About

Jason A. Wertheim is an Edward G. Elcock Professor and Vice Chair for Research in the Department of Surgery at Northwestern University. He is also an Associate Professor of Surgery in the Organ Transplantation Division and an Associate Professor of Biomedical Engineering. His research focuses on discovering new methods to develop liver and kidney tissue in the laboratory to address the organ shortage problem. His multidisciplinary work involves tissue engineering and regenerative medicine approaches to develop organ scaffolds using pluripotent stem and progenitor cells, supporting the growth of new tissues that can become whole organs. Wertheim's background includes a BS in Chemical Engineering from MIT, an MD from the University of Pennsylvania, and a PhD in Bioengineering from the University of Pennsylvania. He completed a fellowship in Transplantation at UCLA and a residency in Surgery at Massachusetts General Hospital. His contributions are centered on advancing organ regeneration techniques to transform medicine.

Research topics

• Medicine
• Biochemistry
• Biology
• Political Science
• Chemistry
• Computer Science
• Psychology
• Medical education
• Internal medicine
• Pathology
• Pedagogy
• Cell biology
• Biophysics
• Virology
• Molecular biology
• Pharmacology

Selected publications

• Ex-vivo bioengineered technology to examine cellular function in metabolic dysfunction-associated steatohepatitis (MASH)

  American Journal of Transplantation · 2026-01-01

  articleOpen accessSenior author
  PublisherOA PDFDOI

• A kidney organoid-based readout to assess disease activity in primary and recurrent focal segmental glomerulosclerosis

  Kidney International · 2025-02-04 · 8 citations

  article
  PublisherDOI

• Polyacrylamide Hydrogels with Reversibly Photocontrolled Stiffness for 2D Mechanobiology

  ACS Applied Materials & Interfaces · 2025-06-06 · 3 citations

  articleOpen access

  We report the development of polyacrylamide hydrogels with photoswitchable stiffness using solely visible light and their application to cell culture. We have previously shown that azobenzenes can control the binding constants of dynamic covalent boronic ester bonds (Chem. Sci. 2018, 9, 5987; J. Am. Chem. Soc. 2020, 142, 19969). Here we show that these photoswitchable dynamic bonds can be incorporated into polyacrylamide hydrogels that are stable for at least 10 days in buffer without changes in stiffness or photoresponse. Reversible stiffening and softening are achieved with green and blue irradiation, respectively. We prepared soft (877 ± 79 Pa) and stiff (8.4 ± 0.3 kPa) hydrogels that undergo rapid, photoreversible changes in modulus over at least 3 light irradiation cycles. In vitro studies show that the hydrogels are nontoxic to HepG2 cells. The cells undergo the expected changes in morphology, actin stress fiber formation, and Yes-associated protein (YAP) subcellular localization upon stiffening and softening the hydrogel substrate with visible light. These results validate the suitability of our visible-light-controlled hydrogel as a versatile platform for cellular mechanotransduction studies.

  PublisherOA PDFDOI

• Reversibly photocontrolled polyacrylamide hydrogels for mechanobiology

  ChemRxiv · 2025-02-12 · 1 citations

  preprintOpen access

  We report the development of polyacrylamide hydrogels with photoswitchable stiffness using solely visible light and their application to cell culture. We have previously shown that azobenzenes can control the binding constants of dynamic covalent boronic ester bonds (Chem. Sci. 2018, 9, 5987; J. Am. Chem. Soc. 2020, 42, 19969). Here we show that these photoswitchable dynamic bonds can be incorporated into polyacrylamide hydrogels that are stable for at least 10 days in buffer without changes in stiffness or photoresponse. Reversible stiffening and softening are achieved with green and blue irradiation, respectively. We prepared soft (877 ± 79 Pa) and stiff (8.4 ± 0.3 kPa) hydrogels that undergo photoreversible changes in modulus over at least 3 light irradiation cycles. In vitro studies show that the hydrogels are nontoxic to HepG2 cells. The cells undergo the expected changes in morphology, actin stress fiber formation, and Yes-associated protein (YAP) subcellular localization upon stiffening and softening the hydrogel substrate with visible light. These results validate the suitability of our visible-light-controlled hydrogel as a versatile platform for cellular mechanotransduction studies.

  PublisherOA PDFDOI

• Parallel multiOMIC analysis reveals glutamine deprivation enhances directed differentiation of renal organoids

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-01 · 1 citations

  preprintOpen accessSenior authorCorresponding

  Metabolic pathways play a critical role in driving differentiation but remain poorly understood in the development of kidney organoids. In this study, parallel metabolite and transcriptome profiling of differentiating human pluripotent stem cells (hPSCs) to multicellular renal organoids revealed key metabolic drivers of the differentiation process. In the early stage, transitioning from hPSCs to nephron progenitor cells (NPCs), both the glutamine and the alanine-aspartate-glutamate pathways changed significantly, as detected by enrichment and pathway impact analyses. Intriguingly, hPSCs maintained their ability to generate NPCs, even when deprived of both glutamine and glutamate. Surprisingly, single cell RNA-Seq analysis detected enhanced maturation and enrichment for podocytes under glutamine-deprived conditions. Together, these findings illustrate a novel role of glutamine metabolism in regulating podocyte development.

  PublisherOA PDFDOI

• Revealing gait as a murine biomarker of injury, disease, and age with multivariate statistics and machine learning

  Scientific Reports · 2025-09-29 · 1 citations

  articleOpen access

  Hundreds of rodent gait studies have been published over the past two decades, according to a PubMed search. Treadmill gait data, for example from the DigiGait system, generates over 30 + spatial and temporal measures. Despite this multi-dimensional data, all but a handful of the published literature on rodent gait has conducted univariate analysis that reveals limited information on the relationships that are characteristic of different gait states. This study conducted rigorous multivariate analysis in the form of sequential feature selection and factor analysis on gait data from a variety of gait deviations (due to injury i.e. peripheral nerve transection and transplantation, disease i.e. IUGR and hyperoxia, and age-related changes) and used machine learning to train a classifier to distinguish among and score different gait states. Treadmill gait data (DigiGait) of three different types of gait deviations were collected. Data were collected from B6 mice using the DigiGait system, with gait measurements taken at standardized treadmill speeds of 10, 17, and 24 cm/s over a period of 3-4 s per observation. Each mouse underwent at least two trials at each speed. Data were collected on B6 mice that were healthy and had various types of gait deficit due to: (a) a peripheral nerve injury model with increasing degrees of damage to the neuromusculoskeletal sequence of gait i.e. nerve transection, total hind limb transplantation, (b) a central nerve injury model of increasing degrees of damage to the motor regions responsible for gait i.e. IUGR, IUGR + hyperoxia, and (c) gait changes due to increasing age. Multivariate factor analysis (using MATLAB's factoran) and forward feature selection (with ten-fold cross-validation) were conducted to identify those features and factors most descriptive of each gait state for comparison. Various machine learning classifier models were trained with ten-fold cross-validation and evaluated (e.g. random forest, regression, discriminant analysis, support vector machine, and ensemble) in a 70 - 30 training-testing split for their accuracy, precision, recall, and F-score. The highest performing model was used to score each type of gait for direct comparison on a scale of -0.5 to 0.5. The score distributions were plotted on a histogram for direct comparisons of score populations among various gait states. Multivariate feature selection revealed that not all 30 + features were relevant to describing the gait states. Plotting misclassification error (MCE) as a function of number of features included revealed that there was a critical number of features (~ 16) that minimized MCE (0.17 via univariate feature selection vs. 0.12 via multivariate feature selection). Incorporating more than 16 features led MCE to increase linearly indicating overfitting. Relationships among the identified features were understood via factor analysis. The factor analysis results were consistent with the biological differences between the groups (e.g. total hind limb transplantation was distinguishable via features descriptive of the positioning of the paw in relation to the body while nerve transection injury alone was distinguishable via features descriptive of changes to fine motor movements). Across all gait states, there was significant conservation of features and factors. This suggests certain relationships may be fundamental to rodent gait analysis regardless of the gait pathology in question. The highest performing machine learning classifier model (ensemble) was able to distinguish between gait deficits with high performance (F-score, recall, precision, and accuracy all > 0.90). This included the ability to distinguish between peripheral vs. central gait deficit, between individual types of peripheral deficit, between individual types of central deficit, and between younger vs. older animals. Using the classifier to score individual animals and plot the scores by group revealed score distributions that were consistent with biological phenomena. For example, the multivariate gait score trends as a result of increasing central nerve injury were consistent with the trends of white matter volume loss in relevant motor regions of the brain as measured via MRI. Finally, the degrees of separation between multivariate gait scores were consistent with the degree of biological difference between gaits (e.g. central injury had greater separation from healthy vs. peripheral injury; older and younger animals had more moderate, yet still statistically significant, separation in scores vs. any of the injury / disease states did with each other). In conclusion, this study establishes a new methodology to quantify and evaluate gait deviations across a variety of different models. Its novelty is in using multivariate statistics to describe the features and factors that characterize gait states due to injury, disease, and age for use in machine learning model training. This includes statistically describing the differences in gait between diseases with vastly different etiologies of gait deficits (peripheral vs. central). In doing so the methodology's novelty includes accounting for relationships between groupings of features in model training; something that traditional univariate analysis is unable to do. It used multivariate statistics and machine learning to reveal gait as a quantifiable, preclinical biomarker of injury, disease, and age. It collapsed a multi-dimensional biological phenomena (gait) into a single score by encoding revealed biological relationships allowing for direct, quantifiable comparisons of function as it pertains to ambulation. It revealed how these multivariate gait scores can visualize biologically consistent separation and combined effects. Finally, we demonstrate the application of this methodology to already published univariate study that is representative of the hundreds of univariate treadmill gait analysis published over the last two decades. Thereby, opening the door to a new class of multivariate gait analyses that provides greater insight and value than the current state-of-the art.

  PublisherOA PDFDOI

• Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma

  PLoS ONE · 2025-01-07 · 1 citations

  articleOpen accessCorresponding

  OBJECTIVE: Animal models of nerve injury are important for studying nerve injury and repair, particularly for interventions that cannot be studied in humans. However, the vast majority of gait analysis in animals has been limited to univariate analysis even though gait data is highly multi-dimensional. As a result, little is known about how various spatiotemporal components of the gait relate to each other in the context of peripheral nerve injury and trauma. We hypothesize that a multivariate characterization of gait will reveal relationships among spatiotemporal components of gait with biological relevance to peripheral nerve injury and trauma. We further hypothesize that legitimate relationships among said components will allow for more accurate classification among distinct gait phenotypes than if attempted with univariate analysis alone. METHODS: DigiGait data was collected of mice across groups representing increasing degrees of damage to the neuromusculoskeletal sequence of gait; that is (a) healthy controls, (b) nerve damage only via total nerve transection + reconnection of the femoral and sciatic nerves, and (c) nerve, muscle, and bone damage via total hind-limb transplantation. Multivariate relationships among the 30+ spatiotemporal measures were evaluated using exploratory factor analysis and forward feature selection to identify the features and latent factors that best described gait phenotypes. The identified features were then used to train classifier models and compared to a model trained with features identified using only univariate analysis. RESULTS: 10-15 features relevant to describing gait in the context of increasing degrees of traumatic peripheral nerve injury were identified. Factor analysis uncovered relationships among the identified features and enabled the extrapolation of a set of latent factors that further described the distinct gait phenotypes. The latent factors tied to biological differences among the groups (e.g. alterations to the anatomical configuration of the limb due to transplantation or aberrant fine motor function due to peripheral nerve injury). Models trained using the identified features generated values that could be used to distinguish among pathophysiological states with high statistical significance (p < .001) and accuracy (>80%) as compared to univariate analysis alone. CONCLUSION: This is the first performance evaluation of a multivariate approach to gait analysis and the first demonstration of superior performance as compared to univariate gait analysis in animals. It is also the first study to use multivariate statistics to characterize and distinguish among different gradations of gait deficit in animals. This study contributes a comprehensive, multivariate characterization pipeline for application in the study of any pathologies in which gait is a quantitative translational outcome metric.

  PublisherDOI

• Microfluidic Flow Promotes a Steatotic Phenotype in Induced Pluripotent Stem Cell–Derived Hepatocytes that is Influenced by Disease State of the Donor

  Gastro Hep Advances · 2024-12-14

  reviewOpen accessSenior author
  PublisherDOI

• Blocking antibodies against integrin-α3, -αM, and -αMβ2 de-differentiate myofibroblasts, and improve lung fibrosis and kidney fibrosis

  Scientific Reports · 2024-09-16 · 3 citations

  articleOpen access

  Fibrosis is involved in 45% of deaths in the United States, and no treatment exists to reverse the progression of lung or kidney fibrosis. Myofibroblasts are key to the progression and maintenance of fibrosis. We investigated features of cell adhesion necessary for monocytes to differentiate into myofibroblasts, seeking to identify pathways key to myofibroblast differentiation. Blocking antibodies against integrins α3, αM, and αMβ2 de-differentiate myofibroblasts in vitro, lower the pro-fibrotic secretome of myofibroblasts, and treat lung fibrosis and inhibit kidney fibrosis in vivo. Decorin's collagen-binding peptide can be used to direct functionalized blocking antibodies (against integrins-α3, -αM, -αMβ2) to both fibrotic lungs and fibrotic kidneys, reducing the dose of antibody necessary to treat fibrosis. This targeted immunotherapy blocking key integrins may be an effective therapeutic for the treatment of fibrosis.

  PublisherOA PDFDOI

• Glia in tissue engineering: From biomaterial tools to transplantation

  Acta Biomaterialia · 2024-10-11 · 2 citations

  review
  PublisherDOI

Recent grants

• Repairing the Kidney Endothelium via Targeted Extracellular Matrix Modifiers

  NIH · $316k · 2020–2020

• Repairing the Kidney Endothelium via Targeted Extracellular Matrix Modifiers

  NIH · $1.2M · 2020–2024

• Extracellular Matrix Induction of Renal Stem and Progenitor Cell Development

  NIH · 2016–2022

• NIH Grant K08DK101757

  NIH · $777k · 2019

Frequent coauthors

• Kyle M. Koss

  University of Arizona

  20 shared

• Zheng Jenny Zhang

  Northwestern University

  19 shared

• Jennifer S. Carew

  University of Arizona Cancer Center

  18 shared

• Trace M. Jones

  University of Arizona Cancer Center

  18 shared

• Warren S. Pear

  18 shared

• Claudia M. Espitia

  University of Arizona

  18 shared

• Steffan T. Nawrocki

  University of Arizona

  18 shared

• Joseph S. Uzarski

  Miromatrix Medical (United States)

  17 shared

Labs

• Wertheim LaboratoryPI

Education

• Fellowship, Transplant Surgery

  University of California Los Angeles David Geffen School of Medicine

  2011

• Residency, General Surgery

  Massachusetts General Hospital

  2009

• MD, Medicine

  University of Pennsylvania

  2004

• PhD, Bioengineering

  University of Pennsylvania

  2002

• BS, Chemical Engineering

  Massachusetts Institute of Technology

  1996