photonics hv Article Dual-Modality Imaging Microfluidic Cytometer for Onsite Detection of Phytoplankton Bo Xiong 1 , Tianqi Hong 1 , Herbert Schellhorn 2 and Qiyin Fang 1,3, *   Citation: Xiong, B.; Hong, T.; Schellhorn, H.; Fang, Q. Dual-Modality Imaging Microfluidic Cytometer for Onsite Detection of Phytoplankton. Photonics 2021, 8, 435. https://doi.org/10.3390/ photonics8100435 Received: 31 August 2021 Accepted: 5 October 2021 Published: 12 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 School of Biomedical Engineering, McMaster University, 1712 Main St W, Hamilton, ON L8S 4L8, Canada; xiongb3@mcmaster.ca (B.X.); hongt6@mcmaster.ca (T.H.) 2 Department of Biology, McMaster University, 1712 Main St W, Hamilton, ON L8S 4L8, Canada; schell@mcmaster.ca 3 Department of Engineering Physics, McMaster University, 1712 Main St W, Hamilton, ON L8S 4L8, Canada * Correspondence: qiyin.fang@mcmaster.ca Abstract: Phytoplankton monitoring is essential for better understanding and mitigation of phyto- plankton bloom formation. We present a microfluidic cytometer with two imaging modalities for onsite detection and identification of phytoplankton: a lensless imaging mode for morphological features, and a fluorescence imaging mode for autofluorescence signal of phytoplankton. Both imaging modes are integrated in a microfluidic device with a field of view (FoV) of 3.7 mm × 2.4 mm and a depth of field (DoF) of 0.8 mm. The particles in the water flow channel can be detected and classified with automated image processing algorithms and machine learning models using their morphology and fluorescence features. The performance of the device was demonstrated by measur- ing Chlamydomonas, Euglena, and non-fluorescent beads in both separate and mixed flow samples. The recall rates for Chlamydomonas and Euglena ware 93.6% and 94.4%. The dual-modality imaging approach enabled observing both morphology and fluorescence features with a large DoF and FoV which contribute to high-throughput analysis. Moreover, this imaging flow cytometer platform is portable, low-cost, and shows potential in the onsite phytoplankton monitoring. Keywords: flow cytometry; phytoplankton; lensless imaging; fluorescence imaging; miniaturiza- tion; microfluidic 1. Introduction Phytoplankton play a vital role in the aquatic ecosystem [1,2]. However, species composition, concentration, and distribution of phytoplankton change frequently while the drivers of these changes are not fully understood [3]. Phytoplankton bloom, a rapid growth in the algal population, can readily occur under favorable environmental conditions, posting a threat to human and ecosystem health, and resulting in economic losses in agriculture [4]. For example, some phytoplankton species produce toxins are harmful to both fish and humans [5]; algae bloom can result in oxygen depletion, killing fish and benthic organisms [6]. Therefore, it is necessary to closely monitor the phytoplankton bloom development for better mitigation strategies [7–9]. Conventional phytoplankton detection relies on its autofluorescence signatures and morphological features. Currently, both methods are performed by laboratory instruments (e.g., fluorometers and microscopes) and require manual sampling handling [10–12]. Such approach is time-consuming and expensive due to the needs of manual steps by experienced technical staff. Thus, there is a pressing need for low-cost and efficient identification techniques that can detect the species composition and concentration of phytoplankton in situ. Several commercial instruments based on fluorescent spectroscopic sensing are avail- able for onsite phytoplankton monitoring where concentration of chlorophyll and other pigments (such as phycocyanin, phycoerythrin, and carotenoids) can be estimated based on their autofluorescence [13,14]. With these instruments, the phytoplankton can be classified Photonics 2021, 8, 435. https://doi.org/10.3390/photonics8100435 https://www.mdpi.com/journal/photonics