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.

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.

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.

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.

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.

Exosomes from different cells: Characteristics, modifications, and therapeutic applications.

Exosomes are cystic vesicles secreted by living cells with a phospholipid bilayer membrane. Importantly, these vesicles could serve to carry lipids, proteins, genetic materials, and transmit biological information in vivo. The cell-specific proteins and genetic materials in exosomes are capable of reflecting their cell origin and physiological status. Based on the different tissues and cells (macrophage, dendritic cells, tumor cells, mesenchymal stem cells, various body fluids, and so on), exosomes exhibit different characteristics and functions. Furthermore, owing to their high delivery efficiency, biocompatibility, and multifunctional properties, exosomes are expected to become a new means of drug delivery, disease diagnosis, immunotherapy, and precise treatment. At the same time, in order to supplement or enhance the therapeutic applicability of exosomes, chemical or biological modifications can be used to broaden, change or improve their therapeutic capabilities. This review focuses on three aspects: the characteristics and original functions of exosomes secreted by different cells, the modification and transformation of exosomes, and the application of exosomes in different diseases.

Accelerating the experimental responses on cell behaviors: a long-term prediction of cell trajectories using Social Generative Adversarial Network.

The incremented uptake provided by time-lapse microscopy in Organ-on-a-Chip (OoC) devices allowed increased attention to the dynamics of the co-cultured systems. However, the amount of information stored in long-time experiments may constitute a serious bottleneck of the experimental pipeline. Forward long-term prediction of cell trajectories may reduce the spatial-temporal burden of video sequences storage. Cell trajectory prediction becomes crucial especially to increase the trustworthiness in software tools designed to conduct a massive analysis of cell behavior under chemical stimuli. To address this task, we transpose here the exploitation of the presence of "social forces" from the human to the cellular level for motion prediction at microscale by adapting the potential of Social Generative Adversarial Network predictors to cell motility. To demonstrate the effectiveness of the approach, we consider here two case studies: one related to PC-3 prostate cancer cells cultured in 2D Petri dishes under control and treated conditions and one related to an OoC experiment of tumor-immune interaction in fibrosarcoma cells. The goodness of the proposed strategy has been verified by successfully comparing the distributions of common descriptors (kinematic descriptors and mean interaction time for the two scenarios respectively) from the trajectories obtained by video analysis and the predicted counterparts.

High-throughput, microscope-based sorting to dissect cellular heterogeneity.

Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence-activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, and then derive their single-cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.

Características citomorfológicas de las alteraciones plaquetarias cuantitativas y su relación con otras alteraciones celulares / Cytomorphological characteristics of quantitative platelet alterations and its association with other cell alterations

Introducción: Las alteraciones cuantitativas de plaquetas son producidas por el incremento o disminución de los conteos globales de plaquetas. El incremento o trombocitosis se produce por redistribución o aumento de la producción medular; la disminución puede ser el resultado de una reducción de la producción, redistribución o acortamiento de la sobrevida de las plaquetas en circulación. Objetivo: Describir los hallazgos citomorfológicos más importantes en las alteraciones cuantitativas de plaquetas. Métodos: Se realizó una revisión de la literatura, en inglés y español, en la base de datos PubMed y el motor de búsqueda Google Académico de artículos publicados en los últimos 10 años. Se hizo un análisis y resumen de la bibliografía revisada. Análisis y síntesis de la información: Las alteraciones cuantitativas de plaquetas se caracterizan por variaciones en el número y morfología de estas células. Estas se asocian a causas congénitas o adquiridas, en la que la detallada anamnesis de los pacientes es un elemento importante en el diagnóstico. En la trombocitosis se debe diferenciar una trombocitosis reactiva de una enfermedad medular primaria; mientras que en la trombocitopenia se debe considerar el origen étnico de los pacientes y la morfología de los leucocitos. Son numerosas las causas hereditarias de trombocitopenia con anomalías morfológicas de plaquetas y granulocitos. Conclusiones: Las alteraciones cuantitativas de plaquetas son un amplio número de entidades con semejanzas y diferencias en cuanto a presentación y manifestaciones clínicas. Los exámenes de laboratorio constituyen una herramienta importante en el diagnóstico, pronóstico y el seguimiento de los pacientes afectados(AU)

Introduction: Quantitative platelet alterations are produced by the increase or decrease in global platelet counts. Platelet count increase or thrombocytosis is produced by redistribution or increased marrow production. Platelet decrease may result from production, redistribution, or shortened survival of circulating platelets. Objective: To describe the most significant cytomorphological findings in quantitative platelet alterations. Methods: A literature review was carried out, in English and in Spanish, in the database PubMed and with the search engine of Google Scholar, of articles published in the last ten years. An analysis and summary of the revised bibliography was made. Information analysis and synthesis: Quantitative platelet alterations are characterized by variations in the number and morphology of these cells. These are associated with congenital or acquired causes, in which detailed anamnesis of patients is an important element in the diagnosis. In thrombocytosis, reactive thrombocytosis must be differentiated from primary marrow disease; while in thrombocytopenia, the ethnic origin of the patients and the morphology of the leukocytes must be considered. Hereditary causes of thrombocytopenia with morphological abnormalities of platelets and granulocytes are numerous. Conclusions: Quantitative platelet alterations are a large number of entities with similarities and differences in terms of presentation and clinical manifestations. Laboratory tests are an important tool for diagnosis, prognosis, and follow-up of affected patients(AU)

Multiscale modelling of motility wave propagation in cell migration.

The collective motion of cell monolayers within a tissue is a fundamental biological process that occurs during tissue formation, wound healing, cancerous invasion, and viral infection. Experiments have shown that at the onset of migration, the motility is self-generated as a polarisation wave starting from the leading edge of the monolayer and progressively propagates into the bulk. However, it is unclear how the propagation of this motility wave is influenced by cellular properties. Here, we investigate this question using a computational model based on the Potts model coupled to the dynamics of intracellular polarisation. The model captures the propagation of the polarisation wave and suggests that the cells cortex can regulate the migration modes: strongly contractile cells may depolarise the monolayer, whereas less contractile cells can form swirling movement. Cortical contractility is further found to limit the cells motility, which (i) decelerates the wave speed and the leading edge progression, and (ii) destabilises the leading edge. Together, our model describes how different mechanical properties of cells can contribute to the regulation of collective cell migration.

Construction of a human cell landscape at single-cell level.

Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems1. However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a 'single-cell HCL analysis' pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology.

Reconstructing temporal and spatial dynamics from single-cell pseudotime using prior knowledge of real scale cell densities.

Modern cytometry methods allow collecting complex, multi-dimensional data sets from heterogeneous cell populations at single-cell resolution. While methods exist to describe the progression and order of cellular processes from snapshots of such populations, these descriptions are limited to arbitrary pseudotime scales. Here we describe MAPiT, an universal transformation method that recovers real-time dynamics of cellular processes from pseudotime scales by utilising knowledge of the distributions on the real scales. As use cases, we applied MAPiT to two prominent problems in the flow-cytometric analysis of heterogeneous cell populations: (1) recovering the kinetics of cell cycle progression in unsynchronised and thus unperturbed cell populations, and (2) recovering the spatial arrangement of cells within multi-cellular spheroids prior to spheroid dissociation for cytometric analysis. Since MAPiT provides a theoretic basis for the relation of pseudotime values to real temporal and spatial scales, it can be used broadly in the analysis of cellular processes with snapshot data from heterogeneous cell populations.

The Physics of Cellular Decision Making During Epithelial-Mesenchymal Transition.

The epithelial-mesenchymal transition (EMT) is a process by which cells lose epithelial traits, such as cell-cell adhesion and apico-basal polarity, and acquire migratory and invasive traits. EMT is crucial to embryonic development and wound healing. Misregulated EMT has been implicated in processes associated with cancer aggressiveness, including metastasis. Recent experimental advances such as single-cell analysis and temporal phenotypic characterization have established that EMT is a multistable process wherein cells exhibit and switch among multiple phenotypic states. This is in contrast to the classical perception of EMT as leading to a binary choice. Mathematical modeling has been at the forefront of this transformation for the field, not only providing a conceptual framework to integrate and analyze experimental data, but also making testable predictions. In this article, we review the key features and characteristics of EMT dynamics, with a focus on the mathematical modeling approaches that have been instrumental to obtaining various useful insights.

Metabolic Regulation of Cell Fate and Function.

Increasing evidence implicates metabolic pathways as key regulators of cell fate and function. Although the metabolism of glucose, amino acids, and fatty acids is essential to maintain overall energy homeostasis, the choice of a given metabolic pathway and the levels of particular substrates and intermediates increasingly appear to modulate specific cellular activities. This connection is likely related to the growing appreciation that molecules such as acetyl-CoA act as a shared currency between metabolic flux and chromatin modification. We review recent evidence for a role of metabolism in modulating cellular function in four distinct contexts. These areas include the immune system, the tumor microenvironment, the fibrotic response, and stem cell function. Together, these examples suggest that metabolic pathways do not simply provide the fuel that powers cellular activities but instead help to shape and determine cellular identity.

Nucleation of antagonistic organisms and cellular competitions on curved, inflating substrates.

We consider the dynamics of spatially distributed, diffusing populations of organisms with antagonistic interactions. These interactions are found on many length scales, ranging from kilometer-scale animal range dynamics with selection against hybrids to micron-scale interactions between poison-secreting microbial populations. We find that the dynamical line tension at the interface between antagonistic organisms suppresses survival probabilities of small clonal clusters: the line tension introduces a critical cluster size that an organism with a selective advantage must achieve before deterministically spreading through the population. We calculate the survival probability as a function of selective advantage δ and antagonistic interaction strength σ. Unlike a simple Darwinian selective advantage, the survival probability depends strongly on the spatial diffusion constant D_{s} of the strains when σ>0, with suppressed survival when both species are more motile. Finally, we study the survival probability of a single mutant cell at the frontier of a growing spherical cluster of cells, such as the surface of an avascular spherical tumor. Both the inflation and curvature of the frontier significantly enhance the survival probability by changing the critical size of the nucleating cell cluster.

Exposure to Static and Extremely-Low Frequency Electromagnetic Fields and Cellular Free Radicals.

This paper summarizes studies on changes in cellular free radical activities from exposure to static and extremely-low frequency (ELF) electromagnetic fields (EMF), particularly magnetic fields. Changes in free radical activities, including levels of cellular reactive oxygen (ROS)/nitrogen (RNS) species and endogenous antioxidant enzymes and compounds that maintain physiological free radical concentrations in cells, is one of the most consistent effects of EMF exposure. These changes have been reported to affect many physiological functions such as DNA damage; immune response; inflammatory response; cell proliferation and differentiation; wound healing; neural electrical activities; and behavior. An important consideration is the effects of EMF-induced changes in free radicals on cell proliferation and differentiation. These cellular processes could affect cancer development and proper growth and development in organisms. On the other hand, they could cause selective killing of cancer cells, for instance, via the generation of the highly cytotoxic hydroxyl free radical by the Fenton Reaction. This provides a possibility of using these electromagnetic fields as a non-invasive and low side-effect cancer therapy. Static- and ELF-EMF probably play important roles in the evolution of living organisms. They are cues used in many critical survival functions, such as foraging, migration, and reproduction. Living organisms can detect and respond immediately to low environmental levels of these fields. Free radical processes are involved in some of these mechanisms. At this time, there is no credible hypothesis or mechanism that can adequately explain all the observed effects of static- and ELF-EMF on free radical processes. We are actually at the impasse that there are more questions than answers.

Single-molecule localization microscopy and tracking with red-shifted states of conventional BODIPY conjugates in living cells.

Single-molecule localization microscopy (SMLM) is a rapidly evolving technique to resolve subcellular structures and single-molecule dynamics at the nanoscale. Here, we employ conventional BODIPY conjugates for live-cell SMLM via their previously reported red-shifted ground-state dimers (DII), which transiently form through bi-molecular encounters and emit bright single-molecule fluorescence. We employ the versatility of DII-state SMLM to resolve the nanoscopic spatial regulation and dynamics of single fatty acid analogs (FAas) and lipid droplets (LDs) in living yeast and mammalian cells with two colors. In fed cells, FAas localize to the endoplasmic reticulum and LDs of ~125 nm diameter. Upon fasting, however, FAas form dense, non-LD clusters of ~100 nm diameter at the plasma membrane and transition from free diffusion to confined immobilization. Our reported SMLM capability of conventional BODIPY conjugates is further demonstrated by imaging lysosomes in mammalian cells and enables simple and versatile live-cell imaging of sub-cellular structures at the nanoscale.

The MYO6 interactome: selective motor-cargo complexes for diverse cellular processes.

Myosins of class VI (MYO6) are unique actin-based motor proteins that move cargo towards the minus ends of actin filaments. As the sole myosin with this directionality, it is critically important in a number of biological processes. Indeed, loss or overexpression of MYO6 in humans is linked to a variety of pathologies including deafness, cardiomyopathy, neurodegenerative diseases as well as cancer. This myosin interacts with a wide variety of direct binding partners such as for example the selective autophagy receptors optineurin, TAX1BP1 and NDP52 and also Dab2, GIPC, TOM1 and LMTK2, which mediate distinct functions of different MYO6 isoforms along the endocytic pathway. Functional proteomics has recently been used to identify the wider MYO6 interactome including several large functionally distinct multi-protein complexes, which highlight the importance of this myosin in regulating the actin and septin cytoskeleton. Interestingly, adaptor-binding not only triggers cargo attachment, but also controls the inactive folded conformation and dimerisation of MYO6. Thus, the C-terminal tail domain mediates cargo recognition and binding, but is also crucial for modulating motor activity and regulating cytoskeletal track dynamics.