This PDF file contains the front matter associated with SPIE Proceedings Volume 12383, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Oblique plane microscopy (OPM) is a light-sheet microscopy technique that illuminates a tilted plane of a specimen and collects the fluorescence with the same high NA objective. Here, we apply OPM with stage-scanned image volume acquisition to perform high-content, 3D time-lapse imaging of up to 180 breast cancer spheroids over eight days every eight hours. Each spheroid comprises a combination of one to three different cell populations, each co-expressing a unique oncogene-fluorescent protein pair. We trained the image segmentation tool Cellpose to segment nuclei in the resulting data in order to measure integrated fluorescence intensity, nuclear size, and nuclear count for each of the different oncogenes to quantify competition between mutations.

Aspergillus niger is a major fungal phytopathogen of agricultural crops, particularly onion (Allium cepa L.) where it is the causative agent of post-harvest black mold contamination of mature onions. Post-harvest fungal pathogens in onion leads to reported losses in marketable crop of between 15 and 30%, but can be as high as 80% in warm climates. However, the relationship between A. niger and the A. cepa seedling is poorly understood as the disease is not commonly observed to manifest above the soil line during plant growth. Here, we present the first timelapse microscopy data of A. cepa seedlings being colonized by soil-dwelling A. niger using high throughput fluorescence microscopy and artificial transparent soil. Our model system mimics the natural environment in terms of soil structure, hydration, essential plant nutrients and light cycles. Using a specimen carousel to image up to ten specimens over time and hence increase the throughput of our study, we determined the anatomical location of initial infection along the hypocotyl-radical axis. We identified this point to be the junction between the hypocotyl and the radicle, i.e the ‘shoot’ and ‘root’. Timelapse microscopy reveals that infection begins at this location before spreading locally along the plant. We characterized this point using colorimetric glucose sensors and determined that this location represents a local peak of glucose exudation in healthy A. cepa seedlings, which is then colonized readily upon the introduction of A. niger.

In this study we propose a new approach to active illumination multispectral imaging with only one detector per spectral channel. Our setup is characterized by a high signal-to-noise ratio, relatively simple design and high spatial resolution. It can be integrated with multiple continuous or pulse light lasers operating at different wavelengths in the near- and mid-infrared spectral regions. We present a demonstrator which works in two spectral ranges (1550 nm and 2992 nm) with scattered and transmitted light. We provide examples of acquired multispectral images. Finally, we put forward proposals for further development which will allow for the simultaneous acquisition of dozens of spectral channels, with additional improvements of the image resolution and quality.

Motility of cells plays an important role to determine the cell growth, health and to monitor the gene transformation. The aim of the current study is to propose a combination of a high-contrast label-free imaging method and a computational approach (conventionally used for super-resolution) which can be used as a tool in tracing the motion of the cells and organelles. Here, we integrate quantitative phase microscopy (QPM) with waveguide-based trapping (WT) and applied multiple signal classification algorithm (MUSICAL) to analyze the motion of the trapped particle. We successfully trapped and displaced a 1 µm polystyrene bead particle on a strip waveguide using a 1064 nm laser beam. While propelling the polystyrene bead particle, we recorded time-lapsed interferometric images using a partially spatially coherent (PSC) light-based off-axis QPM system. The reconstruction of time-lapsed phase images of the trapped particle is accomplished using the Fourier transform and transport of intensity algorithm, which further used in MUSICAL for the motion trace analysis. Here, we traced the motion of a trapped bead particle with scale finer than the size of the object i.e., diffraction limit of the system. We show super-resolved motion trace even though the particle’s image is itself diffraction limited in each frame. The proposed study could be useful in different biological applications such as cell monitoring, cell tracking, manipulation, and classification between healthy and unhealthy cells.

We propose a reinforcement learning based architecture for focusing light from a coherent light source through a thick scattering sample using a spatial light modulator. The light source, modulator, sample and the image sensor are modeled in COMSOL environment. The diffraction pattern recorded at the image sensor is then transformed into MATLAB where an RL agent is used to predict the pattern that will focus the light through the sample. The SLM pattern is then updated accordingly. The SLM is modeled as a discrete set of block designed to introduce a phase shift between zero and two depending on the output of the RL agent. The state space of the RL agent in defined as an array of scalar values in [0,1]. The effect of resolution, pixel pitch and sample thickness are discussed. The samples are modeled as blocks of randomly distributed refractive indices. The model is trained on a set of 40 samples. Then training and test performances are reported. Further, applications on dynamic samples are discussed and results are presented for a number of configurations. The focused and unfocused diffraction patterns are presented for various sample thicknesses and at different epochs.

Here we present a simple, high-throughput, time-resolved flow cytometer to detect changes in NAD(P)H autofluorescence lifetime. The lifetime of this metabolite is characterized by its binding state, when bound to its coenzyme the metabolite exhibits a longer lifetime (1 to 7 ns) indicating a preferential energy generation state of oxidative phosphorylation. A shorter lifetime, 0.1 to 1 ns, however, indicates an unbound coenzyme which has been correlated to the ATP energy pathway of glycolysis. The work here in is to demonstrate the capabilities of simple, time-resolved cytometry to show subtle changes in autofluorescence lifetime detection. Flow cytometry results are validated by the use of the Agilent Seahorse HS Mini. Results include the method of ATP energy generation measured by the Seahorse as a comparison to flow cytometry lifetime shifts. Measurements were made using an estrogen receptor positive breast cancer cell line and the same cell line under developed Tamoxifen resistance. The outcome of this work is a map of the metabolic profile of tamoxifen resistance in breast cancer.

Traditional fluorescence-activated cell sorting (FACS) is commonly used to purify desired cell populations from the bulk cell suspension with high throughput. However, there are many biological applications that are not possible on FACS because they require imaging to classify cell types. One such application is the nuclear translocation assay, where the fluorescent signal moves from the cytosol into the nucleus in activated cells. The total fluorescence signal does not change upon activation so traditionally FACS cannot discern dormant and activated cells. Imaging is required to determine if a cell is activated based on the localization of fluorescence in the cell. With recent development in machine learning, optimized neural networks can learn more effective and compact cell image features than traditional feature design techniques. We have developed a cell image classification workflow that uses unsupervised clustering to identify the subpopulations present in a sample, allows the user to pick which subpopulation to sort, and a supervised classifier that is able to perform real-time cell classification to make sort decisions.

We present results applying this workflow to perform a nuclear translocation assay. The nuclear and cytosolic cells were classified with 98.7% precision and 87.4% recall with a latency of <0.4 ms per cell. This accuracy and speed demonstrate that this classification workflow can be used to perform the nuclear translocation assay in an Image Activated Cell Sorter.

Imaging flow cytometry has enabled high-throughput morphology-based analysis of large cell populations at the singlecell level. However, most of these systems were based on 2D imaging, and with the loss of the spatial information in the depth direction, its accuracy was limited, especially when performing analysis based on subcellular structures. Efforts to develop 3D imaging flow cytometry have been undertaken, but the throughput was limited because of the slow 3D imaging systems. Even by adopting light-sheet microscopy, the throughput was limited to less than 100 cells/s, due to the camera frame rate. To overcome this limitation, we combined oblique-plane microscopy with 1D acoustofluidic particle focusing to image multiple cells in parallel. With this, we were able to overcome the throughput limitation by the camera frame rate and achieve a detection throughput of 2,300 cells/s, the highest to our knowledge for 3D imaging flow cytometry. Furthermore, with such high throughput, we were able to capture 3D images of 4×10⁵ cells in an acquisition time of less than five min and perform subcellular-structure-based analysis of mitotic cells, demonstrating practical 3D imaging flow cytometry for the first time to our knowledge. Result show, that with 3D imaging flow cytometry, we can capture structural information that would be overlooked with its 2D imaging counterpart.

Calcium (Ca²⁺) signaling in endothelial cells plays an important role in regulation of wide range of physiological processes in the pulmonary microcirculation. A dysregulation in the intracellular Ca²⁺ concentration can serve as an initiator of different pathological conditions like lung endothelial barrier disruption, edema, inflammation, etc. Normal physiologic signaling is localized spatio-temporally and signaling ‘signatures’ define the specificity of outcomes within the cells. However, analyzing the signaling dynamics acquired through imaging of live tissues has been challenging owing to the intricate patterns of the distinct signals. Moreover, signal analysis tools based on whole-field or static region of interest (ROI) assessments may under- or over-estimate measurements of signaling parameters with respect to event origination, spread and duration. In the current study we designed an algorithm for detection and analysis of these biological signaling events based on dynamic ROI tracking, where time-dependent polygonal ROIs are automatically assigned to the changing perimeters of detected signaling events. This approach allowed for robust tracking of signals and quantification of the signaling event parameters over time. We next applied this algorithm on image sequences of lung slices isolated from genetically encoded mice expressing the endothelial specific cdh5GCaMP8 (GFP-based) calcium indicator, and observed an inherent dynamic Ca²⁺ signaling profile within the pulmonary microvascular endothelium. To investigate the versatility of our algorithm, we further treated the lung slices with acetylcholine (ACh) or subjected them to Ca²⁺-free (CAF) medium, to examine the emerging change in patterns of the profiles from the inherent basal dynamics. Under both these conditions, our software revealed distinct changes in event parameters with respect to event amplitude, duration and spread of the signaling events. Thus, our algorithm allowed us to identify distinct Ca²⁺ signaling patterns associated with various stimuli, thereby enabling identification of these signatures under a wide variety of sub-cellular/pathologic challenges.

The SARS-CoV-2 virus is still a challenge because of its diversity and mutations. The binding interactions of the angiotensin converting enzyme 2 (ACE2) receptor and the spike protein are relevant for the SARS-CoV-2 virus to enter the cell. Consequently, it is important and helpful to analyze binding activities and the changes in the structure of the ACE2 receptor and the spike protein. Surface enhanced Raman spectroscopy is able to analyze small concentrations of the proteins without contact, non-invasively and label-free. In this work, we present a SERS based approach in the visible wavelength range to analyze and study the binding interactions of the ACE2 receptor and the spike protein. SERS measurements of the ACE2 receptor, the spike protein and the ACE2-spike complex were performed. Additionally, an inhibitor was used to prevent the spike protein from binding to ACE2 and to compare the results. The analysis of the measured SERS spectra reveals structural differences and changes due to binding activities. Thus, we show that the performed SERS based approach can help for rapid and non-invasive analysis of binding interactions of the ACE2-spike complex and also of protein binding in general.

Algal blooms appear most often during the summertime in marine or fresh water resources and can be harmful to humans, animals, and aquatic life. Especially cyanobacteria blooms can be dangerous as they are able to produce cyanotoxins, which can be harmful already at low concentrations. Consequently, it is very important to analyze algae and cyanobacteria blooms quickly, in situ and without complex sample preparation to identify whether the bloom can be harmful or not and to initiate further steps in time. Raman spectroscopy is capable of analyzing organic samples in situ and non-invasively with high specificity. It allows to investigate the structure of algae and cyanobacteria without physical contact and a time effective sample preparation is possible. In this work, we present a Raman based approach to analyze algae and cyanobacteria in the visible wavelength range. We determine components and the structure of the cells to distinguish potentially harmful species and also non-harmful species. Most of the fluorescence signal from the algae is suppressed so that the Raman signals from components inside the cell can be measured. The acquired spectra are processed to compare them and to retrieve information about the differences in the inner structure. Our Raman-based approach is thus suited for fast analysis and distinction of potentially harmful algae and cyanobacteria.

Second messenger signals, e.g., Ca²⁺ and cyclic nucleotides, orchestrate a wide range of cellular events. The methods by which second messenger signals determine specific physiological responses are complex. Recent studies point to the importance of temporal and spatial encoding in determining signal specificity. Studies also indicate the importance of mechanical stimuli, substrate stiffness, and mechanical responses–the “mechanosome”–in regulating physiology. Hence, approaches that probe both chemical and mechanical signals are needed. Here, we report preliminary efforts to combine hyperspectral imaging for second messenger signal measurements, monolayer stress microscopy for mechanical force measurements, and S8 analysis software for quantifying localized signals–specifically, Ca²⁺ dynamics and mechanical forces in human airway smooth muscle cells (HASMCs). HASMCs were prepared as confluent monolayers on 11 kPa gels with embedded fluorescent microparticles that serve as fiducial markers as well as smaller microparticles to measure deformation (strain). Imaging was performed using a custom excitation-scanning hyperspectral microscope. Hyperspectral images were unmixed to identify signals from cellular fluorescent labels (e.g., CAL 590-AM) and fluorescent microparticles. Images were analyzed to quantify localized force dynamics through monolayer stress microscopy. S8 software was used to identify, track, and quantify spatially-localized Ca²⁺ activity. Results indicate that localized and transient cellular signals and forces can be quantified and mapped within cell populations. Importantly, these results establish a method for simultaneous interrogation of cellular signals and mechanical forces that may play synergistic roles in regulating downstream cellular physiology in confluent monolayers. This work was supported by NIH P01HL066299, R01HL137030, R01HL058506, and NSF MRI1725937. Drs. Leavesley and Rich disclose financial interest in a university start-up company, SpectraCyte LLC, to commercialize spectral imaging technologies.

Quantitative oblique back-illumination microscopy (qOBM) is a label-free imaging technique that enables tomographic phase imaging of thick scattering samples with epi-illumination. Here, we apply qOBM to image three-dimensional brain organoid cell cultures of tuberous sclerosis complex (TSC) disease. We identify quantitative differences that occur between the TSC organoids and a control cell line, and discuss the implications of these differences on our understanding the development of TSC organoid cultures. These differences include disruptions in the tubular processes in the organoid, a higher degree of folding and non-spherical cell growth, and differences in the proliferating cell structures between the two groups.

Understanding living cells and imaging intracellular dynamics requires complex and expensive devices. Conventional fluorescence microscopes suffer from poor resolution, while super-resolution comes with its own tradeoffs, making multi-cell studies challenging. We apply computational techniques such as super-resolution radial fluctuations to the tissue micrograph sequences, comparing a variety of numerical modifications and parameter settings to enable imaging of subcellular structures with higher resolution, reduced background noise, and enhanced signal intensity. Discrete organelle structures and dynamics are distinguishable in the final images, despite the original images having poor lateral resolution. The resultant computational techniques are widely available for use in studies anywhere

Single cell analysis of multi-dimensional microscopy images is repetitive, time consuming, and arduous. Numerous analysis steps are required to quantify and visualize cell heterogeneity and trends between experimental groups. The open-source community has created tools to facilitate this process. To further simplify analysis, we created a library of functions called cell-analysis-tools. This library includes functions that can streamline single-cell analysis for faster quality checking and automation. This library also includes example code with randomly generated data for dimensionality reduction [t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), Uniform Manifold Approximation and Projection (UMAP)] and machine learning models [random forest, support vector machine (SVM), linear regression] that scientists can swap with their own data to visualize trends. Lastly, this library includes template scripts for feature extraction that can help identify differences between experimental groups and cell heterogeneity within a group. This library can significantly decrease user time while increasing robustness and reproducibility of results.

In clinical practice, peripheral blood smears are observed with the naked eye to confirm the number and morphology of White Blood Cells (WBC). The pathologist’s proficiency has a great influence on the results because WBCs have diverse morphology. Many studies have been conducted on diagnostic assistance system for pathologists. However, these are still difficult to apply to images of various extracellular environments that change according to acquired conditions. Added to this is the limitation that only one WBC can be classified in one image. Therefore, in this study, we propose a robust segmentation algorithm for WBC nucleus and cytoplasm used color space, superpixel and watershed algorithm. Further, we propose a classification algorithm that classifies five types of normal WBCs using 16 morphological and texture features as well as the K-Nearest Neighbor (KNN) model. The accuracy of segmentation WBC nucleus and cytoplasm was 95.83% and 93.66%, respectively. The p-value for all the 16 features was significant (<0.001). In addition, through this study, research on a diagnostic assistance system that can classify more types of WBCs will be possible in the future.

Recently, as the usage of electronic devices increase, modern people suffer from eye diseases. We analyzed goblet cells of wide-field fluorescence microscopy with a deep learning. In this study, we propose to real-time analysis using knowledge distillation using proposed loss function and optimized network. In the proposed method, residual based UNet was used as the teacher network to distill knowledge into lightweight E-Net. We train the student network using pixelwise loss and . The proposed method showed 4% improvements in dice-score compared to the lightweight E-Net, and the processing time was decreased to 68% compared to the case where only the teacher network was performed.

Dielectrophoresis is a technology that uses the electrical properties of cells to control the movement of cells in a non-contact manner. It is important to observe cell movement in order to analyze cell characteristics using DEP technology. We developed an algorithm that can track the movement of hundreds of unlabeled cells by DEP force. The proposed algorithm consists of a cell detection step using a deep learning detection model and a cell tracking step based on a multiscale region of interest. Cell detection and tracking accuracy using Recall, precision, f-measure, and MOTA on a timelapse microscope image dataset has an accuracy of about 97% or more. In conclusion, by developing an automated tool that can perform imaging-based DEP cell analysis, cell tracking algorithms that can track hundreds of cells simultaneously can reduce cell analysis time and labor.

Results are presented from an experimental proof of concept for a full-spectrum microfluidic flow cytometer. Existing flow cytometers operate on photomultiplier tubes (PMTs) and are restricted in the number of available fluorophores, with newer systems costing $250k to $500k. Current EMCCD technology paired with microfluidics promises to offer superior channel decomposition while allowing a significant reduction in price. A device was constructed from polydimethysiloxane (PDMS) with fiber optics for sample excitation and fluorescence collection, and with an 80 micron channel for sample flow. In the first experiment, we captured spectra of individual fluorescently labeled polystyrene beads and demonstrated classification of beads according to their labeling fluorophore, as well as characterization of anomalous beads. In the second experiment, two stoichiometric mixtures of six dyes were created with the successful decomposition of the mixtures into components, using reference spectra acquired on the system. We anticipate the capability of full spectra to enable the extension to twenty-one fluorophores with the microfluidic design creating a robust system with a total cost of under $50,000.

Light sheet fluorescence microscopy (LSFM) works on fluorescence imaging, employing target-specific fluorescent proteins to label regions of interest in a sample. In this way, analyzing and manipulating information from different targets in a specimen requires labeling the specimen using multiple fluorophores. Multispectral imaging helps identify several target molecules simultaneously at once, thereby retrieving information from the entire volume of the sample. Here we report multispectral imaging studies on mammalian cells using our in-house built multispectral LSFM imaging system. Experimental studies were conducted on mammalian cells in alive conditions, and results are provided.