Biology subjects

Tsia, K. K.

Publications and source records attributed to Tsia, K. K..

7 recordsLinked to original sources

bioRxiv2025

On-The-Fly Live-Cell Intrinsic Morphological Drug and Genetic Screens by Gigapixel-per-second Spinning Arrayed Disk Imaging

Next-generation drug discovery and functional genomics require rapid, unbiased single-cell profiling at scale--demands that exceed the limited speed, throughput, and labor-intensive labeling constraints of conventional high-content image-based screening. We introduce spinning arrayed disk (SpAD), a high-throughput, label-free imaging platform for live-cell imaging that integrates continuous circular scanning, ultrafast quantitative phase imaging (QPI), and a novel circular array of 96 culture chambers. SpAD achieves an order-of-magnitude reduction in imaging time compared to traditional fluorescence-based workflows, while remaining compatible with standard cell culture workflows. By extracting rich biophysical features using intrinsic morphological (InMorph) profiling and machine learning, SpAD enables sensitive, large-scale screening of drug responses and CRISPR gene knockouts without labeling. Critically, label-free biophysical readouts from SpAD reveal mechanism-linked changes in mass, refractive index, subcellular textures, and light scattering that fluorescent labels often obscure. SpAD thereby resolves subtle phenotypes and heterogeneous subpopulations with high reproducibility, providing a robust, scalable foundation for precision cellular morphological assays.

bioengineering↗

bioRxiv2025

FACED 2.0 enables large-scale voltage and calcium imaging in vivo

Monitoring neuronal activity at large scale and high spatiotemporal resolution is crucial for understanding information processing within the brain. We optimized a kilohertz-frame-rate two-photon fluorescence microscope with all-optical megahertz line-scan rate to achieve ultrafast imaging across large areas and volumes at subcellular resolution. Applying this technique to voltage and calcium imaging in vivo, we demonstrated simultaneous recording of voltage activity over 200 neurons and calcium activity over 14,000 neurons.

neuroscience↗

bioRxiv2024

Generalizable Morphological Profiling of Cells by Interpretable Unsupervised Learning

The intersection of advanced microscopy and machine learning is revolutionizing cell biology into a quantitative, data-driven science. While traditional morphological profiling of cells relies on labor-intensive manual feature extraction susceptible to biases, deep learning offers promising alternatives but struggles with the interpretability of its black-box operation and dependency on extensive labeled data. We introduce MorphoGenie, an unsupervised deep-learning framework designed to address these challenges in single-cell morphological profiling. Enabling disentangled representation learning integrated with high-fidelity image reconstructions, MorphoGenie possesses a critical attribute to learn a compact, generalizable and interpretable latent space. This facilitates the extraction of biologically meaningful features without human annotation, additionally overcoming the "curse of dimensionality" inherent in manual methods. Unlike prior models, MorphoGenie introduces a systematic approach to mapping disentangled latent representations to fundamental hierarchical morphological attributes, ensuring both semantic and biological interpretability. Moreover, it adheres to the concept of combinatorial generalization--a core principle of human intelligence-- which greatly enhances the models capacity to generalize across a broad spectrum of imaging modalities (e.g., quantitative phase imaging and fluorescence imaging) and experimental conditions (ranging from discrete cell type/state classification to continuous trajectory inference). The framework offers a new, generalized strategy for unbiased and comprehensive morphological profiling, potentially revealing insights into cellular behavior in health and disease that might be overlooked by expert visual examination.

bioinformatics↗

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

Tomographic-encoded multiphoton (TEMP) microscopy

Axial scanning in multiphoton microscopy (MPM) is typically realized by mechanically shifting either the objective or the sample. However, the scan speed is usually hindered by the mechanical inertia of the bulky mass. Although the extended depth of field provided by the non-diffracting beam allows fast volumetric imaging, it abandons the axial resolution. Here, we demonstrate a novel and powerful tomographic technique using the Bessel droplet in MPM, termed Tomographic-Encoded MultiPhoton (TEMP) microscopy. We show that benefiting from the high-order nonlinear excitation in MPM, the side-lobes cancellation and smaller beam focus of the Bessel droplet realize better image quality. The TEMP microscopy allows fast axial scanning, less risks of photodamage and photobleaching, and high-resolution and high-contrast imaging. Furthermore, fewer raw images are required for the 3D image reconstruction. To demonstrate its usability and advantages for scattering tissues and biomedical applications, we showcase the TEMP microscopy with highly scattering fluorescence microspheres and mouse brain slice. More details can be visualized by the Bessel droplet compared with the conventional Gaussian and Bessel beam. More importantly, the TEMP technique is an easy-plug-in method for the current microscopy system. The TEMP microscopy is promising for fast volumetric multiphoton imaging, especially for highly scattering tissues.

bioengineering↗

bioRxiv2020

starmapVR: immersive visualisation of single cell spatial omic data

MotivationAdvances in high throughput single-cell and spatial omic technologies have enabled the profiling of molecular expression and phenotypic properties of hundreds of thousands of individual cells in the context of their two dimensional (2D) or three dimensional (3D) spatial endogenous arrangement. However, current visualisation techniques do not allow for effective display and exploration of the single cell data in their spatial context. With the widespread availability of low-cost virtual reality (VR) gadgets, such as Google Cardboard, we propose that an immersive visualisation strategy is useful. ResultsWe present starmapVR, a light-weight, cross-platform, web-based tool for visualising single-cell and spatial omic data. starmapVR supports a number of interaction methods, such as keyboard, mouse, wireless controller and voice control. The tool visualises single cells in a 3D space and each cell can be represented by a star plot (for molecular expression, phenotypic properties) or image (for single cell imaging). For spatial transcriptomic data, the 2D single cell expression data can be visualised alongside the histological image in a 2.5D format. The application of starmapVR is demonstrated through a series of case studies. Its scalability has been carefully evaluated across different platforms. Availability and implementationstarmapVR is freely accessible at https://holab-hku.github.io/starmapVR, with the corresponding source code available at https://github.com/holab-hku/starmapVR under the open source MIT license. Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗