Multichannel Impedance Cytometry Downstream of Cell Separation by Deterministic Lateral Displacement to Quantify Macrophage Enrichment in Heterogeneous Samples
Abstract
The integration of on‐chip biophysical cytometry downstream of microfluidic enrichment for inline monitoring of phenotypic and separation metrics at single‐cell sensitivity can allow for active control of separation and its application to versatile sample sets. Integration of impedance cytometry downstream of cell separation by deterministic lateral displacement (DLD) for enrichment of activated macrophages from a heterogeneous sample is presented, without the problems of biased sample loss and sample dilution caused by off‐chip analysis. This requires designs to match cell/particle flow rates from DLD separation into the confined single‐cell impedance cytometry stage, the balancing of flow resistances across the separation array width to maintain unidirectionality, and the utilization of co‐flowing beads as calibrated internal standards for inline assessment of DLD separation and for impedance data normalization. Using a heterogeneous sample with un‐activated and activated macrophages, wherein macrophage polarization during activation causes cell size enlargement, on‐chip impedance cytometry is used to validate DLD enrichment of the activated subpopulation at the displaced outlet, based on the multiparametric characteristics of cell size distribution and impedance phase metrics. This hybrid platform can monitor the separation of specific subpopulations from cellular samples with wide size distributions, for active operational control and enhanced sample versatility.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 29, 2023
- Source ID
- 10.1002/admt.202201463
Entities
People
- Aditya Rane
- Armita Salahi
- Carlos Honrado
- Federica Caselli
- Javad Jarmoshti
- Karina Torres-Castro
- Li Jin
- Nathan S. Swami
- Xiao Li
- Xudong Li
Organizations
- Air Force Office of Scientific Research
- Sapienza University of Rome
- University of Virginia