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NanoLoom: AI-Powered Single-Molecule Analysis of DNA–Protein Interactions
Last updated: · By Emoda Software
NanoLoom is an international R&D project supported by the Eureka Eurostars-3 programme and carried out with technology and research partners from Türkiye and Europe. It aims to study DNA–protein interactions at the single-molecule level and at high throughput for drug development. Its microfluidic chip aligns large numbers of DNA molecules within a single field of view, so that protein movement on DNA can be observed directly with fluorescence microscopy. Emoda Software leads the development of the AI-based image analysis package that extracts protein binding, movement and quantity from these images.
Project facts
| Project name | NanoLoom |
|---|---|
| Programme | Eureka Eurostars-3 – Call 8 |
| Application number | 8981 |
| Start | 2026 |
| Field | Drug discovery, single-molecule biophysics, AI image analysis |
| Emoda's role | Development of the AI-based image and protein quantification analysis package (work package lead); data calibration; AI integration and validation; data management plan |
What is NanoLoom?
Single-molecule approaches have greatly advanced our understanding of DNA processes in academic research. However, traditional single-molecule methods either analyse one DNA molecule at a time or rely on indirect measurements, which has limited their use in drug development. NanoLoom aims to bridge academic molecular research and industrial drug development.
The NanoLoom chip combines the DNA Curtains approach developed at Columbia University with nanofabrication and microfluidics. Large numbers of surface-bound DNA molecules are arranged in a precise array, and protein behaviour on these molecules is imaged simultaneously and in real time with single-molecule fluorescence microscopy. This high-throughput single-molecule imaging format is known as the DNA curtains assay.
The project has three components:
- The NanoLoom chip: a validated platform for investigating DNA–protein interactions.
- A drug screening workflow: a process developed specifically for the chip, focusing on DNA-binding proteins that are hard to target with conventional drugs.
- An AI-driven data analysis platform: extracts kinetic and spatial data from raw images and compares binding kinetics in the presence and absence of potential inhibitors.
Why does it matter?
Drug discovery needs faster, smarter and more reliable tools. Observing many protein–DNA interactions at once helps researchers understand mechanisms of action and drug efficacy earlier in the development pipeline. This is expected to speed up the identification of drug candidates and reduce experimental complexity and R&D costs. NanoLoom has broad application potential, including cancer research, infectious diseases, neurodegenerative disorders and new therapeutic areas.
Emoda's role in NanoLoom
Drawing on its experience in image analysis and data platforms, Emoda leads the development of the project's AI-based protein quantification analysis package:
- Data calibration module: accurate alignment of imaging channels and correction of imaging artefacts.
- Region of interest (ROI) detection: finding, validating and preparing each DNA molecule in the field of view for analysis.
- Protein detection and tracking: locating individual proteins on DNA, following their movement over time and visualising it with kymographs.
- AI integration and validation: ensuring compatibility between experimental data and models, refining outputs and minimising false positive and false negative signals.
- Data management and compliance: a Data Management Plan (DMP), security measures and risk assessments; ethics, IP and data protection compliance together with the partners.
- Project management support.
AI-based analysis pipeline
In the first project period (January–June 2026), the first version of the analysis pipeline was developed on synthetic data. The pipeline has four stages:
| Stage | Steps |
|---|---|
| Input | Loading and calibrating the multi-channel microscope image (TIFF) |
| Regions of interest (ROI) | Detecting, validating and cropping individual DNA molecules |
| Detection | AI-based image processing to detect proteins, applied across the whole field of view |
| Tracking and analysis | Tracking proteins over time and generating kymographs |
The goal is to cut manual analysis time from hours to minutes, detect subtle changes in DNA and protein behaviour, quantify binding events at high throughput and improve accuracy and reproducibility. Many labs still trace kymographs by hand in general-purpose tools such as ImageJ/Fiji; the NanoLoom pipeline aims to automate single-molecule tracking and kymograph analysis across large numbers of DNA molecules.
Who is it for? Single-molecule and drug discovery labs
NanoLoom is designed for single-molecule biophysics labs studying DNA replication, transcription and repair proteins, for teams working on DNA-binding proteins such as transcription factors that are often considered undruggable, for researchers working on DNA–protein interactions, biotech innovators and pharmaceutical teams seeking deeper mechanistic insight, faster data interpretation and more reliable early-stage decisions in drug discovery.
Related services
- AI solutions: detection and tracking models
- Image processing: calibration and analysis of microscope images
- Custom software development: analysis software tailored to research teams
Project kick-off announcement: NanoLoom Eurostars project news.
Sources
Co-funded by the European Union
This project has received funding from the Eurostars-3 Programme with co-funding from the European Union's Horizon Europe research and innovation programme. The views and information expressed are those of the author(s) only; neither the Eureka Secretariat nor the European Commission is responsible for them or for any use made of them.
Frequently Asked Questions
What is the NanoLoom project?
NanoLoom is an international R&D project supported by the Eureka Eurostars-3 programme that aims to study DNA–protein interactions at the single-molecule level and at high throughput for drug development. A microfluidic chip aligns large numbers of DNA molecules in a single field of view, and protein movement on these molecules is imaged directly.
Which programme supports NanoLoom?
NanoLoom was approved in Call 8 of the Eureka Eurostars-3 programme under application number 8981. Eurostars-3 is co-funded by the European Union's Horizon Europe programme and the national funding bodies of participating countries.
What does Emoda do in NanoLoom?
Emoda leads the development of the project's AI-based image and protein quantification analysis package: data calibration, DNA region-of-interest (ROI) detection, protein detection and tracking, AI integration and validation, and the data management plan.
How does NanoLoom's AI analysis work?
The pipeline has four stages: loading and calibrating the multi-channel microscope image, detecting DNA molecules as regions of interest (ROI), detecting proteins with AI-based image processing, and finally tracking proteins over time and visualising them with kymographs.
Who is NanoLoom for?
Researchers working on DNA–protein interactions, biotech companies and pharmaceutical teams that want faster and more reliable early-stage decisions in drug discovery.
Which disease areas could NanoLoom contribute to?
The project aims to help understand drug mechanisms and efficacy earlier in areas including cancer research, infectious diseases, neurodegenerative disorders and new therapeutic areas.
What is the DNA curtains assay?
DNA curtains is a high-throughput single-molecule imaging method in which large numbers of surface-tethered DNA molecules are aligned along nanofabricated barriers and imaged simultaneously by fluorescence microscopy. It was developed at Columbia University and has been used to study DNA replication, transcription and repair proteins. The NanoLoom chip builds on this approach.
Does NanoLoom automate kymograph analysis?
Yes, that is the goal. NanoLoom's AI pipeline detects DNA molecules, finds the proteins on each molecule, tracks them over time and generates kymographs automatically, so kymographs no longer need to be traced by hand in general-purpose tools.
