Single cell flow cytometry and machine learning stratifies patients undergoing transurethral resection of the bladder tumor (TURBT)

2019 ◽  
Vol 18 (2) ◽  
pp. e2406-e2407
Author(s):  
A. Koladiya ◽  
K. Otavová ◽  
V. Adamcová ◽  
J. Stejskal ◽  
B. Ogan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Transurethral Resection ◽  
Bladder Tumor ◽  
Cell Flow Cytometry

scholarly journals New interpretable machine learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

2019 ◽  
Author(s):  
Evan Greene ◽  
Greg Finak ◽  
Leonard A. D’Amico ◽  
Nina Bhardwaj ◽  
Candice D. Church ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
T Cell ◽  
Single Cell ◽  
Cancer Immunotherapy ◽  
Effector Memory ◽  
Machine Learning Method ◽  
Learning Method ◽  
Modeling Framework ◽  
Interpretable Machine Learning

AbstractHigh-dimensional single-cell cytometry is routinely used to characterize patient responses to cancer immunotherapy and other treatments. This has produced a wealth of datasets ripe for exploration but whose biological and technical heterogeneity make them difficult to analyze with current tools. We introduce a new interpretable machine learning method for single-cell mass and flow cytometry studies, FAUST, that robustly performs unbiased cell population discovery and annotation. FAUST processes data on a per-sample basis and returns biologically interpretable cell phenotypes that can be compared across studies, making it well-suited for the analysis and integration of complex datasets. We demonstrate how FAUST can be used for candidate biomarker discovery and validation by applying it to a flow cytometry dataset from a Merkel cell carcinoma anti-PD-1 trial and discover new CD4+ and CD8+ effector-memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. We then use FAUST to validate these correlates in an independent CyTOF dataset from a published metastatic melanoma trial. Importantly, existing state-of-the-art computational discovery approaches as well as prior manual analysis did not detect these or any other statistically significant T cell sub-populations associated with anti-PD-1 treatment in either data set. We further validate our methodology by using FAUST to replicate the discovery of a previously reported myeloid correlate in a different published melanoma trial, and validate the correlate by identifying it de novo in two additional independent trials. FAUST’s phenotypic annotations can be used to perform cross-study data integration in the presence of heterogeneous data and diverse immunophenotyping staining panels, enabling hypothesis-driven inference about cell sub-population abundance through a multivariate modeling framework we call Phenotypic and Functional Differential Abundance (PFDA). We demonstrate this approach on data from myeloid and T cell panels across multiple trials. Together, these results establish FAUST as a powerful and versatile new approach for unbiased discovery in single-cell cytometry.

scholarly journals Infinity Flow: High-Throughput Single-Cell Quantification of 100s of Proteins Using Conventional Flow Cytometry and Machine Learning

2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Cell Quantification

scholarly journals Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Low Cost ◽  
Expression Patterns ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Supervised Machine Learning ◽  
Melanoma Metastasis ◽  
Immunologic Research

AbstractModern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.

Adaptive genetic algorithms combined with high sensitivity single cell-based technology derived urine-based score to differentiate between high-grade and low-grade transitional cell carcinoma of the bladder.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 572-572
Author(s):  
Shaheen Riadh Alanee ◽  
Zade Roumayah ◽  
Musatafa Deebajah ◽  
James O. Peabody ◽  
Rodrigo Mora ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Bladder Cancer ◽  
Single Cell ◽  
Transitional Cell ◽  
Low Grade ◽  
High Grade ◽  
Median Score ◽  
Adaptive Genetic Algorithms ◽  
Cell Flow Cytometry ◽  
Carcinoma Of The Bladder

572 Background: We previously showed that adaptive genetic algorithms (AGA), in combination with single-cell flow cytometry technology, can be used to develop a noninvasive urine-based score to detect bladder cancer with high accuracy. Our aim in this analysis was to investigate if that same score can differentiate between high grade (HG) and low grade (LG) transitional cell carcinoma of the bladder (BC). Methods: We collected urine samples from cystoscopy confirmed HG and LG superficial bladder cancer patients and healthy donors in an optimized urine collection media. We then examined these samples using an assay developed from AGA in combination with single-cell flow cytometry technology. Results: We examined 50 BC and 15 healthy donor urine samples. Patients were majorly White (59.2%), males (61.2%), and had HG BC (66.7%). AGA derived score of 1.1 differentiated between BCa and healthy patients with high precision (AUC 0.92). The median score was 2.8 for LG BC and 6 for LG BC. Mann-Whitney Rank Sum Test indicated that the difference between the median score of HG and LG BC was significant at P value = 0.003. The score performed well independent of patients’ sex or smoking history. Conclusions: Using single-cell technology and machine learning, we developed a new urine-based score that can potentially differentiate between HG and LG bladder cancer. Future studies are planned to validate this score.

scholarly journals Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation

2018 ◽  
Author(s):  
Mohammad Tanhaemami ◽  
Elaheh Alizadeh ◽  
Claire Sanders ◽  
Babetta L. Marrone ◽  
Brian Munsky’
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Lipid Accumulation ◽  
Time Course ◽  
Fluorescent Dyes ◽  
Single Cell Analysis ◽  
Learning Approaches ◽  
Label Free ◽  
Unlabeled Cell

Abstract—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.

Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry

2019 ◽  
Vol 14 (7) ◽  
pp. 1946-1969 ◽  
Author(s):  
Jolanda Brummelman ◽  
Claudia Haftmann ◽  
Nicolás Gonzalo Núñez ◽  
Giorgia Alvisi ◽  
Emilia M. C. Mazza ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Computational Analysis ◽  
Fluorescent Antibody ◽  
High Dimensional ◽  
Cell Flow Cytometry ◽  
Development Application

scholarly journals High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning

2021 ◽  
Vol 7 (39) ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Peter A. Morawski ◽  
Daniel J. Campbell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Cell Quantification

scholarly journals A quantitative single-cell assay for retrograde membrane traffic enables rapid detection of defects in cellular organization

2020 ◽  
Vol 31 (7) ◽  
pp. 511-519 ◽  
Author(s):  
Phi Luong ◽  
Qian Li ◽  
Pin-Fang Chen ◽  
Paul J. Wrighton ◽  
Denis Chang ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Endoplasmic Reticulum ◽  
Single Cell ◽  
Membrane Trafficking ◽  
Rapid Detection ◽  
Cell Function ◽  
Cellular Organization ◽  
Intracellular Protein ◽  
Flow Cytometry Assay ◽  
Cell Flow Cytometry

Retrograde membrane trafficking from plasma membrane to the Golgi and endoplasmic reticulum affects intracellular protein dynamics underlying cell function. Here, we developed split-fluorescent toxin reporters that enable a quantitative, sensitive, and real-time single-cell flow cytometry assay for retrograde membrane transport.

Single-Cell Flow Cytometry Profiling of BAL in Children

2020 ◽  
Vol 63 (2) ◽  
pp. 152-159
Author(s):  
Shivanthan Shanthikumar ◽  
Matthew Burton ◽  
Richard Saffery ◽  
Sarath C. Ranganathan ◽  
Melanie R. Neeland
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Cell Flow Cytometry
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