Label-free single cell analysis in microfluidics devices using self-supervised deep learning

Scientific Context of the Project

Cancer cells have different physiological and biological characteristics. Utilizing these characteristic differences, different technologies could be created for cancer diagnosis and therapeutic purposes. In this proposed project, a new image-based cytometer method will be developed for automated, rapid, and reliable characterization of cells in a microfluidic chip by investigating the morphological, size, deformation, density, and magnetic properties of cells without using any labels. This method will be used for high-efficient detection of rare cancer cells among blood cells for diagnosis and prognosis purposes, as well as enabling the analysis of cancer cell responses to the drugs at the single-cell level.

Related References:

1. PNAS, 2015, 112, E3667-E3668.

2. ACS Sensors, 2021, 6, 2191-2201.

3. SPIE BIOS, LBIS, 2021, 1165509.

Innovative Aspects of the Project 

Novel cytometers will be developed by harmonizing microfluidics technology and deep learning-based data analysis that will enable sensitive and label-free single cell analysis.

Research Environment and Infrastructure

Rapid prototyping tools, cleanroom facility, cell imaging facility, cell culture facility and well-equipped characterization and bioengineering centers (

Project Acronym


Main Supervisor

Assoc. Prof. Cumhur Tekin (IZTECH)


Assoc Prof. Mustafa Ozuysal (IZTECH)

Assoc. Prof. Sinan Guven (IBG)

Recruiting Institution

İzmir Institute of Technology, Graduate School, Urla/İzmir

PhD Awarding Institution

İzmir Institute of Technology, Graduate School

PhD Title

PhD in Bioengineering

International Academic Secondment

Temple University, Philadelphia, USA

Intersectoral Mobility

Siemens Healthineers (TR or GER) and Istanbul Health Industry Cluster (ISEK)