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.