Nanotherapeutic Heterogeneity: Sources, Effects, and Solutions

Nanomaterials have revolutionized medicine by enabling control over drugs' pharmacokinetics, biodistribution, and biocompatibility. However, most nanotherapeutic batches are highly heterogeneous, meaning they comprise nanoparticles that vary in size, shape, charge, composition, and ligand functionalization. Similarly, individual nanotherapeutics often have heterogeneously distributed components, ligands, and charges. This review discusses nanotherapeutic heterogeneity's sources and effects on experimental readouts and therapeutic efficacy. Among other topics, it demonstrates that heterogeneity exists in nearly all nanotherapeutic types, examines how nanotherapeutic heterogeneity arises, and discusses how heterogeneity impacts nanomaterials' in vitro and in vivo behavior. How nanotherapeutic heterogeneity skews experimental readouts and complicates their optimization and clinical translation is also shown. Lastly, strategies for limiting nanotherapeutic heterogeneity are reviewed and recommendations for developing more reproducible and effective nanotherapeutics provided.

On-Chip Photonic Detection Techniques for Non-Invasive In Situ Characterizations at the Microfluidic Scale

Microfluidics has emerged as a robust technology for diverse applications, ranging from bio-medical diagnostics to chemical analysis. Among the different characterization techniques that can be used to analyze samples at the microfluidic scale, the coupling of photonic detection techniques and on-chip configurations is particularly advantageous due to its non-invasive nature, which permits sensitive, real-time, high throughput, and rapid analyses, taking advantage of the microfluidic special environments and reduced sample volumes. Putting a special emphasis on integrated detection schemes, this review article explores the most relevant advances in the on-chip implementation of UV-vis, near-infrared, terahertz, and X-ray-based techniques for different characterizations, ranging from punctual spectroscopic or scattering-based measurements to different types of mapping/imaging. The principles of the techniques and their interest are discussed through their application to different systems.

Unknown cell class distinction via neural network based scattering snapshot recognition

Neural network-based image classification is widely used in life science applications. However, it is essential to extrapolate a correct classification method for unknown images, where no prior knowledge can be utilised. Under a closed set assumption, unknown images will be inevitably misclassified, but this can be genuinely overcome choosing an open-set classification approach, which first generates an in-distribution of identified images to successively discriminate out-of-distribution images. The testing of such image classification for single cell applications in life science scenarios has yet to be done but could broaden our expertise in quantifying the influence of prediction uncertainty in deep learning. In this framework, we implemented the open-set concept on scattering snapshots of living cells to distinguish between unknown and known cell classes, targeting four different known monoblast cell classes and a single tumoral unknown monoblast cell line. We also investigated the influence on experimental sample errors and optimised neural network hyperparameters to obtain a high unknown cell class detection accuracy. We discovered that our open-set approach exhibits robustness against sample noise, a crucial aspect for its application in life science. Moreover, the presented open-set based neural network reveals measurement uncertainty out of the cell prediction, which can be applied to a wide range of single cell classifications.

Particle Counting Methods Based on Microfluidic Devices

Particle counting serves as a pivotal constituent in diverse analytical domains, encompassing a broad spectrum of entities, ranging from blood cells and bacteria to viruses, droplets, bubbles, wear debris, and magnetic beads. Recent epochs have witnessed remarkable progressions in microfluidic chip technology, culminating in the proliferation and maturation of microfluidic chip-based particle counting methodologies. This paper undertakes a taxonomical elucidation of microfluidic chip-based particle counters based on the physical parameters they detect. These particle counters are classified into three categories: optical-based counters, electrical-based particle counters, and other counters. Within each category, subcategories are established to consider structural differences. Each type of counter is described not only in terms of its working principle but also the methods employed to enhance sensitivity and throughput. Additionally, an analysis of future trends related to each counter type is provided.

Effect of wall slip on the viscoelastic particle ordering in a microfluidic channel

The formation of a line of equally spaced particles at the centerline of a microchannel, referred as "particle ordering," is desired in several microfluidic applications. Recent experiments and simulations highlighted the capability of viscoelastic fluids to form a row of particles characterized by a preferential spacing. When dealing with non-Newtonian fluids in microfluidics, the adherence condition of the liquid at the channel wall may be violated and the liquid can slip over the surface, possibly affecting the ordering efficiency. In this work, we investigate the effect of wall slip on the ordering of particles suspended in a viscoelastic liquid by numerical simulations. The dynamics of a triplet of particles in an infinite cylindrical channel is first addressed by solving the fluid and particle governing equations. The relative velocities computed for the three-particle system are used to predict the dynamics of a train of particles flowing in a long microchannel. The distributions of the interparticle spacing evaluated at different slip coefficients, linear particle concentrations, and distances from the channel inlet show that wall slip slows down the self-assembly mechanism. For strong slipping surfaces, no significant change of the initial microstructure is observed at low particle concentrations, whereas strings of particles in contact form at higher concentrations. The detrimental effect of wall slip on viscoelastic ordering suggests care when designing microdevices, especially in case of hydrophobic surfaces that may enhance the slipping phenomenon.

Single cell classification of macrophage subtypes by label-free cell signatures and machine learning

Pro-inflammatory (M1) and anti-inflammatory (M2) macrophage phenotypes play a fundamental role in the immune response. The interplay and consequently the classification between these two functional subtypes is significant for many therapeutic applications. Albeit, a fast classification of macrophage phenotypes is challenging. For instance, image-based classification systems need cell staining and coloration, which is usually time- and cost-consuming, such as multiple cell surface markers, transcription factors and cytokine profiles are needed. A simple alternative would be to identify such cell types by using single-cell, label-free and high throughput light scattering pattern analyses combined with a straightforward machine learning-based classification. Here, we compared different machine learning algorithms to classify distinct macrophage phenotypes based on their optical signature obtained from an ad hoc developed wide-angle static light scattering apparatus. As the main result, we were able to identify unpolarized macrophages from M1- and M2-polarized phenotypes and distinguished them from naive monocytes with an average accuracy above 85%. Therefore, we suggest that optical single-cell signatures within a lab-on-a-chip approach along with machine learning could be used as a fast, affordable, non-invasive macrophage phenotyping tool to supersede resource-intensive cell labelling.

Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56^bright and mature cytotoxic NK CD56^dim. Therefore, the ability to discriminate CD56^dim from CD56^bright could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56^bright to CD56^dim. We trained our ML algorithm with sorted NK cell subclasses (≥86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56^bright to CD56^dim and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy.

CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning

T lymphocytes are a group of cells representing the main effectors of human adaptive immunity. Characterization of the most representative T-lymphocyte subclasses, CD4+ and CD8+, is challenging, but has a significant impact on clinical decisions. Up to now, T lymphocytes have been identified by quite complex cytometric assays, which are based on antibody labeling. However, a label-free approach based on pure biophysical evaluation at a single-cell level could enable the ability to distinguish between these subclasses. Here, we report a light-scattering approach, supported by accurate data mining, to evaluate cell biophysical properties on an integrated microfluidic chip. In order to perform single-cell optical analysis in viscoelastic fluids, such a chip is composed of mixing, alignment, readout and collection sections. In particular, we measured the cell dimensions, the refractive index of the cell nucleus, the refractive index of the cytosol, and the nucleus-to-cytosol ratio. Combining measurement of biophysical properties and machine learning allows us to both distinguish and count human CD4+ and CD8+ cells with an accuracy of 79%. An enhanced identification accuracy of 88% can be achieved by stimulating the cells with a selective anti-apoptotic protein, which results in increased biophysical differences between CD4+ and CD8+ cells. This approach has been successfully validated by analysis of samples that recapitulate physiological and pathological scenarios (CD4+/CD8+ ratios). The results are encouraging for the possible application of our approach in hematological clinical routines, as well as in diagnosis and follow-up of specific pathologies, such as human immunodeficiency virus (HIV) progression.

Biophysical investigation of living monocytes in flow by collaborative coherent imaging techniques

We implemented a completely label-free biophysical (morphometric and optical) property characterization of living monocytes in flow, using measurements obtained from two coherent imaging techniques: a pure light scattering approach to obtain an optical signature (OS) of cells, and a digital holography (DH) approach to achieve optical cell reconstructions in flow. A precise 3D cell alignment platform, taking advantage of viscoelastic fluid properties and microfluidic channel geometry, was used to investigate the OS of cells to achieve their refractive index, ratio of the nucleus over cytoplasm, and overall cell dimension. Further quantitative phase-contrast reconstructions by DH were employed to calculate surface area, dry mass, and biovolume of monocytes by using the OS outcomes as input parameters. The results show significantly different biophysical cell properties, confirming the possibility to differentiate monocytes from other cell classes in flow, thus avoiding chemical cell staining or labeling, which are nowadays used.

Single-cell screening of multiple biophysical properties in leukemia diagnosis from peripheral blood by pure light scattering

Histology and histopathology are based on the morphometric observations of quiescent cells. Their diagnostic potential could largely benefit from a simultaneous screening of intrinsic biophysical properties at single-cell level. For such a purpose, we analyzed light scattering signatures of individual mononuclear blood cells in microfluidic flow. In particular, we extracted a set of biophysical properties including morphometric (dimension, shape and nucleus-to-cytosol ratio) and optical (optical density) ones to clearly discriminate different cell types and stages. By considering distinctive ranges of biophysical properties along with the obtained relative cell frequencies, we can identify unique cell classes corresponding to specific clinical conditions (p < 0.01). Based on such a straightforward approach, we are able to discriminate T-, B-lymphocytes, monocytes and beyond that first results on different stages of lymphoid and myeloid leukemia cells are presented. This work shows that the simultaneous screening of only three biophysical properties enables a clear distinction between pathological and physiological mononuclear blood stream cells. We believe our approach could represent a useful tool for a label-free analysis of biophysical single-cell signatures.

Bioengineering Microgels and Hydrogel Microparticles for Sensing Biomolecular Targets

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.

Label-free analysis of mononuclear human blood cells in microfluidic flow by coherent imaging tools

The investigation of the physical properties of peripheral blood mononuclear cells (PBMC) is of great relevance, as they play a key role in regulating human body health. Here we report the possibility to characterize human PBMC in their physiological conditions in a microfluidic-based measurement system. A viscoelastic polymer solution is adopted for 3D alignment of individual cells inflow. An optical signature (OS) acquisition of each flowing cell is performed using a wide angle light scattering apparatus. Besides, a quantitative phase imaging (QPI) holographic system is employed with the aim (i) to check the position in flow of individual cells using a holographic 3D cell tracking method; and (ii) to estimate their 3D morphometric features, such as their refractive index (RI). Results obtained by combining OS and QPI have been compared with literature values, showing good agreement. The results confirm the possibility to obtain sub-micrometric details of physical cell properties in microfluidic flow, avoiding chemical staining or fluorescent labelling.

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

We present an in-flow ultrasensitive fluorescence detection of microRNAs (miRNAs) using spectrally encoded microgels. We researched and employed a viscoelastic fluid to achieve an optimal alignment of microgels in a straight measurement channel and applied a simple and inexpensive microfluidic layout, allowing continuous fluorescence signal acquisitions with several emission wavelengths. In particular, we chose microgels endowed with fluorescent emitting molecules designed for multiplex spectral analysis of specific miRNA types. We analysed in a quasi-real-time manner circa 80 microgel particles a minute at sample volumes down to a few microliters, achieving a miRNA detection limit of 202 fM in microfluidic flow conditions. Such performance opens up new routes for biosensing applications of particles within microfluidic devices.

Optical signature of erythrocytes by light scattering in microfluidic flows

A camera-based light scattering approach coupled with a viscoelasticity-induced cell migration technique has been used to characterize the morphological properties of erythrocytes in microfluidic flows. We have obtained the light scattering profiles (LSPs) of individual living cells in microfluidic flows over a wide angular range and matched them with scattering simulations to characterize their morphological properties. The viscoelasticity-induced 3D cell alignment in microfluidic flows has been investigated by bright-field and holographic microscopy tracking, where the latter technique has been used to obtain precise cell alignment profiles in-flow. Such information allows variable cell probability control in microfluidic flows at very low viscoelastic polymer concentrations, obtaining cell measurements that are almost physiological. Our results confirm the possibility of precise, label-free analysis of individual living erythrocytes in microfluidic flows.