Computational cytometer based on magnetically modulated coherent imaging and deep learning

Hongjie Zhang ,Danny Kim,Katherine Tsai,Xuewei Liu,Bijie Bai,Alexander Guziak,Dino Di Carlo,C M M Cheung ,Omai B Garner,Mengxing Ouyang,Zhuoran Duan,Tairan Liu,Donghyuk Kim,Sener Yalcin,Hatice Ceylan Koydemir,Aniruddha Ray,Alborz Feizi,Yi Luo,Janay Elise Kong ,Aydogan Özcan

doi:10.1038/s41377-019-0203-5

Abstract

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.

Highlights

Rare cell detection aims to identify a sufficient number of low-abundance cells within a vast majority of background cells, which typically requires the processing of large volumes of biological sample
The presented computational cytometry technique may be applied for the detection of various types of rare cells in blood or other bodily fluids using appropriately selected ligand-coated magnetic beads
The same magnetic beads that are used for capturing and isolating target cells from whole blood are used for periodic cell modulation and specific detection within a dense background

Summary

Introduction

Rare cell detection aims to identify a sufficient number of low-abundance cells within a vast majority of background cells, which typically requires the processing of large volumes of biological sample. The direct detection of rare cells from whole blood requires the processing of large amounts of patient sample (e.g., up to a few hundred millilitres10), which is both unrealistic and time consuming. To alleviate this issue, highly specific labelling methods are often used before detection for sample purification/enrichment to facilitate rapid detection and processing[5,10]. The use of colloidal magnetic particles as labelling reagents offers benefits in forming stable suspensions, fast reaction kinetics[10] and minimum damage to the target cells, with high viability retained[11]

Methods

Results

Conclusion