Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer.

Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.

Live-cell imaging powered by computation.

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

The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas.

Recent large-scale collaborations are generating major surveys of cell types and connections in the mouse brain, collecting large amounts of data across modalities, spatial scales, and brain areas. Successful integration of these data requires a standard 3D reference atlas. Here, we present the Allen Mouse Brain Common Coordinate Framework (CCFv3) as such a resource. We constructed an average template brain at 10 µm voxel resolution by interpolating high resolution in-plane serial two-photon tomography images with 100 µm z-sampling from 1,675 young adult C57BL/6J mice. Then, using multimodal reference data, we parcellated the entire brain directly in 3D, labeling every voxel with a brain structure spanning 43 isocortical areas and their layers, 329 subcortical gray matter structures, 81 fiber tracts, and 8 ventricular structures. CCFv3 can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D and is openly accessible (https://atlas.brain-map.org/).

Genome-Scale Imaging of the 3D Organization and Transcriptional Activity of Chromatin.

The 3D organization of chromatin regulates many genome functions. Our understanding of 3D genome organization requires tools to directly visualize chromatin conformation in its native context. Here we report an imaging technology for visualizing chromatin organization across multiple scales in single cells with high genomic throughput. First we demonstrate multiplexed imaging of hundreds of genomic loci by sequential hybridization, which allows high-resolution conformation tracing of whole chromosomes. Next we report a multiplexed error-robust fluorescence in situ hybridization (MERFISH)-based method for genome-scale chromatin tracing and demonstrate simultaneous imaging of more than 1,000 genomic loci and nascent transcripts of more than 1,000 genes together with landmark nuclear structures. Using this technology, we characterize chromatin domains, compartments, and trans-chromosomal interactions and their relationship to transcription in single cells. We envision broad application of this high-throughput, multi-scale, and multi-modal imaging technology, which provides an integrated view of chromatin organization in its native structural and functional context.

Controlling Visually Guided Behavior by Holographic Recalling of Cortical Ensembles.

Neurons in cortical circuits are often coactivated as ensembles, yet it is unclear whether ensembles play a functional role in behavior. Some ensemble neurons have pattern completion properties, triggering the entire ensemble when activated. Using two-photon holographic optogenetics in mouse primary visual cortex, we tested whether recalling ensembles by activating pattern completion neurons alters behavioral performance in a visual task. Disruption of behaviorally relevant ensembles by activation of non-selective neurons decreased performance, whereas activation of only two pattern completion neurons from behaviorally relevant ensembles improved performance, by reliably recalling the whole ensemble. Also, inappropriate behavioral choices were evoked by the mistaken activation of behaviorally relevant ensembles. Finally, in absence of visual stimuli, optogenetic activation of two pattern completion neurons could trigger behaviorally relevant ensembles and correct behavioral responses. Our results demonstrate a causal role of neuronal ensembles in a visually guided behavior and suggest that ensembles implement internal representations of perceptual states.

Intelligent Image-Activated Cell Sorting.

A fundamental challenge of biology is to understand the vast heterogeneity of cells, particularly how cellular composition, structure, and morphology are linked to cellular physiology. Unfortunately, conventional technologies are limited in uncovering these relations. We present a machine-intelligence technology based on a radically different architecture that realizes real-time image-based intelligent cell sorting at an unprecedented rate. This technology, which we refer to as intelligent image-activated cell sorting, integrates high-throughput cell microscopy, focusing, and sorting on a hybrid software-hardware data-management infrastructure, enabling real-time automated operation for data acquisition, data processing, decision-making, and actuation. We use it to demonstrate real-time sorting of microalgal and blood cells based on intracellular protein localization and cell-cell interaction from large heterogeneous populations for studying photosynthesis and atherothrombosis, respectively. The technology is highly versatile and expected to enable machine-based scientific discovery in biological, pharmaceutical, and medical sciences.

Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging.

A highly multiplexed cytometric imaging approach, termed co-detection by indexing (CODEX), is used here to create multiplexed datasets of normal and lupus (MRL/lpr) murine spleens. CODEX iteratively visualizes antibody binding events using DNA barcodes, fluorescent dNTP analogs, and an in situ polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease (MRL/lpr), extensive and previously uncharacterized splenic cell-interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images.

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.

Helicase-Dependent RNA Decay Illuminated by a Cryo-EM Structure of a Human Nuclear RNA Exosome-MTR4 Complex.

The ribonucleolytic RNA exosome interacts with RNA helicases to degrade RNA. To understand how the 3' to 5' Mtr4 helicase engages RNA and the nuclear exosome, we reconstituted 14-subunit Mtr4-containing RNA exosomes from Saccharomyces cerevisiae, Schizosaccharomyces pombe, and human and show that they unwind structured substrates to promote degradation. We loaded a human exosome with an optimized DNA-RNA chimera that stalls MTR4 during unwinding and determined its structure to an overall resolution of 3.45 Å by cryoelectron microscopy (cryo-EM). The structure reveals an RNA-engaged helicase atop the non-catalytic core, with RNA captured within the central channel and DIS3 exoribonuclease active site. MPP6 tethers MTR4 to the exosome through contacts to the RecA domains of MTR4. EXOSC10 remains bound to the core, but its catalytic module and cofactor C1D are displaced by RNA-engaged MTR4. Competition for the exosome core may ensure that RNA is committed to degradation by DIS3 when engaged by MTR4.

Expansion Microscopy: Scalable and Convenient Super-Resolution Microscopy.

Expansion microscopy (ExM) is a physical form of magnification that increases the effective resolving power of any microscope. Here, we describe the fundamental principles of ExM, as well as how recently developed ExM variants build upon and apply those principles. We examine applications of ExM in cell and developmental biology for the study of nanoscale structures as well as ExM's potential for scalable mapping of nanoscale structures across large sample volumes. Finally, we explore how the unique anchoring and hydrogel embedding properties enable postexpansion molecular interrogation in a purified chemical environment. ExM promises to play an important role complementary to emerging live-cell imaging techniques, because of its relative ease of adoption and modification and its compatibility with tissue specimens up to at least 200 µm thick.

Light-Sheet Microscopy and Its Potential for Understanding Developmental Processes.

The ability to visualize and quantitatively measure dynamic biological processes in vivo and at high spatiotemporal resolution is of fundamental importance to experimental investigations in developmental biology. Light-sheet microscopy is particularly well suited to providing such data, since it offers exceptionally high imaging speed and good spatial resolution while minimizing light-induced damage to the specimen. We review core principles and recent advances in light-sheet microscopy, with a focus on concepts and implementations relevant for applications in developmental biology. We discuss how light-sheet microcopy has helped advance our understanding of developmental processes from single-molecule to whole-organism studies, assess the potential for synergies with other state-of-the-art technologies, and introduce methods for computational image and data analysis. Finally, we explore the future trajectory of light-sheet microscopy, discuss key efforts to disseminate new light-sheet technology, and identify exciting opportunities for further advances.

Single-Particle Cryo-EM at Crystallographic Resolution.

Until only a few years ago, single-particle electron cryo-microscopy (cryo-EM) was usually not the first choice for many structural biologists due to its limited resolution in the range of nanometer to subnanometer. Now, this method rivals X-ray crystallography in terms of resolution and can be used to determine atomic structures of macromolecules that are either refractory to crystallization or difficult to crystallize in specific functional states. In this review, I discuss the recent breakthroughs in both hardware and software that transformed cryo-microscopy, enabling understanding of complex biomolecules and their functions at atomic level.

Identification of Gene Positioning Factors Using High-Throughput Imaging Mapping.

Genomes are arranged non-randomly in the 3D space of the cell nucleus. Here, we have developed HIPMap, a high-precision, high-throughput, automated fluorescent in situ hybridization imaging pipeline, for mapping of the spatial location of genome regions at large scale. High-throughput imaging position mapping (HIPMap) enabled an unbiased siRNA screen for factors involved in genome organization in human cells. We identify 50 cellular factors required for proper positioning of a set of functionally diverse genomic loci. Positioning factors include chromatin remodelers, histone modifiers, and nuclear envelope and pore proteins. Components of the replication and post-replication chromatin re-assembly machinery are prominently represented among positioning factors, and timely progression of cells through replication, but not mitosis, is required for correct gene positioning. Our results establish a method for the large-scale mapping of genome locations and have led to the identification of a compendium of cellular factors involved in spatial genome organization.

Microscopy-Based High-Content Screening.

Image-based screening is used to measure a variety of phenotypes in cells and whole organisms. Combined with perturbations such as RNA interference, small molecules, and mutations, such screens are a powerful method for gaining systematic insights into biological processes. Screens have been applied to study diverse processes, such as protein-localization changes, cancer cell vulnerabilities, and complex organismal phenotypes. Recently, advances in imaging and image-analysis methodologies have accelerated large-scale perturbation screens. Here, we describe the state of the art for image-based screening experiments and delineate experimental approaches and image-analysis approaches as well as discussing challenges and future directions, including leveraging CRISPR/Cas9-mediated genome engineering.

A primer to single-particle cryo-electron microscopy.

Cryo-electron microscopy (cryo-EM) of single-particle specimens is used to determine the structure of proteins and macromolecular complexes without the need for crystals. Recent advances in detector technology and software algorithms now allow images of unprecedented quality to be recorded and structures to be determined at near-atomic resolution. However, compared with X-ray crystallography, cryo-EM is a young technique with distinct challenges. This primer explains the different steps and considerations involved in structure determination by single-particle cryo-EM to provide an overview for scientists wishing to understand more about this technique and the interpretation of data obtained with it, as well as a starting guide for new practitioners.

SPED Light Sheet Microscopy: Fast Mapping of Biological System Structure and Function.

The goal of understanding living nervous systems has driven interest in high-speed and large field-of-view volumetric imaging at cellular resolution. Light sheet microscopy approaches have emerged for cellular-resolution functional brain imaging in small organisms such as larval zebrafish, but remain fundamentally limited in speed. Here, we have developed SPED light sheet microscopy, which combines large volumetric field-of-view via an extended depth of field with the optical sectioning of light sheet microscopy, thereby eliminating the need to physically scan detection objectives for volumetric imaging. SPED enables scanning of thousands of volumes-per-second, limited only by camera acquisition rate, through the harnessing of optical mechanisms that normally result in unwanted spherical aberrations. We demonstrate capabilities of SPED microscopy by performing fast sub-cellular resolution imaging of CLARITY mouse brains and cellular-resolution volumetric Ca(2+) imaging of entire zebrafish nervous systems. Together, SPED light sheet methods enable high-speed cellular-resolution volumetric mapping of biological system structure and function.

A high-resolution imaging approach to investigate chromatin architecture in complex tissues.

We present ChromATin, a quantitative high-resolution imaging approach for investigating chromatin organization in complex tissues. This method combines analysis of epigenetic modifications by immunostaining, localization of specific DNA sequences by FISH, and high-resolution segregation of nuclear compartments using array tomography (AT) imaging. We then apply this approach to examine how the genome is organized in the mammalian brain using female Rett syndrome mice, which are a mosaic of normal and Mecp2-null cells. Side-by-side comparisons within the same field reveal distinct heterochromatin territories in wild-type neurons that are altered in Mecp2-null nuclei. Mutant neurons exhibit increased chromatin compaction and a striking redistribution of the H4K20me3 histone modification into pericentromeric heterochromatin, a territory occupied normally by MeCP2. These events are not observed in every neuronal cell type, highlighting ChromATin as a powerful in situ method for examining cell-type-specific differences in chromatin architecture in complex tissues.

A whole-slide foundation model for digital pathology from real-world data.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles1-3. Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context4. Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data6. With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling.

Cryo-EM in molecular and cellular biology.

This review summarizes the current state of methods and results achievable by cryo-electron microscopy (cryo-EM) imaging for molecular, cell, and structural biologists who wish to understand what is required and how it might help to address their research questions. It covers some of the main issues in sample preparation, microscopes and data collection, image processing, three-dimensional (3D) reconstruction, and validation and interpretation of the resulting EM density maps and atomic models.

Technological advances in super-resolution microscopy to study cellular processes.

Since its initial demonstration in 2000, far-field super-resolution light microscopy has undergone tremendous technological developments. In parallel, these developments have opened a new window into visualizing the inner life of cells at unprecedented levels of detail. Here, we review the technical details behind the most common implementations of super-resolution microscopy and highlight some of the recent, promising advances in this field.