Massively Parallel Rogue Cell Detection Using Serial Time-Encoded Amplified Microscopy of Inertially Ordered Cells in High-Throughput Flow

Abstract

We aim to develop an instrument for high-throughput identification of rare circulating breast cancer cells to enable early detection and analysis of treatment effectiveness. To address this challenge, we have developed an automated flow-through single-cell optical microscopy system that can evaluate, diagnose, and screen a large population of cells in a short time. This method builds on an unique integration of (i) an ultrafast optical imaging modality known as serial time-encoded amplified microscopy (STEAM) [1] for blur-free imaging of cells in high-speed flow, (ii) inertial microfluidic technology for sheath-free focusing and ordering of cells with inertial forces [2-4], and (iii) hybrid optoelectronic image processing circuitry for real-time image processing. The integrated system transforms microfluidic flow into a series of E-slides an electronic version of glass slides on which cells of interest are digitally analyzed. This property enables fully automated real-time image-recording and classification of a large number of cells through their morphological and biochemical features. As our first proof-of-principle demonstration, we have shown non-stop real-time image-based identification and screening of rare MCF7 breast cancer cells in blood with an unprecedented throughput of 100,000 cells/s and false positive rate of 1 in a million.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2011
Accession Number
ADA553950

Entities

People

  • Dino Di Carlo

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Advanced Electronics
  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Analyzers
  • Antigens
  • Blood Cells
  • Breast Cancer
  • Cells
  • Central Processing Units
  • Detection
  • Digital Data
  • Digital Images
  • Field Programmable Gate Arrays
  • Image Processing
  • Images
  • Leukocytes
  • Optical Images
  • Statistical Analysis
  • Supervised Machine Learning

Fields of Study

  • Physics

Readers

  • Oncology and Biomarker-Based Cancer Detection.
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.

Technology Areas

  • Microelectronics