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Background: The field of drug development is witnessing a remarkable surge in the development of innovative strategies. There is a need to develop technological platforms capable of generating human data prior to progressing to clinical trials. Methods: Here we introduce a new flexible solution designed for the comprehensive monitoring of the natural human skin ecosystem's response to immunogenic drugs over time. Based on unique bioengineering to preserve surgical resections in a long survival state, it allows for the first time a comprehensive analysis of resident immune cells response at both organ and single‐cell levels. Results: Upon injection of the mRNA‐1273 COVID‐19 vaccine, we characterized precise sequential molecular events triggered upon detection of the exogenous substance. The vaccine consistently targets DC/macrophages and mast cells, regardless of the administration route, while promoting specific cell–cell communications in surrounding immune cell subsets. Conclusion: Given its direct translational relevance, this approach provides a multiscale vision of genuine human tissue immunity that could pave the way toward the development of new vaccination and drug development strategies. [ABSTRACT FROM AUTHOR]

Multiplexed fluorescence in vivo imaging remains challenging due to the attenuation and scattering of visible and traditional near infrared (NIR‐I, 650–950 nm) wavelengths. Fluorescence imaging using shortwave infrared (SWIR, 1000–1700 nm, a.k.a. NIR‐II) light enables deeper tissue penetration due to reduced tissue scattering as well as minimal background autofluorescence. SWIR‐emitting semiconductor quantum dots (QDs) with tunable emission peaks and optical stability are powerful contrast agents, yet few imaging demonstrations exclusively use SWIR emission beyond two‐color imaging schemes. In this study, we engineered three high quality lead sulfide/cadmium sulfide (PbS/CdS) core/shell QDs with distinct SWIR emission peaks ranging from 1100–1550 nm for simultaneous three‐color imaging in mice. We first use the exceptional photostability of QDs to non‐invasively track lymphatic drainage with longitudinal imaging, highlighting the detailed networks of lymphatic vessels with widefield imaging over a 2 hr period. We then perform multiplexed imaging with all three QDs to distinctly visualize the lymphatic system and spatially overlapping vasculature networks, including clearly distinguishing the liver and spleen. This work establishes optimized SWIR QDs for next generation multiplexed and longitudinal preclinical imaging, unlocking numerous opportunities for preclinical studies of disease progression, drug biodistribution, and cell trafficking dynamics in living organisms. [ABSTRACT FROM AUTHOR]

Anti‐Stokes luminescence (ASL) based on lanthanide nanocrystals holds immense promise for in vivo optical imaging and bio‐detection, which benefits from filtered autofluorescence. However, the current longest emission and excitation wavelengths of lanthanide ASL system were shorter than 1200 nm and 1532 nm, respectively, which limited tissue penetration depth and caused low signal‐to‐noise ratio (SNR) of in vivo imaging due to tissue scattering and water absorption. In this work, we extended the excitation wavelength to 1710 nm with the second near‐infrared (NIR‐II, 1000–1700 nm) emission up to 1650 nm through a novel ASL nanocrystal LiYF4 : 10 %Tm@LiYF4 : 70 %Er@LiYF4. Compared with 1532 nm excited ASL nanoprobes, the 1710 nm excited nanocrystals could improve in vivo imaging SNR by 12.72 folds. Based on this excellent imaging performance of the proposed ASL nanoprobes, three‐channel in vivo dynamic multiplexed imaging was achieved, which quantitatively revealed metabolic rates of intestinal dynamics and liver enrichment under anesthetized and awake states. This innovative ASL nanoprobes and dynamic multiplexed imaging technology would be conducive to optimizing dosing regimen and treatment plans across various physiological conditions. [ABSTRACT FROM AUTHOR]

Expansion Microscopy (ExM) and Light‐Sheet Fluorescence Microscopy (LSFM) are forefront imaging techniques that enable high‐resolution visualization of biological specimens. ExM enhances nanoscale investigation using conventional fluorescence microscopes, while LSFM offers rapid, minimally invasive imaging over large volumes. This review explores the joint advancements of ExM and LSFM, focusing on the excellent performance of the integrated modality obtained from the combination of the two, which is refer to as ExLSFM. In doing so, the chemical processes required for ExM, the tailored optical setup of LSFM for examining expanded samples, and the adjustments in sample preparation for accurate data collection are emphasized. It is delve into various specimen types studied using this integrated method and assess its potential for future applications. The goal of this literature review is to enrich the comprehension of ExM and LSFM, encouraging their wider use and ongoing development, looking forward to the upcoming challenges, and anticipating innovations in these imaging techniques. [ABSTRACT FROM AUTHOR]

Fluorescence lifetime imaging microscopy (FLIM) opens new dimensions for highly multiplexed imaging in live cells and organisms using differences in fluorescence lifetime to distinguish spectrally identical fluorescent probes. Here, a set of fluorescence‐activating and absorption‐shifting tags (FASTs) capable of modulating the fluorescence lifetime of embedded fluorogenic 4‐hydroxybenzylidene rhodanine (HBR) derivatives is described. It is shown that changes in the FAST protein sequence can vary the local environment of the chromophore and lead to significant changes in fluorescence lifetime. These fluorescence lifetime‐modulating tags enable multiplexed imaging of up to three targets in one spectral channel using a single HBR derivative in live cells and live zebrafish larvae. The combination of fluorescence lifetime multiplexing with spectral multiplexing allows to successfully image six targets in live cells, opening great prospects for multicolor fluorescence lifetime multiplexing. [ABSTRACT FROM AUTHOR]

Multiplexed immunofluorescence (IF) can be achieved using different commercially available platforms, often making use of conjugated antibodies detected in iterative cycles. A growing portfolio of pre-conjugated antibodies is offered by the providers, as well as the possibility for in-house conjugation. For many conjugation methods and kits, there are limitations in which antibodies can be used, and conjugation results are sometimes irreproducible. The conjugation process can limit or slow down the progress of studies requiring conjugation of essential markers needed for a given project. Here, we demonstrate a protocol combining manual indirect immunofluorescence (IF) of primary antibodies, followed by antibody elution and staining with multiplexed panels of commercially pre-conjugated antibodies on the PhenoCycler platform. We present detailed protocols for applying the workflow on fresh frozen and formalin fixed paraffin embedded tissue sections. We also provide a ready to use workflow for coregistration of the images and demonstrate this for two examples. [ABSTRACT FROM AUTHOR]

Dendritic cells (DCs) play a pivotal role in immune surveillance, acting as sentinels that coordinate immune responses within tissues. Although differences in the identity and functional states of DC subpopulations have been identified through multiparametric flow cytometry and single‐cell RNA sequencing, these methods do not provide information about the spatial context in which the cells are located. This knowledge is crucial for understanding tissue organisation and cellular cross‐talk. Recent developments in multiplex imaging techniques can now offer insights into this complex spatial and functional landscape. This review provides a concise overview of these imaging methodologies, emphasising their application in identifying DCs to delineate their tissue‐specific functions and aiding newcomers in navigating this field. [ABSTRACT FROM AUTHOR]

ABSTRACT Bacterial communities are highly complex, with interaction networks dictating ecosystem function. Bacterial interactions are constrained by the spatial organization of these microbial communities, yet studying the spatial organization of microbial communities at the single-cell level has been technically challenging. Here, we use the recently developed high-phylogenetic-resolution microbiota mapping by fluorescence in situ hybridization technology to image the gut microbiota at the species and single-cell level. We simultaneously image 63 different bacterial species to spatially characterize the perturbation and recovery of the gut microbiota to ampicillin and vancomycin in the cecum and distal colon of mice. To decipher the biology in this complex imaging data, we developed an analytical framework to characterize the spatial changes of the gut microbiota to a perturbation. The three-tiered analytical approach includes image-level diversity, pairwise colocalization analysis, and hypothesis-driven neighborhood analysis. Through this workflow, we identify biogeographic and antibiotic-based differences in the spatial organization of the gut microbiota. We demonstrate that the cecal microbiota has increased micrometer-scale diversity than the colon at baseline and recovers better from perturbation. Also, we identify potential foundation and keystone species that have high baseline neighborhood richness and that are associated with recovery from antibiotics. Through this workflow, we add a spatial layer to the characterization of bacterial communities and progress toward a better understanding of bacterial interactions leading to improved microbiome modulation strategies.IMPORTANCEAntibiotics have broad off-target effects on the gut microbiome. When the microbial community is unable to recover from antibiotics, it can lead to increased susceptibility to gastrointestinal infections and increased risk of immunological and metabolic diseases. In this study, we work to better understand how the gut microbiota recovers from antibiotics by employing a recent technology to image the entire bacterial community at once. Through this approach, we characterize the spatial changes in the gut microbiota after treatment with model antibiotics in both the cecum and colon of mice. We find antibiotic- and biogeographic-dependent spatial changes between bacterial species and that many of these spatial colocalizations do not recover to baseline levels even 35 days after antibiotic administration.

Fluorescence imaging is an indispensable method for studying biological processes non-invasively in cells and transparent organisms. Extension into the shortwave infrared (SWIR, 1000-2000 nm) region of the electromagnetic spectrum has allowed for imaging in mammals with unprecedented depth and resolution for optical imaging. In this review, we summarize recent advances in imaging technologies, dye scaffold modifications, and incorporation of these dyes into probes for SWIR imaging in mice. Finally, we offer an outlook on the future of SWIR detection in the field of chemical biology.

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.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. PDAC is characterized by a complex tumor microenvironment (TME), that plays a pivotal role in disease progression and resistance to therapy. Investigating the spatial distribution and interaction of TME cells with the tumor is the basis for understanding the mechanisms underlying disease progression and represents a current challenge in PDAC research. Imaging mass cytometry (IMC) is the major multiplex imaging technology for the spatial analysis of tumor heterogeneity. However, there is a dearth of reports of multiplexed IMC panels for different preclinical mouse models, including pancreatic cancer. We addressed this gap by utilizing two preclinical models of PDAC: the genetically engineered, bearing KRAS–TP53 mutations in pancreatic cells, and the orthotopic, and developed a 28–marker panel for single–cell IMC analysis to assess the abundance, distribution and phenotypes of cells involved in PDAC progression and their reciprocal functional interactions. Herein, we provide an unprecedented definition of the distribution of TME cells in PDAC and compare the diversity between transplanted and genetic disease models. The results obtained represent an important and customizable tool for unraveling the complexities of PDAC and deciphering the mechanisms behind therapy resistance. [ABSTRACT FROM AUTHOR]

Diffraction-limited resolution and low penetration depth are fundamental constraints in optical microscopy and in vivo imaging. Recently, liquid-jet X-ray technology has enabled the generation of X-rays with high-power intensities in laboratory settings. By allowing the observation of cellular processes in their natural state, liquid-jet soft X-ray microscopy (SXM) can provide morphological information on living cells without staining. Furthermore, X-ray fluorescence imaging (XFI) permits the tracking of contrast agents in vivo with high elemental specificity, going beyond attenuation contrast. In this study, we established a methodology to investigate nanoparticle (NP) interactions in vitro and in vivo, solely based on X-ray imaging. We employed soft (0.5 keV) and hard (24 keV) X-rays for cellular studies and preclinical evaluations, respectively. Our results demonstrated the possibility of localizing NPs in the intracellular environment via SXM and evaluating their biodistribution with in vivo multiplexed XFI. We envisage that laboratory liquid-jet X-ray technology will significantly contribute to advancing our understanding of biological systems in the field of nanomedical research. [ABSTRACT FROM AUTHOR]

High‐resolution in vivo optical multiplexing in second near‐infrared window (NIR‐II, 1000–1700 nm) is vital to biomedical research. Presently, limited by bio‐tissue scattering, only luminescent probes located at NIR‐IIb (1500–1700 nm) window can provide high‐resolution in vivo multiplexed imaging. However, the number of available luminescent probes in this narrow NIR‐IIb region is limited, which hampers the available multiplexed channels of in vivo imaging. To overcome the above challenges, through theoretical simulation we expanded the conventional NIR‐IIb window to NIR‐II long‐wavelength (NIR‐II‐L, 1500–1900 nm) window on the basis of photon‐scattering and water‐absorption. We developed a series of novel lanthanide luminescent nanoprobes with emission wavelengths from 1852 nm to 2842 nm. NIR‐II‐L nanoprobes enabled high‐resolution in vivo dynamic multiplexed imaging on blood vessels and intestines, and provided multi‐channels imaging on lymph tubes, tumors and intestines. The proposed NIR‐II‐L probes without mutual interference are powerful tools for high‐contrast in vivo multiplexed detection, which holds promise for revealing physiological process in living body. [ABSTRACT FROM AUTHOR]

Driven by the needs of precision medicine, current imaging techniques are under continuous development to offer more accurate and comprehensive information beyond traditional macroscopic anatomical images. Multispectral color-coded (multicolor) 19F magnetic resonance imaging (MRI) is receiving increasing attention owing to its capability for visualizing quantitative and multiplexed molecular information during various biological processes. The chemical design and preparation of 19F probes lie at the core of multicolor 19F MRI since their performance dominates the accomplishment of this technique. Herein, the working principles of multicolor 19F MRI are briefly introduced. Recent progress on multicolor 19F MRI probes for simultaneous in vivo visualization of multiple biological targets is summarized. Finally, current challenges and potential solutions in this fast-developing field are discussed.

Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.

Signal transduction processes are a necessary component of multicellular life, and their dysregulation is the basis for a host of syndromes and diseases. Thus, it is imperative that we discover the complex details of how signal transduction processes result in specific cellular outcomes. One of the primary mechanisms of regulation over signaling pathways is through spatiotemporal control. However, traditional methods are limited in their ability to reveal such details. To overcome these limitations, researchers have developed a variety of genetically encodable, fluorescent protein-based biosensors to study these dynamic processes in real time in living cells. Due to the complexities and interconnectedness of signaling pathways, it is thus desirable to use multiple biosensors in individual cells to better elucidate the relationships between signaling pathways. However, multiplexed imaging with such biosensors has been historically difficult. Nevertheless, recent developments in designs and multiplexing strategies have led to vast improvements in our capabilities. In this review, we provide perspectives on the recently developed biosensor designs and multiplexing strategies that are available for multiplexed imaging of signal transduction pathways.

The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment.​ [ABSTRACT FROM AUTHOR]

Cancer stem cells (CSCs) undergo epithelial-mesenchymal transition (EMT) to drive metastatic dissemination in experimental cancer models. However, tumour cells undergoing EMT have not been observed disseminating into the tissue surrounding human tumour specimens, leaving the relevance to human cancer uncertain. We have previously identified both EpCAM and CD24 as CSC markers that, alongside the mesenchymal marker Vimentin, identify EMT CSCs in human oral cancer cell lines. This afforded the opportunity to investigate whether the combination of these three markers can identify disseminating EMT CSCs in actual human tumours. Examining disseminating tumour cells in over 12,000 imaging fields from 74 human oral tumours, we see a significant enrichment of EpCAM, CD24 and Vimentin co-stained cells disseminating beyond the tumour body in metastatic specimens. Through training an artificial neural network, these predict metastasis with high accuracy (cross-validated accuracy of 87–89%). In this study, we have observed single disseminating EMT CSCs in human oral cancer specimens, and these are highly predictive of metastatic disease.

The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (

Abstract Spatial structure in biology, spanning molecular, organellular, cellular, tissue, and organismal scales, is encoded through a combination of genetic and epigenetic factors in individual cells. Microscopy remains the most direct approach to exploring the intricate spatial complexity defining biological systems and the structured dynamic responses of these systems to perturbations. Genetic screens with deep single‐cell profiling via image features or gene expression programs have the capacity to show how biological systems work in detail by cataloging many cellular phenotypes with one experimental assay. Microscopy‐based cellular profiling provides information complementary to next‐generation sequencing (NGS) profiling and has only recently become compatible with large‐scale genetic screens. Optical screening now offers the scale needed for systematic characterization and is poised for further scale‐up. We discuss how these methodologies, together with emerging technologies for genetic perturbation and microscopy‐based multiplexed molecular phenotyping, are powering new approaches to reveal genotype–phenotype relationships.

The widespread application of laser desorption/ionization mass spectrometry (LDI‐MS) highlights the need for a bright and multiplexable labeling platform. While ligand‐capped Au nanoparticles (AuNPs) have emerged as a promising LDI‐MS contrast agent, the predominant thiol ligands suffer from low ion yields and extensive fragmentation. In this work, we develop a N‐heterocyclic carbene (NHC) ligand platform that enhances AuNP LDI‐MS performance. NHC scaffolds are tuned to generate barcoded AuNPs which, when benchmarked against thiol‐AuNPs, are bright mass tags and form unfragmented ions in high yield. To illustrate the transformative potential of NHC ligands, the mass tags were employed in three orthogonal applications: monitoring a bioconjugation reaction, performing multiplexed imaging, and storing and reading encoded information. These results demonstrate that NHC‐nanoparticle systems are an ideal platform for LDI‐MS and greatly broaden the scope of nanoparticle contrast agents. [ABSTRACT FROM AUTHOR]

Background: The tumor microenvironment (TME) in cholangiocarcinoma (CCA) influences the immune environment. Checkpoint blockade is promising, but reliable biomarkers to predict response to treatment are still lacking. Materials and Methods: The levels of checkpoint molecules (PD-1, PD-L1, PD-L2, LAG-3, ICOS, TIGIT, TIM-3, CTLA-4), macrophages (CD68), and T cells (CD4 and CD8 cells) were assessed by multiplexed immunofluorescence in 50 intrahepatic cases. Associations between marker expression, immune cells, and region of expression were studied in the annotated regions of tumor, interface, sclerotic tumor, and tumor-free tissue. Results: ICCA demonstrated CD4_TIM-3 high densities in the tumor region of interest (ROI) compared to the interface (p = 0.014). CD8_PD-L1 and CD8_ICOS densities were elevated in the sclerotic tumor compared to the interface (p = 0.011 and p = 0.031, respectively). In a multivariate model, high expression of CD8_PD-L2 (p = 0.048) and CD4_ICOS_TIGIT (p = 0.011) was associated with nodal metastases. Conclusions: High densities of PD-L1 were more abundant in the sclerotic tumor region; this is meaningful for the stratification of immunotherapy. Lymph node metastasis correlates with CD4_ICOS_TIGIT co-expression and CD8_PD-L2 expression, indicating the checkpoint expression profile of patients with a poor prognosis. Also, multiple co-expressions occur, and this potentially suggests a role for combination therapy with different immune checkpoint targets than just PD-1 blockade monotherapy. [ABSTRACT FROM AUTHOR]

The tumor microenvironment (TME) is composed of many different cellular and acellular components that together drive tumor growth, invasion, metastasis, and response to therapies. Increasing realization of the significance of the TME in cancer biology has shifted cancer research from a cancer-centric model to one that considers the TME as a whole. Recent technological advancements in spatial profiling methodologies provide a systematic view and illuminate the physical localization of the components of the TME. In this review, we provide an overview of major spatial profiling technologies. We present the types of information that can be extracted from these data and describe their applications, findings and challenges in cancer research. Finally, we provide a future perspective of how spatial profiling could be integrated into cancer research to improve patient diagnosis, prognosis, stratification to treatment and development of novel therapeutics. Elhanani et al. review major spatial profiling technologies and their application, findings, and challenges in cancer research. They propose how spatial profiling could be integrated into cancer research to improve patient diagnosis, prognosis, stratification to treatment, and development of novel therapeutics. [ABSTRACT FROM AUTHOR]

Deciphering the intricate tumor-immune interactions within the microenvironment is crucial for advancing cancer immunotherapy. Here, we introduce mipDVP, an advanced approach integrating highly multiplexed imaging, single-cell laser microdissection, and sensitive mass spectrometry to spatially profile the proteomes of distinct cell populations in a human colorectal and tonsil cancer with high sensitivity. In a colorectal tumor-a representative cold tumor-we uncovered spatial compartmentalization of an immunosuppressive macrophage barrier that potentially impedes T cell infiltration. Spatial proteomic analysis revealed distinct functional states of T cells in different tumor compartments. In a tonsil cancer sample-a hot tumor-we identified significant proteomic heterogeneity among cells influenced by proximity to cytotoxic T cell subtypes. T cells in the tumor parenchyma exhibit metabolic adaptations to hypoxic regions. Our spatially resolved, highly multiplexed strategy deciphers the complex cellular interplay within the tumor microenvironment, offering valuable insights for identifying immunotherapy targets and predictive signatures., Competing Interests: Declaration of interests M.M. is an indirect investor in Evosep Biosystems and OmicVision Biosciences. A.M. is a co-founder and chief scientific officer of OmicVision Biosciences., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Identifying cell types in highly multiplexed images is essential for understanding tissue spatial organization. Current cell type annotation methods often rely on extensive reference images and manual adjustments. In this work, we present a tool, Robust Image-Based Cell Annotator (RIBCA), that enables accurate, automated, unbiased, and fine-grained cell type annotation for images with a wide range of antibody panels, without requiring additional model training or human intervention. Our tool has successfully annotated over 3 million cells, revealing the spatial organization of various cell types across more than 40 different human tissues. It is open-source and features a modular design, allowing for easy extension to additional cell types.

Abstract Background Algorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter tuning, or a sophisticated understanding of programming to adapt the software to the researcher’s need. Here, we present CellSeg, an open-source, pre-trained nucleus segmentation and signal quantification software based on the Mask region-convolutional neural network (R-CNN) architecture. CellSeg is accessible to users with a wide range of programming skills. Results CellSeg performs at the level of top segmentation algorithms in the 2018 Kaggle Data Challenge both qualitatively and quantitatively and generalizes well to a diverse set of multiplexed imaged cancer tissues compared to established state-of-the-art segmentation algorithms. Automated segmentation post-processing steps in the CellSeg pipeline improve the resolution of immune cell populations for downstream single-cell analysis. Finally, an application of CellSeg to a highly multiplexed colorectal cancer dataset acquired on the CO-Detection by indEXing (CODEX) platform demonstrates that CellSeg can be integrated into a multiplexed tissue imaging pipeline and lead to accurate identification of validated cell populations. Conclusion CellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level.

pH (low) insertion peptides (pHLIPs) have been developed for cancer imaging and therapy targeting the acidic extracellular microenvironment. However, the characteristics of intratumoral distribution (ITD) of pHLIPs are not yet fully understood. This study aimed to reveal the details of the ITD of pHLIPs and their spatial relationship with other tumor features of concern. The fluorescent dye-labeled pHLIPs were intravenously administered to subcutaneous xenograft mouse models of U87MG and IGR-OV1 expressing αVβ3 integrins (using large necrotic tumors). The αVβ3 integrin-targeting Cy5.5-RAFT-c(-RGDfK-)4 was used as a reference. In vivo and ex vivo fluorescence imaging, whole-tumor section imaging, fluorescence microscopy, and multiplexed fluorescence colocalization analysis were performed. The ITD of fluorescent dye-labeled pHLIPs was heterogeneous, having a high degree of colocalization with necrosis. A direct one-to-one comparison of highly magnified images revealed the cellular localization of pHLIP in pyknotic, karyorrhexis, and karyolytic necrotic cells. pHLIP and hypoxia were spatially contiguous but not overlapping cellularly. The hypoxic region was found between the ITDs of pHLIP and the cRGD peptide and the Ki-67 proliferative activity remained detectable in the pHLIP-accumulated regions. The results provide a better understanding of the characteristics of ITD of pHLIPs, leading to new insights into the theranostic applications of pHLIPs. [ABSTRACT FROM AUTHOR]

Spatial structure in biology, spanning molecular, organellular, cellular, tissue, and organismal scales, is encoded through a combination of genetic and epigenetic factors in individual cells. Microscopy remains the most direct approach to exploring the intricate spatial complexity defining biological systems and the structured dynamic responses of these systems to perturbations. Genetic screens with deep single‐cell profiling via image features or gene expression programs have the capacity to show how biological systems work in detail by cataloging many cellular phenotypes with one experimental assay. Microscopy‐based cellular profiling provides information complementary to next‐generation sequencing (NGS) profiling and has only recently become compatible with large‐scale genetic screens. Optical screening now offers the scale needed for systematic characterization and is poised for further scale‐up. We discuss how these methodologies, together with emerging technologies for genetic perturbation and microscopy‐based multiplexed molecular phenotyping, are powering new approaches to reveal genotype–phenotype relationships. [ABSTRACT FROM AUTHOR]

Fluorescence imaging is a noninvasive technique that affords real‐time fast feedback, high sensitivity, and harmless radiation and is thus effective in visualizing the anatomy and function of organs. Recently, second near‐infrared (NIR‐II, 1000–1700 nm) fluorescence imaging emerged as a popular imaging technique for both fundamental research and clinical practice, with strong potential for applications in the field of biomedicine. It affords a high signal‐to‐noise ratio and high spatial and temporal resolutions for the imaging of deep tissue owing to reduced scattering, minimal absorption, and negligible autofluorescence. Herein, the performance and advancement of fluorophores for NIR‐II fluorescence imaging are summarized. Further, the challenges to the NIR‐II fluorophores in terms of emission wavelengths, quantum yields, stability, targeting, and biocompatibility are discussed. Finally, perspective on the current development and the orientation for future studies on NIR‐II imaging, such as dual‐mode imaging, multiplexed imaging, surgical navigation, integrated diagnosis and treatment, and biosensing are discussed.

Photon excitation and emission at the NIR‐II spectral window enable high‐contrast deep‐tissue bioimaging. However, multiplexed imaging with NIR‐II excitation and emission has been hampered by the limited chemical strategies to develop bright fluorophores with tunable absorption in this spectral regime. Herein, we developed a series of heptamethine cyanines (HCs) with varied absorption/emission maxima spanning from 1100 to 1600 nm through a physical organic approach. A bulky counterion paired to HCs was found to elicit substantial improvements in absorptivity (7‐fold), brightness (14‐fold), and spectral profiles in water, addressing a notorious quenching problem of NIR‐II cyanines due to aggregation and polarization. We demonstrated the utilities of HC1222 and HC1342 for high‐contrast dual‐color imaging of circulatory system, lymphatic structures, tumor, and organ function in living mice under 1120 nm and 1319 nm excitation, showing HCs as a promising platform for non‐invasive bioimaging. [ABSTRACT FROM AUTHOR]

Increasingly, highly multiplexed tissue imaging methods are used to profile protein expression at the single‐cell level. However, a critical limitation is the lack of robust cell segmentation tools for tissue sections. We present Multiplexed Image Resegmentation of Internal Aberrant Membranes (MIRIAM) that combines (a) a pipeline for cell segmentation and quantification that incorporates machine learning‐based pixel classification to define cellular compartments, (b) a novel method for extending incomplete cell membranes, and (c) a deep learning‐based cell shape descriptor. Using human colonic adenomas as an example, we show that MIRIAM is superior to widely utilized segmentation methods and provides a pipeline that is broadly applicable to different imaging platforms and tissue types. [ABSTRACT FROM AUTHOR]

Effective visualisation of quantitative microscopy data is crucial for interpreting and discovering new patterns from complex bioimage data. Existing visualisation approaches, such as bar charts, scatter plots and heat maps, do not accommodate the complexity of visual information present in microscopy data. Here we develop ShapoGraphy, a first of its kind method accompanied by an interactive web-based application for creating customisable quantitative pictorial representations to facilitate the understanding and analysis of image datasets (www.shapography.com). ShapoGraphy enables the user to create a structure of interest as a set of shapes. Each shape can encode different variables that are mapped to the shape dimensions, colours, symbols, or outline. We illustrate the utility of ShapoGraphy using various image data, including high dimensional multiplexed data. Our results show that ShapoGraphy allows a better understanding of cellular phenotypes and relationships between variables. In conclusion, ShapoGraphy supports scientific discovery and communication by providing a rich vocabulary to create engaging and intuitive representations of diverse data types.

Multiplexed sequential and combinatorial imaging enables the simultaneous detection of multiple biological molecules, e.g. proteins, DNA, or RNA, enabling single-cell spatial multi-omics measurements at sub-cellular resolution. Recently, we designed a multiplexed imaging approach (Hi-M) to study the spatial organization of chromatin in single cells. In order to enable Hi-M sequential imaging on custom microscope setups, we developed Qudi-HiM, a modular software package written in Python 3. Qudi-HiM contains modules to automate the robust acquisition of thousands of three-dimensional multicolor microscopy images, the handling of microfluidics devices, and the remote monitoring of ongoing acquisitions and real-time analysis. In addition, Qudi-HiM can be used as a stand-alone tool for other imaging modalities.

Abstract Cells react to their microenvironment by integrating external stimuli into phenotypic decisions via an intracellular signaling network. To analyze the interplay of environment, local neighborhood, and internal cell state effects on phenotypic variability, we developed an experimental approach that enables multiplexed mass cytometric imaging analysis of up to 240 pooled spheroid microtissues. We quantified the contributions of environment, neighborhood, and intracellular state to marker variability in single cells of the spheroids. A linear model explained on average more than half of the variability of 34 markers across four cell lines and six growth conditions. The contributions of cell‐intrinsic and environmental factors to marker variability are hierarchically interdependent, a finding that we propose has general implications for systems‐level studies of single‐cell phenotypic variability. By the overexpression of 51 signaling protein constructs in subsets of cells, we also identified proteins that have cell‐intrinsic and cell‐extrinsic effects. Our study deconvolves factors influencing cellular phenotype in a 3D tissue and provides a scalable experimental system, analytical principles, and rich multiplexed imaging datasets for future studies.

Molecular pathology, such as high-throughput genomic and proteomic profiling, identifies precise disease targets from biopsies but require tissue dissociation, losing valuable histologic and spatial context. Emerging spatial multi-omic technologies now enable multiplexed visualization of genomic, proteomic, and epigenomic targets within a single tissue slice, eliminating the need for labeling multiple adjacent slices. Although early work focused on RNA (spatial transcriptomics), spatial technologies can now concurrently capture DNA, genome accessibility, histone modifications, and proteins with spatially-resolved single-cell resolution. This review outlines the principles, advantages, limitations, and potential for spatial technologies to advance dermatologic research. By jointly profiling multiple molecular channels, spatial multiomics enables novel studies of copy number variations, clonal heterogeneity, and enhancer dysregulation, replete with spatial context, illuminating the skin's complex heterogeneity., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Although several imaging strategies for dual fluorescence (or Förster) resonance energy transfer (FRET) biosensors have been reported, their implementation is challenging because of the limited performance of fluorescent proteins and the spectral overlap of FRET biosensors. These processes often require additional data calibration to eliminate artifacts. Many CFP/YFP FRET biosensors have been developed. In this study, we introduced the mKate3/HT7(JFX650) FRET pair, which effectively formed two pairs of FRET pairs for dual-FRET imaging when combined with the CFP/YFP FRET pair. The FRET donor mKate3 exhibited higher brightness than its predecessor mKate. The FRET acceptor, HT7(JFX650), is a HaloTag7 protein covalently conjugated with a far-red JFX650-THL ligand. The pair comprising mKate3 and HT7(JFX650) represents an excellent FRET dyad, exhibiting a high FRET efficiency ratio. To use the FRET pair for dual FRET biosensor imaging, we constructed PKA and K + biosensors based on the mKate3/HT7(JFX650) FRET pair. These biosensors can be used along with CFP/YFP biosensors to simultaneously detect the responses of intracellular PKA/Src, PKA/Ca 2+ , and K + /Ca 2+ under different stimuli. The findings revealed that dual FRET biosensors, which are based on the combination of CFP/YFP and mKate3/HT7 (JFX650), exhibit adequate compatibility and can be used to visualize multiple molecular activities in a live cell.

Background: Algorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter tuning, or a sophisticated understanding of programming to adapt the software to the researcher's need. Here, we present CellSeg, an open-source, pre-trained nucleus segmentation and signal quantification software based on the Mask region-convolutional neural network (R-CNN) architecture. CellSeg is accessible to users with a wide range of programming skills. Results: CellSeg performs at the level of top segmentation algorithms in the 2018 Kaggle Data Challenge both qualitatively and quantitatively and generalizes well to a diverse set of multiplexed imaged cancer tissues compared to established state-of-the-art segmentation algorithms. Automated segmentation post-processing steps in the CellSeg pipeline improve the resolution of immune cell populations for downstream single-cell analysis. Finally, an application of CellSeg to a highly multiplexed colorectal cancer dataset acquired on the CO-Detection by indEXing (CODEX) platform demonstrates that CellSeg can be integrated into a multiplexed tissue imaging pipeline and lead to accurate identification of validated cell populations. Conclusion: CellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level. [ABSTRACT FROM AUTHOR]

Summary: Type 2 diabetes mellitus (T2D) is a chronic age-related disorder characterized by hyperglycemia due to the failure of pancreatic beta cells to compensate for increased insulin demand. Despite decades of research, the pathogenic mechanisms underlying T2D remain poorly defined. Here, we use imaging mass cytometry (IMC) with a panel of 34 antibodies to simultaneously quantify markers of pancreatic exocrine, islet, and immune cells and stromal components. We analyze over 2 million cells from 16 pancreata obtained from donors with T2D and 13 pancreata from age-similar non-diabetic controls. In the T2D pancreata, we observe significant alterations in islet architecture, endocrine cell composition, and immune cell constituents. Thus, both HLA-DR-positive CD8 T cells and macrophages are enriched intra-islet in the T2D pancreas. These efforts demonstrate the utility of IMC for investigating complex events at the cellular level in order to provide insights into the pathophysiology of T2D.

Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD1, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

Fresnel incoherent correlation holography (FINCH) was a milestone in incoherent holography. In this roadmap, two pathways, namely the development of FINCH and applications of FINCH explored by many prominent research groups, are discussed. The current state-of-the-art FINCH technology, challenges, and future perspectives of FINCH technology as recognized by a diverse group of researchers contributing to different facets of research in FINCH have been presented. [ABSTRACT FROM AUTHOR]

Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD1, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD. [ABSTRACT FROM AUTHOR]