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scRNA-seq FAQs

What hardware is used for single cell genomics?

We currently have two platforms for single cell genomics:

  • The Chromium Controller from 10X genomics
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  • InDrop from 1CellBio
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How does it work?

During encapsulation, cell suspension with RT reagents, oil, and barcoded beads flow through channels in a chip to form oil-partitioned droplets, called GEMs (Gel Beads in Emulsion). This is the step that the 10X Chromium Controller performs, and it does so by applying pressure to the top of a small โ€˜chipโ€™ (resembles a plate with 8 wells). Each well contains one of your samples, and underlying each well is a single use microfluidic device built into the chip. When pressure is applied to the top of the chip, reagents are pumped through the in-chip fluidics to form droplets. Ideally, each of these droplets contains one cell, along with RT reagents and a single barcoded gel bead.

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The gel beads have poly T primer sequences to capture intact mRNA via their poly-A tails. They also have a cell barcode sequence, which is identical across a single bead, and a Unique Molecular Identifier (UMI), which will be unique for each transcript (differs across a single bead). This description focuses on scRNAseq, but different assays will utilize different capture sequences. After encapsulation, the gel beads dissolve and the primers and RT reagents generate barcoded cDNA.

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The GEM emulsion is broken and the cDNAs go through a standard library preparation, adding sequencing primers and sample indexing. The resulting libraries contain barcodes distinguishing reads by transcript (UMI), cell (GEM barcode), and sample.

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How โ€˜deepโ€™ should I sequence each cell?

For scRNAseq gene expression libraries, 10X recommends a minimum of 20,000 read pairs/cell. We usually have a target recovery of 10,000 cells per sample and a NextSeq high output kit gives about 400 million read pairs, so you can expect about one sequencing run for every two samples if using our NextSeq. Other library types have different depth requirements. See the table below for recommendations for some common assays. Another important consideration is that required depth may vary by cell type and library complexity. 10X describes this as the sequencing saturation and depending on your experiment, it may require optimization.

How much will it cost?

It's really hard to give a simple and direct answer to this question, because the cost of a single cell experiment really depends on multiple factors, including the number of samples you process, how many cells you analyze, how deeply you choose to sequence each cell, and the lengths of reads you want. We can help guide you in making decisions for all of these factors. In our experience, the 'typical' first scRNA-seq experiment costs about $7600. This would be for Tier 3 Service for 2 samples, including encapsulation, library preparation, sequencing on a NextSeq run, and labor. Pricing varies widely by experiment and we provide a detailed quote before work begins.

How do I analyze my data?

We do not yet have a dedicated informatics support person to analyze data for you, however, we have a lot of experience in the analysis of scRNA-seq data and can help guide you through the process. Our Linux server is fully equipped with the software for preprocessing data, and our RStudio Server is a great way to conveniently analyze your processed data directly on your laptop using tools like Seurat or the Bioconductor suite or R packages. Additionally, 10X has comprehensive guides for its analysis software and pipelines.

Is it typical to run replicate samples for single cell experiments?

Ideally, yes, biological replicates would be great, but they present some considerable practical challenges at present. For example, workflow logistics and reagent cost often make replicates either cost prohibitive and/or logistically challenging. Often times, the types of questions one ends up asking in with scRNA-seq data related to comparing the transcriptional state of one cluster of cells within a sample to another cluster of cells in the same sample. In this case, the individual cells in each cluster serve as replicates for statistical testing. In addition, software like Seurat allow you to integrate multiple samples together, making it easier to compare the transcriptional state of cell clusters across samples. Ultimately, whether and how you consider doing replicate samples depends on your budget, sample type, and experimental question, but we can help guide you during your experimental design phase.

Does CHMI provide reagents?

Yes! We will provide all the reagents necessary for your experiments. There are a few exceptions, such as cell prep reagents or TotalSeq antibodies for CITE-Seq, but these are usually specific and custom items that are determined by your individual experiment. Our tiered service model affords flexibility in how we work with you. See the table below for a summary of the tiers. If you are interested in using our reagents, we recommend starting with Tier 2 as you are learning the protocol, then moving to Tier 1 for future projects once you are comfortable. If you have never done any scRNA-seq before, we recommend Tier 3, as the cost and time investment of scRNAseq make it high risk and best for experienced users.

Service Tiers

Service TierDescription
Tier 1
We provide reagents only.
Tier 2
We provide reagents, guided instruction, and lab space.
Tier 3
Fully inclusive - we provide reagents and labor. Note that we typically will still ask you do the cell prep portion of the workflow, but will offer support and provide recommendations for best practices.

How should I digest my tissue for single cell assays?

We generally recommend a 30min digestion at 6ยบC with a cold active protease. See this paper for the method, and a comparison of these two digestions when cells are analyzed by scRNA-seq. Alternatively, if you have difficult-to-digest tissues, you may want to consider using the Worthington Tissue Dissociation Guide as a starting point.

Can I only do RNA-seq on single cells, or are there other single cell assay options?

There has been tremendous growth in the types of single cell assays being developed and commercialized. Currently, we offer scRNA-seq, scATAC-seq, CITE-Seq, and cell hashing on the 10X platform. In addition, a more recently released 'multiome' kit combines scRNA-seq and scATAC-seq to allow simultaneous profiling of transcripts and open/closed chromatin in the same cells at the single cell level. 10X has a wide variety of assays available. If you're interested in working with us on a 10X assay we don't have listed here, feel free to reach out!

While 10X has emerged as the dominant single cell platform, InDrop has advantages in customizability and user control. InDrop is a more open platform with live control of reagent flow rates and direct observation of encapsulation, enabling bespoke applications. This FAQ focuses on 10X, but if your project requires non-standard parameters, InDrop may be a good option.

What is CITE-Seq?

CITE-seq is a technique that allows simultaneous characterization of protein and RNA expression by labeling cells with antibodies conjugated to oligos. This is compatible with the standard 10x single cell expression protocol. We offer two methods of CITE-Seq, each using different varieties of TotalSeq antibodies and having different advantages. Not all antibodies are availability for every kind of TotalSeq, so we recommend checking availability of your desired TotalSeq antibody first before considering other aspects. Additionally, if you plan to label the same cell surface protein for both flow sorting and Feature Barcoding, you'll need to use distinct antibody clones. See here for more information.

CITE-seq and Cell Hashing with 10x single cell RNAseq (TotalSeq A)

ProsCons
Less expensive
Limited 10x support
More customizable
Cell hashing option for multiplexing

Feature Barcoding CITE-Seq with 10x single cell RNAseq (TotalSeq B)

ProsCons
Full 10x support
More expensive