Image-Based Live Cell Sorting

Vibrational imaging of metabolites for improved microbial cell strains

Significance

Biomanufacturing utilizes modified microbial systems to sustainably produce commercially important biomolecules for use in agricultural, energy, food, material, and pharmaceutical industries. However, technological challenges related to non-destructive and high-throughput metabolite screening need to be addressed to fully unlock the potential of synthetic biology and sustainable biomanufacturing.

Aim

This perspective outlines current analytical screening tools used in industrial cell strain development programs and introduces label-free vibrational spectro-microscopy as an alternative contrast mechanism.

Approach

We provide an overview of the analytical instrumentation currently used in the "test" portion of the design, build, test, and learn cycle of synthetic biology. We then highlight recent progress in Raman scattering and infrared absorption imaging techniques, which have enabled improved molecular specificity and sensitivity.

Results

Recent developments in high-resolution chemical imaging methods allow for greater throughput without compromising the image contrast. We provide a roadmap of future work needed to support integration with microfluidics for rapid screening at the single-cell level.

Conclusions

Quantifying the net expression of metabolites allows for the identification of cells with metabolic pathways that result in increased biomolecule production, which is essential for improving the yield and reducing the cost of industrial biomanufacturing. Technological advancements in vibrational microscopy instrumentation will greatly benefit biofoundries as a complementary approach for non-destructive cell screening.

Live cell pool and rare cell isolation using Enrich TROVO system

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.

Accessible high-speed image-activated cell sorting

Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.

Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca2+ imaging and electrophysiology

I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Passive Focusing of Single Cells Using Microwell Arrays for High-Accuracy Image-Activated Sorting

Sorting single cells from a population was of critical importance in areas such as cell line development and cell therapy. Image-based sorting is becoming a promising technique for the nonlabeling isolation of cells due to the capability of providing the details of cell morphology. This study reported the focusing of cells using microwell arrays and the following automatic size sorting based on the real-time recognition of cells. The simulation first demonstrated the converged streamlines to the symmetrical plane contributed to the focusing effect. Then, the influence of connecting microchannel, flowing length, particle size, and the sample flow rate on the focusing effect was experimentally analyzed. Both microspheres and cells could be aligned in a straight line at the Reynolds number (Re) of 0.027-0.187 and 0.027-0.08, respectively. The connecting channel was proved to drastically improve the focusing performance. Afterward, a tapered microwell array was utilized to focus sphere/cell spreading in a wide channel to a straight line. Finally, a custom algorithm was employed to identify and sort the size of microspheres/K562 cells with a throughput of 1 event/s and an accuracy of 97.8/97.1%. The proposed technique aligned cells to a straight line at low Reynolds numbers and greatly facilitated the image-activated sorting without the need for a high-speed camera or flow control components with high frequency. Therefore, it is of enormous application potential in the field of nonlabeled separation of single cells.

Recent advances in deformation-assisted microfluidic cell sorting technologies

Cell sorting is an essential prerequisite for cell research and has great value in life science and clinical studies. Among the many microfluidic cell sorting technologies, label-free methods based on the size of different cell types have been widely studied. However, the heterogeneity in size for cells of the same type and the inevitable size overlap between different types of cells would result in performance degradation in size-based sorting. To tackle such challenges, deformation-assisted technologies are receiving more attention recently. Cell deformability is an inherent biophysical marker of cells that reflects the changes in their internal structures and physiological states. It provides additional dimensional information for cell sorting besides size. Therefore, in this review, we summarize the recent advances in deformation-assisted microfluidic cell sorting technologies. According to how the deformability is characterized and the form in which the force acts, the technologies can be divided into two categories: (1) the indirect category including transit-time-based and image-based methods, and (2) the direct category including microstructure-based and hydrodynamics-based methods. Finally, the separation performance and the application scenarios of each method, the existing challenges and future outlook are discussed. Deformation-assisted microfluidic cell sorting technologies are expected to realize greater potential in the label-free analysis of cells.

COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning

Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without the need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in the ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.

Automated microarray platform for single-cell sorting and collection of lymphocytes following HIV reactivation

A promising strategy to cure HIV-infected individuals is to use latency reversing agents (LRAs) to reactivate latent viruses, followed by host clearance of infected reservoir cells. However, reactivation of latent proviruses within infected cells is heterogeneous and often incomplete. This fact limits strategies to cure HIV which may require complete elimination of viable virus from all cellular reservoirs. For this reason, understanding the mechanism(s) of reactivation of HIV within cellular reservoirs is critical to achieve therapeutic success. Methodologies enabling temporal tracking of single cells as they reactivate followed by sorting and molecular analysis of those cells are urgently needed. To this end, microraft arrays were adapted to image T-lymphocytes expressing mCherry under the control of the HIV long terminal repeat (LTR) promoter, in response to the application of LRAs (prostratin, iBET151, and SAHA). In response to prostratin, iBET151, and SAHA, 30.5%, 11.2%, and 12.1% percentage of cells, respectively. The arrays enabled large numbers of single cells (>25,000) to be imaged over time. mCherry fluorescence quantification identified cell subpopulations with differing reactivation kinetics. Significant heterogeneity was observed at the single-cell level between different LRAs in terms of time to reactivation, rate of mCherry fluorescence increase upon reactivation, and peak fluorescence attained. In response to prostratin, subpopulations of T lymphocytes with slow and fast reactivation kinetics were identified. Single T-lymphocytes that were either fast or slow reactivators were sorted, and single-cell RNA-sequencing was performed. Different genes associated with inflammation, immune activation, and cellular and viral transcription factors were found.

Deep learning with microfluidics for on-chip droplet generation, control, and analysis

Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.

Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data

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.

Automated microarray for single-cell sorting and collection of lymphocytes following HIV reactivation

A promising strategy to cure HIV infected individuals is to use latency reversing agents (LRAs) to reactivate latent viruses, followed by host clearance of infected reservoir cells. However, reactivation of latent proviruses within infected cells is heterogeneous and often incomplete. This fact limits strategies to cure HIV which may require complete elimination of viable virus from all cellular reservoirs. For this reason, understanding the mechanism(s) of reactivation of HIV within cellular reservoirs is critical to achieve therapeutic success. Methodologies enabling temporal tracking of single cells as they reactivate followed by sorting and molecular analysis of those cells are urgently needed. To this end, microraft arrays were adapted to image T-lymphocytes expressing mCherry under the control of the HIV long terminal repeat (LTR) promoter, in response to the application of various LRAs (prostratin, iBET151, and SAHA). In response to prostratin, iBET151, and SAHA, 30.5 %, 11.2 %, and 12.1 % percentage of cells respectively, reactivated similar to that observed in other experimental systems. The arrays enabled large numbers of single cells (>25,000) to be imaged over time. mCherry fluorescence quantification identified cell subpopulations with differing reactivation kinetics. Significant heterogeneity was observed at the single cell level between different LRAs in terms of time to reactivation, rate of mCherry fluorescence increase upon reactivation, and peak fluorescence attained. In response to prostratin, subpopulations of T lymphocytes with slow and fast reactivation kinetics were identified. Single T-lymphocytes that were either fast or slow reactivators were sorted, and single-cell RNA-sequencing was performed. Different genes associated with inflammation, immune activation, and cellular and viral transcription factors were found. These results advance our conceptual understanding of HIV reactivation dynamics at the single-cell level toward a cure for HIV.

New Frontiers in Colorectal Cancer Treatment Combining Nanotechnology with Photo- and Radiotherapy

Colorectal cancer is the third most common cancer worldwide. Despite recent advances in the treatment of this pathology, which include a personalized approach using radio- and chemotherapies in combination with advanced surgical techniques, it is imperative to enhance the performance of these treatments and decrease their detrimental side effects on patients' health. Nanomedicine is likely the pathway towards solving this challenge by enhancing both the therapeutic and diagnostic capabilities. In particular, plasmonic nanoparticles show remarkable potential due to their dual therapeutic functionalities as photothermal therapy agents and as radiosensitizers in radiotherapy. Their dual functionality, high biocompatibility, easy functionalization, and targeting capabilities make them potential agents for inducing efficient cancer cell death with minimal side effects. This review aims to identify the main challenges in the diagnosis and treatment of colorectal cancer. The heterogeneous nature of this cancer is also discussed from a single-cell point of view. The most relevant works in photo- and radiotherapy using nanotechnology-based therapies for colorectal cancer are addressed, ranging from in vitro studies (2D and 3D cell cultures) to in vivo studies and clinical trials. Although the results using nanoparticles as a photo- and radiosensitizers in photo- and radiotherapy are promising, preliminary studies showed that the possibility of combining both therapies must be explored to improve the treatment efficiency.

Droplet Detection and Sorting System in Microfluidics: A Review

Since being invented, droplet microfluidic technologies have been proven to be perfect tools for high-throughput chemical and biological functional screening applications, and they have been heavily studied and improved through the past two decades. Each droplet can be used as one single bioreactor to compartmentalize a big material or biological population, so millions of droplets can be individually screened based on demand, while the sorting function could extract the droplets of interest to a separate pool from the main droplet library. In this paper, we reviewed droplet detection and active sorting methods that are currently still being widely used for high-through screening applications in microfluidic systems, including the latest updates regarding each technology. We analyze and summarize the merits and drawbacks of each presented technology and conclude, with our perspectives, on future direction of development.

Intelligent image-activated sorting of Chlamydomonas reinhardtii by mitochondrial localization

Organelle positioning in cells is associated with various metabolic functions and signaling in unicellular organisms. Specifically, the microalga Chlamydomonas reinhardtii repositions its mitochondria, depending on the levels of inorganic carbon. Mitochondria are typically randomly distributed in the Chlamydomonas cytoplasm, but relocate toward the cell periphery at low inorganic carbon levels. This mitochondrial relocation is linked with the carbon-concentrating mechanism, but its significance is not yet thoroughly understood. A genotypic understanding of this relocation would require a high-throughput method to isolate rare mutant cells not exhibiting this relocation. However, this task is technically challenging due to the complex intracellular morphological difference between mutant and wild-type cells, rendering conventional non-image-based high-event-rate methods unsuitable. Here, we report our demonstration of intelligent image-activated cell sorting by mitochondrial localization. Specifically, we applied an intelligent image-activated cell sorting system to sort for C. reinhardtii cells displaying no mitochondrial relocation. We trained a convolutional neural network (CNN) to distinguish the cell types based on the complex morphology of their mitochondria. The CNN was employed to perform image-activated sorting for the mutant cell type at 180 events per second, which is 1-2 orders of magnitude faster than automated microscopy with robotic pipetting, resulting in an enhancement of the concentration from 5% to 56.5% corresponding to an enrichment factor of 11.3. These results show the potential of image-activated cell sorting for connecting genotype-phenotype relations for rare-cell populations, which require a high throughput and could lead to a better understanding of metabolic functions in cells.

Artificial intelligence-assisted automatic and index-based microbial single-cell sorting system for One-Cell-One-Tube

Identification, sorting, and sequencing of individual cells directly from in situ samples have great potential for in-depth analysis of the structure and function of microbiomes. In this work, based on an artificial intelligence (AI)-assisted object detection model for cell phenotype screening and a cross-interface contact method for single-cell exporting, we developed an automatic and index-based system called EasySort AUTO, where individual microbial cells are sorted and then packaged in a microdroplet and automatically exported in a precisely indexed, "One-Cell-One-Tube" manner. The target cell is automatically identified based on an AI-assisted object detection model and then mobilized via an optical tweezer for sorting. Then, a cross-interface contact microfluidic printing method that we developed enables the automated transfer of cells from the chip to the tube, which leads to coupling with subsequent single-cell culture or sequencing. The efficiency of the system for single-cell printing is >93%. The throughput of the system for single-cell printing is ~120 cells/h. Moreover, >80% of single cells of both yeast and Escherichia coli are culturable, suggesting the superior preservation of cell viability during sorting. Finally, AI-assisted object detection supports automated sorting of target cells with high accuracy from mixed yeast samples, which was validated by downstream single-cell proliferation assays. The automation, index maintenance, and vitality preservation of EasySort AUTO suggest its excellent application potential for single-cell sorting.

Upgraded User-Friendly Image-Activated Microfluidic Cell Sorter Using an Optimized and Fast Deep Learning Algorithm

Image-based cell sorting is essential in biological and biomedical research. The sorted cells can be used for downstream analysis to expand our knowledge of cell-to-cell differences. We previously demonstrated a user-friendly image-activated microfluidic cell sorting technique using an optimized and fast deep learning algorithm. Real-time isolation of cells was carried out using this technique with an inverted microscope. In this study, we devised a recently upgraded sorting system. The cell sorting techniques shown on the microscope were implemented as a real system. Several new features were added to make it easier for the users to conduct the real-time sorting of cells or particles. The newly added features are as follows: (1) a high-resolution linear piezo-stage is used to obtain in-focus images of the fast-flowing cells; (2) an LED strobe light was incorporated to minimize the motion blur of fast-flowing cells; and (3) a vertical syringe pump setup was used to prevent the cell sedimentation. The sorting performance of the upgraded system was demonstrated through the real-time sorting of fluorescent polystyrene beads. The sorter achieved a 99.4% sorting purity for 15 μm and 10 μm beads with an average throughput of 22.1 events per second (eps).

ANN-Based Instantaneous Simulation of Particle Trajectories in Microfluidics

Microfluidics has shown great potential in cell analysis, where the flowing path in the microfluidic device is important for the final study results. However, the design process is time-consuming and labor-intensive. Therefore, we proposed an ANN method with three dense layers to analyze particle trajectories at the critical intersections and then put them together with the particle trajectories in straight channels. The results showed that the ANN prediction results are highly consistent with COMSOL simulation results, indicating the applicability of the proposed ANN method. In addition, this method not only shortened the simulation time but also lowered the computational expense, providing a useful tool for researchers who want to receive instant simulation results of particle trajectories.

Trapping and releasing of single microparticles and cells in a microfluidic chip

A microfluidic device was designed and fabricated to capture single microparticles and cells by using hydrodynamic force and selectively release the microparticles and cells of interest via negative dielectrophoresis by activating selected individual microelectrodes. The trap microstructure was optimized based on numerical simulation of the electric field as well as the flow field. The capture and selective release functions of the device were verified by multi-types microparticles with different diameters and K562 cells. The capture efficiencies/release efficiencies were 95.55% ± 0.43%/96.41% ± 1.08% and 91.34% ± 0.01%/93.67% ± 0.36% for microparticles and cells, respectively. By including more traps and microelectrodes, the device can achieve high throughput and realize the visual separation of microparticles/cells of interest in a large number of particle/cell groups.

1β-Hydroxyalantolactone from Inulae Flos alleviated the progression of pulmonary fibrosis via inhibiting JNK/FOXO1/NF-κB pathway

Inulae Flos was widely distributed throughout Europe, Africa, and Asia, and was commonly used as a folk medicine in clinic for treating various respiratory diseases, including cough, asthma, bronchitis, pulmonary fibrosis, and pneumonia. However, the ingredients responsible for the pharmacology effects of I. Flos and the underlying mechanisms remain unclear. In this study, the effects of 16 known sesquiterpene lactones and flavonoids from I. Flos on TGF-β1-induced fibroblast activation were assessed by phenotypic high-content screening. Among those sixteen compounds, 1β-hydroxy alantolactone (HAL), the main characteristic sesquiterpene lactone from I. Flos, exhibited remarkable inhibitory activity. The further studies showed that HAL significantly inhibited the proliferation and induced the apoptosis of human fibroblast cell lines HELF and MRC-5 in a concentration-dependent manner. It also reduced intracellular ROS production, suppressed the mRNA expressions of E-cad, TGF-β1, Smad3, Col I, α-SMA and TNF-α, and downregulated protein expressions of α-SMA and F-actin. Furthermore, HAL significantly reduced the levels of HA, LN, PC-III and IV-C in serum, TNF-α and IL-6 in BALF, and TGF-β1, HYP and Col I in lung tissues of bleomycin (BLM)-treated rats. HAL significantly downregulated the expressions of p-JNK, FOXO1, p-p65, α-SMA, p-smad3 and Col I but upregulated p-FOXO1, which could be reversed by JNK agonist anisomycin. These results demonstrated that HAL induced the apoptosis of lung fibroblast cells activated by TGF-β1 and improved BLM-induced lung fibrosis in rats via inhibiting JNK/FOXO1/NF-κB pathway.

Cancer nanotechnology: current status and perspectives

Modern medicine has been waging a war on cancer for nearly a century with no tangible end in sight. Cancer treatments have significantly progressed, but the need to increase specificity and decrease systemic toxicities remains. Early diagnosis holds a key to improving prognostic outlook and patient quality of life, and diagnostic tools are on the cusp of a technological revolution. Nanotechnology has steadily expanded into the reaches of cancer chemotherapy, radiotherapy, diagnostics, and imaging, demonstrating the capacity to augment each and advance patient care. Nanomaterials provide an abundance of versatility, functionality, and applications to engineer specifically targeted cancer medicine, accurate early-detection devices, robust imaging modalities, and enhanced radiotherapy adjuvants. This review provides insights into the current clinical and pre-clinical nanotechnological applications for cancer drug therapy, diagnostics, imaging, and radiation therapy.

A technology of a different sort: microraft arrays

A common procedure performed throughout biomedical research is the selection and isolation of biological entities such as organelles, cells and organoids from a mixed population. In this review, we describe the development and application of microraft arrays, an analysis and isolation platform which enables a vast range of criteria and strategies to be used when separating biological entities. The microraft arrays are comprised of elastomeric microwells with detachable polymer bases (microrafts) that act as capture and culture sites as well as supporting carriers during cell isolation. The technology is elegant in its simplicity and can be implemented for samples possessing tens to millions of objects yielding a flexible platform for applications such as single-cell RNA sequencing, subcellular organelle capture and assay, high-throughput screening and development of CRISPR gene-edited cell lines, and organoid manipulation and selection. The transparent arrays are compatible with a multitude of imaging modalities enabling selection based on 2D or 3D spatial phenotypes or temporal properties. Each microraft can be individually isolated on demand with retention of high viability due to the near zero hydrodynamic stress imposed upon the cells during microraft release, capture and deposition. The platform has been utilized as a simple manual add-on to a standard microscope or incorporated into fully automated instruments that implement state-of-the-art imaging algorithms and machine learning. The vast array of selection criteria enables separations not possible with conventional sorting methods, thus garnering widespread interest in the biological and pharmaceutical sciences.

The in vitro micronucleus assay using imaging flow cytometry and deep learning

The in vitro micronucleus (MN) assay is a well-established assay for quantification of DNA damage, and is required by regulatory bodies worldwide to screen chemicals for genetic toxicity. The MN assay is performed in two variations: scoring MN in cytokinesis-blocked binucleated cells or directly in unblocked mononucleated cells. Several methods have been developed to score the MN assay, including manual and automated microscopy, and conventional flow cytometry, each with advantages and limitations. Previously, we applied imaging flow cytometry (IFC) using the ImageStream® to develop a rapid and automated MN assay based on high throughput image capture and feature-based image analysis in the IDEAS® software. However, the analysis strategy required rigorous optimization across chemicals and cell lines. To overcome the complexity and rigidity of feature-based image analysis, in this study we used the Amnis® AI software to develop a deep-learning method based on convolutional neural networks to score IFC data in both the cytokinesis-blocked and unblocked versions of the MN assay. We show that the use of the Amnis AI software to score imagery acquired using the ImageStream® compares well to manual microscopy and outperforms IDEAS® feature-based analysis, facilitating full automation of the MN assay.

Toward deep biophysical cytometry: prospects and challenges

The biophysical properties of cells reflect their identities, underpin their homeostatic state in health, and define the pathogenesis of disease. Recent leapfrogging advances in biophysical cytometry now give access to this information, which is obscured in molecular assays, with a discriminative power that was once inconceivable. However, biophysical cytometry should go 'deeper' in terms of exploiting the information-rich cellular biophysical content, generating a molecular knowledge base of cellular biophysical properties, and standardizing the protocols for wider dissemination. Overcoming these barriers, which requires concurrent innovations in microfluidics, optical imaging, and computer vision, could unleash the enormous potential of biophysical cytometry not only for gaining a new mechanistic understanding of biological systems but also for identifying new cost-effective biomarkers of disease.