Skip to main content
eScholarship
Open Access Publications from the University of California

Node-Pore Sensing: A Robust, High Dynamic Range Method for Multi-Parametric Screening of Biological Samples

  • Author(s): Balakrishnan, Karthik Ratna
  • Advisor(s): Sohn, Lydia L
  • et al.
Abstract

Resistive-pulse sensing is a technique that allows for measurements of particles in a solution. The underlying principle relies on measuring the electrical resistance across a small pore connecting two reservoirs filled with fluid. As an insulating particle enters the pore, the particle displaces the conducting fluid, leading to an increase in the resistance measured. Pulse magnitude and width reveal information about the particle in transit. The size of the pulse magnitude corresponds to the size of the particle, while the width of the pulse represents the length of time the particle takes to transit the pore.

We show that resistive pulse sensing can be used in combination with protein functionalization to achieve surface marker screening of small sample sizes. We analyze single satellite cells from muscle fibers of mice. We also present unique channel geometries that offer advancements to traditional resistive-pulse sensing. This new technique, node-pore sensing, provides unique signal detection benefits and essentially provides particle tracking, allowing us to determine specifically when a particle transits a specific region of a pore. Ultimately, node-pore sensing provides unprecedented sensitivity, which we show can be used to detect human immunodeficiency virus in human plasma. The particle tracking aspect of this technique affords us the capability of probing particle interactions in different regions of the pore, ultimately allowing us to perform single-cell screening of multiple biomarkers in a single device. We demonstrate multi-marker screening on cultured leukemia cells and bone marrow from leukemia patients. These studies showcase the promise of our platform for broad screening and diagnostic applications.

Main Content
Current View