7/ Compared to spray-coating, LTE shows:
✔️ Improved ionization
✔️ Reduced analyte diffusion
✔️ Better image sharpness
✔️ Cleaner baseline

8/ LTE pushes the boundaries of matrix deposition for MALDI-MSI. Better control, better data, better images. #MALDI #MassSpec #MSI #Metabolomics #Lipidomics

5/ LTE purifies the matrix during deposition — great for improving signal-to-noise, even when starting with lower-purity matrix

6/ The matrix stays stable at −80°C for at least 2 weeks. No loss in ionization efficiency or image quality after storage — big win for throughput & experimental planning

3/ We validated LTE using two matrices:
✅ DHB
✅ DAN
Calibrated thickness vs. deposition time = ✔️ reproducibility.

4/ ESEM images showed beautiful, uniform sub-micron matrix crystals across the tissue. Small crystals = better ionization = sharper images.

2/ We adapted Low-Temperature Thermal Evaporation (LTE)—originally used in nanotechnology and solar cell applications—for matrix deposition in #MALDI-MSI. The result: a reproducible, vacuum-based method that offers precise control over matrix thickness and produces ultra-pure coatings.

1/ In MALDI-MSI, matrix deposition is everything. It impacts sensitivity, spatial resolution, and reproducibility. We asked: can we improve matrix application using a dry, solvent-free, controlled method?

5/ Want to learn more? 📄
Read our full paper on #bioRxiv: www.biorxiv.org/content/10.1...
#Metabolomics #MachineLearning #DeepLearning #MSMS
We’d love to hear your thoughts!
This is another successful collaboration with @seeslab.bsky.social at @urv.cat

4/ The results:
✅ ChemEmbed ranks the correct metabolite #1 in 42% of cases in a test dataset.
✅ Finds the correct compound in the top 5 in 76% of cases
✅ Against external benchmarks CASMI 2016 and 2022, and ARUS dataset (unidentified spectra from human plasma & urine), ChemEmbed outperforms #SIRIUS

3/ We enhance MS/MS data by:
✅ Merging spectra from multiple collision energies
✅ Incorporating calculated neutral losses
✅ Training a CNN on a dataset of 38,472 unique compounds from NIST20, MSDIAL, GNPS, and Agilent METLIN metabolomic libraries

2/ Our solution to reduce this problem: #ChemEmbed
We combine enhanced MS/MS spectra with continuous vector representations of molecular structures (300-dimensional embeddings aligned with Mol2vec representations). This gives our CNN-based model richer input, improving annotation accuracy.

1/ The problem:
#Metabolomics relies on MS/MS spectral databases, but most spectra remain unidentified due to limited reference libraries. Computational methods help, but they struggle with high-dimensional and sparse spectral and structural data.

Sorry, now it should work www.biorxiv.org/content/10.1...

www.biorxiv.org/content/10.1...

An ongoing study in our lab focuses on developing new matrices for MALDI-MSI of small molecules (m/z <400) using LTE deposition. We're minimizing matrix background interference while enhancing metabolite ionization and coverage, paving the way for #Spatial #Metabolomics. More exciting results soon!

🔬 Read more about how LTE advances the field of MALDI-MSI, including crystal morphology, stability tests, and comparative analysis with spray-coating, in our new preprint: biorxiv.org/content/10.110…#MALDIMSM#MassSpectrometryr#Lipidomicscs

Spray-coating is the most popular method, but LTE outperforms it by:
🔹 Enhancing ionization efficiency
🔹 Reducing analyte diffusion
🔹 Improving spatial resolution & image quality in MSI images

Stability is another win! After storing mouse brain sections coated with DHB or DAN matrices at -80 °C for two weeks, ionization efficiency, signal intensity, and image quality remained consistent. Robustness is crucial for long-term studies, and LTE delivers.