Low-volume Plasmodium blood sample processing protocols for untargeted transcriptomics optimized using Plasmodium knowlesi
Year: 2025
Type: article
Abstract: As the importance of transcriptional variation and regulation for Plasmodium becomes more apparent, advances for non- falciparum species are hindered by our reliance on natural infections to study parasite biology. Untargeted transcriptomic research is also complicated by low-parasite densities and high proportions of human genetic material, highlighting the need for optimized sample processing protocols. In this study, we used a Plasmodium knowlesi culture diluted in whole blood as a mock Plasmodium vivax natural infection to compare white blood cell (WBC), rRNA and globin depletion methods and RNA-seq library preparation kits to create an optimized protocol for low-volume sample processing. Depletion techniques and library preparation kit selection are both crucial to enrich parasite RNA in low-parasite density and low-volume samples. WBC depletion methods can increase the percentage of mapped Plasmodium paired-end (PE) reads from 5.4 to 37.2–54.5%, depending on the method. Additionally, globin RNA and rRNA depletion significantly decreased the human globin RNA and rRNA combined gene counts up to 90%. When comparing the library preparation kits, the transcription profiles of the mRNA kits were highly correlated and enriched for protein-coding genes. The total RNA library prep kit was dominated by P. knowlesi rRNA, despite having the highest percentage of mapped P. knowlesi PE reads. Parasite sequencing output can be optimized when WBC and globin depletion are used in combination with an mRNA library preparation kit bringing untargeted Plasmodium transcriptomics to resource-limited settings which will lead to new insights in parasite biology.
Source: Microbial Genomics
Institution: Instituut voor Tropische Geneeskunde
Language: English
Topic: Malaria Research and Control
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,839
Award: G2022842445
OpenAlex ID: W4416427340