Open-ST: High-resolution spatial transcriptomics in 3D.

Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.

Deciphering the cancer microenvironment from bulk data with EcoTyper.

In this issue of Cell, Luca, Steen et al. develop the EcoTyper software to deconvolve tumor-microenvironment interactions from high volume bulk transcriptomics data. They demonstrate its effectiveness in improving predictions for tumor progression and patient prognosis for a variety of tumor types from multiple data sources.

NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans.

Comprehensively resolving neuronal identities in whole-brain images is a major challenge. We achieve this in C. elegans by engineering a multicolor transgene called NeuroPAL (a neuronal polychromatic atlas of landmarks). NeuroPAL worms share a stereotypical multicolor fluorescence map for the entire hermaphrodite nervous system that resolves all neuronal identities. Neurons labeled with NeuroPAL do not exhibit fluorescence in the green, cyan, or yellow emission channels, allowing the transgene to be used with numerous reporters of gene expression or neuronal dynamics. We showcase three applications that leverage NeuroPAL for nervous-system-wide neuronal identification. First, we determine the brainwide expression patterns of all metabotropic receptors for acetylcholine, GABA, and glutamate, completing a map of this communication network. Second, we uncover changes in cell fate caused by transcription factor mutations. Third, we record brainwide activity in response to attractive and repulsive chemosensory cues, characterizing multimodal coding for these stimuli.

High-resolution profiling of pathways of escape for SARS-CoV-2 spike-binding antibodies.

Defining long-term protective immunity to SARS-CoV-2 is one of the most pressing questions of our time and will require a detailed understanding of potential ways this virus can evolve to escape immune protection. Immune protection will most likely be mediated by antibodies that bind to the viral entry protein, spike (S). Here, we used Phage-DMS, an approach that comprehensively interrogates the effect of all possible mutations on binding to a protein of interest, to define the profile of antibody escape to the SARS-CoV-2 S protein using coronavirus disease 2019 (COVID-19) convalescent plasma. Antibody binding was common in two regions, the fusion peptide and the linker region upstream of the heptad repeat region 2. However, escape mutations were variable within these immunodominant regions. There was also individual variation in less commonly targeted epitopes. This study provides a granular view of potential antibody escape pathways and suggests there will be individual variation in antibody-mediated virus evolution.

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.

Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome.

The gut microbiome has been implicated in multiple human chronic gastrointestinal (GI) disorders. Determining its mechanistic role in disease has been difficult due to apparent disconnects between animal and human studies and lack of an integrated multi-omics view of disease-specific physiological changes. We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epigenome, and transcriptome in the context of irritable bowel syndrome (IBS) host physiology. We identified IBS subtype-specific and symptom-related variation in microbial composition and function. A subset of identified changes in microbial metabolites correspond to host physiological mechanisms that are relevant to IBS. By integrating multiple data layers, we identified purine metabolism as a novel host-microbial metabolic pathway in IBS with translational potential. Our study highlights the importance of longitudinal sampling and integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets in a comprehensive treatment strategy for chronic GI diseases. VIDEO ABSTRACT.

Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients.

Viruses are a constant threat to global health as highlighted by the current COVID-19 pandemic. Currently, lack of data underlying how the human host interacts with viruses, including the SARS-CoV-2 virus, limits effective therapeutic intervention. We introduce Viral-Track, a computational method that globally scans unmapped single-cell RNA sequencing (scRNA-seq) data for the presence of viral RNA, enabling transcriptional cell sorting of infected versus bystander cells. We demonstrate the sensitivity and specificity of Viral-Track to systematically detect viruses from multiple models of infection, including hepatitis B virus, in an unsupervised manner. Applying Viral-Track to bronchoalveloar-lavage samples from severe and mild COVID-19 patients reveals a dramatic impact of the virus on the immune system of severe patients compared to mild cases. Viral-Track detects an unexpected co-infection of the human metapneumovirus, present mainly in monocytes perturbed in type-I interferon (IFN)-signaling. Viral-Track provides a robust technology for dissecting the mechanisms of viral-infection and pathology.

Integrative Structure Modeling: Overview and Assessment.

Integrative structure modeling computationally combines data from multiple sources of information with the aim of obtaining structural insights that are not revealed by any single approach alone. In the first part of this review, we survey the commonly used sources of structural information and the computational aspects of model building. Throughout the past decade, integrative modeling was applied to various biological systems, with a focus on large protein complexes. Recent progress in the field of cryo-electron microscopy (cryo-EM) has resolved many of these complexes to near-atomic resolution. In the second part of this review, we compare a range of published integrative models with their higher-resolution counterparts with the aim of critically assessing their accuracy. This comparison gives a favorable view of integrative modeling and demonstrates its ability to yield accurate and informative results. We discuss possible roles of integrative modeling in the new era of cryo-EM and highlight future challenges and directions.

Evaluating and Enhancing Target Specificity of Gene-Editing Nucleases and Deaminases.

Programmable nucleases and deaminases, which include zinc-finger nucleases, transcription activator-like effector nucleases, CRISPR RNA-guided nucleases, and RNA-guided base editors, are now widely employed for the targeted modification of genomes in cells and organisms. These gene-editing tools hold tremendous promise for therapeutic applications. Importantly, these nucleases and deaminases may display off-target activity through the recognition of near-cognate DNA sequences to their target sites, resulting in collateral damage to the genome in the form of local mutagenesis or genomic rearrangements. For therapeutic genome-editing applications with these classes of programmable enzymes, it is essential to measure and limit genome-wide off-target activity. Herein, we discuss the key determinants of off-target activity for these systems. We describe various cell-based and cell-free methods for identifying genome-wide off-target sites and diverse strategies that have been developed for reducing the off-target activity of programmable gene-editing enzymes.

Comprehensive Integration of Single-Cell Data.

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

Principles for Integrative Structural Biology Studies.

Integrative structure determination is a powerful approach to modeling the structures of biological systems based on data produced by multiple experimental and theoretical methods, with implications for our understanding of cellular biology and drug discovery. This Primer introduces the theory and methods of integrative approaches, emphasizing the kinds of data that can be effectively included in developing models and using the nuclear pore complex as an example to illustrate the practice and challenges involved. These guidelines are intended to aid the researcher in understanding and applying integrative structural methods to systems of their interest and thus take advantage of this rapidly evolving field.

Cell Type Purification by Single-Cell Transcriptome-Trained Sorting.

Much of current molecular and cell biology research relies on the ability to purify cell types by fluorescence-activated cell sorting (FACS). FACS typically relies on the ability to label cell types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for cell types without a priori knowledge of cell-type markers. Here, we propose GateID, a computational method that combines single-cell transcriptomics with FACS index sorting to purify cell types of choice using only native cellular properties such as cell size, granularity, and mitochondrial content. We validate GateID by purifying various cell types from zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.

Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain.

Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons constitute more than 85 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body.

Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage. VIDEO ABSTRACT.

Benchmarking Metagenomics Tools for Taxonomic Classification.

Metagenomic sequencing is revolutionizing the detection and characterization of microbial species, and a wide variety of software tools are available to perform taxonomic classification of these data. The fast pace of development of these tools and the complexity of metagenomic data make it important that researchers are able to benchmark their performance. Here, we review current approaches for metagenomic analysis and evaluate the performance of 20 metagenomic classifiers using simulated and experimental datasets. We describe the key metrics used to assess performance, offer a framework for the comparison of additional classifiers, and discuss the future of metagenomic data analysis.

exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids.

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

Digital Museum of Retinal Ganglion Cells with Dense Anatomy and Physiology.

When 3D electron microscopy and calcium imaging are used to investigate the structure and function of neural circuits, the resulting datasets pose new challenges of visualization and interpretation. Here, we present a new kind of digital resource that encompasses almost 400 ganglion cells from a single patch of mouse retina. An online "museum" provides a 3D interactive view of each cell's anatomy, as well as graphs of its visual responses. The resource reveals two aspects of the retina's inner plexiform layer: an arbor segregation principle governing structure along the light axis and a density conservation principle governing structure in the tangential plane. Structure is related to visual function; ganglion cells with arbors near the layer of ganglion cell somas are more sustained in their visual responses on average. Our methods are potentially applicable to dense maps of neuronal anatomy and physiology in other parts of the nervous system.

SnapShot: RNA Structure Probing Technologies.

Chemical probing coupled to high-throughput sequencing offers a flexible approach to uncover many aspects of RNA structure relevant to its cellular function. With a wide variety of chemical probes available that each report on different features of RNA molecules, a broad toolkit exists for investigating in vivo and in vitro RNA structure and interactions with other molecules.

Real-Time Genetic Compensation Defines the Dynamic Demands of Feedback Control.

Biological signaling networks use feedback control to dynamically adjust their operation in real time. Traditional static genetic methods such as gene knockouts or rescue experiments can often identify the existence of feedback interactions but are unable to determine what feedback dynamics are required. Here, we implement a new strategy, closed-loop optogenetic compensation (CLOC), to address this problem. Using a custom-built hardware and software infrastructure, CLOC monitors, in real time, the output of a pathway deleted for a feedback regulator. A minimal model uses these measurements to calculate and deliver-on the fly-an optogenetically enabled transcriptional input designed to compensate for the effects of the feedback deletion. Application of CLOC to the yeast pheromone response pathway revealed surprisingly distinct dynamic requirements for three well-studied feedback regulators. CLOC, a marriage of control theory and traditional genetics, presents a broadly applicable methodology for defining the dynamic function of biological feedback regulators.

Pharmacogenomics of GPCR Drug Targets.

Natural genetic variation in the human genome is a cause of individual differences in responses to medications and is an underappreciated burden on public health. Although 108 G-protein-coupled receptors (GPCRs) are the targets of 475 (∼34%) Food and Drug Administration (FDA)-approved drugs and account for a global sales volume of over 180 billion US dollars annually, the prevalence of genetic variation among GPCRs targeted by drugs is unknown. By analyzing data from 68,496 individuals, we find that GPCRs targeted by drugs show genetic variation within functional regions such as drug- and effector-binding sites in the human population. We experimentally show that certain variants of µ-opioid and Cholecystokinin-A receptors could lead to altered or adverse drug response. By analyzing UK National Health Service drug prescription and sales data, we suggest that characterizing GPCR variants could increase prescription precision, improving patients' quality of life, and relieve the economic and societal burden due to variable drug responsiveness. VIDEO ABSTRACT.