A Capillary-Force-Driven, Single-Cell Transfer Method for Studying Rare Cells.
Jacob AmontreeKangfu ChenJose VarillasZ Hugh FanPublished in: Bioengineering (Basel, Switzerland) (2024)
The characterization of individual cells within heterogeneous populations (e.g., rare tumor cells in healthy blood cells) has a great impact on biomedical research. To investigate the properties of these specific cells, such as genetic biomarkers and/or phenotypic characteristics, methods are often developed for isolating rare cells among a large number of background cells before studying their genetic makeup and others. Prior to using real-world samples, these methods are often evaluated and validated by spiking cells of interest (e.g., tumor cells) into a sample matrix (e.g., healthy blood) as model samples. However, spiking tumor cells at extremely low concentrations is challenging in a standard laboratory setting. People often circumvent the problem by diluting a solution of high-concentration cells, but the concentration becomes inaccurate after series dilution due to the fact that a cell suspension solution can be inhomogeneous, especially when the cell concentration is very low. We report on an alternative method for low-cost, accurate, and reproducible low-concentration cell spiking without the use of external pumping systems. By inducing a capillary force from sudden pressure drops, a small portion of the cellular membrane was aspirated into the reservoir tip, allowing for non-destructive single-cell transfer. We investigated the surface membrane tensions induced by cellular aspiration and studied a range of tip/tumor cell diameter combinations, ensuring that our method does not affect cell viability. In addition, we performed single-cell capture and transfer control experiments using human acute lymphoblastic leukemia cells (CCRF-CEM) to develop calibrated data for the general production of low-concentration samples. Finally, we performed affinity-based tumor cell isolation using this method to generate accurate concentrations ranging from 1 to 15 cells/mL.
Keyphrases
- induced apoptosis
- single cell
- cell cycle arrest
- acute lymphoblastic leukemia
- endoplasmic reticulum stress
- oxidative stress
- machine learning
- cell death
- dna methylation
- bone marrow
- deep learning
- electronic health record
- big data
- liquid chromatography tandem mass spectrometry
- artificial intelligence
- pi k akt
- ms ms
- data analysis
- genetic diversity
- solid phase extraction