Diagnostic Potential of Imaging Flow Cytometry

Doan, Minh; Vorobjev, Ivan A.; Rees, Paul; Filby, Andrew; Wolkenhauer, Olaf; Goldfeld, Anne E.; Lieberman, Judy; Barteneva, Natasha S.; Carpenter, Anne E.; Hennig, Holger

doi:10.1016/j.tibtech.2017.12.008

Cited by 143 publications

(88 citation statements)

References 12 publications

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“…Additional nuclear classes such as pH2A.X S139 , p53, cyclin D3 and Ki-67 were analyzed using similar multiparametric clustering approaches (data not shown). The strategy could enable new ways to perform deep proteomic drug screening on different models for translational medicine and preclinical trials [46,47].…”

Section: Discussionmentioning

confidence: 99%

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

Bouzekri

Esch

Ornatsky

2019

Preprint

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In pharmaceutical research, high-content screening is an integral part of lead candidate development. Drug response in vitro over 40 parameters including biomarkers, signaling molecules, cell morphological changes, proliferation indexes and toxicity in a single sample could significantly enhance discovery of new therapeutics. As a proof of concept, we present a workflow for multidimensional Imaging Mass Cytometry™ (IMC™) and data processing with open source computational tools. CellProfiler was used to identify single cells through establishing cellular boundaries, followed by histoCAT™ (histology topography cytometry analysis toolbox) for extracting single-cell quantitative information visualized as t-SNE plots and heatmaps. Human breast cancer-derived cell lines SKBR3, HCC1143 and MCF-7 were screened for expression of cellular markers to generate digital images with a resolution comparable to conventional fluorescence microscopy. Predicted pharmacodynamic effects were measured in MCF-7 cells dosed with three target-specific compounds: growth stimulatory EGF, microtubule depolymerization agent nocodazole and genotoxic chemotherapeutic drug etoposide. We show strong pairwise correlation between nuclear markers pHistone3 S28 , Ki-67 and p4E-BP1 T37/T46 in classified mitotic cells and anti-correlation with cell surface markers. Our study demonstrates that IMC data expands the number of measured parameters in single cells and brings higherdimension analysis to the field of cell-based screening in early lead compound discovery.

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Section: Discussionmentioning

confidence: 99%

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

Bouzekri

Esch

Ornatsky

2019

Preprint

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“…These measurements are usually a combination of fluorescence intensities derived from a stain targeting a cell of interest, and morphological characteristics derived from the stain‐free cell images. Although this approach has led to numerous insights into cell population heterogeneity , it has some serious drawbacks, mainly: manual gating is hard to reproduce, manual gating is subjective and biased, and manual gating is time‐consuming for large experiments . …”

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confidence: 99%

Classification of Human White Blood Cells Using Machine Learning for Stain‐Free Imaging Flow Cytometry

et al. 2019

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Imaging flow cytometry (IFC) produces up to 12 spectrally distinct, information‐rich images of single cells at a throughput of 5,000 cells per second. Yet often, cell populations are still studied using manual gating, a technique that has several drawbacks, hence it would be advantageous to replace manual gating with an automated process. Ideally, this automated process would be based on stain‐free measurements, as the currently used staining techniques are expensive and potentially confounding. These stain‐free measurements originate from the brightfield and darkfield image channels, which capture transmitted and scattered light, respectively. To realize this automated, stain‐free approach, advanced machine learning (ML) methods are required. Previous works have successfully tested this approach on cell cycle phase classification with both a classical ML approach based on manually engineered features, and a deep learning (DL) approach. In this work, we compare both approaches extensively on the problem of white blood cell classification. Four human whole blood samples were assayed on an ImageStream‐X MK II imaging flow cytometer. Two samples were stained for the identification of eight white blood cell types, while two other sample sets were stained for the identification of resting and active eosinophils. For both data sets, four ML classifiers were evaluated on stain‐free imagery with stratified 5‐fold cross‐validation. On the white blood cell data set, the best obtained results were 0.778 and 0.703 balanced accuracy for classical ML and DL, respectively. On the eosinophil data set, this was 0.871 and 0.856 balanced accuracy. We conclude that classifying cell types based on only stain‐free images is possible with all four classifiers. Noteworthy, we also find that the DL approaches tested in this work do not outperform the approaches based on manually engineered features. © 2019 International Society for Advancement of Cytometry

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“…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%

“…Although imaging flow cytometers are capable of capturing information-rich cell images, common analytic pipelines are rarely seen exploiting its high-content potential. 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).…”

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confidence: 99%

Label‐Free Identification of White Blood Cells Using Machine Learning

et al. 2019

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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.

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Diagnostic Potential of Imaging Flow Cytometry

Cited by 143 publications

References 12 publications

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

Classification of Human White Blood Cells Using Machine Learning for Stain‐Free Imaging Flow Cytometry

Label‐Free Identification of White Blood Cells Using Machine Learning

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