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Cell Systems
This journal offers authors two options (open access or subscription) to publish research

Aug 17, 2022

Volume 13Issue 8p589-682
On the cover: Natural killer (NK) cells (pale blue) attacking SARS-CoV-2 viruses (red) with machine learning methodology signified by patterned numerals. In this issue of Cell Systems, Zhang et al. (p. 598) propose a machine learning method that integrates single-cell mult-iomic data with GWAS summary statistics to discover cell-type-specific disease risk genes. Application to severe COVID-19 identifies over 1,000 risk genes in 19 human lung cell types. Genetic risk is found to be enriched within NK cells and CD56 bright cytokine-producing NK cells. Cover art by Lettie Margaret McGuire....
On the cover: Natural killer (NK) cells (pale blue) attacking SARS-CoV-2 viruses (red) with machine learning methodology signified by patterned numerals. In this issue of Cell Systems, Zhang et al. (p. 598) propose a machine learning method that integrates single-cell mult-iomic data with GWAS summary statistics to discover cell-type-specific disease risk genes. Application to severe COVID-19 identifies over 1,000 risk genes in 19 human lung cell types. Genetic risk is found to be enriched within NK cells and CD56 bright cytokine-producing NK cells. Cover art by Lettie Margaret McGuire.

Voices

  • What are the current bottlenecks in developing and applying CRISPR technologies?

    • Elizabeth H. Kellogg,
    • Jonathan Gootenberg,
    • Omar Abudayyeh,
    • Alan S.L. Wong,
    • James E. Dahlman,
    • Audrone Lapinaite,
    • Cameron Myhrvold,
    • Chang C. Liu,
    • Patrick D. Hsu,
    • Prashant Mali,
    • Lei Stanley Qi
    I think of CRISPR as being a subset of “adaptive” bacterial systems that promote host survival. These adaptive systems are macromolecular systems that enhance host survival in a myriad of ways: by destroying foreign DNA, by mediating horizontal gene transfer, or even by altering host metabolism, among many other diverse functions. In this sense, CRISPR represents just the tip of the iceberg; there are likely many unexplored and uncharacterized adaptive systems that would be practically useful for genomic manipulation.

Commentary

    Featured Article
  • More is different with a vengeance

    • Michael P.H. Stumpf
    In his 1972 landmark paper “More is Different,” Philip W. Anderson established “complexity” as a fundamentally important subject of inquiry. He highlighted the profound limitations of reductionist approaches in understanding nature’s complexity, and he set in motion new lines of investigation that have, among other things, led to systems biology.

Articles

  • Multiomic analysis reveals cell-type-specific molecular determinants of COVID-19 severity

    • Sai Zhang,
    • Johnathan Cooper-Knock,
    • Annika K. Weimer,
    • Minyi Shi,
    • Lina Kozhaya,
    • Derya Unutmaz,
    • Calum Harvey,
    • Thomas H. Julian,
    • Simone Furini,
    • Elisa Frullanti,
    • Francesca Fava,
    • Alessandra Renieri,
    • Peng Gao,
    • Xiaotao Shen,
    • Ilia Sarah Timpanaro,
    • Kevin P. Kenna,
    • J. Kenneth Baillie,
    • Mark M. Davis,
    • Philip S. Tsao,
    • Michael P. Snyder
    Open Access
    Zhang et al. apply a machine learning method that integrates single-cell multiomics with GWAS summary statistics for gene discovery. Application to severe COVID-19 identifies >1,000 risk genes, which account for 77% of the observed heritability. Genetic risk is focused within NK cells, CD56bright cytokine-producing NK cells in particular, highlighting the dysfunction of these cells as a determinant of severe disease.
  • Defect-buffering cellular plasticity increases robustness of metazoan embryogenesis

    • Long Xiao,
    • Duchangjiang Fan,
    • Huan Qi,
    • Yulin Cong,
    • Zhuo Du
    Developmental processes are resilient to genetic and environmental changes, a property termed robustness. Xiao et al. reveal that knockdown of conserved genes frequently induces cellular defects, which can be buffered by cellular plasticity through alleviation, correction, and accommodation. Thus, cellular-level compensations are non-negligible contributors to organismal robustness and fitness.
  • A quantitative biophysical principle to explain the 3D cellular connectivity in curved epithelia

    • Pedro Gómez-Gálvez,
    • Pablo Vicente-Munuera,
    • Samira Anbari,
    • Antonio Tagua,
    • Carmen Gordillo-Vázquez,
    • Jesús A. Andrés-San Román,
    • Daniel Franco-Barranco,
    • Ana M. Palacios,
    • Antonio Velasco,
    • Carlos Capitán-Agudo,
    • Clara Grima,
    • Valentina Annese,
    • Ignacio Arganda-Carreras,
    • Rafael Robles,
    • Alberto Márquez,
    • Javier Buceta,
    • Luis M. Escudero
    The complexity of curved epithelia poses a challenge to quantify their organization and the energy cues that regulate the 3D cellular connectivity. Gómez-Gálvez et al. use a biophysical model, based on computational and experimental data, to uncover a quantitative principle that explains the 3D cellular connectivity in tubular epithelia.
  • Featured Article
  • Mapping hormone-regulated cell-cell interaction networks in the human breast at single-cell resolution

    • Lyndsay M. Murrow,
    • Robert J. Weber,
    • Joseph A. Caruso,
    • Christopher S. McGinnis,
    • Kiet Phong,
    • Philippe Gascard,
    • Gabrielle Rabadam,
    • Alexander D. Borowsky,
    • Tejal A. Desai,
    • Matthew Thomson,
    • Thea Tlsty,
    • Zev J. Gartner
    Open Access
    Estrogen and progesterone regulate breast development and modify cancer risk. Using single-cell analysis and leveraging person-to-person variability to identify gene programs that co-vary across individuals, Murrow et al. map the tissue-level response to ovarian hormones. Prior pregnancy and obesity modify hormone responsiveness in the breast through distinct mechanisms.
  • Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity

    • Anoop T. Ambikan,
    • Hong Yang,
    • Shuba Krishnan,
    • Sara Svensson Akusjärvi,
    • Soham Gupta,
    • Magda Lourda,
    • Maike Sperk,
    • Muhammad Arif,
    • Cheng Zhang,
    • Hampus Nordqvist,
    • Sivasankaran Munusamy Ponnan,
    • Anders Sönnerborg,
    • Carl Johan Treutiger,
    • Liam O’Mahony,
    • Adil Mardinoglu,
    • Rui Benfeitas,
    • Ujjwal Neogi
    Open Access
    Ambikan et al. used blood cell transcriptomics, immunophenotyping, plasma metabolomics, and single-cell-type metabolomics of monocytes to identify the system-level metabolic rewiring in COVID-19 patients. Integrative omics improved the clinical definition of the risk group of COVID-19 severity. The personalized and group-specific metabolic models indicated the essential role of transporters and metabolites of central metabolism (TCA cycle) in COVID-19 severity. This can lead to an alternate treatment strategy through metabolic perturbations of central metabolism in severe COVID-19.
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