The worldwide pharmaceutical sector is dynamic and highly invested in innovation. In 2021, the industry spent an impressive 238 billion U.S. dollars on global research and development. This advanced technique explores the molecular composition of individual cells, from DNA to tiny molecules. 

Against this backdrop, single-cell multiomics emerges as a promising innovation, with the multiomics market valued at USD 2.57 billion the same year, showing the significance and growing impact of single-cell multiomics in the industry.

Many scientists recognize single-cell RNA sequencing, but as technology advances multiomics including genomics, epigenomics, proteomics, and metabolomics have come to the forefront. While traditional drug discovery methods serve as a foundation, they often don't achieve the necessary precision and depth. 

By using various omics techniques, single-cell multiomics gives a more complete view of the cellular landscape. This is much like assembling a jigsaw puzzle and signals a new phase in the drug discovery process.


Techniques in Single-Cell Multiomics

Single-cell multiomics covers a broad area, offering many techniques that let researchers study individual cells from multiple perspectives simultaneously. Single-cell RNA sequencing is one key technique, evidenced by the rising interest in single-cell RNA seq studies. This method takes individual cells and sequences their RNA, giving deep insights into their RNA profiles.

However, multiomics goes beyond RNA. It looks at other molecules that scientists also find interesting. For example:

  • Epigenomics looks at changes to DNA and how DNA is accessed. 
  • Genomics explores the DNA of individual cells to find possible changes or errors.
  • Proteomics studies the proteins in the cells, and 
  • Metabolomics examines the small molecules inside the cells.

Additionally, new technologies have pushed single-cell multiomics forward. As researchers learn more, they develop better tools and methods. One popular option is studying chromatin accessibility with single-cell ATAC-seq. This method finds parts of the genome that are open and possibly active, compared to closed or inactive elements.

When it comes to finding new drugs, these techniques transform the process. By looking closely at individual cells, they help create more specific and effective treatments.


Applications in Drug Discovery

Single-cell multiomics doesn't isn’t just for basic research; it has a significant impact on drug discovery. As we study cells more closely, multiomics gives us important information for drug research with many applications.

Target Identification

One of the primary goals of drug discovery is to identify potential targets for therapeutic intervention. Single-cell multiomics helps us pinpoint these uses. By looking at individual cells closely, we can locate specific pathways or molecules that are altered when a disease strikes. This helps develop drugs that specifically target the areas where they're needed, leading to more effective treatments.

Biomarker Discovery

Biomarkers are indicators of normal or pathological processes in the body. Through multiomics, we can discover novel biomarkers that help in disease diagnosis and prognosis, and even monitor the effectiveness of treatments. By understanding the unique molecular signature of a disease, we can develop tests that detect these biomarkers, enabling early diagnosis and timely intervention.

Personalized Medicine

Every individual is unique, and so is their genetic makeup. Single-cell multiomics provides a window into this genetic diversity, allowing for treatments tailored to individual genetic profiles. By understanding the molecular details of a patient's cells, we can predict how they might respond to a particular drug, ensuring they get the most effective treatment for their specific condition. 

Single-cell multiomics enables us to see the cellular world in unprecedented detail and, in doing so, is paving the way for breakthroughs that could change the face of medicine. The significance of personalized medicine is further underscored by the FDA's increasing approval of such treatments, as detailed in a report by the Personalized Medicine Coalition.


"Scaling of scRNA-seq experiments. (a) Key technologies that have allowed jumps in experimental scale. A jump to ∼100 cells was enabled by sample multiplexing, and then a jump to ∼1,000 cells was achieved by large-scale studies using integrated fluidic circuits, followed by a jump to several thousands of cells with liquid-handling robotics. Further orders-of-magnitude increases bringing the number of cells assayed into the tens of thousands were enabled by random capture technologies using nanodroplets and picowell technologies. Recent studies have used in situ barcoding to inexpensively reach the next order of magnitude of hundreds of thousands of cells. (b) Cell numbers reported in representative publications by publication date. Key technologies are indicated.

Image, caption source: Svensson, V., Vento-Tormo, R. & Teichmann, S. Exponential scaling of single-cell RNA-seq in the past decadeNat Protoc 13, 599–604 (2018). Creative Commons license "


Challenges and Responses

Single-cell multiomics holds great promise for drug discovery, but it also brings challenges. Still, the science community keeps pressing forward, finding new ways to push the boundaries of what's possible.

Harnessing Big Data with Advanced Computational Tools

The sheer volume of data generated from single-cell multiomics experiments is enormous. As we move forward, bioinformatics will continue to evolve with more sophisticated algorithms capable of processing and integrating large datasets. These tools will be designed to decipher complex biological networks and pathways, paving the way for the identification of novel drug targets.

Cloud Computing and Collaborative Frameworks

Cloud-based bioinformatics platforms are becoming increasingly crucial, allowing researchers to store, share, and analyze massive datasets effectively. Collaborative frameworks enabled by cloud technologies will facilitate multi-institutional research, leveraging the expertise and computational resources from across the globe to accelerate the research that fuels drug discovery. 

Specialized cloud-based platforms for single-cell analyses will need to be developed that are easy to use, but also flexible enough to adapt to the constant evolution of algorithms and approaches used to analyze these data.

Machine Learning and AI-Driven Predictive Models

Since single-cell data is a collection of many messy, labeled observations, and its format is a sparse matrix. These factors make it an approach particularly well-suited for machine learning (ML) and artificial intelligence (AI) analysis techniques. As the data continues to grow, these technologies will become central to interpreting single-cell multiomics data. Predictive models using AI will help in understanding disease progression and drug response for known and novel cell populations, and new models will be instrumental in predicting the efficacy and toxicity of new drug candidates before they enter clinical trials.

Educational and Skill Development

The demand for skilled bioinformatics, data science, and computational biology professionals is already growing, but the breadth of knowledge required to develop single-cell solutions is daunting. The next generation of biologists, bioinformaticians, data scientists, and software engineers will need to learn to work in interdisciplinary teams to tackle these types of problems.


Solutions and Best Practices

We must establish best practices for single-cell multiomics research. Standardizing protocols, ensuring reproducibility, and fostering collaborations between researchers, clinicians, and bioinformaticians keep the promise of single-cell multiomics alive. As we continue exploring this field, the emphasis on innovation, collaboration, and ethical considerations will guide us.

Using advanced methods, especially with genetic data, raises ethical questions. People worry about keeping their genetic data private, permitting its use, and how others might misuse this information. Establishing strong ethical guidelines and transparent research methods is crucial. Informed consent, data anonymization, and strict data access controls are some steps taken to address these concerns. 

To navigate these complexities, frameworks like the one proposed by the Global Alliance for Genomics and Health offer guidelines for responsible sharing of genomic and health-related data.


Real-World Applications

The promise of single-cell multiomics is undeniably exciting, and real-world possibilities show its full potential. Across the globe, researchers and pharmaceutical companies are exploring multiomics to bring about tangible changes in drug discovery. 

Cancer Research

For instance, by analyzing tumor cells' genetic and molecular profiles, researchers are working to develop targeted therapies for specific cancer subtypes. These drugs, which will be tailored to the unique genetic makeup of the tumor, will offer increased efficacy and reduced side effects compared to traditional chemotherapy. A tangible example of this application is seen in colon cancer research, where single-cell RNA sequencing was utilized to dissect the cellular landscape of tumor-infiltrating myeloid cells, as reported in a study published in Cell.com.

Neurodegenerative Diseases

The study of neurodegenerative diseases is another notable example of this. Traditional drug discovery methods often hit a roadblock due to the complexity of the brain's cellular makeup. However, with single-cell multiomics, researchers have been able to dissect the molecular profiles of individual neurons, identifying specific pathways that are altered in conditions like Alzheimer's or Parkinson's. This is paving the way for drugs that target these particular pathways, offering hope for more effective treatments in the future.

Autoimmune Diseases

In autoimmune diseases like rheumatoid arthritis or lupus, researchers have identified specific molecular signatures that differentiate the diseases. This could improve diagnostic accuracy and lead to the development of drugs targeting the unique signatures, providing more effective treatment options.


Future Perspectives

The journey of single-cell multiomics in drug discovery is just beginning, and the horizon is filled with possibilities. This upward trajectory is expected to continue, with projections showing that the global single-cell analysis market could reach USD 5.6 billion by 2025. As we look to the future, several emerging trends and technologies promise to enhance the impact of multiomics further and reshape the pharmaceutical research landscape.

Emerging Technologies

New techniques are continually developed in biotechnology that promise to refine and expand our understanding of individual cells. For instance, spatial transcriptomics, which provides a spatial context to gene expression data, is gaining traction. This technique allows researchers to understand what genes are expressed and where they are expressed within a tissue, adding another layer of depth to our understanding.

Potential Impact

Single-cell multiomics will likely change more than just drug discovery. As we use this method more, it could change many parts of healthcare. We might see more personalized medicine, where doctors choose treatments based on a person's unique genes and molecules. Also, by finding clear signs of diseases early through multiomics, we could diagnose and treat people earlier, leading to better results and lower costs.

Moreover, as we unravel the human body's mysteries at a cellular level, our understanding of disease mechanisms will become more nuanced. This could pave the way for preventive medicine, where interventions occur even before a disease takes hold, based on the patient’s genetic and molecular risk factors.


Breakthrough Potential

Single-cell multiomics have a bright future in drug discovery and healthcare. At this juncture, the potential for breakthroughs and innovations is immense, promising more accurate, personalized, and proactive healthcare.

However, as with any pioneering technique, challenges exist. But with these challenges come opportunities, and the scientific community's resilience and ingenuity will help find the solutions and expand the limits of what we can achieve.




Mark Kunitomi
Mark Kunitomi is the Chief Scientific Officer at Almaden Genomics. He was a post-doctoral fellow at UC San Francisco with a background in genomics, bioinformatics and microbiology, and he has a Ph.D. in Biochemistry & Molecular Biology from the University of California, San Francisco.