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Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.
Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies
Automated and reproducible cell identification in mass cytometry using neural networks
The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples. To address these limitations, we present two novel developments: (1) a pre-trained cell identification model named Immunopred that allows automated identification of immune cells without user-defined prior knowledge of expected cell types and (2) a fully automated cytometry meta-analysis pipeline built around Immunopred. We evaluated this pipeline on six COVID-19 study data sets comprising 270 unique samples and uncovered novel significant phenotypic changes in the wider immune landscape of COVID-19 that were not identified when each study was analyzed individually. Applied widely, our approach will support the discovery of novel findings in research areas where cytometry data sets are available for integration
A biology-driven deep generative model for cell-type annotation in cytometry
Cytometry enables precise single-cell phenotyping within heterogeneous
populations. These cell types are traditionally annotated via manual gating,
but this method suffers from a lack of reproducibility and sensitivity to
batch-effect. Also, the most recent cytometers - spectral flow or mass
cytometers - create rich and high-dimensional data whose analysis via manual
gating becomes challenging and time-consuming. To tackle these limitations, we
introduce Scyan (https://github.com/MICS-Lab/scyan), a Single-cell Cytometry
Annotation Network that automatically annotates cell types using only prior
expert knowledge about the cytometry panel. We demonstrate that Scyan
significantly outperforms the related state-of-the-art models on multiple
public datasets while being faster and interpretable. In addition, Scyan
overcomes several complementary tasks such as batch-effect removal,
debarcoding, and population discovery. Overall, this model accelerates and
eases cell population characterisation, quantification, and discovery in
cytometry
GateNet: A novel Neural Network Architecture for Automated Flow Cytometry Gating
Flow cytometry is widely used to identify cell populations in patient-derived
fluids such as peripheral blood (PB) or cerebrospinal fluid (CSF). While
ubiquitous in research and clinical practice, flow cytometry requires gating,
i.e. cell type identification which requires labor-intensive and error-prone
manual adjustments. To facilitate this process, we designed GateNet, the first
neural network architecture enabling full end-to-end automated gating without
the need to correct for batch effects. We train GateNet with over 8,000,000
events based on N=127 PB and CSF samples which were manually labeled
independently by four experts. We show that for novel, unseen samples, GateNet
achieves human-level performance (F1 score ranging from 0.910 to 0.997). In
addition we apply GateNet to a publicly available dataset confirming
generalization with an F1 score of 0.936. As our implementation utilizes
graphics processing units (GPU), gating only needs 15 microseconds per event.
Importantly, we also show that GateNet only requires ~10 samples to reach
human-level performance, rendering it widely applicable in all domains of flow
cytometry
Functional Analysis of Immunocompromised Patients’ Leucocytes by Single-cell Mass Cytometry
Immunodeficiencies make up a large group of diseases characterized by heterogeneous clinical
manifestations, including life-threatening infections, autoimmunity, chronic inflammation, allergy
and malignant diseases. They are classically divided in primary (PID) and secondary (SID)
immunodeficiencies and they can be caused by monogenic defects or be secondary to exogenous
factors, malignant or non-malignant diseases. In the last 20 years, accelerating progress has been
made in identifying new forms of PIDs thanks to the advances of molecular and genetic
characterizations. These disorders are either diagnosed early in life or even later, in adults. It is
estimated that 1-2% of the population might be affected with any type of the whole PID
spectrum.
Immune cell characterization, particularly by flow cytometry techniques, has extensively showed
its importance in the clinical management of patients presenting immune deficiencies with
quantitative cell defects, as well as in the understanding of the immune system. It has already
improved the classification of immunological diseases, as well as contributed to improve
treatment efficacy and follow-up. Recently, mass cytometry techniques have been used for
diagnostic purposes, significantly increasing the breadth and depth of the functional and
phenotypic characterization of a patient’s immune cells, in comparison to traditional flow
cytometry techniques. These advancements are driven by the great increase in measurable
parameters provided by mass cytometry, which allows for all major known immune cell
populations and subpopulations to be characterized with a single analysis.
The major contribution of this research resides in directly testing the functional activity and
response of a patient's immune cells to different stimuli. The highly multiparametric nature of
mass cytometry allows for both a broad and in depth characterization of the functional immune
response using only a minimal volume of a patient's blood (1 mL) with results available within
one day, thus drastically improving time to diagnosis. In addition to having a proportional and
phenotypic characterization of a patient's immune cells, identifying the functionally abnormal
cell population(s) will provide the clinicians with an even better understanding of their patient's
immunological defect. Interpretation of the mass cytometry results along with the patient's
clinical data will allow for the identification of signatures associated with specific immunological
defects, new classes of immunodeficiencies and therapies that are best adapted to a specific class
of an immunological disorder, hence improving the diagnosis and the benefits for
immunocompromised patients
flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry
Lux M, Brinkman RR, Chauve C, et al. flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry. Bioinformatics. 2018;34(13):2245-2253.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 data sets, 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
Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data
Clustering high-dimensional data, such as images or biological measurements,
is a long-standingproblem and has been studied extensively. Recently, Deep
Clustering has gained popularity due toits flexibility in fitting the specific
peculiarities of complex data. Here we introduce the Mixture-of-Experts
Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering
model.The model can learn multi-modal distributions of high-dimensional data
and use these to generaterealistic data with high efficacy and efficiency.
MoE-Sim-VAE is based on a Variational Autoencoder(VAE), where the decoder
consists of a Mixture-of-Experts (MoE) architecture. This specific architecture
allows for various modes of the data to be automatically learned by means of
the experts.Additionally, we encourage the lower dimensional latent
representation of our model to follow aGaussian mixture distribution and to
accurately represent the similarities between the data points. Weassess the
performance of our model on the MNIST benchmark data set and challenging
real-worldtasks of clustering mouse organs from single-cell RNA-sequencing
measurements and defining cellsubpopulations from mass cytometry (CyTOF)
measurements on hundreds of different datasets.MoE-Sim-VAE exhibits superior
clustering performance on all these tasks in comparison to thebaselines as well
as competitor methods.Comment: Submitted to PLOS Computational Biolog
An open-source solution for advanced imaging flow cytometry data analysis using machine learning
Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using “user-friendly” platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data set. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery
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