Cell Imaging Data Research Articles

Deep Learning-Based Integration of Morphology and Immunophenotype Predicts Molecular Genetic Subtypes of Multiple Myeloma

Abstract Multiple myeloma, the second most common hematologic malignancy, is characterized by recurrent molecular genetic variants which portend important prognostic and therapeutic implications. These variants, which include chromosomal aneuploidies, translocations, and other structural changes, are currently identified by performing karyotyping and fluorescence in situ hybridization analysis on bone marrow biopsy samples. Comprehensive variant testing for full characterization of multiple myeloma cases can take days to weeks, is costly, and is not available to all patients. Past studies have explored to what extent cellular morphologic information from bone marrow aspirates or immunophenotypic information from flow cytometry can predict these genetic variants via the use of machine learning algorithms. However, the integration of multiple hematopathology data modalities using machine learning towards improving the diagnosis and characterization of hematologic neoplasms has yet to be explored. To this end, we developed a deep learning-based algorithm which, for individual patients, integrates tens-of-thousands of cell images from Wright-stained bone marrow aspirate smears with hundreds-of-thousands of events from myeloma flow cytometry panels to predict the presence or absence of particular molecular genetic variants among 85 cases of multiple myeloma. Attention-based multi-instance learning models were developed utilizing convolutional neural networks to analyze cell image data and multi-layer perceptrons to analyze flow cytometry data. Models trained solely on cell image data showed variable performance in predicting genetic subtypes of multiple myeloma cases: t(11;14), AUROC = 0.795; 1q+, AUROC = 0.743; del(17p), AUROC = 0.520. Similar models trained solely on flow cytometry data showed fair performance for all subtypes: t(11;14), AUROC = 0.761; 1q+, AUROC = 0.761; del(17p), AUROC = 0.787. However, models which combine features among high-attention events from both cell image and flow cytometry data yield significantly improved performance: t(11;14), AUROC = 0.892; 1q+, AUROC = 0.789; del(17p), AUROC = 0.876. Similarly, models trained to predict established prognostic classifications of multiple myeloma cases performed best when cell image and flow cytometry data is integrated: cell images alone, AUROC = 0.721; flow cytometry alone, AUROC = 0.827; both data combined, AUROC = 0.839. Additional improvements in prognostic classification were obtained when utilizing a Kronecker product to more completely utilize pairwise interactions between cell image and flow cytometry features: AUROC = 0.864. This study is, to our knowledge, the first to utilize deep learning to integrate multiple hematopathology data modalities towards automated diagnosis or subclassification of hematologic neoplasms in individual patients. The results of this study demonstrate the feasibility of accurately predicting molecular genetic subtypes of multiple myeloma cases solely from cell morphology and flow cytometric information. With further refinement, these promising models could be applied in low resource settings as well as research settings to better understand the biological relationship between plasma cell genetics with morphology and immunophenotype.

A computational framework to in silico screen for drug-induced hepatocellular toxicity.

Drug-induced liver injury (DILI) is the most common trigger for acute liver failure and the leading cause of attrition in drug development. In this study, we developed an in silico framework to screen drug-induced hepatocellular toxicity (INSIGHT) by integrating the post-treatment transcriptomic data from both rodent models and primary human hepatocytes. We first built an early prediction model using logistic regression with elastic net regularization for 123 compounds and established the INSIGHT framework that can screen for drug-induced hepatotoxicity. The 235 signature genes identified by INSIGHT were involved in metabolism, bile acid synthesis, and stress response pathways. Applying the INSIGHT to an independent transcriptomic dataset treated by 185 compounds predicted that 27 compounds show a high DILI risk, including zoxazolamine and emetine. Further integration with cell image data revealed that predicted compounds with high DILI risk can induce abnormal morphological changes in the endoplasmic reticulum and mitochondrion. Clustering analysis of the treatment-induced transcriptomic changes delineated distinct DILI mechanisms induced by these compounds. Our study presents a computational framework for a mechanistic understanding of long-term liver injury and the prospective prediction of DILI risk.

A generative benchmark for evaluating the performance of fluorescent cell image segmentation

Fluorescent cell imaging technology is fundamental in life science research, offering a rich source of image data crucial for understanding cell spatial positioning, differentiation, and decision-making mechanisms. As the volume of this data expands, precise image analysis becomes increasingly critical. Cell segmentation, a key analysis step, significantly influences quantitative analysis outcomes. However, selecting the most effective segmentation method is challenging, hindered by existing evaluation methods' inaccuracies, lack of graded evaluation, and narrow assessment scope. Addressing this, we developed a novel framework with two modules: StyleGAN2-based contour generation and Pix2PixHD-based image rendering, producing diverse, graded-density cell images. Using this dataset, we evaluated three leading cell segmentation methods: DeepCell, CellProfiler, and CellPose. Our comprehensive comparison revealed CellProfiler's superior accuracy in segmenting cytoplasm and nuclei. Our framework diversifies cell image data generation and systematically addresses evaluation challenges in cell segmentation technologies, establishing a solid foundation for advancing research and applications in cell image analysis.

MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.

Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics. We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen's ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen's potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications. MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics.

Abstract 4699: Evaluating extracellular matrix invasion and cytotoxicity of natural killer cells in a novel co-culture assay

Abstract Background: The complex tumor microenvironment (TME) in solid tumors presents unique challenges for lymphocytes used in cancer immunotherapy. The extracellular matrix (ECM) in TME can limit the recruitment of lymphocytes and restrict access to cancer cells. This study utilized Matrigel to study the kinetics of natural killer cell invasion and cytotoxicity. We hypothesized that the kinetics of tumor cell killing by the NKs would depend on the distance for invasion and rely on matrix-metalloproteinases (MMPs) for remodeling the ECM. Methods: Increasing volumes (50-100 µL/well) of Matrigel (6 mg/mL) representing increasing distance for invasion was layered over MCF7-red target tumor cells expressing nuclear-localized mKate2 (red fluorescent protein). NK92 (Creative Bioarray) or eGFP-NK92 (BPS Bioscience) were seeded over the solidified Matrigel layer at E:T of 3:1. The broad spectrum MMP inhibitor, Ilomostat (GM6001, 2 µM and 10 µM), was added to the wells to investigate to role of MMPs. Label-free impedance and live cell imaging data was collected in real time on xCELLigence RTCA eSight to evaluate NK cell invasion and cytotoxicity. Percent cytolysis was calculated on the RTCA software from normalized cell impedance readings and imaging data using the formula [(1-(treated/untreated)*100]. Results: Impedance reading increases as the MCF7-red cells settle, attach, and proliferate. The readings plateau as the cells reach confluence. The addition of Matrigel modulates the impedance signature of MCF7 cells. Importantly, Matrigel delays cytolysis by NK92 cells added at 24h. The delay increases with increasing volume of Matrigel representing increasing invasion distance. The time to achieve 60% killing with respect to controls (KT60) was 67, 76 and 89 hours for 50, 75 and 100 µL/well, respectively. Consistent with the role of MMPs in invasion, percent cytolysis calculated independently from normalized impedance readings and live cell imaging data confirmed both delayed and reduced target cell killing by GFP-NK92 cells in the presence of Ilomostat. Interestingly, only a few GFP-NK92 were detected by live cell imaging when there was increased target cell death. These included highly active NK cells that made multiple contacts with different MCF7 red targets in clusters suggesting serial killing activity. Conclusions: The observations that only a few NK92 cells are detected during the killing of susceptible target cells and that cytolysis is not abrogated by the inhibition of MMPs suggest a potential role for distal effectors like cytokines, independent of invasion. This is a novel 2.5D assay that demonstrates the potential of RTCA eSight platform for studying the invasion and cytotoxicity function of various immune cells through both real-time impedance recording and live-cell time-lapse imaging simultaneously. (For Research Use Only. Not for use in diagnostic procedures. RA45236.3472453704) Citation Format: Jyothi Thyagabhavan Mony, Xiaoyu Zhang. Evaluating extracellular matrix invasion and cytotoxicity of natural killer cells in a novel co-culture assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4699.

Abstract 3248: Metabolic rewiring shifts engineered TCR T cell bioenergetics to enhance cytotoxicity

Abstract Background: Adoptive T cell-based transfer has shown tremendous success in hematological cancers. However, an important obstacle to using cell and gene therapy for solid tumors is the aggressive tumor microenvironment (TME). To significantly improve prognosis of subjects undergoing immunotherapy, it is essential to incorporate unique methods to drive T cell energy production and metabolism towards empowering T cells to dismantle the suppressive TME. This can promote anti-tumor effects and persistence of the transfused engineered T cells. In the present study we present a simple metabolic preconditioning process that improves T cell performance and bioenergetics. Methods: Using the Agilent xCELLigence RTCA eSight, and Seahorse XF Analyzer we assessed the killing efficiency, and bioenergetics of engineered T-cells after pretreating with Arginine. Briefly, engineered TCR T-cells against MART-1 were generated by transducing CD3+ T-cells (Hemacare, Seattle, WA) with retrovirus SAMEN-DMF5 with a CD34 marker gene, against MART-1. The TCR T cells were pre-conditioned in 6mM Arginine for 7 days, followed by a killing assay using MART-1 expressing melanoma cell line as target cells (624.38) at the E:T ratio of 5:1. Target melanoma cells, 624.38 cells, were engineered to express a red-fluorescent nuclear protein. The comparison was made with transduced T-cells grown in RPMI (no added amino acid supplementation, and non-transduced T cells. The cytotoxicity assay was assessed using real-time impedance and live cell imaging data collected on xCELLigence RTCA eSight. Concurrently, the metabolic profiling of the engineered T-cells was examined by the XF96 Analyzer. Results: Supplementing the growth medium with 6 mM Arginine significantly increased the potency of MART-1 engineered T cells (up to 6-fold) and bioenergetic studies revealed increased spare respiratory capacity, basal respiration, and ATP production via mitochondrial respiration. Conclusions: Arginine pre-conditioning improved TCR T cell potency through metabolic rewiring favoring mitochondrial respiration. For Research Use Only. Not for use in diagnostic procedures RA45236.3483333333 Citation Format: Rashmi Pillai, Xiaoyu Zhang, Yama Abassi. Metabolic rewiring shifts engineered TCR T cell bioenergetics to enhance cytotoxicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3248.

The ability to sense the environment is heterogeneously distributed in cell populations.

Channel capacity of signaling networks quantifies their fidelity in sensing extracellular inputs. Low estimates of channel capacities for several mammalian signaling networks suggest that cells can barely detect the presence/absence of environmental signals. However, given the extensive heterogeneity and temporal stability of cell state variables, we hypothesize that the sensing ability itself may depend on the state of the cells. In this work, we present an information-theoretic framework to quantify the distribution of sensing abilities from single-cell data. Using data on two mammalian pathways, we show that sensing abilities are widely distributed in the population and most cells achieve better resolution of inputs compared to an 'average cell'. We verify these predictions using live-cell imaging data on the IGFR/FoxO pathway. Importantly, we identify cell state variables that correlate with cells' sensing abilities. This information-theoretic framework will significantly improve our understanding of how cells sense in their environment.

DARTS: an open-source Python pipeline for Ca2+ microdomain analysis in live cell imaging data.

Ca2+ microdomains play a key role in intracellular signaling processes. For instance, they mediate the activation of T cells and, thus, the initial adaptive immune system. They are, however, also of utmost importance for activation of other cells, and a detailed understanding of the dynamics of these spatially localized Ca2+ signals is crucial for a better understanding of the underlying signaling processes. A typical approach to analyze Ca2+ microdomain dynamics is live cell fluorescence microscopy imaging. Experiments usually involve imaging a larger number of cells of different groups (for instance, wild type and knockout cells), followed by a time consuming image and data analysis. With DARTS, we present a modular Python pipeline for efficient Ca2+ microdomain analysis in live cell imaging data. DARTS (Deconvolution, Analysis, Registration, Tracking, and Shape normalization) provides state-of-the-art image postprocessing options like deep learning-based cell detection and tracking, spatio-temporal image deconvolution, and bleaching correction. An integrated automated Ca2+ microdomain detection offers direct access to global statistics like the number of microdomains for cell groups, corresponding signal intensity levels, and the temporal evolution of the measures. With a focus on bead stimulation experiments, DARTS provides a so-called dartboard projection analysis and visualization approach. A dartboard projection covers spatio-temporal normalization of the bead contact areas and cell shape normalization onto a circular template that enables aggregation of the spatiotemporal information of the microdomain detection results for the individual cells of the cell groups of interest. The dartboard visualization allows intuitive interpretation of the spatio-temporal microdomain dynamics at the group level. The application of DARTS is illustrated by three use cases in the context of the formation of initial Ca2+ microdomains after cell stimulation. DARTS is provided as an open-source solution and will be continuously extended upon the feedback of the community. Code available at: 10.5281/zenodo.10459243.

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures.

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

Peripheral Blood Leukocyte Detection Based on an Improved Detection Transformer Algorithm.

The combination of a blood cell analyzer and artificial microscopy to detect white blood cells is used in hospitals. Blood cell analyzers not only have large throughput, but they also cannot detect cell morphology; although artificial microscopy has high accuracy, it is inefficient and prone to missed detections. In view of the above problems, a method based on Fourier ptychographic microscopy (FPM) and deep learning to detect peripheral blood leukocytes is proposed in this paper. Firstly, high-resolution and wide-field microscopic images of human peripheral blood cells are obtained using the FPM system, and the cell image data are enhanced with DCGANs (deep convolution generative adversarial networks) to construct datasets for performance evaluation. Then, an improved DETR (detection transformer) algorithm is proposed to improve the detection accuracy of small white blood cell targets; that is, the residual module Conv Block in the feature extraction part of the DETR network is improved to reduce the problem of information loss caused by downsampling. Finally, CIOU (complete intersection over union) is introduced as the bounding box loss function, which avoids the problem that GIOU (generalized intersection over union) is difficult to optimize when the two boxes are far away and the convergence speed is faster. The experimental results show that the mAP of the improved DETR algorithm in the detection of human peripheral white blood cells is 0.936. In addition, this algorithm is compared with other convolutional neural networks in terms of average accuracy, parameters, and number of inference frames per second, which verifies the feasibility of this method in microscopic medical image detection.

Detection Of Malaria Parasites In Human Blood Cells Using Convolutional Neural Network

Malaria is a blood disease caused by the Plasmodium parasite which is transmitted by the bite of the female Anopheles mosquito. The diagnosis of malaria is carried out by a microscopist through examination of human blood cells. Their level of accuracy depends on the quality of the tool, expertise in classifying and counting infected and uninfected parasite cells. The disadvantages of examining this way include the difficulty in making a diagnosis on a large scale and the poor quality of the results. The dataset used in model evaluation is a dataset developed by LHNVBC which contains 27,558 cell image data. The malaria dataset will be processed through data science processing using a Convolutional Neural Network with the ResNet architecture. The model will conduct training on the dataset and then the model will be able to recognize malaria parasites in human blood cells. The model will be trained by optimizing multinomial logistic regression using Stochastic Gradient Descent (SGD) and Nesterov momentum values. The results of training data validation accuracy from model training with 50 epochs were obtained at 96.23% and 97% after being tested on data testing.

Mol* Volumes and Segmentations: visualization and interpretation of cell imaging data alongside macromolecular structure data and biological annotations.

Segmentation helps interpret imaging data in a biological context. With the development of powerful tools for automated segmentation, public repositories for imaging data have added support for sharing and visualizing segmentations, creating the need for interactive web-based visualization of 3D volume segmentations. To address the ongoing challenge of integrating and visualizing multimodal data, we developed Mol* Volumes and Segmentations (Mol*VS), which enables the interactive, web-based visualization of cellular imaging data supported by macromolecular data and biological annotations. Mol*VS is fully integrated into Mol* Viewer, which is already used for visualization by several public repositories. All EMDB and EMPIAR entries with segmentation datasets are accessible via Mol*VS, which supports the visualization of data from a wide range of electron and light microscopy experiments. Additionally, users can run a local instance of Mol*VS to visualize and share custom datasets in generic or application-specific formats including volumes in .ccp4, .mrc, and .map, and segmentations in EMDB-SFF .hff, Amira .am, iMod .mod, and Segger .seg. Mol*VS is open source and freely available at https://molstarvolseg.ncbr.muni.cz/.

Classification of Malaria Cell Image using Inception-V3 Architecture

Malaria is a severe global public health problem caused by the bite of infected mosquitoes. It can be cured, but only with early detection and effective, quick treatment. It can cause severe conditions if not properly diagnosed and treated at an early stage. In the worst scenario, it can cause death. This study aims at focusing on classifying malaria cell images. Malaria is classified as a dangerous disease caused by the bite of the female Anophles mosquito. As such, it leads to mortality when immediate action and treatment fails to be administered. In particular, this study aims to classify malaria cell images by utilizing the Inception-V3 architecture. In this study, training was conducted on 27,558 malaria cell image data through Inception-V3 architecture by proposing 3 scenarios. The proposed scenario 1 model applies the SGD optimizer to generate a loss value of 0.13 and an accuracy value of 0.95; scenario 2 model applies the Adam optimizer to generate a loss value of 0.09 and an accuracy value of 0.96; and lastly scenario 3 implements the RMSprop optimizer to generate a loss value of 0.08 and an accuracy value of 0.97. Applying the three scenarios, the results of the study apparently indicate that the Inception-V3 model using the RMSprop optimizer is capable of providing the best accuracy results with an accuracy of 97% with the lowest loss value, compared to scenario 1 and scenario 2. Further, the test results confirms that the proposed model in this study is capable of classifying malaria cells effectively.

Immersive and interactive visualization of 3D spatio-temporal data using a space time hypercube: Application to cell division and morphogenesis analysis.

The analysis of multidimensional time-varying datasets faces challenges, notably regarding the representation of the data and the visualization of temporal variations. We propose an extension of the well-known Space-Time Cube (STC) visualization technique in order to visualize time-varying 3D spatial data, taking advantage of the interaction capabilities of Virtual Reality (VR). First, we propose the Space-Time Hypercube (STH) as an abstraction for 3D temporal data, extended from the STC concept. Second, through the example of embryo development imaging dataset, we detail the construction and visualization of a STC based on a user-driven projection of the spatial and temporal information. This projection yields a 3D STC visualization, which can also encode additional numerical and categorical data. Additionally, we propose a set of tools allowing the user to filter and manipulate the 3D STC which benefits the visualization, exploration and interaction possibilities offered by VR. Finally, we evaluated the proposed visualization method in the context of 3D temporal cell imaging data analysis, through a user study (n = 5) reporting the feedback from five biologists. These domain experts also accompanied the application design as consultants, providing insights on how the STC visualization could be used for the exploration of complex 3D temporal morphogenesis data.

Steady-state distributions of nascent RNA for general initiation mechanisms

Fluctuations in the number of nascent RNA accurately reflect transcriptional activity. However, mathematical models predicting their distributions are difficult to solve analytically due to their non-Markovian nature stemming from transcriptional elongation. Here we circumvent this problem by deriving an exact relationship between the steady-state distribution of nascent RNA and the distribution of initiation times, which can be computed for any general initiation mechanism described by a set of first-order reactions. We test our theory using simulations and live cell imaging data.

Integrated intracellular organization and its variations in human iPS cells

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine1,2. Here we reduced this complexity by focusing on cellular organization—a key readout and driver of cell behaviour3,4—at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the ‘wiring’ of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring.

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.

Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells.

Stimulated Raman Scattering Microscopy (SRS) is a powerful tool for label-free detailed recognition and investigation of the cellular and subcellular structures of living cells. Determining subcellular protein localization from the cell level of SRS images is one of the basic goals of cell biology, which can not only provide useful clues for their functions and biological processes but also help to determine the priority and select the appropriate target for drug development. However, the bottleneck in predicting subcellular protein locations of SRS cell imaging lies in modeling complicated relationships concealed beneath the original cell imaging data owing to the spectral overlap information from different protein molecules. In this work, a multiple parallel fusion network, MPFnetwork, is proposed to study the subcellular locations from SRS images. This model used a multiple parallel fusion model to construct feature representations and combined multiple nonlinear decomposing algorithms as the automated subcellular detection method. Our experimental results showed that the MPFnetwork could achieve over 0.93 dice correlation between estimated and true fractions on SRS lung cancer cell datasets. In addition, we applied the MPFnetwork method to cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new method for the time-resolved study of subcellular components in different cells, especially cancer cells.

Virtual screening for small-molecule pathway regulators by image-profile matching.

Identifying the chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual, profile-based screening approach instead identifies compounds that match to pathways based on the phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small-molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image-profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds.

A Self-Immolative Fluorescent Probe for Selective Detection of SARS-CoV-2 Main Protease.

Existing tools to detect and visualize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suffer from low selectivity, poor cell permeability, and high cytotoxicity. Here we report a novel self-immolative fluorescent probe (MP590) for the highly selective and sensitive detection of the SARS-CoV-2 main protease (Mpro). This fluorescent probe was prepared by connecting a Mpro-cleavable peptide (N-acetyl-Abu-Tle-Leu-Gln) with a fluorophore (i.e., resorufin) via a self-immolative aromatic linker. Fluorescent titration results show that MP590 can detect Mpro with a limit of detection (LoD) of 35 nM and is selective over interferents such as hemoglobin, bovine serum albumin (BSA), thrombin, amylase, SARS-CoV-2 papain-like protease (PLpro), and trypsin. The cell imaging data indicate that this probe can report Mpro in HEK 293T cells transfected with a Mpro expression plasmid as well as in TMPRSS2-VeroE6 cells infected with SARS-CoV-2. Our results suggest that MP590 can both measure and monitor Mpro activity and quantitatively evaluate Mpro inhibition in infected cells, making it an important tool for diagnostic and therapeutic research on SARS-CoV-2.