation. We want to look at both drug-drug and drug-gene interaction. One can be a normal metabolizer but because an external factor was added, such as smoking or taking another medication, one can phenoconvert to a poor metabolizer. Strong inhibitors such as Prozac can phenoconvert an individual to a PM and the opposite is also true. A strong inducer can turn an individual into an ultra-rapid metabolizer. Therefore, we call this the blind spot of pharmacogenomics (PGx) or what is better know the Achilles' heel of PGx. So, it’s not enough to look at just one medication and see how a person is metabolizing it but rather to look at drug-gene interaction as well. A great example is with the enzyme CYP2D6. Prozac is a strong inhibitor of 2D6 and if we add on Tramadol for pain, as an example, which uses CYP2D6 to activate, we can say we have phenoconverted that individual. The person was a normal metabolizer of CYP2D6 enzyme but adding Tramadol made them a poor metabolizer. This presentation will provide an overview of the concept of phenoconversion and its impact on clinical outcomes.

Learning Objectives:

1. Define the concept of phenoconversion

2. What is the utility of pharmacogenomics in a mental health setting?

Pharmacogenomics has had rapid implementation over the past decade, with recent growth in application in oncology, mental health, solid organ transplantation, and more. There has been much focus on specific drug-gene-action relationships, such that there are now a number of actionable pharmacogenomics examples. With the application of panel testing, there are often additional opportunities to impact a patient’s care, beyond the specific disease focus area.  This shift from the gene/drug pairing to the broader look across the patient’s current and future needs is an inflection point in moving pharmacogenomics into routine usage.

Learning Objectives:

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

2. Identify various challenges in implementing pharmacogenomics from research to practice

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

Advanced cancer patients, individuals that have a <50% chance of 5 year-survival and have exhausted standard of care options, are often seeking for innovative therapies. These drugs may be investigational and only available via clinical trials. Unfortunately, >95% of cancer patients are excluded owing to exclusion criteria, geographic limitations, timing issues, or simply being at the wrong institution at the wrong time. Alternatively, they might be trying off-label treatments or novel combination therapies via a wide variety of means (right-to-try, expanded access, novel combination therapy, novel indications, etc..). Identifying the right cancer treatment is difficult. Cancer is highly personal, and what works for one person might not work for someone else. Unfortunately, unless a paper is published or a case series documented, most of the learning from these n-of-1 experiments is lost. Finally, oncologists often don't have enough time to help patients find the best treatment options due to the increasing demand of oncology services from factors including an aging and growing population. In fact, ASCO estimates that there will be a shortage of >2200 oncologists by 2025.

We will discuss how artificial intelligence (AI) and natural language processing (NLP) tools can be utilized to analyze Real World Data (RWD) and other data sources to empower patients and their oncologists to make more informed and effective treatment decisions. Furthermore, collecting the safety and efficacy information of these therapies can also be beneficial for researchers of investigational drugs, new therapeutic combinations, or novel indications. This RWD can increase the ability to identify causal inferences between treatments and outcomes.

Learning Objectives:

1. Identify challenges advanced cancer patients and community oncologists face in finding treatment options

2. Evaluate how artificial intelligence could empower more advanced cancer patients and their treating oncologists with the best treatments options they should consider

3. Explore how Real World Data (RWD) on the safety and efficacy of these treatment options may be beneficial for researchers of investigational drugs, new therapeutic combinations, or novel indications

Many genomic databases and knowledgebases are available and useful in determining the significance of genetic alterations in cancer. Each one of these resources has unique features that allow the user to answer specific questions about the gene, variant, or alteration in question. In this introduction to resources facilitating cancer variant interpretation, we will review different features of several resources (including COSMIC, cBioPortal, OncoKB, CIViC, PeCan Data Portal, JAX-CKB, the Molecular Oncology Almanac, Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer, Mastermind by Genomenon, and the VICC MetaKB) that make these resources particularly helpful and unique. The goal of this review is to introduce the audience to the landscape of genomic resources available and encourage the use, familiarization, and collaboration with these databases and knowledgebases to ease the burden of interpreting genomic variants in cancer.  In addition to learning about the features incorporated into these resources, the landscape of integration of resources will be reviewed as this field continues to aggregate data and make meaningful connections to benefit the users of this technology.

Learning Objectives:

1. Identify different kinds of genomic resources

2. Learn to apply the features available in genomic resources to cancer variant interpretation

3. Describe the importance of collaboration and aggregation of genomic variant information