ResearcherJohns Hopkins UniversityActive in the last 2 years
Denis Wirtz
Matched on ORCID iD 0000-0001-6147-3045; OpenAlex author ID A5003835601; NIH RePORTER profile ID 3127500. Sources: OpenAlex.
Their work
- Papers
- 7 (first author 0, last author 2)
- Trials
- 0
- Grants
- 6
- Labs led
- 1
- Collaborators
- 24
2012: 1 grant · 2013: none · 2014: 1 grant · 2015: none · 2016: 1 grant · 2017: 1 paper · 2018: none · 2019: none · 2020: none · 2021: none · 2022: 1 paper · 2023: 2 papers · 2024: 1 paper · 2025: none · 2026: 2 papers, 3 grants
Papers 7
- Quantitative 3D histology reveals localized immune remodeling during early pancreatic cancer progression. Last author DOI 10.1016/j.cpblue.2026.100008↗
- Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer Co-author DOI 10.64898/2026.03.03.709150↗
- Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings. Co-author DOI 10.1126/sciadv.ado5103↗
- Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer. Co-author DOI 10.1016/j.medj.2022.11.009↗
- Intraparenchymal metastases as a cause for local recurrence of pancreatic cancer. Last author DOI 10.1111/his.14839↗
- CODA: quantitative 3D reconstruction of large tissues at cellular resolution Co-author DOI 10.1038/s41592-022-01650-9↗
- Computational discovery of tissue morphology biomarkers in very long-term survivors with pancreatic ductal adenocarcinoma Co-author DOI 10.1101/207969↗
Grants 6
- Engineering self-propelled tumor-infiltrating CAR T cells using synthetic velocity receptors R01CA300052↗
- T32: Predoctoral and Postdoctoral Training Program in Nanotechnology for Cancer Research T32CA153952↗
- RTB 1 U54CA268083↗
- Single-cell phenotyping for therapeutic stratification in pancreatic cancer R01CA174388↗
- The Johns Hopkins Cancer Nanotechnology Training Center R25CA153952↗
- T-32 NIH Postdoctoral Training Program in Nanotechnology for Cancer Medicine NTCM T32CA130840↗
Closest collaborators
The 24 strongest of 24 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Laura DeLong Wood · 7 shared papers, 1 shared grant, 2017–2026 · strength 2.21
- Grant RTB 1 U54CA268083↗
- Paper Quantitative 3D histology reveals localized immune remodeling during early pancreatic cancer progression. DOI 10.1016/j.cpblue.2026.100008↗
- Paper Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic… DOI 10.1126/sciadv.ado5103↗
- Pei-Hsun wu · 3 shared papers, 1 shared grant, 2023–2026 · strength 1.56
- Grant RTB 1 U54CA268083↗
- Paper Quantitative 3D histology reveals localized immune remodeling during early pancreatic cancer progression. DOI 10.1016/j.cpblue.2026.100008↗
- Paper Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic ca… DOI 10.1016/j.medj.2022.11.009↗
- Ashley L. Kiemen · 6 shared papers, 2022–2026 · strength 1.14
- Paper Quantitative 3D histology reveals localized immune remodeling during early pancreatic cancer progression. DOI 10.1016/j.cpblue.2026.100008↗
- Paper Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic… DOI 10.1126/sciadv.ado5103↗
- Paper Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in … DOI 10.64898/2026.03.03.709150↗
- Ralph H. Hruban · 6 shared papers, 2017–2026 · strength 0.99
- Paper Quantitative 3D histology reveals localized immune remodeling during early pancreatic cancer progression. DOI 10.1016/j.cpblue.2026.100008↗
- Paper Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic… DOI 10.1126/sciadv.ado5103↗
- Paper Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic ca… DOI 10.1016/j.medj.2022.11.009↗
- Pei-Hsun Wu · 4 shared papers, 2017–2026 · strength 0.65
- Paper Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic… DOI 10.1126/sciadv.ado5103↗
- Paper Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in … DOI 10.64898/2026.03.03.709150↗
- Paper CODA: quantitative 3D reconstruction of large tissues at cellular resolution DOI 10.1038/s41592-022-01650-9↗
- Toby C. Cornish · 3 shared papers, 2022–2026 · strength 0.58
- Paper Quantitative 3D histology reveals localized immune remodeling during early pancreatic cancer progression. DOI 10.1016/j.cpblue.2026.100008↗
- Paper Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic… DOI 10.1126/sciadv.ado5103↗
- Paper CODA: quantitative 3D reconstruction of large tissues at cellular resolution DOI 10.1038/s41592-022-01650-9↗
- André Forjaz · 2 shared papers, 2026 · strength 0.46
- Paper Quantitative 3D histology reveals localized immune remodeling during early pancreatic cancer progression. DOI 10.1016/j.cpblue.2026.100008↗
- Paper Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in … DOI 10.64898/2026.03.03.709150↗
- Won Jin Ho · 2 shared papers, 2026 · strength 0.46
- Paper Quantitative 3D histology reveals localized immune remodeling during early pancreatic cancer progression. DOI 10.1016/j.cpblue.2026.100008↗
- Paper Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in … DOI 10.64898/2026.03.03.709150↗
- David Zwicker · 1 shared paper, 2024 · strength 0.23
- Dimitrios N. Sidiropoulos · 1 shared paper, 2026 · strength 0.22
- Elana Judith Fertig · 1 shared paper, 2026 · strength 0.22
- Paola A. Guerrero · 1 shared paper, 2026 · strength 0.22
- Luciane Tsukamoto Kagohara · 1 shared paper, 2026 · strength 0.22
- Vincent Bernard Pagan · 1 shared paper, 2026 · strength 0.22
- Anirban Maitra · 1 shared paper, 1 shared grant, 2014–2017 · strength 0.22
- Jin Peng He · 1 shared paper, 2023 · strength 0.17
- Alexander Ioannis Damanakis · 1 shared paper, 2023 · strength 0.17
- Nicholas Jason Roberts · 1 shared paper, 2023 · strength 0.17
- Elliot K. Fishman · 1 shared paper, 2023 · strength 0.17
- Daniel A. Laheru · 1 shared paper, 2023 · strength 0.17
- Hai-Quan Mao · 1 shared grant, 2014 · strength 0.14
- Seung‐Mo Hong · 1 shared paper, 2022 · strength 0.12
- Jacob S. Sarnecki · 1 shared paper, 2017 · strength 0.08
- Christopher Lee Wolfgang · 1 shared paper, 2017 · strength 0.08
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.