Single-Cell Proteomics Using Mass Spectrometry. Article Swipe
Single-cell proteomics (SCP) is transforming our understanding of biological complexity by shifting from bulk proteomics, where signals are averaged over thousands of cells, to the proteome analysis of individual cells. This granular perspective reveals distinct cell states, population heterogeneity, and the underpinnings of disease pathogenesis that bulk approaches may obscure. However, SCP demands exceptional sensitivity, precise cell handling, and robust data processing to overcome the inherent challenges of analyzing picogram-level protein samples without amplification. Recent innovations in sample preparation, separations, data acquisition strategies, and specialized mass spectrometry instrumentation have substantially improved proteome coverage and throughput. Approaches that integrate complementary omics, streamline multi-step sample processing, and automate workflows through microfluidics and specialized platforms promise to further push SCP boundaries. Advances in computational methods, especially for data normalization and imputation, address the pervasive issue of missing values, enabling more reliable downstream biological interpretations. Despite these strides, higher throughput, reproducibility, and consensus best practices remain pressing needs in the field. This mini review summarizes the latest progress in SCP technology and software solutions, highlighting how closer integration of analytical, computational, and experimental strategies will facilitate deeper and broader coverage of single-cell proteomes.
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Metadata
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All OpenAlex metadata
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Single-Cell Proteomics Using Mass Spectrometry.Work title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-03-29Full publication date if available
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Amanda Momenzadeh, Jesse G. MeyerList of authors in order
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https://pubmed.ncbi.nlm.nih.gov/40034135Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2502.11982Direct OA link when available
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Proteomics, Mass spectrometry, Computer science, Chemistry, Chromatography, Computational biology, Biology, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.computational | 120 |
| abstract_inverted_index.microfluidics | 108 |
| abstract_inverted_index.normalization | 125 |
| abstract_inverted_index.substantially | 89 |
| abstract_inverted_index.underpinnings | 41 |
| abstract_inverted_index.understanding | 6 |
| abstract_inverted_index.amplification. | 73 |
| abstract_inverted_index.computational, | 176 |
| abstract_inverted_index.heterogeneity, | 38 |
| abstract_inverted_index.picogram-level | 69 |
| abstract_inverted_index.instrumentation | 87 |
| abstract_inverted_index.interpretations. | 140 |
| abstract_inverted_index.reproducibility, | 146 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.05479847 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |