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

Inertial focusing excels at the precise spatial ordering and separation of microparticles by size within fluid flows. However, this advantage, resulting from 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 an inertial focusing system and achieve an efficient focusing of particles ranging from 6 to 30 µm in diameter onto a single plane with less than 3 µm variance and over 95% focusing efficiency at highly scalable throughput (2.4-30 mL h-1) - 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.

StaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases.

Single-cell atlases pose daunting computational challenges pertaining to the integration of spatial and temporal information and the visualization of trajectories across large atlases. We introduce StaVia, a computational framework that synergizes multi-faceted single-cell data with higher-order random walks that leverage the memory of cells' past states, fused with a cartographic Atlas View that offers intuitive graph visualization. This spatially aware cartography captures relationships between cell populations based on their spatial location as well as their gene expression and developmental stage. We demonstrate this using zebrafish gastrulation data, underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts.

Unveiling the impact of interprofessional education on shaping students' interprofessional identity and collaboration perception: a mixed-method study.

BACKGROUND: Interprofessional education (IPE) has the potential to shape students' collaboration perception and interprofessional identity but remains understudied. This study aims to understand the effects of the IPE program as a contextual trigger to promote collaboration perception change and interprofessional identity formation among healthcare professional students. METHODS: Using concurrent triangulation mixed-methods, we examined the relationship between collaboration perception and interprofessional identity change among health profession students (N = 263), and explored their perspectives on how their IPE experiences influenced their perception and identity. Participants completed the Interdisciplinary Education Perception Scale and Extended Professional Identity Scale and responded to open-ended questions before and after the IPE intervention. Pearson's correlation, t-tests, regression (quantitative), and thematic analysis (qualitative) were conducted. RESULTS: Teams with initially lower collaboration perception (M = 3.59) and lower interprofessional identity (M = 3.59) showed a significant increase in collaboration perception (M = 3.76, t = 2.63; p = .02) and interprofessional identity (M = 3.97, t = 4.86; p < .001) after participating in IPE. The positive relationship between collaboration perception and interprofessional identity strengthened after participating in IPE, as evident from the correlation (Time 1: r = .69; p < .001; Time 2: r = .79; p < .001). Furthermore, collaboration perception in Time 1 significantly predicted the variance in interprofessional identity at Time 2 (ß = 0.347, p < .001). Qualitative findings indicated that 85.2% of students expressed that IPE played a role in promoting their interprofessional identity and collaboration attitudes. CONCLUSIONS: Incorporating the IPE program into the curriculum can effectively enhance students' collaboration perception and interprofessional identity, ultimately preparing them for collaborative practice in the healthcare system. By engaging students in interprofessional teamwork, communication, and joint decision-making processes, the IPE program provides a valuable context for students to develop a sense of belonging and commitment to interprofessional collaboration.

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

Image-based cytometry faces 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, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable 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 label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD  also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.

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 lung-cancer cell subtype classification, drug response assays and cell-cycle progression tracking. 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.

StarmapVis: An interactive and narrative visualisation tool for single-cell and spatial data.

Current single-cell visualisation techniques project high dimensional data into 'map' views to identify high-level structures such as cell clusters and trajectories. New tools are needed to allow the transversal through the high dimensionality of single-cell data to explore the single-cell local neighbourhood. StarmapVis is a convenient web application displaying an interactive downstream analysis of single-cell expression or spatial transcriptomic data. The concise user interface is powered by modern web browsers to explore the variety of viewing angles unavailable to 2D media. Interactive scatter plots display clustering information, while the trajectory and cross-comparison among different coordinates are displayed in connectivity networks. Automated animation of camera view is a unique feature of our tool. StarmapVis also offers a useful animated transition between two-dimensional spatial omic data to three-dimensional single cell coordinates. The usability of StarmapVis is demonstrated by four data sets, showcasing its practical usability. StarmapVis is available at: https://holab-hku.github.io/starmapVis.

Optofluidic imaging meets deep learning: from merging to emerging.

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.

EuniceScope: Low-Cost Imaging Platform for Studying Microgravity Cell Biology.

Microgravity is proven to impact a wide range of human physiology, from stimulating stem cell differentiation to confounding cell health in bones, skeletal muscles, and blood cells. The research in this arena is progressively intensified by the increasing promises of human spaceflights. Considering the limited access to spaceflight, ground-based microgravity-simulating platforms have been indispensable for microgravity-biology research. However, they are generally complex, costly, hard to replicate and reconfigure - hampering the broad adoption of microgravity biology and astrobiology. To address these limitations, we developed a low-cost reconfigurable 3D-printed microscope coined EuniceScope to allow the democratization of astrobiology, especially for educational use. EuniceScope is a compact 2D clinostat system integrated with a modularized brightfield microscope, built upon 3D-printed toolbox. We demonstrated that this compact system offers plausible imaging quality and microgravity-simulating performance. Its high degree of reconfigurability thus holds great promise in the wide dissemination of microgravity-cell-biology research in the broader community, including Science, technology, engineering, and mathematics (STEM) educational and scientific community in the future.

Ultrafast two-photon fluorescence imaging of cerebral blood circulation in the mouse brain in vivo.

Characterizing blood flow dynamics in vivo is critical to understanding the function of the vascular network under physiological and pathological conditions. Existing methods for hemodynamic imaging have insufficient spatial and temporal resolution to monitor blood flow at the cellular level in large blood vessels. By using an ultrafast line-scanning module based on free-space angular chirped enhanced delay, we achieved two-photon fluorescence imaging of cortical blood flow at 1,000 two-dimensional (2D) frames and 1,000,000 one-dimensional line scans per second in the awake mouse. This orders-of-magnitude increase in temporal resolution allowed us to measure cerebral blood flow at up to 49 mm/s and observe pulsatile blood flow at harmonics of heart rate. Directly visualizing red blood cell (RBC) flow through vessels down to >800 µm in depth, we characterized cortical layer­dependent flow velocity distributions of capillaries, obtained radial velocity profiles and kilohertz 2D velocity mapping of multifile blood flow, and performed RBC flux measurements from penetrating blood vessels.

890-nm-excited SHG and fluorescence imaging enabled by an all-fiber mode-locked laser.

We demonstrate second-harmonic generation (SHG) microscopy excited by the ∼890-nm light frequency-doubled from a 137-fs, 19.4-MHz, and 300-mW all-fiber mode-locked laser centered at 1780 nm. The mode-locking at the 1.7-µm window is realized by controlling the emission peak of the gain fiber, and uses the dispersion management technique to broaden the optical spectrum up to 30 nm. The spectrum is maintained during the amplification and the pulse is compressed by single-mode fibers. The SHG imaging performance is showcased on a mouse skull, leg, and tail. Two-photon fluorescence imaging is also demonstrated on C. elegans labeled with green and red fluorescent proteins. The frequency-doubled all-fiber laser system provides a compact and efficient tool for SHG and fluorescence microscopy.