An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using “user-friendly” platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data set. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery

Telomerase Activation to Reverse Immunosenescence in Elderly Patients With Acute Coronary Syndrome: Protocol for a Randomized Pilot Trial

Background: Inflammation plays a key role in the pathophysiology of coronary heart disease (CHD) and its acute manifestation, acute coronary syndrome (ACS). Aging is associated with a decline of the immune system, a process known as immunosenescence. This is characterized by an increase in highly proinflammatory T cells that are involved in CHD progression, plaque destabilization, and myocardial ischemia–reperfusion injury. Telomere dysfunction has been implicated in immunosenescence of T lymphocytes. Telomerase is the enzyme responsible for maintaining telomeres during cell divisions. It has a protective effect on cells under oxidative stress and helps regulate flow-mediated dilation in microvasculature. Objective: The TACTIC (Telomerase ACTivator to reverse Immunosenescence in Acute Coronary Syndrome) trial will investigate whether a telomerase activator, TA-65MD, can reduce the proportion of senescent T cells in patients with ACS with confirmed CHD. It will also assess the effect of TA-65MD on decreasing telomere shortening, reducing oxidative stress, and improving endothelial function. Methods: The study was designed as a single-center, randomized, double-blind, parallel-group, placebo-controlled phase II trial. Recruitment started in January 2019. A total of 90 patients, aged 65 years or older, with treated ACS who have had CHD confirmed by angiography will be enrolled. They will be randomized to one of two groups: TA-65MD oral therapy (8 mg twice daily) or placebo taken for 12 months. The primary outcome is the effect on immunosenescence determined by a decrease in the proportion of CD8+ TEMRA (T effector memory cells re-expressing CD45RA [CD45 expressing exon A]) cells at 12 months. Secondary outcomes include leukocyte telomere length, endothelial function, cardiac function as measured by echocardiography and NT-proBNP (N-terminal fragment of the prohormone brain-type natriuretic peptide), systemic inflammation, oxidative stress, and telomerase activity. Results: The study received National Health Service (NHS) ethics approval on August 9, 2018; Medicines and Healthcare products Regulatory Agency approval on October 19, 2018; and NHS Health Research Authority approval on October 22, 2018. The trial began recruiting participants in January 2019 and completed recruitment in March 2020; the trial is due to report results in 2021. Conclusions: This pilot trial in older patients with CHD will explore outcomes not previously investigated outside in vitro or preclinical models. The robust design ensures that bias has been minimized. Should the results indicate reduced frequency of immunosenescent CD8+ T cells as well as improvements in telomere length and endothelial function, we will plan a larger, multicenter trial in patients to determine if TA-65MD is beneficial in the treatment of CHD in elderly patients

Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors

Dendritic cells (DCs) and monocytes play a central role in pathogen sensing, phagocytosis, and antigen presentation and consist of multiple specialized subtypes. However, their identities and interrelationships are not fully understood. Using unbiased single-cell RNA sequencing (RNA-seq) of ~2400 cells, we identified six human DCs and four monocyte subtypes in human blood. Our study reveals a new DC subset that shares properties with plasmacytoid DCs (pDCs) but potently activates T cells, thus redefining pDCs; a new subdivision within the CD1C+ subset of DCs; the relationship between blastic plasmacytoid DC neoplasia cells and healthy DCs; and circulating progenitor of conventional DCs (cDCs). Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease

Diagnostic Potential of Imaging Flow Cytometry

Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25-5.0 μg/mL) and/or carbendazim (0.8-1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the "DeepFlow" neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for 'mononucleates', 'binucleates', 'mononucleates with MN' and 'binucleates with MN', respectively. Successful classifications of 'trinucleates' (90%) and 'tetranucleates' (88%) in addition to 'other or unscorable' phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks

A New Evolutionary Algorithm-Based Home Monitoring Device for Parkinson’s Dyskinesia

Parkinson’s disease (PD) is a neurodegenerative movement disorder. Although there is no cure, symptomatic treatments are available and can significantly improve quality of life. The motor, or movement, features of PD are caused by reduced production of the neurotransmitter dopamine. Dopamine deficiency is most often treated using dopamine replacement therapy. However, this therapy can itself lead to further motor abnormalities referred to as dyskinesia. Dyskinesia consists of involuntary jerking movements and muscle spasms, which can often be violent. To minimise dyskinesia, it is necessary to accurately titrate the amount of medication given and monitor a patient’s movements. In this paper, we describe a new home monitoring device that allows dyskinesia to be measured as a patient goes about their daily activities, providing information that can assist clinicians when making changes to medication regimens. The device uses a predictive model of dyskinesia that was trained by an evolutionary algorithm, and achieves AUC>0.9 when discriminating clinically significant dyskinesia

Distinct lung cell signatures define the temporal evolution of diffuse alveolar damage in fatal COVID-19

Background Lung damage in severe COVID-19 is highly heterogeneous however studies with dedicated spatial distinction of discrete temporal phases of diffuse alveolar damage (DAD) and alternate lung injury patterns are lacking. Existing studies have also not accounted for progressive airspace obliteration in cellularity estimates. We used an imaging mass cytometry (IMC) analysis with an airspace correction step to more accurately identify the cellular immune response that underpins the heterogeneity of severe COVID-19 lung disease. Methods Lung tissue was obtained at post-mortem from severe COVID-19 deaths. Pathologist-selected regions of interest (ROIs) were chosen by light microscopy representing the patho-evolutionary spectrum of DAD and alternate disease phenotypes were selected for comparison. Architecturally normal SARS-CoV-2-positive lung tissue and tissue from SARS-CoV-2-negative donors served as controls. ROIs were stained for 40 cellular protein markers and ablated using IMC before segmented cells were classified. Cell populations corrected by ROI airspace and their spatial relationships were compared across lung injury patterns. Findings Forty patients (32M:8F, age: 22–98), 345 ROIs and >900k single cells were analysed. DAD progression was marked by airspace obliteration and significant increases in mononuclear phagocytes (MnPs), T and B lymphocytes and significant decreases in alveolar epithelial and endothelial cells. Neutrophil populations proved stable overall although several interferon-responding subsets demonstrated expansion. Spatial analysis revealed immune cell interactions occur prior to microscopically appreciable tissue injury. Interpretation The immunopathogenesis of severe DAD in COVID-19 lung disease is characterised by sustained increases in MnPs and lymphocytes with key interactions occurring even prior to lung injury is established

Single-cell insights into immune dysregulation in rheumatoid arthritis flare versus drug-free remission

Immune-mediated inflammatory diseases (IMIDs) are typically characterised by relapsing and remitting flares of inflammation. However, the unpredictability of disease flares impedes their study. Addressing this critical knowledge gap, we use the experimental medicine approach of immunomodulatory drug withdrawal in rheumatoid arthritis (RA) remission to synchronise flare processes allowing detailed characterisation. Exploratory mass cytometry analyses reveal three circulating cellular subsets heralding the onset of arthritis flare – CD45RO+PD1hi CD4+ and CD8+ T cells, and CD27+CD86+CD21- B cells – further characterised by single-cell sequencing. Distinct lymphocyte subsets including cytotoxic and exhausted CD4+ memory T cells, memory CD8+CXCR5+ T cells, and IGHA1+ plasma cells are primed for activation in flare patients. Regulatory memory CD4+ T cells (Treg cells) increase at flare onset, but with dysfunctional regulatory marker expression compared to drug-free remission. Significant clonal expansion is observed in T cells, but not B cells, after drug cessation; this is widespread throughout memory CD8+ T cell subsets but limited to the granzyme-expressing cytotoxic subset within CD4+ memory T cells. Based on our observations, we suggest a model of immune dysregulation for understanding RA flare, with potential for further translational research towards novel avenues for its treatment and prevention

A cell atlas of human thymic development defines T cell repertoire formation.

The thymus provides a nurturing environment for the differentiation and selection of T cells, a process orchestrated by their interaction with multiple thymic cell types. We used single-cell RNA sequencing to create a cell census of the human thymus across the life span and to reconstruct T cell differentiation trajectories and T cell receptor (TCR) recombination kinetics. Using this approach, we identified and located in situ CD8αα+ T cell populations, thymic fibroblast subtypes, and activated dendritic cell states. In addition, we reveal a bias in TCR recombination and selection, which is attributed to genomic position and the kinetics of lineage commitment. Taken together, our data provide a comprehensive atlas of the human thymus across the life span with new insights into human T cell development