In this interview, News Medical-Life Sciences speaks with Dr. Shi-Qi An, Director of Marketing and Science Communications at Novogene Europe, about the growing importance of omics technologies in modern biological research. Drawing on her background in molecular microbiology, antimicrobial research and genomics, Dr. An discusses how integrating different omics approaches can uncover complex biological mechanisms, improve experimental design and support drug discovery.
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Can you please introduce yourself and explain how your background in molecular microbiology has shaped your work at Novogene Europe?
My background is originally in molecular microbiology, and I spent many years working in academic research. During that time, I worked in fundamental science, using different technologies, from genomics and transcriptomics through to structural biology, to understand microbial systems and infection models. Later in my academic career, I moved towards more translational research, particularly antimicrobial target identification and drug discovery. This involved investigating potential therapeutic targets and developing different experimental models to test candidate compounds.
That experience helped me transition from academic research to industry. I discovered that my strength lies in connecting science to practical, commercial applications. In a life sciences company, it is important not only to understand the technology itself but also to understand what customers are trying to achieve. You need to translate a technology or service into a meaningful solution that helps researchers answer their scientific questions.
My international background has also been valuable. I am originally from China, and having experience across different research environments gives me a broader perspective on how scientific technologies are developed, adopted, and applied. At Novogene Europe, I bring those different experiences together to help connect advanced omics technologies with the needs of researchers across academia, biotechnology, and pharmaceutical development.
When you first speak with a new research or biotechnology client, how do you translate their biological questions into an effective multiomics study design?
The starting point is always to understand the customer and their research question. Different customers have different requirements, so we can’t recommend the same technology to everyone. For example, researchers working in fundamental academic science may be exploring a completely new biological mechanism. A small biotechnology company might be developing a new therapeutic candidate, while a larger pharmaceutical company may have very specific requirements around timelines, sample quality, and project delivery.
We usually try to understand the researcher’s background before the conversation. We look at their research area, recent publications, and the scientific questions they are investigating. Then we ask some fundamental questions: What do you already know? What do you not know? What are you trying to achieve? From there, we can start discussing which technologies might help answer those questions.
There are also practical considerations. We need to understand the sample type, sample quality, available budget, and turnaround time. These factors can strongly influence what is realistically achievable. For exploratory research, particularly when customers are working with new sample types or developing new methods, the process can be more collaborative. We might discuss several approaches, set up different experimental phases, and consider what to do if the initial method doesn’t work.
It is important to understand the technology’s limitations and capabilities. Ultimately, the goal is to develop a scientifically meaningful study that also works in practice for the customer.
Introducing the Novogene Falcon III
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Many researchers still treat genomics, transcriptomics, proteomics, and metabolomics as separate disciplines. What makes integrating these different omics technologies so powerful?
I think this is one of the most exciting areas of biological research, although we are still relatively early in fully integrating these different layers. Most multi-omics studies currently combine two or perhaps three types of data. It is still relatively rare to see a study that integrates genomics, epigenomics, transcriptomics, proteomics, and metabolomics together.
The reason integration is so powerful is that each layer tells us something different about the biological system. Genomics tells us what is potentially possible in a cell or organism. Transcriptomics tells us which genes are being expressed. Proteomics gets us closer to understanding which proteins are present and contribute to biological function. Metabolomics takes this another step further by helping us understand what is happening at the biochemical level.
What I find particularly interesting is that these layers don’t always agree. A change at the genomic or transcriptomic level does not necessarily translate directly into the same change at the protein or metabolite level. Those discrepancies can actually be extremely informative. For example, in cancer research, changes within particular cell populations can influence responses to drug treatment. When researchers combine genomic information with single-cell transcriptomics, they can begin to identify specific cell populations associated with treatment resistance.
With traditional bulk approaches, you might miss these differences because you’re looking at an average across all the cells. By combining different omics layers, we can begin to understand not only which biological changes are occurring, but also how those changes relate to cellular function and disease mechanisms.
As a molecular microbiologist, where do you see omics technologies having the greatest impact on microbiology and antimicrobial research?
One of the most important areas is understanding microbial behavior and how microorganisms respond to their environment, particularly during infection.
From my research background, I have been very interested in identifying antimicrobial targets and understanding the biological mechanisms that contribute to infection and drug resistance. Microorganisms are complex systems. Looking at the genome alone does not necessarily tell us how a microorganism will behave under particular conditions. We also need to understand how genes are regulated, which pathways are activated, and how these changes influence the organism’s function. This is where combining different omics technologies becomes particularly valuable.
For antimicrobial research, we want to identify potential targets, understand the mechanisms associated with those targets, and investigate how microorganisms respond to different treatments. But there is also a translational aspect. Once we identify a potentially interesting mechanism, we need to determine whether it can be developed into a practical therapeutic approach. That requires moving beyond basic biological understanding towards experimental validation and drug discovery.
I think the real opportunity is to use these technologies together to connect fundamental microbiology with applications that can address important medical challenges.
As single-cell and spatial omics expand, how should researchers decide whether these higher-resolution approaches are necessary?
A key consideration is whether the added resolution will actually help answer the biological question. Single-cell technologies are particularly powerful when we are investigating heterogeneous populations. If we analyze a tissue or cell population using a bulk approach, we obtain an average measurement across all the cells. That can be useful, but it can also hide important differences between individual cell populations.
For example, in cancer research, a small population of cells may behave differently from the rest of the tumor. Those cells might respond differently to treatment or contribute to drug resistance. If we only examine the average expression profile, we might miss that population completely. Single-cell transcriptomics allows us to separate those different cellular populations and investigate their individual expression patterns.
However, that does not mean single-cell analysis is always necessary. If the research question concerns the overall response of a relatively homogeneous sample, a bulk approach may provide sufficient information.
We also need to consider sample availability, quality, cost, and the complexity of downstream analysis. Spatial technologies introduce another dimension by allowing researchers to investigate biological information in its tissue context. This can be particularly useful when understanding the relationship between different cell populations and their surrounding environment is important. The key is to understand what information is missing from the existing experimental approach and whether the additional technology can provide it.
Learn how Novogene’s omics technologies can support your research.
What are the main challenges researchers face when interpreting complex metabolomics and other omics datasets?
One of the biggest challenges is moving from large amounts of data to meaningful biological interpretation. With omics technologies, we can generate enormous datasets, but producing the data is only one part of the research process. We need to understand what those results mean in the context of the original biological question.
For example, when integrating transcriptomics, proteomics, and metabolomics, we may observe differences between the various layers. Those differences are not necessarily a problem. They may reveal important aspects of how the biological system is regulated. However, interpreting them requires an understanding of the underlying biology as well as the analytical methods.
This is why I think it is important to consider downstream analysis from the start of a project, rather than treating it as something to address after the data is generated. Researchers need to be clear about what they want to investigate, what comparisons are meaningful, and how the results might be validated. The objective is not simply to generate the largest possible dataset. The goal is to produce information that helps answer a scientific question.
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Novogene Europe works with academic researchers, biotechnology companies, and pharmaceutical organizations. How do their expectations differ?
We generally see three broad categories of customers, and their requirements can be quite different.
Academic researchers, particularly those involved in fundamental science, are often interested in exploring new biological questions. They may want to test an emerging technology or work with sample types not previously investigated with a particular method. In those situations, there is often a greater emphasis on collaboration, experimental development, and understanding the limitations of different approaches.
For smaller biotechnology companies, the research may be more closely connected to product development or a particular commercial objective. They still need scientific flexibility, but they also have to consider resources, project milestones, and how the results will support their development program. Larger pharmaceutical companies may have much more defined requirements around turnaround time, consistency, and project delivery. For these customers, timelines can be especially important because the omics work may form part of a much larger development process.
My role is to understand those different priorities and help connect the customer’s scientific objectives with a realistic technical solution. That requires both scientific understanding and an appreciation of how research operates commercially.
How important are sample quality, communication, and local laboratory capabilities when delivering successful omics projects?
We discuss sample quality early with customers because it can determine which approaches are technically feasible. Sometimes researchers have very interesting biological questions, but the available samples may limit what’s possible. We therefore need to understand the sample type, its quality, and what can realistically be achieved before recommending a particular technology.
Communication is equally important, particularly when the research involves new methods or unusual samples. For exploratory projects, we may need to develop the experimental approach in stages. We can discuss an initial method, evaluate its feasibility, and then consider alternatives if needed. This helps researchers understand the technical limitations and make informed decisions about how to proceed.
For European customers, having access to regional scientific support and laboratory capabilities can also help facilitate these discussions. However, the fundamental principle remains the same regardless of where the work is carried out: we need to understand the biological question, the practical requirements, and the limitations of the available material.
Bioinformatics is often considered a bottleneck in multiomics research. How can researchers ensure their data leads to meaningful scientific conclusions?
I think bioinformatics needs to be considered as part of the overall experimental design. There is sometimes a tendency to focus on which sequencing or omics technology to use without thinking enough about what will happen once the data is generated. But the analysis is what allows us to connect the results back to the original research question.
This becomes especially important when we are combining different omics layers. For example, a change in gene expression does not necessarily mean we will observe a corresponding change in protein abundance or biochemical activity. If we analyze each dataset independently, we may miss the relationships between them. By looking at the different layers together, we can begin to identify patterns and potential biological mechanisms that would not be apparent from a single dataset.
We also need to be realistic about what the results can tell us. Not every association represents a direct biological mechanism, and further experimental work may be required to validate a finding. For me, successful omics research is about combining the right technology, appropriate experimental design, and meaningful biological interpretation.
Looking ahead, which developments in omics technologies do you believe will have the greatest impact on life sciences and drug discovery?
I think one of the most exciting developments is the increasing ability to integrate different types of biological information, particularly as single-cell technologies become more widely used. We are already seeing how powerful it can be to combine genomic and transcriptomic information to investigate biological mechanisms that would otherwise remain hidden. The next challenge is to connect those findings more closely with protein function and biochemical activity. That is particularly relevant to drug discovery, where understanding a molecular change is only the beginning. We also need to know how that change influences cellular behavior and responses to treatment.
Single-cell approaches are especially interesting because they allow us to investigate differences between cell populations that might otherwise be averaged together. When these technologies are combined with other omics approaches, they can provide a much more detailed understanding of disease mechanisms and therapeutic responses. However, I think it is important to remember that more complex technology is not automatically better. The value comes from selecting the right combination of methods for the research question and being able to translate the results into meaningful biological knowledge.
That is what makes omics so exciting. We are moving towards a much more integrated understanding of biological systems, and that creates opportunities for both fundamental research and practical applications.
Where can readers find more information?
Readers can visit the Novogene Europe website to learn more about the company’s comprehensive range of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and single-cell sequencing solutions.
For an overview of Novogene’s capabilities, explore the Multi-omics Research Solutions flyer .
About Dr. Shi-Qi An
Dr. Shi-Qi An is Director of Marketing and Science Communications at Novogene Europe, where she works at the intersection of scientific innovation, genomics, multiomics technologies, and commercial strategy. She has more than a decade of experience in molecular microbiology, genomics, and translational life sciences research.
Dr. An earned her PhD in Microbiology through research undertaken at Guangxi University in China and University College Cork in Ireland, as part of an international research collaboration. Her doctoral work investigated bacterial cell-to-cell signaling and virulence regulation in Xanthomonas campestris, a plant pathogen.
Following her doctorate, she held postdoctoral research positions at Queen’s University Belfast and the University of Dundee. She subsequently joined the University of Southampton as an Interdisciplinary Senior Research Fellow with the National Biofilms Innovation Center (NBIC).
Her academic research has focused on microbial signaling, bacterial virulence, biofilm formation, antimicrobial resistance, and identifying new therapeutic targets.
Dr. An later transitioned into the life sciences industry, holding product marketing and scientific leadership positions at Novogene Europe. Her experience combines advanced scientific knowledge with an understanding of the commercial and practical requirements of academic, biotechnology, and pharmaceutical customers.
