Using Biomedical Data Science to Advance Precision Medicine and Digital Health

Join us!

The Aran Lab integrates multidimensional biomedical data — including genomics and clinical data — to advance precision medicine and improve therapeutic strategies.

The core themes of the lab are:

  • Developing computational methods to understand cellular heterogeneity in complex tissues.
  • Incorporating cutting-edge technologies to study cellular dynamics in the tumor-microenvironment affecting response to immunotherapies.
  • Investigating real-world evidence and developing machine-learning models to improve clinical decision-making.

The lab is affiliated with the Faculties of Biology and Computer Science and the Lorry I. Lokey Interdisciplinary Center for Life Sciences and Engineering at the Technion - Israel Institute of Technology, Haifa, Israel.

The Team

Principal Investigator

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Dvir Aran

Lab Manager

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Tal Katz-Ezov

Researchers

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Mahmoud Yazbek

Master’s student

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Ortal Cohen

Master’s student

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Collaborators

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Abraham (Avi) Tsur

Sheba Medical Center

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Mallar Bhattacharya

Associate Professor, UCSF

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Anthem.ai

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Rambam Medical Center

Recent News

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[05/08/21[ To boost or not to boost? Commentary by Dvir on COVID-19 boosters in Nature News.

[27/07/21[ Great news today. We got the Israel Science Foundation (ISF) Research Grant!

[29/06/21] The first Digital Medicine Conference in Israel organised by Anthem Israel and chaired by the Dvir and Ron Shamir was held in Tel-Aviv.

[15/04/21] News & Views on extracting insights from heterogeneous tissues published in Nature Computer Science

[01/04/21] Welcome Ortal and Mahmoud on joining the lab as Master’s students.

Research

The lab focuses on the discovery of predictive biomarkers, improving existing treatments, and developing novel therapeutic strategies. The lab will develop novel computational and experimental cancer immunology methodologies for characterizing the interactions of the tumor and its microenvironment, and specifically focus on the mechanisms enabling immune resistance. By incorporating publicly available datasets with self-generated data, from bulk and single-cell profiling of multiple ‘omics’ types, mass spectrometry cytometry, immune repertoires, and other cutting-edge technologies, we will adequately portray the cellular dynamics of cancer initiation, progression, and response to treatment. The lab branches to both basic and clinical research, and collaborates with computer scientists, bioengineers, basic experimental researchers and clinicians.

Cancer Immunology

The recent breakthroughs in cancer immunology and the development of a newer generation of cancer immunotherapies have opened a brand-new chapter in the war on cancer. The tumor microenvironment composition has a major effect on the response to immunotherapy agents. Thus, an improved understanding of the interactions between the tumor, its microenvironment, and its proximal surroundings is crucial for improving existing treatments and the design of novel immunotherapy strategies.

Cancer Metastasis

Metastasis is the primary cause of mortality in most cancers, yet today, metastatic spread is still poorly understood. In recent years, there is increasing evidence that metastatic colonization depends not just on the inherent properties of cancer cells, but also on properties of the microenvironment in distant sites. Understanding how disseminated cells evade and corrupt the immune system during the metastatic colonization will be pivotal in developing new therapeutic methods to combat metastasis.

Clinical Informatics

Clinicians have been using Electronic Health Records (EHR) for over a decade for organizing and preserving patient information. However, only recently, these rich and valuable datasets became widely accessible for research. EHR analysis is expected to drive future precision medicine efforts and improve healthcare quality. Our lab is collaborating with leading hospitals and research institutions and will enjoy unique access to one of the largest health care datasets in the world.

Single-cell RNA-seq

Until recently, genomic analyses have been performed on heterogeneous populations of cells and thus observed signals represented a combination of the unique characteristics of each individual cell. In the last few years, single-cell techniques combined with high-throughput technologies have revolutionized the field, allowing for high-dimensional analysis of isolated subpopulations of individual cells and enabling an unprecedented level of granularity in characterizing gene expression changes in disease models. Researchers can now address core challenges that bar advancement in the field of onco-immunology, enabling the mapping of the variable spectrum of immune, stromal, and other cell states and ascertaining which of these features predict or explain clinical responses to anticancer agents.

Software

Here are some representative tools developed before the inception of the lab.

SingleR

Reference-Based Single-Cell RNA-Seq Annotation - Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. SingleR is used by thousands of researchers across the world.
Code Project Bioconductor

xCell

xCell is a webtool that performs cell type enrichment analysis from gene expression data for 64 immune and stroma cell types. Over 20,000 researchers world-wide have now used xCell.
Code Webtool

Posts

הרהורים על וריאנט דלתא

An article I wrote for DYOMA on my thoughts about the delta variant.
Jul 10, 2021 1 min read

חוקרים: צינון מפחית את הסיכוי לפתח תסמיני קורונה

An article in Haaretz on our publication in the Journal of Infection
Nov 20, 2020 1 min read

Translate this ... research done to impact

Interview for the mAcademia podcast
Jul 11, 2020 1 min read

שיטות חישוביות בחזית הביולוגיה

פוסט אורח במדע גדול, בקטנה
Jun 11, 2020 1 min read

Life-Threatening Lung Disease Averted in Experimental Models

Jan 14, 2019 0 min read
See all posts

Join us!

Always looking for talented and curious candidates at the postdoctoral, predoctoral, and undergraduate levels.

PhD and Master’s students

The lab is opening in the summer of 2020 and is seeking highly motivated students to develop and study novel approaches in translational bioinformatics, or the application of analytic and interpretive methods to optimize the transformation of genome-scale data of many types into proactive, predictive, preventative, and participatory health.

Ideal candidates will have:

  • Strong background in bioinformatics, computational biology, biostatistics, and genomics.
  • Strong problem-solving skills, creative thinking, and the ability to build new software tools as needed are required.
  • Applicants must possess good communication skills and be fluent in both spoken and written English.
  • A background in molecular biology or medicine or pharmacology will be a strong plus. Prior experience with genetic, drug, or clinical databases, machine-learning, deep-learning, text-mining and knowledge representation is a plus.

To apply, please send your CV and research interests to aran.lab.technion@gmail.com.

Postdoctoral researchers

We are looking for fully-funded postdoctoral researcher to work on some exciting projects. Candidates are expected to have a track record of bioinformatics and or clinical informatics analysis.

There is also an option of co-advising postdoctoral researchers with other computational or experimental labs. An example might be an experimental postdoc with their own biological question for which there are no current analysis tools.

If interested, please send the following to aran.lab.technion@gmail.com:

  • A short research statement on your goals
  • Your CV
  • Contact information for three references

Contact

  • LS&E - Technion Life Sciences and Engineering Infrastructure Center, Haifa, 94305
  • Floor 8, right side of hall