Automated biophysical classification of apoptotic pancreatic cancer cell subpopulations by using machine learning approaches with impedance cytometry

Honrado, Carlos; Salahi, Armita; Adair, Sara J.; Moore, J. H.; Bauer, Todd W.

doi:10.1039/d2lc00304j

Cited by 37 publications

(28 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, determining the correlation between the impedance of urothelial cancer cells and the pathological grade of UCB would be useful for setting up treatment plans and predicting the prognosis of UCB patients. Additionally, a combination method of clustering by unsupervised learning and classification by supervised learning with each population being clustered [ 19 ] will be helpful in determining the discrimination of the minority subpopulation in a heterogeneous sample.…”

Section: Resultsmentioning

confidence: 99%

“…Several recent works have applied ML models to impedance cytometry data for aiding classification; for instance, k-means-based clustering of subpopulations of peripheral blood mononuclear cells [ 17 ], SVM to quantify eight groups of pollen grains [ 18 ], and several ML models for classification of apoptotic pancreatic cancer cell subpopulations [ 19 ]. Furthermore, neural networks have been utilized to characterize intrinsic properties, size, velocity, and cross-sectional position of cells [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification between Normal and Cancerous Human Urothelial Cells by Using Micro-Dimensional Electrochemical Impedance Spectroscopy Combined with Machine Learning

Jeong

Kım

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

Although the high incidence and recurrence rates of urothelial cancer of the bladder (UCB) are heavy burdens, a noninvasive tool for effectively detecting UCB as an alternative to voided urine cytology, which has low sensitivity, is yet to be reported. Herein, we propose an intelligent discrimination method between normal (SV-HUC-1) and cancerous (TCCSUP) urothelial cells by using a combination of micro-dimensional electrochemical impedance spectroscopy (µEIS) with machine learning (ML) for a noninvasive and high-accuracy UCB diagnostic tool. We developed a unique valved flow cytometry, equipped with a pneumatic valve to increase sensitivity without cell clogging. Since contact between a cell and electrodes is tight with a high volume fraction, the electric field can be effectively confined to the cell. This enables the proposed sensor to highly discriminate different cell types at frequencies of 10, 50, 100, 500 kHz, and 1 MHz. A total of 236 impedance spectra were applied to six ML models, and systematic comparisons of the ML models were carried out. The hyperparameters were estimated by conducting a grid search or Bayesian optimization. Among the ML models, random forest strongly discriminated between SV-HUC-1 and TCCSUP, with an accuracy of 91.7%, sensitivity of 92.9%, precision of 92.9%, specificity of 90%, and F1-score of 93.8%.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification between Normal and Cancerous Human Urothelial Cells by Using Micro-Dimensional Electrochemical Impedance Spectroscopy Combined with Machine Learning

Jeong

Kım

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Many other works that apply machine learning to electrical impedance data on single cells rely on impedance cytometry measurements or impedance spectroscopy on a few frequencies [ 42 , 43 ]. While these methods typically have the advantages of larger sample sizes as measurements are faster and in the case of cytometry, the cells move continuously.…”

Section: Discussionmentioning

confidence: 99%

Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning

Ferguson

Hwang

Cheng

2023

Sensors

View full text Add to dashboard Cite

Many recent efforts in the diagnostic field address the accessibility of cancer diagnosis. Typical histological staining methods identify cancer cells visually by a larger nucleus with more condensed chromatin. Machine learning (ML) has been incorporated into image analysis for improving this process. Recently, impedance spectrometers have been shown to generate all-inclusive lab-on-a-chip platforms to detect nucleus abnormities. In this paper, a wideband electrical sensor and data analysis paradigm that can identify nuclear changes shows the realization of a single-cell microfluidic device to detect nuclei of altered sizes. To model cells of altered nucleus, Jurkat cells were treated to enlarge or shrink their nucleus followed by broadband sensing to obtain the S-parameters of single cells. The ability to deduce important frequencies associated with nucleus size is demonstrated and used to improve classification models in both binary and multiclass scenarios, despite a heterogeneous and overlapping cell population. The important frequency features match those predicted in a double-shell circuit model published in prior work, demonstrating a coherent new analytical technique for electrical data analysis. The electrical sensing platform assisted by ML with impressive accuracy of cell classification looks forward to a label-free and flexible approach to cancer diagnosis.

show abstract

“…Schütt et al applied a k-means algorithm for subpopulation clustering of peripheral blood mononuclear cells, based on peak voltage and phase [ 27 ]. As another example, Honrado et al developed an ML-based method of classification of impedance data to distinguish and quantify cellular subpopulations at the early apoptotic versus late apoptotic and necrotic states [ 28 ]. Ahuja et al used a support vector machine (SVM) classifier to discriminate between live and dead breast cancer cells by using the peak impedance magnitude and phase [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Nucleic Acid Quantification by Multi-Frequency Impedance Cytometry and Machine Learning

et al. 2023

View full text Add to dashboard Cite

Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts and its purity in many samples, including in samples with very small input DNA, there is utility of novel machine learning approaches for accurate and high-throughput DNA quantification. Here, we demonstrated the ability of a neural network to predict DNA amounts coupled to paramagnetic beads. To this end, a custom-made microfluidic chip is applied to detect DNA molecules bound to beads by measuring the impedance peak response (IPR) at multiple frequencies. We leveraged electrical measurements including the frequency and imaginary and real parts of the peak intensity within a microfluidic channel as the input of deep learning models to predict DNA concentration. Specifically, 10 different deep learning architectures are examined. The results of the proposed regression model indicate that an R_Squared of 97% with a slope of 0.68 is achievable. Consequently, machine learning models can be a suitable, fast, and accurate method to measure nucleic acid concentration in a sample. The results presented in this study demonstrate the ability of the proposed neural network to use the information embedded in raw impedance data to predict the amount of DNA concentration.

show abstract

Automated biophysical classification of apoptotic pancreatic cancer cell subpopulations by using machine learning approaches with impedance cytometry

Abstract: Machine learning applied to impedance cytometry data enables biophysical recognition of cellular subpopulations over the apoptotic progression after gemcitabine treatment of pancreatic cancer cells from tumor xenografts.

Cited by 37 publications

References 51 publications

Classification between Normal and Cancerous Human Urothelial Cells by Using Micro-Dimensional Electrochemical Impedance Spectroscopy Combined with Machine Learning

Classification between Normal and Cancerous Human Urothelial Cells by Using Micro-Dimensional Electrochemical Impedance Spectroscopy Combined with Machine Learning

Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning

Nucleic Acid Quantification by Multi-Frequency Impedance Cytometry and Machine Learning

Contact Info

Product

Resources

About