Time, a quality-control parameter in flow cytometry

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

BACKGROUND

Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases.

METHODS

Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer.

RESULTS

Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83-0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89-0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71-0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state.

CONCLUSION

CyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis. Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018.

Molecular evolution and genetic variations of V and W proteins derived by RNA editing in Avian Paramyxoviruses

The newly assigned subfamily Avulavirinae in the family Paramyxoviridae includes avian paramyxoviruses (APMVs) isolated from a wide variety of avian species across the globe. Till date, 21 species of APMVs are reported and their complete genome sequences are available in GenBank. The APMV genome comprises of a single stranded, negative sense, non-segmented RNA comprising six transcriptional units (except APMV-6 with seven units) each coding for a structural protein. Additionally, by co-transcriptional RNA editing of phosphoprotein (P) gene, two mRNAs coding for accessory viral proteins, V and W, are generated along with unedited P mRNA. However, in APMV-11, the unedited mRNA codes for V protein while +2 edited mRNA translates to P protein, similar to members of subfamily Rubulavirinae in the same family. Such RNA editing in paramyxoviruses enables maximizing the coding capacity of their smaller genome. The three proteins of P gene: P, V and W, share identical N terminal but varied C terminal sequences that contribute to their unique functions. Here, we analyzed the P gene editing site, V and W sequences of all 21 APMV species known so far (55 viruses) by using bioinformatics and report their genetic variations and molecular evolution. The variations observed in the sequence and hexamer phase positions of the P gene editing sites is likely to influence the levels and relative proportions of P, V and W proteins' expressions which could explain the differences in the pathogenicity of APMVs. The V protein sequences of APMVs had conserved motifs similar to V proteins of other paramyxoviruses including the seven cysteine residues involved in MDA5 interference, STAT1 degradation and interferon antagonism. Conversely, W protein sequences of APMVs were distinct. High sequence homology was observed in both V and W proteins between strains of the same species than between species except in APMV-3 which was the most divergent APMV species. The estimates of synonymous and non-synonymous substitution rates suggested negative selection pressure on the V and W proteins within species indicating their low evolution rate. The molecular clock analysis revealed higher conservation of V protein sequence compared to W protein indicating the important role played by V protein in viral replication, pathogenesis and immune evasion. However, we speculate the genetic diversity of W proteins could impact the degree of pathogenesis, variable interferon antagonistic activity and the wide host range exhibited by APMV species. Phylogenetically, V proteins of APMVs clustered into three groups similar to the recent classification of APMVs into three new genera while no such pattern could be deciphered in the analysis of W proteins except that strains of same species grouped together. This is the first comprehensive study describing in detail the genetic variations and the molecular evolution of P gene edited, accessory viral proteins of Avian paramyxoviruses.

Guidelines for Gating Flow Cytometry Data for Immunological Assays

"Gating" refers to the selection of successive subpopulations of cells for analysis in flow cytometry. It is usually performed manually, based on expert knowledge of cell characteristics. However, there can be considerable disagreement in how gates should be applied, even between individuals experienced in the field. While clinical software often automates gating, and some guidelines do exist (especially for clinical assays), there are no comprehensive guidelines across the various types of immunological assays performed using flow cytometry. Here we attempt to provide such guidelines, focused on the most general and pervasive types of gates, why they are important, and what recommendations can be made regarding their use. We do so through the display of example data, collected by academic, government, and industry representatives. These guidelines should be of value to both novice and experienced flow cytometrists analyzing a wide variety of immunological assays.

Listmode data processing

Most flow cytometry data are acquired and stored in listmode format, whereby all measurements on each cell are stored separately in a list, rather than in histograms. This unit discusses the desirability of using listmode format and techniques for handling such data.

Classmode: a new data format for real-time multiparameter data analysis and data compression

A new data acquisition and analysis format (classmode) was developed that allows real-time data classification in a flow cytometer. In our cytometer, detected events were classified in real time by their presence or absence in a set of look-up tables (LUT). A modification of the cytometer hardware allows the exclusive transfer of the LUT data to the acquisition/storage computer. Using a combination of 8 LUTs, the analyzed events can be classified into 256 subpopulations. Real-time data classification results in an increased data transfer rate and a significant compression of the data.

Rapid multivariate analysis and display of cross-reacting antibodies on human leukocytes

We present an application which can rapidly determine the binding patterns of monoclonal antibodies on mixed populations of cells simultaneously in a single rapid analysis. It is an application of the tube identifier parameter (TIP) system which can provide fully correlated list-mode data of the entire patient phenotype in a single file. Using the phenogram analytical display, we are able to determine the cross-reacting antibodies for an entire antibody panel for each cell type. This information can be displayed in a single plot. Using light scatter gating to select different populations of lymphocytes, monocytes, and neutrophils, phenograms can be simultaneously generated. This provides a directly comparable means of displaying the positive and negative binding characteristics of each antibody on each cell population. Any marker combination that is abnormal will be identifiable in the phenogram. Additionally, by plotting the fluorescence distributions of each marker beside one another (termed overview), quantifiable differences in intensity can be determined. There are 3 major benefits of the proposed analysis. By using the TIP concept, several sets of antibodies can be compared simultaneously. Any light scatter gate can be used and this gate can be changed on one histogram or plot, yet apply to the total analysis. Data analysis is particularly rapid since the entire phenotype of a patient can be evaluated by performing a single rapid analysis.

An innovation in flow cytometry data collection and analysis producing a correlated multiple sample analysis in a single file

The problems associated with rapid analysis and interpretation of data from multicolor immunofluorescence panels have been a formidable barrier to their routine use. Using present flow cytometry concepts, a panel of 11 tubes each containing multiple phenotypic markers or controls requires postdata acquisition manipulation of many multiparameter histogram and listmode files. We have developed a method that compresses all of the information from such a panel into a single listmode data file during run time. A single data file is used to record the entire phenotypic analysis for a particular patient or series within an experiment. This is accomplished by the incorporation of a tube identifier parameter (TIP) as well as the fluorescence and light scatter parameters normally collected. The TIP can then be used for gating discrimination of any tube or set of tubes within a panel. When the TIP is correlated with the PRISM parameter the entire patient phenotypic image can be represented within a single two-parameter histogram we have called a phenogram. This phenogram can be generated in real time, providing on-line preprocessing of a complex multicolor experiment. By examining the image created by the phenogram it is possible to rapidly flag abnormalities such as incorrect gating. This procedure was carried out on an EPICS Elite flow cytometer in its standard configuration with the addition of hardware to provide an input for the TIP.

Flow cytometry: clinical applications in haematology

The time interval between the development of a new technique or methodology and its acceptance, if successful, as a recognized clinical application can be many years. The application of flow cytometry to reticulocyte counting, for example, has taken 8 years from the appearance of the first publication, and in 1990 it is still in its infancy as a clinical method. It is therefore a challenging task to anticipate which of the methodologies currently under development will achieve acceptance. It would be impossible to deal with all the candidates in the space available, and so a review is provided to those methods that may have potential applications in clinical haematology, together with some of the more practical details of methods that have recently been demonstrated to be viable in the clinical laboratory. The first category consists of leukocyte enumeration and studies on bone marrow, neutrophils, platelets and cellular DNA content, whilst the second covers reticulocyte counting and total red cell volume measurement. The contribution of flow cytometry to the field of immunophenotyping haematological disorders is probably unique in already being clinically acceptable. Finally, the question of quality control is addressed, as this is an essential prerequisite to the adoption of any new method in the clinical laboratory.