Emerging Trends in Single-Cell Multiomics
August 29, 2024
Single-cell multiomics research is a rapidly evolving field. Here is a summary of some of the latest trends that are emerging in this field.
Reduced Sequencing Costs
The introduction of newer sequencing technologies, such as the Element AVITI, Ultima UG100, Complete Genomics DNBSeq and Singular Genomics G4 sequencing platforms, has drastically reduced the cost per base of sequencing. This has enabled researchers to generate large volumes of sequencing data at a fraction of the previous cost, making multi- omics studies more accessible and cost- effective.
Learn more about running BD Rhapsody™ Libraries on the Element AVITI.
Advanced Integration Algorithms
Advanced computational methods, including machine learning and network-based approaches, are being used to develop sophisticated integration algorithms that can effectively merge data from different omics layers and extract meaningful biological insight
The output of the BD Rhapsody™ Sequence Analysis Pipeline directly feeds into tools like Seurat which can be used for the seamless storage, analysis, and exploration of diverse multimodal single-cell datasets.
Spatial Multiomics
Spatially resolved omics techniques enable the mapping of molecular profiles within intact tissue sections, preserving the spatial context of cells and biomolecules.
Researchers have developed a novel method called “fragment- sequencing method”, which enables unsupervised, high-throughput and single-cell resolution analysis.1 It takes advantage of single-cell analysis platforms such as the BD Rhapsody™ Single-Cell Analysis System to characterize single-cell transcriptomes within multiple spatially distinct tissue microenvironments.
Multiplexing and Hashing
Multiplexing strategies, such as sample barcoding and indexing, enable the pooling of multiple samples into a single sequencing run, thereby maximizing sequencing throughput and cost efficiency.
Learn more about BD® Flex Single-Cell Multiplexing Kit, which allows you to combine and simultaneously process up to 24 different samples in one single-cell multiomics experiment.
Long-Read Sequencing Technologies
Long-read sequencing technologies, such as Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) sequencing, offer advantages for certain applications, such as de novo genome assembly, structural variant detection, and isoform-level transcriptomics. While long- read sequencing may have higher upfront costs compared to short-read sequencing, improvements in read accuracy and throughput are driving down the overall cost per base, making long-read sequencing increasingly cost-competitive for multi-omics studies.
A method called FLOUR-seq integrates BD Rhapsody™ System with nanopore sequencing to capture the entire spectrum of RNA (encompassing nascent, mature, and degrading RNAs) within cells.2
Open-Source Software and Cloud Computing
The availability of open-source bioinformatics tools and cloud computing platforms has democratized access to computational resources for processing and analyzing multiomics data. Researchers can leverage scalable cloud-based infrastructures, such as Amazon Web Services AWS and Google Cloud Platform GCP , to perform complex bioinformatics analyses without the need for costly on-premises computing infrastructure, reducing the overall cost of multiomics research.
Explore running the BD Rhapsody Sequence Analysis Pipeline on the free cloud-based platform Seven Bridges which requires no previous command line experience.
Want to get started with your single cell multiomics experiment? Contact us.