About the Series
GenomeWeb and the Association of Biomolecular Resource Facilities are partnering for the third year to produce a series of online seminars highlighting methods, techniques, and instrumentation that support life science research.
Special thanks to the series sponsor, Canon BioMedical.
This page will be updated throughout the year as more webinars in the series are scheduled, so please check back regularly!
August 20, 2018 | 1:00 PM ET
Advances in Single-Cell RNA-Seq: Novel Technologies, Practical Applications
This webinar will provide an overview of recent advances in single-cell RNA sequencing from the perspectives of three research organizations.
Our first speaker, Ashley Byrne of the University of California, Santa Cruz, will discuss a long-read cDNA sequencing approach based on Oxford Nanopore sequencing technology to evaluate RNA isoform diversity in single B cells.
Using this approach, Byrne and colleagues have been able to reconstruct isoform-level transcriptomes using their analysis pipeline Mandalorion. They also discovered that much of the RNA isoform diversity observed is found across B cell-specific receptors, which could have implications for immunotherapy design — specifically for targeting B cell lymphomas.
Next, Junyue Cao of the University of Washington will share a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei.
Cao's team used the method, called sci-RNA-seq (single cell combinatorial indexing RNA sequencing) to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 stage, which is over 50-fold “shotgun cellular coverage” of its somatic cell composition. Cao will discuss how the data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.
Our third speaker, Kate Hall of the Stowers Institute for Medical Research, will share two different workflows her team has developed to streamline single-cell RNA-seq services.
The first workflow uses the Single Cell Chromium Controller from 10X Genomics and allows thousands of cells to be pooled together and processed as a single reaction. The high-throughput nature of this method can help identify unique cell populations in a given pool. The second workflow uses the Mantis small-volume pipetting robot from Formulatrix to set up quarter-sized reactions for cDNA synthesis on individual cells. This workflow is more suitable for a smaller sample set as it can help give a more detailed view of specific cell types via full-length transcript sequencing.
August 29, 2018 | 1:00 PM ET
Implementing Novel Technologies in the Core Lab: Spatial Transcriptomics and Sci-MET
This webinar will discuss two potentially disruptive technologies — spatial transcriptomics and single-cell methylome combinatorial indexing (sci-MET) — along with considerations for implementing them in a core facility setting.
Spatial transcriptomics combines traditional histological information with genome-wide transcriptome data by using high-resolution tissue imaging and RNA capture. This is made possible by placing thin tissue sections on microscopic glass slides, which are covered with spatially barcoded cDNA primers. Researchers using this technology have produced high-quality data from a variety of tissue types (for example brain, breast cancer, prostate cancer and heart) and organisms (human, mouse, and plants) and spatially barcoded arrays have also been applied to study single cells in solution.
Single-cell combinatorial indexing (sci-) is a robust and generalizable protocol family developed for the interrogation of single-cell omics. Sci- protocols follow a shared strategy of forming a unique barcode per nuclei through multiple iterations of indexing, pooling, and random sampling. With this method, protocols for single-cell library generation assaying the transcriptome, chromatin accessibility, genome-wide copy-number variation, chromatin conformation, and most recently, the methylome have been described. The methylome protocol (sci-MET) was used to generate 3,282 high-quality single-cell whole-genome methylation libraries. With low coverage, sparse methylation information was shown to be sufficient in discriminating cell types, both in a synthetic mixture of cell lines and in a neuronal subtype from mouse cortical samples. Sci-MET, as well as the other sci- protocols, provide an avenue for high-throughput single-cell omics necessary to interrogate the nuance and complexity of complex and developing tissue.
In this webinar, our panelists will discuss specifics of these technologies and their applications, within a particular focus on their implementation in core facilities.
May 3, 2018 | 11:00 AM ET
Novel Tools for Multidimensional Profiling of Single Cells
This webinar will introduce new technologies that enable multidimensional measurements from single cells to obtain a more complete picture of a cell’s phenotype and gene expression.
The first part of the webinar will describe two recently developed applications that use antibody conjugated oligos to enhance existing single-cell RNA-seq platforms: CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing), which allows measurement of a potentially unlimited number of protein markers in parallel to transcriptomes; and Cell Hashing, which enables sample multiplexing, robust multiple detection, and super-loading of scRNA-seq platforms, allowing confident recovery of four times as many single cells per experiment.
The second part of the webinar will cover the recently developed Patch-seq technique, which combines whole-cell patch clamp recording, immunohistochemistry, and single-cell RNA-sequencing to obtain high-quality morphology, electrophysiology and scRNA-seq data in parallel from single cells.
Our expert panelists will present an overview of the key protocol steps and quality control measures, as well as a discussion of potential applications and ongoing efforts to increase throughput.
July 12, 2018 | 1:00 PM ET
Analyzing the Human Brainome to Understand Alzheimer’s Disease Development: Genome, Transcriptome, Proteome, Phenome Interaction in Human Cortex
This webinar discusses a project that is analyzing the “Human Brainome” – genome, transcriptome, proteome, and phenome interaction data -- to gain insights into Alzheimer’s disease pathogenesis.
Amanda Myers of the University of Miami Miller School of Medicine describes the study, which used two separate sets of human brain tissue for analysis. Genome, transcriptome and proteome data was collected and analyzed to determine key drivers for Alzheimer’s pathology. Both an analysis of single effects (DNA driving downstream expression of one target) as well as multi-target analysis (transcript and peptide networks) was performed.
From a set of ~ 5.2 million SNPs, ~15,000 transcripts and ~2000 peptides a small subset of targets was discovered that are computationally predicted to be crucial to disease processes and replicated between our two sets of tissues. Targets were validated in the wet lab to insure that these targets on their own had effects on the specific Alzheimer’s disease brain pathology. Several targets on their own effected disease processes, demonstrating that our pipelines are robust and nominating these targets as new Alzheimer’s disease candidate genes.