Identifying antibiotic-resistant strains via cell sorting and elastic-light-scatter phenotyping.

The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony's unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern. KEY POINTS: • Individual bacteria cells are placed on gradient agar plates by a cell sorter • Laser-light scatter patterns are used to recognize antibiotic-resistant organisms • Scatter patterns formed by colonies correspond to AMR-associated phenotypes.

Maternal schistosomiasis impairs offspring Interleukin-4 production and B cell expansion.

Epidemiological studies have identified a correlation between maternal helminth infections and reduced immunity to some early childhood vaccinations, but the cellular basis for this is poorly understood. Here, we investigated the effects of maternal Schistosoma mansoni infection on steady-state offspring immunity, as well as immunity induced by a commercial tetanus/diphtheria vaccine using a dual IL-4 reporter mouse model of maternal schistosomiasis. We demonstrate that offspring born to S. mansoni infected mothers have reduced circulating plasma cells and peripheral lymph node follicular dendritic cells at steady state. These reductions correlate with reduced production of IL-4 by iNKT cells, the cellular source of IL-4 in the peripheral lymph node during early life. These defects in follicular dendritic cells and IL-4 production were maintained long-term with reduced secretion of IL-4 in the germinal center and reduced generation of TFH, memory B, and memory T cells in response to immunization with tetanus/diphtheria. Using single-cell RNASeq following tetanus/diphtheria immunization of offspring, we identified a defect in cell-cycle and cell-proliferation pathways in addition to a reduction in Ebf-1, a key B-cell transcription factor, in the majority of follicular B cells. These reductions are dependent on the presence of egg antigens in the mother, as offspring born to single-sex infected mothers do not have these transcriptional defects. These data indicate that maternal schistosomiasis leads to long-term defects in antigen-induced cellular immunity, and for the first time provide key mechanistic insight into the factors regulating reduced immunity in offspring born to S. mansoni infected mothers.

Comparative analysis between zebrafish and an automated live-cell assay to classify developmental neurotoxicant chemicals.

Modern toxicology's throughput has dramatically increased due to alternative models, laboratory automation, and machine learning. This has enabled comparative studies across species and assays to prioritize chemical hazard potential and to understand how different model systems might complement one another. However, such comparative studies of high-throughput data are still in their infancy, with more groundwork needed to firmly establish the approach. Therefore, this study aimed to compare the bioactivity of the NIEHS Division of Translational Toxicology's (DTT) 87-compound developmental neurotoxicant (DNT) library in zebrafish and an in vitro high-throughput cell culture system. The early life-stage zebrafish provided a whole animal approach to developmental toxicity assessment. Chemical hits for abnormalities in embryonic zebrafish morphology, mortality, and behavior (ZBEscreen™) were compared with chemicals classified as high-risk by the Cell Health Index (CHI™), which is an outcome class probability from a machine learning classifier using 12 parameters from the SYSTEMETRIC® Cell Health Screen (CHS). The CHS was developed to assess human toxicity risk using supervised machine learning to classify acute cell stress phenotypes in a human leukemia cell line (HL60 cells) following a 4-h exposure to a chemical of interest. Due to the design of the screen, the zebrafish assays were more exhaustive, yielding 86 total bioactive hits, whereas the SYSTEMETRIC® CHS focusing on acute toxicity identified 20 chemicals as potentially toxic. The zebrafish embryonic and larval photomotor response assays (EPR and LPR, respectively) detected 40 of the 47 chemicals not found by the zebrafish morphological screen and CHS. Collectively, these results illustrate the advantages of using two alternative models in tandem for rapid hazard assessment and chemical prioritization and the effectiveness of CHI™ in identifying toxicity within a single multiparametric assay.

Impact of combination of short lecture and group discussion on the learning of physiology by nonmajor undergraduates.

This study assessed the impact of an "active learning" strategy employed alone or in combination with traditional lectures on the learning of mammalian physiology by undergraduate students. The study investigated the impact of three teaching strategies, namely 1) traditional lecture, 2) group discussion alone, and 3) combination of lecture and group discussion. For all strategies, students were given homework in a textbook and they completed written assignments before each session. Every student led the discussion of at least one assigned theme during each group session. The students had no access to the textbook or notes during group discussions. Four examinations (3 in-semester and a final) assessed the students' knowledge of fundamental concepts of physiology of specific organ systems. Part of the final examination reassessed knowledge of previously tested topics. The results show that the teaching modality employed to introduce physiology topics influenced students' learning. The average marginal effect of the lecture + discussion modality (average improvement linked to lecture + discussion strategy) on students' performance was 6.45% [95% confidence interval (CI95) (4.73, 8.16), P = 1.74 × 10-13], and the average improvement associated with the discussion-only modality was 5.5% [CI95 (3.84, 7.16), P = 7.84 × 10-11]. On average, all class ranks performed better on materials covered under active learning settings than under lecture-only conditions. Moreover, students' performance under combined lecture and discussion conditions is predictive of their overall performance in the course. The results support the positive effect of student-centered learning and demonstrate the efficacy of a combination of lectures and group discussions on learning of physiology by nonmajor students.NEW & NOTEWORTHY The purpose of this study was to evaluate the effect of group discussion on the learning of mammalian physiology by nonmajor undergraduate students. Combining traditional lectures with group discussions increased the active participation of students in class and improved their learning of physiology, as measured by the results of in-semester and final examinations. The active learning technique benefited all class ranks on average.

Surface Environment and Energy Density Effects on the Detection and Disinfection of Microorganisms Using a Portable Instrument.

Real-time detection and disinfection of foodborne pathogens are important for preventing foodborne outbreaks and for maintaining a safe environment for consumers. There are numerous methods for the disinfection of hazardous organisms, including heat treatment, chemical reaction, filtration, and irradiation. This report evaluated a portable instrument to validate its simultaneous detection and disinfection capability in typical laboratory situations. In this challenging study, three gram-negative and two gram-positive microorganisms were used. For the detection of contamination, inoculations of various concentrations were dispensed on three different surface types to estimate the performance for minimum-detectable cell concentration. Inoculations higher than 103~104 CFU/mm2 and 0.15 mm of detectable contaminant size were estimated to generate a sufficient level of fluorescence signal. The evaluation of disinfection efficacy was conducted on three distinct types of surfaces, with the energy density of UVC light (275-nm) ranging from 4.5 to 22.5 mJ/cm2 and the exposure time varying from 1 to 5 s. The study determined the optimal energy dose for each of the microorganisms species. In addition, surface characteristics may also be an important factor that results in different inactivation efficacy. These results demonstrate that the proposed portable device could serve as an in-field detection and disinfection unit in various environments, and provide a more efficient and user-friendly way of performing disinfection on large surface areas.

Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns.

The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473-709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.

Hybrid Raman and Laser-Induced Breakdown Spectroscopy for Food Authentication Applications.

The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries.

Development of a Smartphone-Integrated Reflective Scatterometer for Bacterial Identification.

We present a smartphone-based bacterial colony phenotyping instrument using a reflective elastic light scattering (ELS) pattern and the resolving power of the new instrument. The reflectance-type device can acquire ELS patterns of colonies on highly opaque media as well as optically dense colonies. The novel instrument was built using a smartphone interface and a 532 nm diode laser, and these essential optical components made it a cost-effective and portable device. When a coherent and collimated light source illuminated a bacterial colony, a reflective ELS pattern was created on the screen and captured by the smartphone camera. The collected patterns whose shapes were determined by the colony morphology were then processed and analyzed to extract distinctive features for bacterial identification. For validation purposes, the reflective ELS patterns of five bacteria grown on opaque growth media were measured with the proposed instrument and utilized for the classification. Cross-validation was performed to evaluate the classification, and the result showed an accuracy above 94% for differentiating colonies of E. coli, K. pneumoniae, L. innocua, S. enteritidis, and S. aureus.

Stomach region stimulated determines effects on duodenal motility in rats.

Gastric electrical stimulation (GES) is used clinically to promote proximal GI emptying and motility. In acute experiments, we measured duodenal motor responses elicited by GES applied at 141 randomly chosen electrode sites on the stomach serosal surface. Overnight-fasted (H2O available) anesthetized male rats (n = 81) received intermittent biphasic GES for 5 min (20-s-on/40-s-off cycles; I = 0.3 mA; pw = 0.2 ms; 10 Hz). A strain gauge on the serosal surface of the proximal duodenum of each animal was used to evaluate baseline motor activity and the effect of GES. Using ratios of time blocks compared with a 15-min prestimulation baseline, we evaluated the effects of the 5-min stimulation on concurrent activity, on the 10 min immediately after the stimulation, and on the 15-min period beginning with the onset of stimulation. We mapped the magnitude of the duodenal response (three different motility indices) elicited from the 141 stomach sites. Post hoc electrode site maps associated with duodenal responses suggested three zones similar to the classic regions of forestomach, corpus, and antrum. Maximal excitatory duodenal motor responses were elicited from forestomach sites, whereas inhibitory responses occurred with stimulation of the corpus. Moderate excitatory duodenal responses occurred with stimulation of the antrum. Complex, weak inhibitory/excitatory responses were produced by stimulation at boundaries between stomach regions. Patterns of GES efficacies coincided with distributions of previously mapped vagal afferents, suggesting that excitation of the duodenum is strongest when GES electrodes are situated over stomach concentrations of vagal intramuscular arrays, putative stretch receptors in the muscle wall.

A novel experimental workflow to determine the impact of storage parameters on the mass spectrometric profiling and assessment of representative phosphatidylethanolamine lipids in mouse tissues.

Evaluation of signaling lipids is essential for measuring biological processes. There is a lack of experimental data regarding the proper storage of extracts for signaling lipid analysis, potentially impacting the procedures that can lead to accurate and reproducible evaluation. In this study, the importance of pre-analytical conditions for analyzing ion transitions for phosphatidylethanolamines (PEs), an abundant signaling phospholipid, was systematically assessed. A novel workflow was utilized involving an MRM-based experimental approach followed by statistical analysis. Specifically, lipids were extracted from the brain, heart, lungs, and serum of C57BL/6 mice. Extract subsets were resuspended in organic solvents prior to storage in various temperature conditions. Mass spectrometry analysis by multiple reaction monitoring (MRM) profiling was performed at four time points (1 day, 2 weeks, 2 months, or 6 months) to measure relative amounts of PEs in distinct lipid extract aliquots. We introduce an innovative statistical workflow to measure the changes in relative amounts of PEs in the profiles over time to determine lipid extract storage conditions in which fewer profile changes occur. Results demonstrated that time is the most significant factor affecting the changes in lipid samples, with temperature and solvent having comparatively minor effects. We conclude that for lipid extracts obtained by Bligh & Dyer extraction, storage at - 80.0 °C without solvent for less than 2 weeks before analysis is ideal. By considering the data generated by this study, lipid extract storage practices may be optimized and standardized, enhancing the validity and reproducibility of lipid assessments.

IL-4 promotes stromal cell expansion and is critical for development of a type-2, but not a type 1 immune response.

IL-4 is critical for differentiation of Th2 cells and antibody isotype switching, but our work demonstrated that it is produced in the peripheral LN under both Type 2, and Type 1 conditions, raising the possibility of other functions. We found that IL-4 is vital for proper positioning of hematopoietic and stromal cells in steady state, and the lack of IL-4 or IL-4Rα correlates with disarrangement of both follicular dendritic cells and CD31+ endothelial cells. We observed a marked disorganization of B cells in these mice, suggesting that the lymphocyte-stromal cell axis is maintained by the IL-4 signaling pathway. This study showed that absence of IL-4 correlates with significant downregulation of Lymphotoxin alpha (LTα) and Lymphotoxin beta (LTß), critical lymphokines for the development and maintenance of lymphoid organs. Moreover, immunization of IL-4 deficient mice with Type 2 antigens failed to induce lymphotoxin production, LN reorganization, or germinal center formation, while this process is IL-4 independent following Type 1 immunization. Additionally, we found that Type 1 antigen mediated LN reorganization is dependent on IFN-γ in the absence of IL-4. Our findings reveal a role of IL-4 in the maintenance of peripheral lymphoid organ microenvironments during homeostasis and antigenic challenge.

Detection of E. coli labeled with metal-conjugated antibodies using lateral-flow assay and laser-induced breakdown spectroscopy.

This study explores the adoption of laser-induced breakdown spectroscopy (LIBS) for the analysis of lateral-flow immunoassays (LFIAs). Gold (Au) nanoparticles are standard biomolecular labels among LFIAs, typically detected via colorimetric means. A wide diversity of lanthanide-complexed polymers (LCPs) are also used as immunoassay labels but are inapt for LFIAs due to lab-bound detection instrumentation. This is the first study to show the capability of LIBS to transition LCPs into the realm of LFIAs, and one of the few to apply LIBS to biomolecular label detection in complete immunoassays. Initially, an in-house LIBS system was optimized to detect an Au standard through a process of line selection across acquisition delay times, followed by determining limit of detection (LOD). The optimized LIBS system was applied to Au-labeled Escherichia coli detection on a commercial LFIA; comparison with colorimetric detection yielded similar LODs (1.03E4 and 8.890E3 CFU/mL respectively). Optimization was repeated with lanthanide standards to determine if they were viable alternatives to Au labels. It was found that europium (Eu) and ytterbium (Yb) may be more favorable biomolecular labels than Au. To test whether Eu-complexed polymers conjugated to antibodies could be used as labels in LFIAs, the conjugates were successfully applied to E. coli detection in a modified commercial LFIA. The results suggest interesting opportunities for creating highly multiplexed LFIAs. Multiplexed, sensitive, portable, and rapid LIBS detection of biomolecules concentrated and labeled on LFIAs is highly relevant for applications like food safety, where in-field food contaminant detection is critical. Graphical abstract.

Alternatives to current flow cytometry data analysis for clinical and research studies.

Flow cytometry has well-established methods for data analysis based on traditional data collection techniques. These techniques typically involved manual insertion of tube samples into an instrument that, historically, could only measure 1-3 colors. The field has since evolved to incorporate new technologies for faster and highly automated sample preparation and data collection. For example, the use of microwell plates on benchtop instruments is now a standard on virtually every new instrument, and so users can easily accumulate multiple data sets quickly. Further, because the user must carefully define the layout of the plate, this information is already defined when considering the analytical process, expanding the opportunities for automated analysis. Advances in multi-parametric data collection, as demonstrated by the development of hyperspectral flow-cytometry, 20-40 color polychromatic flow cytometry, and mass cytometry (CyTOF), are game-changing. As data and assay complexity increase, so too does the complexity of data analysis. Complex data analysis is already a challenge to traditional flow cytometry software. New methods for reviewing large and complex data sets can provide rapid insight into processes difficult to define without more advanced analytical tools. In settings such as clinical labs where rapid and accurate data analysis is a priority, rapid, efficient and intuitive software is needed. This paper outlines opportunities for analysis of complex data sets using examples of multiplexed bead-based assays, drug screens and cell cycle analysis - any of which could become integrated into the clinical environment.

flowVS: channel-specific variance stabilization in flow cytometry.

BACKGROUND: Comparing phenotypes of heterogeneous cell populations from multiple biological conditions is at the heart of scientific discovery based on flow cytometry (FC). When the biological signal is measured by the average expression of a biomarker, standard statistical methods require that variance be approximately stabilized in populations to be compared. Since the mean and variance of a cell population are often correlated in fluorescence-based FC measurements, a preprocessing step is needed to stabilize the within-population variances. RESULTS: We present a variance-stabilization algorithm, called flowVS, that removes the mean-variance correlations from cell populations identified in each fluorescence channel. flowVS transforms each channel from all samples of a data set by the inverse hyperbolic sine (asinh) transformation. For each channel, the parameters of the transformation are optimally selected by Bartlett's likelihood-ratio test so that the populations attain homogeneous variances. The optimum parameters are then used to transform the corresponding channels in every sample. flowVS is therefore an explicit variance-stabilization method that stabilizes within-population variances in each channel by evaluating the homoskedasticity of clusters with a likelihood-ratio test. With two publicly available datasets, we show that flowVS removes the mean-variance dependence from raw FC data and makes the within-population variance relatively homogeneous. We demonstrate that alternative transformation techniques such as flowTrans, flowScape, logicle, and FCSTrans might not stabilize variance. Besides flow cytometry, flowVS can also be applied to stabilize variance in microarray data. With a publicly available data set we demonstrate that flowVS performs as well as the VSN software, a state-of-the-art approach developed for microarrays. CONCLUSIONS: The homogeneity of variance in cell populations across FC samples is desirable when extracting features uniformly and comparing cell populations with different levels of marker expressions. The newly developed flowVS algorithm solves the variance-stabilization problem in FC and microarrays by optimally transforming data with the help of Bartlett's likelihood-ratio test. On two publicly available FC datasets, flowVS stabilizes within-population variances more evenly than the available transformation and normalization techniques. flowVS-based variance stabilization can help in performing comparison and alignment of phenotypically identical cell populations across different samples. flowVS and the datasets used in this paper are publicly available in Bioconductor.

Hyperspectral cytometry.

Hyperspectral cytometry is an emerging technology for single-cell analysis that combines ultrafast optical spectroscopy and flow cytometry. Spectral cytometry systems utilize diffraction gratings or prism-based monochromators to disperse fluorescence signals from multiple labels (organic dyes, nanoparticles, or fluorescent proteins) present in each analyzed bioparticle onto linear detector arrays such as multianode photomultipliers or charge-coupled device sensors. The resultant data, consisting of a series of characterizing every analyzed cell, are not compensated by employing the traditional cytometry approach, but rather are spectrally unmixed utilizing algorithms such as constrained Poisson regression or non-negative matrix factorization. Although implementations of spectral cytometry were envisioned as early as the 1980s, only recently has the development of highly sensitive photomultiplier tube arrays led to design and construction of functional prototypes and subsequently to introduction of commercially available systems. This chapter summarizes the historical efforts and work in the field of spectral cytometry performed at Purdue University Cytometry Laboratories and describes the technology developed by Sony Corporation that resulted in release of the first commercial spectral cytometry system-the Sony SP6800. A brief introduction to spectral data analysis is also provided, with emphasis on the differences between traditional polychromatic and spectral cytometry approaches.

A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects.

BACKGROUND: Flow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not accurately identify anomalies, whether previously known or unknown, that may exist in future samples tested. Although one-class classifiers trained using only normal cases would avoid such a bias, robust sample characterization is critical for a generalizable model. Owing to sample heterogeneity and instrumental variability, arbitrary characterization of samples usually introduces feature noise that may lead to poor predictive performance. Herein, we present a non-parametric Bayesian algorithm called ASPIRE (anomalous sample phenotype identification with random effects) that identifies phenotypic differences across a batch of samples in the presence of random effects. Our approach involves simultaneous clustering of cellular measurements in individual samples and matching of discovered clusters across all samples in order to recover global clusters using probabilistic sampling techniques in a systematic way. RESULTS: We demonstrate the performance of the proposed method in identifying anomalous samples in two different FC data sets, one of which represents a set of samples including acute myeloid leukemia (AML) cases, and the other a generic 5-parameter peripheral-blood immunophenotyping. Results are evaluated in terms of the area under the receiver operating characteristics curve (AUC). ASPIRE achieved AUCs of 0.99 and 1.0 on the AML and generic blood immunophenotyping data sets, respectively. CONCLUSIONS: These results demonstrate that anomalous samples can be identified by ASPIRE with almost perfect accuracy without a priori access to samples of anomalous subtypes in the training set. The ASPIRE approach is unique in its ability to form generalizations regarding normal and anomalous states given only very weak assumptions regarding sample characteristics and origin. Thus, ASPIRE could become highly instrumental in providing unique insights about observed biological phenomena in the absence of full information about the investigated samples.

Spectral flow cytometry: Fundamentals and future impact.

Spectral flow cytometry has emerged as a significant player in the cytometry marketplace, with the potential for rapid growth. Despite a slow start, the technology has made significant strides in advancing various areas of single-cell analysis utilized by the scientific community. The integration of spectral cytometry into clinical laboratories and diagnostic processes is currently underway and is expected to garner a significant level of widespread acceptance in the near future. However, incorporating a new methodological approach into existing research programs can lead to misunderstandings or even misuse. This chapter offers an introductory yet comprehensive explanation of the scientific principles that form the foundation of spectral cytometry. Specifically, it delves into the unmixing processes that are utilized for data analysis. This overview is designed for those who are new to the field and seeking an informative guide to this exciting emerging technology.

Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality.

As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.

A versatile information retrieval framework for evaluating profile strength and similarity.

In profiling assays, thousands of biological properties are measured in a single test, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles' high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise. Here, we developed a statistical framework that uses the well-established mean average precision (mAP) as a single, data-driven metric to bridge this gap. We validated the mAP framework against established metrics through simulations and real-world data applications, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we used mAP to assess both phenotypic activity for a given perturbation (or a sample) as well as consistency within groups of perturbations (or samples) across diverse high-dimensional datasets. We evaluated the framework on different profile types (image, protein, and mRNA profiles), perturbation types (CRISPR gene editing, gene overexpression, and small molecules), and profile resolutions (single-cell and bulk). Our open-source software allows this framework to be applied to identify interesting biological phenomena and promising therapeutics from large-scale profiling data.

Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices.

Multispectral and hyperspectral flow cytometry (FC) instruments allow measurement of fluorescence or Raman spectra from single cells in flow. As with conventional FC, spectral overlap results in the measured signal in any given detector being a mixture of signals from multiple labels present in the analyzed cells. In contrast to traditional polychromatic FC, these devices utilize a number of detectors (or channels in multispectral detector arrays) that is larger than the number of labels, and no particular detector is a priori dedicated to the measurement of any particular label. This data-acquisition modality requires a rigorous study and understanding of signal formation as well as unmixing procedures that are employed to estimate labels abundance. The simplest extension of the traditional compensation procedure to multispectral data sets is equivalent to an ordinary least-square (LS) solution for estimating abundance of labels in individual cells. This process is identical to the technique employed for unmixing spectral data in various imaging fields. The present study shows that multispectral FC data violate key assumptions of the LS process, and use of the LS method may lead to unmixing artifacts, such as population distortion (spreading) and the presence of negative values in biomarker abundances. Various alternative unmixing techniques were investigated, including relative-error minimization and variance-stabilization transformations. The most promising results were obtained by performing unmixing using Poisson regression with an identity-link function within a generalized linear model framework. This formulation accounts for the presence of Poisson noise in the model of signal formation and subsequently leads to superior unmixing results, particularly for dim fluorescent populations. The proposed Poisson unmixing technique is demonstrated using simulated 8-channel, 2-fluorochrome data and real 32-channel, 6-fluorochrome data. The quality of unmixing is assessed by computing absolute and relative errors, as well as by calculating the symmetrized Kullback-Leibler divergence between known and approximated populations. These results are applicable to any flow-based system with more detectors than labels where Poisson noise is the dominant contributor to the overall system noise and highlight the fact that explicit incorporation of appropriate noise models is the key to accurately estimating the true label abundance on the cells. © 2013 International Society for Advancement of Cytometry.