Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral proteins bind to host mitochondrial proteins, likely inhibiting oxidative phosphorylation (OXPHOS) and stimulating glycolysis. We analyzed mitochondrial gene expression in nasopharyngeal and autopsy tissues from patients with coronavirus disease 2019 (COVID-19). In nasopharyngeal samples with declining viral titers, the virus blocked the transcription of a subset of nuclear DNA (nDNA)–encoded mitochondrial OXPHOS genes, induced the expression of microRNA 2392, activated HIF-1α to induce glycolysis, and activated host immune defenses including the integrated stress response. In autopsy tissues from patients with COVID-19, SARS-CoV-2 was no longer present, and mitochondrial gene transcription had recovered in the lungs. However, nDNA mitochondrial gene expression remained suppressed in autopsy tissue from the heart and, to a lesser extent, kidney, and liver, whereas mitochondrial DNA transcription was induced and host-immune defense pathways were activated. During early SARS-CoV-2 infection of hamsters with peak lung viral load, mitochondrial gene expression in the lung was minimally perturbed but was down-regulated in the cerebellum and up-regulated in the striatum even though no SARS-CoV-2 was detected in the brain. During the mid-phase SARS-CoV-2 infection of mice, mitochondrial gene expression was starting to recover in mouse lungs. These data suggest that when the viral titer first peaks, there is a systemic host response followed by viral suppression of mitochondrial gene transcription and induction of glycolysis leading to the deployment of antiviral immune defenses. Even when the virus was cleared and lung mitochondrial function had recovered, mitochondrial function in the heart, kidney, liver, and lymph nodes remained impaired, potentially leading to severe COVID-19 pathology.

Mitochondrial dysfunction is considered to be an important mediator of the pro‐aging process in chronic kidney disease, which is continuously increasing worldwide. Although PTEN‐induced kinase 1 (PINK1) regulates mitochondrial function, its role in renal aging remains unclear. We investigated the association between PINK1 and renal aging, especially through the cGAS‐STING pathway, which is known to result in an inflammatory phenotype. Pink1 knockout (Pink1^−/−) C57BL/6 mice and senescence‐induced renal tubular epithelial cells (HKC‐8) treated with H₂O₂ were used as the renal aging models. Extensive analyses at transcriptomic‐metabolic levels have explored changes in mitochondrial function in PINK1 deficiency. To investigate whether PINK1 deficiency affects renal aging through the cGAS‐STING pathway, we explored their expression levels in PINK1 knockout mice and senescence‐induced HKC‐8 cells. PINK1 deficiency enhances kidney fibrosis and tubular injury, and increases senescence and the senescence‐associated secretory phenotype (SASP). These phenomena were most apparent in the 24‐month‐old Pink1^−/− mice and HKC‐8 cells treated with PINK1 siRNA and H₂O₂. Gene expression analysis using RNA sequencing showed that PINK1 deficiency is associated with increased inflammatory responses, and transcriptomic and metabolomic analyses suggested that PINK1 deficiency is related to mitochondrial metabolic dysregulation. Activation of cGAS‐STING was prominent in the 24‐month‐old Pink1^−/− mice. The expression of SASPs was most noticeable in senescence‐induced HKC‐8 cells and was attenuated by the STING inhibitor, H151. PINK1 is associated with renal aging, and mitochondrial dysregulation by PINK1 deficiency might stimulate the cGAS‐STING pathway, eventually leading to senescence‐related inflammatory responses.

Intercellular mitochondrial transfer in the tumor microenvironment (TME) is a paradigm-shifting process that redefines cancer–T cell crosstalk. This review explores its dual nature as both a tumor immune evasion strategy and a promising therapeutic avenue. Crucially, oxidative stress acts as a key regulator, inducing tunneling nanotube (TNT) formation to facilitate this organelle exchange. Tumors exploit this by transferring dysfunctional, reactive oxygen species (ROS) generating mitochondria to T cells to induce senescence while simultaneously hijacking healthy mitochondria from T cells to empower their own metabolism. This directional exchange, quantified by computational tools like mitochondrial-enabled reconstruction of cellular interactions (MERCI), is linked to poor clinical outcomes. Transfer occurs via TNTs, extracellular vesicles, and direct contact. Conversely, the therapeutic transfer of healthy mitochondria from sources like mesenchymal stromal cells can revitalize exhausted T cells, improving chimeric antigen receptor T (CAR-T) cell efficacy. Clinical translation is guided by emerging biomarkers, including circulating mitochondrial DNA (mtDNA), mitochondrial haplogroups, and the tumor mitochondrial transfer (TMT) score. Harnessing this biological axis for next-generation immunotherapies requires overcoming challenges in transfer efficiency and standardization to effectively modulate the tumor redox landscape and immune response.

In single-cell RNA-seq (scRNA-seq) experiments, the number of individual cells has increased exponentially, and the sequencing depth of each cell has decreased significantly. As a result, analyzing scRNA-seq data requires extensive considerations of program efficiency and method selection. In order to reduce the complexity of scRNA-seq data analysis, we present scedar, a scalable Python package for scRNA-seq exploratory data analysis. The package provides a convenient and reliable interface for performing visualization, imputation of gene dropouts, detection of rare transcriptomic profiles, and clustering on large-scale scRNA-seq datasets. The analytical methods are efficient, and they also do not assume that the data follow certain statistical distributions. The package is extensible and modular, which would facilitate the further development of functionalities for future requirements with the open-source development community. The scedar package is distributed under the terms of the MIT license at https://pypi.org/project/scedar.

Spatial transcriptomics (ST) is emerging as a powerful technology that transforms our understanding of thyroid cancer by offering a spatial context of gene expression within the tumor tissue. In this review, we synthesize the recent applications of ST in thyroid cancer research, with a particular focus on the heterogeneity of the tumor microenvironment, tumor evolution, and cellular interactions. Studies have leveraged the spatial information provided by ST to map distinct cell types and expression patterns of genes and pathways across the different regions of thyroid cancer samples. The spatial context also allows a closer examination of invasion and metastasis, especially through the dysregulation at the tumor leading edge. Additionally, signaling pathways are inferred at a more accurate level through the spatial proximity of ligands and receptors. We also discuss the limitations that need to be overcome, including technical limitations like low resolution and sequencing depth, the need for high-quality samples, and complex data handling processes, and suggest future directions for a wider and more efficient application of ST in advancing personalized treatment of thyroid cancer.