Revealing the spatio-phenotypic patterning of cells in healthy and tumor tissues with mLSR-3D and STAPL-3D

A multispectral 3D live organoid imaging platform to screen probes for fluorescence guided surgery

Achieving complete tumor resection is challenging and can be improved by real-time fluorescence-guided surgery with molecular-targeted probes. However, pre-clinical identification and validation of probes presents a lengthy process that is traditionally performed in animal models and further hampered by inter- and intra-tumoral heterogeneity in target expression. To screen multiple probes at patient scale, we developed a multispectral real-time 3D imaging platform that implements organoid technology to effectively model patient tumor heterogeneity and, importantly, healthy human tissue binding.

A general algorithm for consensus 3D cell segmentation from 2D segmented stacks

Cell segmentation is the fundamental task. Only by segmenting, can we define the quantitative spatial unit for collecting measurements to draw biological conclusions. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However 3D cell segmentation, which requires dense annotation of 2D slices still poses significant challenges. Labelling every cell in every 2D slice is prohibitive. Moreover it is ambiguous, necessitating cross-referencing with other orthoviews. Lastly, there is limited ability to unambiguously record and visualize 1000's of annotated cells. Here we develop a theory and toolbox, u-Segment3D for 2D-to-3D segmentation, compatible with any 2D segmentation method. Given optimal 2D segmentations, u-Segment3D generates the optimal 3D segmentation without data training, as demonstrated on 11 real life datasets, >70,000 cells, spanning single cells, cell aggregates and tissue.

Blueprints from plane to space: outlook of next-generation three-dimensional histopathology

Here, we summarize the literature relevant to recent advances in three-dimensional (3D) histopathology in relation to clinical oncology, highlighting serial sectioning, tissue clearing, light-sheet microscopy, and digital image analysis with artificial intelligence. We look forward to a future where 3D histopathology expands our understanding of human pathophysiology and improves patient care through cross-disciplinary collaboration and innovation.

Multiplexed 3D Analysis of Immune States and Niches in Human Tissue

Tissue homeostasis and the emergence of disease are controlled by changes in the proportions of resident and recruited cells, their organization into cellular neighbourhoods, and their interactions with acellular tissue components. Highly multiplexed tissue profiling (spatial omics)1 makes it possible to study this microenvironment in situ, usually in 4-5 micron thick sections (the standard histopathology format)2. Microscopy-based tissue profiling is commonly performed at a resolution sufficient to determine cell types but not to detect subtle morphological features associated with cytoskeletal reorganisation, juxtracrine signalling, or membrane trafficking3. Here we describe a high-resolution 3D imaging approach able to characterize a wide variety of organelles and structures at sub-micron scale while simultaneously quantifying millimetre-scale spatial features. This approach combines cyclic immunofluorescence (CyCIF) imaging4 of over 50 markers with confocal microscopy of archival human tissue thick enough (30-40 microns) to fully encompass two or more layers of intact cells. 3D imaging of entire cell volumes substantially improves the accuracy of cell phenotyping and allows cell proximity to be scored using plasma membrane apposition, not just nuclear position. In pre-invasive melanoma in situ5, precise phenotyping shows that adjacent melanocytic cells are plastic in state and participate in tightly localised niches of interferon signalling near sites of initial invasion into the underlying dermis. In this and metastatic melanoma, mature and precursor T cells engage in an unexpectedly diverse array of juxtracrine and membrane-membrane interactions as well as looser "neighbourhood" associations6 whose morphologies reveal functional states. These data provide new insight into the transitions occurring during early tumour formation and immunoediting and demonstrate the potential for phenotyping of tissues at a level of detail previously restricted to cultured cells and organoids.

Bridging live-cell imaging and next-generation cancer treatment

By providing spatial, molecular and morphological data over time, live-cell imaging can provide a deeper understanding of the cellular and signalling events that determine cancer response to treatment. Understanding this dynamic response has the potential to enhance clinical outcome by identifying biomarkers or actionable targets to improve therapeutic efficacy. Here, we review recent applications of live-cell imaging for uncovering both tumour heterogeneity in treatment response and the mode of action of cancer-targeting drugs. Given the increasing uses of T cell therapies, we discuss the unique opportunity of time-lapse imaging for capturing the interactivity and motility of immunotherapies. Although traditionally limited in the number of molecular features captured, novel developments in multidimensional imaging and multi-omics data integration offer strategies to connect single-cell dynamics to molecular phenotypes. We review the effect of these recent technological advances on our understanding of the cellular dynamics of tumour targeting and discuss their implication for next-generation precision medicine.

How single-cell techniques help us look into lung cancer heterogeneity and immunotherapy

Lung cancer patients tend to have strong intratumoral and intertumoral heterogeneity and complex tumor microenvironment, which are major contributors to the efficacy of and drug resistance to immunotherapy. From a new perspective, single-cell techniques offer an innovative way to look at the intricate cellular interactions between tumors and the immune system and help us gain insights into lung cancer and its response to immunotherapy. This article reviews the application of single-cell techniques in lung cancer, with focuses directed on the heterogeneity of lung cancer and the efficacy of immunotherapy. This review provides both theoretical and experimental information for the future development of immunotherapy and personalized treatment for the management of lung cancer.

Inhibition of cleavage of human complement component C5 and the R885H C5 variant by two distinct high affinity anti-C5 nanobodies

The human complement system plays a crucial role in immune defense. However, its erroneous activation contributes to many serious inflammatory diseases. Since most unwanted complement effector functions result from C5 cleavage into C5a and C5b, development of C5 inhibitors, such as clinically approved monoclonal antibody eculizumab, are of great interest. Here, we developed and characterized two anti-C5 nanobodies, UNbC5-1 and UNbC5-2. Using surface plasmon resonance, we determined a binding affinity of 119.9 pM for UNbC5-1 and 7.7 pM for UNbC5-2. Competition experiments determined that the two nanobodies recognize distinct epitopes on C5. Both nanobodies efficiently interfered with C5 cleavage in a human serum environment, as they prevented red blood cell lysis via membrane attack complexes (C5b-9) and the formation of chemoattractant C5a. The cryo-EM structure of UNbC5-1 and UNbC5-2 in complex with C5 (3.6 Å resolution) revealed that the binding interfaces of UNbC5-1 and UNbC5-2 overlap with known complement inhibitors eculizumab and RaCI3, respectively. UNbC5-1 binds to the MG7 domain of C5, facilitated by a hydrophobic core and polar interactions, and UNbC5-2 interacts with the C5d domain mostly by salt bridges and hydrogen bonds. Interestingly, UNbC5-1 potently binds and inhibits C5 R885H, a genetic variant of C5 that is not recognized by eculizumab. Altogether, we identified and characterized two different, high affinity nanobodies against human C5. Both nanobodies could serve as diagnostic and/or research tools to detect C5 or inhibit C5 cleavage. Furthermore, the residues targeted by UNbC5-1 hold important information for therapeutic inhibition of different polymorphic variants of C5.

Configurational entropy is an intrinsic driver of tissue structural heterogeneity

Tissues comprise ordered arrangements of cells that can be surprisingly disordered in their details. How the properties of single cells and their microenvironment contribute to the balance between order and disorder at the tissue-scale remains poorly understood. Here, we address this question using the self-organization of human mammary organoids as a model. We find that organoids behave like a dynamic structural ensemble at the steady state. We apply a maximum entropy formalism to derive the ensemble distribution from three measurable parameters - the degeneracy of structural states, interfacial energy, and tissue activity (the energy associated with positional fluctuations). We link these parameters with the molecular and microenvironmental factors that control them to precisely engineer the ensemble across multiple conditions. Our analysis reveals that the entropy associated with structural degeneracy sets a theoretical limit to tissue order and provides new insight for tissue engineering, development, and our understanding of disease progression.

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

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

3D imaging for driving cancer discovery

Our understanding of the cellular composition and architecture of cancer has primarily advanced using 2D models and thin slice samples. This has granted spatial information on fundamental cancer biology and treatment response. However, tissues contain a variety of interconnected cells with different functional states and shapes, and this complex organization is impossible to capture in a single plane. Furthermore, tumours have been shown to be highly heterogenous, requiring large-scale spatial analysis to reliably profile their cellular and structural composition. Volumetric imaging permits the visualization of intact biological samples, thereby revealing the spatio-phenotypic and dynamic traits of cancer. This review focuses on new insights into cancer biology uniquely brought to light by 3D imaging and concomitant progress in cancer modelling and quantitative analysis. 3D imaging has the potential to generate broad knowledge advance from major mechanisms of tumour progression to new strategies for cancer treatment and patient diagnosis. We discuss the expected future contributions of the newest imaging trends towards these goals and the challenges faced for reaching their full application in cancer research.

MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images

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.

Tissue clearing to examine tumour complexity in three dimensions

The visualization of whole organs and organisms through tissue clearing and fluorescence volumetric imaging has revolutionized the way we look at biological samples. Its application to solid tumours is changing our perception of tumour architecture, revealing signalling networks and cell interactions critical in tumour progression, and provides a powerful new strategy for cancer diagnostics. This Review introduces the latest advances in tissue clearing and three-dimensional imaging, examines the challenges in clearing epithelia - the tissue of origin of most malignancies - and discusses the insights that tissue clearing has brought to cancer research, as well as the prospective applications to experimental and clinical oncology.