Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67.

Single Cell Spatial Analysis Reveals the Topology of Immunomodulatory Purinergic Signaling in Glioblastoma

Glioblastoma develops an immunosuppressive microenvironment that fosters tumorigenesis and resistance to current therapeutic strategies. Here we use multiplexed tissue imaging and single-cell RNA-sequencing to characterize the composition, spatial organization, and clinical significance of extracellular purinergic signaling in glioblastoma. We show that glioblastoma exhibit strong expression of CD39 and CD73 ectoenzymes, correlating with increased adenosine levels. Microglia are the predominant source of CD39, while CD73 is principally expressed by tumor cells, particularly in tumors with amplification of EGFR and astrocyte-like differentiation. Spatially-resolved single-cell analyses demonstrate strong spatial correlation between tumor CD73 and microglial CD39, and that their spatial proximity is associated with poor clinical outcomes. Together, this data reveals that tumor CD73 expression correlates with tumor genotype, lineage differentiation, and functional states, and that core purine regulatory enzymes expressed by neoplastic and tumor-associated myeloid cells interact to promote a distinctive adenosine-rich signaling niche and immunosuppressive microenvironment potentially amenable to therapeutic targeting.

Optoaccoustic Tomography – good news for microcirculatory research

Tomographic imaging is a well established technology in preventive medicine and biomedical research, although not without limitations and concerns. Optoacoustic tomography (OAT) is a recent development that bridges optical and sonographic techniques to solve spatial resolution in deep-tissue imaging. In addition to safety advantages, OAT allows multiple wavelength readings for natural thermoelastic chromophores. In this study, we explore Multi Spectral Optoacoustic Tomography (MSOT) capacities to simultaneously acquire three independent chromophores – deoxygenated haemoglobin (Hb), oxygenated haemoglobin (HbO2), and melanin, from healthy human volunteers, with maximal molar extinction of HbO2 at 950 nm, Hb at 750 nm and melanin at 680 nm. Later we demonstrate how image stability during acquisition is fundamental for optimal resolution, precision and consistency of high throughout MSOT data collection. From recorded scans, a workflow is layered for data evaluation. With the MSOT dedicated software results were extracted from 3D image analysis of deep (15 mm3) vessels. The possibilities offered by this new system, specially in vascular pathophysiology, are immense and can be extended beyond current knowledge.

Optoaccoustic Tomography – good news for microcirculatory research

Tomographic imaging is a well established technology in preventive medicine and biomedical research, although not without limitations and concerns. Optoacoustic tomography (OAT) is a recent development that bridges optical and sonographic techniques to solve spatial resolution in deep-tissue imaging. In addition to safety advantages, OAT allows multiple wavelength readings for natural thermoelastic chromophores. In this study, we explore Multi Spectral Optoacoustic Tomography (MSOT) capacities to simultaneously acquire three independent chromophores – deoxygenated haemoglobin (Hb), oxygenated haemoglobin (HbO2), and melanin, from healthy human volunteers, with maximal molar extinction of HbO2 at 950 nm, Hb at 750 nm and melanin at 680 nm. Later we demonstrate how image stability during acquisition is fundamental for optimal resolution, precision and consistency of high throughout MSOT data collection. From recorded scans, a workflow is layered for data evaluation. With the MSOT dedicated software results were extracted from 3D image analysis of deep (15 mm3) vessels. The possibilities offered by this new system, specially in vascular pathophysiology, are immense and can be extended beyond current knowledge.

Simultaneous Optimization of Excitation Wavelength and Emission Window for Semiconducting Polymer Nanoparticles to Improve Imaging Quality

Optimized excitation wavelength and emission window are essential for fluorescence imaging with high quality. Semiconducting polymer nanoparticles (SPNs) as fluorescent contrast agents have been extensively studied, but their imaging abilities in the second near infrared IIb window (NIR-IIb, 1500 to 1700 nm) with long excitation wavelength have not been reported yet. Herein, as a proof-of-concept, we demonstrate for the first time that an SPN named L1057 nanoparticles (NPs) exhibit intense NIR-IIb signal due to its ultra-high brightness and broad emission spectrum. After screening 915 nm as an optimal excitation wavelength, we applied L1057 NPs to visualize the whole body vessels, cerebral vessels, gastrointestinal tract, and tumor progression in different stages, achieving superior spatial resolution and signal to background ratio in the NIR-IIb window with respect to NIR-II window (1000 to 1700 nm). This study reveals that simultaneous optimization of excitation wavelength and emission window is an efficient strategy to enhance imaging quality and that L1057 NPs can serve as a promising NIR-IIb contrast agent for high-resolution and deep-tissue imaging.

Revisiting PFA-mediated tissue fixation chemistry: FixEL enables trapping of small molecules in the brain to visualize their distribution dynamics

Various small molecules have been used as functional probes for tissue imaging in medical diagnosis and pharmaceutical drugs for disease treatment. The spatial distribution, target selectivity, and diffusion/extrusion kinetics of small molecules in structurally complicated specimens are critical for function. However, robust methods for precisely evaluating these parameters in the brain have been limited. Herein we report a new method termed "Fixation-driven chemical crosslinking of exogenous ligands (FixEL)" which traps and images exogenously administered molecules-of-interest (MOI) in complex tissues. This method relies on proteins-MOI interactions, and chemical crosslinking of amine-tethered MOI with paraformaldehyde used for perfusion fixation. FixEL is used to obtain images of the distribution of the small molecules and their dynamics, which addresses selective/nonselective binding to proteins, time-dependent localization changes, and diffusion/retention kinetics of MOI such as PET tracer derivatives or drug-like small molecules. Clear imaging of a nanobody distributed in the whole brain was also achieved with high spatial resolution using 2D/3D mode.

MIAAIM: Multi-omics image integration and tissue state mapping using topological data analysis and cobordism learning

High-parameter tissue imaging enables detailed molecular analysis of single cells in their spatial environment. However, the comprehensive characterization and mapping of tissue states through multimodal imaging across different physiological and pathological conditions requires data integration across multiple imaging systems. Here, we introduce MIAAIM (Multi-omics Image Alignment and Analysis by Information Manifolds) a modular, reproducible computational framework for aligning data across bioimaging technologies, modeling continuities in tissue states, and translating multimodal measures across tissue types. We demonstrate MIAAIM's workflows across diverse imaging platforms, including histological stains, imaging mass cytometry, and mass spectrometry imaging, to link cellular phenotypic states with molecular microenvironments in clinical biopsies from multiple tissue types with high cellular complexity. MIAAIM provides a robust foundation for the development of computational methods to integrate multimodal, high-parameter tissue imaging data and enable downstream computational and statistical interrogation of tissue states.