Literature DB >> 35273392

Museum of spatial transcriptomics.

Lambda Moses1, Lior Pachter2,3.   

Abstract

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/ .
© 2022. Springer Nature America, Inc.

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Year:  2022        PMID: 35273392     DOI: 10.1038/s41592-022-01409-2

Source DB:  PubMed          Journal:  Nat Methods        ISSN: 1548-7091            Impact factor:   28.547


  124 in total

1.  Localisation of cellular globin messenger RNA by in situ hybridisation to complementary DNA.

Authors:  P R Harrison; D Conkie; J Paul; K Jones
Journal:  FEBS Lett       Date:  1973-05-15       Impact factor: 4.124

2.  Formation and detection of RNA-DNA hybrid molecules in cytological preparations.

Authors:  J G Gall; M L Pardue
Journal:  Proc Natl Acad Sci U S A       Date:  1969-06       Impact factor: 11.205

3.  RNA-DNA hybrids at the cytological level.

Authors:  H A John; M L Birnstiel; K W Jones
Journal:  Nature       Date:  1969-08-09       Impact factor: 49.962

Review 4.  The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing.

Authors:  Ed Lein; Lars E Borm; Sten Linnarsson
Journal:  Science       Date:  2017-10-06       Impact factor: 47.728

5.  Spatially Resolved Transcriptomes-Next Generation Tools for Tissue Exploration.

Authors:  Michaela Asp; Joseph Bergenstråhle; Joakim Lundeberg
Journal:  Bioessays       Date:  2020-05-04       Impact factor: 4.345

Review 6.  Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics.

Authors:  Jie Liao; Xiaoyan Lu; Xin Shao; Ling Zhu; Xiaohui Fan
Journal:  Trends Biotechnol       Date:  2020-06-03       Impact factor: 19.536

7.  High resolution detection of DNA-RNA hybrids in situ by indirect immunofluorescence.

Authors:  G T Rudkin; B D Stollar
Journal:  Nature       Date:  1977-02-03       Impact factor: 49.962

8.  Immunological method for mapping genes on Drosophila polytene chromosomes.

Authors:  P R Langer-Safer; M Levine; D C Ward
Journal:  Proc Natl Acad Sci U S A       Date:  1982-07       Impact factor: 11.205

9.  A non-radioactive in situ hybridization method for the localization of specific RNAs in Drosophila embryos reveals translational control of the segmentation gene hunchback.

Authors:  D Tautz; C Pfeifle
Journal:  Chromosoma       Date:  1989-08       Impact factor: 4.316

Review 10.  Single-cell genomics and spatial transcriptomics: Discovery of novel cell states and cellular interactions in liver physiology and disease biology.

Authors:  Antonio Saviano; Neil C Henderson; Thomas F Baumert
Journal:  J Hepatol       Date:  2020-06-10       Impact factor: 25.083

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  15 in total

Review 1.  What is a cell type and how to define it?

Authors:  Hongkui Zeng
Journal:  Cell       Date:  2022-07-21       Impact factor: 66.850

Review 2.  The emerging landscape of spatial profiling technologies.

Authors:  Jeffrey R Moffitt; Emma Lundberg; Holger Heyn
Journal:  Nat Rev Genet       Date:  2022-07-20       Impact factor: 59.581

Review 3.  A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.

Authors:  Jiawen Chen; Weifang Liu; Tianyou Luo; Zhentao Yu; Minzhi Jiang; Jia Wen; Gaorav P Gupta; Paola Giusti; Hongtu Zhu; Yuchen Yang; Yun Li
Journal:  Brief Bioinform       Date:  2022-07-18       Impact factor: 13.994

4.  Light-Seq: light-directed in situ barcoding of biomolecules in fixed cells and tissues for spatially indexed sequencing.

Authors:  Jocelyn Y Kishi; Ninning Liu; Emma R West; Kuanwei Sheng; Jack J Jordanides; Matthew Serrata; Constance L Cepko; Sinem K Saka; Peng Yin
Journal:  Nat Methods       Date:  2022-10-10       Impact factor: 47.990

5.  Spatially informed cell-type deconvolution for spatial transcriptomics.

Authors:  Ying Ma; Xiang Zhou
Journal:  Nat Biotechnol       Date:  2022-05-02       Impact factor: 68.164

6.  RNA velocity unraveled.

Authors:  Gennady Gorin; Meichen Fang; Tara Chari; Lior Pachter
Journal:  PLoS Comput Biol       Date:  2022-09-12       Impact factor: 4.779

7.  spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data.

Authors:  Brenda Pardo; Abby Spangler; Lukas M Weber; Stephanie C Page; Stephanie C Hicks; Andrew E Jaffe; Keri Martinowich; Kristen R Maynard; Leonardo Collado-Torres
Journal:  BMC Genomics       Date:  2022-06-10       Impact factor: 4.547

Review 8.  Computational solutions for spatial transcriptomics.

Authors:  Iivari Kleino; Paulina Frolovaitė; Tomi Suomi; Laura L Elo
Journal:  Comput Struct Biotechnol J       Date:  2022-09-01       Impact factor: 6.155

Review 9.  Cell-by-Cell: Unlocking Lung Cancer Pathogenesis.

Authors:  Ansam Sinjab; Zahraa Rahal; Humam Kadara
Journal:  Cancers (Basel)       Date:  2022-07-14       Impact factor: 6.575

Review 10.  Statistical and machine learning methods for spatially resolved transcriptomics data analysis.

Authors:  Zexian Zeng; Yawei Li; Yiming Li; Yuan Luo
Journal:  Genome Biol       Date:  2022-03-25       Impact factor: 13.583

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