To explore some biological processes, scientists analyze single cells. That analysis could include examining a cell’s genes, proteins, metabolites, and more. Even in the recent past, scientists could not analyze single cells in enough detail. To really get something out of this technology, scientists needed sensitive methods that could be scaled up, and various advances make that possible.
The majority of today’s sequencing studies use bulk cell analysis. Although useful and relatively easy to perform, bulk studies can miss crucial information. For example, U.K.-based scientists wrote that traditional studies of hematopoiesis “involve purification of cell populations based on a small number of surface markers” and “such population-based analysis obscures underlying heterogeneity contained within any phenotypically defined cell population.” To better understand disease-related changes in hematopoiesis, these scientists applied single-cell methods.
As shown in the following examples, scientists can often learn more from less—single cells instead of a collection of them—but studies can still require the analysis of a large number of single cells.
Documenting DNA
Many studies of DNA use samples generated from a collection of cells. To connect changes in DNA to disease, though, some researchers explore molecular differences—such as structural variants (SVs)—in single cells. “SVs are a major class of genetic variation connected with the development of human diseases,” says Jan Korbel, co-director of the molecular medicine partnership unit at the European Molecular Biology Laboratory. “Particularly, SVs emerge somatically during the clonal evolution of cancer, where they comprise the primary form of somatic driver mutations in many cancers.” In addition, he notes that SVs “form in rapid bursts resulting in vast genetic heterogeneity, contribute to tumorigenesis and metastasis, and can affect therapy responses.”
Traditional forms of sequencing, which analyze collections of cells, miss most SVs. Korbel and his colleagues solved that problem with an approach called single-cell tri-channel processing (scTRIP). This computational method combines signals from three distinct channels of information in the genomic code of an individual cell: 1) read depth, which is the number of reads falling into a certain genomic interval; 2) strand orientation, which reveals the DNA strand that a read comes from; and 3) haplotype phase, which shows whether the strands come from the maternal or paternal copy of the DNA. Plus, this method can analyze the DNA in single cells with 200-kilobase resolution.
Image: This artistic representation of the scTRIP technology—developed by Jan Korbel and his colleagues at EMBL—shows the three distinct channels of information in the genomic code of an individual cell that this method analyses. Image courtesy of Tobias Wüstenfeld/EMBL.
The scTRIP technology creates useful new opportunities. “scTRIP provides, for the first time, the ability to measure the full spectrum of SVs in hundreds of single cells,” Korbel explains. “In contrast to prior single-cell methods, scTRIP is not limited to detecting SVs causing copy-number alterations, but also readily detects copy-number balanced SVs, such as inversions and translocations.”
The advances from this method could also change the way that clinicians assess cancer. “In contrast to prior single-cell sequencing methods, scTRIP can be used to systematically identify complex DNA rearrangement mechanisms, such as chromothripsis and breakage-fusion-bridge cycles in individual cells,” Korbel explains. “In the future this may, for example, enable diagnosis of cancer and tumor classification.” This method could also be used to study the very origin of mutational mechanisms in cancer.
Plus, scTRIP offers other benefits. In comparison to previous methods of sequencing single cells, scTRIP can sequence 50-fold—or even more—cells, and it’s 50-fold less expensive.
Processing proteins
In addition to DNA, scientists study proteins, because they are the result of gene expression. Some new tools make it easier to do that in single cells. As an example, Miguel Tam, senior manager of product realization and marketing at BioLegend, says, “We offer a comprehensive suite of oligo-conjugated antibodies, named TotalSeq, designed to integrate into existing single-cell sequencing protocols.”
BioLegend offers this product in various forms for different applications, depending on the platform being used. “The main application is single-cell proteomics, but done hundreds of times more efficiently than what has been traditionally used to study proteins in single cells,” Tam says. “Using next-generation sequencing as a readout for the protein-detection signal, we can now multiplex hundreds of antibodies and generate incredibly valuable datasets.”
The company also offers a pre-optimized panel to conveniently analyze the most common immune cell types, including T and B cells, natural killers cells, and monocytes. “The panel is offered as lyophilized, single-use vials and eliminates the need for researchers to titrate and optimize the signal for each individual antibody contained in the cocktail,” Tam points out. “It also provides a much more attractive cost for a novel reagent, which will help researchers move their projects forward faster.”
Moving to multi-omics
In some situations, scientists might want to look at more than one kind of molecule from the same cell, and some platforms make that possible. As an example, CEO Charlie Silver describes Mission Bio as “the only single-cell multi-omics company to sequence both genotype and phenotype.”
The company’s Tapestri Platform can analyze the DNA and protein in the same cell. This method arises from hardware, reagents, and software that work with a next-generation sequencer from Illumina. In addition, this method works on a range of samples, such as liquids, like bone marrow or peripheral blood, and tissue slices, which can be fresh or cryopreserved.
Currently, the Tapestri Platform focuses on oncology. Scientists can apply this platform to biomarker discovery, clinical development, and even product quality control. “In the cell-therapy space,” Silver explains, “scientists can use this technology to characterize a product, such as how many viral vectors are integrated and the protein content on the surface.” These features can determine the activity of a product.
This technology can also be applied to archived materials. “Lots of our customers are running large studies of samples—tens of thousands of samples—in which the diagnosis is matched with the outcome.” Such studies can be used to see how the DNA and protein information in the samples might have been used to change the diagnosis—and hopefully improved the outcome.
With some diseases, such as cancer, scientists seek the source. Cancer starts in one cell as a mutation that can spread through mitosis and metastasis. To stop or limit—maybe even prevent—cancer and other diseases, scientists need to dig in and see just what goes on in single cells. Now, that’s possible in faster and more sensitive ways.