An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Hennig, Holger; Rees, Paul; Blasi, Thomas; Kamentsky, Lee; Hung, Jia‐Jang; Dao, David; Carpenter, Anne E.; Filby, Andrew

doi:10.1016/j.ymeth.2016.08.018

Cited by 96 publications

(92 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous studies have established the capabilities of such techniques in label-free cell cycle analysis (50,51) and combined with the application of powerful IFC software platforms such as CellProfiler and CellProfiler Analyst (52). Approaches such as machine learning and deep learning techniques can potentially be used to explore all intuitive and non-intuitive measures of classifying RBC images.…”

Section: Practical Implementationmentioning

confidence: 99%

“…Approaches such as machine learning and deep learning techniques can potentially be used to explore all intuitive and non-intuitive measures of classifying RBC images. Previous studies have established the capabilities of such techniques in label-free cell cycle analysis (50,51) and combined with the application of powerful IFC software platforms such as CellProfiler and CellProfiler Analyst (52). We are currently investigating the application of such techniques to our RBC IFC data and have recently showcased potential in the use of neural networks to classify IFC-captured RBC images (53).…”

Section: Practical Implementationmentioning

confidence: 99%

See 1 more Smart Citation

Label‐Free Analysis of Red Blood Cell Storage Lesions Using Imaging Flow Cytometry

et al. 2019

View full text Add to dashboard Cite

Deleterious changes, collectively termed as storage lesions, alter the characteristics of red blood cell (RBC) morphology during in vitro storage. Due to gradual loss of cellular membrane, RBCs lose their original biconcave disk shape and begin a process of spherical deformation that is characterized by well-defined morphological criteria. At the spheroechinocyte stage, the structure of RBC is irreversibly damaged and lacks the elasticity necessary to efficiently deliver oxygen. Quantifying the prevalence of spheroechinocytes could provide an important morphological measure of the quality of stored blood products. Unlike the conventional RBC morphology characterization assay involving light microscopy, we introduce a label-free assay using imaging flow cytometry (IFC). The technique captures 100,000 images of a sample and calculates a relative measure of spheroechinocyte population in a fraction of the time required by the conventional method. A comparative method study, measuring a morphological index for 11 RCC units through storage, found that the two techniques measured similar trends with IFC reporting the metric at an average of 3.9% higher. We monitored 18 RCC units between Weeks 1 and 6 of storage and found that the spheroechinocyte population increased by an average of 26.2%. The large (3.5-64.1%) variation between the units' spheroechinocyte population percentage at Week 1 suggests a possible dependence of blood product quality on donor characteristics. Given our method's ability to rapidly monitor large samples and refine morphological characterization beyond conventional methods, we believe our technique offers good potential for studying the underlying relationships between RBC morphology and blood storage lesions.Key terms deformability; erythrocyte; imaging flow cytometry; red blood cell morphology; storage lesion CURRENTLY, blood banks and hospital laboratories are devoid of convenient, label-free techniques to measure the morphological distribution of red blood cells (RBCs) stored in vitro. Parameters such as membrane shape and deformability are critical for understanding an RBC's functional efficacy as an oxygen transporter (1). Microscopy-related assays, considered the gold standard for RBC morphology analysis, stain and smear samples prior to manual characterization. High-throughput assays that can measure RBCs within environments closely resembling in vivo conditions could obtain a more insightful assessment of the morphology distribution.RBCs are stored in vitro primarily to enhance the oxygen carrying capacity of vulnerable patients via transfusions (2). Blood banks extract RBCs from donated blood using customized separation techniques and suspend the cells in a preservative fluid to increase its shelf-life (3). During storage of this red cell concentrate (RCC), typically set at a maximum of 6 weeks, RBCs undergo a sequence of significant biochemical and biomechanical changes, which have been collectively termed hypothermic storage lesions (4,5). RBC storage lesions cause the formation of m...

show abstract

Section: Practical Implementationmentioning

confidence: 99%

Section: Practical Implementationmentioning

confidence: 99%

Label‐Free Analysis of Red Blood Cell Storage Lesions Using Imaging Flow Cytometry

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Better approaches have been proposed, where data-driven feature extraction, feature selection, and machine learning have been applied to unbiasedly identify morphological profiles that are subtle and less visible to human observation (3)(4)(5). Better approaches have been proposed, where data-driven feature extraction, feature selection, and machine learning have been applied to unbiasedly identify morphological profiles that are subtle and less visible to human observation (3)(4)(5).…”

mentioning

confidence: 99%

Label‐Free Identification of White Blood Cells Using Machine Learning

et al. 2019

Self Cite

View full text Add to dashboard Cite

White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state‐of‐the‐art method for determining WBC differential counts. However, this process requires several sample preparation steps and may adversely disturb the cells. We present a novel label‐free approach using an imaging flow cytometer and machine learning algorithms, where live, unstained WBCs were classified. It achieved an average F1‐score of 97% and two subtypes of WBCs, B and T lymphocytes, were distinguished from each other with an average F1‐score of 78%, a task previously considered impossible for unlabeled samples. We provide an open‐source workflow to carry out the procedure. We validated the WBC analysis with unstained samples from 85 donors. The presented method enables robust and highly accurate identification of WBCs, minimizing the disturbance to the cells and leaving marker channels free to answer other biological questions. It also opens the door to employing machine learning for liquid biopsy, here, using the rich information in cell morphology for a wide range of diagnostics of primary blood. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

show abstract

“…Eine klare Verbesserung der theoretischen Modelle ist notwendig, welche dafür sorgen würden, dass experimentelle Daten sporadisch zu quantitativen Parametern führen . Außerdem sollten Data‐Mining‐Ansätze für z.…”

Section: Diskussionunclassified

“…Eine klare Verbesserung der theoretischen Modelle ist notwendig, welche dafürsorgen würden, dass experimentelle Daten sporadisch zu quantitativen Parametern führen. [20,30,65] Außerdem sollten Data-Mining-Ansätze fürz .B.T oxizitätsstudien [66] hilfreich sein, um mehr quantitative Daten aus der überwältigenden Menge experimenteller Daten, die in der Literatur zu finden sind, zu gewinnen.…”

Section: Diskussionunclassified

Analyse quantitativer Partikelaufnahme von Zellen über verschiedene Messmethoden

et al. 2019

View full text Add to dashboard Cite

Im letzten Jahrzehnt gab es eine große Anzahl publizierter zweidimensionaler und zeitunabhängiger In‐vitro‐Studien über die Aufnahme kolloidaler Nano‐ und Mikropartikel in Zellen. In diesem Kurzaufsatz werden unterschiedliche angewandte Methoden solcher Untersuchungen zusammengefasst und kritisch diskutiert. Ergänzende experimentelle Daten erlauben einen direkten Vergleich zwischen diesen angewandten Techniken. Das Hauptaugenmerk wird darauf liegen, quantitative Parameter aus solchen Studien zu gewinnen, in denen mehrere verschiedene experimentelle Messmethoden genutzt wurden, mit dem Ziel eine bessere Vergleichbarkeit der Daten zu gewährleisten.

show abstract

An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Cited by 96 publications

References 31 publications

Label‐Free Analysis of Red Blood Cell Storage Lesions Using Imaging Flow Cytometry

Label‐Free Analysis of Red Blood Cell Storage Lesions Using Imaging Flow Cytometry

Label‐Free Identification of White Blood Cells Using Machine Learning

Analyse quantitativer Partikelaufnahme von Zellen über verschiedene Messmethoden

Contact Info

Product

Resources

About