High-throughput physical phenotyping of cell differentiation

In this report,we present multiparameter deformability cytometry(m-DC),in which we explore a large set of parameters describing the physical phenotypes of pluripotent cells and their derivatives.m-DC utilizes microfluidic inertial focusing and hydrodynamic stretching of single cells in conjunction with high-speed video recording to realize high-throughput characterization of over 20 different cell motion and morphology-derived parameters.Parameters extracted from videos include size,deformability,deformation kinetics,and morphology.We train support vector machines that provide evidence that these additional physical measurements improve classification of induced pluripotent stem cells,mesenchymal stem cells,neural stem cells,and their derivatives compared to size and deformability alone.In addition,we utilize visual interactive stochastic neighbor embedding to visually map the high-dimensional physical phenotypic spaces occupied by these stem cells and their progeny and the pathways traversed during differentiation.This report demonstrates the potential of m-DC for improving understanding of physical differences that arise as cells differentiate and identifying cell subpopulations in a label-free manner.Ultimately,such approaches could broaden our understanding of subtle changes in cell phenotypes and their roles in human biology.

Computational cytometer based on magnetically modulated coherent imaging and deep learning

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 magneticbead-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.