25 Proteomics, Genomics & Microarrays


omics analyses are vital to understanding disease pathophysiology and identifying early-stage disease markers. SCIEX, a global leader in life science analytical technologies, plays a pivotal role in AD research by providing advanced mass spectrometry tools that facilitate the discovery and quantitation of biomarkers associated with the disease. These systems enable ultra-sensitive quantitation of proteins and their modifications in various samples. Innovations such as fast scanning quantitative lipidomics analysis can detect lipid isomers such as lysophospholipids, a class of lipid that may be implicated in AD.


Figure 2: AI aided analysis of cellular phenotypes reacting in response to a panel of 23 experimental compounds, to identify the potential neurotoxic effects of each of these substances. Some substances caused severe disintegration of neural networks and cell death; others caused mostly moderate perturbations. The compounds and concentrations are displayed with a heatmap overlay representing viability (fraction of live cells) scored by the ML model. Credit: Molecular Devices.


The central role of omics in understanding and treating AD


Genomic technologies are key to unlocking the complexity of AD. While early studies focused on well-known genetic mutations, like the APOE ε4 allele, today’s research has expanded to include polygenic risk scores and gene-environment interactions. These insights are revealing how disruptions in metabolic and immune-related genes may accelerate neurodegeneration.


To identify the right genomic targets, from individual alleles to polygenic risk markers, systems biology approaches founded on next-generation sequencing (NGS), genome-wide association studies (GWAS) and in silico disease models are being utilised18,19. These models require high-quality, precision tools and reagents and next-generation data analytical models. AI is accelerating progress in these technologies and approaches, for instance, by enhancing the design, optimisation and analysis of DNA complex sequences. Tools like Integrated DNA Technologies (IDT)’s SciToolsTM Plus API enable researchers to design genes with high precision while identifying and resolving sequence complexities, such as high guanine and cytosine (GC) content or secondary structures. This integration streamlines workflows in applications ranging from gene editing to synthetic circuit design, allowing for faster, more reliable construction of DNA constructs.


For example, through its partnership with IGI, the Danaher-IGI Beacon for CRISPR Cures aims to develop therapeutic platforms for over 500 genetic diseases, with many applications potentially extending to neurodegeneration.20 CRISPR gene editing - supported by IDT’s high-fidelity nucleic acids and current Good Manufacturing Practice (cGMP)-grade CRISPR guide RNAs - is enabling scientists to target genes regulating insulin signaling and neuroinflammation in the brain. Complementing this, Aldevron provides custom plasmids and mRNA to fuel preclinical and clinical gene-editing studies21. The recently announced personalised gene-editing treatment for an infant with a rare disease at the Children’s Hospital of Philadelphia was jointly manufactured by IDT and Aldevron, in collaboration with Acuitas Therapeutics. The case is viewed as a historic breakthrough in precision medicine, offering new hope for those with rare genetic disorders22.


While genomics reveals the blueprint, proteomics uncovers the real-time changes driving disease progression. In AD, proteomics analyses illuminate key post- translational modifications of tau and insulin receptor substrates, as well as accumulation of oxidatively modified proteins. These data and that from other


Leica Microsystems, renowned for high-resolution imaging, provides spatial proteomics tools for the topological assessment of protein expression in specific brain regions - bridging molecular data with histopathological findings. Moreover, organoids - three dimensional (3D) cell models that mimic biological structures and interactions better than 2D cell cultures - such as Molecular Devices’ neurospheres formed using human induced pluripotent stem cell (iPSC)-derived glutamatergic neurons, GABAergic neurons, and astrocytes, can be used to successfully model AD for functional characterisation23. Molecular Devices and Leica Microsystems have developed AI-enabled solutions for characterisation of organoids, including the detection of morphological changes to cells that would otherwise be difficult to detect and characterise24,25,26. Leica Biosystems is also developing AI-driven digital pathology tools and biomarker assays for high throughput diagnostic screening27,28.


AI, Machine Learning and Deep Learning: From pattern recognition to predictive power


With the proliferation of multi-omics data, AI is fast becoming indispensable. Algorithms in machine learning (ML) and deep learning (DL) are now being deployed to accelerate the discovery cycle, from therapeutic target discovery to early diagnosis. As multiple biomarkers and multimodal signatures of AD are discovered and translated, there is potential to leverage AI to predict the risk of disease onset and progression, guide patients throughout their journey, and help accelerate drug development. Moreover, high content screening and AI are being used to analyse cellular responses to genetic perturbations in preclinical models of AD. These screens combine CRISPR knockouts created using IDT solutions, and imaging using Molecular Devices tools, building functional genomics maps of neuronal pathways. AI then assembles these datasets into gene interaction networks, highlighting high-priority targets for further investigation.


At the same time, Indica Labs and Leica Biosystems are applying an AI-powered digital pathology platform to accelerate the advent of next-generation companion diagnostics29. These systems leverage deep learning to provide reproducible, scalable assessments that support both clinical trials and basic research30.


The development of AI-enabled diagnostics relies on biomedical research looking to classify AD tissue samples, quantify amyloid/tau burden, and detect microglial activation.


Collaborative synergies: Academia, industry, and innovation


The scale and complexity of AD demands interdisciplinary collaboration. Danaher is leading the path to advance science via the Beacon program, establishing partnerships with the Innovative Genomics Institute (IGI), Stanford University, Cincinnati Children’s Hospital, Duke University and many others.


Figure 3: AI-powered segmentation of microscope images (breast cancer exmple), using pre-trained AI-based networks for nuclear and membrane segmentation (available in both brightfi eld and fl uorescence imaging). Credit: Leica Biosystems.


One notable collaboration is the Bio-Hermes-002 study, conducted in partnership between Beckman Coulter Diagnostics, Global Alzheimer’s Platform Foundation (GAP) and other collaborators31.Through these and other efforts, our ambition is to help support not only earlier detection but also stratifi ed intervention. Our collaborative momentum supports a shift from symptomatic treatment to preventive precision medicine - a paradigm where asymptomatic individuals at high risk are identifi ed early through biomarkers and genetic profi ling and managed proactively.


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