Using Single Cell RNA Sequencing (scRNA-Seq) to Examine What Makes Cancer Possible
In this webinar, Dr. Katerina Gurova, Roswell Park Cancer Institute, describes how she used scRNA-Seq to examine whether the transition between one epigenetic state into another makes cells vulnerable to oncogene induced transformation.
High Sensitivity Single Cell RNA Sequencing with Evercode Whole Transcriptome v2
Single cell RNA sequencing has become a core tool for researchers to understand biology. As scRNA-seq has become more ubiquitous, many applications demand higher scalability and sensitivity. To meet this need, we developed the Evercode Whole Transcriptome v2 solution, a combinatorial barcoding workflow for scRNA-seq with dramatically improved sensitivity, robustness, and unbiased gene expression.
Single-cell Transcriptomic Landscape in Alzheimer's Disease
The gene-regulatory landscape of the brain is highly dynamic in health and disease, coordinating a menagerie of biological processes across distinct cell types. Understanding these regulatory programs requires a holistic experimental and analytical approach. Here, we present a single-cell study of 380,000 nuclei in late-stage Alzheimer’s Disease (AD) using parse biosciences whole transcriptome kit, profiling gene expression in thousands of genes and uncovering vast neuronal and glial heterogeneity in late-stage AD.
Mapping and Modeling the Genomic Basis of Differential RNA Isoform Expression at Single-cell Resolution
In this informative webinar recording, Elisabeth Rebboah, UC Irvine, discusses overcoming these obstacles to perform differential RNA isoform expression at single-cell resolution using the split pool combinatorial barcoding protocol from Parse Biosciences along with a combination of short read sequencing to characterize cell types and long read sequencing to reveal full-length isoforms.
Single Cell RNA-Seq on the Frontlines: High Multiplex and Time-course Experiments Expand Utility
Experimental designs are growing alongside the increased throughput requirements of single-cell RNA-Seq assays. Replicates, disease states, and time-course designs dramatically increase the number of samples and the burden of information gleaned from a single experiment. Alongside these considerations is a substantial increase in the implications of the results. Our speakers discuss the ramifications of higher-order studies on needs for data quality, throughput attenuation, and sample longevity.