Genome-level sequencing and analysis has exploded in popularity since the completion of the Human Genome Project in 2004. Growth in this field has been aided by a bevy of technological improvements facilitating the development of “next-generation sequencing (NGS)” techniques, which has made sequencing faster, easier, more sensitive, and more accessible. This progress has allowed scientists to shift away from population- and individual-level gene profiling to move towards identifying DNA and RNA variations at the cellular level—spurring the advent of single-cell sequencing (SCS).
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Single-cell sequencing allows scientists to characterize cellular heterogeneity. Since SCS generates individual profiles of each cell, the technique can identify differences in cellular sub-populations that may be pivotal in homeostasis and pathogenesis. Furthermore, SCS can characterize how individual cells respond to the same stimuli, enhancing our understanding of the role individual cell phenotypes effect organismal function. In 2017, the Human Cell Atlas Project was assembled by a global community of leading scientists with the aim of using SCS to build a comprehensive map of the human body, including unique ID profiles for each cell type, 3-D information on how cell types work together to form tissues, and insights into how all body systems are connected and how changes in these relationships underlie health and disease.
Any scientist seeking to use SCS in their research needs to consider several experimental parameters.
Scope: Am I looking at the genome, epigenome, or transcriptome? Can I study multiple "-omes"?
Currently, the most established SCS approach is to profile the whole transcriptome. In doing this, scientists can link cell states and fates to phenotypes and roles. A number of different protocols for this process have been established. Some use oligo dT primers to specifically target polyadenylated (polyA+) nucleic acids. While this approach captures mRNAs, it excludes non-polyadenylated (polyA-) nucleic acids such as non-coding RNAs and circular RNAs, which can be captured using random hexamer primers. Specifically engineered primers incorporating random hexamers and oligo dTs have proven effective in capturing both polyA+ and – RNAs. Different techniques have also been developed to profile other “-omes”, including the genome, epigenome, and proteome. Sequencing an entire “-ome” remains a labor- and resource-intensive prospect. Despite these challenges, methods currently exist that enable the profiling of multiple “-omes” in a single experiment.
Source Material: How can I obtain single cells without contaminating my samples?
Obtaining enough raw genetic material in order to generate a comprehensive and representative amplified sample is critical for any sequencing experiment, but even more so for SCS experiments, given the smaller sample amounts and increased impact of any potential contamination. Single-cell extraction techniques can range from basic (mouth pipetting and serial dilution) to highly advanced (microfluidic platforms which use channels to isolate single cells).
Data Precision and Depth: How accurate is my amplification? Am I only looking at specific genes or am I trying to construct a full genomic profile?
Despite technological advancements, sequencing an entire genome, epigenome, or transcriptome is still a labor and resource intensive prospect. Issues such as artifact generation and amplification bias are more prominent when sequencing is not targeted towards specific regions or genes. The various amplification techniques researchers have developed thus far have their own respective strengths and weaknesses, and scientists need to pay careful attention to factors such as copy number fidelity, coverage uniformity, read length/depth, and the potential introduction of any amplification artifacts such as indels, SNVs, or inversions. Targeted gene sequencing can provide deeper read depths for specific genes known to be associated with a given disease. However, this is not as useful for exploratory basic science experiments as it is for diagnostic or translational applications.
Throughput: How can I generate sufficiently large datasets in reasonable amounts of time?
The biggest logistical obstacle facing SCS has always been the issue of throughput. Obtaining cell specific data is highly informative, but it is more time consuming than bulk-based assays. Microfluidic platforms can now be used to separate multi-cellular samples into thousands of individual cells, each contained within its own droplet or microwell. The cells are then processed, and their nucleic acids are tagged with unique molecular identifiers (UMIs)—“barcodes” allowing researchers to trace a particular sequence back to its origin cell. The UMI-tagged nucleic acids are then pooled, amplified, and sequenced, allowing for thousands of cells to be studied in parallel.
SCS is a powerful tool in the scientist’s arsenal not only for uncovering the genetic properties of individual cells, but also for linking genetic variation to disease and pathogenesis. Designing the right SCS protocol for a specific research need will take careful consideration of a number of variables, both experimental and technical. Finding the right instrument is pivotal for obtaining the data you want, for the answers you seek.
From: Mission Bio
Tapestri: The Precision Genomics PlatformTM is a high-throughput single-cell DNA analysis solution designed to help researchers and clinicians unlock-single-cell biology for the discovery, development, and delivery of precision medicine. Utilizing a two-step process incorporating microfluidics, the Tapestri maximizes DNA capture efficiency and minimizes allele dropout, making it capable of identifying co-occurring mutations, zygosity, and rare cell populations.
RNA and protein expression levels do not always correlate perfectly. Milo is a single-cell Western blotting platform that can measure protein expression in thousands of individual cells in a single run. The platform extracts, separates, and immobilizes proteins in 5 minutes, giving you something that can be probed with standard off-the-shelf fluorescent antibodies and analyzed using ProteinSimple's Scout software. Since the total process takes 4-6 hours with no overnight steps, you'll be able to bridge the gap between single-cell translation and transcription in no time!
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