Innovating in a bioimaging core through instrument development

Developing devices and instrumentation in a bioimaging core facility is an important part of the innovation mandate inherent in the core facility model but is a complex area due to the required skills and investments, and the impossibility of a universally applicable model. Here, we seek to define technological innovation in microscopy and situate it within the wider core facility innovation portfolio, highlighting how strategic development can accelerate access to innovative imaging modalities and increase service range, and thus maintain the cutting edge needed for sustainability. We consider technology development from the perspective of core facility staff and their stakeholders as well as their research environment and aim to present a practical guide to the 'Why, When, and How' of developing and integrating innovative technology in the core facility portfolio. Core facilities need to innovate to stay up to date. However, how to carry out the innovation is not very obvious. One area of innovation in imaging core facilities is the building of optical setups. However, the creation of optical setups requires specific skill sets, time, and investments. Consequently, the topic of whether a core facility should develop optical devices is discussed as controversial. Here, we provide resources that should help get into this topic, and we discuss different options when and how it makes sense to build optical devices in core facilities. We discuss various aspects, including consequences for staff and the relation of the core to the institute, and also broaden the scope toward other areas of innovation.

Optical coherence tomography for multicellular tumor spheroid category recognition and drug screening classification via multi-spatial-superficial-parameter and machine learning

Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies.

Imaging Flow Cytometry: Development, Present Applications, and Future Challenges

Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the detailed imaging characteristics of microscopy. In our comprehensive review, we adopt a historical perspective to chart the development of ImFC, highlighting its origins and current state of the art and forecasting potential future advancements. The genesis of ImFC stemmed from merging the hydraulic system of a flow cytometer with advanced camera technology. This synergistic coupling facilitates the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. Nevertheless, ImFC's implementation has encountered hurdles, particularly in developing software capable of managing its sophisticated data acquisition and analysis needs. The scale and complexity of the data generated by ImFC necessitate the creation of novel analytical tools that can effectively manage and interpret these data, thus allowing us to unlock the full potential of ImFC. Notably, artificial intelligence (AI) algorithms have begun to be applied to ImFC, offering promise for enhancing its analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from the high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories, but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.

Light-field flow cytometry for high-resolution, volumetric and multiparametric 3D single-cell analysis

Imaging flow cytometry (IFC) combines flow cytometry and fluorescence microscopy to enable high-throughput, multiparametric single-cell analysis with rich spatial details. However, current IFC techniques remain limited in their ability to reveal subcellular information with a high 3D resolution, throughput, sensitivity, and instrumental simplicity. In this study, we introduce a light-field flow cytometer (LFC), an IFC system capable of high-content, single-shot, and multi-color acquisition of up to 5,750 cells per second with a near-diffraction-limited resolution of 400-600 nm in all three dimensions. The LFC system integrates optical, microfluidic, and computational strategies to facilitate the volumetric visualization of various 3D subcellular characteristics through convenient access to commonly used epi-fluorescence platforms. We demonstrate the effectiveness of LFC in assaying, analyzing, and enumerating intricate subcellular morphology, function, and heterogeneity using various phantoms and biological specimens. The advancement offered by the LFC system presents a promising methodological pathway for broad cell biological and translational discoveries, with the potential for widespread adoption in biomedical research.

Structured-light-sheet imaging in an integrated optofluidic platform

Heterogeneity investigation at the single-cell level reveals morphological and phenotypic characteristics in cell populations. In clinical research, heterogeneity has important implications in the correct detection and interpretation of prognostic markers and in the analysis of patient-derived material. Among single-cell analysis, imaging flow cytometry allows combining information retrieved by single cell images with the throughput of fluidic platforms. Nevertheless, these techniques might fail in a comprehensive heterogeneity evaluation because of limited image resolution and bidimensional analysis. Light sheet fluorescence microscopy opened new ways to study in 3D the complexity of cellular functionality in samples ranging from single-cells to micro-tissues, with remarkably fast acquisition and low photo-toxicity. In addition, structured illumination microscopy has been applied to single-cell studies enhancing the resolution of imaging beyond the conventional diffraction limit. The combination of these techniques in a microfluidic environment, which permits automatic sample delivery and translation, would allow exhaustive investigation of cellular heterogeneity with high throughput image acquisition at high resolution. Here we propose an integrated optofluidic platform capable of performing structured light sheet imaging flow cytometry (SLS-IFC). The system encompasses a multicolor directional coupler equipped with a thermo-optic phase shifter, cylindrical lenses and a microfluidic network to generate and shift a patterned light sheet within a microchannel. The absence of moving parts allows a stable alignment and an automated fluorescence signal acquisition during the sample flow. The platform enables 3D imaging of an entire cell in about 1 s with a resolution enhancement capable of revealing sub-cellular features and sub-diffraction limit details.

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae

Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.

Advanced flow cytometry for biomedical applications

Flow cytometry (FC) is a versatile tool with excellent capabilities to detect and measure multiple characteristics of a population of cells or particles. Notable advancements in in vivo photoacoustic FC, coherent Raman FC, microfluidic FC, and so on, have been achieved in the last two decades, which endows FC with new functions and expands its applications in basic research and clinical practice. Advanced FC broadens the tools available to researchers to conduct research involving cancer detection, microbiology (COVID-19, HIV, bacteria, etc.), and nucleic acid analysis. This review presents an overall picture of advanced flow cytometers and provides not only a clear understanding of their mechanisms but also new insights into their practical applications. We identify the latest trends in this area and aim to raise awareness of advanced techniques of FC. We hope this review expands the applications of FC and accelerates its clinical translation.

Volumetric imaging of human mesenchymal stem cells (hMSCs) for non-destructive quantification of 3D cell culture growth

The adoption of cell-based therapies into the clinic will require tremendous large-scale expansion to satisfy future demand, and bioreactor-microcarrier cultures are best suited to meet this challenge. The use of spherical microcarriers, however, precludes in-process visualization and monitoring of cell number, morphology, and culture health. The development of novel expansion methods also motivates the advancement of analytical methods used to characterize these microcarrier cultures. A robust optical imaging and image-analysis assay to non-destructively quantify cell number and cell volume was developed. This method preserves 3D cell morphology and does not require membrane lysing, cellular detachment, or exogenous labeling. Complex cellular networks formed in microcarrier aggregates were imaged and analyzed in toto. Direct cell enumeration of large aggregates was performed in toto for the first time. This assay was successfully applied to monitor cellular growth of mesenchymal stem cells attached to spherical hydrogel microcarriers over time. Elastic scattering and fluorescence lightsheet microscopy were used to quantify cell volume and cell number at varying spatial scales. The presented study motivates the development of on-line optical imaging and image analysis systems for robust, automated, and non-destructive monitoring of bioreactor-microcarrier cell cultures.

Virtual-freezing fluorescence imaging flow cytometry with 5-aminolevulinic acid stimulation and antibody labeling for detecting all forms of circulating tumor cells

Circulating tumor cells (CTCs) are precursors to cancer metastasis. In blood circulation, they take various forms such as single CTCs, CTC clusters, and CTC-leukocyte clusters, all of which have unique characteristics in terms of physiological function and have been a subject of extensive research in the last several years. Unfortunately, conventional methods are limited in accurately analysing the highly heterogeneous nature of CTCs. Here we present an effective strategy for simultaneously analysing all forms of CTCs in blood by virtual-freezing fluorescence imaging (VIFFI) flow cytometry with 5-aminolevulinic acid (5-ALA) stimulation and antibody labeling. VIFFI is an optomechanical imaging method that virtually freezes the motion of fast-flowing cells on an image sensor to enable high-throughput yet sensitive imaging of every single event. 5-ALA stimulates cancer cells to induce the accumulation of protoporphyrin (PpIX), a red fluorescent substance, making it possible to detect all cancer cells even if they show no expression of the epithelial cell adhesion molecule, a typical CTC biomarker. Although PpIX signals are generally weak, VIFFI flow cytometry can detect them by virtue of its high sensitivity. As a proof-of-principle demonstration of the strategy, we applied cancer cells spiked in blood to the strategy to demonstrate image-based detection and accurate classification of single cancer cells, clusters of cancer cells, and clusters of a cancer cell(s) and a leukocyte(s). To show the clinical utility of our method, we used it to evaluate blood samples of four breast cancer patients and four healthy donors and identified EpCAM-positive PpIX-positive cells in one of the patient samples. Our work paves the way toward the determination of cancer prognosis, the guidance and monitoring of treatment, and the design of antitumor strategies for cancer patients.

Portable light-sheet optofluidic microscopy for 3D fluorescence imaging flow cytometry

Imaging flow cytometry (IFC) combines conventional flow cytometry with optical microscopy, allowing for high-throughput, multi-parameter screening of single-cell specimens with morphological and spatial information. However, current 3D IFC systems are limited by instrumental complexity and incompatibility with available microfluidic devices or operations. Here, we report portable light-sheet optofluidic microscopy (PLSOM) for 3D fluorescence cytometric imaging. PLSOM exploits a compact, open-top light-sheet configuration compatible with commonly adopted microfluidic chips. The system offers a subcellular resolution (2-4 μm) in all three dimensions, high throughput (∼1000 cells per s), and portability (30 cm (l) × 10 cm (w) × 26 cm (h)). We demonstrated PLSOM for 3D IFC using various phantom and cell systems. The low-cost and custom-built architecture of PLSOM permits easy adaptability and dissemination for broad 3D flow cytometric investigations.

Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry

Quantitative Phase Imaging (QPI) has gained popularity in bioimaging because it can avoid the need for cell staining, which in some cases is difficult or impossible. However, as a result, QPI does not provide labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for QPI techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed through the tomographic phase microscopy in flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy (FM) data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting specific three-dimensional intracellular structures directly from the phase-contrast data in a typical flow cytometry configuration.

Microscopic Imaging Methods for Organ-on-a-Chip Platforms

Microscopic imaging is essential and the most popular method for in situ monitoring and evaluating the outcome of various organ-on-a-chip (OOC) platforms, including the number and morphology of mammalian cells, gene expression, protein secretions, etc. This review presents an overview of how various imaging methods can be used to image organ-on-a-chip platforms, including transillumination imaging (including brightfield, phase-contrast, and holographic optofluidic imaging), fluorescence imaging (including confocal fluorescence and light-sheet fluorescence imaging), and smartphone-based imaging (including microscope attachment-based, quantitative phase, and lens-free imaging). While various microscopic imaging methods have been demonstrated for conventional microfluidic devices, a relatively small number of microscopic imaging methods have been demonstrated for OOC platforms. Some methods have rarely been used to image OOCs. Specific requirements for imaging OOCs will be discussed in comparison to the conventional microfluidic devices and future directions will be introduced in this review.

Modular multimodal platform for classical and high throughput light sheet microscopy

Light-sheet fluorescence microscopy (LSFM) has become an important tool for biological and biomedical research. Although several illumination and detection strategies have been developed, the sample mounting still represents a cumbersome procedure as this is highly dependent on the type of sample and often this might be time consuming. This prevents the use of LSFM in other promising applications in which a fast and straightforward sample-mounting procedure and imaging are essential. These include the high-throughput research fields, e.g. in drug screenings and toxicology studies. Here we present a new imaging paradigm for LSFM, which exploits modularity to offer multimodal imaging and straightforward sample mounting strategy, enhancing the flexibility and throughput of the system. We describe its implementation in which the sample can be imaged either as in any classical configuration, as it flows through the light-sheet using a fluidic approach, or a combination of both. We also evaluate its ability to image a variety of samples, from zebrafish embryos and larvae to 3D complex cell cultures.

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models

Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.

Review: tomographic imaging flow cytometry

Within the last decades, conventional flow cytometry (FC) has evolved as a powerful measurement method in clinical diagnostics, biology, life sciences and healthcare. Imaging flow cytometry (IFC) extends the power of traditional FC by adding high resolution optical and spectroscopic information. However, the conventional IFC only provides a 2D projection of a 3D object. To overcome this limitation, tomographic imaging flow cytometry (tIFC) was developed to access 3D information about the target particles. The goal of tIFC is to visualize surfaces and internal structures in a holistic way. This review article gives an overview of the past and current developments in tIFC.

Progressive use of multispectral imaging flow cytometry in various research areas

Multi-spectral imaging flow cytometry (MIFC) has become one of the most powerful technologies for investigating general analytics, molecular and cell biology, biotechnology, medicine, and related fields. It combines the capabilities of the morphometric and photometric analysis of single cells and micrometer-sized particles in flux with regard to thousands of events. It has become the tool of choice for a wide range of research and clinical applications. By combining the features of flow cytometry and fluorescence microscopy, it offers researchers the ability to couple the spatial resolution of multicolour images of cells and organelles with the simultaneous analysis of a large number of events in a single system. This provides the opportunity to visually confirm findings and collect novel data that would otherwise be more difficult to obtain. This has led many researchers to design innovative assays to gain new insight into important research questions. To date, it has been successfully used to study cell morphology, surface and nuclear protein co-localization, protein-protein interactions, cell signaling, cell cycle, cell death, and cytotoxicity, intracellular calcium, drug uptake, pathogen internalization, and other applications. Herein we describe some of the recent advances in the field of multiparametric imaging flow cytometry methods in various research areas.

Automatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images

Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular, we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms. The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results.

Recent advances in point spread function engineering and related computational microscopy approaches: from one viewpoint

This personal hybrid review piece, written in light of my recipience of the UIPAB 2020 young investigator award, contains a mixture of my scientific biography and work so far. This paper is not intended to be a comprehensive review, but only to highlight my contributions to computation-related aspects of super-resolution microscopy, as well as their origins and future directions.

Three-dimensional imaging of intact porcine cochlea using tissue clearing and custom-built light-sheet microscopy

Hearing loss is a prevalent disorder that affects people of all ages. On top of the existing hearing aids and cochlear implants, there is a growing effort to regenerate functional tissues and restore hearing. However, studying and evaluating these regenerative medicine approaches in a big animal model (e.g. pigs) whose anatomy, physiology, and organ size are similar to a human is challenging. In big animal models, the cochlea is bulky, intricate, and veiled in a dense and craggy otic capsule. These facts complicate 3D microscopic analysis that is vital in the cochlea, where structure-function relation is time and again manifested. To allow 3D imaging of an intact cochlea of newborn and juvenile pigs with a volume up to ∼ 250 mm3, we adapted the BoneClear tissue clearing technique, which renders the bone transparent. The transparent cochleae were then imaged with cellular resolution and in a timely fashion, which prevented bubble formation and tissue degradation, using an adaptive custom-built light-sheet fluorescence microscope. The adaptive light-sheet microscope compensated for deflections of the illumination beam by changing the angles of the beam and translating the detection objective while acquiring images. Using this combination of techniques, macroscopic and microscopic properties of the cochlea were extracted, including the density of hair cells, frequency maps, and lower frequency limits. Consequently, the proposed platform could support the growing effort to regenerate cochlear tissues and assist with basic research to advance cures for hearing impairments.

AI on a chip

Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as drug design, material discovery, speech recognition, self-driving cars, advertising, finance, medical imaging, and astronomical observation, where AI-produced outcomes have been proven to be comparable or even superior to the performance of human experts. In these applications, what is essentially important for the development of AI is the data needed for machine learning. Despite its prominent importance, the very first process of the AI development, namely data collection and data preparation, is typically the most laborious task and is often a limiting factor of constructing functional AI algorithms. Lab-on-a-chip technology, in particular microfluidics, is a powerful platform for both the construction and implementation of AI in a large-scale, cost-effective, high-throughput, automated, and multiplexed manner, thereby overcoming the above bottleneck. On this platform, high-throughput imaging is a critical tool as it can generate high-content information (e.g., size, shape, structure, composition, interaction) of objects on a large scale. High-throughput imaging can also be paired with sorting and DNA/RNA sequencing to conduct a massive survey of phenotype-genotype relations whose data is too complex to analyze with traditional computational tools, but is analyzable with the power of AI. In addition to its function as a data provider, lab-on-a-chip technology can also be employed to implement the developed AI for accurate identification, characterization, classification, and prediction of objects in mixed, heterogeneous, or unknown samples. In this review article, motivated by the excellent synergy between AI and lab-on-a-chip technology, we outline fundamental elements, recent advances, future challenges, and emerging opportunities of AI with lab-on-a-chip technology or "AI on a chip" for short.

High-throughput 3D imaging of single cells with light-sheet fluorescence microscopy on chip

Single-cell analysis techniques are fundamental to study the heterogeneity of cellular populations, which is the basis to understand several biomedical mechanisms. Light-sheet fluorescence microscopy is a powerful technique for obtaining high-resolution imaging of individual cells, but the complexity of the setup and the sample mounting procedures limit its overall throughput. In our work, we present an optofluidic microscope-on-chip with integrated microlenses for light-sheet shaping and with a fluidic microchannel that allows the automatic and continuous delivery of samples of a few tens of microns in size. The device is used to perform dual-color fluorescence analysis and 3D reconstruction of xenograft-derived mouse breast cancer cells.

Reducing data acquisition for light-sheet microscopy by extrapolation between imaged planes

Light-sheet fluorescence microscopy (LSFM) is a powerful technique that can provide high-resolution images of biological samples. Therefore, this technique offers significant improvement for three-dimensional (3D) imaging of living cells. However, producing high-resolution 3D images of a single cell or biological tissues, normally requires high acquisition rate of focal planes, which means a large amount of sample sections. Consequently, it consumes a vast amount of processing time and memory, especially when studying real-time processes inside living cells. We describe an approach to minimize data acquisition by interpolation between planes using a phase retrieval algorithm. We demonstrate this approach on LSFM data sets and show reconstruction of intermediate sections of the sparse samples. Since this method diminishes the required amount of acquisition focal planes, it also reduces acquisition time of samples as well. Our suggested method has proven to reconstruct unacquired intermediate planes from diluted data sets up to 10× fold. The reconstructed planes were found correlated to the original preacquired samples (control group) with correlation coefficient of up to 90%. Given the findings, this procedure appears to be a powerful method for inquiring and analyzing biological samples.

Three-dimensional localization microscopy in live flowing cells

Capturing the dynamics of live cell populations with nanoscale resolution poses a significant challenge, primarily owing to the speed-resolution trade-off of existing microscopy techniques. Flow cytometry would offer sufficient throughput, but lacks subsample detail. Here we show that imaging flow cytometry, in which the point detectors of flow cytometry are replaced with a camera to record 2D images, is compatible with 3D localization microscopy through point-spread-function engineering, which encodes the depth of the emitter into the emission pattern captured by the camera. The extraction of 3D positions from sub-cellular objects of interest is achieved by calibrating the depth-dependent response of the imaging system using fluorescent beads mixed with the sample buffer. This approach enables 4D imaging of up to tens of thousands of objects per minute and can be applied to characterize chromatin dynamics and the uptake and spatial distribution of nanoparticles in live cancer cells.

3-Step flow focusing enables multidirectional imaging of bioparticles for imaging flow cytometry

Multidirectional imaging flow cytometry (mIFC) extends conventional imaging flow cytometry (IFC) for the image-based measurement of 3D-geometrical features of particles. The innovative core is a flow rotation unit in which a vertical sample lamella is incrementally rotated by 90 degrees into a horizontal lamella. The required multidirectional views are generated by guiding all particles at a controllable shear flow position of the parabolic velocity profile of the capillary slit detection chamber. All particles pass the detection chamber in a two-dimensional sheet under controlled rotation while each particle is imaged multiple times. This generates new options for automated particle analysis. In an experimental application, we used our system for the accurate classification of 15 species of pollen based on 3D-morphological information. We demonstrate how the combination of multi directional imaging with advanced machine learning algorithms can improve the accuracy of automated bio-particle classification. As an additional benefit, we significantly decrease the number of false positives in the classification of foreign particles, i.e. those elements which do not belong to one of the trained classes by the 3D-extension of the classification algorithm.

Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches

Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection of rare pathogenic objects in patient blood. These objects can be circulating tumor cells, very rare during the early stages of cancer development, various microorganisms and parasites in the blood during acute blood infections. All of these rare diagnostic objects can be detected and identified very rapidly to save a patient's life. This review outlines the main techniques of visualization of rare objects in the blood flow, methods for extraction of such objects from the blood flow for further investigations and new approaches to identify the objects automatically with the modern deep learning methods.

Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.

Microfluidic Based Optical Microscopes on Chip

Last decade's advancements in optofluidics allowed obtaining an ever increasing integration of different functionalities in lab on chip devices to culture, analyze, and manipulate single cells and entire biological specimens. Despite the importance of optical imaging for biological sample monitoring in microfluidics, imaging is traditionally achieved by placing microfluidics channels in standard bench-top optical microscopes. Recently, the development of either integrated optical elements or lensless imaging methods allowed optical imaging techniques to be implemented in lab on chip systems, thus increasing their automation, compactness, and portability. In this review, we discuss known solutions to implement microscopes on chip that exploit different optical methods such as bright-field, phase contrast, holographic, and fluorescence microscopy.

Image-Based Profiling of Synaptic Connectivity in Primary Neuronal Cell Culture

Neurological disorders display a broad spectrum of clinical manifestations. Yet, at the cellular level, virtually all these diseases converge into a common phenotype of dysregulated synaptic connectivity. In dementia, synapse dysfunction precedes neurodegeneration and cognitive impairment by several years, making the synapse a crucial entry point for the development of diagnostic and therapeutic strategies. Whereas high-resolution imaging and biochemical fractionations yield detailed insight into the molecular composition of the synapse, standardized assays are required to quickly gauge synaptic connectivity across large populations of cells under a variety of experimental conditions. Such screening capabilities have now become widely accessible with the advent of high-throughput, high-content microscopy. In this review, we discuss how microscopy-based approaches can be used to extract quantitative information about synaptic connectivity in primary neurons with deep coverage. We elaborate on microscopic readouts that may serve as a proxy for morphofunctional connectivity and we critically analyze their merits and limitations. Finally, we allude to the potential of alternative culture paradigms and integrative approaches to enable comprehensive profiling of synaptic connectivity.

In Vitro Tissue Microarrays for Quick and Efficient Spheroid Characterization

Three-dimensional (3D) in vitro microphysiological cultures, such as spheroids and organoids, promise increased patient relevance and therapeutic predictivity compared with reductionist cell monolayers. However, high-throughput characterization techniques for 3D models are currently limited to simplistic live/dead assays. By sectioning and staining in vitro microtissues, researchers can examine their structure; detect DNA, RNA, and protein targets; and visualize them at the level of single cells. The morphological examination and immunochemistry staining for in vitro cultures has historically been done in a laborious manner involving testing one set of cultures at a time. We have developed a technology to rapidly screen spheroid phenotype and protein expression by arranging 66 spheroids in a gel array for paraffin embedding, sectioning, and immunohistochemsitry. The process is quick, mostly automatable, and uses 11 times less reagents than conventional techniques. Here we showcase the capabilities of the technique in an array made up of 11 different cell lines stained in conventional hematoxylin and eosin (H&E) staining, as well as immunohistochemistry staining for estrogen (ER), progesterone (PR), and human epidermal growth factor (Her-2) receptors, and TP53. This new methodology can be used in optimizing stem cell-based models of disease and development, for tissue engineering, safety screening, and efficacy screens in cancer research.

Nanoscale Plasmonic V-Groove Waveguides for the Interrogation of Single Fluorescent Bacterial Cells

We experimentally demonstrate the interrogation of an individual Escherichia coli cell using a nanoscale plasmonic V-groove waveguide. Several different configurations were studied. The first involved the excitation of the cell in a liquid environment because it flows on top of the waveguide nanocoupler, while the obtained fluorescence is coupled into the waveguide and collected at the other nanocoupler. The other two configurations involved the positioning of the bacterium within the nanoscale waveguide and its excitation in a dry environment either directly from the top or through waveguide modes. This is achieved by taking advantage of the waveguide properties not only for light guiding but also as a mechanical tool for trapping the bacteria within the V-grooves. The obtained results are supported by a set of numerical simulations, shedding more light on the mechanism of excitation. This demonstration paves the way for the construction of an efficient bioplasmonic chip for diverse cell-based sensing applications.

Simultaneous dual-color light sheet fluorescence imaging flow cytometry for high-throughput marine phytoplankton analysis

This paper reports the development of a dual-color light sheet fluorescence imaging flow cytometer exclusively designed for rapid phytoplankton analysis. By simultaneously exciting chlorophyll and phycoerythrin fluorescence, the system is enabled to discriminate phycoerythrin-containing and phycoerythrin-lacking phytoplankton groups through simultaneous two-channel spectral imaging-in-flow. It is demonstrated the system has good sensitivity and resolution to detect picophytoplankton down to the size of ~1μm, high throughput of 1.3 × 10⁵cells/s and 5 × 10³cells/s at 100μL/min and 3mL/min volume flow rates for cultured picophytoplankton and nanophytoplankton detection, respectively, and a broad imaging range from ~1μm up to 300μm covering most marine phytoplankton cell sizes with just one 40 × objective. The simultaneous realization of high resolution, high sensitivity and high throughput with spectral resolving power of the system is expected to promote the technology towards more practical applications that demand automated phytoplankton analysis.