Seven-color fluorescence imaging of tissue samples based on Fourier spectroscopy and singular value decomposition

Temporally multiplexed imaging of dynamic signaling networks in living cells

Molecular signals interact in networks to mediate biological processes. To analyze these networks, it would be useful to image many signals at once, in the same living cell, using standard microscopes and genetically encoded fluorescent reporters. Here, we report temporally multiplexed imaging (TMI), which uses genetically encoded fluorescent proteins with different clocklike properties-such as reversibly photoswitchable fluorescent proteins with different switching kinetics-to represent different cellular signals. We linearly decompose a brief (few-second-long) trace of the fluorescence fluctuations, at each point in a cell, into a weighted sum of the traces exhibited by each fluorophore expressed in the cell. The weights then represent the signal amplitudes. We use TMI to analyze relationships between different kinase activities in individual cells, as well as between different cell-cycle signals, pointing toward broad utility throughout biology in the analysis of signal transduction cascades in living systems.

Untangling Alzheimer's disease with spatial multi-omics: a brief review

Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.

Types of Neurons in the Human Colonic Myenteric Plexus Identified by Multilayer Immunohistochemical Coding

BACKGROUND AND AIMS

Gut functions including motility, secretion, and blood flow are largely controlled by the enteric nervous system. Characterizing the different classes of enteric neurons in the human gut is an important step to understand how its circuitry is organized and how it is affected by disease.

METHODS

Using multiplexed immunohistochemistry, 12 discriminating antisera were applied to distinguish different classes of myenteric neurons in the human colon (2596 neurons, 12 patients) according to their chemical coding. All antisera were applied to every neuron, in multiple layers, separated by elutions.

RESULTS

A total of 164 combinations of immunohistochemical markers were present among the 2596 neurons, which could be divided into 20 classes, with statistical validation. Putative functions were ascribed for 4 classes of putative excitatory motor neurons (EMN1-4), 4 inhibitory motor neurons (IMN1-4), 3 ascending interneurons (AIN1-3), 6 descending interneurons (DIN1-6), 2 classes of multiaxonal sensory neurons (SN1-2), and a small, miscellaneous group (1.8% of total). Soma-dendritic morphology was analyzed, revealing 5 common shapes distributed differentially between the 20 classes. Distinctive baskets of axonal varicosities surrounded 45% of myenteric nerve cell bodies and were associated with close appositions, suggesting possible connectivity. Baskets of cholinergic terminals and several other types of baskets selectively targeted ascending interneurons and excitatory motor neurons but were significantly sparser around inhibitory motor neurons.

CONCLUSIONS

Using a simple immunohistochemical method, human myenteric neurons were shown to comprise multiple classes based on chemical coding and morphology and dense clusters of axonal varicosities were selectively associated with some classes.

HyU: Hybrid Unmixing for longitudinal in vivo imaging of low signal-to-noise fluorescence

The expansion of fluorescence bioimaging toward more complex systems and geometries requires analytical tools capable of spanning widely varying timescales and length scales, cleanly separating multiple fluorescent labels and distinguishing these labels from background autofluorescence. Here we meet these challenging objectives for multispectral fluorescence microscopy, combining hyperspectral phasors and linear unmixing to create Hybrid Unmixing (HyU). HyU is efficient and robust, capable of quantitative signal separation even at low illumination levels. In dynamic imaging of developing zebrafish embryos and in mouse tissue, HyU was able to cleanly and efficiently unmix multiple fluorescent labels, even in demanding volumetric timelapse imaging settings. HyU permits high dynamic range imaging, allowing simultaneous imaging of bright exogenous labels and dim endogenous labels. This enables coincident studies of tagged components, cellular behaviors and cellular metabolism within the same specimen, providing more accurate insights into the orchestrated complexity of biological systems.

MALDI HiPLEX-IHC: multiomic and multimodal imaging of targeted intact proteins in tissues

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is one of the most widely used methods for imaging the spatial distribution of unlabeled small molecules such as metabolites, lipids and drugs in tissues. Recent progress has enabled many improvements including the ability to achieve single cell spatial resolution, 3D-tissue image reconstruction, and the precise identification of different isomeric and isobaric molecules. However, MALDI-MSI of high molecular weight intact proteins in biospecimens has thus far been difficult to achieve. Conventional methods normally require in situ proteolysis and peptide mass fingerprinting, have low spatial resolution, and typically detect only the most highly abundant proteins in an untargeted manner. In addition, MSI-based multiomic and multimodal workflows are needed which can image both small molecules and intact proteins from the same tissue. Such a capability can provide a more comprehensive understanding of the vast complexity of biological systems at the organ, tissue, and cellular levels of both normal and pathological function. A recently introduced top-down spatial imaging approach known as MALDI HiPLEX-IHC (MALDI-IHC for short) provides a basis for achieving this high-information content imaging of tissues and even individual cells. Based on novel photocleavable mass-tags conjugated to antibody probes, high-plex, multimodal and multiomic MALDI-based workflows have been developed to image both small molecules and intact proteins on the same tissue sample. Dual-labeled antibody probes enable multimodal mass spectrometry and fluorescent imaging of targeted intact proteins. A similar approach using the same photocleavable mass-tags can be applied to lectin and other probes. We detail here several examples of MALDI-IHC workflows designed to enable high-plex, multiomic and multimodal imaging of tissues at a spatial resolution as low as 5 µm. This approach is compared to other existing high-plex methods such as imaging mass cytometry, MIBI-TOF, GeoMx and CODEX. Finally, future applications of MALDI-IHC are discussed.

Spatiotemporal organisation of protein processing in the kidney

The kidney regulates plasma protein levels by eliminating them from the circulation. Proteins filtered by glomeruli are endocytosed and degraded in the proximal tubule and defects in this process result in tubular proteinuria, an important clinical biomarker. However, the spatiotemporal organization of renal protein metabolism in vivo was previously unclear. Here, using functional probes and intravital microscopy, we track the fate of filtered proteins in real time in living mice, and map specialized processing to tubular structures with singular value decomposition analysis and three-dimensional electron microscopy. We reveal that degradation of proteins requires sequential, coordinated activity of distinct tubular sub-segments, each adapted to specific tasks. Moreover, we leverage this approach to pinpoint the nature of endo-lysosomal disorders in disease models, and show that compensatory uptake in later regions of the proximal tubule limits urinary protein loss. This means that measurement of proteinuria likely underestimates severity of endocytotic defects in patients.

Polesel et al. visualize plasma protein filtration, uptake and metabolism in the kidneys of living mice in real-time. They reveal coordinated activity of different specialized tubular segments, with major compensatory adaptations occurring in disease states.

Real-time high-spectral-resolution mid-infrared spectroscopy with a signal-to-noise ratio of ten thousand

We developed a mid-infrared spectroscopy system with high spectral resolution and a high signal-to-noise ratio using an extremely high-order germanium immersion grating. The spectroscopic system covers wavelengths from 3 to 5 µm and has a spectral resolution of 1 GHz with a single-shot bandwidth of 2 THz. We proposed a method of improving the signal-to-noise ratio and achieved a ratio of over 3000 with a data acquisition rate of 125 Hz in the presence of fluctuations in the light source and environment. A signal-to-noise ratio of 10,000 was achieved with 0.1-s integration for 100-µW mid-infrared light.

Live-cell fluorescence spectral imaging as a data science challenge

Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.

The Intriguing Landscape of Single-Cell Protein Analysis

Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

Normalization of optical fluence distribution for three-dimensional functional optoacoustic tomography of the breast

SIGNIFICANCE

In three-dimensional (3D) functional optoacoustic tomography (OAT), wavelength-dependent optical attenuation and nonuniform incident optical fluence limit imaging depth and field of view and can hinder accurate estimation of functional quantities, such as the vascular blood oxygenation. These limitations hinder OAT of large objects, such as a human female breast.

AIM

We aim to develop a measurement-data-driven method for normalization of the optical fluence distribution and to investigate blood vasculature detectability and accuracy for estimating vascular blood oxygenation.

APPROACH

The proposed method is based on reasonable assumptions regarding breast anatomy and optical properties. The nonuniform incident optical fluence is estimated based on the illumination geometry in the OAT system, and the depth-dependent optical attenuation is approximated using Beer-Lambert law.

RESULTS

Numerical studies demonstrated that the proposed method significantly enhanced blood vessel detectability and improved estimation accuracy of the vascular blood oxygenation from multiwavelength OAT measurements, compared with direct application of spectral linear unmixing without optical fluence compensation. Experimental results showed that the proposed method revealed previously invisible structures in regions deeper than 15 mm and/or near the chest wall.

CONCLUSIONS

The proposed method provides a straightforward and computationally inexpensive approximation of wavelength-dependent effective optical attenuation and, thus, enables mitigation of the spectral coloring effect in functional 3D OAT imaging.

Highly-multiplexed volumetric mapping with Raman dye imaging and tissue clearing

Mapping the localization of multiple proteins in their native three-dimensional (3D) context would be useful across many areas of biomedicine, but multiplexed fluorescence imaging has limited intrinsic multiplexing capability, and most methods for increasing multiplexity can only be applied to thin samples (<100 µm). Here, we harness the narrow spectrum of Raman spectroscopy and introduce Raman dye imaging and tissue clearing (RADIANT), an optical method that is capable of imaging multiple targets in thick samples in one shot. We expanded the range of suitable bioorthogonal Raman dyes and developed a tissue-clearing strategy for them (Raman 3D imaging of solvent-cleared organs (rDISCO)). We applied RADIANT to image up to 11 targets in millimeter-thick brain slices, extending the imaging depth 10- to 100-fold compared to prior multiplexed protein imaging methods. We showcased the utility of RADIANT in extracting systems information, including region-specific correlation networks and their topology in cerebellum development. RADIANT will facilitate the exploration of the intricate 3D protein interactions in complex systems.

Spatial Multiplex In Situ Tagging (MIST) Technology for Rapid, Highly Multiplexed Detection of Protein Distribution on Brain Tissue

Highly multiplexed analysis of biospecimens significantly advances the understanding of biological basics of diseases, but these techniques are limited by the number of multiplexity and the speed of processing. Here, we present a rapid multiplex method for quantitative detection of protein markers on brain sections with the cellular resolution. This spatial multiplex in situ tagging (MIST) technology is built upon a MIST microarray that contains millions of small microbeads carrying barcoded oligonucleotides. Using antibodies tagged with UV cleavable oligonucleotides, the distribution of protein markers on a tissue slice could be "printed" on the MIST microarray with high fidelity. The performance of this technology in detection sensitivity, resolution, and signal-to-noise level has been fully characterized by detecting brain cell markers. We showcase the codetection of 31 proteins simultaneously within 2 h, which is about 10 times faster than the other immunofluorescence-based approaches of similar multiplexity. A full set of computational toolkits was developed to segment the small regions and identify the regional differences across the entire mouse brain. This technique enables us to rapidly and conveniently detect dozens of biomarkers on a tissue specimen, and it can find broad applications in clinical pathology and disease mechanistic studies.

Deep-learning-assisted Fourier transform imaging spectroscopy for hyperspectral fluorescence imaging

Hyperspectral fluorescence imaging is widely used when multiple fluorescent probes with close emission peaks are required. In particular, Fourier transform imaging spectroscopy (FTIS) provides unrivaled spectral resolution; however, the imaging throughput is very low due to the amount of interferogram sampling required. In this work, we apply deep learning to FTIS and show that the interferogram sampling can be drastically reduced by an order of magnitude without noticeable degradation in the image quality. For the demonstration, we use bovine pulmonary artery endothelial cells stained with three fluorescent dyes and 10 types of fluorescent beads with close emission peaks. Further, we show that the deep learning approach is more robust to the translation stage error and environmental vibrations. Thereby, the He-Ne correction, which is typically required for FTIS, can be bypassed, thus reducing the cost, size, and complexity of the FTIS system. Finally, we construct neural network models using Hyperband, an automatic hyperparameter selection algorithm, and compare the performance with our manually-optimized model.

Highly multiplexed immunofluorescence of the human kidney using co-detection by indexing

The human kidney is composed of many cell types that vary in their abundance and distribution from normal to diseased organ. As these cell types perform unique and essential functions, it is important to confidently label each within a single tissue to accurately assess tissue architecture and microenvironments. Towards this goal, we demonstrate the use of co-detection by indexing (CODEX) multiplexed immunofluorescence for visualizing 23 antigens within the human kidney. Using CODEX, many of the major cell types and substructures, such as collecting ducts, glomeruli, and thick ascending limb, were visualized within a single tissue section. Of these antibodies, 19 were conjugated in-house, demonstrating the flexibility and utility of this approach for studying the human kidney using custom and commercially available antibodies. We performed a pilot study that compared both fresh frozen and formalin-fixed paraffin-embedded healthy non-neoplastic and diabetic nephropathy kidney tissues. The largest cellular differences between the two groups was observed in cells labeled with aquaporin 1, cytokeratin 7, and α-smooth muscle actin. Thus, our data show the power of CODEX multiplexed immunofluorescence for surveying the cellular diversity of the human kidney and the potential for applications within pathology, histology, and building anatomical atlases.

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models

Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.

Imaging mass cytometry for high-dimensional tissue profiling in the eye

Background

Imaging mass cytometry (IMC) combines the principles of flow cytometry and mass spectrometry (MS) with laser scanning spatial resolution and offers unique advantages for the analysis of tissue samples in unprecedented detail. In contrast to conventional immunohistochemistry, which is limited in its application by the number of possible fluorochrome combinations, IMC uses isoptope-coupled antibodies that allow multiplex analysis of up to 40 markers in the same tissue section simultaneously.

Methods

In this report we use IMC to analyze formalin-fixed, paraffin-embedded conjunctival tissue. We performed a 18-biomarkers IMC analysis of conjunctival tissue to determine and summarize the possibilities, relevance and limitations of IMC for deciphering the biology and pathology of ocular diseases.

Results

Without modifying the manufacturer’s protocol, we observed positive and plausible staining for 12 of 18 biomarkers. Subsequent bioinformatical single-cell analysis and phenograph clustering identified 24 different cellular clusters with distinct expression profiles with respect to the markers used.

Conclusions

IMC enables highly multiplexed imaging of ocular samples at subcellular resolution. IMC is an innovative and feasible method, providing new insights into ocular disease pathogenesis that will be valuable for basic research, drug discovery and clinical diagnostics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12886-021-02099-8.

Instant three-color multiplane fluorescence microscopy

One of the most widely used microscopy techniques in biology and medicine is fluorescence microscopy, offering high specificity in labeling as well as maximal sensitivity. For live-cell imaging, the ideal fluorescence microscope should offer high spatial resolution, fast image acquisition, three-dimensional sectioning, and multicolor detection. However, most existing fluorescence microscopes have to compromise between these different requirements. Here, we present a multiplane, multicolor wide-field microscope that uses a dedicated beam splitter for recording volumetric data in eight focal planes and for three emission colors with frame rates of hundreds of volumes per second. We demonstrate the efficiency and performance of our system by three-dimensional imaging of multiply labeled fixed and living cells. The use of commercially available components makes our proposed microscope straightforward for implementation, thus promising for widely used applications.

High-dimensional immune-profiling in cancer: implications for immunotherapy

Immunotherapy is a rapidly growing field for cancer treatment. In contrast to conventional cancer therapies, immunotherapeutic strategies focus on reactivating the immune system to mount an antitumor response. Despite the encouraging outcome in clinical trials, a large proportion of patients still do not respond to treatment and many experience different degrees of immune-related adverse events. Furthermore, it is now increasingly appreciated that even many conventional cancer therapies such as radiotherapy could have a positive impact on the host immune system for better clinical response. Hence, there is a need to better understand tumor immunity in order to design immunotherapeutic strategies, especially evidence-based combination therapies, for improved clinical outcomes. With this aim, cancer research turned its attention to profiling the immune contexture of either the tumor microenvironment (TME) or peripheral blood to uncover mechanisms and biomarkers which might aid in precision immunotherapeutics. Conventional technologies used for this purpose were limited by the depth and dimensionality of the data. Advances in newer techniques have, however, greatly improved the breadth and depth, as well as the quantity and quality of data that can be obtained. The result of these advances is a wealth of new information and insights on how the TME could be affected by various immune cell-types, and how this might in turn impact the clinical outcome of cancer patients . We highlight herein some of the high-dimensional technologies currently employed in immune profiling in cancer and summarize the insights and potential benefits they could bring in designing better cancer immunotherapies.

Enhanced Multiplexing of Immunofluorescence Microscopy Using a Long-Stokes-Shift Fluorophore

Immunofluorescence labeling and microscopy offer a highly specific means to visualize proteins or other molecular species in a sample by labeling target antigens with fluorescent probes. These fluorescent probes can then be visualized using a fluorescence microscope, allowing their relative spatial relationships to be determined. Due to spectral overlap of common fluorophores, however, it can be challenging to analyze more than three antigens in a single sample with standard imaging approaches. This article describes multiplexed labeling and imaging of four target antigens through the use of a long-Stokes-shift fluorophore-a fluorophore with an unusually large gap between its excitation and emission maxima-in tandem with three conventional fluorophores. This combination allows for multiplexed imaging of four antigens in a single sample with excellent spectral discrimination suitable for sensitive analyses using standard imaging hardware. Particular advantages of this approach are its flexibility in terms of target antigens and the lack of any specialized procedures, reagents, or equipment beyond the commercially available labeling reagent coupled to the long-Stokes-shift fluorophore. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Four-probe immunofluorescence labeling Basic Protocol 2: Four-probe immunofluorescence imaging.

Protocol for Creating Antibodies with Complex Fluorescence Spectra

Fluorescent antibodies are a workhorse of biomedical science, but fluorescence multiplexing has been notoriously difficult due to spectral overlap between fluorophores. We recently established proof-of-principal for fluorescence Multiplexing using Spectral Imaging and Combinatorics (MuSIC), which uses combinations of existing fluorophores to create unique spectral signatures for increased multiplexing. However, a method for labeling antibodies with MuSIC probes has not yet been developed. Here, we present a method for labeling antibodies with MuSIC probes. We conjugate a DBCO-Peg5-NHS ester linker to antibodies and a single-stranded DNA "docking strand" to the linker and, finally, hybridize two MuSIC-compatible, fluorescently labeled oligos to the docking strand. We validate the labeling protocol with spin-column purification and absorbance measurements. We demonstrate the approach using (i) Cy3, (ii) Tex615, and (iii) a Cy3-Tex615 combination as three different MuSIC probes attached to three separate batches of antibodies. We created single-, double-, and triple-positive beads that are analogous to single cells by incubating MuSIC probe-labeled antibodies with protein A beads. Spectral flow cytometry experiments demonstrate that each MuSIC probe can be uniquely distinguished, and the fraction of beads in a mixture with different staining patterns are accurately inferred. The approach is general and might be more broadly applied to cell-type profiling or tissue heterogeneity studies in clinical, biomedical, and drug discovery research.

High-speed compressed-sensing fluorescence lifetime imaging microscopy of live cells

We present high-resolution, high-speed fluorescence lifetime imaging microscopy (FLIM) of live cells based on a compressed sensing scheme. By leveraging the compressibility of biological scenes in a specific domain, we simultaneously record the time-lapse fluorescence decay upon pulsed laser excitation within a large field of view. The resultant system, referred to as compressed FLIM, can acquire a widefield fluorescence lifetime image within a single camera exposure, eliminating the motion artifact and minimizing the photobleaching and phototoxicity. The imaging speed, limited only by the readout speed of the camera, is up to 100 Hz. We demonstrated the utility of compressed FLIM in imaging various transient dynamics at the microscopic scale.

Fluorophore unmixing based on bleaching and recovery kinetics using MCR-ALS

Fluorescence microscopy is a key technology in the life sciences, though its performance is constrained by the number of labels that can be recorded. We propose to use the kinetics of fluorophore photodestruction and subsequent fluorescence recovery to distinguish multiple spectrally-overlapping emitters in fixed cells, thus enhancing the information that can be obtained from a single measurement. We show that the data can be directly processed using multivariate curve resolution - alternating least squares (MCR-ALS) to deliver distinct images for each fluorophore in their local environment, and apply this methodology to membrane imaging using DiBAC₄(3) and concanavalin A - Alexa Fluor 488 as the fluorophores. We find that the DiBAC₄(3) displays two distinct degradation/recovery kinetics that correspond to two different label distributions, allowing us to simultaneously distinguish three different fluorescence distributions from two spectrally overlapping fluorophores. We expect that our approach will scale to other dynamically-binding dyes, leading to similarly increased multiplexing capability.

Highly Multiplexed Immunohistochemical MALDI-MS Imaging of Biomarkers in Tissues

Immunohistochemistry (IHC) combined with fluorescence microscopy provides an important and widely used tool for researchers and pathologists to image multiple biomarkers in tissue specimens. However, multiplex IHC using standard fluorescence microscopy is generally limited to 3-5 different biomarkers, with hyperspectral or multispectral methods limited to 8. We report the development of a new technology based on novel photocleavable mass-tags (PC-MTs) for facile antibody labeling, which enables highly multiplexed IHC based on MALDI mass spectrometric imaging (MALDI-IHC). This approach significantly exceeds the multiplexity of both fluorescence- and previous cleavable mass-tag-based methods. Up to 12-plex MALDI-IHC was demonstrated on mouse brain, human tonsil, and breast cancer tissues specimens, reflecting the known molecular composition, anatomy, and pathology of the targeted biomarkers. Novel dual-labeled fluorescent PC-MT antibodies and label-free small-molecule mass spectrometric imaging greatly extend the capability of this new approach. MALDI-IHC shows promise for use in the fields of tissue pathology, tissue diagnostics, therapeutics, and precision medicine.

Advances in stimulated Raman scattering imaging for tissues and animals

Stimulated Raman scattering (SRS) microscopy has emerged in the last decade as a powerful optical imaging technology with high chemical selectivity, speed, and subcellular resolution. Since the invention of SRS microscopy, it has been extensively employed in life science to study composition, structure, metabolism, development, and disease in biological systems. Applications of SRS in research and the clinic have generated new insights in many fields including neurobiology, tumor biology, developmental biology, metabolomics, pharmacokinetics, and more. Herein we review the advances and applications of SRS microscopy imaging in tissues and animals, as well as envision future applications and development of SRS imaging in life science and medicine.

Hyperspectral Luminescence Imaging in Combination with Signal Deconvolution Enables Reliable Multi-Indicator-Based Chemical Sensing

Although real-time monitoring of individual analytes using reversible optical chemical sensors (optodes) is well established, it remains a challenge in optical sensing to monitor multiple analyte concentrations simultaneously. Here, we present a novel sensing approach using hyperspectral imaging in combination with signal deconvolution of overlapping emission spectra of multiple luminescent indicator dyes, which facilitates multi-indicator-based chemical imaging. The deconvolution algorithm uses a linear combination model to describe the superimposed sensor signals and employs a sequential least-squares fit to determine the percent contribution of the individual indicator dyes to the total measured signal. As a proof of concept, we used the algorithm to analyze the measured response of an O₂ sensor composed of red-emitting Pd(II)/Pt(II) porphyrins and NIR-emitting Pd(II)/Pt(II) benzoporphyrins with different sensitivities. This facilitated chemical imaging of O₂ over a wide dynamic range (0-950 hPa) with a hyperspectral camera system (470-900 nm). The applicability of the novel method was demonstrated by imaging the O₂ distribution in the heterogeneous microenvironment around the roots of the aquatic plant Littorella uniflora. The presented approach of combining hyperspectral sensing with signal deconvolution is flexible and can easily be adapted for use of various multi-indicator- or even multianalyte-based optical sensors with different spectral characteristics, enabling high-resolution simultaneous imaging of multiple analytes.

Ultra-small four-emission-point spectral-detection system using seven-dichroic-mirror array

An ultra-small and highly efficient spectral-detection system for four emission points was developed by integrating an injection-molded-plastic four-lens array, a seven-dichroic-mirror array, and an image sensor as one device. The seven-dichroic-mirror array was further miniaturized compared to our previous four-dichroic-mirror array by measures including reduction of the thickness of each dichroic mirror from 1.0 to 0.5 mm. As a result, the system enables highly sensitive and low-crosstalk seven-color detection of laser-induced fluorescence from four emission points of a four-capillary array. This capability allows simultaneous quantification of up-to-seven fluorophores concurrently present in each capillary. Sanger DNA sequencing and STR genotyping by four-capillary-array electrophoresis were experimentally demonstrated by the system.

Cyclodextrin-Based Synthesis and Host-Guest Chemistry of Plasmonic Nanogap Particles with Strong, Quantitative, and Highly Multiplexable Surface-Enhanced Raman Scattering Signals

We developed a synthetic strategy to form cyclodextrin-based intrananogap particles (CIPs) with a well-defined ∼1 nm interior gap in a high yield (∼97%), and were able to incorporate 10 different Raman dyes inside the gap using the cyclodextrin-based host-guest chemistry, leading to strong, reproducible, and highly multiplexable surface-enhanced Raman scattering (SERS) signals. The average SERS enhancement factor (EF) for CIPs was 3.0 × 10⁹ with a very narrow distribution of the EFs that range from 9.5 × 10⁸ to 9.5 × 10⁹ for ∼95% of the measured particles. Remarkably, 10 different Raman dyes can be loaded within the nanogap of CIPs, and 6 different Raman dye-loaded CIPs with little spectral overlaps were distinctly detected for cancer cell imaging applications with a single excitation source. Our synthetic strategy provides new platforms in precisely forming plasmonic nanogap structures with all key features for widespread use of SERS including strong signal intensity, reliability in quantification of signal and multiplexing capability.

Histone modification dynamics as revealed by multicolor immunofluorescence-based single-cell analysis

Post-translational modifications on histones can be stable epigenetic marks or transient signals that can occur in response to internal and external stimuli. Levels of histone modifications fluctuate during the cell cycle and vary among different cell types. Here, we describe a simple system to monitor the levels of multiple histone modifications in single cells by multicolor immunofluorescence using directly labeled modification-specific antibodies. We analyzed histone H3 and H4 modifications during the cell cycle. Levels of active marks, such as acetylation and H3K4 methylation, were increased during the S phase, in association with chromatin duplication. By contrast, levels of some repressive modifications gradually increased during G2 and the next G1 phases. We applied this method to validate the target modifications of various histone demethylases in cells using a transient overexpression system. In extracts of marine organisms, we also screened chemical compounds that affect histone modifications and identified psammaplin A, which was previously reported to inhibit histone deacetylases. Thus, the method presented here is a powerful and convenient tool for analyzing the changes in histone modifications.

UNMIX-ME: spectral and lifetime fluorescence unmixing via deep learning

Hyperspectral fluorescence lifetime imaging allows for the simultaneous acquisition of spectrally resolved temporal fluorescence emission decays. In turn, the acquired rich multidimensional data set enables simultaneous imaging of multiple fluorescent species for a comprehensive molecular assessment of biotissues. However, to enable quantitative imaging, inherent spectral overlap between the considered fluorescent probes and potential bleed-through must be considered. Such a task is performed via either spectral or lifetime unmixing, typically independently. Herein, we present "UNMIX-ME" (unmix multiple emissions), a deep learning-based fluorescence unmixing routine, capable of quantitative fluorophore unmixing by simultaneously using both spectral and temporal signatures. UNMIX-ME was trained and validated using an in silico framework replicating the data acquisition process of a compressive hyperspectral fluorescent lifetime imaging platform (HMFLI). It was benchmarked against a conventional LSQ method for tri and quadri-exponential simulated samples. Last, UNMIX-ME's potential was assessed for NIR FRET in vitro and in vivo preclinical applications.

Fluorescent Multiplex Cell Rolling Assay: Simultaneous Capturing up to Seven Samples in Real-Time Using Spectral Confocal Microscopy

The parallel plate flow chamber assay is widely utilized to study physiological cell-cell adhesive interactions under dynamic flow that mimics the bloodstream. In this technique, the cells are perfused under defined shear stresses over a monolayer of endothelial cells (expressing homing molecules, e.g., selectins) or a surface (expressing recombinant homing molecules). However, with the need to study multiple samples and multiple parameters per sample, using a traditional bright-field microscope-based flow assay allows only one sample at a time to be analyzed, resulting in high interexperiment variability, the need for normalization, waste of materials, and significant consumption of time. We developed a multiplexing approach using a three-color fluorescence staining method, which allowed for up to seven different combination signatures to be run at one time. Using this fluorescent multiplex cell rolling (FMCR) assay, each sample is labeled with a different signature of emission wavelengths and mixed with other samples just minutes before the flow run. Subsequently, real-time images are acquired in a single pass using a line-scanning spectral confocal microscope. To illustrate the glycan-dependent binding of E-selectin, a central molecule in cell migration, to its glycosylated ligands expressed on myeloid-leukemic cells in flow, the FMCR assay was used to analyze E-selectin-ligand interactions following the addition (fucosyltransferase-treatment) or removal (deglycosylation) of key glycans on the flowing cells. The FMCR assay allowed us to analyze the cell-adhesion events from these different treatment conditions simultaneously in a competitive manner and to calculate differences in rolling frequency, velocity, and tethering capability of cells under study.

Oligonucleotide conjugated antibodies permit highly multiplexed immunofluorescence for future use in clinical histopathology

SIGNIFICANCE

Advanced genetic characterization has informed cancer heterogeneity and the challenge it poses to effective therapy; however, current methods lack spatial context, which is vital to successful cancer therapy. Conventional immunolabeling, commonplace in the clinic, can provide spatial context to protein expression. However, these techniques are spectrally limited, resulting in inadequate capacity to resolve the heterogenous cell subpopulations within a tumor.

AIM

We developed and optimized oligonucleotide conjugated antibodies (Ab-oligo) to facilitate cyclic immunofluorescence (cyCIF), resulting in high-dimensional immunostaining.

APPROACH

We employed a site-specific conjugation strategy to label antibodies with unique oligonucleotide sequences, which were hybridized in situ with their complementary oligonucleotide sequence tagged with a conventional fluorophore. Antibody concentration, imaging strand concentration, and configuration as well as signal removal strategies were optimized to generate maximal staining intensity using our Ab-oligo cyCIF strategy.

RESULTS

We successfully generated 14 Ab-oligo conjugates and validated their antigen specificity, which was maintained in single color staining studies. With the validated antibodies, we generated up to 14-color imaging data sets of human breast cancer tissues.

CONCLUSIONS

Herein, we demonstrated the utility of Ab-oligo cyCIF as a platform for highly multiplexed imaging, its utility to measure tumor heterogeneity, and its potential for future use in clinical histopathology.

Multi-target immunofluorescence by separation of antibody cross-labelling via spectral-FLIM-FRET

In biomedical research, indirect immunofluorescence labelling by use of primary and secondary antibodies is central for revealing the spatial distribution of multiple cellular antigens. However, labelling is regularly restricted to few antigens since species variation of primary and corresponding secondary antibodies is limited bearing the risk of unspecific cross-labelling. Here, we introduce a novel microscopic procedure for leveraging undesirable cross-labelling effects among secondary antibodies thereby increasing the number of fluorophore channels. Under cross-labelling conditions, commonly used fluorophores change chemical-physical properties by ‘Förster resonance energy transfer’ leading to defined changes in spectral emission and lifetime decay. By use of spectral fluorescence lifetime imaging and pattern-matching, we demonstrate precise separation of cross-labelled cellular antigens where conventional imaging completely fails. Consequently, this undesired effect serves for an innovative imaging procedure to separate critical antigens where antibody species variation is limited and allows for multi-target labelling by attribution of new fluorophore cross-labelling channels.

Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning

Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluorophores, and the situation is complicated even further when imaged under a multiphoton microscope. Several methods have been developed and commonly used by biologists for fluorescence microscopy spectral unmixing, such as linear unmixing, non-negative matrix factorization, deconvolution, and principal component analysis. However, they either require pre-knowledge of emission spectra or restrict the number of fluorophores to be the same as detection channels, which highly limits the real-world applications of those spectral unmixing methods. In this paper, we developed a robust and flexible spectral unmixing method: Learning Unsupervised Means of Spectra (LUMoS), which uses an unsupervised machine learning clustering method to learn individual fluorophores’ spectral signatures from mixed images, and blindly separate channels without restrictions on the number of fluorophores that can be imaged. This method highly expands the hardware capability of two-photon microscopy to simultaneously image more fluorophores than is possible with instrumentation alone. Experimental and simulated results demonstrated the robustness of LUMoS in multi-channel separations of two-photon microscopy images. We also extended the application of this method to background/autofluorescence removal and colocalization analysis. Lastly, we integrated this tool into ImageJ to offer an easy to use spectral unmixing tool for fluorescence imaging. LUMoS allows us to gain a higher spectral resolution and obtain a cleaner image without the need to upgrade the imaging hardware capabilities.

Multiplexed in-gel microfluidic immunoassays: characterizing protein target loss during reprobing of benzophenone-modified hydrogels

From whole tissues to single-cell lysate, heterogeneous immunoassays are widely utilized for analysis of protein targets in complex biospecimens. Recently, benzophenone-functionalized hydrogel scaffolds have been used to immobilize target protein for immunoassay detection with fluorescent antibody probes. In benzophenone-functionalized hydrogels, multiplex target detection occurs via serial rounds of chemical stripping (incubation with sodium-dodecyl-sulfate (SDS) and β-mercaptoethanol at 50-60 °C for ≥1 h), followed by reprobing (interrogation with additional antibody probes). Although benzophenone facilitates covalent immobilization of proteins to the hydrogel, we observe 50% immunoassay signal loss of immobilized protein targets during stripping rounds. Here, we identify and characterize signal loss mechanisms during stripping and reprobing. We posit that loss of immobilized target is responsible for ≥50% of immunoassay signal loss, and that target loss is attributable to disruption of protein immobilization by denaturing detergents (SDS) and incubation at elevated temperatures. Furthermore, our study suggests that protein losses under non-denaturing conditions are more sensitive to protein structure (i.e., hydrodynamic radius), than to molecular mass (size). We formulate design guidance for multiplexed in-gel immunoassays, including that low-abundance proteins be immunoprobed first, even when targets are covalently immobilized to the gel. We also recommend careful scrutiny of the order of proteins targets detected via multiple immunoprobing cycles, based on the protein immobilization buffer composition.

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Several techniques rely on detection of fluorescence signals to identify or study phenomena or to elucidate functions. Separation of these fluorescence signals were proven cumbersome until the advent of hyperspectral imaging, in which fluorescence sources can be separated from each other as well as from background signals and autofluorescence (given knowledge of their spectral signatures). However, traditional, emission-scanning hyperspectral imaging suffers from slow acquisition times and low signal-to-noise ratios due to the necessary filtering of both excitation and emission light. It has been previously shown that excitation-scanning hyperspectral imaging reduces the necessary acquisition time while simultaneously increasing the signal-to-noise ratio of acquired data. Using commercially available equipment, this protocol describes how to assemble, calibrate, and use an excitation-scanning hyperspectral imaging microscopy system for separation of signals from several fluorescence sources in a single sample. While highly applicable to microscopic imaging of cells and tissues, this technique may also be useful for any type of experiment utilizing fluorescence in which it is possible to vary excitation wavelengths, including but not limited to: chemical imaging, environmental applications, eye care, food science, forensic science, medical science, and mineralogy.

Exploring single cells in space and time during tissue development, homeostasis and regeneration

Complex 3D tissues arise during development following tightly organized events in space and time. In particular, gene regulatory networks and local interactions between single cells lead to emergent properties at the tissue and organism levels. To understand the design principles of tissue organization, we need to characterize individual cells at given times, but we also need to consider the collective behavior of multiple cells across different spatial and temporal scales. In recent years, powerful single cell methods have been developed to characterize cells in tissues and to address the challenging questions of how different tissues are formed throughout development, maintained in homeostasis, and repaired after injury and disease. These approaches have led to a massive increase in data pertaining to both mRNA and protein abundances in single cells. As we review here, these new technologies, in combination with in toto live imaging, now allow us to bridge spatial and temporal information quantitatively at the single cell level and generate a mechanistic understanding of tissue development.

Probe design for super-multiplexed vibrational imaging

Optical microscopy has served biomedical research for decades due to its high temporal and spatial resolutions. Among various optical imaging techniques, fluorescence imaging offers superb sensitivity down to single molecule level but its multiplexing capacity is limited by intrinsically broad bandwidth. To simultaneously capture a vast number of targets, the newly emerging vibrational microscopy technique draws increasing attention as vibration spectroscopy features narrow transition linewidth. Nonetheless, unlike fluorophores that have been studied for centuries, a systematic investigation on vibrational probes is underemphasized. Herein, we reviewed some of the recent developments of vibrational probes for multiplex imaging applications, particularly those serving stimulated Raman scattering (SRS) microscopy, which is one of the most promising vibrational imaging techniques. We wish to summarize the general guidelines for developing bioorthogonal vibrational probes with high sensitivity, chemical specificity and most importantly, tunability to fulfill super-multiplexed optical imaging. Future directions to significantly improve the performance are also discussed.

Stokes shift microscopy by excitation and emission imaging

In this contribution, we present a new method, based on a tunable excitation laser source and a robust common path interferometer in the detection channel. Its purpose is to image spectral excitation and emission information on a monochrome complementary metal oxide semiconductor (CMOS) camera. This allows us to spatially obtain both excitation and emission spectra of the whole imaged area and create derived images such as red-green-blue (RGB), excitation and emission maxima, and Stokes shift images. Our presented method is a further development of hyperspectral imaging that usually is limited to recording spatially resolved emission spectra. Taking advantage of the full camera chip should speed up the acquisition versus line scan or pointwise hyperspectral imaging.

Optimizing channel selection for excitation-scanning hyperspectral imaging

A major benefit of fluorescence microscopy is the now plentiful selection of fluorescent markers. These labels can be chosen to serve complementary functions, such as tracking labeled subcellular molecules near demarcated organelles. However, with the standard 3 or 4 emission channels, multiple label detection is restricted to segregated regions of the electromagnetic spectrum, as in RGB coloring. Hyperspectral imaging allows the user to discern many fluorescence labels by their unique spectral properties, provided there is significant differentiation of their emission spectra. The cost of this technique is often an increase in gain or exposure time to accommodate the signal reduction from separating the signal into many discrete excitation or emission channels. Recent advances in hyperspectral imaging have allowed the acquisition of more signal in a shorter time period by scanning the excitation spectra of fluorophores. Here, we explore the selection of optimal channels for both significant signal separation and sufficient signal detection using excitation-scanning hyperspectral imaging. Excitation spectra were obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with a Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) Tunable filters had bandwidths between 13 and 17 nm. Scans utilized excitation wavelengths between 340 nm and 550 nm. Hyperspectral image stacks were generated and analyzed using ENVI and custom MATLAB scripts. Among channel consideration criteria were: number of channels, spectral range of scan, spacing of center wavelengths, and acquisition time.

Signal removal methods for highly multiplexed immunofluorescent staining using antibody conjugated oligonucleotides

Successful cancer treatment continues to elude modern medicine and its arsenal of therapeutic strategies. Therapy resistance is driven by significant tumor heterogeneity, complex interactions between malignant, microenvironmental and immune cells and cross talk between signaling pathways. Advances in molecular characterization technologies such as next generation sequencing have helped unravel this network of interactions and have vastly affected how cancer is diagnosed and treated. However, the translation of complex genomic analyses to pathological diagnosis remains challenging using conventional immunofluorescence (IF) staining, which is typically limited to 2-5 antigens. Numerous strategies to increase distinct antigen detection on a single sample have been investigated, but all have deleterious effects on the tissue limiting the maximum number of biomarkers that can be imaged on a single sample and none can be seamlessly integrated into routine clinical workflows. To facilitate ready integration into clinical histopathology, we have developed a novel cyclic IF (cycIF) technology based on antibody conjugated oligonucleotides (Ab-oligos). In situ hybridization of complementary oligonucleotides (oligos) facilitates biomarker labeling for imaging on any conventional fluorescent microscope. We have validated a variety of oligo configurations and their respective signal removal strategies capable of diminishing fluorescent signal to levels of autofluorescence before subsequent staining cycles. Robust signal removal is performed without the employment of harsh conditions or reagents, maintaining tissue integrity and antigenicity for higher dimensionality immunostaining of a single sample. Our platform Ab-oligo cycIF technology uses conventional fluorophores and microscopes, allowing for dissemination to a broad audience and congruent integration into clinical histopathology workflows.

Fluorescence Multiplexing with Spectral Imaging and Combinatorics

Ultraviolet-to-infrared fluorescence is a versatile and accessible assay modality but is notoriously hard to multiplex due to overlap of wide emission spectra. We present an approach for fluorescence called multiplexing using spectral imaging and combinatorics (MuSIC). MuSIC consists of creating new independent probes from covalently linked combinations of individual fluorophores, leveraging the wide palette of currently available probes with the mathematical power of combinatorics. Probe levels in a mixture can be inferred from spectral emission scanning data. Theory and simulations suggest MuSIC can increase fluorescence multiplexing ∼4-5 fold using currently available dyes and measurement tools. Experimental proof-of-principle demonstrates robust demultiplexing of nine solution-based probes using ∼25% of the available excitation wavelength window (380-480 nm), consistent with theory. The increasing prevalence of white lasers, angle filter-based wavelength scanning, and large, sensitive multianode photomultiplier tubes make acquisition of such MuSIC-compatible data sets increasingly attainable.

Improving the spectral resolution in fluorescence microscopy through shaped-excitation imaging

The visualization of distinct molecular species represents an important challenge of bio-imaging research. In past decades, the development of multicolor fluorescent (FL) labels has greatly improved our ability to track biological analytes, paving the way for important advances in understanding the cell dynamics. It remains challenging, however, to visualize a large number of different fluorephores simultaneously. Owing to a spectrally broad absorption of fluorescent dyes, only up to five color categories can be resolved at once. Here, we demonstrate a general strategy for distinguishing between multiple fluorescent targets in acquired microscopy images with improved accuracy. The present strategy is enabled through spectral shaping of the excitation light with an optical filter that uniquely attenuates the light absorption of each fluorophore in the investigated sample. The resulting emission changes, induced by such excitation modulation, are therefore target-specific and can be used for identifying various fluorescent species. The technique is demonstrated through an accurate identification of 8 different CdSe dyes with absorption maxima spanning the 520-620 spectral range. It is subsequently applied for accurate measurements of the pH balance in buffers emulating a metabolism of tumor cells.

Morphologically constrained spectral unmixing by dictionary learning for multiplex fluorescence microscopy

Motivation

Current spectral unmixing methods for multiplex fluorescence microscopy have a limited ability to cope with high spectral overlap as they only analyze spectral information over individual pixels. Here, we present adaptive Morphologically Constrained Spectral Unmixing (MCSU) algorithms that overcome this limitation by exploiting morphological differences between sub-cellular structures, and their local spatial context.

Results

The proposed method was effective at improving spectral unmixing performance by exploiting: (i) a set of dictionary-based models for object morphologies learned from the image data; and (ii) models of spatial context learned from the image data using a total variation algorithm. The method was evaluated on multi-spectral images of multiplex-labeled pancreatic ductal adenocarcinoma (PDAC) tissue samples. The former constraint ensures that neighbouring pixels correspond to morphologically similar structures, and the latter constraint ensures that neighbouring pixels have similar spectral signatures. The average Mean Squared Error (MSE) and Signal Reconstruction Error (SRE) ratio of the proposed method was 39.6% less and 8% more, respectively, compared to the best of all other algorithms that do not exploit these spatial constraints.

Availability and Implementation

Open source software (MATLAB).

Contact

broysam@central.uh.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Mobile Technologies for the Discovery, Analysis, and Engineering of the Global Microbiome

The microbiome has been heralded as a gauge of and contributor to both human health and environmental conditions. Current challenges in probing, engineering, and harnessing the microbiome stem from its microscopic and nanoscopic nature, diversity and complexity of interactions among its members and hosts, as well as the spatiotemporal sampling and in situ measurement limitations induced by the restricted capabilities and norm of existing technologies, leaving some of the constituents of the microbiome unknown. To facilitate significant progress in the microbiome field, deeper understanding of the constituents' individual behavior, interactions with others, and biodiversity are needed. Also crucial is the generation of multimodal data from a variety of subjects and environments over time. Mobile imaging and sensing technologies, particularly through smartphone-based platforms, can potentially meet some of these needs in field-portable, cost-effective, and massively scalable manners by circumventing the need for bulky, expensive instrumentation. In this Perspective, we outline how mobile sensing and imaging technologies could lead the way to unprecedented insight into the microbiome, potentially shedding light on various microbiome-related mysteries of today, including the composition and function of human, animal, plant, and environmental microbiomes. Finally, we conclude with a look at the future, propose a computational microbiome engineering and optimization framework, and discuss its potential impact and applications.

In Situ Hybridization and Double Immunohistochemistry for the Detection of VEGF-A mRNA and CD34/Collagen IV Proteins in Renal Transplant Biopsies

Quantitative metrics on the tissue distribution of different cell phenotypes, extracellular matrix components, and signaling/cell cycle markers hold the promise for the advent of new-generation tissue-based predictive/prognostic biomarkers in clinical diagnostics. The workflow of this approach is composed of three major phases: (1) detection of multiple molecular targets on a single histologic section, (2) image acquisition, and (3) digital image processing and analysis. Here, we present the most prevalent current alternatives for step (1) and describe a three-plex staining and image acquisition platform that captures the spatial distribution of macromolecules from two different species.

Deblurring Signal Network Dynamics

To orchestrate the function and development of multicellular organisms, cells integrate intra- and extracellular information. This information is processed via signal networks in space and time, steering dynamic changes in cellular structure and function. Defects in those signal networks can lead to developmental disorders or cancer. However, experimental analysis of signal networks is challenging as their state changes dynamically and differs between individual cells. Thus, causal relationships between network components are blurred if lysates from large cell populations are analyzed. To directly study causal relationships, perturbations that target specific components have to be combined with measurements of cellular responses within individual cells. However, using standard single-cell techniques, the number of signal activities that can be monitored simultaneously is limited. Furthermore, diffusion of signal network components limits the spatial precision of perturbations, which blurs the analysis of spatiotemporal processing in signal networks. Hybrid strategies based on optogenetics, surface patterning, chemical tools, and protein design can overcome those limitations and thereby sharpen our view into the dynamic spatiotemporal state of signal networks and enable unique insights into the mechanisms that control cellular function in space and time.

The dipole potential correlates with lipid raft markers in the plasma membrane of living cells

The dipole potential generating an electric field much stronger than any other type of membrane potential influences a wide array of phenomena, ranging from passive permeation to voltage-dependent conformational changes of membrane proteins. It is generated by the ordered orientation of lipid carbonyl and membrane-attached water dipole moments. Theoretical considerations and indirect experimental evidence obtained in model membranes suggest that the dipole potential is larger in liquid-ordered domains believed to correspond to lipid rafts in cell membranes. Using three different dipole potential-sensitive fluorophores and four different labeling approaches of raft and nonraft domains, we showed that the dipole potential is indeed stronger in lipid rafts than in the rest of the membrane. The magnitude of this difference is similar to that observed between the dipole potential in control and sphingolipid-enriched cells characteristic of Gaucher's disease. The results established that the heterogeneity of the dipole potential in living cell membranes is correlated with lipid rafts and imply that alterations in the lipid composition of the cell membrane in human diseases can lead to substantial changes in the dipole potential.