Single Cell Genomics Platform

The Technology: Single-Cell Combinatorial Indexing (SCI)

Advances in genomic technologies have made it possible to characterize molecular profiles, such as the transcriptome or epigenome, of individual cells. The discovery of rare but important cell types, delineation of cell trajectories in development, and the importance of cell type proportions in health and disease are among the many discoveries enabled by these technological advances. While many commercial single-cell molecular profiling technologies are available, they remain prohibitively expensive for large-scale projects. To address this limitation, single cell combinatorial indexing (sci-) based methods were developed. Sci-based methods leverage a ‘split and pool’ molecular barcoding strategy to enable exponentially large numbers of nuclei to be molecularly profiled at a low cost-per-cell, albeit at a high cost-per-experiment.

Example application of sci-RNA-seq3.

BBI BAT-Lab Services

By performing large-scale single cell molecular profiling experiments in a centralized lab, we aim to produce high quality, consistent data while keeping costs for individual researchers to a minimum. The BBI Advanced Technology Lab (BAT-Lab) currently offers services for two molecular profiling technologies: optimized sci-RNA-seq3 and sci-ATAC-seq3. Services range from in-house tissue dissociation and nuclei isolation (optional), through single-cell sequencing and basic data analysis. Of note, we currently perform sci-experiments only on fixed nuclei, and the sci-RNA-seq is a polyA capture based method (3’-end counting). Both raw sequencing and analysis-ready data (demultiplexed, cell by gene count matrices) are returned, along with helpful QC and preliminary analyses. See below for more information about our bioinformatics services and support.

Our pricing structure depends on the number of samples, whether the sample prep is to be performed in house (i.e. nuclei isolation), which technology is requested, and the targeted number of nuclei to be sequenced. Please contact us at sci-help@brotmanbaty.org to discuss your project and our current rates.

Are sci-based methods right for you?

The strength of sci-based methods lie in their ability to profile exponentially large numbers of nuclei, enabling for the average cost-per-cell to fall well below that of commercial methods. Investigators can choose to profile tens-of-thousands to millions of nuclei. In order to reach production scale, we profile nuclei from multiple experiments together and set minimums on the number of nuclei per sample.

As with all single-cell technologies, results vary from organism to organism and tissue to tissue, and we cannot guarantee results. Our team is adept at optimizing tissue dissociation and nuclei isolation from a variety of organisms and tissue types, and we offer QC samples at reduced rates for new specimen types. It should be noted that the average UMIs-per-nuclei may also be lower than would be achieved with commercial droplet-based technologies.

For these reasons, our services are ideal for those favoring ‘breadth’ over ‘depth’ in their single cell projects. If you are looking for lower throughput single-cell sequencing, Fluent BioSciences (instrument free) and 10X Genomics offer commercial solutions, and the Genomics Cores at Fred Hutch Cancer Center and the Institute for Stem Cell & Regenerative Medicine (ISCRM) offer 10X support services. If you would like to see us offer support for these or other technologies in the future please let us know at sci-help@brotmanbaty.org. Our mission is to make cutting-edge ‘-omics’ technologies, including many developed by labs at our member institutions, broadly available to the Institute’s research community!

Bioinformatics Training Support

The Brotman Baty bioinformatics team pre-processes all single-cell sequencing data and includes a web dashboard that shows the results of some basic quality control metrics, clustering, and automated cell type identification with Garnett (for model organisms). All pipelines are open source and can be found on our GitHub page. Raw data is returned along with a premade Cell Data Set (cds) object to use with Monocle3, a well-developed toolkit for analyzing single-cell gene expression experiments. Investigators with limited R programming experience are encouraged to follow our recorded tutorials to kick-start their downstream data exploration.

Training Session December 2021

BBI Tutorial Resources: This repository stores code used in the quarterly BBI single-cell data analysis tutorials.

Training Session February 2021

BBI Tutorial Resources: This repository stores code used in the quarterly BBI single-cell data analysis tutorials.

Training Session December 2020

BBI Tutorial Resources: This repository stores code used in the quarterly BBI single-cell data analysis tutorials.

Training Session September 2020

BBI Tutorial Resources: This repository stores code used in the quarterly BBI single-cell data analysis tutorials.