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.

What is a cell type?

A next step for cell atlases should be to chart perturbations in human model systems.

PhaSepDB in 2022: annotating phase separation-related proteins with droplet states, co-phase separation partners and other experimental information.

Phase separation (PS) proteins form droplets to regulate myriad membraneless organelles (MLOs) and cellular pathways such as transcription, signaling transduction and protein degeneration. PS droplets are usually liquid-like and can convert to hydrogel/solid-like under certain conditions. The PS behavior of proteins is regulated by co-PS partners and mutations, modifications, oligomerizations, repeat regions and alternative splicing of the proteins. With growing interest in PS condensates and associated proteins, we established PhaSepDB 1.0, which provided experimentally verified PS proteins and MLO-related proteins. The past few years witnessed a surge in PS-related research works; thus, we kept updating PhaSepDB. The current PhaSepDB contains 1419 PS entries, 770 low-throughput MLO-related entries and 7303 high-throughput MLO-related entries. We provided more detailed annotations of PS proteins, including PS verification experiments, regions used in experiments, phase diagrams of different experimental conditions, droplet states, co-PS partners and PS regulatory information. We believe that researchers can go further in studying PS proteins with the updated PhaSepDB (http://db.phasep.pro/).

CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data.

CellMarker 2.0 (http://bio-bigdata.hrbmu.edu.cn/CellMarker or http://117.50.127.228/CellMarker/) is an updated database that provides a manually curated collection of experimentally supported markers of various cell types in different tissues of human and mouse. In addition, web tools for analyzing single cell sequencing data are described. We have updated CellMarker 2.0 with more data and several new features, including (i) Appending 36 300 tissue-cell type-maker entries, 474 tissues, 1901 cell types and 4566 markers over the previous version. The current release recruits 26 915 cell markers, 2578 cell types and 656 tissues, resulting in a total of 83 361 tissue-cell type-maker entries. (ii) There is new marker information from 48 sequencing technology sources, including 10X Chromium, Smart-Seq2 and Drop-seq, etc. (iii) Adding 29 types of cell markers, including protein-coding gene lncRNA and processed pseudogene, etc. Additionally, six flexible web tools, including cell annotation, cell clustering, cell malignancy, cell differentiation, cell feature and cell communication, were developed to analysis and visualization of single cell sequencing data. CellMarker 2.0 is a valuable resource for exploring markers of various cell types in different tissues of human and mouse.

A Flexible Chamber for Time-Lapse Live-Cell Imaging with Stimulated Raman Scattering Microscopy.

Stimulated Raman scattering (SRS) microscopy is a label-free chemical imaging technology. Live-cell imaging with SRS has been demonstrated for many biological and biomedical applications. However, long-term time-lapse SRS imaging of live cells has not been widely adopted. SRS microscopy often uses a high numerical aperture (NA) water-immersion objective and a high NA oil-immersion condenser to achieve high-resolution imaging. In this case, the gap between the objective and the condenser is only a few millimeters. Therefore, most commercial stage-top environmental chambers cannot be used for SRS imaging because of their large thickness with a rigid glass cover. This paper describes the design and fabrication of a flexible chamber that can be used for time-lapse live-cell imaging with transmitted SRS signal detection on an upright microscope frame. The flexibility of the chamber is achieved by using a soft material - a thin natural rubber film. The new enclosure and chamber design can be easily added to an existing SRS imaging setup. The testing and preliminary results demonstrate that the flexible chamber system enables stable, long-term, time-lapse SRS imaging of live cells, which can be used for various bioimaging applications in the future.

Correlative all-optical quantification of mass density and mechanics of subcellular compartments with fluorescence specificity.

Quantitative measurements of physical parameters become increasingly important for understanding biological processes. Brillouin microscopy (BM) has recently emerged as one technique providing the 3D distribution of viscoelastic properties inside biological samples - so far relying on the implicit assumption that refractive index (RI) and density can be neglected. Here, we present a novel method (FOB microscopy) combining BM with optical diffraction tomography and epifluorescence imaging for explicitly measuring the Brillouin shift, RI, and absolute density with specificity to fluorescently labeled structures. We show that neglecting the RI and density might lead to erroneous conclusions. Investigating the nucleoplasm of wild-type HeLa cells, we find that it has lower density but higher longitudinal modulus than the cytoplasm. Thus, the longitudinal modulus is not merely sensitive to the water content of the sample - a postulate vividly discussed in the field. We demonstrate the further utility of FOB on various biological systems including adipocytes and intracellular membraneless compartments. FOB microscopy can provide unexpected scientific discoveries and shed quantitative light on processes such as phase separation and transition inside living cells.

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.

A roadmap for the Human Developmental Cell Atlas.

The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development.

Exploring rare cellular activity in more than one million cells by a transscale scope.

In many phenomena of biological systems, not a majority, but a minority of cells act on the entire multicellular system causing drastic changes in the system properties. To understand the mechanisms underlying such phenomena, it is essential to observe the spatiotemporal dynamics of a huge population of cells at sub-cellular resolution, which is difficult with conventional tools such as microscopy and flow cytometry. Here, we describe an imaging system named AMATERAS that enables optical imaging with an over-one-centimeter field-of-view and a-few-micrometer spatial resolution. This trans-scale-scope has a simple configuration, composed of a low-power lens for machine vision and a hundred-megapixel image sensor. We demonstrated its high cell-throughput, capable of simultaneously observing more than one million cells. We applied it to dynamic imaging of calcium ions in HeLa cells and cyclic-adenosine-monophosphate in Dictyostelium discoideum, and successfully detected less than 0.01% of rare cells and observed multicellular events induced by these cells.

Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry.

Most high-dimensional datasets are thought to be inherently low-dimensional-that is, data points are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here, we study the viability of two approaches from differential geometry to estimate the Riemannian curvature of these low-dimensional manifolds. The intrinsic approach relates curvature to the Laplace-Beltrami operator using the heat-trace expansion and is agnostic to how a manifold is embedded in a high-dimensional space. The extrinsic approach relates the ambient coordinates of a manifold's embedding to its curvature using the Second Fundamental Form and the Gauss-Codazzi equation. We found that the intrinsic approach fails to accurately estimate the curvature of even a two-dimensional constant-curvature manifold, whereas the extrinsic approach was able to handle more complex toy models, even when confounded by practical constraints like small sample sizes and measurement noise. To test the applicability of the extrinsic approach to real-world data, we computed the curvature of a well-studied manifold of image patches and recapitulated its topological classification as a Klein bottle. Lastly, we applied the extrinsic approach to study single-cell transcriptomic sequencing (scRNAseq) datasets of blood, gastrulation, and brain cells to quantify the Riemannian curvature of scRNAseq manifolds.

Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.

With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating high-throughput analysis of image data in a variety of biomedical imaging disciplines, ranging from radiology and pathology to cancer biology and immunology. Specifically, a growth in research on deep learning has led to the widespread application of computer-visualization techniques for analyzing and mining data from biomedical images. The availability of open-source software packages and the development of novel, trainable deep neural network architectures has led to increased accuracy in cell detection and segmentation algorithms. By automating cell segmentation, it is now possible to mine quantifiable cellular and spatio-cellular features from microscopy images, providing insight into the organization of cells in various pathologies. This mini-review provides an overview of the current state of the art in deep learning- and artificial intelligence-based methods of segmentation and data mining of cells in microscopy images of tissue.

Correlative fluorescence microscopy, transmission electron microscopy and secondary ion mass spectrometry (CLEM-SIMS) for cellular imaging.

Electron microscopy (EM) has been employed for decades to analyze cell structure. To also analyze the positions and functions of specific proteins, one typically relies on immuno-EM or on a correlation with fluorescence microscopy, in the form of correlated light and electron microscopy (CLEM). Nevertheless, neither of these procedures is able to also address the isotopic composition of cells. To solve this, a correlation with secondary ion mass spectrometry (SIMS) would be necessary. SIMS has been correlated in the past to EM or to fluorescence microscopy in biological samples, but not to CLEM. We achieved this here, using a protocol based on transmission EM, conventional epifluorescence microscopy and nanoSIMS. The protocol is easily applied, and enables the use of all three technologies at high performance parameters. We suggest that CLEM-SIMS will provide substantial information that is currently beyond the scope of conventional correlative approaches.

An evolutionary perspective on the interplays between hydrogen sulfide and oxygen in cellular functions.

The physiological effects of the endogenously generated hydrogen sulfide (H2S) have been extensively studied in recent years. This review summarized the role of H2S in the origin of life and H2S metabolism in organisms from bacteria to vertebrates, examined the relationship between H2S and oxygen from an evolutionary perspective and emphasized the oxygen-dependent manner of H2S signaling in various physiological and pathological processes. H2S and oxygen are inextricably linked in various cellular functions. H2S is involved in aerobic respiration and stimulates oxidative phosphorylation and ATP production within the cell. Besides, H2S has protective effects on ischemia and reperfusion injury in several organs by acting as an oxygen sensor. Also, emerging evidence suggests the role of H2S is in an oxygen-dependent manner. All these findings indicate the subtle relationship between H2S and oxygen and further explain why H2S, a toxic molecule thriving in an anoxia environment several billion years ago, still affects homeostasis today despite the very low content in the body.

Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks.

Cells interpret cues from and interact with fibrous microenvironments through the body based on the mechanics and organization of these environments and the phenotypic state of the cell. This in turn regulates mechanoactive pathways, such as the localization of mechanosensitive factors. Here, we leverage the microscale heterogeneity inherent to engineered fiber microenvironments to produce a large morphologic data set, across multiple cells types, while simultaneously measuring mechanobiological response (YAP/TAZ nuclear localization) at the single cell level. This dataset describing a large dynamic range of cell morphologies and responses was coupled with a machine learning approach to predict the mechanobiological state of individual cells from multiple lineages. We also noted that certain cells (e.g., invasive cancer cells) or biochemical perturbations (e.g., modulating contractility) can limit the predictability of cells in a universal context. Leveraging this finding, we developed further models that incorporate biochemical cues for single cell prediction or identify individual cells that do not follow the established rules. The models developed here provide a tool for connecting cell morphology and signaling, incorporating biochemical cues in predictive models, and identifying aberrant cell behavior at the single cell level.

Diffusive Interface Model for Actomyosin Driven Cell Oscillations.

In this paper, we build phase-field models for the actomyosin driven cell oscillations. In our modeling, an oscillation starts from an actin cortex breakage. After the breakage, due to the unbalanced distribution of actin and myosin, there is unbalanced contraction force in different membrane components, which then results in the lipids transferring to the bulged membrane compartment. As such we can observe a cell oscillation. During the whole process, the actin and myosin polymerization and depolymerization play important roles. We give detailed formulations under the framework of phase-field methodology, in which phase-field functions are used to describe different parts of the cell membrane, integrated with the distribution of the actin and myosin at different components. The whole system is described as a set of time-dependent partial differential equations in three-dimensional space. Forward Euler method is used to solve the system. The spectral method is used for spatial discretizations for efficiency and accuracy purpose. Given carefully selected parameters, three-dimensional simulations are performed and compared with biological images. The simulations prove that actomyosin dynamics are the major reasons for cell oscillations. Further, our method can be easily extended into the simulations of cell polarization. We also compared our numerical simulations with biological experiments. This modeling gives an example of applying diffusive interface methods toward complex biology experiments.

Predicting cell health phenotypes using image-based morphology profiling.

Genetic and chemical perturbations impact diverse cellular phenotypes, including multiple indicators of cell health. These readouts reveal toxicity and antitumorigenic effects relevant to drug discovery and personalized medicine. We developed two customized microscopy assays, one using four targeted reagents and the other three targeted reagents, to collectively measure 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species, DNA damage, and cell cycle stage. We then tested an approach to predict multiple cell health phenotypes using Cell Painting, an inexpensive and scalable image-based morphology assay. In matched CRISPR perturbations of three cancer cell lines, we collected both Cell Painting and cell health data. We found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost. We hypothesized that these models can be applied to accurately predict cell health assay outcomes for any future or existing Cell Painting dataset. For Cell Painting images from a set of 1500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts. We provide a web app to browse predictions: http://broad.io/cell-health-app. Our approach can be used to add cell health annotations to Cell Painting datasets.

Integrating abiotic chemical catalysis and enzymatic catalysis in living cells.

Life emerges from networks of multiple chemical reactions mediated by enzymes. If abiotic chemical catalysis is implanted into the reaction network of life, such an integration would produce organisms generating unique secondary metabolites and value-added materials from feedstocks or even air, or new diagnostics and therapeutics against diseases. In this review, we introduce selected papers in this emerging field of catalysis research.

Extracellular Matrix Mimics Using Hyaluronan-Based Biomaterials.

Hyaluronan (HA) is a critical element of the extracellular matrix (ECM). The regulated synthesis and degradation of HA modulates the ECM chemical and physical properties that, in turn, influence cellular behavior. HA triggers signaling pathways associated with the adhesion, proliferation, migration, and differentiation of cells, mediated by its interaction with specific cellular receptors or by tuning the mechanical properties of the ECM. This review summarizes the recent advances on strategies used to mimic the HA present in the ECM to study healthy or pathological cellular behavior. This includes the development of HA-based 2D and 3D in vitro tissue models for the seeding and encapsulation of cells, respectively, and HA particles as carriers for the targeted delivery of therapeutic agents.