October 1, 2021 – Genome Research (https://genome.org) publishes a special issue highlighting novel advances and insights in Single-cell Genomics.
This special issue is Guest-Edited by Dr. Nicholas Navin, Dr. Orit Rozenblatt-Rosen, and Dr. Nancy Zhang, whose expertise across statistics, single-cell methods development, cancer biology, and computational approaches, directed the compilation of what Dr. Navin, Grady Saunders Distinguished Professor and Director of CPRIT Single Cell Genomics Center at the MD Anderson Cancer Center, describes as "animportant collection of articles and reviews, covering the latest cutting-edge developments in the field in areas such as computational methods, basic research, and biomedical applications."
Dr. Zhang, Ge Li and Ning Zhao Professor of Statistics and Vice Dean of Wharton Doctoral Programs at The Wharton School at University of Pennsylvania, explains that "with the rapid evolution of technology, the demand for computational tools to harness the technology and mine the complex high dimensional data sets is growing." This issue highlights research on the development of such novel computational methods for cell type identification and classification (Aevermann et al. 2021; Kimmel and Kelley 2021), single-cell epigenetics (Ohnuki et al. 2021; Ku et al. 2021), and spatial transcriptomics (Miller et al. 2021) among other areas of single-cell genomics.
Dr. Rozenblatt-Rosen, Head of Cellular and Tissue Genomics and Senior Fellow at Genentech, a member of the Roche Group, underscores the application of novel tools to a broad range of longstanding questions;"This special issue provides important insights for the community across many aspects of the single-cell genomics field, including how advances in technology and computation have driven impactful scientific discoveries in numerous areas of biology." Examples in the application of such advances applied to decipher biological function are also featured (Wang et al. 2021b; Xu et al. 2021), as well as single-cell reference atlases, which are community resources expected to be useful in future population-wide studies of disease (Nieto et al. 2021). "Single-cell genomics has given us a novel lens for better understanding health and disease," Rozenblatt-Rosen further emphasizes.
In addition to original research, four Perspective review articles provide insights into the past, present, and future of the field, including the use of transposase to develop single-cell genomics approaches (Adey 2021), computational approaches to study spatial transcriptomics (Dries et al. 2021), cancer cell states in tumors and properties of cancer phenotypes (Barkley et al. 2021), and the application of single-cell genomic methods to study common diseases and population-level variation (Auerbach et al. 2021). Dr. Zhang summarizes, "Thus, this special issue is giving a perspective of this dynamic field at the crossroads of single-cell technology development and high-throughput application to gain deeper understanding of biology and disease".
References:
Aevermann B, Zhang Y, Novotny M, Keshk M, Bakken T, Miller J, Hodge R, Lelieveldt B, Lein E, Scheuermann RH. 2021. A machine learning method for the discovery of minimum marker gene combinations for cell-type identification from single-cell RNA sequencing. Genome Res.https://doi.org/10.1101/gr.275569.121
Kimmel JC, Kelley DR. 2021. Semisupervised adversarial neural networks for single-cell classification.Genome Res. https://doi.org/10.1101/gr.268581.120
Ohnuki H, Venzon DJ, Lobanov A, Tosato G. 2021. Iterative epigenomic analyses in the same single cell. Genome Res. https://doi.org/10.1101/gr.269068.120
Ku WL, Pan L, Cao Y, Gao W, Zhao K. 2021. Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing. Genome Res. https://doi.org/10.1101/gr.260893.120
Miller BF, Bambah-Mukku D, Dulac C, Zhuang X, Fan J. 2021. Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities. Genome Res. https://doi.org/10.1101/gr.271288.120
Wang S, Lee MP, Jones S, Liu J, Waldhaus J. 2021b. Mapping the regulatory landscape of auditory hair cells from single-cell multi-omics data. Genome Res. https://doi.org/10.1101/gr.271080.120
Xu J, Zhang P, Huang Y, Zhou Y, Hou Y, Bekris LM, Lathia J, Chiang C-W, Li L, Pieper AA, et al. 2021. Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease. Genome Res. https://doi.org/10.1101/gr.272484.120
Nieto P, Elosua-Bayes M, Trincado JL, Marchese D, Massoni-Badosa R, Salvany M, Henriques A, Nieto J, Aguilar-Fernández S, Mereu E, et al. 2021. A single-cell tumor immune atlas for precision oncology. Genome Res. https://doi.org/10.1101/gr.273300.120
Adey AC. 2021. Tagmentation-based single-cell genomics. Genome Res. https://doi.org/10.1101/gr.275223.121
Dries R, Chen J, del Rossi N, Khan MM, Sistig A, Yuan G-C. 2021. Advances in spatial transcriptomic data analysis. Genome Res. https://doi.org/10.1101/gr.275224.121
Barkley D, Rao A, Pour M, Franca GS, Yanai I. 2021. Cancer cell states and emergent properties of the dynamic tumor system. Genome Res. https://doi.org/10.1101/gr.275308.121
Auerbach BJ, Hu J, Reilly MP, Li M. 2021. Applications of single-cell genomics and computational strategies to study common disease and population-level variation. Genome Res. https://doi.org/10.1101/gr.275430.121
In addition to the articles highlighted above, the following will also appear in the issue:
Heiser CN, Wang VM, Chen B, Hughey JJ, Lau KS. 2021. Automated quality control and cell identification of droplet-based single-cell data using dropkick. Genome Res. https://doi.org/10.1101/gr.271908.120
Lakkis J, Wang D, Zhang Y, Hu G, Wang K, Pan H, Ungar L, Reilly MP, Li X, Li M. 2021. A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics. Genome Res. https://doi.org/10.1101/gr.271874.120
Erdmann-Pham DD, Fischer J, Hong J, Song YS. 2021. A likelihood-based deconvolution of bulk gene expression data using single-cell references. Genome Res. https://doi.org/10.1101/gr.272344.120
Wang J, Roeder K, Devlin B. 2021a. Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data. Genome Res. https://doi.org/10.1101/gr.268722.120
Gao Y, Li L, Amos CI, Li W. 2021. Analysis of alternative polyadenylation from single-cell RNA-seq using scDaPars reveals cell subpopulations invisible to gene expression. Genome Res. https://doi.org/10.1101/gr.271346.120
Alghamdi N, ChangW, Dang P, Lu X, Wan C, Gampala S, Huang Z, Wang J, Ma Q, Zang Y, et al. 2021. A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data. Genome Res. https://doi.org/10.1101/gr.271205.120
Baker BM, Mokashi SS, Shankar V, Hatfield JS, Hannah RC, Mackay TFC, Anholt RRH. 2021. The Drosophila brain on cocaine at single-cell resolution. Genome Res. https://doi.org/10.1101/gr.268037.120
Slaidina M, Gupta S, Banisch TU, Lehmann R. 2021. A single-cell atlas reveals unanticipated cell type complexity in Drosophila ovaries. Genome Res. https://doi.org/10.1101/gr.274340.120
Durham TJ, Daza RM, Gevirtzman L, Cusanovich DA, Bolonduro O, Noble WS, Shendure J, Waterston RH. 2021. Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes. Genome Res. https://doi.org/10.1101/gr.271791.120
