The potential of multispectral imaging flow cytometry for environmental monitoring

Abstract

Environmental monitoring involves the quantification of microscopic cells and particles such as algae, plant cells, pollen, or fungal spores. Traditional methods using conventional microscopy require expert knowledge, are time‐intensive and not well‐suited for automated high throughput. Multispectral imaging flow cytometry (MIFC) allows measurement of up to 5000 particles per second from a fluid suspension and can simultaneously capture up to 12 images of every single particle for brightfield and different spectral ranges, with up to 60x magnification. The high throughput of MIFC has high potential for increasing the amount and accuracy of environmental monitoring, such as for plant‐pollinator interactions, fossil samples, air, water or food quality that currently rely on manual microscopic methods. Automated recognition of particles and cells is also possible, when MIFC is combined with deep‐learning computational techniques. Furthermore, various fluorescence dyes can be used to stain specific parts of the cell to highlight physiological and chemical features including: vitality of pollen or algae, allergen content of individual pollen, surface chemical composition (carbohydrate coating) of cells, DNA‐ or enzyme‐activity staining. Here, we outline the great potential for MIFC in environmental research for a variety of research fields and focal organisms. In addition, we provide best practice recommendations.

Best practices in plant cytometry: The second tranche

“…Dunker et al [14] describes the potential of multispectral imaging flow cytometry for improving our knowledge about environmental and focal organisms that currently mostly depend on manual microscopic methods. In addition, best practice recommendations are provided.…”

“…Dunker et al [14] describes the potential of multispectral imaging flow cytometry for improving our knowledge about environmental monitoring, with increasing accuracy, in a variety of research fields (such as plant–pollinator interactions, fossil samples, air, water, or food quality) and focal organisms that currently mostly depend on manual microscopic methods. In addition, best practice recommendations are provided.…”

On the potential for GWAS with phenotypic population means and allele-frequency data (popGWAS)

“…GWAS with wild populations has been advocated for some time (Santure & Garant, 2018). However, despite recent progress in high throughput, automated phenotyping (Dunker et al, 2022; Tills et al, 2023; Xie & Yang, 2020), the advances of biodiversity genomics in obtaining high quality reference genomes for almost every species (Exposito-Alonso et al, 2020; Formenti et al, 2022) and the possibility to gain cost-effective genome-wide population data (Czech, Peng, Spence, Lang, Bellagio, Hildebrandt, Fritschi, Schwab, Rowan, & Weigel, 2022; Schlötterer et al, 2014), relatively few empirical studies are currently available. This gap between the possibilities and actual practical application in biodiversity conservation (Heuertz et al, 2023; Hogg, 2023) is probably as much due to the still existing logistic and financial challenges as to a lack of data- and resource-efficient methods.…”

Comparison of Deep Learning Architectures for three different Multispectral Imaging Flow Cytometry Datasets

“…M ULTISPECTRAL imaging flow cytometry (MIFC) has been recently shown to be useful for environmental monitoring of plant-pollinator interactions and assessment of food and water quality [1], [2], [3]. This measuring technique, originally designed for immunological analyses as blood cell analysis, allows to separate single cells from a fluid suspension by hydro-dynamically focusing of a sample stream in a narrowing flow cell, surrounded by a sheath stream.…”

“…The different mentioned application examples have different numbers of classes which need to be differentiated. For immunological applications, a limited set of cell types needs to be distinguished, while in environmental monitoring, potentially several thousands of classes need to be differentiated eventually [3].…”