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

Abstract 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

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

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

Forty years on: clathrin-coated pits continue to fascinate

Clathrin mediated endocytosis (CME) is a fundamental process in cell biology and has been extensively investigated throughout the last several decades. Every cell biologist learns about it at some point during their education and the beauty of this process has led many of us to go deeper and make it the topic of our own research. Great progress has been made towards elucidating the mechanisms of CME and the field is becoming increasingly complex with several hundred new publications every year. This makes it easy to get lost in the vast amount of literature and to forget about the fundamentals of the field, based on the careful interpretation of simple observations made over 40 years ago. A study performed by Anderson, Brown and Goldstein in 1977 (Anderson et al., 1977) is a prime example of this. We therefore want to take a step back and examine how this seminal study was pivotal to our understanding of CME and its progression into ever increasing complexity over the last four decades

Small angle light scattering apparatus for analysis of single micrometric particles in microfluidic flows

The fast characterization of micrometric particle is becoming of increasing importance. The measurement of shape and the index of refraction of a particle allows very accurate characterization of the analysed material. A CCD-camera based small angle light scattering (SALS) apparatus has been developed to characterize single micrometric particles. The measured scattering vector spans the range 0.02 - 6.8 1/µm. The incident laser light is collimated to a spot of about 50 µm in diameter at the sample position with a divergence lower than 0.045 rad. Such a small collimated laser beam distinguishes this system from previous small angle light scattering instruments described in literature and opens the possibility to perform SALS in quiescent and in-flow conditions in small microfluidic channels. By properly designing the micro-channel and using a viscoelastic liquid as the suspending medium, it is able to realize a precise 3D focusing of the target particles. The forward scattering emitted from the particle is collected by a lens with high numerical aperture. Further, at the focal point of that lens a homemade beam stop blocks the incident light. Finally, a second lens maps the scattered light on the CCD sensor, allowing for the collection of far field images on short distances. Measurements with mono-disperse polystyrene particles, having nominal radii of 0.95, 1.64, 2.08, 2.90, 3.04, and 4.01 µm, both in quiescent and in-flow conditions have been realized. Experiments in-flow conditions allow the measurement of the single particle scattering profile. Results are validated by comparison with calculations based on the Lorenz-Mie theory. Measurements of real multiplexed particle solutions, with particles down to 1 µm in radius confirmed the possibility to use this SALS apparatus. Moreover, initial analyses of microgel particle structures in quiescent conditions over time have been carried out. This analysis can be extended for more complex systems, like multi-shell, or non spherical particles in terms of single particle characterization

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

Multiplex single particle analysis in microfluidics

A straightforward way to measure separated micrometric sized particles in microfluidic flow is reported. The light scattering profile (LSP) of each single particle is fully characterized by using a CMOS-camera based small angle light scattering (SALS) apparatus, ranging from 2° up to 30°. To ensure controlled particle passage through the incident laser, a viscoelastic 3D alignment effect by viscoelastic induced particle migration has been implemented in a simple and cost-effective microfluidic device. Different polystyrene particle sizes are measured in microfluidic flows and the obtained scattering signatures are matched with the Lorenz-Mie based scattering theory. The results confirm the possibility of using this apparatus for real multiplex particle analyses in microfluidic particle flow