Dielectric characterization of Plasmodium falciparum infected red blood cells using microfluidic impedance cytometry

Although malaria is the world’s most life-threatening parasitic disease, there is no clear understanding of how certain biophysical properties of infected cells change during the malaria infection cycle. In this article, we use microfluidic impedance cytometry to measure the dielectric properties of Plasmodium falciparum-infected red blood cells (i-RBCs) at specific time-points during the infection cycle. Individual parasites were identified within i-RBCs using Green Fluorescent Protein (GFP) emission. The dielectric properties of cell sub-populations were determined using the multi-shell model. Analysis showed that the membrane capacitance and cytoplasmic conductivity of i-RBCs increased along the infection time-course, due to membrane alterations caused by parasite infection. The volume ratio occupied by the parasite was estimated to vary from <10% at earlier stages, to ~90% at later stages. This knowledge could be used to develop new label-free cell sorting techniques for sample pre-enrichment, improving diagnosis

Dataset for "Dielectric characterization of Plasmodium falciparum infected red blood cells using microfluidic impedance cytometry" article

The dataset contains the experimental data and Matlab codes needed to generate the figures of the article &#39;Dielectric characterization of Plasmodium falciparum infected red blood cells using microfluidic impedance cytometry&#39; by C. Honrado, L. Ciuffreda, D. Spencer, L. Ranford-Cartwright and H. Morgan in Royal Society Interface.</span

Dataset for &quot;Analysis of Parasitic Protozoa at the Single-cell Level using Microfluidic Impedance Cytometry&quot; article

Dataset for: McGrath, J. S. et al (2017). Analysis of parasitic protozoa at the single-cell level using microfluidic impedance cytometry. Scientific Reports. In the article associated with the dataset, we use Microfluidic Impedance Cytometry (MIC) to characterise the AC electrical (or dielectric) properties of single protozoan parasites (Cryptosporidium and/or Giardia (oo)cysts) and demonstrate rapid discrimination based on viability and species. Specifically, MIC was used to identify live and inactive C. parvum oocysts with over 90% certainty, whilst also detecting damaged and/or excysted oocysts. Furthermore, discrimination of Cryptosporidium parvum, Cryptosporidium muris and Giardia lamblia, with over 92% certainty was achieved. The data and code necessary to generate the full results can be found in this dataset.</span

Dataset for &quot;Integrated Separation and Readout &ndash; Towards Field-diagnosis of Trypanosomiasis&quot;

Assigned DOI is https://doi.org/10.5258/SOTON/D0589 The dataset contains the experimental data and Matlab codes needed to generate the figures of the article &#39;Integrated Separation and Readout &ndash; Towards Field-diagnosis of Trypanosomiasis &#39; by C. Honrado, S. Holm, J. Beech, B. Ho, D. Spencer, M. Barrett, J. Tegenfeldt and H. Morgan. </span