A New Image for Cell Sorting

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.

Single cell RNA-seq: a novel tool to unravel virus-host interplay

Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.

Olfactory Ensheathing Cell Ameliorate Neuroinflammation Following Spinal Cord Injury Through Upregulating REV-ERBα in Microglia

Circadian dysregulation involved in the pathophysiology of spinal cord injury (SCI). Modulation of circadian rhythms hold promise for the SCI treatment. Here, we aim to investigated the mechanism of olfactory ensheathing cells (OEC) in alleviating neuroinflammation via modulating clock gene expression in microglia. In this study, SCI rats were randomly divided into OEC group and vehicle group. At 1 day after the surgery, OECs were intravenously transplanted into OEC group SCI rat, while the rats in vehicle group received culture medium. After 7 days post of OEC transplantation, tissues were collected from the brain (prefrontal cortex, hypothalamus, spinal cord) for PCR, western blotting and immunohistochemistry (IHC) assay at zeitgeber time (ZT) 6, ZT 12, ZT 18, and ZT 24. The roles of OEC in modulating REV-ERBα in microglia were studied by experimental inhibition of gene expression and the co-culture experiment. In the vehicle group, IHC showed a significant increase of Iba-1 expression in the cerebral white matter and spinal cord compared with control group (P < 0.0001 for all comparisons). The expression of Iba-1 was significantly decreased (P < 0.0001 for all comparisons). In the OEC group, the expression of PER 1, PER 2, CLOCK, and REV-ERBα was in a rhythmical manner in both spinal cord and brain regions. SCI disrupted their typical rhythms. And OECs transplantation could modulate those dysregulations by upregulating REV-ERBα. In vitro study showed that OECs couldn't reduce the activation of REV-ERBα inhibited microglia. The intravenous transplantation of OECs can mediate cerebral and spinal microglia activation through upregulation REV-ERBα after SCI.

Image-Enabled Cell Sorting Using the BD CellView Technology

This chapter is an extension of the original publication by Schraivogel et al. (Science 375:315-320, 2022) which described, for the first time, image-enabled and high-speed cell sorting based on the BD CellView technology. It summarizes the technical aspects of the instrument in an easy-to-digest form and provides example-based guidance toward implementation of the CellView-based image cell sorting technology. As an example, it explains how to use the image-enabled cell sorter to analyze the chemically induced fragmentation of the Golgi apparatus in HeLa cells-an experiment that was alluded to in the original publication but was not included in the manuscript due to space constraints. The chemically induced Golgi fragmentation sort illustrates an elegant example of the utility of image-enabled cell sorting as a significant expansion of the single-cell toolbox. It is such a striking phenotype when analyzed with image cytometry but undetectable when using conventional flow cytometry. Described in a straightforward and concise manner, this experiment serves as a standard system assurance for image-based cell sorters.

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.

Machine learning implementation strategy in imaging and impedance flow cytometry

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

Microfluidic pumps for cell sorting

Microfluidic cell sorting has shown promising advantages over traditional bulky cell sorting equipment and has demonstrated wide-reaching applications in biological research and medical diagnostics. The most important characteristics of a microfluidic cell sorter are its throughput, ease of use, and integration of peripheral equipment onto the chip itself. In this review, we discuss the six most common methods for pumping fluid samples in microfluidic cell sorting devices, present their advantages and drawbacks, and discuss notable examples of their use. Syringe pumps are the most commonly used method for fluid actuation in microfluidic devices because they are easily accessible but they are typically too bulky for portable applications, and they may produce unfavorable flow characteristics. Peristaltic pumps, both on- and off-chip, can produce reversible flow but they suffer from pulsatile flow characteristics, which may not be preferable in many scenarios. Gravity-driven pumping, and similarly hydrostatic pumping, require no energy input but generally produce low throughputs. Centrifugal flow is used to sort cells on the basis of size or density but requires a large external rotor to produce centrifugal force. Electroosmotic pumping is appealing because of its compact size but the high voltages required for fluid flow may be incompatible with live cells. Emerging methods with potential for applications in cell sorting are also discussed. In the future, microfluidic cell sorting methods will trend toward highly integrated systems with high throughputs and low sample volume requirements.

NF-κB dynamics in the language of immune cells

Biological discovery has been driven by advances in throughput and resolution of analysis technologies. They have also created an indelible bias for snapshot-based knowledge. Even though recent methods such as multi-omics single-cell assays have empowered immunological investigations, they still provide snapshots of cellular behaviors and thus, have inherent limitations in reconstructing unsynchronized dynamic events across individual cells. Here, we present a rationale for how NF-κB may convey specificity of contextual information through subtle quantitative features of its signaling dynamics. The next frontier of predictive understanding should involve functional characterization of NF-κB signaling dynamics and their immunological implications. This may help solve the apparent paradox that a ubiquitously activated transcription factor can shape accurate responses to different immune challenges.