The contrast and sensitivity of in vivo fluorescence imaging has been revolutionized by molecular fluorophores operating in the second near-infrared window (NIR-II; 1000-1700 nm), but an ongoing challenge is the solvatochromism-caused quenching in aqueous solution for the long-wavelength absorbing fluorophores. Herein, we develop a series of anti-quenching pentamethine cyanine fluorophores that significantly overcome the severe solvatochromism, thus affording stable absorption/emission beyond 1000 nm with up to ~ 44-fold enhanced brightness and superior photostability in aqueous solution. These advantages allow for deep optical penetration (8 mm) as well as high-contrast and highly-stable lymphatic imaging superior to clinical-approved indocyanine green. Additionally, these fluorophores exhibit pH-responsive fluorescence, allowing for noninvasive ratiometric fluorescence imaging and quantification of gastric pH in vivo. The results demonstrate reliable accuracy in tissue as deep as 4 mm, comparable to standard pH electrode method. This work unlocks the potential of anti-quenching pentamethine cyanines for NIR-II biological applications. Fluorophores operating in the second near-infrared window suffer from solvatochromism-caused fluorescence quenching in biological aqueous solution. Here, the authors synthesized a series of pH-responsive pentamethine cyanine fluorophores that afford stable absorption/emission beyond 1000 nm.

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Anti-quenching NIR-II molecular fluorophores for in vivo high-contrast imaging and pH sensing.

Deep learning is becoming an increasingly important tool for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration and super-resolution imaging, and discuss how the latest deep learning research could be applied to other image reconstruction tasks. Despite its successes, deep learning also poses substantial challenges and has limits. We discuss key questions, including how to obtain training data, whether discovery of unknown structures is possible, and the danger of inferring unsubstantiated image details.

Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction

INTRODUCTION Neural circuits across the brain are composed of structures spanning seven orders of magnitude in size that are assembled from thousands of distinct protein types. Electron microscopy has imaged densely labeled brain tissue at nanometer-level resolution over near-millimeter-level dimensions but lacks the contrast to distinguish specific proteins and the speed to readily image multiple specimens. Conversely, confocal fluorescence microscopy offers molecular contrast but has insufficient resolution for dense neural tracing or the precise localization of specific molecular players within submicrometer-sized structures. Last, superresolution fluorescence microscopy bleaches fluorophores too quickly for large-volume imaging and also lacks the speed for effective brain-wide or cortex-wide imaging of multiple specimens. RATIONALE We combined two imaging technologies to address these issues. Expansion microscopy (ExM) creates an expanded, optically clear phantom of a fluorescent specimen that retains its original relative distribution of fluorescent tags. Lattice light-sheet microscopy (LLSM) then images this phantom in three dimensions with minimal photobleaching at speeds sufficient to image the entire Drosophila brain or across the width of the mouse cortex in ∼2 to 3 days, with multiple markers at an effective resolution of ∼60 by 60 by 90 nm for 4× expansion. RESULTS We applied expansion/LLSM (ExLLSM) to study a variety of subcellular structures in the brain. In the mouse cortex, we quantified the volume of organelles, measured morphological parameters of ~1500 dendritic spines, determined the variation of distances between pre- and postsynaptic proteins, observed large differences in postsynaptic expression at adjacent pyramidal neurons, and studied both the azimuthal asymmetry and layer-specific longitudinal variation of axonal myelination. In Drosophila , we traced the axonal branches of olfactory projection neurons across one hemisphere and studied the stereotypy of their boutons at the calyx and lateral horn across five animals. We also imaged all dopaminergic neurons (DANs) across the brain of another specimen, visualized DAN morphologies in all major brain regions, and traced a cluster of eight DANs to their termini to determine their respective cell types. In the same specimen, we also determined the number of presynaptic active zones (AZs) across the brain and the local density of all AZs and DAN-associated AZs in each brain region. CONCLUSION With its high speed, nanometric resolution, and ability to leverage genetically targeted, cell type–specific, and protein-specific fluorescence labeling, ExLLSM fills a valuable niche between the high throughput of conventional optical pipelines of neural anatomy and the ultrahigh resolution of corresponding EM pipelines. Assuming the development of fully validated, brain-wide isotropic expansion at 10× or beyond and sufficiently dense labeling, ExLLSM may enable brainwide comparisons of even densely innervated neural circuits across multiple specimens with protein-specific contrast at 25-nm resolution or better.

Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution

Cell adhesion to the extracellular matrix is fundamental to metazoan multicellularity and is accomplished primarily through the integrin family of cell-surface receptors. Integrins are internalized and enter the endocytic-exocytic pathway before being recycled back to the plasma membrane. The trafficking of this extensive protein family is regulated in multiple context-dependent ways to modulate integrin function in the cell. Here, we discuss recent advances in understanding the mechanisms and cellular roles of integrin endocytic trafficking.

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Integrin trafficking in cells and tissues

Brief update on endocytosis of nanomedicines.

True physiological imaging of subcellular dynamics requires studying cells within their parent organisms, where all the environmental cues that drive gene expression, and hence the phenotypes that we actually observe, are present. A complete understanding also requires volumetric imaging of the cell and its surroundings at high spatiotemporal resolution, without inducing undue stress on either. We combined lattice light-sheet microscopy with adaptive optics to achieve, across large multicellular volumes, noninvasive aberration-free imaging of subcellular processes, including endocytosis, organelle remodeling during mitosis, and the migration of axons, immune cells, and metastatic cancer cells in vivo. The technology reveals the phenotypic diversity within cells across different organisms and developmental stages and may offer insights into how cells harness their intrinsic variability to adapt to different physiological environments.

Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms

True physiological imaging of subcellular dynamics requires studying cells within their parent organisms, where all the environmental cues that drive gene expression, and hence the phenotypes we actually observe, are present. A complete understanding also requires volumetric imaging of the cell and its surroundings at high spatiotemporal resolution without inducing undue stress on either. We combined lattice light sheet microscopy with two-channel adaptive optics to achieve, across large multicellular volumes, noninvasive aberration-free imaging of subcellular processes, including endocytosis, organelle remodeling during mitosis, and the migration of axons, immune cells, and metastatic cancer cells in vivo. The technology reveals the phenotypic diversity within cells across different organisms and developmental stages, and may offer insights into how cells harness their intrinsic variability to adapt to different physiological environments.

/pdf/observing-the-cell-in-its-native-state-imaging-subcellular-h9vfo3s43u.pdf

Yuan Ruan

Papers

Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms

Observing the Cell in Its Native State: Imaging Subcellular Dynamics in Multicellular Organisms