This is particularly critical when studying temporally dynamic processes, such as progenitor cell development into terminally differentiated populations via multiple transitional stages. Consequently, biologically significant heterogeneity within a population can be masked, and relevant information averaged with irrelevant signals from contaminating cells ( 6). Nonetheless, this type of analysis does not consider variability in gene expression between individual cells, or the influence of sample contamination with unrelated cell types that share overlapping phenotypic characteristics. During this process, new and unique population markers were identified that can more effectively resolve different immune cell compartments. Until recently, gene expression studies were performed on bulk populations of sorted or purified immune cells in attempt to better understand their transcriptomes. However, not all immune cell types can be fully resolved by the sole analysis of phenotypic markers, since many of these are expressed by multiple cell lineages, or are differentially regulated during inflammation ( 3– 5). With the aid of microscopy and flow cytometry, immune cells can be readily classified into distinct types based on specific surface markers. To process the samples appropriately, users need to add the oligonucleotide sequence used for each sample to the sample submission form they deliver to the IGL.The immune system comprises a network of cells, tissues and organs that mediate host defense against pathogens, but this network also plays a critical role in homeostatic activities, such as tissue development ( 1), and metabolism ( 2). The IGL is able to complete the libraries for these protocols. It is the responsibility of each user to select their tagging method, to treat their cells, and to mix them prior to delivery to the IGL for processing. Cells that have hashtags are identified informatically after alignment to the reference genome. Details on each of these are available from the appropriate vendor. Alternatively, a lipid-based oligonucleotide tagging system is available from 10x Genomics. Oligonucleotide-antibody conjugates are available for this from external vendors, for example, BioLegend (TotalSeq A and B). It is possible to combine these samples into one library by tagging the cells in each sample with a unique identifier. Occasionally users may have a set of samples that individually have fewer than 10,000 cells each, but that can add up to 10,000 cells (so, for example, sample A has 2000 cells, sample B has 3000 cells, and sample C has 5000 cells). Generally, most users will assay 10,000 cells from one sample. The standard 10x Genomics recommendation for the library preparation protocol is that the maximum number of cells per library is 10,000. See below for more information on services and the 10x Genomics workflow in the IGL. The MPSSR also accepts cDNAs from users and can finish the library preparation for them, and can prepare hashtag and CITE-seq libraries. Training on the Chromium Controller is provided through the GPSR following which researchers can schedule use of the Chromium system for their direct use. If you prefer to prepare your own single cell DNA and libraries, the IGL Chromium platform is available for Shared Access use once you have received training from a member of the IGL. 10x Genomics library preparation and sequencing is performed through the Massively Parallel Sequencing Shared Resource.įor a Full Service 10x Genomics single cell analysis project from sample submission through sequencing, please go to the MPSSR iLab page to request a project. The Gene Profiling Shared Resource maintains the Chromium Controller system and provides support for preparation of single cell cDNA and gDNA for 10x Genomics applications. The Integrated Genomics Lab (IGL) provides support for single cell and nuclei DNA preparation and sequencing using the 10x Genomics Chromium single cell platform.
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