A new method using machine learning for automated image analysis applied to chip-based digital assays.

Chip-based digital assays such as the digital polymerase chain reaction (digital PCR), digital loop-mediated amplification (digital LAMP), digital enzyme-linked immunosorbent assay (digital ELISA) and digital proximity ligation assay (digital PLA) need high-throughput quantification of the captured fluorescence image data. However, traditional methods that are mainly based on image segmentation using either a fixed threshold or an automated hard threshold failed to extract valid signals over a broad range of image characteristics. In this study, we introduce a new method for automated image analysis to extract signals applied to chip-based digital assays. This approach precisely locates each micro-compartment based on the structure design of the chip, thereby eliminating the interference of non-signal noise in the image. Utilizing the principle that the human eyes can distinguish between the positive micro-compartments and the negative micro-compartments, we take the parameters of each micro-compartment together with its surrounding micro-compartments as the training dataset of the Random Forest classifier to classify the micro-compartments and extract valid signals, thus solving the problem caused by the differences among images. Furthermore, we adopted the iteration methodology that adds the output of a model's prediction to the input of the next model's training dataset, until the output of a model's prediction reaches the accuracy we expected, which improves the work efficiency during data training greatly. We demonstrate the method on the dPCR dataset and it performs well without any manual adjustment of settings. The results show that our proposed method can recognize the positive signals from the fluorescence images with an accuracy of 97.78%. With minor modification, bio-instrument companies or researchers can integrate this method into their digital assay devices' software conveniently.

[1]  Yusi Fu,et al.  Digital polymerase chain reaction in an array of femtoliter polydimethylsiloxane microreactors. , 2012, Analytical chemistry.

[2]  Anupam Singhal,et al.  Megapixel digital PCR , 2011, Nature Methods.

[3]  Derek Tseng,et al.  Targeted DNA sequencing and in situ mutation analysis using mobile phone microscopy , 2017, Nature Communications.

[4]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[5]  Aydogan Ozcan,et al.  Research highlights: digital assays on chip. , 2015, Lab on a chip.

[6]  Daniel T. Chiu,et al.  Self-Digitization Microfluidic Chip for Absolute Quantification of mRNA in Single Cells , 2014, Analytical chemistry.

[7]  Rustem F Ismagilov,et al.  Digital, ultrasensitive, end-point protein measurements with large dynamic range via Brownian trapping with drift. , 2014, Journal of the American Chemical Society.

[8]  David M. Rissin,et al.  Single-Molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations , 2010, Nature Biotechnology.

[9]  Wei Jin,et al.  Self-priming compartmentalization digital LAMP for point-of-care. , 2012, Lab on a chip.

[10]  Ioannis Pratikakis,et al.  Adaptive degraded document image binarization , 2006, Pattern Recognit..

[11]  Gang Li,et al.  Absolute quantification of DNA methylation using microfluidic chip-based digital PCR. , 2017, Biosensors & bioelectronics.

[12]  Mustafa Khammash,et al.  Digital Quantification of Proteins and mRNA in Single Mammalian Cells. , 2016, Molecular cell.

[13]  Weibo Fang,et al.  Smartphone-based mobile digital PCR device for DNA quantitative analysis with high accuracy. , 2018, Biosensors & bioelectronics.

[14]  Yaoh-Shiang Lin,et al.  Nonadhesive Culture System as a Model of Rapid Sphere Formation with Cancer Stem Cell Properties , 2012, PloS one.

[15]  Alfred M. Bruckstein,et al.  A new method for image segmentation , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[16]  David R Walt,et al.  Protein Counting in Single Cancer Cells. , 2016, Analytical chemistry.

[17]  James W. Tung,et al.  Non-Small Cell Lung Cancer Cells Expressing CD44 Are Enriched for Stem Cell-Like Properties , 2010, PloS one.

[18]  Steve Feng,et al.  Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning. , 2015, Lab on a chip.

[19]  Stephen Bustin,et al.  Homogeneous and digital proximity ligation assays for the detection of Clostridium difficile toxins A and B , 2016, Biomolecular detection and quantification.

[20]  Tza-Huei Wang,et al.  Ultra-thin, evaporation-resistent PDMS devices for absolute quantification of DNA using digital PCR , 2015, 2015 Transducers - 2015 18th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS).

[21]  Bingwen Yu,et al.  Digital PCR on an integrated self-priming compartmentalization chip. , 2014, Lab on a chip.

[22]  Hiroyuki Noji,et al.  Large-scale femtoliter droplet array for digital counting of single biomolecules. , 2012, Lab on a chip.

[23]  Ying Mu,et al.  Co-detection of ALDH1A1, ABCG2, ALCAM and CD133 in three A549 subpopulations at the single cell level by one-step digital RT-PCR. , 2018, Integrative biology : quantitative biosciences from nano to macro.

[24]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[25]  Lin Qiu,et al.  A scalable self-priming fractal branching microchannel net chip for digital PCR. , 2017, Lab on a chip.

[26]  Kuangwen Hsieh,et al.  Simple and Precise Counting of Viable Bacteria by Resazurin-Amplified Picoarray Detection. , 2018, Analytical chemistry.

[27]  Luke P. Lee,et al.  Digital LAMP in a sample self-digitization (SD) chip. , 2012, Lab on a chip.