Automated classification of bacterial particles in flow by multiangle scatter measurement and support vector machine classifier

An automatic analysis and quality assurance method for lymphocyte subset identification

OBJECTIVES

Lymphocyte subsets are the predictors of disease diagnosis, treatment, and prognosis. Determination of lymphocyte subsets is usually carried out by flow cytometry. Despite recent advances in flow cytometry analysis, most flow cytometry data can be challenging with manual gating, which is labor-intensive, time-consuming, and error-prone. This study aimed to develop an automated method to identify lymphocyte subsets.

METHODS

We propose a knowledge-driven combined with data-driven method which can gate automatically to achieve subset identification. To improve accuracy and stability, we have implemented a Loop Adjustment Gating to optimize the gating result of the lymphocyte population. Furthermore, we have incorporated an anomaly detection mechanism to issue warnings for samples that might not have been successfully analyzed, ensuring the quality of the results.

RESULTS

The evaluation showed a 99.2 % correlation between our method results and manual analysis with a dataset of 2,000 individual cases from lymphocyte subset assays. Our proposed method attained 97.7 % accuracy for all cases and 100 % for the high-confidence cases. With our automated method, 99.1 % of manual labor can be saved when reviewing only the low-confidence cases, while the average turnaround time required is only 29 s, reducing by 83.7 %.

CONCLUSIONS

Our proposed method can achieve high accuracy in flow cytometry data from lymphocyte subset assays. Additionally, it can save manual labor and reduce the turnaround time, making it have the potential for application in the laboratory.

Flow Cytometry: The Next Revolution

Unmasking the subtleties of the immune system requires both a comprehensive knowledge base and the ability to interrogate that system with intimate sensitivity. That task, to a considerable extent, has been handled by an iterative expansion in flow cytometry methods, both in technological capability and also in accompanying advances in informatics. As the field of fluorescence-based cytomics matured, it reached a technological barrier at around 30 parameter analyses, which stalled the field until spectral flow cytometry created a fundamental transformation that will likely lead to the potential of 100 simultaneous parameter analyses within a few years. The simultaneous advance in informatics has now become a watershed moment for the field as it competes with mature systematic approaches such as genomics and proteomics, allowing cytomics to take a seat at the multi-omics table. In addition, recent technological advances try to combine the speed of flow systems with other detection methods, in addition to fluorescence alone, which will make flow-based instruments even more indispensable in any biological laboratory. This paper outlines current approaches in cell analysis and detection methods, discusses traditional and microfluidic sorting approaches as well as next-generation instruments, and provides an early look at future opportunities that are likely to arise.

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring

In recent years, the bioprocessing industry has experienced significant growth and is increasingly emerging as an important economic sector. Here, efficient process management and constant control of cellular growth are essential. Good product quality and yield can only be guaranteed with high cell density and high viability. Whereas the on-line measurement of physical and chemical process parameters has been common practice for many years, the on-line determination of viability remains a challenge and few commercial on-line measurement methods have been developed to date for determining viability in industrial bioprocesses. Thus, numerous studies have recently been conducted to develop sensors for on-line viability estimation, especially in the field of optical spectroscopic sensors, which will be the focus of this review. Spectroscopic sensors are versatile, on-line and mostly non-invasive. Especially in combination with bioinformatic data analysis, they offer great potential for industrial application. Known as soft sensors, they usually enable simultaneous estimation of multiple biological variables besides viability to be obtained from the same set of measurement data. However, the majority of the presented sensors are still in the research stage, and only a few are already commercially available.

Recent advances in microbial community analysis from machine learning of multiparametric flow cytometry data

Dynamic analysis of microbial composition is crucial for understanding community functioning and detecting dysbiosis. Compositional information is mostly obtained through sequencing of taxonomic markers or whole meta-genomes, which may be productively complemented by real-time quantitative community multiparametric flow cytometry data (FCM). Patterns and clusters in FCM community data can be distinguished and compared by unsupervised machine learning. Alternatively, FCM data from preselected individual strain phenotypes can be used for supervised machine-training in order to differentiate similar cell types within communities. Both types of machine learning can quantitatively deconvolute community FCM data sets and rapidly analyse global changes in response to treatment. Procedures may further be optimized for recurrent microbiome samples to simultaneously quantify physiological and compositional states.

Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification

Flow cytometers are robust and ubiquitous tools of biomedical research, as they enable high-throughput fluorescence-based multi-parametric analysis and sorting of single cells. However, analysis is often constrained by the availability of detection reagents or functional changes of cells caused by fluorescent staining. Here, we introduce MAPS-FC (multi-angle pulse shape flow cytometry), an approach that measures angle- and time-resolved scattered light for high-throughput cell characterization to circumvent the constraints of conventional flow cytometry. In order to derive cell-specific properties from the acquired pulse shapes, we developed a data analysis procedure based on wavelet transform and k-means clustering. We analyzed cell cycle stages of Jurkat and HEK293 cells by MAPS-FC and were able to assign cells to the G1, S, and G2/M phases without the need for fluorescent labeling. The results were validated by DNA staining and by sorting and re-analysis of isolated G1, S, and G2/M populations. Our results demonstrate that MAPS-FC can be used to determine cell properties that are otherwise only accessible by invasive labeling. This approach is technically compatible with conventional flow cytometers and paves the way for label-free cell sorting.

Computational Analysis of Microbial Flow Cytometry Data

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data

The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flow cytometry data using a supervised machine learning algorithm of standard cell type recognition (CellCognize). As a proof-of-concept, we trained neural networks with 32 microbial cell and bead standards. The resulting classifiers were extensively validated in silico on known microbiota, showing on average 80% prediction accuracy. Furthermore, the classifiers could detect shifts in microbial communities of unknown composition upon chemical amendment, comparable to results from 16S-rRNA-amplicon analysis. CellCognize was also able to quantify population growth and estimate total community biomass productivity, providing estimates similar to those from ¹⁴C-substrate incorporation. CellCognize complements current sequencing-based methods by enabling rapid routine cell diversity analysis. The pipeline is suitable to optimize cell recognition for recurring microbiota types, such as in human health or engineered systems.

Duygan et al. develop a supervised machine learning algorithm, CellCognize, to quantify cell type diversity from multidimensional flow cytometry data. Their model achieves 80% prediction accuracy, detects shifts in microbial communities of unknown composition and quantifies population growth and biomass productivity. Their work will be useful to study microbiota in human health or engineered systems.

Fast Pathogen Identification Using Single-Cell Matrix-Assisted Laser Desorption/Ionization-Aerosol Time-of-Flight Mass Spectrometry Data and Deep Learning Methods

In diagnostics of infectious diseases, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) can be applied for the identification of pathogenic microorganisms. However, to achieve a trustworthy identification from MALDI-TOF MS data, a significant amount of biomass should be considered. The bacterial load that potentially occurs in a sample is therefore routinely amplified by culturing, which is a time-consuming procedure. In this paper, we show that culturing can be avoided by conducting MALDI-TOF MS on individual bacterial cells. This results in a more rapid identification of species with an acceptable accuracy. We propose a deep learning architecture to analyze the data and compare its performance with traditional supervised machine learning algorithms. We illustrate our workflow on a large data set that contains bacterial species related to urinary tract infections. Overall we obtain accuracies up to 85% in discriminating five different species.

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.

Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation

Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method’s accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes

BACKGROUND

Flow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cytometry data will result in greater insights and actionability as compared to traditional tools used in the analysis of microbiomes. This paper is a demonstration of the fruitfulness of machine learning in analyzing microbial flow cytometry data generated in anaerobic microbiome perturbation experiments.

RESULTS

Autoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticles and carbon sources perturbed anaerobic microbiomes but was marginal in predicting perturbations due to antibiotics. A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers (DL) were marginally better under tested conditions at predicting overall community structure while distributed random forests (DRF) worked better for predicting the most important putative microbial group(s) in the anaerobic digesters viz. methanogens, and it can be optimized with better parameter tuning. Predictive classification patterns with DL (feed forward artificial neural network with three hidden layers) were found to be comparable to previously demonstrated multivariate analysis. The potential applications of this approach have been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiomes perturbed by synthetic nanoparticles as well as antibiotics.

CONCLUSION

Machine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes to several stimuli.

Automated analysis of acute myeloid leukemia minimal residual disease using a support vector machine

We investigated the ability of support vector machines (SVM) to analyze minimal residual disease (MRD) in flow cytometry data from patients with acute myeloid leukemia (AML) automatically, objectively and standardly. The initial disease data and MRD review data in the form of 159 flow cytometry standard 3.0 files from 36 CD7-positive AML patients in whom MRD was detected more than once were exported. SVM was used for training with setting the initial disease data to 1 as the flag and setting 15 healthy persons to set 0 as the flag. Based on the two training groups, parameters were optimized, and a predictive model was built to analyze MRD data from each patient. The automated analysis results from the SVM model were compared to those obtained through conventional analysis to determine reliability. Automated analysis results based on the model did not differ from and were correlated with results obtained through conventional analysis (correlation coefficient c = 0.986, P > 0.05). Thus the SVM model could potentially be used to analyze flow cytometry-based AML MRD data automatically, objectively, and in a standardized manner.

Single Cell and Population Level Analysis of HCA Data

High Content Analysis instrumentation has undergone tremendous hardware advances in recent years. It is now possible to obtain images of hundreds of thousands to millions of individual objects, across multiple wells, channels, and plates, in a reasonable amount of time. In addition, it is possible to extract dozens, or hundreds, of features per object using commonly available software tools. Analyzing this data provides new challenges to the scientists. The magnitude of these numbers is reminiscent of flow cytometer, where practitioners have long been taking what effectively amounted to very low resolution, multi-parametric measurements from individual cells for many decades. Flow cytometrists have developed a wide range of tools to effectively analyze and interpret these types of data. This chapter will review the techniques used in flow cytometry and show how they can easily and effectively be applied to High Content Analysis.

Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities

Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative approach for analyzing synthetic microbial communities. We created so-called in silico communities, which allow us to explore the possibilities of bacterial flow cytometry data using supervised machine learning techniques. We can identify single cells with an accuracy >90% for more than half of the communities consisting out of two bacterial populations. In order to assess to what extent an in silico community is representative for its synthetic counterpart, we created so-called abundance gradients, a combination of synthetic (i.e., in vitro) communities containing two bacterial populations in varying abundances. By showing that we are able to retrieve an abundance gradient using a combination of in silico communities and supervised machine learning techniques, we argue that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of synthetic communities and bacterial flow cytometry data in general.

Scattering pulse of label free fine structure cells to determine the size scale of scattering structures

Scattering pulse is sensitive to the morphology and components of each single label-free cell. The most direct detection result, label free cell's scattering pulse is studied in this paper as a novel trait to recognize large malignant cells from small normal cells. A set of intrinsic scattering pulse calculation method is figured out, which combines both hydraulic focusing theory and small particle's scattering principle. Based on the scattering detection angle ranges of widely used flow cytometry, the scattering pulses formed by cell scattering energy in forward scattering angle 2°-5° and side scattering angle 80°-110° are discussed. Combining the analysis of cell's illuminating light energy, the peak, area, and full width at half maximum (FWHM) of label free cells' scattering pulses for fine structure cells with diameter 1-20 μm are studied to extract the interrelations of scattering pulse's features and cell's morphology. The theoretical and experimental results show that cell's diameter and FWHM of its scattering pulse agree with approximate linear distribution; the peak and area of scattering pulse do not always increase with cell's diameter becoming larger, but when cell's diameter is less than about 16 μm the monotone increasing relation of scattering pulse peak or area with cell's diameter can be obtained. This relationship between the features of scattering pulse and cell's size is potentially a useful but very simple criterion to distinguishing malignant and normal cells by their sizes and morphologies in label free cells clinical examinations.

High-throughput screening of high Monascus pigment-producing strain based on digital image processing

This work proposed a new method which applied image processing and support vector machine (SVM) for screening of mold strains. Taking Monascus as example, morphological characteristics of Monascus colony were quantified by image processing. And the association between the characteristics and pigment production capability was determined by SVM. On this basis, a highly automated screening strategy was achieved. The accuracy of the proposed strategy is 80.6 %, which is compatible with the existing methods (81.1 % for microplate and 85.4 % for flask). Meanwhile, the screening of 500 colonies only takes 20–30 min, which is the highest rate among all published results. By applying this automated method, 13 strains with high-predicted production were obtained and the best one produced as 2.8-fold (226 U/mL) of pigment and 1.9-fold (51 mg/L) of lovastatin compared with the parent strain. The current study provides us with an effective and promising method for strain improvement.

Label-free identification of individual bacteria using Fourier transform light scattering

Rapid identification of bacterial species is crucial in medicine and food hygiene. In order to achieve rapid and label-free identification of bacterial species at the single bacterium level, we propose and experimentally demonstrate an optical method based on Fourier transform light scattering (FTLS) measurements and statistical classification. For individual rod-shaped bacteria belonging to four bacterial species (Listeria monocytogenes, Escherichia coli, Lactobacillus casei, and Bacillus subtilis), two-dimensional angle-resolved light scattering maps are precisely measured using FTLS technique. The scattering maps are then systematically analyzed, employing statistical classification in order to extract the unique fingerprint patterns for each species, so that a new unidentified bacterium can be identified by a single light scattering measurement. The single-bacterial and label-free nature of our method suggests wide applicability for rapid point-of-care bacterial diagnosis.

Promoter activity dynamics in the lag phase of Escherichia coli

Background

Lag phase is a period of time with no growth that occurs when stationary phase bacteria are transferred to a fresh medium. Bacteria in lag phase seem inert: their biomass does not increase. The low number of cells and low metabolic activity make it difficult to study this phase. As a consequence, it has not been studied as thoroughly as other bacterial growth phases. However, lag phase has important implications for bacterial infections and food safety. We asked which, if any, genes are expressed in the lag phase of Escherichia coli, and what is their dynamic expression pattern.

Results

We developed an assay based on imaging flow cytometry of fluorescent reporter cells that overcomes the challenges inherent in studying lag phase. We distinguish between lag1 phase- in which there is no biomass growth, and lag2 phase- in which there is biomass growth but no cell division. We find that in lag1 phase, most promoters are not active, except for the enzymes that utilize the specific carbon source in the medium. These genes show promoter activities that increase exponentially with time, despite the fact that the cells do not measurably increase in size. An oxidative stress promoter, katG, is also active. When cells enter lag2 and begin to grow in size, they switch to a full growth program of promoter activity including ribosomal and metabolic genes.

Conclusions

The observed exponential increase in enzymes for the specific carbon source followed by an abrupt switch to production of general growth genes is a solution of an optimal control model, known as bang-bang control. The present approach contributes to the understanding of lag phase, the least studied of bacterial growth phases.

Automated bacterial identification by angle resolved dark-field imaging

We propose and demonstrate a dark-field imaging technique capable of automated identification of individual bacteria. An 87-channel multispectral system capable of angular and spectral resolution was used to measure the scattering spectrum of various bacteria in culture smears. Spectra were compared between various species and between various preparations of the same species. A 15-channel system was then used to prove the viability of bacterial identification with a relatively simple microscope system. A simple classifier was able to identify four of six bacterial species with greater than 90% accuracy in bacteria-by-bacteria testing.

Determine the quality of human embryonic stem colonies with laser light scattering patterns

Background

With the prompt developments of regenerative medicine, the potential clinical applications of human embryonic stem cells have attracted intense attention. However, the labor-intensive and complex manual cell selection processes required during embryonic stem cell culturing have seriously limited large-scale production and broad applications. Thus, availability of a label-free, non-invasive platform to replace the current cumbersome manual selection has become a critical need.

Results

A non-invasive, label-free, and time-efficient optical platform for determining the quality of human embryonic stem cell colonies was developed by analyzing the scattering signals from those stem cell colonies. Additionally, confocal microscopy revealed that the cell colony morphology and surface structures were correlated with the resulting characteristic light scattering patterns. Standard immunostaining assay (Oct-4) was also utilized to validate the quality-determination from this light scattering protocol. The platform developed here can therefore provide identification accuracy of up to 87% for colony determination.

Conclusions

Our study here demonstrated that light scattering patterns can serve as a feasible alternative approach to replace conventional manual selection for human embryonic stem cell cultures.

High-throughput secondary screening at the single-cell level

We have developed an automated system for drug screening using a single-cell-multiple functional response technology. The approach uses a semiautomated preparatory system, high-speed sample collection, and a unique analytical tool that provides instantaneous results for compound dilutions using 384-well plates. The combination of automation and rapid robotic sampling increases quality control and robustness. High-speed flow cytometry is used to collect single-cell results together with a newly defined analytical tool for extraction of IC(50) curves for multiple assays per cell. The principal advantage is the extreme speed of sample collection, with results from a 384-well plate being completed for both collection and data processing in less than 10 min. Using this approach, it is possible to extract detailed drug response information in a highly controlled fashion. The data are based on single-cell results, not populations. With simultaneous assays for different functions, it is possible to gain a more detailed understanding of each drug/compound interaction. Combined with integrated advanced data processing directly from raw data files, the process from sampling to analytical results is highly intuitive. Direct PubMed links allow review of drug structure and comparisons with similar compounds.

Computational analysis of high-throughput flow cytometry data

INTRODUCTION

Flow cytometry has been around for over 40 years, but only recently has the opportunity arisen to move into the high-throughput domain. The technology is now available and is highly competitive with imaging tools under the right conditions. Flow cytometry has, however, been a technology that has focused on its unique ability to study single cells and appropriate analytical tools are readily available to handle this traditional role of the technology.

AREAS COVERED

Expansion of flow cytometry to a high-throughput (HT) and high-content technology requires both advances in hardware and analytical tools. The historical perspective of flow cytometry operation as well as how the field has changed and what the key changes have been discussed. The authors provide a background and compelling arguments for moving toward HT flow, where there are many innovative opportunities. With alternative approaches now available for flow cytometry, there will be a considerable number of new applications. These opportunities show strong capability for drug screening and functional studies with cells in suspension.

EXPERT OPINION

There is no doubt that HT flow is a rich technology awaiting acceptance by the pharmaceutical community. It can provide a powerful phenotypic analytical toolset that has the capacity to change many current approaches to HT screening. The previous restrictions on the technology, based on its reduced capacity for sample throughput, are no longer a major issue. Overcoming this barrier has transformed a mature technology into one that can focus on systems biology questions not previously considered possible.

Application of nanoparticles for the detection and sorting of pathogenic bacteria by flow-cytometry

In this paper we will describe a new developed contribution of fluorescence nano-crystal (q-dots) as a fluorescence label for detecting pathogenic bacteria by flow cytometry (FCM) and the use of nano-magnetic particles to improve bacterial sorting by Flow cytometry cell sorting (FACS).FCM or FACS systems are based upon single cell detection by light scatter and Immunofluorescence labeling signals. The common FACS systems are based upon single or dual excitation as excitation source both for light scatter parameters and for several fluorescence detectors. Hence, for multi-labeling detection, there is a need for fluorophores with broad excitation wave length and sharp emission bands. Moreover, such fluorophores should be with high fluorescence efficiency, stable, and available for bio-molecules conjugation. Q-dots benefit from practical features which meet those -criteria. We will describe the use of q-dots as fluorescence labels for specific conjugates against Bacillus anthracis spores and Yersinia pestis bacteria, which enable the specific detection of the different species. A specific and sensitive multiplex analysis procedure for both pathogens was achieved, with high sensitivity down to 10(3) bacteria per ml in the sample.Sorting bacteria by FACS has a tremendous advantage for sensitive and selective analysis and sorting of sub-populations. However it has always been a difficult task due to the fact that bacteria are small particles (usually 1-3 μm). For such small particles, light scatter signal is on the threshold level, and many positive events may be lost. Here we will present the development of a procedure for sorting of the gram negative bacteria Y. pestis from environment samples. We will show that the application of nano-magnetic particles, as a tool for the immunomagnetic labeling and separation of the bacteria, enables fast sorting in high and low bacterial concentration down to 10  (5) cfu/ml. The nano-metric physical size of the immunospecific labeling particles disguises them from the FACS detectors; hence the bacterial population becomes the major population as opposed to being "rare events population" when using standard micro-magnetic beads for pre-enrichment.The procedure of separation and collection of bacteria enables sensitive detection and characterization methods of bacteria from complex samples.

Hyperspectral cytometry at the single-cell level using a 32-channel photodetector

Despite recent progress in cell-analysis technology, rapid classification of cells remains a very difficult task. Among the techniques available, flow cytometry (FCM) is considered especially powerful, because it is able to perform multiparametric analyses of single biological particles at a high flow rate-up to several thousand particles per second. Moreover, FCM is nondestructive, and flow cytometric analysis can be performed on live cells. The current limit for simultaneously detectable fluorescence signals in FCM is around 8-15 depending upon the instrument. Obtaining multiparametric measurements is a very complex task, and the necessity for fluorescence spectral overlap compensation creates a number of additional difficulties to solve. Further, to obtain well-separated single spectral bands a very complex set of optical filters is required. This study describes the key components and principles involved in building a next-generation flow cytometer based on a 32-channel PMT array detector, a phase-volume holographic grating, and a fast electronic board. The system is capable of full-spectral data collection and spectral analysis at the single-cell level. As demonstrated using fluorescent microspheres and lymphocytes labeled with a cocktail of antibodies (CD45/FITC, CD4/PE, CD8/ECD, and CD3/Cy5), the presented technology is able to simultaneously collect 32 narrow bands of fluorescence from single particles flowing across the laser beam in <5 μs. These 32 discrete values provide a proxy of the full fluorescence emission spectrum for each single particle (cell). Advanced statistical analysis has then been performed to separate the various clusters of lymphocytes. The average spectrum computed for each cluster has been used to characterize the corresponding combination of antibodies, and thus identify the various lymphocytes subsets. The powerful data-collection capabilities of this flow cytometer open up significant opportunities for advanced analytical approaches, including spectral unmixing and unsupervised or supervised classification.

Discovering the unknown: detection of emerging pathogens using a label-free light-scattering system

A recently introduced technique for pathogen recognition called BARDOT (BActeria Rapid Detection using Optical scattering Technology) belongs to the broad class of optical sensors and relies on forward-scatter phenotyping (FSP). The specificity of FSP derives from the morphological information that bacterial material encodes on a coherent optical wavefront passing through the colony. The system collects elastically scattered light patterns that, given a constant environment, are unique to each bacterial species and serovar. The notable similarity between FSP technology and spectroscopies is their reliance on statistical machine learning to perform recognition. Currently used methods utilize traditional supervised techniques which assume completeness of training libraries. However, this restrictive assumption is known to be false for most experimental conditions, resulting in unsatisfactory levels of accuracy, poor specificity, and consequently limited overall performance for biodetection and classification tasks. The presented work demonstrates application of the BARDOT system to classify bacteria belonging to the Salmonella class in a nonexhaustive framework, that is, without full knowledge about all the possible classes that can be encountered. Our study uses a Bayesian approach to learning with a nonexhaustive training dataset to allow for the automated detection of unknown bacterial classes.

Introduction A: recent advances in cytometry instrumentation, probes, and methods--review

Cytometric techniques are continually being improved, refined, and adapted to new applications. This chapter briefly outlines recent advances in the field of cytometry with the main focus on new instrumentations in flow and image cytometry as well as new probes suitable for multiparametric analyses. There is a remarkable trend for miniaturizing cytometers, developing label-free and fluorescence-free analytical approaches, and designing "intelligent" probes. Furthermore, new methods for analyzing complex data for extracting relevant information are reviewed.

A Machine-Learning Approach to Detecting Unknown Bacterial Serovars

Technologies for rapid detection of bacterial pathogens are crucial for securing the food supply. A light-scattering sensor recently developed for real-time identification of multiple colonies has shown great promise for distinguishing bacteria cultures. The classification approach currently used with this system relies on supervised learning. For accurate classification of bacterial pathogens, the training library should be exhaustive, i.e., should consist of samples of all possible pathogens. Yet, the sheer number of existing bacterial serovars and more importantly the effect of their high mutation rate would not allow for a practical and manageable training. In this study, we propose a Bayesian approach to learning with a nonexhaustive training dataset for automated detection of unmatched bacterial serovars, i.e., serovars for which no samples exist in the training library. The main contribution of our work is the Wishart conjugate priors defined over class distributions. This allows us to employ the prior information obtained from known classes to make inferences about unknown classes as well. By this means, we identify new classes of informational value and dynamically update the training dataset with these classes to make it increasingly more representative of the sample population. This results in a classifier with improved predictive performance for future samples. We evaluated our approach on a 28-class bacteria dataset and also on the benchmark 26-class letter recognition dataset for further validation. The proposed approach is compared against state-of-the-art involving density-based approaches and support vector domain description, as well as a recently introduced Bayesian approach based on simulated classes.

Image processing guided analysis for estimation of bacteria colonies number by means of optical transforms

A novel method for evaluation of bacterial colonies number (Colony Forming Units--CFU), is described. Proposed algorithm, based on the Mellin transform, allows the CFU evaluation, invariant for the spatial orientation and scale changes. The proposed method involves image recording of bacteria grown in Petri dishes, calculation of the Fourier spectrum followed by coordinates transformation, and determination of the Mellin transform. It was proved that there is a high correlation between CFU and maxima of Mellin spectra. The method was practically implemented for evaluation of antibacterial activity of silver-based nanomaterials and the effect of an additional laser light irradiation.

Very small embryonic-like stem cells are present in adult murine organs: ImageStream-based morphological analysis and distribution studies

Recently, we purified a population of CXCR4+/Oct-4+/SSEA-1+/Sca-1+/Lin(-)/CD45(-) very small embryonic-like stem cells (VSELs) from adult murine bone marrow (BM). After using flow cytometry, ImageStream analysis, confocal microscopy, and real time RT-PCR, we report that similar cells could be also identified and isolated from several organs in adult mice. The highest total numbers of Oct-4+ VSELs were found in the brain, kidneys, muscles, pancreas, and BM. These observations support our hypothesis that a population of very primitive cells expressing germ line/epiblast markers (Oct-4, SSEA-1) is deposited early during embryogenesis in various organs and survives into adulthood. Further studies are needed to determine whether these cells, after being isolated from various adult human organs similarly to their murine BM-derived counterparts, are endowed with pluripotent stem cell properties.

Cytomics and nanobioengineering

The finding that an individual's genome differs as much as by many million variants from that of the human reference assembly diminished the great enthusiasm that every disease could be predicted based on nucleotide polymorphisms. Even individual cells of an organ may be specifically equipped to perform specific tasks and that the information of individual cells in a cell system is key information to understand function or dysfunction. Therefore, cytomics received great attention during the last years as it allows to quantitatively and qualitatively analyzing great number of individual cells, cell constituents, and of their intracellular and functional interactions in a cellular system and also giving the concept of analysis of these data.Exhaustive data extraction from multiparametric assays and multiple tests are the prerequisite for prediction of drug toxicity. Cytomics, as novel approach for unsupervised data analysis give a chance to find the most predictive parameters, which describe best the toxicity of a chemical. Cytomics is intrinsically connected to drug development and drug discovery.Focused on small structures, nanobioengineering is the ideal partner of cytomics, the systems biological discipline for cell population analysis. Realizing the idea "from the molecule to the patient" develops and offers chemical compounds, proteins, and other biomolecules, cells as well as tissues as instruments and products for a wide variety of biotechnological and biomedical applications.The integrative nanobioengineering combining different disciplines of nanotechnology will promote the development of innovative therapies and diagnostic methods. It can improve the precision of the measurements with focus on single cell analysis. By nanobioengineering and whole body imaging techniques, cytomics covers the field from molecules through bacterial cells, eukaryotic tissues, and organs to small animal live analysis. Toxicological testing and medical drug development are currently strongly broadening. It harbors the promise to substantially impact on various fields of biomedicine, drug discovery, and predictive medicine.As the number of scientific data is rising exponentially, new data analysis tools and strategies like cytomics and nanobioengineering take a lead and get closer to application. Bionanoengineering may strongly support the quantitative data supply, thus strengthening the rational for cytomics approach.