Image-Based Live Cell Sorting

doi:10.1016/j.tibtech.2020.10.006

Trends in Biotechnology

Volume 39, Issue 6, June 2021, Pages 613-623

https://doi.org/10.1016/j.tibtech.2020.10.006 Get rights and content

Highlights

Advancements in imaging and computational decision-making have given rise to a new method for classifying and isolating single cells from samples, which addresses major limitations of commonly used technologies, for example, fluorescence activated cell sorting (FACS).
Microfluidic flow, microfluidic containment, and microarray-based systems have been utilized to separate cells utilizing high-spatial resolution and real-time kinetic information.
From inexpensive and flexible in-laboratory devices to precise clinical tools, image-based cell sorting (IBCS) systems have broad potential.
Machine learning and deep neural networks can be combined with IBCS for intelligent cell selection and isolation.
Microfluidics and microarrays can be combined in single IBCS platforms, taking advantage of both technologies.

Technologies capable of cell separation based on cell images provide powerful tools enabling cell selection criteria that rely on spatially or temporally varying properties. Image-based cell sorting (IBCS) systems utilize microfluidic or microarray platforms, each having unique characteristics and applications. The advent of IBCS marks a new paradigm in which cell phenotype and behavior can be explored with high resolution and tied to cellular physiological and omics data, providing a deeper understanding of single-cell physiology and the creation of cell lines with unique properties. Cell sorting guided by high-content image information has far-reaching implications in biomedical research, clinical medicine, and pharmaceutical development.

Section snippets

Single-Cell Analysis and Sorting Paves the Way for Biomedical Breakthroughs

The ability to analyze individual cells has revolutionized biomedical research and clinical medicine, leading to innovations that encompass genetic engineering, regenerative medicine, and cancer immunotherapies. While cell population data provide a wealth of information, there is an inherent loss of information from bulk cell samples. Small variations between adjacent cells are known to lead to profound physiologic differences, for example, immune cells vary in their ability to combat

Microfluidic Flow IBCS Platforms

Microfluidic flow IBCS platforms image moving cells within a flow stream and, of the IBCS methods described here, are most closely related to FACS. Although powerful, FACS is limited to single time point measurements of light scatter or fluorescence emission collected from an entire cell. A FACS-like sorting system paired with cell image acquisition has proven quite challenging due to: (i) the limited time the cell spends in the interrogation beam (~10 μs), and (ii) the very short transit time

Microfluidic Containment for Imaging and Sorting

A second class of IBCS systems slows or stops cells using cell-sized, multifunctional containers such as droplets or microchambers to control cell motion and minimize image blur (Figure 3 and Table 1). Cell containment is an especially useful strategy for IBCS when sample sizes are small and efficient retrieval of viable cells is required, as these methods enable throughputs of 0.2–5 cells/s with high sorting accuracies (often >98%). Unlike flow IBCS devices, which image cells that are moving

Microarray IBCS Platforms

Cell-based microarrays enable high-throughput and high-content screening for a variety of single-cell applications. IBCS arrays employ wells, traps, or patterning to array cells that are typically plated in a stochastic fashion followed by manual or automated assays [39,40]. Positioning cells at fixed addresses solves multiple imaging challenges. Very high-quality images using standard imaging hardware and algorithms along with facile data processing are a distinct advantage of imaging

Concluding Remarks and Future Directions

The platforms developed for IBCS come in many forms, including microfluidic flow, microfluidic containment, and microarray systems, each having unique performance characteristics (Table 1). Microfluidic flow IBCS are attractive for quickly sorting large input samples, with an emphasis on throughput and speed. Microfluidic containment IBCS analyze smaller sample sizes with intermediate throughput but provide high flexibility in imaging strategies and sample manipulation. Microarray IBCS allow

Acknowledgments

This work was supported by the National Cancer Institute awards CA224763 and CA233811.

Disclaimer Statement

N.L.A. and C.E.S. disclose a financial interest in Cell Microsystems, Inc. All other authors declare no conflicts.

Glossary

Confocal imaging: an optical imaging device that scans highly focused light across an object to collect a 3D representation.
Deep neural networks: form of artificial neural network with three or more hidden layers connecting the input and output layers, often used for classification and prediction modeling.
Dielectrophoresis (DEP): a process in which a non-uniform electric field exerts a force on an object, guiding it to a specific location or holding the object in place.
Fluorescence-activated cell

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Although several technologies have been developed to isolate cells of interest from a heterogenous sample, clogging and impaired cell viability limit such isolation. We have developed the Enrich TROVO system as a novel, nonfluidic technology to sort live cells. The TROVO system combines imaging-based cell selection and photo-crosslinking of (gelatin methacrylate) gelMA-hydrogel to capture cells. After capture, cells are released by enzymatic digestion of the hydrogel and then retrieved for downstream analysis or further cell culturing. The system can capture cells with a recovery rate of 48% while maintaining 90% viability. Moreover, TROVO can enrich rare cells 506-fold with 93% efficiency using single step isolation from a 1:10⁴ cell mixture, and can also capture one target cell from 1 million cells, reaching an enrichment ratio of 9128. In addition, 100% purity and 49% recovery rate can be achieved by a following negative isolation process. Compared to existing technologies, the TROVO system is clog-resistant, highly biocompatible, and can process a wide range of sample sizes.
Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data
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Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM) that uses real-time population-wide object characterization to enable data-driven high-fidelity imaging of relevant phenotypes based on the population context. DDM combines data-independent and data-dependent steps to synergistically enhance data acquired using different imaging modalities. As a proof of concept, we develop and apply DDM with plugins for improved high-content screening and live adaptive microscopy for cell migration and infection studies that capture events of interest, rare or common, with high precision and resolution. We propose that DDM can reduce human bias, increase reproducibility, and place single-cell characteristics in the context of the sample population when interpreting microscopy data, leading to an increase in overall data fidelity.
Low-latency label-free image-activated cell sorting using fast deep learning and AI inferencing
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Citation Excerpt :
Image-based detection, classification, and separation of target cells among the total cell population can bring phenomenal insight to biomedical research and application (Blasi et al., 2016; Brasko et al., 2018; Doan et al., 2018; Gu et al., 2019; LaBelle et al., 2021; Roberts and Tesdorpf, 2015; Schraivogel et al., 2022).
Classification and sorting of cells using image-activated cell sorting (IACS) systems can bring significant insight to biomedical sciences. Incorporating deep learning algorithms into IACS enables cell classification and isolation based on complex and human-vision uninterpretable morphological features within a heterogeneous cell population. However, the limited capabilities and complicated implementation of deep learning–assisted IACS systems reported to date hinder the adoption of the systems for a wide range of biomedical research. Here, we present image-activated cell sorting by applying fast deep learning algorithms to conduct cell sorting without labeling. The overall sorting latency, including signal processing and AI inferencing, is less than 3 ms, and the training time for the deep learning model is less than 30 min with a training dataset of 20,000 images. Both values set the record for IACS with sorting by AI inference. . We demonstrated our system performance through a 2-part polystyrene beads sorting experiment with 96.6% sorting purity, and a 3-part human leukocytes sorting experiment with 89.05% sorting purity for monocytes, 92.00% sorting purity for lymphocytes, and 98.24% sorting purity for granulocytes. The above performance was achieved with simple hardware containing only 1 FPGA, 1 PC and GPU, as a result of an optimized custom CNN UNet and efficient use of computing power. The system provides a compact, sterile, low-cost, label-free, and low-latency cell sorting solution based on real-time AI inferencing and fast training of the deep learning model.
Study on the shear stress and interfacial friction of droplets moving on a superhydrophobic surface
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Citation Excerpt :
Microdroplets rested on a superhydrophobic material surface often shows high contact angle, low rolling angle and interfacial friction. Therefore, superhydrophobic materials have been widely used in Lab-on-a-chip [1], self-cleaning materials [2,3], biological cell sorting technologies [4,5], dynamic display technologies et al. [6,7] to reduce the residue of fluid on the material surface and to lower the energy dissipation at the liquid/solid interface when a droplet moves on the superhydrophobic surface. Previous studies suggested that the energy dissipation at the liquid/solid interface was closely related to the rolling and sliding behavior of droplet [8].
The variation of velocity field inside a droplet was investigated by particle image velocimetry when the droplet moved on a superhydrophobic surface. Results showed that shear stress increased with the increase of the motion velocity of droplet and droplet viscosity when the motion velocity of droplet increased from 0.3 to 1.1 mm/s and the droplet viscosity increased from 2.69 to 17.41 mPa·s. Moreover, friction force at liquid/solid interface was independent on droplet volume, viscosity and motion velocity on the superhydrophobic surface. It is indicated that the variation of velocity field and shear stress inside droplet was closely associated with the change of the influence depth and the center of mass inside the droplet caused by the substrate. The findings are helpful to understand the motion behaviors of a droplet on the superhydrophobic surface to reduce the energy dissipation when a droplet transport on the superhydrophobic surfaces.
Microfluidic platforms for the manipulation of cells and particles
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Citation Excerpt :
Fast cell manipulation is of great importance for increasing throughput and decreasing the diagnosis time [168,169]. Hence, high-speed imaging techniques have been established to integrate with conventional and microfluidic-based cell sorting methods [170,171]. Moreover, Raman imaging is a new method to improve the characterization abilities of the conventional flow cytometry techniques, which rely on indirect chemical characterization [172,173].
The vast majority of researches in cell biology, biomedicine, biotechnology, and chemistry require cell/particle separation from a pool of samples for disease diagnostics and clinical applications. Hence, diverse techniques are proposed to effectively manipulate cells and particles in macro and microsystems. Conventional techniques are time-consuming and hand-laborious, bringing about the advent of new microfluidic techniques used for cell/particle handling. Several high-throughput, label-free, and reliable techniques are proposed that accurately manipulate cells and particles in microsystems. Herein, we present a concise review on the operation principles of various cell/particle manipulation methods and explain their merits and limitations. First, an overview of the conventional techniques currently being utilized in clinical applications is given, and then novel microfluidic and lab-on-a-chip techniques, including hydrodynamics, acoustics, electric, magnetic, optical, and thermal, are introduced. Finally, the future trends of this field are briefly studied, and some of the novel mechanisms that exhibit great potential in the cell or particle manipulation are presented.
Organogenesis of Plant Tissues in Colchicine Allows Selecting in Field Trial Blueberry (Vaccinium spp. cv Duke) Clones with Commercial Potential
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^@: Twitter: @belencorlla (B. Cortés-Llanos) and @AngeloMassaro19 (A. Massaro).

⁴: https://sites.bioe.uw.edu/allbritton/

View full text

Trends in Biotechnology

ReviewImage-Based Live Cell Sorting

Highlights

Section snippets

Single-Cell Analysis and Sorting Paves the Way for Biomedical Breakthroughs

Microfluidic Flow IBCS Platforms

Microfluidic Containment for Imaging and Sorting

Microarray IBCS Platforms

Concluding Remarks and Future Directions

Acknowledgments

Disclaimer Statement

Glossary

Micro Nano Eng.

Trends Biotechnol.

Trends Biotechnol.

Adv. Drug Deliv. Rev.

Cell Rep.

Biosens. Bioelectron.

Methods Cell Biol.

Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells

Nat. Commun.

Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation

Cell

Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing

Cell

Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors

Leukemia

High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy

Nat. Med.

Platforms for single-cell collection and analysis

Int. J. Mol. Sci.

A review of sorting, separation and isolation of cells and microbeads for biomedical applications: microfluidic approaches

Analyst

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Lab Chip

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Nature

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Lab Chip

In flow cytometry, image is everything

Cytom. Part A

High-throughput imaging flow cytometry by optofluidic time-stretch microscopy

Nat. Protoc.

Cameraless high-throughput three-dimensional imaging flow cytometry

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Intelligent image-activated cell sorting

Cell

A practical guide to intelligent image-activated cell sorting

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Ghost cytometry

Science

Machine learning based real-time image-guided cell sorting and classification

Cytom. Part A

Raman image-activated cell sorting

Nat. Commun.

Intelligent image-activated cell sorting 2.0

Lab Chip

Intelligent image-based deformation-assisted cell sorting with molecular specificity

Nat. Methods

Review
Image-Based Live Cell Sorting