Single-cell profiling of the human decidual immune microenvironment in patients with recurrent pregnancy loss

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Abstract

Maintaining homeostasis of the decidual immune microenvironment at the maternal–fetal interface is essential for placentation and reproductive success. Although distinct decidual immune cell subpopulations have been identified under normal conditions, systematic understanding of the spectrum and heterogeneity of leukocytes under recurrent miscarriage in human deciduas remains unclear. To address this, we profiled the respective transcriptomes of 18,646 primary human decidual immune cells isolated from patients with recurrent pregnancy loss (RPL) and healthy controls at single-cell resolution. We discovered dramatic differential distributions of immune cell subsets in RPL patients compared with the normal decidual immune microenvironment. Furthermore, we found a subset of decidual natural killer (NK) cells that support embryo growth were diminished in proportion due to abnormal NK cell development in RPL patients. We also elucidated the altered cellular interactions between the decidual immune cell subsets in the microenvironment and those of the immune cells with stromal cells and extravillous trophoblast under disease state. These results provided deeper insights into the RPL decidual immune microenvironment disorder that are potentially applicable to improve the diagnosis and therapeutics of this disease.

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Fast gapped-read alignment with Bowtie 2.

Ben Langmead, Steven L Salzberg (2022)

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

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BEDTools: a flexible suite of utilities for comparing genomic features

Aaron Quinlan, Ira Hall (2010)

Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Damian Szklarczyk, Annika L Gable, David Lyon … (2018)

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

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Author and article information

Contributors

Kun Qu:

ORCID: http://orcid.org/0000-0002-5555-8437

qukun@ustc.edu.cn

Journal

Journal ID (nlm-ta): Cell Discov

Journal ID (iso-abbrev): Cell Discov

Title: Cell Discovery

Publisher: Springer Singapore (Singapore )

ISSN (Electronic): 2056-5968

Publication date (Electronic): 4 January 2021

Publication date PMC-release: 4 January 2021

Publication date Collection: 2021

Volume: 7

Electronic Location Identifier: 1

Affiliations

[1 ]GRID grid.59053.3a, ISNI 0000000121679639, Department of Oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, Division of Life Sciences and Medicine, , University of Science and Technology of China, ; Hefei, Anhui 230027 China

[2 ]HanGene Biotech, Xiaoshan Innovation Polis, Hangzhou, Zhejiang 311200 China

[3 ]GRID grid.59053.3a, ISNI 0000000121679639, The First Affiliated Hospital of USTC, , University of Science and Technology of China, ; Hefei, Anhui 230021 China

[4 ]GRID grid.59053.3a, ISNI 0000000121679639, CAS Center for Excellence in Molecular Cell Sciences, The CAS Key Laboratory of Innate Immunity and Chronic Disease, , University of Science and Technology of China, ; Hefei, Anhui 230027 China

[5 ]GRID grid.59053.3a, ISNI 0000000121679639, School of Data Science, , University of Science and Technology of China, ; Hefei, Anhui 230027 China

Author information

Chuang Guo http://orcid.org/0000-0002-3912-0793

Kun Qu http://orcid.org/0000-0002-5555-8437

Article

Publisher ID: 236

DOI: 10.1038/s41421-020-00236-z

PMC ID: 7779601

PubMed ID: 33390590

SO-VID: 59448311-959a-4a12-a506-129e377d2c79

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 4 September 2020

Date accepted : 23 November 2020

Funding

Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);

Award ID: 81788101

Award ID: 31970858

Award ID: 31771428

Award ID: 91940306

Award ID: 91640113

Award ID: 31700796

Award ID: 81871479

Award Recipient : Chuang Guo Jun Lin Kun Qu

Funded by: the National Key R&D Program of China 2017YFA0102900 the Fundamental Research Funds for the Central Universities YD2070002019

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Keywords: immunology,gene expression profiling

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Keywords: immunology, gene expression profiling

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Single-cell profiling of the human decidual immune microenvironment in patients with recurrent pregnancy loss

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Most cited references 55

Fast gapped-read alignment with Bowtie 2.

BEDTools: a flexible suite of utilities for comparing genomic features

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

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Cited by 102

Most referenced authors 2,450