Biology subjects

Lee, K. C. M.

Publications and source records attributed to Lee, K. C. M..

4 recordsLinked to original sources

bioRxiv2024

Dispersion-free inertial focusing (DIF) for high-yield polydisperse micro-particles filtration and analysis

Inertial focusing excels at the precise spatial ordering and separation of microparticles by size within fluid flows. However, this advantage, brought by its inherent size-dependent dispersion, could turn into a drawback that challenges applications requiring consistent and uniform positioning of polydisperse particles, such as microfiltration and flow cytometry. To overcome this fundamental challenge, we introduce Dispersion-Free Inertial Focusing (DIF). This new method minimizes particle size-dependent dispersion while maintaining the high throughput and precision of standard inertial focusing, even in a highly polydisperse scenario. We demonstrate a rule-of-thumb principle to reinvent inertial focusing system and achieve an efficient focusing of particles ranging from 6 to 30 {micro}m in diameter onto a single plane with less than 3 {micro}m variance and over 95% focusing efficiency at highly scalable throughput (2.4-30 mL/hr) - a stark contrast to existing technologies that struggle with polydispersity. We demonstrated that DIF could be applied in a broad range of applications, particularly enabling high-yield continuous microparticle filtration and large-scale high-resolution single-cell morphological analysis of heterogeneous cell populations. This new technique is also readily compatible with the existing inertial microfluidic design and thus could unleash more diverse systems and applications.

cell biology↗

bioRxiv2023

Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity

Image-based cytometry faces constant challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, we introduce Cyto-Morphology Adversarial Distillation (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, enabling integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation - offering additional interpretable insights into the label-free morphological profiles. We demonstrate the versatile efficacy of CytoMAD in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of different human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. We also applied CytoMAD to jointly analyze tumor biopsies across different non-small-cell lung cancer patients and reveal previously unexplored biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD holds promises to substantiate the wide adoption of biophysical cytometry for cost-effective diagnostic and screening applications.

bioengineering↗

bioRxiv2022

Morphological profiling by high-throughput single-cell biophysical fractometry

Complex and irregular cell architecture is known to statistically exhibit fractal geometry, i.e., a pattern resembles a smaller part of itself. Although fractal variations in cells are proven to be closely associated with the disease-related phenotypes that are otherwise obscured in the standard cell-based assays, fractal analysis with single-cell precision remains largely unexplored. To close this gap, here we develop an image-based approach that quantifies a multitude of single-cell biophysical fractal-related properties at subcellular resolution. Taking together with its high-throughput single-cell imaging performance (~10,000 cells/sec), this technique, termed single-cell biophysical fractometry, offers sufficient statistical power for delineating the cellular heterogeneity, in the context of classification of lung-cancer cell subtypes and tracking of cell-cycle progression. Further correlative fractal analysis shows that single-cell biophysical fractometry can enrich the standard morphological profiling depth and spearhead systematic fractal analysis of how cell morphology encodes cellular health and pathological conditions.

cell biology↗

bioRxiv2019

PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

MotivationNew single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity.\n\nResultsWe introduce a highly scalable graph-based clustering algorithm PARC - phenotyping by accelerated refined community-partitioning - for ultralarge-scale, high-dimensional single-cell data (> 1 million cells). Using large single cell mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without sub-sampling of cells, including Phenograph, FlowSOM, and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single cell data set of 1.1M cells within 13 minutes, compared to >2 hours to the next fastest graph-clustering algorithm, Phenograph. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis.\n\nAvailability and Implementationhttps://github.com/ShobiStassen/PARC

bioinformatics↗