A Protocol for Low-Input RNA-Sequencing of Patients with Febrile Neutropenia Captures Relevant Immunological Information

Abstract

Improved methods are needed for diagnosing infectious diseases in children with cancer. Most children have fever for other reasons than bacterial infection and are exposed to unnecessary antibiotics and hospital admission. Recent research has shown that host whole blood RNA transcriptomic signatures can distinguish bacterial infection from other causes of fever. Implementation of this method in clinics could change the diagnostic approach for children with cancer and suspected infection. However, extracting sufficient mRNA to perform transcriptome profiling by standard methods is challenging due to the patient's low white blood cell (WBC) counts. In this prospective cohort study, we succeeded in sequencing 95% of samples from children with leukaemia and suspected infection by using a low-input protocol. This could be a solution to the issue of obtaining sufficient RNA for sequencing from patients with low white blood cell counts. Further studies are required to determine whether the captured immune gene signatures are clinically valid and thus useful to clinicians as a diagnostic tool for patients with cancer and suspected infection.

Keywords: febrile neutropenia; low-input RNA-sequencing; neutropenia; paediatrics; transcriptomics; white blood cells.

Figure 1

Overview of the study population sequenced either by the low-input protocol or by both low-input and standard protocols as described in the methods. A total of 88 samples from 22 patients with leukaemia and suspected infection was sequenced by the low-input protocol (Takara SMART-seq HT) (96% succeeded) and 15 of these were also processed by the standard protocol (Truseq). Samples stated by the laboratory as WBC < 0.1 × 10⁹/L and absolute neutrophil count (ANC) < 0.1 × 10⁹/L were defined as WBC = 0 and ANC = 0, respectively. * Median (IQR) × 10⁹/L, ** median (range) × 10⁹/L.

Figure 2

Comparative metrics from 15 samples processed with both the low-input and standard protocols. Each dot represents one sample, shaded violins represent their density distribution. ***: p < 0.001. (A) Library size (Millions) reads captured by the protocols. (B) Total expressed genes (thousands) captured by the protocols. (C) Total immune signature genes (hundreds) captured by the protocols. Immune signature genes were retrieved from the Molecular Signature Database (MSigDB).

Figure 3

Heatmaps illustrating the correlation between gene expression levels from samples originating from the same patient analysed using a standard protocol and low-input protocol, respectively. (A) Union of 15 most differentially expressed genes between for each sample and the rest of the samples in the protocol. (B) Immune signature genes retrieved from MSigDB, and (C) the 38-gene signature by Herberg et al. (2016) [7]. Sample correlation is presented by the Spearman correlation coefficient and coloured according to the legend. pHeatmap (v1.0.12).

Figure 4

(A) Venn diagram illustrating the overlap of total captured genes between protocols and the genes captured uniquely in each protocol. (B) Bar chart visualising the proportion of different transcript types in each library. Non-coding transcript types constituted, e.g., tRNA, snRNA, snoRNA, miRNA, miscRNA, lincRNA. N.S is not significant at cut-off alpha 0.05.

Figure 5

Comparative metrics from patients processed with the low-input protocol. Samples were grouped according to RIN ≥ 5 and RIN < 5. Each dot represents one sample. Significance level cut-off: NS.: p > 0.05, **: p < 0.01, ***: p < 0.001, (A,B) Total WBC count (×10⁹/L). (C,D) Library size (Millions), reads captured in each sample, (E,F) Immune signature genes (hundreds) retrieved from MSigDB, (G,H) total genes (thousands).

Figure 6

(A) Venn diagram illustrating the overlap of total captured genes between samples grouped according to RIN ≥ 5 and RIN < 5, as well as the genes captured uniquely in each protocol. (B) Dot plot visualising the relationship in the number of expressed genes (thousands) vs. number of unmapped reads (percentage) (log-scale) per sample. Each dot corresponds to one sample. Samples are coloured according to their RIN value as presented in the legend.

Figure 7

Workflow of the low-input protocol. (A) Patient blood samples were retrieved in PAXgene tubes and (B) RNA was automatically purified by QiaQube. (C) Reverse transcription (RT) and cDNA amplification were performed in a single step using SMART-Seq^® HT kit, minimising sample handling and hands-on time. (D) Following amplification, the samples were cleaned up and prepared for sequencing using a Nextera XT DNA library preparation kit and sequenced on (E) Miseq Benchtop Sequencer (Illumina, San Diego, California, USA). Illustrated using Biorender (https://biorender.com/ accessed on 1 June 2023).