Identification of Plasmodium sporozoites using angle-resolved scattered light pulse shapes
- 1Flow Cytometry Core Facility, German Rheumatology Research Center (DRFZ), Berlin, Germany
- 2Vector Biology Research Unit, Max Planck Institute for Infection Biology (MPIIB), Berlin, Germany
Abstract
Flow cytometry is widely used to identify and sort rare cells, but conventional approaches rely on fluorescent labels. Label-free discrimination of rare targets in complex, heterogeneous samples remains challenging. Here, we introduce a label-free strategy that combines multi-angle pulse shape flow cytometry (MAPS-FC), which exploits the full temporal structure of the signal pulses during cell or particles transit through the laser spot, with a deep learning–based analysis. As a biologically relevant test case, we focus on Plasmodium falciparum sporozoites, the infectious stage of the malaria parasite, which need to be isolated from mosquito salivary glands and are embedded in a heterogeneous mosquito-derived background. Using a GFP-expressing reporter line for validation, we trained a long short-term memory (LSTM) neural network to classify sporozoites solely from their scattering pulse shapes captured through MAPS-FC. Using training data from multiple days and setting a threshold on the classification confidence, the network can distinguish sporozoites from mosquito debris without fluorescent labels. This work establishes MAPS-FC with deep learning as a platform for label-free identification of rare parasites in complex biological samples.
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BibTeX
@article{devaraj2026identification,
title = {{Identification of Plasmodium sporozoites using angle-resolved scattered light pulse shapes}},
author = {Vinod Devaraj and Sophia Kahler and Daniel Kage and Elena A. Levashina and Toralf Kaiser},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0354060},
url = {https://doi.org/10.1371/journal.pone.0354060},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Identification of Plasmodium sporozoites using angle-resolved scattered light pulse shapes
AU - Vinod Devaraj
AU - Sophia Kahler
AU - Daniel Kage
AU - Elena A. Levashina
AU - Toralf Kaiser
PY - 2026
DA - 2026/11/05
JO - PLOS One
DO - 10.1371/journal.pone.0354060
UR - https://doi.org/10.1371/journal.pone.0354060
AB - Flow cytometry is widely used to identify and sort rare cells, but conventional approaches rely on fluorescent labels. Label-free discrimination of rare targets in complex, heterogeneous samples remains challenging. Here, we introduce a label-free strategy that combines multi-angle pulse shape flow cytometry (MAPS-FC), which exploits the full temporal structure of the signal pulses during cell or particles transit through the laser spot, with a deep learning–based analysis. As a biologically relevant test case, we focus on Plasmodium falciparum sporozoites, the infectious stage of the malaria parasite, which need to be isolated from mosquito salivary glands and are embedded in a heterogeneous mosquito-derived background. Using a GFP-expressing reporter line for validation, we trained a long short-term memory (LSTM) neural network to classify sporozoites solely from their scattering pulse shapes captured through MAPS-FC. Using training data from multiple days and setting a threshold on the classification confidence, the network can distinguish sporozoites from mosquito debris without fluorescent labels. This work establishes MAPS-FC with deep learning as a platform for label-free identification of rare parasites in complex biological samples.
SN - 1932-6203
ER -