Computational approaches in rheumatic diseases – Deciphering complex spatio-temporal cell interactions Article Swipe
C. Hegarty
,
Nuno Neto
,
Paul A. Cahill
,
Achilleas Floudas
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.csbj.2023.08.005
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.csbj.2023.08.005
Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accelerated primarily due to the utilisation of bulk and single cell RNA sequencing. Particularly the capacity to generate cell-cell interaction networks could reveal evidence of previously unappreciated processes leading to disease. However, there is currently a lack of universal nomenclature as a result of varied experimental and technological approaches that discombobulates the study of synovial inflammation. While spatial transcriptomic analysis that combines anatomical information with transcriptomic data of synovial tissue biopsies promises to provide more insights into disease pathogenesis, in vitro functional assays with single-cell resolution will be required to validate current bioinformatic applications. In order to provide a comprehensive approach and translate experimental data to clinical practice, a combination of clinical and molecular data with machine learning has the potential to enhance patient stratification and identify individuals at risk of arthritis that would benefit from early therapeutic intervention. This review aims to provide a comprehensive understanding of the effect of computational approaches in deciphering synovial inflammation pathogenesis and discuss the impact that further experimental and novel computational tools may have on therapeutic target identification and drug development.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.csbj.2023.08.005
- http://www.csbj.org/article/S2001037023002805/pdf
- OA Status
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- Cited By
- 4
- References
- 177
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385615069
All OpenAlex metadata
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https://openalex.org/W4385615069Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.csbj.2023.08.005Digital Object Identifier
- Title
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Computational approaches in rheumatic diseases – Deciphering complex spatio-temporal cell interactionsWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
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C. Hegarty, Nuno Neto, Paul A. Cahill, Achilleas FloudasList of authors in order
- Landing page
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https://doi.org/10.1016/j.csbj.2023.08.005Publisher landing page
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https://www.csbj.org/article/S2001037023002805/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.csbj.org/article/S2001037023002805/pdfDirect OA link when available
- Concepts
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Computational biology, Rheumatoid arthritis, Inflammation, Pathogenesis, Disease, Medicine, Arthritis, Bioinformatics, Transcriptome, Psoriatic arthritis, Stromal cell, Immunology, Biology, Pathology, Gene, Gene expression, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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