American Journal of Pathology最新文献

The recent arrival of high-resolution spatial transcriptomics (ST) technologies is generating a veritable revolution in life sciences, enabling biomolecules to be measured in their native spatial context. By integrating morphology and molecular biology, ST technologies offer the potential of improving the understanding of tissue biology and disease and may also provide meaningful clinical insights. In this review, we describe the main ST technologies currently available and the computational analysis for data interpretation and visualization, and illustrate their scientific and potential medical interest in the context of kidney disease. Finally, we discuss the perspectives and challenges of these booming new technologies.

Cholangiocarcinoma (CCA) and other liver cancer subtypes often develop in damaged organs. Physiological agents or extrinsic factors, like toxins, can induce cell death in such tissues, triggering compensatory proliferation and inflammation. Depending on extracellular and intracellular factors, different mechanisms, like apoptosis, necroptosis, ferroptosis, or autophagy, can be triggered. Each of them can lead to protumorigenic or anti-tumorigenic events within a cell or through regulation of the microenvironment. However, the exact role of each cell death mechanism in CCA onset, progression, and treatment is not well known. Here, we summarize current knowledge of different cell death mechanisms in patients with CCA and preclinical CCA research. We discuss cell death-related drugs with relevance to CCA treatment and how they could be used in the future to improve targeted CCA therapy.

Cholangiocarcinomas (CCAs) are a heterogeneous group of malignant tumors that originate from the biliary tract. They are usually diagnosed in advanced stages, leading to a dismal prognosis for affected patients. As CCA often arises as a sporadic cancer in individuals lacking specific risk factors or with heterogeneous backgrounds, and there are no defined high-risk groups, the implementation of effective surveillance programs for CCA is problematic. The identification and validation of new biomarkers useful for risk stratification, diagnosis, prognosis, and prediction of treatment response remains an unmet need for patients with CCA, even though numerous studies have been conducted lately to try to discover and validate CCA biomarkers. In this review, we overview the available information about the different types of biomarkers that have been investigated in recent years using minimally invasive biospecimens (blood, serum/plasma, bile, and urine) and their potential usefulness in diagnosis, prognosis, and risk stratification. It is widely accepted that early detection of CCA will impact patients' outcomes, by improving survival rates, quality of life, and the possibility of less invasive and/or curative treatments; however, challenges to its translation and clinical application for patients with CCA need to be resolved.

Chronic kidney disease (CKD) and its subset diabetic kidney disease are progressive conditions that affect >850 million people worldwide. Diabetes, hypertension, and glomerulonephritis are the most common causes of CKD, which is associated with significant patient morbidity and an increased risk of cardiovascular events, such as heart failure, ultimately leading to premature death. Despite newly approved drugs, increasing evidence shows that patients respond to treatment differently given the complexity of disease heterogeneity and complicated pathophysiology. This review article presents an integrative approach to understanding and addressing CKD through the lens of precision medicine and therapeutics. Leveraging advancements in single-cell omics technologies and artificial intelligence, we can explore the intricate cellular mechanisms underlying CKD and diabetic kidney disease pathogenesis. By dissecting the cellular heterogeneity and identifying rare cell populations using single-cell approaches, it will be possible to uncover novel therapeutic targets and biomarkers for personalized treatment strategies. Finally, we discuss the potential of artificial intelligence-driven analyses in predicting disease progression and treatment response, thereby paving the way for tailored interventions.

The application of spatial transcriptomics (ST) technologies is booming and has already yielded important insights across many different tissues and disease models. In nephrology, ST technologies have helped to decipher the cellular and molecular mechanisms at work in kidney diseases and have allowed the recent creation of spatially anchored human kidney atlases in healthy and diseased kidney tissues. During ST data analysis, the obtained computationally annotated clusters are often superimposed on a histologic image without their initial identification being based on the morphologic and spatial analyses of the tissues and lesions. In this study, we conduct a histopathologic-based analysis of ST data on a human kidney sample corresponding as closely as possible to the reality of the interpretation of a kidney biopsy sample in a health care or research context. This study shows the feasibility of a morphology-based approach to interpreting ST data, helping to improve our understanding of the lesion phenomena at work in chronic kidney disease at both the cellular and the molecular level. Finally, we show that our newly identified pathology-based clusters can be accurately projected onto other slides from nephrectomy or needle biopsy samples. They thus serve as a reference for analyzing other kidney tissues, paving the way for the future of molecular microscopy and precision pathology.