[Preparation of monoclonal antibody against cystatin C and establishment of its immunoassay system].

OBJECTIVE: To prepare human cystatin C( CysC) recombinant protein and produce monoclonal antibodies with high affinity and specificity. Develop a competitive ELISA detection system to detect of CysC in human serum. METHODS: The CysC gene sequence was found on NCBI. The optimized gene fragments were synthesized and the recombinant CysC protein was expressed in Escherichia coli then used to immunize Balb/c mice. The positive hybridoma cell lines were obtained by hybridoma cell fusion techniques and ascites monoclonal antibody was prepared and purified. Affinity of the antibody was measured by indirect ELISA. Then competitive ELISA detection system was established, and 52 cases of human serum samples were detected by the detection system. RESULTS: Four stable cell lines secreting CysC monoclonal antibodies were obtained. Antibody Ab3 was used as a detection antibody and HRP labeling was performed. Its affinity constant was 4. 26 × 10~6L/mol. The linear range of detection was 0. 011-1. 924 µg/mL. The detection limit was 4. 598 ng/mL and IC_(50) was 0. 145 µg/mL. The established competitiveELISA serum detection system could accurately detect those 52 serum samples. CONCLUSION: The monoclonal antibody against CysC with high affinity and specificity has been successfully obtained. A reliable competitive ELISA serum detection system is established. The method provides a basis for the development of CysC rapid immunoassay kit.

Evaluation of Cellpose segmentation with sequential thresholding for instance segmentation of cytoplasms within autofluorescence images.

BACKGROUND: Autofluorescence imaging of the coenzyme, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), provides a label-free technique to assess cellular metabolism. Because NAD(P)H is localized in the cytosol and mitochondria, instance segmentation of cell cytoplasms from NAD(P)H images allows quantification of metabolism with cellular resolution. However, accurate cytoplasmic segmentation of autofluorescence images is difficult due to irregular cell shapes and cell clusters. METHOD: Here, a cytoplasm segmentation method is presented and tested. First, autofluorescence images are segmented into cells via either hand-segmentation or Cellpose, a deep learning-based segmentation method. Then, a cytoplasmic post-processing algorithm (CPPA) is applied for cytoplasmic segmentation. CPPA uses a binarized segmentation image to remove non-segmented pixels from the NAD(P)H image and then applies an intensity-based threshold to identify nuclei regions. Errors at cell edges are removed using a distance transform algorithm. The nucleus mask is then subtracted from the cell segmented image to yield the cytoplasm mask image. CPPA was tested on five NAD(P)H images of three different cell samples, quiescent T cells, activated T cells, and MCF7 cells. RESULTS: Using POSEA, an evaluation method tailored for instance segmentation, the CPPA yielded F-measure values of 0.89, 0.87, and 0.94 for quiescent T cells, activated T cells, and MCF7 cells, respectively, for cytoplasm identification of hand-segmented cells. CPPA achieved F-measure values of 0.84, 0.74, and 0.72 for Cellpose segmented cells. CONCLUSION: These results exceed the F-measure value of a comparative cell segmentation method (CellProfiler, ∼0.50-0.60) and support the use of artificial intelligence and post-processing techniques for accurate segmentation of autofluorescence images for single-cell metabolic analyses.

POSEA: A novel algorithm to evaluate the performance of multi-object instance image segmentation.

Many techniques and software packages have been developed to segment individual cells within microscopy images, necessitating a robust method to evaluate images segmented into a large number of unique objects. Currently, segmented images are often compared with ground-truth images at a pixel level; however, this standard pixel-level approach fails to compute errors due to pixels incorrectly assigned to adjacent objects. Here, we define a per-object segmentation evaluation algorithm (POSEA) that calculates segmentation accuracy metrics for each segmented object relative to a ground truth segmented image. To demonstrate the performance of POSEA, precision, recall, and f-measure metrics are computed and compared with the standard pixel-level evaluation for simulated images and segmented fluorescence microscopy images of three different cell samples. POSEA yields lower accuracy metrics than the standard pixel-level evaluation due to correct accounting of misclassified pixels of adjacent objects. Therefore, POSEA provides accurate evaluation metrics for objects with pixels incorrectly assigned to adjacent objects and is robust for use across a variety of applications that require evaluation of the segmentation of unique adjacent objects.

Label-free spatially maintained measurements of metabolic phenotypes in cells.

Metabolic reprogramming at a cellular level contributes to many diseases including cancer, yet few assays are capable of measuring metabolic pathway usage by individual cells within living samples. Here, autofluorescence lifetime imaging is combined with single-cell segmentation and machine-learning models to predict the metabolic pathway usage of cancer cells. The metabolic activities of MCF7 breast cancer cells and HepG2 liver cancer cells were controlled by growing the cells in culture media with specific substrates and metabolic inhibitors. Fluorescence lifetime images of two endogenous metabolic coenzymes, reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD), were acquired by a multi-photon fluorescence lifetime microscope and analyzed at the cellular level. Quantitative changes of NADH and FAD lifetime components were observed for cells using glycolysis, oxidative phosphorylation, and glutaminolysis. Conventional machine learning models trained with the autofluorescence features classified cells as dependent on glycolytic or oxidative metabolism with 90%-92% accuracy. Furthermore, adapting convolutional neural networks to predict cancer cell metabolic perturbations from the autofluorescence lifetime images provided improved performance, 95% accuracy, over traditional models trained via extracted features. Additionally, the model trained with the lifetime features of cancer cells could be transferred to autofluorescence lifetime images of T cells, with a prediction that 80% of activated T cells were glycolytic, and 97% of quiescent T cells were oxidative. In summary, autofluorescence lifetime imaging combined with machine learning models can detect metabolic perturbations between glycolysis and oxidative metabolism of living samples at a cellular level, providing a label-free technology to study cellular metabolism and metabolic heterogeneity.

Cytokinesis machinery promotes cell dissociation from collectively migrating strands in confinement.

Cells tune adherens junction dynamics to regulate epithelial integrity in diverse (patho)physiological processes, including cancer metastasis. We hypothesized that the spatially confining architecture of peritumor stroma promotes metastatic cell dissemination by remodeling cell-cell adhesive interactions. By combining microfluidics with live-cell imaging, FLIM/FRET biosensors, and optogenetic tools, we show that confinement induces leader cell dissociation from cohesive ensembles. Cell dissociation is triggered by myosin IIA (MIIA) dismantling of E-cadherin cell-cell junctions, as recapitulated by a mathematical model. Elevated MIIA contractility is controlled by RhoA/ROCK activation, which requires distinct guanine nucleotide exchange factors (GEFs). Confinement activates RhoA via nucleocytoplasmic shuttling of the cytokinesis-regulatory proteins RacGAP1 and Ect2 and increased microtubule dynamics, which results in the release of active GEF-H1. Thus, confining microenvironments are sufficient to induce cell dissemination from primary tumors by remodeling E-cadherin cell junctions via the interplay of microtubules, nuclear trafficking, and RhoA/ROCK/MIIA pathway and not by down-regulating E-cadherin expression.

Autofluorescence Imaging to Evaluate Cellular Metabolism.

Cellular metabolism is the process by which cells generate energy, and many diseases, including cancer, are characterized by abnormal metabolism. Reduced nicotinamide adenine (phosphate) dinucleotide (NAD(P)H) and oxidized flavin adenine dinucleotide (FAD) are coenzymes of metabolic reactions. NAD(P)H and FAD exhibit autofluorescence and can be spectrally isolated by excitation and emission wavelengths. Both coenzymes, NAD(P)H and FAD, can exist in either a free or protein-bound configuration, each of which has a distinct fluorescence lifetime-the time for which the fluorophore remains in the excited state. Fluorescence lifetime imaging (FLIM) allows quantification of the fluorescence intensity and lifetimes of NAD(P)H and FAD for label-free analysis of cellular metabolism. Fluorescence intensity and lifetime microscopes can be optimized for imaging NAD(P)H and FAD by selecting the appropriate excitation and emission wavelengths. Metabolic perturbations by cyanide verify autofluorescence imaging protocols to detect metabolic changes within cells. This article will demonstrate the technique of autofluorescence imaging of NAD(P)H and FAD for measuring cellular metabolism.

Fluorescence intensity and lifetime redox ratios detect metabolic perturbations in T cells.

The auto-fluorescent coenzymes reduced nicotinamide dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD) allow label-free detection of cellular metabolism. The optical redox ratio, which is traditionally computed as the ratio of NADH and FAD intensities, allows quantification of cell redox state. In addition to multiple formulations of the optical redox ratio from NADH and FAD intensity measurements, a fluorescence lifetime redox ratio (FLIRR) based on the fractions of protein-bound NADH and FAD was developed to overcome the limitations of experimental factors that influence fluorescence intensity measurements. In this paper, we compare fluorescence-intensity computations of the optical redox ratio with the fluorescence lifetime redox ratio for quiescent and activated T cells. Fluorescence lifetime images of NAD(P)H and FAD of T cells were acquired with a two-photon fluorescence lifetime microscope. Metabolic perturbation experiments, including inhibition of glycolysis, oxidative phosphorylation, glutaminolysis, and fatty acid synthesis revealed differences between the intensity and lifetime redox ratios. Statistical analysis reveals that the FLIRR has a lower standard deviation and skewness (two-tail T-test, P value = 0.05) than the intensity redox ratio. Correlation analysis revealed a weak relationship between FLIRR and intensity redox ratio for individual cells, with a stronger correlation identified for activated T cells (Linear regression, R-value = 0.450) than quiescent T cells (R-value = 0.172). Altogether, the results demonstrate that while both the fluorescence lifetime and intensity redox ratios resolve metabolic perturbations in T cells, the endpoints are influenced by different metabolic processes.