ReviewHuman molecular genetics2016
Genetics and immunity in the era of single-cell genomics.
Review in Human molecular genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Oxidative Stress and Epigenetics: miRNA Involvement in Rare Autoimmune Diseases.Antioxidants (Basel, Switzerland) · 2023Review
- Genetic Susceptibility to Fungal Infections.Advanced biomedical research · 2023Review
- Multi-Dimensional Gene Regulation in Innate and Adaptive Lymphocytes: A View From Regulomes.Frontiers in immunology · 2021Review
- Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy.Frontiers in cell and developmental biology · 2021Review
- Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing.Frontiers in genetics · 2021Article
- SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq.Computational and structural biotechnology journal · 2021Article
- The impact of single-cell RNA sequencing on understanding the functional organization of the immune system.Briefings in functional genomics · 2018Review
- Impact of sequencing depth and read length on single cell RNA sequencing data of T cells.Scientific reports · 2017Article
- Leukocyte diversity in resolving and nonresolving mechanisms of cardiac remodeling.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2017Review
- Computational approaches for interpreting scRNA-seq data.FEBS letters · 2017Review
- New Directions for Natural Killer T Cells in the Immunotherapy of Cancer.Frontiers in immunology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent developments in the field of single-cell genomics (SCG) are changing our understanding of how functional phenotypes of cell populations emerge from the behaviour of individual cells. Some of the applications of SCG include the discovery of new gene networks and novel cell subpopulations, fine mapping of transcription kinetics, and the relationships between cell clonality and their functional phenotypes. Immunology is one of the fields that is benefiting the most from such advancements, providing us with completely new insights into mammalian immunity. In this review, we start by covering new immunological insights originating from the use of single-cell genomic tools, specifically single-cell RNA-sequencing. Furthermore, we discuss how new genetic study designs are starting to explain inter-individual variation in the immune response. We conclude with a perspective on new multi-omics technologies capable of integrating several readouts from the same single cell and how such techniques might push our biological understanding of mammalian immunity to a new level.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.