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Adding a cationic helper lipid to a lipid nanoparticle (LNP) can increase lung delivery and decrease liver delivery. However, it remains unclear whether charge-dependent tropism is universal or, alternatively, whether it depends on the component that is charged. Here, we report evidence that cationic cholesterol-dependent tropism can differ from cationic helper lipid-dependent tropism. By testing how 196 LNPs delivered mRNA to 22 cell types, we found that charged cholesterols led to a different lung:liver delivery ratio than charged helper lipids. We also found that combining cationic cholesterol with a cationic helper lipid led to mRNA delivery in the heart as well as several lung cell types, including stem cell-like populations. These data highlight the utility of exploring charge-dependent LNP tropism.

Imaging flow cytometry (IFC) combines flow cytometry and fluorescence microscopy to enable high-throughput, multiparametric single-cell analysis with rich spatial details. However, current IFC techniques remain limited in their ability to reveal subcellular information with a high 3D resolution, throughput, sensitivity, and instrumental simplicity. In this study, we introduce a light-field flow cytometer (LFC), an IFC system capable of high-content, single-shot, and multi-color acquisition of up to 5,750 cells per second with a near-diffraction-limited resolution of 400-600 nm in all three dimensions. The LFC system integrates optical, microfluidic, and computational strategies to facilitate the volumetric visualization of various 3D subcellular characteristics through convenient access to commonly used epi-fluorescence platforms. We demonstrate the effectiveness of LFC in assaying, analyzing, and enumerating intricate subcellular morphology, function, and heterogeneity using various phantoms and biological specimens. The advancement offered by the LFC system presents a promising methodological pathway for broad cell biological and translational discoveries, with the potential for widespread adoption in biomedical research.

Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.

This study introduces a rapid, volumetric live-cell imaging technique for visualizing autofluorescent sub-cellular structures and their dynamics by employing high-resolution Fourier light-field microscopy. We demonstrated this method by capturing lysosomal autofluorescence in fibroblasts and HeLa cells. Additionally, we conducted multicolor imaging to simultaneously observe lysosomal autofluorescence and fluorescently-labeled organelles such as lysosomes and mitochondria. We further analyzed the data to quantify the interactions between lysosomes and mitochondria. This research lays the foundation for future exploration of native cellular states and functions in three-dimensional environments, effectively reducing photodamage and eliminating the necessity for exogenous labels.

Live-cell imaging reveals the phenotypes and mechanisms of cellular function and their dysfunction that underscore cell physiology, development, and pathology. Here, we report a 3D super-resolution live-cell microscopy method by integrating radiality analysis and Fourier light-field microscopy (rad-FLFM). We demonstrated the method using various live-cell specimens, including actins in Hela cells, microtubules in mammary organoid cells, and peroxisomes in COS-7 cells. Compared with conventional wide-field microscopy,rad-FLFM realizes scanning-free, volumetric 3D live-cell imaging with sub-diffraction-limited resolution of ∼150 nm (x-y) and 300 nm (z), milliseconds volume acquisition time, six-fold extended depth of focus of ∼6 µm, and low photodamage. The method provides a promising avenue to explore spatiotemporal-challenging subcellular processes in a wide range of cell biological research.

Volumetric interrogation of the organization and processes of intracellular organelles and molecules in cellular systems with a high spatiotemporal resolution is essential for understanding cell physiology, development, and pathology. Here, we report high-resolution Fourier light-field microscopy (HR-FLFM) for fast and volumetric live-cell imaging. HR-FLFM transforms conventional cell microscopy and enables exploration of less accessible spatiotemporal-limiting regimes for single-cell studies. The results present a near-diffraction-limited resolution in all three dimensions, a five-fold extended focal depth to several micrometers, and a scanning-free volume acquisition time up to milliseconds. The system demonstrates instrumentation accessibility, low photo damage for continuous observation, and high compatibility with general cell assays. We anticipate HR-FLFM to offer a promising methodological pathway for investigating a wide range of intracellular processes and functions with exquisite spatiotemporal contextual details.

The rapid development of scientific CMOS (sCMOS) technology has greatly advanced optical microscopy for biomedical research with superior sensitivity, resolution, field-of-view, and frame rates. However, for sCMOS sensors, the parallel charge-voltage conversion and different responsivity at each pixel induces extra readout and pattern noise compared to charge-coupled devices (CCD) and electron-multiplying CCD (EM-CCD) sensors. This can produce artifacts, deteriorate imaging capability, and hinder quantification of fluorescent signals, thereby compromising strategies to reduce photo-damage to live samples. Here, we propose a content-adaptive algorithm for the automatic correction of sCMOS-related noise (ACsN) for fluorescence microscopy. ACsN combines camera physics and layered sparse filtering to significantly reduce the most relevant noise sources in a sCMOS sensor while preserving the fine details of the signal. The method improves the camera performance, enabling fast, low-light and quantitative optical microscopy with video-rate denoising for a broad range of imaging conditions and modalities.