PICASSO allows ultra-multiplexed fluorescence imaging of spatially overlapping proteins without reference spectra measurements

Multiplexed sequential imaging in living cells with orthogonal fluorogenic RNA aptamer/dye pairs

Detecting multiple targets in living cells is important in cell biology. However, multiplexed fluorescence imaging beyond two-to-three targets remains a technical challenge. Herein, we introduce a multiplexed imaging strategy, 'sequential Fluorogenic RNA Imaging-Enabled Sensor' (seqFRIES), which enables live-cell target detection via sequential rounds of imaging-and-stripping. In seqFRIES, multiple orthogonal fluorogenic RNA aptamers are genetically encoded inside cells, and then the corresponding cell membrane permeable dye molecules are added, imaged, and rapidly removed in consecutive detection cycles. As a proof-of-concept, we have identified in this study four fluorogenic RNA aptamer/dye pairs that can be used for highly orthogonal and multiplexed imaging in living bacterial and mammalian cells. After further optimizing the cellular fluorescence activation and deactivation kinetics of these RNA/dye pairs, the whole four-color semi-quantitative seqFRIES process can be completed in ∼20 min. Meanwhile, seqFRIES-mediated simultaneous detection of critical signalling molecules and mRNA targets was also achieved within individual living cells. We expect our validation of this new seqFRIES concept here will facilitate the further development and potential broad usage of these orthogonal fluorogenic RNA/dye pairs for multiplexed and dynamic live-cell imaging and cell biology studies.

Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications

There is a significant demand for multiplexed fluorescence sensing and detection across a range of applications. Yet, the development of portable and compact multiplexable systems remains a substantial challenge. This difficulty largely stems from the inherent need for spectrum separation, which typically requires sophisticated and expensive optical components. Here, we demonstrate a compact, lens-free, and cost-effective fluorescence sensing setup that incorporates machine learning for scalable multiplexed fluorescence detection. This method utilizes low-cost optical components and a pretrained machine learning (ML) model to enable multiplexed fluorescence sensing without optical adjustments. Its multiplexing capability can be easily scaled up through updates to the machine learning model without altering the hardware. We demonstrate its real-world application in a probe-based multiplexed Loop-Mediated Isothermal Amplification (LAMP) assay designed to simultaneously detect three common respiratory viruses within a single reaction. The effectiveness of this approach highlights the system's potential for point-of-care applications that require cost-effective and scalable solutions. The machine learning-enabled multiplexed fluorescence sensing demonstrated in this work would pave the way for widespread adoption in diverse settings, from clinical laboratories to field diagnostics.

Fluorescence Lifetime Multiplexing (FLEX) for simultaneous high dimensional spatial biology in 3D

Immunohistochemistry is a crucial method for detecting specific proteins within tissue samples, yet constrained to one biomarker per tissue section. Multiplexed immunofluorescence, while allowing simultaneous visualization of multiple proteins, faces limitations in the number of simultaneous fluorescent labels due to spectral overlap. Although cyclic immunofluorescence techniques have successfully broadened antibody staining capacities in a single tissue sample, they are plagued by time-consuming and labor-intensive procedures, sample degradation risks, and inability to scale beyond thin sections. In this study, we introduce the use of 3D confocal Fluorescence Lifetime Imaging Microscopy as a high-throughput, multiplexed immunofluorescence platform that can differentiate 11 or more biomarkers in 3D tissue volumes. Leveraging both spectral and lifetime information, this approach allows for practical spatial biology in thin sections that can readily scale to larger volumes of tissue. We believe that this highly multiplexed and versatile biomarker imaging platform will significantly expedite cancer research and enable new translational approaches in the future.

Multiplexed volumetric CLEM enabled by scFvs provides insights into the cytology of cerebellar cortex

Mapping neuronal networks is a central focus in neuroscience. While volume electron microscopy (vEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide molecular information to identify cell types or functions. We developed an approach that uses fluorescent single-chain variable fragments (scFvs) to perform multiplexed detergent-free immunolabeling and volumetric-correlated-light-and-electron-microscopy on the same sample. We generated eight fluorescent scFvs targeting brain markers. Six fluorescent probes were imaged in the cerebellum of a female mouse, using confocal microscopy with spectral unmixing, followed by vEM of the same sample. The results provide excellent ultrastructure superimposed with multiple fluorescence channels. Using this approach, we documented a poorly described cell type, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

From Blur to Brilliance: The Ascendance of Advanced Microscopy in Neuronal Cell Biology

The intricate network of the brain's neurons and synapses poses unparalleled challenges for research, distinct from other biological studies. This is particularly true when dissecting how neurons and their functional units work at a cell biological level. While traditional microscopy has been foundational, it was unable to reveal the deeper complexities of neural interactions. However, an imaging renaissance has transformed our capabilities. Advancements in light and electron microscopy, combined with correlative imaging, now achieve unprecedented resolutions, uncovering the most nuanced neural structures. Maximizing these tools requires more than just technical proficiency. It is crucial to align research aims, allocate resources wisely, and analyze data effectively. At the heart of this evolution is interdisciplinary collaboration, where various experts come together to translate detailed imagery into significant biological insights. This review navigates the latest developments in microscopy, underscoring both the promise of and prerequisites for bending this powerful tool set to understanding neuronal cell biology.

Inspiring a convergent engineering approach to measure and model the tissue microenvironment

Understanding the molecular and physical complexity of the tissue microenvironment (TiME) in the context of its spatiotemporal organization has remained an enduring challenge. Recent advances in engineering and data science are now promising the ability to study the structure, functions, and dynamics of the TiME in unprecedented detail; however, many advances still occur in silos that rarely integrate information to study the TiME in its full detail. This review provides an integrative overview of the engineering principles underlying chemical, optical, electrical, mechanical, and computational science to probe, sense, model, and fabricate the TiME. In individual sections, we first summarize the underlying principles, capabilities, and scope of emerging technologies, the breakthrough discoveries enabled by each technology and recent, promising innovations. We provide perspectives on the potential of these advances in answering critical questions about the TiME and its role in various disease and developmental processes. Finally, we present an integrative view that appreciates the major scientific and educational aspects in the study of the TiME.

A Guide to Biodetection in Droplets

Droplet-based methods for optical biodetection enable unprecedented high-throughput experimental parameters. The methods, however, remain underused due to the accompanying multidisciplinary and complicated experimental workflows. Here, we provide a tutorial for droplet-based optical biodetection workflows with a focus on the key aspect of label selection. By discussing and guiding readers through recent state-of-the-art studies, we aim to make droplet-based approaches more accessible to the general scientific public.

Two-site microendoscopic imaging probe for simultaneous three-dimensional imaging at two anatomic locations in tissues

Systems that can image in three dimensions at cellular resolution and across different locations within an organism may enable insights into complex biological processes, such as immune responses, for which a single location measurement may be insufficient. In this Letter, we describe an in vivo two-site imaging probe (TIP) that can simultaneously image two anatomic sites with a maximum separation of a few centimeters. The TIP consists of two identical bendable graded index (GRIN) lenses and is demonstrated by a two-photon two-color fluorescence imaging system. Each GRIN lens has a field of view of 162 × 162 × 170 µm3, a nominal numerical aperture of 0.5, a magnification of 0.7, and working distances of 0.2 mm in air for both ends. A blind linear unmixing algorithm is applied to suppress bleedthrough between channels. We use this system to successfully demonstrate two-site two-photon two-color imaging of two biomedically relevant samples, i.e., (1) a mixture of two autofluorescent anti-cancer drugs and (2) a live hybrid tumor consisting of two spectrally distinct fluorescent cell lines.

Live-cell imaging powered by computation

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

Opto-combinatorial indexing enables high-content transcriptomics by linking cell images and transcriptomes

We introduce a simple integrated analysis method that links cellular phenotypic behaviour with single-cell RNA sequencing (scRNA-seq) by utilizing a combination of optical indices from cells and hydrogel beads. With our method, the combinations, referred to as joint colour codes, enable the link via matching the optical combinations measured by conventional epi-fluorescence microscopy with the concatenated DNA molecular barcodes created by cell-hydrogel bead pairs and sequenced by next-generation sequencing. We validated our approach by demonstrating an accurate link between the cell image and scRNA-seq with mixed species experiments, longitudinal cell tagging by electroporation and lipofection, and gene expression analysis. Furthermore, we extended our approach to multiplexed chemical transcriptomics, which enabled us to identify distinct phenotypic behaviours in HeLa cells treated with various concentrations of paclitaxel, and determine the corresponding gene regulation associated with the formation of a multipolar spindle.

Per-pixel unmixing of spectrally overlapping fluorophores using intra-exposure excitation modulation

Multilabel fluorescence imaging is essential for the visualization of complex systems, though a major challenge is the limited width of the useable spectral window. Here, we present a new method, exNEEMO, that enables per-pixel quantification of spectrally-overlapping fluorophores based on their light-induced dynamics, in a way that is compatible with a very broad range of timescales over which these dynamics may occur. Our approach makes use of intra-exposure modulation of the excitation light to distinguish the different emitters given their reference responses to this modulation. We use the approach to simultaneously image four green photochromic fluorescent proteins at the full spatial resolution of the imaging.

Phenotypic characterization of liver tissue heterogeneity through a next-generation 3D single-cell atlas

Three-dimensional (3D) geometrical models are potent tools for quantifying complex tissue features and exploring structure-function relationships. However, these models are generally incomplete due to experimental limitations in acquiring multiple (> 4) fluorescent channels in thick tissue sections simultaneously. Indeed, predictive geometrical and functional models of the liver have been restricted to few tissue and cellular components, excluding important cellular populations such as hepatic stellate cells (HSCs) and Kupffer cells (KCs). Here, we combined deep-tissue immunostaining, multiphoton microscopy, deep-learning techniques, and 3D image processing to computationally expand the number of simultaneously reconstructed tissue structures. We then generated a spatial single-cell atlas of hepatic architecture (Hep3D), including all main tissue and cellular components at different stages of post-natal development in mice. We used Hep3D to quantitatively study 1) hepatic morphodynamics from early post-natal development to adulthood, and 2) the effect on the liver's overall structure when changing the hepatic environment after removing KCs. In addition to a complete description of bile canaliculi and sinusoidal network remodeling, our analysis uncovered unexpected spatiotemporal patterns of non-parenchymal cells and hepatocytes differing in size, number of nuclei, and DNA content. Surprisingly, we found that the specific depletion of KCs results in morphological changes in hepatocytes and HSCs. These findings reveal novel characteristics of liver heterogeneity and have important implications for both the structural organization of liver tissue and its function. Our next-gen 3D single-cell atlas is a powerful tool to understand liver tissue architecture, opening up avenues for in-depth investigations into tissue structure across both normal and pathological conditions.

BINGO: a blind unmixing algorithm for ultra-multiplexing fluorescence images

MOTIVATION

Spectral imaging is often used to observe different objects with multiple fluorescent labels to reveal the development of the biological event. As the number of observed objects increases, the spectral overlap between fluorophores becomes more serious, and obtaining a "pure" picture of each fluorophore becomes a major challenge. Here, we propose a blind spectral unmixing algorithm called BINGO (Blind unmixing via SVD-based Initialization Nmf with project Gradient descent and spare cOnstrain), which can extract all kinds of fluorophores more accurately from highly overlapping multichannel data, even if the spectra of the fluorophores are extremely similar or their fluorescence intensity varies greatly.

RESULTS

BINGO can isolate up to 10 fluorophores from spectral imaging data for a single excitation. nine-color living HeLa cells were visualized distinctly with BINGO. It provides an important algorithmic tool for multiplex imaging studies, especially in intravital imaging. BINGO shows great potential in multicolor imaging for biomedical sciences.

AVAILABILITY AND IMPLEMENTATION

The source code used for this paper is available with the test data at https://github.com/Xinyuan555/BINGO_unmixing.

Mosaic-PICASSO: accurate crosstalk removal for multiplex fluorescence imaging

MOTIVATION

Ultra-multiplexed fluorescence imaging has revolutionized our understanding of biological systems, enabling the simultaneous visualization and quantification of multiple targets within biological specimens. A recent breakthrough in this field is PICASSO, a mutual-information-based technique capable of demixing up to 15 fluorophores without their spectra, thereby significantly simplifying the application of ultra-multiplexed fluorescence imaging. However, this study has identified a limitation of mutual information (MI)-based techniques. They do not differentiate between spatial colocalization and spectral mixing. Consequently, MI-based demixing may incorrectly interpret spatially co-localized targets as non-colocalized, leading to overcorrection.

RESULTS

We found that selecting regions within a multiplex image with low-spatial similarity for measuring spectroscopic mixing results in more accurate demixing. This method effectively minimizes overcorrections and promises to accelerate the broader adoption of ultra-multiplex imaging.

AVAILABILITY AND IMPLEMENTATION

The codes are available at https://github.com/xing-lab-pitt/mosaic-picasso.

Chromatically Corrected Multicolor Multiphoton Microscopy

Simultaneous imaging of multiple labels in tissues is key to studying complex biological processes. Although strategies for color multiphoton excitation have been established, chromatic aberration remains a major problem when multiple excitation wavelengths are used in a scanning microscope. Chromatic aberration introduces a spatial shift between the foci of beams of different wavelengths that varies across the field of view, severely degrading the performance of color imaging. In this work, we propose an adaptive correction strategy that solves this problem in two-beam microscopy techniques. Axial chromatic aberration is corrected by a refractive phase mask that introduces pure defocus into one beam, while lateral chromatic aberration is corrected by a piezoelectric mirror that dynamically compensates for lateral shifts during scanning. We show that this light-efficient approach allows seamless chromatic correction over the entire field of view of different multiphoton objectives without compromising spatial and temporal resolution and that the effective area for beam-mixing processes can be increased by more than 1 order of magnitude. We illustrate this approach with simultaneous three-color, two-photon imaging of developing zebrafish embryos and fixed Brainbow mouse brain slices over large areas. These results establish a robust and efficient method for chromatically corrected multiphoton imaging.

Deep Learning Pipeline for Automated Cell Profiling from Cyclic Imaging

Recent advances in microscopy allow scientists to generate vast amounts of biological data from a single biopsy sample. Cyclic fluorescence microscopy, in particular, enables multiple targets to be detected simultaneously. This, in turn, has deepened our understanding of tissue composition, cell-to-cell interactions, and cell signaling. Unfortunately, analysis of these datasets can be time-prohibitive due to the sheer volume of data. In this paper, we present CycloNET, a computational pipeline tailored for analyzing raw fluorescent images obtained through cyclic immunofluorescence. The automated pipeline pre-processes raw image files, quickly corrects for translation errors between imaging cycles, and leverages a pre-trained neural network to segment individual cells and generate single-cell molecular profiles. We applied CycloNET to a dataset of 22 human samples from head and neck squamous cell carcinoma patients and trained a neural network to segment immune cells. CycloNET efficiently processed a large-scale dataset (17 fields of view per cycle and 13 staining cycles per specimen) in 10 minutes, delivering insights at the single-cell resolution and facilitating the identification of rare immune cell clusters. We expect that this rapid pipeline will serve as a powerful tool to understand complex biological systems at the cellular level, with the potential to facilitate breakthroughs in areas such as developmental biology, disease pathology, and personalized medicine.

Enablers and challenges of spatial omics, a melting pot of technologies

Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.

A large-scale volumetric correlated light and electron microscopy study localizes Alzheimer's disease-related molecules in the hippocampus

Connectomics is a nascent neuroscience field to map and analyze neuronal networks. It provides a new way to investigate abnormalities in brain tissue, including in models of Alzheimer's disease (AD). This age-related disease is associated with alterations in amyloid-β (Aβ) and phosphorylated tau (pTau). These alterations correlate with AD's clinical manifestations, but causal links remain unclear. Therefore, studying these molecular alterations within the context of the local neuronal and glial milieu may provide insight into disease mechanisms. Volume electron microscopy (vEM) is an ideal tool for performing connectomics studies at the ultrastructural level, but localizing specific biomolecules within large-volume vEM data has been challenging. Here we report a volumetric correlated light and electron microscopy (vCLEM) approach using fluorescent nanobodies as immuno-probes to localize Alzheimer's disease-related molecules in a large vEM volume. Three molecules (pTau, Aβ, and a marker for activated microglia (CD11b)) were labeled without the need for detergents by three nanobody probes in a sample of the hippocampus of the 3xTg Alzheimer's disease model mouse. Confocal microscopy followed by vEM imaging of the same sample allowed for registration of the location of the molecules within the volume. This dataset revealed several ultrastructural abnormalities regarding the localizations of Aβ and pTau in novel locations. For example, two pTau-positive post-synaptic spine-like protrusions innervated by axon terminals were found projecting from the axon initial segment of a pyramidal cell. Three pyramidal neurons with intracellular Aβ or pTau were 3D reconstructed. Automatic synapse detection, which is necessary for connectomics analysis, revealed the changes in density and volume of synapses at different distances from an Aβ plaque. This vCLEM approach is useful to uncover molecular alterations within large-scale volume electron microscopy data, opening a new connectomics pathway to study Alzheimer's disease and other types of dementia.

Recent methodological advances towards single-cell proteomics

Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and provide an outlook on this research field.

Quantitative assessment of near-infrared fluorescent proteins

Recent progress in fluorescent protein development has generated a large diversity of near-infrared fluorescent proteins (NIR FPs), which are rapidly becoming popular probes for a variety of imaging applications. However, the diversity of NIR FPs poses a challenge for end-users in choosing the optimal one for a given application. Here we conducted a systematic and quantitative assessment of intracellular brightness, photostability, oligomeric state, chemical stability and cytotoxicity of 22 NIR FPs in cultured mammalian cells and primary mouse neurons and identified a set of top-performing FPs including emiRFP670, miRFP680, miRFP713 and miRFP720, which can cover a majority of imaging applications. The top-performing proteins were further validated for in vivo imaging of neurons in Caenorhabditis elegans, zebrafish, and mice as well as in mice liver. We also assessed the applicability of the selected NIR FPs for multicolor imaging of fusions, expansion microscopy and two-photon imaging.

AutoUnmix: an autoencoder-based spectral unmixing method for multi-color fluorescence microscopy imaging

Multiplexed fluorescence microscopy imaging is widely used in biomedical applications. However, simultaneous imaging of multiple fluorophores can result in spectral leaks and overlapping, which greatly degrades image quality and subsequent analysis. Existing popular spectral unmixing methods are mainly based on computational intensive linear models, and the performance is heavily dependent on the reference spectra, which may greatly preclude its further applications. In this paper, we propose a deep learning-based blindly spectral unmixing method, termed AutoUnmix, to imitate the physical spectral mixing process. A transfer learning framework is further devised to allow our AutoUnmix to adapt to a variety of imaging systems without retraining the network. Our proposed method has demonstrated real-time unmixing capabilities, surpassing existing methods by up to 100-fold in terms of unmixing speed. We further validate the reconstruction performance on both synthetic datasets and biological samples. The unmixing results of AutoUnmix achieve the highest SSIM of 0.99 in both three- and four-color imaging, with nearly up to 20% higher than other popular unmixing methods. For experiments where spectral profiles and morphology are akin to simulated data, our method realizes the quantitative performance demonstrated above. Due to the desirable property of data independency and superior blind unmixing performance, we believe AutoUnmix is a powerful tool for studying the interaction process of different organelles labeled by multiple fluorophores.

Expandable ELAST for super-resolution imaging of thick tissue slices using a hydrogel containing charged monomers

Hydrogels have been utilized extensively as a material for retaining position information in tissue imaging procedures, such as tissue clearing and super-resolution imaging. Immunostaining thick biological tissues, however, poses a bottleneck that restricts sample size. The recently developed technique known as entangled link-augmented stretchable tissue-hydrogel (ELAST) accelerates the immunostaining process by embedding specimens in long-chain polymers and stretching them. A more advanced version of ELAST, magnifiable entangled link-augmented stretchable tissue-hydrogel (mELAST), achieves rapid immunostaining and tissue expansion by embedding specimens in long-chain neutral polymers and subsequently hydrolyzing them. Building on these techniques, we introduce a variant of mELAST called ExELAST. This approach uses charged monomers to stretch and expand tissue slices. Using ExELAST, we first tested two hydrogel compositions that could permit uniform expansion of biological specimens. Then, we apply the tailored hydrogel to the 500-μm-thick mouse brain slices and demonstrated that they can be stained within two days and imaged with a resolution below the diffraction limit of light.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

Multiplex Protein Imaging through PACIFIC: Photoactive Immunofluorescence with Iterative Cleavage

Multiplex protein imaging technologies enable deep phenotyping and provide rich spatial information about biological samples. Existing methods have shown great success but also harbored trade-offs between various pros and cons, underscoring the persisting necessity to expand the imaging toolkits. Here we present PACIFIC: photoactive immunofluorescence with iterative cleavage, a new modality of multiplex protein imaging methods. PACIFIC achieves iterative multiplexing by implementing photocleavable fluorophores for antibody labeling with one-step spin-column purification. PACIFIC requires no specialized instrument, no DNA encoding, or chemical treatments. We demonstrate that PACIFIC can resolve cellular heterogeneity in both formalin-fixed paraffin-embedded (FFPE) samples and fixed cells. To further highlight how PACIFIC assists discovery, we integrate PACIFIC with live-cell tracking and identify phosphor-p70S6K as a critical driver that governs U87 cell mobility. Considering the cost, flexibility, and compatibility, we foresee that PACIFIC can confer deep phenotyping capabilities to anyone with access to traditional immunofluorescence platforms.

Multiplexed Sequential Imaging in Living Cells with Orthogonal Fluorogenic RNA Aptamer/Dye Pairs

Single-cell detection of multiple target analytes is an important goal in cell biology. However, due to the spectral overlap of common fluorophores, multiplexed fluorescence imaging beyond two-to-three targets inside living cells remains a technical challenge. Herein, we introduce a multiplexed imaging strategy that enables live-cell target detection via sequential rounds of imaging-and-stripping process, which is named as "sequential Fluorogenic RNA Imaging-Enabled Sensor" (seqFRIES). In seqFRIES, multiple orthogonal fluorogenic RNA aptamers are genetically encoded inside cells, and then the corresponding cell membrane permeable dye molecules are added, imaged, and rapidly removed in consecutive detection cycles. As a proof-of-concept, we have identified in this study five in vitro orthogonal fluorogenic RNA aptamer/dye pairs (>10-fold higher fluorescence signals), four of which can be used for highly orthogonal and multiplexed imaging in living bacterial and mammalian cells. After further optimizing the cellular fluorescence activation and deactivation kinetics of these RNA/dye pairs, the whole four-color semi-quantitative seqFRIES process can now be completed in ~20 min. Meanwhile, seqFRIES-mediated simultaneous detection of two critical signaling molecules, guanosine tetraphosphate and cyclic diguanylate, was also achieved within individual living cells. We expect our validation of this new seqFRIES concept here will facilitate the further development and potential broad usage of these orthogonal fluorogenic RNA/dye pairs for highly multiplexed and dynamic cellular imaging and cell biology studies.

Precancerous Lesions of the Head and Neck Region and Their Stromal Aberrations: Piecemeal Data

Head and neck squamous cell carcinomas (HNSCCs) develop through a series of precancerous stages from a pool of potentially malignant disorders (PMDs). Although we understand the genetic changes that lead to HNSCC, our understanding of the role of the stroma in the progression from precancer to cancer is limited. The stroma is the primary battleground between the forces that prevent and promote cancer growth. Targeting the stroma has yielded promising cancer therapies. However, the stroma at the precancerous stage of HNSCCs is poorly defined, and we may miss opportunities for chemopreventive interventions. PMDs already exhibit many features of the HNSCC stroma, such as inflammation, neovascularization, and immune suppression. Still, they do not induce cancer-associated fibroblasts or destroy the basal lamina, the stroma's initial structure. Our review aims to summarize the current understanding of the transition from precancer to cancer stroma and how this knowledge can reveal opportunities and limitations for diagnostic, prognostic, and therapeutic decisions to benefit patients. We will discuss what may be needed to fulfill the promise of the precancerous stroma as a target to prevent progression to cancer.

Linguistic Analysis Identifies Emergent Biomaterial Fabrication Trends for Orthopaedic Applications

The expanse of publications in tissue engineering (TE) and orthopedic TE (OTE) over the past 20 years presents an opportunity to probe emergent trends in the field to better guide future technologies that can make an impact on musculoskeletal therapies. Leveraging this trove of knowledge, a hierarchical systematic search method and trend analysis using connected network mapping of key terms is developed. Within discrete time intervals, an accelerated publication rate for anatomic orthopedic tissue engineering (AOTE) of osteochondral defects, tendons, menisci, and entheses is identified. Within these growing fields, the top-listed key terms are extracted and stratified into evident categories, such as biomaterials, delivery method, or 3D printing and biofabrication. It is then identified which categories decreased, remained constant, increased, or emerged over time, identifying the specific emergent categories currently driving innovation in orthopedic repair technologies. Together, these data demonstrate a significant convergence of material types and descriptors used across tissue types. From this convergence, design criteria to support future research of anatomic constructs that mimic both the form and function of native tissues are formulated. In summary, this review identifies large-scale trends and predicts new directions in orthopedics that will define future materials and technologies.

The localization of molecularly distinct microglia populations to Alzheimer's disease pathologies using QUIVER

New histological techniques are needed to examine protein distribution in human tissues, which can reveal cell shape and disease pathology connections. Spatial proteomics has changed the study of tumor microenvironments by identifying spatial relationships of immunomodulatory cells and proteins and contributing to the discovery of new cancer immunotherapy biomarkers. However, the fast-expanding toolkit of spatial proteomic approaches has yet to be systematically applied to investigate pathological alterations in the aging human brain in health and disease states. Moreover, post-mortem human brain tissue presents distinct technical problems due to fixation procedures and autofluorescence, which limit fluorescence methodologies. This study sought to develop a multiplex immunohistochemistry approach (visualizing the immunostain with brightfield microscopy). Quantitative multiplex Immunohistochemistry with Visual colorimetric staining to Enhance Regional protein localization (QUIVER) was developed to address these technical challenges. Using QUIVER, a ten-channel pseudo-fluorescent image was generated using chromogen removal and digital microscopy to identify unique molecular microglia phenotypes. Next, the study asked if the tissue environment, specifically the amyloid plaques and neurofibrillary tangles characteristic of Alzheimer's disease, has any bearing on microglia's cellular and molecular phenotypes. QUIVER allowed the visualization of five molecular microglia/macrophage phenotypes using digital pathology tools. The recognizable reactive and homeostatic microglia/macrophage phenotypes demonstrated spatial polarization towards and away from amyloid plaques, respectively. Yet, microglia morphology appearance did not always correspond to molecular phenotype. This research not only sheds light on the biology of microglia but also offers QUIVER, a new tool for examining pathological alterations in the brains of the elderly.

Multispectral confocal 3D imaging of intact healthy and tumor tissue using mLSR-3D

Revealing the 3D composition of intact tissue specimens is essential for understanding cell and organ biology in health and disease. State-of-the-art 3D microscopy techniques aim to capture tissue volumes on an ever-increasing scale, while also retaining sufficient resolution for single-cell analysis. Furthermore, spatial profiling through multi-marker imaging is fast developing, providing more context and better distinction between cell types. Following these lines of technological advance, we here present a protocol based on FUnGI (fructose, urea and glycerol clearing solution for imaging) optical clearing of tissue before multispectral large-scale single-cell resolution 3D (mLSR-3D) imaging, which implements 'on-the-fly' linear unmixing of up to eight fluorophores during a single acquisition. Our protocol removes the need for repetitive illumination, thereby allowing larger volumes to be scanned with better image quality in less time, also reducing photo-bleaching and file size. To aid in the design of multiplex antibody panels, we provide a fast and manageable intensity equalization assay with automated analysis to design a combination of markers with balanced intensities suitable for mLSR-3D. We demonstrate effective mLSR-3D imaging of various tissues, including patient-derived organoids and xenografted tumors, and, furthermore, describe an optimized workflow for mLSR-3D imaging of formalin-fixed paraffin-embedded samples. Finally, we provide essential steps for 3D image data processing, including shading correction that does not require pre-acquired shading references and 3D inhomogeneity correction to correct fluorescence artefacts often afflicting 3D datasets. Together, this provides a one-week protocol for eight-fluorescent-marker 3D visualization and exploration of intact tissue of various origins at single-cell resolution.

Multiplexed bioluminescence imaging with a substrate unmixing platform

Bioluminescent tools can illuminate cellular features in whole organisms. Multi-component tracking remains challenging, though, owing to a lack of well-resolved probes and long imaging times. To address the need for more rapid, quantitative, and multiplexed bioluminescent readouts, we developed an analysis pipeline featuring sequential substrate administration and serial image acquisition. Light output from each luciferin is layered on top of the previous image, with minimal delay between substrate delivery. A MATLAB algorithm was written to analyze bioluminescent images generated from the rapid imaging protocol and deconvolute (i.e., unmix) signals from luciferase-luciferin pairs. Mixtures comprising three to five luciferase reporters were readily distinguished in under 50 min; this same experiment would require days using conventional workflows. We further showed that the algorithm can be used to accurately quantify luciferase levels in heterogeneous mixtures. Based on its speed and versatility, the multiplexed imaging platform will expand the scope of bioluminescence technology.

Super-multiplexing excitation spectral microscopy with multiple fluorescence bands

Fluorescence microscopy, with high molecular specificity and selectivity, is a valuable tool for studying complex biological systems and processes. However, the ability to distinguish a large number of distinct subcellular structures in a single sample is impeded by the broad spectra of molecular fluorescence. We have recently shown that excitation spectral microscopy provides a powerful means to unmix up to six fluorophores in a single fluorescence band. Here, by working with multiple fluorescence bands, we extend this approach to the simultaneous imaging of up to ten targets, with the potential for further expansions. By covering the excitation/emission bandwidth across the full visible range, an ultra-broad 24-wavelength excitation scheme is established through frame-synchronized scanning of the excitation wavelength from a white lamp via an acousto-optic tunable filter (AOTF), so that full-frame excitation-spectral images are obtained every 24 camera frames, offering superior spectral information and multiplexing capability. With numerical simulations, we validate the concurrent imaging of 10 fluorophores spanning the visible range to achieve exceptionally low (∼0.5%) crosstalks. For cell imaging experiments, we demonstrate unambiguous identification of up to eight different intracellular structures labeled by common fluorophores of substantial spectral overlap with minimal color crosstalks. We thus showcase an easy-to-implement, cost-effective microscopy system for visualizing complex cellular components with more colors and lower crosstalks.