Multiomics Analysis of Structural Magnetic Resonance Imaging of the Brain and Cerebrospinal Fluid Metabolomics in Cognitively Normal and Impaired Adults

In-Depth Chemical Analysis of the Brain Extracellular Space Using In Vivo Microdialysis with Liquid Chromatography-Tandem Mass Spectrometry

Metabolomic analysis of samples acquired in vivo from the brain extracellular space by microdialysis sampling can provide insights into chemical underpinnings of a given brain state and how it changes over time. Small sample volumes and low physiological concentrations have limited the identification of compounds from this compartment, so at present, we have scant knowledge of its composition. As a result, most in vivo measurements have limited depth of analysis. Here, we describe an approach to (1) identify hundreds of compounds in brain dialysate and (2) routinely detect many of these compounds in 5 μL microdialysis samples to enable deep monitoring of brain chemistry in time-resolved studies. Dialysate samples collected over 12 h were concentrated 10-fold and then analyzed using liquid chromatography with iterative tandem mass spectrometry (LC-MS/MS). Using this approach on dialysate from the rat striatum with both reversed-phase and hydrophilic interaction liquid chromatography yielded 479 unique compound identifications. 60% of the identified compounds could be detected in 5 μL of dialysate without further concentration using a single 20 min LC-MS analysis, showing that once identified, most compounds can be detected using small sample volumes and shorter analysis times compatible with routine in vivo monitoring. To detect more neurochemicals, LC-MS analysis of dialysate derivatized with light and isotopically labeled benzoyl chloride was employed. 872 nondegenerate benzoylated features were detected with this approach, including most small-molecule neurotransmitters and several metabolites involved in dopamine metabolism. This strategy allows deeper annotation of the brain extracellular space than previously possible and provides a launching point for defining the chemistry underlying brain states.

Multi-omics analysis of the gut microbiome and metabolites associated with the psychoneurological symptom cluster in children with cancer receiving chemotherapy

BACKGROUND

Children with cancer receiving chemotherapy commonly report a cluster of psychoneurological symptoms (PNS), including pain, fatigue, anxiety, depression, and cognitive dysfunction. The role of the gut microbiome and its functional metabolites in PNS is rarely studied among children with cancer. This study investigated the associations between the gut microbiome-metabolome pathways and PNS in children with cancer across chemotherapy as compared to healthy children.

METHODS

A case-control study was conducted. Cancer cases were recruited from Children's Healthcare of Atlanta and healthy controls were recruited via flyers. Participants reported PNS using the Pediatric Patient-Reported Outcomes Measurement Information System. Data for cases were collected pre-cycle two chemotherapy (T0) and post-chemotherapy (T1), whereas data for healthy controls were collected once. Gut microbiome and its metabolites were measured using fecal specimens. Gut microbiome profiling was performed using 16S rRNA V4 sequencing, and metabolome was performed using an untargeted liquid chromatography-mass spectrometry approach. A multi-omics network integration program analyzed microbiome-metabolome pathways of PNS.

RESULTS

Cases (n = 21) and controls (n = 14) had mean ages of 13.2 and 13.1 years. For cases at T0, PNS were significantly associated with microbial genera (e.g., Ruminococcus, Megasphaera, and Prevotella), which were linked with carnitine shuttle (p = 0.0003), fatty acid metabolism (p = 0.001) and activation (p = 0.001), and tryptophan metabolism (p = 0.008). Megasphaera, clustered with aspartate and asparagine metabolism (p = 0.034), carnitine shuttle (p = 0.002), and tryptophan (p = 0.019), was associated with PNS for cases at T1. Gut bacteria with potential probiotic functions, along with fatty acid metabolism, tryptophan, and carnitine shuttle, were more clustered in cancer cases than the control network and this linkage with PNS needs further studies.

CONCLUSIONS

Using multi-omics approaches, this study indicated specific microbiome-metabolome pathways linked with PNS in children with cancer across chemotherapy. Due to limitations such as antibiotic use in cancer cases, these findings need to be further confirmed in a larger cohort.

Omics-based biomarkers discovery for Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorders presenting with the pathological hallmarks of amyloid plaques and tau tangles. Over the past few years, great efforts have been made to explore reliable biomarkers of AD. High-throughput omics are a technology driven by multiple levels of unbiased data to detect the complex etiology of AD, and it provides us with new opportunities to better understand the pathophysiology of AD and thereby identify potential biomarkers. Through revealing the interaction networks between different molecular levels, the ultimate goal of multi-omics is to improve the diagnosis and treatment of AD. In this review, based on the current AD pathology and the current status of AD diagnostic biomarkers, we summarize how genomics, transcriptomics, proteomics and metabolomics are all conducing to the discovery of reliable AD biomarkers that could be developed and used in clinical AD management.

Global research trends and foci of artificial intelligence-based tumor pathology: a scientometric study

Background

With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous researches have demonstrated that AI-based tumor pathology may help to solve the challenges faced by traditional pathology. This technology has attracted the attention of scholars in many fields and a large amount of articles have been published. This study mainly summarizes the knowledge structure of AI-based tumor pathology through bibliometric analysis, and discusses the potential research trends and foci.

Methods

Publications related to AI-based tumor pathology from 1999 to 2021 were selected from Web of Science Core Collection. VOSviewer and Citespace were mainly used to perform and visualize co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references and keywords in this field.

Results

A total of 2753 papers were included. The papers on AI-based tumor pathology research had been continuously increased since 1999. The United States made the largest contribution in this field, in terms of publications (1138, 41.34%), H-index (85) and total citations (35,539 times). We identified the most productive institution and author were Harvard Medical School and Madabhushi Anant, while Jemal Ahmedin was the most co-cited author. Scientific Reports was the most prominent journal and after analysis, Lecture Notes in Computer Science was the journal with highest total link strength. According to the result of references and keywords analysis, “breast cancer histopathology” “convolutional neural network” and “histopathological image” were identified as the major future research foci.

Conclusions

AI-based tumor pathology is in the stage of vigorous development and has a bright prospect. International transboundary cooperation among countries and institutions should be strengthened in the future. It is foreseeable that more research foci will be lied in the interpretability of deep learning-based model and the development of multi-modal fusion model.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03615-0.