Single-cell transcriptomic characterization of immune cell responses to chemokines 9342

Abstract Description Chemokines are critical regulators of immune cell movement, making them promising therapeutic targets. However, a comprehensive understanding of how individual immune cell types respond to specific chemokines and the interplay between chemokines and cytokines remains incomplete. To address this gap, we created a single-cell RNA-sequencing-based dictionary to systematically measure cellular responses to 40 chemokines in mouse lymph nodes in vivo. We observed the expected chemotactic activities of various chemokines on immune cells. Furthermore, our analysis revealed that chemokine stimulation induced distinct gene signatures in diverse immune cells, revealing additional roles beyond cell trafficking. For example, CX3CL1 was found to upregulate Egr1, a key regulator of cell growth and differentiation. A cell-type-centric perspective further uncovered unidentified chemokine-driven polarization states. Integrating chemokine datasets with spatial transcriptomics datasets revealed tissue-specific chemokine functions, advancing our understanding of chemokine spatial dynamics. Furthermore, incorporating chemokine datasets into our Immune Response Enrichment Analysis (IREA) software enables simultaneous prediction of cytokine and chemokine activities on immune cells. This chemokine dictionary serves as a valuable resource for unraveling immune cell activities influenced by chemokines, paving the way for innovative immunotherapeutic strategies. Funding Sources Harvard School of Dental Medicine start-up funds Topic Categories Computational and Systems Immunology (COMP)

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Publication Details

Journal
The Journal of Immunology
Published
2025-11-01
DOI
https://doi.org/10.1093/jimmun/vkaf283.2756
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

Single-cell transcriptomic characterization of immune cell responses to chemokines 9342

Xi Lu, Ang Cui, Mei Zhou, Nicholas Lai et al.
The Journal of Immunology
Single-cell and spatial transcriptomics
article

Single-cell transcriptomic characterization of immune cell responses to chemokines 9342

Xi Lu, Ang Cui, Mei Zhou, Nicholas Lai, Chrispus Ngule, Brianna S. Yao, Shuqiang Li, Wesley S. Lu, Nehal Singh, McKayla Van Orden, Pooja Paresh Savla, Thomas Kunow
article en

Abstract

Abstract Description Chemokines are critical regulators of immune cell movement, making them promising therapeutic targets. However, a comprehensive understanding of how individual immune cell types respond to specific chemokines and the interplay between chemokines and cytokines remains incomplete. To address this gap, we created a single-cell RNA-sequencing-based dictionary to systematically measure cellular responses to 40 chemokines in mouse lymph nodes in vivo. We observed the expected chemotactic activities of various chemokines on immune cells. Furthermore, our analysis revealed that chemokine stimulation induced distinct gene signatures in diverse immune cells, revealing additional roles beyond cell trafficking. For example, CX3CL1 was found to upregulate Egr1, a key regulator of cell growth and differentiation. A cell-type-centric perspective further uncovered unidentified chemokine-driven polarization states. Integrating chemokine datasets with spatial transcriptomics datasets revealed tissue-specific chemokine functions, advancing our understanding of chemokine spatial dynamics. Furthermore, incorporating chemokine datasets into our Immune Response Enrichment Analysis (IREA) software enables simultaneous prediction of cytokine and chemokine activities on immune cells. This chemokine dictionary serves as a valuable resource for unraveling immune cell activities influenced by chemokines, paving the way for innovative immunotherapeutic strategies. Funding Sources Harvard School of Dental Medicine start-up funds Topic Categories Computational and Systems Immunology (COMP)

The Journal of ImmunologyVol. 214(Supplement_1)
Harvard University (US), Dana-Farber/Harvard Cancer Center (US)
Openalex Percentile: Top 68%
Single-cell and spatial transcriptomics
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