Machine learning integrative approaches to advance computational immunology.

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

[Impact of cardiovascular diseases on fitness to drive: mobility limitations in the elderly]. / Einfluss kardiovaskulärer Erkrankungen auf die Fahreignung: Mobilitätseinschränkungen im Alter.

The prevalence of cardiovascular diseases increases with age. Common symptoms such as dyspnea, chest pain, dizziness, or syncope can impact driving fitness. Due to a growing number of private drivers aged 65 and older and an increasing prevalence of cardiovascular diseases, questions regarding driving fitness restrictions for cardiological patients are gaining prominence in clinical settings. This article aims to summarize current recommendations for driving fitness in the context of cardiovascular diseases. The basis for the guidelines includes the Driving License Ordinance, the expert assessment guidelines of the Federal Highway Research Institute, and the guidelines of the German Society of Cardiology on driving fitness. Original literature on this topic is limited.Emphasizing an individualized assessment, clear guidelines for driving fitness in cardiac diseases or their symptoms and treatments are formulated. Regardless of the cardiac condition, the symptoms and likelihood of sudden loss of consciousness play a leading role in driving fitness assessment. Resulting impairments can range from a few weeks to a complete revocation of driving fitness. Regular examinations and differentiated assessments by medical professionals are prerequisites for maintaining driving fitness.The driving fitness of older private drivers is a significant and practical topic in cardiology. Current guidelines support the treating physicians in providing appropriate recommendations.

Getting Hold of the Tobamovirus Particle-Why and How? Purification Routes over Time and a New Customizable Approach.

This article develops a multi-perspective view on motivations and methods for tobamovirus purification through the ages and presents a novel, efficient, easy-to-use approach that can be well-adapted to different species of native and functionalized virions. We survey the various driving forces prompting researchers to enrich tobamoviruses, from the search for the causative agents of mosaic diseases in plants to their increasing recognition as versatile nanocarriers in biomedical and engineering applications. The best practices and rarely applied options for the serial processing steps required for successful isolation of tobamoviruses are then reviewed. Adaptations for distinct particle species, pitfalls, and 'forgotten' or underrepresented technologies are considered as well. The article is topped off with our own development of a method for virion preparation, rooted in historical protocols. It combines selective re-solubilization of polyethylene glycol (PEG) virion raw precipitates with density step gradient centrifugation in biocompatible iodixanol formulations, yielding ready-to-use particle suspensions. This newly established protocol and some considerations for perhaps worthwhile further developments could serve as putative stepping stones towards preparation procedures appropriate for routine practical uses of these multivalent soft-matter nanorods.

GraphCompass: spatial metrics for differential analyses of cell organization across conditions.

SUMMARY: Spatial omics technologies are increasingly leveraged to characterize how disease disrupts tissue organization and cellular niches. While multiple methods to analyze spatial variation within a sample have been published, statistical and computational approaches to compare cell spatial organization across samples or conditions are mostly lacking. We present GraphCompass, a comprehensive set of omics-adapted graph analysis methods to quantitatively evaluate and compare the spatial arrangement of cells in samples representing diverse biological conditions. GraphCompass builds upon the Squidpy spatial omics toolbox and encompasses various statistical approaches to perform cross-condition analyses at the level of individual cell types, niches, and samples. Additionally, GraphCompass provides custom visualization functions that enable effective communication of results. We demonstrate how GraphCompass can be used to address key biological questions, such as how cellular organization and tissue architecture differ across various disease states and which spatial patterns correlate with a given pathological condition. GraphCompass can be applied to various popular omics techniques, including, but not limited to, spatial proteomics (e.g. MIBI-TOF), spot-based transcriptomics (e.g. 10× Genomics Visium), and single-cell resolved transcriptomics (e.g. Stereo-seq). In this work, we showcase the capabilities of GraphCompass through its application to three different studies that may also serve as benchmark datasets for further method development. With its easy-to-use implementation, extensive documentation, and comprehensive tutorials, GraphCompass is accessible to biologists with varying levels of computational expertise. By facilitating comparative analyses of cell spatial organization, GraphCompass promises to be a valuable asset in advancing our understanding of tissue function in health and disease. .

Sex differences in lipidomic and bile acid plasma profiles in patients with and without coronary artery disease.

BACKGROUND: Lipids, including phospholipids and bile acids, exert various signaling effects and are thought to contribute to the development of coronary artery disease (CAD). Here, we aimed to compare lipidomic and bile acid profiles in the blood of patients with and without CAD stratified by sex. METHODS: From 2015 to 2022, 3,012 patients who underwent coronary angiography were recruited in the INTERCATH cohort. From the overall cohort, subgroups were defined using patient characteristics such as CAD vs. no CAD, 1st vs. 3rd tertile of LDL-c, and female vs. male sex. Hereafter, a matching algorithm based on age, BMI, hypertension status, diabetes mellitus status, smoking status, the Mediterranean diet score, and the intake of statins, triglycerides, HDL-c and hs-CRP in a 1:1 ratio was implemented. Lipidomic analyses of stored blood samples using the Lipidyzer platform (SCIEX) and bile acid analysis using liquid chromatography with tandem mass spectrometry (LC‒MS/MS) were carried out. RESULTS: A total of 177 matched individuals were analyzed; the median ages were 73.5 years (25th and 75th percentile: 64.1, 78.2) and 71.9 years (65.7, 77.2) for females and males with CAD, respectively, and 67.6 years (58.3, 75.3) and 69.2 years (59.8, 76.8) for females and males without CAD, respectively. Further baseline characteristics, including cardiovascular risk factors, were balanced between the groups. Women with CAD had decreased levels of phosphatidylcholine and diacylglycerol, while no differences in bile acid profiles were detected in comparison to those of female patients without CAD. In contrast, in male patients with CAD, decreased concentrations of the secondary bile acid species glycolithocholic and lithocholic acid, as well as altered levels of specific lipids, were detected compared to those in males without CAD. Notably, male patients with low LDL-c and CAD had significantly greater concentrations of various phospholipid species, particularly plasmalogens, compared to those in high LDL-c subgroup. CONCLUSIONS: We present hypothesis-generating data on sex-specific lipidomic patterns and bile acid profiles in CAD patients. The data suggest that altered lipid and bile acid composition might contribute to CAD development and/or progression, helping to understand the different disease trajectories of CAD in women and men. REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT04936438 , Unique identifier: NCT04936438.

The future of rapid and automated single-cell data analysis using reference mapping.

As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery.

Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein-ligand complexes in explicit water is included, accumulating over 170 µs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models.

Link between supercurrent diode and anomalous Josephson effect revealed by gate-controlled interferometry.

In Josephson diodes the asymmetry between positive and negative current branch of the current-phase relation leads to a polarity-dependent critical current and Josephson inductance. The supercurrent nonreciprocity can be described as a consequence of the anomalous Josephson effect -a φ0-shift of the current-phase relation- in multichannel ballistic junctions with strong spin-orbit interaction. In this work, we simultaneously investigate φ0-shift and supercurrent diode efficiency on the same Josephson junction by means of a superconducting quantum interferometer. By electrostatic gating, we reveal a direct link between φ0-shift and diode effect. Our findings show that spin-orbit interaction in combination with a Zeeman field plays an important role in determining the magnetochiral anisotropy and the supercurrent diode effect.

Morphological and Molecular Identification of Ulva spp. (Ulvophyceae; Chlorophyta) from Algarrobo Bay, Chile: Understanding the Composition of Green Tides.

Green algae blooms of the genus Ulva are occurring globally and are primarily attributed to anthropogenic factors. At Los Tubos beach in Algarrobo Bay along the central Chilean coast, there have been blooms of these algae that persist almost year-round over the past 20 years, leading to environmental, economic, and social issues that affect the local government and communities. The objective of this study was to characterize the species that form these green tides based on a combination of ecological, morpho-anatomical, and molecular information. For this purpose, seasonal surveys of beached algal fronds were conducted between 2021 and 2022. Subsequently, the sampled algae were analyzed morphologically and phylogenetically using the molecular markers ITS1 and tufA, allowing for the identification of at least five taxa. Of these five taxa, three (U. stenophylloides, U. uncialis, U. australis) have laminar, foliose, and distromatic morphology, while the other two (U. compressa, U. aragoensis) have tubular, filamentous, and monostromatic fronds. Intertidal surveys showed that U. stenophylloides showed the highest relative coverage throughout the seasons and all intertidal levels, followed by U. uncialis. Therefore, we can establish that the green tides on the coast of Algarrobo in Chile are multispecific, with differences in relative abundance during different seasons and across the intertidal zone, opening opportunities for diverse future studies, ranging from ecology to algal biotechnology.

hadge: a comprehensive pipeline for donor deconvolution in single-cell studies.

Single-cell multiplexing techniques (cell hashing and genetic multiplexing) combine multiple samples, optimizing sample processing and reducing costs. Cell hashing conjugates antibody-tags or chemical-oligonucleotides to cell membranes, while genetic multiplexing allows to mix genetically diverse samples and relies on aggregation of RNA reads at known genomic coordinates. We develop hadge (hashing deconvolution combined with genotype information), a Nextflow pipeline that combines 12 methods to perform both hashing- and genotype-based deconvolution. We propose a joint deconvolution strategy combining best-performing methods and demonstrate how this approach leads to the recovery of previously discarded cells in a nuclei hashing of fresh-frozen brain tissue.

Shared inflammatory glial cell signature after stab wound injury, revealed by spatial, temporal, and cell-type-specific profiling of the murine cerebral cortex.

Traumatic brain injury leads to a highly orchestrated immune- and glial cell response partially responsible for long-lasting disability and the development of secondary neurodegenerative diseases. A holistic understanding of the mechanisms controlling the responses of specific cell types and their crosstalk is required to develop an efficient strategy for better regeneration. Here, we combine spatial and single-cell transcriptomics to chart the transcriptomic signature of the injured male murine cerebral cortex, and identify specific states of different glial cells contributing to this signature. Interestingly, distinct glial cells share a large fraction of injury-regulated genes, including inflammatory programs downstream of the innate immune-associated pathways Cxcr3 and Tlr1/2. Systemic manipulation of these pathways decreases the reactivity state of glial cells associated with poor regeneration. The functional relevance of the discovered shared signature of glial cells highlights the importance of our resource enabling comprehensive analysis of early events after brain injury.

Defining and benchmarking open problems in single-cell analysis.

With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.

Benefits of subthalamic nucleus deep brain stimulation on visually-guided saccades depend on stimulation side and classic paradigm in Parkinson's disease.

OBJECTIVE: We aimed to gain further insight into previously reported beneficial effects of subthalamic nucleus deep brain stimulation (STN-DBS) on visually-guided saccades by examining the effects of unilateral compared to bilateral stimulation, paradigm, and target eccentricity on saccades in individuals with Parkinson's disease (PD). METHODS: Eleven participants with PD and STN-DBS completed the visually-guided saccade paradigms with OFF, RIGHT, LEFT, and BOTH stimulation. Rightward saccade performance was evaluated for three paradigms and two target eccentricities. RESULTS: First, we found that BOTH and LEFT increased gain, peak velocity, and duration compared to OFF stimulation. Second, we found that BOTH and LEFT stimulation decreased latency during the gap and step paradigms but had no effect on latency during the overlap paradigm. Third, we found that RIGHT was not different compared to OFF at benefiting rightward saccade performance. CONCLUSIONS: Left unilateral and bilateral stimulation both improve the motor outcomes of rightward visually-guided saccades. Additionally, both improve latency, a cognitive-motor outcome, but only in paradigms when attention does not require disengagement from a present stimulus. SIGNIFICANCE: STN-DBS primarily benefits motor and cognitive-motor aspects of visually-guided saccades related to reflexive attentional shifting, with the latter only evident when the fixation-related attentional system is not engaged.

Determining mammalian cells state by fractal micromotion.

We applied four fractal dimension estimation algorithms on the temporal electrical impedance signal of normal MDCK type II cell cultures monitored by ECIS technique and showed that the fractal dimension due to micromotion allows discriminating processes not sensed by the spectral impedance of the culture. In this work we subjected cell cultures to electric current damage and drug exposure to analyze the changes in the fractal structure of the temporal signal. Among the changes presented and detected are the differentiation between a healthy monolayer and one exposed to a drug, as well as the distinction between a seeding process and a wound-healing process performed by electric current. The four algorithms used were validated by applying them on topological functions of known fractal dimension, a study that determined the necessary conditions for a correct estimation.

SpatialData: an open and universal data framework for spatial omics.

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.

longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types.

SUMMARY: Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization. AVAILABILITY AND IMPLEMENTATION: The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.

Procedural and one-year outcomes of robotic-assisted versus manual percutaneous coronary intervention.

BACKGROUND: Robotic-assisted percutaneous coronary intervention (rPCI) has proven to be feasible and safe. Comparative analyses of rPCI versus manual PCI (mPCI) are scarce. AIMS: We aimed to investigate procedural aspects and outcomes of rPCI using the second-generation CorPath GRX Vascular Robotic System compared with mPCI in patients with chronic coronary syndrome and non-ST-segment elevation myocardial infarction acute coronary syndrome. METHODS: From January to April 2021, 70 patients underwent rPCI at the University Heart & Vascular Center Hamburg-Eppendorf and were recruited into the INTERCATH study. By propensity score matching, a control cohort of 210 patients who underwent mPCI from 2015-2021 was identified. Co-primary endpoints were one-year all-cause mortality and major adverse cardiovascular events (MACE) as a composite of cardiovascular death, unplanned target lesion revascularisation, myocardial infarction, and stroke. RESULTS: The median age of the patients (n=280) was 70.7 (25th percentile-75th percentile: 62.0-78.0) years, and 24.6% were female. The Gensini score (28.5 [16.2-48.1] vs 28.0 [15.5-47.0]; p=0.78) was comparable between rPCI versus mPCI. During the PCI procedure, total contrast fluid volume did not differ, whilst longer fluoroscopy times (20.4 min [13.8-27.2] vs 14.4 min [10.4-24.3]; p=0.001) were documented in the rPCI versus mPCI cohort. After 12 months of follow-up, neither all-cause mortality (p=0.22) nor MACE (p=0.25) differed between the groups. CONCLUSIONS: rPCI was associated with longer fluoroscopy times compared with mPCI, though without increased use of contrast medium. One-year follow-up revealed no differences in all-cause mortality or MACE, supporting the safety of a robotic-assisted approach.

Toward accurate and fast velocity quantification with 3D ultrashort TE phase-contrast imaging.

PURPOSE: Traditional phase-contrast MRI is affected by displacement artifacts caused by non-synchronized spatial- and velocity-encoding time points. The resulting inaccurate velocity maps can affect the accuracy of derived hemodynamic parameters. This study proposes and characterizes a 3D radial phase-contrast UTE (PC-UTE) sequence to reduce displacement artifacts. Furthermore, it investigates the displacement of a standard Cartesian flow sequence by utilizing a displacement-free synchronized-single-point-imaging MR sequence (SYNC-SPI) that requires clinically prohibitively long acquisition times. METHODS: 3D flow data was acquired at 3T at three different constant flow rates and varying spatial resolutions in a stenotic aorta phantom using the proposed PC-UTE, a Cartesian flow sequence, and a SYNC-SPI sequence as reference. Expected displacement artifacts were calculated from gradient timing waveforms and compared to displacement values measured in the in vitro flow experiments. RESULTS: The PC-UTE sequence reduces displacement and intravoxel dephasing, leading to decreased geometric distortions and signal cancellations in magnitude images, and more spatially accurate velocity quantification compared to the Cartesian flow acquisitions; errors increase with velocity and higher spatial resolution. CONCLUSION: PC-UTE MRI can measure velocity vector fields with greater accuracy than Cartesian acquisitions (although pulsatile fields were not studied) and shorter scan times than SYNC-SPI. As such, this approach is superior to traditional Cartesian 3D and 4D flow MRI when spatial misrepresentations cannot be tolerated, for example, when computational fluid dynamics simulations are compared to or combined with in vitro or in vivo measurements, or regional parameters such as wall shear stress are of interest.

C-reactive protein modifies lipoprotein(a)-related risk for coronary heart disease: the BiomarCaRE project.

BACKGROUND AND AIMS: Recent investigations have suggested an interdependence of lipoprotein(a) [Lp(a)]-related risk for cardiovascular disease with background inflammatory burden. The aim the present analysis was to investigate whether high-sensitive C-reactive protein (hsCRP) modulates the association between Lp(a) and coronary heart disease (CHD) in the general population. METHODS: Data from 71 678 participants from 8 European prospective population-based cohort studies were used (65 661 without/6017 with established CHD at baseline; median follow-up 9.8/13.8 years, respectively). Fine and Gray competing risk-adjusted models were calculated according to accompanying hsCRP concentration (<2 and ≥2 mg/L). RESULTS: Among CHD-free individuals, increased Lp(a) levels were associated with incident CHD irrespective of hsCRP concentration: fully adjusted sub-distribution hazard ratios [sHRs (95% confidence interval)] for the highest vs. lowest fifth of Lp(a) distribution were 1.45 (1.23-1.72) and 1.48 (1.23-1.78) for a hsCRP group of <2 and ≥2 mg/L, respectively, with no interaction found between these two biomarkers on CHD risk (Pinteraction = 0.82). In those with established CHD, similar associations were seen only among individuals with hsCRP ≥ 2 mg/L [1.34 (1.03-1.76)], whereas among participants with a hsCRP concentration <2 mg/L, there was no clear association between Lp(a) and future CHD events [1.29 (0.98-1.71)] (highest vs. lowest fifth, fully adjusted models; Pinteraction = 0.024). CONCLUSIONS: While among CHD-free individuals Lp(a) was significantly associated with incident CHD regardless of hsCRP, in participants with CHD at baseline, Lp(a) was related to recurrent CHD events only in those with residual inflammatory risk. These findings might guide adequate selection of high-risk patients for forthcoming Lp(a)-targeting compounds.