Understanding how the immune system responds to infection requires tracking how cells change over time. Researchers at the Chinese Academy of Sciences have developed a new method to observe how immune cells activate and respond during infection at the single-cell level.

Traditional RNA sequencing methods measure total RNA in cells, which provides a snapshot but can miss how gene activity is changing in real time. The new approach, called scIVNL-seq, focuses on newly made RNA, allowing researchers to track gene expression as it happens. This provides a more accurate picture of how cells respond during infection.

scIVNL-seq detects single-cell new RNA in vivo

A Overview of scIVNL-seq. S⁴U was injected into mice via tail vein injection. BM cells were collected and re-suspended. Single-cell suspensions were loaded onto microfluidic device, followed by cell lysis and mRNA capture. S⁴U integrated in new RNA was converted into cytidine analogs and was further recognized as cytosine by reverse transcriptase. cDNAs were amplified and libraries were sequenced. New RNA was identified by T-to-C substitutions. The schematic diagrams were created with Adobe Photoshop (Version 22.0.0) (B) New RNA (red) imaging of femur sections, co-stained with CD45 (green), CD31 (cyan) antibodies and DAPI (blue). Scale bar, 100 μm. C UMAP showing cell type clusters (left), UMI counts of new RNA and NTRs (right) in bone marrow CD45⁺ immune cells. The uniform manifold approximation and projection (UMAP) method was used for dimension reduction. Unique molecular identifier (UMI) counts per cell are shown as ln(count+1). Ratios of new RNA to total RNA are shown as NTRs. HSPC, hematopoietic stem and progenitor cell; Mono, monocyte; Mφ, macrophage; Neu, neutrophil; Baso, basophil; NK, natural killer cell; DC, dendritic cell; Pre B, Pre-B cell; B, B cell; T, T cell. D Violin plot of NTR for each BM cell type. E Signature gene expression of new RNA (left) and total RNA (right) for each cell type. Genes in the heatmap were the same between new RNA and total RNA. Expressions were log-normalized. F Total RNA and new RNA expression (log-normalized) of Ptprc, Ccr2, Camp and Rpl13. G Scatter plot of transcription rate (α, normalized RNA counts per cells/h) and RNA half-life (t_1/2, h) of genes expressed in BM cells. H UMAP projection of BM cell types with new RNA. Cells with new RNA data were clustered by unsupervised classification. Cell types identified with total RNA data were mapped back on UMAP projection. 

Using this method, the team studied how immune cells react to Salmonella infection in mice. They found that bone marrow macrophages respond very early, becoming primed for action shortly after infection begins. In contrast, macrophages in the intestine showed a more limited response.

At the same time, certain adaptive immune cells, including CD8 positive T cells and plasma cells, were activated much earlier than expected. This challenges the traditional view that adaptive immunity takes longer to respond compared to innate immunity.

Another important finding was that intestinal cells known as enterocytes can present antigens to immune cells, helping trigger an immune response. This suggests that non-immune cells can play a direct role in activating immune defenses.

By using RNA sequencing to monitor newly synthesized RNA, the researchers uncovered how gene expression is tightly controlled during infection. The balance between RNA production and degradation was shown to shape how immune cells behave over time.

Overall, this work provides new insight into how the immune system coordinates its response to infection. It also highlights the value of advanced RNA sequencing methods for studying dynamic biological processes in real time.