High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation.

By developing a high-density murine immunophenotyping platform compatible with high-throughput genetic screening, we have established profound contributions of genetics and structure to immune variation (http://www.immunophenotype.org). Specifically, high-throughput phenotyping of 530 unique mouse gene knockouts identified 140 monogenic 'hits', of which most had no previous immunologic association. Furthermore, hits were collectively enriched in genes for which humans show poor tolerance to loss of function. The immunophenotyping platform also exposed dense correlation networks linking immune parameters with each other and with specific physiologic traits. Such linkages limit freedom of movement for individual immune parameters, thereby imposing genetically regulated 'immunologic structures', the integrity of which was associated with immunocompetence. Hence, we provide an expanded genetic resource and structural perspective for understanding and monitoring immune variation in health and disease.

flowSim: Near duplicate detection for flow cytometry data.

The analysis of large amounts of data is important for the development of machine learning (ML) models. flowSim is the first algorithm designed to visualize, detect and remove highly redundant information in flow cytometry (FCM) training sets to decrease the computational time for training and increase the performance of ML algorithms by reducing overfitting. flowSim performs near duplicate image detection by combining community detection algorithms with the density analysis of the marker expression values. flowSim clustering compared to consensus manual clustering on a dataset composed of 160 images of bivariate FCM data had a mean Adjusted Rand Index of 0.90, demonstrating its efficiency in identifying similar patterns. flowSim selectively discarded near duplicate files in datasets constructed with known redundancy, and removed 92.6% of FCM images in a dataset of over 500,000 drawn from public repositories.

flowCut: An R package for automated removal of outlier events and flagging of files based on time versus fluorescence analysis.

Technical artifacts such as clogging that occur during the data acquisition process of flow cytometry data can cause spurious events and fluorescence intensity shifting that impact the quality of the data and its analysis results. These events should be identified and potentially removed before being passed to the next stage of analysis. flowCut, an R package, automatically detects anomaly events in flow cytometry experiments and flags files for potential review. Its results are on par with manual analysis and it outperforms existing automated approaches.

Automated identification of maximal differential cell populations in flow cytometry data.

We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e.g., disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice-based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub (github.com/aya49/flowGraph) and on BioConductor.

The miR-185/PAK6 axis predicts therapy response and regulates survival of drug-resistant leukemic stem cells in CML.

Overcoming drug resistance and targeting cancer stem cells remain challenges for curative cancer treatment. To investigate the role of microRNAs (miRNAs) in regulating drug resistance and leukemic stem cell (LSC) fate, we performed global transcriptome profiling in treatment-naive chronic myeloid leukemia (CML) stem/progenitor cells and identified that miR-185 levels anticipate their response to ABL tyrosine kinase inhibitors (TKIs). miR-185 functions as a tumor suppressor: its restored expression impaired survival of drug-resistant cells, sensitized them to TKIs in vitro, and markedly eliminated long-term repopulating LSCs and infiltrating blast cells, conferring a survival advantage in preclinical xenotransplantation models. Integrative analysis with mRNA profiles uncovered PAK6 as a crucial target of miR-185, and pharmacological inhibition of PAK6 perturbed the RAS/MAPK pathway and mitochondrial activity, sensitizing therapy-resistant cells to TKIs. Thus, miR-185 presents as a potential predictive biomarker, and dual targeting of miR-185-mediated PAK6 activity and BCR-ABL1 may provide a valuable strategy for overcoming drug resistance in patients.

Data File Standard for Flow Cytometry, Version FCS 3.2.

FCS 3.2 is a revision of the flow cytometry data standard based on a decade of suggested improvements from the community as well as industry needs to capture instrument conditions and measurement features more precisely. The unchanged goal of the standard is to provide a uniform file format that allows files created by one type of acquisition hardware and software to be analyzed by any other type. The standard retains the overall FCS file structure and most features of previous versions, but also contains a few changes that were required to support new types of data and use cases efficiently. These changes are incompatible with existing FCS file readers. Notably, FCS 3.2 supports mixed data types to, for example, allow FCS measurements that are intrinsically integers (e.g., indices or class assignments) or measurements that are commonly captured as integers (e.g., time ticks) to be more represented as integer values, while capturing other measurements as floating-point values in the same FCS data set. In addition, keywords explicitly specifying dyes, detectors, and analytes were added to avoid having to extract those heuristically and unreliably from measurement names. Types of measurements were formalized, several keywords added, others removed, or deprecated, and various aspects of the specification were clarified. A reference implementation of the cyclic redundancy check (CRC) calculation is provided in two programming languages since a correct CRC implementation was problematic for many vendors. © 2020 International Society for Advancement of Cytometry.

ISAC Probe Tag Dictionary: Standardized Nomenclature for Detection and Visualization Labels Used in Cytometry and Microscopy Imaging.

Since the advent of microscopy imaging and flow cytometry, there has been an explosion in the number of probes, consisting of a component binding to an analyte and a detectable tag, to mark areas of interest in or on cells and tissue. Probe tags have been created to detect and/or visualize probes. Over time, these probe tags have increased in number. The expansion has resulted in arbitrarily created synonyms of probe tags used in publications and software. The synonyms are problematic for readability of publications, accuracy of text/data mining, and bridging data from multiple platforms, protocols, and databases for Big Data analysis. Development and implementation of a universal language for probe tags will ensure equivalent quality and level of data being reported or extracted for clinical/scientific evaluation as well as help connect data from many platforms. The International Society for Advancement of Cytometry Data Standards Task Force composed of academic scientists and industry hardware/software/reagent manufactures have developed recommendations for a standardized nomenclature for probe tags used in cytometry and microscopy imaging. These recommendations are shared in this technical note in the form of a Probe Tag Dictionary. © 2020 International Society for Advancement of Cytometry.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Single Cell Phenotypic Profiling of 27 DLBCL Cases Reveals Marked Intertumoral and Intratumoral Heterogeneity.

Diffuse large B-cell lymphoma (DLBCL) is the most common histologic subtype of non-Hodgkin lymphoma and is notorious for its clinical heterogeneity. Patient outcomes can be predicted by cell-of-origin (COO) classification, demonstrating that the underlying transcriptional signature of malignant B-cells informs biological behavior in the context of standard combination chemotherapy regimens. In the current study, we used mass cytometry (CyTOF) to examine tumor phenotypes at the protein level with single cell resolution in a collection of 27 diagnostic DLBCL biopsy specimens from treatment naïve patients. We found that malignant B-cells from each patient occupied unique regions in 37-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Interestingly, variable MHC class II expression was found to be the greatest contributor to phenotypic diversity. Within individual tumors, a subset of cases showed multiple phenotypic subpopulations, and in one case, we were able to demonstrate direct correspondence between protein-level phenotypic subsets and DNA mutation-defined subclones. In summary, CyTOF analysis can resolve both intertumoral and intratumoral heterogeneity among primary samples and reveals that each case of DLBCL is unique and may be comprised of multiple, genetically distinct subclones. © 2019 International Society for Advancement of Cytometry.

Improving the Quality and Reproducibility of Flow Cytometry in the Lung. An Official American Thoracic Society Workshop Report.

Defining responses of the structural and immune cells in biologic systems is critically important to understanding disease states and responses to injury. This requires accurate and sensitive methods to define cell types in organ systems. The principal method to delineate the cell populations involved in these processes is flow cytometry. Although researchers increasingly use flow cytometry, technical challenges can affect its accuracy and reproducibility, thus significantly limiting scientific advancements. This challenge is particularly critical to lung immunology, as the lung is readily accessible and therefore used in preclinical and clinical studies to define potential therapeutics. Given the importance of flow cytometry in pulmonary research, the American Thoracic Society convened a working group to highlight issues and technical challenges to the performance of high-quality pulmonary flow cytometry, with a goal of improving its quality and reproducibility.

ddPCRclust: an R package and Shiny app for automated analysis of multiplexed ddPCR data.

Motivation: Droplet digital PCR (ddPCR) is an emerging technology for quantifying DNA. By partitioning the target DNA into ∼20 000 droplets, each serving as its own PCR reaction compartment, a very high sensitivity of DNA quantification can be achieved. However, manual analysis of the data is time consuming and algorithms for automated analysis of non-orthogonal, multiplexed ddPCR data are unavailable, presenting a major bottleneck for the advancement of ddPCR transitioning from low-throughput to high-throughput. Results: ddPCRclust is an R package for automated analysis of data from Bio-Rad's droplet digital PCR systems (QX100 and QX200). It can automatically analyze and visualize multiplexed ddPCR experiments with up to four targets per reaction. Results are on par with manual analysis, but only take minutes to compute instead of hours. The accompanying Shiny app ddPCRvis provides easy access to the functionalities of ddPCRclust through a web-browser based GUI. Availability and implementation: R package: https://github.com/bgbrink/ddPCRclust; Interface: https://github.com/bgbrink/ddPCRvis/; Web: https://bibiserv.cebitec.uni-bielefeld.de/ddPCRvis/. Supplementary information: Supplementary data are available at Bioinformatics online.

flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry.

Motivation: Identification of cell populations in flow cytometry is a critical part of the analysis and lays the groundwork for many applications and research discovery. The current paradigm of manual analysis is time consuming and subjective. A common goal of users is to replace manual analysis with automated methods that replicate their results. Supervised tools provide the best performance in such a use case, however they require fine parameterization to obtain the best results. Hence, there is a strong need for methods that are fast to setup, accurate and interpretable. Results: flowLearn is a semi-supervised approach for the quality-checked identification of cell populations. Using a very small number of manually gated samples, through density alignments it is able to predict gates on other samples with high accuracy and speed. On two state-of-the-art datasets, our tool achieves median(F1)-measures exceeding 0.99 for 31%, and 0.90 for 80% of all analyzed populations. Furthermore, users can directly interpret and adjust automated gates on new sample files to iteratively improve the initial training. Availability and implementation: FlowLearn is available as an R package on https://github.com/mlux86/flowLearn. Evaluation data is publicly available online. Details can be found in the Supplementary Material. Supplementary information: Supplementary data are available at Bioinformatics online.

Implementation and Validation of an Automated Flow Cytometry Analysis Pipeline for Human Immune Profiling.

Automated reagent preparation, sample processing, and data acquisition have increased the rate at which flow cytometry data can be generated. Furthermore, advances in technology and flow cytometry instrumentation continually increase the complexity and dimensionality of this data. Together, this leads to increased pressure on manual data analysis, which has inherent limitations including subjectivity of the analyst and the length of time needed for data processing. These issues can create bottlenecks in the data processing workflow and potentially compromise data quality. To address these issues, as well as the challenges associated with manual gating in a high-volume human immune profiling laboratory, we sought to implement an automated analysis pipeline. In this report, we discuss considerations for selecting an automated analysis method, the process of implementing an automated pipeline, and detail our successful incorporation of an automated gating strategy with flowDensity into our analysis workflow. This validated pipeline augments our laboratory's ability to provide rapid high-throughput immune profiling for patients participating in cancer immunotherapy clinical trials. © International Society for Advancement of Cytometry.

High throughput automated analysis of big flow cytometry data.

The rapid expansion of flow cytometry applications has outpaced the functionality of traditional manual analysis tools used to interpret flow cytometry data. Scientists are faced with the daunting prospect of manually identifying interesting cell populations in 50-dimensional datasets, equalling the complexity previously only reached in mass cytometry. Data can no longer be analyzed or interpreted fully by manual approaches. While automated gating has been the focus of intense efforts, there are many significant additional steps to the analytical pipeline (e.g., cleaning the raw files, event outlier detection, extracting immunophenotypes). We review the components of a customized automated analysis pipeline that can be generally applied to large scale flow cytometry data. We demonstrate these methodologies on data collected by the International Mouse Phenotyping Consortium (IMPC).

Methodology for evaluating and comparing flow cytometers: A multisite study of 23 instruments.

We demonstrate improved methods for making valid and accurate comparisons of fluorescence measurement capabilities among instruments tested at different sites and times. We designed a suite of measurements and automated data processing methods to obtain consistent objective results and applied them to a selection of 23 instruments at nine sites to provide a range of instruments as well as multiple instances of similar instruments. As far as we know, this study represents the most accurate methods and results so far demonstrated for this purpose. The first component of the study reporting improved methods for photoelectron scale (Spe) evaluations, which was published previously (Parks, El Khettabi, Chase, Hoffman, Perfetto, Spidlen, Wood, Moore, and Brinkman: Cytometry A 91 (2017) 232-249). Those results which were within themselves are not sufficient for instrument comparisons, so here, we use the Spe scale results for the 23 cytometers and combine them with additional information from the analysis suite to obtain the metrics actually needed for instrument evaluations and comparisons. We adopted what we call the 2+2SD limit of resolution as a maximally informative metric, for evaluating and comparing dye measurement sensitivity among different instruments and measurement channels. Our results demonstrate substantial differences among different classes of instruments in both dye response and detection sensitivity and some surprisingly large differences among similar instruments, even among instruments with nominally identical configurations. On some instruments, we detected defective measurement channels needing service. The system can be applied in shared resource laboratories and other facilities as an aspect of quality assurance, and accurate instrument comparisons can be valuable for selecting instruments for particular purposes and for making informed instrument acquisition decisions. An institutionally supported program could serve the cytometry community by facilitating access to materials, and analysis and maintaining an archive of results. © 2018 International Society for Advancement of Cytometry.

Evaluating flow cytometer performance with weighted quadratic least squares analysis of LED and multi-level bead data.

We developed a fully automated procedure for analyzing data from LED pulses and multilevel bead sets to evaluate backgrounds and photoelectron scales of cytometer fluorescence channels. The method improves on previous formulations by fitting a full quadratic model with appropriate weighting and by providing standard errors and peak residuals as well as the fitted parameters themselves. Here we describe the details of the methods and procedures involved and present a set of illustrations and test cases that demonstrate the consistency and reliability of the results. The automated analysis and fitting procedure is generally quite successful in providing good estimates of the Spe (statistical photoelectron) scales and backgrounds for all the fluorescence channels on instruments with good linearity. The precision of the results obtained from LED data is almost always better than that from multilevel bead data, but the bead procedure is easy to carry out and provides results good enough for most purposes. Including standard errors on the fitted parameters is important for understanding the uncertainty in the values of interest. The weighted residuals give information about how well the data fits the model, and particularly high residuals indicate bad data points. Known photoelectron scales and measurement channel backgrounds make it possible to estimate the precision of measurements at different signal levels and the effects of compensated spectral overlap on measurement quality. Combining this information with measurements of standard samples carrying dyes of biological interest, we can make accurate comparisons of dye sensitivity among different instruments. Our method is freely available through the R/Bioconductor package flowQB. © 2017 International Society for Advancement of Cytometry.

Critical assessment of automated flow cytometry data analysis techniques.

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.

SUMMARY: flowDensity facilitates reproducible, high-throughput analysis of flow cytometry data by automating a predefined manual gating approach. The algorithm is based on a sequential bivariate gating approach that generates a set of predefined cell populations. It chooses the best cut-off for individual markers using characteristics of the density distribution. The Supplementary Material is linked to the online version of the manuscript. AVAILABILITY AND IMPLEMENTATION: R source code freely available through BioConductor (http://master.bioconductor.org/packages/devel/bioc/html/flowDensity.html.). Data available from FlowRepository.org (dataset FR-FCM-ZZBW). CONTACT: rbrinkman@bccrc.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep profiling of multitube flow cytometry data.

MOTIVATION: Deep profiling the phenotypic landscape of tissues using high-throughput flow cytometry (FCM) can provide important new insights into the interplay of cells in both healthy and diseased tissue. But often, especially in clinical settings, the cytometer cannot measure all the desired markers in a single aliquot. In these cases, tissue is separated into independently analysed samples, leaving a need to electronically recombine these to increase dimensionality. Nearest-neighbour (NN) based imputation fulfils this need but can produce artificial subpopulations. Clustering-based NNs can reduce these, but requires prior domain knowledge to be able to parameterize the clustering, so is unsuited to discovery settings. RESULTS: We present flowBin, a parameterization-free method for combining multitube FCM data into a higher-dimensional form suitable for deep profiling and discovery. FlowBin allocates cells to bins defined by the common markers across tubes in a multitube experiment, then computes aggregate expression for each bin within each tube, to create a matrix of expression of all markers assayed in each tube. We show, using simulated multitube data, that flowType analysis of flowBin output reproduces the results of that same analysis on the original data for cell types of >10% abundance. We used flowBin in conjunction with classifiers to distinguish normal from cancerous cells. We used flowBin together with flowType and RchyOptimyx to profile the immunophenotypic landscape of NPM1-mutated acute myeloid leukemia, and present a series of novel cell types associated with that mutation.