Single-Cell Experiments: The Power of Pooling and Multiplexing Samples

mulitplex sample prep singlecell


In this post, I will look at two strategies for lowering expenses, increase sample throughput, and reducing bias in single-cell experiments: sample pooling and multiplexing. I'll go over these approaches, their benefits, uses, applications and best practices in single-cell research. Let's start than with the concepts.


Imagine carpooling to save on transportation costs. Similarly, pooling samples involves combining different samples into a single tube and treating them as a complex sample. By doing so, we simplify the experimental process and reduce variability and bias. When samples are processed individually, even minor differences in handling can accumulate and introduce bias. However, by processing multiple samples together, we minimize technical variability, ensuring a more accurate representation of the underlying biology. Important also is the experimental cost reduction.


Multiplexing takes pooling a step further by allowing us to identify the original sample of each individual cell within a complex sample. For that we must label the cells of each individual sample with a unique identifier, what will allow us at the data analysis to distinguish the origin of each individual cell. This extra step is crucial when we need to maintain sample identity while processing multiple samples simultaneously.

Methods for Multiplexing

1. Natural genetic variation: If our samples possess unique natural tags, such as differing genotypes, we can leverage this inherent variation for multiplexing. By genotyping our individual samples previously, we can subsequently identify specific single-nucleotide polymorphisms (SNPs) within our single-cell sequencing data, thereby determining the source of each cell.

2. Antibody-based multiplexing: Using specifically designed antibodies with unique oligonucleotide barcodes, we can label individual samples. These barcodes serve as synthetic DNA or RNA sequences that are amplified during the single-cell experiment. This method allows for effective multiplexing and works well for cell-based experiments, although nuclei-based experiments may encounter limitations. The antibodies used for multiplexing are mainly commercialized by Biolegend.

3. Lipid-based multiplexing: A highly efficient method involves using lipid tags to label cells by directly entering their cellular membranes. Unlike antibody-based methods, lipid-based multiplexing is not reliant on protein expression, resulting in faster staining times and cleaner data. MULTI-seq lipids offered by companies like Sigma are a popular choice for this approach.


- Increased Throughput: Pooling or multiplexing enables us to process a larger number of cells in a single experiment, thus increasing our sample throughput. This is particularly valuable when the available number of cells is limited.

- Sample Representation: By pooling or multiplexing samples, we ensure that the variability present in the individual samples is accurately reflected in our single-cell data. This is especially important when studying biological systems with inherent heterogeneity.

- Technical Bias Reduction: Pooling or multiplexing different samples allows us to process them using the same laboratory equipment, which reduces technical variability.

- Cost-effective: Pooling or multiplexing allows us to analyze a higher number of samples without incurring significant additional costs. This is particularly advantageous when working with large sample sets and limited budgets.


1. Optimization: Each multiplexing method requires optimization to minimize batch effects and maximize data quality. This involves determining optimal antibody concentrations, incubation times, and washing steps to ensure accurate and reliable results.

2. Sample Size: Assess the minimum cell requirement for a specific assay and determine if pooling or multiplexing is necessary. Smaller sample sizes may require multiplexing to achieve the desired cell numbers, while larger samples may benefit from pooling for increased efficiency.

3. Quality Control: Regularly monitor and assess the quality of pooled and multiplexed samples throughout the process. This includes evaluating cell viability, ensuring barcode or tag integrity, and performing appropriate controls to validate the accuracy of demultiplexing algorithms during data analysis.

4. Demultiplexing Analysis: Use robust computational tools or pipelines for demultiplexing and accurately assigning individual cells to their respective samples of origin. This step is critical for downstream analysis and interpretation of the data.

You can find more details about this topic at the Episode 18: Pooling or Multiplexing Samples? from the Single-Cell World Podcast.


Pooling and multiplexing samples for single-cell investigations are good strategies for increasing sample throughput, lowering expenses, and analyzing many samples in a single run. Besides these benefits, there are also and drawbacks. You should carefully plan your experiments and select the strategy that best meets your needs.

Remember to stay curious, optimize your protocols, and use pooling and multiplexing to their greatest capacity in your single-cell research.

Good luck with your single-cell experiments!

Thanks for reading.



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