Construction of a bacteriophage-derived vector with potential applications in targeted drug delivery and cell imaging.

There is a strong relationship between the dysregulation of epidermal growth factor receptor (EGFR) and the development of epithelial-derived cancers. Therefore, EGFR has usually been considered the desired target for gene therapy. Here, we propose an approach for targeting EGFR-expressing cells by phage particles capable of displaying EGF and GFP as tumor-targeting and reporting elements, respectively. For this purpose, the superfolder GFP-EGF (sfGFP-EGF) coding sequence was inserted at the N-terminus of the pIII gene in the pIT2 phagemid. The capability of the constructed phage to recognize EGFR-overexpressing cells was monitored by fluorescence microscopy, fluorescence-activated cell sorting (FACS), and cell-based ELISA experiments. FACS analysis showed a significant shift in the mean fluorescence intensity (MFI) of the cells treated with phage displaying sfGFP-EGF compared to phage displaying only sfGFP. The binding of phage displaying sfGFP-EGF to A-431 cells, monitored by fluorescence microscopy, indicated the formation of the sfGFP-EGF-EGFR complex on the surface of the treated cells. Cell-based ELISA experiments showed that phages displaying either EGF or sfGFP-EGF can specifically bind EGFR-expressing cells. The vector constructed in the current study has the potential to be engineered for gene delivery purposes as well as cell-based imaging for tumor detection.

Dilated Heterogeneous Convolution for Cell Detection and Segmentation Based on Mask R-CNN.

Owing to the variable shapes, large size difference, uneven grayscale, and dense distribution among biological cells in an image, it is very difficult to accurately detect and segment cells. Especially, it is a serious challenge for some microscope imaging devices with limited resources owing to a large number of learning parameters and computational burden when using the standard Mask R-CNN. In this work, we propose a mask R-DHCNN for cell detection and segmentation. More specifically, Dilation Heterogeneous Convolution (DHConv) is proposed by designing a novel convolutional kernel structure (i.e., DHConv), which integrates the strengths of the heterogeneous kernel structure and dilated convolution. Then, the traditional homogeneous convolution structure of the standard Mask R-CNN is replaced with the proposed DHConv module to it adapt to shape and size differences encountered in cell detection and segmentation tasks. Finally, a series of comparison and ablation experiments are conducted on various biological cell datasets (such as U373, GoTW1, SIM+, and T24) to verify the effectiveness of the proposed method. The results show that the proposed method can obtain better performance than some state-of-the-art methods in multiple metrics (including AP, Precision, Recall, Dice, and PQ) while maintaining competitive FLOPs and FPS.

Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights.

AIM: Analysis of cerebrospinal fluid (CSF) is essential for diagnostic workup of patients with neurological diseases and includes differential cell typing. The current gold standard is based on microscopic examination by specialised technicians and neuropathologists, which is time-consuming, labour-intensive and subjective. METHODS: We, therefore, developed an image analysis approach based on expert annotations of 123,181 digitised CSF objects from 78 patients corresponding to 15 clinically relevant categories and trained a multiclass convolutional neural network (CNN). RESULTS: The CNN classified the 15 categories with high accuracy (mean AUC 97.3%). By using explainable artificial intelligence (XAI), we demonstrate that the CNN identified meaningful cellular substructures in CSF cells recapitulating human pattern recognition. Based on the evaluation of 511 cells selected from 12 different CSF samples, we validated the CNN by comparing it with seven board-certified neuropathologists blinded for clinical information. Inter-rater agreement between the CNN and the ground truth was non-inferior (Krippendorff's alpha 0.79) compared with the agreement of seven human raters and the ground truth (mean Krippendorff's alpha 0.72, range 0.56-0.81). The CNN assigned the correct diagnostic label (inflammatory, haemorrhagic or neoplastic) in 10 out of 11 clinical samples, compared with 7-11 out of 11 by human raters. CONCLUSIONS: Our approach provides the basis to overcome current limitations in automated cell classification for routine diagnostics and demonstrates how a visual explanation framework can connect machine decision-making with cell properties and thus provide a novel versatile and quantitative method for investigating CSF manifestations of various neurological diseases.

Glycopolymer Decorated pH-Dependent Ratiometric Fluorescent Probe Based on Förster Resonance Energy Transfer for the Detection of Cancer Cells.

Development of fluorescent imaging probes is an important topic of research for the early diagnosis of cancer. Based on the difference between the cellular environment of tumor cells and normal cells, several "smart" fluorescent probes have been developed. In this work, a glycopolymer functionalized Förster resonance energy transfer (FRET) based fluorescent sensor is developed, which can monitor the pH change in cellular system. One-pot sequential reversible addition-fragmentation chain transfer (RAFT)polymerization technique is employed to synthesize fluorescent active triblock glycopolymer that can undergo FRET change on the variation of pH. A FRET pair, fluorescein o-acrylate (FA) and 7-amino-4-methylcoumarin (AMC) is linked via a pH-responsive polymer poly [2-(diisopropylamino)ethyl methacrylate] (PDPAEMA), which can undergo reversible swelling/deswelling under acidic/neutral condition. The presence of glycopolymer segment provides stability, water solubility, and specificity toward cancer cells. The cellular FRET experiments on cancer cells (MDA MB 231) and normal cells (3T3 fibroblast cells) demonstrate that the material is capable of distinguishing cells as a function of pH change.

Relationship between a deep learning model and liquid-based cytological processing techniques.

OBJECTIVE: Artificial intelligence (AI)-based cytopathology studies conducted using deep learning have enabled cell detection and classification. Liquid-based cytology (LBC) has facilitated the standardisation of specimen preparation; however, cytomorphology varies according to the LBC processing technique used. In this study, we elucidated the relationship between two LBC techniques and cell detection and classification using a deep learning model. METHODS: Cytological specimens were prepared using the ThinPrep and SurePath methods. The accuracy of cell detection and cell classification was examined using the one- and five-cell models, which were trained with one and five cell types, respectively. RESULTS: When the same LBC processing techniques were used for the training and detection preparations, the cell detection and classification rates were high. The model trained on ThinPrep preparations was more accurate than that trained on SurePath. When the preparation types used for training and detection were different, the accuracy of cell detection and classification was significantly reduced (P < 0.01). The model trained on both ThinPrep and SurePath preparations exhibited slightly reduced cell detection and classification rates but was highly accurate. CONCLUSIONS: For the two LBC processing techniques, cytomorphology varied according to cell type; this difference affects the accuracy of cell detection and classification by deep learning. Therefore, for highly accurate cell detection and classification using AI, the same processing technique must be used for both training and detection. Our assessment also suggests that a deep learning model should be constructed using specimens prepared via a variety of processing techniques to construct a globally applicable AI model.

Fluorescence anisotropy cytosensing of folate receptor positive tumor cells using 3D polyurethane-GO-foams modified with folic acid: molecular dynamics and in vitro studies.

Integrated polyurethane (PU)-based foams modified with PEGylated graphene oxide and folic acid (PU@GO-PEG-FA) were developed with the goal of capturing and detecting tumor cells with precision. The detection of the modified PU@GO-PEG surface through FA against folate receptor-overexpressed tumor cells is the basis for tumor cell capture. Molecular dynamics (MD) simulations were applied to study the strength of FA interactions with the folate receptor. Based on the obtained results, the folate receptor has intense interactions with FA, which leads to the reduction in the FA interactions with PEG, and so decreases the fluorescence intensity of the biosensor. The synergistic interactions offer the FA-modified foams a high efficiency for capturing the tumor cell. Using a turn-off fluorescence technique based on the complicated interaction of FA-folate receptor generated by target recognition, the enhanced capture tumor cells could be directly read out at excitation-emission wavelengths of 380-450 nm. The working range is between 1×10 2 to 2×10 4 cells mL -1 with a detection limit of 25 cells mL -1 and good reproducibility with relative standard deviation of 2.35%. Overall, findings demonstrate that the fluorescence-based biosensor has a significant advantage for early tumor cell diagnosis.

Improved YOLOv7 Algorithm for Detecting Bone Marrow Cells.

The detection and classification of bone marrow (BM) cells is a critical cornerstone for hematology diagnosis. However, the low accuracy caused by few BM-cell data samples, subtle difference between classes, and small target size, pathologists still need to perform thousands of manual identifications daily. To address the above issues, we propose an improved BM-cell-detection algorithm in this paper, called YOLOv7-CTA. Firstly, to enhance the model's sensitivity to fine-grained features, we design a new module called CoTLAN in the backbone network to enable the model to perform long-term modeling between target feature information. Then, in order to cooperate with the CoTLAN module to pay more attention to the features in the area to be detected, we integrate the coordinate attention (CoordAtt) module between the CoTLAN modules to improve the model's attention to small target features. Finally, we cluster the target boxes of the BM cell dataset based on K-means++ to generate more suitable anchor boxes, which accelerates the convergence of the improved model. In addition, in order to solve the imbalance between positive and negative samples in BM-cell pictures, we use the Focal loss function to replace the multi-class cross entropy. Experimental results demonstrate that the best mean average precision (mAP) of the proposed model reaches 88.6%, which is an improvement of 12.9%, 8.3%, and 6.7% compared with that of the Faster R-CNN model, YOLOv5l model, and YOLOv7 model, respectively. This verifies the effectiveness and superiority of the YOLOv7-CTA model in BM-cell-detection tasks.

Multienzyme System in Amorphous Metal-Organic Frameworks for Intracellular Lactate Detection.

Lactate is an important downstream product of glycolysis in living cells, and its level is highly related with diseases. On the basis of amorphous metal-organic frameworks (aMOFs), a multienzyme system consisting of lactate oxidase (LOx) and horseradish peroxidase (HRP) was established for intracellular lactate detection. By coencapsulation in aMOFs with proximity, LOx and HRP were delivered into cells, serving as artificially constructed organelles, exhibiting high activity and selectivity for the intracellular detection of the important metabolite lactate, which improved the signal to noise ratio by ∼650-fold. As demonstrated by both experimental and simulation results, the high efficiency was attributed to the short distance between the two types of enzymes coencapsulated in aMOFs. The concept of constructing multienzyme systems in this study shows promise for the detection of various intracellular metabolites.

A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations.

The automatic detection of cells in microscopy image sequences is a significant task in biomedical research. However, routine microscopy images with cells, which are taken during the process whereby constant division and differentiation occur, are notoriously difficult to detect due to changes in their appearance and number. Recently, convolutional neural network (CNN)-based methods have made significant progress in cell detection and tracking. However, these approaches require many manually annotated data for fully supervised training, which is time-consuming and often requires professional researchers. To alleviate such tiresome and labor-intensive costs, we propose a novel weakly supervised learning cell detection and tracking framework that trains the deep neural network using incomplete initial labels. Our approach uses incomplete cell markers obtained from fluorescent images for initial training on the Induced Pluripotent Stem (iPS) cell dataset, which is rarely studied for cell detection and tracking. During training, the incomplete initial labels were updated iteratively by combining detection and tracking results to obtain a model with better robustness. Our method was evaluated using two fields of the iPS cell dataset, along with the cell detection accuracy (DET) evaluation metric from the Cell Tracking Challenge (CTC) initiative, and it achieved 0.862 and 0.924 DET, respectively. The transferability of the developed model was tested using the public dataset FluoN2DH-GOWT1, which was taken from CTC; this contains two datasets with reference annotations. We randomly removed parts of the annotations in each labeled data to simulate the initial annotations on the public dataset. After training the model on the two datasets, with labels that comprise 10% cell markers, the DET improved from 0.130 to 0.903 and 0.116 to 0.877. When trained with labels that comprise 60% cell markers, the performance was better than the model trained using the supervised learning method. This outcome indicates that the model's performance improved as the quality of the labels used for training increased.

Examination of blood samples using deep learning and mobile microscopy.

BACKGROUND: Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards. RESULTS: Instance segmentation models such as Mask R-CNN, Mask Scoring R-CNN, D2Det and YOLACT were trained and optimised for the detection and classification of all blood cell types. These networks were not designed to detect very small objects in large numbers, so extensive modifications were necessary. Thus, segmentation of all blood cell types and their classification was feasible with great accuracy: qualitatively evaluated, mean average precision of 0.57 and mean average recall of 0.61 are achieved for all blood cell types. Quantitatively, 93% of ground truth blood cells can be detected. CONCLUSIONS: Mobile blood testing as a point-of-care system can be performed with diagnostic accuracy using deep learning methods. In the future, this application could enable very fast, cheap, location- and knowledge-independent patient care.

Mapping chromatin modifications at the single cell level.

Understanding chromatin regulation holds enormous promise for controlling gene regulation, predicting cellular identity, and developing diagnostics and cellular therapies. However, the dynamic nature of chromatin, together with cell-to-cell heterogeneity in its structure, limits our ability to extract its governing principles. Single cell mapping of chromatin modifications, in conjunction with expression measurements, could help overcome these limitations. Here, we review recent advances in single cell-based measurements of chromatin modifications, including optimization to reduce DNA loss, improved DNA sequencing, barcoding, and antibody engineering. We also highlight several applications of these techniques that have provided insights into cell-type classification, mapping modification co-occurrence and heterogeneity, and monitoring chromatin dynamics.

Sensitive visual detection of intracellular zinc ions based on signal-on polydopamine carbon dots.

The concentration of intracellular zinc ions is a significant clinical parameter for diagnosis. However, it is still a challenge for direct visual detection of zinc ions in cells at single-cell level. To address this issue, herein, water-soluble amino-rich polydopamine carbon quantum dots (PDA-CQDs) were successfully synthesized, with strong blue-green fluorescence as the probes for zinc ions detection in cells. The structure and properties of PDA-CQDs were confirmed by transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transformed infrared (FT-IR), UV-visible spectrophotometry (UV-vis), and fluorescence spectroscopy. Importantly, by successfully linking salicylaldehyde (SA) to PDA-CQDs via nucleophilic reaction, the FL quenching and Zn ions induced FL-recovering system was built up, thus offering a signal-on platform for the detection of zinc ions. This PDA-CQDs-SA nanoprobe can be applied for the detection of Zn2+with a detection limit of 0.09µM, with good biocompatibility confirmed using cytotoxicity assay. Of significance, the results of fluorescence bioimaging showed that PDA-CQDs-SA is able to detect Zn2+in single-cell visually, with the detection limit of Zn ions in cells as low as 0.11µM per cell, which was confirmed using flow cytometry. Therefore, this work offers a potential probe for Zn2+detection in cells at single-cell level, towards the precise diagnosis of zinc ions related diseases.

Rethinking the Dilated Encoder in TE-YOLOF: An Approach Based on Attention Mechanism to Improve Performance for Blood Cell Detection.

Blood cell detection is an essential branch of microscopic imaging for disease diagnosis. TE-YOLOF is an effective model for blood cell detection, and was recently found to have an outstanding trade-off between accuracy and model complexity. However, there is a lack of understanding of whether the dilated encoder in TE-YOLOF works well for blood cell detection. To address this issue, we perform a thorough experimental analysis and find the interesting fact that the dilated encoder is not necessary for TE-YOLOF to perform the blood cell detection task. For the purpose of increasing performance on blood cell detection, in this research, we use the attention mechanism to dominate the dilated encoder place in TE-YOLOF and find that the attention mechanism is effective to address this problem. Based upon these findings, we propose a novel approach, named Enhanced Channel Attention Module (ECAM), based on attention mechanism to achieve precision improvement with less growth on model complexity. Furthermore, we examine the proposed ECAM method compared with other tip-top attention mechanisms and find that the proposed attention method is more effective on blood cell detection task. We incorporate the spatial attention mechanism in CBAM with our ECAM to form a new module, which is named Enhanced-CBAM. We propose a new network named Enhanced Channel Attention Network (ENCANet) based upon Enhanced-CBAM to perform blood cell detection on BCCD dataset. This network can increase the accuracy to 90.3 AP while the parameter is only 6.5 M. Our ENCANet is also effective for conducting cross-domain blood cell detection experiments.

Deep learning based digital cell profiles for risk stratification of urine cytology images.

Urine cytology is a test for the detection of high-grade bladder cancer. In clinical practice, the pathologist would manually scan the sample under the microscope to locate atypical and malignant cells. They would assess the morphology of these cells to make a diagnosis. Accurate identification of atypical and malignant cells in urine cytology is a challenging task and is an essential part of identifying different diagnosis with low-risk and high-risk malignancy. Computer-assisted identification of malignancy in urine cytology can be complementary to the clinicians for treatment management and in providing advice for carrying out further tests. In this study, we presented a method for identifying atypical and malignant cells followed by their profiling to predict the risk of diagnosis automatically. For cell detection and classification, we employed two different deep learning-based approaches. Based on the best performing network predictions at the cell level, we identified low-risk and high-risk cases using the count of atypical cells and the total count of atypical and malignant cells. The area under the receiver operating characteristic (ROC) curve shows that a total count of atypical and malignant cells is comparably better at diagnosis as compared to the count of malignant cells only. We obtained area under the ROC curve with the count of malignant cells and the total count of atypical and malignant cells as 0.81 and 0.83, respectively. Our experiments also demonstrate that the digital risk could be a better predictor of the final histopathology-based diagnosis. We also analyzed the variability in annotations at both cell and whole slide image level and also explored the possible inherent rationales behind this variability.

Real-time monitoring of single circulating tumor cells with a fluorescently labeled deoxy-glucose by in vivo flow cytometry.

Circulating tumor cells (CTCs) play an essential role in metastasis and serve as an important prognostic biomarker. The technology of CTC labeling and detection in vivo can greatly improve the research of cancer metastasis and therapy. However, there is no in vivo technology to detect CTCs in clinic. In this study, we demonstrate that 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino]-2-deoxy-d-glucose (2-NBDG), a 2-deoxy-glucose analog, can work in vivo to indicate CTCs and metastases fluorescently by direct intravenous injection. During the development of an implanted tumor in mice, the spontaneous CTCs released from the primary tumor into blood vessels can be labeled by 2-NBDG due to the abnormal metabolism of CTCs. The green fluorescence of 2-NBDG from CTCs is then noninvasively detected by an in vivo flow cytometry system. Due to the high uptake of glucose by tumor cells, the CTCs in mice can maintain a high 2-NBDG level and thus be distinguished by 2-NBDG fluorescence in vivo efficiently, enabling tumor detection in vivo like positron emission tomography (PET) but at the single-cell resolution. Our results suggest 2-NBDG, a glucose analog with high biosafety, holds promising potential in clinical applications, similar to the widely-used contrast medium 2-F18 -fluorodeoxyglucose in PET.

Risks for cervical abnormalities in women with non-16/18 high-risk human papillomavirus infections in south Shanghai, China.

The study was aimed to analyze the prevalence characteristics of non-16/18 high-risk human papillomaviruses (HR-HPV) and the related risks for cervical abnormalities in south Shanghai. A total of 2291 HPV women who had been referred for a colposcopy due to HPV infection from @@@@@2016.12 to 2019.6 were enrolled. Combined with liquid-based thin-layer cell test (TCT) and pathological results of cervical biopsy, the infection spectrum and pathogenic risk of non-16/18 HR-HPV in local population were investigated. The results showed that the single HR-HPV infection rate was significantly higher than that of multiple infection, and the five most frequently detected types were HPV16, HPV52, HPV18, HPV53, HPV58 in the group. The total proportion of non-16/18 HR-HPV infection was 68.22%, more than twice of HPV16/18. In cases with high-grade cervical intraepithelial lesions (HSIL) or cervical cancer, non-16/18 HR-HPV infections account for 50.84% (single infection: 28.57%, multiple infection: 22.27%). The risk of cervical abnormalities caused by single HPV infection was ranked as HPV16 > HPV52 > HPV18 = HPV58 > HPV51 > HPV53 = HPV56 > others. Notably, among non-16/18 HR-HPV infected patients with HSIL/cancer lesions, the omission diagnostic rate of TCT was 62.81%. The infection rate of non-16/18 HR-HPV in whole study population was much higher than that of 16/18 type, and the infection rate of the former was also slightly higher in patients with HSIL and cancer. Due to the high omission diagnostic rate of TCT, we suggest patients with persistent non-16/18 HPV infection should undergo colposcopy biopsy to reduce missed detection of HSIL and cancers.

Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy.

Medical imaging is a growing field that has stemmed from the need to conduct noninvasive diagnosis, monitoring, and analysis of biological systems. With the developments and advances in the medical field and the new techniques that are used in the intervention of diseases, very soon the prevalence of implanted biomedical devices will be even more significant. The implanted materials in a biological system are used in diverse fields, which require lengthy evaluation and validation processes. However, currently the evaluation of the toxicity of biomaterials has not been fully automated yet. Moreover, image analysis is an integral part of biomaterial research, but it is not within the core capacities of a significant portion of biomaterial scientists, which results in the use of predominantly ready-made tools. The detailed image analysis can be conducted once all the relevant parameters including the inherent characteristics of image acquisition techniques are considered. Herein, we cover the currently used image analysis-based techniques for assessment of biomaterial/cell interaction with a specific focus on unstained brightfield microscopy acquired mostly in but not limited to microfluidic systems, which serve as multiparametric sensing platforms for noninvasive experimental measurements. We present the major imaging acquisition techniques that enable point-of-care testing when incorporated with microfluidic cells, discuss the constraints enforced by the geometry of the system and the material that is analyzed, and the challenges that rise in the image analysis when unstained cell imaging is employed. Emerging techniques such as utilization of machine learning and cell-specific pattern recognition algorithms and potential future directions are discussed. Automation and optimization of biomaterial assessment can facilitate the discovery of novel biomaterials together with making the validation of biomedical innovations cheaper and faster.

Deep Learning-Based Detection of Endothelial Tip Cells in the Oxygen-Induced Retinopathy Model.

Proliferative retinopathies, such as diabetic retinopathy and retinopathy of prematurity, are leading causes of vision impairment. A common feature is a loss of retinal capillary vessels resulting in hypoxia and neuronal damage. The oxygen-induced retinopathy model is widely used to study revascularization of an ischemic area in the mouse retina. The presence of endothelial tip cells indicates vascular recovery; however, their quantification relies on manual counting in microscopy images of retinal flat mount preparations. Recent advances in deep neural networks (DNNs) allow the automation of such tasks. We demonstrate a workflow for detection of tip cells in retinal images using the DNN-based Single Shot Detector (SSD). The SSD was designed for detection of objects in natural images. We adapt the SSD architecture and training procedure to the tip cell detection task and retrain the DNN using labeled tip cells in images of fluorescently stained retina flat mounts. Transferring knowledge from the pretrained DNN and extensive data augmentation reduced the amount of required labeled data. Our system shows a performance comparable to the human level, while providing highly consistent results. Therefore, such a system can automate counting of tip cells, a readout frequently used in retinopathy research, thereby reducing routine work for biomedical experts.

A single-cell analytical approach to quantify activated caspase-3/7 during osteoblast proliferation, differentiation, and apoptosis.

The protein heterogeneity at the single-cell level has been recognized to be vital for an understanding of various life processes during animal development. In addition, the knowledge of accurate quantity of relevant proteins at cellular level is essential for appropriate interpretation of diagnostic and therapeutic results. Some low-copy-number proteins are known to play a crucial role during cell proliferation, differentiation, and also in apoptosis. The fate decision is often based on the concentration of these proteins in the individual cells. This is likely to apply also for caspases, cysteine proteases traditionally associated with cell death via apoptosis but recently being discovered also as important factors in cell proliferation and differentiation. The hypothesis was tested in bone-related cells, where modulation of fate from apoptosis to proliferation/differentiation and vice versa is particularly challenging, e.g., towards anti-osteoporotic treatments and anti-cancer strategies. An ultrasensitive and highly selective method based on bioluminescence photon counting was used to quantify activated caspase-3/7 in order to demonstrate protein-level heterogeneity in individual cells within one population and to associate quantitative measurements with different cell fates (proliferation, differentiation, apoptosis). The results indicate a gradual increase of caspase-3/7 activation from the proliferative status to differentiation (more than three times) and towards apoptosis (more than six times). The findings clearly support one of the putative key mechanisms of non-apoptotic functions of pro-apoptotic caspases based on fine-tuning of their activation levels.

An easy-to-operate method for single-cell isolation and retrieval using a microfluidic static droplet array.

In-depth study of cellular heterogeneity of rare cells (e.g. circulating tumour cells (CTCs) and circulating foetal cells (CFCs)) is greatly needed in disease management but has never been completely explored due to the current technological limitations. We have developed a retrieval method for single-cell detection using a static droplet array (SDA) device through liquid segmentation with almost no sample loss. We explored the potential of using SDA for low sample input and retrieving the cells of interest using everyday laboratory equipment for downstream molecular analysis. This single-cell isolation and retrieval method is low-cost, rapid and provides a solution to the remaining challenge for single rare cell detection. The entire process takes less than 15 min, is easy to fabricate and allows for on-chip analysis of cells in nanolitre droplets and retrieval of desired droplets. To validate the applicability of our device and method, we mimicked detection of single CTCs by isolating and retrieving single cells and perform real-time PCR on their mRNA contents.