Variable selection for nonlinear dimensionality reduction of biological datasets through bootstrapping of correlation networks.

Identifying the most relevant variables or features in massive datasets for dimensionality reduction can lead to improved and more informative display, faster computation times, and more explainable models of complex systems. Despite significant advances and available algorithms, this task generally remains challenging, especially in unsupervised settings. In this work, we propose a method that constructs correlation networks using all intervening variables and then selects the most informative ones based on network bootstrapping. The method can be applied in both supervised and unsupervised scenarios. We demonstrate its functionality by applying Uniform Manifold Approximation and Projection for dimensionality reduction to several high-dimensional biological datasets, derived from 4D live imaging recordings of hundreds of morpho-kinetic variables, describing the dynamics of thousands of individual leukocytes at sites of prominent inflammation. We compare our method with other standard ones in the field, such as Principal Component Analysis and Elastic Net, showing that it outperforms them. The proposed method can be employed in a wide range of applications, encompassing data analysis and machine learning.

Strategies of neutrophil diversification.

Neutrophils are formidable defenders. Their vast numbers, constant production, high cytotoxicity and capacity to produce extracellular traps, underlie their ability to efficiently protect in a microorganism-rich world. However, neutrophils are much more than immune sentinels, as evidenced by the expanding repertoire of functions discovered in the context of tissue homeostasis, regeneration or chronic pathologies. In this Perspective, we discuss general functional features of the neutrophil compartment that may be relevant in most, if not all, physiological scenarios in which they participate, including specialization in naïve tissues, transcriptional noise in the bloodstream as a potential strategy for diversification and functional bias in inflammatory sites. We intentionally present the reader with more questions than answers and propose models and approaches that we hope will shed new light onto the biology of these fascinating cells and spark new directions of research.

Insights into the Complexity of a Dormant Mycobacterium tuberculosis Cluster Once Transmission Is Resumed.

Genotyping tools help identify the complexity in Mycobacterium tuberculosis transmission clusters. We carried out a thorough analysis of the epidemiological and bacteriological complexity of a cluster in Almería, Spain. The cluster, initially associated with Moroccan migrants and with no secondary cases identified in 4 years, then reappeared in Spanish-born individuals. In one case, two Mycobacterium tuberculosis clonal variants were identified. We reanalyzed the cluster, supported by the characterization of multiple cultured isolates and respiratory specimens, whole-genome sequencing, and epidemiological case interviews. Our findings showed that the cluster, which was initially thought to have restarted activity with just a single case harboring a small degree of within-host diversity, was in fact currently growing due to coincidental reactivation of past exposures, with clonal diversity transmitted throughout the cluster. In one case, within-host diversity was amplified, probably due to prolonged diagnostic delay. IMPORTANCE The precise study of the dynamics of tuberculosis transmission in socio-epidemiologically complex scenarios may require more thorough analysis than the standard molecular epidemiology strategies. Our study illustrates the epidemiological and bacteriological complexity present in a transmission cluster in a challenging epidemiological setting with a high proportion of migrant cases. The combination of whole-genome sequencing, refined and refocused epidemiological interviews, and in-depth analysis of the bacterial composition of sputa and cultured isolates was crucial in order to correctly reinterpret the true nature of this cluster. Our global approach allowed us to reinterpret correctly the unnoticed epidemiological and bacteriological complexity involved in the Mycobacterium tuberculosis transmission event under study, which had been overlooked by the usual molecular epidemiology approaches.

Behavioural immune landscapes of inflammation.

Transcriptional and proteomic profiling of individual cells have revolutionized interpretation of biological phenomena by providing cellular landscapes of healthy and diseased tissues1,2. These approaches, however, do not describe dynamic scenarios in which cells continuously change their biochemical properties and downstream 'behavioural' outputs3-5. Here we used 4D live imaging to record tens to hundreds of morpho-kinetic parameters describing the dynamics of individual leukocytes at sites of active inflammation. By analysing more than 100,000 reconstructions of cell shapes and tracks over time, we obtained behavioural descriptors of individual cells and used these high-dimensional datasets to build behavioural landscapes. These landscapes recognized leukocyte identities in the inflamed skin and trachea, and uncovered a continuum of neutrophil states inside blood vessels, including a large, sessile state that was embraced by the underlying endothelium and associated with pathogenic inflammation. Behavioural screening in 24 mouse mutants identified the kinase Fgr as a driver of this pathogenic state, and interference with Fgr protected mice from inflammatory injury. Thus, behavioural landscapes report distinct properties of dynamic environments at high cellular resolution.

ACME: Automatic feature extraction for cell migration examination through intravital microscopy imaging.

Cell detection and tracking applied to in vivo fluorescence microscopy has become an essential tool in biomedicine to characterize 4D (3D space plus time) biological processes at the cellular level. Traditional approaches to cell motion analysis by microscopy imaging, although based on automatic frameworks, still require manual supervision at some points of the system. Hence, when dealing with a large amount of data, the analysis becomes incredibly time-consuming and typically yields poor biological information. In this paper, we propose a fully-automated system for segmentation, tracking and feature extraction of migrating cells within blood vessels in 4D microscopy imaging. Our system consists of a robust 3D convolutional neural network (CNN) for joint blood vessel and cell segmentation, a 3D tracking module with collision handling, and a novel method for feature extraction, which takes into account the particular geometry in the cell-vessel arrangement. Experiments on a large 4D intravital microscopy dataset show that the proposed system achieves a significantly better performance than the state-of-the-art tools for cell segmentation and tracking. Furthermore, we have designed an analytical method of cell behaviors based on the automatically extracted features, which supports the hypotheses related to leukocyte migration posed by expert biologists. This is the first time that such a comprehensive automatic analysis of immune cell migration has been performed, where the total population under study reaches hundreds of neutrophils and thousands of time instances.

Recurrences of multidrug-resistant tuberculosis: Strains involved, within-host diversity, and fine-tuned allocation of reinfections.

Recurrent tuberculosis occurs due to exogenous reinfection or reactivation/persistence. We analysed 90 sequential MDR Mtb isolates obtained in Argentina from 27 patients with previously diagnosed MDR-TB that recurred in 2018 (1-10 years, 2-10 isolates per patient). Three long-term predominant strains were responsible for 63% of all MDR-TB recurrences. Most of the remaining patients were infected by strains different from each other. Reactivation/persistence of the same strain caused all but one recurrence, which was due to a reinfection with a predominant strain. One of the prevalent strains showed marked stability in the recurrences, while in another strain higher SNP-based diversity was observed. Comparisons of intra- versus inter-patient SNP distances identified two possible reinfections with closely related variants circulating in the community. Our results show a complex scenario of MDR-TB infections in settings with predominant MDR Mtb strains.

Complete Analysis of the Epidemiological Scenario around a SARS-CoV-2 Reinfection: Previous Infection Events and Subsequent Transmission.

The first descriptions of reinfection by SARS-CoV-2 have been recently reported. However, these studies focus exclusively on the reinfected case, without considering the epidemiological context of the event. Our objectives were to perform a complete analysis of the sequential infections and community transmission events around a SARS-CoV-2 reinfection, including the infection events preceding it, the exposure, and subsequent transmissions. Our analysis was supported by host genetics, viral whole-genome sequencing, phylogenomic viral population analysis, and refined epidemiological data obtained from interviews with the involved subjects. The reinfection involved a 53-year-old woman with asthma (Case A), with a first COVID-19 episode in April 2020 and a much more severe second episode 4-1/2 months later, with SARS-CoV-2 seroconversion in August, that required hospital admission. An extended genomic analysis allowed us to demonstrate that the strain involved in Case A's reinfection was circulating in the epidemiological context of Case A and was also transmitted subsequently from Case A to her family context. The reinfection was also supported by a phylogenetic analysis, including 348 strains from Madrid, which revealed that the strain involved in the reinfection was circulating by the time Case A suffered the second episode, August-September 2020, but absent at the time range corresponding to Case A's first episode. IMPORTANCE We present the first complete analysis of the epidemiological scenario around a reinfection by SARS-CoV-2, more severe than the first episode, including three cases preceding the reinfection, the reinfected case per se, and the subsequent transmission to another seven cases.

Different dynamics of mean SARS-CoV-2 RT-PCR Ct values between the first and second COVID-19 waves in the Madrid population.

SARS-CoV-2 RT-PCR cycle threshold values from 18,803 cases (2 March-4 October) in Madrid define three stages: (i) initial ten weeks with sustained reduction in viral load (Ct: 23.4-32.3), (ii) stability with low viral loads (Ct: 31.9-35.5) in the next nine weeks and (iii) sudden increase with progressive higher viral loads until reaching stability at high levels in the next twelve weeks, coinciding with an increased percentage of positive cases and reduced median age. These data indicate differential virological/epidemiological patterns between the first and second COVID-19 waves in Madrid.