Digital Molecular Analytics: Digitising the World of Biology

16 May 2024

Authors: Dr Andrew Breeson and Prof Atul N. Parikh

Graphics: Dr Foo Yong Hwee and Dr Andrew Breeson

Digital Molecular Analytics is an emerging science that explores the most fundamental elements of life at a molecular level. It is transforming our approach to diagnosing diseases and reshaping healthcare in the digital age by allowing researchers to detect and analyse single molecules in a biological environment. Furthermore, it has the potential to provide affordable, sensitive, and rapid diagnostic devices whenever and wherever they are needed.

This blog post will outline the principles of this exciting new field and show how the Institute for Digital Molecular Analytics and Science (IDMxS) is pioneering techniques with far-reaching implications for diagnostics and health.

Traditional assays: The Foundation of Analogue Analytics

For centuries, scientists have explored ways to detect the presence, amount, or activity of target molecules using procedures called assays. Scientists typically conduct these assay experiments in a single reaction vessel (like a test tube), and based on measurable responses from a bioactive substance added to the vessel, they can infer the presence or concentration of a target substance (the analyte). For example, an assay might investigate how quickly bacteria grow when exposed to a nutrient or quantify the death of cancer cells treated with a chemotherapeutic agent. In each case, the bioactive substance will produce a signal directly proportional to the analyte’s concentration (or activity), which the scientist then measures.

While these traditional assays have long been the gold standard in scientific research and clinical diagnostics, they provide a signal that averages the responses (or behaviours) of many millions of molecules. Therefore, these assays can’t offer any insights into the response of individual molecules. Moreover, the traditional assays are burdened with many practical limitations that affect their accuracy, efficiency, and scalability.

• Traditional assays can’t detect very low concentrations of analyte. The sensitivity is affected because measurements can be influenced by similar substances present in the sample.
• Traditional assays provide a range or an approximation of the quantity of the target analyte, which might be influenced by external factors and often needs calibration against known standards.
• Scaling up traditional assays can be cumbersome and error-prone because they often require linear increases in sample volume and reagents and can be limited by the physical constraints of the assay setup (like the vessel size).
• Traditional assays are difficult to automate fully because they often involve manual setup, measurement, and interpretation steps, which can also introduce variability and human error.
• The data from traditional assays can be complex to analyse and interpret. Experts are often needed to understand the nuances and potential interferences in the readouts.
• Traditional assays have a limited dynamic range, which can restrict the upper and lower limits of detection. This limitation can make it difficult to measure very high or very low concentrations of an analyte without multiple dilutions or concentrations.

Because traditional assays measure analytes indirectly, it’s a bit like counting the average number of people at a party by measuring the total amount of food consumed. While this method gives a good ballpark estimation, it can lead to inaccuracies, especially if some guests are much hungrier than others… But imagine if you could check each guest as they entered the party instead of guessing based on food. Not only would you know the exact number of people at the party, but you would also know precisely who showed up. Welcome to the world of digital assays!

Next-Generation Analytics: A Transition from Traditional to Digital Assays

How can we digitise an assay? The method is surprisingly simple. Rather than conducting the experiment in the same large vessel like traditional assays, digital assays partition the experimental sample into thousands or even millions of “microreactors”. In these tests, each tiny partition is a reaction vessel that either contains the analyte (we call this a “1”) or it doesn’t (a “0”). There’s no ambiguity—each reaction is either positive or negative. Just as digital cameras transform a visual image into a series of pixels, each represented by 0s and 1s, digital assays transform a sample into a series of clear, discrete test results.

The Advantages of Going Digital

1. By simply counting how many partitions contain the analyte, digital assays can be incredibly accurate, even when detecting molecules at minuscule concentrations.
2. They are absolute, without requiring reference measurements, and sensitive, even in the presence of interfering components. For example, they can detect low concentrations of rare markers in blood that indicate early-stage diseases.
3. These tests use very small amounts of sample and reagents, making them efficient and less wasteful.
4. Digital assays are often faster, as they can be automated using advanced technologies like microfluidics, which handle liquids at incredibly small scales.
5. Just like adding more pixels to a digital image can improve its clarity, increasing the number of tiny reactions in a digital assay can enhance its precision without a significant increase in cost or complexity.

For these reasons, digital molecular analytics has profound implications for global health. By enabling precise quantification of pathogens, cancer biomarkers, and genetic mutations – long before the earliest symptoms of the disease become evident – these technologies can lead to earlier intervention and more personalised treatment strategies. Digital assays can detect viruses at very low concentrations for infectious diseases, making it possible to identify infections before they become widespread. These technologies thus may herald a new era in individual and public health – characterised by very early detection and pre-symptomatic intervention – thereby delaying (if not entirely eliminating!) the disease states.

In addition, quick and cost-effective analyses mean that high-quality diagnostics can be brought to resource-limited settings where traditional laboratory infrastructure is lacking. This democratisation of advanced diagnostic tools has the potential to transform public health landscapes across the globe and is the overarching goal of IDMxS.

The Four Pillars of IDMxS: Advancing Digital Molecular Analytics Through Integrated Research Clusters

IDMxS is a collaborative environment that integrates various aspects of biological and digital sciences to push the boundaries of molecular diagnostics. The institute is organised into several clusters, each focusing on different stages of the analytics process.

1. Detection – This cluster focuses on developing new generic platform technologies to selectively capture and sensitively measure low-abundance biomolecules. It uses engineered nanostructures, sophisticated molecular recognition, and novel signal enhancement or amplification techniques that can identify single molecules of DNA, RNA, or protein in complex biological samples partitioned into FemtoLitre (a volume of 0.000000000000001 Litres) droplets, chambers, or wells.
2. Transduction – This cluster transforms the biochemical interactions detected by the arrays of partitioned sensors into readable digital signals. It involves converting changes in molecular interactions into optical or electrical signals that can be easily quantified and analysed.
3. Analytics – This cluster interprets the generated digital data using advanced data analysis techniques, including artificial intelligence and machine learning. This allows for the extraction of meaningful insights from complex datasets, facilitating rapid and accurate diagnostics.
4. Translation – This cluster works on two parallel fronts. One goal of the translation cluster is to integrate these technologies into practical, user-friendly devices that can be deployed in various settings, including clinical laboratories and point-of-care locations. The goal is to make advanced diagnostics accessible and feasible for widespread use. The second goal of the translation cluster is to apply the generic platform technologies developed in other clusters to specific biomarkers associated with ageing and infectious diseases – two broad classes of medical challenges of our time.

By converting the complexity of molecular interactions into digital data, the IDMxS approach promises to enhance our understanding of the biological and chemical world around us, leading to significant advances in medicine, environmental science, and beyond.

Click here to learn more about our research and how we are unlocking the power of every molecule in the digital age.