Profiling the heterogeneous phenotypes of rare circulating tumour cells (CTCs) in whole blood is critical to unravelling the complex and dynamic properties of these potential clinical markers. This task is challenging because these cells are present at parts per billion levels among normal blood cells. Here we report a new nanoparticle-enabled method for CTC characterization, called magnetic ranking cytometry, which profiles CTCs on the basis of their surface expression phenotype. We achieve this using a microfluidic chip that successfully processes whole blood samples. The approach classifies CTCs with single-cell resolution in accordance with their expression of phenotypic surface markers, which is read out using magnetic nanoparticles. We deploy this new technique to reveal the dynamic phenotypes of CTCs in unprocessed blood from mice as a function of tumour growth and aggressiveness. We also test magnetic ranking cytometry using blood samples collected from cancer patients.

Biotemplated nanomaterials offer versatile functionality for multimodal imaging, biosensing, and drug delivery. There remains an unmet need for traceable and biocompatible nanomaterials that can be synthesized in a precisely controllable manner. Here, we report self-assembled quantum dot DNA hydrogels that exhibit both size and spectral tunability. We successfully incorporate DNA-templated quantum dots with high quantum yield, long-term photostability, and low cytotoxicity into a hydrogel network in a single step. By leveraging DNA-guided interactions, we introduce multifunctionality for a variety of applications, including enzyme-responsive drug delivery and cell-specific targeting. We report that quantum dot DNA hydrogels can be used for delivery of doxorubicin, an anticancer drug, to increase potency 9-fold against cancer cells. This approach also demonstrated high biocompatibility, trackability, and in vivo therapeutic efficacy in mice bearing xenografted breast cancer tumors. This work paves the way for the development of new tunable biotemplated nanomaterials with multiple synergistic functionalities for biomedical applications.

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The development of strategies for isolating rare cells from complex matrices like blood is important for a wide variety of applications including the analysis of bloodborne cancer cells, infectious pathogens, and prenatal testing. Due to their high colloidal stability and surface-to-volume ratio, antibody-coated magnetic nanoparticles are excellent labels for cellular surface markers. Unfortunately, capture of nanoparticle-bound cells at practical flow rates is challenging due to the small volume, and thus low magnetic susceptibility, of magnetic nanoparticles. We have developed a means to capture nanoparticle-labeled cells using microstructures which create pockets of locally low linear velocity, termed velocity valleys. Cells that enter a velocity valley slow down momentarily, allowing the magnetic force to overcome the reduced drag force and trap the cells. Here, we describe a model for this mechanism of cell capture and use this model to guide the rational design of a device that efficiently captures rare cells and sorts them according to surface expression in complex matrices with greater than 10,000-fold specificity. By analysing the magnetic and drag forces on a cell, we calculate a threshold linear velocity for capture and relate this to the capture efficiency. We find that the addition of X-shaped microstructures enhances capture efficiency 5-fold compared to circular posts. By tuning the linear velocity, we capture cells with a 100-fold range of surface marker expression with near 100% efficiency and sort these cells into spatially distinct zones. By tuning the flow channel geometry, we reduce non-specific cell adhesion by 5-fold.

The analysis of heterogeneous subpopulations of circulating tumor cells (CTCs) is critical to enhance our understanding of cancer metastasis and enable non-invasive cancer diagnosis and monitoring. The phenotypic variability and plasticity of these cells – properties closely linked to their clinical behavior – demand techniques that isolate viable, discrete fractions of tumor cells for functional assays of their behavior and detailed analysis of biochemical properties. Here, we introduce the Prism Chip, a high-resolution immunomagnetic profiling and separation chip which harnesses a cobalt-based alloy to separate a flowing stream of nanoparticle-bound tumor cells with differential magnetic loading into ten discrete streams. Using this approach, we achieve exceptional purity (5.7 log white blood cell depletion) of isolated cells. We test the differential profiling function of the integrated device using prostate cancer blood samples from a mouse xenograft model. Using integrated graphene Hall sensors, we demonstrate concurrent automated profiling of single cells and CTC clusters that belong to distinct subpopulations based on protein surface expression.

Image-reversal soft lithography enables the straightforward fabrication of high-performance biosensors without requiringhigh-resolution photolitography.

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Genome-scale functional genetic screens can be used to interrogate determinants of protein expression modulation of a target of interest. Such phenotypic screening approaches typically require sorting of large numbers of cells (>108). In conventional cell sorting techniques (i.e. fluorescence-activated cell sorting), sorting time, associated with high instrument and operating costs and loss of cell viability, are limiting to the scalability and throughput of these screens. We recently established a rapid and scalable high-throughput microfluidic cell sorting platform (MICS) using immunomagnetic nanoparticles to sort cells in parallel capable of sorting more than 108 HAP1 cells in under one hour while maintaining high levels of cell viability (Ref. 1). This protocol outlines how to set-up MICS for large-scale phenotypic screens in mammalian cells. We anticipate this platform being used for genome-wide functional genetic screens as well as other applications requiring the sorting of large numbers of cells based on protein expression.