ReviewKidney3602021
Myeloid Heterogeneity in Kidney Disease as Revealed through Single-Cell RNA Sequencing.
Review in Kidney360, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 7 citations in OpenAlex.
- The role of interleukin-4 in acute kidney injury and chronic kidney disease: a literature review.BMC nephrology · 2026Review
- Novel insights into kidney disease: the scRNA-seq and spatial transcriptomics approaches: a literature review.BMC nephrology · 2025Review
- Identification of Shared Biomarkers in Chronic Kidney Disease and Diabetic Nephropathy Using Single-Cell Sequencing.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Single nuclei transcriptomics delineates complex immune and kidney cell interactions contributing to kidney allograft fibrosis.Kidney international · 2023Article
- Cell-type-specific molecular characterization of cells from circulation and kidney in IgA nephropathy with nephrotic syndrome.Frontiers in immunology · 2023Article
- Advanced methods and novel biomarkers in autoimmune diseases ‑ a review of the recent years progress in systemic lupus erythematosus.Frontiers in medicine · 2023Review
- Single-cell transcriptomics: A new tool for studying diabetic kidney disease.Frontiers in physiology · 2022Review
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
Abstract
Kidney disease represents a global health burden of increasing prevalence and is an independent risk factor for cardiovascular disease. Myeloid cells are a major cellular compartment of the immune system; they are found in the healthy kidney and in increased numbers in the damaged and/or diseased kidney, where they act as key players in the progression of injury, inflammation, and fibrosis. They possess enormous plasticity and heterogeneity, adopting different phenotypic and functional characteristics in response to stimuli in the local milieu. Although this inherent complexity remains to be fully understood in the kidney, advances in single-cell genomics promise to change this. Specifically, single-cell RNA sequencing (scRNA-seq) has had a transformative effect on kidney research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput, and subsequent generation of cell atlases. Moving forward, combining scRNA- and single-nuclear RNA-seq with greater-resolution spatial transcriptomics will allow spatial mapping of kidney disease of varying etiology to further reveal the patterning of immune cells and nonimmune renal cells. This review summarizes the roles of myeloid cells in kidney health and disease, the experimental workflow in currently available scRNA-seq technologies, and published findings using scRNA-seq in the context of myeloid cells and the kidney.
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.