Cell Reports Methods最新文献

We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

In a recent issue of Nature Nanotechnology, Zeng et al. report that arraying immuno-stimulatory CpG molecules with specific nanoscale spacing on DNA origami nanoparticles enhanced Th1-polarized immune responses. These results highlight spatial presentation of adjuvants as a design strategy to optimize cancer vaccine efficacy, safety, and tolerability.

Tracking the lineage relationships of cell populations is of increasing interest in diverse biological contexts. In this issue of Cell Reports Methods, Holze et al. present a suite of computational tools to facilitate such analyses and encourage their broader application.

Human brain tissue models and organoids are vital for studying and modeling human neurological disease. However, the high cost of long-term cultured organoids inhibits their wide-ranging application. It is therefore urgent to develop methods for the cryopreservation of brain tissue and organoids. Here, we establish a method using methylcellulose, ethylene glycol, DMSO, and Y27632 (termed MEDY) for the cryopreservation of cortical organoids without disrupting the neural cytoarchitecture or functional activity. MEDY can be applied to multiple brain-region-specific organoids, including the dorsal/ventral forebrain, spinal cord, optic vesicle brain, and epilepsy patient-derived brain organoids. Additionally, MEDY enables the cryopreservation of human brain tissue samples, and pathological features are retained after thawing. Transcriptomic analysis shows that MEDY can protect synaptic function and inhibit the endoplasmic reticulum-mediated apoptosis pathway. MEDY will enable the large-scale and reliable storage of diverse neural organoids and living brain tissue and will facilitate wide-ranging research, medical applications, and drug screening.

To address the limitation of overlooking crucial ecological interactions due to relying on single time point samples, we developed a computational approach that analyzes individual samples based on the interspecific microbial relationships. We verify, using both numerical simulations as well as real and shuffled microbial profiles from the human oral cavity, that the method can classify single samples based on their interspecific interactions. By analyzing the gut microbiome of people with autistic spectrum disorder, we found that our interaction-based method can improve the classification of individual subjects based on a single microbial sample. These results demonstrate that the underlying ecological interactions can be practically utilized to facilitate microbiome-based diagnosis and precision medicine.

Localized cutaneous neurofibromas (cNFs) are benign tumors that arise in the dermis of patients affected by neurofibromatosis type 1 syndrome. cNFs are benign lesions: they do not undergo malignant transformation or metastasize. Nevertheless, they can cover a significant proportion of the body, with some individuals developing hundreds to thousands of lesions. cNFs can cause pain, itching, and disfigurement resulting in substantial socio-emotional repercussions. Currently, surgery and laser desiccation are the sole treatment options but may result in scarring and potential regrowth from incomplete removal. To identify effective systemic therapies, we introduce an approach to establish and screen cNF organoids. We optimized conditions to support the ex vivo growth of genomically diverse cNFs. Patient-derived cNF organoids closely recapitulate cellular and molecular features of parental tumors as measured by immunohistopathology, methylation, RNA sequencing, and flow cytometry. Our cNF organoid platform enables rapid screening of hundreds of compounds in a patient- and tumor-specific manner.

Alcohol-associated liver disease (ALD) is a prevalent liver disease, yet research is hampered by the lack of suitable and reliable human ALD models. Herein, we generated human adipose stromal/stem cell (hASC)-derived hepatocellular organoids (hAHOs) and hASC-derived liver organoids (hALOs) in a three-dimensional system using hASC-derived hepatocyte-like cells and endodermal progenitor cells, respectively. The hAHOs were composed of major hepatocytes and cholangiocytes. The hALOs contained hepatocytes and nonparenchymal cells and possessed a more mature liver function than hAHOs. Upon ethanol treatment, both steatosis and inflammation were present in hAHOs and hALOs. The incubation of hALOs with ethanol resulted in increases in the levels of oxidative stress, the endoplasmic reticulum protein thioredoxin domain-containing protein 5 (TXNDC5), the alcohol-metabolizing enzymes ADH1B and ALDH1B1, and extracellular matrix accumulation, similar to those of liver tissues from patients with ALD. These results present a useful approach for understanding the pathogenesis of ALD in humans, thus facilitating the discovery of effective treatments.

The role of protein turnover in pancreatic ductal adenocarcinoma (PDA) metastasis has not been previously investigated. We introduce dynamic stable-isotope labeling of organoids (dSILO): a dynamic SILAC derivative that combines a pulse of isotopically labeled amino acids with isobaric tandem mass-tag (TMT) labeling to measure proteome-wide protein turnover rates in organoids. We applied it to a PDA model and discovered that metastatic organoids exhibit an accelerated global proteome turnover compared to primary tumor organoids. Globally, most turnover changes are not reflected at the level of protein abundance. Interestingly, the group of proteins that show the highest turnover increase in metastatic PDA compared to tumor is involved in mitochondrial respiration. This indicates that metastatic PDA may adopt alternative respiratory chain functionality that is controlled by the rate at which proteins are turned over. Collectively, our analysis of proteome turnover in PDA organoids offers insights into the mechanisms underlying PDA metastasis.

Co-assembling enzymes with nanoparticles (NPs) into nanoclusters allows them to access channeling, a highly efficient form of multienzyme catalysis. Using pyruvate kinase (PykA) and lactate dehydrogenase (LDH) to convert phosphoenolpyruvic acid to lactic acid with semiconductor quantum dots (QDs) confirms how enzyme cluster formation dictates the rate of coupled catalytic flux (k_flux) across a series of differentially sized/shaped QDs and 2D nanoplatelets (NPLs). Enzyme kinetics and coupled flux were used to demonstrate that by mixing different NP systems into clusters, a >10× improvement in k_flux is observed relative to free enzymes, which is also ≥2× greater than enhancement on individual NPs. Cluster formation was characterized with gel electrophoresis and transmission electron microscopy (TEM) imaging. The generalizability of this mixed-NP approach to improving flux is confirmed by application to a seven-enzyme system. This represents a powerful approach for accessing channeling with almost any choice of enzymes constituting a multienzyme cascade.