Democratising Live-Cell Adaptive Super-Resolution Microscopy based on SRRF

Lead Research Organisation: University College London
Department Name: MRC Laboratory of Molecular Cell Biology


Human perception depends heavily on the capacity of the visual cortex to compensate for flaws in the structure of the eye. Evolutionarily, the architecture of the eye changed little over time while neuronal computation has taken on a major role in compensating for the optical limitations imposed by the `hardware' and optimising its performance under different conditions.

Microscopy is following a similar shift. The basic optical design of microscopes has changed little over the last 20 years. However, we have achieved remarkable advancements in bypassing their physical limitations by finding new ways to collect and analyse images. Through computational-assisted approaches we are now able to achieve a massive increase in the resolution of these imaging systems (by 10-fold or more) and considerably reduce optical aberrations.

Over a decade ago Super-Resolution Microscopy, a new field dedicated to increasing resolution in light microscopy, was conceived. In these techniques, methods such as PALM, STORM and SRRF, use advanced spatio-temporal analysis of the data-feed in microscopes to estimate the location of molecules, achieving a resolution near the molecular scale itself (<50nm). These technologies are revolutionizing biological and biomedical research. They allow us to observe for the first time the dynamic structure of cells at the nanoscale, beyond the capacity of any other method.

Super-Resolution Microscopy is however limited by its complexity. The quality and resolution of these methods depends heavily on having considerable knowledge of the photophysics of the fluorophores used and of the characteristics of the sample being imaged. Non-artefactual high-resolution imaging depends on correctly exploiting these traits. Live-cell imaging entails a further degree of difficulty, as one needs to take also into account and minimize the toxic light burden imposed into cells by the existing methods.

Here we propose iSRRF, a paradigm shift in Super-Resolution Microscopy where the decisions regarding imaging conditions are not determined by the researcher, but by an artificial intelligence engine that studies the sample and learns how to best optimize imaging. This computational approach will maximize the image quality and resolution while limiting the sample illumination to minimise live-cell phototoxicity. It will replace human intuition with empirical decisions based on mathematically well-defined actions.

At its base, this approach will be based on the recently developed Super-Resolution Radial Fluctuations (SRRF) method, taking advantage of its capacity to enable live-cell Super-Resolution imaging in most modern light microscopes, even those not initially designed for Super-Resolution. SRRF is currently transforming the microscopy field, leading to the development of the first Super-Resolution cameras (iXon SRRF-Stream by Andor Technology). It is set to be integrated into the microscopes of most of the leading imaging companies. Following a similar model, iSRRF will be provided as an open-source, easy-to-use, easy-to-implement algorithm compatible with the extremely popular ImageJ and Micro-Manager image analysis and acquisition platforms, enabling the widest possible uptake both by companies and individual users.

As a pilot project demonstrating iSRRF, we will study cell division. Cell division is often used as the benchmark for phototoxicity in microscopy as photodamage disrupts cell division, preventing it entirely if the damage is sufficiently high. It has, thus far, been a struggle to apply Super-Resolution Microscopy to study cell division due to the requirement for high laser illumination intensities when compared to conventional imaging methods. Our preliminary data shows however that iSRRF will be capable of following cell division over several hours while achieving a 2-to-10 fold increase in resolution. This capacity is beyond any other existing Super-Resolution method.

Technical Summary

Despite over 10-years of research on the Super-Resolution field, Super-Resolution microscopy remains fairly inaccessible to non-expert researchers and most biological problems. While in part this is due to a lack of access to Super-Resolution systems, it is dominantly because of a steep learning curve and inherent difficulty in setting up experiments compared conventional microscopy.

Recently we have developed Super-Resolution Radial Fluctuations (SRRF - pronounced surf). SRRF allows microscopists to achieve optical resolutions of few tens of nanometres in live cells without causing meaningful light-induced damage. Where Super-Resolution light microscopy methods such as PALM, STORM and STED use intense sample illuminations that can disrupt the normal behaviour of the sample or even cause cell death, SRRF requires >100-fold less illumination.

In parallel, we developed a second technology, SQUIRREL, as an approach to quantitatively map the quality and resolution of Super-Resolution images. SQUIRREL uses the robust assumption that Super-Resolution images should be higher-resolution representations of their diffraction-limited counterparts. Local deviations from this immediately highlight decay in image quality (i.e. image artefacts).

By combining SRRF and SQUIRREL in a real-time analysis framework, it becomes possible to generate Super-Resolution images of known resolution and quality. This ability immediately opens the door to establish a machine learning approach that adapts the microscope acquisition and SRRF analysis settings to maximise these two values. This enables the microscopy system to: i) learn from the sample about how to improve imaging in real time, and ii) in a dynamic manner adapt to changes in the imaging properties of a living sample.

With iSRRF, Super-Resolution will become considerably simpler and accessible to researchers. Achieving quality and resolution in images beyond the capacity of a system purely driven by a researcher.

Planned Impact

Our project will have a considerable impact on the microscopy, cell biology and biophysics academic communities. Our recent biotechnology developments - SRRF and SQUIRREL - of which iSRRF will be based on, are already used across the international community (see support letters). Thus, there are already large numbers of researchers primed and ready to rapidly exploit iSRRF in their research. We expect the project to be appealing not only as a cutting-edge piece of research but also as a tool that can be leveraged in many biological research projects. Our direct collaborators in the fields of cell division, cancer, host-pathogen interaction and immunobiology will particularly benefit from early access to the iSRRF capacity. The developments of this project will be communicated through peer-reviewed publications, conferences and through social media. Both Dr. Ricardo Henriques (RH) and Prof. Buzz Baum (BB) have a long history of publishing in these domains.

-- Training --
The PDRA to be recruited will receive continuous cross-disciplinary training and mentoring, which will aid his/her career progression. The skills learned over the course of this project, namely image analysis and optical biophysics, are very rare to find in the UK and are in high demand. We expect that training of researchers skilled in this area will contribute immensely to the UK competitiveness in these areas, both in academia and industry. In addition, the PDRA will benefit from generic skills gained on training courses at UCL, through co-authoring papers, grants and reviews and by presenting this work at international meetings. RH and BB are actively involved in interdisciplinary training activities at UCL (via the Natural sciences undergraduate programme, and the CoMPLEX, IPLS and LiDo Masters/PhD programmes). The project described here will be ideal for introducing students from different backgrounds to interdisciplinary re-search in life and physical sciences.

-- Translational and Industrial --
It will be possible to rapidly translate the analytical and imaging developments from this project into
applications of interest to biotechnology, microscopy and imaging companies. The data generated will be of high-value to the pharma industry as it has the potential to identify fundamental cell behaviour in the presence of disease, allowing for the tailored design of new drug targets. Most importantly, we are closely collaborating with Andor Technology, Intelligent Imaging Innovations and Cairn Research to ensure that our developments can be translated into a potential commercial solutions.

-- Societal and Economical --
Microscopy approaches and instrumentation is one of the fastest growing areas in biomedical research, critical not only for cell biology, but also for the direct discovery of new molecular targets mitigating human disease. Over the past decades, the UK has been a world leader in these developments. This project will enable and supply new approaches crucial for enhancing microscopy and cell biology studies, supplying UK research with novel state-of-the-art and experimental methods - a key attractor for industrial R\&D collaborations - and empowering research in the UK with some of the most advanced imaging facilities in Europe. Locally, UCL (where this project will be housed) has made a commitment to make the interface between medicine, biophysics and imaging a key priority, contributing to the long-term sustainability of these studies and serving as a seeding source for collaborations with our partners such as the Francis Crick Institute and NHS Hospitals (e.g. Royal Free Hospital).

-- Outreach --
RH and BB have been involved in interactions with the wider community through media appearances, public discussions and school visits. Through this type of outreach we expect this work to reach a wide audience; giving the public a better understanding of how next-generation imaging technology may impact health and disease.


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Description This project aims to provide high-performance data analysis software for the biomedical research community.
Exploitation Route This project aims to provide high-performance data analysis software for the biomedical research community.
Sectors Education,Healthcare,Manufacturing, including Industrial Biotechology

Description Andor Technology 
Organisation Andor Technology
Country United Kingdom 
Sector Private 
PI Contribution R&D partnership to implement analytical technologies developed into commercial turnkey systems.
Collaborator Contribution R&D partnership to implement analytical technologies developed into commercial turnkey systems.
Impact This collaboration has led to the translation of UCL developed technology into turn-key commercial hardware for biomedical research. As part of this collaboration Andor is bringing experimental kit and software to UCL that local researchers in biology and physics can use in their research.
Start Year 2017
Description Intelligent Imaging Innovations 
Organisation Intelligent Imaging Innovations Ltd
Country United Kingdom 
Sector Private 
PI Contribution R&D partnership to implement analytical technologies developed into commercial turnkey systems.
Collaborator Contribution R&D partnership to implement analytical technologies developed into commercial turnkey systems.
Impact This collaboration has led to the establishment of UCL as the R&D reference site for 3i in the UK. As part of this collaboration 3i is bringing experimental kit and software to UCL that local researchers in biology and physics can use in their research.
Start Year 2016
Title NanoJ 
Description In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). 
Type Of Technology Software 
Year Produced 2018 
Open Source License? Yes  
Impact NanoJ provides the technological basis to a series of high-performance analytical algorithm for super-resolution microscopy data analysis 
Title NanoJ-Fluidics 
Description Fluorescence microscopy can reveal all aspects of cellular mechanisms, from molecular details to dynamics, thanks to approaches such as super-resolution and live-cell imaging. Each of its modalities requires specific sample preparation and imaging conditions to obtain high-quality, artefact-free images, ultimately providing complementary information. Combining and multiplexing microscopy approaches is crucial to understand cellular events, but requires elaborate workflows involving multiple sample preparation steps. We present a robust fluidics approach to automate complex sequences of treatment, labelling and imaging of live and fixed cells. Our open-source NanoJ-Fluidics system is based on low-cost LEGO hardware controlled by ImageJ-based software and can be directly adapted to any microscope, providing easy-to-implement high-content, multimodal imaging with high reproducibility. 
Type Of Technology Software 
Year Produced 2018 
Open Source License? Yes  
Impact NanoJ-Fluidics is now the basis for commercial technology in development by Abbelight 
Description Host laboratory for In2ScienceUK 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Schools
Results and Impact We are a volunteer laboratory for the In2ScienceUK programme, an award winning initiative which empowers students from disadvantaged backgrounds to achieve their potential and progress to STEM and research careers through high quality work placements and careers guidance
Year(s) Of Engagement Activity 2016,2017,2018