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Cell Systems
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Jun 16, 2021

Volume 12Issue 6p463-688
Open Archive
On the cover: Cells occupy a diverse range of single cell states. In this issue of Cell Systems, a team from the Allen Institute for Cell Science (Gerbin et al., 670–687) classified thousands of single cells to quantify the relationship between cell organization and gene expression. Here, the diversity of cell organization and gene expression is shown in hiPSC-derived cardiomyocytes, where the sarcomere is labeled with alpha-actinin-2-mEGFP (white). Cells are colored by the combined organizational score. Image credit: Thao Do and the authors....
On the cover: Cells occupy a diverse range of single cell states. In this issue of Cell Systems, a team from the Allen Institute for Cell Science (Gerbin et al., 670–687) classified thousands of single cells to quantify the relationship between cell organization and gene expression. Here, the diversity of cell organization and gene expression is shown in hiPSC-derived cardiomyocytes, where the sarcomere is labeled with alpha-actinin-2-mEGFP (white). Cells are colored by the combined organizational score. Image credit: Thao Do and the authors.
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Editorial

  • Celebrating five full years of Cell Systems

    • Quincey Justman
    This issue of Cell Systems is a milestone for us: we have published five full years of science that we love. If I were to distill this moment into one observation that illustrates how the field has changed since our inaugural issue in July of 2015, it would be this: the number of labs with access to technology that makes a very large number of precise measurements on individual biological units, such as cells, proteins, and genomes, has skyrocketed. If the last five years were about generating data, I anticipate that the next five years will be about computer-aided exploration of them: mapping their peaks, valleys, and open expanses, encountering species and phenomena that haven’t yet been named in our language.

Essay

  • Schrödinger’s What Is Life? at 75

    • Rob Phillips
    “These facts are easily the most interesting that science has revealed in our day.”—Erwin Schrödinger in What is Life?, discussing how the “dislocation of just a few atoms” in a gene can bring about a “well-defined change in the large-scale hereditary characteristics of the organism.”

Perspectives

  • Biological feedback control—Respect the loops

    • Hana El-Samad
    El-Samad presents a brief narration of the discovery and salient roles of cellular feedback loops and highlights a number of gaps in our understanding of biological feedback control structures. The discussion identifies the need for a systematic framework to probe feedback loops, unravel their versatile roles in biological organization, and understand the tradeoffs they induce for cellular function. Feedback loops are a fundamental underpinning of life, and revealing their quantitative secrets is bound to be a large piece of the puzzle of health and disease.

  • A forecast for large-scale, predictive biology: Lessons from meteorology

    • Markus W. Covert,
    • Taryn E. Gillies,
    • Takamasa Kudo,
    • Eran Agmon
    In this perspective, Covert et al. draw on experience gained in meteorology to forecast how mathematical modeling may transform biological research and applications in the coming years.

  • IonoBiology: The functional dynamics of the intracellular metallome, with lessons from bacteria

    • Leticia Galera-Laporta,
    • Colin J. Comerci,
    • Jordi Garcia-Ojalvo,
    • Gürol M. Süel
    Metal ions are essential for life and represent the second most abundant constituent (after water) of any living cell. While the biological importance of inorganic ions has been appreciated for over a century, we are far from a comprehensive understanding of the functional roles that ions play in cells and organisms. In particular, recent advances are challenging the traditional view that cells maintain constant levels of ion concentrations (ion homeostasis). In fact, the ionic composition (metallome) of cells appears to be purposefully dynamic.

  • Perfect adaptation in biology

    • Mustafa H. Khammash
    In this perspective, Khammash illustrates the structural constraints that networks executing robust perfect adaptation cannot avoid and argues that understanding them offers a compelling means to unravel regulatory biological complexity.

  • Machine learning for perturbational single-cell omics

    • Yuge Ji,
    • Mohammad Lotfollahi,
    • F. Alexander Wolf,
    • Fabian J. Theis
    Single-cell data from perturbation experiments enable the use of deep learning approaches to characterize perturbed cellular phenotypes and predict drug properties and effects of treatment. We first define objectives in learning perturbation response from single-cell data; purvey existing approaches, resources, and datasets; and discuss how a perturbation atlas can enable deep learning models. We then cover deep learning concepts and deep neural networks that the field might employ to create more powerful and explainable models.

  • Conservation of metabolic regulation by phosphorylation and non-covalent small-molecule interactions

    • Christoph H. Gruber,
    • Maren Diether,
    • Uwe Sauer
    In this Perspective, Uwe Sauer and colleagues review extant observations of protein phosphorylation and small molecule interactions in metabolism and ask which of their specific regulatory functions are conserved between Escherichia coli and Homo sapiens. They discuss the fact that phosphorylation in human does not appear to replace the metabolite-protein interactions and regulatory logic observed in both species, but rather seems to add additional opportunities for fine-tuning and more complex responses.

Reviews

  • Fundamentals to function: Quantitative and scalable approaches for measuring protein stability

    • Beatriz Atsavapranee,
    • Catherine D. Stark,
    • Fanny Sunden,
    • Samuel Thompson,
    • Polly M. Fordyce
    Atsavapranee and Stark et al. review methods for measuring protein stability at a variety of resolutions and throughputs, including calorimetry, spectroscopy, mass spectrometry, gel electrophoresis, and sequencing approaches. The authors highlight benefits and limitations of each method and discuss why quantitative and systematic measurements are essential for understanding the relationship between protein sequence, stability, and function.

  • Context-aware synthetic biology by controller design: Engineering the mammalian cell

    • Nika Shakiba,
    • Ross D. Jones,
    • Ron Weiss,
    • Domitilla Del Vecchio
    The rise of systems biology has ushered a new paradigm: the view of the cell as a system that processes environmental inputs to drive phenotypic outputs. Synthetic biology provides a complementary approach, allowing us to program cell behavior through the addition of synthetic genetic devices into the cellular processor. These devices, and the complex genetic circuits they compose, are engineered using a design-prototype-test cycle, allowing for predictable device performance to be achieved in a context-dependent manner.

  • Engineering molecular translation systems

    • Camila Kofman,
    • Joongoo Lee,
    • Michael C. Jewett
    Engineering molecular translation systems provides exciting opportunities for expanding the chemistry of life. Here, we present current chemical and biological strategies to evolve, diversify, and repurpose the ribosome and its peripheral machinery to synthesize novel sequence-defined polymers. These efforts are transforming chemical and synthetic biology.

  • Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

    • Mojca Mattiazzi Usaj,
    • Clarence Hue Lok Yeung,
    • Helena Friesen,
    • Charles Boone,
    • Brenda J. Andrews
    Single cells that are genetically identical can have important differences in the genes they express, their metabolism, the health of their organelles, their cell-cycle position, and their age. Single-cell heterogeneity is seen in microbial populations and is an important feature of development and differentiation in multicellular organisms, often leading to differences in how cells respond to their environment. In this review, we focus on single-cell image analysis, which is one of the most useful ways to explore single-cell heterogeneity.

  • Mapping the multiscale structure of biological systems

    • Leah V. Schaffer,
    • Trey Ideker
    In this review, Schaffer and Ideker discuss concepts and progress toward the goal of creating unified multiscale models of biological structure and function. Many experimental technologies measure physical proximity or functional similarity among biological entities at different scales—including amino acids within a protein, proteins within an enzymatic complex, and cells within a tissue. This review discusses use of these proximity networks to create multiscale models, along with major applications, visualization techniques, and current challenges.

  • Advances in systems biology modeling: 10 years of crowdsourcing DREAM challenges

    • Pablo Meyer,
    • Julio Saez-Rodriguez
    Ten years of DREAM challenges in systems biology have shown the power of this crowdsourcing competition approach to advance not only the predictive power, reproducibility, and reliability of computational and biophysical models but also biology’s conceptual understanding. It has proven to be a good way to marry forward and reverse modeling, where predictions are respectively constructed using existing concepts or blindly data driven. Going outward from the nucleus to cell tissues and lineages, we revisit the most current systems biology problems.

Synthesis

  • Learning the protein language: Evolution, structure, and function

    • Tristan Bepler,
    • Bonnie Berger
    In this synthesis, Bepler and Berger discuss recent advances in protein language modeling and their applications to downstream protein property prediction problems. They consider how these models can be enriched with prior biological knowledge and introduce an approach for encoding protein structural knowledge into the learned representations.

Article

  • Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived cardiomyocytes

    • Kaytlyn A. Gerbin,
    • Tanya Grancharova,
    • Rory M. Donovan-Maiye,
    • Melissa C. Hendershott,
    • Helen G. Anderson,
    • Jackson M. Brown,
    • Jianxu Chen,
    • Stephanie Q. Dinh,
    • Jamie L. Gehring,
    • Gregory R. Johnson,
    • HyeonWoo Lee,
    • Aditya Nath,
    • Angelique M. Nelson,
    • M. Filip Sluzewski,
    • Matheus P. Viana,
    • Calysta Yan,
    • Rebecca J. Zaunbrecher,
    • Kimberly R. Cordes Metzler,
    • Nathalie Gaudreault,
    • Theo A. Knijnenburg,
    • Susanne M. Rafelski,
    • Julie A. Theriot,
    • Ruwanthi N. Gunawardane
    This study establishes a framework for multidimensional analysis in single cells to study the relationship between gene expression and cell organization. The quantitative and automated image analysis tools developed in the study were applied to thousands of single cells, and the results suggest that gene expression alone is not sufficient to classify cell states.
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