Single-Cell Proteogenomics Completes the Puzzle

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December 16, 2021

Cells are loaded bags of DNA, RNA, and proteins. Scientists often study each in isolation, but in the last few years, single-cell proteogenomics has enabled a more holistic look at individual cells. A slew of technologies can now integrate transcriptome and proteomic data to further our understanding of how cellular networks shift and drive diseases.

Method explosion

Single-cell proteogenomics is not a single method—it is a massive and quickly growing ecosystem of tools and technologies to interrogate cells. There are methods available to measure epigenetic features, DNA sequences, gene expression levels, and cell-surface proteins, all in a single experiment.¹ Methods like DR-seq and G&T-seq can simultaneously sequence the genome and profile the transcriptome of individual cells, for instance, while scNOMeRe-seq can measure the occupancy of nucleosomes, in individual cells, while profiling the methylome and quantifying RNA expression levels.

Why does this repertoire of methods, with their tongue-tying acronyms, matter? One reason is because all cells in the human body share roughly the same genome with some cell-to-cell genetic variation, but give rise to completely different cell types. And each of those cell types and genetic variants might play an important role in determining why some tumors grow resistant to a therapeutic, for example, or why certain people have a weaker immune response to COVID-19, according to Aaron Llanso, Senior Director for Clinical Applications at Mission Bio.²

To understand this cellular heterogeneity, researchers need to look at the whole puzzle, not just individual pieces.

At a high level, “single-cell biology, and being able to leverage the same cell to produce protein maps as well as genetic maps, are incredibly important for finally being able to connect one to the other,” says Sean Mackay, CEO and Co-founder of IsoPlexis. Single-cell proteogenomics methods can reveal “direct pathways that would be missed or averaged if you were looking across the bulk.”

Many of the techniques built around single-cell proteogenomics, which was Nature Methods’ Method of the Year in 2019, have been developed by individual, academic labs. That means there’s an entry barrier for other groups to use the techniques in a replicable manner. And measuring so many molecules at once, in individual cells, is inherently complex and thus hard to standardize. To take the technology into the clinic, and lower the entry barrier, multiple companies have developed automated solutions for some of the more widely used proteogenomics methods.

Commercial drive

Consider a tumor. It might be made up of thousands of cells, each of which is slightly different. Some therapeutics might shrink the tumor and kill off, say, 90% of the cancer cells. But the 10% that remain could be resistant. The tumor could regrow. Single-cell proteogenomics offers an ideal way to study tumor cells, one by one, to understand why some become resistant.

To study cancer-specific cell-surface proteins that could be targeted by therapeutics—such as the protein Ak2, which is associated with metastasis to the liver—and genomic variations simultaneously, next-generation sequencing (NGS) techniques and flow cytometry just won’t do.

“Characterizing cellular heterogeneity is important for understanding disease state, progress, and treatment response,” says Dipesh Risal, Senior Market Manager for Proteogenomics at BioLegend. “Single-cell RNA-seq has been incredibly successful at characterizing heterogenous cell populations at a transcriptomic level,” he says, “but the phenotyping of cell-surface proteins has historically been limited to only a handful of targets due to the limited number of instrument channels, fluorophores, or metals available in methods like flow cytometry and mass cytometry.”

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Mission Bio offers a platform, called Tapestri, that “yields single-cell resolution proteogenomic data across thousands of cells from a single sample with an integrated workflow,” says Llanso. It removes the pitfalls baked into flow cytometry, he adds. Hundreds of cell-surface epitopes, along with thousands of mutations, can be characterized in a single experiment using this technology.

Another method, called CITE-seq, was developed in 2017 by a team at the New York Genome Center. It can measure hundreds of cell-surface epitopes too by using oligonucleotide-labeled antibodies to simultaneously measure cellular proteins and the transcriptome in individual cells, according to Risal.³

In a CITE-seq experiment, for instance, the oligonucleotide attached to each antibody has “a barcode unique to that particular antibody, as well as a capture sequence complementary to capture sequences utilized in single-cell encapsulation platforms,” says Risal. The oligonucleotides also have a PCR handle that can be used with next-generation sequencing.

Staining samples with a mixture of oligonucleotide-antibody molecules, which BioLegend sells by the name of TotalSeq, “at the beginning of a single-cell experiment means that you not only get a transcriptomic profile of the samples at single-cell resolution, but also information about cell-surface protein expression,” says Risal.

Multiple samples, such as from different cancer patients, can also be pooled together and run in a single experiment, according to Risal, by using a pool of antibodies with unique barcode sequences. BioLegend calls these “hashtag antibodies,” and says they help “save consumable costs and reduce batch effects between experiments.”

Mission Bio has collaborated with BioLegend using these antibodies in their Tapestri platform to profile cell-surface proteins and DNA variations simultaneously. The oligonucleotide-tagged antibodies enable each “protein tag to be associated with an individual cell. NGS libraries are prepared for both DNA and protein analytes, which can then be pooled, sequenced, and bioinformatically deconvolved into single-cell profiles,” explains Llanso.

Research breakthroughs

Breast cancers are heterogeneous; different types of cells respond to therapeutics in different ways. Methods like CITE-seq, which is used for surface protein and transcriptome profiling, can help develop personalized therapies.

For one study, published in September, authors used CITE-seq on cells found in 26 different tumors, creating an in-depth atlas of cell-surface proteins and genetic variants that underpin 29 to 49 unique cellular states, each of which can respond in different ways to a tumor’s microenvironment.⁴

In acute myeloid leukemia, two types of cells behave completely differently, but cannot be reliably identified from one another with DNA sequencing alone. Those two cell types are called nonmalignant clonal hematopoiesis, or CHIP, clones and acute myeloid leukemia, or AML, clones. “The DNA mutations that most commonly define CHIP clones are also frequently observed within AML clones,” says Llanso. For a recent study, researchers profiled both cell-surface proteins and the genotype in these cancer cells to clearly distinguish the two, to a degree that was previously impossible with NGS and flow cytometry alone.⁵ Their data could be used to predict which patients in remission have a high risk of relapsing, says Llanso, and to potentially develop personalized therapies for each person’s unique tumor.

“I believe that single-cell proteogenomics will allow us to better understand the biology underlying these cases in order to dramatically improve clinical decision making in this setting,” he says. “Single-cell proteogenomics puts a face on the genotype, giving us a much clearer picture of the enemies we must go after.”

IsoPlexis also recently unveiled Duomic, a proteomics platform that can “simultaneously measure the expression levels of functional proteins and genes in the same cell,” according to a press release issued in September. Duomic builds upon the company’s proteomically driven single-cell analysis, which has been used in a number of clinical research studies where the readout was predictive of patient response attributes, especially in cell therapy and immune-oncology. In three recent research articles—two in Nature Medicine and another in the Journal of Clinical Oncology—Duomic was used to identify which highly functional cells predict the potency of CAR-T therapies, how metastatic lung cancers respond to tumor-infiltrating lymphocyte treatments, and also find unique blood-based biomarkers correlating with patient response and progression-free survival, says Mackay.^6,7,8

Measuring “functional proteins” (regulated by phosphorylation, for example) and genomic variants together is key to understanding “the onset of resistance to small molecule therapeutics,” says Mackay. “For the first time, we’re able to connect those phosphoprotein analytes from those potentially highly resistant single cells to the genetic drivers,” he says. In the future, researchers could adjust those genes synthetically, or find other pathways that could be manipulated, to halt a tumor’s resistance.

“Complex somatic mosaicism is present in virtually all of our tissues, and the next challenge is understanding how to map these genetic variants into dynamic and competitive clonal ecosystems,” explains Llanso. “By adding proteins to the equation, we can now attach phenotypic profiles to genetic profiles across the clonal architecture in a sample.”

And sometimes, research breakthroughs are made even when researchers aren’t looking for them, simply because single-cell proteogenomics offers more data, at a higher resolution, than individual methods.

In a study from February, researchers integrated proteomics and transcriptomics for cells at various stages of the cell cycle. They found that several of these proteins have oncogenic functions, a finding that wouldn’t have materialized if they only looked at how the proteome changed over time.⁹

Single-cell proteogenomics is ushering in a data explosion and putting all the pieces of the cellular puzzle together, finally, to reveal its beautiful whole.