Cell Nuclei In Images Research Articles

Towards a More Objective and High-throughput Spheroid Invasion Assay Quantification Method.

Multicellular spheroids embedded in 3D hydrogels are prominent in vitro models for 3D cell invasion. Yet, quantification methods for spheroid cell invasion that are high-throughput, objective and accessible are still lacking. Variations in spheroid sizes and the shapes of the cells within render it difficult to objectively assess invasion extent. The goal of this work is to develop a high-throughput quantification method of cell invasion into 3D matrices that minimizes sensitivity to initial spheroid size and cell spreading and provides precise integrative directionally-dependent metrics of invasion. By analyzing images of fluorescent cell nuclei, invasion metrics are automatically calculated at the pixel level. The initial spheroid boundary is segmented and automated calculations of the nuclear pixel distances from the initial boundary are used to compute common invasion metrics (i.e., the change in invasion area, mean distance) for the same spheroid at a later timepoint. We also introduce the area moment of inertia as an integrative metric of cell invasion that considers the invasion area as well as the pixel distances from the initial spheroid boundary. Further, we show that principal component analysis can be used to quantify the directional influence of a stimuli to invasion (e.g., due to a chemotactic gradient or contact guidance). To demonstrate the power of the analysis for cell types with different invasive potentials and the utility of this method for a variety of biological applications, the method is used to analyze the invasiveness of five different cell types. In all, implementation of this high-throughput quantification method results in consistent and objective analysis of 3D multicellular spheroid invasion. We provide the analysis code in both MATLAB and Python languages as well as a GUI for ease of use for researchers with a range of computer programming skills and for applications in a variety of biological research areas such as wound healing and cancer metastasis.

Spatial heterogeneity analysis of seeding of human induced pluripotent stem cells for neuroectodermal differentiation

IntroductionPreparing a uniform cell population in high–density seeding of adherent human induced pluripotent stem cells (hiPSC) requires stable culture conditions and consistent culture operation. In this study, we evaluated cell distribution patterns by changing cell seeding operations and their impact on differentiation toward the neuroectodermal lineage. MethodsThe hiPSC line 201B7 was seeded at 1.23 × 105 cells/cm2 following a conventional operation, prolongated time of cell seeding suspension or vessel tilting during cell seeding operation. Fluorescent imaging of cell nuclei was performed 24 h following cell seeding and used for spatial heterogeneity analysis. Flow cytometric analysis was also performed seven days after cell differentiation induction toward neuroectodermal lineage. ResultsIndices for spatial heterogeneity following high–density cell seeding were proposed to assess cell distribution patterns. Global heterogeneity (HG) was shown to be mostly affected by vessel tilting during cell seeding operation, while local heterogeneity (HL) was affected by prolongated time of cell seeding suspension. Changes in both spatial heterogeneities in the hiPSC population resulted in a lower yield of target neuroectodermal cells compared with the control operation. ConclusionHigh–density hiPSC seeding is critical for achieving a higher yield of target cells of neuroectodermal lineage. Understanding the spatial heterogeneity in early stages detects errors in cell culture motion and predicts cell fate in later stages of cell culture.

Synergistically enhanced water-resistive perovskite nanocrystals for cell nucleus imaging and acid phosphatase detection

Despite the impressive success of metal halide perovskite nanocrystals (NCs) in diverse fields, such as solar cells, light-emitting diodes, photodetectors, and so forth, their intrinsic sensitivity to water limits the extending applications in aqueous systems. Herein, enhanced water stability is achieved for NH2-PEG-COOH capped Mn2+-doped CsPbCl3/CsPb2Cl5 core/shell NCs through the synergistic effect of ion doping, core/shell construction and polymer encapsulation. The NH2-PEG-COOH encapsulation is validated crucial for the formation of CsPbCl3/CsPb2Cl5 core/shell structure, since it retains contact for CsPbCl3 NCs with trace water, and guarantees a slow water-etching process, triggering the CsPbCl3 to CsPb2Cl5 transformation. Thanks to the high formation energy induced by Mn2+ doping, CsPbCl3/CsPb2Cl5 hetero core/shell construction and NH2-PEG-COOH protective barrier, the obtained NCs exhibit unattenuated photoluminescence quantum yield after dipped in water for 15 days. Based on the robust water stability, the NH2-PEG-COOH capped Mn2+-doped CsPbCl3/CsPb2Cl5 NCs work well in cell nucleus imaging and acid phosphatase detection. This work not only produces a novel perovskite fluorescence probe in aqueous systems but also provides a general and effective combination strategy to enhance the water stability of perovskite materials.

Automatic classification and segmentation of blast cells using deep transfer learning and active contours.

Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, although widely practiced, suffers from significant limitations including labor-intensity and susceptibility to human error, particularly in distinguishing the subtle differences between normal and leukemic cells. To overcome these limitations, our research introduces the ALLDet classifier, an innovative tool employing deep transfer learning for the automated analysis and categorization of ALL from White Blood Cell (WBC) nuclei images. Our investigation encompassed the evaluation of nine state-of-the-art pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, ResNet50, ResNet101, DenseNet121, DenseNet201, Xception, MobileNet, and EfficientNetB3. We augmented this approach by incorporating a sophisticated contour-based segmentation technique, derived from the Chan-Vese model, aimed at the meticulous segmentation of blast cell nuclei in blood smear images, thereby enhancing the accuracy of our analysis. The empirical assessment of these methodologies underscored the superior performance of the EfficientNetB3 model, which demonstrated exceptional metrics: a recall specificity of 98.5%, precision of 95.86%, F1-score of 97.16%, and an overall accuracy rate of 97.13%. The Chan-Vese model's adaptability to the irregular shapes of blast cells and its noise-resistant segmentation capability were key to capturing the complex morphological changes essential for accurate segmentation. The combined application of the ALLDet classifier, powered by EfficientNetB3, with our advanced segmentation approach, emerges as a formidable advancement in the early detection and accurate diagnosis of ALL. This breakthrough not only signifies a pivotal leap in leukemia diagnostic methodologies but also holds the promise of significantly elevating the standards of patient care through the provision of timely and precise diagnoses. The implications of this study extend beyond immediate clinical utility, paving the way for future research to further refine and enhance the capabilities of artificial intelligence in medical diagnostics.

Photophysical image analysis: Unsupervised probabilistic thresholding for images from electron-multiplying charge-coupled devices.

We introduce the concept photophysical image analysis (PIA) and an associated pipeline for unsupervised probabilistic image thresholding for images recorded by electron-multiplying charge-coupled device (EMCCD) cameras. We base our approach on a closed-form analytic expression for the characteristic function (Fourier-transform of the probability mass function) for the image counts recorded in an EMCCD camera, which takes into account both stochasticity in the arrival of photons at the imaging camera and subsequent noise induced by the detection system of the camera. The only assumption in our method is that the background photon arrival to the imaging system is described by a stationary Poisson process (we make no assumption about the photon statistics for the signal). We estimate the background photon statistics parameter, λbg, from an image which contains both background and signal pixels by use of a novel truncated fit procedure with an automatically determined image count threshold. Prior to this, the camera noise model parameters are estimated using a calibration step. Utilizing the estimates for the camera parameters and λbg, we then introduce a probabilistic thresholding method, where, for the first time, the fraction of misclassified pixels can be determined a priori for a general image in an unsupervised way. We use synthetic images to validate our a priori estimates and to benchmark against the Otsu method, which is a popular unsupervised non-probabilistic image thresholding method (no a priori estimates for the error rates are provided). For completeness, we lastly present a simple heuristic general-purpose segmentation method based on the thresholding results, which we apply to segmentation of synthetic images and experimental images of fluorescent beads and lung cell nuclei. Our publicly available software opens up for fully automated, unsupervised, probabilistic photophysical image analysis.

Extraction and evaluation of cell nuclei images in label-free phase contrast microscopy enabled by machine learning using a data analysis platform Usiigaci

Extraction and evaluation of cell nuclei images in label-free phase contrast microscopy enabled by machine learning using a data analysis platform Usiigaci

Gold-Nanorod-Assisted Live Cell Nuclear Imaging Based on Near-Infrared II Dark-Field Microscopy.

Dark-field microscopy offers several advantages, including high image contrast, minimal cell damage, and the absence of photobleaching of nanoprobes, which make it highly advantageous for cell imaging. The NIR-II window has emerged as a prominent research focus in optical imaging in recent years, with its low autofluorescence background in biological samples and high imaging SBR. In this study, we initially compared dark-field imaging results of colorectal cancer cells in both visible and NIR-II wavelengths, confirming the superior performance of NIR-II imaging. Subsequently, we synthesized gold nanorods with localized surface plasmon resonance (LSPR) absorption peaks in the NIR-II window. After bio-compatible modification, we non-specifically labeled colorectal cancer cells for NIR-II dark-field scattering imaging. The imaging results revealed a sixfold increase in SBR, especially in the 1425-1475 nm wavelength range. Finally, we applied this imaging system to perform dark-field imaging of cell nuclei in the NIR-II region and used GNRs for specific nuclear labeling in colorectal cancer cells. The resulting images exhibited higher SBR than non-specifically-labeled cell imaging, and the probe's labeling was precise, confirming the potential application of this system in photothermal therapy and drug delivery for cancer cells.

High-Content and High-Throughput Clonogenic Survival Assay Using Fluorescence Barcoding.

The Clonogenic Survival Assay (CSA) is a fundamental tool employed to assess cell survival and proliferative potential in cancer research. Despite its importance, CSA faces limitations, primarily its time- and labor-intensive nature and its binary output. To overcome these limitations and enhance CSA's utility, several approaches have been developed, focusing on increasing the throughput. However, achieving both high-content and high-throughput analyses simultaneously has remained a challenge. In this paper, we introduce LeGO-CSA, an extension of the classical CSA that employs the imaging of cell nuclei barcoded with fluorescent lentiviral gene ontology markers, enabling both high-content and high-throughput analysis. To validate our approach, we contrasted it with results from a classical assay and conducted a proof-of-concept screen of small-molecule inhibitors targeting various pathways relevant to cancer treatment. Notably, our results indicate that the classical CSA may underestimate clonogenicity and unveil intriguing aspects of clonal cell growth. We demonstrate the potential of LeGO-CSA to offer a robust approach for assessing cell survival and proliferation with enhanced precision and throughput, with promising implications for accelerating drug discovery and contributing to a more comprehensive understanding of cellular behavior in cancer.

SiR-DNA/SiR-Hoechst-induced chromosome entanglement generates severe anaphase bridges and DNA damage.

SiR-DNA/SiR-Hoechst is a far-red fluorescent DNA probe that is routinely used for live-cell imaging of cell nuclei in interphase and chromosomes during mitosis. Despite being reported to induce DNA damage, SiR-DNA has been used in more than 300 research articles, covering topics like mitosis, chromatin biology, cancer research, cytoskeletal research, and DNA damage response. Here, we used live-cell imaging to perform a comprehensive analysis of the effects of SiR-DNA on mitosis of four human cell lines (RPE-1, DLD-1, HeLa, and U2OS). We report a dose-, time-, and light-dependent effect of SiR-DNA on chromosome segregation. We found that, upon the exposure to light during imaging, nanomolar concentrations of SiR-DNA induce non-centromeric chromosome entanglement that severely impairs sister chromatid segregation and spindle elongation during anaphase. This causes DNA damage that is passed forward to the following cell cycle, thereby having a detrimental effect on genome integrity. Our findings highlight the drawbacks in using SiR-DNA for investigation of late mitotic events and DNA damage-related topics and urge the use of alternative labeling strategies to study these processes.

4D segmentation algorithm with application to 3D+time image segmentation

In this paper, we introduce and study a novel segmentation method for 4D images based on surface evolution governed by a nonlinear partial differential equation, the generalized subjective surface equation. The new method uses 4D digital image information and information from a thresholded 4D image in a local neighborhood. Thus, the 4D image segmentation is accomplished by defining the edge detector function’s input as the weighted sum of the norm of gradients of presmoothed 4D image and norm of presmoothed thresholded 4D image in a local neighborhood. Additionally, we design and study a numerical method based on the finite volume approach for solving the new model. The reduced diamond cell approach is used for approximating the gradient of the solution. We use a semi-implicit finite volume scheme for the numerical discretization and show that our numerical scheme is unconditionally stable. The new 4D method was tested on artificial data and applied to real data representing 3D+time microscopy images of cell nuclei within the zebrafish pectoral fin and hind-brain. In a real application, processing 3D+time microscopy images amounts to solving a linear system with several billion unknowns and requires over 1000 GB of memory; thus, it may not be possible to process these images on a serial machine without parallel implementation utilizing the MPI. Consequently, we develop and present in the paper OpenMP and MPI parallel implementation of designed algorithms. Finally, we include cell tracking results to show how our new method serves as a basis for finding trajectories of cells during embryogenesis.

A Deep Learning Model for Automated Segmentation of Fluorescence Cell images

Deep learning techniques bring together key advantages in biomedical image segmentation. They speed up the process, increase the reproducibility, and reduce the workload in segmentation and classifcation. Deep learning techniques can be used for analysing cell concentration, cell viability, as well as the size and form of each cell. In this study, we develop a deep learning model for automated segmentation of fuorescence cell images, and apply it to fuorescence images recorded with a home-built epi-fuorescence microscope. A deep neural network model based on U-Net architecture was built using a publicly available dataset of cell nuclei images [1]. A model accuracy of 97.3% was reached at the end of model training. Fluorescence cell images acquired with our home-built microscope were then segmented using the developed model. 141 of 151 cells in 5 images were successfully segmented, revealing a segmentation success rate of 93.4%. This deep learning model can be extended to the analysis of diferent cell types and cell viability.

Label-Free and In Situ Identification of Cells via Combinational Machine Learning Models.

Cell identification and counting in living and coculture systems are crucial in cell interaction studies, but current methods primarily rely on complicated and time-consuming staining techniques. Here, a label-free method to precisely recognize, identify, and instantly count cells in situ in coculture systems via combinational machine learning models s presented. A convolutional neural network (CNN) model is first used to generate virtual images of cell nuclei based on unlabeled phase-contrast images. Coordinates of all the cells are then returned according to the virtual nucleus images using two clustering algorithms. Finally, phase-contrast images of single cells are cropped based on the coordinates and sent into another CNN model for cell-type identification. This combinational approach is highly automatic and efficient, which requires few to no manual annotations of images in the training phase. It shows practical performance in different cell culture conditions including cell ratios, densities, and substrate materials, having great potential in real-time cell tracking and analyzing.

Multimodal 3D photoacoustic remote sensing and confocal fluorescence microscopy imaging.

.Significance: Complementary absorption and fluorescence contrast could prove useful for a wide range of biomedical applications. However, current absorption-based photoacoustic microscopy systems require the ultrasound transducers to physically touch the samples, thereby increasing contamination and limiting strong optical focusing in reflection mode.Aim: We sought to develop an all-optical system for imaging cells and tissues using the three combined imaging modalities: photoacoustic remote sensing (PARS), epifluorescence, and confocal laser scanning microscopy (CLSM).Approach: A PARS subsystem with ultraviolet excitation was used to obtain label-free absorption-contrast images of nucleic acids in ex vivo tissue samples. Co-integrated epifluorescence and CLSM subsystems were used to verify the 2D and 3D nuclei distribution.Results: Complementary absorption and fluorescence contrast were demonstrated in phantom imaging experiments and subsequent cell and tissue imaging experiments. Lateral and axial resolution of ultraviolet-PARS (UV-PARS) is shown to be 0.39 and , respectively, with 266-nm light. CLSM lateral and axial resolution was measured as 0.97 and , respectively. This resolution is sufficient to image individual cell layers with fine optical sectioning. UV-PARS images of cell nuclei are validated in thick tissue using CLSM.Conclusions: Multimodal absorption and fluorescence contrast are obtained with a non-contact all-optical microscopy system for the first time and utilized to obtain images of cells and tissues with subcellular resolution.

Recent Advance in Carbon Dots: From Properties to Applications

AbstractAs a new type of nanomaterial, carbon dots (CDs) have been recognized as a versatile nanoplatform with enormous potential for biomedical and optoelectronic applications because of low cytotoxicity, photostability, biocompatibility and versatility in addition to their tunable photoluminescence. In this review, we aim at updating the latest research on preparation, optoelectronic and chemical properties of CDs as well as their biomedical and optoelectronic applications. We highlight the applications of CDs for stem cell imaging, cell nucleus imaging, two‐photon fluorescence imaging, tumor diagnosis and therapy and their optoelectronic applications including light‐emitting diodes (LEDs), solar cells and electrocatalysis. Furthermore, we also discuss current challenges and breakthroughs on machine‐learning‐driven synthesis of CDs as well as nucleic acid detection with an eye towards the future. Finally, we hope that this review will give further impulses to target‐oriented research on CDs for biomedical and optoelectronic applications.

Abstract PO-048: Visual nucleotyping identifies chromatin phenotypes triggered by genome editing

Abstract Optical microscopy has the potential to provide rapid phenotypic readout of cellular states. Here we show that automated optical phenotyping of nuclei is capable of rapidly and reliably discriminating the effects of targeted mutations to chromatin remodelers, which are major players in oncogenesis and targets for therapeutic intervention. Smarca4 is a chromatin remodeler frequently mutated in cancer. We investigated whether nuclear morphometry and concomitant supervised machine learning approaches on standard optical microscopy images of nuclear-DNA stained cells could reliably discern Smarca4-/- lung cancer cells from isogenic Smarca4+/+ clones that were derived by precision genome editing. We used TALEN-based editing on A549 lung cancer cells to generate isogenic Smarca4+/+ clones, performed conventional 2D 40x optical imaging of DAPI-stained cell nuclei from parental (Smarca4-/-) and edited (Smarca4+/+) cells, quantified a set of numerical parameters (features) that reflected various intuitive attributes of nuclear morphology and chromatin texture of the cell nuclei, and finally tested the efficacy of supervised machine learning algorithms containing these features to distinguish between the two cell categories. We found that the parental and edited A549 cells exhibited similar nuclear size and shape attributes but showed measurable differences in subtle chromatin texture. Additionally, classifiers based on these chromatin texture features were able to accurately distinguish the smarca4-rescued cells from the smarca4-mutant cells. Our results thus demonstrate the promise of quantitative chromatin phenotyping for genome editing applications in oncology. Citation Format: Vivek Nandakumar, Sandra Stehling-Sun, William Kerwin, Alister Funnell, John Stamatoyannopoulos. Visual nucleotyping identifies chromatin phenotypes triggered by genome editing [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-048.

TEM study of chronic alcoholism effects on early carcinogenesis by probing the nanoscale structural alterations of cell nuclei

Nanoscale structural alteration in the nuclei of cells with the progression of carcinogenesis is due to the rearrangements of the basic building blocks in the cell such as DNA, RNA, lipids, etc. Although epigenetic modifications underlie the development of cancer, exposure to carcinogenic chemicals such as alcohol also enhances the development of cancer. We report the effects of chronic alcoholism on early-carcinogenesis based on changes in the degree of nanoscale structural alterations (L d) in nuclei. For this, transmission electron microscopy (TEM) imaging of the nuclei of colonic cells is performed for the following four mouse models: control mice; chronic alcoholic mice treated with ethanol (i.e., EtOH mice); mice treated with colonic carcinogen azoxymethane (AOM) and dextran sulfate sodium (DSS) that induced colitis (i.e., AOM + DSS mice); and chronic alcoholic or EtOH treated mice, together with AOM and DSS treatment (i.e., AOM + DSS + EtOH mice). The disordered optical lattices are constructed from their respective TEM images of thin colonic cell nuclei and the L d values are calculated using the inverse participation ratio (IPR) technique from the spatially localized eigenfunctions of these lattices. Results show no significant difference in the average L d value of the colon cell nuclei of alcohol treated mice relative to its control [i.e., L d(C) ∼ L d(EtOH)]; however, an increase in the L d value of alcohol treated precancerous cells [i.e., L d(AOM + DSS + EtOH) > L d(AOM + DSS)], indicating that alcohol accelerates the early carcinogenic process.

Hybrid adversarial-discriminative network for leukocyte classification in leukemia.

Leukemia is a lethal disease that is harmful to bone marrow and overall blood health. The classification of white blood cell images is crucial for leukemia diagnosis. The purpose of this study is to classify white blood cells by extracting discriminative information from cell segmentation and combining it with the fine-grained features. We propose a hybrid adversarial residual network with support vector machine (SVM), which utilizes the extracted features to improve the classification accuracy for human peripheral white cells. Firstly, we segment the cell and nucleus by utilizing an adversarial residual network, which contains a segmentation network and a discriminator network. To extract features that can handle the inter-class consistency problem effectively, we introduce the adversarial residual network. Then, we utilize convolutional neural network (CNN) features and histogram of oriented gradient (HOG) features, which can extract discriminative features from images of segmented cell nuclei. To utilize the representative features fully, a discriminative network is introduced to deal with neighboring information at different scales. Finally, we combine the vectors of HOG features with those of CNN features and feed them into a linear SVM to classify white blood cells into six types. We used three methods to evaluate the effect of leukocyte classification based on 5000 leukocyte images acquired from a local hospital. The first approach is to use the CNN features as the input of SVM to classify leukocytes, which achieved 94.23% specificity, 95.10% sensitivity, and 94.41% accuracy. The use of the HOG features for SVM achieved 83.50% specificity, 87.50% sensitivity, and 85.00% accuracy. The use of combined CNN and HOG features achieved 94.57% specificity, 96.11% sensitivity, and 95.93% accuracy. We propose a novel hybrid adversarial-discriminative network for the classification of microscopic leukocyte images. It improves the accuracy of cell classification, reduces the difficulty and time pressure of doctors' work, and economizes the valuable time of doctors in daily clinical diagnosis.

Green Synthesis of Superior Molecular Fluorophores from Chitosan Assisted with Cellulase for Cell Nucleus Imaging and Photosensitive Printing

In this study, a facile and green route was designed using hydrolyzed chitosan as precursor to fabricate superior molecular state fluorophores based on the Maillard reaction (MR). The fluorophores exhibit excitation-dependent photoluminescence behavior, emitting multicolor fluorescence. The CCK-8 assay shows the low cytotoxicity of the Maillard reaction products (MRPs). The properties of the MRPs were associated with the molecular size. The high-molecular-weight fraction (>10 kDa) contains a large quantity of brown products which cause fluorescence quenching by an inner-filter effect. The medium-molecular-weight fraction (5–10 kDa) is able to permeate into the living cells and specifically stains the nucleus for stable imaging. The low-molecular-weight fraction (<3 kDa) possesses a dramatically high quantum yield of 30.4% and excellent ion stability, as well as a pH-responsive property; it can serve as a potential pH sensor and fluorescent ink for photosensitive printing. The potential applications of MRPs fractions in different fields are proposed, significantly improving the utilization efficiency. Additionally, this work provides a new perspective on designing and preparing large-scale metal-free fluorescence materials with high safety and good water solubility in a green and mild manner.

3D Cell Image Segmentation by Modified Subjective Surface Method

Abstract In this work, we focused on 3D image segmentation where the segmented surface is reconstructed by the use of 3D digital image information and information from thresholded 3D image in a local neighbourhood. To this end, we applied a mathematical model based on the level set formulation and numerical method which is based on the so-called reduced diamond cell approach. The segmentation approach is based on surface evolution governed by a nonlinear PDE, the modified subjective surface equation. This is done by defining the input to the edge detector function as the weighted sum of norm of presmoothed 3D image and norm of presmoothed thresholded 3D image in a local neighbourhood. For the numerical discretization, we used a semi-implicit finite volume scheme. The method was applied to real data representing 3D microscopy images of cell nuclei within the zebrafish pectoral fin.

Polypeptide-Templated Au Nanoclusters with Red and Blue Fluorescence Emissions for Multimodal Imaging of Cell Nuclei.

Noble metal nanoclusters (<2 nm) prepared using special molecules such as proteins and peptides as templates have been attracting increasing attention in biomedical applications and cell imaging because of their excellent biocompatibility, size-dependent fluorescence properties, and relatively simple synthesis. Although cell imaging using metal clusters has developed rapidly in recent years, multimodal imaging of the cell nucleus remains challenging. Herein, we report the successful synthesis of two different types of gold nanoclusters, one with blue fluorescence emission (λex 320 nm, λem 405 nm) and the other with red fluorescence emission (λex 560 nm, λem 657 nm), using the same polypeptide SV (sequence: NH2-CCYGGPKKKRKVG-COOH) as a template. The synthesis of these two optically distinct Au nanoclusters (Au NCs) was achieved through the optimization of synthesis conditions, including light, pH, the Au: polypeptide ratio, reaction time, and temperature. The multimodal imaging ability of the Au NCs were subsequently explored, with particular emphasis on the enzyme-like catalysis of the NCs and their use in the optical imaging of cell nuclei. Results show that the Au NCs possessed similar biological activities as the parent peptide used in their synthesis as well as demonstrating peroxidase-like activity due to the Au nanocluster core. Further, the Au NCs targeted the cell nucleus, with their strong fluorescence allowing cell imaging (cells containing Au clusters could be distinguished by eye, eliminating the need for additional labels). Finally, the Au NCs exhibited excellent multimodal cell imaging abilities. The results suggest that the Au NCs introduced here are promising candidates for the labeling and analysis of pathological samples.