Single cell transcriptome signatures and cell-cell interactions associated with sarcoidosis in lung immune cell populations - Scorecard - MDSpire

Single cell transcriptome signatures and cell-cell interactions associated with sarcoidosis in lung immune cell populations

  • By

  • Camille M. Moore

  • Shu-Yi Liao

  • Cheyret Wood

  • Arunangshu Sarkar

  • Jonathan H. Cardwell

  • Kristyn MacPhail

  • Margaret M. Mroz

  • Christina Riley

  • Kara Mould

  • Clara I. Restrepo

  • Li Li

  • Lisa A. Maier

  • Ivana V. Yang

  • May 26, 2026

  • 0 min

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Clinical Scorecard: Transcriptomic Profiles and Intercellular Interactions in Lung Immune Cells Related to Sarcoidosis

At a Glance

CategoryDetail
ConditionSarcoidosis
Key MechanismsImmune dysregulation and chronic inflammation, primarily affecting the lungs.
Target PopulationPatients with pulmonary sarcoidosis and healthy controls.
Care SettingNational Jewish Health

Key Highlights

  • Significant differential expression of genes in macrophages associated with sarcoidosis.
  • Reduction in B cell populations observed in sarcoidosis patients.
  • Distinct transcriptional alterations in CD4+ T cells.
  • Increased CD4+ T cell interactions in sarcoidosis patients.
  • Downregulation of LGALS9-CD45 signaling noted.

Guideline-Based Recommendations

Diagnosis

  • Diagnosis confirmed by tissue biopsy according to ATS/ERS criteria.

Management

  • Progressive disease defined by specific pulmonary function test declines and imaging changes.

Monitoring & Follow-up

  • Clinical features including acuity of presentation, organ involvement, and pulmonary function tests should be monitored.

Risks

  • Potential for tissue damage and fibrosis due to immune dysregulation.

Patient & Prescribing Data

Patients with progressive and non-progressive sarcoidosis.

Immunosuppressive treatment may be required for progressive cases.

Clinical Best Practices

  • Utilize single-cell RNA sequencing for detailed analysis of immune cell populations.
  • Integrate clinical phenotyping with molecular data for better understanding of disease progression.

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