Description

Title

Spatial molecular analysis of tissue sections using microfluidic probe

Description

Every organism is an expression of the fine balance between order and chaos. Tumor biology, not being exempt from this complexity, expresses ‘chaos’ in the form of heterogeneity, and overlooking it by averaging data across whole tumors often makes profiles inaccurate and their treatment ineffective. To address this particular challenge from a technological perspective, our group introduced an openspace microfluidic technology called the microfluidic probe (MFP)1. The MFP enables biochemical assays at the microscale that can both deposit biomarker specific ligands and extract cells from tissue sections using hydrodynamic confinements. These comprise single or multiple biochemicals being injected and aspirated simultaneously so as to localize nanoliter volume liquids on umlength scale on an immersed surface without mechanical contact to the tissue. The continuous flow inherent to this system allows for rapid reaction kinetics and dynamic control over the location and area of interaction. By means of using MFP to implement the most critical steps in current pathology workflows (e.g. tissue lysis and staining)2 we can accelerate the protocol implementation and obtain quantifiable genomic, transcriptomic and proteomic information from the same tissue sample, with interactive control over the ‘scale/spatial resolution’ of analysis. µIHC, the currently implemented biomarker expression assay is contingent on availability of appropriate antibodies and has a limited role to play in discovery-based biomarker studies.

Tasks

The primary task of the thesis is to develop assays and analytics for biomarker discovery integrating spatial lysis with DNA and protein microarray techniques. Specifically, the student will:

  • Develop/adapt current microscale lysis assays for profiling the genome and proteome by delivering them onto commercially available DNA and proteomic microarray panels.
  • Develop the assay for both targeted (clinically relevant) and discovery based downstream analysis.
  • Develop metrics for quantitative evaluation of heterogeneity within tumor tissue sections.

Requirements

The student must be strong in one of the following disciplines: biomedical engineering or molecular/cell biology with some experience in bioinformatics. The student must be motivated, self-driven and keen to work in an interdisciplinary environment. Time frame & Contact Fall 2020. Starting date is flexible, and the project duration is six months. Application should be addressed to Govind Kaigala (gov@zurich.ibm.com).