6 questions to help you get started with Chromium single cell sequencing in 2022
Did you spend the holidays thinking about your 2022 experiments? Science is awesome—we get it! That’s why we created a helpful webinar series designed to cover all things Chromium single cell sequencing, including the basics of how 10x Genomics single cell technology works and the insights it can provide; how to design your experiments; and best practices for data analysis. In this blog, check out some of the top questions from the Q&A sessions following each webinar, then, watch the on-demand series for yourself. Whether you’ve been planning to start your first Chromium experiment or you’re a seasoned single cell veteran, we hope you have a great year and can get back into the lab with everything you need to be successful.
Overview of Chromium single cell sequencing technology
What types of cellular analyses can I perform using a [10x Genomics] single cell approach?
The transcriptome of a single cell can often hold the key to cellular identity and function. Measuring gene expression at single cell resolution is therefore a powerful approach to dissect cellular heterogeneity from complex samples, and is a foundational single cell technique that has transformed science in recent years. Our Chromium Single Cell Gene Expression assay can allow you to look at the RNA content of each individual cell from your sample.
Single cell gene expression helps you access the “what”—but sometimes it’s important to take a step back and look for the “how.” For example, how is gene expression controlled? If you're interested in chromatin accessibility and how it can regulate gene expression, Chromium Single Cell ATAC (Assay for Transposase Accessible Chromatin) helps you identify regions of open chromatin in the genome within individual cells.
Through single cell multiomics—the analysis and integration of datasets from different omic groups—scientists can measure multiple cellular features from the same single source, in addition to whole transcriptome gene expression. Our Chromium Single Cell Immune Profiling product provides access to cell surface protein expression and immune repertoire sequences, including T- and B-cell receptors, so you can get clonal type information from the immune cells in your sample. Chromium Single Cell Multiome ATAC + Gene Expression allows you to simultaneously profile gene expression and open chromatin from the same single cells to characterize cell types and states, and uncover gene regulatory programs. Chromium Single Cell CRISPR Screening lets you perform CRISPR screens at single cell resolution, accessing a simultaneous readout of single guide RNA and the transcriptional profiles of single cells as a result of perturbation.
Though not a comprehensive list of 10x Genomics technologies, you can see there are a number of different options depending on what your experimental goals are. Explore our Product Selector to find the right solution to match your needs.
What are some of the advantages and disadvantages of the different single cell approaches?
10x Genomics assays are certainly only one of many different approaches for single cell analysis. For example, there are plate-based approaches and microwell-based approaches. These approaches are typically lower throughput compared to the throughput provided by 10x Genomics products, which are microfluidics-based. Another approach, called combinatorial barcoding, allows you to scale up the number of cells that can be analyzed, but uses a more time consuming and pipetting intensive workflow. With these approaches in mind, we believe our technology is both scalable and streamlined in terms of the workflow.
For a more detailed breakdown of the differences between these approaches for single cell analysis, explore Chapter 3 of our eBook, Demystifying single cell sequencing.
Designing your first single cell experiment
How do I start planning my first single cell experiment?
There are a number of considerations when you are in the early planning phases, but sample preparation is an important starting point. One thing to think about is the sample type you’re working with. Is it tissue, a cell suspension, or whole blood? Does it require dissociation, enrichment, or cleanup steps? Additionally, you should consider the required number of samples per experimental condition and whether or not technical replicates are necessary. Narrowing in on individual samples, you should consider how many cells are necessary per sample to get the results you want, which will also inform your approach to sample enrichment, cleanup, and quality control.
From these initial considerations, you can find the right protocols for your samples and experimental goals. We recommend exploring our Getting Started with Single Cell Gene Expression guide for more details.
You can also speak with a member of our technical support team—we're always here and available to help walk scientists through the planning phases of their experiments, as well as address your specific experimental needs. Email us at [email protected] with your questions!
I am starting with a small number of cells. Are there any specific things I should take into account?
It’s very important to take into account the number of cells that you're starting with, particularly when considering cleanup methods for sample preparation. Some cleanup methods, such as density centrifugation or dead cell removal, require a higher starting cell number and incur significant sample loss. If you're working with a limited sample, it might be best to do a few washes and spins rather than investing time into doing a more complex cleanup that could risk sample loss.
It may also be possible to perform fluorescence-activated cell sorting (FACS) to retain more of the cells. Sample loss will be determined by the percentage of dead cells present in your sample and the population of cells you may be trying to enrich for.
We also have recommendations for how to sort directly into one of the buffers and subsequently run the sample on our single cell assays. This could be a useful strategy for sorting low-input samples.
For more guidance on sample prep with low-input samples, check out these technical documents:
- Single Cell Protocols - Cell Preparation Guide
- What is the minimum number of cells that can be profiled? - KB Article
- Nuclei Isolation for Single Cell Multiome ATAC + Gene Expression Sequencing - Demonstrated Protocol
Getting started with analyzing single cell data
If my two samples were processed in parallel, should I still use batch effect correction when I integrate my datasets?
That is a very good question! When we talk about batch effects, we are generally referring to the technical variation between samples that arises when they are processed on different technologies or different platforms. This can also be prevented if you have very good experimental design. For example, when you are combining two samples and processing them in parallel, that's actually a great way to prevent batch effects. If you have done that, there should be no or very minimal batch effects.
When you are integrating your two data sets, you might only need to normalize them if there are any differences in sequencing depth between the two samples. Fortunately, this normalization is performed automatically in our aggr pipeline within Cell Ranger. At this point, you shouldn’t have to worry about batch effects. In fact, you should be careful to avoid overcorrecting your data so as to match your true biological signal.
Can you share any recommended resources for marker genes of different cell types?
Cell type annotation according to marker genes is usually done via literature search, and many great databases are available online where you can find genes for the specific tissues you are working with. For example, if you are working with brain tissues, the Allen Brain Atlas is a great resource with a lot of datasets and marker genes.
In our Analysis Guide, we also provide a helpful introduction to some of these databases where you can explore a list of marker genes for different tissues. View guides →
2022—A year of single cell success
Want to get more insights to prepare for your single cell experiments and data analysis in 2022? Watch our full, on-demand webinar series, Single cell sequencing: From concept to benchtop, at your convenience, covering topics such as:
- What is single cell sequencing and why does it matter?
- Designing your first single cell experiment
- Getting started with analyzing single cell data
We wish you the best in 2022, including a year of many successful single cell experiments!