Introduction
Single-cell RNA sequencing technology (scRNA-seq) is a powerful tool for studying gene expression within individual cells. scRNA-seq analyzes each cell separately, allowing researchers to characterize complex processes, identify rare cell types, investigate gene regulation, and trace cell lineage development, providing valuable insights into cellular diversity and function.
The scRNA-seq process consists of several key steps. First, individual cells are isolated, followed by cell lysis to release their RNA. This RNA is then reverse-transcribed into complementary DNA (cDNA), which is subsequently amplified through polymerase chain reaction (PCR) to create a sufficient material for high throughput sequencing . The cDNA library is then prepared, sequenced and analyzed using computational methods.
Recent technological advancements have led to the development of many commercial scRNA sequencing platforms, each employing different methods for single-cell transcriptome profiling. These variations result in capacity, sensitivity, and reproducibility differences across platforms.
In this blog, we will compare four available commercial platforms for scRNA-seq—10x Genomics Chromium, Fluidigm C1, ddSEQ, and Wafergen ICELL8—examining their capabilities, sensitivity, and reproducibility to guide you in choosing the best option for your research.
10x Genomics Chromium: Efficient Large-Scale scRNA-Seq
The 10x Genomics Chromium system uses droplet microfluidics to partition thousands of single cells into individual droplets, with each droplet containing reagents for reverse transcription and barcoding. This enables the generation of single-cell libraries, which are then sequenced using Illumina platforms. The library construction process is highly efficient, with a 55-65% capturing efficiency, utilizing barcoded beads within droplets to perform cDNA synthesis and preparation in the same droplets, followed by sequencing. The 10x Genomics system is known for its high throughput, capturing tens of thousands of cells in a single run with a high rate of successful single-cell partitioning. The 10x platform has a much lower bias for high-GC content genes compared to other technologies, making it more similar to bulk RNA-seq data. Some doublets can occur but are generally minimized through optimized protocols.
The cost per cell is economical due to high throughput sequencing, ranging from a few cents to a few dollars per cell depending on the project scale and specific reagents. This makes it ideal for high-throughput single-cell RNA-seq, cell atlas projects, immune profiling, tumor heterogeneity studies, and developmental biology research.
Fluidigm C1: High Read Depth per Cell with Lower Cell Capture
The Fluidigm C1 Single-Cell Auto Prep System isolates single cells into individual nanochannels using integrated fluidic circuits. Following isolation, the system performs cell lysis, cDNA conversion, pre-amplification, and retrieval for library construction and sequencing. The automated library construction process provides high-quality, consistent results with minimal manual intervention. This platform simplifies single-cell isolation, enables comprehensive transcriptome analysis, and outperforms other technologies at the recommended sequencing depth.
While Fluidigm C1 has more reads per cell, it captures fewer cells overall. The system typically processes dozens to a few hundred cells per run, making it suitable for detailed but smaller-scale studies. Moreover, capturing efficiency can be limited by cell size and distribution, with some chambers remaining empty or containing multiple cells. This results in a higher cost per cell compared to droplet-based systems, as proprietary IFCs and reagents are required.
Fluidigm C1 can be a good option for validating results from larger-scale single-cell RNA-seq studies by performing deep sequencing on a subset of cells. It is also useful for characterizing subtle cell state changes, such as differentiation processes or treatment responses.
ddSEQ : Efficient and Accessible Single-Cell Analysis
The Bio-Rad ddSEQ Single-Cell Isolator utilizes droplet microfluidics to partition single cells into individual droplets, where reverse transcription and barcoding occur, similar to the 10x Genomics Chromium system. The ddSEQ system is designed to provide a straightforward and efficient way to capture and process single cells, making it accessible for various laboratory settings. One of its key strengths is its ease of use and integration into existing workflows, offering a user-friendly interface that simplifies the process of single-cell RNA sequencing.
Bio-Rad ddSEQ was demonstrated to have the highest overlap in detecting highly variable genes with 10X Genomics, compared to Fluidigm C1 and ICELL8. Moreover, ddSEQ has been shown to outperform competitors detecting micro RNAs. However, the Bio-Rad ddSEQ system does have its limitations. While it is cost-effective for a moderate number of cells, it captures fewer cells per run compared to high-capacity systems like 10x Genomics Chromium. This may limit its use in large-scale studies where processing tens of thousands of cells is necessary. The ddSEQ platform showed reduced capture efficiency for both high-GC and low-GC content genes.
While generally reliable, the capturing efficiency in ddSEQ can vary depending on the sample type and preparation, thereby potentially impacting the consistency of single-cell partitioning.
Overall, ddSEQ can be a good fit for studies where moderate cell numbers and read depth are required, such as differential expression analysis in moderately heterogeneous tissues.
ICELL8- High-Precision Single-Cell RNA Sequencing
The Wafergen ICELL8 Single-Cell System (ICELL8) employs a nanowell-based approach to capture individual cells. It involves dispensing cells into nano wells, imaging to identify wells containing single cells, and then proceeding with cell lysis, cDNA synthesis, and library preparation. ICELL8 supports medium-throughput single-cell RNA sequencing, making it suitable for large-scale studies. Its key strength lies in its high precision for single-cell capture, facilitated by imaging and selecting wells, ensuring high accuracy and reducing doublets.
This system has a capturing efficiency of 24-35% and is highly flexible, accommodating various cell types and sizes, which can be an advantage over the more standardized droplet-based approach of 10x Genomics. ICELL8 displayed higher efficiency in detecting long non-coding RNAs (lincRNA). However, it showed lower capture efficiency for high-GC content genes and higher capture efficiency for low-GC genes compared to other technologies. Moreover, ICELL8 demonstrated the lowest correlation with bulk sequencing compared to the rest.
ICELL8 can be a good fit for experiments requiring precise control over which cells are sequenced, such as selecting specific cell types or conditions. It is also suitable for studies with limited cell numbers, such as rare cell populations.
Summary
The choice of single-cell RNA sequencing platforms should be based on the specific needs of your biological research question. A high-throughput, cost-effective system like the 10-x Genomics Chromium is ideal for large-scale studies. If your research focuses on detailed transcriptome analysis with high read depth per cell, the Fluidigm C1 is a better fit despite its lower cell capture efficiency. Bio-Rad ddSEQ system is easy to use, integrates nicely into existing workflows, and is suitable for labs with medium cell processing needs. The Wafergen ICELL8 single-Cell System is a valuable choice for studies requiring precise Single-cell capture across various cell types. The choice of the appropriate technology will ensure the best results for your specific research objectives since each platform has its strengths and limitations.
Table 1. Comparison of the single cell RNA sequencing platforms.
| Platform | Strategy | Throughput | Cost |
| 10X Genomics | Microdrop | 1,000-80,000 cells | $$ |
| ddSEQ | Microdrop | 1000-10000 cells | $$$ |
| Fluidigm C1 | Microfluidic | 100-800 cells | $$$$$ |
| Wafergen ICELL8 | Microwell | 500 -1,800 cells | $$$$ |
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Shahrzad Ghazisaeidi, PhD, Data Scientist, Bridge Informatics
Shahrzad specializes in high-throughput sequencing, data pre-processing, and downstream analysis of transcriptomic and epigenetic landscapes. She is particularly passionate about developing innovative tools for drug repurposing.
Prior to joining Bridge Informatics, Shahrzad served as a Postdoctoral Associate at the Hospital for Sick Children in Toronto, Canada where she researched the epigenetics of peripheral nerve injury models.
Shahrzad earned her Ph.D. in Physiology and Neuroscience from the University of Toronto. Her studies focused on the sex-dependent and independent gene regulation of peripheral nerve injury. Currently based in Toronto, Shahrzad balances her professional pursuits with personal interests by making time for yoga, martial arts, and poetry.
