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Renal fibrosis, a common pathological manifestation of virtually all types of chronic kidney disease (CKD), often results in diffuse kidney scarring and predisposes to end-stage renal disease. Currently, there is no effective therapy against renal fibrosis. Recently, our laboratory identified an ER-resident protein, thioredoxin domain containing 5 (TXNDC5), as a critical mediator of cardiac fibrosis. Transcriptome analyses of renal biopsy specimens from patients with CKD revealed marked TXNDC5 upregulation in fibrotic kidneys, suggesting a potential role of TXNDC5 in renal fibrosis. Employing multiple fluorescence reporter mouse lines, we showed that TXNDC5 was specifically upregulated in collagen-secreting fibroblasts in fibrotic mouse kidneys. In addition, we showed that TXNDC5 was required for TGF-β1-induced fibrogenic responses in human kidney fibroblasts (HKFs), whereas TXNDC5 overexpression was sufficient to promote HKF activation, proliferation, and collagen production. Mechanistically, we showed that TXNDC5, transcriptionally controlled by the ATF6-dependent ER stress pathway, mediated its profibrogenic effects by enforcing TGF-β signaling activity through posttranslational stabilization and upregulation of type I TGF-β receptor in kidney fibroblasts. Using a tamoxifen-inducible, fibroblast-specific Txndc5 knockout mouse line, we demonstrated that deletion of Txndc5 in kidney fibroblasts mitigated the progression of established kidney fibrosis, suggesting the therapeutic potential of TXNDC5 targeting for renal fibrosis and CKD.

Pulmonary fibrosis (PF) is a major public health problem with limited therapeutic options. There is a clear need to identify novel mediators of PF to develop effective therapeutics. Here we show that an ER protein disulfide isomerase, thioredoxin domain containing 5 (TXNDC5), is highly upregulated in the lung tissues from both patients with idiopathic pulmonary fibrosis and a mouse model of bleomycin (BLM)-induced PF. Global deletion of Txndc5 markedly reduces the extent of PF and preserves lung function in mice following BLM treatment. Mechanistic investigations demonstrate that TXNDC5 promotes fibrogenesis by enhancing TGFβ1 signaling through direct binding with and stabilization of TGFBR1 in lung fibroblasts. Moreover, TGFβ1 stimulation is shown to upregulate TXNDC5 via ER stress/ATF6-dependent transcriptional control in lung fibroblasts. Inducing fibroblast-specific deletion of Txndc5 mitigates the progression of BLM-induced PF and lung function deterioration. Targeting TXNDC5, therefore, could be a novel therapeutic approach against PF., Pulmonary fibrosis is a major public health problem with unclear mechanism and limited therapeutic options. Here the authors show that a fibroblast-enriched endoplasmic reticulum protein, TXNDC5, promotes pulmonary fibrosis by stabilizing TGFBR1 and show the potential of TXNDC5 as a therapeutic target against pulmonary fibrosis.

Cell image segmentation is an important topic since it benefits for medical research and diagnosis. It is more challenging than other segmentation problems because cells have similar colors and their boundaries are not always obvious. In recent years, deep learning based methods, including the V-net, play an important role in image segmentation. In this paper, we proposed several techniques to further improve the performance of the V-net for cell segmentation. First, in addition to (i) cells and (ii) background, we add two labels: (iii) the edge between cells and background and (iv) the edges among cells. Since the properties of cell edges are quite different from those of cell bodies and background, adding these labels are helpful for improving the performance. Moreover, several morphology-based post-processing algorithms are applied for boundary refinement. With these techniques, the accuracies of cell segmentation can be much improved and the cells with poor contrast can still be well segmented.

Cell segmentation has attracted increasing interests since it is helpful for medical research and automatic diagnosis. In this paper, we proposed a method that can well segment cells by applying two stages of thresholds, a global one and an adaptive one, with morphology operations to overcome overlapping problems of cells.

In biology and medicine, it is critical to study the amount, distribution, movement, status, and behavior of cells. To do this, one should well segment cells from three dimensional (3D) cell images in prior and precisely extract the moving path of each cell. In this work, an advanced 3D cell segmentation and tracing algorithm is proposed. It applies local histograms to perform adaptive thresholding. Moreover, based on the observation that a cell is always nearly convex, the technique of shortest path segmentation is applied to disconnect the over-merged cells. Furthermore, the overlapping cell areas between two adjacent layers are adopted to refine segmentation results. Then, the moving path of a cell can be traced by determining the cell with the shortest distance and the most similar characteristics in the next 3D image. With the proposed algorithm, 3D cell images can be accurately segmented and each cell can be well traced.