Multi-Cellular Immunological Interactions Associated With COVID-19 Infections

Jitender S. Verma, Claudia R. Libertin, Yash Gupta, Geetika Khanna, Rohit Kumar, Balvinder S. Arora, Loveneesh Krishna, Folorunso O. Fasina, James B. Hittner, Athos Antoniades, Marc H.V. van Regenmortel, Ravi Durvasula, Prakasha Kempaiah, Ariel L. Rivas

Research output: Contribution to journalArticlepeer-review


To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a “reductionist” one, which analyzes each cell type separately, and (ii) a “non-reductionist” method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern’s immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.

Original languageEnglish (US)
Article number794006
JournalFrontiers in immunology
StatePublished - Feb 24 2022


  • COVID-19
  • biological complexity
  • cutoff-free
  • error prevention
  • multi-cellularity
  • pattern recognition
  • personalized medicine
  • personalized methods

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology


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