Periodic Reporting for period 1 - imATLAS (Multimodal imaging of lung tissue based on LIBS, MALDI and histology fusion to unravel sarcoidosis causes)
Période du rapport: 2023-10-01 au 2025-09-30
The imATLAS project's main objective was to develop and validate a novel multimodal imaging workflow by fusing two powerful analytical techniques: Laser-Induced Breakdown Spectroscopy (LIBS) for elemental mapping (e.g. metals) and Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MSI) for molecular mapping (e.g. lipids, peptides). The ultimate goal was to co-localize these elemental and molecular signals in human lung tissue to identify specific "fingerprints" of exposure and unravel the causes of idiopathic diseases, particularly those triggered by an exogenous agent. Identifying the specific triggering agent is critical for the diagnosis and treatment of these diseases, yet effective diagnostic tools to do this are lacking.
The imATLAS project has pushed the state-of-the-art in two key areas. First, the dual-detection kHz-LIBS prototype represents a significant hardware innovation for elemental bio-imaging, solving a key limitation of standard systems. Second, the "feature-level" LIBS-MALDI fusion workflow provides a viable new method for correlating elemental and molecular data in complex tissues.
The existing kHz-LIBS system was substantially re-engineered for biological analysis. A new optical microscopy path was integrated for precise focusing on transparent tissue. Most critically, a novel dual-detection (ICCD/CMOS) system was designed and implemented, allowing the system to simultaneously detect trace-level elements (like particles) and high-concentration matrix elements. The new instrument achieved a 10 µm spatial resolution—a 2x improvement on the original goal—and successfully detected metallic nanoparticles in the rodent lung models.
Data processing became the project's principal focus. When a simple "low-level" fusion failed, a successful "mid-level" (or "feature-level") fusion strategy was developed. This workflow statistically correlates 2D elemental maps (from LIBS) with thousands of molecular spectra (from MALDI) to find significant co-localized signals. This work, which included the first-time application of PCA for LIBS denoising in this context, led to two peer-reviewed publications.
A key result from the imATLAS project was the successful alignment and correlation of elemental LIBS images with various molecular MALDI-MSI images from a rodent tissue model, which validated this new workflow.
The imATLAS project has pushed the state-of-the-art in two key areas. First, the dual-detection kHz-LIBS prototype represents a significant hardware innovation for elemental bio-imaging, solving a key limitation of standard systems. Second, the "feature-level" LIBS-MALDI fusion workflow provides a viable new method for correlating elemental and molecular data in complex tissues.
The existing kHz-LIBS system was substantially re-engineered for biological analysis. A new optical microscopy path was integrated for precise focusing on transparent tissue. Most critically, a novel dual-detection (ICCD/CMOS) system was designed and implemented, allowing the system to simultaneously detect trace-level elements (like particles) and high-concentration matrix elements. The new instrument achieved a 10 µm spatial resolution—a 2x improvement on the original goal—and successfully detected metallic nanoparticles in the rodent lung models.
Data processing became the project's principal focus. When a simple "low-level" fusion failed, a successful "mid-level" (or "feature-level") fusion strategy was developed. This workflow statistically correlates 2D elemental maps (from LIBS) with thousands of molecular spectra (from MALDI) to find significant co-localized signals. This work, which included the first-time application of PCA for LIBS denoising in this context, led to two peer-reviewed publications.
A key result from the imATLAS project was the successful alignment and correlation of elemental LIBS images with various molecular MALDI-MSI images from a rodent tissue model, which validated this new workflow.
The project's technical work was structured around four main work packages, which encountered significant methodological challenges that led to strategic pivots and important new developments.
The project's most significant methodological challenge was the preparation of formalin-fixed and paraffin-embedded (FFPE) human tissue samples for multimodal analysis.
Work on LIBS (Laser-Induced Breakdown Spectroscopy) preparation (WP1) determined that direct analysis on bulk FFPE blocks was unfeasible due to high spectral interferences from the paraffin matrix (e.g. C2 and CN bands). Analysis on thin sections was successful, but the substrates required for MALDI-MSI (glass or conductive ITO-coated slides) produced high background signals that saturated the LIBS detectors. This critical finding indicated that a co-located measurement on the exact same sample was not viable
This use of the kHz-LIBS system required a substantial adaptation of the preceding kHz-LIBS system to make it suitable for biological analysis. The previous setup was designed for geology and used a laser distance sensor for focusing, which failed to respond to translucent tissue on transparent slides. The main technical achievements were:
1. New Focusing System
2. Dual-Detection System: A critical challenge was "dynamic range saturation," where major elements saturated the detectors, hiding the trace elements of interest. This was solved by re-engineering the system to include two simultaneous detection paths.
3. Hardware Integration: A new sample holder was developed, and the entire system was precisely synchronized.
The outcome was a "functional kHz system" that successfully detected inorganic Ti nanoparticles in the rodent tissue models. A strategic deviation was made prioritize spatial resolution (10 µm), a 2x improvement on the original project’s goal
This Data Processing and Fusion Workflow became the principal focus of the project's efforts in order to build a data treatment pipeline.
The initial "low-level" fusion strategy (concatenating datasets) failed. The corrective action was to pivot to a "mid-level" (or "feature-level") fusion strategy. This involved developing discrete, functional scripts for preprocessing (e.g. 'Rolling Ball' background removal, 'cross-correlation' image alignment). This workflow statistically correlates 2D elemental maps from LIBS with thousands of individual molecular (m/z) signals from MALDI.
This work was supported by two key collaborations that produced new data science methods:
Hyperspectral Denoising: An investigation into denoising techniques (e.g. Savitzky-Golay, FFT, PCA, Whittaker Smoothing). The key discovery was that PCA is the most effective method for this LIBS framework, providing a 5-fold SNR enhancement with no signal distortion. This was the first known application of PCA and Whittaker Smoothing to LIBS data in this context. The Automated Peak Identification was developed to automatically identify emission lines in LIBS spectra.
The project's most significant methodological challenge was the preparation of formalin-fixed and paraffin-embedded (FFPE) human tissue samples for multimodal analysis.
Work on LIBS (Laser-Induced Breakdown Spectroscopy) preparation (WP1) determined that direct analysis on bulk FFPE blocks was unfeasible due to high spectral interferences from the paraffin matrix (e.g. C2 and CN bands). Analysis on thin sections was successful, but the substrates required for MALDI-MSI (glass or conductive ITO-coated slides) produced high background signals that saturated the LIBS detectors. This critical finding indicated that a co-located measurement on the exact same sample was not viable
This use of the kHz-LIBS system required a substantial adaptation of the preceding kHz-LIBS system to make it suitable for biological analysis. The previous setup was designed for geology and used a laser distance sensor for focusing, which failed to respond to translucent tissue on transparent slides. The main technical achievements were:
1. New Focusing System
2. Dual-Detection System: A critical challenge was "dynamic range saturation," where major elements saturated the detectors, hiding the trace elements of interest. This was solved by re-engineering the system to include two simultaneous detection paths.
3. Hardware Integration: A new sample holder was developed, and the entire system was precisely synchronized.
The outcome was a "functional kHz system" that successfully detected inorganic Ti nanoparticles in the rodent tissue models. A strategic deviation was made prioritize spatial resolution (10 µm), a 2x improvement on the original project’s goal
This Data Processing and Fusion Workflow became the principal focus of the project's efforts in order to build a data treatment pipeline.
The initial "low-level" fusion strategy (concatenating datasets) failed. The corrective action was to pivot to a "mid-level" (or "feature-level") fusion strategy. This involved developing discrete, functional scripts for preprocessing (e.g. 'Rolling Ball' background removal, 'cross-correlation' image alignment). This workflow statistically correlates 2D elemental maps from LIBS with thousands of individual molecular (m/z) signals from MALDI.
This work was supported by two key collaborations that produced new data science methods:
Hyperspectral Denoising: An investigation into denoising techniques (e.g. Savitzky-Golay, FFT, PCA, Whittaker Smoothing). The key discovery was that PCA is the most effective method for this LIBS framework, providing a 5-fold SNR enhancement with no signal distortion. This was the first known application of PCA and Whittaker Smoothing to LIBS data in this context. The Automated Peak Identification was developed to automatically identify emission lines in LIBS spectra.
The imATLAS project successfully pushed beyond the state-of-the-art in two primary, interconnected areas:
- Instrumental Innovation: The development of a novel kHz-LIBS prototype specifically re-engineered and optimized for elemental bio-imaging .
- Methodological Innovation: The creation of new data processing methodologies for hyperspectral denoising and multimodal fusion, resulting in two peer-reviewed publications
- Instrumental Innovation: The development of a novel kHz-LIBS prototype specifically re-engineered and optimized for elemental bio-imaging .
- Methodological Innovation: The creation of new data processing methodologies for hyperspectral denoising and multimodal fusion, resulting in two peer-reviewed publications