Evidence map›Paper›PMID 32265898›Full record

ArticleFrontiers in immunology2020

Deep Phenotyping of Urinary Leukocytes by Mass Cytometry Reveals a Leukocyte Signature for Early and Non-Invasive Prediction of Response to Treatment in Active Lupus Nephritis.

Martina Bertolo, Sabine Baumgart, Pawel Durek, Anette Peddinghaus, Henrik Mei, Thomas Rose, Philipp Enghard, Andreas Grützkau

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 31 citations in OpenAlex.

  1. Review
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  10. Tissue-resident memory T cells in the kidney.Seminars in immunopathology · 2022
    Review
  11. Review
  12. Article
  13. Immune-Related Urine Biomarkers for the Diagnosis of Lupus Nephritis.International journal of molecular sciences · 2021
    Review
  14. Review
  15. eLife · 2021
    Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Martina BertoloDepartment of Nephrology, Charité Universitätsmedizin Berlin, Berlin, Germany.
Sabine BaumgartDeutsches Rheuma-Forschungszentrum Berlin (DRFZ), a Leibniz-Institute, Berlin, Germany.
Pawel DurekDeutsches Rheuma-Forschungszentrum Berlin (DRFZ), a Leibniz-Institute, Berlin, Germany.
Anette PeddinghausDeutsches Rheuma-Forschungszentrum Berlin (DRFZ), a Leibniz-Institute, Berlin, Germany.
Henrik MeiDeutsches Rheuma-Forschungszentrum Berlin (DRFZ), a Leibniz-Institute, Berlin, Germany.
Thomas RoseDepartment of Rheumatology and Clinical Immunology, Charité Universitätsmedizin Berlin, Berlin, Germany.
Philipp EnghardDepartment of Nephrology, Charité Universitätsmedizin Berlin, Berlin, Germany.
Andreas GrützkauDeutsches Rheuma-Forschungszentrum Berlin (DRFZ), a Leibniz-Institute, Berlin, Germany.
German Rheumatism Research Centre · DECharité - Universitätsmedizin Berlin · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-invasive biomarkers are necessary for diagnosis and monitoring disease activity in lupus nephritis (LN) to circumvent risks and limitations of renal biopsies. To identify new non-invasive cellular biomarkers in the urine sediment of LN patients, which may reflect kidney inflammation and can be used to predict treatment outcome, we performed in-depth urinary immune cell profiling by mass cytometry. We established a mass cytometric workflow to comparatively analyze the cellular composition of urine and peripheral blood (PB) in 13 patients with systemic lupus erythematosus (SLE) with active, biopsy-proven proliferative LN. Clinical and laboratory data were collected at the time of sampling and 6 months after induction of therapy in order to evaluate the clinical response of each patient. Six patients with different acute inflammatory renal diseases were included as comparison group. Leukocyte phenotypes and composition differed significantly between urine and paired PB samples. In urine, neutrophils and monocytes/macrophages were identified as the most prominent cell populations comprising together about 30%-83% of nucleated cells, while T and B lymphocytes, eosinophils, and natural killer (NK) cells were detectable at frequencies of <10% each. The majority of urinary T cells showed phenotypical characteristics of activated effector memory T cells (EM) as indicated by the co-expression of CD38 and CD69 - a phenotype that was not detectable in PB. Kidney inflammation was also reflected by tissue-imprinted macrophages, which phenotypically differed from PB monocytes by an increased expression of HLA-DR and CD11c. The presence of activated urinary T cells and macrophages could be used for differential diagnosis of proliferative LN forms and other renal pathologies. Most interestingly, the amount of EM in the urine sediment could be used as a biomarker to stratify LN patients in terms of response to induction therapy. Deep immunophenotypic profiling of urinary cells in LN allowed us to identify a signature of activated T cells and macrophages, which appear to reflect leukocytic infiltrates in the kidney. This explorative study has not only confirmed but also extended the knowledge about urinary cells as a future non-invasive biomarker platform for diagnosis and precision medicine in inflammatory renal diseases.

Indexed as

AdultBiomarkersBiopsyDiagnosis, DifferentialDisease ProgressionEarly DiagnosisFemaleHumansImmunologic MemoryImmunophenotypingKidneyLupus Erythematosus, SystemicLupus NephritisLymphocyte ActivationLymphocyte CountMacrophagesBiomarkerslupus nephritislupus nephritis biomarkermass cytometrysystemic lupus – erythematosusurinary leukocytes

Identifiers

PMID32265898
PMCPMC7105605
OpenAlexW3013776576

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.