Our research program develops new technologies to interrogate and manipulate biological systems at the scale of individual cells by leveraging advances in materials, fabrication, instrumentation, and computation across multiple domains and length scales. Current research projects include the following:

Microscopy-based Cell Sorting

Separating specific cells from a heterogeneous mixture is a fundamentally important part of many biological assays and processes. Currently methods rely on fluorescence labeling of specific markers and flow cytometry, which limits acceptable cell types and separation parameters. We are developing new approaches to separate cells based on function and behaviour observed using microscopy, as well as scaling these methods to ultimately compete with flow cytometry. Applications of our work include cell line development for bioproduction, as well as cell therapy research and manufacturing.

Selective Single Cell Sequencing

Biological cells can be thought of as individual computational units (i.e. CPUs) that make decisions based on external stimuli and internal state. Recent technological advances have enabled single cell transcriptomic sequencing. However, current methods do not provide robust ways to select target cells based on observed behaviour and function. We are developing selective single cell sequencing technologies to elucidate transcriptional programs associated with specific behaviours and functions. This capability is akin to a debugging tool for microprocessors to examine memory and program execution.

Red Blood Cell Biomechanics

Red blood cells perform the critical function of transporting oxygen and carbon dioxide between tissues in the body. This capability is enabled in part by their extraordinary mechanical deformability, which allows these cells to repeatedly squeeze through capillaries many times smaller than their diameter. We are studying the loss of red blood cell deformability as a potential biomarker for measuring antimalarial drug efficacy, as well as for assessing the quality of donor blood in transfusion medicine.

Deformability-based Cell Sorting

One of the most interesting and frustrating characteristics of low-Reynolds number flow is its kinematic reversibility. This characteristic is especially problematic for cell separation where discriminating properties of target cells must be translated into forces that against the viscous forces imposed by the carrier fluid. To overcome this problem, we developed the microfluidic ratchet mechanism to overcome kinematic reversibility using the asymmetry in the deformation single cells through micrometer tapered constrictions. Oscillatory flow through such constrictions enables selective separation of cells based on squeezability. This mechanism enables deformability-based sorting of high-density cell samples (e.g. whole blood) with high selectivity.

Image Cytometry and Machine Learning

Cytometry is the quantitative assessment of cells based on morphological characteristics such as size, shape, internal structure, as well as the presence and location of markers identified using fluorescence labels. Advances in microscopy has been steadily improving the quality and quantity of images that could be acquired on cellular samples. We are using recent advances in machine learning to identify cells with specific behaviours and functions from microscopy images.