Danielle Dionne

23.5k total citations · 7 hit papers
16 papers, 4.5k citations indexed

About

Danielle Dionne is a scholar working on Molecular Biology, Immunology and Neurology. According to data from OpenAlex, Danielle Dionne has authored 16 papers receiving a total of 4.5k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 6 papers in Immunology and 4 papers in Neurology. Recurrent topics in Danielle Dionne's work include Single-cell and spatial transcriptomics (7 papers), Immune Cell Function and Interaction (5 papers) and Neuroinflammation and Neurodegeneration Mechanisms (3 papers). Danielle Dionne is often cited by papers focused on Single-cell and spatial transcriptomics (7 papers), Immune Cell Function and Interaction (5 papers) and Neuroinflammation and Neurodegeneration Mechanisms (3 papers). Danielle Dionne collaborates with scholars based in United States, Israel and Germany. Danielle Dionne's co-authors include Aviv Regev, Orit Rozenblatt–Rosen, Lan Nguyễn, Eric S. Lander, Vijay K. Kuchroo, Jenny Chen, Britt Adamson, Livnat Jerby‐Arnon, Raktima Raychowdhury and Charles P. Fulco and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Danielle Dionne

16 papers receiving 4.5k citations

Hit Papers

Perturb-Seq: Dissecting Molecular Circuits with Scalable ... 2016 2026 2019 2022 2016 2020 2019 2019 2019 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Danielle Dionne United States 16 2.4k 1.2k 833 733 543 16 4.5k
Klemens Ruprecht Germany 50 1.9k 0.8× 760 0.6× 773 0.9× 573 0.8× 823 1.5× 211 8.0k
Mohsen Khademi Sweden 47 1.7k 0.7× 2.5k 2.0× 1.0k 1.2× 901 1.2× 344 0.6× 130 6.7k
Shigetomo Fukuhara Japan 41 4.4k 1.8× 988 0.8× 725 0.9× 300 0.4× 607 1.1× 97 6.5k
Markus Krumbholz Germany 36 1.5k 0.6× 2.9k 2.3× 688 0.8× 915 1.2× 508 0.9× 69 6.3k
Tal Shay Israel 23 2.1k 0.9× 2.5k 2.0× 815 1.0× 495 0.7× 641 1.2× 39 5.0k
Ted Yednock United States 38 1.9k 0.8× 2.3k 1.8× 1.0k 1.3× 697 1.0× 317 0.6× 78 5.9k
Rosetta Pedotti Italy 27 1.5k 0.6× 2.0k 1.6× 575 0.7× 458 0.6× 270 0.5× 40 4.9k
Mirko H. H. Schmidt Germany 36 2.5k 1.1× 572 0.5× 647 0.8× 589 0.8× 780 1.4× 73 4.5k
Pietro Luigi Poliani Italy 42 2.1k 0.9× 2.2k 1.8× 1.1k 1.3× 708 1.0× 603 1.1× 145 5.8k
Zeynep Kalender Atak Belgium 17 3.4k 1.4× 1.4k 1.1× 893 1.1× 231 0.3× 831 1.5× 23 4.9k

Countries citing papers authored by Danielle Dionne

Since Specialization
Citations

This map shows the geographic impact of Danielle Dionne'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 Danielle Dionne with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danielle Dionne more than expected).

Fields of papers citing papers by Danielle Dionne

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Danielle Dionne. 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 Danielle Dionne. The network helps show where Danielle Dionne may publish in the future.

Co-authorship network of co-authors of Danielle Dionne

This figure shows the co-authorship network connecting the top 25 collaborators of Danielle Dionne. A scholar is included among the top collaborators of Danielle Dionne 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 Danielle Dionne. Danielle Dionne is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Ximerakis, Methodios, Kristina M. Holton, Ceren Ozek, et al.. (2023). Heterochronic parabiosis reprograms the mouse brain transcriptome by shifting aging signatures in multiple cell types. Nature Aging. 3(3). 327–345. 39 indexed citations
2.
Hodis, Eran, Elena Torlai Triglia, John Kwon, et al.. (2022). Stepwise-edited, human melanoma models reveal mutations’ effect on tumor and microenvironment. Science. 376(6592). eabi8175–eabi8175. 32 indexed citations
3.
He, Danyang, Heping Xu, Huiyuan Zhang, et al.. (2022). Disruption of the IL-33-ST2-AKT signaling axis impairs neurodevelopment by inhibiting microglial metabolic adaptation and phagocytic function. Immunity. 55(1). 159–173.e9. 94 indexed citations
4.
Pawlak, Mathias, David DeTomaso, Alexandra Schnell, et al.. (2022). Induction of a colitogenic phenotype in Th1-like cells depends on interleukin-23 receptor signaling. Immunity. 55(9). 1663–1679.e6. 27 indexed citations
5.
Schnell, Alexandra, Linglin Huang, Meromit Singer, et al.. (2021). Stem-like intestinal Th17 cells give rise to pathogenic effector T cells during autoimmunity. Cell. 184(26). 6281–6298.e23. 157 indexed citations
6.
Dixon, Karen O., Marcin Tabaka, Markus A. Schramm, et al.. (2021). TIM-3 restrains anti-tumour immunity by regulating inflammasome activation. Nature. 595(7865). 101–106. 255 indexed citations breakdown →
7.
Habib, Naomi, Cristin McCabe, Daniel Kitsberg, et al.. (2020). Disease-associated astrocytes in Alzheimer’s disease and aging. Nature Neuroscience. 23(6). 701–706. 587 indexed citations breakdown →
8.
Drokhlyansky, Eugene, Christopher S. Smillie, Nicholas Van Wittenberghe, et al.. (2020). The Human and Mouse Enteric Nervous System at Single-Cell Resolution. Cell. 182(6). 1606–1622.e23. 337 indexed citations breakdown →
9.
Subramanian, Ayshwarya, Eriene-Heidi Sidhom, Maheswarareddy Emani, et al.. (2019). Single cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation. Nature Communications. 10(1). 5462–5462. 139 indexed citations
10.
Ximerakis, Methodios, Scott Lipnick, Brendan T. Innes, et al.. (2019). Single-cell transcriptomic profiling of the aging mouse brain. Nature Neuroscience. 22(10). 1696–1708. 439 indexed citations breakdown →
11.
Baryawno, Ninib, Dariusz Przybylski, Monika S. Kowalczyk, et al.. (2019). A Cellular Taxonomy of the Bone Marrow Stroma in Homeostasis and Leukemia. Cell. 177(7). 1915–1932.e16. 558 indexed citations breakdown →
12.
Kurtuluş, Sema, Asaf Madi, Giulia Escobar, et al.. (2019). Checkpoint Blockade Immunotherapy Induces Dynamic Changes in PD-1−CD8+ Tumor-Infiltrating T Cells. Immunity. 50(1). 181–194.e6. 401 indexed citations breakdown →
13.
Gaublomme, Jellert T., Bo Li, Cristin McCabe, et al.. (2019). Nuclei multiplexing with barcoded antibodies for single-nucleus genomics. Nature Communications. 10(1). 2907–2907. 88 indexed citations
14.
Tekin, Halil, Sean Simmons, Beryl B. Cummings, et al.. (2018). Effects of 3D culturing conditions on the transcriptomic profile of stem-cell-derived neurons. Nature Biomedical Engineering. 2(7). 540–554. 72 indexed citations
15.
Dixit, Atray, Oren Parnas, Jenny Chen, et al.. (2016). Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell. 167(7). 1853–1866.e17. 1027 indexed citations breakdown →
16.
Costello, Maura, Trevor J. Pugh, Chip Stewart, et al.. (2013). Discovery and characterization of artifactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation. Nucleic Acids Research. 41(6). e67–e67. 252 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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