When it comes to single-cell sequencing, it's important to start your project on the right foot. Proper experimental design and preparation are often the difference between success and failure. Key decision-making factors in a single-cell project include:
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What you hope to learn from your research.
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The quality and amount of your sample available.
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What kind and number of cells you’re investigating.
You can learn more about these factors in our last single-cell blog post, where we cover six questions you should ask yourself in designing a single-cell experiment.
In this post, we discuss factors that may discourage users from starting a single-cell project. Advancements in single-cell sequencing technologies are making it more accessible, affordable, and possible for researchers. Take a look at these five common roadblocks to single-cell and see how underutilized technologies can help you overcome them.
Common Roadblocks in Single-Cell Sequencing
I don’t have a lot of cells
Cell capture rates vary from platform to platform. When counting and loading cells into an assay, it is important to consider this capture rate to ensure enough cells are loaded at the beginning to yield the target cell number at the end. Take a look at our last single-cell blog post for more details.
Additionally, cryopreservation for shipment and storage of samples will result in significant cell loss. Cryopreservation is stressful on cells and, combined with the necessary washing steps to rehydrate the cells, can diminish the starting viable cell count by over 50%.
With Parse Biosciences’ single-cell workflow, you can fix cells and load them directly into the combinatorial barcoding assay. The stress and cell loss of cryopreservation are avoided, enabling you to start from fewer cells compared to other storage and fixation methods.
My sample quality is poor
Sample quality is the most important factor in the success of a single-cell project. A typical sample will include viable cells, but will also contain dead cells, ambient RNA, and debris. These factors can muddle the results of sequencing, as droplet based methods will capture these unwanted components along with your target cells.
The LeviCell EOS from LevitasBio can enrich your sample for a more successful sequencing run. The LeviCell separates viable cells from up to 85% of dead or dying cells and debris. This is an excellent option for projects that require:
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Cryopreservation of live cells, which can lead to decreased viability and additional debris.
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Freezing tissue, which requires nuclei isolation to proceed with single-cell sequencing after the fact. LeviCell EOS nuclei isolation and cleanup create a high-quality nuclei suspension.
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Barcoding cells, which leaves behind excess antibodies. These antibodies are not removed during standard washing steps, but can be removed during the LeviCell enrichment process.
Without LeviCell enrichment, droplet based methods will capture the debris, dead cells, and even antibodies from barcoding. This will decrease the downstream success of the experiment. When this happens, ambient RNA can be amplified, along with the RNA from the captured cell. LeviCell is a critical tool to precede any droplet sequencing methods that use preserved cells or tissues.
If using a non-droplet-based technology like Parse, the cell becomes the reaction site. Ambient RNA is washed away and does not get carried along with the cells, yielding less ambiguous data.
My needs are multiomic
There are more RNA than DNA single-cell protocols available. This can limit your options when choosing a single-cell platform if you need to capture both. Whole-genome and transcriptome single-cell is possible with BioSkryb ResolveOME. In this case, Psomagen will work together with BioSkryb to get you the data you need.
If you're looking for cell surface protein expression, both 10x Genomics and Parse Biosciences library preparation methods allow for the capture of mRNA as well as enrichment of TCR and BCR regions from human and mouse samples.
If your project would benefit from an even broader view of your sample contents, 10x Genomics may be the better choice. These protocols also support gene expression paired with cell surface protein analysis or ATAC.
I’m investigating low expressing genes
During bulk sequencing, any genes expressed in any cell type can be represented in your sequencing output — albeit as part of an average of the sample, rather than a single-cell resolution readout. If you’re interested in fragile cells, rare transcripts, or cells with low RNA content, you might miss important details. For example, if only one low percentage cell type in your sample expresses a gene, the signal from that gene may be lost in background signal when mixed with all the transcripts from all the other more abundant cell types.
Single-cell, however, offers a more real-world readout. The difference in gene expression between individual cells is used to cluster cells within heterogeneous tissue. In a tissue with 20 different cell types, single cell analysis allows you to cluster based on gene expression, so the rare cell type that expresses a particular gene is no longer obscured by the gene expression of the other 19 cell types.
Improved cell type calling, including separation of subtypes and identification of cell state, is directly related to the efficiency of gene capture. If you can only look at half the genes in a cell, you cannot fully understand that cell’s gene expression, making it potentially difficult to differentiate between another cluster. A new technology for improving gene capture is the GEM-X technology from 10x Genomics. This method can yield a two-fold increase in gene detection, thanks to improved capture of rare transcripts and delicate cell types.
Another alternative is the “one cell per well” methods, like BioSkryb Genomics ResolveDNA or ResolveOME platforms. Although these platforms may offer lower cell throughput, they yield improved RNA (and DNA) capture, allowing for easier cell type identification using fewer cells.
I don’t have the budget for single-cell
Compared to bulk RNA sequencing, single-cell is in another pricing tier. This sticker shock can deter researchers from making the jump. However, there are ways to manage your budget effectively and still get results at single-cell resolution.
The easiest option is to reduce your initial investment. If you’re just testing the waters of single-cell sequencing, it probably doesn’t make sense to bring single-cell methods in-house. Working with a trusted contract research organization or core facility can save you the cost of instrumentation and the training of personnel associated with it.
Many sequencing partners bring multiple technologies in-house, making them a one-stop service provider for researchers. Our lab is also a 10x Genomics Certified Service Provider for single-cell applications, including:
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3’ and 5’ gene expression
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Immune profiling
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ATAC library preparation
Psomagen also offers Parse Biosciences workflows and sample enrichment on the LeviCell EOS. Parse workflows make it possible to generate high throughput single-cell libraries with a lower price-to-entry, as it is an instrument-free process performed with only standard lab equipment. The protocol is inherently multiplexed, allowing researchers to target up to one million cells or analyze up to 96 samples in a single reaction, at a single price point. This may be an option for your project if you are investigating:
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3’ gene expression
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VDJ enrichment
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CRISPR screening
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Targeted gene capture
Other options for multiplexing samples take advantage of BioLegend’s TotalSeq cell hashing antibodies. For use with human or mouse samples, these labeled antibodies can tag cells from different samples with a unique sequence. Samples can then be pooled, and the tag sequenced, to yield not only the cell-specific sequence, but the sample-specific sequence as well. Newer 10x technologies, including HT and GEM-X protocols, allow for the capture of up to as many as 60k cells in a single reaction. Combining this higher throughput with multiplexing can reduce per sample library prep costs by 50-80%.
The complexities of single cell sequencing are wide ranging. It is essential to understand the goals of the experiment, limitations imposed by the sample type, the pros and cons of each technology, and whether it's better to have an experienced partner conduct the experiments or bring the technology in-house . By considering all of these factors, researchers can make a decision to generate the most successful single-cell datasets.
