Since their discovery in the 16th century, living cells have ignited a wave of technological advancements and breakthroughs, ranging from basic to revolutionary.

Despite the fact that nearly all cells in the human body share the same genetic materials, the transcriptome in each cell showcases the exceptional activity of a select few genes. This makes profiling gene expression activity one of the most powerful tools for delving into the distinctive identity, state, function, and responses of cells. In just over a decade, the single-cell RNA sequencing (scRNA-seq) technique has taken the scientific community by storm, becoming increasingly precise and capturing vast amounts of data. Since this technology is relatively new, the reference standards for annotation and the associated analysis tools are still being optimized.

In this blog, we'll delve into the cutting-edge developments and trends in scRNA-seq technology that are propelling it into the forefront of modern research.

Sc RNA Seq Technique and the Data Generated: A Timeline (2009-Present)

In 2009, a groundbreaking report detailing the characterization of cells during early developmental stages using a next-generation sequencing platform sparked a surge of interest in obtaining high-resolution views of single-cell heterogeneity on a global scale. Previous experiments offered vital insights into cellular processes but were limited by the low sensitivity of microarray technology. The concept and technology brought out by the 2009 Nature study opened a new avenue to scale up the number of cells and make compatible high‐throughput RNA sequencing possible for the first time. Since then, numerous modified and improved single-cell RNAseq technologies have been developed, revolutionizing sample collection, single-cell capture, barcoded reverse transcription, cDNA amplification, library preparation, sequencing, and streamlined bioinformatics analysis. While these steps have resulted in more mature scRNA-seq methods, the fundamental concept of the technique remains the same.

Recent Developments in Single-cell RNA Sequencing

In recent years, scRNA-seq has seen many advancements, and new techniques have emerged, pushing the boundaries of what is possible with this technology. The major developments in the past decade are captured in this image sourced from a 2022 article.

Let’s look at some of the most exciting developments in scRNA-seq in the past few years.

1. Increased Throughput in Terms of the Number of Cells Generated per Experiment

With an increasing number of cells, sample matrices become well-conditioned, and novel data analyses become possible. The advent of new techniques like drop-seq, seq-well, DNBelabC4, etc., has increased the throughput of scRNA-seq experiments and has made it possible to extract and process the data from a large number of cells (in millions).

2. Improved Data Analysis Tools

Advancements in single-cell RNA seq technology have generated vast amounts of data. Researchers have developed new tools and techniques to extract meaningful insights from complex and heterogeneous cell populations. Tools such as Scanpy, Cell Ranger, and Monocle have been developed to perform quality control, clustering, and gene expression analysis. The Seurat package, which is widely used for the analysis of scRNA-seq data, has recently been updated to include improved algorithms for dimensionality reduction, clustering, and gene set enrichment analysis. Additionally, advances in machine learning have enabled the development of new tools, such as UMAP and PHATE, that can visualize single-cell data more intuitively.

3. Automation of Data Processing Pipelines

Traditional cell-type identification methods for scRNA-seq data analysis are time-consuming and knowledge-dependent for manual annotation. In contrast, automatic cell-type identification methods may have the advantages of being faster and more user-friendly. R packages like ILoReg have been developed to improve the identification of subpopulations of cells through an additional probabilistic feature extraction step applied before clustering and visualization.

4. Reduced Cost with the Advancement of Technology

Traditional one-cell-one-sample approaches were constrained fundamentally in scale by costs, time, and labor. However, massively-parallel methods developed over the past decade enable ensemble processing while retaining single-cell resolution. Such technological advancements have led to a significant reduction in the cost of single-cell RNA seq. However, it is important to note that the cost can vary depending on factors such as the sequencing depth, the number of cells sequenced, and the quality of the data generated and that though the cost per cell has been reduced significantly, the cost per sample (including the library preparation and sequencing) is still substantially high.

5. Increased Availability of Single-Cell Sequencing Data

A bibliometric analysis (2021) on global research trends on single-cell sequencing technology reported that with the advancement of high-throughput sequencing and new computer algorithms, the development trend of publications changed from 2010 to 2019, showing a clear upward trend since 2014. Collaboration networks were formed between various institutes from the United States, China, the United Kingdom, and Germany, with Harvard University, Stanford University, Karolinska Institutes, Peking University, and the University of Washington forming the biggest nodes in every cluster of the collaboration network. Multiple single-cell repositories like Single cell portal, Human cell Atlas, etc. have been set up and are expanding each day, providing researchers access to the data from millions of cells.

6. Integration with Other Data Types

Combining scRNA‐seq with other large-scale genetic screening tools is further expanding the applications of the technology. One such combinational technology is combining scRNA‐seq and CRISPR‐based genome‐scale genetic screening, such as Perturb‐seq, to enable the assessment of transcriptional effects of knocking out several genes with CRISPR and LinTIMaT that integrates single‐cell transcriptome data and mutation data for lineage tracing. Such combinational applications allow us to investigate the genetic effect on the cellular transcriptome and functions on a large scale. A major requirement here is the availability of curated data that is consistently processed. The rise of the use of single-cell RNA seq data has promoted the growth of platforms such as Polly, which provides highly curated ML-ready data that is ready for consumption.

Single-cell RNA seq has seen tremendous growth and advancement in recent years. The improved sample preparation methods, increased data resolution, and new data analysis tools have enabled researchers to gain a deeper understanding of cellular processes and gene regulation. As scRNA-seq continues to evolve, it will play an increasingly important role in improving our understanding of biological systems and disease.  

We, at Elucidata have the world’s largest collection of highly curated ML-ready single-cell and bulk RNA seq data. In our data warehouse (aka OmixAtlas), the metadata is harmonized, data is standardized and normalized through consistent pipelines, cell types are accurately expert-annotated, and standard ontologies are followed to ensure reliable results and empower scientists in achieving their research goals.

‍Reach out to us to learn more!