Doing more with less

How single-cell hashing can reduce experimental cost

21 Feb 2022

In a joint effort between VIB Tech Watch, the VIB labs of Yvan Saeys (VIB-UGhent) and Stein Aerts (VIB-KU Leuven), and the R&D team of Janssen Pharmaceutica NV, researchers compared the cost-effectiveness of different multiplexing strategies for single-cell experiments. By determining the optimal method for different types of tissue, the results provide a big step forward for anyone working in the single-cell field keen to reduce experimental cost and sample variation.  

The recent advent of single-cell technology has led to a real revolution in biological research. Current methods make it possible to routinely assay many thousands or even millions of cells at once, providing unprecedented insights into heterogeneous cell populations. 

In standard single-cell workflows, individual samples need to be processed in parallel, which limits the throughput, increases reagent costs, and could introduce batch effects. To address this issue, the research field is looking into reliable approaches to pool samples. 

“Multiplexing samples by labeling cells with barcodes before pooling and single-cell compartmentalization, a technique called ‘hashing’, allows for accurate detection of cells originating from different samples but captured and processed as a single sample,” explains Viacheslav Mylka, Sequencing Technology Specialist at VIB. 

Many ways for hashing cells 

Several approaches exist to introduce sample-specific barcodes, including oligo-labeled antibodies, oligo-labeled lipid anchors, chemical labeling with oligos, or genetic cell labeling. ​ 

Mylka: “The success of using antibodies for hashing depends on the ubiquitous expression of the corresponding target antigens, which can be problematic for some samples or species. An elegant way to overcome this limitation can be the use of lipid anchors that are antigen-independent and in theory bind universally to the cell or nucleus membrane.” 

“Both antibody-based and lipid-based methods are simple, straightforward, and generally applicable to a wide range of single-cell applications and platforms,” explains Irina Matetovici, Mylka’s colleague at VIB. “That’s why we compared antibody-based and lipid-based sample barcoding methods by multiplexing four distinct human cancer cell lines.” ​ 

Cells and nuclei 

“Single-cell suspensions are most commonly prepared from fresh tissues,” explains Yvan Saeys, group leader at the VIB-UGhent Center for inflammation Research. “This is a major roadblock for clinical samples, archived materials, and tissues such as the brain.” To overcome these limitations, researchers extract and analyze single-nuclei instead, using similar workflows. ​ ​ 

Saeys: “That is why we also included nuclei samples in our comparison of hashing methods. We also compared methods for different mouse tissues, as well as peripheral blood mononuclear cells from healthy individuals and SARS-CoV-2 patients.” 




Different hashing techniques for different samples 

According to the teams’ results, the best method depends on the type of tissue at hand, says Stein Aerts, group leader at the VIB-KU Leuven Center for Brain & Disease Research: “We found that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brains. However, antibodies demonstrate better results on tissues like spleen or lung.” 

Silvie Van den Hoecke (VIB Tech Watch): “This is a milestone in the VIB Janssen Single Cell Tech collaboration, which has been running for the past couple of years. The focus on technology in this collaboration enables us to join forces between teams with complementary expertise, making this program unique.” 

Aerts: “Multiple research lines within our institute but also the single-cell field, in general, will now benefit from the results of this hashing benchmark study.” 

“I am personally very excited about the publication of the first wet-lab paper of our (computational) lab. It will positively impact all downstream single-cell analyses, drastically reducing the effect of batch and other technical artifacts,” adds Saeys. 

Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNAseq ​
Mylka et al. Genome Biology 2022 


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