What is cancer metabolism?

The uptake and metabolism of nutrients support fundamental cellular process from bioenergetics to biomass production and cell fate regulation. While many studies of cell metabolism focus on cancer cells, the principles of metabolism elucidated in cancer cells apply to a wide range of mammalian cells. The goal of this review is to discuss how the field of cancer metabolism provides a framework for revealing principles of cell metabolism and for dissecting the metabolic networks that allow cells to meet their specific demands. Understanding context-specific metabolic preferences and liabilities will unlock new approaches to target cancer cells to improve patient care.

Fundamental behaviors emerge from simulations of a living minimal cell.

We present a whole-cell fully dynamical kinetic model (WCM) of JCVI-syn3A, a minimal cell with a reduced genome of 493 genes that has retained few regulatory proteins or small RNAs. Cryo-electron tomograms provide the cell geometry and ribosome distributions. Time-dependent behaviors of concentrations and reaction fluxes from stochastic-deterministic simulations over a cell cycle reveal how the cell balances demands of its metabolism, genetic information processes, and growth, and offer insight into the principles of life for this minimal cell. The energy economy of each process including active transport of amino acids, nucleosides, and ions is analyzed. WCM reveals how emergent imbalances lead to slowdowns in the rates of transcription and translation. Integration of experimental data is critical in building a kinetic model from which emerges a genome-wide distribution of mRNA half-lives, multiple DNA replication events that can be compared to qPCR results, and the experimentally observed doubling behavior.

Deciphering cell signaling networks with massively multiplexed biosensor barcoding.

Genetically encoded fluorescent biosensors are powerful tools for monitoring biochemical activities in live cells, but their multiplexing capacity is limited by the available spectral space. We overcome this problem by developing a set of barcoding proteins that can generate over 100 barcodes and are spectrally separable from commonly used biosensors. Mixtures of barcoded cells expressing different biosensors are simultaneously imaged and analyzed by deep learning models to achieve massively multiplexed tracking of signaling events. Importantly, different biosensors in cell mixtures show highly coordinated activities, thus facilitating the delineation of their temporal relationship. Simultaneous tracking of multiple biosensors in the receptor tyrosine kinase signaling network reveals distinct mechanisms of effector adaptation, cell autonomous and non-autonomous effects of KRAS mutations, as well as complex interactions in the network. Biosensor barcoding presents a scalable method to expand multiplexing capabilities for deciphering the complexity of signaling networks and their interactions between cells.

Atoms to Phenotypes: Molecular Design Principles of Cellular Energy Metabolism.

We report a 100-million atom-scale model of an entire cell organelle, a photosynthetic chromatophore vesicle from a purple bacterium, that reveals the cascade of energy conversion steps culminating in the generation of ATP from sunlight. Molecular dynamics simulations of this vesicle elucidate how the integral membrane complexes influence local curvature to tune photoexcitation of pigments. Brownian dynamics of small molecules within the chromatophore probe the mechanisms of directional charge transport under various pH and salinity conditions. Reproducing phenotypic properties from atomistic details, a kinetic model evinces that low-light adaptations of the bacterium emerge as a spontaneous outcome of optimizing the balance between the chromatophore's structural integrity and robust energy conversion. Parallels are drawn with the more universal mitochondrial bioenergetic machinery, from whence molecular-scale insights into the mechanism of cellular aging are inferred. Together, our integrative method and spectroscopic experiments pave the way to first-principles modeling of whole living cells.

TOR, the Gateway to Cellular Metabolism, Cell Growth, and Disease.

Michael N. Hall is this year's recipient of the Lasker Basic Medical Research Award for the identification of the target of rapamycin, TOR. TOR is a master regulator of the cell's growth and metabolic state, and its dysregulation contributes to a variety of diseases, including diabetes, obesity, neurodegenerative disorders, aging, and cancer, making the TOR pathway an attractive therapeutic target.

On a Quest for Principles, Big Data in Hand.

Cell editor Karen Carniol discusses the excitement and challenges of the "big data" era and how quantitative approaches reveal important biological principles with Galit Lahav, Gürol Süel, and Olga Troyanskaya. Annotated excerpts from this conversation are presented below, and the full conversation is available with the article online.

Design and Analysis of Single-Cell Sequencing Experiments.

Recent advances in single-cell sequencing hold great potential for exploring biological systems with unprecedented resolution. Sequencing the genome of individual cells can reveal somatic mutations and allows the investigation of clonal dynamics. Single-cell transcriptome sequencing can elucidate the cell type composition of a sample. However, single-cell sequencing comes with major technical challenges and yields complex data output. In this Primer, we provide an overview of available methods and discuss experimental design and single-cell data analysis. We hope that these guidelines will enable a growing number of researchers to leverage the power of single-cell sequencing.

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.

SnapShot: antiviral restriction factors.

Restriction factors are cellular proteins that inhibit viruses at different steps of their replication cycle and represent an important first line of defense against viral pathogens. This SnapShot provides an overview of cell-intrinsic antiviral factors, describes their properties, and illustrates the striking variety of antiviral mechanisms as well the sophisticated viral countermeasures. To view this SnapShot, open or download the PDF.

Bridging structural and cell biology with cryo-electron microscopy.

Most life scientists would agree that understanding how cellular processes work requires structural knowledge about the macromolecules involved. For example, deciphering the double-helical nature of DNA revealed essential aspects of how genetic information is stored, copied and repaired. Yet, being reductionist in nature, structural biology requires the purification of large amounts of macromolecules, often trimmed off larger functional units. The advent of cryogenic electron microscopy (cryo-EM) greatly facilitated the study of large, functional complexes and generally of samples that are hard to express, purify and/or crystallize. Nevertheless, cryo-EM still requires purification and thus visualization outside of the natural context in which macromolecules operate and coexist. Conversely, cell biologists have been imaging cells using a number of fast-evolving techniques that keep expanding their spatial and temporal reach, but always far from the resolution at which chemistry can be understood. Thus, structural and cell biology provide complementary, yet unconnected visions of the inner workings of cells. Here we discuss how the interplay between cryo-EM and cryo-electron tomography, as a connecting bridge to visualize macromolecules in situ, holds great promise to create comprehensive structural depictions of macromolecules as they interact in complex mixtures or, ultimately, inside the cell itself.

Deciphering the impact of genomic variation on function.

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

Cell-type-directed design of synthetic enhancers.

Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes1. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here we show that deep learning models2-6, can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution. We evaluate the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We further exploit enhancer design to create 'dual-code' enhancers that target two cell types and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the state space searches towards local optima, we characterize enhancer codes through the strength, combination and arrangement of transcription factor activator and transcription factor repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to enhancer rules similar to those of Drosophila enhancers. Enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Multiscale topology classifies cells in subcellular spatial transcriptomics.

Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue1, hitherto with some trade-off between transcriptome depth, spatial resolution and sample size2. Although integration of image-based segmentation has enabled impactful work in this context, it is limited by imaging quality and tissue heterogeneity. By contrast, recent array-based technologies offer the ability to measure the entire transcriptome at subcellular resolution across large samples3-6. Presently, there exist no approaches for cell type identification that directly leverage this information to annotate individual cells. Here we propose a multiscale approach to automatically classify cell types at this subcellular level, using both transcriptomic information and spatial context. We showcase this on both targeted and whole-transcriptome spatial platforms, improving cell classification and morphology for human kidney tissue and pinpointing individual sparsely distributed renal mouse immune cells without reliance on image data. By integrating these predictions into a topological pipeline based on multiparameter persistent homology7-9, we identify cell spatial relationships characteristic of a mouse model of lupus nephritis, which we validate experimentally by immunofluorescence. The proposed framework readily generalizes to new platforms, providing a comprehensive pipeline bridging different levels of biological organization from genes through to tissues.

Sex differences orchestrated by androgens at single-cell resolution.

Sex differences in mammalian complex traits are prevalent and are intimately associated with androgens1-7. However, a molecular and cellular profile of sex differences and their modulation by androgens is still lacking. Here we constructed a high-dimensional single-cell transcriptomic atlas comprising over 2.3 million cells from 17 tissues in Mus musculus and explored the effects of sex and androgens on the molecular programs and cellular populations. In particular, we found that sex-biased immune gene expression and immune cell populations, such as group 2 innate lymphoid cells, were modulated by androgens. Integration with the UK Biobank dataset revealed potential cellular targets and risk gene enrichment in antigen presentation for sex-biased diseases. This study lays the groundwork for understanding the sex differences orchestrated by androgens and provides important evidence for targeting the androgen pathway as a broad therapeutic strategy for sex-biased diseases.

How Bacteria Subvert Animal Cell Structure and Function.

Bacterial pathogens encode a wide variety of effectors and toxins that hijack host cell structure and function. Of particular importance are virulence factors that target actin cytoskeleton dynamics critical for cell shape, stability, motility, phagocytosis, and division. In addition, many bacteria target organelles of the general secretory pathway (e.g., the endoplasmic reticulum and the Golgi complex) and recycling pathways (e.g., the endolysosomal system) to establish and maintain an intracellular replicative niche. Recent research on the biochemistry and structural biology of bacterial effector proteins and toxins has begun to shed light on the molecular underpinnings of these host-pathogen interactions. This exciting work is revealing how pathogens gain control of the complex and dynamic host cellular environments, which impacts our understanding of microbial infectious disease, immunology, and human cell biology.

Cell Competition: Mechanisms and Physiological Roles.

Cell-competitive interactions are widespread in nature and determine the outcome of a vast variety of biological processes. A particular class of competitive interactions takes place when alterations in intrinsic cellular properties are sensed nonautonomously by comparison between neighboring cells, resulting in the selective elimination of one cell population. This type of cell competition was first described four decades ago in developing epithelia of Drosophila. In the last 15 years, further molecular and cellular analyses have provided essential knowledge about the mechanisms, universality, and physiological relevance of cell competition. The two main phenomena triggering cell competition are alterations in cellular metabolic status and alterations in epithelial apico-basal polarity, while other reported pathways are less characterized. Cell competition plays essential roles in quality control, homeostasis, and repair of developing and adult tissues, and depending on the context, it may function as a tumor-suppressing or tumor-promoting mechanism.

Transcriptional Control of Developmental Cell Behaviors.

Tissue-specific transcription regulators emerged as key developmental control genes, which operate in the context of complex gene regulatory networks (GRNs) to coordinate progressive cell fate specification and tissue morphogenesis. We discuss how GRNs control the individual cell behaviors underlying complex morphogenetic events. Cell behaviors classically range from mesenchymal cell motility to cell shape changes in epithelial sheets. These behaviors emerge from the tissue-specific, multiscale integration of the local activities of universal and pleiotropic effectors, which underlie modular subcellular processes including cytoskeletal dynamics, cell-cell and cell-matrix adhesion, signaling, polarity, and vesicle trafficking. Extrinsic cues and intrinsic cell competence determine the subcellular spatiotemporal patterns of effector activities. GRNs influence most subcellular activities by controlling only a fraction of the effector-coding genes, which we argue is enriched in effectors involved in reading and processing the extrinsic cues to contextualize intrinsic subcellular processes and canalize developmental cell behaviors. The properties of the transcription-cell behavior interface have profound implications for evolution and disease.

Chemical Principles in Tissue Clearing and Staining Protocols for Whole-Body Cell Profiling.

Mammalian bodies have more than a billion cells per cubic centimeter, which makes whole-body cell (WBC) profiling of an organism one of the ultimate challenges in biology and medicine. Recent advances in tissue-clearing technology have enabled rapid and comprehensive cellular analyses in whole organs and in the whole body by a combination of state-of-the-art technologies of optical imaging and image informatics. In this review, we focus mainly on the chemical principles in currently available techniques for tissue clearing and staining to facilitate our understanding of their underlying mechanisms. Tissue clearing is usually conducted by the following steps: (a) fixation, (b) permeabilization, (c) decolorizing, and (d) refractive index (RI) matching. To phenotype individual cells after tissue clearing, it is important to visualize genetically encoded fluorescent reporters and/or to stain tissues with fluorescent dyes, fluorescent labeled antibodies, or nucleic acid probes. Although some technical challenges remain, the chemical principles in tissue clearing and staining for WBC profiling will enable various applications, such as identifying cellular circuits across multiple organs and measuring their dynamics in stochastic and proliferative cellular processes, for example, autoimmune and malignant neoplastic diseases.

Single-cell phenotyping within transparent intact tissue through whole-body clearing.

Understanding the structure-function relationships at cellular, circuit, and organ-wide scale requires 3D anatomical and phenotypical maps, currently unavailable for many organs across species. At the root of this knowledge gap is the absence of a method that enables whole-organ imaging. Herein, we present techniques for tissue clearing in which whole organs and bodies are rendered macromolecule-permeable and optically transparent, thereby exposing their cellular structure with intact connectivity. We describe PACT (passive clarity technique), a protocol for passive tissue clearing and immunostaining of intact organs; RIMS (refractive index matching solution), a mounting media for imaging thick tissue; and PARS (perfusion-assisted agent release in situ), a method for whole-body clearing and immunolabeling. We show that in rodents PACT, RIMS, and PARS are compatible with endogenous-fluorescence, immunohistochemistry, RNA single-molecule FISH, long-term storage, and microscopy with cellular and subcellular resolution. These methods are applicable for high-resolution, high-content mapping and phenotyping of normal and pathological elements within intact organs and bodies.