The Chondrocyte Channelome: A Novel Ion Channel Candidate in the Pathogenesis of Pectus Deformities

Costal cartilage is a type of rod-like hyaline cartilage connecting the ribs to the sternum. The chest wall deformities pectus excavatum (PE) and pectus carinatum (PC) involve displacement of the sternum causing a depression or protrusion of the chest. There is little knowledge about costal cartilage and pectus deformities with much of its understanding based on assumptions from articular cartilage. Chondrocytes are subjected to a constantly changing environment with fluctuations in pH and osmolarity. Ion channels detect these changes and in turn regulate proliferation, differentiation, and extracellular matrix production. Using ion channel qPCR arrays, we produced expression profiles for normal, fetal, PE-affected, and PC-affected costal chondrocytes as well as articular chondrocytes. Costal and articular chondrocytes had many commonly expressed ion channels with certain channels specific to each cartilage type. The discrepancy in ion channel expression is likely to be a reflection of the functional differences between the two cartilage types. Additionally, fetal costal chondrocytes had several other distinct ion channels possibly due to the differentiation status of the cells. In PC and PE chondrocytes, ACCN1 (ASIC2) and KCNN2 (SK2) were consistently down-regulated compared to normal costal chondrocytes. However, Western blot analysis found deceases only in ASIC2 protein levels. ASIC2 is a proton-gated ion channel involved in cell response to extracellular pH changes. Calcium monitoring revealed a delay in the formation calcium transients in PC cells when challenged with low pH which may be caused by aberrant signaling from ASIC channels. Immunofluorescent analysis of connexins found that Cx43 was present in chondrocytes with phosphorylated Cx43 localizing in and around the nucleus. Analysis of ATP release found that release is likely a connexin-mediated process, though external acidosis did not induce ATP release. Analysis of microRNAs found upregulation and down-regulation of several microRNAs in PC versus control cells, though further studies are needed to identify a possible microRNA signature for pectus deformities. Overall, we have generated a comprehensive ion channel profile for the costal chondrocytes, as well as identified a possible contributing factor for pectus deformities

Transient ALT Activation Protects Human Primary Cells From Chromosome Instability Induced by Low Chronic Oxidative Stress

Cells are often subjected to the effect of reactive oxygen species (ROS) as a result of both intracellular metabolism and exposure to exogenous factors. ROS-dependent oxidative stress can induce 8-oxodG within the GGG triplet found in the G-rich human telomeric sequence (TTAGGG), making telomeres highly susceptible to ROS-induced oxidative damage. Telomeres are nucleoprotein complexes that protect the ends of linear chromosomes and their dysfunction is believed to affect a wide range of cellular and/or organismal processes. Acute oxidative stress was shown to affect telomere integrity, but how prolonged low level oxidative stress, which may be more physiologically relevant, affects telomeres is still poorly investigated. Here, we explored this issue by chronically exposing human primary fibroblasts to a low dose of hydrogen peroxide. We observed fluctuating changes in telomere length and fluctuations in the rates of chromosome instability phenotypes, such that when telomeres shortened, chromosome instability increased and when telomeres lengthened, chromosome instability decreased. We found that telomere length fluctuation is associated with transient activation of an alternative lengthening of telomere (ALT) pathway, but found no evidence of cell death, impaired proliferation, or cell cycle arrest, suggesting that ALT activation may prevent oxidative damage from reaching levels that threaten cell survival

Differential Dielectric Responses of Chondrocyte and Jurkat Cells in Electromanipulation Buffers

Electromanipulation of cells as a label-free cell manipulation and characterization tool has gained particular interest recently. However, the applicability of electromanipulation, particularly dielectrophoresis (DEP), to biological cells is limited to cells suspended in buffers containing lower amounts of salts relative to the physiological buffers. One might question the use of low conductivity buffers (LCBs) for DEP separation, as cells are stressed in buffers lacking physiological levels of salt. In LCB, cells leak ions and undergo volume regulation. Therefore, cells exhibit time-dependent DEP response in LCB. In this work, cellular changes in LCB are assessed by dielectric spectroscopy, cell viability assay, and gene expression of chondrocytes and Jurkats. Results indicate leakage of ions from cells, increases in cytoplasmic conductivity, membrane capacitance, and conductance. Separability factor, which defines optimum conditions for DEP cell separation, for the two cell types is calculated using the cellular dielectric data. Optimum DEP separation conditions change as cellular dielectric properties evolve in LCB. Genetic analyses indicate no changes in expression of ionic channel proteins for chondrocytes suspended in LCB. Retaining cellular viability might be important during dielectrophoretic separation, especially when cells are to be biologically tested at a downstream microfluidic component

Concordance between nuclei detected manually, by automated analysis of GFP fluorescence image, and by AI of the phase image.

Three sets of detected nuclei data are shown: Nuclei detected by manual inspection of GFP fluorescence images (red dots), nuclei detected by classical image analysis of GFP fluorescence images (green dots) and nuclei detected by AI-based analysis of the phase contrast images (yellow dots). Scale bar = 25 μm. Many image regions illustrated high concordance between the three datasets, whereas the inset square highlighted in orange illustrates a region of high discordance (scale bar = 10 μm). The GFP fluorescence-based automated image analysis tends to merge nuclear objects compared to the manual annotations and the AI-based analysis of the phase contrast images. (PDF)</p

Training and evaluation of 2D U-Nets.

(A) Schematic describing the process for model training and evaluation of 2D U-Nets for iPSC nuclear segmentation. (i) A single timepoint image set consists of a phase contrast image created by stitching 100 fields of view (FOV) and a corresponding fluorescence image where nuclei are labeled with GFP. FOV are acquired and pre-processed to identify the best in-focus z plane and then stitched (see Methods). (ii) The fluorescence images are processed to produce a foreground and background mask. (iii) The processed fluorescence image is combined with the unlabeled image for (iv) training the 2D U-Net. (v) The accuracy of inferencing individual nuclei from phase contrast images is evaluated by comparing the locations of inferenced nuclei with fluorescent nuclei in reference images. (B) Representative phase contrast images of images of cells seeded at relatively low, medium, and high densities. Scalebar shown is 250 μm. (C) The ‘fraction of additional objects’, ‘fraction of missing objects’ and the F1 score determined for each frame of one image dataset collected over 20 h. Accuracy metrics are plotted versus the measured number of cell nuclei/mm2 per frame. The inferenced results were compared for the three models trained on either low (i), medium (ii), or high (iii) cell densities. (D) Using the high-cell-density model, the ‘fraction of additional objects’, ‘fraction of missing objects and the F1 scores are plotted for five replicate datasets. The first dataset in the plot are the data shown in panel 2C. Each datapoint represents the corresponding error rate for that frame, and the dot color indicates the density of cells in the frame. Tukey box plots indicate summary statistics for each time-lapse dataset.</p

Tracking of segmented iPS cells; comparison of the 3 different segmentation models.

Labels “High”, “Medium” and “Low” refer to high-cell-density, medium-cell-density, and low-cell-density U-Net models. (A) After tracking, eliminating objects with short track lengths reduces segmentation errors. Including only objects that were successfully tracked for times greater than the minimum track length results in datasets with fewer segmentation errors. The high-cell-density segmentation model (which was created with training data produced with the highest number of cells) performed better overall with respect to fraction of missing objects and F1 scores than the other two models after track length filtering. (B) Examining inferenced results from the high-cell-density model indicates that eliminating objects that were tracked for less than 20 consecutive minutes appears to produce a more accurate cell count. The green line is the number of segmented fluorescent cells in a colony that were counted in the corresponding GFP image; that number is nearly identical to the inferred data when a filter of 20 sequential minutes is applied. (C) Tukey boxplot of Linkage Error Rate per track computed by comparing inferenced data from the high-cell-density, medium-cell-density and low-cell-density segmentation models with reference tracks created from GFP fluorescence data. Lines in boxes indicate the median error for inferenced tracks was slightly lower when the high-cell-density model was applied to the data. (D) Tracking error rate computed for the high-cell-density model as a function of track length. (E) Cumulative probability distribution of track lengths inferenced from the high-, medium-, and low-cell-density models and from auto segmentation of GFP. (F) interdivision times (hours from a division event to a subsequent division of those daughter cells) for cell tracks computed using the different cell-density models or auto GFP segmentation. Interdivision times that appear to occur within 9 hours are considered physiologically improbable and are likely to be tracking errors. Auto GFP segmentation resulted in 8 of these events compared to 91, 103, and 151 such events for the high-cell-density, medium-cell-density, and low-cell-density models. The numbers of interdivision times that were measured at >9 hours were 441 for the auto GFP segmentation, and 405, 291 and 228 for the high-cell-density, medium-cell-density and low-cell-density U-Net models.</p

Effect of excitation light exposure on cell division.

(A) Cells were exposed only to transmitted light (indicted by grey lines) or to transmitted light and excitation light (indicated by green lines) for 1 sec at 2 min intervals. The relative excitation light is indicated in each panel; 1x is the level of excitation light at which training data were acquired; 1.4x, 2.1x, 3.6x refer to the fold increase in excitation light to which cells were exposed. The number of cells is reported over time relative to the number of starting cells. Shadows around lines reflect standard deviations from replicate samples (n = 3). (B) Mitosis rates were determined from the samples analyzed in 6A using the 3D U-Net mitosis detector to determine the total number of mitotic events that occurred within 60 min divided by the initial number of cells in the image in a moving 2-minute time window followed by a 20-frame moving average to reduce noise. (C) Tukey box plots of doubling times calculated from the cell count data corresponding to the relative exposure levels shown in each panel of 6A. Each data point represents a doubling time calculated every two minutes from the numbers of cells in 30 sequential frames (60 min). For the samples exposed to relative light levels that were greater than 1x exposure (green indicators), doubling times are significantly longer than for the other samples. Doubling Time = ln(2)/(ln(Nt/N0)/t). (D) Tukey box plots of doubling times calculated from the numbers of mitotic events determined in samples shown in each panel of Fig 6B corresponding to their relative exposure levels. Doubling Time = ln(2)/(ln(Nt0+#mitoses)/N0)/t). (E) Tukey box plots of interdivision times determined from the samples that were subjected to the relative light exposure indicated. Each dot represents the interdivision time for a single cell. The data associated with exposure to higher levels of excitation light resulted in very few cells that progressed from one cell cycle to another, although interdivision times were not different. All data are based on a stitched image of 2x2 fields of view with a minimum track length of 100 minutes. All statistical tests were unpaired t-tests using the mean of each replicate comparing within the same experiment.</p

Inference performance when a threshold of multiple models is applied.

A threshold was applied to the image data in exp0 (a representative sub-image shown in S2 Fig) and the model performance scores: ‘Fraction of Missing Objects’, ‘Fraction of Additional Objects’ and ‘F1 Score’ are plotted as a function of the threshold value. As expected, the ‘Fraction of Missing Objects’ increases with threshold value, the ‘Fraction of Additional Objects’ decreases with threshold value, and the ‘F1 Score’ is highest for intermediate threshold values. (PDF)</p