ReviewMolecular & cellular proteomics : MCP2023
Challenges and Opportunities for Single-cell Computational Proteomics.
Review in Molecular & cellular proteomics : MCP, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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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.
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Who cites it
13 citing papers in PubMed.
- Unpaired data as a first-order challenge in single-cell and spatial proteomics.Nature biotechnology · 2026Article
- What Are the Practical Applications of Single-Cell Proteomics?Proteomes · 2026Article
- scProfiterole: Clustering of Single-Cell Proteomic Data Using Graph Contrastive Learning via Spectral Filters.bioRxiv : the preprint server for biology · 2026Article
- Proteomic Studies in Absence Epilepsy: A Systematic Review of Methodological Diversity and Implications for Data Interpretation.Current issues in molecular biology · 2026Review
- Sample Preparation for Multi-Omics Analysis: Considerations and Guidance for Identifying the Ideal Workflow.Proteomics · 2025Review
- Spatial biology using single-cell mass spectrometry imaging and integrated microscopy.Nature communications · 2025Article
- Single-cell proteomics using mass spectrometry.Cell genomics · 2025Review
- Mass Spectrometry-based Solutions for Single-cell Proteomics.Genomics, proteomics & bioinformatics · 2025Review
- Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.Journal of proteome research · 2025Review
- Attracting Computational Researchers to Proteomics.Journal of the American Society for Mass Spectrometry · 2024Article
- A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics.ACS measurement science au · 2024Review
- Comprehensive Micro-SPE-Based Bottom-Up Proteomic Workflow for Sensitive Analysis of Limited Samples.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Evaluating Proteomics Imputation Methods with Improved Criteria.Journal of proteome research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.
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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.