Periodic Reporting for period 1 - ROICAM (Revisiting the origin of intrinsic cellular autofluorescence in metabolic imaging)
Berichtszeitraum: 2023-09-18 bis 2025-09-17
Metabolic molecules such as NADH and FAD emit light when excited at specific wavelengths by a laser, through a process known as fluorescence. Because it requires no dyes or labels, multiphoton fluorescence lifetime imaging microscopy (MPM-FLIM) can use this autofluorescence to monitor how cells use energy in real time. However, in skin and other complex tissues, structural proteins like keratin are also fluorescent and can confuse the metabolic readout.
ROICAM set out to clarify where the autofluorescence signal really comes from, quantify it more reliably, and demonstrate practical imaging workflows that work in realistic biological samples. The project focused on three goals:
RO1: Systematically measure how NADH, FAD and keratin fluoresce under different conditions.
RO2: Build robust analysis methods and software for quantitative fluorescence lifetime imaging, with particular emphasis on phasor analysis.
RO3: Test the approach in relevant skin cell models and under controlled metabolic perturbations.
By the end of the project, ROICAM delivered new measurements and analysis tools that improve the interpretation of label free metabolic imaging, upgraded a multiphoton microscope to collect cleaner data, and showed how different skin cell types respond to metabolic challenges in ways that can now be distinguished more confidently. These outcomes support future applications in non invasive diagnostics and basic research on cell metabolism.
ROICAM set out to clarify where the autofluorescence signal really comes from, quantify it more reliably, and demonstrate practical imaging workflows that work in realistic biological samples. The project focused on three goals:
RO1: Systematically measure how NADH, FAD and keratin fluoresce under different conditions.
RO2: Build robust analysis methods and software for quantitative fluorescence lifetime imaging, with particular emphasis on phasor analysis.
RO3: Test the approach in relevant skin cell models and under controlled metabolic perturbations.
By the end of the project, ROICAM delivered new measurements and analysis tools that improve the interpretation of label free metabolic imaging, upgraded a multiphoton microscope to collect cleaner data, and showed how different skin cell types respond to metabolic challenges in ways that can now be distinguished more confidently. These outcomes support future applications in non invasive diagnostics and basic research on cell metabolism.
• Established reference measurements: We measured fluorescence spectra and lifetimes of NADH under a range of pH, temperature and viscosity conditions, and in mixtures with key metabolic enzymes as well as with keratin. A clear dependence of NADH lifetime on viscosity was confirmed.
• Advanced open software for the community: The project extended the open-source PAM software with reconvolution fitting, improved phasor lifetime analysis, and region of interest handling compatible with deep learning-based cell segmentation tools. These additions enable reproducible, batch analysis of large imaging datasets and are available for others to use and build upon.
• Validated in biological models: Using primary human keratinocytes and fibroblasts, we applied mitochondrial inhibitors and hypoxia to perturb metabolism and recorded distinct lifetime responses between cell types. This supports the idea that background keratin and true metabolic signals can be better separated with the improved workflow.
• Upgraded imaging hardware: Detector and optics improvements increased photon collection and time resolution on the multiphoton microscope, raising overall image quality for current and future projects at the host institution.
• Dissemination and exploitation: Results were shared through conference and workshop presentations and manuscripts prepared or published during 2025. The software modules are released as open source. Hardware upgrades remain in use as shared research infrastructure. Public outreach is planned through local science festival activities. These routes ensure continued uptake of methods after the end of the project.
• Advanced open software for the community: The project extended the open-source PAM software with reconvolution fitting, improved phasor lifetime analysis, and region of interest handling compatible with deep learning-based cell segmentation tools. These additions enable reproducible, batch analysis of large imaging datasets and are available for others to use and build upon.
• Validated in biological models: Using primary human keratinocytes and fibroblasts, we applied mitochondrial inhibitors and hypoxia to perturb metabolism and recorded distinct lifetime responses between cell types. This supports the idea that background keratin and true metabolic signals can be better separated with the improved workflow.
• Upgraded imaging hardware: Detector and optics improvements increased photon collection and time resolution on the multiphoton microscope, raising overall image quality for current and future projects at the host institution.
• Dissemination and exploitation: Results were shared through conference and workshop presentations and manuscripts prepared or published during 2025. The software modules are released as open source. Hardware upgrades remain in use as shared research infrastructure. Public outreach is planned through local science festival activities. These routes ensure continued uptake of methods after the end of the project.
ROICAM advances label free metabolic imaging in three key ways:
• Sharper interpretation of autofluorescence: By isolating how environment factors such as viscosity alter NADH lifetime, the project reduces ambiguity when lifetimes change in cells and tissues.
• Improved, reusable analysis workflows: The combination of reconvolution fitting and phasor analysis tied to segmentation driven regions enables cell wise and subcellular mapping at scale. This meets a growing need for robust, open and automated pipelines in bioimaging.
• Better data at the source: Hardware upgrades deliver cleaner, higher time resolution lifetime data, improving accuracy for all users of the system.
These advances can help researchers study disease mechanisms and drug responses with fewer reagents and less sample preparation, and they point toward non invasive metabolic profiling of skin. Potential beneficiaries include academic labs, clinical research units and companies developing imaging based diagnostics.
• Sharper interpretation of autofluorescence: By isolating how environment factors such as viscosity alter NADH lifetime, the project reduces ambiguity when lifetimes change in cells and tissues.
• Improved, reusable analysis workflows: The combination of reconvolution fitting and phasor analysis tied to segmentation driven regions enables cell wise and subcellular mapping at scale. This meets a growing need for robust, open and automated pipelines in bioimaging.
• Better data at the source: Hardware upgrades deliver cleaner, higher time resolution lifetime data, improving accuracy for all users of the system.
These advances can help researchers study disease mechanisms and drug responses with fewer reagents and less sample preparation, and they point toward non invasive metabolic profiling of skin. Potential beneficiaries include academic labs, clinical research units and companies developing imaging based diagnostics.