Microphysiological Systems and Quantitative Biology

“Our research aims to solve the ethical and practical problems caused by the use of laboratory animals, by providing alternative solutions based on lab-on-chip bioengineered cellular models”

DR. CARLOS ORTIZ DE SOLÓRZANO AURUSA

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Microphysiological Systems and Quantitative Biology

The group, integrated in the Cancer Center Clínica Universidad de Navarra, develops microphysiological systems to validate hypotheses and test drug effectiveness. These systems, which simulate the cellular composition and physiology of specific tissues through the development of three-dimensional cultures in microfluidic support (organ-on-chip), are useful in a wide variety of biomedical research areas.

In our laboratory these systems are applied to the study of pancreatic cancer and infectious processes in the airways of patients with chronic obstructive pulmonary disease (COPD).

On the other hand, the group develops tools for analysis and quantification of biomedical images, aimed at improving research and diagnosis of different diseases. In particular, these tools are applied to the analysis and interpretation of multidimensional microscopy images, which can be included within the concept of Computational Pathology, mainly in the context of the study of cancer, as well as radiological images for non-invasive diagnosis and monitoring of prevalent diseases (COPD, lung cancer, Parkinson's disease, Lewy Body dementia).

Dr. Carlos Ortiz de Solórzano

GROUP LEADER

	+34 948 194 700 \| Ext. 815019
	codesolorzano@unav.es
	Research profile

Oncology research integrated in the
Cancer Center Clinica Universidad de Navarra

Learn about the cancer center

Microphysiological Systems and Quantitative Biology
Research Group Objectives

We are developing tools that are useful in many areas of our business.

Develop "organ-on-chip" tools
for the study of diseases and drug validation.

Develop and validate image analysis tools
for biomedical image enhancement and analysis (multidimensional microscopy, microCT, nuclear magnetic resonance).

Develop Artificial Intelligence (AI)
based image understanding and predictive tools to support the diagnosis and stratification of patients.

Impact of our research

Using microfabrication, microfluidic, microscopy and image analysis techniques we have studied the impact of extracellular matrix properties on cancer cell migration.

The group has coordinated an international competition aimed at promoting the development and objective validation of cell tracking algorithms from multidimensional microscopy videos.

We have developed a microfluidic system for the detection of viable circulating tumor cells in peripheral blood of cancer patients.

We have developed NaroNet, an assembly of deep learning models that is able to learn the spatial relationships between cell types within the tumor microenvironment, and associate these relationships to relevant clinical labels.

An airway-infection-on-chip microfluidic system has been developed to study the dynamics of chronic airway infection processes in a controlled manner.

We have developed an image segmentation and interpretation system, based on the use of anatomical atlases, which has allowed us to discover the diagnostic value of nuclear magnetic resonance, applied to the study of neuromelanin-rich structures located in the brainstem, for the diagnosis of Parkinson's disease.

TRANSLATIONAL RESEARCH

Analysis of medical images

By using NaroNet, an ensemble of Deep Leaning models applied to the analysis of cancer tissues stained by multiplex immunofluorescence, we have found the most frequent cell phenotypes for different types of endometrial cancers, and more importantly, which are the spatial associations between those phenotypes, at three levels of spatial complexity. Using NaroNet, we have discovered which spatial relationships found in the tumor microenvironment allow us to classify the histological type, and other clinical characteristics of these tumors.

Figure taken from Jiménez-Sánchez et al. Medical Image Analysis 78, 102384 (2022

Research lines in
Microphysiological Systems and Quantitative Biology

IPs: Carlos Ortiz de Solórzano e Iván Cortés Domínguez

Description:

Our overarching goal is to understand the role of the biomechanical properties of the tumor microenvironment in the development of cancer.

Objectives:

Develop biomimetic matrices to simulate the composition and mechanical properties of the extracellular matrix of tumors.
Develop organ-on-chip microfluidic devices to help creating and monitoring the growth and phenotype of tumor organoids within biomimetic matrices.
To setup an intravital microscopy system to monitor key elements of the immune system, in vivo, in tumor organoids of murine origin implanted subcutaneously in syngeneic mice.
Study the role of the presence of selected elements of the extracellular matrix, selected elements of the immune system and relevant metabolic constraints, in the phenotype of tumor organoids grown within microfluidic devices

IPs: Carlos Ortiz de Solórzano e Iván Cortés Domínguez

Description:

Our overarching goal is to study the dynamics of mono and multibacterial infections of Haemophilus influenzae in the lower respiratory tract of COPD patients.

Objectives:

Develop an airway-infection-on-chip system, composed of cell layers that simulate the lower respiratory airways, and of microfluidic channels and chambers to generate biofilms made of different strains of Haemophilus influenzae and drive their infection of the cell layers.
Develop an organ-infection-on-chip system to simulate bacterial infectious processes of lung organoids grown from the lung epithelium of COPD patients.
Develop multidimensional microscopy protocols to visualize the infectious processes analyzed in airway-infection-on-chip and an organ-infection-on-chip systems.
Develop image analysis algorithms to quantify the microscopu images captured during the experiment that simulate bacterial infections.

IPs: Carlos Ortiz de Solórzano

Description:

The goal of this line of work is to develop AI tools that allow us to study the characteristics and interactions between different cell types of the immune system, and relate those to clinical parameters, most relevantly the predicted efficiency of immune based anticancer therapies.

Objectives:

Develop a weakly supervised Deep Learning model able to learn the phenotypes present in the tumor microenvironment, along with the spatial interactions between them, using tumor tissue sections stained using multiplex immunofluorescence.
Setup a tissue simulator that creates images of tissues stained by multiplex immunofluorescence, with the goal of using synthetically generated images for data augmentation of the model mentioned in the previous point.
Adapt the above mentioned model to its use in unsupervised scenarios, and adapt it to its use for other staining modalities (simple immunofluorescence, H&E, etc.)
Adapt the above mentioned model to integrate visual conventional histology and data from spatial sequencing.

IPs: Carlos Ortiz de Solórzano

Description:

Our goa list is to identify image-based biomarkers that could be used for the early diagnosis and follow-up of neurodegenerative diseases, specifically Parkinson’s disease and Lewy body dementia.

Objectives:

Develop anatomical atlases of the brainstem from MRI images that enhance the presence of neuromelanin or the accumulation of iron.
Use the above mentioned anatomical atlases to segment structures of interest: Substantial nigra pars compact, locus coeruleus, nigrosome, red nuclei, etc…
Quantify the accumulation of neuromelanin and iron the these structures in patients that suffer from Parkinson’s disease and Lewy body dementia.
Determine the diagnostic value of the quantifications perform and their potential association with clinical parameters.