CytoPy: An autonomous cytometry analysis framework Burton, Ross J; Ahmed, Raya; Cuff, Simone M ...
PLOS computational biology/PLoS computational biology,
06/2021, Letnik:
17, Številka:
6
Journal Article
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Odprti dostop
Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there ...has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.
A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield ...negative culture results. By reducing the number of query samples to be cultured and enabling diagnostic services to concentrate on those in which there are true microbial infections, a significant improvement in efficiency of the service is possible.
Screening process for urine samples prior to culture was modelled in a single clinical microbiology laboratory covering three hospitals and community services across Bristol and Bath, UK. Retrospective analysis of all urine microscopy, culture, and sensitivity reports over one year was used to compare two methods of classification: a heuristic model using a combination of white blood cell count and bacterial count, and a machine learning approach testing three algorithms (Random Forest, Neural Network, Extreme Gradient Boosting) whilst factoring in independent variables including demographics, historical urine culture results, and clinical details provided with the specimen.
A total of 212,554 urine reports were analysed. Initial findings demonstrated the potential for using machine learning algorithms, which outperformed the heuristic model in terms of relative workload reduction achieved at a classification sensitivity > 95%. Upon further analysis of classification sensitivity of subpopulations, we concluded that samples from pregnant patients and children (age 11 or younger) require independent evaluation. First the removal of pregnant patients and children from the classification process was investigated but this diminished the workload reduction achieved. The optimal solution was found to be three Extreme Gradient Boosting algorithms, trained independently for the classification of pregnant patients, children, and then all other patients. When combined, this system granted a relative workload reduction of 41% and a sensitivity of 95% for each of the stratified patient groups.
Based on the considerable time and cost savings achieved, without compromising the diagnostic performance, the heuristic model was successfully implemented in routine clinical practice in the diagnostic laboratory at Severn Pathology, Bristol. Our work shows the potential application of supervised machine learning models in improving service efficiency at a time when demand often surpasses resources of public healthcare providers.
Abstract
Motivation
Clustering is an unsupervised method for identifying structure in unlabelled data. In the context of cytometry, it is typically used to categorize cells into subpopulations of ...similar phenotypes. However, clustering is greatly dependent on hyperparameters and the data to which it is applied as each algorithm makes different assumptions and generates a different ‘view’ of the dataset. As such, the choice of clustering algorithm can significantly influence results, and there is often not one preferred method but different insights to be obtained from different methods. To overcome these limitations, consensus approaches are needed that directly address the effect of competing algorithms. To the best of our knowledge, consensus clustering algorithms designed specifically for the analysis of cytometry data are lacking.
Results
We present a novel ensemble clustering methodology based on geometric median clustering with weighted voting (GeoWaVe). Compared to graph ensemble clustering methods that have gained popularity in single-cell RNA sequencing analysis, GeoWaVe performed favourably on different sets of high-dimensional mass and flow cytometry data. Our findings provide proof of concept for the power of consensus methods to make the analysis, visualization and interpretation of cytometry data more robust and reproducible. The wide availability of ensemble clustering methods is likely to have a profound impact on our understanding of cellular responses, clinical conditions and therapeutic and diagnostic options.
Availability and implementation
GeoWaVe is available as part of the CytoCluster package https://github.com/burtonrj/CytoCluster and published on the Python Package Index https://pypi.org/project/cytocluster. Benchmarking data described are available from https://doi.org/10.5281/zenodo.7134723.
Supplementary information
Supplementary data are available at Bioinformatics online.
Infection remains a major cause of morbidity, mortality and technique failure in patients with end stage kidney failure who receive peritoneal dialysis (PD). Recent research suggests that the early ...inflammatory response at the site of infection carries diagnostically relevant information, suggesting that organ and pathogen-specific "immune fingerprints" may guide targeted treatment decisions and allow patient stratification and risk prediction at the point of care. Here, we recorded microRNA profiles in the PD effluent of patients presenting with symptoms of acute peritonitis and show that elevated peritoneal miR-223 and reduced miR-31 levels were useful predictors of bacterial infection. Cell culture experiments indicated that miR-223 was predominantly produced by infiltrating immune cells (neutrophils, monocytes), while miR-31 was mainly derived from the local tissue (mesothelial cells, fibroblasts). miR-223 was found to be functionally stabilised in PD effluent from peritonitis patients, with a proportion likely to be incorporated into neutrophil-derived exosomes. Our study demonstrates that microRNAs are useful biomarkers of bacterial infection in PD-related peritonitis and have the potential to contribute to disease-specific immune fingerprints. Exosome-encapsulated microRNAs may have a functional role in intercellular communication between immune cells responding to the infection and the local tissue, to help clear the infection, resolve the inflammation and restore homeostasis.
Sepsis is characterized by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis ...of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility of identifying integrative patterns from clinical parameters, plasma biomarkers, and extensive phenotyping of blood immune cells. While no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90-day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90-day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T-cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical, and clinical parameters.
NKG2D plays a major role in controlling immune responses through the regulation of natural killer (NK) cells, αβ and γδ T-cell function. This activating receptor recognizes eight distinct ligands ...(the MHC Class I polypeptide-related sequences (MIC) A andB, and UL16-binding proteins (ULBP)1-6) induced by cellular stress to promote recognition cells perturbed by malignant transformation or microbial infection. Studies into human cytomegalovirus (HCMV) have aided both the identification and characterization of NKG2D ligands (NKG2DLs). HCMV immediate early (IE) gene up regulates NKGDLs, and we now describe the differential activation of ULBP2 and MICA/B by IE1 and IE2 respectively. Despite activation by IE functions, HCMV effectively suppressed cell surface expression of NKGDLs through both the early and late phases of infection. The immune evasion functions UL16, UL142, and microRNA(miR)-UL112 are known to target NKG2DLs. While infection with a UL16 deletion mutant caused the expected increase in MICB and ULBP2 cell surface expression, deletion of UL142 did not have a similar impact on its target, MICA. We therefore performed a systematic screen of the viral genome to search of addition functions that targeted MICA. US18 and US20 were identified as novel NK cell evasion functions capable of acting independently to promote MICA degradation by lysosomal degradation. The most dramatic effect on MICA expression was achieved when US18 and US20 acted in concert. US18 and US20 are the first members of the US12 gene family to have been assigned a function. The US12 family has 10 members encoded sequentially through US12-US21; a genetic arrangement, which is suggestive of an 'accordion' expansion of an ancestral gene in response to a selective pressure. This expansion must have be an ancient event as the whole family is conserved across simian cytomegaloviruses from old world monkeys. The evolutionary benefit bestowed by the combinatorial effect of US18 and US20 on MICA may have contributed to sustaining the US12 gene family.
CD200 receptor (CD200R) negatively regulates peripheral and mucosal innate immune responses. Viruses, including herpesviruses, have acquired functional CD200 orthologs, implying that viral ...exploitation of this pathway is evolutionary advantageous. However, the role that CD200R signaling plays during herpesvirus infection in vivo requires clarification. Utilizing the murine cytomegalovirus (MCMV) model, we demonstrate that CD200R facilitates virus persistence within mucosal tissue. Specifically, MCMV infection of CD200R-deficient mice (CD200R(-/-)) elicited heightened mucosal virus-specific CD4 T cell responses that restricted virus persistence in the salivary glands. CD200R did not directly inhibit lymphocyte effector function. Instead, CD200R(-/-) mice exhibited enhanced APC accumulation that in the mucosa was a consequence of elevated cellular proliferation. Although MCMV does not encode an obvious CD200 homolog, productive replication in macrophages induced expression of cellular CD200. CD200 from hematopoietic and non-hematopoietic cells contributed independently to suppression of antiviral control in vivo. These results highlight the CD200-CD200R pathway as an important regulator of antiviral immunity during cytomegalovirus infection that is exploited by MCMV to establish chronicity within mucosal tissue.
Human CMV (HCMV)-encoded NK cell-evasion functions include an MHC class I homolog (UL18) with high affinity for the leukocyte inhibitory receptor-1 (CD85j, ILT2, or LILRB1) and a signal peptide ...(SP(UL40)) that acts by upregulating cell surface expression of HLA-E. Detailed characterization of SP(UL40) revealed that the N-terminal 14 aa residues bestowed TAP-independent upregulation of HLA-E, whereas C region sequences delayed processing of SP(UL40) by a signal peptide peptidase-type intramembrane protease. Most significantly, the consensus HLA-E-binding epitope within SP(UL40) was shown to promote cell surface expression of both HLA-E and gpUL18. UL40 was found to possess two transcription start sites, with utilization of the downstream site resulting in translation being initiated within the HLA-E-binding epitope (P2). Remarkably, this truncated SP(UL40) was functional and retained the capacity to upregulate gpUL18 but not HLA-E. Thus, our findings identify an elegant mechanism by which an HCMV signal peptide differentially regulates two distinct NK cell-evasion pathways. Moreover, we describe a natural SP(UL40) mutant that provides a clear example of an HCMV clinical virus with a defect in an NK cell-evasion function and exemplifies issues that confront the virus when adapting to immunogenetic diversity in the host.
ABSTRACT
Death receptor 3 (DR3, TNFRSF25), the closest family relative to tumor necrosis factor receptor 1, promotes CD4+ T‐cell‐driven inflammatory disease. We investigated the in vivo role of DR3 ...and its ligand TL1A in viral infection, by challenging DR3‐deficient (DR3KO) mice and their DR3WT littermates with the β‐herpesvirus murine cytomegalovirus or the poxvirus vaccinia virus. The phenotype and function of splenic T‐cells were analyzed using flow cytometry and molecular biological techniques. We report surface expression of DR3 by naive CD8+ T cells, with TCR activation increasing its levels 4‐fold and altering the ratio of DR3 splice variants. T‐cell responses were reduced up to 90% in DR3KO mice during acute infection. Adoptive transfer experiments indicated this was dependent on T‐cell‐restricted expression of DR3. DR3‐dependent CD8+ T‐cell expansion was NK and CD4 independent and due to proliferation, not decreased cell death. Notably, impaired immunity in DR3KO hosts on a C57BL/6 background was associated with 4‐ to 7‐fold increases in viral loads during the acute phase of infection, and in mice with suboptimal NK responses was essential for survival (37.5%). This is the first description of DR3 regulating virus‐specific T‐cell function in vivo and uncovers a critical role for DR3 in mediating antiviral immunity.—Twohig, J. P., Marsden, M., Cuff, S. M., Ferdinand, J. R., Gallimore, A M., Perks, W. V., Al‐Shamkhani, A., Humphreys, I. R., Wang, E. C. Y. The death receptor 3/TL1A pathway is essential for efficient development of antiviral CD4+ and CD8+ T‐cell immunity. FASEB J. 26, 3575–3586 (2012). www.fasebj.org
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