ReviewMolecules and cells2023
Single-Cell Genomics for Investigating Pathogenesis of Inflammatory Diseases.
Review in Molecules and cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 13 citations in OpenAlex.
- Type 1 Diabetes Mellitus Pathogenesis: Mechanisms, Early Diagnostic Strategies, and Emerging Therapeutic Approaches.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026Review
- Roles of PEG10 in cancer and neurodegenerative disorder (Review).Oncology reports · 2025Review
- Inflammatory Cell Interactions in the Rotator Cuff Microenvironment: Insights From Single-Cell Sequencing.International journal of genomics · 2025Review
- Differential pathological changes in colon microenvironments in acute and chronic mouse models of inflammatory bowel disease.Animal cells and systems · 2025Article
- Molecular profiling and therapeutic tailoring to address disease heterogeneity in systemic lupus erythematosus.Clinical and experimental medicine · 2024Review
- Comparative Assessment of Risk and Turn-Around Time between Sequence-Based Typing and Next-Generation Sequencing for HLA Typing.Diagnostics (Basel, Switzerland) · 2024Article
- Unlocking biological mechanisms with integrative functional genomics approaches.Molecules and cells · 2024Review
- Psoriasis and Psoriatic Arthritis-Associated Genes, Cytokines, and Human Leukocyte Antigens.Medicina (Kaunas, Lithuania) · 2024Review
- Adipose tissue macrophage heterogeneity in the single-cell genomics era.Molecules and cells · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent technical advances have enabled unbiased transcriptomic and epigenetic analysis of each cell, known as "single-cell analysis". Single-cell analysis has a variety of technical approaches to investigate the state of each cell, including mRNA levels (transcriptome), the immune repertoire (immune repertoire analysis), cell surface proteins (surface proteome analysis), chromatin accessibility (epigenome), and accordance with genome variants (eQTLs; expression quantitative trait loci). As an effective tool for investigating robust immune responses in coronavirus disease 2019 (COVID-19), many researchers performed single-cell analysis to capture the diverse, unbiased immune cell activation and differentiation. Despite challenges elucidating the complicated immune microenvironments of chronic inflammatory diseases using existing experimental methods, it is now possible to capture the simultaneous immune features of different cell types across inflamed tissues using various single-cell tools. In this review, we introduce patient-based and experimental mouse model research utilizing single-cell analyses in the field of chronic inflammatory diseases, as well as multi-organ atlas targeting immune cells.
Indexed as
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.