ArticleACS measurement science au2022
High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling.
Article in ACS measurement science au, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mass spectrometry-based human spatial omics: fundamentals, innovations, and applications.Journal of biomedical science · 2026Pooled it
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Pneumatically Assisted Microfluidic Probe for Enhanced Mass Spectrometry Imaging Performance.Journal of the American Society for Mass Spectrometry · 2025Article
- A sulfatide-centered ultra-high-resolution magnetic resonance MALDI imaging benchmark dataset for MS1-based lipid annotation tools.GigaScience · 2025Article
- Recent Applications of Artificial Intelligence and Related Technical Challenges in MALDI MS and MALDI-MSI: A Mini Review.Mass spectrometry (Tokyo, Japan) · 2025Review
- Hardware and software solutions for implementing nanospray desorption electrospray ionization (nano-DESI) sources on commercial mass spectrometers.Journal of mass spectrometry : JMS · 2024Article
- [Research progress of deep learning applications in mass spectrometry imaging data analysis].Se pu = Chinese journal of chromatography · 2024Review
- Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry.Nature methods · 2024Article
- Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry.bioRxiv : the preprint server for biology · 2023Article
- High-resolution integrated microfluidic probe for mass spectrometry imaging of biological tissues.Analytica chimica acta · 2023Article
- Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy.Nature communications · 2023Article
- Applications of ambient ionization mass spectrometry in 2022: An annual review.Analytical science advances · 2023Review
- High-Throughput Mass Spectrometry Imaging of Biological Systems: Current Approaches and Future Directions.Trends in analytical chemistry : TRAC · 2023Article
- Mass Spectrometry Imaging for Single-Cell or Subcellular Lipidomics: A Review of Recent Advancements and Future Development.Molecules (Basel, Switzerland) · 2023Review
- Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging.IEEE transactions on computational imaging · 2023Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Rapid, Sensitive, and Versatile: A 5-Year Perspective on Applications of Ambient Ionization Mass Spectrometry in Bioanalytical Science.Mass spectrometry reviewsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Mass spectrometry imaging (MSI) enables label-free mapping of hundreds of molecules in biological samples with high sensitivity and unprecedented specificity. Conventional MSI experiments are relatively slow, limiting their utility for applications requiring rapid data acquisition, such as intraoperative tissue analysis or 3D imaging. Recent advances in MSI technology focus on improving the spatial resolution and molecular coverage, further increasing the acquisition time. Herein, a deep learning approach for dynamic sampling (DLADS) was employed to reduce the number of required measurements, thereby improving the throughput of MSI experiments in comparison with conventional methods. DLADS trains a deep learning model to dynamically predict molecularly informative tissue locations for active mass spectra sampling and reconstructs high-fidelity molecular images using only the sparsely sampled information. Experimental hardware and software integration of DLADS with nanospray desorption electrospray ionization (nano-DESI) MSI is reported for the first time, which demonstrates a 2.3-fold improvement in throughput for a linewise acquisition mode. Meanwhile, simulations indicate that a 5-10-fold throughput improvement may be achieved using the pointwise acquisition mode.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.