Background
The use of quantitative morphological features has been gaining traction as an additional source of information that can be used for cell characterization alongside genomic, transcriptomic, and proteomic data. New technologies such as image-based cytometry or ghost cytometry capture cellular and sub-cellular information which can be used as inputs to machine learning models.1 Classifiers built on morphological features have been used to analyze cellular subsets and even to sort them in real time. In immuno-oncology, morphology has been used to study tumorigenicity and metastatic burden, tumor heterogeneity within or across samples, treatment response, and tumor microenvironment.2–4 Despite the interest in morphological features for cell characterization, there are currently no standardized controls for these types of systems. To address this need, we have developed customizable synthetic control cells with visible surface and subcellular features suitable for morphology-based technologies.
Methods
We created spherical hydrogel particles with size and optical properties to mimic the visual phenotypes of different cell types. To accomplish this, high contrast components were incorporated into our hydrogel particles to simulate cellular granularity. These particles have visible contrast when compared to a ‘smooth’ particle of the same size. Granular features can be tuned to match the surface texture of different cell types, for example red blood cells, or added as larger internal features to mimic organelles.
Results
Granular and smooth synthetic cells imaged on Deepcell’s REM-I image-based flow cytometer show consistent size and morphology across samples (figure 1). SEM images of a synthetic red blood cell mimic demonstrates granular features on the particle surface and an average particle size of 8.09 +/- 0.17µm (n=10), consistent with biological red blood cells (figure 2). We also show 20µm synthetic cells with organelle mimics which contain internal features of varying size scales stained with FITC (figure 3a) or Rhodamide B (figure 3b).
Conclusions
We have demonstrated the ability to create synthetic hydrogel controls with visible morphological features for image-based cytometry platforms. Our synthetic cell mimic platform can also be combined with biomarkers, DNA, or fluorescent dyes to enable complete control of synthetic cellular compositions.
Acknowledgements
Images in figure 1 were provided by Deepcell, Inc.
References
Ota S, et al. Ghost Cytometry. Science. 2018;360:1246–1251.
Wu P, et al. Single-cell morphology encodes metastatic potential. Sci Adv. 2020;6(4).
Kusumoto D, et al. Anti-senescent drug screening by deep learning-based morphology senescence scoring. Nat Commun. 2021;12, Article 257.
Pratapa A, et al. Image-based cell phenotyping with deep learning. 161urrO pin Chem Biol. 2021;65:9–17.
Abstract 76 Figure 1
Synthetic cells of various sizes and granularities. Images of (A) 25µm granular, (B) 10µm granular, and (C) 10µm smooth particles acquired on the REM-I Image Cytometry Platform from Deepcell.
Abstract 76 Figure 2
Synthetic red blood cell mimics display granular material (indicated by pink arrows). Representative SEM images at (A) intermediate and (B) high magnification. Particles are adhered onto poly-I-lysine.
Abstract 76 Figure 3
20µm synthetic cells with internal features of varying sizes. (A) 3–10µm internal features containing FITC. (B) 2–5µm internal features containing Rhodamine B.