Immurai headshotsLooking deeper into single-cell genomics, scientists find new approaches to improving health. Those expanding avenues in medicine range from drug development to regenerative medicine. Turning new information from single-cell genomics into applications depends on a collection of new technologies, some of which are explored here.

Hendrik Knoetgen, PhD.—head of genomic medicine and therapeutics, at Roche Pharma Research & Early Development in Switzerland—says, “We do see a great potential in the field of single-cell genomics—an exciting and rapidly evolving technology that will potentially help us to advance therapeutics with a higher precision and speed.”

Given the broad and fast pace of this field, Knoetgen doesn’t point out just one key advance. Instead he says, “Recent technological advances include spatial omics, multi-omics, cellular indexing of transcriptomes and epitopes by sequencing, and new sequencing protocols that cover the full-length transcripts, not only the 3’ ends.

Knoetgen further says, “We think that data management is crucial and requires highly standardized and robust software workflows, and Roche experts are involved to advance the field, engaging in a continuous exchange with academic and other industrial groups through shared tools and analysis workflows.” He adds, “As an example, the Roche co-developed BESCA toolkit was recently made publicly available.”

Large scale, low cost

When feasible, scientists like a platform that provides a range of benefits. As an example, Jens Durruthy, PhD.—associate director, product management – Single Cell at 10x Genomics in the San Francisco Bay area—says, “In July 2021, we launched Chromium X, our next-generation single-cell analysis platform.” He adds, “The platform—together with our new high-throughput assays—enables cost-effective, large-scale, single-cell experiments.” This platform can be used in drug and CRISPR screens, large-scale translational studies, epigenetic and immune profiling, cell mapping, antibody discovery, and biomarker identification. “Researchers also have the flexibility to cost-effectively expand from standard or low-throughput experiments to much higher throughput projects, even million-cell experiments,” Durruthy explains.

Creating such capabilities in single-cell genomics depends on design for purpose. Scientists at 10x Genomics spent more than two years designing and building the Chromium X. Key goals in that development included making it easy to use and cost-effective. According to Durruthy, Chromium X “makes high-throughput single-cell experiments routine for everyone,” and it “can deliver routine million-cell experiments for a cost as low as two cents per cell.”

The ability to run such high-scale experiments allows other benefits. As one example, Durruthy mentions “hypothesis-free, unbiased, explorative studies that can elevate our current understanding of biology and human health.”

As an example of using the Chromium X in healthcare, Durruthy mentions the application of single-cell CRISPR screens for drug development. With this type of screening, he says, scientists can collect “more information-rich data on gene function and disease pathways, characterize hundreds of promising targets in parallel with higher content readouts, improve target selection by efficiently characterizing regulators of disease progression and resistance at single-cell resolution, and help understand the mechanism of action to prioritize targets with the most favorable safety profiles and phenotypic effects.”

With Immunai’s technology, a variety of multi-omic single-cell data and metadata can be analyzed through unsupervised algorithms and then reviewed by experts to produce the final results.

Exploring the immune system

At New York-based Immunai, scientists are mapping the immune system with multi-omic resolution. As Danny Wells, PhD.—senior vice president of strategic research and scientific cofounder—explains, “For each single cell that we profile, we measure multiple layers of biology that span the central dogma from the top level of proteins—the actors in the cell—to the transcriptome—the RNAs—and down to even the DNA level, where we’re trying to understand the architecture of DNA and its structure that is so associated with gene regulation.”

Effectively exploring the immune system depends on such a wide approach. “Much of the information in the immune system is captured at different levels,” Wells says. “There are some proteins in multiple different forms that are not encoded really anywhere in the genome or the transcriptome of the cell.” Instead, those proteins arise from post-transcriptional modifications.

Tracking all of those changes creates a technological challenge. “Really being able to measure all of the omics takes a lot of work

on the molecular biology side,” Wells explains. “At the same time, these assays generate a huge amount of data, and we’re really trying to extract as much value from those data as we possibly can.”

This immune-system information can be applied to better understanding a wide range of diseases. “The immune system really underlies the vast majority of human health and disease states, from the progression of cancer via immune evasion to autoimmunity and infectious disease,” Wells says.

Data like Immunai is collecting could change the future of healthcare. “You’re really going to start seeing more and more advances as the data revolution in the omics takes off in life sciences,” Wells says. “Having a culture that really empowers both sides to make discoveries and empowers communication between them is a recipe for success.”

Many other researchers also apply single-cell genomics to cancer. Recently, Kenneth Livak of the Dana-Farber Cancer Institute in Boston and his colleagues reported: “Advances in molecular biology, microfluidics and bioinformatics have empowered the study of thousands or even millions of individual cells from malignant tumours at the single-cell level of resolution.” They added, “This high-dimensional, multi-faceted characterization of the genomic, transcriptomic, epigenomic and proteomic features of the tumour and/or the associated immune and stromal cells enables the dissection of tumour heterogeneity, the complex interactions between tumour cells and their microenvironment, and the details of the evolutionary trajectory of each tumour.”

Collaborating on data

Other companies, including Seven Bridges in Cambridge, MA, also see the ongoing revolution in life science data that could enhance health. Brandi Davis-Dusenbery, PhD. chief scientific officer at Seven Bridges, says this company “really serves to provide a complete bioinformatics and biomedical data-analysis ecosystem for researchers across the world.” She adds, “That spans the gamut from clinical researchers, the bench biologist, epidemiologists—so really this wide swath of researchers.” New technologies, like single-cell genomics, require systems that enable researchers with different backgrounds and expertise to work together effectively.

The challenges associated with collaborating on data are amplified by the scale of single-cell genomics. “One of the challenges specific to single-cell work, RNA-seq in particular, is that—instead of having a single sample that you need to process from bulk RNA—the data can explode, and it becomes this interesting challenge to manage all of that data,” she says.

To help companies work with so much data, she and her colleagues create a secure, cloud-based bioinformatics solution. “A pharmaceutical company can leverage this flexible infrastructure in ways that really support their researchers,” she explains. “Single-cell analysis is still very much an emergent technology, and different organizations work with different workflows depending on their specific needs.”

Davis-Dusenbery sees a transition going on in the pharmaceutical industry. “Pharma is transitioning from a place where each therapeutic area built their own workflows and did the analysis as they want,” she says. “Now, we’re seeing more consolidatio—or at least a high hybrid mode—where there’s a need for more standard workflows that can be propagated across all therapy areas. This supports data sharing, re-analysis, and overall greater impact from every experiment.”

In addition, she sees the technology changing. “There is a movement toward spatial transcriptomics—getting more and more fine-grained information about the tissue, about the organization of the cell,” she says. This will add even more data, and pharmaceutical scientists will seek ways to integrate that with other information. As a result, she says, “Having a platform that can support many types of data is more critical than ever before.”

One example of commercial technology available for spatial transcriptomics is the Rebus Esper. This platform uses synthetic aperture optics, which uses 3D light patterns to illuminate targets. In imaging a volume of a sample, this technology provides 80-nanometer resolution. At the same time, this platform captures information on gene expression via cyclic single-molecule fluorescent in situ hybridization. One key to this technology is simplicity, at least for the user. A sample is sectioned, loaded in a fluidics cell where a reagent kit labels specific RNA scripts, and then data can be collected. So, the molecular biology lies in the platform. The output allows a user to perform single-cell analysis and spatial mapping.

Reaching for regeneration

Single-cell genomics can also contribute to very advanced forms of healthcare. At the Washington University School of Medicine in St. Louis, Samantha Morris, PhD. associate professor of developmental biology and genetics, explores some of the possibilities in regenerative medicine.

“One challenge is using existing single-cell datasets to act as a reference of cell identity,” Morris explains. “For example, from single-cell RNA-seq profiling of an unknown cell type or population, existing annotated references can be used to reveal the identity of the query cells.” This approach gets very complex with cells in development, as the cells transition from one state to another. “This challenge applies to health in the context of regenerative medicine, where cell identity is being engineered, either by differentiating stem cells or by directly reprogramming cell identity,” she explains. “In this context, it is essential to accurately measure cell identity to understand how efficient our cell engineering protocols are and refine them if necessary.”

One interest of Morris is lineage reprogramming, which she describes as “the direct conversion of cell identity between fully differentiated cell types, driven by transcription factor overexpression.” Although this method holds promise to replace cell types that are deficient or defective in diseased tissues, Morris says that “reprogramming is typically very inefficient, and the resulting cell types do not closely resemble the in vivo target identities.” When reprogramming is successful, which is a rare event, single-cell genomics provide a more accurate picture of the process.

Morris’s lab developed CellTagging, which uses lentivirus to label cells and capture lineage information in parallel with single-cell transcriptomes. “We applied CellTagging to a reprogramming protocol designed to generate cells that repair the liver and intestine, using fibroblasts as the source cell type,” she says.

Credit: Samantha Morris

Single-cell genomics also helps Morris get a better understanding of the transition process. “The evidence suggests that cells transition through a transient ‘reprogramming-permissive’ state, where they respond to transcription factor overexpression and change identity,” she explains. “If we can force cells into these malleable states, we will significantly improve reprogramming efficiency to increase the yield of target cell types.”

Touching much of medicine

From regenerative medicine to more effective drugs, single-cell genomics provide the data that promises to improve healthcare. In many cases, this information will be combined with other types of data. As time goes on, even more complex datasets will be developed to delve ever deeper into the cause of disease and ways to treat or even prevent it.

That transition in clinical research and healthcare depends on advanced data-collecting platforms, as well as more effective ways to analyze the data. That combination could provide insight into human biology in ways that scientists could not even imagine just a decade ago. Then, a decade in the future, this work could improve or protect the health of people around the world.


Mike May, PhD. is a freelance science writer and editor based near Seattle.

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