Beyond 40 fluorescent probes for deep phenotyping of blood mononuclear cells, using spectral technology

The analytical capability of flow cytometry is crucial for differentiating the growing number of cell subsets found in human blood. This is important for accurate immunophenotyping of patients with few cells and a large number of parameters to monitor. Here, we present a 43-parameter panel to analyze peripheral blood mononuclear cells from healthy individuals using 41 fluorescence-labelled monoclonal antibodies, an autofluorescent channel, and a viability dye. We demonstrate minimal population distortions that lead to optimized population identification and reproducible results. We have applied an advanced approach in panel design, in selection of sample acquisition parameters and in data analysis. Appropriate autofluorescence identification and integration in the unmixing matrix, allowed for resolution of unspecific signals and increased dimensionality. Addition of one laser without assigned fluorochrome resulted in decreased fluorescence spill over and improved discrimination of cell subsets. It also increased the staining index when autofluorescence was integrated in the matrix. We conclude that spectral flow cytometry is a highly valuable tool for high-end immunophenotyping, and that fine-tuning of major experimental steps is key for taking advantage of its full capacity.

Phenotypic Analysis of Hematopoietic Stem and Progenitor Cell Populations in Acute Myeloid Leukemia Based on Spectral Flow Cytometry, a 20-Color Panel, and Unsupervised Learning Algorithms

The analysis of hematopoietic stem and progenitor cell populations (HSPCs) is fundamental in the understanding of normal hematopoiesis as well as in the management of malignant diseases, such as leukemias, and in their diagnosis and follow-up, particularly the measurement of treatment efficiency with the detection of measurable residual disease (MRD). In this study, I designed a 20-color flow cytometry panel tailored for the comprehensive analysis of HSPCs using a spectral cytometer. My investigation encompassed the examination of forty-six samples derived from both normal human bone marrows (BMs) and patients with acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) along with those subjected to chemotherapy and BM transplantation. By comparing my findings to those obtained through conventional flow cytometric analyses utilizing multiple tubes, I demonstrate that my innovative 20-color approach enables a more in-depth exploration of HSPC subpopulations and the detection of MRD with at least comparable sensitivity. Furthermore, leveraging advanced analytical tools such as t-SNE and FlowSOM learning algorithms, I conduct extensive cross-sample comparisons with two-dimensional gating approaches. My results underscore the efficacy of these two methods as powerful unsupervised alternatives for manual HSPC subpopulation analysis. I expect that in the future, complex multi-dimensional flow cytometric data analyses, such as those employed in this study, will be increasingly used in hematologic diagnostics.

A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups

Falls and frailty status are often associated with a decline in physical capacity and multifactorial assessment is highly recommended. Based on the functional and biomechanical parameters measured during clinical tests with an accelerometer integrated into smart eyeglasses, the purpose was to characterize a population of older adults through an unsupervised analysis into different physical performance groups. A total of 84 participants (25 men and 59 women) over the age of sixty-five (age: 74.17 ± 5.80 years; height: 165.70 ± 8.22 cm; body mass: 68.93 ± 13.55 kg) performed a 30 s Sit-to-Stand test, a six-minute walking test (6MWT), and a 3 m Timed Up and Go (TUG) test. The acceleration data measured from the eyeglasses were processed to obtain six parameters: the number of Sit-to-Stands, the maximal vertical acceleration values during Sit-to-Stand movements, step duration and length, and the duration of the TUG test. The total walking distance covered during the 6MWT was also retained. After supervised analyses comparison (i.e., ANOVAs), only one of the parameters (i.e., step length) differed between faller groups and no parameters differed between frail and pre-frail participants. In contrast, unsupervised analysis (i.e., clustering algorithm based on K-means) categorized the population into three distinct physical performance groups (i.e., low, intermediate, and high). All the measured parameters discriminated the low- and high-performance groups. Four of the measured parameters differentiated the three groups. In addition, the low-performance group had a higher proportion of frail participants. These results are promising for monitoring activities in older adults to prevent the decline of physical capacities.

Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses

BACKGROUND

Social media is an important information source for a growing subset of the population and can likely be leveraged to provide insight into the evolving drug overdose epidemic. Twitter can provide valuable insight into trends, colloquial information available to potential users, and how networks and interactivity might influence what people are exposed to and how they engage in communication around drug use.

OBJECTIVE

This exploratory study was designed to investigate the ways in which unsupervised machine learning analyses using natural language processing could identify coherent themes for tweets containing substance names.

METHODS

This study involved harnessing data from Twitter, including large-scale collection of brand name (N=262,607) and street name (N=204,068) prescription drug-related tweets and use of unsupervised machine learning analyses (ie, natural language processing) of collected data with data visualization to identify pertinent tweet themes. Latent Dirichlet allocation (LDA) with coherence score calculations was performed to compare brand (eg, OxyContin) and street (eg, oxys) name tweets.

RESULTS

We found people discussed drug use differently depending on whether a brand name or street name was used. Brand name categories often contained political talking points (eg, border, crime, and political handling of ongoing drug mitigation strategies). In contrast, categories containing street names occasionally referenced drug misuse, though multiple social uses for a term (eg, Sonata) muddled topic clarity.

CONCLUSIONS

Content in the brand name corpus reflected discussion about the drug itself and less often reflected personal use. However, content in the street name corpus was notably more diverse and resisted simple LDA categorization. We speculate this may reflect effective use of slang terminology to clandestinely discuss drug-related activity. If so, straightforward analyses of digital drug-related communication may be more difficult than previously assumed. This work has the potential to be used for surveillance and detection of harmful drug use information. It also might be used for appropriate education and dissemination of information to persons engaged in drug use content on Twitter.

An interactive web application for processing, correcting, and visualizing genome-wide pooled CRISPR-Cas9 screens

A limitation of pooled CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes arising from copy-number-amplified genomics regions. To solve this issue, we previously developed CRISPRcleanR: a computational method implemented as R/python package and in a dockerized version. CRISPRcleanR detects and corrects biased responses to CRISPR-Cas9 targeting in an unsupervised fashion, accurately reducing false-positive signals while maintaining sensitivity in identifying relevant genetic dependencies. Here, we present CRISPRcleanR WebApp , a web application enabling access to CRISPRcleanR through an intuitive interface. CRISPRcleanR WebApp removes the complexity of R/python language user interactions; provides user-friendly access to a complete analytical pipeline, not requiring any data pre-processing and generating gene-level summaries of essentiality with associated statistical scores; and offers a range of interactively explorable plots while supporting a more comprehensive range of CRISPR guide RNAs' libraries than the original package. CRISPRcleanR WebApp is available at https://crisprcleanr-webapp.fht.org/.

Unsupervised analysis of COVID-19 pandemic evolution in brazilian states: Vaccination Scenario

Brazil is one of the countries with the worst response against the pandemic scenario of coronavírus. At the beginning we were on average with 4000 deaths in a 24 hours period. In the course of this situation, large amounts of health and medicine datasets were being generated in real time, requiring effective ways to extract information and discover patterns that can help in the fight against this disease. And even more important is to monitor the progress of prophylactic measures and whether they are being effective in reducing the spread of the virus. Thus, the aim of this study is to analyze how the coronavirus has different ways to evolve in each Brazilian state with the influences of the vaccination process. To achieve this goal, the time series Clustering Technique based on a K-Means variation was applied, with the similarity metric Dynamic Time Warping (DTW). We produced this study using the data reported by the Ministry of Health in Brazil, referring to deaths per 100k inhabitants and all vaccination data available. Our results indicate an unevenly occurring vaccination and the need to identify other associated patterns with human development indices and other socio-economic indicators, being this the first analysis developed in the country, under the goals above.

In-Depth Immunophenotyping With Mass Cytometry During TB Treatment Reveals New T-Cell Subsets Associated With Culture Conversion

Tuberculosis (TB) is a difficult-to-treat infection because of multidrug regimen requirements based on drug susceptibility profiles and treatment observance issues. TB cure is defined by mycobacterial sterilization, technically complex to systematically assess. We hypothesized that microbiological outcome was associated with stage-specific immune changes in peripheral whole blood during TB treatment. The T-cell phenotypes of treated TB patients were prospectively characterized in a blinded fashion using mass cytometry after Mycobacterium tuberculosis (Mtb) antigen stimulation with QuantiFERON-TB Gold Plus, and then correlated to sputum culture status. At two months of treatment, cytotoxic and terminally differentiated CD8⁺ T-cells were under-represented and naïve CD4⁺ T-cells were over-represented in positive- versus negative-sputum culture patients, regardless of Mtb drug susceptibility. At treatment completion, a T-cell immune shift towards differentiated subpopulations was associated with TB cure. Overall, we identified specific T-cell profiles associated with slow sputum converters, which brings new insights in TB prognostic biomarker research designed for clinical application.

Unsupervised flow cytometry analysis in hematological malignancies: A new paradigm

Ever since hematopoietic cells became "events" enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large array of solutions was then developed to identify at best the numerous cell subsets that can be delineated, notably among hematopoietic cells. As instruments became more and more stable and robust, the focus moved to analytic software. Almost concomitantly, the capacity increased to use large panels (both with mass and classical cytometry) and to apply artificial intelligence/machine learning for their analysis. The combination of these concepts raised new analytical possibilities, opening an unprecedented field of subtle exploration for many conditions, including hematopoiesis and hematological disorders. In this review, the general concepts and progress achieved in the development of new analytical approaches for exploring high-dimensional data sets at the single-cell level will be described as they appeared over the past few years. A larger and more practical part will detail the various steps that need to be mastered, both in data acquisition and in the preanalytical check of data files. Finally, a step-by-step explanation of the solution in development to combine the Bioconductor clustering algorithm FlowSOM and the popular and widely used software Kaluza® (Beckman Coulter) will be presented. The aim of this review was to point out that the day when these progresses will reach routine hematology laboratories does not seem so far away.

A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods

Classification of asthma phenotypes has a potentially relevant impact on the clinical management of the disease. Methods for statistical classification without a priori assumptions (data-driven approaches) may contribute to developing a better comprehension of trait heterogeneity in disease phenotyping. This study aimed to summarize and characterize asthma phenotypes derived by data-driven methods. We performed a systematic review using three scientific databases, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We included studies reporting adult asthma phenotypes derived by data-driven methods using easily accessible variables in clinical practice. Two independent reviewers assessed studies. The methodological quality of included primary studies was assessed using the ROBINS-I tool. We retrieved 7446 results and included 68 studies of which 65% (n = 44) used data from specialized centers and 53% (n = 36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis (n = 19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal (n = 49), functional (n = 48) and clinical (n = 47). The identified asthma phenotypes varied according to the sample’s characteristics, variables included in the model, and data availability. Overall, the most frequent phenotypes were related to atopy, gender, and severe disease. This review shows a large variability of asthma phenotypes derived from data-driven methods. Further research should include more population-based samples and assess longitudinal consistency of data-driven phenotypes.

Unsupervised Flow Cytometry Analysis Allows for an Accurate Identification of Minimal Residual Disease Assessment in Acute Myeloid Leukemia

The assessment of minimal residual disease (MRD) is increasingly considered to monitor response to therapy in hematological malignancies. In acute myeloblastic leukemia (AML), molecular MRD (mMRD) is possible for about half the patients while multiparameter flow cytometry (MFC) is more broadly available. However, MFC analysis strategies are highly operator-dependent. Recently, new tools have been designed for unsupervised MFC analysis, segregating cell-clusters with the same immunophenotypic characteristics. Here, the Flow-Self-Organizing-Maps (FlowSOM) tool was applied to assess MFC-MRD in 96 bone marrow (BM) follow-up (FU) time-points from 40 AML patients with available mMRD. A reference FlowSOM display was built from 19 healthy/normal BM samples (NBM), then simultaneously compared to the patient's diagnosis and FU samples at each time-point. MRD clusters were characterized individually in terms of cell numbers and immunophenotype. This strategy disclosed subclones with varying immunophenotype within single diagnosis and FU samples including populations absent from NBM. Detectable MRD was as low as 0.09% in MFC and 0.051% for mMRD. The concordance between mMRD and MFC-MRD was 80.2%. MFC yielded 85% specificity and 69% sensitivity compared to mMRD. Unsupervised MFC is shown here to allow for an easy and robust assessment of MRD, applicable also to AML patients without molecular markers.

A precision medicine framework using artificial intelligence for the identification and confirmation of genomic biomarkers of response to an Alzheimer's disease therapy: Analysis of the blarcamesine (ANAVEX2-73) Phase 2a clinical study

INTRODUCTION

The search for drugs to treat Alzheimer's disease (AD) has failed to yield effective therapies. Here we report the first genome-wide search for biomarkers associated with therapeutic response in AD. Blarcamesine (ANAVEX2-73), a selective sigma-1 receptor (SIGMAR1) agonist, was studied in a 57-week Phase 2a trial (NCT02244541). The study was extended for a further 208 weeks (NCT02756858) after meeting its primary safety endpoint.

METHODS

Safety, clinical features, pharmacokinetic, and efficacy, measured by changes in the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL), were recorded. Whole exome and transcriptome sequences were obtained for 21 patients. The relationship between all available patient data and efficacy outcome measures was analyzed with unsupervised formal concept analysis (FCA), integrated in the Knowledge Extraction and Management (KEM) environment.

RESULTS

Biomarkers with a significant impact on clinical outcomes were identified at week 57: mean plasma concentration of blarcamesine (slope MMSE:P < .041), genomic variants SIGMAR1 p.Gln2Pro (ΔMMSE:P < .039; ΔADCS-ADL:P < .063) and COMT p.Leu146fs (ΔMMSE:P < .039; ΔADCS-ADL:P < .063), and baseline MMSE score (slope MMSE:P < .015). Their combined impact on drug response was confirmed at week 148 with linear mixed effect models.

DISCUSSION

Confirmatory Phase 2b/3 clinical studies of these patient selection markers are ongoing. This FCA/KEM analysis is a template for the identification of patient selection markers in early therapeutic development for neurologic disorders.

Top-down machine learning approach for high-throughput single-molecule analysis

Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events often requires extensive sampling. Although emerging experimental techniques can generate such large datasets, existing analysis tools are not suitable to process the large volume of data obtained in high-throughput paradigms. Here, we present a new analysis platform (DISC) that accelerates unsupervised analysis of single-molecule trajectories. By merging model-free statistical learning with the Viterbi algorithm, DISC idealizes single-molecule trajectories up to three orders of magnitude faster with improved accuracy compared to other commonly used algorithms. Further, we demonstrate the utility of DISC algorithm to probe cooperativity between multiple binding events in the cyclic nucleotide binding domains of HCN pacemaker channel. Given the flexible and efficient nature of DISC, we anticipate it will be a powerful tool for unsupervised processing of high-throughput data across a range of single-molecule experiments.

Two Distinct Subtypes Revealed in Blood Transcriptome of Breast Cancer Patients With an Unsupervised Analysis

Background: Breast cancer (BC) is a highly heterogeneous cancer. The interaction between immune system and BC is complex, widespread yet unclear. In this study, we aimed to reveal the heterogeneity of host systemic immune response to BC and understand the possible mechanisms that may drive the heterogeneity using transcriptomic data from peripheral blood mononuclear cells (PBMCs). Methods: Transcriptome-wide gene expressions of PBMCs in 33 BC patients were generated by RNA sequencing. An unsupervised clustering algorithm was employed to discover PBMC transcriptome subtypes among BC patients. Association analysis between PBMC subtypes and age, clinical stage, abundance of immune cells, and other clinical factors was performed to understand the underlying biological processes that may drive this heterogeneity. Immune gene signature identification and in silico survival analysis were performed to investigate the potential clinical implications of these PBMC subtypes. The findings were validated using the whole blood transcriptomes of an independent cohort. Results: We observed that established BC subtypes were not associated with PBMC gene expression profiles. Instead, we discovered and validated two new BC subtypes using PBMC transcriptome, which have distinct immune cell proportions, especially for lymphocytes (P = 5.22 × 10^-12) and neutrophils (P = 1.13 × 10^-14). Enrichment analysis of differentially expressed genes revealed that these two subtypes had distinct patterns of immune responses, including osteoclast differentiation and interleukin-10 signaling pathway. We developed two immune gene signatures that can differentiate these two BC PBMC subtypes. Further analysis suggested they had the ability to predict the clinical outcome of BC patients. Conclusions: PBMC transcriptome profiles can classify BC patients into two distinct subtypes. These two subtypes are mainly shaped by different immune cell abundance, which may have implications on clinical outcomes.

Theiler's Virus-Mediated Immunopathology in the CNS and Heart: Roles of Organ-Specific Cytokine and Lymphatic Responses

Theiler's murine encephalomyelitis virus (TMEV) induces different diseases in the central nervous system (CNS) and heart, depending on the mouse strains and time course, with cytokines playing key roles for viral clearance and immune-mediated pathology (immunopathology). In SJL/J mice, TMEV infection causes chronic TMEV-induced demyelinating disease (TMEV-IDD) in the spinal cord about 1 month post-inoculation (p.i.). Unlike other immunopathology models, both pro- and anti-inflammatory cytokines can play dual roles in TMEV-IDD. Pro-inflammatory cytokines play beneficial roles in viral clearance while they are also detrimental in immune-mediated demyelination. Anti-inflammatory cytokines suppress not only protective anti-viral immune responses but also detrimental autoreactive immune responses. Conversely, in C3H mice, TMEV infection induces a non-CNS disease, myocarditis, with three distinctive phases: phase I, viral pathology with interferon and chemokine responses; phase II, immunopathology mediated by acquired immune responses; and phase III, cardiac fibrosis. Although the exact mechanism(s) by which a single virus, TMEV, induces these different diseases in different organs is unclear, our bioinformatics approaches, especially principal component analysis (PCA) of transcriptome data, allow us to identify the key factors contributing to organ-specific immunopathology. The PCA demonstrated that in vitro infection of a cardiomyocyte cell line reproduced the transcriptome profile of phase I in TMEV-induced myocarditis; distinct interferon/chemokine-related responses were induced in vitro in TMEV-infected cardiomyocytes, but not in infected neuronal cells. In addition, the PCA of the in vivo CNS transcriptome data showed that decreased lymphatic marker expressions were weakly associated with inflammation in TMEV infection. Here, dysfunction of lymphatic vessels is shown to potentially contribute to immunopathology by delaying the clearance of cytokines and immune cells from the inflammatory site, although this can also confine the virus at these sites, preventing virus spread via lymphatic vessels. On the other hand, in the heart, dysfunction of lymphatics was associated with reduced lymphatic muscle contractility provoked by pro-inflammatory cytokines. Therefore, TMEV infection may induce different patterns of cytokine expressions as well as lymphatic vessel dysfunction by rather different mechanisms between the CNS and heart, which might explain observed patterns of organ-specific immunopathology.

Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks

Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the Populus tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems.

Informatics and machine learning to define the phenotype

INTRODUCTION

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

Influenza vaccine response profiles are affected by vaccine preparation and preexisting immunity, but not HIV infection

Vaccines dramatically reduce infection-related morbidity and mortality. Determining factors that modulate the host response is key to rational vaccine design and demands unsupervised analysis. To longitudinally resolve influenza-specific humoral immune response dynamics we constructed vaccine response profiles of influenza A- and B-specific IgM and IgG levels from 42 healthy and 31 HIV infected influenza-vaccinated individuals. Pre-vaccination antibody levels and levels at 3 predefined time points after vaccination were included in each profile. We performed hierarchical clustering on these profiles to study the extent to which HIV infection associated immune dysfunction, adaptive immune factors (pre-existing influenza-specific antibodies, T cell responses), an innate immune factor (Mannose Binding Lectin, MBL), demographic characteristics (gender, age), or the vaccine preparation (split vs. virosomal) impacted the immune response to influenza vaccination. Hierarchical clustering associated vaccine preparation and pre-existing IgG levels with the profiles of healthy individuals. In contrast to previous in vitro and animal data, MBL levels had no impact on the adaptive vaccine response. Importantly, while HIV infected subjects with low CD4 T cell counts showed a reduced magnitude of their vaccine response, their response profiles were indistinguishable from those of healthy controls, suggesting quantitative but not qualitative deficits. Unsupervised profile-based analysis ranks factors impacting the vaccine-response by relative importance, with substantial implications for comparing, designing and improving vaccine preparations and strategies. Profile similarity between HIV infected and HIV negative individuals suggests merely quantitative differences in the vaccine response in these individuals, offering a rationale for boosting strategies in the HIV infected population.