A Single-Organelle Optical Omics Platform for Cell Science and Biomarker Discovery

Extracellular vesicles from activated platelets possess a phospholipid-rich biomolecular profile and enhance prothrombinase activity

BACKGROUND

Extracellular vesicles (EVs), in particular those derived from activated platelets, are associated with a risk of future venous thromboembolism.

OBJECTIVES

To study the biomolecular profile and function characteristics of EVs from control (unstimulated) and activated platelets.

METHODS

Biomolecular profiling of single or very few (1-4) platelet-EVs (control/stimulated) was performed by Raman tweezers microspectroscopy. The effects of such EVs on the coagulation system were comprehensively studied.

RESULTS

Raman tweezers microspectroscopy of platelet-EVs followed by biomolecular component analysis revealed for the first time 3 subsets of EVs: (i) protein rich, (ii) protein/lipid rich, and (iii) lipid rich. EVs from control platelets presented a heterogeneous biomolecular profile, with protein-rich EVs being the main subset (58.7% ± 3.5%). Notably, the protein-rich subset may contain a minor contribution from other extracellular particles, including protein aggregates. In contrast, EVs from activated platelets were more homogeneous, dominated by the protein/lipid-rich subset (>85%), and enriched in phospholipids. Functionally, EVs from activated platelets increased thrombin generation by 52.4% and shortened plasma coagulation time by 34.6% ± 10.0% compared with 18.6% ± 13.9% mediated by EVs from control platelets (P = .015). The increased procoagulant activity was predominantly mediated by phosphatidylserine. Detailed investigation showed that EVs from activated platelets increased the activity of the prothrombinase complex (factor Va:FXa:FII) by more than 6-fold.

CONCLUSION

Our study reports a novel quantitative biomolecular characterization of platelet-EVs possessing a homogenous and phospholipid-enriched profile in response to platelet activation. Such characteristics are accompanied with an increased phosphatidylserine-dependent procoagulant activity. Further investigation of a possible role of platelet-EVs in the pathogenesis of venous thromboembolism is warranted.

Identification of extracellular vesicles from their Raman spectra via self-supervised learning

Extracellular vesicles (EVs) released from cells attract interest for their possible role in health and diseases. The detection and characterization of EVs is challenging due to the lack of specialized methodologies. Raman spectroscopy, however, has been suggested as a novel approach for biochemical analysis of EVs. To extract information from the spectra, a novel deep learning architecture is explored as a versatile variant of autoencoders. The proposed architecture considers the frequency range separately from the intensity of the spectra. This enables the model to adapt to the frequency range, rather than requiring that all spectra be pre-processed to the same frequency range as it was trained on. It is demonstrated that the proposed architecture accepts Raman spectra of EVs and lipoproteins from 13 biological sources and from two laboratories. High reconstruction accuracy is maintained despite large variances in frequency range and noise level. It is also shown that the architecture is able to cluster the biological nanoparticles by their Raman spectra and differentiate them by their origin without pre-processing of the spectra or supervision during learning. The model performs label-free differentiation, including separating EVs from activated vs. non-activated blood platelets and EVs/lipoproteins from prostate cancer patients versus non-cancer controls. The differentiation is evaluated by creating a neural network classifier that observes the features extracted by the model to classify the spectra according to their sample origin. The classification reveals a test sensitivity of 92.2 % and selectivity of 92.3 % over 769 measurements from two labs that have different measurement configurations.

Mitochondrial Dysfunction and Apoptosis in Brain Microvascular Endothelial Cells Following Blast Traumatic Brain Injury

Blood brain barrier (BBB) breakdown is a key driver of traumatic brain injury (TBI), contributing to prolonged neurological deficits and increased risk of death in TBI patients. Strikingly, the role of endothelium in the progression of BBB breakdown has not been sufficiently investigated, even though it constitutes the bulk of BBB structure. In the current study, we investigate TBI-induced changes in the brain endothelium at the subcellular level, particularly focusing on mitochondrial dysfunction, using a combination of confocal imaging, gene expression analysis, and molecular profiling by Raman spectrometry. Herein, we developed and applied an in-vitro blast-TBI (bTBI) model that employs an acoustic shock tube to deliver injury to cultured human brain microvascular endothelial cells (HBMVEC). We found that this injury results in aberrant expression of mitochondrial genes, as well as cytokines/ inflammasomes, and regulators of apoptosis. Furthermore, injured cells exhibit a significant increase in reactive oxygen species (ROS) and in Ca2+ levels. These changes are accompanied by overall reduction of intracellular proteins levels as well as profound transformations in mitochondrial proteome and lipidome. Finally, blast injury leads to a reduction in HBMVEC cell viability, with up to 50% of cells exhibiting signs of apoptosis following 24 h after injury. These findings led us to hypothesize that mitochondrial dysfunction in HBMVEC is a key component of BBB breakdown and TBI progression.

Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning

Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study human hepatic tissue samples, developing and validating a workflow for in vitro and intraoperative pathological diagnosis of liver cancer. We distinguish carcinoma tissues from adjacent non-tumour tissues in a rapid, non-disruptive, and label-free manner by using Raman spectroscopy combined with deep learning, which is validated by tissue metabolomics. This technique allows for detailed pathological identification of the cancer tissues, including subtype, differentiation grade, and tumour stage. 2D/3D Raman images of unprocessed human tissue slices with submicrometric resolution are also acquired based on visualization of molecular composition, which could assist in tumour boundary recognition and clinicopathologic diagnosis. Lastly, the potential for a portable handheld Raman system is illustrated during surgery for real-time intraoperative human liver cancer diagnosis.

Single cell metabolism: current and future trends

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Biomarkers of mitochondrial origin: a futuristic cancer diagnostic

Cancer is a highly fatal disease without effective early-stage diagnosis and proper treatment. Along with the oncoproteins and oncometabolites, several organelles from cancerous cells are also emerging as potential biomarkers. Mitochondria isolated from cancer cells are one such biomarker candidates. Cancerous mitochondria exhibit different profiles compared with normal ones in morphology, genomic, transcriptomic, proteomic and metabolic landscape. Here, the possibilities of exploring such characteristics as potential biomarkers through single-cell omics and Artificial Intelligence (AI) are discussed. Furthermore, the prospects of exploiting the biomarker-based diagnosis and its futuristic utilization through circulatory tumor cell technology are analyzed. A successful alliance of circulatory tumor cell isolation protocols and a single-cell omics platform can emerge as a next-generation diagnosis and personalized treatment procedure.

Mitochondrial Dysfunction: A Prelude to Neuropathogenesis of SARS-CoV-2

The SARS-CoV-2 virus is notorious for its neuroinvasive capability, causing multiple neurological conditions. The neuropathology of SARS-CoV-2 is increasingly attributed to mitochondrial dysfunction of brain microglia cells. However, the changes in biochemical content of mitochondria that drive the progression of neuro-COVID remain poorly understood. Here we introduce a Raman microspectrometry approach that enables the molecular profiling of single cellular organelles to characterize the mitochondrial molecular makeup in the infected microglia cells. We found that microglia treated with either spike protein or heat-inactivated SARS-CoV-2 trigger a dramatic reduction in mtDNA content and an increase in phospholipid saturation levels. At the same time, no significant changes were detected in Golgi apparatus and in lipid droplets, the organelles that accommodate biogenesis and storage of lipids. We hypothesize that transformations in mitochondria are caused by increased synthesis of reactive oxygen species in these organelles. Our findings call for the development of mitochondria-targeted therapeutic approaches to limit neuropathology associated with SARS-CoV-2.

Fluorescent probes for imaging bioactive species in subcellular organelles

Luminescent molecular probes and nanoscale materials have become important tools in biosensing and bioimaging applications because of their high sensitivity, fast response, specificity, and methodological simplicity. In recent years, there has been a notable advancement in fluorescent probes that respond to the subtle changes in subcellular microenvironments (e.g., polarity, pH, and viscosity) or distribution of certain crucial biomarkers (e.g., reactive oxygen species, ions, amino acids, and enzymes). The dynamic fluctuations of these bio-molecules in subcellular microenvironments control cellular homeostasis, immunity, signal conduction, and metabolism. Their abnormal expressions are linked to various biological disorders and disease states. Thus, the real-time monitoring of such bioactive species is intimately linked to clinical diagnostics. Appropriately designed luminescent probes are ideally suited for desired organelle specificity, as well as for reporting intracellular changes in biochemicals/microenvironmental factors with the luminescence ON response. In this perspective, we review our recent work on the development of fluorescent probes for sensing and imaging within sub-cellular organelles. We have also discussed the design aspects for developing a prodrug with a fluorescent probe as an integral part of possible theranostic applications. An overview of the design principles, photophysical properties, detection mechanisms, current challenges, and potential future directions of fluorescent probes is presented in this feature article. We have also discussed the limitations and challenges of developing the solution platform for sensing technologies in clinical diagnostics.