In most European countries, the diagnosis of cancer is achieved by examination of haematoxylin-eosin (HE) staining by an experienced pathologist. Nevertheless, several other diagnostic approaches exist (e.g., immunohistochemical staining) which are not applied routinely for all cases due to their technical complexity, duration, and cost. Therefore, an important unmet medical need for fast, non-invasive, and label-free immunohistochemical staining based on molecular imaging without laborious sample treatment exists. This demanding challenge will be tackled in STAIN-IT using a non-invasive label-free measurement technique called multimodal imaging (e.g., the combination of coherent anti-Stokes Raman scattering, second harmonic generation, and two-photon-excited fluorescence). The multimodal images will be analysed using deep learning approaches, such as convolution neural networks (CNNs). These CNNs are utilized to mimic immunohistochemical stainings. CNNs are neural networks that learn the feature representation of the data, which is optimally suited to model a specific immunohistochemical staining. In STAIN-IT, the staining models will be developed along with the methods to quantitatively understand the nonlinear behaviour of the CNNs. With the envisioned approximation approaches for CNNs, these models no longer act as ‘black box’ systems, and a quantification of tissue changes associated with the staining models can be achieved. For the very first time, STAIN-IT will develop a label-free, non-invasive, labour-inexpensive, and fast computational immunohistochemical staining, which can be easily implemented into clinical routine yielding increased diagnostic reliability and a better understanding of disease pathogenesis. A fast test of the antigen KI-67 in an intraoperative frozen section consultation situation or the use of Collagen IV as a quality control marker of tissue-engineered medicines are some of the exciting application possibilities of such staining model.

Neurological disorders have emerged as a significant global societal burden, exemplified by afflictions like Alzheimer's and Parkinson's, impacting over one billion individuals globally and surpassing the combined economic burden of cancer and diabetes. This has spurred a concerted global effort, with increased support for neuroscience research. These disorders often target deep brain regions and profoundly influence the structural connectivity of neuronal cells within functional circuits. Synapses, where neurons exchange information, exhibit plasticity, altering information transmission efficiency, shape, and position. Understanding the mechanisms underlying these structural changes, especially in neuronal circuits, remains limited in both healthy and affected individuals. The ERC PoC project STEDGate seeks to advance our understanding of neuronal connectivity and plasticity by developing STED-enabled holographic endo-nanoscopy for neuroscience. This ground-breaking technology promises atraumatic nanoscale in-vivo imaging of deep brain structures reaching depths up to 5 mm beneath the brain's surface. Collaborating with the start-up endeavour DeepEn, the team aims to facilitate the commercial transition of this technology. Making deep-tisue nanoscopy available globally will revolutionize our ability to monitor and understand neurological disorders and, ultimately, offer new avenues for intervention and treatment..

Bladder cancer is among the most expensive diseases in oncology in terms of treatment costs; each procedure requires days of hospitalisation and recurrence rates are high. Current unmet clinical needs can be addressed by optical methods due to the combination of non-invasive and real-time capture of unprecedented biomedical information. The MIB objective is to provide robust, easy-to-use, cost-effective optical methods with superior sensitivity and specificity to enable a step-change in point-of-care diagnostics of bladder cancer. The concept relies on combining optical methods (optical coherence tomography, multi-spectral opto-acoustic tomography, shifted excitation Raman difference spectroscopy, and multiphoton microscopy) providing structural, biochemical and functional information. The hypothesis is that such combination enables in situ diagnosis of bladder cancer with superior sensitivity and specificity due to unprecedented combined anatomic, biochemical and molecular tissue information. The step-change is that this hybrid concept is provided endoscopically for in vivo clinical use. The project relies on development of new light sources, high-speed imaging systems, unique imaging fibre bundles, and endoscopes, combined and applied clinically. The consortium comprises world-leading academic organisations in a strong partnership with innovative SMEs and clinical end-users. Through commercialization of this novel imaging platform, MIB is expected to reinforce leading market positions in medical devices and healthcare for the SMEs in areas where European industry is already strong. The impact is that improved diagnostic procedures facilitate earlier onset of effective treatment, thus recurrence and follow-up procedures would be reduced by 10%, i.e., reducing costs. Using MIB technology, healthcare cost savings in the order of 360M€ are expected for the whole EU. Equally important, prognosis and patient quality of life would improve drastically.