AI Article Synopsis

  • Scientists want to connect how genes affect traits by analyzing proteins from cells, but it's hard because there's no perfect method for studying proteins yet.
  • New technology has improved how we prepare and separate tiny samples for analysis, but we still need better sensitivity and speed in these tests.
  • This article discusses new techniques for analyzing proteins without labels, helping researchers pick the best methods for their work while also pointing out future possibilities and limitations.

Article Abstract

The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10909491PMC
http://dx.doi.org/10.1002/pmic.202200162DOI Listing

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