Cell Reports Medicine最新文献

Spatially resolved multi-omics revolutionizes cancer therapy by decoding the cellular and molecular heterogeneity of the tumor microenvironment through spatial coordinates. This commentary discusses the roles of spatial multi-omics in identifying precise therapeutic targets and predicting treatment responses while also highlighting the challenges that impede its integration into precision medicine.

Given expanding studies in epidemiology and disease-oriented human studies offering hundreds of associations between the human "ome" and disease, prioritizing molecules relevant to disease mechanisms among this growing breadth is important. Here, we link the circulating proteome to human heart failure (HF) propensity (via echocardiographic phenotyping and clinical outcomes) across the lifespan, demonstrating key pathways of fibrosis, inflammation, metabolism, and hypertrophy. We observe a broad array of genes encoding proteins linked to HF phenotypes and outcomes in clinical populations dynamically expressed at a transcriptional level in human myocardium during HF and cardiac recovery (several in a cell-specific fashion). Many identified targets do not have wide precedent in large-scale genomic discovery or human studies, highlighting the complementary roles for proteomic and tissue transcriptomic discovery to focus epidemiological targets to those relevant in human myocardium for further interrogation.

Indoleamine-2,3-dioxygenase (IDO)1 degrades tryptophan, obtained through dietary intake, into immunoregulatory metabolites of the kynurenine pathway. Deficiency or blockade of IDO1 results in the enhancement of autoimmune severity in rodent models and increased susceptibility to developing autoimmunity in humans. Despite this, therapeutic modalities that leverage IDO1 for the treatment of autoimmunity remain limited. Here, we use messenger (m)RNA formulated in lipid nanoparticles (LNPs) to deliver a human IDO1 variant containing the myristoylation site of Src to anchor the protein to the inner face of the plasma membrane. This membrane-anchored IDO1 has increased protein production, leading to increased metabolite changes, and ultimately ameliorates disease in three models of T cell-mediated autoimmunity: experimental autoimmune encephalomyelitis (EAE), rat collagen-induced arthritis (CIA), and acute graft-versus-host disease (aGVHD). The efficacy of IDO1 is correlated with hepatic expression and systemic tryptophan depletion. Thus, the delivery of membrane-anchored IDO1 by mRNA suppresses the immune response in several well-characterized models of autoimmunity.

Cryptorchidism, commonly known as undescended testis, affects 1%-9% of male newborns, posing infertility and testis tumor risks. Despite its prevalence, the detailed pathophysiology underlying male infertility within cryptorchidism remains unclear. Here, we profile and analyze 46,644 single-cell transcriptomes from individual testicular cells obtained from adult males diagnosed with cryptorchidism and healthy controls. Spermatogenesis compromise in cryptorchidism links primarily to spermatogonium self-renewal and differentiation dysfunctions. We illuminate the involvement of testicular somatic cells, including immune cells, thereby unveiling the activation and degranulation of mast cells in cryptorchidism. Mast cells are identified as contributors to interstitial fibrosis via transforming growth factor β1 (TGF-β1) and cathepsin G secretion. Furthermore, significantly increased levels of secretory proteins indicate mast cell activation and testicular fibrosis in the seminal plasma of individuals with cryptorchidism compared to controls. These insights serve as valuable translational references, enriching our comprehension of testicular pathogenesis and informing more precise diagnosis and targeted therapeutic strategies for cryptorchidism.

Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall short in visualizing such confounding factors. We propose DiffChest, a self-conditioned diffusion model trained on 515,704 chest radiographs from 194,956 patients across the US and Europe. DiffChest provides patient-specific explanations and visualizes confounding factors that might mislead the model. The high inter-reader agreement, with Fleiss' kappa values of 0.8 or higher, validates its capability to identify treatment-related confounders. Confounders are accurately detected with 10%-100% prevalence rates. The pretraining process optimizes the model for relevant imaging information, resulting in excellent diagnostic accuracy for 11 chest conditions, including pleural effusion and heart insufficiency. Our findings highlight the potential of diffusion models in medical image classification, providing insights into confounding factors and enhancing model robustness and reliability.

Since the nineties, the incidence of sporadic early-onset (EO) cancers has been rising worldwide. The underlying reasons are still unknown. However, identifying them is vital for advancing both prevention and intervention. Here, we exploit available knowledge derived from clinical observations to formulate testable hypotheses aimed at defining the causal factors of this epidemic and discuss how to experimentally test them. We explore the potential impact of exposome changes from the millennials to contemporary young generations, considering both environmental exposures and enhanced susceptibilities to EO-cancer development. We emphasize how establishing the time required for an EO cancer to develop is relevant to defining future screening strategies. Finally, we discuss the importance of integrating multi-dimensional data from international collaborations to generate comprehensive knowledge and translate these findings back into clinical practice.

Lung parenchyma destruction represents a severe condition commonly found in chronic obstructive pulmonary disease (COPD), a leading cause of morbidity and mortality worldwide. Promoting lung regeneration is crucial for achieving clinical improvement. However, no therapeutic drugs are approved to improve the regeneration capacity due to incomplete understanding of the underlying pathogenic mechanisms. Here, we identify a positive feedback loop formed between adipose triglyceride lipase (ATGL)-mediated lipolysis and overexpression of CD36 specific to lung epithelial cells, contributing to disease progression. Genetic deletion of CD36 in lung epithelial cells and pharmacological inhibition of either ATGL or CD36 effectively reduce COPD pathogenesis and promote lung regeneration in mice. Mechanistically, disruption of the ATGL-CD36 loop rescues Z-DNA binding protein 1 (ZBP1)-induced cell necroptosis and restores WNT/β-catenin signaling. Thus, we uncover a crosstalk between lipolysis and lung epithelial cells, suggesting the regenerative potential for therapeutic intervention by targeting the ATGL-CD36-ZBP1 axis in COPD.

The potential of serum extracellular vesicles (EVs) as non-invasive biomarkers for diagnosing colorectal cancer (CRC) remains elusive. We employed an in-depth 4D-DIA proteomics and machine learning (ML) pipeline to identify key proteins, PF4 and AACT, for CRC diagnosis in serum EV samples from a discovery cohort of 37 cases. PF4 and AACT outperform traditional biomarkers, CEA and CA19-9, detected by ELISA in 912 individuals. Furthermore, we developed an EV-related random forest (RF) model with the highest diagnostic efficiency, achieving AUC values of 0.960 and 0.963 in the train and test sets, respectively. Notably, this model demonstrated reliable diagnostic performance for early-stage CRC and distinguishing CRC from benign colorectal diseases. Additionally, multi-omics approaches were employed to predict the functions and potential sources of serum EV-derived proteins. Collectively, our study identified the crucial proteomic signatures in serum EVs and established a promising EV-related RF model for CRC diagnosis in the clinic.

Pancreatic ductal adenocarcinoma (PDAC) still has a poor response to therapies, partly due to their cancer-associated fibroblasts (CAFs). Here, we investigate the synergistic impact of a combinatory approach between a known chemotherapy agent, such as gemcitabine (GEM), and gene-modified human mesenchymal stromal/stem cells (MSCs) secreting the pro-apoptotic soluble (s)TRAIL (sTRAIL MSCs) on both PDAC cells and CAFs. The combo significantly impacts on PDAC survival in 2D and 3D models. In orthotopic xenograft models, GEM and sTRAIL MSCs induce tumor architecture shredding with a reduction of CK7- and CK8/18-positive cancer cells and the abrogation of spleen metastases. A cytotoxic effect on primary human CAFs is also observed along with an alteration of their transcriptome and a reduction of the related desmoplasia. Collectively, we demonstrate a promising therapeutic profile of combining GEM and sTRAIL MSCs to target both tumoral and stromal compartments in PDAC.