Evidence map›Paper›PMID 37206615›Full record

ArticleTrends in analytical chemistry : TRAC2023

High-Throughput Mass Spectrometry Imaging of Biological Systems: Current Approaches and Future Directions.

Li-Xue Jiang, Manxi Yang, Syeda Nazifa Wali, Julia Laskin

Abstract read
In one paragraph

Article in Trends in analytical chemistry : TRAC, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Spatial pharmacology using mass spectrometry imaging.Trends in pharmacological sciences · 2024
    Review
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Li-Xue JiangDepartment of Chemistry, Purdue University, West Lafayette, Indiana, 47907, United States.
Manxi YangDepartment of Chemistry, Purdue University, West Lafayette, Indiana, 47907, United States.
Syeda Nazifa WaliDepartment of Chemistry, Purdue University, West Lafayette, Indiana, 47907, United States.
Julia LaskinDepartment of Chemistry, Purdue University, West Lafayette, Indiana, 47907, United States.

Funding

Development of a high throughput system for molecular imaging of different cell types in mouse brain tissuesRF1MH128866 · NIMH · PURDUE UNIVERSITY · PI CHOPRA, GAURAV, LASKIN, JULIA · 2021 to 2021
$1.5M
Novel Platform for Quantitative Subcellular Resolution Imaging of Human Tissues Using Mass SpectrometryUH3CA255132 · NCI · PURDUE UNIVERSITY · PI LASKIN, JULIA · 2020 to 2021
$1.3M
NCI NIH HHS UH3 CA255132NIMH NIH HHS RF1 MH128866
6 · The paper itself

Abstract

In the past two decades, the power of mass spectrometry imaging (MSI) for the label free spatial mapping of molecules in biological systems has been substantially enhanced by the development of approaches for imaging with high spatial resolution. With the increase in the spatial resolution, the experimental throughput has become a limiting factor for imaging of large samples with high spatial resolution and 3D imaging of tissues. Several experimental and computational approaches have been recently developed to enhance the throughput of MSI. In this critical review, we provide a succinct summary of the current approaches used to improve the throughput of MSI experiments. These approaches are focused on speeding up sampling, reducing the mass spectrometer acquisition time, and reducing the number of sampling locations. We discuss the rate-determining steps for different MSI methods and future directions in the development of high-throughput MSI techniques.

Indexed as

Continuous scanningMSIMultimodalSparse samplingThroughput

Identifiers

PMID37206615
PMCPMC10191415

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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.