High-throughput Imaging Research Articles

Lensless On-Chip Chemiluminescence Imaging for High-Throughput Single-Cell Heterogeneity Analysis.

High-throughput single-cell heterogeneity imaging and analysis is essential for understanding complex biological systems and for advancing personalized precision disease diagnosis and treatment. Here, we present a miniaturized lensless chemiluminescence chip for high-throughput single-cell functional imaging with subcellular resolution. With the sensitive chemiluminescence sensing and wide field of view of contact lensless imaging, we demonstrated the chemiluminescent imaging of over 1000 single cells, and their membrane glycoprotein and the high-throughput single-cell heterogeneity of membrane protein imaging were examined for precision analysis. Furthermore, the functional adhesion and heterogeneity of single live cells were imaged and explored. This miniaturized lensless on-chip CL-CMOS imaging platform enables high-throughput single-cell imaging and analysis with high sensitivity and subcellular resolution, providing new techniques for the cellular study of biological heterogeneity and has potential application in precision disease diagnosis and treatment at the point-of-care settings.

Integrating dynamic high-throughput phenotyping and genetic analysis to monitor growth variation in foxtail millet

BackgroundFoxtail millet [Setaria italica (L.) Beauv] is a C4 graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation.ResultsThis study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH.ConclusionThe HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.

Correction: High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Correction: High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Universal high-throughput image quantification of subcellular structure dynamics and spatial distributions within cells.

Current methods for image analysis in microscopy are tailored to specific biological contexts, which creates challenges in implementation and efficiency for researchers studying a diverse range of subcellular processes. Our application of dispersion indices, traditionally used in economics, offers a universal framework for high throughput quantification of biological structures, enabling easier and more consistent analysis across various biological contexts. By transforming pixel intensity and count into meaningful statistical measures, our method simplifies the quantification of subcellular structures such as autophagic puncta, microtubule dynamics, and mitochondrial clustering. This approach accelerates the quantitative analysis of sub-cellular processes in disease as well as imaged-based drug discovery. Classification: Bioengineering, Cell Biology, Applied Biological Sciences.

An Omni-Mesoscope for multiscale high-throughput quantitative phase imaging of cellular dynamics and high-content molecular characterization.

The mesoscope has emerged as a powerful imaging tool in biomedical research, yet its high cost and low resolution have limited its broader application. Here, we introduce the Omni-Mesoscope, a high-spatial-temporal and multimodal mesoscopic imaging platform built from cost-efficient off-the-shelf components. This system uniquely merges the capabilities of label-free quantitative phase microscopy to capture live-cell morphodynamics across thousands of cells with highly multiplexed fluorescence imaging for comprehensive molecular characterization. This Omni-Mesoscope offers a mesoscale field of view of ~5 square millimeters with a high spatial resolution down to 700 nanometers, enabling the capture of detailed subcellular features. We demonstrate its capability in delineating molecular characteristics underlying rare morphodynamic cellular phenomena, including cancer cell responses to chemotherapy and the emergence of polyploidy in drug-resistant cells. We also integrate expansion technique to enhance three-dimensional volumetric super-resolution imaging of thicker tissues, opening the avenues for biological exploration at unprecedented scales and resolutions.

High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Multicolor iLIFE (m-iLIFE) volume cytometry for high-throughput imaging of multiple organelles

To be able to resolve multiple organelles at high throughput is an incredible achievement. This will have immediate implications in a range of fields ranging from fundamental cell biology to translational medicine. To realize such a high-throughput multicolor interrogation modality, we have developed a light-sheet based flow imaging system that is capable of visualizing multiple sub-cellular components with organelle-level resolution. This is possible due to the unique optical design that combines an illumination system comprising two collinear light sheets illuminating the flowing cells and a dedicated dual-color 4f-detection, enabling simultaneous recording of multiple organelles. The system PSF sections up to 4 parallel microfluidic channels through which cells are flowing, and multicolor images of cell cross-sections are recorded. The data is then computationally processed (filtered using ML algorithm, shift-corrected, and merged) and combined to reconstruct the 3D multicolor volume. System testing is conducted using multicolor fluorescent nano-beads (size ∼175\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim 175$$\\end{document} nm) and flow-based imaging parameters (PSF size, motion-blur, flow rate, frame rate, and number of cell-sections) are determined for quality imaging. Drug treatment studies were carried out for healthy and cancerous HeLa cells to check the performance of the proposed system. The cells were treated with a drug (Vincristine, which is known to promote mitochondrial fission in cells), and the same is compared with untreated control cells. The proposed multicolor iLIFE system could screen ∼800\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim 800$$\\end{document} cells/min (at a flow speed of 2490μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2490 ~\\upmu$$\\end{document}m/s), and the drug treatment studies were carried out up to 24 h. Studies showed the disintegration of mitochondrial network and dysfunctional lysosomes and their accumulation at the cell membrane, which is a clear indication of cell apoptosis. Compared to control cells (untreated), the mortality is highest at a concentration of 500 nM post 12 h of drug treatment. With the capability of multiorganelle interrogation and organelle-level resolution, the multicolor iLIFE cytometry system is suitably placed to assist optical imaging and biomedical research.

Automated multimodal imaging of Caenorhabditis elegans behavior in multi-well plates.

Assays of behavior in model organisms play an important role in genetic screens, drug testing, and the elucidation of gene-behavior relationships. We have developed an automated, high-throughput imaging and analysis method for assaying behaviors of the nematode C. elegans. We use high-resolution optical imaging to longitudinally record the behaviors of 96 animals at a time in multi-well plates, and computer vision software to quantify the animals' locomotor activity, behavioral states, and egg laying events. To demonstrate the capabilities of our system we used it to examine the role of serotonin in C. elegans behavior. We found that egg-laying events are preceded by a period of reduced locomotion, and that this decline in movement requires serotonin signaling. In addition, we identified novel roles of serotonin receptors SER-1 and SER-7 in regulating the effects of serotonin on egg laying across roaming, dwelling, and quiescent locomotor states. Our system will be useful for performing genetic or chemical screens for modulators of behavior.

A High-Content and High-Throughput Imaging Screen to Identify a Small Molecule Inhibitor for the Lipid Uptake Function of KIM-1

A High-Content and High-Throughput Imaging Screen to Identify a Small Molecule Inhibitor for the Lipid Uptake Function of KIM-1

Design and Implementation of a Proof-of-Concept Robotic-Microfluidic Interface to Bridge Spatial Comprehensive Three-Dimensional Liquid Chromatography with Mass Spectrometry.

A proof-of-concept system is presented for the hyphenation of spatial comprehensive three-dimensional liquid chromatography (3D-LC) to mass spectrometry (MS) detection via a robotic-microfluidic interface. A three-dimensional fractal microflow distributor, incorporating 16 parallel RP monolithic capillary columns arranged in a 4 × 4 configuration, was connected to an X-Y-Z robotic system. This setup facilitated the deposition of successive arrays of microdroplets onto an MS target plate. To minimize carryover during droplet deposition, a strategy was implemented in which the distance between the target plate and the capillary was gradually increased during the deposition process. System-level variation in travel time and subsequent flow rates across parallel columns was assessed and translated in retention alignment based on injection of a protein standard. The successful separation of intact proteins was demonstrated through a parallel 4 × 4 column configuration, applying MALDI-MS detection after microdroplet spotting on an MS target plate. Furthermore, the discussion encompasses high-throughput MS imaging detection within the framework of spatial 3D-LC.

HiExM: high-throughput expansion microscopy enables scalable super-resolution imaging.

Expansion microscopy (ExM) enables nanoscale imaging using a standard confocal microscope through the physical, isotropic expansion of fixed immunolabeled specimens. ExM is widely employed to image proteins, nucleic acids, and lipid membranes in single cells; however, current methods limit the number of samples that can be processed simultaneously. We developed High-throughput Expansion Microscopy (HiExM), a robust platform that enables expansion microscopy of cells cultured in a standard 96-well plate. Our method enables ~4.2x expansion of cells within individual wells, across multiple wells, and between plates. We also demonstrate that HiExM can be combined with high-throughput confocal imaging platforms to greatly improve the ease and scalability of image acquisition. As an example, we analyzed the effects of doxorubicin, a known cardiotoxic agent, on human cardiomyocytes (CMs) as measured by Hoechst signal across the nucleus. We show a dose dependent effect on nuclear DNA that is not observed in unexpanded CMs, suggesting that HiExM improves the detection of cellular phenotypes in response to drug treatment. Our method broadens the application of ExM as a tool for scalable super-resolution imaging in biological research applications.

Digitising Kew's Herbarium: Do We Have a Protocol for That?

How do you get 8.5 million plant and fungal collections imaged, transcribed and online for global consumption? Digitisation staff at the Royal Botanic Gardens, Kew are currently seeking to answer this question to make the vast herbarium and fungarium collections freely available. Two years into the project and with 3.4 million collections fully digitised (as of July 2024), we reflect here on the trials and challenges faced by one team in particular, the digitisation team’s curator-botanists in the herbarium. Kew Gardens is thought to host the world’s oldest purpose-built herbarium. The plant collections housed within, comprise one of the largest collections in the world—collections that underpin much of Kew’s scientific research. Along with Kew's Fungarium, Kew's Herbarium collections include over 95% of known vascular plant genera and 60% of described fungi, representing data collected from more than 260 years of exploration and observation. Earlier projects have ensured the digitisation of all 330,000 type specimens and smaller, limited, family or area projects, with images and data being made available via platforms such as Global Biodiversity Information Facility (GBIF) and Global Plants on JSTOR (Journal Storage). The current digitisation project, running from 2022–26, ambitiously aims to create a complete catalogue of all remaining collections at Kew. Specimen data transcriptions and high-resolution images will be freely available online via the new Kew Data Portal, accessible externally to researchers around the globe. The strength of this project lies in the dedicated teams of specialists involved, including imaging experts, data managers, quality assurance specialists, and curators of the herbarium and fungarium. Staff in the role of curator-botanist (digitisation), a role created at the start of the project, function as a buffer between the high throughput image capturers and the curator-botanists (collections) and researchers looking to work on the collections whilst the project is ongoing. They have become experts in multi-tasking and problem solving and have developed processes and workflows to respond to the needs of all involved. Scrutinising every herbarium folder, some that may have remained closed for years, has brought delights and complications at every turn. At the same time, workspaces have had to be found for numerous imaging stations within the herbarium wings, allowing the imaging team to meet their targets, whilst being ever aware of the need for work space in the collections. Visitors have not been prevented from coming to Kew during the digitisation project, and the work of Kew’s own researchers and curators does not simply stop. It is important to leave space available for study and curation, and microscopes accessible around the collections to ensure research can continue. The curator-botanists in the digitisation team assist heavily in the logistics planning and keep communications among all parties flowing. Aside from the technical details, which naturally come to the fore in such a project (camera specifications, data cleaning, transcription criteria etc.), the very basics of herbaria and curation, first needed to be addressed. Examples of the challenges met, and the solutions put forward include: Query: What is a standard herbarium sheet? Solution: ‘Anatomy of a specimen’ fact sheet produced with explanations of commonly found elements of a collection along with frequently encountered Latin phrases and abbreviations. Query: Shouldn’t this be recurated? Solution: Achievable recurations pre-imaging were assessed and tackled with herbarium curators; bigger tasks were logged for later attention. Anomalies identified during imaging would remain in situ so as not to affect later processes (transcription, quality assurance). Query: Is a spider a pest? Solution: No…well, not usually. An identification sheet for common herbarium pests was produced with information on spiders and other non-pest herbarium visitors. Query: Is Sellotape archival? Solution: No, just no. A system for reporting and removing specimens in need of repair or remounting has been implemented. Query: What is a standard herbarium sheet? Solution: ‘Anatomy of a specimen’ fact sheet produced with explanations of commonly found elements of a collection along with frequently encountered Latin phrases and abbreviations. Query: Shouldn’t this be recurated? Solution: Achievable recurations pre-imaging were assessed and tackled with herbarium curators; bigger tasks were logged for later attention. Anomalies identified during imaging would remain in situ so as not to affect later processes (transcription, quality assurance). Query: Is a spider a pest? Solution: No…well, not usually. An identification sheet for common herbarium pests was produced with information on spiders and other non-pest herbarium visitors. Query: Is Sellotape archival? Solution: No, just no. A system for reporting and removing specimens in need of repair or remounting has been implemented. The team set up regular ‘exceptions’ lectures and refreshers to deal with these and many other queries as they arose. In presenting these and other matters we hope to share lessons learnt and gain further insight into how other institutes have or will undertake such projects.

Spatial and signaling overlap of growth factor receptor systems at clathrin-coated sites.

Cellular communication is regulated at the plasma membrane by the interactions of receptor, adhesion, signaling, exocytic,and endocytic proteins. Yet, the composition and control of these complexes in response to external cues remain unclear. We use high-resolution and high-throughput fluorescence imaging to map the localization of growth factor receptors and related proteins at single clathrin-coated structures in human squamous HSC3 cells. We find distinct protein signatures between control cells and cells stimulated with growth factors. Clathrin sites at the plasma membrane are preloaded with some receptors but not others. Stimulation with epidermal growth factor induces capture and concentration of epidermal growth factor, fibroblast growth factor1, and low-density lipoprotein receptor (EGFR, FGFR1, and LDLR). Regulatory proteins including ubiquitin ligase Cbl, the scaffold Grb2, and the mechanoenzyme dynamin2 are also recruited. Disrupting FGFR1 or EGFR activity with drugs prevents the recruitment of both EGFR and FGFR1. EGF was able to activate FGFR1 phosphorylation. Our data reveal novel coclustering and activation of receptors and regulatory factors at clathrin-coated sites in response to stimulation by a single growth factor, EGF or FGF. This behavior integrates growth factor signaling and allows for complex responses to extracellular cues and drugs at the plasma membrane of human cells.

High-throughput optimized prime editing mediated endogenous protein tagging for pooled imaging of protein localization.

The subcellular organization of proteins carries important information on cellular state and gene function, yet currently there are no technologies that enable accurate measurement of subcellular protein localizations at scale. Here we develop an approach for pooled endogenous protein tagging using prime editing, which coupled with an optical readout and sequencing, provides a snapshot of proteome organization in a manner akin to perturbation-based CRISPR screens. We constructed a pooled library of 17,280 pegRNAs designed to exhaustively tag 60 endogenous proteins spanning diverse localization patterns and explore a large space of genomic and pegRNA design parameters. Pooled measurements of tagging efficiency uncovered both genomic and pegRNA features associated with increased efficiency, including epigenetic states and interactions with transcription. We integrate pegRNA features into a computational model with predictive value for tagging efficiency to constrain the design space of pegRNAs for large-scale peptide knock-in. Lastly, we show that combining in-situ pegRNA sequencing with high-throughput deep learning image analysis, enables exploration of subcellular protein localization patterns for many proteins in parallel following a single pooled lentiviral transduction, setting the stage for scalable studies of proteome dynamics across cell types and environmental perturbations.

Image Analysis Artificial Intelligence Technologies for Plant Phenotyping: Current State of the Art

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended.

Abstract C058: A novel approach to understanding pancreas cancer risk through enhancer- promoter interactions

Abstract Genetic or epigenetic variations in regulatory enhancer elements increase susceptibility to a range of pathologies, including pancreas cancer. Pinpointing genes affecting pancreas cancer risk holds promise for early detection, prevention, and effective therapies. Despite recent advances, linking enhancer elements to target genes and predicting the transcriptional outcome of enhancer dysfunction remain significant challenges. Using 3D chromatin assays, we generated an extensive enhancer interaction dataset for the human pancreas, spanning more than 20 donors and five major cell types, including both the exocrine and endocrine compartments. We employed a network approach to parse chromatin interactions into enhancer-promoter tree models, facilitating quantitative, genome-wide analysis of enhancer connectivity. Using the tree models, we developed a machine learning algorithm capable of estimating the impact of enhancer perturbations on cell-type specific gene expression in the human pancreas. Complementing this computational approach, we streamlined an experimental platform using CRISPR-interference and RNA-FISH coupled with high-throughput imaging to quantitatively measure the effect size of enhancer perturbations in primary human pancreas cells. Our enhancer tree models enabled functional annotation of genetic variants associated with pancreas diseases, revealing novel target genes in specific cell types. For pancreas cancer (PDAC), our data suggests a stronger association of disease susceptibility variants with acinar cells, even though ductal cells historically have been the focus of PDAC. This discovery linked three potentially functional variants associated with PDAC risk to acinar cell enhancers, revealing novel target genes to further investigation. By integrating genomics with mechanistic findings into enhancer function, our work offers a framework for understanding genetic basis of pancreas cancer disease risk. Citation Format: Li Wang, Songjoon Baek, Gauri Prasad, Thucnhi Truongvo, Jason Hoskins, Laufey Amundadottir, Efsun Arda. A novel approach to understanding pancreas cancer risk through enhancer- promoter interactions [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C058.

Predictive Prioritization of Enhancers Associated with Pancreas Disease Risk.

Genetic and epigenetic variations in regulatory enhancer elements increase susceptibility to a range of pathologies. Despite recent advances, linking enhancer elements to target genes and predicting transcriptional outcomes of enhancer dysfunction remain significant challenges. Using 3D chromatin conformation assays, we generated an extensive enhancer interaction dataset for the human pancreas, encompassing more than 20 donors and five major cell types, including both exocrine and endocrine compartments. We employed a network approach to parse chromatin interactions into enhancer-promoter tree models, facilitating a quantitative, genome-wide analysis of enhancer connectivity. With these tree models, we developed a machine learning algorithm to estimate the impact of enhancer perturbations on cell type-specific gene expression in the human pancreas. Orthogonal to our computational approach, we perturbed enhancer function in primary human pancreas cells using CRISPR interference and quantified the effects at the single-cell level through RNA FISH coupled with high-throughput imaging. Our enhancer tree models enabled the annotation of common germline risk variants associated with pancreas diseases, linking them to putative target genes in specific cell types. For pancreatic ductal adenocarcinoma, we found a stronger enrichment of disease susceptibility variants within acinar cell regulatory elements, despite ductal cells historically being assumed as the primary cell-of-origin. Our integrative approach-combining cell type-specific enhancer-promoter interaction mapping, computational models, and single-cell enhancer perturbation assays-produced a robust resource for studying the genetic basis of pancreas disorders.

Computational Methods for Image Analysis in Craniofacial Development and Disease.

Observation is at the center of all biological sciences. Advances in imaging technologies are therefore essential to derive novel biological insights to better understand the complex workings of living systems. Recent high-throughput sequencing and imaging techniques are allowing researchers to simultaneously address complex molecular variations spatially and temporarily in tissues and organs. The availability of increasingly large dataset sizes has allowed for the evolution of robust deep learning models, designed to interrogate biomedical imaging data. These models are emerging as transformative tools in diagnostic medicine. Combined, these advances allow for dynamic, quantitative, and predictive observations of entire organisms and tissues. Here, we address 3 main tasks of bioimage analysis, image restoration, segmentation, and tracking and discuss new computational tools allowing for 3-dimensional spatial genomics maps. Finally, we demonstrate how these advances have been applied in studies of craniofacial development and oral disease pathogenesis.

Substrate heterogeneities cause root growth trajectories to switch from vertical to oblique.

Hard pans, soil compaction, soil aggregation and stones create physical barriers that can affect the development of a root system. Roots are known to exploit paths of least resistance to avoid such obstacles, but the mechanism through which this is achieved is not well understood. Here, we combined 3D-printed substrates with a high-throughput live imaging platform to study the responses of plant roots to a range of physical barriers. Using image analysis algorithms, we determined the properties of growth trajectories and identified how the presence of rigid circular obstacles affects the ability of a primary root to maintain its vertical trajectory. Results showed the types of growth responses were limited, both vertical and oblique trajectories were found to be stable and influenced by the size of the obstacles. When obstacles were of intermediate sizes, trajectories were unstable and changed in nature through time. We formalised the conditions for root trajectory to change from vertical to oblique, linking the angle at which the root detaches from the obstacle to the root curvature due to gravitropism. Exploitation of paths of least resistance by a root may therefore be constrained by the ability of the root to curve and respond to gravitropic signals.

High-throughput fluorescence lifetime imaging flow cytometry

Flow cytometry is a vital tool in biomedical research and laboratory medicine. However, its accuracy is often compromised by undesired fluctuations in fluorescence intensity. While fluorescence lifetime imaging microscopy (FLIM) bypasses this challenge as fluorescence lifetime remains unaffected by such fluctuations, the full integration of FLIM into flow cytometry has yet to be demonstrated due to speed limitations. Here we overcome the speed limitations in FLIM, thereby enabling high-throughput FLIM flow cytometry at a high rate of over 10,000 cells per second. This is made possible by using dual intensity-modulated continuous-wave beam arrays with complementary modulation frequency pairs for fluorophore excitation and acquiring fluorescence lifetime images of rapidly flowing cells. Moreover, our FLIM system distinguishes subpopulations in male rat glioma and captures dynamic changes in the cell nucleus induced by an anti-cancer drug. FLIM flow cytometry significantly enhances cellular analysis capabilities, providing detailed insights into cellular functions, interactions, and environments.