Literature DB >> 31918114

The emergence and promise of single-cell temporal-omics approaches.

Alex R Lederer1, Gioele La Manno2.   

Abstract

Single-cell transcriptomics enables the measurement of gene expression in complex biological systems at the resolution of individual cells. Multivariate analysis of single-cell data helps describe the variation in expression accompanying cellular processes during embryonic development, disease progression and in response to stimuli. Likewise, new methods have extended the possibilities of single-cell analysis by measuring the transcriptome while simultaneously capturing information on lineage or past molecular events. These emerging approaches have the common strategy of querying a static snapshot for information related to different temporal stages. Single-cell temporal-omics methods open new possibilities such as extrapolating the future or correlating past events to present gene expression. We highlight advancements in the single-cell field, describe novel toolkits for investigation, and consider the potential impact of temporal-omics approaches for the study of disease progression.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Year:  2020        PMID: 31918114     DOI: 10.1016/j.copbio.2019.12.005

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  11 in total

1.  TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics.

Authors:  Alexander Tong; Jessie Huang; Guy Wolf; David van Dijk; Smita Krishnaswamy
Journal:  Proc Mach Learn Res       Date:  2020-07

Review 2.  Tools and Concepts for Interrogating and Defining Cellular Identity.

Authors:  Kara L McKinley; David Castillo-Azofeifa; Ophir D Klein
Journal:  Cell Stem Cell       Date:  2020-05-07       Impact factor: 24.633

Review 3.  The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues.

Authors:  Joseph Swift; Kathleen Greenham; Joseph R Ecker; Gloria M Coruzzi; C Robertson McClung
Journal:  Plant J       Date:  2021-12-06       Impact factor: 7.091

Review 4.  RNA velocity-current challenges and future perspectives.

Authors:  Volker Bergen; Ruslan A Soldatov; Peter V Kharchenko; Fabian J Theis
Journal:  Mol Syst Biol       Date:  2021-08       Impact factor: 11.429

5.  ACME dissociation: a versatile cell fixation-dissociation method for single-cell transcriptomics.

Authors:  Helena García-Castro; Nathan J Kenny; Marta Iglesias; Patricia Álvarez-Campos; Vincent Mason; Anamaria Elek; Anna Schönauer; Victoria A Sleight; Jakke Neiro; Aziz Aboobaker; Jon Permanyer; Manuel Irimia; Arnau Sebé-Pedrós; Jordi Solana
Journal:  Genome Biol       Date:  2021-04-08       Impact factor: 17.906

Review 6.  Connecting past and present: single-cell lineage tracing.

Authors:  Cheng Chen; Yuanxin Liao; Guangdun Peng
Journal:  Protein Cell       Date:  2022-04-19       Impact factor: 15.328

7.  RNA-sequencing and microarray data mining revealing: the aberrantly expressed mRNAs were related with a poor outcome in the triple negative breast cancer patients.

Authors:  Hongjun Fei; Songchang Chen; Chenming Xu
Journal:  Ann Transl Med       Date:  2020-03

8.  Preprocessing choices affect RNA velocity results for droplet scRNA-seq data.

Authors:  Charlotte Soneson; Avi Srivastava; Rob Patro; Michael B Stadler
Journal:  PLoS Comput Biol       Date:  2021-01-11       Impact factor: 4.475

9.  Molecular profiling of stem cell-derived retinal pigment epithelial cell differentiation established for clinical translation.

Authors:  Sandra Petrus-Reurer; Alex R Lederer; Laura Baqué-Vidal; Iyadh Douagi; Belinda Pannagel; Irina Khven; Monica Aronsson; Hammurabi Bartuma; Magdalena Wagner; Andreas Wrona; Paschalis Efstathopoulos; Elham Jaberi; Hanni Willenbrock; Yutaka Shimizu; J Carlos Villaescusa; Helder André; Erik Sundstrӧm; Aparna Bhaduri; Arnold Kriegstein; Anders Kvanta; Gioele La Manno; Fredrik Lanner
Journal:  Stem Cell Reports       Date:  2022-06-14       Impact factor: 7.294

Review 10.  How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data.

Authors:  Lea D Schlieben; Holger Prokisch; Vicente A Yépez
Journal:  Front Mol Biosci       Date:  2021-06-01
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