About

Denis Wirtz is the vice provost for research at Johns Hopkins University and the Theophilus Halley Smoot Professor in the Department of Chemical and Biomolecular Engineering at the Whiting School. His research spans the interface of physics, biology, and oncology, with seminal contributions to understanding cancer cell migration, cytoskeleton biophysics, and the emerging field of mechanobiology. Wirtz has developed quantitative methods such as particle-tracking microrheology, which are widely used in academia and industry, and has pioneered research in cell migration in 3D settings, bacterial cell division, and high-throughput cell phenotyping. Recently, he developed CODA, an AI-based imaging method for large tissue and tumor volumes in 3D. He co-founded the Johns Hopkins Institute for NanoBioTechnology (INBT) and directs several NCI-funded programs, including a postdoctoral training program in nanotechnology for oncology, the Physical Sciences-Oncology Center (PS-OC), and the Cellular Cancer Biology Imaging Cancer (CCBIR) Center.

Research topics

• Cell biology
• Biology
• Chemistry
• Cancer research
• Biophysics

Selected publications

• Mechano-induced patterned domain formation by monocytes

  Nature Materials · 2025-11-05 · 1 citations

  articleSenior author
  PublisherDOI

• Supplemental Figure 1 from Age-Related Increases in IGFBP2 Increase Melanoma Cell Invasion and Lipid Synthesis

  2025-04-01

  preprintOpen access

  <p>Overview of IGFBP2 expression in patient data bases, additional pAKT staining, and oil red O zoom out.</p>

  PublisherOA PDFDOI

• Selecting the optimal cell migration assay: fundamentals and practical guidelines

  Nature Methods · 2025-12-18 · 2 citations

  reviewOpen accessSenior author
  PublisherOA PDFDOI

• Senescence-Linked Fibrosis in the Aging Human Ovary Revealed by p16-Based Histological Profiling and Spatial Transcriptomics

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-08 · 5 citations

  articleOpen access

  protein expression, a canonical senescence marker, we identified and mapped senescent cells in postmenopausal ovaries. We integrated p16 immunohistochemistry, multiplexed immunofluorescence, spatial transcriptomics, and AI-guided digital pathology to map senescent microenvironments. p16-positive cells formed discrete stromal, vascular, and cyst-associated clusters that increased with age and were enriched for macrophages and myofibroblast-like cells. Wholetranscriptome profiling of 92 spatial regions uncovered a 32-gene p16-associated signature, BuckSenOvary, that distinguished p16-positive regions across cortex and medulla. BuckSenOvary is characterized by suppression of cell-cycle regulators and activation of inflammatory and extracellular-matrix remodelling genes. AI-based collagen matrix analysis confirmed that p16-positive regions exhibit more architecturally complex collagen, demonstrating that focal senescent microenvironments are fibro-inflammatory. These findings position senescent ovarian niches as therapeutic targets to preserve ovarian function.

  PublisherOA PDFDOI

• Spatial proteomics and transcriptomics reveal early immune cell organization in pancreatic intraepithelial neoplasia

  JCI Insight · 2025-06-26 · 9 citations

  articleOpen access

  Pancreatic ductal adenocarcinoma (PDAC) has a poor survival rate due to late detection. PDAC arises from precursor microscopic lesions, termed pancreatic intraepithelial neoplasia (PanIN), that develop at least a decade before overt disease; this provides an opportunity to intercept PanIN-to-PDAC progression. However, immune interception strategies require full understanding of PanIN and PDAC cellular architecture. Surgical specimens containing PanIN and PDAC lesions from a unique cohort of 5 treatment-naive patients with PDAC were surveyed using spatial omics (proteomic and transcriptomic). Findings were corroborated by spatial proteomics of PanIN and PDAC from tamoxifen-inducible KPC mice. We uncovered the organization of lymphoid cells into tertiary lymphoid structures (TLSs) adjacent to PanIN lesions. These TLSs lacked CD21+CD23+ B cells compared with more mature TLSs near the PDAC border. PanINs harbored mostly CD4+ T cells, with fewer Tregs and exhausted T cells than PDAC. Peritumoral space was enriched with naive CD4+ and central memory T cells. These observations highlight the opportunity to modulate the immune microenvironment in PanINs before immune exclusion and immunosuppression emerge during progression into PDAC.

  PublisherOA PDFDOI

• InterpolAI: deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping

  Nature Methods · 2025-05-28 · 12 citations

  articleOpen accessSenior author

  Recent advances in imaging and computation have enabled analysis of large three-dimensional (3D) biological datasets, revealing spatial composition, morphology, cellular interactions and rare events. However, the accuracy of these analyses is limited by image quality, which can be compromised by missing data, tissue damage or low resolution due to mechanical, temporal or financial constraints. Here, we introduce InterpolAI, a method for interpolation of synthetic images between pairs of authentic images in a stack of images, by leveraging frame interpolation for large image motion, an optical flow-based artificial intelligence (AI) model. InterpolAI outperforms both linear interpolation and state-of-the-art optical flow-based method XVFI, preserving microanatomical features and cell counts, and image contrast, variance and luminance. InterpolAI repairs tissue damages and reduces stitching artifacts. We validated InterpolAI across multiple imaging modalities, species, staining techniques and pixel resolutions. This work demonstrates the potential of AI in improving the resolution, throughput and quality of image datasets to enable improved 3D imaging. InterpolAI leverages optimal flow-based artificial intelligence to produce synthetic images between pairs of images for diverse three-dimensional image types. InterpolAI is more robust and accurate than existing methods, improving data quality for downstream analysis.

  PublisherOA PDFDOI

• Optimization of velocity receptor transduction in CAR T cells

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-06

  preprintOpen accessSenior authorCorresponding

  Limited infiltration capacity significantly limits the effectiveness of chimeric antigen receptor (CAR) T cells for solid tumors. We have recently developed a large family of highly modular synthetic cytokine receptors termed velocity receptors (VRs), capable of binding key inflammatory cytokines, such as IL5, IL8, and TNFα, which drive CAR T cells into an elevated motility state. These new CAR T cells sense and amplify these autocrine secreted cytokines, thereby maintaining a self-propelled, high migratory state, facilitating penetration into dense tumor cores. In this study, we systematically evaluated key factors influencing VR transduction in order to improve their stable integration and expression. We established a dual-fluorescence reporter system to allow simultaneous monitoring of both VR and CAR constructs, and while evaluating modifications to the vector construct and generating standardized infectious unit (IFU) curves under various conditions. Our results demonstrate that the attempt to reduce overall lentiviral vector size by eliminating non coding sections upstream of the central polypurine tract (cPPT) do not yield better transduction efficiency, though it is unclear if the effect is due to viral production or integration impairment. We also observed a log-linear relationship between viral dose and transduction efficiency for a subset of VRs previously tested in various mouse models of human cancer, with VR5αIL8 and VR5αTNFα VRs consistently outperforming VR5αIL5 and V5 (full length native IL5 receptor). Overall, these findings establish an optimized and reproducible framework that offers valuable guidance for the future development and functional study of VR-CAR T cells in cellular therapies for solid tumors.

  PublisherOA PDFDOI

• Supplemental Figure 2 from Age-Related Increases in IGFBP2 Increase Melanoma Cell Invasion and Lipid Synthesis

  2025-04-01

  preprintOpen access

  <p>Supplemental Figure 2 shows melanoma cells treated with Albumax, as well as an additional cell line showing the contribution of IGFBP2 to invasion</p>

  PublisherOA PDFDOI

• Abstract A046: Quantitative, multiplex assessment of the pancreatic microenvironment in individuals at high-risk of pancreatic cancer reveals differences in tumorigenesis

  Cancer Research · 2025-09-28

  article

  Abstract Pancreatic Ductal Adenocarcinoma (PDAC) is the deadliest form of pancreatic cancer. The lifetime risk of an individual in the United States developing pancreatic cancer is low, at an estimated 1.7%, but the 5-year survival rate is a dismal 13%. High-risk individuals (HRIs) possess a heightened predisposition to the development of PDAC due to an inherited deleterious germline variant or due to a strong family history of pancreatic cancer. Pancreatic surveillance of HRIs leads to the detection of pancreatic precancerous abnormalities and early-stage cancer with improved survival. Most PDACs derive from microscopic precancerous lesions called pancreatic intraepithelial neoplasia, or PanIN. PanINs are undetectable with current non-invasive diagnostic tools. The absence of a reliable method to non-invasively assess PanIN burden complicates clinical decision-making, particularly regarding the timing of surgical intervention in HRIs. A deeper understanding of PanIN biology across diverse high-risk populations could improve patient outcomes by refining screening criteria and treatment guidelines. In this study, we employ a novel AI-powered digital pathology pipeline to analyze the organization of resected pancreata comparing familial and germline cases to age-, BMI-, and sex-matched controls. We assess histological sections of resected pancreata from 45 HRI patients who had surgery for suspected neoplasms and 90 matched controls who had pancreatic resection for non-ductal pancreatic conditions. By training a deep-learning algorithm to recognize 11 key pancreatic microanatomical features (including PanINs) in pathology slides from surgical resections, we find that HRIs contain a higher average neoplastic burden, higher interlobular fat, stromal and immune content, and lower acinar composition, reflecting acinar atrophy, fatty replacement, and fibrosis, possibly secondary to pancreatic ductal blockage due to PanINs. In addition to significantly greater PanIN burden than matched controls, PanIN from HRI were more likely to have high-grade dysplasia compared to matched controls. Notably, familial HRIs demonstrated a higher PanIN burden and higher pancreatic fat and fibrosis than germline variant carriers. Overall, HRIs displayed increased proportions of PanINs, adipose tissue, fibrosis and parenchymal atrophy, contributing to pronounced pancreatic tissue heterogeneity. Through ongoing molecular profiling using transcriptomic, proteomic and spatially-guided DNA sequencing, we aim to reveal relevant mechanisms of pancreatic tumorigenesis in high-risk populations. Citation Format: Lucie Dequiedt, Zhiyuan Ding, Brian A. Pedro, Youran Li, William Dhana, Kurtis Campbell, Mulan Bell, Courtney Cannon, Valentina Matos-Romero, Hassan Sinan, Ali Dbouk, Elizabeth Abou Diwan, Mohamad Dbouk, Ralph H. Hruban, Won Jin Ho, Luciane T. Kagohara, Michael G. Goggins, Denis Wirtz, Laura D. Wood, Marcia I. Canto, Ashley L. Kiemen. Quantitative, multiplex assessment of the pancreatic microenvironment in individuals at high-risk of pancreatic cancer reveals differences in tumorigenesis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr A046.

  PublisherDOI

• 3D map-guided modeling of functional endometrial tissue using multi-compartment assembloids

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-23

  preprintOpen accessSenior authorCorresponding

  The human endometrium is a dynamic tissue that lines the uterus and undergoes constant remodeling, making it especially susceptible to gynecological diseases like endometriosis and endometrial cancer. The molecular mechanisms of these conditions are not well understood, partly due to the lack of in vitro models that mimic endometrial physiology, which limits options for targeted intervention and treatment of these diseases. Mouse models are also inadequate, as common laboratory strains do not naturally undergo a menstrual cycle comparable to that of humans. This study addresses this need by developing a 3D multi-compartment assembloid that mimics the architecture of endometrial tissue and recapitulates all three phases of the menstrual cycle (proliferative, secretory, and menstrual regression) within a single platform. The cellular and extracellular matrix (ECM) components in each compartment are carefully tuned based on a 3D spatial cellular map of endometrial tissue. The model contains endometrial epithelial cells enveloped in a basement membrane and endometrial stromal cells in a surrounding collagen-rich layer; this architecture allows realistic interactions between these cells and their respective ECMs. This assembloid successfully supports the controlled growth and organization of these cells, revealing reciprocal regulation of cell behavior and exhibiting compartment-specific hormonal responses, i.e., stromal decidualization. This platform enables the study of dynamic, phase-resolved, and compartment-specific paracrine signaling in human endometrial biology. By combining tissue-informed design, modular fabrication, and full-cycle hormonal responsiveness, this model sets a new benchmark for blastocyst implantation studies, organ modeling, and precision diagnostics in human reproductive health.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R21CA137891

  NIH · $357k · 2012

• NIH Grant R21EB006890

  NIH · $443k · 2009

• NIH Grant R21CA137686

  NIH · $826k · 2012

• Single-cell phenotyping for therapeutic stratification in pancreatic cancer

  NIH · $3.8M · 2012–2018

• NIH Grant R03TW008718

  NIH · $161k · 2014

Frequent coauthors

• Ashley Kiemen

  Johns Hopkins Medicine

  310 shared

• Pei-Hsun Wu

  Johns Hopkins University

  221 shared

• Sashank Reddy

  Johns Hopkins Medicine

  147 shared

• Kyu Sang Han

  Johns Hopkins University

  145 shared

• Pei‐Hsun Wu

  Johns Hopkins University

  141 shared

• Laura D. Wood

  Johns Hopkins Medicine

  141 shared

• Joel C. Sunshine

  Johns Hopkins University

  131 shared

• Ralph H. Hruban

  Cancer Research Center

  124 shared

Labs

• Denis Wirtz LabPI

Education

• PhD, Chemical Engineering

  Stanford University

  1993

Awards & honors

• NSF CAREER award (1995)
• Fellow of the Institute for Medical and Biological Engineeri…
• Fellow of the American Association for the Advancement of Sc…
• Fellow of the American Physical Society (APS) (2010)
• Theophilus H. Smoot Professor of Engineering and Science (20…