Analysis of red blood cells’ dynamic status in a simulated blood circulation system using an ultrahigh-speed simultaneous framing optical electronic camera

Blood quality evaluation via on-chip classification of cell morphology using a deep learning algorithm

The quality of red blood cells (RBCs) in stored blood has a direct impact on the recovery of patients treated by blood transfusion, which directly reflects the quality of blood. The traditional means for blood quality evaluation involve the use of reagents and multi-step and time-consuming operations. Here, a low-cost, multi-classification, label-free and high-precision method is developed, which combines microfluidic technology and a deep learning algorithm together to recognize and classify RBCs based on morphology. The microfluidic channel is designed to effectively and controllably solve the problem of cell overlap, which has a severe negative impact on the identification of cells. The object detection model in the deep learning algorithm is optimized and used to recognize multiple RBCs simultaneously in the whole field of view, so as to classify them into six morphological subcategories and count the numbers in each subgroup. The mean average precision of the developed object detection model reaches 89.24%. The blood quality can be evaluated by calculating the morphology index (MI) according to the numbers of cells in subgroups. The validation of the method is verified by evaluating three blood samples stored for 7 days, 21 days and 42 days, which have MIs of 84.53%, 73.33% and 24.34%, respectively, indicating good agreement with the actual blood quality. This method has the merits of cell identification in a wide channel, no need for single cell alignment as the image cytometry does and it is not only applicable to the quality evaluation of RBCs, but can also be used for general cell identifications with different morphologies.

Recent advances in non-optical microfluidic platforms for bioparticle detection

The effective analysis of the basic structure and functional information of bioparticles are of great significance for the early diagnosis of diseases. The synergism between microfluidics and particle manipulation/detection technologies offers enhanced system integration capability and test accuracy for the detection of various bioparticles. Most microfluidic detection platforms are based on optical strategies such as fluorescence, absorbance, and image recognition. Although optical microfluidic platforms have proven their capabilities in the practical clinical detection of bioparticles, shortcomings such as expensive components and whole bulky devices have limited their practicality in the development of point-of-care testing (POCT) systems to be used in remote and underdeveloped areas. Therefore, there is an urgent need to develop cost-effective non-optical microfluidic platforms for bioparticle detection that can act as alternatives to optical counterparts. In this review, we first briefly summarise passive and active methods for bioparticle manipulation in microfluidics. Then, we survey the latest progress in non-optical microfluidic strategies based on electrical, magnetic, and acoustic techniques for bioparticle detection. Finally, a perspective is offered, clarifying challenges faced by current non-optical platforms in developing practical POCT devices and clinical applications.

Microtechnologies for Cell Microenvironment Control and Monitoring

A great breadth of questions remains in cellular biology. Some questions cannot be answered using traditional analytical techniques and so demand the development of new tools for research. In the near future, the development of highly integrated microfluidic analytical platforms will enable the acquisition of unknown biological data. These microfluidic systems must allow cell culture under controlled microenvironment and high throughput analysis. For this purpose, the integration of a variable number of newly developed micro- and nano-technologies, which enable control of topography and surface chemistry, soluble factors, mechanical forces and cell–cell contacts, as well as technology for monitoring cell phenotype and genotype with high spatial and temporal resolution will be necessary. These multifunctional devices must be accompanied by appropriate data analysis and management of the expected large datasets generated. The knowledge gained with these platforms has the potential to improve predictive models of the behavior of cells, impacting directly in better therapies for disease treatment. In this review, we give an overview of the microtechnology toolbox available for the design of high throughput microfluidic platforms for cell analysis. We discuss current microtechnologies for cell microenvironment control, different methodologies to create large arrays of cellular systems and finally techniques for monitoring cells in microfluidic devices.

Insights into red blood cell storage lesion: Toward a new appreciation

Red blood cell storage lesion (RSL) is a multifaceted biological phenomenon. It refers to deterioration in RBC quality that is characterized by lethal and sub-lethal, reversible and irreversible defects. RSL is influenced by prestorage variables and it might be associated with variable clinical outcomes. Optimal biopreservation conditions are expected to offer maximum levels of RBC survival and acceptable functionality and bioreactivity in-bag and in vivo; consequently, full appraisal of RSL requires understanding of how RSL changes interact with each other and with the recipient. Recent technological innovation in MS-based omics, imaging, cytometry, small particle and systems biology has offered better understanding of RSL contributing factors and effects. A number of elegant in vivo and in vitro studies have paved the way for the identification of quality control biomarkers useful to predict RSL profile and posttransfusion performance. Moreover, screening tools for the early detection of good or poor "storers" and donors have been developed. In the light of new perspectives, storage time is not the touchstone to rule on the quality of a packed RBC unit. At least by a biochemical standpoint, the metabolic aging pattern during storage may not correspond to the currently fresh/old distinction of stored RBCs. Finally, although each unit of RBCs is probably unique, a metabolic signature of RSL across storage variables might exist. Moving forward from traditional hematologic measures to integrated information on structure, composition, biochemistry and interactions collected in bag and in vivo will allow identification of points for intervention in a transfusion meaningful context.