Single cell genomics: from phenomenology to mechanism

By Amos Tanay, Aviv Regev. Nature, 2017

705 citations recorded by the source corpus.

Three of the most fundamental questions in biology are how individual cells differentiate to form tissues, how tissues function in a coordinated and flexible fashion and which gene regulatory mechanisms support these processes. Single-cell genomics is opening up new ways to tackle these questions by combining the comprehensive nature of genomics with the microscopic resolution that is required to describe complex multicellular systems. Initial single-cell genomic studies provided a remarkably rich phenomenology of heterogeneous cellular states, but transforming observational studies into models of dynamics and causal mechanisms in tissues poses fresh challenges and requires stronger integration of theoretical, computational and experimental frameworks.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6c39174febcec75b71adc33114d3ffd37b48344a

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Citations in the corpus

Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, background): “Single-cell RNA sequencing (scRNA-seq) is an increasingly popular tool that opens the way for studying cellular heterogeneity at a high resolution [1], thus shedding new light on fundamental questions in areas such as development [2], autoimmunity [3], and cancer [4].”
MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions (2019, methodology): “These phenomenological observations are used for creating cell type atlases and as a starting point for analysis of different cellular processes, including differentiation, cell cycle, and response to stimuli [1 – 9] (reviewed in [10]).”
Background fluorescence and spreading error are major contributors of variability in high‐dimensional flow cytometry data visualization by t‐distributed stochastic neighboring embedding (2018, background): “THE advent of powerful single-cell technologies, such as single-cell RNA-seq (scRNAseq) and cytometry by time-of-flight (CyTOF), and the recent improvement of polychromatic flow cytometry to measure up to 30 parameters simultaneously, required the development of new tools to visualize complex multid”
Systems Biology of Cancer Metastasis (2019, neutral): “…useful data analysis techniques for single-cell genomics, transcriptomics, and proteomics, and the subsequent incorporation of these data into predictive computational models is now beginning(Liu et al., 2017a; Marcotte et al., 2016; Setty et al., 2016; Tanay and Regev, 2017; Villani et al., 2017).”
Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues (2023, neutral): “S10b, left lower panel), we found a PSTS graph that appearedtoaccuratelyre ﬂ ecttherelationship(s)betweenDCISandIDC clones (sub-clusters 6, 13, 16); this could be due to the edges from sub-clusters 6 and 16 in the initial graphs before optimisation (i.e., PSTS distance) already being stable, but…”
Comprehensive integration of single-cell data (2018, neutral): “Our approach consists of four broad steps, as explained in detail below: (1) data preprocessing and feature selection, (2) dimension reduction and identification of "anchor" correspondences between datasets, (3) filtering, scoring, and weighting of anchor correspondences, (4) data matrix correction,”
A Single-Cell Atlas of Cell Types, States, and Other Transcriptional Patterns from Nine Regions of the Adult Mouse Brain (2018, background): “The systematic recognition of more-subtle (yet consistent) patterns of variation among cells of the same class presents formidable analytical challenges – particularly when these distinctions are to be made in systematic, inductive (unsupervised), data-driven ways (Mayer et al., 2015; Satija et al.,”
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models (2019, positive): “Recent technological improvements in microfluidics and low volume sample handling (Tanay & Regev, 2017) have enabled the emergence of single-cell transcriptomics (Macosko et al , 2017; Zheng et al , 2017) as a popular tool for analyzing biological systems (Patel et al , 2014; Gaublomme et al ,…”
Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer (2019, background): “However, transforming observational studies from single cell analysis into mechanical understanding of cancer evolution poses many challenges (Tanay and Regev, 2017).”
A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes (2017, background): “Single-cell RNA sequencing (scRNA-Seq) is a revolutionary technology, which allows studying fundamental biological questions that were previously out of reach [1, 2].”
stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues (2020, methodology): “Run Louvain or K-means clustering 6.”
Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain (2018, background): “Inference of Cell Types and States Using Independent Components Analysis The recognition of subtle variation among cells of the same class presents a formidable analytical challenge for unsupervised analysis (Mayer et al., 2015; Satija et al., 2015; Shekhar et al., 2016; Tanay and Regev, 2017; Tasic”
Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer (2017, background): “Recent advances in single-cell genomics provide an avenue to explore genetic and functional heterogeneity at a cellular resolution (Navin, 2015; Tanay and Regev, 2017).”
Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics (2018, background): “Single cell genomics offers enormous promise to transform our understanding of heterogeneous processes and to reconstruct unsupervised taxonomies of cell types [1, 2].”