Review on Photomicrography based Full Blood Count (FBC) Testing and Recent Advancements

With advancements in related sub-fields, research on photomicrography in life science is emerging and this is a review on its application towards human full blood count testing which is a primary test in medical practices. For a prolonged period of time, analysis of blood samples is the basis for bio medical observations of living creatures. Cell size, shape, constituents, count, ratios are few of the features identified using DIP based analysis and these features provide an overview of the state of human body which is important in identifying present medical conditions and indicating possible future complications. In addition, functionality of the immune system is observed using results of blood tests. In FBC tests, identification of different blood cell types and counting the number of cells of each type is required to obtain results. Literature discuss various techniques and methods and this article presents an insightful review on human blood cell morphology, photomicrography, digital image processing of photomicrographs, feature extraction and classification, and recent advances. Integration of emerging technologies such as microfluidics, micro-electromechanical systems, and artificial intelligence based image processing algorithms and classifiers with cell sensing have enabled exploration of novel research directions in blood testing applications.

Machine learning issues and opportunities in ultrafast particle classification for label-free microflow cytometry

Machine learning offers promising solutions for high-throughput single-particle analysis in label-free imaging microflow cytomtery. However, the throughput of online operations such as cell sorting is often limited by the large computational cost of the image analysis while offline operations may require the storage of an exceedingly large amount of data. Moreover, the training of machine learning systems can be easily biased by slight drifts of the measurement conditions, giving rise to a significant but difficult to detect degradation of the learned operations. We propose a simple and versatile machine learning approach to perform microparticle classification at an extremely low computational cost, showing good generalization over large variations in particle position. We present proof-of-principle classification of interference patterns projected by flowing transparent PMMA microbeads with diameters of 15.2 mu m and 18.6 mu m. To this end, a simple, cheap and compact label-free microflow cytometer is employed. We also discuss in detail the detection and prevention of machine learning bias in training and testing due to slight drifts of the measurement conditions. Moreover, we investigate the implications of modifying the projected particle pattern by means of a diffraction grating, in the context of optical extreme learning machine implementations

Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System

Automated white blood cell (WBC) counting systems process an extracted whole blood sample and provide a cell count. A step that would not be ideal for onsite screening of individuals in triage or at a security gate. Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering co-registered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, specifically the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained and sealed blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera as a platform to build an automated blood cell counting system. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyperspectral datacube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells\u27 features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. The system has shown to successfully segment blood cells based on their spectral-spatial information. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting

Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning

While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities are not available, the task remains time-consuming and prone to human inconsistencies. This has an impact especially in cases of Acute Promyelocytic Leukemia (APL) that require urgent treatment. Integration of automated computational hematopathology into clinical workflows can improve the throughput of these services and reduce cognitive human error. However, a major bottleneck in deploying such systems is a lack of sufficient cell morphological object-labels annotations to train deep learning models. We overcome this by leveraging patient diagnostic labels to train weakly-supervised models that detect different types of acute leukemia. We introduce a deep learning approach, Multiple Instance Learning for Leukocyte Identification (MILLIE), able to perform automated reliable analysis of blood films with minimal supervision. Without being trained to classify individual cells, MILLIE differentiates between acute lymphoblastic and myeloblastic leukemia in blood films. More importantly, MILLIE detects APL in blood films (AUC 0.94 ± 0.04) and in bone marrow aspirates (AUC 0.99 ± 0.01). MILLIE is a viable solution to augment the throughput of clinical pathways that require assessment of blood film microscopy

High-Resolution Imaging of Natural Killer Cell Immunological Synapses

The first observations of the immunological synapse have demonstrated that immune-cell signalling in situ does not simply depend on protein structures and signalling pathways but also on temporal and spatial coordinates. With the advent of new live-cell, three-dimensional fluorescence microscopy techniques our understanding of the relationship between the formation of the immunological synapse and the development of an immune response has been greatly improved. Using artificial activating substrates as surrogate target cells or antigen presenting cells in conventional microscopes has so far been the state-of-the-art to obtain high-resolution images of immunological synapses. However, such artificial substrates may not fully recapitulate the complexity of intercellular interactions. Newly developed super-resolution techniques are very promising, but they remain inadequate for live-cell imaging. Technical improvements are therefore crucially needed to address these bottlenecks and improve our understanding of immune-cell signalling. In this report we achieve high-speed high-resolution imaging of live intercellular immunological synapses by combining confocal microscopy with optical tweezers. We design, build and demonstrate the performance and flexibility of the instrument by imaging a variety of molecules at T cell and NK cell synapses. NKG2D is an important receptor that allows NK cells to recognise and kill tumour cells. Due to the lack of suitable imaging technology, NKG2D signalling at the synapse remains unclear. We specifically use our new instrument to gain further understanding of NKG2D signalling, signal integration, and NKG2D-mediated cytotoxicity. For the first time at live intercellular NK-cell immunological synapses, we describe the formation and the dynamics of NKG2D microclusters. We show that these microclusters actively signal and that they coalesce around a secretory domain through which lytic secretions are delivered. Importantly, these results suggest that the physical distribution of NKG2D at the immunological synapse may play an important role in directing lytic-secretion delivery at the NK cell synapse

Neutrophil biomechanical properties and immune function in health and inflammatory disease

Low density granulocytes (LDGs) are a poorly understood class of immune cells found in patients with chronic inflammatory diseases including psoriasis and systemic lupus erythematosus (SLE). Research completed at the National Institutes of Health (NIH) revealed that in the context of SLE, LDGs release higher levels of type 1 interferons, undergo increased NETosis, and accordingly drive inflammation. Meanwhile, advances in mechanical phenotyping at the University of Cambridge have driven hypotheses of neutrophil trafficking and immune function being intimately linked to cellular biomechanical properties (e.g. density, stiffness, morphology). This thesis analyses the intersection of immune cellular biomechanical phenotypes and their function. Specifically, it focuses on the role of neutrophils and LDGs in inflammatory diseases. In this thesis, real-time deformability cytometry (RT-DC) was optimised as a high-throughput mechanical phenotyping technique for the analysis of neutrophils. This enabled development of a protocol to recover purified neutrophils to their whole blood mechanical phenotype. Neutrophil biomechanical properties were analysed by RT-DC, lattice light-sheet microscopy, confocal microscopy and scanning electron microscopy. Neutrophil immunologic functions (e.g. NETosis, macropinocytosis) were imaged using florescence microscopy. To analyse the contribution of biomechanical properties to neutrophil trafficking, a novel microfluidic microvasculature mimetic was developed. An endothelial flow assay was used to image neutrophils interacting with endothelial cells. Finally, the complete proteomes and phosphoproteomes of LDGs and normal dense neutrophils (NDNs) were obtained from five healthy donors and five SLE patients. Several key insights were gained. Firstly, hypotonic lysis and magnetic column-based isolation techniques are damaging to neutrophil biomechanical properties, but purification of neutrophils retaining their biomechanical properties can be achieved by using gradients and column-free magnetic systems followed by recovery at 37 degrees Celsius. Secondly, the biphasic biomechanical kinetics of neutrophil priming were described; cells contract briefly before immediately expanding. The expansion phase was determined to be macropinocytosis dependent. Thirdly, SLE LDGs are phenotypically rougher than autologous SLE NDNs or healthy LDGs. This appears to impact their microvasculature trafficking abilities, as SLE LDGs were increasingly trapped in the narrow channels of a three- dimensional microvasculature mimetic. These results suggest a role for biomechanical properties in modulation of neutrophil trafficking, indicating that SLE LDGs may be increasingly retained in microvasculature networks, similar to what has been described for primed neutrophils. Finally, unbiased proteomics quantified 4109 proteins and 875 phosphoproteins in four neutrophil subsets (healthy unstimulated NDNs, healthy primed NDNs, SLE NDNs, and SLE LDG). This shed new light into neutrophil heterogeneity at the protein level and to my knowledge, is the first proteomic profile of the SLE LDG. In addition to findings pertaining to SLE LDG biology and function, differential phosphorylation of proteins associated with cytoskeletal organisation were identified in SLE LDGs relative to SLE NDNs, suggesting a phosphoproteomic explanation for the SLE LDGs’ distinct biomechanical phenotype. When taken together, this work could have important pathogenic implications in the context of SLE manifestations in various organs and the development of small vessel vasculopathy.Kathleen Bashant was funded by the NIH-Cambridge Scholars Program This research was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014*). The views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care