Ronald Moore

21.6k total citations · 3 hit papers
189 papers, 12.3k citations indexed

About

Ronald Moore is a scholar working on Spectroscopy, Molecular Biology and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Ronald Moore has authored 189 papers receiving a total of 12.3k indexed citations (citations by other indexed papers that have themselves been cited), including 125 papers in Spectroscopy, 119 papers in Molecular Biology and 15 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Ronald Moore's work include Advanced Proteomics Techniques and Applications (109 papers), Mass Spectrometry Techniques and Applications (100 papers) and Metabolomics and Mass Spectrometry Studies (40 papers). Ronald Moore is often cited by papers focused on Advanced Proteomics Techniques and Applications (109 papers), Mass Spectrometry Techniques and Applications (100 papers) and Metabolomics and Mass Spectrometry Studies (40 papers). Ronald Moore collaborates with scholars based in United States, China and South Korea. Ronald Moore's co-authors include Richard Smith, David Camp, Weijun Qian, Jean D. Wilson, Matthew Monroe, Yufeng Shen, Tao Liu, Ljiljana Paša‐Tolić, Ryan Kelly and Marina Gritsenko and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Ronald Moore

189 papers receiving 12.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Toward a Human Blood Serum Proteome

2002 655 citations Joshua Adkins, Ronald Moore et al. Molecular & Cellular Proteomics profile →
Reversed‐phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells

2011 454 citations Feng Yang, Marina Gritsenko et al. PROTEOMICS profile →
Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells

2018 417 citations Ying Zhu, Paul Piehowski et al. Nature Communications profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ronald Moore United States	63	7.7k	6.3k	1.3k	675	658	189	12.3k
Kenneth B. Tomer United States	63	5.0k 0.7×	3.8k 0.6×	937 0.7×	1.2k 1.8×	584 0.9×	259	12.4k
Alexander Makarov Russia	56	8.9k 1.2×	8.3k 1.3×	907 0.7×	203 0.3×	645 1.0×	299	15.5k
N. Leigh Anderson United States	45	7.5k 1.0×	6.0k 1.0×	1.2k 0.9×	249 0.4×	562 0.9×	97	11.4k
Lingjun Li United States	59	8.2k 1.1×	6.1k 1.0×	1.1k 0.8×	202 0.3×	562 0.9×	507	15.4k
Kevin M. Brindle United Kingdom	63	6.4k 0.8×	4.9k 0.8×	1.7k 1.3×	196 0.3×	633 1.0×	269	16.5k
Jean‐Charles Sanchez Switzerland	61	8.3k 1.1×	4.5k 0.7×	607 0.5×	347 0.5×	793 1.2×	225	13.3k
Christoph H. Borchers Canada	64	11.0k 1.4×	5.3k 0.8×	740 0.6×	304 0.5×	1.1k 1.7×	372	16.6k
Lloyd M. Smith United States	64	10.8k 1.4×	3.7k 0.6×	3.3k 2.5×	495 0.7×	827 1.3×	349	21.8k
Gary J. Patti United States	53	8.4k 1.1×	2.3k 0.4×	1.3k 1.0×	325 0.5×	334 0.5×	160	12.3k
Jürgen Schiller Germany	52	5.5k 0.7×	3.1k 0.5×	746 0.6×	318 0.5×	1.1k 1.6×	271	9.6k

Countries citing papers authored by Ronald Moore

Since Specialization

Citations

This map shows the geographic impact of Ronald Moore's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ronald Moore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ronald Moore more than expected).

Fields of papers citing papers by Ronald Moore

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ronald Moore. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ronald Moore. The network helps show where Ronald Moore may publish in the future.

Co-authorship network of co-authors of Ronald Moore

This figure shows the co-authorship network connecting the top 25 collaborators of Ronald Moore. A scholar is included among the top collaborators of Ronald Moore based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ronald Moore. Ronald Moore is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Kitata, Reta Birhanu, Marija Veličković, Zhangyang Xu, et al.. (2025). Robust collection and processing for label-free single voxel proteomics. Nature Communications. 16(1). 547–547. 3 indexed citations

Fulcher, James, Ashley N. Ives, Shinya Tasaki, et al.. (2025). Discovery of Proteoforms Associated With Alzheimer's Disease Through Quantitative Top-Down Proteomics. Molecular & Cellular Proteomics. 24(6). 100983–100983. 5 indexed citations

Zemaitis, Kevin, James Fulcher, Rashmi Kumar, et al.. (2025). Spatial top-down proteomics for the functional characterization of human kidney. Clinical Proteomics. 22(1). 9–9. 1 indexed citations

Fulcher, James, Lye Meng Markillie, Hugh Mitchell, et al.. (2024). Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting. Nature Communications. 15(1). 10614–10614. 13 indexed citations

Liao, Yen‐Chen, James Fulcher, Sarah Williams, et al.. (2023). Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform. Molecular & Cellular Proteomics. 22(2). 100491–100491. 17 indexed citations

Yu, Fengchao, James Fulcher, Sarah Williams, et al.. (2023). Evaluating Linear Ion Trap for MS3-Based Multiplexed Single-Cell Proteomics. Analytical Chemistry. 95(3). 1888–1898. 12 indexed citations

Day, Nicholas, Tong Zhang, Matthew Gaffrey, et al.. (2022). A deep redox proteome profiling workflow and its application to skeletal muscle of a Duchenne Muscular Dystrophy model. Free Radical Biology and Medicine. 193(Pt 1). 373–384. 14 indexed citations

Woo, Jongmin Jacob, Sarah Williams, Lye Meng Markillie, et al.. (2021). High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip. Nature Communications. 12(1). 119 indexed citations

Li, Xiaolu, Nicholas Day, Song Feng, et al.. (2021). Mass spectrometry-based direct detection of multiple types of protein thiol modifications in pancreatic beta cells under endoplasmic reticulum stress. Redox Biology. 46. 102111–102111. 30 indexed citations

10.

Fulcher, James, Aman Makaju, Ronald Moore, et al.. (2021). Enhancing Top-Down Proteomics of Brain Tissue with FAIMS. Journal of Proteome Research. 20(5). 2780–2795. 48 indexed citations

11.

Piehowski, Paul, Ying Zhu, Lisa Bramer, et al.. (2020). Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution. Nature Communications. 11(1). 8–8. 202 indexed citations

12.

Stanfill, Bryan, Ernesto Nakayasu, Lisa Bramer, et al.. (2018). Quality Control Analysis in Real-time (QC-ART): A Tool for Real-time Quality Control Assessment of Mass Spectrometry-based Proteomics Data. Molecular & Cellular Proteomics. 17(9). 1824–1836. 23 indexed citations

13.

Zhu, Ying, Paul Piehowski, Rui Zhao, et al.. (2018). Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells. Nature Communications. 9(1). 882–882. 417 indexed citations breakdown →

14.

Piehowski, Paul, Mowei Zhou, Grant M. Fujimoto, et al.. (2017). Informed-Proteomics: open-source software package for top-down proteomics. Nature Methods. 14(9). 909–914. 118 indexed citations

15.

Guo, Jia, Ziyu Dai, Dian Su, et al.. (2014). Proteome-wide Light/Dark Modulation of Thiol Oxidation in Cyanobacteria Revealed by Quantitative Site-specific Redox Proteomics. Molecular & Cellular Proteomics. 13(12). 3270–3285. 76 indexed citations

16.

Sigdel, Tara K., Nathan Salomonis, Carrie Nicora, et al.. (2013). The Identification of Novel Potential Injury Mechanisms and Candidate Biomarkers in Renal Allograft Rejection by Quantitative Proteomics. Molecular & Cellular Proteomics. 13(2). 621–631. 69 indexed citations

17.

Ding, Shi-Jian, Wei Wang, Jon Jacobs, et al.. (2007). Profiling signaling polarity in chemotactic cells. Proceedings of the National Academy of Sciences. 104(20). 8328–8333. 62 indexed citations

18.

Fang, Ruihua, Dwayne A. Elias, Matthew Monroe, et al.. (2006). Differential Label-free Quantitative Proteomic Analysis of Shewanella oneidensis Cultured under Aerobic and Suboxic Conditions by Accurate Mass and Time Tag Approach. Molecular & Cellular Proteomics. 5(4). 714–725. 78 indexed citations

19.

Liu, Tao, Weijun Qian, Jon Jacobs, et al.. (2005). Improved proteome coverage by using high efficiency cysteinyl peptide enrichment: The human mammary epithelial cell proteome. PROTEOMICS. 5(5). 1263–1273. 53 indexed citations

20.

Moore, Ronald, et al.. (1979). Dihydrotestosterone accumulation is the cause of prostatic hypertrophy in the dog. Clinical research. 27(2). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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