Dual spatially resolved transcriptomics for human host–pathogen colocalization studies in FFPE tissue sections

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Technologies to study localized host–pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host–pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.

\mentary Information

The online version contains supplementary material available at 10.1186/s13059-023-03080-y.

Related collections

Most cited references 71

Record: found
Abstract: found
Article: not found

Proteomics. Tissue-based map of the human proteome.

Mathias Uhlén, Linn Fagerberg, Björn Hallström … (2015)

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.

0 comments Cited 4004 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Integrated analysis of multimodal single-cell data

Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen … (2021)

Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

0 comments Cited 3989 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Fast, sensitive, and accurate integration of single cell data with Harmony

Ilya Korsunsky, Nghia Millard, Jean Fan … (2019)

The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.

0 comments Cited 2284 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Stefania Giacomello:

ORCID: http://orcid.org/0000-0003-0738-1574

stefania.giacomello@scilifelab.se

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 19 October 2023

Publication date PMC-release: 19 October 2023

Publication date Collection: 2023

Volume: 24

Electronic Location Identifier: 237

Affiliations

[1 ]GRID grid.5037.1, ISNI 0000000121581746, Department of Gene Technology, , KTH Royal Institute of Technology, SciLifeLab, ; Stockholm, Sweden

[2 ]Department of Cell and Molecular Biology, Karolinska Institutet, ( https://ror.org/056d84691) Stockholm, Sweden

[3 ]2nd Department of Pathology, Semmelweis University, ( https://ror.org/01g9ty582) Budapest, Hungary

[4 ]10X Genomics, ( https://ror.org/01sxvbm95) Pleasanton, CA USA

[5 ]Department of Laboratory Medicine, Karolinska Institutet, ( https://ror.org/056d84691) Stockholm, Sweden

[6 ]Department of Oncology-Pathology, Karolinska Institutet, ( https://ror.org/056d84691) 17177 Stockholm, Sweden

[7 ]GRID grid.517451.3, ISNI 0000 0000 8775 5756, Center for Regenerative Therapies Dresden (CRTD), ; TU Dresden, Dresden, Germany

[8 ]Universitätsmedizin Göttingen, Institute of Pharmacology and Toxicology, ( https://ror.org/021ft0n22) Göttingen, Germany

Author information

Stefania Giacomello http://orcid.org/0000-0003-0738-1574

Article

Publisher ID: 3080

DOI: 10.1186/s13059-023-03080-y

PMC ID: 10588020

PubMed ID: 37858234

SO-VID: ba96b76c-f382-468b-86d3-970e2445cb81

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 15 June 2022

Date accepted : 2 October 2023

Funding

Funded by: Royal Institute of Technology

Open Access :

Open access funding provided by Royal Institute of Technology.

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: spatial transcriptomics,host–pathogen interactions,colocalization analysis,formalin-fixed paraffin-embedded (ffpe) tissues

Data availability:

ScienceOpen disciplines: Genetics

Keywords: spatial transcriptomics, host–pathogen interactions, colocalization analysis, formalin-fixed paraffin-embedded (ffpe) tissues

Dual spatially resolved transcriptomics for human host–pathogen colocalization studies in FFPE tissue sections

Read this article at

Abstract

\mentary Information

Related collections

Novel Coronavirus Disease COVID-19

Most cited references 71

Proteomics. Tissue-based map of the human proteome.

Integrated analysis of multimodal single-cell data

Fast, sensitive, and accurate integration of single cell data with Harmony

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 237

Most referenced authors 3,075