Classification of human white blood cells using machine learning for stain-free imaging flow cytometry

Lippeveld, Maxim; Knill, Carly; Ladlow, Emma; Fuller, Andrew; Michaelis, Louise J.; Saeys, Yvan; Filby, Andrew; Peralta, Daniel

doi:10.1101/680975

Cited by 4 publications

(4 citation statements)

References 35 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, lower resolution imaging and the flowing nature of the cells may make it more difficult to detect the intracellular structures of small T cells. Although the resulting images may be different enough to require a distinct CNN, recent advances in CNNs for imaging flow cytometry suggest our pipeline could be optimized for this practical setting. Overall, our strong results demonstrate the feasibility of classifying T cells directly from autofluorescence intensity images, which can guide future work to bring this technology to pre‐clinical and clinical applications.…”

Section: Discussionmentioning

confidence: 99%

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

Wang

Walsh

Skala

et al. 2019

Journal of Biophotonics

View full text Add to dashboard Cite

The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen‐induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states in a non‐destructive manner by detecting endogenous changes in metabolic co‐enzymes such as NAD(P)H. However, recognizing robust activity patterns is computationally challenging in the absence of exogenous labels. We demonstrate machine learning methods that can accurately classify T cell activity across human donors from NAD(P)H intensity images. Using 8260 cropped single‐cell images from six donors, we evaluate classifiers ranging from traditional models that use previously‐extracted image features to convolutional neural networks (CNNs) pre‐trained on general non‐biological images. Adapting pre‐trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. Our image processing and classifier training software is available at https://github.com/gitter-lab/t-cell-classification.

show abstract

Section: Discussionmentioning

confidence: 99%

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

Wang

Walsh

Skala

et al. 2019

Journal of Biophotonics

View full text Add to dashboard Cite

show abstract

“…For instance, to sort T cells in practice, the classifier would need to be coupled to a flow sorter. The flowing nature of the cells may make the resulting images different enough to require a distinct CNN, which is not an obstacle given recent advances in CNNs for imaging flow cytometry 10,21,22,34,48,49 . Overall, our strong results demonstrate the feasibility of classifying T cells directly from autofluorescence intensity images, which can guide future work to bring this technology to pre-clinical and clinical applications.…”

Section: Discussionmentioning

confidence: 99%

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

Wang

Walsh

Skala

et al. 2019

Preprint

View full text Add to dashboard Cite

The importance of T cells in immunotherapy has motivated developing technologies to better characterize T cells and improve therapeutic efficacy. One specific objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states of individual cells in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust patterns of T cell activity is computationally challenging in the absence of exogenous labels or information-rich autofluorescence lifetime measurements. We demonstrate that advanced machine learning can accurately classify T cell activity from NAD(P)H intensity images and that those image-based signatures transfer across human donors. Using a dataset of 8,260 cropped single-cell images from six donors, we meticulously evaluate multiple machine learning models. These range from traditional models that represent images using summary statistics or extract image features with CellProfiler to deep convolutional neural networks (CNNs) pre-trained on general non-biological images. Adapting pre-trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. However, we observe that fine-tuning all layers of the pre-trained CNN does not provide a classification performance boost commensurate with the additional computational cost. Our software detailing our image processing and model training pipeline is available as Jupyter notebooks at https: //github.com/gitter-lab/t-cell-classification. lifetime imaging to identify macrophages within the tumor microenvironment in vivo 7 and classified activation state of T cells in vitro 6 . The fluorescence lifetime of NAD(P)H and FAD is highly sensitive to the microenvironment and binding of NAD(P)H and FAD. Thus, this fluorescence lifetime can be used to resolve metabolic differences between functional states of immune cells. However, fluorescence lifetime imaging requires specialized and expensive microscope components, limiting its use to particular labs. Although autofluorescence intensity images lack the depth of information provided by the fluorescence lifetime, intensity images can easily and quickly be acquired on almost any commercial fluorescence microscope, allowing widespread adoption and seamless integration of a new technique into existing protocols of live cell assessment. Here, we develop a computational framework that uses autofluorescence intensity images to assess T cell activation state at the single-cell level.Machine learning is promising for classifying cell subtypes from label-free images. For example, Pavillon et al. used regularized logistic regression to predict macrophage activation state 8 , and Yoo...

show abstract

“…Furthermore, online image analysis often requires a too high computational power such that real-time cell sorting cannot easily be done. Several machine learning approaches have recently been proposed to automatically analyze the big amount of data generated by label-free imaging flow cytometry 8,[13][14][15][16][17][18][19] , although in most of them the image processing is carried out offline. Exceptions are 15,16,20 , where single-particle classifications respectively took < 1 ms , 0.2 ms and 3.6 ms when accelerated by a GPU.…”

Section: Flow Cytometers Are Instruments Able To Analyze and Charactementioning

confidence: 99%

Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry

Lugnan

Gooskens

Vatin

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Machine learning offers promising solutions for high-throughput single-particle analysis in label-free imaging microflow cytomtery. However, the throughput of online operations such as cell sorting is often limited by the large computational cost of the image analysis while offline operations may require the storage of an exceedingly large amount of data. Moreover, the training of machine learning systems can be easily biased by slight drifts of the measurement conditions, giving rise to a significant but difficult to detect degradation of the learned operations. We propose a simple and versatile machine learning approach to perform microparticle classification at an extremely low computational cost, showing good generalization over large variations in particle position. We present proof-of-principle classification of interference patterns projected by flowing transparent PMMA microbeads with diameters of $${15.2}\,\upmu \text {m}$$ 15.2 μ m and $${18.6}\,\upmu \text {m}$$ 18.6 μ m . To this end, a simple, cheap and compact label-free microflow cytometer is employed. We also discuss in detail the detection and prevention of machine learning bias in training and testing due to slight drifts of the measurement conditions. Moreover, we investigate the implications of modifying the projected particle pattern by means of a diffraction grating, in the context of optical extreme learning machine implementations.

show abstract

Classification of human white blood cells using machine learning for stain-free imaging flow cytometry

Cited by 4 publications

References 35 publications

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry

Contact Info

Product

Resources

About