In-flow real-time detection of spectrally encoded microgels for miRNA absolute quantification

Dannhauser, David; Causa, Filippo; Battista, Edmondo; Cusano, A; Rossi, Domenico; Netti, Paolo A.

doi:10.1063/1.4967489

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…1). [19][20][21][22][23][24][25][26] Figure 1. Experimental setup and typical scattering pattern snapshots.…”

Section: Scattering Apparatusmentioning

confidence: 99%

Out of distribution detection in deep learning-based scattering pattern classification

Dannhauser,

Netti,

Causa

2023

Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI

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Intrinsic biophysical cell properties hold an enormous potential for cell class and state classification in microfluidics, allowing to avoid the need of cost intensive fluorescence labelling. Several methods can accomplish cell identification, while convolutional neural networks show an outstanding performance compared to other state-of-the-art classification methods, regarding accuracy and speed. In fact, neural networks show high performance for known image class prediction but struggles when unknown (out of distribution) image classes need to be identified. In such a scenario no prior knowledge of the unknown cell class can be used for the model training, which inevitably results in image misclassification. In fact, to distinguish unknown cell classes, a neural network must first construct an in-distribution of known images to afterwards detect out of distribution as unknowns, which is also called open-set classification assumption. Ones, a new cell class is identified, the neural network can be retrained with the obtained knowledge to dynamically update its cell class database. This process can be simply repeated for each new detected cell class. We applied this open-set idea to scattering pattern snapshots of different classes of living cells obtained in microfluidics. Our outcome shows a proof-of-concept for openset based convolutional neural network for cell image classification, which can be applied to a wide range of single cell classification approaches to reduce uncertainty in machine learning based technologies.

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“…1). [19][20][21][22][23][24][25][26] Figure 1. Experimental setup and typical scattering pattern snapshots.…”

Section: Scattering Apparatusmentioning

confidence: 99%

Out of distribution detection in deep learning-based scattering pattern classification

Dannhauser,

Netti,

Causa

2023

Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI

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“…According to the scheme of encoded core/shell microgels for oligonucleotide, Dannhauser et al [105] provided an easy tool for a quasi real-time fluorescence detection of single microgels in microfluidic flows using non-Newtonian fluids at a low concentration level is recently reported. In particular a cost-effective and biocompatible viscoelastic fluid was used to achieve optimal microgel alignment in the centerline of a straight channel.…”

Section: Microgels and Hydrogel Microparticles In Multiplex Assaysmentioning

confidence: 99%

Bioengineering Microgels and Hydrogel Microparticles for Sensing Biomolecular Targets

Battista

Causa

Netti

2017

Gels

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Hydrogels, and in particular microgels, are playing an increasingly important role in a diverse range of applications due to their hydrophilic, biocompatible, and highly flexible chemical characteristics. On this basis, solution-like environment, non-fouling nature, easy probe accessibility and target diffusion, effective inclusion of reporting moieties can be achieved, making them ideal substrates for bio-sensing applications. In fact, hydrogels are already successfully used in immunoassays as well as sensitive nucleic acid assays, also enabling hydrogel-based suspension arrays. In this review, we discuss key parameters of hydrogels in the form of micron-sized particles to be used in sensing applications, paying attention to the protein and oligonucleotides (i.e., miRNAs) targets as most representative kind of biomarkers.

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“…As a result, microgels with core–shell architecture or barcoded hydrogel particles are capable of scalable, sensitive, and multiplex optical detection . Their particle nature allows for assay versatility, multiplex assay, , easy manipulation in assay steps, and fast analysis in flow, also compatible with cytometers …”

Section: Introductionmentioning

confidence: 99%

“…As a result, microgels with core−shell architecture or barcoded hydrogel particles are capable of scalable, 44 sensitive, and multiplex optical detection. 45 Their particle nature 46 allows for assay versatility, multiplex assay, 40,47 easy manipulation in assay steps, and fast analysis in flow, 48 also compatible with cytometers. 49 Therefore, the combination of well-known mb with microgels or hydrogel particles could represent a very promising new interface, combining the performance of high specificity of mb with antifouling properties and high chemical structure flexibility of microgels.…”

Section: ■ Introductionmentioning

confidence: 99%

Supramolecular Microgels with Molecular Beacons at the Interface for Ultrasensitive, Amplification-Free, and SNP-Selective miRNA Fluorescence Detection

Caputo

Battista

Netti

et al. 2019

ACS Appl. Mater. Interfaces

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In this study, a supramolecular structure with femtomolar biorecognition properties is proposed for use in analytical devices. It is obtained by an innovative interface between synthetic hydrogel polymers and molecular beacon (mb) probes. Supramolecularly structured microgels are synthetized with a core−shell architecture with specific dyes polymerized in a desired compartment. Mb probes are opportunely conjugated at the microgel interface so that their recognition mechanism is preserved and their spatial distribution is optimized to avoid crowding effects. The miR-21, a microRNA involved in various biological processes and usually used as a biomarker in early cancer diagnosis, has been selected as the target. The results demonstrate that by tuning the spatial distribution of molecular probes immobilized on the microgel and/or the amount of microgels, the assay shows scalable sensitivity reaching a limit of detection down to about 10 fM, without amplification steps and with detection time as short as 1 h. The assay results specific toward single mutated targets, and it is stable in the presence of high-interfering oligonucleotides concentrations. The miRNA target is also detected in human serum with performances similar to those observed in PBS buffer because of microgel antifouling properties without the need of any surface treatment. All tests were performed in a low sample volume (20 μL). As a result, mb-microgel represents an innovative biosensor to precisely quantify microRNAs in a direct (mix&read), scalable, and selective way. Such an approach paves the way for creating innovative biosensing interfaces with other probes, such as hairpins, aptamers, and PNA.

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In-flow real-time detection of spectrally encoded microgels for miRNA absolute quantification

Cited by 9 publications

References 42 publications

Out of distribution detection in deep learning-based scattering pattern classification

Out of distribution detection in deep learning-based scattering pattern classification

Bioengineering Microgels and Hydrogel Microparticles for Sensing Biomolecular Targets

Supramolecular Microgels with Molecular Beacons at the Interface for Ultrasensitive, Amplification-Free, and SNP-Selective miRNA Fluorescence Detection

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