The human brain vasculature is of great medical importance: its dysfunction causes disability and death
, and the specialized structure it forms-the blood-brain barrier-impedes the treatment of ...nearly all brain disorders
. Yet so far, we have no molecular map of the human brain vasculature. Here we develop vessel isolation and nuclei extraction for sequencing (VINE-seq) to profile the major vascular and perivascular cell types of the human brain through 143,793 single-nucleus transcriptomes from 25 hippocampus and cortex samples of 9 individuals with Alzheimer's disease and 8 individuals with no cognitive impairment. We identify brain-region- and species-enriched genes and pathways. We reveal molecular principles of human arteriovenous organization, recapitulating a gradual endothelial and punctuated mural cell continuum. We discover two subtypes of human pericytes, marked by solute transport and extracellular matrix (ECM) organization; and define perivascular versus meningeal fibroblast specialization. In Alzheimer's disease, we observe selective vulnerability of ECM-maintaining pericytes and gene expression patterns that implicate dysregulated blood flow. With an expanded survey of brain cell types, we find that 30 of the top 45 genes that have been linked to Alzheimer's disease risk by genome-wide association studies (GWASs) are expressed in the human brain vasculature, and we confirm this by immunostaining. Vascular GWAS genes map to endothelial protein transport, adaptive immune and ECM pathways. Many are microglia-specific in mice, suggesting a partial evolutionary transfer of Alzheimer's disease risk. Our work uncovers the molecular basis of the human brain vasculature, which will inform our understanding of overall brain health, disease and therapy.
Ageing is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death
. Despite rapid advances over recent years, many of the ...molecular and cellular processes that underlie the progressive loss of healthy physiology are poorly understood
. To gain a better insight into these processes, here we generate a single-cell transcriptomic atlas across the lifespan of Mus musculus that includes data from 23 tissues and organs. We found cell-specific changes occurring across multiple cell types and organs, as well as age-related changes in the cellular composition of different organs. Using single-cell transcriptomic data, we assessed cell-type-specific manifestations of different hallmarks of ageing-such as senescence
, genomic instability
and changes in the immune system
. This transcriptomic atlas-which we denote Tabula Muris Senis, or 'Mouse Ageing Cell Atlas'-provides molecular information about how the most important hallmarks of ageing are reflected in a broad range of tissues and cell types.
In this paper we analyze the effect of shocks in production networks. Our work is based on a rich dataset that contains information about companies from Slovenia right after the financial crisis of ...2008. The processed data spans for 8 years and covers the transaction history as well as performance indicators and various metadata of the companies. We define sales shocks at different levels, and identify companies impacted by them. Next we investigate stress, the potential immediate upstream and downstream impact of a shock within the production network. We base our main findings on a matched pairs analysis of stressed companies. We find that both shock and stress are associated with reporting bankruptcy in the future and that stress foremost impacts the future sales of customers. Furthermore, we find evidence that stress not only results in performance losses but the reconfiguration of the production network as well. We show that stressed companies actively seek for new trading partners, and that these new links often share the industry of the shocked company. These results suggest that both stressed customers and suppliers react quickly to stress and adjust their trading relationships.
A plethora of centrality measures or rankings have been proposed to account for the importance of the nodes of a network. In the seminal study of Boldi and Vigna (2014), the comparative evaluation of ...centrality measures was termed a difficult, arduous task. In networks with fast dynamics, such as the Twitter mention or retweet graphs, predicting emerging centrality is even more challenging.
Our main result is a new, temporal walk based dynamic centrality measure that models temporal information propagation by considering the order of edge creation. Dynamic centrality measures have already started to emerge in publications; however, their empirical evaluation is limited. One of our main contributions is creating a quantitative experiment to assess temporal centrality metrics. In this experiment, our new measure outperforms graph snapshot based static and other recently proposed dynamic centrality measures in assigning the highest time-aware centrality to the actually relevant nodes of the network. Additional experiments over different data sets show that our method perform well for detecting concept drift in the process that generates the graphs.
Aging is the key risk factor for cognitive decline, yet the molecular changes underlying brain aging remain poorly understood. Here, we conducted spatiotemporal RNA sequencing of the mouse brain, ...profiling 1,076 samples from 15 regions across 7 ages and 2 rejuvenation interventions. Our analysis identified a brain-wide gene signature of aging in glial cells, which exhibited spatially defined changes in magnitude. By integrating spatial and single-nucleus transcriptomics, we found that glial aging was particularly accelerated in white matter compared with cortical regions, whereas specialized neuronal populations showed region-specific expression changes. Rejuvenation interventions, including young plasma injection and dietary restriction, exhibited distinct effects on gene expression in specific brain regions. Furthermore, we discovered differential gene expression patterns associated with three human neurodegenerative diseases, highlighting the importance of regional aging as a potential modulator of disease. Our findings identify molecular foci of brain aging, providing a foundation to target age-related cognitive decline.
Display omitted
•Brain-wide gene signature of aging in glial cells with spatially defined changes•Glial cell aging is accelerated in white matter•Rejuvenation interventions have region-specific effects on gene expression•Genes implicated in neurodegenerative diseases show regional aging patterns
A spatiotemporal transcriptome map of the aging mouse brain identifies region-specific acceleration of glial aging, particularly in white matter, distinctive regional responses to rejuvenation interventions, and regional age-associated expression patterns of genes tied to human neurodegenerative diseases.
Animal studies show aging varies between individuals as well as between organs within an individual
, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized ...levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer's disease (AD) progression independently from and as strongly as plasma pTau-181 (ref.
), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.
Node embeddings in dynamic graphs Béres, Ferenc; Kelen, Domokos M.; Pálovics, Róbert ...
Applied network science,
08/2019, Letnik:
4, Številka:
1
Journal Article
Recenzirano
Odprti dostop
In this paper, we present algorithms that learn and update temporal node embeddings on the fly for tracking and measuring node similarity over time in graph streams. Recently, several representation ...learning methods have been proposed that are capable of embedding nodes in a vector space in a way that captures the network structure. Most of the known techniques extract embeddings from static graph snapshots. By contrast, modeling the dynamics of the nodes in temporal networks requires evolving node representations. In order to update node representations that reflect the temporal changes in the local graph structure, we rely on ideas for data stream algorithms. For example, we assess neighborhood overlap by a MinHash fingerprint-based algorithm.
To evaluate our methods, in addition to the standard link prediction task, we provide dynamic ground truth data for the quantitative evaluation of similarity search by using online updated node embeddings. In our experiments, we constructed tennis tournament Twitter mention graphs as edge streams and compiled dynamic ground truth by using tournament schedule as external source. Our new algorithms outperformed snapshot-based batch methods for both link prediction and similarity search.
Several genetic risk factors for Alzheimer's disease implicate genes involved in lipid metabolism and many of these lipid genes are highly expressed in glial cells
. However, the relationship between ...lipid metabolism in glia and Alzheimer's disease pathology remains poorly understood. Through single-nucleus RNA sequencing of brain tissue in Alzheimer's disease, we have identified a microglial state defined by the expression of the lipid droplet-associated enzyme ACSL1 with ACSL1-positive microglia being most abundant in patients with Alzheimer's disease having the APOE4/4 genotype. In human induced pluripotent stem cell-derived microglia, fibrillar Aβ induces ACSL1 expression, triglyceride synthesis and lipid droplet accumulation in an APOE-dependent manner. Additionally, conditioned media from lipid droplet-containing microglia lead to Tau phosphorylation and neurotoxicity in an APOE-dependent manner. Our findings suggest a link between genetic risk factors for Alzheimer's disease with microglial lipid droplet accumulation and neurotoxic microglia-derived factors, potentially providing therapeutic strategies for Alzheimer's disease.
We address the problem of recommending highly volatile items for users, both with potentially ambiguous location that may change in time. The three main ingredients of our method include (1) using ...online machine learning for the highly volatile items; (2) learning the personalized importance of hierarchical geolocation (for example, town, region, country, continent); finally (3) modeling temporal relevance by counting recent items with an exponential decay in recency.
For (1), we consider a time-aware setting, where evaluation is cumbersome by traditional measures since we have different top recommendations at different times. We describe a time-aware framework based on individual item discounted gain. For (2), we observe that trends and geolocation turns out to be more important than personalized user preferences: user–item and content-item matrix factorization improves in combination with our geo-trend learning methods, but in itself, they are greatly inferior to our location based models. In fact, since our best performing methods are based on spatiotemporal data, they are applicable in the user cold start setting as well and perform even better than content based cold start methods. Finally for (3), we estimate the probability that the item will be viewed by its previous views to obtain a powerful model that combines item popularity and recency.
To generate realistic data for measuring our new methods, we rely on Twitter messages with known GPS location and consider hashtags as items that we recommend the users to be included in their next message.
In a previous result, we showed that the influence of social contacts spreads information about new artists through the Last.fm social network. We successfully decomposed influence from effects of ...trends, global popularity, and homophily or shared environment of friends. In this paper, we present our new experiments that use a mathematically sound formula for defining and measuring the influence in the network. We provide new baseline and influence models and evaluation measures, both batch and online, for real-time recommendations with very strong temporal aspects. Our experiments are carried over the 2-year “scrobble” history of 70,000 Last.fm users. In our results, we formally define and distil the effect of social influence. In addition, we provide new models and evaluation measures for real-time recommendations with very strong temporal aspects.
PDF
