Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging

The diversification of methods for studying cell-cell interactions and communication

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

Gábor Transform-Based Signal Isolation, Rapid Deconvolution, and Quantitation of Intact Protein Ions with Mass Spectrometry

High-resolution mass spectrometry (HRMS) is a powerful technique for the characterization and quantitation of complex biological mixtures, with several applications including clinical monitoring and tissue imaging. However, these medical and pharmaceutical applications are pushing the analytical limits of modern HRMS techniques, requiring either further development in instrumentation or data processing methods. Here, we demonstrate new developments in the interactive Fourier-transform analysis for mass spectrometry (iFAMS) software including the first application of Gábor transform (GT) to protein quantitation. Newly added automation tools detect signals from minimal user input and apply thresholds for signal selection, deconvolution, and baseline correction to improve the objectivity and reproducibility of deconvolution. Additional tools were added to improve the deconvolution of highly complex or congested mass spectra and are demonstrated here for the first time. The "Gábor Slicer" enables the user to explore trends in the Gábor spectrogram with instantaneous ion mass estimates accurate to 10 Da. The charge adjuster allows for easy visual confirmation of accurate charge state assignments and quick adjustment if necessary. Deconvolution refinement utilizes a second GT of isotopically resolved data to remove common deconvolution artifacts. To assess the quality of deconvolution from iFAMS, several comparisons are made to deconvolutions using other algorithms such as UniDec and an implementation of MaxEnt in Agilent MassHunter BioConfirm. Lastly, the newly added batch processing and quantitation capabilities of iFAMS are demonstrated and compared to a common extracted ion chromatogram approach.

Application of a variational autoencoder for clustering and analyzing in situ articular cartilage cellular response to mechanical stimuli

In various biological systems, analyzing how cell behaviors are coordinated over time would enable a deeper understanding of tissue-scale response to physiologic or superphysiologic stimuli. Such data is necessary for establishing both normal tissue function and the sequence of events after injury that lead to chronic disease. However, collecting and analyzing these large datasets presents a challenge-such systems are time-consuming to process, and the overwhelming scale of data makes it difficult to parse overall behaviors. This problem calls for an analysis technique that can quickly provide an overview of the groups present in the entire system and also produce meaningful categorization of cell behaviors. Here, we demonstrate the application of an unsupervised method-the Variational Autoencoder (VAE)-to learn the features of cells in cartilage tissue after impact-induced injury and identify meaningful clusters of chondrocyte behavior. This technique quickly generated new insights into the spatial distribution of specific cell behavior phenotypes and connected specific peracute calcium signaling timeseries with long term cellular outcomes, demonstrating the value of the VAE technique.

A Far-Red Fluorescent Probe to Visualize Gram-Positive Bacteria in Patient Samples

Gram-positive bacteria, in particular Staphylococcus aureus (S. aureus), are the leading bacterial cause of death in high-income countries and can cause invasive infections at various body sites. These infections are associated with prolonged hospital stays, a large economic burden, considerable treatment failure, and high mortality rates. So far, there is only limited knowledge about the specific locations where S. aureus resides in the human body during various infections. Hence, the visualization of S. aureus holds significant importance in microbiological research. Herein, we report the development and validation of a far-red fluorescent probe to detect Gram-positive bacteria, with a focus on staphylococci, in human biopsies from deep-seated infections. This probe displays strong fluorescence and low background in human tissues, outperforming current tools for S. aureus detection. Several applications are demonstrated, including fixed- and live-cell imaging, flow cytometry, and super-resolution bacterial imaging.

Intraocular immune response in human uveitis: time to look beyond animal models

PURPOSE

To review the current and future approaches to investigating the intraocular immune response in human uveitis.

DESIGN

Perspective METHODS: Review of currently available methods for investigating the immune response in ocular tissues and fluids in patients with intraocular inflammation/ uveitis. The advantages and disadvantages of human studies have been compared to those of animal models of uveitis.

RESULTS

Animal models, while being excellent tools for mechanistic studies, do not replicate the clinical and immunological heterogeneity of human uveitis. Opportunities for immunological studies in human uveitis are mostly limited to histological studies, or sampling of intraocular fluids and peripheral blood. Histopathological studies can be enhanced by revisiting published historical data, tissue repositories or autopsy specimens. Intraocular fluids can be investigated by a variety of techniques. Among these, flow cytometry and single cell RNA sequencing (scRNAseq) provide single cell resolution. While the current technology is costly and labor-intensive, scRNAseq is less limited by the low cellular yield from intraocular fluids and allows unbiased immune profiling enabling discovery of new cellular subsets. Immunological phenotypes uncovered from human data can be further investigated in animal studies.

CONCLUSION

The diversity of the intraocular immune response in uveitis patients remains challenging but can be studied by multiple techniques including histopathology, flow cytometry and scRNAseq. Human data can be combined with animal studies for translating uveitis research into novel therapies.

Spatial Biology of Breast Cancer

Spatial findings have shaped on our understanding of breast cancer. In this review, we discuss how spatial methods, including spatial transcriptomics and proteomics and the resultant understanding of spatial relationships, have contributed to concepts regarding cancer progression and treatment. In addition to discussing traditional approaches, we examine how emerging multiplex imaging technologies have contributed to the field and how they might influence future research.

Increased Multiplexity in Optical Tissue Clearing-Based Three-Dimensional Immunofluorescence Microscopy of the Tumor Microenvironment by Light-Emitting Diode Photobleaching

Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy is transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only 3 or 4 cellular and noncellular TME components can be localized in cleared tumor tissue. Here we report a light-emitting diode (LED) photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through 3 work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within the supervised learning paradigm, deep learning based segmentation methods demonstrating human level performance have emerged. However, limited work has been done in developing such generalist methods within the label-free unsupervised context. Here we present an unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data. UNSEG leverages a Bayesian-like framework and the specificity of nucleus and cell membrane markers to construct an a posteriori probability estimate of each pixel belonging to the nucleus, cell membrane, or background. It uses this estimate to segment each cell into its nuclear and cell-membrane compartments. We show that UNSEG is more internally consistent and better at generalizing to the complexity of tissue morphology than current deep learning methods. This allows UNSEG to unambiguously identify the cytoplasmic compartment of a cell, which we employ to demonstrate its use in an exemplar biological scenario. Within the UNSEG framework, we also introduce a new perturbed watershed algorithm capable of stably and automatically segmenting a cluster of cell nuclei into individual cell nuclei that increases the accuracy of classical watershed. Perturbed watershed can also be used as a standalone algorithm that researchers can incorporate within their supervised or unsupervised learning approaches to extend classical watershed, particularly in the multiplexed imaging context. Finally, as part of developing UNSEG, we have generated a high-quality annotated gastrointestinal tissue (GIT) dataset, which we anticipate will be useful for the broader research community. We demonstrate the efficacy of UNSEG on the GIT dataset, publicly available datasets, and on a range of practical scenarios. In these contexts, we also discuss the possibility of bias inherent in quantification of segmentation accuracy based on F 1 score. Segmentation, despite its long antecedents, remains a challenging problem, particularly in the context of tissue samples. UNSEG, an easy-to-use algorithm, provides an unsupervised approach to overcome this bottleneck, and as we discuss, can help improve deep learning based segmentation methods by providing a bridge between unsupervised and supervised learning paradigms.

Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy

Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.

Systematic Review of Cerebrospinal Fluid Biomarker Discovery in Neuro-Oncology: A Roadmap to Standardization and Clinical Application

Effective diagnosis, prognostication, and management of CNS malignancies traditionally involves invasive brain biopsies that pose significant risk to the patient. Sampling and molecular profiling of cerebrospinal fluid (CSF) is a safer, rapid, and noninvasive alternative that offers a snapshot of the intracranial milieu while overcoming the challenge of sampling error that plagues conventional brain biopsy. Although numerous biomarkers have been identified, translational challenges remain, and standardization of protocols is necessary. Here, we systematically reviewed 141 studies (Medline, SCOPUS, and Biosis databases; between January 2000 and September 29, 2022) that molecularly profiled CSF from adults with brain malignancies including glioma, brain metastasis, and primary and secondary CNS lymphomas. We provide an overview of promising CSF biomarkers, propose CSF reporting guidelines, and discuss the various considerations that go into biomarker discovery, including the influence of blood-brain barrier disruption, cell of origin, and site of CSF acquisition (eg, lumbar and ventricular). We also performed a meta-analysis of proteomic data sets, identifying biomarkers in CNS malignancies and establishing a resource for the research community.

Quantitative performance assessment of Ultivue multiplex panels in formalin-fixed, paraffin-embedded human and murine tumor specimens

We present a rigorous validation strategy to evaluate the performance of Ultivue multiplex immunofluorescence panels. We have quantified the accuracy and precision of four different multiplex panels (three human and one mouse) in tumor specimens with varying levels of T cell density. Our results show that Ultivue panels are typically accurate wherein the relative difference in cell proportion between a multiplex image and a 1-plex image is less than 20% for a given biomarker. Ultivue panels exhibited relatively high intra-run precision (CV ≤ 25%) and relatively low inter-run precision (CV >> 25%) which can be remedied by using local intensity thresholding to gate biomarker positivity. We also evaluated the reproducibility of cell-cell distance estimates measured from multiplex images which show high intra- and inter-run precision. We introduce a new metric, multiplex labeling efficiency, which can be used to benchmark the overall fidelity of the multiplex data across multiple batch runs. Taken together our results provide a comprehensive characterization of Ultivue panels and offer practical guidelines for analyzing multiplex images.

Multimodal mass spectrometry imaging identifies cell-type-specific metabolic and lipidomic variation in the mammalian liver

Spatial single-cell omics provides a readout of biochemical processes. It is challenging to capture the transient lipidome/metabolome from cells in a native tissue environment. We employed water gas cluster ion beam secondary ion mass spectrometry imaging ([H2O]n>28K-GCIB-SIMS) at ≤3 μm resolution using a cryogenic imaging workflow. This allowed multiple biomolecular imaging modes on the near-native-state liver at single-cell resolution. Our workflow utilizes desorption electrospray ionization (DESI) to build a reference map of metabolic heterogeneity and zonation across liver functional units at tissue level. Cryogenic dual-SIMS integrated metabolomics, lipidomics, and proteomics in the same liver lobules at single-cell level, characterizing the cellular landscape and metabolic states in different cell types. Lipids and metabolites classified liver metabolic zones, cell types and subtypes, highlighting the power of spatial multi-omics at high spatial resolution for understanding celluar and biomolecular organizations in the mammalian liver.

Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling

PURPOSE OF REVIEW

Single-cell profiling, either in suspension or within the tissue context, is a rapidly evolving field. The purpose of this review is to outline recent advancements and emerging trends with a specific focus on studies in spondyloarthritis.

RECENT FINDINGS

The introduction of sequencing-based approaches for the quantification of RNA, protein, or epigenetic modifications at single-cell resolution has provided a major boost to discovery-driven research. Fluorescent flow cytometry, mass cytometry, and image-based cytometry continue to evolve. Spatial transcriptomics and imaging mass cytometry have extended high-dimensional analysis to cells in tissues. Applications in spondyloarthritis include the indexing and functional characterization of cells, discovery of disease-associated cell states, and identification of signatures associated with therapeutic responses. Single-cell TCR-seq has provided evidence for clonal expansion of CD8+ T cells in spondyloarthritis. The use of single-cell profiling approaches in spondyloarthritis research is still in its early stages. Challenges include high cost and limited availability of diseased tissue samples. To harness the full potential of the rapidly expanding technical capabilities, large-scale collaborative efforts are imperative.

Applicability of Spatial Technology in Cancer Research

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

A critical evaluation of ultrasensitive single-cell proteomics strategies

Success of mass spectrometry characterization of the proteome of single cells allows us to gain a greater understanding than afforded by transcriptomics alone but requires clear understanding of the tradeoffs between analytical throughput and precision. Recent advances in mass spectrometry acquisition techniques, including updated instrumentation and sample preparation, have improved the quality of peptide signals obtained from single cell data. However, much of the proteome remains uncharacterized, and higher throughput techniques often come at the expense of reduced sensitivity and coverage, which diminish the ability to measure proteoform heterogeneity, including splice variants and post-translational modifications, in single cell data analysis. Here, we assess the growing body of ultrasensitive single-cell approaches and their tradeoffs as researchers try to balance throughput and precision in their experiments.

In situ characterization of the tumor microenvironment

The development of new therapies for cancer is underpinned by an increasing need to comprehensively characterize the tumor microenvironment (TME). While traditional approaches have relied on bulk or single-cell approaches, these are limited in their ability to provide cellular context. Deconvolution of the complex TME is fundamental to understanding tumor dynamics and treatment resistance. Spatially resolved characterization of the TME is likely to provide greater insights into the cellular architecture, tumor-immune cell interactions, receptor-ligand interactions, and cell niches. In turn, these aid in dictating the optimal way in which to target each patient's individual cancer. In this review, we discuss a number of cutting-edge in situ spatial profiling methods giving us new insights into tumor biology.

DELVE: feature selection for preserving biological trajectories in single-cell data

Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .

Unraveling cellular complexity with transient adapters in highly multiplexed super-resolution imaging

Mapping the intricate spatial relationships between the many different molecules inside a cell is essential to understanding cellular functions in all their complexity. Super-resolution fluorescence microscopy offers the required spatial resolution but struggles to reveal more than four different targets simultaneously. Exchanging labels in subsequent imaging rounds for multiplexed imaging extends this number but is limited by its low throughput. Here, we present a method for rapid multiplexed super-resolution microscopy that can, in principle, be applied to a nearly unlimited number of molecular targets by leveraging fluorogenic labeling in conjunction with transient adapter-mediated switching for high-throughput DNA-PAINT (FLASH-PAINT). We demonstrate the versatility of FLASH-PAINT with four applications: mapping nine proteins in a single mammalian cell, elucidating the functional organization of primary cilia by nine-target imaging, revealing the changes in proximity of thirteen different targets in unperturbed and dissociated Golgi stacks, and investigating and quantifying inter-organelle contacts at 3D super-resolution.

The follicular lymphoma tumor microenvironment at single-cell and spatial resolution

ABSTRACT

Follicular lymphoma (FL) is a generally incurable malignancy that originates from developmentally blocked germinal center B cells residing, primarily, within lymph nodes (LNs). During the long natural history of FL, malignant B cells often disseminate to multiple LNs and can affect virtually any organ. Nonmalignant LNs are highly organized structures distributed throughout the body, in which they perform functions critical for host defense. In FL, the malignant B cells "re-educate" the lymphoid environment by altering the phenotype, distribution, and abundance of other cells such as T cells, macrophages, and subsets of stromal cells. Consequently, dramatic anatomical changes occur and include alterations in the number, shape, and size of neoplastic follicles with an accompanying attenuation of the T-cell zone. Ongoing and dynamic interactions between FL B cells and the tumor microenvironment (TME) result in significant clinical heterogeneity observed both within and across patients. Over time, FL evolves into pathological variants associated with distinct outcomes, ranging from an indolent disease to more aggressive clinical courses with early death. Given the importance of both cell-intrinsic and -extrinsic factors in shaping disease progression and patient survival, comprehensive examination of FL tumors is critical. Here, we describe the cellular composition and architecture of normal and malignant human LNs and provide a broad overview of emerging technologies for deconstructing the FL TME at single-cell and spatial resolution. We additionally discuss the importance of capturing samples at landmark time points as well as longitudinally for clinical decision-making.

Selective induction of programmed cell death using synthetic biology tools

Regulated cell death (RCD) controls the removal of dispensable, infected or malignant cells, and is thus essential for development, homeostasis and immunity of multicellular organisms. Over the last years different forms of RCD have been described (among them apoptosis, necroptosis, pyroptosis and ferroptosis), and the cellular signaling pathways that control their induction and execution have been characterized at the molecular level. It has also become apparent that different forms of RCD differ in their capacity to elicit inflammation or an immune response, and that RCD pathways show a remarkable plasticity. Biochemical and genetic studies revealed that inhibition of a given pathway often results in the activation of back-up cell death mechanisms, highlighting close interconnectivity based on shared signaling components and the assembly of multivalent signaling platforms that can initiate different forms of RCD. Due to this interconnectivity and the pleiotropic effects of 'classical' cell death inducers, it is challenging to study RCD pathways in isolation. This has led to the development of tools based on synthetic biology that allow the targeted induction of RCD using chemogenetic or optogenetic methods. Here we discuss recent advances in the development of such toolset, highlighting their advantages and limitations, and their application for the study of RCD in cells and animals.

3DCellComposer - A Versatile Pipeline Utilizing 2D Cell Segmentation Methods for 3D Cell Segmentation

Background

Cell segmentation is crucial in bioimage informatics, as its accuracy directly impacts conclusions drawn from cellular analyses. While many approaches to 2D cell segmentation have been described, 3D cell segmentation has received much less attention. 3D segmentation faces significant challenges, including limited training data availability due to the difficulty of the task for human annotators, and inherent three-dimensional complexity. As a result, existing 3D cell segmentation methods often lack broad applicability across different imaging modalities.

Results

To address this, we developed a generalizable approach for using 2D cell segmentation methods to produce accurate 3D cell segmentations. We implemented this approach in 3DCellComposer, a versatile, open-source package that allows users to choose any existing 2D segmentation model appropriate for their tissue or cell type(s) without requiring any additional training. Importantly, we have enhanced our open source CellSegmentationEvaluator quality evaluation tool to support 3D images. It provides metrics that allow selection of the best approach for a given imaging source and modality, without the need for human annotations to assess performance. Using these metrics, we demonstrated that our approach produced high-quality 3D segmentations of tissue images, and that it could outperform an existing 3D segmentation method on the cell culture images with which it was trained.

Conclusions

3DCellComposer, when paired with well-trained 2D segmentation models, provides an important alternative to acquiring human-annotated 3D images for new sample types or imaging modalities and then training 3D segmentation models using them. It is expected to be of significant value for large scale projects such as the Human BioMolecular Atlas Program.

Multi-omic profiling of follicular lymphoma reveals changes in tissue architecture and enhanced stromal remodeling in high-risk patients

Follicular lymphoma (FL) is a generally incurable malignancy that evolves from developmentally blocked germinal center (GC) B cells. To promote survival and immune escape, tumor B cells undergo significant genetic changes and extensively remodel the lymphoid microenvironment. Dynamic interactions between tumor B cells and the tumor microenvironment (TME) are hypothesized to contribute to the broad spectrum of clinical behaviors observed among FL patients. Despite the urgent need, existing clinical tools do not reliably predict disease behavior. Using a multi-modal strategy, we examined cell-intrinsic and -extrinsic factors governing progression and therapeutic outcomes in FL patients enrolled onto a prospective clinical trial. By leveraging the strengths of each platform, we identify several tumor-specific features and microenvironmental patterns enriched in individuals who experience early relapse, the most high-risk FL patients. These features include stromal desmoplasia and changes to the follicular growth pattern present 20 months before first progression and first relapse.

A Hitchhiker's guide to high-dimensional tissue imaging with multiplexed ion beam imaging

Advancements in multiplexed tissue imaging technologies are vital in shaping our understanding of tissue microenvironmental influences in disease contexts. These technologies now allow us to relate the phenotype of individual cells to their higher-order roles in tissue organization and function. Multiplexed Ion Beam Imaging (MIBI) is one of such technologies, which uses metal isotope-labeled antibodies and secondary ion mass spectrometry (SIMS) to image more than 40 protein markers simultaneously within a single tissue section. Here, we describe an optimized MIBI workflow for high-plex analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues following antigen retrieval, metal isotope-conjugated antibody staining, imaging using the MIBI instrument, and subsequent data processing and analysis. While this workflow is focused on imaging human FFPE samples using the MIBI, this workflow can be easily extended to model systems, biological questions, and multiplexed imaging modalities.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

A Foundation Model for Cell Segmentation

Cells are a fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, most models in use are specialist models that work well for specific domains. Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive. In this work, we present CellSAM, a foundation model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells (in tissues and cell culture), yeast, and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. We also show that CellSAM can unify bioimaging analysis workflows such as spatial transcriptomics and cell tracking. A deployed version of CellSAM is available at https://cellsam.deepcell.org/.

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

Multiplex protein imaging in tumour biology

Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.

Sequentially-Activated Antibodies Based on Programmable DNA Tags for Rapid Multiplexed Protein Imaging

The achievement of rapid multiplexed protein imaging is limited by the use of stimulating reagents, extensive incubating and washing steps, and the low fluorescence intensity of targets. In this study, sequentially-activated DNA tags are developed and combined them with primary antibodies using signal enhancement strategies to create sequentially-activated antibodies (SAAs). These SAAs enable rapid, wash-free sequential imaging of different protein targets. The samples are pre-processed to label all targets of interest with SAAs simultaneously, and the signal is turned ON for only one target in each stage. The sequential imaging of multiple targets is achieved through wash-free strand displacement reactions that exhibit rapid kinetics with t1/2  < 10 s in a cellular context. Remarkably, this method successfully demonstrates sequential imaging of nine different protein targets within just a few minutes. This all-in-one platform for multiplexed protein imaging holds great promise for diverse applications in immunofluorescence imaging.

Semi-automated approaches for interrogating spatial heterogeneity of tissue samples

Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components' interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues' composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition.

Glycan-specific molecularly imprinted polymers towards cancer diagnostics: merits, applications, and future perspectives

Aberrant glycans are a hallmark of cancer states. Notably, emerging evidence has demonstrated that the diagnosis of cancers with tumour-specific glycan patterns holds great potential to address unmet medical needs, especially in improving diagnostic sensitivity and selectivity. However, despite vast glycans having been identified as potent markers, glycan-based diagnostic methods remain largely limited in clinical practice. There are several reasons that prevent them from reaching the market, and the lack of anti-glycan antibodies is one of the most challenging hurdles. With the increasing need for accelerating the translational process, numerous efforts have been made to find antibody alternatives, such as lectins, boronic acids and aptamers. However, issues concerning affinity, selectivity, stability and versatility are yet to be fully addressed. Molecularly imprinted polymers (MIPs), synthetic antibody mimics with tailored cavities for target molecules, hold the potential to revolutionize this dismal progress. MIPs can bind a wide range of glycan markers, even those without specific antibodies. This capacity effectively broadens the clinical applicability of glycan-based diagnostics. Additionally, glycoform-resolved diagnosis can also be achieved through customization of MIPs, allowing for more precise diagnostic applications. In this review, we intent to introduce the current status of glycans as potential biomarkers and critically evaluate the challenges that hinder the development of in vitro diagnostic assays, with a particular focus on glycan-specific recognition entities. Moreover, we highlight the key role of MIPs in this area and provide examples of their successful use. Finally, we conclude the review with the remaining challenges, future outlook, and emerging opportunities.

Extended correlation functions for spatial analysis of multiplex imaging data

Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.

In Vivo Stimulation of Therapeutic Antigen-Specific T Cells in an Artificial Lymph Node Matrix

T cells are critical mediators of antigen-specific immune responses and are common targets for immunotherapy. Biomaterial scaffolds have previously been used to stimulate antigen-presenting cells to elicit antigen-specific immune responses; however, structural and molecular features that directly stimulate and expand naïve, endogenous, tumor-specific T cells in vivo have not been defined. Here, an artificial lymph node (aLN) matrix is created, which consists of an extracellular matrix hydrogel conjugated with peptide-loaded-MHC complex (Signal 1), the co-stimulatory signal anti-CD28 (Signal 2), and a tethered IL-2 (Signal 3), that can bypass challenges faced by other approaches to activate T cells in situ such as vaccines. This dynamic immune-stimulating platform enables direct, in vivo antigen-specific CD8+ T cell stimulation, as well as recruitment and coordination of host immune cells, providing an immuno-stimulatory microenvironment for antigen-specific T cell activation and expansion. Co-injecting the aLN with naïve, wild-type CD8+ T cells results in robust activation and expansion of tumor-targeted T cells that kill target cells and slow tumor growth in several distal tumor models. The aLN platform induces potent in vivo antigen-specific CD8+ T cell stimulation without the need for ex vivo priming or expansion and enables in situ manipulation of antigen-specific responses for immunotherapies.

A perspective on FAIR quality control in multiplexed imaging data processing

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.

Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

Tissue niches and immunopathology through the lens of spatial tissue profiling techniques

Spatial organization plays a fundamental role in biology, influencing the function of biological structures at various levels. The immune system, in particular, relies on the orchestrated interactions of immune cells with their microenvironment to mount protective or pathogenic immune responses. The COVID-19 pandemic has underscored the significance of studying immunity within target organs to understand disease progression and severity. To achieve this, multiplex histology and spatial transcriptomics have proven indispensable in providing a spatial context to protein and gene expression patterns. By combining these techniques, researchers gain a more comprehensive understanding of the complex interactions at the cellular and molecular level in distinct tissue niches, key functional units modulating health and disease. In this review, we discuss recent advances in spatial tissue profiling techniques, highlighting their advantages over traditional histopathology studies. The insights gained from these approaches have the potential to revolutionize the diagnosis and treatment of various diseases including cancer, autoimmune disorders, and infectious diseases. However, we also acknowledge their challenges and limitations. Despite these, spatial tissue profiling offers promising opportunities to improve our understanding of how tissue niches direct regional immunity, and their relevance in tissue immunopathology, as a basis for novel therapeutic strategies and personalized medicine.

Expanding the coverage of spatial proteomics: a machine learning approach

MOTIVATION

Multiplexed protein imaging methods use a chosen set of markers and provide valuable information about complex tissue structure and cellular heterogeneity. However, the number of markers that can be measured in the same tissue sample is inherently limited.

RESULTS

In this paper, we present an efficient method to choose a minimal predictive subset of markers that for the first time allows the prediction of full images for a much larger set of markers. We demonstrate that our approach also outperforms previous methods for predicting cell-level protein composition. Most importantly, we demonstrate that our approach can be used to select a marker set that enables prediction of a much larger set than could be measured concurrently.

AVAILABILITY AND IMPLEMENTATION

All code and intermediate results are available in a Reproducible Research Archive at https://github.com/murphygroup/CODEXPanelOptimization.

Mapping cancer biology in space: applications and perspectives on spatial omics for oncology

Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data

BACKGROUND

Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R.

RESULTS

Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples.

CONCLUSIONS

The cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis. It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html . The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer .

MAPS: pathologist-level cell type annotation from tissue images through machine learning

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

T cell-mediated curation and restructuring of tumor tissue coordinates an effective immune response

Antigen-specific T cells traffic to, are influenced by, and create unique cellular microenvironments. Here we characterize these microenvironments over time with multiplexed imaging in a melanoma model of adoptive T cell therapy and human patients with melanoma treated with checkpoint inhibitor therapy. Multicellular neighborhood analysis reveals dynamic immune cell infiltration and inflamed tumor cell neighborhoods associated with CD8+ T cells. T cell-focused analysis indicates T cells are found along a continuum of neighborhoods that reflect the progressive steps coordinating the anti-tumor immune response. More effective anti-tumor immune responses are characterized by inflamed tumor-T cell neighborhoods, flanked by dense immune infiltration neighborhoods. Conversely, ineffective T cell therapies express anti-inflammatory cytokines, resulting in regulatory neighborhoods, spatially disrupting productive T cell-immune and -tumor interactions. Our study provides in situ mechanistic insights into temporal tumor microenvironment changes, cell interactions critical for response, and spatial correlates of immunotherapy outcomes, informing cellular therapy evaluation and engineering.

Deep Learning Pipeline for Automated Cell Profiling from Cyclic Imaging

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

Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data

Oral squamous cell carcinoma (OSCC), a prevalent and aggressive neoplasm, poses a significant challenge due to poor prognosis and limited prognostic biomarkers. Leveraging highly multiplexed imaging mass cytometry, we investigated the tumor immune microenvironment (TIME) in OSCC biopsies, characterizing immune cell distribution and signaling activity at the tumor-invasive front. Our spatial subsetting approach standardized cellular populations by tissue zone, improving feature reproducibility and revealing TIME patterns accompanying loss-of-differentiation. Employing a machine-learning pipeline combining reliable feature selection with multivariable modeling, we achieved accurate histological grade classification (AUC = 0.88). Three model features correlated with clinical outcomes in an independent cohort: granulocyte MAPKAPK2 signaling at the tumor front, stromal CD4+ memory T cell size, and the distance of fibroblasts from the tumor border. This study establishes a robust modeling framework for distilling complex imaging data, uncovering sentinel characteristics of the OSCC TIME to facilitate prognostic biomarkers discovery for recurrence risk stratification and immunomodulatory therapy development.

Integrating Multiplexed Imaging and Multiscale Modeling Identifies Tumor Phenotype Transformation as a Critical Component of Therapeutic T Cell Efficacy

Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here we integrated CODEX multiplexed tissue imaging with multiscale modeling software, to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface.

Immunotherapy combination approaches: mechanisms, biomarkers and clinical observations

The approval of the first immune checkpoint inhibitors provided a paradigm shift for the treatment of malignancies across a broad range of indications. Whereas initially, single-agent immune checkpoint inhibition was used, increasing numbers of patients are now treated with combination immune checkpoint blockade, where non-redundant mechanisms of action of the individual agents generally lead to higher response rates. Furthermore, immune checkpoint therapy has been combined with various other therapeutic modalities, including chemotherapy, radiotherapy and other immunotherapeutics such as vaccines, adoptive cellular therapies, cytokines and others, in an effort to maximize clinical efficacy. Currently, a large number of clinical trials test combination therapies with an immune checkpoint inhibitor as a backbone. However, proceeding without inclusion of broad, if initially exploratory, biomarker investigations may ultimately slow progress, as so far, few combinations have yielded clinical successes based on clinical data alone. Here, we present the rationale for combination therapies and discuss clinical data from clinical trials across the immuno-oncology spectrum. Moreover, we discuss the evolution of biomarker approaches and highlight the potential new directions that comprehensive biomarker studies can yield.

Increased multiplexity in optical tissue clearing-based 3D immunofluorescence microscopy of the tumor microenvironment by LED photobleaching

Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy have been transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only three or four cellular and non-cellular TME components can be localized in a cleared tumor tissue. Here we report a LED photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through three work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.

Imaging in Type 1 Diabetes, Current Perspectives and Directions

Type 1 diabetes (T1D) is characterized by the autoimmune-mediated attack of insulin-producing beta cells in the pancreas, leading to reliance on exogenous insulin to control a patient's blood glucose levels. As progress is being made in understanding the pathophysiology of the disease and how to better develop therapies to treat it, there is an increasing need for monitoring technologies to quantify beta cell mass and function throughout T1D progression and beta cell replacement therapy. Molecular imaging techniques offer a possible solution through both radiologic and non-radiologic means including positron emission tomography, magnetic resonance imaging, electron paramagnetic resonance imaging, and spatial omics. This commentary piece outlines the role of molecular imaging in T1D research and highlights the need for further applications of such methodologies in T1D.

Multimodal spatiotemporal phenotyping of human retinal organoid development

Organoids generated from human pluripotent stem cells provide experimental systems to study development and disease, but quantitative measurements across different spatial scales and molecular modalities are lacking. In this study, we generated multiplexed protein maps over a retinal organoid time course and primary adult human retinal tissue. We developed a toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components and global patterning in each organoid and primary tissue. In addition, we generated a single-cell transcriptome and chromatin accessibility timecourse dataset and inferred a gene regulatory network underlying organoid development. We integrated genomic data with spatially segmented nuclei into a multimodal atlas to explore organoid patterning and retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death and showing that mosaic genetic perturbations in retinal organoids provide insight into cell fate regulation.

Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images

Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.