Mapping Infant Immunity with Minimal Input: Integrative Single-cell and Multiomic Profiling
At birth, the neonatal immune system is abruptly confronted with a radically different environment, rich in microbial and environmental stimuli. Increasing evidence suggests that early-life immunity is not merely immature, but rather uniquely adapted to meet the distinct demands of this developmental window. During this period, the immune system is highly dynamic, undergoing rapid changes to accommodate the shifting external landscape. Understanding this complex and evolving immune landscape requires direct study in neonates. However, a major limitation in such investigations is the low total blood volume in neonates (~100 mL/kg), which restricts the amount that can be safely collected for research, particularly in extremely low birth weight (ELBW) or extremely premature infants. In our recent work, we address this limitation by demonstrating how high-dimensional immune profiling can be performed using minute volumes of neonatal blood. We introduce a set of optimized protocols for longitudinal sampling across early life and apply advanced techniques, including flow cytometry, proteomics, and single-cell RNA sequencing, to maximize the information obtained from minimal input. Together, these methods enable a comprehensive view of the transcriptomic and proteomic signatures of circulating immune cells in neonates, offering new insights into the unique trajectory of early-life immunity.
Authors
- Long Phan (ORCID: https://orcid.org/0000-0003-0980-7057)
- Oluwabunmi Olaloye (ORCID: https://orcid.org/0000-0002-9217-3333)
- Liza Konnikova (ORCID: https://orcid.org/0000-0003-4804-5497)
- Nina N. Brodsky (ORCID: https://orcid.org/0000-0003-2472-7397)
- Magdalena Coman
- Monisha Chakder
- Yi Cao
- Carlos R. Oliveira
- Kalida Gawon
Institutions
- Yale University (US)
- Yalova University (TR)
Publication Details
- Journal
- Journal of Visualized Experiments
- Published
- 2026-04-03
- DOI
- https://doi.org/10.3791/70279
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00